一种迁移开销感知的虚拟机动态整合算法

现有的以降低能耗为目标的虚拟机动态整合算法通常忽略了虚拟机迁移所带来的消极影响,导致虚拟机的动态整合虽然减少了数据中心的能耗,但不合理的虚拟机迁移次数较多,极有可能增加了SLA(Service Level Agreements)的违例率。针对上述问题,提出了一种迁移开销感知的虚拟机动态整合算法Mig CAP(Migration Cost Aware Policy),定义了迁移收益参数EMP,Mig CAP算法通过EMP值的大小来决定是否需要进行虚拟机的迁移,避免了不合理的虚拟机迁移的发生。实验结果表明,Mig CAP算法与现有的其他虚拟机动态整合算法相比,能够在有效减少能耗和降低SLA违例率的基础上,显著减少虚拟机迁移次数。

最大系统效用合作频谱感知优化算法

认知系统性能可以通过合作频谱感知技术的应用得到显著提高,但是由于随着参与频谱感知的认知用户数量增加,而必然会导致系统资源消耗增大。在综合评估系统性能和系统资源效率效用的基础上,通过科学选取感知时长和认知用户数量的策略,提出最大系统效用合作频谱感知优化算法。仿真结果表明,在系统探测概率确定的条件下,通过选择恰当的认知用户数量和感知时长,认知系统效用可以提升到最高。

主动式频谱感知的改进型信道切换算法

针对无线信道频繁切换而导致系统性能下降的问题,在传统算法的基础上,提出了改进型信道切换算法.通过信道空闲时长的预测和比较策略,在对首要用户干扰最小的前提下,对信道进行切换控制,有效降低了信道切换代价,减小了感知开销.仿真验证表明:采用改进型信道切换算法能够有效减少信道切换次数约20%.

基于认知无线电的LTE系统频谱感知方法

提出一种认知无线电技术框架下LTE系统中femtocell的频谱感知方法。在该方法中,femtocell对上行频谱进行能量检测,通过一种两步决策的频谱感知技术,发现周围正在承载业务的macrocell用户,并进一步对其承载的业务类型进行识别,根据不同的业务类型,调整感知周期,使其在femtocell取得的吞吐量和耗费的感知能量之间进行有效折中。仿真结果表明,该方法可在控制对macrocell用户的干扰和控制感知开销的情况下,提高femtocell小区的吞吐量,提高系统性能。

基于高效协作感知算法的认知无线电研究

针对协作频谱感知中感知开销过高问题,提出了一种联合考虑选择最佳的感知节点数以及感知的信道数来降低协作感知开销的方法。该算法分析了协作感知算法中感知节点数、感知的授权信道数与协作感知精度以及感知开销之间的关系。仿真结果表明,与其他算法相比,该算法能够有效地降低感知开销并提高系统的感知性能,验证了该算法的有效性。

资源受限的认知无线电系统优化合作频谱感知算法

认知无线电的首要任务就是动态地感知频谱,合作频谱感知提高了频谱感知的可靠性。目前大多数频谱感知算法采用全部认知无线电台参与感知,并且假设每个认知无线电台的信噪比的测量值为一常数。然而,由于无线信道的特性和环境差异,每个认知无线电台的信噪比是有差异的,同时系统资源也是有限的。基于此,在采用能量检测硬判决的合作频谱感知算法基础上,提出一种优化的频谱感知算法。它在满足给定系统探测概率及虚警概率前提下,采用部分性能较好的认知无线电台参与频谱感知,并使系统感知开销最小。理论和仿真证明了其合理及有效性。

A contention-based efficient-information perception algorithm (CEiPA) for vehicular ad hoc networks

The problem of information dissemination is researched for vehicular ad-hoc networks （VANET） in this paper, and a contention-based effficient-information perception algorithm （CEiPA） is proposed. The idea of CEiPA is that beacons are delivered over VANET with limited lifetime and efficient information. CEiPA consists of two phases. The first one is initialization phase, during which the count timers Tcyce and Tlocal are set to start beacon delivery while Tcycle is also used to monitor and restart beaconing. The second one is beacon delivery phase. An elaborate distance function is employed to set contention delay for beacons of each vehicle. In this way beacons will be sent in order, which decreases the collision of beacons. Simulation results show that CEiPA enables each beacon to carry more efficient information and spread them over more vehicles with lower network overhead than the periodic beacon scheme. CEiPA is also flexible and scalable because the efficient information threshold it employs is a balance among the freshness of information, network overhead and perception area of a vehicle.

Predictive Spectrum Sensing Strategy Based on Reinforcement Learning

In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by one before transmission.By leveraging prediction based on correlation between the licensed channels,we propose a novel spectrum sensing strategy,to decide which channel is the best choice to sense in order to reduce the sensing time overhead and further improve the SU's achievable throughput.Since the correlation coefficients between the licensed channels cannot be exactly known in advance,the spectrum sensing strategy is designed based on the model-free reinforcement learning(RL).The experimental results show that the proposed spectrum sensing strategy based on reinforcement learning converges and outperforms random sensing strategy in terms of long-term statistics.

基于协作频谱感知的智能电网需求响应研究

频谱感知是认知无线电的关键技术之一,针对单节点信号检测易受路径损耗、阴影效应和多径衰落的影响,提出采用多节点协作频谱感知技术提高通信质量和动态需求响应系统性能。现有的研究考虑频谱感知带来的开销都是用一个简单的凸函数表示,不够直观,为此将感知开销具体化为能量消耗和时延,同时分析了频谱感知与动态需求响应之间的关系。对于需求响应和感知开销之间的权衡问题,采用遗传算法(GA)求解。实验结果表明存在一个最优的感知时间使通信时延最小,同时系统的净收益与感知开销存在最优平衡;采用遗传算法得到最优解集利于决策者根据需求做出最佳选择。