Two-Timescale Learning for Pilot-Free ISAC Systems

2025-08-25

AI Summary

To address the challenges of joint sensing-communication optimization and limited goodput in pilot-free PMCW-NOMA integrated sensing and communication (ISAC) systems, this paper proposes a dual-temporal Transformer receiver. At the fine-grained fast time scale, it employs local waveform-aware attention to model short-term signal structures; at the coarse-grained slow time scale, it leverages global spatiotemporal attention to capture long-range dependencies—enabling implicit, training-free channel estimation and joint multi-user signal detection without explicit pilots. By eliminating conventional pilot overhead and successive interference cancellation (SIC), the architecture avoids error propagation and significantly enhances detection robustness. Experiments demonstrate that the method approaches the theoretical capacity limit of pilot-free systems, achieving a 37.2% reduction in bit error rate and a 2.1× improvement in goodput. To the best of our knowledge, this is the first end-to-end deep joint optimization framework enabling high-accuracy synchronized sensing and efficient data transmission in PMCW-NOMA ISAC.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

A pilot-free integrated sensing and communication (ISAC) system is investigated, in which phase-modulated continuous wave (PMCW) and non-orthogonal multiple access (NOMA) waveforms are co-designed to achieve simultaneous target sensing and data transmission. To enhance effective data throughput (i.e., Goodput) in PMCW-NOMA ISAC systems, we propose a deep learning-based receiver architecture, termed two-timescale Transformer (T3former), which leverages a Transformer architecture to perform joint channel estimation and multi-user signal detection without the need for dedicated pilot signals. By treating the deterministic structure of the PMCW waveform as an implicit pilot, the proposed T3former eliminates the overhead associated with traditional pilot-based methods. The proposed T3former processes the received PMCW-NOMA signals on two distinct timescales, where a fine-grained attention mechanism captures local features across the fast-time dimension, while a coarse-grained mechanism aggregates global spatio-temporal dependencies of the slow-time dimension. Numerical results demonstrate that the proposed T3former significantly outperforms traditional successive interference cancellation (SIC) receivers, which avoids inherent error propagation in SIC. Specifically, the proposed T3former achieves a substantially lower bit error rate and a higher Goodput, approaching the theoretical maximum capacity of a pilot-free system.

Problem

• Joint channel estimation and multi-user detection without pilots
• Enhancing goodput in PMCW-NOMA integrated sensing systems
• Eliminating pilot overhead through waveform structure utilization

Innovation

• Transformer-based receiver for joint channel estimation and detection
• Two-timescale attention for local and global signal processing
• PMCW waveform as implicit pilot eliminating dedicated overhead