  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.
