Toward the Age of Semantic Information: A Deep Learning-Enabled Generalized Deduplication-Based Semantic Transmission Mechanism

In the upcoming global-coverage 6G networks, high packet loss and long latency in long-distance transmissions exacerbate the trade-off between data timeliness and integrity, particularly in time-sensitive applications involving time-series data with stringent integrity requirements. This challenge exposes the limitations of existing transmission systems, such as source-channel coding and semantic communication, which fail to jointly address both dimensions. In this paper, we propose a deep learning (DL)-enabled generalized deduplication (GD)-based semantic transmission (DLGD-ST) mechanism for time-series data. By leveraging GD to address the impact of semantic ambiguity on data integrity, DLGD-ST exploits the semantic recovery and temporal discreteness of the data to effectively mitigate the conflict between integrity and timeliness. In particular, a well-designed long-short-term memory (LSTM)-based GD algorithm is developed to separate shallow semantic components and supplementary components, ensuring the integrity of semantic transmission. A deep semantic encoding process is then performed using a double-layer progressive dimension reduction (DPDR) and adaptive quantization (AQ) scheme, which capitalizes on the channel robustness of semantics to reduce transmission rounds and improve timeliness. Furthermore, an incremental dimension hybrid automatic repeat request (ID-HARQ) mechanism is introduced to improve semantic reliability by retransmitting high-dimensional semantics, thereby further minimizing end-to-end transmission rounds. To accurately evaluate performance, we introduce the Age of Semantic Information (AoSI), which incorporates integrity constraints into the generalized Age of Information (AoI) to jointly assess integrity and timeliness. Simulation results demonstrate that the proposed DLGD-ST mechanism, enabled by accurate data recovery and reduced transmission rounds, achieves better AoSI performance compared to existing communication systems under both high and low signal-to-noise ratio (SNR) conditions.