技术领域Technical Field
本发明涉及认知无线电网络频谱感知技术领域，具体涉及一种认知无线电网络中优化能效与感知性能的动态分组方法。The present invention relates to the technical field of cognitive radio network spectrum perception, and in particular to a dynamic grouping method for optimizing energy efficiency and perception performance in a cognitive radio network.
背景技术Background technique
随着无线通信技术的发展，通信设备迅速增长，用于无线通信的频谱资源日益紧缺。无线通信技术当前面临的挑战是如何高效地利用稀缺的频谱资源。美国联邦通信委员会的报告称，在所有可用的无线电频谱中，只有一小部分得到了有效利用，其余的未被使用或未被充分使用。因此，有必要提高频谱利用率和动态频谱接入的机会。为了在动态变化的场景中增加频谱接入的机会，需要将认知无线电技术合并到现有的无线电接入网中。认知无线电网络可根据自己的认知来适应周围的无线电环境，它可以智能访问已授权的频谱，有效提高频谱利用率。一个认知无线电网络由主用户(PU)和认知用户(CU)组成，认知无线电网络启动频谱感知，为认知用户检测空闲频谱并使其能随机地访问授权频谱做准备。随着无线电技术的发展，绿色通信要求已越来越高，因此能量效率是通信技术发展需要重点考虑的技术参数。With the development of wireless communication technology, communication devices have grown rapidly, and spectrum resources for wireless communication are becoming increasingly scarce. The current challenge facing wireless communication technology is how to efficiently utilize scarce spectrum resources. According to a report by the Federal Communications Commission of the United States, only a small portion of all available radio spectrum is effectively utilized, and the rest is not used or not fully utilized. Therefore, it is necessary to improve spectrum utilization and opportunities for dynamic spectrum access. In order to increase spectrum access opportunities in dynamically changing scenarios, cognitive radio technology needs to be incorporated into existing radio access networks. Cognitive radio networks can adapt to the surrounding radio environment based on their own cognition. They can intelligently access the authorized spectrum and effectively improve spectrum utilization. A cognitive radio network consists of primary users (PUs) and cognitive users (CUs). The cognitive radio network starts spectrum sensing to prepare cognitive users to detect idle spectrum and enable them to randomly access the authorized spectrum. With the development of radio technology, green communication requirements have become increasingly high, so energy efficiency is a technical parameter that needs to be considered in the development of communication technology.
能量效率是认知无线电网络的一个重要参数。例如：在文献[Zhu S,Cai Z,Hu H,et al.zkCrowd:A Hybrid Blockchain-based Crowdsourcing Platform[J].IEEETransactions on Industrial Informatics,2020,16(6):4196-4205.]中，采用了基于最优资源分配和能量捕获的协作频谱感知来提高能量效率。随着5G技术的发展，能量效率的重要性也随之提高。文献[Huang X,Zhang D,Tang S,et al.Fairness-Based DistributedResource Allocation in Two-Tier Heterogeneous Networks[J].IEEE Access,2019,7(2):40000-40012.]提出了无线功率传输方案以节省能耗，解决了高数据速率导致的能耗大的问题。为了提高5G技术中的频谱感知效率，文献[Huang T,Li X,Cao Q.Research onan Evaluation Algorithm of Sensing Node Reliability in Cognitive Networks[J].IEEE Access,2020,8(1):11848-11855.]中作者提出了在衰落信道中具有高效频谱感知的基于多模态的合作频谱感知方案。在文献[黄堂森,李小武,曹庆皎.认知网络中无线电信号智能感知方法研究[J].应用科学学报,2020,38(5):410-418.]中所做的工作表明，电路的能量消耗是评估整个系统的能量效率时要考虑的重要因素。为在文献[Cui Z,Xue F,Zhang S,et al.A Hybid BlockChain-Based Identity Authentication Scheme forMulti-WSN[J].IEEE Transactions on Services Computing,2019,13(2):241-251.]的基础上降低能耗，文献[Salah M,Omer O A,Mohammed U S.Spectral EfficiencyEnhancement Based on Sparsely Indexed Modulation for Green RadioCommunication[J].IEEE Access,2019,7(1):31913-31925.]中考虑了系统参数的功率优化，但这仅适用于传统无线电，不适用于认知无线电网络。文献[Chen A,Shi Z,XiongJ.Generalized Real-Valued Weighted Covariance-Based Detection Methods forCognitive Radio Networks With Correlated Multiple Antennas[J].IEEE Access,2019,7(2):34373-34382.]中的工作只关注感知和传输过程中的能量消耗，而忽略了电路的能量消耗。Energy efficiency is an important parameter of cognitive radio networks. For example, in the literature [Zhu S, Cai Z, Hu H, et al. zkCrowd: A Hybrid Blockchain-based Crowdsourcing Platform [J]. IEEE Transactions on Industrial Informatics, 2020, 16 (6): 4196-4205.], collaborative spectrum sensing based on optimal resource allocation and energy capture is used to improve energy efficiency. With the development of 5G technology, the importance of energy efficiency has also increased. The literature [Huang X, Zhang D, Tang S, et al. Fairness-Based Distributed Resource Allocation in Two-Tier Heterogeneous Networks [J]. IEEE Access, 2019, 7 (2): 40000-40012.] proposed a wireless power transmission scheme to save energy consumption, solving the problem of high energy consumption caused by high data rates. In order to improve the efficiency of spectrum sensing in 5G technology, the authors proposed a multi-modal cooperative spectrum sensing scheme with efficient spectrum sensing in fading channels in the literature [Huang T, Li X, Cao Q. Research on an Evaluation Algorithm of Sensing Node Reliability in Cognitive Networks[J]. IEEE Access, 2020, 8(1): 11848-11855.]. The work done in the literature [Huang Tangsen, Li Xiaowu, Cao Qingjiao. Research on Intelligent Sensing Methods of Radio Signals in Cognitive Networks[J]. Journal of Applied Sciences, 2020, 38(5): 410-418.] shows that the energy consumption of the circuit is an important factor to be considered when evaluating the energy efficiency of the entire system. In order to reduce energy consumption based on the literature [Cui Z, Xue F, Zhang S, et al. A Hybid BlockChain-Based Identity Authentication Scheme for Multi-WSN [J]. IEEE Transactions on Services Computing, 2019, 13 (2): 241-251.], the power optimization of system parameters is considered in the literature [Salah M, Omer O A, Mohammed U S. Spectral Efficiency Enhancement Based on Sparsely Indexed Modulation for Green Radio Communication [J]. IEEE Access, 2019, 7 (1): 31913-31925.], but this is only applicable to traditional radios and not to cognitive radio networks. The work in the literature [Chen A, Shi Z, Xiong J. Generalized Real-Valued Weighted Covariance-Based Detection Methods for Cognitive Radio Networks With Correlated Multiple Antennas [J]. IEEE Access, 2019, 7 (2): 34373-34382.] only focuses on the energy consumption in the perception and transmission process, but ignores the energy consumption of the circuit.
