Multiobjective Reinforcement Learning Based Energy Consumption in C-RAN enabled Massive MIMO

TL;DR: In this paper , the authors analyzed how the energy consumption of future mobile networks can be minimised by using the right RAN architecture, sharing the network with other operators and implementing the most efficient energy minimising technologies in the RAN.

TL;DR: In this article , the authors provide a high-level review of the crucial enabling 5G technologies and an exhaustive review of C-RAN resource allocation algorithms for 5G networks with emphasis on resource allocation metrics/parameters.

TL;DR: An evolutionary multi-objective optimization algorithm using deep neural network for resource allocation in ultra-dense networks improves energy efficiency, spectrum efficiency, and fairness.

Abstract: In this study, we developed an energy-efficient multi-user-associated optimization method involving a massive multi-input multi-output (M-MIMO) system-enabled Cloud Radio Access Network (C-RAN) in Full-Duplex (FD) mode. Maximization of energy efficiency (EE) was achieved with user association. We compose the non-convex multi-objective optimization (MOO) problem for resource allocation and user association in C-RAN. The resultant non-convex MOO problem is non-deterministic polynomial (NP) hard. To tackle this complexity, we find a trade-off between achievable rate and energy consumption. We first reaffirm the problem as an MOO targeting high throughput and minimizing energy consumption instantaneously. By using the epsilon (ε)-constraint method, we transform MOO to an equivalent single objective optimization (SOO) problem by majorization–minimization (MM) approach that enables the transformation of binaries into continuous variables. Further, we propose a multi-objective resource allocation algorithm to obtain a Pareto optimal solution. The simulation results show a significant gain in EE of C-RAN achieved through our proposed MOO algorithm. Our results also show remarkable trade-offs between EE and spectral efficiency (SE).

TL;DR: This monograph summarizes many years of research insights in a clear and self-contained way and providest the reader with the necessary knowledge and mathematical toolsto carry out independent research in this area.

TL;DR: This text introduces the intuitions and concepts behind Markov decision processes and two classes of algorithms for computing optimal behaviors: reinforcement learning and dynamic programming, and surveys efficient extensions of the foundational algorithms.

TL;DR: The basic architecture, research topics, and naïve solutions of MORL are introduced at first and several representative MORL approaches and some important directions of recent research are comprehensively reviewed.

TL;DR: In this paper, the authors proposed a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard.