LLM-enabled Antenna Partitioning and Beamforming Optimization for Segmented Pinching

TL;DR

This paper introduces a learning framework using large language models for optimizing antenna deployment, partitioning, and beamforming in segmented pinching ISAC systems, enhancing adaptability and reducing training costs.

Contribution

It proposes a novel LLM-based approach with a CSI-induced self-graph and transfer mechanism for flexible, site-independent antenna and beamforming optimization.

Findings

Achieves higher communication rates and reliable sensing accuracy.

Deployment policy remains stable across different user counts.

Reduces training cost by enabling policy transfer instead of retraining.

Abstract

Integrated sensing and communication (ISAC) requires spatial architectures that can flexibly balance data transmission and environment sensing. Segmented pinching antenna-assisted ISAC provides such flexibility by allowing different waveguide segments to be dynamically configured for transmission and reception. However, its design involves the joint optimization of antenna deployment, segment partitioning, and beamforming under coupled communication and sensing constraints, which becomes particularly challenging when the numbers of communication users and sensing targets vary across scenarios. To endow the system with stronger adaptability to changing user and target configurations, we propose a general learning framework for segmented pinching antenna-assisted ISAC systems. Specifically, a channel state information (CSI)-induced self-graph is constructed to produce permutation-invariant representations of user-target interactions, and the resulting features are processed by a large language model (LLM) backbone with two task-specific heads for jointly predicting antenna deployment, segment partitioning, and ISAC beamforming. In addition, a user count transfer mechanism is developed to examine whether the learned deployment policy is site-specific and reusable under changed user configurations. Simulation results show that the proposed framework achieves higher communication rates while maintaining reliable sensing accuracy. Moreover, the learned deployment policy remains highly stable when transferring to other user counts, which reduces the training cost from full model retraining to beamforming head adaption.