Reliable Online Resource Allocation for Multi-User Semantic Communications: A Constraint Bayesian Optimization Approach

TL;DR

This paper introduces a Bayesian optimization-based framework for reliable online resource allocation in multi-user semantic communication systems, improving reconstruction quality and reducing latency.

Contribution

It develops a novel online algorithm that jointly optimizes semantic compression and transmission rates using Gaussian-process models under quality constraints.

Findings

Achieves 98.03% constraint-satisfaction rate in simulations.

Reduces transmission latency by over 45% compared to fixed schemes.

Effectively selects near-optimal compression ratios using GP surrogate models.

Abstract

Semantic communication has been increasingly integrated into edge computing systems for reconstruction tasks, owing to its advantages in source compression, robustness to channel noise, and task execution efficiency. However, the black-box nature of neural-network (NN)-based semantic codecs, together with the noisy transmission of semantic features, makes it difficult to allocate transmission resources and guarantee reconstruction quality for multiple users. In this paper, we propose a reliable online resource allocation framework for a semantic-driven multi-user edge computing system, where multiple users encode source information into semantic features and offload reconstruction to an edge server. We formulate a multi-user resource optimization problem whose objective jointly accounts for system-wide reconstruction performance and transmission latency, under constraints that guarantee each user's minimum reconstruction quality. To solve this problem, we develop a Bayesian optimization (BO)-based online algorithm that enables flexible control of the user-side semantic compression ratio (CR) and allocation of transmission rates. The edge server jointly determines each user's CR and transmission rate by exploiting Gaussian-process (GP) models that capture the relationship between reconstruction performance, signal-to-noise ratio (SNR), and CR, and by employing an acquisition function to select CRs that satisfy the performance quality constraints while maximizing the objective. Simulation results on high-resolution video-frame reconstruction datasets demonstrate that the proposed method selects near-optimal CRs via the GP surrogate and acquisition function, achieving a 98.03% constraint-satisfaction rate and reducing transmission latency by more than 45% compared with fixed-CR schemes.