在认知无线电网络中，由于频谱感知本身消耗了大量的能量，而无效的频谱感知则又增加了能量消耗。因此，需要改进频谱感知算法以提高能量效率。例如：在文献[WangY,Liu M,Yang J,et al.Data-driven deep learning for automatic modulationrecognition in cognitive radios[J].IEEE Transactions on Vehicular Technology,2019,68(4):4074-4077.]中，作者提出了一种基于凸优化的分组感知方案，通过对感知时间和功率分配进行联合优化，提升认知无线电网络中的能量效率，并将频谱感知问题实现为凸优化。在文献[Zhou X,Liang W,Wang K,et al.Multi-Modality BehavioralInfluence Analysis for Personalized Recommendations in Health Social MediaEnvironment[J].IEEE Transactions on Computational Social Systems,2019,6(5):888-897.]中，隐马尔可夫模型和多层感知器被用于预测频谱，提升了频谱效率和能量效率，但增加了计算量，因此该算法并不是提升能量效率的有效方法。在文献[黄堂森、王结太.基于监督学习的自适应协作频谱感知算法[J].光电子·激光，2016，27(9):1010-1016.]中，作者采用凸变换迭代算法，提出了实现能源效率的功率分配方案，将该问题表述为分式规划问题。文献[Li X,Huang H,Xiao F,et al.ABlockchain-Based TrustManagement With Conditional Privacy-Preserving Announcement Scheme for VANETs[J].IEEE Internet of Things Journal,2020,7(5):4101-4112.]中认知无线电网络的能量效率提升方法是通过基于信道状态进行鲁棒功率分配来实现，将优化问题转化为考虑能量和吞吐量的极值问题。在文献[Wang F,Ma J,Han G,Dong L,et al.InvestigatingFactors Influencing Moment Tensor Inversion of Induced Seismicity in VirtualIoT[J].IEEE Access,2019,7(2):34238-34251.]中将能效优化公式化为非凸函数，但仅考虑了功率分配的因素。由于无线电环境在动态变化，能效在非确定性多项式优化问题上更为复杂。In cognitive radio networks, spectrum sensing itself consumes a lot of energy, and ineffective spectrum sensing increases energy consumption. Therefore, it is necessary to improve the spectrum sensing algorithm to improve energy efficiency. For example, in the literature [Wang Y, Liu M, Yang J, et al. Data-driven deep learning for automatic modulation recognition in cognitive radios [J]. IEEE Transactions on Vehicular Technology, 2019, 68 (4): 4074-4077.], the authors proposed a group sensing scheme based on convex optimization, which improves the energy efficiency in cognitive radio networks by jointly optimizing the sensing time and power allocation, and implements the spectrum sensing problem as a convex optimization. In the literature [Zhou X, Liang W, Wang K, et al. Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment [J]. IEEE Transactions on Computational Social Systems, 2019, 6 (5): 888-897.], hidden Markov models and multilayer perceptrons are used to predict spectrum, which improves spectrum efficiency and energy efficiency, but increases the amount of computation, so the algorithm is not an effective way to improve energy efficiency. In the literature [Huang Tangsen, Wang Jietai. Adaptive collaborative spectrum sensing algorithm based on supervised learning [J]. Optoelectronics Laser, 2016, 27 (9): 1010-1016.], the authors use a convex transformation iterative algorithm to propose a power allocation scheme to achieve energy efficiency, and formulate the problem as a fractional programming problem. In the literature [Li X, Huang H, Xiao F, et al. ABlockchain-Based Trust Management With Conditional Privacy-Preserving Announcement Scheme for VANETs[J]. IEEE Internet of Things Journal, 2020, 7(5): 4101-4112.], the energy efficiency improvement method of cognitive radio networks is achieved by robust power allocation based on channel state, which transforms the optimization problem into an extreme value problem considering energy and throughput. In the literature [Wang F, Ma J, Han G, Dong L, et al. Investigating Factors Influencing Moment Tensor Inversion of Induced Seismicity in VirtualIoT[J]. IEEE Access, 2019, 7(2): 34238-34251.], the energy efficiency optimization formula is formulated as a non-convex function, but only the power allocation factor is considered. Since the radio environment is changing dynamically, energy efficiency is more complicated in non-deterministic polynomial optimization problems.
综上可知，现有技术将频谱感知看作一个凸优化问题，但采用凸优化方法无法区分工作用户和休息用户，不能解决认知用户间的平等性问题，无法在兼顾用户平等工作的前提下提高频谱感知性能和能量效率。In summary, the existing technology regards spectrum sensing as a convex optimization problem, but the convex optimization method cannot distinguish between working users and resting users, cannot solve the equality problem between cognitive users, and cannot improve spectrum sensing performance and energy efficiency while taking into account the equal work of users.
发明内容Summary of the invention
本发明的目的在于提供一种认知无线电网络中优化能效与感知性能的动态分组方法，以解决背景技术中提出的问题。The purpose of the present invention is to provide a dynamic grouping method for optimizing energy efficiency and perception performance in a cognitive radio network, so as to solve the problems raised in the background technology.
为实现上述目的，本发明提供了一种认知无线电网络中优化能效与感知性能的动态分组方法，包括以下步骤：To achieve the above object, the present invention provides a dynamic grouping method for optimizing energy efficiency and perception performance in a cognitive radio network, comprising the following steps:
S1、由认知无线电路由器对所有的认知用户进行训练，得到每个认知用户的初始可靠度值；S1, the cognitive radio router trains all cognitive users to obtain the initial reliability value of each cognitive user;
S2、融合中心从经过训练的认知用户中选择可靠度值排名靠前的M个认知用户参与协作感知，并将所选中的M个认知用户交错分成两组，两组认知用户交替执行频谱感知工作；其中，M为偶数；S2, the fusion center selects M cognitive users with the highest reliability values from the trained cognitive users to participate in collaborative sensing, and divides the selected M cognitive users into two groups in an interlaced manner. The two groups of cognitive users perform spectrum sensing work alternately; where M is an even number;
S3、设所选中的M个认知用户均参与协作感知，则一个感知周期T内所有被选中的认知用户消耗的总能量为：S3. Assume that all the selected M cognitive users participate in collaborative sensing. Then the total energy consumed by all selected cognitive users in a sensing cycle T is:
设每个被选中的认知用户与融合中心之间的信道增益均为常数h，则式(4)可简写为：Assuming that the channel gain between each selected cognitive user and the fusion center is a constant h, equation (4) can be simplified as:
则所有被选中的M个认知用户总的能量效率表示为：Then the total energy efficiency of all selected M cognitive users is expressed as:
每组认知用户的检测概率表示为：The detection probability of each group of cognitive users is expressed as:
式(7)中，X＝1表示认知用户判决主用户在工作；H1表示主用户处于工作状态；wj表示第j个认知用户的权值，其计算式表示为：In formula (7), X = 1 means that the cognitive user determines that the primary user is working; H1 means that the primary user is in working state; wj represents the weight of the jth cognitive user, and its calculation formula is expressed as:
式(8)中，Ej表示第j个认知用户在一个感知周期内收集的能量，λh,j表示双阈值能量检测的高阈值，λl,j表示双阈值能量检测的低阈值；In formula (8),Ej represents the energy collected by the jth cognitive user in one sensing cycle, λh,j represents the high threshold of dual-threshold energy detection, and λl,j represents the low threshold of dual-threshold energy detection;
每组认知用户的虚警概率表示为：The false alarm probability of each group of cognitive users is expressed as:
式(9)中，H0表示主用户处于非工作状态；In formula (9), H0 indicates that the primary user is in a non-working state;
其中，每组认知用户的能量消耗表示为：Among them, the energy consumption of each group of cognitive users is expressed as:
式(10)中，L表示每组中认知用户的数量，L＝M/2；k是组号索引；In formula (10), L represents the number of cognitive users in each group, L = M/2; k is the group number index;
S4、将感知性能和能量消耗的联合优化等效替换为约束优化问题，表示为：S4. The joint optimization of perceived performance and energy consumption is equivalently replaced by a constrained optimization problem, expressed as:
式(11)中，Pd,k(T)、Pf,k(T)和ET,k(T)分别表示第k组认知用户的检测概率、虚警概率和能量消耗；γ1、γ2和Eγ分别表示各组认知用户的检测概率阈值、虚警概率阈值和能量消耗阈值；In formula (11), Pd,k (T), Pf,k (T) and ET,k (T) represent the detection probability, false alarm probability and energy consumption of the kth group of cognitive users respectively; γ1 , γ2 and Eγ represent the detection probability threshold, false alarm probability threshold and energy consumption threshold of each group of cognitive users respectively;
S5、通过贪婪启发式算法对式(11)进行求解，最终求得最优感知周期T。S5. Solve equation (11) through a greedy heuristic algorithm and finally obtain the optimal sensing period T.
