认知网中感知时间和功率控制的联合优化机制

针对认知无线网中为了最大化认知用户的吞吐量问题,提出了一种感知时间和功率控制的联合优化机制。该机制保证认知系统在低于一定干扰限制下,将认知用户吞吐量描述成为一个多约束优化问题,从理论上分析了最优功率分配方案与最优感知时间分配方案。根据理论分析结果,设计了联合迭代机制通过确定合适的感知参数从而达到最大化认知用户吞吐量的目的。仿真结果表明：提出的联合优化机制复杂度较低,并且该方案的认知吞吐量性能最接近理论最优方案的性能。     

联合网络与用户最优化的认知无线电功率控制

针对认知用户的功率控制在实现用户效用的最大化时牺牲网络收益进行了研究,联合网络和用户两端对用户进行功率控制可以实现网络收益和用户效用的平衡。联合算法的实现可以采用认知用户单位价格的多维搜索方式,但是该方式计算量大,为了减少计算的复杂度,文章提出一种渐进分析法求解最优单位价格。首先确定使得网络收益最大化的单位价格,并基于该单位价格,采用博弈论分析方法,对认知用户进行功率控制。该算法表明在实现网络收益最大化的同时,用户的收益达到最优化。通过仿真分析,验证了联合算法的有效性。     

A joint sensing and transmission power control policy for RF energy harvesting cognitive radio networks

We consider a radio frequency energy harvesting cognitive radio network in which a secondary user (SU) can opportunistically access channel to transmit packets or harvest radio frequency energy when the channel is idle or occupied by a primary user. The channel occupancy state and the channel fading state are both modeled as finite state Markov chains. At the beginning of each time slot, the SU should determine whether to harvest energy for future use or sense the primary channel to acquire the current channel occupancy state. It then needs to select an appropriate transmission power to execute the packet transmission or harvest energy if the channel is detected to be idle or busy, respectively. This sequential decision‐making, done to maximize the SU's expected throughput, requires to design a joint spectrum sensing and transmission power control policy based on the amount of stored energy, the retransmission index, and the belief on the channel state. We formulate this as a partially observable Markov decision process and use a computationally tractable point‐based value iteration algorithm. Section 5 illustrate the significant outperformance of the proposed optimal policy compared with the optimal fixed‐power policy and the greedy fixed‐power policy.     

认知无线网络中基于Stackelberg博弈的功率控制新算法

针对认知无线网络中存在的干扰问题和功率消耗过大问题,提出了一种新的基于斯坦克尔伯格(Stackelberg)博弈的功率控制算法。建立了主次用户双层网络模型,将主用户设置为领导者,次用户设置为追随者,并对次用户产生的总干扰进行定量分析。在次用户多次博弈的过程中,动态地调整主用户单位干扰价格,在保证主用户自身正常通信前提下,尽量使得收益最大化,提高主用户的参与积极性。仿真实验表明,该算法在保证主次用户的服务质量（QoS）前提下,有效地减小了次用户的发射功率,而且能获得更高的系统容量。     

认知无线网络中一种非合作博弈功率控制算法

针对现有的功率控制算法中存在的干扰问题和功率消耗过大问题,设计出一种新的效用函数,并根据此效用函数提出了一个基于非合作博弈的新的功率控制算法。首先,在效用函数中分别为信干噪比和功率设定了不同的代价因子,并将信道状态概念引入到代价因子里面,使其能够更加合理地控制用户,避免用户过度增加发射功率,同时减小了用户间的干扰;其次,证明了该算法纳什均衡的存在性和唯一性;最后,给出了所提算法的流程图。仿真结果表明,与Nash算法相比,在保证非授权用户服务质量( QoS)前提下,该算法功率消耗明显降低,并且具有较好的抗背景噪声性能;与K-G( Koskie-Gajic)算法相比,该算法保证了所有的用户的信干噪比满足上下限阈值要求,并且提高了系统容量。     

基于感知无线电非合作博弈功率控制算法的研究

本文基于Goodman提出的非合作博弈功率控制模型改进了代价函数。针对感知无线电系统（CR）中各用户的通信需求,采用多载波码分多址（MC-CDMA）感知无线电系统,解决感知用户对主用户干扰和通信中断等问题,为实现感知频谱资源的有效分配,提出了一种新的感知无线电系统功率控制博弈算法。通过仿真表明,该算法同几种经典算法相比,既满足不同种类用户SIR要求,又提高了系统吞吐量,实现了对不同用户发射功率的有效控制,且系统性能明显提高。     

