CN103702340A - Parameter adjustment method and system of distributed cognitive radio network - Google Patents

Abstract

Translated fromChinese

本发明公开了一种分布式认知无线电网络的参数调整方法及系统，涉及无线电通信技术领域，所述方法包括：S1：从授权信道中选择Ｎs个作为待检测信道；S2：对待检测信道进行检测，并按照数据发送概率p_tra在检测出的空闲信道上进行数据包的发送，统计数据包成功发送的个数；S3：对实际的空闲信道数量和接入认知用户数量进行估算；S4：计算新的信道检测数量N_s′和新的数据发送概率p_tra′，用N_s′的值为N_s进行赋值，并用p_tra′的值为p_tra进行赋值，以实现参数调整提高认知用户吞吐量。本发明通过估算实际的空闲信道数量和接入认知用户数量以计算能使系统吞吐量最大的信道检测数量和数据发送概率，使得在不存在中央控制信道的情况下，保证了系统吞吐量最大化。

The invention discloses a method and system for adjusting parameters of a distributed cognitive radio network, and relates to the technical field of radio communication. The method includes: S1: selecting Ns channels from authorized channels as channels to be detected; S2: performing a process on the channels to be detected Detect, and send data packets on the detected idle channel according to the data sending probability p_tra , and count the number of successfully sent data packets; S3: estimate the actual number of idle channels and the number of access cognitive users; S4 : Calculate the new channel detection number N_s ′ and the new data transmission probability p_tra ′, assign N_s with the value of N_s ′, and assign the value of p_tra with the value of p_tra ′, so as to realize parameter adjustment and improve recognition know the user throughput. The present invention calculates the number of channel detections and data transmission probability that can maximize the system throughput by estimating the actual number of idle channels and the number of access cognitive users, so that the maximum system throughput is guaranteed when there is no central control channel change.

Description

Translated fromChinese

分布式认知无线电网络的参数调整方法及系统Parameter adjustment method and system for distributed cognitive radio network

技术领域technical field

本发明涉及无线电通信技术领域，特别涉及一种分布式认知无线电网络的参数调整方法及系统。 The present invention relates to the technical field of radio communication, in particular to a method and system for adjusting parameters of a distributed cognitive radio network. the

背景技术Background technique

无线电频谱作为一种宝贵资源，目前只有政府授权的用户才能够使用某段特定的频段。随着无线电通信对频谱需求的提高，如何有效利用有限的频谱资源变得越来越重要，从而成为近年来的研究热点之一。然而，在近年通过对授权频谱使用效率的监测，目前的频谱管理方法还存在很多缺陷。美国联邦通信委员会（Federal Communications Commission，FCC）针对目前频谱使用的现状，建议使用更加灵活的频谱分配方法，由此，新的频谱使用方法——“认知无线电”应运而生，即允许用户设备检测其周围的频谱环境，并且动态接入所检测到空闲频谱，这种用户就被称为次用户或者认知用户（Secondary User,SU）。在认知无线电系统中，认知用户在不影响主用户/授权用户（Primary User，PU）性能的前提下，动态机会接入原本授权给主用户的频谱。其中，授权用户对于频谱使用具有较高的优先级，通常为已有网络中的一些经过授权的设备并且不会和认知用户之间有协同关系，因此，我们所考虑的认知无线电网络大多为授权网络和认知网络重叠的网络。 As a precious resource, radio spectrum is currently only available to users authorized by the government to use a specific frequency band. With the increasing demand for spectrum in radio communication, how to effectively use limited spectrum resources has become more and more important, thus becoming one of the research hotspots in recent years. However, in recent years, through the monitoring of the efficiency of authorized spectrum use, there are still many defects in the current spectrum management method. The Federal Communications Commission (FCC) of the United States proposes a more flexible spectrum allocation method in response to the current status of spectrum use. As a result, a new spectrum use method - "cognitive radio" came into being, which allows user equipment Detect the surrounding spectrum environment and dynamically access the detected idle spectrum. Such users are called secondary users or cognitive users (Secondary User, SU). In a cognitive radio system, cognitive users can dynamically access the spectrum originally authorized to the primary user without affecting the performance of the primary user/authorized user (PU). Among them, authorized users have a higher priority for spectrum use, usually some authorized devices in the existing network and will not have a cooperative relationship with cognitive users. Therefore, most of the cognitive radio networks we consider A network that overlaps the authorization network and the cognitive network. the

在目前的研究中，授权用户和认知用户既可以共存于集中式系统，也能够同时工作在分布式系统中。在集中式系统中，中央控制信道可以对认知用户所进行的频谱检测和数据包接入进行集中调度。但在分布式系统中，由于缺少中央控制信道和集中调度机制，快速可靠的频谱检测策略对于提高系统性能显得尤为重要。 In the current study, authorized users and cognitive users can both coexist in a centralized system and work simultaneously in a distributed system. In a centralized system, the central control channel can centrally schedule the spectrum detection and data packet access performed by cognitive users. However, in a distributed system, due to the lack of a central control channel and a centralized scheduling mechanism, a fast and reliable spectrum detection strategy is particularly important for improving system performance. the

分布式系统中，有两个参数能够直接影响系统吞吐量，分别为认知用户的信道检测数量和数据发送概率，但这两个参数需要由接入认知用户数量及系统空闲信道数量来计算获得，而接入认知用户数量及系统空闲信道数量的信息，在系统不存在中央控制信道的情况下，认知用户不能直接获取，导致系统吞吐量无法最大化。 In a distributed system, there are two parameters that can directly affect the system throughput, namely, the number of channel detections of cognitive users and the probability of data transmission, but these two parameters need to be calculated by the number of accessing cognitive users and the number of idle channels in the system However, when there is no central control channel in the system, cognitive users cannot directly obtain information about the number of access cognitive users and the number of idle channels in the system, resulting in failure to maximize system throughput. the

发明内容Contents of the invention

（一）要解决的技术问题 (1) Technical problems to be solved

本发明要解决的技术问题是：如何在不存在中央控制信道的情况下，保证系统吞吐量最大化。 The technical problem to be solved by the present invention is: how to ensure the maximum throughput of the system in the absence of a central control channel. the

（二）技术方案 (2) Technical plan

为解决上述技术问题，本发明提供了一种分布式认知无线电网络的参数调整方法，所述方法包括以下步骤： In order to solve the above technical problems, the present invention provides a parameter adjustment method of a distributed cognitive radio network, the method comprising the following steps:

S1：从授权信道中选择Ｎ_s个作为待检测信道，所述Ｎ_s为信道检测数量且为不小于1的整数； S1: Select N_s from the authorized channels as channels to be detected, where N_s is the number of channel detections and is an integer not less than 1;

S2：对所述待检测信道进行检测，并按照数据发送概率p_tra在检测出的空闲信道上进行数据包的发送，统计数据包成功发送的个数，所述p_tra的取值范围为0＜p_tra≤1； S2: Detect the channel to be detected, and transmit data packets on the detected idle channel according to the data transmission probability p_tra , and count the number of successfully transmitted data packets, and the value range of p_tra is 0 < p_tra ≤ 1;

S3：根据检测出的空闲信道数量及所述数据包成功发送的个数对实际的空闲信道数量和接入认知用户数量进行估算； S3: Estimate the actual number of idle channels and the number of access cognitive users according to the detected number of idle channels and the number of successfully sent data packets;

S4：根据估算出的所述实际的空闲信道数量和接入认知用户数量计算新的信道检测数量N_s′和新的数据发送概率p_tra′，用N_s′的值为N_s进行赋值，并用p_tra′的值为p_tra进行赋值，以实现参数调整，使得认知用户吞吐量最大化。 S4: Calculate the new channel detection number N_s ′ and the new data transmission probability p_tra ′ based on the estimated actual number of idle channels and the number of access cognitive users, and use the value of N_s ′ to assign N_s , and use the value of p_tra ′ to assign p_tra to achieve parameter adjustment and maximize the cognitive user throughput.

其中，步骤S1之前还包括： Wherein, before step S1 also includes:

S001：对所述信道检测数量Ｎ_s和数据发送概率p_tra进行初始化。 S001: Initialize the channel detection number N_s and data transmission probability p_tra .

其中，步骤S1之前还包括： Wherein, before step S1 also includes:

S002：将所述检测出的空闲信道数量M_idl置为0，并将所述数据 包成功发送的个数S_suc置为0； S002: the number of idle channels M_idl detected is set to 0, and the number S_suc of the successfully sent data packets is set to 0;

步骤S2进一步包括： Step S2 further comprises:

S201：对所述待检测信道进行逐个检测，每次检测到空闲信道时，将所述空闲信道数量M_idl加1，并将所述空闲信号对应的频段加入空闲信道子集； S201: Detect the channels to be detected one by one, and add 1 to the number of idle channels M_idl each time an idle channel is detected, and add the frequency band corresponding to the idle signal to the idle channel subset;

S202：按照数据发送概率p_tra在所述空闲信道子集中选取一个频段进行数据包发送； S202: Select a frequency band in the idle channel subset according to the data transmission probability p_tra to transmit data packets;

S203：每发送成功一个数据包，则将所述数据包成功发送的个数S_suc加1。 S203: Add 1 to the number S_suc of the successfully sent data packets each time a data packet is successfully sent.

