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.

翻译：弹性被定义为网络抵抗、适应并快速从中断中恢复，以及从用户视角持续维持可接受服务水平的能力。随着未来无线网络（包括先进的5G及即将到来的6G）的到来，关键服务成为未来网络不可或缺的组成部分，要求为终端用户提供不间断的服务交付。然而，随着网络复杂性、用户移动性和多样性的日益增长，当前依赖局部优化的弹性管理技术难以扩展至大规模密集网络部署。本文旨在通过基于多智能体深度强化学习的密集多小区网络弹性全局优化来解决这一问题。具体而言，我们提出的解决方案能够动态调整小区天线倾角并重新配置发射功率，以缓解中断并同时提升覆盖范围与服务可用性。本文构建了一个多目标优化问题，在满足弹性约束的同时最大化网络区域的服务质量，从而最小化中断对相邻小区的影响。大量仿真实验表明，采用我们提出的解决方案，用户吞吐量层面的平均服务可用性最高可平均提升50-60%，同时在最佳情况下覆盖可用性可达99%。