The field of semantic communication is undergoing a significant transformation, with a growing focus on transmitting task-relevant semantic information and intent-oriented communication. Researchers are exploring innovative approaches to improve the efficiency and effectiveness of semantic communication systems, including the development of adaptive deep understanding models and joint source-channel coding schemes. These advancements aim to enable more accurate and robust transmission of semantic information, even in varying channel conditions. Noteworthy papers in this area include:

• User-Intent-Driven Semantic Communication via Adaptive Deep Understanding, which proposes a system that interprets diverse abstract intents and achieves deep intent understanding.
• Adaptive Source-Channel Coding for Semantic Communications, which presents an adaptive source-channel coding scheme that outperforms typical deep joint source-channel coding and separate source-channel coding schemes.
• Experimental Validation of Provably Covert Communication Using Software-Defined Radio, which demonstrates the feasibility of provably-secure covert radio-frequency communication using software-defined radios.
• Semantic Communication with Distribution Learning through Sequential Observations, which investigates distribution learning in semantic communication and establishes fundamental conditions for learning source statistics when priors are unknown.