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.     

认知无线多跳网中保证信干噪比的频谱分配算法

为解决无线多跳网络在固定频谱分配方式下所固有的信道冲突等问题,利用认知无线电的动态频谱分配技术,提出了一种适用于次用户组成的无线多跳网络的、underlay方式下的全分布式频谱分配算法。该算法将频谱分配问题建模成静态非合作博弈,证明了纳什均衡点的存在,并给出了一种求解纳什均衡点的迭代算法。大量仿真实验证明,该算法能实现信道与功率的联合分配,在满足主用户干扰功率限制的同时,保证次用户接收信干噪比要求。     

无线传感器网络基于容量和传输能耗的功率与信道联合博弈算法

针对无线传感器网络(WSNs)日益增大的干扰导致网络容量下降的问题,同时考虑到网络能量有限性,该文综合网络容量和链路传输能耗,构建了高容量低传输能耗的功率控制与信道分配联合博弈模型,并通过理论分析证明该模型存在最优功率和最优信道。继而采用最佳响应策略,在该博弈模型基础上提出了一种功率控制与信道分配联合优化算法(PCOA),理论证明其能收敛到纳什均衡状态,且具有较小的信息复杂度。最后,仿真结果表明,PCOA算法能够达到降低网络干扰和链路能耗,增大网络容量的目的。     

认知MIMO系统中波束成形和功率的联合控制博弈算法

针对认知MIMO系统中波束成形和功率的联合优化问题,建立了非合作可分离对策联合博弈模型,提出了新的代价函数,根据KT条件证明了该模型中的各个子博弈均存在最优纳什均衡,从而得到联合博弈存在最优纳什均衡的结论,并提出了新的交替迭代算法。仿真结果表明,该算法有较快的收敛速度;同时,在动态环境下,算法也能达到最优纳什均衡,具有较好的稳定性。     

认知无线电系统中一种改进的功率控制博弈算法

分布式功率控制是认知无线电(CR)系统中的关键技术之一,它直接影响到无线系统的性能。本文采用了博弈论的方法来实现对CR用户的分布式功率控制,在David Goodman的非协作博弈算法的基础上,给出了一种改进的效用函数,它使各用户在满足要求信干比条件下发送功率最小,同时使整个系统内由各种干扰引起的失真最小。本文通过理论推导证明了新的效用函数存在纳什均衡,并且均衡点唯一,同时仿真验证了该算法的收敛性,仿真结果表明这种兼顾用户自身利益以及用户间公平性的效用函数能降低发送功率,并且有效提高CR系统的性能。     

一种基于不完美信息博弈的多冲突域信道分配算法

无线网络中信道分配的好坏将极大地影响网络整体性能,为了解决和优化此问题,提出了一种基于不完美信息博弈的信道分配算法。假设网络场景为多跳Ad Hoc网络,因此对其采用多冲突域建模更符合实际。算法通过不断循环改变各用户无线电的信道试图接近纳什均衡点,仿真结果表明算法能够使信道分配策略接近纳什均衡信道分配,同时相比单冲突域情况在多冲突域中更能最大化信道使用率。     

博弈条件下雷达波形设计策略研究

为提高电子战中弹载雷达检测性能,该文提出基于纳什均衡的雷达波形设计方法。首先建立电子战条件下雷达与干扰信号博弈模型,基于最大化信干噪比(SINR)准则,分别设计了雷达和干扰的波形策略;然后通过数学推导论证了博弈纳什均衡解的存在性,设计了一种重复剔除严格劣势的多次迭代注水方法来实现纳什均衡;通过二步注水法推导了非均衡的maxmin优化方案;最后通过仿真实验测试不同策略下雷达检测性能。仿真结果证明,基于纳什均衡的雷达信号设计有助于提升博弈条件下雷达检测性能,对比未博弈时,雷达检测概率最高可提升12.02%,较maxmin策略最高可提升3.82%,证明所设计的纳什均衡策略更接近帕累托最优。     

DF协作中继网络基于买者/卖者博弈的中继选择和功率分配策略

该文针对采用解码-转发(DF)协议的协作中继网络,提出了一种基于买者-卖者博弈的中继选择和功率分配策略,通过将用户建模为买者,可以以最大效用为标准选择最优中继和确定最佳的购买功率;将中继建模为卖者,可通过先市场后利润的功率价格调整策略获得最大的利润。分析了两者博弈达到平衡的条件并进行了仿真,结果验证了纳什均衡点的存在并表明,该策略计算量少,收敛速度快,实用性强,在兼顾用户和中继节点的利益的同时可以有效提高用户的传输速率,扩大基站的覆盖范围,提高功率利用效率。     