其中，步骤203之后还包括： Wherein, after step 203 also includes:

S204：判断是否已经执行了预设次数，若没有，则返回步骤S201，否则执行步骤S3，所述预设次数为预设的接入帧的个数。 S204: Determine whether the preset number of times has been executed, and if not, return to step S201; otherwise, execute step S3, where the preset number of times is the number of preset access frames. the

其中，步骤S3中实际的空闲信道数量通过以下公式估算： Wherein, the actual number of idle channels in step S3 is estimated by the following formula:

$\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>=</span>=</span>{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>p</span>p</span>}}}_{\mathrm{<span><span>selidl</span>selidl</span>}}<span><span>*</span>*</span>\mathrm{<span><span>N</span>N</span>}$

其中，

为估算出的实际的空闲信道数量，

N_IAP为预设的接入帧的个数，M_idl为所述检测出的空闲信道数量，N为授权信道的总个数。 in,

is the estimated actual number of idle channels,

N_IAP is the number of preset access frames, M_idl is the number of detected idle channels, and N is the total number of authorized channels.

其中，步骤S3中接入认知用户数量通过以下公式估算： Among them, the number of access cognitive users in step S3 is estimated by the following formula:

$\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>=</span>=</span>\mathrm{<span><span>1</span>1</span>}<span><span>+</span>+</span>\frac{\mathrm{<span><span>ln</span>ln</span>}<span><span>(</span>(</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>I</span>I</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>=</span>=</span>\mathrm{<span><span>1</span>1</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}<span><span>)</span>)</span><span><span>\&CenterDot;</span>\&CenterDot;</span>\frac{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}<span><span>+</span>+</span>\mathrm{<span><span>1</span>1</span>}}{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}}<span><span>\&CenterDot;</span>\&CenterDot;</span>\frac{\mathrm{<span><span>N</span>N</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>)</span>)</span>}{\mathrm{<span><span>ln</span>ln</span>}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>\mathrm{<span><span>1</span>1</span>}<span><span>/</span>/</span>\mathrm{<span><span>N</span>N</span>}<span><span>)</span>)</span>}$

其中，

为估算出的接入认知用户数量，S_I(N_s＝1,p_tra)＝S_suc/N_IAP，Ｎ_p为一个接入帧中数据发送时隙的个数，η为常数。 in,

is the estimated number of access cognitive users, S_I (N_s =1,p_tra )=S_suc /N_IAP , N_p is the number of data transmission time slots in an access frame, and η is a constant.

其中，步骤S4中新的信道检测数量Ｎ_s’和新的数据发送概率p_tra’通过以下公式估算： Among them, the new channel detection number N_s ' and the new data transmission probability p_tra ' in step S4 are estimated by the following formula:

$<span><span>(</span>(</span>{{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}}^{<span><span>\&prime;</span>\&prime;</span>}<span><span>,</span>,</span>{{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}}^{<span><span>\&prime;</span>\&prime;</span>}<span><span>)</span>)</span><span><span>=</span>=</span>\left\{\begin{array}{cc}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>1</span>1</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>1</span>1</span>}}^{<span><span>*</span>*</span>}<span><span>)</span>)</span><span><span>,</span>,</span>& \stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>\&le;</span>\&le;</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>;</span>;</span>\\ <span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>2</span>2</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>2</span>2</span>}}^{<span><span>*</span>*</span>}<span><span>)</span>)</span><span><span>,</span>,</span>& \stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>\&GreaterEqual;</span>\&Greater\; Equal;</span>\mathrm{<span><span>N</span>N</span>}<span><span>;</span>;</span>\\ <span><span>(</span>(</span>{\mathrm{<span><span>X</span>x</span>}}_{\mathrm{<span><span>1</span>1</span>}}<span><span>,</span>,</span>{\mathrm{<span><span>X</span>x</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>others</span>others</span>}<span><span>;</span>;</span>\end{array}\right.$

其中，$(X_1,X_2)=\left\{\begin{array}{cc}{(N_{s1}^{*},p_{\mathrm{tra}}^{*}),}_1& S_{\mathrm{sys}}(N_s^{*},p_{\mathrm{tra}1}^{*})\&GreaterEqual;S_{\mathrm{sys}1}(N_s^{*},p_{\mathrm{tra}}^{*})\\ (N_{s3}^{*},p_{\mathrm{tra}3}^{*}),& S_{\mathrm{sys}}(N_{s1}^{*},p_{\mathrm{tra}1}^{*})<S_{\mathrm{sys}}(N_{s3}^{*},p_{\mathrm{tra}3}^{*})\end{array}\right.,$$(N_{s1}^{*},p_{\mathrm{tra}1}^{*})=\left(\underset{N_s}{\arg }\underset{p_{\mathrm{tra}}=1}{\min }\right\{S_{\mathrm{sys}}\left(N_s\right)-S_{\mathrm{sys}}\left({\hat{N}}_s\right)\},1),$$(N_{s2}^{*},p_{\mathrm{tra}2}^{*})=(1,N/\hat{K})$以及 

并且( in,$(x_1,x_2)=\left\{\begin{array}{cc}{(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},p_{\mathrm{tra}}^{*}),}_1& S_{\mathrm{sys}}(N_{\mathrm{the\; s}}^{*},p_{\mathrm{tra}1}^{*})\&Greater\; Equal;S_{\mathrm{sys}1}(N_{\mathrm{the\; s}}^{*},p_{\mathrm{tra}}^{*})\\ (N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}3}^{*},p_{\mathrm{tra}3}^{*}),& S_{\mathrm{sys}}(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},p_{\mathrm{tra}1}^{*})<S_{\mathrm{sys}}(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}3}^{*},p_{\mathrm{tra}3}^{*})\end{array}\right.,$$(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},p_{\mathrm{tra}1}^{*})=\left(\underset{N_{\mathrm{the\; s}}}{\arg }\underset{p_{\mathrm{tra}}=1}{\min }\right\{S_{\mathrm{sys}}\left(N_{\mathrm{the\; s}}\right)-S_{\mathrm{sys}}\left({\hat{N}}_{\mathrm{the\; s}}\right)\},1),$$(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}2}^{*},p_{\mathrm{tra}2}^{*})=(1,N/\hat{K})$ as well as

and(

${\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>sys</span>sys</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}<span><span>)</span>)</span><span><span>=</span>=</span>\frac{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}}{{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>+</span>+</span>\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}}\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>sac</span>sac</span>}}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}{<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>sac</span>sac</span>}}<span><span>)</span>)</span>}^{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>-</span>-</span>\mathrm{<span><span>1</span>1</span>}}<span><span>,</span>,</span>$

N为授权信道总数，N_p为发送数据包时隙数量，

为估算出的接入认知用户数量，

为估算出的实际的空闲信道数量，η为常数，其中， N is the total number of authorized channels, N_p is the number of time slots for sending data packets,

is the estimated number of access cognitive users,

For the estimated actual number of idle channels, η is a constant, where,

${\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>sac</span>sac</span>}}<span><span>=</span>=</span>\left\{\begin{array}{cc}\frac{\mathrm{<span><span>1</span>1</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>\frac{{<span><span>(</span>(</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>)</span>)</span>}^{<span><span>[</span>[</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>]</span>]</span>}}{{\mathrm{<span><span>N</span>N</span>}}^{<span><span>[</span>[</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>]</span>]</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>if</span>if</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>\&le;</span>\&le;</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>;</span>;</span>\\ \frac{\mathrm{<span><span>1</span>1</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>,</span>,</span>& \mathrm{<span><span>if</span>if</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>\&GreaterEqual;</span>\&Greater\; Equal;</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>+</span>+</span>\mathrm{<span><span>1</span>1</span>}<span><span>,</span>,</span>\end{array}\right.$

为使得${\&PartialD;S}_{\mathrm{sys}}/\&PartialD;{\stackrel{\&OverBar;}{N}}_s{|}_{{\hat{N}}_s}=0$成立，且${\stackrel{\&OverBar;}{N}}_s\&Element;[0,N-\hat{M}+1]$的值。 

to make${\&PartialD;S}_{\mathrm{sys}}/\&PartialD;{\stackrel{\&OverBar;}{N}}_{\mathrm{the\; s}}{|}_{{\hat{N}}_{\mathrm{the\; s}}}=0$ established, and${\stackrel{\&OverBar;}{N}}_{\mathrm{the\; s}}\&Element;[0,N-\hat{m}+1]$ value.

