  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.
