Deep Reinforcement Learning for Resource Management in Device-to-Device (D2D)-Assisted 6G Networks: Current Solutions, Open Issues, and Future Directions

Sixth-generation (6G) wireless networks have the potential to offer several emerging technologies that require efficient resource allocation. However, traditional resource allocation methods have limited performance in mitigating interference, which hinders scalability and compromises joint optimization of spectral, energy, and computational efficiency. This motivates the implementation of deep reinforcement learning (DRL) techniques for resource management in device-to-device (D2D)-enabled 6G systems towards self-sustainable networks (SSNs). Hence, this paper presents a review of DRL algorithms for resource management in D2D networks. The state-of-the-art DRL algorithms, including value-based, policy-based, and hybrid methods, are reviewed. It highlights their strengths and limitations for resource allocation and power control. Moreover, DRL-based solutions are discussed for mode selection, spectrum allocation, and power allocation, as well as the joint optimization of resource management to enhance network performance in terms of both energy and spectral efficiency. It also presents advanced learning techniques, such as multi-agent DRL and federated DRL, as potential solutions to improve network scalability and preserve users’ privacy. Finally, the paper summarizes the open issues and future research directions for adaptive and explainable resource management techniques in D2D-enabled Industrial Internet of Things (IIoT) and Integrated Sensing and Communication (ISAC) systems in the upcoming 6G networks.