【作者】 李晴；

【导师】 崔琪楣；

【作者基本信息】 北京邮电大学 ， 信息与通信工程， 2020， 硕士

【摘要】 随着信息通信技术的快速发展,第五代移动通信系统(the Fifth Generation Mobile Communication System,5G)面临诸多挑战:一方面,无线数据流量呈指数级爆炸式增长,不断挑战网络的容量极限;另一方面,大规模机器通信、高可靠低时延等通信场景下,网络需支持多样化服务质量(Quality of Service,QoS)需求业务。为了满足海量数据需求,5G采用超密集化部署提升网络容量。在超密集网络(Ultra-Dense Network,UDN)场景下,随着小基站密度的增加,小区间干扰将愈加严重且复杂;大量的基站设备也会造成能源消耗剧增、环境污染等问题。非连续发送机制作为5G关键技术之一,基于网络负载情况动态调整小基站工作状态,从而降低小区间干扰水平,提高网络能源效率。但是工作状态的转换会造成小区间干扰动态耦合,加大了分析超密集多用户系统性能的难度。此外,基于物理层遍历容量的理论模型,难以精确分析链路层缓存队列长度分布、平均时延等QoS性能,无法有效指导保障业务差异化QoS的资源分配设计。针对上述问题,本文重点开展非连续发送机制下5G多用户超密集网络有效容量分析及资源管理优化研究。文中首先建模该机制下动态耦合的小区间干扰,基于有效容量理论分析多用户场景下小基站总服务速率及链路层QoS性能,最后提出QoS约束下最大化网络能效的最优资源分配。本文主要研究内容如下:首先,本文提出了一种基于多维有效容量的跨层分析模型。在非连续发送机制下,网络内小区间动态耦合的干扰使得各小基站系统性能互相关。文中拓展传统一维有效容量模型至多维空间,推导出小基站总服务速率、缓存队列溢出概率、时延中断概率等跨层系统性能;然后基于多维有效容量理论,设计多维求根算法分析上述系统性能,并证明其收敛性。其次,基于上述模型,本文首先推导出N个小基站多用户场景下采用轮询或者最大信干噪比调度机制时,各小基站用户信干噪比、有效容量的近似表达式;然后基于上述结论进一步求解了多用户场景不同调度机制下的跨层系统性能;最后,通过蒙特卡洛仿真验证了上述分析模型及结论的准确性,还显示了在低负载情况下,非连续发送机制与全缓存机制相比,可以提升约29.6%网络容量,数据平均时延降低约 37.2%。最后,结合非连续发送机制下双模电路系统的实现方式,本文首先基于有效容量推导出小基站有效能效的表达式;然后构建保障平均时延约束下最大化系统总有效能效的资源优化问题;最后,设计基于遗传算法的最优资源分配算法求解了上述问题。仿真结果验证了上述算法的有效性及收敛性,并进一步对比分析不同调度方式和不同时延约束条件下网络内最大总有效能效。更多还原

【Abstract】 With the rapid development of information and communication technology,the Fifth Generation Mobile Communication System(5G)is facing many challenges:On one hand,wireless data traffic has grown exponentially,constantly challenging the capacity limit of the network.On the other hand,under massive Machine Type of Communication and Ultra Reliable&Low Latency Communication scenarios,the network needs to support services with diversified quality of service(QoS)demand.In order to meet the massive data requirement,5G applies ultra-dense network(UDN)technology to increase network capacity.However,as the density of small base stations(SBSs)increases,the inter-cell interference will become more serious and complicated;a large amount of base station equipment also causes problems such as a dramatic increase of energy consumption and environmental pollution.As one of the key technologies of 5G,the Cell discontinuous transmission(Cell DTx)dynamically adjusts the working states of SBSs based on traffic load,reduces the inter-cell interference,and improves network energy efficiency.But the state transformation in Cell DTx will cause dynamic coupled inter-cell interference,which brings great challenges to the performance analysis of multiuser UDN.Moreover,the traditional theoretical model is based on the physical-layer ergodic capacity,which is difficult to accurately analyze the QoS performance such as link-layer queue length distribution and average delay,or efficiently guide the design the resource optimization that guarantees diverse QoS requirements.To solve the above problems,this thesis focuses on the effective capacity and resource optimization for 5G multiuser ultra-dense networks.Specifically,this thesis models the dynamic coupled inter-cell interference,applies and extends the effective capacity theory to analyze the sum service rate of the physical layer and the QoS performance of the link layer in the multiuser UDN,develops a resource optimization scheme to maximize network energy efficiency under QoS constraints.The main research contents and innovations of this paper are summarized as follows:First,we propose a cross-layer analysis framework based on a multi-dimensional effective capacity model.The transition between modes makes the performance of small base stations correlated.Therefore,we extend the traditional one-dimensional effective capacity to multi-dimensional space to approximate the queue length distribution,and delay distribution.Based on the key properties of multi-dimensional effective capacity,we develop a new iterative bisection search algorithm that is capable of approximating the above performance.The convergence of this algorithm to a unique QoS exponent vector is later proved.Secondly,based on the above analysis framework,we derive the approximate expressions of signal-to-interference ratio,effective capacity under N-SBS scenarios with the Round Robin and Max-C/I scheduling policies.Monte Carlo simulation results verify the accuracy of the above analysis framework and results,and further show that under low load condition,Cell DTx have approximately 29.6%higher sum rate and 37.2%smaller average delay than those under full-buffer scenarios.Finally,we derive the expression of the effective energy efficiency.Based on the genetic algorithm,we develop a resource optimization algorithm to maximize the sum effective energy efficiency under average delay constraints.Moreover,we build a simulation platform to verify the effectiveness and convergence of the above algorithm.Furthermore,we compare and analyze the maximum sum effective energy efficiency that can be achieved under different scheduling and different delay constraints.更多还原