进一步的，所述步骤S1中，对所有的认知用户进行训练的方法为：Furthermore, in step S1, the method for training all cognitive users is:
S1.1、将N个认知用户布设在一个正方形区域中，并在所述正方形区域的中心设置一个主用户，主用户和所有的认知用户之间都保持相对静止，主用户与各认知用户之间的距离不等，且存在路径损耗和瑞利衰落；S1.1. N cognitive users are arranged in a square area, and a master user is set in the center of the square area. The master user and all cognitive users remain relatively stationary. The distances between the master user and each cognitive user are not equal, and there is path loss and Rayleigh fading.
S1.2、对主用户进行预设次数的发射信号-关停循环训练；训练过程中认知用户的工作规律与主用户一致，主用户关停以后，认知用户执行判决操作；S1.2. Perform a preset number of transmission signal-shutdown cycle training on the primary user; during the training process, the working rules of the cognitive user are consistent with those of the primary user. After the primary user is shut down, the cognitive user performs the judgment operation;
S1.3、在所有认知用户训练执行完以后，认知无线电路由器对各认知用户的判决结果进行统计，以判决准确率为统计标准，得到每个认知用户的初始可靠度值。S1.3. After all cognitive users have completed training, the cognitive radio router counts the judgment results of each cognitive user, and uses the judgment accuracy rate as the statistical standard to obtain the initial reliability value of each cognitive user.
进一步的，所述步骤S1.2中，对主用户进行5000～20000次循环训练，主用户发射信号的持续时间为10毫秒，发射功率为100毫瓦，主用户的工作概率为50％。Furthermore, in step S1.2, the primary user is trained 5000 to 20000 times in a cycle, the duration of the primary user transmitting a signal is 10 milliseconds, the transmission power is 100 milliwatts, and the working probability of the primary user is 50%.
进一步的，所述步骤S1中，由于无线电环境是变化的，认知无线电路由器在得到认知用户的初始可靠度值后，会按照下式(1)定期对认知用户的可靠度值进行更新：Furthermore, in step S1, since the radio environment is changing, after obtaining the initial reliability value of the cognitive user, the cognitive radio router will periodically update the reliability value of the cognitive user according to the following formula (1):
式(1)中，Pj表示第j个认知用户的学习强度，N表示认知用户的数量。In formula (1),Pj represents the learning intensity of the jth cognitive user, and N represents the number of cognitive users.
进一步的，所述步骤S2中，两组认知用户交替工作的具体方式为：当其中一组认知用户执行频谱感知操作时，另一组认知用户进行自身的数据传输操作，或者处于静默状态以保存有限的能量。Furthermore, in step S2, the specific manner in which the two groups of cognitive users work alternately is: when one group of cognitive users performs spectrum sensing operations, the other group of cognitive users performs its own data transmission operations, or is in a silent state to save limited energy.
进一步的，所述步骤S2中，在两组认知用户交替工作过程中，一个感知周期T内，每组认知用户执行感知操作的时间均为t1，每组认知用户传输数据的时间均为t2，且一个感知周期内第j个认知用户所消耗的能量表示为：Furthermore, in step S2, during the alternating operation of the two groups of cognitive users, within a sensing cycle T, the time for each group of cognitive users to perform sensing operations is t1 , the time for each group of cognitive users to transmit data is t2 , and The energy consumed by the jth cognitive user in a sensing cycle is expressed as:
式(2)中，fs是认知用户的采样频率，且假设所有认知用户的采样频率一致；e0表示单个认知用户采样一次消耗的能量；nj的取值为0或1，取值为1时表示认知用户在传输数据，取值为0时表示认知用户未进行数据传输操作；C0表示单位距离内传输数据所需要的发射功率。In formula (2),fs is the sampling frequency of the cognitive user, and it is assumed that the sampling frequency of all cognitive users is the same;e0 represents the energy consumed by a single cognitive user for sampling once;nj is 0 or 1, when it is 1, it means that the cognitive user is transmitting data, and when it is 0, it means that the cognitive user is not performing data transmission operations;C0 represents the transmission power required to transmit data within a unit distance.
进一步的，所述步骤S2中，第j个认知用户在一个感知周期内执行数据传输所消耗的能量与其在整个感知周期中所消耗的能量之比为认知用户的能量效率，表示为：Furthermore, in step S2, the ratio of the energy consumed by the jth cognitive user in performing data transmission in one sensing cycle to the energy consumed in the entire sensing cycle is the energy efficiency of the cognitive user, which is expressed as:
式(3)中，hj表示第j个认知用户数据传输信道的信道增益，dj表示第j个认知用户的数据传输距离。In formula (3),hj represents the channel gain of the data transmission channel of the jth cognitive user, anddj represents the data transmission distance of the jth cognitive user.
进一步的，所述步骤S5中，利用贪婪启发式算法求解得到最优感知周期T的具体步骤如下：Furthermore, in step S5, the specific steps of using the greedy heuristic algorithm to solve and obtain the optimal sensing period T are as follows:
S5.1、对所选中的M个认知用户进行一次分组称为一个事件，记为P；针对某P事件，将Pd,k(T)、Pf,k(T)和ET,k(T)分别与对应的阈值进行比较，若两组认知用户的检测概率、虚警概率和能量消耗均满足最低要求，则存在感知周期T的最优解，该事件被记录，并跳转至步骤S5.2；否则排除该P事件，改变T值，重新执行步骤S5.1；S5.1. Grouping the selected M cognitive users is called an event, recorded as P. For a certain P event, Pd,k (T), Pf,k (T) and ET,k (T) are compared with the corresponding thresholds respectively. If the detection probability, false alarm probability and energy consumption of the two groups of cognitive users meet the minimum requirements, there is an optimal solution for the perception period T, the event is recorded, and jump to step S5.2; otherwise, exclude the P event, change the T value, and re-execute step S5.1.