本发明还公开了一种分布式认知无线电网络的参数调整系统，所述系统包括： The present invention also discloses a parameter adjustment system of a distributed cognitive radio network, the system comprising:

选择模块，用于从授权信道中选择Ｎ_s个作为待检测信道，所述Ｎ_s为信道检测数量且为不小于1的整数； A selection module, configured to select N_s from authorized channels as channels to be detected, where N_s is the number of channel detections and is an integer not less than 1;

检测与接入系统，用于对所述待检测信道进行检测，并按照数据发送概率p_tra在检测出的空闲信道上进行数据包的发送，统计数据包成功发送的个数，所述p_tra的取值范围为0＜p_tra≤1； The detection and access system is used to detect the channel to be detected, and transmit data packets on the detected idle channel according to the data transmission probability p_tra , and count the number of successfully transmitted data packets, the p_tra The value range of is 0<p_tra ≤1;

估算模块，用于根据检测出的空闲信道数量及所述数据包成功发送的个数对实际的空闲信道数量和接入认知用户数量进行估算； An estimation module, used to estimate the actual number of idle channels and the number of access cognitive users according to the detected number of idle channels and the number of successfully sent data packets;

参数调整模块，用于根据估算出的所述实际的空闲信道数量和接入认知用户数量计算新的信道检测数量N_s′和新的数据发送概率p_tra′，用N_s′的值为N_s进行赋值，并用p_tra′的值为p_tra进行赋值，以实现参数调整，使得认知用户吞吐量最大化。 The parameter adjustment module is used to calculate the new channel detection number N_s ′ and the new data transmission probability p_tra ′ according to the estimated actual number of idle channels and the number of access cognitive users, and the value of N_s ′ is N_s is assigned a value, and the value of p_tra ′ is used to assign a value to p_tra to realize parameter adjustment and maximize cognitive user throughput.

（三）有益效果 (3) Beneficial effects

本发明通过估算实际的空闲信道数量和接入认知用户数量以计算能使系统吞吐量最大的信道检测数量和数据发送概率，使得在不存 在中央控制信道的情况下，保证了系统吞吐量最大化。 The present invention calculates the number of channel detections and data transmission probability that can maximize the system throughput by estimating the actual number of idle channels and the number of access cognitive users, so that the system throughput is guaranteed in the absence of a central control channel maximize. the

附图说明Description of drawings

图1是分布式认知无线电网络中一个接入帧内的授权用户的信道状态示意图。 Fig. 1 is a schematic diagram of channel states of authorized users in an access frame in a distributed cognitive radio network. the

图2是分布式认知无线电网络中认知用户对图1所示的信道进行接入的接入结果示意图。 FIG. 2 is a schematic diagram of an access result of a cognitive user accessing the channel shown in FIG. 1 in a distributed cognitive radio network. the

图3是本发明一种实施例的分布式认知无线电网络的参数调整方法的流程图。 Fig. 3 is a flowchart of a method for adjusting parameters of a distributed cognitive radio network according to an embodiment of the present invention. the

图4是图3所示的方法所计算的估计值

与实际值M的归一化MSE。 Figure 4 is the estimated value calculated by the method shown in Figure 3

Normalized MSE with actual value M.

图5是图3所示的方法所计算的估计值

与实际值K的归一化MSE Figure 5 is the estimated value calculated by the method shown in Figure 3

Normalized MSE with actual value K

图6是图3所示的方法所计算的估计值与实际值达到的最大平均系统吞吐量的归一化MSE。 FIG. 6 is the normalized MSE of the maximum average system throughput achieved by the estimated value and the actual value calculated by the method shown in FIG. 3 . the

图7是本发明一种实施例的分布式认知无线电网络的参数调整系统的结构框图。 Fig. 7 is a structural block diagram of a system for adjusting parameters of a distributed cognitive radio network according to an embodiment of the present invention. the

具体实施方式Detailed ways

以下将结合附图及实施例来详细说明本发明的实施方式，以下实施例用于说明本发明，但不用来限制本发明的范围，借此对本发明如何应用技术手段来解决技术问题，并达成技术效果的实现过程能充分理解并据以实施。需要说明的是，只要不构成冲突，本发明中的各个实施例以及各实施例中的各个特征可以相互结合，所形成的技术方案均在本发明的保护范围之内。 The following examples will be used to illustrate the present invention, but not to limit the scope of the present invention, so that how to apply technical means to the present invention to solve technical problems, and achieve The realization process of technical effects can be fully understood and implemented accordingly. It should be noted that, as long as there is no conflict, each embodiment and each feature in each embodiment of the present invention can be combined with each other, and the formed technical solutions are all within the protection scope of the present invention. the

另外，在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。 In addition, the steps shown in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and, although a logical order is shown in the flow diagrams, in some cases, the sequence may be different. The steps shown or described are performed in the order herein. the

图1是分布式认知无线电网络中一个接入帧内的授权用户的信道状态示意图；图1中包含N个授权信道的频谱，其中存在任意M个信 道为空闲信道，即有M个信道不被授权用户占用。假设系统中包含K个认知用户且每个认知用户均与授权用户同步，并通过检测估计授权信道的状态机会性地接入授权信道而不会对授权用户造成干扰。 Figure 1 is a schematic diagram of the channel status of authorized users in an access frame in a distributed cognitive radio network; Figure 1 contains the spectrum of N authorized channels, in which there are any M channels that are idle channels, that is, there are M channels Not occupied by authorized users. Assume that the system contains K cognitive users and each cognitive user is synchronized with the authorized user, and opportunistically accesses the authorized channel by detecting and estimating the state of the authorized channel without causing interference to the authorized user. the

假设认知用户的接入帧（SU Access Frame，SAF）具有固定的时间长度T_SAF。其中，一个T_SAF包括两个时隙，分别为频谱检测时隙T_S和数据传输时隙T_P。 It is assumed that an access frame (SU Access Frame, SAF) of a cognitive user has a fixed time length T_SAF . Wherein, a T_SAF includes two time slots, which are spectrum detection time slot T_S and data transmission time slot T_P .

在频谱检测时隙T_S内，每个认知用户对授权频谱进行检测，且在一个单位检测时间T_Sm内完成一个授权信道的检测。每个认知用户从N个授权信道中任意选择N_s个信道进行逐一检测，并可以从所检测结果中得到一个空闲信道的子集。 In the spectrum detection time slot T_S , each cognitive user detects the licensed spectrum, and completes the detection of a licensed channel within a unit detection time T_Sm . Each cognitive user arbitrarily selects N_s channels from N authorized channels to detect one by one, and can obtain a subset of idle channels from the detected results.

将这种频谱检测方法定义为“固定时长检测策略”（FTSS）。根据对FTSS的定义，可得到检测所有N_s个信道的时间为T_S＝T_Sm·N_s,其中定义第k个认知用户通过频谱扫描检测到的空闲信道的个数为

且

为随机变量。由于在分布式系统中缺少中央控制信道，因此任何一个认知用户都无法知道其他用户所检测到的空闲信道。 This spectrum detection method is defined as "fixed duration detection strategy" (FTSS). According to the definition of FTSS, the time to detect all N_s channels can be obtained as T_S =T_Sm N_s , where the number of idle channels detected by the k-th cognitive user through spectrum scanning is defined as

and

is a random variable. Due to the absence of a central control channel in a distributed system, any one cognitive user cannot know about the free channels detected by other users.

如图2所示，第k个认知用户从在检测时隙T_S内所检测到的

个空闲信道中任意选择一个空闲信道，并在此空闲信道上以发送概率 

在每个单位发送时隙T_Pm的开始时刻发送一个数据包进行数据接入。由于发送概率因此每个单位发送时隙内各认知用户所发送的数据包的总数U是一个随机变化的。考虑到各认知用户数据包接入的公平性，假设各认知用户具有相同的数据包发送概率p_tra且(p_tra∈(0,1])。由于每个认知用户发送数据包的概率服从伯努利分布，且各认知用户数据包发送概率分布为独立同分布，因此，我们可以得到每个发送时隙的平均数据包的发送个数为E[U]＝Kp_tra。 As shown in Figure 2, the kth cognitive user is detected from the detection time slot T_S

Randomly select an idle channel from the idle channels, and send on this idle channel with the probability

A data packet is sent at the beginning of each unit sending time slot T_Pm for data access. Due to the sending probability Therefore, the total number U of data packets sent by each cognitive user in each unit sending time slot changes randomly. Considering the fairness of each cognitive user's data packet access, it is assumed that each cognitive user has the same data packet transmission probability p_tra and (p_tra ∈ (0,1]). Since each cognitive user sends a data packet The probability obeys the Bernoulli distribution, and the sending probability distribution of each cognitive user's data packets is independent and identically distributed. Therefore, we can obtain the average number of data packets sent in each sending time slot as E[U]=Kp_tra .

由于认知用户之间不存在相互协作，因此每个认知用户随机从其所检测到的空闲信道的集合中选择一个空闲信道进行接入。在此，将 一个（第k个）认知用户在一个发送时隙内成功发送数据定义为必须同时满足以下两个条件： Since there is no mutual cooperation among cognitive users, each cognitive user randomly selects an idle channel from the set of detected idle channels to access. Here, a (kth) cognitive user is defined to successfully send data within a sending slot as the following two conditions must be met at the same time:

一、在其所检测的N_s个信道中至少存在一个空闲信道，即

1. There is at least one idle channel among the N_s channels detected by it, that is,

二、第k个认知用户从所检测到的

个空闲信道中任选一个空闲信道并在此信道上发送数据包进行接入，同时在此时隙内没有其他认知用户在此空闲信道上进行数据包发送。 Second, the kth cognitive user is detected from the

Select an idle channel among the idle channels and send data packets on this channel for access, and at the same time, no other cognitive users send data packets on this idle channel during this time slot.

