绿色认知无线电信道分配与功率控制技术研究

发布时间：2018-01-04 21:43

本文关键词：绿色认知无线电信道分配与功率控制技术研究　出处：《西南科技大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着无线通信技术的飞速发展,认知无线电技术作为当前缓解无线频谱资源紧张局面的有效方法,已成为通信领域的研究热点。伴随移动网络的扩张,网络能耗随之增加,因此,认知无线电在绿色节能领域开展研究,目的是节约无线网络系统资源和减少设备能源消耗,提高频谱利用率和能量效率,达到绿色节能通信的目标。首先,本文对粒子群算法中惯性权重引入指数函数进行优化,发展了一种改进粒子群(Improve Particle Swarm Optimization,IPSO)算法,该算法收敛速度更快。然后给出了惯性权重收敛性分析。其次,针对分布式频谱共享Underlay模式,在保证主用户正常通信和认知用户基本通信的条件下,导出了功率优化模型与相关约束条件。以认知用户最小化发射功率为目标,改进了一种基于IPSO优化的功率控制算法,通过粒子群算法和惩罚函数理论将优化问题转化成适应度函数。实验结果表明,与粒子群算法和自适应粒子群算法对比,本文的IPSO算法可获得更小的总发射功率,并具有较高的传输速率和全局搜索能力。在此基础上结合能量检测模型,对认知用户的发射功率进行能量限定,进而采用能量检测的功率控制方法,实现最小化发射功率的节能通信。最后,针对信道分配以分布式频谱共享异构双层网络为研究对象,研究了信道非重叠与信道重叠两种情况下的优化模型。在保证主用户正常通信与认知用户基本通信的条件下,导出了功率去耦合信道分配能量效率模型,及其相关约束条件。以认知用户最大化能量效率为目标,在功率与信道去耦合分配基础上,发展了一种改进粒子群和遗传算法(Genetic Algorithm,GA)相结合的信道分配优化,本文简称CH-GAIPSO算法,用以实现最优信道分配策略。在此基础上结合能量检测模型,对认知用户的发射功率进行能量限定,进而采用能量限定的信道分配方法,使认知用户能量效率得到一定提高。仿真研究表明了本文方法的有效性。
[Abstract]:With the rapid development of wireless communication technology, cognitive radio technology, as an effective method to alleviate the shortage of wireless spectrum resources, has become a research hotspot in the field of communication, along with the expansion of mobile networks. Network energy consumption increases, therefore, cognitive radio in the field of green energy conservation research, the purpose is to save wireless network system resources and reduce equipment energy consumption, improve spectrum efficiency and energy efficiency. To achieve the goal of green energy saving communication. Firstly, this paper introduces the exponential function to optimize the inertia weight in particle swarm optimization (PSO). An improved particle swarm optimization Particle Swarm optimization (IPSO) algorithm is developed. The convergence speed of the algorithm is faster. Then the convergence analysis of inertial weight is given. Secondly, the distributed spectrum sharing Underlay mode is proposed. Under the condition of ensuring the normal communication between the primary user and the cognitive user, the power optimization model and the related constraints are derived. The objective is to minimize the transmit power of the cognitive user. A power control algorithm based on IPSO optimization is improved. The optimization problem is transformed into fitness function by particle swarm optimization and penalty function theory. Compared with particle swarm optimization (PSO) and adaptive particle swarm optimization (APSO), the IPSO algorithm in this paper can achieve smaller total transmit power. And it has high transmission rate and global search ability. Based on the energy detection model, the transmission power of cognitive users is limited, and then the power control method of energy detection is adopted. Energy saving communication is realized to minimize the transmission power. Finally, the distributed spectrum sharing heterogeneous double-layer network is the research object for channel allocation. In this paper, the optimization model of channel nonoverlapping and channel overlap is studied. Under the condition that the primary user can communicate with the cognitive user, the energy efficiency model of power decoupling channel allocation is derived. With the aim of maximizing the energy efficiency of cognitive users, and on the basis of power and channel decoupling allocation. An improved particle swarm optimization (PSO) combined with genetic algorithm (GA) is developed for channel allocation optimization, which is referred to as CH-GAIPSO algorithm. In order to achieve the optimal channel allocation strategy, combined with the energy detection model, the transmission power of cognitive users is limited, and then the energy limited channel allocation method is adopted. The energy efficiency of cognitive users is improved to some extent. The simulation results show the effectiveness of the proposed method.
【学位授予单位】：西南科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN925

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