S5.2、对各组认知用户的检测概率和虚警概率做加权平均，即用加权平均检测概率和加权平均虚警概率来近似该P事件的整体检测概率和虚警概率；依据该P事件的认知用户交错分组工作模型，设：S5.2. Take a weighted average of the detection probability and false alarm probability of each group of cognitive users, that is, use the weighted average detection probability and weighted average false alarm probability To approximate the overall detection probability and false alarm probability of the P event; according to the cognitive user staggered grouping working model of the P event, let:
则和的计算分别如式(13)和式(14)所示：but and The calculations of are shown in formula (13) and formula (14) respectively:
该P事件中，一个感知周期内所有被选中的认知用户总的能量消耗重新表述为ET,k(T)：In this P event, the total energy consumption of all selected cognitive users in a sensing cycle is reformulated as ET,k (T):
当被记录的所有T值计算完和ET(T)后，执行步骤S5.3；When all the recorded T values are calculated AfterET (T), execute step S5.3;
S5.3、利用动态规划算法对式(5)进行求解，即可求得最优感知周期T值为：S5.3. Solve equation (5) using the dynamic programming algorithm to obtain the optimal sensing period T value:
式(16)中，α1、α2和α3是加权因子，且α1+α2+α3＝1，δ()是评价指数，获得最优T值后，可同时获得ET、和In formula (16), α1 , α2 and α3 are weighting factors, and α1 + α2 + α3 = 1, δ() is the evaluation index. After obtaining the optimal T value,ET , and
相比于现有技术，本发明具有以下有益效果：Compared with the prior art, the present invention has the following beneficial effects:
(1)、本发明的动态分组方法，将选定的认知用户交错分组分成两组，在一个感知周期内，其中一组认知用户执行感知操作，另一组的认知用户执行数据传输或处于静默状态，在下一个周期，两组交换工作职能，如此不断交替，可持续对目标频段进行探测，在有效保护主用户的同时提高频谱感知性能和能量效率。(1) The dynamic grouping method of the present invention divides the selected cognitive users into two groups in an interlaced manner. In one sensing cycle, one group of cognitive users performs sensing operations, and the other group of cognitive users performs data transmission or is in a silent state. In the next cycle, the two groups exchange their work functions. In this way, the target frequency band can be continuously detected, thereby effectively protecting the primary users while improving the spectrum sensing performance and energy efficiency.
(2)、本发明的动态分组方法，选择偶数个可靠度值考前的认知用户参与协作感知，排除性能不可靠的认知用户，不但有效提升了频谱感知性能，也极大的提高了能量效率。实验结果表明本发明的动态分组算法在信噪比等于-20dB时，频谱检测概率高于传统算法50％，虚警概率低于传统算法10％，能量效率高于传统算法15％，因此本发明的动态分组算法具有优越的感知性能和极高的能量效率。(2) The dynamic grouping method of the present invention selects cognitive users with an even number of reliability values before the test to participate in collaborative perception, and excludes cognitive users with unreliable performance, which not only effectively improves the spectrum perception performance, but also greatly improves the energy efficiency. The experimental results show that when the signal-to-noise ratio of the dynamic grouping algorithm of the present invention is equal to -20dB, the spectrum detection probability of the dynamic grouping algorithm of the present invention is 50% higher than that of the traditional algorithm, the false alarm probability is 10% lower than that of the traditional algorithm, and the energy efficiency is 15% higher than that of the traditional algorithm. Therefore, the dynamic grouping algorithm of the present invention has superior perception performance and extremely high energy efficiency.
除了上面所描述的目的、特征和优点之外，本发明还有其它的目的、特征和优点。下面将参照图，对本发明作进一步详细的说明。In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
附图是用来提供对本发明实施例的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本发明实施例，但并不构成对本发明实施例的限制。在附图中：The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:
图1是本发明优选实施例中认知用户交错分组的工作模型；FIG1 is a working model of cognitive user staggered grouping in a preferred embodiment of the present invention;
图2是本发明优选实施例中两组认知用户的交替工作结构示意图；FIG2 is a schematic diagram of the alternating working structure of two groups of cognitive users in a preferred embodiment of the present invention;
图3是本发明的动态分组算法与现有技术的等增益结合算法和节点选择算法的频谱感知性能随信噪比变化的比较图；其中，图3(a)是三种算法的检测概率随信噪比变化曲线图；图3(b)是三种算法的虚警概率随信噪比变化曲线图；FIG3 is a comparison diagram of spectrum sensing performance of the dynamic grouping algorithm of the present invention and the equal gain combination algorithm and node selection algorithm of the prior art as the signal-to-noise ratio changes; wherein FIG3(a) is a curve diagram of detection probability of the three algorithms as the signal-to-noise ratio changes; FIG3(b) is a curve diagram of false alarm probability of the three algorithms as the signal-to-noise ratio changes;
图4是本发明的动态分组算法与现有技术的等增益结合算法和节点选择算法的能量效率随信噪比变化的比较图；4 is a comparison diagram of energy efficiency of the dynamic grouping algorithm of the present invention and the equal gain combination algorithm and node selection algorithm of the prior art as the signal-to-noise ratio changes;
图5是本发明的动态分组算法与现有技术的等增益结合算法和节点选择算法的剩余能量随感知周期数量的增加而变化的比较图。FIG5 is a comparison diagram showing how the residual energy of the dynamic grouping algorithm of the present invention, the equal gain combination algorithm and the node selection algorithm of the prior art change with the increase in the number of sensing cycles.
具体实施方式Detailed ways
以下结合附图对本发明的实施例进行详细说明，但是本发明可以根据权利要求限定和覆盖的多种不同方式实施。The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.
