基于潜在博弈的认知无线电频谱分配研究

为解决认知无线电网络中认知用户、授权用户共存情况下的频谱分配问题,提出了一种基于潜在博弈的认知无线电频谱分配模型,该模型以最小化系统总干扰水平为目标,认知用户采用避免机会浪费的改进型策略动态调整规则进行频谱选择,经过有限改进路径快速收敛到博弈的纳什均衡点。在30个认知用户和2个授权用户共存的场景下进行仿真实验,算法以较快的收敛速度实现了频谱分配的目标,证明了模型及算法的可行有效。     

基于非合作博弈论的未充分使用频谱定价算法

提出一种适用于＂未充分使用＂频段的定价算法。该算法基于非合作博弈模型,引入未充分使用频谱的收益因子,各授权用户在保证现有通信质量前提下以最大化收益为目标,通过用户之间的定价竞争,实现了一种分布式的动态频谱分配。仿真结果表明,该算法在频谱需求较大的情况下能有效提高＂未充分使用＂频谱的效益,同时算法收敛速度较快,便于实际应用。     

基于频谱数据库的分布式动态频谱接入算法

频谱数据库是一种直接获得频谱信息的方式,针对在复杂多变的网络环境中用户之间缺少信息交互,研究了数据库协助和没有数据库情况下的动态频谱接入算法。一方面,次用户通过历史感知数据估计信道的可用性而进行信道选择,另一方面,次用户通过数据库获得更加可靠的频谱信息从而做出策略。证明了用户之间的博弈是一个超模博弈,通过提出的分布式学习算法能收敛到一个纯策略纳什均衡点。仿真结果表明,提出的联合数据库感知算法和数据库协助算法比依靠感知的结果收敛更快,获得的系统吞吐量接近最大,减少了用户决策的时延,提高了频谱利用率。     

一种利用优先经验回放深度Q-Learning的频谱接入算法

针对认知无线传感器网络中频谱接入算法的频谱利用率不高、重要经验利用率不足、收敛速度慢等问题,提出了一种采用优先经验回放双深度Q-Learning的动态频谱接入算法。该算法的次用户对经验库进行抽样时,采用基于优先级抽样的方式,以打破样本相关性并充分利用重要的经验样本,并采用一种非排序批量删除方式删除经验库的无用经验样本,以降低能量开销。仿真结果表明,该算法与采用双深度Q-Learning的频谱接入算法相比提高了收敛速度;与传统随机频谱接入算法相比,其阻塞概率降低了6%~10%,吞吐量提高了18%~20%,提高了系统的性能。     

基于Stackelberg博弈的动态频谱接入控制

为了解决多主用户和多次级用户共存网络的频谱资源分配问题,提出了一种基于斯塔科尔伯格（Stackelberg）博弈的动态频谱接入控制算法。该算法通过三阶段Stackelberg博弈模拟主用户频谱竞价,博弈过程中次级用户以最大化传输速率为目的接入主用户频谱,同时设计了一种迭代过程来求解纳什均衡。实验计算与结果分析证明了纳什均衡唯一存在性的充要条件,并说明了迭代过程的收敛性以及主用户最佳效用的影响因素。     

一种支持服务区分的动态频谱接入机制

动态频谱接入技术是实现认知无线电(CR)网络的重要技术之一,其可以灵活地对频谱空洞加以利用并能够有效地缓解频谱紧缺的窘境。文章设计了一种支持服务区分的动态频谱接入机制,将次用户分为高优先级次用户和低优先级次用户;然后利用强制优先排队理论对认知无线电网络中有服务区分的动态频谱接入过程进行模拟建模,并对高优先级次用户和低优先级次用户的切换率、接入失败率进行了仿真分析,这些因子对于用户的接入性能起着关键性作用。     

分枝定界在多级动态频谱分配中的应用

针对认知无线电动态频谱分配中认知用户较多,传统优化算法收敛时间较长的问题,本文结合分枝定界原理提出一种多级动态频谱分配算法。首先建立基于用户需求的多级动态频谱分配模型,然后借助图着色理论,将问题转化为函数优化问题,最后借助分枝定界算法,通过把全部可行的解空间不断分割为越来越小的子集,从而实现了对该多级模型的频谱分配。仿真实验采用与遗传算法比较,通过对二者认知用户接入量和系统网络效益的分析比较,表明该算法对处理多级DSA分配问题的优越性,且所提算法具有较小的计算复杂度,具有较高的应用价值。      

一种基于认知无线电的动态频谱接入MAC方案

在传统的无线通信系统中,频谱的分配是固定的。但是由于通信过程的突发性,这些频谱的使用率很低。另一方面,随着无线通信和多媒体的高速发展和广泛应用,无线频谱资源日趋紧张。如何提高频谱利用率已经成为迫切需要解决的问题。一种可行的思路是把这些授权频谱向未授权用户开放,未授权用户采用动态频谱接入技术,在不对授权用户造成干扰的前提下使用频谱。本文以认知无线电技术(Cognitive Radio,CR)为基础,提出了一种基于CR的动态频谱接入MAC方案(CR-Ad Hoc-MAC)。该方案允许未授权用户自适应地选取可用带宽,实现了动态频谱接入,有效地提高了频谱利用率。     

