Energy-Efficient Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning

TL;DR

This paper proposes an energy-efficient data offloading strategy in a massive-MIMO enabled edge network, balancing latency and energy consumption through data partitioning, power control, and CPU scaling, with a novel optimization algorithm.

Contribution

It introduces a new optimization framework for joint offloading, power, and CPU frequency control in massive-MIMO edge networks, solving it with a nested primal-dual algorithm.

Findings

Partial offloading reduces energy consumption compared to binary offloading.

Larger requests lead to more data offloaded to MECs to meet latency.

Channel estimation errors increase offloading to MECs.

Abstract

We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multi-access edge computing (MEC) server. Massive-MIMO enables simultaneous uplink transmissions from all users, significantly shortening the data offloading time compared to sequential protocols, and makes the three phases of data offloading, computing, and downloading have comparable durations. Based on this three-phase structure, we formulate a novel problem to minimize a weighted sum of the energy consumption at both the users and the MEC server under a round-trip latency constraint, using a combination of data partitioning, transmit power control and CPU frequency scaling at both the user and server ends. We design a novel nested primal-dual algorithm using two different methods to solve this problem efficiently. Optimized solutions show that for larger requests, more data is offloaded to the MECs to reduce local computation time in order to meet the latency constraint, despite higher energy cost of wireless transmissions. Massive-MIMO channel estimation errors under pilot contamination also causes more data to be offloaded to the MECs. Compared to binary offloading, partial offloading with data partitioning is superior and leads to significant reduction in the overall energy consumption.