Deep reinforcement learning empowered joint mode selection and resource allocation for RIS-aided D2D communications

Guo, Liang; Jia, Jie; Chen, Jian; Du, An; Wang, Xingwei

doi:10.1007/s00521-023-08745-0

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…Deep reinforcement learning (DRL) is investigated in [18] as a potential solution for tackling network dynamics, resource diversity, and the integration of managing resources with mode selection in mobile edge computing networks (MECNs). In [19], the authors presented a joint optimization challenge, where they addressed mode selection, channel assignment, power allocation, and discrete phase shift selection to optimize the average sum data rate of device-to-device pairs. In [12], the authors explore using a DRL approach to managing computation offloading and multi-user scheduling in internet of things (IoT) edge computing systems.…”

Section: Related Workmentioning

confidence: 99%

Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic

Khan,

Raza,

Khan

et al. 2023

Electronics

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Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of IoT entities, AI-based solutions, particularly the deep reinforcement learning (DRL) strategy, have been intensively studied in both the academic and industrial fields. However, there are still many open challenges, namely, the lengthening convergence phenomena of the agent, network dynamics, resource diversity, and mode selection, which need to be tackled. A mixed integer non-linear fractional programming (MINLFP) problem is formulated to maximize computing and radio resources while maintaining quality of service (QoS) for every user’s equipment. We adopt the advanced asynchronous advantage actor-critic (A3C) approach to take full advantage of distributed multi-agent-based solutions for achieving energy efficiency in MECNs. The proposed approach, which employs A3C for computing offloading and resource allocation, is shown through numerical results to significantly reduce energy consumption and improve energy efficiency. This method’s effectiveness is further shown by comparing it to other benchmarks.

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Section: Related Workmentioning

confidence: 99%

Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic

Khan,

Raza,

Khan

et al. 2023

Electronics

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“…In [19], a multi-agent reinforcement learning (MARL)-based scheme was proposed for the joint design of passive beamforming and power control. Furthermore, to address the optimization problem of the mixed action space in IRS-assisted D2D communication networks, the authors of [20] proposed a novel multi-agent multipass deep Q-networks algorithm using centralized training and a decentralized execution (CTDE) framework.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Device-to-Device Communications for Green Internet of Things Using Unmanned Aerial Vehicle-Mounted Intelligent Reflecting Surface

Tan,

Pang,

Cao

et al. 2024

Drones

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The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance.

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FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource Allocation Scheme for Energy Efficiency Maximization in D2D-Assisted 6G Networks

Muhammad Fahad Noman,

Dimyati,

Ariffin Noordin

et al. 2024

IEEE Access

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Deep reinforcement learning empowered joint mode selection and resource allocation for RIS-aided D2D communications

Cited by 6 publications

References 45 publications

Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic

Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic

Energy-Efficient Device-to-Device Communications for Green Internet of Things Using Unmanned Aerial Vehicle-Mounted Intelligent Reflecting Surface

FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource Allocation Scheme for Energy Efficiency Maximization in D2D-Assisted 6G Networks

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