Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering enhanced efficiency by reducing transmission data. However, implementing semantic communication faces significant challenges, particularly in resource-constrained devices that cannot support the complex artificial intelligence (AI) models required for semantic extraction. This paper addresses this challenge by proposing a novel unmanned aerial vehicle (UAV)-enabled semantic-bit coexisting relay system, where the UAV serves as intermediate nodes to assist transmissions from resource-limited users to the base station. By deploying semantic extraction models at the UAV, the proposed system solves the computational resource limitations for user devices while minimizing transmission latency via data size reduction. In such a system, we formulate a system latency minimization problem that jointly considers semantic compression model selection and bandwidth allocation. To address this complex problem, we develop an effective solution method by decomposing the original problem into a semantic compression model selection based on performance-latency trade-offs and a bandwidth-allocation optimization via convex optimization techniques. Extensive numerical evaluations demonstrate that the proposed framework consistently outperforms conventional schemes across diverse network settings and compression parameters, significantly reducing end-to-end latency while maintaining high-quality semantic communication.

The domain adaptation issues in semantic communications become critical when transmitter and receiver operate across different multiple domains or when input data during inference have different distributional characteristics than the data used to train semantic encoders and decoders. In this paper, we introduce the Multidomain Adaptive Deep Semantic Communication (MA-DeepSC) framework, designed to enhance semantic communications across multiple domains. Our framework consists of two core components: the Multidomain Adaptive Semantic Coding Network (MASCN), inherently designed to adapt semantic encoding and decoding across multiple domains, and the multidomain data adaptation network (MDAN), which transforms actual observable data into the data on which the system was initially trained, thus obviating the need for retraining the existing pre-trained semantic coding network. We validate our approach through experiments on digit datasets and CelebA, observing significant outperformance over existing techniques. In addition, we analyze the strategic benefits and drawbacks of both MASC and MDAN, assessing their applicability under various scenarios. The source code for MA-DeepSC is available at https://github.com/wongdongwook/JSAC_MA-DeepSC.

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) techniques show promise in enhancing spectral efficiency in sixth-generation Internet of Things (IoT) networks. However, optimizing the synergy between these two methods is challenging due to the complex and dynamic environment. This study focuses on maximizing the sum-rate metric in RIS-assisted uplink multiantenna RSMA IoT networks to address this problem. We jointly optimized the base station beamforming design, power allocation, and RIS phase shifts to enhance the spectral efficiency with multiple mobile IoT devices present. The controlled parameters are continuous variables and the mathematical problem is nonconcave. Therefore, we formulated the problem as a Markov decision process and used the deep deterministic policy gradient (DDPG) to determine the optimal joint actions. We proposed a safe action shaping process for the decision-making actor network to address constraint violations. Through a rigorous performance evaluation, we demonstrated that the DDPG approach with action shaping outperforms the current DDPG algorithm regarding the maximum achievable sum rate.