可扩展无蜂窝无线接入网性能分析:一种随机几何方法

Performance Analysis of Scalable Cell-Free RAN:A Stochastic Geometry Approach

无蜂窝无线接入网(Cell-Free Radio Access Network,CF-RAN)打破了传统的蜂窝组网方式,形成网络节点规模可扩展的无线接入网架构。CF-RAN在传统无蜂窝大规模MIMO(Cell-Free massive Multiple Input Multiple Output,CF-mMIMO)系统的基础上,对物理层功能进行了合理划分,包括远程射频单元(Remote Radio Unit,RRU)、边缘分布式单元(Edge Distributed Unit,EDU)和以用户为中心的分布式单元(User-Centered Distributed Unit,UCDU)以及中央处理器(Central Processing Unit,CPU),实现了CF-mMIMO系统协作传输中复杂度和性能之间的权衡。在该网络架构中,RRU负责完成射频信号的发送和接收以及数模/模数转换;EDU负责完成基带信号处理中的信道估计、多用户/多数据流检测、多用户/多数据流预编码、上下行信道互易校准等;UCDU负责将接收到的多个EDU发送的属于同一用户的上行数据进行合并,将多个下行数据流分发给对应的EDU,以及RRU之间校准所需的信道估计;CPU负责确定EDU和UCDU之间的数据流对应关系。作为一种新型无蜂窝网络架构下,有必要对其系统级性能进行理论分析,特别是同时考虑信道随机性和空间位置随机性的性能分析,这是本文的主要研究动机。首先,考虑信道估计误差和导频污染的影响,采用可扩展的全导频迫零(Full-pilot Zero-Forcing,FZF)合并预编码,本文给出了系统上下行可达信号干扰噪声比(Signal-to-Interference-Noise Ratio,SINR)的闭合表达式。在此基础上,考虑用户设备(User Equipment,UE)和RRU的空间位置随机性,本文将UE和RRU的位置建模为有限区域内的两个独立二项式点过程(Binomial Point Process,BPP),推导出了用户速率覆盖概率的理论表达式,通过仿真验证了理论结果的准确性。在仿真中,本文在EDU-RRU关联方案上分别采用了基于遗传算法(Genetic Algorithm,GA)的节点交织部署,基于k-means算法的节点聚类部署和随机节点部署。根据数值分析结果,可扩展CF-RAN架构实现了集中式处理和分布式处理之间的灵活权衡,在具备可扩展性的同时,系统性能方面优于全分布式架构,接近全集中式架构。此外,相比其他两种部署方案,基于GA的节点交织部署可以获得最好的性能,而基于k-means算法的节点聚类部署导致的性能损失最大。最后,根据随机几何分析结果,CF-RAN用户下行覆盖概率分别会随着EDU数量和UE数量的增加而降低。

Cell-free radio access network(CF-RAN)breaks the traditional cellular networking method and forms a radio access network architecture with a scalable network node size.Based on the traditional cell-free massive MIMO(CFmMIMO)systems,CF-RAN divides the physical layer functions,including remote radio unit(RRU),edge distributed unit(EDU),user-centered distributed unit(UCDU)and central processing unit(CPU),which enable CF-mMIMO systems to achieve a trade-off between complexity and performance in cooperative transmission.In this network architecture,RRUs are responsible for completing the transmission and reception of radio frequency signals and digital-toanalog/analog-to-digital conversion;EDUs are responsible for completing channel estimation,multi-user/multi-data stream detection,multi-user/multi-data stream precoding,and reciprocity calibration of uplink and downlink channels in baseband signal processing;UCDUs are responsible for combining the received uplink data belonging to the same user sent by multiple EDUs,distributing multiple downlink data streams to the corresponding EDU,and channel estimation required for calibration between RRUs;and CPU is responsible for determining the data flow correspondence between EDUs and UCDUs.As a novel cell-free network architecture,a theoretical analysis of its system-level performance must be conducted,especially performance analysis that simultaneously considers the randomness of channel fading and the randomness of node spatial positions.This is the major research motivation of this study.We used a scalable full-pilot zero-forcing(FZF)system combining/precoding in uplink/downlink and considered the impact of channel estimation error and pilot contamination under the assumption of independent Rayleigh fading channels.We gave closedform expressions of the uplink/downlink achievable signal-to-interference-noise ratio(SINR)of the CF-RAN system according to random matrix theory.Considering the spatial location randomness of user equipments(UE)and RRUs,we modeled the locations of UEs and RRUs as two independent binomial point processes(BPP)within a limited area,according to the distribution of various coherence distances between the UEs and RRUs.We derived the expression of the user downlink rate coverage probability of our scalable CF-RAN system,and the accuracy of our theoretical results was verified through Monte Carlo simulation.In simulation,we used genetic algorithm(GA)-based interleaving deployment,k-means algorithm-based clustering deployment,and random deployment on the EDU-RRU association scheme.According to the numerical analysis results,the scalable CF-RAN architecture achieved a flexible trade-off between centralized processing and distributed processing.The system performance was better than the fully distributed architecture and was close to the fully centralized architecture,while being scalable.In addition,compared with the other two node deployment schemes,the interleaved deployment based on GA achieved the best performance,while the clustering deployment based on the k-means algorithm caused the largest performance loss.Finally,according to the stochastic geometric analysis results,the user downlink coverage probability of scalable CF-RAN decreased as the number of EDUs and UEs increased.This research on the performance analysis of the scalable CF-RAN architecture provides some theoretical basis for the future development of user-centered network architecture and gives a new direction for cell-free research.However,this study has some shortcomings that need to be further improved.For example,after obtaining the closed-form expression of the uplink SINR,the uplink performance was not analyzed based on stochastic geometry theory.