  This paper studies a wireless powered mobile edge computing (MEC) system with
device-to-device (D2D)-enabled task offloading. In this system, a set of
distributed multi-antenna energy transmitters (ETs) use collaborative energy
beamforming to wirelessly charge multiple users. By using the harvested energy,
the actively computing user nodes can offload their computation tasks to nearby
idle users (as helper nodes) via D2D communication links for self-sustainable
remote computing. We consider the frequency division multiple access (FDMA)
protocol, such that the D2D communications of different user-helper pairs are
implemented over orthogonal frequency bands. Furthermore, we focus on a
particular time block for task execution, which is divided into three slots for
computation task offloading, remote computing, and result downloading,
respectively, at different user-helper pairs. Under this setup, we jointly
optimize the collaborative energy beamforming at ETs, the communication and
computation resource allocation at users and helpers, and the user-helper
pairing, so as to maximize the sum computation rate (i.e., the number of task
input-bits executed over this block) of the users, subject to individual energy
neutrality constraints at both users and helpers. First, we consider the
computation rate maximization problem under any given user-helper pairs, for
which an efficient solution is proposed by using the techniques of alternating
optimization and convex optimization. Next, we develop the optimal user-helper
pairing scheme based on exhaustive search and a low-complexity scheme based on
greedy selection. Numerical results show that the proposed design significantly
improves the sum computation rate at users, as compared to benchmark schemes
without such joint optimization.
