5G Radio Resource Allocation for Communication and Computation Offloading

Abstract

Edge computing is envisioned as a key enabler in future cellular networks by bringing the computing, networking and storage resources closer to the end users and enabling offloading for computation-intensive or latency-critical tasks coming from the emerging 5G/6G applications. Such technology also introduces additional challenges when it comes to deciding when to offload or not since the dynamic wireless environment plays a significant role in the overall communication and computation costs when offloading workload to the nearby edge nodes. In this paper, we focus on the communication cost in computation offloading via wireless channels, by formulating an α -fair utility-based radio resource allocation (RRA) problem tailored for offloading in a multi-user urban scenario where the uplink connection is the main focus. We begin by modeling the wireless channel with large- and small-scale fading at both lower and millimetre-wave frequencies, followed by data rate calculation based on 3GPP for a more realistic approach. Then, while assessing the fairness of the RRA, we simulate the resource allocation framework while taking into account both users who need to offload and users who are only interested in high downlink data rates. Simulation results show that the weighted proportional fairness method adapted for computation offloading can provide a good trade-off between fairness and performance compared to other benchmark schemes.