本发明实施例提供一种认知无线电网络中优化能效与感知性能的动态分组方法，认知无线电网络包括一个认知无线电路由器(CRR)、多个认知用户(CU)和多个主用户(Primary PU)，都分布在认知无线电网络区域。认知无线电网络在执行频谱感知的过程中，需要多用户协作感知来提升感知性能，但感知性能不随感知用户数量的增加而线性增长。因此，本发明实施例的动态分组方法将所选中的M个认知用户分成两组，两组认知用户交替执行频谱感知工作，即当其中一组认知用户工作时，另一组认知用户处于数据传输或休息状态，这可极大减少频谱感知操作上的能量消耗。具体地，本发明实施例的认知无线电网络中优化能效与感知性能的动态分组方法包括以下步骤：The embodiment of the present invention provides a dynamic grouping method for optimizing energy efficiency and perception performance in a cognitive radio network. The cognitive radio network includes a cognitive radio router (CRR), multiple cognitive users (CU) and multiple primary users (Primary PU), all of which are distributed in the cognitive radio network area. In the process of performing spectrum sensing, the cognitive radio network requires multi-user collaborative sensing to improve the perception performance, but the perception performance does not increase linearly with the increase in the number of sensing users. Therefore, the dynamic grouping method of the embodiment of the present invention divides the selected M cognitive users into two groups, and the two groups of cognitive users perform spectrum sensing work alternately, that is, when one group of cognitive users is working, the other group of cognitive users is in a data transmission or resting state, which can greatly reduce the energy consumption of spectrum sensing operations. Specifically, the dynamic grouping method for optimizing energy efficiency and perception performance in a cognitive radio network of the embodiment of the present invention includes the following steps:
步骤一、由认知无线电路由器对所有的认知用户进行训练，得到每个认知用户的初始可靠度值；训练方法如下：Step 1: The cognitive radio router trains all cognitive users to obtain the initial reliability value of each cognitive user. The training method is as follows:
步骤1.1、设有N个认知用户，将这N个认知用户均匀布设在一个正方形区域中，并在该正方形区域的中心设置一个主用户，主用户和所有的认知用户之间都保持相对静止，主用户与各认知用户之间的距离不等，且存在路径损耗和瑞利衰落。Step 1.1, assume that there are N cognitive users, and these N cognitive users are evenly distributed in a square area, and a master user is set in the center of the square area. The master user and all cognitive users remain relatively stationary, the distances between the master user and each cognitive user are not equal, and there is path loss and Rayleigh fading.
步骤1.2、对主用户进行预设次数的发射信号-关停循环训练；在训练过程中认知用户的工作规律与主用户一致，主用户的每一次发射，认知用户都会执行频谱感知操作，主用户关停以后，认知用户执行判决操作，即判断主用户信号是否存在，并把判决结果报告给认知无线电路由器。在一种优选的实施例中，主用户发射信号的持续时间为10毫秒，发射功率为100毫瓦，发射一次信号后关停10毫秒，即主用户工作概率为50％，主用户发射信号-关停循环工作5000～20000次，也即训练5000～20000次。Step 1.2, the main user is trained with a preset number of transmission signal-shutdown cycles; during the training process, the working rules of the cognitive user are consistent with the main user. Each time the main user transmits, the cognitive user will perform spectrum sensing operations. After the main user shuts down, the cognitive user performs a judgment operation, that is, judges whether the main user signal exists, and reports the judgment result to the cognitive radio router. In a preferred embodiment, the duration of the main user transmitting the signal is 10 milliseconds, the transmission power is 100 milliwatts, and it shuts down for 10 milliseconds after transmitting a signal, that is, the main user has a working probability of 50%, and the main user transmits the signal-shutdown cycle 5000 to 20000 times, that is, the training is 5000 to 20000 times.
步骤1.3、所有的认知用户执行完预定次数的训练以后，认知无线电路由器对各认知用户的判决结果进行统计，以判决准确率为统计标准，得到每个认知用户的初始可靠度值。Step 1.3: After all cognitive users have completed the predetermined number of trainings, the cognitive radio router counts the judgment results of each cognitive user, and uses the judgment accuracy rate as a statistical standard to obtain the initial reliability value of each cognitive user.
在一种优选的实施例中，由于无线电环境是变化的，认知无线电路由器在得到认知用户的初始可靠度值以后，会定期照按下式(1)对认知用户的可靠度值进行更新：In a preferred embodiment, since the radio environment is changing, after obtaining the initial reliability value of the cognitive user, the cognitive radio router will periodically update the reliability value of the cognitive user according to the following formula (1):
上式(1)中，Pj表示第j个认知用户的学习强度，N表示认知用户的数量。In the above formula (1),Pj represents the learning intensity of the jth cognitive user, and N represents the number of cognitive users.
步骤二、融合中心从N个认知用户中选择可靠度值排名靠前的M个认知用户参与协作感知，并将所选中的M个认知用户交错分成两组；其中，M为偶数，且M≤N。两组认知用户等概率交替执行频谱感知工作，具体认知用户交错分组工作模型如图1所示。Step 2: The fusion center selects M cognitive users with the highest reliability values from N cognitive users to participate in collaborative sensing, and divides the selected M cognitive users into two groups in an interlaced manner; where M is an even number and M≤N. The two groups of cognitive users perform spectrum sensing work alternately with equal probability. The specific cognitive user interlaced grouping work model is shown in Figure 1.
图1的认知用户交错分组工作模型中两组认知用户数相同，采取该分组方式的目的是为了让两组用户的感知性能近似相等。在一个感知周期内，两组等时间交替工作，可对主用户频段进行无差别探测，这可在有效保护主用户的同时提高发现频谱空洞的概率。两组用户的具体工作方式如图2所示，当其中一组认知用户执行频谱感知操作时，另一组认知用户进行自身的数据传输操作，或者处于静默状态以保存有限的能量，进而达到提高能量效率的目的。In the cognitive user staggered grouping working model of Figure 1, the number of cognitive users in the two groups is the same. The purpose of adopting this grouping method is to make the perception performance of the two groups of users approximately equal. In one perception cycle, the two groups work alternately at equal times, and can perform indiscriminate detection on the primary user's frequency band, which can effectively protect the primary user while increasing the probability of discovering spectrum holes. The specific working mode of the two groups of users is shown in Figure 2. When one group of cognitive users performs spectrum sensing operations, the other group of cognitive users performs its own data transmission operations, or is in a silent state to save limited energy, thereby achieving the purpose of improving energy efficiency.
如图2所示，在两组认知用户交替工作过程中，一个感知周期T内，每组认知用户执行感知操作的时间均为t1，每组认知用户传输数据的时间均为t2，一个感知周期内第j个认知用户所消耗的能量表示为：As shown in FIG2 , during the alternating operation of two groups of cognitive users, within a sensing cycle T, the time for each group of cognitive users to perform sensing operations is t1 , and the time for each group of cognitive users to transmit data is t2 . The energy consumed by the jth cognitive user in a sensing cycle is expressed as:
上式(2)中，fs是认知用户的采样频率，且假设所有认知用户的采样频率一致；e0表示单个认知用户采样一次消耗的能量，也称为基准能耗单位，记为1焦耳；nj的取值为0或1，取值为1时表示用户在传输数据，取值为0时表示用户未进行数据传输操作；C0表示单位距离内传输数据所需要的发射功率，此处单位距离设置为1米。In the above formula (2),fs is the sampling frequency of the cognitive user, and it is assumed that the sampling frequency of all cognitive users is the same;e0 represents the energy consumed by a single cognitive user for sampling once, also known as the benchmark energy consumption unit, recorded as 1 joule;nj takes the value of 0 or 1, when the value is 1, it means that the user is transmitting data, and when the value is 0, it means that the user is not performing data transmission operations;C0 represents the transmission power required for transmitting data within a unit distance, where the unit distance is set to 1 meter.
