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.     

基于信道预测的认知无线电混合频谱切换算法

针对认知无线电网络,提出了一种将被动式频谱切换与主动式频谱切换相结合的混合频谱切换算法。该算法基于主用户信道的连续时间马尔可夫链模型,预测出信道的未来状态信息,根据该预测结果周期性地对正在通信的认知用户执行主动式频谱切换。该算法对于由于碰撞而退出信道的认知用户执行被动式频谱切换。仿真结果表明,相对于被动频谱切换算法,混合频谱切换算法在保持认知用户阻塞概率和中断概率不变的前提下可显著减少认知用户和主用户间的碰撞次数,能够提高认知无线电网络的频谱利用率。     

认知无线电网络信道交汇加速算法

在认知无线电网络中,二级用户需要首先发现邻居信息并形成通信链路,被称为信道交汇过程。有很多的信道跳频算法,但它们的目标是要形成一个集合点图案或保证在有限时间内会合。在这项研究中,提出了一种算法,以加快与多个用户认知无线电网络的交汇过程,即最近最少使用策略。其基本思路是减少已经交汇用户之间交汇的重复。为了评估所提出的方案,进行了大量的实验。     

基于遗传算法的全双工认知无线电网络信道分配策略研究

随着无线通信业务的不断增长和智能终端的日益多样化,无线频谱资源变得愈加紧缺,认知无线电作为一项能够有效解决频谱利用率过低的技术,近年来引起了学术界和工程界的广泛关注。在认知无线电网络中,次用户以合作频谱感知的方式智能判断主用户的工作状态,以便在授权频谱的空闲时段内接入并加以使用。将全双工通信技术引入到传统的认知无线电网络中,实现了频谱感知和数据传输的同步,既能确保数据传输的连续性,又能减少对主用户的干扰,对提升无线频谱利用率具有重要作用。对于多信道全双工认知无线电网络的信道分配,建立了次级网络吞吐量最大化模型,并通过惩罚函数法,将混合整数非线性规划问题转换成不带约束条件的非线性规划问题,提出了基于遗传算法的全双工认知无线电网络信道分配策略。仿真实验结果表明,该算法能在综合考虑算法复杂度和性能的情况下,完成信道分配并保证次级网络吞吐量的最大化。     

能量效率和碰撞概率联合优化的协作频谱感知算法

协作频谱感知能提高认知无线电网络的感知性能。随着认知网络频谱感知性能的提高,一方面感知网络将会消耗更多的能量;另一方面次用户拥有更多的机会接入授权频谱,次用户的吞吐量不断增加,同时在通信过程中主用户与次用户发生碰撞的概率也不断增大。本文提出了一种联合优化能量效率和碰撞概率的协作频谱感知算法,通过最优感知检测点判断节点所处信道状态,融合中心舍弃信道状态不好的节点使其不参与数据融合,既消除了信道状态不好的节点对全局判决的不良影响,又提高了能量利用率。仿真结果表明,该算法提高了网络的频谱检测性能,并延长了感知网络的生命周期。     

认知无线Mesh网络中基于概率的贪婪频谱决策技术研究

认知无线电网络传统的频谱决策方法中次用户根据不同的判据选择信道,会引起信道竞争和拥塞。针对认知无线Mesh网络中基于概率的频谱决策方法,提出一种贪婪信道选择算法,当发生频谱切换时,其结合改进的抢占优先(相似文献   

应急通信系统中基于集群智能的动态信道分配

应急通信系统中频谱资源在时间和空间上的动态变化,使得寻找一条稳定的公共控制信道十分困难。为此,将认知无线电技术引入应急通信系统中,建立了应急认知无线电网络模型,提出一种基于集群智能的动态信道分配算法。系统中每个节点根据检测到的可用信道信噪比和邻居节点对信道的选择情况,采用信道选择更新函数,选择公共控制信道。应急通信系统中的所有节点构成一个集群,节点进行各自的信道更新并与其他节点交互形成集群智能。各节点周期性地进行消息发送与监听,依据频谱资源的动态变化及时更新公共控制信道的选择,最终达到全局公共控制信道的动态选择。仿真结果表明,算法能够达到公共控制信道的有效收敛,有很好的可扩展性,并且能够快速地适应频谱资源的动态变化。     

