认知网络中的能量回收技术：性能分析及优化设计

通信网络中有限的能源和频带资源限制了网络容量的进一步提升.对能量回收技术在认知网络中的应用进行研究,量化评估用户可回收的能量以及可达吞吐率,并进行优化设计很有必要.在所分析的系统中,当授权用户进行通信时,非授权用户可回收无线信号中所带有的能量,并利用回收的能量进行频谱检测;当检测到授权用户空闲时,非授权用户将接入频谱,利用回收到的能量进行数据传输.采用马尔科夫链模型对通信场景进行描述分析,发现授权用户的活跃程度对非授权用户可回收的能量、获得的传输机会带来影响,进而决定了非授权用户的可达吞吐量.在此基础上,提出一种通过控制授权用户业务量,以最大化网络能量效用和频谱效用的优化方案,并通过仿真证实了理论分析的正确性.

Energy Harvesting in Cognitive Radio Networks:Performance Analysis and Optimization

The limited energy and spectrum resources in wireless networks constrain the further enhance-ment of network capacity. Therefore, considering the application of energy harvesting technique in cogni-tive radio networks, it is necessary to analyze and optimize the harvested energy and achievable throughput of cognitive users. In the proposed system, the secondary user ( SU) , which has no authorization to access the spectrum when the primary user ( PU) is active, can harvest and store the radio frequency energy from PU's signal. Meanwhile, the harvested energy can be used to assist SU's information transmission when the spectrum is idle. Instead of traditional queuing theory, Markov chain model is used to analyze states transi-tion process of SU and quantify the steady state probabilities, and it is found hat the active degree of PU can affect the throughput and harvested energy of the system. An optimal scheme is proposed to maximize the energy-efficiency and spectrum-efficiency by controlling the traffic density of the PU. Numerical re-sults proves the theoretical analysis.