Parallel Processing at the Edge in Dense Wireless Networks

This paper investigates the gain of parallel processing, when mobile edge computing (MEC) is implemented in dense wireless networks. In this scenario users connect to several access points (APs), and utilize the computation capability of multiple servers at the same time. This allows a balanced load at the servers, with the eventual cost of decreased spectrum efficiency. The problem of sum transmission energy minimization under response time constraints is considered and proved to be non-convex. The complexity of optimizing a part of the system parameters is investigated, and based on these results an iterative resource allocation procedure is proposed that converges to a local optimum. The performance of the joint resource allocation is evaluated by comparing it to lower and upper bounds defined by less or more flexible multi-cell MEC architectures. The results show that the free selection of the AP is crucial for achieving decent system performance. The average level of parallel processing is in general low in dense systems, but it is an important option for the rare, highly unbalanced instances.