本文選題：C-RAN + Markov轉移模型�。� 參考：《重慶郵電大學》2016年碩士論文

【摘要】：移動通信網(wǎng)絡具有典型的“潮汐效應”,即網(wǎng)絡流量在一天中的不同時刻起伏較大�，F(xiàn)有通信網(wǎng)絡主要按照用戶的最大化需求來進行設計和建設,網(wǎng)絡資源利用率偏低。在C-RAN網(wǎng)絡中,通過基帶處理單元對各個小區(qū)進行統(tǒng)一管理,為頻譜資源的集中化處理提供了條件,以此實現(xiàn)頻譜資源的動態(tài)分配,提高頻譜資源使用效率。本文研究C-RAN網(wǎng)絡中的頻譜資源分配,主要工作如下:1.設計了一種基于Markov轉移模型的小區(qū)用戶數(shù)預測機制,命名為HCPTI。將復雜移動模型進行簡化,利用排隊論,建立基于小區(qū)用戶數(shù)的Markov轉移模型。分析用戶到達率和離開率,并對其予以求解。求解排隊模型不同時刻的瞬態(tài)解,得出不同時刻小區(qū)用戶數(shù)的期望值。綜合考慮預測誤差和系統(tǒng)開銷,求出HCPTI預測機制的最佳預測周期,把最佳預測周期下求得的用戶數(shù)期望值作為預測結果。在最佳預測周期下把HCPTI預測機制與時間序列自回歸預測(AR)方案作進行仿真性能比較,結果表明,HCPTI預測機制的預測性能更優(yōu)。2.基于小區(qū)用戶數(shù)預測機制,設計了頻譜資源初次分配方案(命名為FABDR)和相應的調整分配方案(命名為INRA)。根據(jù)用戶日常使用網(wǎng)絡資源概率的變化情況,求出各個小區(qū)在不同時刻的帶寬需求。綜合考慮載波大小、頻譜資源利用率和分配次數(shù)這三個因素,確定頻譜資源分配的最小分配單元(MAU)。在分配周期的起始時刻對各小區(qū)進行頻譜資源的初次預分配,當頻譜資源供需不匹配時,進行頻譜資源分配的調整。綜合考察頻譜資源利用率、系統(tǒng)容量、系統(tǒng)阻塞率和能耗,把本文分配方案與固定分配方案和周期批處理動態(tài)分配方案進行仿真性能比較,結果表明,本文分配方案綜合性能更優(yōu)。
[Abstract]:Mobile communication network has typical tidal effect, that is, the network flow fluctuates greatly at different times of day. The existing communication network is mainly designed and constructed according to the maximum demand of users, and the utilization ratio of network resources is on the low side. In C-RAN network, each cell is managed uniformly by baseband processing unit, which provides the condition for the centralized processing of spectrum resources, so as to realize the dynamic allocation of spectrum resources and improve the efficiency of spectrum resources. In this paper, spectrum resource allocation in C-RAN networks is studied. The main work is as follows: 1. A cell user prediction mechanism based on Markov transfer model is designed and named HCPTI. The complex mobile model is simplified and the Markov transfer model based on the number of cell users is established by using queuing theory. The user arrival rate and departure rate are analyzed and solved. The transient solution of the queuing model at different times is solved, and the expected value of the number of cell users at different times is obtained. Considering the prediction error and system overhead, the optimal prediction period of HCPTI prediction mechanism is obtained, and the expected number of users under the optimal forecasting period is taken as the prediction result. The HCPTI prediction mechanism is compared with the time series autoregressive prediction scheme under the best forecasting period. The results show that the prediction performance of the HCPTI prediction mechanism is better than that of the time series autoregressive prediction scheme. Based on the cell user number prediction mechanism, a spectrum resource allocation scheme (named FABDR) and a corresponding adjusted allocation scheme (named INRAA) are designed. According to the change of the probability of users using network resources, the bandwidth requirements of each cell at different times are calculated. Considering the carrier size, spectrum resource efficiency and allocation times, the minimum allocation unit of spectrum resource allocation is determined. At the beginning of the allocation period, the spectrum resources of each cell are preallocated for the first time, and when the supply and demand of the spectrum resources are not matched, the spectrum resource allocation is adjusted. The spectrum resource efficiency, system capacity, system blocking rate and energy consumption are comprehensively investigated. The simulation performance of the proposed allocation scheme is compared with that of the fixed allocation scheme and the periodic batch dynamic allocation scheme. The results show that, The comprehensive performance of the allocation scheme in this paper is better.
【學位授予單位】：重慶郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN929.5

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