认知无线电系统中协同能量检测算法的性能研究

该文首先研究了单认知用户能量检测算法的检测性能和检测灵敏度与检测时长和噪声短时间内平均功率波动性之间的关系,得出检测性能和检测灵敏度随噪声短时间内平均功率波动性的增大而急剧下降的结论,在低信噪比时尤为明显;针对检测性能和检测灵敏度随噪声短时间内平均功率波动性的增大而急剧下降,提出了基于多用户协同能量检测算法。仿真表明,在低信噪比环境下,即使噪声短时间内的平均功率存在较大的波动性,只要适当地增加协同检测的用户数,仍可获得较为准确的检测性能。     

一种认知无线电系统的传输调度方案

将认知无线电系统中的传输调度方案建模为一个约束马尔科夫决策过程（CMDP）,即在满足缓存器内包数约束的情况下最小化发送数据包消耗的平均功率。因为在认知无线电系统中,环境参数无法预先得知,为此利用R学习来自适应地获取CMDP的近似最优策略。在仿真结果中,对基于R学习的传榆调度方案的性能进行了比较和分析,结果显示该方案能适用于参数未知的环境且有效地降低平均功率。     

Reinforcement Learning Based Q-Routing: Performance Evaluation on Cognitive Radio Network Topologies

Wireless Personal Communications - The present work explores the scope of cognitive radio networks (CRNs) to support the spectrum demand on future diverse application specific wireless services...     

网络编码在认知无线电中的应用

研究了网络编码应用到认知无线电中的可能,发现在认知无线电中应用网络编码技术,可以减少非授权用户和授权用户的冲突概率,增加无线网络的实际吞吐率.最后通过仿真验证了网络编码优化认知无线电网络性能的有效性.     

频谱池在认知无线电中的应用

认知无线电CR（Cognitive Radio）能够有效解决频率资源的稀缺性和低利用率之间的矛盾,使得频率资源得到更好的利用。在CR系统中使用频谱池可以进一步提高频谱利用率。本文首先介绍了频谱池的概念和原理,然后对频谱池实现相关问题进行了探讨,并分析了频谱池的应用前景。     

认知网络下TCP协议性能分析

首先在无线环境下的基础上搭建认知平台,当主用户想要占次用户信道时,通过基站的频谱监测,频谱分配,分配次用户到其他空闲信道继续通信.其次,在此平台下分析TCP Reno,TCP Newreno,TCP Sackl和TCP vegas协议的性能,包括拥塞窗口、吞吐量.最后定量分析主用户活跃程度、切换后信道带宽和信道误比特率...     

认知无线电网络安全研究

分析了认知无线电网络中存在的两类安全隐患,归纳了针对这些安全隐患的网络攻击行为,介绍了目前安全领域的研究成果,并探讨了认知无线电网络安全技术未来的发展方向.     

非理想SIC下SWIPT-CR-NOMA网络中断性能分析

针对采用无线携能通信的多中继底层协作认知非正交多址接入网络,提出一种两阶段中继选择策略。认知中继执行功率划分的无线携能通信协议为次级用户提供解码转发服务,其能量开销源于所采集到的能量。考虑了实际的非正交多址接入网络中,中继节点与次级用户均无法完全消除多址干扰,即无法实现理想连续干扰消除。在干扰阈值约束下,推导了非理想连续干扰消除下两次级用户端中断概率的精确表达式,并通过蒙特卡洛仿真验证其正确性。此外,定量分析了各系统相关参数（最大发射功率、干扰阈值、功率分配系数等）的选取对次级用户中断性能的具体影响。结果表明,在相同的系统参数设置下,所提方案次级用户中断性能远优于现有部分中继选择方案。      

认知无线电环境下TCP性能分析

首先讨论分析了认知无线电环境下影响TCP性能的关键因素,并通过NS2仿真软件搭建认知网络环境,仿真分析了不同TCP版本协议在认知网络环境下的性能状况,包括reno,newreno,westwood,vegas和sack。仿真结果表明,在认知网络环境下,westwood性能最好而vegas最差。     

Semi-supervised and Unsupervised Privacy-Preserving Distributed Transfer Learning Approach in HAR Systems

Wireless Personal Communications - One of the challenges faced by machine learning in human activity recognition systems is the different distributions of the training and test samples. Transfer...     

基于中继协作的认知无线电性能研究

频谱感知是认知无线电技术的关键。协作频谱感知能够充分利用网络资源,提高网络中认知用户的检测概率,降低认知用户的虚警概率。基于两个认知用户之间的中继协作,研究了协作频谱感知的感知性能与吞吐量的折衷。仿真结果表明：中继协作的频谱感知方法能够使得在充分保证对授权用户不造成干扰即检测概率一定时,能够有效地降低认知用户感知授权用户的虚警概率,缩短＂弱＂认知用户获得最大吞吐量的最优感知时间,提高其最大吞吐量,并提高认知系统的最大吞吐量。     

Applications of Machine Learning to Cognitive Radio Networks

Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.     

