Fluid Antenna-Enabled Near-Field Integrated Sensing, Computing, and Semantic Communication for Emerging Applications
Yinchao Yang, Jingxuan Zhou, Zhaohui Yang, Mohammad Shikh‐Bahaei · IEEE Transactions on Cognitive Communications and Networking · 2025
The integration of sensing and communication (ISAC) has been recognised as a key enabler for next-generation technologies. Meanwhile, with the adoption of high-frequency bands and large-scale antenna arrays, the extension of the Rayleigh distance necessitates the consideration of near-field (NF) models, where signal waves are spherical. While NF-ISAC enhances both sensing and communication, it introduces significant challenges, including increased data volume and potential privacy concerns. To address these issues, this paper introduces a novel framework: near-field integrated sensing, computing, and semantic communication (NF-ISCSC), which incorporates semantic communication to transmit only contextual information, thereby reducing data overhead and improving system efficiency. However, the sensitivity of semantic communication to channel conditions underscores the need for adaptive solutions. To this end, fluid antennas (FAs) are proposed to assist the NF-ISCSC system, offering dynamic adaptability to channel variations. The proposed FA-enabled NF-ISCSC framework takes into account multiple communication users, and extended targets which are composed of a series of scatterers. A joint optimisation problem is formulated to maximise data rate while accounting for sensing performance, computational constraints, and the power budget. By using an alternating optimisation (AO) approach, the original optimisation problem is decomposed into three sub-problems to iteratively optimise ISAC beamforming, FA positioning, and semantic extraction ratio. The beamforming optimisation is solved using the successive convex approximation method. The FA positioning problem is solved via a computationally efficient projected Broyden–Fletcher–Goldfarb–Shann (projected BFGS) algorithm. The semantic extraction ratio optimisation employs a bisection search method. Simulation results validate the proposed framework, highlighting the significant benefits of achieving higher data rates and enhanced privacy.