基于地理位置的频谱共享系统功率分配

针对次用户地理位置分布的随机性,提出一种基于地理位置的多信道underlay频谱共享模型,并设计了一种基于凸优化的功率分配算法以提高频谱资源利用率。首先根据不同地理位置的次用户对主用户干扰的差异,设计一种基于产出投入比的公平性信道分配准则,将复杂的多信道资源分配问题转化为单信道功率分配问题;继而采用一种基于凸优化的迭代寻优方式进行功率分配,最终获得次用户的系统信道容量。仿真结果表明,相比按接入时间先后分配信道的方法,基于地理位置的多信道underlay频谱共享模型可以获得更高的信道容量。     

一种基于合同理论的动态频谱分配模型

针对认知无线电技术中的频谱分配问题,考虑一个主用户和多个次用户的认知场景,提出一种基于合同理论的动态频谱分配模型,并引入粒子群优化算法对模型进行求解。模型中,主用户对不同次用户提供不同质量的频谱资源以获得收益最大化,达到优化资源分配、提高频谱利用率的目的。经仿真分析验证,该模型下获得的系统效用接近理论上可获得的最大系统效用,具有实际应用的可行性。     

基于鲶鱼粒子群优化算法的认知无线电频谱分配

目前亟待解决如何获得认知无线电系统效益最大化问题,而求解最优频谱分配方法是一项关键技术,针对传统粒子群(PSO)算法收敛速度慢、易陷入局部最优解等缺陷,提出一种基于鲶鱼粒子群算法(CE-PSO)的认知无线电频谱分配方法。首先建立认知无线电频谱分配优化的数学模型,然后以用户取得的效益最大化为优化目标,引入"鲶鱼效应",保持粒子群的多样性,通过粒子间信息交流找到空闲频谱最优分配方案,最后采用仿真实验测试CE-PSO算法的有效性。结果表明,CE-PSO算法克服了PSO算法的缺陷,可以快速、准确地寻找到最优频谱分配方案,更好地实现系统效益的最大化,可以满足认知无线电系统的应用需求。     

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。     

认知无线电中一种基于干扰温度的功率控制算法

基于干扰温度模型,应将认知无线电网络中认知用户与主用户共享频谱时应满足各自的用户服务质量（QoS）问题转化为有约束的非线性功率控制优化问题。运用迭代算法和拉格朗日相关理论探讨出一种近似最优的功率分配算法。理论分析和仿真表明,该算法既能满足主用户的干扰温度容限,同时又能使认知用户获得很好的信噪干扰比,增大系统的吞吐量。     

联合信号子空间和功率分配的认知干扰对齐传输方案设计

本文针对由一条授权通信链路和多条次用户干扰信道组成的认知多输入多输出（Multiple Input Multiple Output,MIMO）系统,首先提出了基于信号子空间的认知干扰对齐迭代优化算法,并且利用单调有界理论证明了该算法可以收敛到稳定点。为了进一步提升系统的和速率性能,提出了一种联合信号子空间和功率分配的增强认知干扰对齐算法。该算法通过在每个次用户的多个数据流之间进行自适应功率分配,解决了次用户的有用信号空间中总是有残余的干扰信号的问题。数值仿真结果表明,相对于传统的认知干扰对齐算法,所提的算法能够获得较为明显的性能提升。      

基于粒子群算法的下行CR-NOMA网络资源分配

将非正交多址接入(Non-orthogonal Multiple Access,NOMA)技术应用于认知无线电(Cognitive Radio,CR)次网络,使次用户的信号在功率域叠加,可以进一步提高次网络的吞吐量。为此,将粒子群算法(Particle Swarm Optimization,PSO)应用于底层模式的CR-NOMA网络进行资源分配,并分为子信道分配和功率分配两个步骤。在子信道分配中,使用结合遗传算法思想的粒子群算法提高算法的全局搜索能力。在此基础上,使用基于罚函数的粒子群算法对子信道功率和信道内用户功率进行分配。仿真结果表明,提出的基于粒子群算法的CR-NOMA网络资源分配相比以往算法能获得更高的次网络吞吐量。     