相对应的，将以下两种情况定义为失败发送数据： Correspondingly, the following two situations are defined as failure to send data:

一、在一个时隙内，同一个空闲信道上有多个（大于1个）数据包接入（例如图2中的第一个时隙内SU₁和SU₃同时在ch1上进行数据包接入）。 1. In a time slot, there are multiple (more than 1) data packet accesses on the same idle channel (for example, in the first time slot in Figure 2, SU₁ and SU₃ perform data packet access on ch1 at the same time. enter).

二、当用户在其所检测的N_s个信道中不存在空闲信道时，所有的数据包发送将被延迟发送（例如：图2中第K个认知用户）。 2. When the user does not have an idle channel among the N_s channels detected by the user, all data packet transmission will be delayed (for example: the Kth cognitive user in FIG. 2 ).

综上所述，可定义上述MAC机制为时隙化机会接入Aloha（SOA-Aloha）。 To sum up, the above MAC mechanism can be defined as slotted opportunistic access Aloha (SOA-Aloha). the

假设每一个认知用户都配有一个计时器，如果计时器超时则认为所发送的数据包发送碰撞。基于此，每个认知用户都会获知其发送的数据包是否成功/失败接入。发生碰撞的数据包将在后续的发送时隙中被重新发送，被延迟发送的数据包将在用户检测到空闲信道的时候进行发送。而新的数据包只有在之前的数据包进行成功发送之后才能在认知用户生成，这意味着每个认知用户的缓存里面一直会至少存在一个待发送的数据包。进一步，我们假设授权用户的信道状态变化缓慢，即在一个接入帧内不发生变化，因此授权用户的信道状态可被视为在一个T_SAF内保持恒定。 It is assumed that each cognitive user is equipped with a timer, and if the timer expires, it is considered that the transmitted data packets collide. Based on this, each cognitive user will know whether the data packets sent by it are successful/failed to access. The collided data packets will be resent in subsequent sending time slots, and the delayed data packets will be sent when the user detects an idle channel. A new data packet can only be generated in a cognitive user after the previous data packet is successfully sent, which means that there will always be at least one data packet to be sent in the cache of each cognitive user. Further, we assume that the channel state of the licensed user changes slowly, that is, does not change within one access frame, so the channel state of the licensed user can be considered to be constant within a T_SAF .

基于以上系统模型介绍，可以得到： Based on the introduction of the above system model, it can be obtained:

T_SAF＝T_S+T_P＝N_sT_Sm+N_pT_PmT_SAF ＝T_S +T_P ＝N_s T_Sm +N_p T_Pm

其中假设T_Pm＝ηT_Sm，同时为了达到较高的系统吞吐量，通常有N_p＞＞N_s。 It is assumed that T_Pm =ηT_Sm , and in order to achieve a higher system throughput, N_p >>N_s is usually provided.

图3是本发明一种实施例的分布式认知无线电网络的参数调整方法的流程图；参照图3，所述方法包括以下步骤： Fig. 3 is a flowchart of a parameter adjustment method of a distributed cognitive radio network according to an embodiment of the present invention; referring to Fig. 3, the method includes the following steps:

S1：从授权信道中选择Ｎ_s个作为待检测信道，所述Ｎ_s为信道检测数量且为不小于1的整数； S1: Select N_s from the authorized channels as channels to be detected, where N_s is the number of channel detections and is an integer not less than 1;

S2：对所述待检测信道进行检测，并按照数据发送概率p_tra在检测出的空闲信道上进行数据包的发送，统计数据包成功发送的个数，所述p_tra的取值范围为0＜p_tra≤1； S2: Detect the channel to be detected, and transmit data packets on the detected idle channel according to the data transmission probability p_tra , and count the number of successfully transmitted data packets, and the value range of p_tra is 0 < p_tra ≤ 1;

S3：根据检测出的空闲信道数量及所述数据包成功发送的个数对实际的空闲信道数量和接入认知用户数量进行估算； S3: Estimate the actual number of idle channels and the number of access cognitive users according to the detected number of idle channels and the number of successfully sent data packets;

S4：根据估算出的所述实际的空闲信道数量和接入认知用户数量计算新的信道检测数量N_s′和新的数据发送概率p_tra′，用N_s′的值为N_s进行赋值，并用p_tra′的值为p_tra进行赋值，以实现参数调整，使得认知用户吞吐量最大化。 S4: Calculate the new channel detection number N_s ′ and the new data transmission probability p_tra ′ based on the estimated actual number of idle channels and the number of access cognitive users, and use the value of N_s ′ to assign N_s , and use the value of p_tra ′ to assign p_tra to achieve parameter adjustment and maximize the cognitive user throughput.

本实施例通过估算实际的空闲信道数量和接入认知用户数量以计算能使系统吞吐量最大的信道检测数量和数据发送概率，使得在不存在中央控制信道的情况下，保证了系统吞吐量最大化。 In this embodiment, by estimating the actual number of idle channels and the number of access cognitive users, the number of channel detections and data transmission probability that can maximize the system throughput are calculated, so that the system throughput is guaranteed in the absence of a central control channel maximize. the

由于在方法运行之前需要有一个初值，优选地，步骤S1之前还包括： Since an initial value is required before the method runs, preferably, before step S1, it also includes:

S001：对所述信道检测数量Ｎ_s和数据发送概率p_tra进行初始化。 S001: Initialize the channel detection number N_s and data transmission probability p_tra .

为便于统计所述空闲信道数量和数据包成功发送的个数，优选地，步骤S1之前还包括： For the convenience of counting the number of the number of idle channels and data packets successfully sent, preferably, before step S1 also includes:

S002：将所述检测出的空闲信道数量M_idl置为0，并将所述数据包成功发送的个数S_suc置为0； S002: Set the detected number of idle channels M_idl to 0, and set the number S_suc of the successfully sent data packets to 0;

步骤S2进一步包括： Step S2 further comprises:

S201：对所述待检测信道进行逐个检测，每次检测到空闲信道时，将所述空闲信道数量M_idl加1，并将所述空闲信号对应的频段加入空闲信道子集，直至所有待检测信道均进行了检测； S201: Detect the channels to be detected one by one, and add 1 to the number of idle channels M_idl each time an idle channel is detected, and add the frequency band corresponding to the idle signal to the idle channel subset until all the idle channels are detected Channels are checked;

S202：按照数据发送概率p_tra在所述空闲信道子集中选取一个频段进行数据包发送； S202: Select a frequency band in the idle channel subset according to the data transmission probability p_tra to transmit data packets;

S203：每发送成功一个数据包，则将所述数据包成功发送的个数S_suc加1。 S203:Add 1 to the number S_suc of the successfully sent data packets each time a data packet is successfully sent.

优选地，步骤203之后还包括： Preferably, after step 203, it also includes:

S204：判断是否已经执行了预设次数，若没有，则返回步骤S201，否则执行步骤S3，所述预设次数为预设的接入帧的个数。 S204: Determine whether the preset number of times has been executed, and if not, return to step S201; otherwise, execute step S3, where the preset number of times is the number of preset access frames. the

优选地，步骤S3中实际的空闲信道数量通过以下公式估算： Preferably, the actual number of idle channels in step S3 is estimated by the following formula:

$\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>=</span>=</span>{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>p</span>p</span>}}}_{\mathrm{<span><span>selidl</span>selidl</span>}}<span><span>*</span>*</span>\mathrm{<span><span>N</span>N</span>}$

其中，为估算出的实际的空闲信道数量，

N_AP为预设的接入帧的个数，M_idl为所述检测出的空闲信道数量，N为授权信道的总个数。 in, is the estimated actual number of idle channels,

N_AP is the number of preset access frames, M_idl is the number of detected idle channels, and N is the total number of authorized channels.

优选地，步骤S3中接入认知用户数量通过以下公式估算： Preferably, the number of access cognitive users in step S3 is estimated by the following formula:

$\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>=</span>=</span>\mathrm{<span><span>1</span>1</span>}<span><span>+</span>+</span>\frac{\mathrm{<span><span>ln</span>ln</span>}<span><span>(</span>(</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>I</span>I</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>=</span>=</span>\mathrm{<span><span>1</span>1</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}<span><span>)</span>)</span><span><span>\&CenterDot;</span>\&Center\; Dot;</span>\frac{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}<span><span>+</span>+</span>\mathrm{<span><span>1</span>1</span>}}{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}}<span><span>\&CenterDot;</span>\&Center\; Dot;</span>\frac{\mathrm{<span><span>N</span>N</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>)</span>)</span>}{\mathrm{<span><span>ln</span>ln</span>}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>\mathrm{<span><span>1</span>1</span>}<span><span>/</span>/</span>\mathrm{<span><span>N</span>N</span>}<span><span>)</span>)</span>}$

其中，

为估算出的接入认知用户数量，S_I(N_s＝1,p_tra)＝S_suc/N_IAP，Ｎ_p为一个接入帧中数据发送时隙的个数，η为常数。 in,

is the estimated number of access cognitive users, S_I (N_s =1,p_tra )=S_suc /N_IAP , N_p is the number of data transmission time slots in an access frame, and η is a constant.

