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

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 “JEPG+LDPC+QAM” and 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.