Linear MMSE Channel Estimation for Underlay Cognitive Radio Networks

Channel estimation techniques applied to cognitive radio networks (CRN) are analyzed for simultaneously primary and secondary channel estimations operating in underlay cognitive radio networks (uCRN). A complete base-band transmission including pilot sequence transmission, channel matrix estimation and optimal precoder matrix generation based on imperfect channel estimation are described. Also, the effect of imperfect channel estimation has been studied to provide means of developing techniques to overcome problems while enhancing the MIMO communication performance.

A Fault-Tolerant Cooperative Spectrum Sensing Algorithm over Cognitive Radio Network Based on Wireless Sensor Network

A serious threat to cognitive radio networks that sense the spectrum in a cooperative manner is the transmission of false spectrum sensing data by malicious sensor nodes. SNR fluctuations due to wireless channel effects complicate handling such attackers even further. This enforces the system to acquire authentication. Actually, the decision maker needs to determine the reliability or trustworthiness of the shared data. In this paper, the evaluation process is considered as an estimation dilemma on a set of evidences obtained through sensor nodes that are coordinated in an underlying wireless sensor network. Then, a likelihood-based computational trust evaluation algorithm is proposed to determine the trustworthiness of each sensor node's data. The proposed procedure just uses the information which is obtained from the sensor nodes without any presumptions about node’s reliability. Numerical results confirm the effectiveness of the algorithm in eliminating malicious nodes or faulty nodes which are not necessarily conscious attackers.

WORST SUBCARRIER AVOIDING WATER-FILLING SUBCARRIER ALLOCATION SCHEME FOR OFDM-BASED CRN

Efficient and reliable subcarrier power joint allocation is served as a promising problem in cognitive OFDM-based Cognitive Radio Networks (CRN). This paper focuses on optimal subcarrier allocation for OFDM-based CRN. We mainly propose subcarrier allocation scheme denoted as Worst Subcarrier Avoiding Water-filling (WSAW), which is based on Rate Adaptive (RA) criterion and three constraints are considered in CRN. The algorithm divides the assignment procedure into two phases. The first phase is an initial subcarrier allocation based on the idea of avoiding selecting the worst subcarrier in order to maximize the transmission rate; while the second phase is an iterative adjustment process which is realized by swapping pairs of subcarriers between arbitrary users. The proposed scheme could assign subcarriers in accordance with channel coherence time. Hence, real time subcarrier allocation could be implemented. Simulation results show that, comparing with the similar existing algorithms, the proposed scheme could achieve larger capacity and a near-optimal BER performance.

A Novel Cooperative Spectrum Sharing Algorithm Based on Optimal Cognitive Radio User Selection

In this paper, we consider the problem of cognitive radio (CR) user selection to maximize overall CR network (CRN) throughput when the available spectrum bandwidth is less than the demand by all CR users. We formulate optimal CR user selection problem. Then, based on approximation of the average received signal to interference plus noise ratio (SINR) and adaptive modulation and coding (AMC), we estimate the required bandwidth of CR users with different required quality of services (QoSs). Using the principle of optimality, we propose a novel cooperative spectrum sharing algorithm for a CRN. The proposed algorithm not only achieves exhaustive search performance but also its complexity is in the order of N × M versus 2N for exhaustive search, where N is the number of CR users, and M is the spectrum pool size. Extensive simulation results illustrate that the proposed algorithm significantly outperforms the existing algorithms that ignore optimal CR user selection. Also, these results illustrate a better fairness criterion than those of previous works.

Optimized in-band control channel with channel selection scheduling and network coding in distributed cognitive radio networks

This article proposes an optimized in-band control channel scheme with channel selection scheduling algorithm and network coding based transmission paradigm in the distributed cognitive radio network （CRN）. As well known, control channel plays an important role for establishment of wireless transmission. In order to improve spectrum efficiency in CRN, control channel is preferred to be deployed without dedicated spectrum allocation, i.e. the in-band way. In this study, the time slot division and dynamic channel selection scheduling algorithm is proposed to realize the in-band control channel with improved spectrum efficiency in the distributed CRN. Furthermore, to adapt to dynamic behavior of the primary users, network coding technology is employed to optimize the overhead of control information transmission so that the control information can be efficiently and reliably transmitted. The performance of the proposed in-band control channel scheme is verified by the extensive simulation results.

