A MULTI-RESOLUTION SPECTRUM SENSING (MRSS) SCHEME UNDER MEASUREMENT-BASED CHANNEL MODELS IN COGNITIVE RADIO

Spectrum sensing is one of the key technologies in Cognitive Radios(CRs).Previous works are accomplished under simple channel models,which may lead to unreliable results when it applied to the over-the-air systems.In this paper,we investigate the performance of a Multi-Resolution Spectrum Sensing(MRSS) algorithm under measurement-based channel models in China.MRSS is a wavelet based algorithm which is suitable for non-stationary,wideband signal analysis.Using statistical mod-eling,measurement-based channel models are presented under typical urban and suburban scenarios in Shanghai,China.Then,the performance of the MRSS algorithm is evaluated under the measure-ment-based channel models.Simulation results show that,using MRSS,the performance is always better in the scenarios where Line-Of-Sight(LOS) path exist;also,in LOS scenarios,rich scattering effect helps to increase the performance.

Media Access with Spatial Reuse for Cooperative Spectrum Sensing

The increasing interest for wireless communication services and scarcity of radio spectrum resources have created the need for a more flexible and efficient usage of the radio frequency bands. Cognitive Radio (CR) emerges as an important trend for a solution to this problem. Spectrum sensing is a crucial function in a CR system. Cooperative spectrum sensing can overcome fading and shadowing effects, and hence increase the reliability of primary user detection. In this paper we consider a system model of a dedicated detect-andforward wireless sensor network (DetF WSN) for cooperative spectrum sensing with k-out-of-n decision fusion in the presence of reporting channels errors. Using this model we consider the design of a spatial reuse media access control (MAC) protocol based on TDMA/OFDMA to resolve conflicts and conserve resources for intra-WSN communication. The influence of the MAC protocol on spectrum sensing performance of the WSN is a key consideration. Two design approaches, using greedy and adaptive simulated annealing (ASA) algorithms, are considered in detail. Performance results assuming a grid network in a Rician fading environment are presented for the two design approaches.

Space-Time Correlation Based Fast Regional Spectrum Sensing in Cognitive Radio

In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.

An Intelligent Fuzzy Based Energy Detection Approach for Cooperative Spectrum Sensing

Cognitive radio systems are helpful to access the unused spectrum using the popular technique, referred to as spectrum sensing. Spectrum sensing involves the detection of primary user (PU) signal using dynamic spectrum access. Cooperative spectrum sensing takes advantage of the spatial diversity in multiple cognitive radio user networks to improve the sensing accuracy. Though the cooperative spectrum sensing schemes significantly improve the sensing accuracy, it requires the noise variance and channel state information which may lead to transmission overhead. To overcome the drawbacks in conventional cooperative spectrum sensing, this paper proposes a fuzzy system based cooperative spectrum sensing. Selection combining (SC) and maximum ratio combining (MRC) are used at fuzzy based fusion center to obtain the value of the sensing energy. These energy values are utilized in finding the presence of PU, results in improved sensing accuracy. In addition, an intelligent fuzzy fusion algorithm determines the PU presence without the channel state information based on multiple threshold values. Simulation results show that the proposed scheme outperforms the existing schemes in terms of sensing accuracy.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

Hybrid spectrum access model for cognitive femtocell networks

In order to mitigate the interference for macrocell users caused by deploying femtocells （home base-station） in a long term evolution （LTE） system, a hybrid spectrum access model is proposed by means of applying the cognitive radio technology to the femtocell. The femtocell periodically senses the radio environment and opportunistically accesses the usable frequency band so that the frequency spectrum resource is used intelligently. The sensing process is performed in two stages, which are principal sensing in the downlink and assisted sensing in the uplink, respectively. Based on the information obtained from the sensing results, the frequency spectrum can be used flexibly in underlay or overlay modes in the femtocell. Simulation results show that by using the proposed model, the throughput of a femtocell is greatly improved with tolerable interference to macrocell users.

Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication

Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.

Phase type delay distribution with PU return and sensing error in CR networks

Many studies have been done in cognitive radios to maximize the network efficiency. There is always a trade-off between sensing and transmission time which has been addressed fully in the literature. There is also some missed idle times during the waiting phase when secondary user finds the channel busy. Therefore, the longer the transmission time is, the higher the successfully delivered bits would be and the higher the missed idle times in the waiting stage would be expected. In this work, we formulate these missed idle times to addressed this trade-off. Furthermore, interference due to PU re-occupancy is modelled in successful transmitted time and in collision probability. Moreover, we derive secondary user's packet delay distribution using phase type model at which retransmission due to collision results from both sensing error and PU re-occupancy is considered. Thanks to derived delay distribution, any statistical moments of delay could be found as a closed form mathematical expression. These moments can be used as constraints for applications with delay sensitive packets. Numerical results are given to clarify the network metrics. Finally, the optimized values for sensing and transmission time are computed using genetic algorithm in order to maximize access efficiency.

