Despite the advent of fifth-generation mobile networks in the next years, the Long-Term Evolution system still have an important role in the mobile communication scenario. Moreover, the development of solutions that fulfill the demand of new multimedia applications as well as provide an efficient use of the energy resources is one of the drivers of future networks. Therefore, in this article, we deal with the total energy efficiency maximization problem subject to minimum satisfaction constraints per service and quality of service in the uplink of Long-Term Evolution using single-carrier frequency-division multiple access. Although the formulated problem is a nonlinear combinatorial optimization problem, we obtain the optimal solution through algebraic manipulations and by using an iterative method. Motivated by the high computational complexity to obtain the optimal solution, we propose a heuristic solution with lower computational complexity. Realistic system-level simulations indicate that the proposed solution presents good performance regarding both outage rate and energy efficiency.Trans Emerging Tel Tech. 2019;30:e3674.wileyonlinelibrary.com/journal/ett

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.

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