An energy-efficient resource allocation strategy in massive MIMO-enabled vehicular edge computing networks

The vehicular edge computing (VEC) is a new paradigm that allows vehicles to offload computational tasks to base stations (BSs) with edge servers for computing. In general, the VEC paradigm uses the 5G for wireless communications, where the massive multi-input multi-output (MIMO) technique will be used. However, considering in the VEC environment with many vehicles, the energy consumption of BS may be very large. In this paper, we study the energy optimization problem for the massive MIMO-based VEC network. Aiming at reducing the relevant BS energy consumption, we first propose a joint optimization problem of computation resource allocation, beam allocation and vehicle grouping scheme. Since the original problem is hard to be solved directly, we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them. Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.