第j个认知用户在一个感知周期内执行数据传输所消耗的能量与其在整个感知周期中所消耗的能量之比为认知用户的能量效率，表示为：The ratio of the energy consumed by the jth cognitive user to perform data transmission in one sensing cycle to the energy consumed in the entire sensing cycle is the energy efficiency of the cognitive user, which is expressed as:
上式(3)中，hj表示第j个认知用户数据传输信道的信道增益，dj表示第j个认知用户数据传输距离。In the above formula (3),hj represents the channel gain of the j-th cognitive user data transmission channel, anddj represents the j-th cognitive user data transmission distance.
步骤三、假设所选中的M个认知用户均参与协作感知，由上式(2)可推导出一个感知周期T内所有被选中的认知用户消耗的总能量如(4)式所示：Step 3: Assuming that all the selected M cognitive users participate in collaborative sensing, the total energy consumed by all selected cognitive users in a sensing cycle T can be derived from the above formula (2) as shown in formula (4):
设每个被选中的认知用户与融合中心之间的信道增益均为常数h，则(4)式可简写为：Assuming that the channel gain between each selected cognitive user and the fusion center is a constant h, equation (4) can be simplified as:
则所有被选中的认知用户总的能量效率表示为：Then the total energy efficiency of all selected cognitive users is expressed as:
每组认知用户的检测概率表示为：The detection probability of each group of cognitive users is expressed as:
上式(7)中，X＝1表示认知用户判决主用户在工作；H1表示主用户处于工作状态；wj表示第j个认知用户的权值，其计算式表示为：In the above formula (7), X = 1 means that the cognitive user determines that the primary user is working; H1 means that the primary user is in working state; wj represents the weight of the jth cognitive user, and its calculation formula is expressed as:
式(8)中，Ej表示第j个认知用户在一个感知周期内收集的能量，λh,j表示双阈值能量检测的高阈值，λl,j表示双阈值能量检测的低阈值；同样，每组认知用户的虚警概率表示为：In formula (8),Ej represents the energy collected by the jth cognitive user in a sensing cycle, λh,j represents the high threshold of dual-threshold energy detection, and λl,j represents the low threshold of dual-threshold energy detection. Similarly, the false alarm probability of each group of cognitive users is expressed as:
式(9)中，H0表示主用户处于非工作状态；In formula (9), H0 indicates that the primary user is in a non-working state;
其中，每组的能量消耗表示为：The energy consumption of each group is expressed as:
式(10)中，L表示每组中认知用户的数量，L＝M/2；k是组号索引。In formula (10), L represents the number of cognitive users in each group, L=M/2; k is the group number index.
步骤四、ET,K是感知周期T的函数，将感知性能和能量消耗的联合优化问题等效为约束优化问题，表示为：Step 4:ET,K is a function of the sensing period T. The joint optimization problem of sensing performance and energy consumption is equivalent to a constrained optimization problem, which can be expressed as:
式(11)中，Pd,k(T)、Pf,k(T)和ET,k(T)分别表示第k组认知用户的检测概率、虚警概率和能量消耗，k∈(1,2)；γ1、γ2和Eγ分别表示各组认知用户的检测概率阈值、虚警概率阈值和能量消耗阈值。In formula (11), Pd,k (T), Pf,k (T) andET,k (T) represent the detection probability, false alarm probability and energy consumption of the kth group of cognitive users, respectively, k∈(1,2); γ1 , γ2 and Eγ represent the detection probability threshold, false alarm probability threshold and energy consumption threshold of each group of cognitive users, respectively.
步骤五、通过贪婪启发式算法对(11)式进行求解，最终求得最优感知周期T，进而在感知性能与能量消耗满足最低要求的同时，实现感知性能和能量消耗之间的平衡。Step 5: Solve equation (11) through a greedy heuristic algorithm to finally obtain the optimal sensing period T, thereby achieving a balance between sensing performance and energy consumption while meeting the minimum requirements.
在一种优选的实施例中，利用贪婪启发式算法求解得到最优感知周期T的具体步骤如下：In a preferred embodiment, the specific steps of using the greedy heuristic algorithm to solve and obtain the optimal sensing period T are as follows:
步骤5.1、对所选中的M个认知用户进行一次分组称为一个事件，记为P(以下统称为P事件)；某P事件后，需检验各组的检测概率、虚警概率和能量消耗是否满足最低要求。针对某P事件，将Pd,k(T)、Pf,k(T)和ET,k(T)分别与对应的阈值进行比较，若两组认知用户的检测概率、虚警概率和能量消耗均满足最低要求，则必定存在感知周期T的最优解，该事件被记录，并跳转至步骤5.2。否则排除该P事件，重新对所选中的M个认知用户进行分组，改变T值，再次执行步骤5.1。Step 5.1: Grouping the selected M cognitive users once is called an event, recorded as P (hereinafter referred to as P event); after a P event, it is necessary to check whether the detection probability, false alarm probability and energy consumption of each group meet the minimum requirements. For a P event, Pd,k (T), Pf,k (T) and ET,k (T) are compared with the corresponding thresholds respectively. If the detection probability, false alarm probability and energy consumption of the two groups of cognitive users meet the minimum requirements, there must be an optimal solution for the perception period T. The event is recorded and jumps to step 5.2. Otherwise, exclude the P event, regroup the selected M cognitive users, change the T value, and execute step 5.1 again.
