MAC与TCP跨层的认知无线网络传输性能增强

认知无线网络中,认知用户需要具备频谱感知的功能,在主用户到来时,可以实现动态频谱切换,从而不影响主用户的通信.但是频谱感知和切换带来的时延可能使认知用户TCP频繁的启动慢启动,而影响传输性能.提出的TCP-CWN(TCP for cognitive wireless network认知无线网络TCP)算法,通过TCP-MAC跨层可以消除由于频谱感知和切换带来的慢启动问题.本方案在NS2上进行仿真,实验结果显示在认知环境下TCP-CWN能很好的缓解由于频谱感知对传输的影响,提高认知用户的传输吞吐量.     

认知无线电网络中分布式频谱分配策略的研究

研究认知无线电网络中的分布式频谱资源分配策略对提高频谱资源利用率具有重要的意义.基于动态议价博弈模型并结合最佳单位带宽定价,对认知无线电网络中主用户和认知用户的收益进行建模,在此基础上提出一种高效的基于议价博弈的分布式频谱分配策略(Bargaining Game-Distributed Spectrum Allocation Strategy,BG-DSAS).分别分析了两用户和N用户条件下的策略方案,并给出了实现BG-DSAS频谱分配策略的算法流程.仿真实验从多个角度分析了策略的合理性和有效性.     

认知无线网络中基于进化博弈的动态频谱接入*

研究了认知无线网络环境中基于价格动态性的动态频谱接入,即不同的授权网络服务商以不同的价格将空闲频谱出售给认知网络,且认知用户可以根据自己获得的报酬动态地接入不同的网络。为最大化认知网络的效用,提出了基于进化博弈的动态频谱接入方案。仿真结果表明,当认知用户群体到达进化均衡时,接入每个主网络的认知用户数量的比例达到稳定状态,最大化了认知用户和网络的效用。     

认知无线电网络信道交汇研究综述

认知无线电技术被认为是解决目前频谱资源利用率低下问题最有前景的技术,基于该技术,认知无线电网络采用动态频谱接入方式有效地提高了授权频段的利用率.然而,动态变化的信道可用性极大地增加了认知无线电网络组网的难度.信道交汇旨在为用户通信提供公共传输媒介,是实现无线网络组网的基础.介绍了认知无线电网络信道交汇的基本概念和特点,并阐述了信道交汇策略设计面临的挑战以及应考虑的性能指标.提出了信道交汇策略的分类标准和系统模型,根据该分类标准,详细剖析了当前信道交汇策略相关的研究工作.最后,讨论了认知无线电网络信道交汇研究的开放性问题,以期为未来的研究指出可能的方向和重点.     

认知无线网络中动态频谱接入性能分析

认知无线电技术利用频谱空洞进行通信,有效缓解了频谱资源紧缺问题,动态频谱接入是其核心技术。网络中主用户对授权频谱的使用效率较高时,次用户接入网络无法完成符合QoS要求的通信,只有当主用户频谱效率在一定门限值下时网络才适合次用户接入。有限频谱空洞资源只能满足有限次用户的通信需求,为了保证通信质量,网络在固定的主用户频谱效率下只能接入适量的次用户。提出用强制优先排队理论对认知无线网络中的动态频谱接入过程进行模拟,通过仿真对次用户的切换概率、阻塞概率两个QoS因子进行分析,在给定的QoS条件下,得到了网络适合次用户接入的主用户频谱效率门限值,以及在固定的主用户频谱效率下网络适合接入次用户的量。     

基于频谱预测的不完美条件下的能量有效性设计

在认知无线电网络中,次级用户在实施频谱预测和频谱感知的过程中,存在频谱预测错误和频谱感知错误,而这些因素往往会影响整个网络的性能。分析了在电池供电能量有限的认知无线电网络中,存在频谱预测错误和频谱感知错误的条件下次级用户的能量有效性,设计了频谱预测归一化能量有效性公式,仿真了在频谱预测能量消耗、频谱预测错误概率、通信强度、信道数量不同时的归一化能量有效性,并与完美条件下的归一化能量有效性做了比较,仿真结果更符合实际情况,具有较好的理论与工程应用价值。     

多层网络层次下资源高效分配策略算法研究

随着近年无线通信技术的不断发展,包括3G通信、WIFI通信和蓝牙等多种通信技术的兴起,需要为用户在在多无线网络通信环境下提供实时有效的通信服务。随着多无线电/多频谱技术的普及面积越来越广,越来越多的用户关注最佳通信服务,因而支持多网络下无线电通信技术成为当今研究的重点领域。以信息论为基础,在认知无线电技术中引入协同通信的思想,构建协作认知无线电网络模型,研究多个认知用户协作感测策略和基于协作竞争的频谱接入机制,不仅具有很大的学术价值,同时也必然会很大的社会和商业价值。     

