Cooperative offloading multi-access edge computing (COMEC) for cell-edge users in heterogeneous dense networks

Multi-access edge computing (MEC) addresses the rising computational demands of advanced applications by bringing processing closer to users. Yet, cell-edge users often face high latency and low throughput—challenges that can be mitigated by deploying multiple MEC servers for simultaneous task offloading in dense heterogeneous networks. This paper investigates the performance gains of collaborative computing and presents a novel Cooperative Offloading Multi-access Edge Computing (COMEC) scheme. The COMEC aims to optimize resource allocation for cell-edge users by reducing latency and maximizing energy efficiency (EE). In this way, cell-edge users with limited battery and computational power can sustain low-latency applications for a longer time. A bi-objective optimization problem is formulated to maximize the EE of edge users while simultaneously minimizing the latency. We propose an iterative algorithm named ORA-ETO to solve the mixed integer non-linear fractional (MINLF) problem. The proposed scheme has been evaluated using both the Rayleigh and WINNER-II propagation models within an asymmetric cell configuration. The obtained results validate the efficacy of the proposed COMEC scheme for cell-edge users, achieving performance gains of over 55% compared to dense multi-server-assisted MEC and CoMP-assisted MEC architectures. The COMEC scheme is statistically more significant ($p<0.01$) and has more stable performance with standard deviation less than $0.082$ kbps/J, making it a superior choice for cell edge users. The effect size (${\eta }^2=0.71$) confirms that the choice of scheme has a considerable impact on EE of cell-edge users.