  Energy-efficient computation is an inevitable trend for mobile edge computing
(MEC) networks. Resource allocation strategies for maximizing the computation
efficiency are critically important. In this paper, computation efficiency
maximization problems are formulated in wireless-powered MEC networks under
both partial and binary computation offloading modes. A practical non-linear
energy harvesting model is considered. Both time division multiple access
(TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated
for offloading. The energy harvesting time, the local computing frequency, and
the offloading time and power are jointly optimized to maximize the computation
efficiency under the max-min fairness criterion. Two iterative algorithms and
two alternative optimization algorithms are respectively proposed to address
the non-convex problems formulated in this paper. Simulation results show that
the proposed resource allocation schemes outperform the benchmark schemes in
terms of user fairness. Moreover, a tradeoff is elucidated between the
achievable computation efficiency and the total number of computed bits.
Furthermore, simulation results demonstrate that the partial computation
offloading mode outperforms the binary computation offloading mode and NOMA
outperforms TDMA in terms of computation efficiency.
