认知引擎中决策方法的研究

认知无线电(Cognitive Radio,CR)技术通过智能的频谱管理来解决频谱资源"短缺"问题,它能够感知到授权用户的空闲频谱,并有效地加以利用,从而减少与授权用户的冲突。现有无线电参数调整策略无法根据环境变化和用户需求进行智能调整,认知引擎中的决策方法能够解决该问题。遗传算法(Genetic Algorithm,GA)和二进制粒子群算法是实现认知引擎决策的典型算法,在对2种算法进行了介绍之后,仿真比较了2种算法在性能方面的差异。     

基于扩散机制的分布式宽带压缩频谱感知算法

针对认知无线电网络共享频谱资源的特征,本文提出一种基于扩散机制的分布式宽带压缩频谱感知方法。该算法包含两个工作阶段。在第一个阶段,每个认知用户对观测信号进行压缩感知和独立重构,产生本地频谱估计;在第二阶段,各个认知用户根据扩散机制协作更新频谱估计信息,实现最优估计。仿真结果表明,该算法与一致性分布式压缩频谱感知方法相比,可以快速增强认知无线电网络频谱感知能力,可应用于动态拓扑结构的认知无线电网络。     

认知无线电技术综述

认知无线电是一种智能频谱共享技术,通过它授权用户的频谱资源得以利用.认知无线电具备感知无线通信环境、并可根据一定的学习和决策算法,实时、自适应地改变系统工作参数的能力,通过对它的利用实现了对无线频谱资源的动态使用,提高频谱资源的利用率.在对认知无线电技术提出的背景、认知无线电技术概念的发展和主要研究内容进行了介绍.     

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.     

填充式频谱共享中的功率分配算法分析

动态频谱共享技术允许认知用户接入未授权的频谱,可以有效地提高频谱资源的利用率。在分析了频谱共享的2种接入模式的基础上,提出了填充式频谱共享的系统模型。根据认知用户的效用函数,通过注水算法得到了用户间的功率迭代公式,进而求得了认知用户的功率和载波分配结果。仿真表明,该迭代算法在有效完成用户间功率与载波分配的同时能够在较短的时间内收敛。     

认知无线电的频谱感知算法研究

频谱感知技术、频谱共享技术、频谱管理技术是当前认知无线电的三大核心技术,而频谱感知技术是实现后续的频谱共享和频谱管理的前提。因此主要对当前认知无线电频谱感知技术的基础理论、算法模型以及频谱感知的具体算法做了详尽的分类,并利用Matlab仿真平台对主要的频谱感知算法进行了仿真,并根据仿真结果,比较了各个算法的性能优劣性,可为认知无线电用户的算法选择提供参考,同时也为认知无线电后续的频谱共享和频谱管理工作奠定一定基础。     

非实时业务下认知网络的频谱共享

根据某种特定业务的QoS要求制定相应的频谱共享策略,可以在这种情况下提高系统的频谱利用率,增加用户平均收益。对非实时业务下的认知网络的频谱共享策略进行了研究,考虑各非授权用户业务量和信道质量对系统频谱效率的影响,提出了一种非实时业务专用的NRT-dedicate频谱共享算法。仿真结果表明,在非实时业务情况下,该算法可以获得比通用业务的频谱共享算法的频谱利用率。     

认知无线电中的频谱共享技术

由于认知无线电网络的一些独有的特性,比如,可用频谱资源的动态变化以及与第一类用户的共存,使得认知无线电网络中的频谱共享技术面临着许多新的挑战。当前关于频谱共享技术的研究旨在应对这些挑战,并且主要从以下四个方面着手：网络架构,频谱分配行为,频谱接入技术和共享频谱的范围。     

基于认知无线电系统的新型合作功率控制博弈算法

以改进的空时分集OFDM-CDMA系统为基础，研究已认知频谱资源的公平分配问题，根据信道状况，吞吐量最大化和功率限制需求，提出了一种合作功率控制纳什议价博弈算法，以有效地降低认知用户（secondary users）的发射功率，提高网络吞吐量。仿真结果表明该算法满足了认知用户公平共享频谱资源的需求，在相同功率消耗情况下，网络吞吐量显著提高。     