车联网中V2R通信功率分配策略

针对车载应用对车辆与路侧单元(V2R)的通信质量及系统吞吐量要求越来越高,提出V2R通信功率分配策略,以提高通信链路质量及系统的吞吐量。该策略利用博弈论对功率分配过程进行分析并找出最优分配方案。当效用函数经过多次迭代而达到纳什均衡时,表明系统已经获得最优的功率分配方案。仿真结果表明,本文提出的车联网功率分配策略有利于减小各通信链路之间的干扰,且达到了提升系统吞吐量的目的。     

异构无线网络中基于Stackelberg博弈的分布式定价和资源分配算法

针对异构无线网络资源管理问题,结合多主多从Stackelberg博弈模型,提出了一种同时满足网络运营商和用户效用最大的异构无线网络定价和资源分配方案。首先设计了一种基于收益和花费的移动用户效用函数,并证明在运营商的价格确定后,效用函数满足凹函数的条件,保证了移动用户间非合作博弈的纳什均衡点存在。为了获取移动用户的最优带宽策略和运营商的最优价格策略,提出了一种分布式迭代算法。最后通过仿真实验取得了参与者的最优策略和子博弈完美纳什均衡。     

Repeated game theory as a framework for algorithm development in communication networks

This article presents a tutorial on how to use repeated game theory as a framework for algorithm development in communication networks. The article starts by introducing the basis of one‐stage games and how the outcome of such games can be predicted, through iterative elimination and Nash equilibrium. In communication networks, however, not all problems can be modeled using one‐stage games. Some problems can be better modeled through multi‐stage games, as many problems in communication networks consist of several iterations or decisions that need to be made over time. Of all the multi‐stage games, the infinite‐horizon repeated games were chosen to be the focus in this tutorial, because optimal equilibrium settings can be achieved, contrarily to the suboptimal equilibria achieved in other types of game. With the theoretical concepts introduced, it is then shown how the developed game theoretical model, and devised equilibrium, can be used as a basis for the behavior of an algorithm, which is supposed to solve a particular problem and will be running at specific network devices. Copyright © 2015 John Wiley & Sons, Ltd.     

Non-cooperative uplink power control in cellular radio systems

This paper presents uplink power control in cellular radio systems from an economic point of view. A utility function is defined for each mobile user, which reflects the user's preference regarding the carrier-to-interference ratio (CIR) and the transmitter power. We observe that, on one hand, mobile users prefer to transmit at a lower power for a fixed CIR. On the other hand, for a given transmitter power, users prefer to obtain a better CIR. Based on this observation, we make two fundamental assumptions about the utility function. We formulate the uplink power control problem as a non-cooperative N-person game. Under the two assumptions that we make about the utility function, there exists a Nash equilibrium. To show the generality of the framework, we study one special case by defining the utility as a linear function. This model encompasses many of the widely studied power control problems. A more general case is also studied by defining utility as an exponential function. This paper establishes a general economic-based framework for studying resource management in wireless networks and points out new research directions.     

Joint route selection and resource allocation in multihop wireless networks based on a game theoretic approach

In this work we aim to design simple, distributed self-configuring solutions for the problem of route selection and channel and power allocation in multihop autonomous wireless systems using a game theoretic perspective. We propose and compare three games with different levels of complexity: a potential flow game where players need complete network knowledge, a local flow game requiring full information of the flow and a low complexity cooperative link game which works with partial information of the flow. All these games have been designed to always assure the convergence to a stable point in order to be implemented as distributed algorithms. To evaluate their quality, we also obtain the best achievable performance in the system using mathematical optimization. The system is modeled with the physical interference model and two different definitions of the network utility are considered: the number of active flows and the aggregated capacity in bps. Results show that the proposed games approach the centralized solution, and specially, that the simpler cooperative link game provides a performance close to that of the flow games.     

