基于次用户分级的PRP M/G/m排队论频谱切换模型

为满足次用户不同时延需求并提高信道利用率,该文提出一种基于次用户分级的PRP M/G/m排队论频谱切换模型。首先,根据次用户主要业务的时延需求不同,对次用户进行分级评价,将次用户分为时延敏感型和非时延敏感型用户,时延敏感型次用户具有更高的优先级来接入信道。其次,采用PRP M/G/m排队论来建立基于用户分级的频谱切换模型。同时推导和分析了该模型下不同优先级次用户扩展数据传输时间,进而提出了面向各级次用户自适应频谱切换策略。仿真结果表明,相较于已有频谱切换策略,该模型能有效降低次用户切换时延以及扩展数据传输时间,保证高优先级次用户的服务质量,提升次用户频谱切换的综合性能。     

基于信道获益和业务实时性的频谱切换策略

为了尽可能地降低认知无线电频谱切换的次数,更加全面地考虑通信环境里的因素,引入模糊控制技术,提出一个可行的频谱切换策略。首先,认知用户接收方根据信噪比和主用户活动性估计信道获益,再通过信道反馈机制将结果值告知认知用户发送方;最后,认知用户发送方将业务分成两类：实时业务与非实时业务,并将信道获益与业务实时性作为两个新参数纳入频谱切换策略。仿真结果显示,与随机选择策略相比,根据信道获益选择信道可以明显降低认知用户的切换率。另外,将业务进行分类,非实时用户与实时用户相比有更低的切换可能性,这种差距可以达到32%。     

认知无线网络中基于信道感知排序的频谱切换

在认知无线网络中,一旦主用户出现,正在使用该频段的认知用户需退出该频段,寻找另外一个空闲的频段继续未完成的传输,这就是认知无线网络中的频谱切换。评价认知用户频谱切换性能有两个重要的参数:切换时延和切换次数。本文提出了一种基于信道感知排序的频谱切换方法,以同时减少切换时延和切换次数。在进行信道感知排序时,本文使用的方法同时考虑了切换发生时信道空闲概率以及使用该信道时不再发生切换的概率。仿真结果表明,与传统信道排序方法相比,本方法的切换时延可以达到与传统方法一样的性能,然而切换次数却大大地减少了。     

混合频谱共享方式下多用户动态频谱分配算法

为解决混合overlay/underlay频谱共享方式下多用户动态频谱分配问题,构建了混合频谱共享方式下动态频谱分配模型,提出了基于Q学习的多用户动态频谱分配算法. 该算法在不对主用户产生有害干扰的前提下,以最大化次用户总吞吐量为目标,构建了与次用户相对应的虚拟次用户作为智能体. 通过与环境交互学习,进行信道和共享方式初选;频谱分配系统根据冲突情况和各智能体的学习结果调整信道分配策略直至次用户间无冲突. 仿真结果表明,该算法在无信道检测和信道先验知识的条件下,能根据前一时隙信道状态和次用户传输速率需求,实现动态信道分配和频谱共享方式确定,避免次用户间冲突,减少主次用户间冲突,有效提升次用户总吞吐量.     

认知无线电频谱切换目标信道访问机制

针对认知无线电先应式决策频谱切换,该文提出按照信道平均空闲时间递减的顺序对目标信道进行访问的机制,证明了该机制在主用户信道空闲时间服从均匀分布、Rayleigh分布以及Weibull分布下均能够保证频谱切换失败概率最小。仿真结果表明所提机制得到的频谱切换失败概率要远远小于随机访问机制的切换失败概率。另外,当能够获得数十次信道空闲时间观测样本情况下,虽然存在估计误差,但所得频谱切换失败概率与理想最优频谱切换失败概率非常接近。     

基于模糊层次—熵权法的自适应垂直切换算法

针对当前异构网络中,采用多决策属性的垂直切换算法难以动态调整网络属性的权重值,从而无法满足用户服务质量(QoS)的需求问题,文章以接入最优的异构无线网络和保障用户QoS为目标,提出基于模糊层次—熵权法的自适应垂直切换算法.首先,根据用户对网络属性的不同要求,采用主观模糊层次赋权与客观熵权法结合的方式构建网络属性优化模型...     