一种认知无线电网络频谱分配策略

频谱分配技术是认知无线电的关键技术之一,为适应认知无线电系统的时变特性,频谱分配算法必须有较快的收敛速度。在干扰温度模型下,提出一种基于Kuhn-Munkras算法的认知无线电频谱分配策略。该策略利用Kuhn-Munkras算法可以实现最佳匹配并且收敛速度快的特性,根据不同的用户在不同信道上所产生的效益的差异性,实现认知用户和信道的最佳匹配。仿真表明,基于Kuhn-Munkras算法的频谱分配在性能上优于传统的配对算法和greedy算法。     

认知无线电动态频谱分配新算法

动态频谱分配技术是认知无线电的一项关键技术,本文针对以OFDM为传输技术的认知无线电系统,提出了固定速率认知用户和变速率认知用户并存情况下的动态频谱分配新算法。本文算法以最小化每比特发射功率为目标,运用最小最大准则与分步求解的方法,对系统目标函数予以简化,在最大公平意义上对认知小区的空闲频谱资源进行动态分配。仿真结果表明,该算法具有较好的公平性,在固定速率用户功率最小化需求和变速率用户吞吐量最大化的需求之间取得了比较好的平衡,适用于两种需求用户并存环境下的认知无线电系统。      

Improved list coloring algorithm in cognitive radio based on time cost and demand satisfaction

For the cognitive users to access the licensed frequency bands opportunistically, cognitive radio is the best solution to mitigate the spectrum resource scarcity at the moment. Dynamic spectrum allocation is a key technology to implement cognitive radio efficiently on the condition that the licensed users must not be interfered. Up to now, the spectrum allocation algorithms based on the graph model seldom focus on enhancing spectrum utilization, speedy accomplishing and requirement satisfaction simultaneously. In this paper, an improved algorithm is proposed to decrease time cost and increase demand satisfaction simultaneously based on a List-Coloring algorithm provided by Wei Wang research group. This improved algorithm was studied by handling connected component in parallel and depressing user’s priority which demand has been met, respectively. And the spectrum utilization is effectively guaranteed at the same time. Our experimental results show that the proposed algorithm is an efficient method. Compared with the original List-Coloring algorithm, the time cost is reduced obviously and the demand satisfaction is greatly improved while only a few bandwidth rewards are lost by using the algorithm proposed in our study.     

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。      

基于强化学习的合作频谱分配算法

为了解决认知无线电网络中的频谱分配问题,提出了一种基于用户体验质量的合作强化学习频谱分配算法,将认知网络中的次用户模拟为强化学习中的智能体,并在次用户间引入合作机制,新加入用户可以吸收借鉴其他用户的强化学习经验,能够以更快的速度获得最佳的频谱分配方案;并且在频谱分配过程中引入了主用户和次用户之间的价格博弈因素,允许主用...     

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。     

认知无线网络频谱感知与节点协同问题综述

认知无线电是被公认的解决移动无线网络频谱资源短缺和频谱资源动态分配问题的最有效和最有潜力的技术。基于认知无线电技术目前在移动无线网络中的实际应用情况,详细分析了认知无线网络目前所存在的无线频谱资源感知、探测和接入机制以及多用户节点协同等问题,并综述了频谱资源分配算法和认知无线网络容量问题的最新研究进展,最后提出用博弈论来研究解决认知无线网络多用户协同问题中的用户自私行为。     

Opportunistic power allocation strategies and fair subcarrier allocation in OFDM-based cognitive radio networks

In this paper we have studied the subcarrier and optimal power allocation strategy for OFDM-based cognitive radio (CR) networks. Firstly, in order to protect the primary user communication from the interference of the cognitive user transmissions in fading wireless channels, we design an opportunistic power control scheme to maximize the cognitive user capacity without degrading primary user’s QoS. The mathematical optimization problem is formulated as maximizing the capacity of the secondary users under the interference constraint at the primary receiver and the Lagrange method is applied to obtain the optimal solution. Secondly, in order to limit the outage probability within primary user’s tolerable range we analyze the outage probability of the primary user with respect to the interference power of the secondary user for imperfect CSI. Finally, in order to get the better tradeoff between fairness and system capacity in cognitive radio networks, we proposed an optimal algorithm of jointing subcarrier and power allocation scheme among multiple secondary users in OFDM-based cognitive radio networks. Simulation results demonstrate that our scheme can improve the capacity performance and efficiently guarantee the fairness of secondary users.     

