The field of wireless communication is undergoing significant transformations, driven by advancements in reconfigurable intelligent surfaces (RISs), semantic communication, wireless communication systems, wireless networks, and communication and reinforcement learning. A common theme among these areas is the focus on improving efficiency, reliability, and spectral efficiency in data transmission.
Researchers in RISs are exploring innovative solutions to overcome key challenges, including severe double-pathloss and complex multi-user scheduling. Noteworthy papers in this area include Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems, Fluid Reconfigurable Intelligent Surface with Element-Level Pattern Reconfigurability: Beamforming and Pattern Co-Design, and Space-time Coded Differential Modulation for Reconfigurable Intelligent Surfaces.
In semantic communication, researchers are developing adaptive deep understanding models and joint source-channel coding schemes to improve the efficiency and effectiveness of semantic communication systems. Noteworthy papers include User-Intent-Driven Semantic Communication via Adaptive Deep Understanding, Adaptive Source-Channel Coding for Semantic Communications, Experimental Validation of Provably Covert Communication Using Software-Defined Radio, and Semantic Communication with Distribution Learning through Sequential Observations.
The development of more efficient and spectrally efficient methods for data transmission is a key focus in wireless communication systems. Researchers are improving the performance of existing systems, such as OFDM-based grant-free access, and exploring new waveform designs, like non-orthogonal affine frequency division multiplexing. Noteworthy papers include a proposal for AMP-based joint activity detection and channel estimation for massive grant-free access in OFDM-based wideband systems, a random modulation technique that guarantees asymptotic replica optimality, and a novel non-orthogonal affine frequency division multiplexing waveform for reliable high-mobility communications.
In wireless networks, researchers are addressing the challenges posed by underwater and mmWave networks, including limited energy supply, harsh communication conditions, and unexpected node malfunctions. Noteworthy papers include Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks and Joint Scheduling and Resource Allocation in mmWave IAB Networks Using Deep RL.
Finally, the field of communication and reinforcement learning is witnessing significant developments, with a focus on efficient communication, adaptive learning, and robust decision-making. Noteworthy papers include In-Context Reinforcement Learning via Communicative World Models, Toward Goal-Oriented Communication in Multi-Agent Systems: An overview, and Semantic-Aware LLM Orchestration for Proactive Resource Management in Predictive Digital Twin Vehicular Networks.
Overall, these advancements have the potential to significantly enhance the performance of next-generation wireless networks, enable reliable high-mobility communications, support massive connectivity, and reduce power consumption.
