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。     

主用户干扰约束下的机会频谱接入感知−传输时隙优化调度

提出一种基于主用户干扰约束的机会频谱接入感知-传输时隙调度优化方案。首先,推导切换机制下认知系统的吞吐量和主用户干扰率,建立感知时间和感知周期联合优化模型;然后,在主用户干扰率和次用户感知质量双重约束下,推导了可最大化认知系统吞吐量的最优感知时间和感知周期的闭合表达式;最后,阐述了时隙优化调度方案并计算了认知系统可获得的最大吞吐量。仿真结果表明,所提出的时隙调度方案可以为认知系统提供更高的吞吐量,并更好地适应主用户干扰率和感知质量约束的变化。     

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。     

基于最优停止的认知无线网络下行频谱接入算法

为有效平衡认知无线网络中次用户系统中接入信道的感知时间和信道总的吞吐量之间的矛盾,提出了一种只对部分信道进行感知的频谱接入策略,联合总的感知信道数和各条信道的感知时间进行优化,并通过最优停止算法求解,在最大化节省感知时间的同时,取得较大的平均吞吐量。与HC-MAC(Hardware Constrained- Media Access Control)算法的仿真分析对比表明,该算法在相同给定条件下感知时间更短,吞吐量更大,具有明显的优势。     

全双工中继协作下的认知MIMO系统吞吐量最大化研究

本文研究了全双工中继协作下的认知MIMO系统的平均吞吐量最大化问题。与传统的中继协作认知无线电系统不同的是,该系统模型中的双工中继节点既能协助认知用户源节点进行多天线频谱感知以提高频谱检测性能,也能解码转发认知用户源节点的发送信号以获得更大的系统吞吐量。为使系统平均吞吐量最大,首先,本文以认知用户能获得的最大平均频谱空洞被发现的概率为目标,对系统的帧结构进行优化以获得最佳的感知时间,接着对多个发送天线进行优化以选择出最佳的发送天线,并推导出了在总的发送功率和对主用户干扰受限条件下的认知用户源节点和双工中继节点的最佳功率分配方案。最后的仿真结果表明本文提出的系统模型和优化方案相比传统的双工等功率分配方案以及单工功率分配方案能够获得更大的系统平均吞吐量。      

主用户活跃性对新的认知无线电系统容量的影响

在机会频谱接入认知无线电系统中,认知用户只有在通过感知确定信道空闲时才可以接入授权信道,因此频谱感知对于系统性能影响非常重要。本文提出了基于新帧结构的四元频谱感知模型,考虑主用户活跃性对认知网络吞吐量的影响,采用可同时最大化感知和数据传输时间的新帧结构模型,不需要考虑感知和吞吐量的均衡。理论分析新模型下感知时间,主用户活跃性,目标检测概率,主用户接收信噪比对系统吞吐量的影响,并与传统模型进行对比。     

基于传输中断概率的认知无线电系统性能分析

如何设计最优的频谱感知与传输框架是认知无线电技术的重要环节。考虑频谱接入过程中数据传输中断对认知网络性能影响的问题,提出了一种新的基于传输中断概率的频谱感知与传输模型,联合优化频谱感知和数据传输两个阶段,将问题建模为对主用户的干扰量约束条件下的非凸优问题,以最大化认知网络吞吐量为目标联合优化感知时间、传输速率,并通过数值计算方法对其进行求解与仿真。数值分析表明,引入传输中断概率后,在满足上述约束条件的同时,在保护主用户和认知网络吞吐量、传输时延之间有了更好的权衡。     

一种用于建立噪声监测系统的优化节点布署方法

噪声污染已成为当今世界性的问题。文章设计出一种利用无线传感器网络平台实现的噪声监测系统。该噪声监测系统的监控范围广、可以实现全天候、实时监控、支持多种接入方式等优点。文章给出一种基于监控区域权重的传感器节点位置布署优化算法,该方法首先确定布署质量最优化问题描述模型,然后依据各个区域的节点布署数量与权重的关系,把该约束优化问题属于极值优化求解问题,并采用拉格朗日极值法求得最优解。并利用OMNET++仿真验证,获得较高的布署效果。     

多无人机协作的认知通信网络中能/谱效折中优化

在无人机（Unmanned Aerial Vehicle,UAV）认知通信网络中,其能量受限和通信高吞吐量问题备受关注。然而,能量效率（Energy Efficiency,EE）的提升可能会导致频谱效率（Spectrum Efficiency,SE）的下降。针对此问题,对UAV协作认知通信网络中EE和SE的折中优化进行了研究。首先,进行了感知时间、UAV通信的发射功率和判决门限各自对SE与EE两者的优化;其次,通过二分法求解使得EE和SE最大化的感知时间值,并通过穷尽搜索法分别求解感知时间、UAV通信的发射功率和判决门限对EE和SE折中优化问题的最优参数值。在此基础上,提出一种联合参数迭代优化算法,求解EE和SE的折中优化问题。仿真实验表明,SE和EE之间存在折中的权衡,并验证了所提优化方案的有效性。     

