认知无线电中基于中继位置和主用户位置的功率分配

针对认知无线电网络,提出了一种基于组间协作的联合直接传输和协作传输的传输机制,在满足干扰约束和峰值功率约束的情况下,通过分析瑞利衰落环境下放大转发协作单中继系统的性能,研究了中继位置和主用户位置对于系统的最优功率分配和系统吞吐量的影响,并对最优功率分配方案和等功率分配方案的性能进行了比较。仿真结果表明,中继位置和主用户的位置对于系统性能起着关键的作用,最优功率分配方案取得相对于等功率分配方案更优的性能,采用最优功率分配方案时,中继节点位于源节点和目的节点连线的中点且靠近认知用户时的系统性能最优。     

认知无线电中基于Stackelberg博弈的分布式功率分配算法

在underlay认知无线电场景中,为了让认知用户能随机地接入主用户正在使用的授权频段,且对主用户产生的干扰不高于主用户能够容忍的干扰温度门限,该文采用Stackelberg博弈机制进行认知用户的发射功率分配。将主用户作为模型中的leader,认知用户作为follower,认知用户使用主用户的授权频段时需以干扰功率为单位支付给主用户相应的费用,而主用户则可以通过调整价格,限制认知用户产生的总干扰功率不高于其所能容忍的干扰温度门限,以便获得最大收益。同时,不同认知用户间根据主用户制定的价格,进行非协作博弈。仿真结果表明,与集中式的最优功率分配算法相比,该文可通过简单的分布式功率分配算法获得与其相近的系统性能,且主用户与认知用户间只需进行少量的信息交互,这与需进行大量信息交互的集中式最优算法相比,具有较大的优势。     

认知无线电网络中基于协作水平的协作门限与功率联合优化

将协作传输技术用于认知无线电网络不仅增强了系统抗衰落能力,而且有效提高了频带利用率。但是,中继节点何时必须参与协助源节点传输信息是一个值得考虑的问题。该文提出一种基于协作水平的中继协作准则。首先给出协作水平的定义和表达式,并基于此式推导了系统平均带宽利用率和误符号率公式,然后由二者的折中得到关于协作门限和功率比的性能函数。为了使系统获得最优性能,文中采用粒子群优化算法联合优化协作门限和功率比。仿真结果表明,良好的源节点到目的节点的链路状态和尽量使用中继参与协作均有助于系统性能的最优化。     

认知无线电网络中最优分组长度设计

为满足不同种类多媒体应用的服务质量需求,研究了一个多等级多信道认知无线电网络中的资源分配问题。根据时延和吞吐量为该网络模型定义一个效用函数并最大化这个效用函数的期望值。首先用不考虑传输功率分配的"浪费功率的方式"设计分组长度。然后在选择分组长度时考虑传输功率分配提出一种基于贪婪算法的改进方式,得到了最优分组长度和相应的传输功率。MATLAB的仿真结果表明,在传输功率受限下,当达到相同效用值时,改进方式和"浪费功率的方式"相比能减小传输功率,并取得更加合理的分组长度,而且能更好地满足带宽需求,这是由于改进方式不仅考虑了各个信道上数据包的到达时间间隔,还考虑了信道增益。     

基于认知无线电系统的协作中继分布式功率分配算法

协作通信与直接通信相比能够显著地提高系统性能。协作通信中的一个关键问题是管理中继节点及有效地进行功率分配。尤其对于频谱共享的认知无线电(Cognitive Radio,CR)系统,协作方案的设计不仅要最大限度地提高认知网络协作的功率效率,而且需要最小化对主系统的干扰。该文针对认知无线电系统的协作通信问题,在多个中继节点与源节点协同通信的场景下,提出了一种基于放大转发(Amplify and Forward,AF)模式下的功率分配及联合优化算法,在保证主系统传输性能不受影响的前提下,提高认知系统的传输速率。仿真结果表明该文提出的自适应协作传输方案,和直接传输及等功率传输方案相比获得了进一步的性能增益,中断概率显著下降。     

基于OFDM的认知无线电系统功率分配研究

分析了基于OFDM的认知无线电系统中认知用户和授权用户之间的相互干扰,以及下行链路对认知用户进行功率分配的优化目标,并给出了速率最大化优化目标下的最优功率分配方案。授权用户能够承受的干扰功率成为认知无线电系统功率分配问题的主要限制因素,其限制了认知用户的发射功率和传输速率,认知用户传输产生的干扰必须满足授权用户能够承受的干扰功率限制。     

认知无线电网络中基于随机学习博弈的信道分配与功率控制

传统的认知无线电频谱分配算法往往忽略节点的传输功率对网络干扰的影响,且存在节点间交互成本高的问题.为此,通过量化传输功率等级,以最大化弹性用户收益为目标,构建联合频谱分配与功率控制非合作博弈模型,证明了该博弈为严格潜在博弈且收敛到纳什均衡点.进一步,将随机学习理论引入博弈模型,提出了基于随机学习的策略选择算法,并给出了该算法收敛到纯策略纳什均衡点的充分条件及严格证明.仿真结果表明,所提算法在少量信息交互前提下能获得较高的传输速率,并提升用户满意度.     

