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Online Learning and Heuristic Algorithms for 5G Cloud-RAN Load Balance
Abstract
The rapidly evolving 5G cellular system adapts cloud computing technology in its radio access networks (RAN), namely Cloud RAN or CRAN. CRAN enables better scalability, flexibility, and performance that allows 5G to provide connectivity for the vast volume of IoT devices. This chapter presents two major research results addressing the load balance (LB) problem in CRAN. First, the authors propose a generic online learning (GOL) system; GOL integrates reinforcement learning (RL) with deep learning method for an environment not fully visible, changing over time, while receiving feedbacks of fluctuating delays. Simulation results show that GOL successfully achieves the LB objectives of reducing both cache-misses and communication load. Next, they study eight practical LB based on real cellular network traffic characteristics provided by Nokia Research. Experiment results of these algorithms on queue-length analysis show that the simple, light-weight queue-based LB is almost as effectively as the much more complex waiting-time-based LB.
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