Multi-Access Edge Computation Offloading Using Massive MIMO

We present a comprehensive Multi-access Edge Computing (MEC) system model with massive Multiple Input Multiple Output (MIMO) access points, each with an integrated edge computing server to allow multiple users to simultaneously offload computationally intensive tasks. We formulate the problem of energy-efficient partial computation offloading under a round trip latency constraint including data offloading time, computation time, and downloading time. A novel and efficient algorithm is designed to minimize a weighted sum of the energy consumed at both the users and the MEC server under a maximum latency constraint, using a combination of power control, partial data offloading, and frequency scaling at both the user and server ends. Numerical results verify how an optimal balance between data offloaded and computed locally is necessary to meet the latency requirement, while minimizing the energy consumption for both wireless transmission and computation. These results also show that with massive MIMO, the times spent for data offloading, computation, and downloading are comparable and grow almost linearly with the amount of data to be computed.