认知无线网络中功率和信道接入的联合优化

在认知无线网络中,针对系统中所有用户能否接入同一信道进行通信的问题,提出了适用于不同情形的联合优化方案.在系统不可行的情形下,提出了联合优化功率和接入控制的方案.由于接入控制是NP问题,于是利用所提LP-PSO算法来实现优化控制,并分析和证明了所提算法的收敛性.在系统可行的情形下,提出了联合优化功率和传输速率分配的方案,通过改进拉格朗日对偶(Lagrange duality)算法来实现优化控制.数值结果分析表明,相比其他接入控制算法,虽然所提算法的时间复杂度有所增加但能够快速收敛,在有效地提高接入量的同时还能降低功率消耗,并能使传输速率得到更公平的分配.     

基于认知无线电的新型非合作功率控制博弈算法

从认知无线电技术特性出发,在干扰温度门限的限制条件下,提出了基于新的代价函数功率控制博弈论算法,以有效地提高认知用户的信干噪比。根据信道状况、吞吐量最大化和功率限制的需求,将功率控制问题转化为一个干扰受限的多约束非线性规划问题。结合博弈论和最优化理论,证明了其纳什均衡的存在性和唯一性,给出了该模型的分布式求解算法。仿真实验表明,该算法可以有效提高认知用户的QoS和系统性能,充分利用无线资源。     

异构蜂窝网络中用户关联与基站功率的协同优化

为实现异构蜂窝网络中宏基站和小基站之间的负载均衡,提出了一种基于效用函数最大化模型的用户关联机制和基站功率控制的协同优化方案.通过迭代算法求解该协同优化问题,首先在基站功率固定的情况下求得最佳用户关联策略,然后在所得的用户关联策略基础上通过Zoutendijk可行方向法求得基站最佳功率.通过协同优化获得的用户关联策略和基站功率控制实现了基站之间的负载均衡,通过降低宏基站功率和关闭闲置小基站降低了基站的能耗.仿真实验表明,所提方案和不实施功率控制的用户关联策略相比,实现了宏基站与小基站之间的负载均衡,降低了宏基站对小基站用户的干扰,提升了小基站用户的信号干扰噪声比,用户速率中位值提高了20％.     

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

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。     

Securing cognitive radio networks

Cognitive radio is a promising technology aiming to improve the utilization of the radio electromagnetic spectrum. A cognitive radio device uses general purpose computer processors that run radio applications software to perform signal processing. The use of this software enables the device to sense and understand its environment and actively change its mode of operation based on its observations. Unfortunately, this solution entails new security challenges. Our objective in this paper is to analyze the security issues of the main recent developments and architectures of cognitive radio networks. We present vulnerabilities inherent to those systems, identify novel types of abuse, classify attacks, and analyze their impact on the operation of cognitive radio‐based systems. Moreover, we discuss and propose security solutions to mitigate such threats. Copyright © 2010 John Wiley & Sons, Ltd.     

认知网络中协作感知吞吐量的优化

为了提高集中式认知网络的吞吐量,提出了基于信任度的吞吐量优化算法.该算法在主用户充分保护的前提下,以认知用户的吞吐量为目标函数,融合中心采用双门限值对本地感知结果进行融合.从理论上证明了吞吐量是全局漏检概率的增函数,当全局漏检概率等于门限值时,吞吐量达到最大值.并利用牛顿迭代法求出单节点概率,然后采用遍历法可得到认知用户吞吐量最大值.仿真结果表明,当信噪比为-14 dB时认知用户融合优化算法相对"AND准则"OR准则"以及"HALF准则"归一化吞吐量分别提高了0.62、0.3和0.09.     

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.     

