Abstract
Device-to-device (D2D) communication was originally presented as an efficient solution to boost conventional cellular network performance. Since the signals degrade among devices when separated by long distances, the relay-aided D2D communication appeared to enhance the coverage quality in either one-way relaying (OWR) or two-way relaying (TWR). To achieve reliable relay-aided D2D communication, the spectrum and power resources should be allocated optimally. Also, the energy harvesting (EH) technology can play an important role to improve the system energy efficiency (EE) by exploiting the radio frequency (RF) and renewable energy (RE) sources. In this paper, a comprehensive literature on the state-of-art OWR and TWR D2D communication techniques is presented. This includes the most recent power allocation (PA), resource allocation (RA), relay selection (RS), and EH techniques. The paper also shows that machine learning (ML) is the future of sixth-generation (6G) networks due to its intelligence facilities. Accordingly, we shed the light on the most important contributions of using reinforcement learning (RL) in relay-aided D2D communication. Last but not least, we highlight the research challenges and future directions for the next generation relay-aided D2D communication.
Original language | English |
---|---|
Article number | 103657 |
Journal | Journal of Network and Computer Applications |
Volume | 216 |
DOIs | |
State | Published - Jul 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 Elsevier Ltd
Keywords
- Device-to-device
- Energy harvesting
- Machine learning
- Relay selection
- Relay-aided
- Resource allocation
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications