  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.
