Title | ||
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Data-Driven C-RAN Optimization Exploiting Traffic and Mobility Dynamics of Mobile Users |
Abstract | ||
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The surging traffic volumes and dynamic user mobility patterns pose great challenges for cellular network operators to reduce operational costs and ensure service quality. Cloud-radio access network (C-RAN) aims to address these issues by handling traffic and mobility in a centralized manner, separating baseband units (BBUs) from base stations (RRHs) and sharing BBUs in a pool. The key problem in C-RAN optimization is to dynamically allocate BBUs and map them to RRHs under cost and quality constraints, since real-world traffic and mobility are difficult to predict, and there are enormous numbers of candidate RRH-BBU mapping schemes. In this work, we propose a data-driven framework for C-RAN optimization. First, we propose a deep-learning-based Multivariate long short term memory (MuLSTM) model to capture the spatiotemporal patterns of traffic and mobility for accurate prediction. Second, we formulate RRH-BBU mapping with cost and quality objectives as a set partitioning problem, and propose a resource-constrained label-propagation (RCLP) algorithm to solve it. We show that the greedy RCLP algorithm is monotone suboptimal with worst-case approximation guarantee to optimal. Evaluations with real-world datasets from Ivory Coast and Senegal show that our framework achieves a BBU utilization above 85.2 percent, with over 82.3 percent of mobility events handled with high quality, outperforming the traditional and the state-of-the-art baselines. |
Year | DOI | Venue |
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2021 | 10.1109/TMC.2020.2971470 | IEEE Transactions on Mobile Computing |
Keywords | DocType | Volume |
Handover,Optimization,Cellular networks,Computer architecture,Mobile computing,Base stations | Journal | 20 |
Issue | ISSN | Citations |
5 | 1536-1233 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Longbiao Chen | 1 | 123 | 10.60 |
Thi Mai Trang Nguyen | 2 | 139 | 19.47 |
Dingqi Yang | 3 | 542 | 28.79 |
Michele Nogueira | 4 | 131 | 23.46 |
Cheng Wang | 5 | 218 | 32.63 |
Daqing Zhang | 6 | 3619 | 217.31 |