认知无线网络中频谱切换算法研究综述

无线频谱资源的匮乏和多用户争用环境,导致认知无线网络中频谱切换研究面临严峻挑战。本文阐述了频谱切换的基本原理,包括频谱切换的概念和特征、频谱切换的主动决策和被动决策分类、频谱切换过程及建模抽象的方法等几个方面。其次,重点以数学建模工具为主线,综述了基于概率论、马尔可夫过程、排队论、模糊逻辑、模糊神经网络等5类代表性的频谱切换算法,评述了学术界在各类频谱切换算法上的重要研究成果;最后,基于对已有算法和研究成果的分析,总结了当前研究中存在的非理想频谱检测、目标信道选择、空闲信道动态性等主要问题,预测了频谱切换的未来研究方向。     

认知无线电网络中基于包调度算法的主动频谱切换

认知无线电网络中,主用户出现在自己的授权频段,但被次用户占用,此时次用户进行频谱切换.主动频谱切换机制是一种可以提高网络的带宽利用率,同时降低丢包率的方法.基于一个有效的包调度算法,提出一个主动频谱切换机制,以期减少频谱切换发生时的不可用信道.该包调度算法有效地集成了两个算法:频谱空洞填充算法和包迁移算法,来减少丢包率和带宽碎片.实验仿真结果验证了该主动频谱切换机制的有效性.     

基于自适应小区选择的切换控制算法研究

针对现有切换控制算法的不足,提出了一种基于自适应小区选择的优先级信道预留切换算法。该算法在切换时根据各小区的负载情况,通过调整导频功率使小区间负载均衡,以系统效用最大化为目标,进行基于预留信道的优先级切换。系统效用通过多选择多维背包算法建模实现,预留信道的优先级切换通过马尔科夫排队模型实现。通过多选择多维背包建模、马尔科夫排队建模与实验仿真表明,该算法的性能在相邻小区间负载不均衡的情况下比目前切换控制所用的优先级信道预留切换算法明显提高。在负载不均衡度为6时,话音业务阻塞率与数据业务分组丢失率比目前算法分别降低0.2%和3%左右,数据分组平均最大等待时间比目前算法降低0.15s,系统小区平均吞吐量最大时升高80kbit/s/cell/MHz。     

感知不确定下卫星认知无线网络多频谱接入优化策略

针对卫星认知无线网络频谱感知不确定性较大导致传统频谱接入机制效率降低的问题,该文提出一种基于动态多频谱感知的信道接入优化策略。认知LEO卫星根据频谱检测概率与授权用户干扰门限之间的关系,实时调整不同频谱感知结果下的信道接入概率。在此基础上以系统吞吐量最大化为目标,设计了一种基于频谱检测概率和虚警概率联合优化的判决门限选取策略,并推导了最佳感知频谱数量。仿真结果表明,认知用户能够在不大于授权用户最大干扰门限的前提下,根据授权信道空闲状态动态选择最佳频谱感知策略,且在检测信号信噪比较低时以更加积极的方式接入授权频谱,降低了频谱感知不确定性对信道接入效率的影响,提高了认知系统吞吐量。     

Artificial bee colony–based spectrum handoff algorithm in wireless cognitive radio networks

In this work, we proposed a new artificial bee colony–based spectrum handoff algorithm for wireless cognitive radio systems. In our wireless cognitive radio system, primary users, secondary users, and related base stations exist in the same communication environment. For our artificial bee colony–based algorithm, secondary users that always struggle to discover an idle channel have a leading role. While honey bees work hard to find the best‐quality nectar source for foraging, secondary users try to find idle channels for making communication. In this way, secondary users are organized for different missions such as sensing and handoff similar to honey bees to minimize spectrum handoff delay by working together. In the spectrum handoff stage, some secondary users must sense the spectrum so that the interrupted secondary user may perform the spectrum handoff process. In our developed spectrum handoff algorithm, the spectrum availability characteristic is observed on the basis of the missions of the bees in the artificial bee colony algorithm with the aim of minimizing the spectrum handoff delay and maximizing probability of finding an idle channel. With the help of the algorithm that is developed using the artificial bee colony, spectrum handoff delay of secondary users is considerably decreased for different number of users without reducing probability of finding an available channel.     

