Optimized power and cell individual offset for cellular load balancing via reinforcement learning

Author's Department

Electronics & Communications Engineering Department

Third Author's Department

Electronics & Communications Engineering Department

Fourth Author's Department

Electronics & Communications Engineering Department

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https://doi.org/10.1109/WCNC49053.2021.9417360

Document Type

Research Article

Publication Title

IEEE Wireless Communications and Networking Conference, WCNC

Publication Date

1-1-2021

doi

10.1109/WCNC49053.2021.9417360

Abstract

We consider the problem of jointly optimizing the transmission power and cell individual offsets (CIOs) in the downlink of cellular networks using reinforcement learning. To that end, we reformulate the problem as a Markov decision process (MDP). We abstract the cellular network as a state, which comprises of carefully selected key performance indicators (KPIs). We present a novel reward function, namely, the penalized throughput, to reflect the tradeoff between the total throughput of the network and the number of covered users. We employ the twin deep delayed deterministic policy gradient (TD3) technique to learn how to maximize the proposed reward function through the interaction with the cellular network. We assess the proposed technique by simulating an actual cellular network, whose parameters and base station placement are derived from a 4G network operator, using NS-3 and SUMO simulators. Our results show the following: 1) Optimizing one of the controls is significantly inferior to jointly optimizing both controls; 2) our proposed technique achieves 18.4% throughput gain compared with the baseline of fixed transmission power and zero CIOs; 3) there is a tradeoff between the total throughput of the network and the number of covered users.

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