Semantic-Cognitive 6G Networks for Meaning-Centric Terahertz Communication

Abstract

The 6G networks of the future require the paradigm shift in approach to transmission of data by means of bits, towards one based on meaning, particularly use of terahertz frequency ranges in which the bandwidth is bountiful, but the channel change is extreme. It is a proposal of a semanticcognitive 6G framework, which is a joint optimization of meaning extraction, intent preservation and adaptive transmission. The designed system presents a semantic relevance encoding algorithm called Semantic State Compression Learning which is referred to as S2CL. In order to overcome terahertz channel uncertainty, the Meaning-Aware Cognitive Adaptation of Terahertz (MACA-T) algorithm makes real time semantically-driven waveform adaptation possible. Besides, the Intent-Entropy Minimization Optimizer (IEMO) minimizes unwarranted semantic redundancy by matching transmitted information to receiver intent. Lastly, there is a Cognitive Deep Feedback Encoder (C-DFE), which keeps refining the semantic representations through a bi-directional intelligence. The simulation has shown a considerable decrease in latency, energy usage, and semantic distortion making the suggested framework a baseline framework of next-generation 6G semantic communication systems. This research aligns with Sustainable Development Goal (SDG 9): Industry, Innovation and Infrastructure and (SDG 11): Sustainable Cities and Communities, by enabling intelligent, energy-efficient, and adaptive communication infrastructures for future digital ecosystems.