认知星地一体化网络中能效最大化的鲁棒功率控制算法

如何提高能量效率是当前认知星地一体化网络研究的热点问题之一,而功率控制是提高能量效率的重要方法。另外,在实际中,由于信道信息评估误差和反馈时延致使所获得信道增益信息是不完美的,从而使得已有基于完美信息的功率算法不能满足系统性能。为此,针对认知星地一体化网络中无线信道信息不确定的情况,进行鲁棒功率控制算法的研究。首先,针对认知用户的瞬时信噪比和对主用户的干扰受信道衰落的影响而具有不确定性的情况,建立了最差情况下的鲁棒优化问题;其次,利用分式规划和拉格朗日对偶分解理论提出了一个低复杂度的鲁棒功率控制算法。仿真分析表明,提出的鲁棒功率控制算法不仅能够严格保证主用户的通信质量,并且与传统的非鲁棒功率控制算法相比能够提高网络能量效率。     

认知无线电网络安全综述

认知无线电网络通过认知用户对无线环境的感知,获得频谱空洞信息,在不干扰主用户的前提下,伺机接入空闲频谱,从而满足更多用户的频谱需求,提高频谱资源的利用率,然而,也带来了前所未有的安全挑战。依托认知环,从数据信道、控制信道和终端设备三方面介绍其安全性所面临的问题,以及现有的解决方案,最后给出认知无线电网络的安全建议。     

认知无线电网络中最稳定频谱分配算法

认知无线电网络中,主用户和认知用户的行为均会给认知节点间的频谱分配带来重大影响。在考虑认知用户间相互干扰时,提出了认知用户可用信道的潜在竞争节点数这一概念。在此基础上,将主用户对认知用户的影响考虑进去,定义了信道稳定度并将其作为衡量频谱质量的一个新的度量衡,以及给出了最稳定信道分配算法。性能评估的结果表明,最稳定信道分配算法可以有效的降低端到端平均时延以及显著的提高端到端吞吐量。     

非理想信道状态信息对频谱共享认知中继网络性能的影响

在频谱共享认知中继网络中,非理想信道状态信息可能导致次级用户的干扰超过主用户所能容忍的最大干扰值,从而影响到主用户的正常通信。为了衡量次级用户的传输对主用户性能的影响,该文提出了干扰概率这一性能指标。在非理想的信道状态信息条件下,推导了次级用户采用机会中继与选择协作协议时的干扰概率闭式解析式。理论分析表明,从干扰概率角度看,选择协作协议优于机会中继协议,但二者的极限干扰概率相同。此外,在非理想信道状态信息条件下,无论是采用机会中继还是选择协作协议,增加中继数量都会对主用户造成更大的干扰。最后,仿真结果验证了理论分析的正确性。     

认知网络主用户吞吐量受限下的传输半径分析与仿真

该文针对认知无线电中认知用户对主用户接收机造成的并发通信干扰问题,采用信息论的观点,在主用户吞吐量门限和主用户通信中断概率的限制条件下,首先分析了认知用户对主用户接收机的干扰功率,并引用马尔科夫不等式,推导出主用户传输半径范围,同时数值分析表明:在其他条件一定时,主用户平均吞吐量、认知用户接入数目和主用户传输半径之间是相互量化制约的关系。其次,改进开发了一个基于中断概率的认知无线电网络仿真平台,分析了在不同认知用户密度下,主用户吞吐量和传输半径之间的实际作用关系,验证了所提模型的合理性和正确性。     

认知无线电系统的顽健资源分配算法

摘 要：针对多用户下垫式认知无线电网络中参数不确定性问题,提出了一种顽健分布式功率控制算法。在干扰温度门限和次用户信干噪比（SINR）的约束下,考虑信道不确定性,实现认知系统功率消耗最小化。基于欧几里得球形不确定性描述,利用拉格朗日对偶分解理论给出了顽健功率控制问题的解。仿真结果表明,该顽健功率分配算法能同时满足主用户和次用户的QoS需求,与非顽健算法和传统SOCP算法对比可提升系统性能。     