基于天线选择的CRN下行波束赋形方案

针对underlay模式下认知无线网络(CRN)的性能提升问题,提出一种基于天线选择的下行波束赋形方案。方案对由CRN模型得到的优化问题的非凸约束条件,包括秩1约束、二进制整数型变量和非线性约束等,分别采用双线性等效变换、松弛变量替换及添加惩罚项等方法转换为凸约束条件,得到了双凸优化问题。并给出了基于交替优化方法的问题求解算法。数值仿真结果表明,相对于固定天线,采用天线选择优化的CRN随着可选天线数目的增加,能量效率增大;在相同的服务质量下,下行功率减小,优化算法的可行解区域增大,而且对主网络基站的干扰具有鲁棒性。

适用于频谱重叠共享CRN的分组调度算法

为在降低认知用户对主用户干扰的同时保障认知业务的服务质量(QoS),提出一种基于干扰控制的分组调度算法,适用于以频谱重叠共享方式接入的认知无线电网络。该算法在MLWDF-IM算法的基础上进行改进,定义干扰控制因子和服务紧迫性函数,以此设计调度策略。仿真结果表明,该算法能在干扰抑制性能略有降低的情况下提高业务的QoS。

基于改进Steiner树的CRN双信道连通拓扑控制

当认知无线电网络中的主用户活动时,网络连通性较差。针对该问题,结合功率控制和信道分配技术,提出使用最小数目信道构造双信道连通无冲突拓扑的方案。生成基本拓扑,使用图着色理论为每个次级用户分配信道。在此基础上,考虑到删除节点后局部冲突图可能不连通,利用改进MPH算法给最短路径密集经过的节点分配路径权值。同时为避免删除节点后拓扑被分为两部分,取切割部分点间最短距离的一半位置添加节点,从而实现双信道连通。理论分析和仿真结果表明,在任意主用户引起的单信道中断情况下,该方案能够保持网络连通,同时减少所需信道数和网络花费。

CRN中基于单位圆盘图模型的广播调度算法

广播调度是目前认知无线电网络中的研究热点之一,现有广播调度算法主要为近似算法,存在方案性能与最优解方案差距太大的问题。为此,提出一种基于单位圆盘图模型的广播调度算法BS-UDGM。构建一棵基于连通支配集的广播树,作为调度的基础结构,采用平面细分和着色技术对广播树进行优化,通过混合使用单播和广播通信模式,完成广播任务。仿真实验结果表明,相比其他调度算法,该算法在延时和冗余方面的性能明显提高。

Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

As huge users are involved,there is a difficulty in spectrum allocation and scheduling in Cognitive Radio Networks(CRNs).Collision increases when there is no allocation of spectrum and these results in huge drop rate and network performance degradation.To solve these problems and allocate appropriate spectrum,a novel method is introduced termed as Quality of Service(QoS)Improvement Proper Scheduling(QIPS).The major contribution of the work is to design a new cross layer QoS Aware Scheduling based on Loss-based Proportional Fairness with Multihop(QoSAS-LBPFM).In Medium Access Control(MAC)multi-channel network environment mobile nodes practice concurrent broadcast between several channels.Acquiring the advantage of introduced cross layer design,the real-time channel conditions offered by Cognitive Radio(CR)function allows adaptive sub channel choice for every broadcast.To optimize the resources of network,the LBPFM adaptively plans the radio resources for allocating to diverse services without lessening the quality of service.Results obtained from simulation proved that QoSAS-LBPFM provides enhanced QoS guaranteed performance against other existing QIPS algorithm.