具有非线性能量收集的RIS辅助无线供电通信网络的吞吐量优化

为了提高无线供电通信网络（WPCN）的信息吞吐量及传能效率并实现系统可持续工作，研究可重构智能表面（RIS）辅助WPCN的吞吐量优化问题。采用非线性能量收集模型，并对RIS进行能量分配，通过联合优化RIS的反射相位和能量分配系数，基站的发射波束赋形，基站能量传输，下行能量传输和上行信息传输的时间分配，以最大限度提高网络用户信息传输的吞吐量。所考虑的优化问题的优化变量高度耦合，难以直接求解。提出基于分块优化、松弛变量引入和半正定松弛方法的高效算法求解该问题。仿真结果表明，与现有的基准方案对比，所提算法能显著提高系统吞吐量和可靠性。     

Robust energy efficiency power allocation for relay-assisted uplink cognitive radio networks

In this paper, we investigate a worst-case robust power allocation scheme to improve energy efficiency (EE) for an amply-and-forward relaying uplink underlay OFDM cognitive radio system with imperfect channel situation information about the channel between primary user (PU) and secondary user (SU) and the channel between SU and corresponding relay. Specifically, a max–min problem is formulated to transform the original optimization problem into maximum EE on minimum throughout channel, and an epigraph problem is introduced to obtain analytical expressions of objective power allocation. Simulation results show that the proposed EE power allocation scheme is valid and effective in EE and robustness.     

Adaptive code symbol assignment in a rateless coded multichannel cognitive radio network

Because of its robustness to packet loss and adaptivity to channel conditions, rateless codes have been used in cognitive radio networks to improve the secondary system performance. In this paper, we investigate an adaptive code symbol assignment scheme for the secondary user (SU) in a multichannel cognitive radio network based on rateless coding. In particular, the SU transmitter first encodes its information data through rateless coding and then assigns the unceasingly generated code symbols adaptively to each available channel obtained by spectrum sensing. Thanks to the forward incremental redundancy provided by rateless codes, it is unnecessary for the SU receiver to request the retransmission of lost symbols and the code symbols collected from any channel at any time contribute to the final data recovery. With an alternating channel occupancy model of the primary user (PU), we conduct a weight enumerator analysis to derive the optimal number of code symbols to be assigned to each available channel, thus to maximize the system throughput while protecting PU from interference. Both theoretical analysis and numerical results demonstrate the good performance of our proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction based channel allocation performance for cognitive radio

The interdependency, in a cognitive radio (CR) network, of spectrum sensing, occupancy modelling, channel switching and secondary user (SU) performance, is investigated. Achievable SU data throughput and primary user (PU) disruption rate have been examined for both theoretical test data as well as data obtained from real-world spectrum measurements done in Pretoria, South Africa. A channel switching simulator was developed to investigate SU performance, where a hidden Markov model (HMM) was employed to model and predict PU behaviour, from which proactive channel allocations could be made. Results show that CR performance may be improved if PU behaviour is accurately modelled, since accurate prediction allows the SU to make proactive channel switching decisions. It is further shown that a trade-off may exist between achievable SU throughput and average PU disruption rate. When using the prediction model, significant performance improvements, particularly under heavy traffic density conditions, of up to double the SU throughput and half the PU disruption rate were observed. Results obtained from a measurement campaign were comparable with those obtained from theoretical occupancy data, with an average similarity score of 95% for prediction accuracy, 90% for SU throughput and 70% for PU disruption rate.     

Optimal precoder design for non‐regenerative multiple‐input multiple‐output cognitive relay systems with perfect and imperfect channel state information

This paper studies optimal precoder design for non‐regenerative multiple‐input multiple‐output (MIMO) cognitive relay systems, where the secondary user (SU) and relay station (RS) share the same spectrum with the primary user (PU). We aim to maximize the system capacity subject to the transmit power constraints at the SU transmitter (SU‐Tx) and RS, and the interference power constraint at the PU. We jointly optimize precoders for the SU‐Tx and RS with perfect and imperfect channel state information (CSI) between the SU‐Tx/RS and PU, where our design approach is based on the alternate optimization method. With perfect CSI, we derive the optimal structures of the RS and SU‐Tx precoding matrices and develop the gradient projection algorithm to find numerical solution of the RS precoder. Under imperfect CSI, we derive equivalent conditions for the interference power constraints and convert the robust SU‐Tx precoder optimization into the form of semi‐definite programming. For the robust RS precoder optimization, we relax the interference power constraint related with the RS precoder to be convex by using an upper bound and apply the gradient projection algorithm to deal with it. Simulation results demonstrate the effectiveness of the proposed schemes. Copyright © 2013 John Wiley & Sons, Ltd.     

