Rate-Splitting and Common Message Decoding in Hybrid Cloud/Mobile Edge Computing Networks

This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) mobile edge computing (MEC) platform, especially introduced to balance network resources for joint communication computation. The transmission is further empowered by splitting users’ messages into private common parts, mitigate interference within CC MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at cell-edge boost connectivity relieve part of its computation burden, connects base-stations via capacity-limited fronthauls. then considers problem maximizing weighted sum-rate subject fronthaul capacity, achievable rates, power, delay, data-split constraints. Thereby determining beamforming vectors associated with messages, allocations, factors. Such intricate non-convex optimization tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, fractional programming, can be compellingly implemented in distributed fashion. simulations illustrate proposed algorithm’s capabilities empowering computation, highlight pronounced role rate-splitting message decoding alleviating large-scale CC/MEC networks.