基于干扰消减的认知无线电频谱分配算法

在认知无线电网络的频谱分配过程基础上,提出了一种基于干扰消减的频谱分配算法.该算法通过将可用频谱分配给能够同时无干扰地接入同一频谱的所有认知用户来提高授权频谱的使用率.同时,该算法参考各个认知用户在初始阶段的可用频谱数量来为未分配到频谱资源的认知用户进行频谱分配,对频谱分配过程的公平性进行了优化.仿真结果表明,该算法能够在认知用户数量较多、可用频谱紧张的情况下获得较高的吞吐量.     

基于空间位置的混合频谱共享系统功率分配研究简

针对次用户空间位置分布的随机性,提出空域混合Overlay/Underlay频谱共享模型以提高无线频谱利用率。根据次用户在不同空间位置对主用户的干扰,在主用户干扰容限约束下推导出次用户工作于Overlay状态和Underlay状态的空间区域;以最大化系统容量为准则建立认知系统功率优化分配模型,推导出次用户的最优功率分配方案,进而得到认知系统可获得的最大容量;理论和仿真结果表明,基于空间位置的混合频谱共享系统可以获得比Overlay系统更高的容量。     

一种认知无线电系统双门限频谱分配算法

基于认知无线电系统中Underlay频谱共享模型,提出了一种双门限频谱分配算法.给定两个信噪比门限,当信噪比低于较小的门限时,系统选择认知用户做中继协助授权用户进行数据传输,以授权用户信道容量最大化为优化目标进行频谱分配;当信噪比大于较大的门限时,系统允许认知用户之间进行低功率数据传输,以认知用户接入数量最大化为优化目标进行频谱分配;当信噪比介于两门限之间时,由授权用户单独进行数据传输.理论分析和仿真表明,当授权用户信噪比低于较小门限时,所提方法能提高授权用户的信道容量;当授权用户信噪比大于较大门限时,所提方法可以提高认知用户的接入数量.     

Power allocation in multibeam satellites based on particle swarm optimization

In this paper, a power allocation in multibeam satellite (MBS) communication based on heuristic particle swarm optimization (PSO) is proposed. The PSO algorithm is evocated to solve the problem of power allocation in the multiple narrow spotbeams aiming to provide the minimum signal-to-noise plus interference ratio (SNIR) required by Earth station users to establish reliable communication. In the developed model it is considered the multibeam interference and the different channel conditions of each beam by rain attenuation. The numerical results have been generated taking into account different sky situations, including clear and rainy scenarios; such results have revealed the viability and accuracy of the PSO algorithm deployment in solving the power allocation problem. In addition, with the proposed scheme it is observed the decrement of transmitted power for non-rainy beams with guarantee of the minimum SNIR at the Earth receivers input, while increase the power availability to the rainy beams; as a consequence, the overall energy efficiency of the MBS system has been improved substantially. Moreover, the convergence of the proposed heuristic PSO-based algorithm is discussed while such heuristic approach comes with computational complexity reduction when compared with others efficient power allocation schemes.     

Joint power allocation and beamforming with users selection for cognitive radio networks via discrete stochastic optimization

Cognitive radio is a promising technique to dynamic utilize the spectrum resource and improve spectrum efficiency. In this paper, we study the problem of mutual interference cancellation among secondary users (SUs) and interference control to primary users (PUs) in spectrum sharing underlay cognitive radio networks. Multiple antennas are used at the secondary base station to form multiple beams towards individual SUs, and a set of SUs are selected to adapt to the beams. For the interference control to PUs, we study power allocation among SUs to guarantee the interference to PUs below a tolerable level while maximizing SUs?? QoS. Based on these conditions, the problem of joint power allocation and beamforming with SUs selection is studied. Specifically, we emphasize on the condition of imperfect channel sensing due to hardware limitation, short sensing time and network connectivity issues, which means that only the noisy estimate of channel information for SUs can be obtained. We formulate the optimization problem to maximize the sum rate as a discrete stochastic optimization problem, then an efficient algorithm based on a discrete stochastic optimization method is proposed to solve the joint power allocation and beamforming with SUs selection problem. We verify that the proposed algorithm has fast convergence rate, low computation complexity and good tracking capability in time-varying radio environment. Finally, extensive simulation results are presented to demonstrate the performance of the proposed scheme.     