步骤5.2、从步骤5.1中只能知晓某P事件是否满足最低要求，但不能获得该P事件的整体感知性能，为求解(11)式中感知周期T的最优解，需评估事件的整体感知性能和一个周期内的总能量消耗。为评估总体感知性能，在某P事件中，对各组的检测概率和虚警概率做加权平均，即用加权平均检测概率和加权平均虚警概率来近似P事件的整体检测概率和虚警概率；依据图1中的交错分组模型，设：Step 5.2: From step 5.1, we can only know whether a certain P event meets the minimum requirements, but we cannot obtain the overall perception performance of the P event. To solve the optimal solution of the perception period T in formula (11), we need to evaluate the overall perception performance of the event and the total energy consumption within a period. To evaluate the overall perception performance, in a certain P event, the detection probability and false alarm probability of each group are weighted averaged, that is, the weighted average detection probability is used. and weighted average false alarm probability To approximate the overall detection probability and false alarm probability of event P; according to the staggered grouping model in Figure 1, let:
则和的计算分别如式(13)和式(14)所示：but and The calculations of are shown in formula (13) and formula (14) respectively:
而P事件一个感知周期内总的能量消耗重新表述为ET,k(T)：The total energy consumption of a P event in a sensing cycle is restated asET,k (T):
当被记录的所有T值计算完和ET(T)后，执行步骤S5.3；When all the recorded T values are calculated AfterET (T), execute step S5.3;
步骤5.3、求解最优感知周期T的问题，可转换为最优分配感知时间的问题，根据(11)式的要求，分组的标准可分为：①一个周期内较小的能量消耗ET(T)；②较大的检测概率③较小的虚警概率为取得ET(T)、和之间的平衡，利用动态规划算法对式(5)进行求解，即可获得最优感知周期为：Step 5.3: Solving the problem of the optimal sensing period T can be converted into the problem of optimally allocating sensing time. According to the requirements of formula (11), the grouping criteria can be divided into: ① Smaller energy consumptionET (T) within a period; ② Larger detection probability ③ Smaller false alarm probability To obtainET (T), and The balance between them can be achieved by solving equation (5) using the dynamic programming algorithm to obtain the optimal sensing period:
式(16)中，α1、α2和α3是加权因子，且α1+α2+α3＝1，δ()是评价指数，获得最优T值后，可同时获得ET、和In formula (16), α1 , α2 and α3 are weighting factors, and α1 + α2 + α3 = 1, δ() is the evaluation index. After obtaining the optimal T value,ET , and
为了验证本发明的一种认知无线电网络中优化能效与感知性能的动态分组算法性能，设置了三组实验。其中第一组实验是验证本发明动态分组算法的感知性能，通过检测概率和虚警概率两项核心指标来描述感知性能。第二组实验是验证本发明动态分组算法的能量效率，通过不同算法的能量效率对比来描述分组算法的性能，具体采用现有的等增益结合算法和节点选择算法与本发明的动态分组算法进行对比。第三组实验是寻找在本发明的动态分组算法的基础上的最优感知周期，以及在获得最优感知周期后认知无线电网络的工作寿命。所有实验均在考虑路径损耗和加性高斯白噪声的条件下进行的蒙特卡罗仿真。In order to verify the performance of a dynamic grouping algorithm that optimizes energy efficiency and perception performance in a cognitive radio network of the present invention, three groups of experiments were set up. The first group of experiments is to verify the perception performance of the dynamic grouping algorithm of the present invention, and the perception performance is described by two core indicators: detection probability and false alarm probability. The second group of experiments is to verify the energy efficiency of the dynamic grouping algorithm of the present invention, and to describe the performance of the grouping algorithm by comparing the energy efficiency of different algorithms. Specifically, the existing equal gain combination algorithm and node selection algorithm are used to compare with the dynamic grouping algorithm of the present invention. The third group of experiments is to find the optimal perception period based on the dynamic grouping algorithm of the present invention, and the working life of the cognitive radio network after obtaining the optimal perception period. All experiments are Monte Carlo simulations conducted under the conditions of considering path loss and additive Gaussian white noise.
在仿真实验中，设有36个认知用户随机分布在边长为1000米的正方形区域内，主用户工作频率为50％，主用户发射功率设定为常数，认知用户的采用频率为1MHz，频谱感知的初始周期设定为10毫秒，后续感知周期的时长由周期优化算法确定。仿真实验用的计算机性能：处理器为Intel(R) Core(TM) i5-8500 CPU @3.00GHz，RAM为8.00GB，64位操作系统，基于x64的处理器，Windows 10专业版系统。In the simulation experiment, 36 cognitive users are randomly distributed in a square area with a side length of 1000 meters. The main user's working frequency is 50%, the main user's transmission power is set to a constant, the cognitive user's adopted frequency is 1MHz, the initial cycle of spectrum sensing is set to 10 milliseconds, and the duration of subsequent sensing cycles is determined by the cycle optimization algorithm. The computer performance used in the simulation experiment: the processor is Intel(R) Core(TM) i5-8500 CPU @3.00GHz, RAM is 8.00GB, 64-bit operating system, x64-based processor, Windows 10 Professional system.
在一种优选的实施例中，为了证明本发明的动态分组算法的频谱感知性能的优越性，将动态分组算法与等增益结合算法和节点选择算法进行了性能比较，并采用了频谱检测概率和虚警概率两项感知性能的核心指标来证明分组算法的性能。从图3(a)中可看出本实施例中的分组算法的检测概率(Pd)在信噪比(SNR)低于-10dB时高于等增益结合算法和节点选择算法，这是因为本实施例中的分组算法是对选定的认知用户分成两组，两组交替工作，不会间断对主用户的保护，所以检测概率比其它算法更高。而节点选择算法的检测概率之所以高于等增益结合算法是因为节点选择算法只选择性能优良的部分用户参与协作感知操作，而等增益结合算法是所有认知用户均参与协作感知，该算法中部分用户由于地理位置不佳或设备损坏等原因而不能获得理想的感知结果，这样的用户对协作感知没有贡献，所以节点选择算法的检测概率会高于等增益结合算法。从图3(b)中可看出本实施例中的虚警概率(Pf)在信噪比低于-20dB时均低于等增益结合算法和节点选择算法，这是因为本实施例中的分组算法事先对认知用户进行了可靠度评估，在分组之前对所有用户进行可靠度值排名，只选择排名靠前的M个用户参与协作感知，这极大的降低了虚警概率。In a preferred embodiment, in order to prove the superiority of the spectrum sensing performance of the dynamic grouping algorithm of the present invention, the dynamic grouping algorithm is compared with the equal gain combination algorithm and the node selection algorithm, and the two core indicators of the sensing performance, spectrum detection probability and false alarm probability, are used to prove the performance of the grouping algorithm. It can be seen from FIG3 (a) that the detection probability (Pd ) of the grouping algorithm in this embodiment is higher than that of the equal gain combination algorithm and the node selection algorithm when the signal-to-noise ratio (SNR) is lower than -10dB. This is because the grouping algorithm in this embodiment divides the selected cognitive users into two groups, and the two groups work alternately without interrupting the protection of the main user, so the detection probability is higher than that of other algorithms. The reason why the detection probability of the node selection algorithm is higher than that of the equal gain combination algorithm is that the node selection algorithm only selects some users with good performance to participate in the collaborative sensing operation, while the equal gain combination algorithm is that all cognitive users participate in the collaborative sensing. In this algorithm, some users cannot obtain ideal sensing results due to poor geographical location or equipment damage. Such users do not contribute to the collaborative sensing, so the detection probability of the node selection algorithm will be higher than that of the equal gain combination algorithm. It can be seen from Figure 3(b) that the false alarm probability (Pf ) in this embodiment is lower than that of the equal gain combination algorithm and the node selection algorithm when the signal-to-noise ratio is lower than -20dB. This is because the grouping algorithm in this embodiment performs a reliability assessment on the cognitive users in advance, ranks all users by reliability values before grouping, and only selects the top M users to participate in collaborative sensing, which greatly reduces the false alarm probability.