Beamforming and power allocation in the downlink of MIMO cognitive radio systems based on multiobjective optimization

In this paper, power allocation and beamforming are considered in a multiple input multiple output (MIMO) downlink cognitive radio (CR) communication system, which a base station (BS) serves one primary user (PU) and one secondary user (SU). In order to design the CR system, a constrained multiobjective optimization problem is presented. Two objectives are the signal to noise plus interference ratios (SINRs) of PU and SU. Since PU has a spectrum license for data communication, a constraint in the optimization problem is that the SINR of PU must be greater than a predefined threshold based on the PU demand requirement. Another constraint is a limitation on power in BS. By considering the mentioned model, three iterative algorithms are proposed. At each iteration of all algorithms, the receiver beamforming vectors are derived based on the maximization of PU and SU SINRs, by assuming that the allocated powers and BS beamforming vectors are known. Also, power is assigned to users such that the constraint of power limitation is satisfied. The difference between the algorithms is in the obtaining of transmitter beamforming parameters. We evaluate the performance of the proposed algorithms in terms of bit error rate (BER) in simulations. Also, the computational complexity of the proposed algorithms is obtained.     

Primary user supported routing protocol for cognitive radio ad hoc networks in search of higher throughput

A routing protocol for cognitive radio ad hoc networks (CRAHNs), namely, primary user supported routing (PSR) is demonstrated in this research. Unlike existing routing protocols for CRAHN, where routing of cognitive users (CUs) is accomplished within CUs, in this proposed protocol, some of the primary users (PUs) support CUs to communicate, by forwarding CU packets. This service provided by PU is of voluntary nature. However, such assistance shall be provided by the PUs, only when they are idle, thereby, preserving the principles of cognitive radio networks (CRNs), which indicates that the operation of PU should not be disturbed by the CU activities. The proposed work is compared with cognitive ad hoc on-demand distance vector (CAODV) protocol. The performance parameters considered are routing overhead, rate of loss of packets, and e2e packet delay. PSR outperforms CAODV in all these performance parameters. There has been on average 26.25% improvement in routing overhead, 34.12% decrease in loss, and 27.01% improvement in e2e delivery in the proposed PSR.     

Joint power and spectrum allocation in multi-hop cognitive radio networks

Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network （CRN） with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony （ABC） algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.     

基于局部拓扑控制的认知网络路由方法

针对认知网络研究一种融合主、次用户多因素优化的局部拓扑控制和路由方法。该方法综合考虑主用户频谱使用情况以及次用户对主用户干扰影响,预测认知链路的稳定性,结合链路功耗,定义一种联合链路代价,提出链路代价最小的局部认知拓扑控制路由 (LCTCR)算法,优化网络拓扑,并在优化后的拓扑上进行网络路由的选择。算法分析和仿真实验证明,在进行认知网络路径选择时,链路功耗和链路稳定性均为重要参数,需联合优化以保证选择更优的实际路由。     

认知型飞蜂窝网络中的子信道分配与功率控制

为了解决宏蜂窝与飞蜂窝构成的两层异构网络上行干扰与资源分配问题,提出了一种在认知型飞蜂窝的双层异构网中结合子信道分配和功率控制进行资源分配的框架。通过对异构网中跨层干扰问题进行分析与建模,将求解最优子信道分配矩阵和用户发射功率矩阵作为干扰管理问题的解决方法。模型中认知型飞蜂窝网络子信道和飞蜂窝网络用户构成非合作博弈,双方利用效用函数最优值进行匹配,构成初始信道分配矩阵;再由接入控制器根据接入条件从初始信道分配矩阵中筛选用户,并优化接入用户的发射功率矩阵,得到最优子信道分配矩阵和功率矩阵。仿真结果表明,优化框架提高了双层异构网络中飞蜂窝网络用户的吞吐量和接入率,降低了异构网中跨层干扰。     

Throughput performance under primary user emulation attack in cognitive radio networks

Spectrum sensing in cognitive radio networks imposes some security threats to the secondary users (SUs) such as primary user emulation attack (PUEA). In the present paper, throughput of an SU is studied for cognitive radio network under PUEA where SU spectrum access is hybrid, ie, either in overlay mode or in underlay mode. A novel analytical expression for throughput of an SU in presence of PUEA is developed. Impact of several parameters such as sensing time, attacker strength, attacker's presence probabilities, maximum allowable SU transmit power, and tolerable interference limit at primary user on the throughput of an SU is investigated. The throughput performance under PUEA and without PUEA is compared.