步骤S4中新的信道检测数量Ｎ_s’和新的数据发送概率p_tra’通过以下公式估算： In step S4, the new channel detection number N_s ' and the new data transmission probability p_tra ' are estimated by the following formula:

$<span><span>(</span>(</span>{{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}}^{<span><span>\&prime;</span>\&prime;</span>}<span><span>,</span>,</span>{{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}}^{<span><span>\&prime;</span>\&prime;</span>}<span><span>)</span>)</span><span><span>=</span>=</span>\left\{\begin{array}{cc}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>1</span>1</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout></span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}"></figure-callout>}}_{\mathrm{<span><span></span></span>}}^{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>\&le;</span>\&le;</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>;</span>;</span>\\ <span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>2</span>2</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout></span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}"></figure-callout>}}_{\mathrm{<span><span></span></span>}}^{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>\&GreaterEqual;</span>\&Greater\; Equal;</span>\mathrm{<span><span>N</span>N</span>}<span><span>;</span>;</span>\\ <span><span>(</span>(</span>{\mathrm{<span><span>X</span><figure-callout\; label="x"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">x</figure-callout></span>}}_{\mathrm{<span><span>1</span>1</span>}}<span><span>,</span>,</span>{\mathrm{<span><span>X</span>x</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>others</span>others</span>}<span><span>;</span>;</span>\end{array}\right.$

其中，$(X_1,X_2)=\left\{\begin{array}{cc}{(N_{s1}^{*},p_{\mathrm{tra}}^{*}),}_1& S_{\mathrm{sys}}(N_s^{*},p_{\mathrm{tra}1}^{*})\&GreaterEqual;S_{\mathrm{sys}1}(N_s^{*},p_{\mathrm{tra}}^{*})\\ (N_{s3}^{*},p_{\mathrm{tra}3}^{*}),& S_{\mathrm{sys}}(N_{s1}^{*},p_{\mathrm{tra}1}^{*})<S_{\mathrm{sys}}(N_{s3}^{*},p_{\mathrm{tra}3}^{*})\end{array}\right.,$$(N_{s1}^{*},p_{\mathrm{tra}1}^{*})=\left(\underset{N_s}{\arg }\underset{p_{\mathrm{tra}}=1}{\min }\right\{S_{\mathrm{sys}}\left(N_s\right)-S_{\mathrm{sys}}\left({\hat{N}}_s\right)\},1),$$(N_{s2}^{*},p_{\mathrm{tra}2}^{*})=(1,N/\hat{K})$以及 

s并且( in,$(x_1,x_2)=\left\{\begin{array}{cc}{(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},p_{\mathrm{tra}}^{*}),}_1& S_{\mathrm{sys}}(N_{\mathrm{the\; s}}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^1)\&Greater\; Equal;S_{\mathrm{sys}1}(N_{\mathrm{the\; s}}^{*},p_{\mathrm{tra}}^{*})\\ (N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}3}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^3),& S_{\mathrm{sys}}(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^1)<S_{\mathrm{sys}}(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}3}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^3)\end{array}\right.,$$(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^1)=\left(\underset{N_{\mathrm{the\; s}}}{\arg }\underset{p_{\mathrm{tra}}=1}{\min }\right\{S_{\mathrm{sys}}\left(N_{\mathrm{the\; s}}\right)-S_{\mathrm{sys}}\left({\hat{N}}_{\mathrm{the\; s}}\right)\},1),$$(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}2}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^2)=(1,N/\hat{K})$ as well as

s and (

$S_{\mathrm{sys}}(N_s,p_{\mathrm{tra}})=\frac{\mathrm{\&eta;}{N}_p}{N_s+\mathrm{\&eta;}{N}_p}\hat{M}\hat{K}{p}_{\mathrm{sac}}{p}_{\mathrm{tra}}{(1-p_{\mathrm{tra}}{p}_{\mathrm{sac}})}^{\hat{K}-1},$（本公式中，S_sys(N_s,p_tra)中 的(N_s,p_tra)为变量，其可以取值为(N_s′,p_tra′)等参数，在代入其他参数后，只需将公式中的N_s,p_tra进行相应替换即可），N为授权信道总数，N_p为发送数据包时隙数量，

为估算出的接入认知用户数量，

为估算出的实际的空闲信道数量，η为常数，其中， $S_{\mathrm{sys}}(N_{\mathrm{the\; s}},p_{\mathrm{tra}})=\frac{\mathrm{\&eta;}{N}_p}{N_{\mathrm{the\; s}}+\mathrm{\&eta;}{N}_p}\hat{m}\hat{K}{p}_{\mathrm{sac}}{p}_{\mathrm{tra}}{(1-p_{\mathrm{tra}}{p}_{\mathrm{sac}})}^{\hat{K}-1},$ (In this formula, (N_s , p_tra ) in S_sys (N_s , p_tra ) is a variable, which can take the value of (N_s ′, p_tra ′) and other parameters. After substituting other parameters, only N_s and p_tra in the formula need to be replaced accordingly), N is the total number of authorized channels, N_p is the number of time slots for sending data packets,

is the estimated number of access cognitive users,

For the estimated actual number of idle channels, η is a constant, where,

${\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>sac</span>sac</span>}}<span><span>=</span>=</span>\left\{\begin{array}{cc}\frac{\mathrm{<span><span>1</span>1</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>\frac{{<span><span>(</span>(</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>)</span>)</span>}^{<span><span>[</span>[</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>]</span>]</span>}}{{\mathrm{<span><span>N</span>N</span>}}^{<span><span>[</span>[</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>]</span>]</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>if</span>if</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>\&le;</span>\&le;</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>;</span>;</span>\\ \frac{\mathrm{<span><span>1</span>1</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>,</span>,</span>& \mathrm{<span><span>if</span>if</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>\&GreaterEqual;</span>\&Greater\; Equal;</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>+</span>+</span>\mathrm{<span><span>1</span>1</span>}<span><span>,</span>,</span>\end{array}\right.$

为使得${\&PartialD;S}_{\mathrm{sys}}/\&PartialD;{\stackrel{\&OverBar;}{N}}_s{|}_{{\hat{N}}_s}=0$成立，且${\stackrel{\&OverBar;}{N}}_s\&Element;[0,N-\hat{M}+1]$的值。 

to make${\&PartialD;S}_{\mathrm{sys}}/\&PartialD;{\stackrel{\&OverBar;}{N}}_{\mathrm{the\; s}}{|}_{{\hat{N}}_{\mathrm{the\; s}}}=0$ established, and${\stackrel{\&OverBar;}{N}}_{\mathrm{the\; s}}\&Element;[0,N-\hat{m}+1]$ value.

实施例1 Example 1

下面以一个初始接入阶段的调整方法的实施例来说明本发明，但不限定本发明的保护范围。 The following describes the present invention with an embodiment of an adjustment method in the initial access phase, but does not limit the protection scope of the present invention. the

步骤101：对所述信道检测数量Ｎ_s和数据发送概率p_tra进行初始化，本实施例中，设置Ｎ_s=1，p_tra为预设值

例如可将其设置为0.5；并将设置N_f=0，N_f为初始接入阶段（Initial Access Period，IAP）内所进行到的帧数，N_IAP为初始接入阶段内的接入帧的个数。 Step 101: Initialize the channel detection number N_s and the data transmission probability p_tra , in this embodiment, set N_s =1, and p_tra is a preset value

For example, it can be set to 0.5; and N_f =0, N_f is the number of frames in the initial access period (Initial Access Period, IAP), and N_IAP is the access frame in the initial access period the number of .

步骤102：将所述空闲信道数量M_idl置为0，并将所述数据包成功发送的个数S_suc置为0；（步骤101和步骤102之间并没有先有顺序） Step 102: Set the number of idle channels M_idl to 0, and set the number S_suc of the successfully sent data packets to 0; (there is no prior sequence between steps 101 and 102)

步骤103：从授权信道中选择Ｎ_s个作为待检测信道； Step 103: selecting N_s channels from authorized channels as channels to be detected;

步骤104：对所述待检测信道进行逐个检测（本实施例中，认知用户检测信道通过射频天线对所述待检测信道所对应的频段发送检测信号，以判断所述待检测信道的状态是否为空闲信道），每次检测到空闲信道时，将所述空闲信道数量M_idl加1，并将所述空闲信号对应的频段加入空闲信道子集，直至所有待检测信道均进行了检测； Step 104: Detect the channels to be detected one by one (in this embodiment, the cognitive user detection channel sends a detection signal to the frequency band corresponding to the channel to be detected through a radio frequency antenna to determine whether the state of the channel to be detected is is an idle channel), when an idle channel is detected each time, the number of idle channels M_idl is added by 1, and the frequency band corresponding to the idle signal is added to the subset of idle channels until all channels to be detected are detected;

步骤105：按照数据发送概率

在所述空闲信道子集中选取一个频段进行数据包发送； Step 105: Send probability according to data

Selecting a frequency band in the idle channel subset to send data packets;

步骤106：每发送成功一个数据包，则将所述数据包成功发送的个数S_suc加1； Step 106:Add 1 to the number S_suc of the successfully sent data packets for each successfully sent data packet;

步骤107：判断是否已经执行了预设次数，若没有，则返回步骤 104，否则执行步骤108，所述预设次数为初始接入阶段内的接入帧的个数N_IAP； Step 107: Determine whether the preset number of times has been executed, if not, return to step 104, otherwise execute step 108, the preset number of times is the number N_IAP of access frames in the initial access phase;

