TY - GEN
T1 - Wireless Resource Management in Intelligent Semantic Communication Networks
AU - Xia, Le
AU - Sun, Yao
AU - Li, Xiaoqian
AU - Feng, Gang
AU - Imran, Muhammad Ali
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 ISCHetNet, 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.
AB - 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 ISCHetNet, 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.
UR - https://www.scopus.com/pages/publications/85131623286
U2 - 10.1109/INFOCOMWKSHPS54753.2022.9797984
DO - 10.1109/INFOCOMWKSHPS54753.2022.9797984
M3 - Conference contribution
AN - SCOPUS:85131623286
T3 - INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops
BT - INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022
Y2 - 2 May 2022 through 5 May 2022
ER -