超密集网络中联合资源优化关键技术研究

发布时间：2018-06-03 15:51

本文选题：超密集网络 + 基站连接　；参考：《南京邮电大学》2017年硕士论文

【摘要】：随着互联网应用和智能设备的飞速发展,推动了用户对更大网络容量、更高数据速率的需求,第五代移动通信系统(the 5th Generation mobile communication technology,5G)应运而生。5G将在数据传输速率、网络容量、覆盖范围、时延、保密性、节能等多方面体现出优越性,而超密集网络(Ultra Dense Network,UDN)作为有效解决网络无缝衔接的部署方案,已成为5G通信关键技术之一。超密集网络能够有效提高网络吞吐量,减少覆盖盲区,并且为宏基站分担一部分流量负荷,实现了负载均衡。然而,网络的超密集部署容易造成干扰严重、时延加大、网络拥塞等问题,无线资源的联合优化是解决这些问题的关键所在。针对无线网络中面临的容量瓶颈问题,本文建立了基于基站连接、信道分配、功率控制的联合优化模型,优化目标是最大网络吞吐量,同时增加了单个用户的服务质量约束,保证了用户公平性。为解决联合优化模型提出了内外交替循环算法,分析了算法的收敛性、可行性。仿真结果表明,本文提出的算法比传统算法提高了大约30%网络容量。针对网络中存在的时延大、能源利用率低的问题,本文建立了基于能效、时延、流量分配的联合优化模型,优化目标是最小网络时延和电能使用率。提出了基于能效感知和流量感知的用户连接算法,该算法包括用户端算法和基站端算法两部分,分析了算法的收敛性、可行性。仿真结果表明,本文提出的算法降低了大约36%的平均流量传输时延。针对网络中出现的流量拥塞问题,本文建立了基于流量路由和功率分配的联合优化模型,优化目标是最小总功率消耗,以最小的代价实现了更大的网络容量和更广的覆盖范围。同时提出了改进的路由方法,把网络流分解成若干子网络流,允许子网络流在不同路径上传输,但是数据始终在相同的源-目的节点间传输。仿真结果表明,节点数目越多,网络总功率消耗越小。
[Abstract]:With the rapid development of Internet applications and intelligent devices, the fifth generation mobile communication systems (the 5th Generation mobile communication technology, 5G) have come into being at the historic moment of the data transmission rate, network capacity, coverage, time delay, secrecy, energy saving and so on. Ultra Dense Network (UDN) is one of the key technologies of 5G communication, which can effectively improve the network throughput, reduce the coverage of the blind area, share a part of the traffic load for the Acer station, and realize the load balancing. However, network The ultra dense deployment of the collaterals can cause serious interference, time delay and network congestion. Joint optimization of wireless resources is the key to solve these problems. In this paper, a joint optimization model based on base station connection, channel allocation and power control is established for the capacity bottleneck in wireless networks. The optimization target is the maximum network. In order to solve joint optimization model, the convergence and feasibility of the algorithm are analyzed. The simulation results show that the algorithm proposed in this paper is higher than the traditional algorithm by about 30% network capacity. In this paper, a joint optimization model based on energy efficiency, delay and flow allocation is established in this paper. The optimization target is the minimum network delay and the use rate of power. A user connection algorithm based on energy efficiency perception and traffic perception is proposed. The algorithm includes two parts of the user end algorithm and the base station algorithm, and the algorithm is analyzed. Convergence and feasibility. The simulation results show that the proposed algorithm reduces the average traffic delay of about 36%. In this paper, a joint optimization model based on traffic routing and power allocation is established for traffic congestion in the network. The optimization target is the minimum total power consumption, and a greater network capacity is realized at the minimum cost. At the same time, an improved routing method is proposed, and an improved routing method is proposed to decompose the network flow into a number of subnetwork flows, allowing the subnetwork flow to be transmitted on different paths, but the data is always transmitted between the same source and destination nodes. The simulation results show that the more the number of nodes, the smaller the total power consumption of the network.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5

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