认知无线电网络中频谱分配算法

 随着新型无线业务的不断发展,频谱供需矛盾日益明显.认知无线电网络被认为是实现动态频谱共享、缓解频谱供需矛盾的重要途径,近年来相关研究受到了广泛关注.本文对认知无线电网络中的频谱分配研究进展进行了分析.论文首先介绍了认知无线电网络的技术背景,分析了认知无线电网络中频谱分配的关键问题和算法设计目标.在此基础上总结了主流频谱分配模型的设计思想与技术特点,并详细描述了各模型经典分配算法的实现机制.最后,对频谱分配研究趋势进行了展望.     

绿色认知无线电网络能量采集的最优功率控制

认知无线电与能量的结合提高了频谱的利用效率和对绿色能源的使用。为减弱对主用户的干扰以及次用户功率分配的有峰值限制,文章首先分析了认知无线电网络的系统模型,进而阐述了动态吞吐量最大化分配算法,最后通过实验分析和验证说明了该课题的最优功率控制的独特性和完备性。     

多信道认知用户能量高效的最优功率分配

针对多信道认知用户能量效率问题,提出认知用户能量效率优化模型,进而优化传输功率和感知帧长。首先,多信道认知用户基于多带宽联合能量检测方案,在一定传输功率和干扰功率限制下,建立了以单位数据能耗为目标的最优化问题;其次,通过非线性分式规划的对偶优化将目标函数转换为内点法求解形式;然后,结合内点法和二分法给出算法流程,进而配置最优功率和感知时间。仿真结果表明,对应不同通信环境采用该优化方法都能通过配置最优功率和感知时间达到能量效率最优。     

A New Power Allocation Algorithm in Cognitive Radio Networks

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks. Instead of using conventional primary user interference constraint, we give a new criterion called allowable signal to interference plus noise ratio （SINR） loss constraint in cognitive transmission to protect primary users. Considering power allocation problem for cognitive users over flat fading channels, in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user, we propose a new power allocation algorithm. The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.     

基于QoS和干扰温度约束的多用户认知无线电网络最优功率分配(英文)

Power allocation is an important issue for Cognitive Radio Networks(CRNs),since it needs to consider the Quality of Service(QoS) for Secondary Users(SUs) while maintaining the interference power to Primary User(PU) below the Interference Temperature(IT) threshold. In this paper, based on Euclidean projection, we propose a distributed power control algorithm with QoS requirements to minimise the total power consumption of SUs under the time-varying channel scenario. Considering the maximum transmit power constraints and the minimum signal to interference plus noise constraints for each SU, together with the IT constraints for each PU, the power allocation problem is transformed into a convex optimization problem without auxiliary variables, and is solved by the Lagrangian dual method with less information exchange.Simulation results demonstrate that the proposed scheme is superior to the Iterative Water-Filling Algorithm(IWFA).     

认知无线电网络中基于协作中继的资源分配算法

在认知无线电网络的协作中继机制下,中继节点利用其和源节点以及目的节点的不同公共信道为二者的通信转发数据,可以有效解决次用户的通信需求和可用带宽之间的矛盾,提高频谱利用率和系统吞吐量。基于协作中继的认知无线电网络中,不同通信链路上可能存在公共可用信道,使信道和中继的分配问题变得复杂。本文研究了公共信道存在的情况下系统的资源分配问题,基于网络最大流理论提出了两种算法:并行算法和贪婪算法,并分析了算法复杂度。仿真结果表明,两种算法都能够更有效地分配资源,提高频谱利用率,改善网络吞吐量。并行算法可以得到最优解,但其复杂度随信道公用程度的上升增长迅速,受节点并行处理能力的限制,只适用于信道公用程度较低的情况。贪婪算法不一定能得到最优解,但其复杂度较低,并且信道公用程度高时接近最优解,因此超出节点的并行处理能力后,可以选择贪婪算法。      

基于OFDM的认知无线电功率分配方法研究

针对认知无线电(Cognitive Radio,CR)需要提高频谱利用率,而正交频分复用(Orthogonal FrequencyDivision Multiplexing,OFDM)技术系统中针对子载波的功率分配算法计算复杂度高的缺点,对认知无线电中的频谱资源和功率分配方法进行了研究。提出了一种将整个信道分为多个子载波块进行功率分配的算法,该算法结合频谱检测和信道估计的结果,选取可用的通信频段并采用注水算法为每一个子载波段分配功率,并通过自适应调制实现吞吐量最大化。仿真结果表明,该算法可以有效提高频谱利用效率。     

Energy‐Efficiency Power Allocation for Cognitive Radio MIMO‐OFDM Systems

This paper studies energy‐efficiency (EE) power allocation for cognitive radio MIMO‐OFDM systems. Our aim is to minimize energy efficiency, measured by “Joule per bit” metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non‐convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy‐ efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.     

Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.

Energy‐Efficient Power Allocation for Cognitive Radio Networks with Joint Overlay and Underlay Spectrum Access Mechanism

Traditional designs of cognitive radio (CR) focus on maximizing system throughput. In this paper, we study the joint overlay and underlay power allocation problem for orthogonal frequency‐division multiple access–based CR. Instead of maximizing system throughput, we aim to maximize system energy efficiency (EE), measured by a “bit per Joule” metric, while maintaining the minimal rate requirement of a given CR system, under the total power constraint of a secondary user and interference constraints of primary users. The formulated energy‐efficient power allocation (EEPA) problem is nonconvex; to make it solvable, we first transform the original problem into a convex optimization problem via fractional programming, and then the Lagrange dual decomposition method is used to solve the equivalent convex optimization problem. Finally, an optimal EEPA allocation scheme is proposed. Numerical results show that the proposed method can achieve better EE performance.