基于呼叫接纳控制（CAC）的动态频谱接入机制

在认知无线电系统中,认知用户利用授权用户的频谱空洞进行通信,达到提高频谱利用率的目的。动态频谱接入技术是认知无线电系统的关键技术,在文中我们提出了一种使用呼叫接纳控制（CAC）的动态频谱接入机制,经过仿真验证该机制能有效地减小认知无线电系统中认知用户的切换概率,同时次用户对信道的使用率会略有下降。     

基于K-均值聚类的动态频谱接入技术

为了有效降低次用户接入授权信道时对主用户造成的干扰以及次用户被阻塞的概率,提出了一种基于K-均值聚类的动态频谱接入方法.该方法首先将关注的频谱划分为若干子信道,尔后对每个子信道进行特征提取,借助频谱特征,用K-均值算法对子信道进行聚类,将子信道分别纳入白色、灰色和黑色频谱池.次用户在接入信道时,重点选择白色频谱池中的信道接入.理沦分析和仿真结果表明,利用该方法能够有效降低次用户对主用户的干扰和次用户被阻塞的概率,提高信道接入效率.     

未知环境中基于图型博弈和Multi-Q学习的动态信道选择算法

研究了分布式无线网络中,没有任何信息交换、也没有环境变化先验知识情况下的动态信道接入算法。运用图型博弈模型对用户的实际拓扑进行建模分析,证明了此博弈模型存在纯策略纳什均衡并且此纳什均衡是全局最优解。同时,采用multi-Q学习求解模型的纯策略纳什均衡解。仿真实验验证了multi-Q学习能获得较高的系统容量以及在图型博弈模型中用户的效用主要由节点的度决定,而与用户数量无直接关系。     

Hybrid model of inter-stage spectrum trading in multistage game-theoretic framework

Dynamic spectrum access(DSA),consisting of spectrum sharing and spectrum trading stage,becomes a promising approach to increase the efficiency of spectrum usage and system performance.In this paper,from the perspective of individual interest optimization,we focus on strategy adaptation of network users and their interaction in spectrum trading process.Considering adverse effects on decision-making accuracy and the fairness among network users via local information acquirement,a hybrid game model based on global information of relevant spectrum is proposed to formulate intelligent behaviors of both primary and secondary users.Specifically,by using the evolutionary game theory,a spectrum-selection approach for the evolution process of secondary users is designed to converge to the evolutionary equilibrium gradually.Moreover,competition among primary users is modeled as a non-cooperative game and an iterative algorithm is employed to achieve the Nash equilibrium.The simulation results show that the proposed hybrid game model investigates network dynamics under different network parameter settings.     

Reinforcement-Learning-Based Double Auction Design for Dynamic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, an important issue is to share the detected available spectrum among different secondary users to improve the network performance. Although some work has been done for dynamic spectrum access, the learning capability of cognitive radio networks is largely ignored in the previous work. In this paper, we propose a reinforcement-learning-based double auction algorithm aiming to improve the performance of dynamic spectrum access in cognitive radio networks. The dynamic spectrum access process is modeled as a double auction game. Based on the spectrum access history information, both primary users and secondary users can estimate the impact on their future rewards and then adapt their spectrum access or release strategies effectively to compete for channel opportunities. Simulation results show that the proposed reinforcement-learning-based double auction algorithm can significantly improve secondary users’ performance in terms of packet loss, bidding efficiency and transmission rate or opportunity access.     

填充式频谱共享中的功率分配算法分析

动态频谱共享技术允许认知用户接入未授权的频谱,可以有效地提高频谱资源的利用率。在分析了频谱共享的2种接入模式的基础上,提出了填充式频谱共享的系统模型。根据认知用户的效用函数,通过注水算法得到了用户间的功率迭代公式,进而求得了认知用户的功率和载波分配结果。仿真表明,该迭代算法在有效完成用户间功率与载波分配的同时能够在较短的时间内收敛。     

Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion

We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With a Bertrand game model, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future.     

Privacy risk adaptive access control model via evolutionary game

Aiming at the problem that in the private sensitive date centralized and opening information systems,a fine-grained and self-adaptive access control model for privacy preserving is desperately needed,thus the balance between privacy preserving and data access utility should be achieved,a rational multi-player risk-adaptive based access control model for privacy preserving was proposed.Firstly,the privacy risk values of access request and requester were formulized by the private information quantity of the requested dataset,and by using Shannon information.Secondly,a risk-adaptive based access control evolutionary game model was constructed by using evolutionary game under the supposing of bounded rational players.Furthermore,dynamic strategies of participants were analyzed by using replicator dynamics equation,and the method of choosing evolutionary stable strategy was proposed.Simulation and comparison results show that,the proposed model is effective to dynamically and adaptively preserve privacy and more risk adaptive,and dynamic evolutionary access strategies of the bounded rational participants are more suitable for practical scenarios.     

动态频谱接入中的侦测与避让技术

动态频谱接入技术对频谱实行动态管理,允许未授权用户在无干扰的情况下使用已授权用户的闲置频带,是解决现今无线频谱资源紧缺和利用率低等难题的有效途径,已成为无线通信领域的研究热点。本文介绍了动态频谱接入的侦测与避让方法,分析和讨论了各自的优缺点,并指出动态频谱接入需要进一步研究的问题和可能的发展方向。