Fluid Antenna-enabled Integrated Sensing, Communication, and Computing Systems

TL;DR

The paper introduces a fluid antenna (FA) enabled ISCC framework for vehicular networks to address inefficiencies of fixed-antenna ISCC systems. It provides detailed models for uplink communication and radar-like sensing using a common FA array, and formulates an integrated latency minimization problem that jointly optimizes antenna positions ${\bf d}(t)$, receive combining ${\bf W}_c(t), {\bf W}_s(t)$, and computing resources ${\bf f}_c^{(t)}, {\bf f}_s^{(t)}$, under regional and latency constraints. The optimization is tackled via an IDPSO-based alternating algorithm that decomposes the problem into three subproblems (computing, combining, and FA positioning) solved with interior-point/SDP/PSO techniques, respectively; the approach achieves rapid convergence and outperforms baseline schemes in total latency and resource utilization. Numerical results with $M=4$, $N=3$, and $L_n=3$ demonstrate substantial latency reductions as FA positions are optimized and resources are jointly allocated. The work highlights the practical potential of movable antennas to enhance ISCC performance in autonomous-vehicle ecosystems by enabling coordinated sensing, communication, and computing operations.

Abstract

The current integrated sensing, communication, and computing (ISCC) systems face significant challenges in both efficiency and resource utilization. To tackle these issues, we propose a novel fluid antenna (FA)-enabled ISCC system, specifically designed for vehicular networks. We develop detailed models for the communication and sensing processes to support this architecture. An integrated latency optimization problem is formulated to jointly optimize computing resources, receive combining matrices, and antenna positions. To tackle this complex problem, we decompose it into three sub-problems and analyze each separately. A mixed optimization algorithm is then designed to address the overall problem comprehensively. Numerical results demonstrate the rapid convergence of the proposed algorithm. Compared with baseline schemes, the FA-enabled vehicle ISCC system significantly improves resource utilization and reduces latency for communication, sensing, and computation.

Figures (4)

• Figure 1: The architecture of FA-enabled vehicle ISCC systems
• Figure 2: Convergence performance of the proposed Algorithm \ref{['Algorithm_Overall']}.
• Figure 3: Average latency in different schemes.
• Figure 4: Comparisons of FA-Enabled ISCC and baseline schemes.