Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks.

Hyebin Park,Yujin Lim

doi:10.3390/s20082361

Abstract

Increased data traffic resulting from the increase in the deployment of connected vehicles has become relevant in vehicular social networks (VSNs). To provide efficient communication between connected vehicles, researchers have studied device-to-device (D2D) communication. D2D communication not only reduces the energy consumption and loads of the system but also increases the system capacity by reusing cellular resources. However, D2D communication is highly affected by interference and therefore requires interference-management techniques, such as mode selection and power control. To make an optimal mode selection and power control, it is necessary to apply reinforcement learning that considers a variety of factors. In this paper, we propose a reinforcement-learning technique for energy optimization with fifth-generation communication in VSNs. To achieve energy optimization, we use centralized Q-learning in the system and distributed Q-learning in the vehicles. The proposed algorithm learns to maximize the energy efficiency of the system by adjusting the minimum signal-to-interference plus noise ratio to guarantee the outage probability. Simulations were performed to compare the performance of the proposed algorithm with that of the existing mode-selection and power-control algorithms. The proposed algorithm performed the best in terms of system energy efficiency and achievable data rate.

Highlights

With the dynamic increase in data traffic in connected vehicles and wireless networks, satisfying cellular data traffic has become relevant
To tackle the energy consumption problem, the traditional base station structure is changed to a centralized structure, such as a heterogeneous cloud radio access network (H-CRAN)
In the H-CRAN structure, a traditional base station is divided into a signal-processing part as the baseband unit (BBU) and a signal transceiver part as the radio remote head (RRH)

Summary

Introduction

With the dynamic increase in data traffic in connected vehicles and wireless networks, satisfying cellular data traffic has become relevant. To satisfy these requirements, vehicular social networks (VSNs) have been studied [1,2]. One of the challenges is to ensure communication quality and reduce delays in VSNs. To guarantee a high data rate in a VSN, base stations are densely deployed and overlapped. Densely deployed base stations cause higher system energy consumption. To tackle the energy consumption problem, the traditional base station structure is changed to a centralized structure, such as a heterogeneous cloud radio access network (H-CRAN). The macro base station only serves voice services to users, and the RRHs are densely overlapped in macro cells

Methods

Results

Conclusion