步骤108：此时N_f＝N_IAP，根据检测出的空闲信道数量及所述数据包成功发送的个数对实际的空闲信道数量和接入认知用户数量进行估算，实际的空闲信道数量通过以下公式估算： Step 108: N_f =N_IAP at this time, estimate the actual number of idle channels and the number of access cognitive users according to the number of detected idle channels and the number of successfully sent data packets, and the actual number of idle channels is passed through Estimated by the following formula:

$\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>=</span>=</span>{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>p</span>p</span>}}}_{\mathrm{<span><span>selidl</span>selidl</span>}}<span><span>*</span>*</span>\mathrm{<span><span>N</span>N</span>}$

其中，

为实际的空闲信道数量，

N_IAP为初始接入阶段内的接入帧的个数，M_idl为所述检测出的空闲信道数量，N为授权信道的总个数； in,

is the actual number of idle channels,

N_IAP is the number of access frames in the initial access phase, M_idl is the number of idle channels detected, and N is the total number of authorized channels;

接入认知用户数量通过以下公式估算： The number of access cognitive users is estimated by the following formula:

$\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>=</span>=</span>\mathrm{<span><span>1</span>1</span>}<span><span>+</span>+</span>\frac{\mathrm{<span><span>ln</span>ln</span>}<span><span>(</span>(</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>I</span>I</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>=</span>=</span>\mathrm{<span><span>1</span>1</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}<span><span>)</span>)</span><span><span>\&CenterDot;</span>\&CenterDot;</span>\frac{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}<span><span>+</span>+</span>\mathrm{<span><span>1</span>1</span>}}{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}}<span><span>\&CenterDot;</span>\&Center\; Dot;</span>\frac{\mathrm{<span><span>N</span>N</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>)</span>)</span>}{\mathrm{<span><span>ln</span>ln</span>}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>\mathrm{<span><span>1</span>1</span>}<span><span>/</span>/</span>\mathrm{<span><span>N</span>N</span>}<span><span>)</span>)</span>}$

其中，

为接入认知用户数量，S_I(N_s＝1,p_tra)，Ｎp为一个接入帧中数据发送时隙的个数，η为常数； in,

is the number of access cognitive users, S_I (N_s =1,p_tra ), Np is the number of data transmission time slots in an access frame, and η is a constant;

步骤109：根据估算出的所述实际的空闲信道数量和接入认知用户数量计算新的信道检测数量N_s′和新的数据发送概率p_tra′，用N_s′的值为N_s进行赋值，并用p_tra′的值为p_tra进行赋值，以实现参数调整； Step 109: Calculate the new channel detection number N_s ′ and the new data transmission probability p_tra ′ based on the estimated actual number of idle channels and the number of access cognitive users, and use the value of N_s ′ to be N_s assignment, and use the value of p_tra ′ to assign value to p_tra to realize parameter adjustment;

新的信道检测数量Ｎ_s’和新的数据发送概率p_tra’通过以下公式估算： The new channel detection number N_s ' and the new data transmission probability p_tra ' are estimated by the following formula:

$<span><span>(</span>(</span>{{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}}^{<span><span>\&prime;</span>\&prime;</span>}<span><span>,</span>,</span>{{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}}^{<span><span>\&prime;</span>\&prime;</span>}<span><span>)</span>)</span><span><span>=</span>=</span>\left\{\begin{array}{cc}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>1</span>1</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout></span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}"></figure-callout>}}_{\mathrm{<span><span></span></span>}}^{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>\&le;</span>\&le;</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>;</span>;</span>\\ <span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>2</span>2</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout></span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}"></figure-callout>}}_{\mathrm{<span><span></span></span>}}^{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>\&GreaterEqual;</span>\&Greater\; Equal;</span>\mathrm{<span><span>N</span>N</span>}<span><span>;</span>;</span>\\ <span><span>(</span>(</span>{\mathrm{<span><span>X</span><figure-callout\; label="x"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">x</figure-callout></span>}}_{\mathrm{<span><span>1</span>1</span>}}<span><span>,</span>,</span>{\mathrm{<span><span>X</span>x</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>others</span>others</span>}<span><span>;</span>;</span>\end{array}\right.$

其中，$(X_1,X_2)=\left\{\begin{array}{cc}{(N_{s1}^{*},p_{\mathrm{tra}}^{*}),}_1& S_{\mathrm{sys}}(N_s^{*},p_{\mathrm{tra}1}^{*})\&GreaterEqual;S_{\mathrm{sys}1}(N_s^{*},p_{\mathrm{tra}}^{*})\\ (N_{s3}^{*},p_{\mathrm{tra}3}^{*}),& S_{\mathrm{sys}}(N_{s1}^{*},p_{\mathrm{tra}1}^{*})<S_{\mathrm{sys}}(N_{s3}^{*},p_{\mathrm{tra}3}^{*})\end{array}\right.,$$(N_{s1}^{*},p_{\mathrm{tra}1}^{*})=\left(\underset{N_s}{\arg }\underset{p_{\mathrm{tra}}=1}{\min }\right\{S_{\mathrm{sys}}\left(N_s\right)-S_{\mathrm{sys}}\left({\hat{N}}_s\right)\},1),$$(N_{s2}^{*},p_{\mathrm{tra}2}^{*})=(1,N/\hat{K})$以及 

s并且( in,$(x_1,x_2)=\left\{\begin{array}{cc}{(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},p_{\mathrm{tra}}^{*}),}_1& S_{\mathrm{sys}}(N_{\mathrm{the\; s}}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^1)\&Greater\; Equal;S_{\mathrm{sys}1}(N_{\mathrm{the\; s}}^{*},p_{\mathrm{tra}}^{*})\\ (N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}3}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^3),& S_{\mathrm{sys}}(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^1)<S_{\mathrm{sys}}(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}3}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^3)\end{array}\right.,$$(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}1}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^1)=\left(\underset{N_{\mathrm{the\; s}}}{\arg }\underset{p_{\mathrm{tra}}=1}{\min }\right\{S_{\mathrm{sys}}\left(N_{\mathrm{the\; s}}\right)-S_{\mathrm{sys}}\left({\hat{N}}_{\mathrm{the\; s}}\right)\},1),$$(N_{\mathrm{the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout>}2}^{*},{\mathrm{<figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout>}}_{\mathrm{tra}}^2)=(1,N/\hat{K})$ as well as

s and (

${\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>sys</span>sys</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}<span><span>)</span>)</span><span><span>=</span>=</span>\frac{\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}}{{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>+</span>+</span>\mathrm{<span><span>\&eta;</span>\&eta;</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>p</span>p</span>}}}\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>sac</span>sac</span>}}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}{<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tra</span>tra</span>}}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>sac</span>sac</span>}}<span><span>)</span>)</span>}^{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>K</span>K</span>}}<span><span>-</span>-</span>\mathrm{<span><span>1</span>1</span>}}<span><span>,</span>,</span>$

N为授权信道总数，N_p为发送数据包时隙数量，

为估算出的接入认知用户数量，

为估算出的实际的空闲信道数量，η为常数，其中， N is the total number of authorized channels, N_p is the number of time slots for sending data packets,

is the estimated number of access cognitive users,

For the estimated actual number of idle channels, η is a constant, where,

${\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>sac</span>sac</span>}}<span><span>=</span>=</span>\left\{\begin{array}{cc}\frac{\mathrm{<span><span>1</span>1</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>-</span>-</span>\frac{{<span><span>(</span>(</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>)</span>)</span>}^{<span><span>[</span>[</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>]</span>]</span>}}{{\mathrm{<span><span>N</span>N</span>}}^{<span><span>[</span>[</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>]</span>]</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>if</span>if</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>\&le;</span>\&le;</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>;</span>;</span>\\ \frac{\mathrm{<span><span>1</span>1</span>}}{\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}}<span><span>,</span>,</span>& \mathrm{<span><span>if</span>if</span>}{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the\; s</span>}}<span><span>\&GreaterEqual;</span>\&Greater\; Equal;</span>\mathrm{<span><span>N</span>N</span>}<span><span>-</span>-</span>\stackrel{<span><span>^</span>^</span>}{\mathrm{<span><span>M</span>m</span>}}<span><span>+</span>+</span>\mathrm{<span><span>1</span>1</span>}<span><span>,</span>,</span>\end{array}\right.$

为使得${\&PartialD;S}_{\mathrm{sys}}/\&PartialD;{\stackrel{\&OverBar;}{N}}_s{|}_{{\hat{N}}_s}=0$成立，且${\stackrel{\&OverBar;}{N}}_s\&Element;[0,N-\hat{M}+1]$的值。 

to make${\&PartialD;S}_{\mathrm{sys}}/\&PartialD;{\stackrel{\&OverBar;}{N}}_{\mathrm{the\; s}}{|}_{{\hat{N}}_{\mathrm{the\; s}}}=0$ established, and${\stackrel{\&OverBar;}{N}}_{\mathrm{the\; s}}\&Element;[0,N-\hat{m}+1]$ value.

当系统中授权信道个数为N＝10时，并设置在认知用户初始接入阶段中，在一个接入帧内的数据发送时隙数量N_p为1，且设置η＝2和 

在仿真中，我们对授权信道静止和动态变化两种情况分别进行了仿真。 When the number of authorized channels in the system is N=10, and it is set in the cognitive user initial access stage, the number of data transmission time slots N_p in one access frame is 1, and η=2 and

In the simulation, we simulated the two situations of the authorized channel being static and dynamically changing respectively.

