Abstract
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.
Highlights
The overall performance of wireless telecommunications systems directly depends on how efficiently the available resources are managed, e.g., subcarriers, time slots, transmit power, antennas, among others
Motivated by the benefits of deep Reinforcement Learning (RL), in this paper, we revisit the problem of maximizing system throughput subject to minimum satisfaction constraints per service, as in [1] and [2], and we propose a new near-optimal Resource Allocation (RRA) solution based on multiple agent deep RL
In [13], we have proposed a Q-learning based solution to the same problem that we address in the present paper, i.e., schedule frequency resources to User Equipment (UE) in order to maximize the system throughput subject to users’ Quality of Service (QoS) requirements, in terms of UE throughput
Summary
The overall performance of wireless telecommunications systems directly depends on how efficiently the available resources are managed, e.g., subcarriers, time slots, transmit power, antennas, among others. Applying deep RL to cellular mobile networks can lead to the following main advantages: (1) a DNN with a moderate size can quickly perform predictions as only a small number of simple operations are needed to obtain an output This is interesting and helps the deep RL agent to get to know his environment faster; (2) the fact that deep RL agent learns directly from the raw collected network data with high dimension in large environments is not a problem due to the powerful representation capabilities of DNNs; (3) by exploiting distributed and/or parallel computing employing multiple machines and multiple cores, the response time of deep RL-based schemes can be greatly reduced and its performance increased; (4) deep RL-based schemes can .
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