Energy-Efficient Resource Allocation Based on Hypergraph 3D Matching for D2D-Assisted mMTC Networks

Energy efficiency is essential for massive machine-type communication (mMTC), because of the limited energy in internet of things (IoT) devices. We consider a two-hop amplify-and-forward (AF) relay communication, and allow IoT devices with inferior channel conditions to connect relays by using device-to-device (D2D) technology. This paper proposes to jointly optimize relay selection, channel allocation and power control, so that the total energy efficiency is maximized while guaranteeing the signal to interference plus noise ratio (SINR) requirements of relays and BSs. The formulated joint optimization problem involves a nonlinear fractional programming (NFP) problem and a user-relay-channel matching problem which is NP-hard. Therefore, we propose a two-stage approach composed of the Dinkelbach method and a hypergraph-based 3D matching (HGM). Simulation results show that the total energy efficiency under the HGM is 8.41% and 59.85% higher than the iterative Hungarian method (IHM) and the minimum zero surface prioritized allocation (MZPA), respectively.