机会频谱接入系统中基于次用户容量分析的检测参数设计

该文对认知网络中基于机会频谱接入(OSA)技术的认知用户的信道容量表达式及其检测参数进行了研究。首先讨论了次用户信号干扰噪声比(SINR)的特性,得到存在干扰和不存在干扰两种状况下的SINR的均值表达式,探讨SINR对次用户信道容量的影响, 以及次用户的信道容量在OSA方式下的变化。并在此基础上,结合信道统计特性,运用跨层设计思想,设计次用户的发送数据帧长和检测频率。从分析结果可以看出,主用户对授权信道的占用模型影响次用户的SINR,进而影响到次用户检测频率和传输帧长的设计,结果表明从次用户物理层特性和主用户业务特性结合角度设计次用户检测参数的方案,在保证次用户吞吐量要求的同时,降低了主次用户之间的干扰,同时提高了系统对频谱的利用率。     

新的感知无线电信道模型及其容量分析

将新的感知无线电信道模型将交织型信道模型和干扰型信道模型结合在一起,引入开关的概念,将频谱空洞的检测融合在信道中,形成有部分主用户信息的感知无线电双用户信道模型.在这种新的信道模型中,将主用户和感知用户之间的信道看作是一个压缩信道,引入压缩率,感知用户通过一个压缩信道来感知主用户的部分信息.针对新的感知信道模型,分析并计算了相应信道容量.最后,仿真验证了理论模型的有效性和容量优势.     

基于放大重传的认知共存网络传输容量分析

采用随机几何方法对认知共存网络中认知用户对主用户造成的累加干扰进行建模,并在无衰落信道和瑞利衰落信道中分别分析了主用户采用放大重传协作通信模式下的中断概率和传输容量。仿真结果表明,放大重传模式可以有效降低共存网络中主用户的中断概率、提高主用户的传输容量,且易于实现。     

认知MIMO系统基于正交投影的SLNR收发联合设计算法

为实现认知MIMO系统中认知网络与主用户网络的高效频谱共享,提出一种基于正交投影的SLNR收发联合设计算法。该算法首先通过正交投影预处理将信号投射到干扰信道的零空间从而消除对主用户的干扰,然后针对认知网络,以改进的信漏噪比为优化指标,利用交替迭代的思想联合设计收发处理矩阵,提高认知网络的性能。仿真结果表明,算法能有效抑制用户间的共信道干扰,在保证主用户正常通信的同时,有效提高了认知网络的误码率及和容量性能。      

Robust beamforming in cognitive radio

This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worstcase based systems.     

基于业务请求数据包长度的机会频谱接入算法

针对非时隙主用户网络,研究了单个次用户在周期性感知框架下的机会频谱接入问题。通过建立次用户信道感知和接入模型,提出了一种基于次用户请求业务数据包长度的机会频谱接入算法。该算法根据每个时隙分配给次用户业务数据包长度,自适应调整机会频谱接入策略。仿真结果表明,所提算法能够在干扰水平要求较高情况下,提高次用户平均有效传输吞吐量的同时,实现有效吞吐量与碰撞概率的折中;同时当外部环境发生变化时算法具有较强的鲁棒性。     

认知无线网络中信道选择算法研究

研究认知无线网络中认知用户（secondary user,SU）的信道选择策略。在每个信道上,由于主用户（primary user,PU）返回的概率不相同,因此SU需要接入一个成功传输概率最大的信道,以尽量避免与PU发生冲突。提出了一个基于EWA学习的信道选择算法,仿真结果表明,SU通过学习历史信道选择的经验,能自适应地选择可用性最好的信道,从而最小化与PU发生冲突的概率,有效地降低了SU进行信道切换的可能性。     

基于分层和模拟退火算法的动态信道分配方案

This paper proposes a dynamic channel allocation scheme based on cognitive radio （CR）. Firstly, the channel probing based on MMSE criterion is implemented, with which the probability distribution of channels in use by the primary user is given. Next, take the distances between the CR users and the primary user as basis to stratify the CR users, among the layers; the simulated annealing （SA） algorithm is used to implement the channel assigmnent. This algorithm differs from the well-known 0-1 matrix based allocation scheme, and keeps a good tradeoff between complexity, capacity as well as the fairness problems. The simulation results show that this algorithm can improve the allocation efficiency effectively.     