基于SumTree采样结合Double DQN的非合作式多用户动态功率控制方法

为了保证认知无线网络中次用户本身的通信服务质量,同时降低次用户因发射功率不合理而造成的功率损耗,提出了一种基于SumTree采样结合深度双Q网络(Double Deep Q Network,Double DQN)的非合作式多用户动态功率控制方法。通过这种方法,次用户可以不断与辅助基站进行交互,在动态变化的环境下经过不断的学习,选择以较低的发射功率完成功率控制任务。其次,该方法可以解耦目标Q值动作的选择和目标Q值的计算,能够有效减少过度估计和算法的损失。并且,在抽取经验样本时考虑到不同样本之间重要性的差异,采用了结合优先级和随机抽样的SumTree采样方法,既能保证优先级转移也能保证最低优先级的非零概率采样。仿真结果表明,该方法收敛后的算法平均损失值能稳定在0.04以内,算法的收敛速度也至少快了10个训练回合,还能提高次用户总的吞吐量上限和次用户功率控制的成功率,并且将次用户的平均功耗降低了0.5 mW以上。

基于免疫克隆选择的认知无线网络频谱分配研究

该文提出了图论和免疫优化理论相结合的认知无线网络频谱分配方法。采用基于矩阵的二进制抗体编码,从效率和公平性考虑设计了随机约束满足算子和公平约束满足算子,提出一种基于免疫克隆选择(ICS)的频谱分配算法。对经典克隆选择算法进行改进,增加约束满足操作保证种群编码能够满足频谱干扰限制,实现了频谱分配的约束优化。理论证明了约束满足操作的有效性,分析了算法的计算复杂性和适用性。仿真结果表明:ICS算法与颜色敏感图论着色算法相比,网络带宽明显提高;在频谱干扰严重的情况下,采用公平约束满足算子的ICS算法能显著提高网络的公平性;与参考智能算法相比,ICS算法具有较快的收敛速度。

认知无线电网络基于空分复用的机会频谱接入

传统的认知无线电技术采用机会频谱接入,认知用户的通信质量难以得到保证。通过利用多天线技术提供的空间信号处理能力,该文提出一种基于空分复用的机会频谱接入方法,当存在频谱空洞时,采用传统的机会频谱接入;无空闲频率资源可用时,认知系统利用空域信息完成发射预编码与接收滤波,从而以空分复用的方式实现通信。文中对认知系统天线配置要求进行了分析,当满足该要求时,认知系统能够与授权系统在同一授权频道实现无互扰共存。仿真结果表明,与传统的机会频谱接入相比,所提方法能够在不影响授权系统性能的情况下有效改善认知系统的通信性能。

面向信道跳转认知无线电网络的CSMA/CA MAC协议

现有基于信道跳转序列（CHS）的盲信道交汇策略研究多针对认知无线电网络媒体访问控制（MAC）协议的接入协商问题,较少关注接入控制方面。为此,分析载波侦听多路访问/冲突避免（CSMA/CA）MAC协议面临的假冲突问题,提出一种概率性加倍竞争窗口调节机制p-DOUBLE。根据假冲突不会在收发双方实现信道交汇的跳转时槽内出现的特点,利用现有基于CHS的盲信道交汇策略在每个跳转时槽内实现信道交汇的概率加倍竞争窗口大小,以降低假冲突对延长回退倒数过程的影响,提高收发双方在实现信道交汇时抓住传输机会的概率。实验结果表明,与传统确定性加倍竞争窗口调节机制1-DOUBLE相比,p-DOUBLE能将报文投递延迟降低14%~20%,网络吞吐量提高17.1%~29.3%。

基于辅助节点的安全协作频谱感知

在认知无线电网络中,恶意用户会占用授权频段,模仿主用户的信号特征发送信号,导致其他认知用户误认为主用户存在。针对该问题,提出一种基于辅助节点的安全感知机制,在网络中部署若干辅助节点,利用链路特征和加密机制对主用户的活动情况进行识别。设计适用于该机制的融合判决算法,不对授权网络做任何形式的改动即能有效识别恶意用户,并在融合时采用信任节点的感知数据,从而提高融合结果的鲁棒性。仿真结果表明,与K秩、And和Or算法相比,在恶意环境下该算法具有较好的检测性能。

