Chain store game based channel allocation iu cognitive radio system

In adaptive channel allocation for secondary user (SU) of cognitive radio (CR) system, it is necessary to consider allocation process from the temporal perspective. In this article, a chain store game is modeled to achieve SU's equilibrium state. Due to the computational complexity of solving equilibrium states, the authors explore the correlated equilibrium (CE) by importing signal mechanisms based on time and sequence number. Also, correlated equilibrium based game algorithms are presented. Simulations show that these algorithms are superior to other allocation algorithms both in channel utilization and communication time.     

CR系统中基于博弈论的频谱分配算法及仿真

在分析了无线电频谱管理现状及频谱资源紧张的前提下,引出频谱管理中的认知无线电技术,同时提出了基于博弈论的电磁频谱管理的模型化方法,深入地分析了博弈论算法的收敛性及效用函数的选择,并且通过Matlab仿真以最小化系统间总干扰为目标描述了博弈论模型在电磁频谱动态管理中的应用。     

无线网络中基于图型演化博弈的动态频谱接入机制

无线网络中,为了实现用户高效的数据传输,针对用户的有限理性特性,提出了一种基于图型演化博弈的动态频谱接入机制,而图型博弈可以较好地反映用户之间真实的博弈关系。同时设计了一种动态频谱接入算法和与之对应的动态方程以降低博弈的复杂度,而且能以较快的速度收敛到Nash均衡并获得较高的系统吞吐量和效用。理论证明该动态方程具有全局逐步稳定性,当用户发生局部的理性偏移时依然能够保证较快收敛和较小性能偏离。仿真对比验证了该机制的上述优势。     

Distributed joint spectrum detection and power allocation in multi-band cognitive radio networks

This article proposes a novel dynamic spectrum sharing scheme in distributed multi-band cognitive radio networks. A non-cooperative game has been utilized to model the spectrum sharing among secondary base stations (SBSs). A distributed joint spectrum detection and power allocation algorithm is designed for maximizing the downlink throughput of secondary networks. Simulation results demonstrate that the proposed algorithm converges fast and achieves a better throughput performance than uniform threshold case. Meanwhile, the convergence of algorithm is proved by Nikaido-Isoda (N-I) function method.     

认知小蜂窝网络中基于能效的下行资源分配算法

在认知小蜂窝网络框架下,对基于OFDMA技术的下行联合频谱资源块和功率分配问题进行了研究。小蜂窝基站在分布式结构下采用开放式接入方式共享空闲频谱资源以最大化其能量效率,基站间的竞争关系使系统资源的动态分配过程可建模为非合作博弈模型。由于最大化具有多个限制条件的分数形势的能量效用函数属于非凸最优问题,可通过将其转化为等价的减数形势,并从串行和并行迭代的角度进行求解。在给定资源块分配策略后,原有博弈模型可被重新建模为便于独立求解发射功率的等价子博弈模型。仿真结果表明,所提算法在干扰受限的通信环境下能收敛到纳什均衡,并有效提高了系统资源利用率和能量效率。     

Multi-cell uplink power allocation game for user minimum performance guarantee in OFDMA systems

The multi-cell uplink power allocation problem for orthogonal frequency division multiplexing access (OFDMA) cellular networks is investigated with the uplink transmission power allocation on each co-frequency subchannel being defined as a multi-cell non-cooperative power allocation game (MNPG). The principle of the design oftbe utility function is given and a novel utility function is proposed for MNPG. By using this utility function, the minimum signal to interference plus noise ratio (SINR) requirement of a user can be guaranteed. It can be shown that MNPG will converge to the Nash equilibrium and that this Nash equilibrium is unique. In considering the simulation results, the effect of the algorithm parameters on the system performance is discussed, and the convergence of the MNPG is verified. The performance of MNPG is compared with that of traditional power allocation schemes, the simulation results showing that the proposed algorithm increases the cell-edge user throughput greatly with only a small decrease in cell total throughput; this gives a good tradeoff between the throughput of cell-edge users and the system spectrum efficiency.     

