In this article, we study a Radio Resource Allocation (RRA) that was
formulated as a non-convex optimization problem whose main aim is to maximize
the spectral efficiency subject to satisfaction guarantees in multiservice
wireless systems. This problem has already been previously investigated in the
literature and efficient heuristics have been proposed. However, in order to
assess the performance of Machine Learning (ML) algorithms when solving
optimization problems in the context of RRA, we revisit that problem and
propose a solution based on a Reinforcement Learning (RL) framework.
Specifically, a distributed optimization method based on multi-agent deep RL is
developed, where each agent makes its decisions to find a policy by interacting
with the local environment, until reaching convergence. Thus, this article
focuses on an application of RL and our main proposal consists in a new deep RL
based approach to jointly deal with RRA, satisfaction guarantees and Quality of
Service (QoS) constraints in multiservice celular networks. Lastly, through
computational simulations we compare the state-of-art solutions of the
literature with our proposal and we show a near optimal performance of the
latter in terms of throughput and outage rate.

本文旨在研究一种利用强化学习算法在无线多业务系统中处理无线电资源分配问题的方法，通过在本地环境中与局部互动来寻找策略，实现了关于 9LTE 系统满足保证和 QoS 约束的无线资源分配。此外，通过计算模拟，本文与文献中的最新解决方案进行比较，并展示了后者在吞吐量和故障率方面近乎最优的性能。