Stochastic spectrum handoff protocols for partially observable cognitive radio networks

Recent studies have been conducted to indicate the ineffective usage of licensed bands due to static spectrum allocation. In order to improve spectrum utilization, cognitive radio (CR) is therefore suggested to dynamically exploit the opportunistic primary frequency spectrums. How to provide efficient spectrum handoff has been considered a crucial issue in the CR networks. Existing spectrum handoff algorithms assume that all the channels can be correctly sensed by the CR users in order to perform appropriate spectrum handoff process. However, this assumption is impractical since excessive time will be required for the CR user to sense the entire spectrum space. In this paper, the partially observable Markov decision process (POMDP) is applied to estimate the network information by partially sensing the frequency spectrums. A POMDP-based spectrum handoff (POSH) scheme is proposed to determine the optimal target channel for spectrum handoff according to the partially observable channel state information. Moreover, a POMDP-based multi-user spectrum handoff (M-POSH) protocol is proposed to exploit the POMDP policy into multi-user CR networks by distributing CR users to frequency spectrum bins opportunistically. By adopting the policies resulted from the POSH and M-POSH algorithms for target channel selection, minimal waiting time at each occurrence of spectrum handoff can be achieved which will be feasible for multimedia applications. Numerical results illustrate that the proposed spectrum handoff protocols can effectively minimize the required waiting time for spectrum handoff in the CR networks.     

一种基于TOPSIS信道排序的新频谱切换方案

将接近理想方案的序数偏好法(TOPSIS)应用于认知无线电网络的频谱切换中,通过对 影响切换性能的4个参数的融合,设计了一种基于TOPSIS算法的信道排序方案,通过仿真验 证,设计了各参数的权值。统计结果表明,提出的算法在80%的情况下要优于传统的切换算 法,可以有效减少认知用户的切换次数,提高有效数据传输速率,降低认知用户的通信中断 概率。     

Reinforcement-Learning-Based Double Auction Design for Dynamic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, an important issue is to share the detected available spectrum among different secondary users to improve the network performance. Although some work has been done for dynamic spectrum access, the learning capability of cognitive radio networks is largely ignored in the previous work. In this paper, we propose a reinforcement-learning-based double auction algorithm aiming to improve the performance of dynamic spectrum access in cognitive radio networks. The dynamic spectrum access process is modeled as a double auction game. Based on the spectrum access history information, both primary users and secondary users can estimate the impact on their future rewards and then adapt their spectrum access or release strategies effectively to compete for channel opportunities. Simulation results show that the proposed reinforcement-learning-based double auction algorithm can significantly improve secondary users’ performance in terms of packet loss, bidding efficiency and transmission rate or opportunity access.     

认知无线电网络中抗追踪用户攻击的隐式信道选择方案(英文)

For the discontinuous occupancy of primary users in cognitive radio networks(CRN),the time-varying of spectrum holes becomes more and more highlighted.In the dynamic environment,cognitive users can access channels that are not occupied by primary users,but they have to hand off to other spectrum holes to continue communication when primary users come back,which brings new security problems.Tracking user attack(TUA) is a typical attack during spectrum handoff,which will invalidate handoff by preventing user accessing,and break down the whole network.In this paper,we propose a Channel Selection Information Hiding scheme(CSIH) to defense TUA.With the proposed scheme,we can destroy the routes to the root node of the attack tree by hiding the information of channel selection and enhance the security of cognitive radio networks.     

基于动态信道预约的自适应QoS切换算法

未来无线网络将为固定和移动用户提供多媒体通信和计算业务．为移动用户提供无线多媒体业务的一个最关键的挑战是保证端到端连接的业务质量．通过重复使用无线频谱的微蜂窝或微微蜂窝结构是一个有前途的改善移动多媒体网络容量的方式．但切换次数随着蜂窝大小的降低而增加．移动多媒体网络的一个至关重要的问题是需要可以满足各种QoS需要且有更高资源利用率的有效切换方式．该文提出了一种称为基于动态信道预约的自适应QoS切换算法,并与其它切换方式进行了性能比较．     

基于BP神经网络的认知无线电疑似跳转信道预测研究

为了在认知无线电环境中进行跟踪干扰,需要预测认知用户在遭受干扰后进行频谱切换的目标信道——疑似跳转信道,以便在这些疑似跳转信道上对其进行跟踪检测和干扰。提出了一种在多信道多用户条件下,认知用户在感知得到的空闲信道上智能决策并选择合适信道接入的频谱模型,并用基于BP神经网络的预测算法预测疑似跳转信道,为衡量预测效果,提出了预测切准率和预测精度两个参考标准,仿真结果验证了该算法的可行性。