In this article, we study Radio Resource Allocation (RRA) as a non-convex optimization problem, aiming at maximizing the spectral efficiency subject to satisfaction guarantees in multiservice wireless systems. This problem has already been previously investigated 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, our proposal is based on the Q-learning technique, where an agent gradually learns a policy by interacting with its local environment, until reaching convergence. Thus, in this article, the task of searching for an optimal solution in a combinatorial optimization problem is transformed into finding an optimal policy in Q-learning. Lastly, through computational simulations we compare the state-of-art proposals of the literature with our approach and we show a near optimal performance of the latter for a well-trained agent.
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.
Resumo-Neste artigo estudamos alocação de recursos de rádio para redes cooperativas com a presença de múltiplos relays em que a adaptação da taxa de transmissão em relação a qualidade de canal dar-se por um mapeamento discreto. Em especial, realizamos a otimização da taxa de transmissão em um sistema cooperativo através da seleção de relays, pareamento de subportadoras e alocação de potência de transmissão. O problema estudadoé formulado como um problema de programação linear inteira cuja soluçãoótima pode ser obtida através de algoritmos conhecidos sob o custo de uma alta complexidade computacional. Motivados por tal fato, propomos uma solução alternativa de baixo custo computacional que apresenta-se como um bom compromisso entre desempenho e complexidade computacional. Palavras-Chave-seleção de relays, pareamento de subportadora, alocação de potência, programação linear inteira.
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