Multiobjective Resource Allocation for mmWave MEC Offloading Under Competition of Communication and Computing Tasks

Toward 6G networks, such as virtual reality (VR) applications, Industry 4.0, and automated driving, demand mobile-edge computing (MEC) techniques to offload computing tasks to nearby servers, which, however, causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, enabling low latency and high throughput. For this sake, this article investigates the resource management for the offload transmission of the mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation, and power allocation is formulated as a multiobjective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using the E -constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that the gap between Pareto front generated by the three-stage iterative resource allocation algorithm and the real Pareto front is less than 0.16%. Furthermore, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.

Automated summary

This article investigates resource management for the offload transmission of the mmWave MEC system, considering both communication-oriented and computing-oriented users. A three-stage iterative resource allocation algorithm is proposed, and simulation results show its superior performance.