Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

     

OFDMA毫微微小区网络中基于效用公平的功率控制策略

毫微微小区(Femtocell)网络能够增强室内覆盖,提高系统容量,但是在频谱共享正交频分多址(OFDMA) Femtocell网络中,同频干扰严重限制了网络的性能。针对频谱共享Femtocell网络中的上行链路,基于网络效率和毫微微小区用户间的公平性,该文提出合作纳什议价功率控制博弈模型,该博弈模型不仅考虑了对宏基站的干扰,而且考虑了毫微微小区用户最小信干噪比(SINR)需求。根据该博弈模型,进一步分析了具有帕累托(Pareto)最优的Kalai-Smorodingsky(KS)议价解。仿真结果表明,该策略既能保证用户公平性、最小SINR需求,又能够有效提高网络频谱利用率。     

Femtocell网络中博弈式频率复用算法

提出了一种适用于Femtocell网络的博弈式频率复用算法。在Femtocell网络中,首先Femtocell基站通过感知其无线环境选择临时子频带,然后通过对分簇后的Femtocell网络执行簇内协调和簇间博弈两步算法,消除相邻Femtocell之间的频谱冲突,使Femtocell网络合理地复用频谱资源。在Femtocell随机部署的网络中,该方法解决了Femtocell网络中的频谱冲突问题。仿真表明,采用该算法后,Femtocell网络的频谱冲突得到有效改善,Femtocell系统平均信道容量明显提高。     

Game-theoretical frequency reuse algorithm in Femtocell network

A game-theoretical frequency reuse algorithm has been proposed for Femtocell network.Firstly,each Femtocell tries to get an available sub-band by sensing its radio circumstance.Then,inner-cluster frequency collisions are avoided by implementing a negotiation mechanism.Lastly,each cluster is regarded as a game player to get the largest utility by using its optimal strategy.Despite Femtocell base stations are randomly installed by users,the proposed method performs well in Femtocell network.Simulation results show that frequency collisions are dramatically reduced and the average capacity of Femtocells is considerably enhanced.     

Game Bot Detection Approach Based on Behavior Analysis and Consideration of Various Play Styles

An approach for game bot detection in massively multiplayer online role‐playing games (MMORPGs) based on the analysis of game playing behavior is proposed. Since MMORPGs are large‐scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. To cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.     

Hierarchical game based spectrum access optimization for anti-jamming in UAV network

For the anti-jamming spectrum access optimization problem in unmanned aerial vehicle (UAV) communication networks,considering the complex and diverse malicious jamming from jammers,a Bayesian Stackelberg game was proposed to formulate the competitive relations between UAV users and jammers.Specifically,jammers acted as the leader,whereas users acted as followers of the proposed game.Based on their different utility functions,the jammer and users independently and selfishly selected their optimal strategies and obtained the optimal channels selection.Due to the NP-hard nature,it was challenging to obtain the Stackelberg Equilibrium of the proposed game.To this end,a hierarchical learning framework was formulated,and a hierarchical channel selection-learning algorithm was proposed.Simulations demonstrate that with the proposed hierarchical learning algorithm,UAV nodes can adjust their channel selection and obtain superior performance.     

改进布谷鸟算法在协同干扰资源分配中的应用

针对敌方雷达网的多干扰机协同干扰资源分配问题,提出了一种基于二维整数编码的改进布谷鸟(ICS)算法。首先,根据不同干扰样式在不同恒虚警检测器中的干扰效果差异,从压制概率公式出发,构建目标函数;然后,根据分配原则建立干扰资源分配模型;最后,利用ICS算法对模型寻优求解,并给出具体求解步骤。仿真结果表明:ICS算法收敛速度快,全局寻优能力强,能很好地解决雷达干扰资源协同优化分配问题。     

Fair and Efficient Spectrum Resource Allocation and Admission Control for Multi-user and Multi-relay Cellular Networks

This paper studies the joint relay selection and spectrum allocation problem for multi-user and multi-relay cellular networks, and per-user fairness and system efficiency are both emphasized. First, we propose a new data-frame structure for relaying resource allocation. Considering each relay can support multiple users, a \(K\) -person Nash bargaining game is formulated to distribute the relaying resource among the users in a fair and efficient manner. To solve the Nash bargaining solution (NBS) of the game, an iterative algorithm is developed based on the dual decomposition method. Then, in view of the selection cooperation (SC) rule could help users achieve cooperation diversity with minimum network overhead, the SC rule is applied for the user-relay association which restricts relaying for a user to only one relay. By using the Langrangian relaxation and the Karush–Kuhn–Tucker condition, we prove that the NBS result of the proposed game just complies with the SC rule. Finally, to guarantee the minimum rate requirements of the users, an admission control scheme is proposed and is integrated with the proposed game. By comparing with other resource allocation schemes, the theoretical analysis and the simulation results testify the effectiveness of the proposed game scheme for efficient and fair relaying resource allocation.     

