Optimal Resource Allocation for Multi-user OFDMA-URLLC MEC Systems

TL;DR

This paper develops a globally optimal resource allocation algorithm for multi-user OFDMA-URLLC MEC systems, addressing delay and reliability constraints through joint uplink-downlink optimization and finite blocklength transmission.

Contribution

It introduces a novel optimization framework with a branch-and-bound solution for power minimization under URLLC constraints, and proposes efficient suboptimal algorithms for practical use.

Findings

The proposed algorithm achieves significant power savings over baseline schemes.

Suboptimal algorithms provide near-optimal performance with lower complexity.

Joint uplink-downlink resource allocation effectively meets URLLC requirements.

Abstract

In this paper, we study resource allocation algorithm design for multi-user orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To meet the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we propose joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we employ a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces a new time causality constraint. The proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted power consumption of the network under a constraint on the number of URLLC user bits computed within the maximum allowable computation time, i.e., the end-to-end delay of a computation task. Despite the non-convexity of the formulated optimization problem, we develop a globally optimal solution using a branch-and-bound approach based on discrete monotonic optimization theory. The branch-and-bound algorithm minimizes an upper bound on the total power consumption until convergence to the globally optimal value. Furthermore, to strike a balance between computational complexity and performance, we propose two efficient suboptimal algorithms based on successive convex approximation and second-order cone techniques. Our simulation results reveal that the proposed resource allocation algorithm design facilitates URLLC in MEC systems, and yields significant power savings compared to three baseline schemes. Moreover, our simulation results show that the proposed suboptimal algorithms offer different trade-offs between performance and complexity and attain a close-to-optimal performance at comparatively low complexity.