基于边缘智能的频谱地图构建与分发方法

频谱地图可协助认知用户准确感知和利用频谱空洞,实现网络节点间的干扰协调,提升无线网络的频谱效率和鲁棒性。然而,当认知用户在利用和共享频谱地图时,面临着计算复杂度高和分发时延开销大的问题,限制了认知用户对空间频谱态势的实时感知能力。为了解决该问题,提出了一种边缘智能网络中基于强化学习的频谱地图构建与分发方法。首先,在频谱地图构建上,采用了一种克里金插值和超分辨率相结合的低复杂度构建技术;其次,通过引入边缘计算,将频谱地图构建与分发过程中的计算迁移策略选择问题建模为一个混合整数非线性规划问题;最后,将人工智能和边缘计算相结合,采用了一种集中式训练、分布式执行的强化学习框架,对不同网络场景下的频谱地图构建和分发策略进行学习。实验结果表明,所提方法具备良好的适应性,可有效降低频谱地图构建与分发的能耗和时延,支持认知用户在移动边缘网络场景下对频谱地图的近实时级应用。     

一种认知无线电的分布式合作频谱检测方法

在认知无线电的实际应用场景中,频谱检测技术至关重要。针对具有阴影衰落,多径衰落及噪声变化的无线通信环境,分析了噪声的不确定性对能量检测的影响,并根据每个认知用户接收到信噪比[γ]的不同,采用一种动态调整判决门限的合作检测方法来提高频谱检测率。仿真实验表明,该方法比一般的合作检测方案在避免对第一用户产生干扰的条件下,明显提高了频谱检测效率,使授权用户网络和认知用户网络都能得到可靠的通信。     

基于混合重引力搜索的认知无线电网络协作通信算法

为了提高认知无线电网络的频谱利用效率,提出一种基于混合重引力搜索的认知无线电协作通信算法。将主用户队列平均时延、主用户队列稳定性、次用户平均发送能耗考虑为约束条件,建模次用户数据速率关于传输带宽与传输时长为最大化优化问题。将重引力搜索算法与遗传算法融合,实现高性能的优化算法,对认知无线电协作通信问题进行优化。在重引力搜索的每次迭代中,对每个粒子进行遗传算子的处理,保留重引力搜索健壮性高以及易于实现的优点,同时增强全局搜索能力。仿真实验的结果显示,该算法提高了认知无线电的频带利用率,并且提高了次用户数据传输的连续性与稳定性。     

Game theory for cognitive radio networks: An overview

Cognitive radio technology, a revolutionary communication paradigm that can utilize the existing wireless spectrum resources more efficiently, has been receiving a growing attention in recent years. As network users need to adapt their operating parameters to the dynamic environment, who may pursue different goals, traditional spectrum sharing approaches based on a fully cooperative, static, and centralized network environment are no longer applicable. Instead, game theory has been recognized as an important tool in studying, modeling, and analyzing the cognitive interaction process. In this tutorial survey, we introduce the most fundamental concepts of game theory, and explain in detail how these concepts can be leveraged in designing spectrum sharing protocols, with an emphasis on state-of-the-art research contributions in cognitive radio networking. Research challenges and future directions in game theoretic modeling approaches are also outlined. This tutorial survey provides a comprehensive treatment of game theory with important applications in cognitive radio networks, and will aid the design of efficient, self-enforcing, and distributed spectrum sharing schemes in future wireless networks.     

A cross-layer protocol of spectrum mobility and handover in cognitive LTE networks

Cognitive radio technique is the next step toward efficient wireless bandwidth utilization. While some of the spectrum bands (unlicensed band) have been increasingly used, most of the other spectrum resources (licensed band) are underutilized. This drives the challenges of open spectrum and dynamic spectrum access concepts, which allows unlicensed users (or called secondary users, SUs) equipped with cognitive radios to opportunistically access the spectrum not used by licensed users (or called primary users, PUs). Most existing results mainly focus on designing the lower-layer cognitive radio problems. In the literature, this is the first result to investigate the higher-layer solution for cognitive radio networks. In this paper, we present a cross-layer protocol of spectrum mobility (layer-2) and handover (layer-3) in cognitive LTE networks. With the consideration of the Poisson distribution model of spectrum resources, a cross-layer handoff protocol with the minimum expected transmission time is developed in cognitive LTE networks. Performance analysis of the proposed handoff protocol is investigated. Finally, simulation results illustrates the proposed handoff protocol significantly reduces the expected transmission time and the spectrum mobility ratio.     

RAC: Range Adaptive Cognitive Radio Networks

In recent years, cognitive radio has received a great attention due to tremendous potential to improve the utilization of the radio spectrum by efficiently reusing and sharing the licensed spectrum bands, as long as the interference power inflicted on the primary users of the band remains below a predefined threshold level. Cognitive radio allows the secondary users in the cognitive radio network to access the licensed spectrum of the primary users opportunistically. In this paper, an autonomous distributed adaptive transmission range control scheme for cognitive radio networks which is called the RAC is proposed. The RAC considers the QoS requirements of both the primary and the secondary users simultaneously. The cognitive user's maximization of its achievable throughput without interfering the primary user by adapting transmission range of the secondary users dynamically is the key feature of the RAC. One of the advantages of using the proposed scheme is its implementation simplicity. The RAC is compared to other cognitive radio schemes in a simulation environment by using ns2. Simulations indicate that, the RAC can well fit into the mobile cognitive radio ad hoc networks and improve the network performance. Having compared to the other schemes utilizing contemporary cognitive radio technology, the RAC provides better adaptability to the environment and maximizes throughput and minimizes data delivery latency.     

