  This paper proposes a novel user cooperation approach in both computation and
communication for mobile edge computing (MEC) systems to improve the energy
efficiency for latency-constrained computation. We consider a basic three-node
MEC system consisting of a user node, a helper node, and an access point (AP)
node attached with an MEC server, in which the user has latency-constrained and
computation-intensive tasks to be executed. We consider two different
computation offloading models, namely the partial and binary offloading,
respectively. Under this setup, we focus on a particular finite time block and
develop an efficient four-slot transmission protocol to enable the joint
computation and communication cooperation. Besides the local task computing
over the whole block, the user can offload some computation tasks to the helper
in the first slot, and the helper cooperatively computes these tasks in the
remaining time; while in the second and third slots, the helper works as a
cooperative relay to help the user offload some other tasks to the AP for
remote execution in the fourth slot. For both cases with partial and binary
offloading, we jointly optimize the computation and communication resources
allocation at both the user and the helper (i.e., the time and transmit power
allocations for offloading, and the CPU frequencies for computing), so as to
minimize their total energy consumption while satisfying the user's computation
latency constraint. Although the two problems are non-convex in general, we
propose efficient algorithms to solve them optimally. Numerical results show
that the proposed joint computation and communication cooperation approach
significantly improves the computation capacity and energy efficiency at the
user and helper nodes, as compared to other benchmark schemes without such a
joint design.