Power allocation games in wireless networks of multi-antenna terminals

We consider wireless networks that can be modeled by multiple access channels in which all the terminals are equipped with multiple antennas. The propagation model used to account for the effects of transmit and receive antenna correlations is the unitary-invariant-unitary model, which is one of the most general models available in the literature. In this context, we introduce and analyze two resource allocation games. In both games, the mobile stations selfishly choose their power allocation policies in order to maximize their individual uplink transmission rates; in particular they can ignore some specified centralized policies. In the first game considered, the base station implements successive interference cancellation (SIC) and each mobile station chooses his best space-time power allocation scheme; here, a coordination mechanism is used to indicate to the users the order in which the receiver applies SIC. In the second framework, the base station is assumed to implement single-user decoding. For these two games a thorough analysis of the Nash equilibrium is provided: the existence and uniqueness issues are addressed; the corresponding power allocation policies are determined by exploiting random matrix theory; the sum-rate efficiency of the equilibrium is studied analytically in the low and high signal-to-noise ratio regimes and by simulations in more typical scenarios. Simulations show that, in particular, the sum-rate efficiency is high for the type of systems investigated and the performance loss due to the use of the proposed suboptimum coordination mechanism is very small.     

Bottleneck Routing Games in Communication Networks

We consider routing games where the performance of each user is dictated by the worst (bottleneck) element it employs. We are given a network, finitely many (selfish) users, each associated with a positive flow demand, and a load-dependent performance function for each network element; the social (i.e., system) objective is to optimize the performance of the worst element in the network (i.e., the network bottleneck). Although we show that such "bottleneck" routing games appear in a variety of practical scenarios, they have not been considered yet. Accordingly, we study their properties, considering two routing scenarios, namely when a user can split its traffic over more than one path (splittable bottleneck game) and when it cannot (unsplittable bottleneck game). First, we prove that, for both splittable and unsplittable bottleneck games, there is a (not necessarily unique) Nash equilibrium. Then, we consider the rate of convergence to a Nash equilibrium in each game. Finally, we investigate the efficiency of the Nash equilibria in both games with respect to the social optimum; specifically, while for both games we show that the price of anarchy is unbounded, we identify for each game conditions under which Nash equilibria are socially optimal.     

Joint drift analysis for multigroup slotted ALOHA: stability withmaximum utilization

In this paper, a slotted ALOHA system is considered where the users in the system are divided into two power groups. At the mobile base station, members of one group are received at a higher power level than those in the second group. This power division can be used to increase the overall throughput of the ALOHA system from 0.368 to 0.53. The two-group ALOHA system is stabilized by extending the idea of joint drift analysis, originally devised for a single group to the two-group case and the paper proposes a criterion for choosing a set of control parameters. The control parameter selection criterion guarantees one stable equilibrium point for each group, which enables the system to operate at the maximum throughput point. Combinations of the "optimal" control parameters are used to update the estimated number of users in the backlog, which is required in calculating the retransmission probability for users in each group. Simulation results demonstrate the superiority of the proposed control strategy over existing stability schemes     

CR系统中基于微分博弈的功率控制算法

该文针对认知无线电系统动态性的特点,将微分博弈理论应用在认知无线电系统的功率控制中,建立了功率控制的非合作微分博弈模型,提出了一种基于微分博弈的分布式非合作功率控制算法。该算法在满足认知用户平均功率门限和QoS需求的基础上,实现了分布式动态功率控制,获得了反馈纳什均衡解析解。仿真结果表明,该算法可有效控制各认知用户的发射功率,增加系统吞吐量,提高系统性能。     

认知无线网络基于信号博弈的分布式功率控制算法

针对认知无线网络分布式环境下信道信息不对称导致资源分配冲突的问题,该文提出一种基于信号博弈的分布式功率分配算法。考虑具有竞争关系的次用户在不使用控制信道的情况下,通过信号博弈策略依次选择功率分配策略,达到信道信息共享的目的,能有效避免对主用户正常传输的干扰以及竞争次用户之间功率分配冲突。该文对均衡结果进行分析,仿真结果表明该算法可以有效估计信道增益,次用户的吞吐量相对于已有研究得到明显提升。     

CDMA Uplink Power Control as a Noncooperative Game

We present a game-theoretic treatment of distributed power control in CDMA wireless systems. We make use of the conceptual framework of noncooperative game theory to obtain a distributed and market-based control mechanism. Thus, we address not only the power control problem, but also pricing and allocation of a single resource among several users. A cost function is introduced as the difference between the pricing and utility functions, and the existence of a unique Nash equilibrium is established. In addition, two update algorithms, namely, parallel update and random update, are shown to be globally stable under specific conditions. Convergence properties and robustness of each algorithm are also studied through extensive simulations.     

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.