Intelligent cognitive spectrum collaboration:Convergence of spectrum sensing,spectrum access,and coding technology

For a future scenario where everything is connected,cognitive technology can be used for spectrum sensing and access,and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios.Machine learning technologies are being increasingly used in the implementation of smart networks.In this paper,after an overview of several key technologies in the cognitive spectrum collaboration,a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning,and the effectiveness of the algorithm is verified by simulations,thus providing a feasible research direction for the realization of cognitive spectrum collaboration.

IRS辅助SIMO-MAC认知无线电系统频谱感知优化策略

认知无线电(Cognitive Radio, CR)通过允许次用户与主用户共享频谱,在提高无线通信的频谱效率方面具有重要意义。最近,智能反射表面(Intelligent Reflecting Surface,IRS)的提出,使我们可以通过IRS反射元件重构信道环境来提高无线通信系统的通信质量。本文提出了一个IRS辅助的单输入多输出多址接入信道(Single Input Multiple Output Multiple Access Channels, SIMO-MAC) CR网络,并结合一种新型的IRS辅助多用户协作频谱感知方案来增强感知性能。目标是在次用户的功率约束以及对主用户的干扰约束下,通过联合优化次用户发射功率分配和IRS相位,最大化次用户的和速率。为了解决所建立的变量耦合的非凸问题,我们采用基于块坐标下降的高效交替优化算法,首先通过拉格朗日乘子法和注水功率分配算法以及Karush-Kuhn-Tucker (KKT)算法求解得到最优的功率分配,然后构造多目标优化问题,利用连续凸逼近、半定松弛方法将非凸问题转化为一个半正定规划的凸问题,从而求解出IRS相移矩阵的近似最优解。仿真结果表明,本文所提方案可以极大地提高认知无线电网络的频谱感知性能以及频谱效率。

基于竞争双深度Q网络的频谱感知和接入

认知用户通过频谱感知和接入过程识别频谱状态并占用空闲频谱,可有效利用频谱资源。针对频谱感知中存在感知错误和频谱接入中存在用户碰撞的问题,首先建立多用户多信道模型,设计频谱感知和频谱接入过程;然后通过结合双深度Q网络和竞争Q网络,设计竞争双深度Q网络,解决过估计问题的同时优化网络结构;最后通过智能体与所设计模型中状态、观测、回报和策略的交互,完成使用竞争双深度Q网络解决频谱感知和接入问题的一体化研究。仿真结果表明,相比于已有深度强化学习方法,使用竞争双深度Q网络得到的数值结果更稳定且感知正确率和信道利用率都提高了4%。

基于深度强化学习的能量采集认知无线电动态频谱接入

为解决认知无线电(Cognitive Radio, CR)中频谱和能量短缺的问题,提出一种基于深度Q网络(Deep Q-Network, DQN)的动态频谱接入算法。次级用户(Secondary User, SU)通过基站射频信号采集能量,并在频谱感知后实现信道的自主接入。模型通过DQN训练,并使用奖励机制和训练算法优化,SU能够根据环境信息作出合适的接入策略。仿真结果表明,提出的深度强化学习(Deep Reinforcement Learning, DRL)模型性能优于无学习模型,提高了频谱感知准确率及用户吞吐量,对比结果证明了模型的适用性及合理的虚警率可以提升模型的学习性能。

基于可靠次用户信息的协作频谱感知算法研究

对认知无线电系统最基本要求之一就是次用户必须有能力以高的精确率来确定主用户是否存在。而以前对认知无线电频谱感知的研究表明:在实际认知网络中次用户之间的相互协作可以提高其频谱检测性能。然而,对于协作频谱感知而言,随着协作次用户数目的增加,势必会增大用于传输本地检测结果到融合中心的专用控制信道带宽,从而增加系统开销。该文在控制信道带宽有限的约束条件下,提出一种通过考虑可靠次用户信息的协作频谱感知算法来进一步改善频谱检测的性能。该算法的基本思想:只有具有可靠的本地检测结果的次用户才发送自己的检测结果到融合中心,否则,该次用户不发送任何信息。同时,对提出的该算法在理论上进行了推导,通过仿真结果表明:在控制信道带宽受限的约束下,相比于传统的"或"门协作频谱感知算法,提出的算法能够大大改善对主用户的检测性能。