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。     

Distributed Channel Allocation Using Kernel Density Estimation in Cognitive Radio Networks

Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.     

Interference reduction in cognitive networks via scheduling

In this letter, we first define a cognitive network to be useful if at least one node can be scheduled to transmit without causing significant simultaneous interference to any primary user and then investigate the interaction between secondary network size and the probability of the secondary network being useful. First the size of the primary network is fixed, and we analyze how quickly the interference threshold limit of the primary network can be reduced as a function of secondary network size. Here there is a tradeoff between the rate of interference threshold reduction and the probability that the secondary network is useful which is completely characterized for Rician fading. We then allow both networks to grow simultaneously. Here the tradeoff is determined in the regime that the interference decreases sufficiently fast for Rayleigh fading. We also investigate the effect of primary channel correlation. Finally, we say that the secondary network is -useful provided at least one of any secondary nodes can be scheduled. We show that in the asymptotic regime, the probabilities of the secondary network being -useful are uniquely related and do not depend on the asymptotic behavior of the interference threshold, the rates at which the networks grow or even the distribution of the fading.     

A topology preserving cluster‐based channel assignment for wireless mesh networks

Wireless Mesh Networks (WMN) with multiple radios and multiple channels are expected to resolve the capacity limitation problem of simpler wireless networks. However, optimal WMN channel assignment (CA) is NP complete, and it requires an optimal mapping of available channels to interfaces mounted over mesh routers. Acceptable solutions to CA must minimize network interference and maximize available network throughput. In this paper, we propose a CA solution called as cluster‐based channel assignment (CBCA). CBCA aims at minimizing co‐channel interference yet retaining topology through non‐default CA. Topology preservation is important because it avoids network partitions and is compatible with single‐interface routers in the network. A ‘non‐default’ CA solution is desired because it uses interfaces over different channels and reduces medium contention among neighbors. To the best of our knowledge, CBCA is a unique cluster‐based CA algorithm that addresses topology preservation using a non‐default channel approach. The main advantage of CBCA is it runs in a distributed manner by allowing cluster heads to perform CA independently. CBCA runs in three stages, where first the WMN nodes are partitioned into clusters. The second stage performs binding of interfaces to neighbors and third stage performs CA. The proposed algorithm improves over previous work because it retains network topology and minimizes network interference, which in turn improves available network throughput. Further, when compared with two other CBCA algorithms, CBCA provides better performance in terms of improved network interference, throughput, delay, and packet delivery ratios when tested upon network topologies with various network densities and traffic loads. Copyright © 2014 John Wiley & Sons, Ltd.     

Prediction‐based MAC‐layer sensing in cognitive radio networks

We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.     

改进粒子群算法在频谱功率分配中的应用

在认知无线电中,由于次用户干扰门限要求的存在,传统频谱功率分配方式获得的次用户有效信道容量较低。针对这一问题,提出了一种基于粒子群算法的频谱功率分配算法。首先建立基于干扰距离的认知网络干扰模型,将频谱功率分配问题转化为函数优化问题,并借助混合随机变异思想的粒子群算法进行求解;针对寻优过程中的约束问题,提出了一种基于投入产出比的外点法,保证粒子群在可行域中寻优,最终获得频谱功率分配。仿真结果表明,与传统算法相比,所提算法能够获得较高的次用户有效信道容量。