一种基于干扰温度限制的信道与功率联合分配新算法

随着无线通信业务的不断增长,频谱资源越来越紧缺,然而另一方面大量授权的无线频谱却被闲置或者利用率极低,于是认知无线电技术应运而生,已成为无线通信领域的研究热点。认知无线电的基本思想是次用户（认知用户）利用主用户（授权用户）未占用的空闲频谱进行通信,其可用无线资源是根据授权用户的频谱使用情况而动态变化的。因此,能否实现对系统可用无线资源的合理有效管理,对整个认知无线电系统性能的优劣起着决定性作用。本文提出了一种在干扰温度限制下基于公平的功率与信道联合分配算法,该算法在主用户干扰温度及次用户发射功率的双重限制下,以最大化系统容量为基本目标,实现信道与功率的联合分配,并且引入贫困线来保证各个用户信道分配的公平性。论文建立了该问题的非线性规划数学模型,给出了模型的求解方法,并进一步设计了具体分配算法及其步骤。论文对干扰门限分别为-90dBm、-95dBm、-100dBm、-105dBm、-110dBm时的系统归一化容量累积分布函数进行了仿真比较,发现当干扰门限越低时,本文算法的优势越明显。这是因为在干扰门限较低时,干扰温度限制是功率分配的主要制约因素,而本文的算法正是基于干扰门限进行分配的。因此基于干扰温度限制的公平的功率与信道联合分配算法具有良好的性能,在保证了系统的公平性效益的同时,提高了系统的归一化容量。      

Cognitive radio spectrum allocation using evolutionary algorithms

Cognitive radio has been regarded as a promising technology to improve spectrum utilization significantly. In this letter, spectrum allocation model is presented firstly, and then spectrum allocation methods based on genetic algorithm (GA), quantum genetic algorithm (QGA), and particle swarm optimization (PSO), are proposed. To decrease the search space we propose a mapping process between the channel assignment matrix and the chromosome of GA, QGA, and the position of the particle of PSO, respectively, based on the characteristics of the channel availability matrix and the interference constraints. Results show that our proposed methods greatly outperform the commonly used color sensitive graph coloring algorithm.     

基于合作博弈的多信道认知无线网络中的频谱共享算法简

采用合作博弈对多信道认知无线网络中的频谱共享问题进行了建模分析,提出了次用户在各信道上的信干噪比乘积作为合作博弈的效用函数。次用户在各信道上保证对主用户的干扰小于一定门限的要求下,通过最大化各自效用函数的乘积来进行功率分配。由于最大化次用户效用函数的乘积问题是非凸的,通过变量替换将其转化为了一个等价的凸优化问题,利用该凸优化问题的对偶分解,提出了一种次用户间的频谱共享算法。仿真结果表明,所提算法在次用户和速率与公平性之间进行了有效折中。     

Optimized channel prediction and auction-based channel allocation for personal cognitive networks

Cognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bio-inspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy-based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm.     

MIMO系统中改进的二进制粒子群天线选择算法

在多输入多输出系统中,发射端和接收端的多天线配置提高了信道容量和传输可靠性,而天线选择技术能在保持系统优点的同时有效地降低运算复杂度以及硬件成本。为了能在时变的信道条件下快速地选择出一组最优的天线子集,提出了一种基于二进制粒子群算法的改进的天线选择算法。推导出了二进制粒子群联合收发端天线选择的信道容量公式,并将其作为粒子群算法的适应度函数,使天线选择问题转换成二进制编码串的组合优化问题。通过改进模糊函数提高粒子群算法的收敛性,让二进制粒子群尽可能地收敛于全局最优位置。仿真结果表明,改进的算法能在降低运算复杂度的同时提高收敛性,且系统信道容量趋近于最优算法。