Data-Driven C-RAN Optimization Exploiting Traffic and Mobility Dynamics of Mobile Users

Abstract

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 labelpropagation (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.

A Survey on Applications of Deep Learning in Cloud Radio Access Network

“…In [111], Chen et al utilized a DL-based method for predicting network traffic volume and handover count followed by a greedy optimization algorithm for optimal BBU-RRH mapping. The objective of this work was to improve the cost-effectiveness and QoS of the network by increasing the utilization rate of BBUs and decreasing handover delay.…”

“…Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3074180, IEEE Access FIGURE 9: DL-based traffic and handover prediction for optimal BBU-RRH mapping in C-RAN proposed in [111].…”

“…However, to evaluate the performance of the network in [111], the authors utilized two large-scale real-world datasets for traffic volume and handover count prediction. Model training was conducted with 70% of the datasets and the remaining 30% were used for testing the model.…”

Anticipatory Allocation of Communication and Computational Resources at the Edge Using Spatio-Temporal Dynamics of Mobile Users

“…Differently, the knowledge (i.e., prediction) of both users' mobility and communication and computational resources they request over time within a given geographical area could significantly improve network optimization mechanisms [23]- [25]. The current state of the art proposes various instruments to forecast the movements of users [26]- [37], their requests [38]- [44], or both [45] (see Section II for more details). Solutions based on machine and deep learning also promise to better anticipate network behaviors and dynamics in heterogeneous and large scale scenarios [46], [47].…”

“…Solutions based on machine and deep learning also promise to better anticipate network behaviors and dynamics in heterogeneous and large scale scenarios [46], [47]. Nevertheless, resulting network optimization problems (including those presented in [26]- [28], [30]- [33], [35], [40]- [43], [45]) fail to take advantage of the joint prediction of both users' mobility and service demands over a look-ahead temporal horizon and within a standard compliant ETSI-MEC context.…”