认知无线电网络中次用户平均吞吐量的优化

协作频谱感知的认知无线电网络中,已有研究表明增加参与协作频谱感知的次用户数量能够提高感知性能,进而提高信道吞吐量。然而,由于信道容量的限制,不断增加参与协作感知的次用户数量并不会使信道吞吐量无限提高,反而会使次用户平均可获得的吞吐量不断降低。针对上述问题,该文以次用户平均吞吐量为优化目标,证明多信道条件下,对于任意给定的融合参数,次用户的平均吞吐量是感知时间的凸函数,并提出交叉迭代算法进行2维优化。仿真结果表明,当信噪比为-10 dB时,次用户使用交叉迭代算法获得的平均吞吐量较已有算法可提高20%以上。

认知无线电网络中基于和声搜索的频谱分配与功率控制

针对认知无线电网络中功率控制和频谱分配过程存在互相影响的问题,提出一种基于和声搜索的频谱分配和功率控制算法。通过综合分析频谱分配和功率控制过程中的各种约束条件,建立了联合功率控制和频谱分配的系统模型。算法根据系统模型特点设计和声搜索算法下相应编码方案,采用多目标优化方法处理复杂的约束条件,并赋予可行解适当的优先权重。仿真实验表明,算法可以很好地解决认知无线电网络中的频谱分配和功率控制问题。

基于量子果蝇优化的认知无线网络频谱分配

将频谱分配的二进制编码转换为量子序列编码,提出一种基于量子果蝇优化的认知无线网络频谱分配方法。首先,将果蝇优化算法(FOA)转换为量子果蝇优化算法(QFOA),拓展FOA的应用范围;然后,采用选择、交叉、变异操作改进QFOA,提高算法收敛速度,增加样本种群多样性,避免算法陷入局部最优;最后,利用改进QFOA对频谱分配的量子序列进行寻优,寻求最优的网络效益或者用户公平性,得到网络整体性能最优的频谱分配策略。仿真结果表明,改进的QFOA收敛速度快且跳出局部最优能力强,应用到认知无线网络频谱分配中,增加了网络资源利用率,提高了网络的整体性能。

一种基于FBMC-OQAM干扰抑制的功率资源分配新算法

基于FBMC-OQAM(filter bank multicarrier-offset quadrature amplitude modulation)的多用户频谱共享的认知无线电网络中的资源分配问题,在此提出了一种干扰抑制的功率资源分配算法(powerresource allocation algorithm,PAA).引入跨层干扰限制来保护网络中的次用户免受过多的干扰.引入虚拟队列的概念,将多用户争用信道资源导致的额外分组时延转化为在信道对应的虚拟队列中的排队时延.该算法以系统能效为目标函数、以时延和传输功率为约束条件,提出一个非线性约束下的非线性分式规划问题.设计了一种迭代算法,先通过一些变换将分式目标函数变为多项式形式,降低其实现难度后迭代求其全局最优解.此外设计了一种次优算法,以部分性能换取更低的计算复杂度.经实验仿真对比,最优算法具有高性能,次优算法在低计算复杂度的基础上具有较高性能,2种算法均具有很好的实用价值.

认知无线网络MAC协议的控制信道建模

针对认知无线网络媒体接入控制(CRN-MAC)协议的控制信道饱和问题,提出一种控制信道建模方案。引入控制信道门限的概念,根据协议超帧、控制子帧、数据子帧的时间约束关系,建立控制信道门限的定量分析模型,得出门限值的数学表达式。应用定量分析模型评价CCMAC协议的理论网络吞吐量。仿真结果验证了该模型的有效性,并给出网络吞吐量与CCMAC协议参数之间的数值关系。