协作认知无线网络中的功率优化及中继选择策略

如何在协作认知网络中有效地实现主要用户和认知用户的频谱共享,即如何在众多认知用户中选择合适的认知中继集是一个基本问题。通过确定并优化主要用户和认知用户效用函数来解决该问题,因采用了纳什均衡理论,故称之为基于博弈论的多中继选择算法(multiple relay selection based on game theory,GTMRS)。在任一认知中继集合中,认知用户之间能够形成非合作功率的博弈模型,可基于纳什均衡得到认知用户的优化协作功率分配算法。在寻找一组确定的中继集合来实现主要用户效用的最大化过程中,引入了修改的信道调和平均数因子,其目的是移除信噪比较小的中继节点,以最大化系统的信噪比。仿真结果显示,该算法能够使更多的认知用户接入到授权频谱中,同时使得主要用户获得更大的效用以及传输速率。因此,基于博弈的多中继选择算法能够有效选择合适的认知中继,并获得主要用户和认知用户在效用上的最优化。

协作认知无线网络中基于优先级队列的两级中心频谱共享机制

针对协作认知无线网络(CCRN)设计了一种基于优先级队列的两级中心协作频谱共享机制(PQTL-CSS)。通过招募认知用户作为中继,协助完成授权通信,并将传统的数据协作拓展至管理协作,由协作认知节点协调其他认知节点的接入,形成由主用户和协作认知节点构成的两级中心管理结构。在保障主用户最高优先级的同时,作为对认知节点协助授权业务传输的回报,赋予其高于非协作认知节点的信道接入权限。本文对不同用户的时延和吞吐量性能进行仿真,结果表明,PQTL-CSS能够在业务随机性较强的情况下,实现多种类型节点的动态、高效频谱共享。

Bidirectional secondary transmissions with energy harvesting in cognitive wireless sensor networks

To the existing spectrum sharing schemes in wireless-powered cognitive wireless sensor networks,the protocols are limited to either separate the primary and the secondary transmission or allow the secondary user to transmit signals in a time slot when it forwards the primary signal.In order to address this limitation,a novel cooperative spectrum sharing scheme is proposed,where the secondary transmission is multiplexed with both the primary transmission and the relay transmission.Specifically,the process of transmission is on a three-phase time-switching relaying basis.In the first phase,a cognitive sensor node SU1 scavenges energy from the primary transmission.In the second phase,another sensor node SU2 and primary transmitter simultaneously transmit signals to the SU1.In the third phase,the node SU1 can assist the primary transmission to acquire the opportunity of spectrum sharing.Joint decoding and interference cancellation technique is adopted at the receivers to retrieve the desired signals.We further derive the closed-form expressions for the outage probabilities of both the primary and secondary systems.Moreover,we address optimization of energy harvesting duration and power allocation coefficient strategy under performance criteria.An effective algorithm is then presented to solve the optimization problem.Simulation results demonstrate that with the optimized solutions,the sensor nodes with the proposed cooperative spectrum sharing scheme can utilize the spectrum in a more efficient manner without deteriorating the performance of the primary transmission,as compared with the existing one-directional scheme in the literature.

A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.