SINR-Adaptive and CBR-Controllable Semantic Cellular Communication Considering Imperfect CSI and Inter-Cell Co-channel Interference

作     者：Niu, Haiwen Wang, Luhan Lu, Zhaoming Wu, Celimuge Wen, Xiangming 

作者机构：Beijing University Of Posts And Telecommunications Beijing Key Laboratory Of Network System Architecture And Convergence Beijing Laboratory Of Advanced Information Networks Beijing100876 China The University Of Electro-Communications Meta-Networking Research Center 1-5-1 Chofugaoka Chofu-shi Tokyo182-8585 Japan 

出 版 物：《IEEE Transactions on Cognitive Communications and Networking》 (IEEE Trans. Cogn. Commun. Netw.)

年 卷 期：2024年

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0836[工学-生物工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Bandwidth 

摘      要：Deep learning (DL)-enabled semantic communications offer promising prospects for higher data transmission efficiency. However, most existing studies focus on point-to-point transmissions with perfect channel state information (CSI) and fixed channel bandwidth ratio (CBR). This paper investigates multi-point transmission in cellular networks with spectrum resource sharing and proposes a signal-to-interference plus noise ratio (SINR) adaptive and CBR-controllable semantic cellular communication system (SACC). In this system, we design a semantic encoder by a Transformer-convolutional neural network (CNN) mixture block to capture non-local and local latent image features simultaneously and a SINR-adaptive module based on the channel-spatial soft attention mechanism to scale image features according to SINR conditions. A model adaption strategy is proposed to handle varying inter-cell co-channel interference proportions under the same SINR. Additionally, a semantic-channel encoder-decoder forwarding paradigm, termed semantic translation, enables dynamic networking among intra-cell users. Extensive simulation results show that the proposed SACC achieves graceful degradation and better image reconstruction performance versus the current engineered JPEG+LDPC+QAMand DeepJSCC benchmarks across different wireless environments and CBRs. Furthermore, experimental results demonstrate substantial improvements in FLOPs, model sizes, and computing latency over the state-of-the-art ADJSCC and WITT with comparable performance. © 2015 IEEE.