Wireless Resource Management in Intelligent Semantic Communication Networks

TL;DR

This paper addresses wireless resource management challenges in intelligent semantic communication networks by optimizing user association and bandwidth allocation to maximize system throughput, introducing a new performance metric and a two-stage solution.

Contribution

It introduces a novel system model with an auxiliary knowledge base and develops a joint optimization framework for resource management in ISC-HetNets, combining stochastic programming and heuristic algorithms.

Findings

Proposed solution outperforms baseline algorithms in system throughput.

Introduced the system throughput in message (STM) as a new performance metric.

Demonstrated reliability and superiority of the approach through numerical results.

Abstract

The prosperity of artificial intelligence (AI) has laid a promising paradigm of communication system, i.e., intelligent semantic communication (ISC), where semantic contents, instead of traditional bit sequences, are coded by AI models for efficient communication. Due to the unique demand of background knowledge for semantic recovery, wireless resource management faces new challenges in ISC. In this paper, we address the user association (UA) and bandwidth allocation (BA) problems in an ISC-enabled heterogeneous network (ISC-HetNet). We first introduce the auxiliary knowledge base (KB) into the system model, and develop a new performance metric for the ISC-HetNet, named system throughput in message (STM). Joint optimization of UA and BA is then formulated with the aim of STM maximization subject to KB matching and wireless bandwidth constraints. To this end, we propose a two-stage solution, including a stochastic programming method in the first stage to obtain a deterministic objective with semantic confidence, and a heuristic algorithm in the second stage to reach the optimality of UA and BA. Numerical results show great superiority and reliability of our proposed solution on the STM performance when compared with two baseline algorithms.