图4给出了估计结果和实际参数M之间的归一化的均方误差（Mean Squared Error，MSE）。从图中可以看出，关于

的归一化MSE随着N_IAP的增加而减少，并趋近于0。另外，随着M的增加，估计结果

更加准确。这是因为当M比较大的时候，认知用户检测到空闲信道的次数增多。 Figure 4 gives the estimated results and the normalized mean squared error (Mean Squared Error, MSE) between the actual parameter M. It can be seen from the figure that the

The normalized MSE of decreases with the increase of N_IAP and tends to 0. Also, as M increases, the estimated results

more precise. This is because when M is relatively large, the number of times cognitive users detect idle channels increases.

在图5中，分别给出了估计值

与实际值K的归一化MSE。从图中可以很直观的看出其MSE的值随着N_IAP的增加而降低。在实际值K相同的条件下，随着M的增长，估计值

更接近于实际参数，即估计值

变得更精确。这是由于在较大的M下检测到空闲信道的次数的增多会给认知用户带来更多的机会发送数据包，从而获得更加精确的S_suc。进一步地，通过观察可以发现，在相同的M下，当K增大时，其估计值会更加准确，其原因在于K增大的情况下，数据包成功发送的概率增加，对S_suc的统计更加准确。 In Fig. 5, estimated values are given respectively

Normalized MSE with actual value K. It can be seen intuitively from the figure that the value of its MSE decreases with the increase of N_IAP . Under the condition that the actual value K is the same, with the increase of M, the estimated value

is closer to the actual parameter, the estimated value

become more precise. This is because the cognitive user will have more chances to send data packets when the number of detected idle channels increases with a larger M, thereby obtaining a more accurate S_suc . Further, through observation, it can_be found that under the same M, when K increases, the estimated value will be more accurate. The reason is that when K increases, the probability of successful data packet transmission increases. more precise.

图6给出了基于参数估计结果的最大平均系统吞吐量

与基于实际参数下达到的最大平均系统吞吐量S_opt的归一化MSE。基于参数估计结果和实际参数的最优的

可以根据下式可得， Figure 6 shows the maximum average system throughput based on the parameter estimation results

The normalized MSE with the maximum average system throughput S_opt achieved under the actual parameters. Optimal based on parameter estimation results and actual parameters

It can be obtained according to the following formula,

${\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>max</span>max</span>}}<span><span>=</span>=</span>\left\{\begin{array}{cc}{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>sys</span>sys</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>1</span>1</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>tral</span>tral</span>}}^{<span><span>*</span>*</span>}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>K</span>K</span>}<span><span>\&le;</span>\&le;</span>\mathrm{<span><span>M</span>m</span>}<span><span>;</span>;</span>\\ {\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>sys</span>sys</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>2</span>2</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout></span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}"></figure-callout>}}_{\mathrm{<span><span></span></span>}}^{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>K</span>K</span>}<span><span>\&GreaterEqual;</span>\&Greater\; Equal;</span>\mathrm{<span><span>N</span>N</span>}\\ \mathrm{<span><span>ax</span>ax</span>}<span><span>(</span>(</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>sys</span>sys</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>1</span>1</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout></span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}"></figure-callout>}}_{\mathrm{<span><span></span></span>}}^{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>)</span>)</span><span><span>,</span>,</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>sys</span>sys</span>}}<span><span>(</span>(</span>{\mathrm{<span><span>N</span>N</span>}}_{\mathrm{<span><span>s</span>the<figure-callout\; label="s"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">s</figure-callout></span>}\mathrm{<span><span>2</span>2</span>}}^{<span><span>*</span>*</span>}<span><span>,</span>,</span>{\mathrm{<span><span>p</span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}">p</figure-callout></span><figure-callout\; label="p\; tra"\; filenames="HDA0000431996560000011.png,HDA0000431996560000012.png"\; state="\{\{state\}\}"></figure-callout>}}_{\mathrm{<span><span></span></span>}}^{\mathrm{<span><span>tra</span>tra</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>)</span>)</span><span><span>)</span>)</span><span><span>,</span>,</span>& \mathrm{<span><span>others</span>others</span>}<span><span>,</span>,</span>\end{array}<span><span>;</span>;</span>\right.$

其中

并且 

值得注意的是，尽管在图5中，当K＝16时，

并没有完全与实际参数K重合，但是根据参数估计结果通过调整(N_s,p_tra)而达到的最大系统平均吞吐量如图6所示，与基于实际参数所达到的最大系统平均吞吐量非常接近。 in

and

It is worth noting that although in Figure 5, when K=16,

It does not completely coincide with the actual parameter K, but the maximum average system throughput achieved by adjusting (N_s ,p_tra ) according to the parameter estimation results is shown in Figure 6, which is very close to the maximum system average throughput achieved based on the actual parameters near.

本发明还公开了一种分布式认知无线电网络的参数调整系统，参照图7，所述系统包括： The present invention also discloses a parameter adjustment system of a distributed cognitive radio network. Referring to FIG. 7, the system includes:

选择模块，用于从授权信道中选择Ｎ_s个作为待检测信道，所述Ｎ_s为信道检测数量且为不小于1的整数； A selection module, configured to select N_s from authorized channels as channels to be detected, where N_s is the number of channel detections and is an integer not less than 1;

检测系统，用于对所述待检测信道进行检测，并按照数据发送概率ptra在检测出的空闲信道上进行数据包的发送，统计数据包成功发送的个数，所述p_tra的取值范围为0＜p_tra≤1； The detection system is used to detect the channel to be detected, and transmit data packets on the detected idle channel according to the data transmission probability ptra, and count the number of successfully transmitted data packets, and the value range of_ptra is 0<p_tra ≤1;

估算模块，用于根据检测出的空闲信道数量及所述数据包成功发送的个数对实际的空闲信道数量和接入认知用户数量进行估算； An estimation module, used to estimate the actual number of idle channels and the number of access cognitive users according to the detected number of idle channels and the number of successfully sent data packets;

参数调整模块，用于根据估算出的所述实际的空闲信道数量和接入认知用户数量计算新的信道检测数量N_s′和新的数据发送概率p_tra′，用N_s′的值为N_s进行赋值，并用p_tra′的值为p_tra进行赋值，以实现参数调整。 The parameter adjustment module is used to calculate the new channel detection number N_s ′ and the new data transmission probability p_tra ′ according to the estimated actual number of idle channels and the number of access cognitive users, and the value of N_s ′ is N_s is assigned, and the value of p_tra ′ is assigned to p_tra to realize parameter adjustment.

本领域的技术人员应该明白，上述的本发明的各模块或各步骤可以用通用的计算装置来实现，它们可以集中在单个的计算装置上，或者分布在多个计算装置所组成的网络上，可选地，它们可以用计算装置可执行的程序代码来实现，从而，可以将它们存储在存储装置中由计算装置来执行，或者将它们分别制作成各个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。这样，本发明不限制于任何特定的硬件和软件结合。 Those skilled in the art should understand that each module or each step of the present invention described above can be realized by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed on a network formed by a plurality of computing devices, Optionally, they can be implemented with program codes executable by computing devices, thus, they can be stored in storage devices and executed by computing devices, or they can be made into individual integrated circuit modules, or multiple of them Each module or step is realized as a single integrated circuit module. As such, the present invention is not limited to any specific combination of hardware and software. the

以上实施方式仅用于说明本发明，而并非对本发明的限制，有关技术领域的普通技术人员，在不脱离本发明的精神和范围的情况下，还可以做出各种变化和变型，因此所有等同的技术方案也属于本发明的范畴，本发明的专利保护范围应由权利要求限定。 The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Those of ordinary skill in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all Equivalent technical solutions also belong to the category of the present invention, and the scope of patent protection of the present invention should be defined by the claims. the

Claims (8)

1. A parameter adjustment method of a distributed cognitive radio network is characterized by comprising the following steps:

s1: selecting N from grant channels_sEach as a channel to be detected, N_sThe number of channel detections is an integer not less than 1;

s2: detecting the channel to be detected and sending the probability p according to the data_traSending data packets on the detected idle channel, counting the number of successfully sent data packets, and p_traValue range ofThe circumference is more than 0_tra≤1；

S3: estimating the actual number of idle channels and the number of access cognitive users according to the detected number of idle channels and the number of successfully transmitted data packets;

s4: calculating new channel detection number N according to the estimated actual idle channel number and the number of the accessed cognitive users_s' and a new data transmission probability p_tra', with N_sA value of N_sAssign a value and use p_traA value of p_traAnd assigning to realize parameter adjustment so as to maximize the throughput of the cognitive user.

2. The method of claim 1, wherein step S1 is preceded by:

s001: detecting the number N of the channels_sAnd a data transmission probability p_traInitialization is performed.

3. The method of claim 1, wherein step S1 is preceded by:

s002: detecting the number M of idle channels_idlSetting the number of successfully sent data packets to be 0_sucSetting to 0;

step S2 further includes:

s201: detecting the channels to be detected one by one, and counting the number M of idle channels each time when an idle channel is detected_idlAdding 1, and adding the frequency band corresponding to the idle signal into an idle channel subset;

s202: according to the data transmission probability p_traSelecting a frequency band from the idle channel subset to transmit a data packet;

s203: the number S of successfully transmitted data packets is determined every time one data packet is successfully transmitted_sucAnd adding 1.

