Energy-efficient resource allocation in cognitive D2D communications: A game-theoretical and matching approach

Energy-efficiency (EE) is critical for cognitive device-to-device (D2D) communications due to limited battery capacity of user equipments (UEs) and hash quality of service (QoS) requirements. In this paper, we address the EE optimization problem by proposing a game theory and matching based resource allocation algorithm. Noncooperative game is adopted to analyze the interactions among UEs and establish mutual preferences, both of which vary dynamically with channel states and interference levels. We then employs the Gale-Shapley (GS) algorithm to match D2D pairs with cellular UEs (CUs), which is proved to be stable and weak Pareto optimal. We also extend the algorithm to address scalability issues in large-scale networks by introducing some tie-breaking and preference deletion rules. Simulation results demonstrate that the proposed algorithm achieves significant EE performance and UE satisfaction gains compared to heuristic algorithms.