基于生存系统模型的认知无线电网络频谱管理自治系统设计

针对动态频谱接入和认知无线电技术对频谱管理的新要求,采用分层的频谱管理理念,基于生存系统模型(Viable System Model,VSM)设计适合认知无线电网络的频谱管理自治系统,该系统通过节点参与频谱管理,网络自我约束等措施,实现认知无线电网络对频谱的自治。最后,将该系统应用于一种异构网络共存的频管案例设计中,仿真分析表明:本文提出的基于VSM的频谱管理自治系统响应时间大幅度降低;认证效率为O(1),较传统方式性能有很大提升。     

Low-complexity joint beamforming and power allocation for SINR balancing in cognitive radio networks

We consider the Signal-to-Interference plus Noise Ratio (SINR) balancing problem involving joint beamfoming and power allocation in the Cognitive Radio (CR) network, wherein the Single-Input Multi-Output Multiple Access Channels (SIMO-MAC) are assumed. Subject to two sets of constraints: the interference temperature constraints of Primary Users (PUs) and the peak power constraints of Cognitive Users (CUs), a low-complexity joint beamforming and power allocation algorithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection (SDMCPA-CP) for SINR balancing is proposed. Compared with the existing algorithm, the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions significantly, especially when large numbers of PUs and CUs are active, while still providing the optimal balanced SINR level for all the CUs.     

Null-space projection and waterfilling resource allocation in multi-antenna cognitive radio networks

Cognitive Radio (CR) is a promising technique for the next generation mobile communication system for its capability to solve the conflicts between the scarcity and underutilization of spectrum. In this paper, aiming at maximizing the system capacity of a multi-antenna CR system on the premise that avoid interference to the primary system in the same band simultaneously, a resource allocation method which is able to avoid interference between PRimary (PR) and CR users by projecting the transmit signals of CR users on the null space of the PR users’ channels is proposed. CR users with better channel condition are selected, and the interference from CR system to PR users can be removed completely by projecting the transmit signals of CR system on the null-space of PR users’ channels. Parallel sub-channels are constructed for CR users through Singular Value Decomposition (SVD). At last, waterfilling is also adopted to increase the CR users’ capacity. Simulation result demonstrates that compared with existing methods, our method can improve the achievable sum rate of CR users as well as reduce the outage probability of PR users.     

Sensing tasks allocation in cellular-based cognitive radio network

In Cognitive Radio (CR) Networks, especially in Cellular-based CR networks, in order to obtain the precise spectrum state which is available in the whole coverage, the spectrum sensing function need to be accomplished by the cooperation of multi-sensing nodes, i.e., cooperative spectrum sensing. This paper mainly focuses on the key question of the cooperative spectrum sensing: How many sensing nodes are required and how to allocate the sensing tasks to these sensing nodes in an effective way? In this paper, we classify the spectrum bands into three categories by a pre-sensing mechanism, in order to firstly exclude unavailable channels and select candidate channels for fine sensing in the next stage. Then the required number of sensing node for each candidate channel is determined by the proposed scheme. Through the scheme proposed in this paper, the sensing tasks allocation in cellular-based CR network can be accomplished in a high effective way and the sensing overhead could keep a low level.     

基于非理想感知的认知无线电系统感知时间与频谱分配联合优化算法

针对非理想感知情况下感知时间与频谱分配联合优化问题,同时考虑漏检与主用户重新占用频谱两种场景所造成的主次用户碰撞,并通过量化主用户对认知用户的干扰,给出有无主用户存在时认知系统可获得的吞吐量。在总传输功率约束以及对主用户的最大干扰功率约束两个限制条件下,以最大化系统平均吞吐量为优化目标,给出感知时间与频谱分配联合优化算法。算法首先通过折半法搜索最优感知时间,在既定的感知时间下,将子信道分配给能获得最大平均吞吐量的认知用户,在此基础上,利用凸优化相关理论求得最优功率分配。仿真结果表明,本文所提算法相比于传统频谱分配算法系统平均吞吐量性能提升了10%左右。     

Optimal resource allocation scheme for cognitive radio networks with relay selection based on game theory

In this paper,we present a non-transferable utility coalition graph game(NTU-CGG) based resource allocation scheme with relay selection for a downlink orthogonal frequency division multiplexing(OFDMA) based cognitive radio networks to maximize both system throughput and system fairness.In this algorithm,with the assistance of others SUs,SUs with less available channels to improve their throughput and fairness by forming a directed tree graph according to spectrum availability and traffic demands of SUs.So this scheme can effectively exploit both space and frequency diversity of the system.Performance results show that,NTU-CGG significantly improves system fairness level while not reducing the throughput comparing with other existing algorithms.     

认知无线电频谱分配新算法研究

构造了一个基于合作博奕的非对称纳什协商效能函数,并通过2个认知用户间协商子载波的使用,实现了基于感知贡献加权的比例公平性频谱分配的新算法。仿真结果表明提出的方案不仅实现了频谱资源的公平有效分配,而且有利于频谱感知的最大化。     

基于改进遗传算法的认知无线传感网动态频谱分配方案

将认知无线电中的动态频谱分配技术应用在无线传感网中,针对工作在ISM(industrial,scientific and medical)频段的无线传感网面临的频谱资源紧缺问题,提出一种基于改进自适应遗传算法的动态频谱分配方案.该算法以图论着色模型为基础,以最大带宽收益和最小切换频率为目标函数,在交叉和变异过程中采用自适应交叉概率和变异概率代替固定的交叉概率和变异概率.仿真结果表明,与传统遗传算法和颜色敏感图论着色算法相比,该算法可以实现提高频谱利用率、降低能量消耗的预期目标.     