认知无线电智能学习决策研究现状

认知无线电由于其灵活的频谱使用和智能参数重配置能力,引起了国内外的广泛关注。目前认知无线电（CR）大部分的研究都集中在动态频谱访问上,而认知无线电智能学习与决策研究正起步并逐步受到重视。现对认知无线电智能技术,包括学习方法、知识表示、智能决策方法等进行了描述,并介绍了CR智能学习决策的研究现状。通过对两类CR智能学习决策方法的分析研究,指出基于学习的决策方法能更好地实现认知无线电适应新环境的能力。     

Optimal Spectrum Sensing in Energy Harvesting Cognitive Radio Systems

In this paper,we consider a cognitive radio system with energy harvesting,in which the secondary user operates in a saving-sensing-transmitting（SST） fashion.We investigate the tradeoff between energy harvesting,channel sensing and data transmission and focus on the optimal SST structure to maximize the SU＇s expected achievable throughput.We consider imperfect knowledge of energy harvesting rate,which cannot be exactly known and only its statistical information is available.By formulating the problem of expected achievable throughput optimization as a mixed-integer non-linear programming one,we derive the optimal saveratio and number of sensed channels with indepth analysis.Simulation results show that the optimal SST structure outperforms random one and performance gain can be enhanced by increasing the SU＇s energy harvesting rate.     

认知无线电关键技术及应用的研究现状

归纳了认知无线电功能的演进,讨论了其相关频谱政策和标准化工作的进展,并重点对频谱侦听和主用户检测、动态频谱分配、功率控制等关键技术及认知无线电在无线区域网、ad hoc网络、UWB系统中应用的研究现状进行了分析.在此基础上探讨了认知无线电技术未来发展值得关注的热点问题.     

认知无线网络中的频谱切换性能分析

在认知无线网络中,次用户的频谱切换性能不仅和主用户的通信行为关系密切,而且也和次用户本身的频谱感知精度以及频谱切换策略密切相关。该文采用连续时间马尔科夫模型对次用户在理想感知和非理想感知情况下的频谱切换进行建模,进一步分析与比较不同频谱感知精度对频谱切换性能的影响。为了有效降低次用户在频谱切换过程中的强制中断概率,提出基于信道预留机制的频谱切换策略。数值结果表明,次用户对主用户信号的感知精度明显会影响次用户的频谱切换性能,而有效的频谱切换策略则可以以较小的代价获得更好的频谱切换性能。     

Adaptation in Convolutionally Coded MIMO-OFDM Wireless Systems Through Supervised Learning and SNR Ordering

Multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems use link adaptation to exploit the dynamic nature of wireless environments. Link adaptation maximizes throughput while maintaining target reliability by adaptively selecting the modulation order and coding rate. Link adaptation is extremely challenging, however, due to the difficulty in predicting error rates in OFDM with binary convolutional codes, bit interleaving, MIMO processing, and real channel impairments. This paper proposes a new machine-learning framework that exploits past observations of the error rate and the associated channel-state information to predict the best modulation order and coding rate for new realizations of the channel state without modeling the input–output relationship of the wireless transceiver. Our approach is enabled through our new error-rate expression that is only parameterized by postprocessing signal-to-noise ratios (SNRs), ordered over subcarriers and spatial streams. Using ordered SNRs, we propose a low-dimensional feature set that enables machine learning to increase the accuracy of link adaptation. An IEEE 802.11n simulation study validates the application of this machine-learning framework in real channels and demonstrates the improved performance of SNR ordering as it compares with competing link-quality metrics.      

A Robust Cooperative Spectrum Sensing Scheme in Cognitive Radio Systems

Performance of cluster-based cooperative spectrum sensing is more greatly improved than that of the traditional cooperative spectrum sensing.However,the style of clusters is not pointed out on traditional cluster-based method.In view of all the fading in the cooperative spectrum sensing,space diversity,and selective diversity,a robust cooperative spectrum sensing scheme based on clusters is proposed.Simulation results show that the detection performance of this new scheme is more close to that of the ideal cluster-based cooperative spectrum sensing scheme,and the excellent performance with higher reliability is gained relative to the actual cluster-based cooperative spectrum sensing scheme.Finally,the number of users in a cluster of the proposed scheme is discussed.The two users in a cluster are not the optimal solution to the overall implementation of cognitive radio.