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。     

混合保护准则下基于NC-OFDM的认知无线电系统功率分配

针对主用户采用OFDM通信体制,认知用户采用NC-OFDM通信体制,认知用户共享主用户授权频谱的工作场景,研究了认知无线电系统功率分配策略。提出依据主用户不同工作状态,采用不同的约束准则:对认知用户工作信道,引入功率泄漏干扰因子约束认知用户的发射功率,以限制对主用户工作信道的泄漏功率强度;对主用户工作信道,设置主用户速率损失上限,以确保主用户正常工作。功率分配算法以混合约束准则为基础,最大化认知无线电系统的传输速率。仿真结果表明,与干扰功率约束机制相比较,本算法能显著提升传输速率。     

基于认知无线电的接入控制研究

研究认知无线电网络（CRN）中CR用户机会式占用频谱的接入控制。阐明CR用户与授权用户合作接入控制策略,针对实际网络环境分析CR用户基于Markov模型和具有预测机制的透明接入控制策略。对于CR用户不同业务需求以及网络中存在的各种干扰,总结了基于功率控制的透明接入控制策略。提出基于CRN的接入控制进一步研究的关键问题。     

Analytical modeling for spectrum handoff decision in cognitive radio networks

Cognitive Radio (CR) is an emerging technology used to significantly improve the efficiency of spectrum utilization. Although some spectrum bands in the primary user’s licensed spectrum are intensively used, most of the spectrum bands remain underutilized. The introduction of open spectrum and dynamic spectrum access lets the secondary (unlicensed) users, supported by cognitive radios; opportunistically utilize the unused spectrum bands. However, if a primary user returns to a band occupied by a secondary user, the occupied spectrum band is vacated immediately by handing off the secondary user’s call to another idle spectrum band. Multiple spectrum handoffs can severely degrade quality of service (QoS) for the interrupted users. To avoid multiple handoffs, when a licensed primary user appears at the engaged licensed band utilized by a secondary user, an effective spectrum handoff procedure should be initiated to maintain a required level of QoS for secondary users. In other words, it enables the channel clearing while searching for target vacant channel(s) for completing unfinished transmission. This paper proposes prioritized proactive spectrum handoff decision schemes to reduce the handoff delay and the total service time. The proposed schemes have been modeled using a preemptive resume priority (PRP) M/G/1 queue to give a high priority to interrupted users to resume their transmission ahead of any other uninterrupted secondary user. The performance of proposed handoff schemes has been evaluated and compared against the existing spectrum handoff schemes. Experimental results show that the schemes developed here outperform the existing schemes in terms of average handoff delay and total service time under various traffic arrival rates as well as service rates.     

基于数据库驱动认知无线电网络的位置隐私保护方案

动态频谱共享能够解决由于互联无线设备快速增长导致的频谱资源短缺问题,但用户需要向数据库提交位置信息来查询频谱的可用性,造成用户的隐私泄露,而多数位置信息保护方案较少同时考虑对主要用户（PU）和二级用户（SU）的位置隐私保护。提出一种基于盲签名和秘密共享的数据库驱动认知无线电网络隐私保护方案。通过对PU和SU的双重隐私保护,使用盲签名和匿名来确保匿名验证用户身份,同时运用秘密共享避免泄露用户信息。仿真结果表明,与PeDSS和LP-Goldberg等方案相比,该方案具有较好的评估性能和更高的安全性,能够更好地应用于移动环境中。     

Securing Privacy Using Optimization and Statistical Models in Cognitive Radio Networks

Cognitive Radio Networks (CRN) are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems. CRN is a promising alternative approach that allows spectrum sharing in many applications. The licensed users considered Primary Users (PU) and unlicensed users as Secondary Users (SU). Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service (QoS). Irrespective of using different optimization techniques, the same methodology is to be updated for the task. So that, learning and optimization go hand in hand. It ensures the security in CRN, risk factors in spectrum sharing to SU for secure communication. The objective of the proposed work is to preserve the location of the SU from attackers and attain the clustering of SU to utilize the resource. Ant Colony Optimization (ACO) is implemented to increase the overall efficiency and utilization of the CRN. ACO is used to form clusters of SUs in the co-operative spectrum sensing technique. This paper deals with threat detection and classifying threats using parameters such as unlikability, context privacy, anonymity, conditional traceability, and trade-off. In this privacy-preserving model, overall accuracy is 97.4%, and it is 9% higher than the conventional models without Privacy-Preserving Architecture (PPA).     