Distributed Power Allocation With Rate Constraints in Gaussian Parallel Interference Channels

This paper considers the minimization of transmit power in Gaussian parallel interference channels, subject to a rate constraint for each user. To derive decentralized solutions that do not require any cooperation among the users, we formulate this power control problem as a (generalized) Nash equilibrium (NE) game. We obtain sufficient conditions that guarantee the existence and nonemptiness of the solution set to our problem. Then, to compute the solutions of the game, we propose two distributed algorithms based on the single user water-filling solution: The sequential and the simultaneous iterative water-filling algorithms, wherein the users update their own strategies sequentially and simultaneously, respectively. We derive a unified set of sufficient conditions that guarantee the uniqueness of the solution and global convergence of both algorithms. Our results are applicable to all practical distributed multipoint-to-multipoint interference systems, either wired or wireless, where a quality of service in (QoS) terms of information rate must be guaranteed for each link.     

干扰样式选择方法研究

针对干扰样式的选择问题,提出一种基于多决策准则的干扰样式选择方法。运用博弈论思想,建立雷达对抗盈利矩阵,可以解决在不确定条件下的干扰样式选择问题。以往的研究主要是基于最大最小决策准则,对其他准则的分析同样值得研究。为提高决策的准确性,文中首先对干扰样式选择问题采用不同准则分别进行决策分析;然后,对得到的结果进行综合,将不同准则中选优次数最多的干扰样式确定为最佳干扰样式;最后,结合实例对所提方法进行运用。结果表明：该方法可有效得出最佳干扰样式,从而实现最优的干扰效果。     

Joint random access and power control game in ad hoc networks with noncooperative users

We consider a distributed joint random access and power control scheme for interference management in wireless ad hoc networks. To derive decentralized solutions that do not require any cooperation among the users, we formulate this problem as noncooperative joint random access and power control game, in which each user minimizes its average transmission cost with a given rate constraint. Using supermodular game theory, the existence and uniqueness of Nash equilibrium are established. Furthermore, we present an asynchronous distributed algorithm to compute the solution of the game based on myopic best response updates, which converges to Nash equilibrium globally. Finally, a link admission algorithm is carried out to guarantee the reliability of the active users. Performance evaluations via simulations show that the game-theoretical based cross-layer design achieves high performance in terms of energy consumption and network stability.     

基于博弈论的二次博弈波束成形算法

针对波束成形算法中,用户的信号方向估计值和用户之间的功率分配存在着相互矛盾,本文提出了一种基于博弈论的二次博弈波束成形算法,构建了波束成形博弈算法数学模型,首先在第一次博弈的时候,将波束成形算法中的信号方向和功率分配映射为博弈论数学模型中的局中人,将其建模为函数的极大极小值求解问题,先求解出信号方向;然后在第二次博弈的时候,将不同用户的功率分配过程描述为一个多用户的博弈过程,设计了功率分配更新算法,通过数学推导论证了纳什平衡点的存在性和唯一性。最后在仿真中,与传统最大信噪比算法进行比较。结果表明该文算法的性能要优于最大信噪比算法,并且讨论了不同参数对该文算法的影响。     

Caching resource management of mobile edge network based on Stackelberg game

Mobile edge caching technology is gaining more and more attention because it can effectively improve the Quality of Experience (QoE) of users and reduce backhaul burden. This paper aims to improve the utility of mobile edge caching technology from the perspectie of caching resource management by examining a network composed of one operator, multiple users and Content Providers (CPs). The caching resource management model is constructed on the premise of fully considering the QoE of users and the servicing capability of the Base Station (BS). In order to create the best caching resource allocation scheme, the original problem is transformed into a multi-leader multi-follower Stackelberg game model through the analysis of the system model. The strategy combinations and the utility functions of players are analyzed. The existence and uniqueness of the Nash Equilibrium (NE) solution are also analyzed and proved. The optimal strategy combinations and the best responses are deduced in detail. Simulation results and analysis show that the proposed model and algorithm can achieve the optimal allocation of caching resource and improve the QoE of users.     

