DRL-Based Resource Allocation for NOMA-Enabled D2D Communications Underlay Cellular Networks
Since the emergence of device-to-device (D2D) communications, an efficient resource allocation (RA) scheme with low-complexity suited for high variability of network environments has been continuously demanded. As a solution, we propose a RA scheme based on deep reinforcement learning (DRL) for D2D...
Main Authors: | Yun Jae Jeong, Seoyoung Yu, Jeong Woo Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10355922/ |
Similar Items
-
Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks
by: Seoyoung Yu, et al.
Published: (2022-12-01) -
Energy-Efficient Power Allocation for Full-Duplex Device-to-Device Underlaying Cellular Networks with NOMA
by: Xu Zhao, et al.
Published: (2023-08-01) -
Trade-Off Analysis of NOMA-D2D and OFDMA-D2D Systems: Resource Allocation Perspective
by: Najmeh Madani, et al.
Published: (2024-01-01) -
D2D user and cellular user communication scheme based on NOMA
by: Zhu Liyuan, et al.
Published: (2023-03-01) -
Resource Allocation for Downlink Full-Duplex Cooperative NOMA-Based Cellular System with Imperfect SI Cancellation and Underlaying D2D Communications
by: Asmaa Amer, et al.
Published: (2021-04-01)