ICSSSS: An intelligent channel selection scheme for cognitive radio ad hoc networks using a self organized map followed by simple segregation

In Cognitive Radio Ad Hoc Networks (CRAHNS), several spectrum bands with different channel characteristics may be available over a large frequency range. It is essential to identify the most appropriate spectrum band correctly which allow the Secondary Users (SUs) to exploit the band without disturbing the Primary Users (PUs). Many channel selection solutions, based on cooperative spectrum sensing, have been employed for this purpose depending on their prediction models for primary users’ activities. In practice, cooperative spectrum sensing cannot completely solve the sensing problems which are false alarm and miss detection, especially in heavily shadowed or fading environment. This paper presents, ICSSSS, as an Intelligent Channel Selection Scheme for cognitive radio ad hoc network using Self organized map followed by simple Segregation. The contribution of the proposed scheme is twofold: using an unsupervised learnable Self Organizing Map (SOM) method to efficiently minimize the probability of the sensing errors (false alarm and miss detection), in addition to segregated channel selection strategy to speed up the search for the available best channel. Simulation results based on NS2 simulations show that the proposed scheme can be used with the advantage of better performance than other existing channel selection strategies.     

Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach

Consider a multi-channel Cognitive Radio Network (CRN) with multiple Primary Users (PUs), and multiple Secondary Users (SUs) competing for access to the channels. In this scenario, it is essential for SUs to avoid collision among one another while maintaining efficient usage of the available transmission opportunities. We investigate two channel access schemes. In the first model, an SU selects a channel and sends a packet directly without Carrier Sensing (CS) whenever the PU is absent on this channel. In the second model, an SU invokes CS in order to avoid collision among co-channel SUs. For each model, we analyze the channel selection problem and prove that it is a so-called “Exact Potential” game. We also formally state the relationship between the global optimal point and the Nash Equilibrium (NE) point as far as system capacity is concerned. Thereafter, to facilitate the SU to select a proper channel in the game in a distributed manner, we design a Bayesian Learning Automaton (BLA)-based approach. Unlike many other Learning Automata (LA), a key advantage of the BLA is that it is learning-parameter free. The performance of the BLA-based approach is evaluated through rigorous simulations and this has been compared with the competing LA-based solution reported for this application, whence we confirm the superiority of our BLA approach.     

基于协作频谱预测的认知网络吞吐率分析

频谱预测是将预测结果传递给次级用户（Secondary User,SU）,使SU有选择性地实施频谱感知,提高频谱感知的有效性。但是存在预测结果不准确的情况,影响整个网络的吞吐率。在基于遗传算法优化的神经网络预测模型基础上,提出了SU进行协作的频谱预测方法,提高了SU预测空闲信道的准确率。讨论了协作频谱预测条件下,在通信强度、协作用户数量、信道数量不同时的系统吞吐率。仿真结果表明协作频谱预测比传统非协作频谱预测系统吞吐率有较大提升。     

基于智能分析的分布式频谱共享技术研究

次用户以跳频序列的方式共享动态的频谱是分布式认知无线电网络频谱共享技术的解决方法之一。在此类方法中,次用户跟随特定的跳频序列接入到它的所有可用信道中,并在此过程中实现与其他次用户的交会。根据分布式认知网络的组网特性,提出了一种针对交会操作的MAC层超帧结构,使得整个认知网络在此结构支撑下可以取得更好的系统性能。根据次用户的频谱感知和使用信道情况的积累与分析,建立次用户的智能频谱库,为次用户的信道接入提供策略支持。仿真结果表明,提出的MAC协议能在高网络负载情况下能获得较好的综合性能。     

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.