D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

Abstract

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading.

Initial Access & Beam Alignment for mmWave and Terahertz Communications

“…However, the design of the environment characteristics was inexplicit, as well as the practical implementation issues were disregarded. In the same context, to achieve a high communication link of D2D devices, authors in [81] propose a NOMA-based reinforcement learning approach to maximize the throughput and minimize the outage probability. They propose two RL algorithms to allow devices to self-organize and learn pure/mixed equilibrium strategies in a wholly dispersed manner.…”

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

“…7. These networks can better match the needs of customers and improve standard mobile network throughput by [45] using smaller and more sophisticated cells, according to [25], [12], as is one over the latest 5G institutionalization of benchmark body engineering even though mm-Wave developments at 28 GHz have been used for rapid 5G affiliation in South Korea, the United States, and Japan, there is a need for more in-depth discussions about the circumstances,…”