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。     

Auction–Stackelberg game framework for access permission in femtocell networks with multiple network operators

With the explosive growth of indoor data traffic in forthcoming fifth generation cellular networks, it is imperative for mobile network operators to improve network coverage and capacity. Femtocells are widely recognized as a promising technology to address these demands. As femtocells are sold or loaned by a mobile network operator (MNO) to its residential or enterprise customers, MNOs usually employ refunding scheme to compensate the femtocell holders (FHs) providing indoor access to other subscribers by configuring the femtocell to operate in open or hybrid access mode. Due to the selfishness nature, competition between network operators as well as femtocell holders makes it challenging for operators to select appropriate FHs for trading access resources. This inspires us to develop an effective refunding framework, with aim to improve overall network resource utilization, through promoting FHs to make reasonable access permission for well-matched macro users. In this paper, we develop a two-stage auction–Stackelberg game (ASGF) framework for access permission in femtocell networks, where MNO and mobile virtual network operator lease access resources from multiple FHs. We first design an auction mechanism to determine the winner femtocell that fulfils the access request of macro users. We next formulate the access permission problem between the winner femtocell and operators as a Stackelberg game, and theoretically prove the existence of unique equilibrium. As a higher system payoff can be gained by improving individual players’ payoff in the game, each player can choose the best response to others’ action by implementing access permission, while avoiding solving a complicated optimization problem. Numerical results validate the effectiveness of our proposed ASGF based refunding framework and the overall network efficiency can be improved significantly.     

带精英集并行遗传算法的无人机干扰资源调度

在中大规模无人机干扰资源调度中,针对现有模型约束条件简单、调度算法适用规模较小的问题,该文提出了带最少任务数约束的资源调度模型,以最大化干扰效益和最小化成本为目标,用层次分析法对效益与成本指标赋权,并设计了一种用精英集加快收敛的改进并行遗传算法。在中等规模和500:500(干扰资源数:目标数)的更大规模仿真实验中,所提算法与遗传算法、非支配排序遗传算法II、修复遗传算法、基于岛屿模型的并行遗传算法和自适应模拟退火遗传禁忌搜索算法的性能相比,能在更短的时长内达到较优的目标函数值。     

基于网络选择的视频通信带宽博弈算法

针对异构无线网络的视频通信提出一种基于网络选择的带宽博弈算法(BAG-NS)。该算法首先根据不同用户视频特性及当前网络状况,采用层次分析法和熵值法确定网络评价参数的权重,选择最佳网络;其次构建了基于收益和代价的用户效用函数,通过分布式迭代算法得到其纳什均衡解,并证明了纳什均衡的存在性和唯一性。仿真结果表明,该算法使不同特性的视频用户合理地分布在不同网络,能兼顾用户公平性和网络拥塞控制,增加网络资源利用率,减小用户视频失真,提高用户视频质量。     

Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach

We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation.     

相控阵雷达噪声干扰博弈分析

针对传统基于博弈论的干扰决策方法先验知识依赖性强、博弈过程理想、实战指导性差、决策结论粗糙的问题,文中提出了一种全新的博弈论分析方法。首先构建了能够体现博弈结果的博弈盈利函数,随后利用闭合分析和蒙特卡洛实验两种方法全面分析了噪声干扰与相控阵雷达之间的博弈过程,最后结合实例对所提方法进行了运用,分别给出了噪声干扰和相控阵雷达的博弈最优策略,证明了所提博弈分析方法的有效性和实用性,为博弈论在雷达对抗领域应用提供了一个新思路。     

Improving physical layer security and efficiency in D2D underlay communication

Device-to-device (D2D) communication is a core technology for expanding the next generation wireless cellular network. To deal with the security challenges and optimize the system communication quality, this paper investigates the security and efficiency problem of D2D underlay communication in a base station cell area with the presence of malicious eavesdroppers. Fairness and strategy space of both D2D User Equipment and Cellular User Equipment are taken into consideration under the control of Efficiency Functions. The optimization problems are formulated as a game model series of utility functions built on the unit price of jamming power and the amount of jamming service. We extracting the system into a price bargain game with a buyer and a seller both desiring maximum profits, a bargaining game approach is adopted to solve this problem by reaching an agreement of unit price. The step number of bargain process is also a restriction under consideration. For the non-steps model, an Evaluation Function and a Comprehensive Utility Function are demonstrated to analyze the bargain process. For steps-contained model, the step number of iteration is involved and an attenuation function is introduced to modify the bargaining game. The algorithms of two models are proposed to derive the equilibrium point for reaching an agreement. Finally, extensive simulations are illustrated for verifying proposed theory.