Nonconvex dynamic spectrum allocation for cognitive radio networks via particle swarm optimization and simulated annealing

Dynamic spectrum access is a promising technique designed to meet the challenge of rapidly growing demands for broadband access in cognitive radio networks. By utilizing the allocated spectrum, cognitive radio devices can provide high throughput and low latency communications. This paper introduces an efficient dynamic spectrum allocation algorithm in cognitive radio networks based on the network utility maximization framework. The objective function in this optimization problem is always nonconvex, which makes the problem difficult to solve. Prior works on network resource optimization always transformed the nonconvex optimization problem into a convex one under some strict assumptions, which do not meet the actual networks. We solve the nonconvex optimization problem directly using an improved particle swarm optimization (PSO) method. Simulated annealing (SA), combined with PSO to form the PSOSA algorithm, overcomes the inherent defects and disadvantages of these two individual components. Simulations show that the proposed solution achieves significant throughput compared with existing approaches, and it is efficient in solving the nonconvex optimization problem.     

Emerging cognitive radio technology: Principles, challenges and opportunities

Due to the increasing demand for new wireless services and applications as well as the increasing number of wireless users, the available spectrum is becoming increasingly scarce. As a result, the federal communications commission (FCC) has been investigating new ways to manage the radio frequency resources. Cognitive radio (CR) technology is an innovative radio design philosophy which aims to increase spectrum utilization by exploiting unused and under-utilized spectrum in dynamically changing environments. The basic idea is to let unlicensed users use licensed frequencies, provided they can guarantee minimum interference perceived by the primary licensed users. However, allowing opportunistic use of the wireless spectrum creates new problems such as peaceful coexistence with other wireless technologies as well as understanding the influence of interference that each of these networks can create. In this article, we discuss the key CR operations and principles, and then discuss some of the main challenges and research opportunities that exist in CR-based emerging wireless networks.     

认知无线电网络组播路由算法和协议综述

认知无线电网络（CRN）在实现更好的无线带宽利用率和提高无线应用质量方面发挥着至关重要的作用。由于认知用户可用频谱机会的动态特性,认知无线电网络中的组播是一个具有挑战性的问题。研究者们已经提出了多种在认知无线电网络中进行有效组播的方案,包括基于优化理论、网络编码、机器学习、博弈论的方案等。总结了解决组播问题有效的算法和技术,并对已有的无线电网络中的组播协议进行了全面的综述,最后给出了未来的研究方向。     

动态阈值的双门限协作感知方法

随着通信技术的提高和通信应用的增多,无线频谱资源越来越匮乏.许多分配到资源的主用户利用率很低,认知无线电技术希望没有分配到资源的次用户合理使用主用户频谱来提高频谱的利用率.频谱感知技术关系到认知无线电中次用户对空闲频谱的使用机会,是认知无线电的关键技术之一.就是在已有的感知技术的基础上,提出了新的感知手段.在认知无线电网络中,不同的次用户由于与主用户距离以及自身所处环境的噪声影响不同,它们的信噪比一般是不一样的.这里采用的动态阈值的双门限协作感知方法,是对传统双门限能量感知方法的改进,通过充分考虑次用户之间的差异性来提高感知的性能.首先,根据对检测和虚警概率的要求,过滤掉一部分检测能力不满足条件的次用户.同时,每个次用户的用于能量感知的双门限会根据它们各自信噪比的不同而动态变化.通过与单门限能量感知以及传统双门限能量感知方法进行比较,可以看出本文提出的感知方法一定程度上提高了系统整体感知的性能.     

Multi-leader multi-follower Stackelberg model for cognitive radio spectrum sharing scheme

Radio spectrum is one of the most scarce and variable resources for wireless communications. Therefore, the proliferation of devices and rapid growth of wireless services continue to strain the limited radio spectrum resource. Cognitive Radio (CR) paradigm is a promising technology to solve the problem of spectrum scarcity. In this paper, a new fair-efficient spectrum sharing scheme is proposed for cognitive radio networks. Based on the multiple-leader multiple-follower Stackelberg game model, the proposed scheme increases opportunistic use of the licensed radio spectrum. To adaptively use the spectrum resource, control decisions are coupled with one another; the result of the each user’s decisions is the input back to the other user’s decision process. Under widely diverse network environments, this adaptive feedback process approach can provide an effective way of finding a suitable solution. The simulation results demonstrate that the proposed scheme has excellent network performance, while other schemes cannot offer such an attractive performance balance.