CRC-Assisted Channel Codes for Integrated Passive Sensing and Communications

2024-11-08

AI Summary

Conventional integrated sensing and communication (ISAC) schemes for multi-antenna base stations rely solely on pilot symbols, resulting in weak coupling between sensing and communication performance. Method: This paper proposes a coded ISAC framework based on OFDM waveforms. It introduces CRC-assisted channel coding to enable bidirectional enhancement between decoding reliability and parameter estimation priors; advocates a data-pilot joint sensing paradigm to overcome pilot-exclusive resource allocation; and designs a learnable near-orthogonal superposition (NOS) short code for joint optimization, trained via weighted multi-task loss. Contribution/Results: The proposed iterative parameter sensing and channel decoding (IPSCD) algorithm achieves substantial gains in both target parameter estimation accuracy and block error rate (BLER) with only a few iterations. Simulations demonstrate that the NOS code outperforms LDPC and Polar codes significantly under short blocklengths. Ablation studies validate the effectiveness of each component.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We propose a novel coded integrated passive sensing and communication (CIPSAC) system with orthogonal frequency division multiplexing (OFDM), where a multi-antenna base station (BS) passively senses the parameters of the targets and decodes the information bit sequences transmitted by a user. The transmitted signal is comprised of pilot and data OFDM symbols where the data symbols adopt cyclic redundancy check (CRC)-assisted channel codes to facilitate both the decoding and sensing procedures. In the proposed scheme, CRC not only enhances the reliability of communication but also provides guidance to the parameter sensing procedure at the BS. In particular, a novel iterative parameter sensing and channel decoding (IPSCD) algorithm is proposed, where the correctly decoded codewords that pass CRC are utilized for sensing to improve the parameter estimation accuracy, and in return, more accurate parameter estimates lead to a larger number of correctly decoded data symbols. Conventional sensing algorithms rely only on the received pilot signals, while we utilize both the data and pilot signals for sensing. We provide a detailed analysis of the optimal strategy, in which the wrongly decoded data packets are replaced by zero codewords. To further improve the performance, we introduce learning-based near-orthogonal superposition (NOS) codes, which exhibit superior error correction capability especially in the short block length regime. NOS codes are trained using a weighted loss function, where a hyper parameter is introduced to balance the sensing and the communication losses. Simulation results show the effectiveness of the proposed CIPSAC system and the IPSCD algorithm, where both the sensing and decoding performances are significantly improved with a few iterations. We also carry out extensive ablation studies for a comprehensive understanding of the proposed scheme.

Problem

• Enhance sensing and communication using CRC-assisted codes
• Improve parameter estimation with iterative algorithm
• Optimize system performance with learning-based NOS codes

Innovation

• CRC-assisted channel codes
• Iterative sensing and decoding
• Learning-based NOS codes