A Survey on Device-to-Device Communication in Cellular Networks
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Abstract (arXiv)
Device-to-Device (D2D) communication was initially proposed in cellular networks as a new paradigm to enhance network performance. The emergence of new applications such as content distribution and location-aware advertisement introduced new use-cases for D2D communications in cellular networks. The initial studies showed that D2D communication has advantages such as increased spectral efficiency and reduced communication delay. However, this communication mode introduces complications in terms of interference control overhead and protocols that are still open research problems. The feasibility of D2D communications in LTE-A is being studied by academia, industry, and the standardization bodies. To date, there are more than 100 papers available on D2D communications in cellular networks and, there is no survey on this field. In this article, we provide a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy. Moreover, we provide new insights to the over-explored and under-explored areas which lead us to identify open research problems of D2D communication in cellular networks.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2013.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 3; 2025: 1.
10 papers.
- 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
- 2022Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relayingcited by 6
- 2024MP-SL: Multihop Parallel Split Learningcited by 3
- 2024Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learningcited by 1
- 2021GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platformcited by 0
- 2021Cellular traffic offloading via Opportunistic Networking with Reinforcement Learningcited by 0
- 2022FiFo: Fishbone Forwarding in Massive IoT Networkscited by 0
- 2023FedDec: Peer-to-peer Aided Federated Learningcited by 0
- 2023Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approachcited by 0
- 2025CBPNet: A Continual Backpropagation Prompt Network for Alleviating Plasticity Loss on Edge Devicescited by 0
- 2025CBPNet: A Continual Backpropagation Prompt Network for Alleviating Plasticity Loss on Edge Devices19 Sep 2025
- 2024Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning23 Oct 2024
- 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories22 Apr 2024
- 2024MP-SL: Multihop Parallel Split Learning31 Jan 2024
- 2023Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach15 Aug 2023
- 2023FedDec: Peer-to-peer Aided Federated Learning11 Jun 2023
- 2022FiFo: Fishbone Forwarding in Massive IoT Networks2 Nov 2022
- 2022Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying9 Jun 2022
- 2021Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning1 Oct 2021
- 2021GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform27 Jan 2021