在一种优选的实施例中，为了证明本发明的动态分组算法在能量效率上的优越性，将本发明的动态分组算法与等增益结合算法和节点选择算法进行了比较。这三种算法的比较在同一条件下进行。为了显示本发明的动态分组算法在不同信噪比下优越性能，在实验过程中改变信噪比，以验证在不同信噪比下能量效率。能量效率实验以满足感知性能为前提，以一个感知周期为计量单位。从图4中可看出本实施例中的分组算法在不同信噪比下的能量效率均高于节点选择算法和等增益结合算法，这是因为动态分组算法只是其中一组认知用户在执行频谱感知操作，而另一组认知用户在执行数据传输操作或处于静默状态，这可极大减少频谱感知的能耗，增加用于数据传输的能量或者延长认知无线电网络的工作寿命，能量效率得到极大提高。只有当信噪比低于-25dB时，频谱感知操作时间增加，数据传输时间或静默时间减少，能效会低于50％，当信噪比高于-20dB时，分组算法能效高于50％，随着信噪比的增加，分组算法的能效逐步提升，当信噪比达到0dB时，能效可趋向于1，因为这时感知操作时间极少，而数据传输时间或静默时间近似占据整个周期。节点选择算法的能效在不同信噪比下均高于等增益结合算法，这是因为节点选择算法并不是所有认知用户均参与频谱感知操作，而等增益结合算法是所有用户均参与频谱感知操作，节点选择算法在频谱感知操作上消耗的能量小于等增益结合算法，因此能效更高。In a preferred embodiment, in order to prove the superiority of the dynamic grouping algorithm of the present invention in energy efficiency, the dynamic grouping algorithm of the present invention is compared with the equal gain combination algorithm and the node selection algorithm. The comparison of these three algorithms is carried out under the same conditions. In order to show the superior performance of the dynamic grouping algorithm of the present invention under different signal-to-noise ratios, the signal-to-noise ratio is changed during the experiment to verify the energy efficiency under different signal-to-noise ratios. The energy efficiency experiment is based on the premise of meeting the perception performance and takes one perception cycle as the unit of measurement. It can be seen from Figure 4 that the energy efficiency of the grouping algorithm in this embodiment under different signal-to-noise ratios is higher than that of the node selection algorithm and the equal gain combination algorithm. This is because the dynamic grouping algorithm is only one group of cognitive users performing spectrum sensing operations, while the other group of cognitive users is performing data transmission operations or is in a silent state, which can greatly reduce the energy consumption of spectrum sensing, increase the energy used for data transmission or extend the working life of the cognitive radio network, and the energy efficiency is greatly improved. Only when the signal-to-noise ratio is lower than -25dB, the spectrum sensing operation time increases, the data transmission time or silence time decreases, and the energy efficiency is lower than 50%. When the signal-to-noise ratio is higher than -20dB, the energy efficiency of the grouping algorithm is higher than 50%. As the signal-to-noise ratio increases, the energy efficiency of the grouping algorithm gradually increases. When the signal-to-noise ratio reaches 0dB, the energy efficiency can approach 1, because the sensing operation time is very short at this time, and the data transmission time or silence time occupies approximately the entire cycle. The energy efficiency of the node selection algorithm is higher than that of the equal-gain combination algorithm at different signal-to-noise ratios. This is because the node selection algorithm does not have all cognitive users participating in the spectrum sensing operation, while the equal-gain combination algorithm is that all users participate in the spectrum sensing operation. The node selection algorithm consumes less energy in the spectrum sensing operation than the equal-gain combination algorithm, so the energy efficiency is higher.
在一种优选的实施例中，为了证明本发明的动态分组算法在延长认知无线电网络工作寿命上的优越性，与等增益结合算法和节点选择算法进行了比较。这三种算法的比较是在同一条件下进行。图5中的横坐标表示感知周期数量，纵坐标表示认知无线电网络的剩余能量，单位为焦耳(J)，从图5中可以看出，随着感知周期数量的增加，这三种算法的剩余能量均在迅速减少，但本实施例的动态分组算法的剩余能量始终高于其它两种算法，在工作155个周期时，分组算法的剩余能量比节点选择算法高出38.5％，比等增益结合算法高出101.2％，这充分证明了本实施例的动态分组算法对节省能量的有效性。这是因为发明的动态分组算法同时参与协作感知的认知用户数相比其它两种算法更少，则剩余能量更多，可执行的感知周期更多，即延长了认知无线电网络的工作寿命。节点选择算法因为只选择了部分认知用户参与协作感知，所消耗的能量小于等增益结合算法，所以其剩余能量始终高于等增益结合算法。当大于650个感知周期数时，三种算法的剩余能量近似相等，并随着感知周期数的持续增大剩余能量均趋向于零，这也说明不论采用哪种算法，在持续不断的长时间工作后，认知无线电网络的剩余能量终会耗尽，这时就需要更换新的电池，认知无线电网络才能重新开始工作。In a preferred embodiment, in order to prove the superiority of the dynamic grouping algorithm of the present invention in extending the working life of the cognitive radio network, it is compared with the equal gain combination algorithm and the node selection algorithm. The comparison of these three algorithms is carried out under the same conditions. The horizontal axis in Figure 5 represents the number of perception cycles, and the vertical axis represents the residual energy of the cognitive radio network, in joules (J). It can be seen from Figure 5 that with the increase in the number of perception cycles, the residual energy of the three algorithms is rapidly decreasing, but the residual energy of the dynamic grouping algorithm of this embodiment is always higher than that of the other two algorithms. When working for 155 cycles, the residual energy of the grouping algorithm is 38.5% higher than that of the node selection algorithm and 101.2% higher than that of the equal gain combination algorithm, which fully proves the effectiveness of the dynamic grouping algorithm of this embodiment in saving energy. This is because the number of cognitive users participating in the collaborative perception of the dynamic grouping algorithm of the invention is less than that of the other two algorithms, so the residual energy is more and the executable perception cycles are more, that is, the working life of the cognitive radio network is extended. Because the node selection algorithm only selects some cognitive users to participate in collaborative perception, the energy consumed is less than the equal gain combination algorithm, so its residual energy is always higher than the equal gain combination algorithm. When the number of sensing cycles is greater than 650, the residual energy of the three algorithms is approximately equal, and as the number of sensing cycles continues to increase, the residual energy tends to zero. This also shows that no matter which algorithm is used, after continuous long-term work, the residual energy of the cognitive radio network will eventually be exhausted. At this time, new batteries need to be replaced before the cognitive radio network can start working again.
能量效率是认知无线电网络发展的重要指标，为了提高认知无线电网络的能量效率，本发明中提出了一种认知无线电网络中优化能效与感知性能的动态分组算法，该算法只选择偶数个可靠度值靠前的认知用户参与协作感知，排除性能不可靠的认知用户，不但有效提升了频谱感知性能，也极大的提高了能量效率。实验结果表明本发明的分组算法在信噪比等于-20dB时，频谱检测概率高于传统算法50％，虚警概率低于传统算法10％，能量效率高于传统算法15％，这有利证明了本发明中的算法具有优越的感知性能和极高的能量效率。Energy efficiency is an important indicator for the development of cognitive radio networks. In order to improve the energy efficiency of cognitive radio networks, the present invention proposes a dynamic grouping algorithm for optimizing energy efficiency and perception performance in cognitive radio networks. The algorithm only selects cognitive users with even reliability values to participate in collaborative perception, and excludes cognitive users with unreliable performance, which not only effectively improves the spectrum perception performance, but also greatly improves the energy efficiency. The experimental results show that when the signal-to-noise ratio is equal to -20dB, the spectrum detection probability of the grouping algorithm of the present invention is 50% higher than that of the traditional algorithm, the false alarm probability is 10% lower than that of the traditional algorithm, and the energy efficiency is 15% higher than that of the traditional algorithm, which proves that the algorithm in the present invention has superior perception performance and extremely high energy efficiency.
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
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