Synesthesia of Machine (SoM)-Driven Analog Precoder Optimization for Enhanced ISAC Performance in Sub-THz Systems

TL;DR

This paper introduces a novel analog precoder optimization method for sub-THz ISAC systems, leveraging synesthesia-inspired neural networks to enhance dual sensing and communication performance amid beam squint challenges.

Contribution

It proposes a lightweight complex-valued neural network inspired by synesthesia to optimize analog precoders, addressing beam squint effects in sub-THz ISAC systems.

Findings

Enhanced dual-functional gains through analog precoder adjustments

Reduced algorithmic complexity with CSP-Net

Validated performance improvements via simulations

Abstract

Integrated sensing and communication (ISAC) is anticipated to be widely used in future sub-terahertz (sub-THz) systems. With the line-of-sight (LoS) propagation characteristics of sub-THz channels, ISAC transmitter design largely parallels analog precoder optimization. However, balancing both sensing and communication functionalities is challenging due to the beam squint effect in sub-THz systems, limiting ISAC performance gains. To overcome this, the unique design flexibility of sub-THz analog hardware is explored to better adapt to the electromagnetic characteristics of sub-THz channels. It is demonstrated that adjusting the equivalent channel through the analog precoder enhances dual-functional gains. Based on this, a near-optimal benchmark for analog precoder optimization is proposed. To address excessive algorithmic complexity, inspiration is drawn from the synesthesia of machine (SoM) to develop a lightweight complex-valued squint-aware network (CSP-Net). This network reduces complexity by utilizing both communication and sensing channel data, with an architecture tailored to specific data and task characteristics. The effectiveness of the proposed schemes is validated through simulations.