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.

本文针对智能语义通信中的用户关联和带宽分配问题，引入辅助知识库，在系统模型中设计了 “系统吞吐量” 的性能指标，通过两阶段解，提出了一个新的决策方案，与两款基线算法相比，提出的解决方案具有更高的性能表现和可靠性。