An uplink resource allocation scheme for OFDMA‐based cognitive radio networks

Cognitive radio makes it possible for an unlicensed user to access a spectrum unoccupied by licensed users. In cognitive radio networks, extra constraints on interference temperature need to be introduced into radio resource allocation. In this paper, the uplink radio resource allocation is investigated for OFDMA‐based cognitive radio networks. In consideration of the characteristics of cognitive radio and OFDMA, an improved water‐filling power allocation scheme is proposed under the interference temperature constraints for optimal performance. Based on the improved water‐filling power allocation, a simple subcarrier allocation algorithm for uplink is proposed. The subcarrier allocation rules are obtained by theoretical deduction. In the uplink subcarrier allocation algorithm, the subcarriers are allocated to the users with the best channel quality initially and then adjusted to improve the system performance. A cursory water‐filling level estimation method is used to decrease the complexity of the algorithm. Asymptotic performance analysis gives a lower bound of the stability of the water‐filling level estimation. The complexity and performance of the proposed radio resource allocation scheme are investigated by theoretical analysis and numerical results. Copyright © 2008 John Wiley & Sons, Ltd.     

The Master-Slave Stochastic Knapsack Modeling for Fully Dynamic Spectrum Allocation

Scarcity problem of radio spectrum resource stimulates the research on cognitive radio technology, in which dynamic spectrum allocation attracts lots of attention. For higher access efficiency in cognitive radio context, we suggest a fully dynamic access scheme for primary and secondary users, which is modeled by a master-slave stochastic knapsack process. Equilibrium behavior of this knapsack model is analyzed: expressions of blocking probability of both master and slave classes are derived as performance criterion, as well as forced termination probability for the slave class. All the theoretic results are verified by numeric simulations. Compared to traditional opportunistic spectrum access (OSA), which can be regarded as half dynamic due to primary users?? rough preemption, our scheme leads to less termination events for the secondary users while keeping the same performance for the primary class, thus promotes the system access performance. Nonideal spectrum sensing algorithm with detection error is also taken into consideration to evaluate its impact on system access performance, which is a practical issue for implementation.     

Auction-Based Throughput Maximization in Cognitive Radio Networks Under Interference Constraint

Cognitive radio is an emerging technique to improve the utilization of radio frequency spectrum in wireless communication networks. That is, spectrum efficiency can be increased significantly by giving opportunistic access of the frequency bands to a group of cognitive users to whom the band has not been licensed. In this paper, as a cross layer application (MAC and physical layers) of graph theory, we consider the problem of throughput maximization of spectrum allocation in cognitive radio networks under interference constraint. We propose a novel auction-based channel allocation mechanism which tries to maximize both total and primary users’ utilities while satisfying signal to interference ratio constraint on primary receivers so that transmitted packets will be successfully received, without controlling secondary user powers. For comparison we discuss a greedy algorithm as well, however, one that does not handle interference issue. In order to compare results of proposed and greedy algorithms, we propose net throughput by taking into account outage probability of primary receiver. Simulation results show that exposing higher SINR (outage) threshold not only decreases total gain and primary users’ utilities but also worsens channel distribution performance. On the other hand adding auction mechanism significantly increases total gain throughput and primary user’ s utility. Particularly, up to SINR threshold values of 20 dBs, auction provides outstanding performance and proposed algorithm has total throughput results close to those of the greedy one even though no interference constraint is applied in the greedy algorithm. Another noticeable point of simulation results is crossover of net throughputs of proposed and greedy algorithms at a SINR threshold level after which results of ABSA-UNIC and NASA-UNIC are much better. This clearly shows superiority of proposed mechanism.