IEEE Wireless Communications · 2022 · 124 citations · 17 references
ComputingCommunicationComputer ScienceDigital CommunicationMachine LearningSemanticsSemantic NetworksUncertainty RepresentationCodingCommunication EncodingCommunication System SecurityImplementation Cost AnalysisPerformance OverheadSemantic InformationSemantic Knowledge RepresentationSemantic Security
Modern communications prioritize bit‑level precision and efficiency, yet next‑generation semantic‑aware systems face challenges in information overhead, security, and implementation cost. The paper proposes a new semantics‑aware communication framework that integrates semantic encoding with the semantic communication problem, redefining the traditional focus on bit precision. The authors develop a confidence‑based distillation mechanism for joint semantics‑noise coding and an RL‑powered paradigm that enables systems to transmit semantics rather than bit‑level accuracy. The study demonstrates that the proposed framework improves semantic processing and representation, yielding significant performance benefits.
Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that characterizes a new kind of semantics-aware communication framework, incorporating both the semantic encoding and the semantic communication problem. After analyzing the underlying defects of existing semantics-aware techniques, we establish a confidence-based distillation mechanism for the joint semantics-noise coding (JSNC) problem and a reinforcement learning (RL)-powered semantic communication paradigm that endows a system the ability to convey the semantics instead of pursuing the bit level accuracy. On top of these technical contributions, this work provides a new insight to understand how the semantics are processed and represented in a semantics-aware coding and communication system, and verifies the significant benefits of doing so. Targeted on the next generation's semantics-aware communication, some critical concerns and open challenges such as the information overhead, semantic security and implementation cost are also discussed and envisioned.
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A Mathematical Theory of Communication
Claude E. Shannon · Bell System Technical Journal · 1948 · 80.7K citations
Deep Learning Enabled Semantic Communication Systems
Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li et al. · IEEE Transactions on Signal Processing · 2021 · 1.3K citations · Full text