4. The method of claim 3, wherein step 203 is further followed by:

s204: and judging whether the preset times are executed, if not, returning to the step S201, otherwise, executing the step S3, wherein the preset times are the preset number of access frames.

5. The method of claim 4, wherein the actual number of free channels in step S3 is estimated by the following formula:

$\hat{M}={\hat{p}}_{\mathrm{selidl}}*N$

wherein,to estimate the actual number of free channels,

N_IAPfor a predetermined number of access frames, M_idlAnd N is the total number of the authorized channels for the detected number of the idle channels.

6. The method as claimed in claim 5, wherein the number of the accessed cognitive users in step S3 is estimated by the following formula:

<math> <mrow> <mover> <mi>K</mi> <mo>^</mo> </mover> <mo>=</mo> <mn>1</mn> <mo>+</mo> <mfrac> <mrow> <mi>ln</mi> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mi>I</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>p</mi> <mi>tra</mi> </msub> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <mfrac> <mrow> <mi>&eta;</mi> <msub> <mi>N</mi> <mi>p</mi> </msub> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>&eta;</mi> <msub> <mi>N</mi> <mi>p</mi> </msub> </mrow> </mfrac> <mo>&CenterDot;</mo> <mfrac> <mi>N</mi> <mover> <mi>M</mi> <mo>^</mo> </mover> </mfrac> <mo>)</mo> </mrow> </mrow> <mrow> <mi>ln</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>1</mn> <mo>/</mo> <mi>N</mi> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow></math>

wherein,

for the estimated number of access cognitive users, S_I(N_s＝1,p_tra)＝S_suc/N_IAP，Ｎ_pη is a constant number of data transmission slots in an access frame.

7. The method of claim 1, wherein the new number of channel detections N in step S4_s' and a new data transmission probability p_tra' is estimated by the following equation:

<math> <mrow> <mrow> <mo>(</mo> <msup> <msub> <mi>N</mi> <mi>s</mi> </msub> <mo>&prime;</mo> </msup> <mo>,</mo> <msup> <msub> <mi>p</mi> <mi>tra</mi> </msub> <mo>&prime;</mo> </msup> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mrow> <mi>s</mi> <mn>1</mn> </mrow> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mrow> <mi>tra</mi> <mn>1</mn> </mrow> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> </mtd> <mtd> <mover> <mi>K</mi> <mo>^</mo> </mover> <mo>&le;</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mo>;</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mrow> <mi>s</mi> <mn>2</mn> </mrow> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mrow> <mi>tra</mi> <mn>2</mn> </mrow> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> </mtd> <mtd> <mover> <mi>K</mi> <mo>^</mo> </mover> <mo>&GreaterEqual;</mo> <mi>N</mi> <mo>;</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>X</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> </mtd> <mtd> <mi>others</mi> <mo>;</mo> </mtd> </mtr> </mtable> </mfenced> </mrow></math>

wherein, <math> <mrow> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>X</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <msub> <mrow> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mrow> <mi>s</mi> <mn>1</mn> </mrow> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mi>tra</mi> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> </mrow> <mn>1</mn> </msub> </mtd> <mtd> <msub> <mi>S</mi> <mi>sys</mi> </msub> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mi>s</mi> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mrow> <mi>tra</mi> <mn>1</mn> </mrow> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> <mo>&GreaterEqual;</mo> <msub> <mi>S</mi> <mrow> <mi>sys</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mi>s</mi> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mi>tra</mi> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mrow> <mi>s</mi> <mn>3</mn> </mrow> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mrow> <mi>tra</mi> <mn>3</mn> </mrow> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> </mtd> <mtd> <msub> <mi>S</mi> <mi>sys</mi> </msub> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mrow> <mi>s</mi> <mn>1</mn> </mrow> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mrow> <mi>tra</mi> <mn>1</mn> </mrow> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> <mo>&lt;</mo> <msub> <mi>S</mi> <mi>sys</mi> </msub> <mrow> <mo>(</mo> <msubsup> <mi>N</mi> <mrow> <mi>s</mi> <mn>3</mn> </mrow> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mrow> <mi>tra</mi> <mn>3</mn> </mrow> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow></math>$(N_{s1}^{*},p_{\mathrm{tra}1}^{*})=\left(\underset{N_s}{\arg }\underset{p_{\mathrm{tra}}=1}{\min }\right\{S_{\mathrm{sys}}\left(N_s\right)-S_{\mathrm{sys}}\left({\hat{N}}_s\right)\},1),$$(N_{s2}^{*},p_{\mathrm{tra}2}^{*})=(1,N/\hat{K})$and$(N_{s3}^{*},p_{\mathrm{tra}3}^{*})=\left(\min \underset{N_s}{\arg }\right\{p_{\mathrm{sac}}>1/\hat{K}\},1/\hat{K}{p}_{\mathrm{sac}}\left(N_{s3}^{*}\right)),$and is

<math> <mrow> <msub> <mi>S</mi> <mi>sys</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>s</mi> </msub> <mo>,</mo> <msub> <mi>p</mi> <mi>tra</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>&eta;</mi> <msub> <mi>N</mi> <mi>p</mi> </msub> </mrow> <mrow> <msub> <mi>N</mi> <mi>s</mi> </msub> <mo>+</mo> <mi>&eta;</mi> <msub> <mi>N</mi> <mi>p</mi> </msub> </mrow> </mfrac> <mover> <mi>M</mi> <mo>^</mo> </mover> <mover> <mi>K</mi> <mo>^</mo> </mover> <msub> <mi>p</mi> <mi>sac</mi> </msub> <msub> <mi>p</mi> <mi>tra</mi> </msub> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>p</mi> <mi>tra</mi> </msub> <msub> <mi>p</mi> <mi>sac</mi> </msub> <mo>)</mo> </mrow> <mrow> <mover> <mi>K</mi> <mo>^</mo> </mover> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>,</mo> </mrow></math>

N is the total number of authorized channels, N_pIn order to transmit the number of time slots of a data packet,

for the estimated number of access to the cognitive users,

η is a constant for the estimated actual number of free channels, wherein,

<math> <mrow> <msub> <mi>p</mi> <mi>sac</mi> </msub> <mo>=</mo> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mfrac> <mn>1</mn> <mover> <mi>M</mi> <mo>^</mo> </mover> </mfrac> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <mi>N</mi> <mo>-</mo> <msub> <mi>N</mi> <mi>s</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>[</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mo>]</mo> </mrow> </msup> <msup> <mi>N</mi> <mrow> <mo>[</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mo>]</mo> </mrow> </msup> </mfrac> <mo>)</mo> </mrow> <mo>,</mo> </mtd> <mtd> <mi>if</mi> <msub> <mi>N</mi> <mi>s</mi> </msub> <mo>&le;</mo> <mi>N</mi> <mo>-</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mo>;</mo> </mtd> </mtr> <mtr> <mtd> <mfrac> <mn>1</mn> <mover> <mi>M</mi> <mo>^</mo> </mover> </mfrac> <mo>,</mo> </mtd> <mtd> <mi>if</mi> <msub> <mi>N</mi> <mi>s</mi> </msub> <mo>&GreaterEqual;</mo> <mi>N</mi> <mo>-</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mo>+</mo> <mn>1</mn> <mo>,</mo> </mtd> </mtr> </mtable> </mfenced> </mrow></math>

to make it possible to <math> <mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>S</mi> </mrow> <mi>sys</mi> </msub> <mo>/</mo> <mo>&PartialD;</mo> <msub> <mover> <mi>N</mi> <mo>&OverBar;</mo> </mover> <mi>s</mi> </msub> <msub> <mo>|</mo> <msub> <mover> <mi>N</mi> <mo>^</mo> </mover> <mi>s</mi> </msub> </msub> <mo>=</mo> <mn>0</mn> </mrow></math>Is established, and <math> <mrow> <msub> <mover> <mi>N</mi> <mo>&OverBar;</mo> </mover> <mi>s</mi> </msub> <mo>&Element;</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mi>N</mi> <mo>-</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mo>+</mo> <mn>1</mn> <mo>]</mo> </mrow></math>the value of (c).

8. A system for adjusting parameters of a distributed cognitive radio network, the system comprising:

a selection module for selecting N from the grant channels_sEach as a channel to be detected, N_sThe number of channel detections is an integer not less than 1;

detection and access system for the premisesDetecting the channel to be detected according to the data transmission probability p_traSending data packets on the detected idle channel, counting the number of successfully sent data packets, and p_traHas a value range of 0 < p_tra≤1；

The estimation module is used for estimating the actual number of idle channels and the number of the access cognitive users according to the detected number of the idle channels and the number of the successful sending of the data packets;

a parameter adjusting module for calculating a new channel detection number N according to the estimated actual idle channel number and the number of the accessed cognitive users_s' and a new data transmission probability p_tra', with N_sA value of N_sAssign a value and use p_traA value of p_traAnd assigning to realize parameter adjustment so as to maximize the throughput of the cognitive user.

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