基于合作博弈的多虚拟机实时迁移带宽分配机制

IaaS云计算平台采用虚拟机实时迁移技术进行资源动态调度和管理。在实际应用场景下,需要并行实时迁移多个虚拟机。由于实时迁移算法本身以最大利用带宽的方式进行数据传输,存在着迁移进程间竞争带宽的问题,无法保证带宽全局最优分配,影响整体迁移的性能。提出一种基于合作博弈的多虚拟机实时迁移带宽分配机制,将带宽分配问题建模为一个纳什议价,通过求解纳什议价解得到帕累托最优的带宽分配方案,并在实际的虚拟化平台上进行了实现。实验结果表明,相比标准的并行实时迁移,所提出的带宽分配机制能够公平有效地分配带宽,提高了并行实时迁移的性能。     

Modeling radio resource management in cognitive radio system based on OFDMA using colored Petri Net

Cognitive Radio (CR) system based on Orthogonal Frequency Division Multiple Access (OFDMA), such as Wireless Regional Area Networks (WRAN) and Worldwide Interoperability for Microwave Access (WiMAX), often attempt to improve performance via dynamic radio resource management, which is characterized as concurrent processing of different traffic and nondeterministic system capacity. It is essential to design and evaluate such complex system using proper modeling and analysis tools. In the previous work, most of the communication systems were modeled as Markov Chain (MC) and Stochastic Petri Nets (SPN), which have the explicit limitation in evaluating adaptive OFDMA CR system with wide area traffic. In this paper, we develop an executable top-down hierarchical Colored Petri Net (CPN) model for adaptive OFDMA CR system, and analyze its performance using CPN tools. The results demonstrate that the CPN can model different radio resource management algorithms in CR Systems, and the CPN tools require less computational effort than Markov model using Matlab, with its flexibility and adaptability to the traffics which arrival interval and processing time are not exponentially distributed.     

A stochastic game framework for joint frequency and power allocation in dynamic decentralized cognitive radio networks

Cognitive radio networks (CRNs) have been recognized as a promising solution to improve the radio spectrum utilization. This article investigates a novel issue of joint frequency and power allocation in decentralized CRNs with dynamic or time-varying spectrum resources. We firstly model the interactions between decentralized cognitive radio links as a stochastic game and then proposed a strategy learning algorithm which effectively integrates multi-agent frequency strategy learning and power pricing. The convergence of the proposed algorithm to Nash equilibrium is proofed theoretically. Simulation results demonstrate that the throughput performance of the proposed algorithm is very close to that of the centralized optimal learning algorithm, while the proposed algorithm could be implemented distributively and reduce information exchanges significantly.     

基于合作博弈的CPS通信资源分配算法

为了提高信息物理融合系统的通信速率,同时保证各用户的QoS需求,提出了一种基于合作博弈的CPS通信资源分配算法。将CPS中OFDMA网络下行链路的资源分配过程建模为多用户间的合作博弈,通过求解纳什议价解,实现用户间的Pareto最优性。仿真结果表明,该算法在系统速率最大化和用户公平性上获得了很好的折中,与最大化系统速率算法相比更具有公平性,与最大化最小公平性算法相比速率提高了34%,在一定程度上提高了CPS通信网络的性能。     

An effective suboptimal power loading scheme in OFDM-based cognitive radio systems

Orthogonal frequency division multiplexing (OFDM) is an attractive modulation candidate for Cognitive Radio (CR) networks. Effective and reliable subcarrier power allocation in OFDM-based Cognitive Radio (CR) networks is a challenging problem. This paper focuses on the power allocation for OFDM-based Cognitive Radio (CR) networks. Our objective is to maximize the total transmission rates of Secondary Users (SU) by adjusting the power of subcarrier while the interference introduced to the Primary User (PU) is within a certain range and the total power of subcarrier is not beyond the total power constraint. We investigate the optimal power allocation algorithm for OFDM-based Cognitive Radio (CR) based on convex optimization theory. Then, because of high complexity of the optimal power allocation algorithm, we propose an effective suboptimal power loading scheme. Theory analysis and simulation results show that the performance of the suboptimal power allocation algorithm is close to the performance of the optimal power allocation algorithm, while the complexity of the suboptimal power allocation algorithm is much lower.