能量收集认知无线电网络下的多接入用户信息年龄研究

针对信道资源有限的多接入信道无线传感器网络场景,实时信息的传送需要考虑信道环境和信息新鲜度问题。该文基于认知无线电物联网（Cognitive Radio-Internet of Things, CR-IoT）系统,构建了一个具有频谱访问权限的主用户（Primary User, PU）和两个可共享PU频谱次用户（Secondary User, SU）的网络模型。在考虑PU工作状态和SU数据队列稳定的条件下,提出了一个以最小化节点平均AoI为目标的优化问题。其次使用两种策略进行优化,包括概率随机接入策略（Probabilistic Random Access Policy, PRA）,该策略下两个SU节点根据相应的概率分布做出独立的传输决策;以及基于李雅普诺夫优化框架优化时隙内调度决策的漂移加罚策略（Drift Plus Penalty Policy, DPP）。仿真结果可知,DPP策略下得到的平均AoI的值要明显低于PRA策略,表明使用DPP策略对平均AoI的优化更加显著,可以有效提升数据包的时效性和新鲜度。      

基于时间估计的动态频谱接入算法

基于先验知识模型,设计了基于信道剩余空闲时间估计的动态频谱接入算法：每个次用户根据感知历史维护信道剩余空闲时间的估计向量并周期进行更新,每个时隙开始时次用户选择剩余空闲时间估计最大的信道接入。对动态频谱接入算法的适应性问题进行了分析,并求得了次用户的最优传输时间长度。仿真结果表明,在给定的参数下,新算法的信道利用率比其他算法提高约5%-10%,同时对主用户的干扰保持最低。     

Robust Cognitive Beamforming With Bounded Channel Uncertainties

This paper studies the robust beamforming design for a multi-antenna cognitive radio (CR) network, which transmits to multiple secondary users (SUs) and coexists with a primary network of multiple users. We aim to maximize the minimum of the received signal-to-interference-plus-noise ratios (SINRs) of the SUs, subject to the constraints of the total SU transmit power and the received interference power at the primary users (PUs) by optimizing the beamforming vectors at the SU transmitter based on imperfect channel state information (CSI). To model the uncertainty in CSI, we consider a bounded region for both cases of channel matrices and channel covariance matrices. As such, the optimization is done while satisfying the interference constraints for all possible CSI error realizations. We shall first derive equivalent conditions for the interference constraints and then convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation, which leads to iterative algorithms for obtaining the robust optimal beamforming solution. Results demonstrate the achieved robustness and the performance gain over conventional approaches and that the proposed algorithms can obtain the exact robust optimal solution with high probability.      

Overhead-Throughput Tradeoff Under a Novel Frame Structure in Centralized Cooperative Cognitive Networks

Cooperative spectrum sensing plays an important role in cognitive radio networks since it improves the detection performance by exploiting spatial diversity. However, the cooperation among terminals also brings additional communication overhead. In this paper, overhead-throughput tradeoff issues are investigated in four scenarios namely (1) identical sensing channel and perfect reporting channel, (2) identical sensing channel and imperfect reporting channel, (3) different sensing channel and perfect reporting channel, (4) different sensing channel and imperfect reporting channel of each secondary user (SU). Taking the reporting overhead into consideration, a novel frame structure consisting of an initial subframe and M consecutive subframes, is proposed to maximize the achievable throughput of the secondary network. And for each scenario, the overhead-throughput tradeoff is formulated as an optimization problem with respect to the number of reporting SUs. A brute-force approach is then used to resolve such optimization problem. Given the optimal number of reporting SUs, a set of candidate SUs is then selected according to the probability of detection, the probability of false alarm and the probability of reporting error. Numerical results show that an optimal overhead-throughput tradeoff is achieved given the optimal number of reporting SUs. In addition, the probability of false alarm is shown to be the most important factor affecting the performance of achievable throughput within the secondary network because the lower probability of false alarm corresponds to the case that the secondary network can use the channel with a higher chance.     

Resource allocation for multiuser cognitive OFDM networks with proportional rate constraints

In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd.     

Dynamic Spectrum Sharing for OFDMA-Based Multi-hop Cognitive Radio Networks with Priority

In this paper, we propose a resource assignment scheme of a secondary user (SU) for a multi-hop cognitive radio network. In multi-hop networks, since each link has different SNR because of their different distance between stations and multipath fading, the link of the smallest SNR is the bottleneck. For overcoming this problem, it is proposed to give a priority to each link based on the link SNR and to assign the resource blocks (RBs) in ordering of instantaneous SNR on the link. This link priority information is shared among nodes through a control channel. Then, we assign the selected SU RB to each SU node according to the SNR ordering with distributed manner. We confirm the effectiveness of the proposed SU RB assignment by using computer simulation.