基于OFDM的认知无线电资源分配研究

发布时间：2018-04-27 02:26

本文选题：认知无线电 + OFDM　；参考：《南京航空航天大学》2017年硕士论文

【摘要】：随着无线通信技术的发展,频谱资源逐渐匮乏。当前静态的频谱资源分配方案已经造成了频谱稀缺和利用率不足的困境。于是,认知无线电(CR)凭借其动态频谱接入的特性,可以提高频谱利用率,被认为是未来无线通信网络中富有前景的技术。正交频分复用(OFDM)技术凭借其灵活地动态分配频谱,已经被广泛应用在CR系统的空间接入上,成为衬于底层的技术。认知无线电技术和OFDM技术的结合能够充分发挥两者的优点。因此,如何动态地分配资源来获得较高的CR系统性能成为当前热门的研究方向之一。本文针对不同优化目标建立系统模型,研究了基于OFDM的认知无线电的资源分配算法。本文的主要工作和贡献如下:1.建立了理想感知条件下基于频谱效率的CR-OFDM资源分配系统模型,考虑主用户与次用户之间相互干扰,以系统容量最大化为优化目标。为了避免较高的计算复杂度,提出一种寻求次优解的分步式资源分配算法,包括子载波分配算法以及次优的功率分配算法。仿真结果表明,所提出的算法获得的系统容量可以逼近最优功率分配算法,并且大于传统功率分配算法,而且其算法复杂度小,可作为CR-OFDM资源分配中一种次优算法。2.建立了非理想感知条件下基于能量效率的CR-OFDM资源分配系统模型,提出一种分步算法,包括子载波分配算法和最优的功率分配算法。其中,子载波分配算法以最大化系统能效为基准,在功率分配中采用了分式规划理论,再结合子梯度算法设计了功率分配方案。数值仿真结果表明,相比于最大容量算法,所提出的资源分配算法可以获得更高的能量效率,同时,相比于未考虑感知错误的资源分配方案,此算法可以保证各主用户间正常通信。3.建立了非理想感知条件下基于中继协作的CR-OFDM资源分配系统模型,采用了AF传输协议,以最大化系统的能量效率为目标,建立了目标函数,提出了最优的功率分配算法和次优的功率分配算法。仿真结果表明,在考虑了系统不理想的频谱感知条件下,系统性能不可避免的有些许下降,但是最优的功率分配算法仍可以获得理想的系统能效。相比容量最大化算法,本算法可以提高系统的能量效率。此外,次优的功率分配算法也可以在降低计算复杂度的同时取得较好的系统表现。
[Abstract]:With the development of wireless communication technology, spectrum resources are gradually scarce. The current static spectrum resource allocation scheme has caused the difficulty of spectrum scarcity and insufficient utilization. As a result, cognitive radio (CR) can improve spectrum efficiency by virtue of its dynamic spectrum access, and is considered to be a promising technology in future wireless communication networks. Orthogonal Frequency Division Multiplexing (OFDM) technology has been widely used in the space access of CR system because of its flexible and dynamic spectrum allocation. The combination of cognitive radio technology and OFDM technology can give full play to the advantages of both. Therefore, how to dynamically allocate resources to achieve higher CR system performance has become one of the hot research directions. In this paper, the resource allocation algorithm of cognitive radio based on OFDM is studied by establishing the system model for different optimization objectives. The main work and contributions of this paper are as follows: 1. The CR-OFDM resource allocation system model based on spectrum efficiency under ideal perception condition is established. Considering the interference between primary and secondary users, the optimal goal is to maximize the system capacity. In order to avoid high computational complexity, a sub-optimal resource allocation algorithm is proposed, which includes sub-carrier allocation algorithm and sub-optimal power allocation algorithm. Simulation results show that the proposed algorithm can approximate the optimal power allocation algorithm, and is larger than the traditional power allocation algorithm, and its complexity is small, so it can be used as a sub-optimal algorithm in CR-OFDM resource allocation. In this paper, a CR-OFDM resource allocation system model based on energy efficiency under non-ideal perception is established, and a step by step algorithm is proposed, which includes subcarrier allocation algorithm and optimal power allocation algorithm. The sub-carrier allocation algorithm is based on maximizing the energy efficiency of the system. The fractional programming theory is used in the power allocation and the sub-gradient algorithm is used to design the power allocation scheme. The numerical simulation results show that compared with the maximum capacity algorithm, the proposed resource allocation algorithm can achieve higher energy efficiency, at the same time, compared with the resource allocation scheme without perceptual errors, the proposed algorithm can achieve higher energy efficiency. This algorithm can ensure the normal communication between the main users. 3. The model of CR-OFDM resource allocation system based on relay cooperation under non-ideal perception condition is established. AF transport protocol is adopted to maximize the energy efficiency of the system and the objective function is established. The optimal power allocation algorithm and the suboptimal power allocation algorithm are proposed. The simulation results show that the performance of the system will inevitably decrease under the condition that the spectrum sensing is not ideal, but the optimal power allocation algorithm can still achieve the ideal system energy efficiency. Compared with the capacity maximization algorithm, this algorithm can improve the energy efficiency of the system. In addition, the sub-optimal power allocation algorithm can reduce the computational complexity and achieve better system performance.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN925

【参考文献】