Proportional Fairness in Wireless Powered Mobile Edge Computing Networks.

For the first time in the literature, we develop an efficient online fairness-aware resource allocation for wireless-powered mobile edge computing (MEC) networks with time division multiple access (TDMA) and partial offloading. The proposed scheme optimizes in each fading state the durations of the energy harvesting (EH) and offloading transmissions, the transmit powers of EH users (EHUs), and the clock frequencies of their processors. Leveraging the convex optimization, we obtain an analytical solution to the system’s proportional fairness maximization problem, which facilitates a simple implementation at a common node (e.g., the BS). The EHUs placed closer to the BS should offload the computation tasks to the MEC BS, since the local processing is more energy-intensive than the wireless transmission. This effect is further emphasized with the increase of the computation load of the EHUs needed to locally process a single bit of raw data. The system’s fairness level increases with decreasing computational loads, because the locally processed data suffer from the “conventional” near-far effect, whereas offloaded data suffer from the “double” near far-effect.