认知无线电网络频谱检测研究

为解决认知无线电中空闲频谱的识别问题,综述了近期世界范围内的频谱检测方法。分析了基于信道衰落、噪声特性等因素对频谱检测性能的影响,指出了物理层检测、MAC(Media Access Control)层自适应检测以及多用户合作检测的关键技术问题及其分析处理方法,并对检测性能进行了定性和定量的分析和探讨。

无线认知网络中一种团划分的频谱感知算法

提出了一种基于节点之间竞争关系以及感知代价的分布式频谱感知算法.该算法基于分布式最小团划分理论,将节点划分至不同的团中,然后再根据团的结构来计算最优频谱感知分配.该算法的时间复杂度仅为O(N2+M×N),其中,N为网络中节点数,M为信道数目.大量的仿真实验结果表明,该算法能够有效地提高网络性能.

认知无线电中频谱感知技术

固定频谱分配政策导致频谱使用率低下,为克服这一缺点,人们提出使用认知无线电技术实现动态频谱接入,从而有效利用频谱空穴。频谱感知是认知无线电的关键技术。文中介绍了目前已提出的频谱感知技术:发射机检测(包括匹配滤波器检测、能量检测和周期平稳特征检测)、合作检测、接收机检测及基于干扰温度的检测,仿真分析了各种方法的优缺点及有待解决的问题。为满足多用户环境和实时性要求,仍需探讨新的频谱感知方法,提高检测性能。

多信道频谱资源优化分配仿真研究

现有无线频谱资源分配的研究,大多利用频谱感知的理想结果,以静态且与资源分配相独立的假设为主,在现实条件下很难满足。针对上述问题,提出多信道频谱感知与资源分配联合优化方法,通过引入一个通用的矢量接入模型,以描述单用户和多用户两种情景下多信道接入方式,设计在对授权用户一定干扰水平下的满足非授权用户最大吞吐量的最优策略,以权衡频谱感知参量、信道接入方式与功率分配策略之间的关系。仿真结果表明,多信道频谱感知与资源分配最优方法,不仅弱化非理想上报信道对频谱感知性能的影响,而且提高了信道频谱资源分配性能。

基于动态多频谱感知的认知无线网络信道接入策略

针对固定频谱感知信道接入机制授权信道分配效率低的问题,提出一种多用户场景下基于动态多频谱感知的认知无线网络信道接入策略.该策略根据认知用户接入请求等级与空闲频谱发现概率为认知用户动态分配感知信道数量,首先分析了认知用户的最佳感知频谱分配数量,在此基础上通过建立认知用户信道接入模型和授权信道可用度更新机制,设计了一种动态感知频谱分配策略,并采用短视策略对网络吞吐量进行了分析.仿真结果表明,所提信道接入策略要比现有认知无线网络信道接入策略提高了网络吞吐量和认知用户接入的公平性,降低了认知用户的平均接入时延.

认知无线电中接入机会的建模与优化

针对认知无线电系统中次级用户的感知接入问题,提出了一种频谱感知与接入机会联合建模的优化方法.该方法考虑了频谱感知对次级用户接入机会的影响,并分析了用户间分布对次级用户接入机会的作用,使次级用户系统能够根据不同的应用场景工作在最佳状态.而目前现有的相关工作忽视主次用户之间在空间分布上的关系,使理论分析不能够匹配实际问题.仿真结果验证了联合建模方法能够匹配实际情况,利用此方法设置次级用户系统可以使其频谱利用效率最高.

认知雷达中协作频谱感知技术研究

频谱感知通过搜索空间中的空闲频段或干扰较少的频段,为未授权用户提供频谱接入的机会,是认知雷达中的一项关键技术。众所周知,协作感知可以提高频谱感知的精度,但是往往需要设置专门的控制信道来汇报协作节点的本地感知结果。这种汇报方式不利于节约信道资源。为了解决这一难题,提出了一种直接利用感知信道进行感知结果汇报的协作感知机制。该机制通过本地感知和协作感知的自适应切换及汇报节点的自适应功率控制,不仅提高了频谱感知的精度,而且节约了汇报信道资源。最后,通过仿真实验验证了该机制的有效性。文中提出的协作感知机制对认知雷达系统的设计具有重要的理论和实践意义。