Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

TL;DR

This paper introduces a multi-agent deep reinforcement learning approach to optimize resilience in dense 5G networks by dynamically adjusting antenna tilt and transmit power, significantly improving service availability and coverage.

Contribution

It presents a novel multi-agent deep reinforcement learning framework for global resilience optimization in 5G networks, addressing scalability issues of local optimization methods.

Findings

Service availability increased by up to 60%.

Coverage availability reached 99% in best cases.

Demonstrated effectiveness through extensive simulations.

Abstract

Resilience is defined as the ability of a network to resist, adapt, and quickly recover from disruptions, and to continue to maintain an acceptable level of services from users' perspective. With the advent of future radio networks, including advanced 5G and upcoming 6G, critical services become integral to future networks, requiring uninterrupted service delivery for end users. Unfortunately, with the growing network complexity, user mobility and diversity, it becomes challenging to scale current resilience management techniques that rely on local optimizations to large dense network deployments. This paper aims to address this problem by globally optimizing the resilience of a dense multi-cell network based on multi-agent deep reinforcement learning. Specifically, our proposed solution can dynamically tilt cell antennas and reconfigure transmit power to mitigate outages and increase both coverage and service availability. A multi-objective optimization problem is formulated to simultaneously satisfy resiliency constraints while maximizing the service quality in the network area in order to minimize the impact of outages on neighbouring cells. Extensive simulations then demonstrate that with our proposed solution, the average service availability in terms of user throughput can be increased by up to 50-60% on average, while reaching a coverage availability of 99% in best cases.