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[2511.19943] AI/ML based Joint Source and Channel Coding for HARQ-ACK Payload
Summary
This paper presents a novel approach to joint source and channel coding for HARQ-ACK payloads using AI/ML techniques, demonstrating significant performance improvements in 5G communications.
Why It Matters
As 5G technology evolves, optimizing data transmission efficiency is crucial. This research addresses the non-uniform distribution of HARQ-ACK bits, proposing a deep learning-based solution that enhances error protection and reduces power consumption, which is vital for improving network performance and coverage.
Key Takeaways
- Introduces a transformer-based encoder for HARQ-ACK payloads.
- Achieves significant power savings (3-6 dB) while maintaining error rates.
- Implements a novel 'free-lunch' training algorithm for improved performance.
- Develops a low-complexity coherent approximation for optimal receiver design.
- Demonstrates the potential for enhanced coverage in 5G networks.
Electrical Engineering and Systems Science > Signal Processing arXiv:2511.19943 (eess) [Submitted on 25 Nov 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:AI/ML based Joint Source and Channel Coding for HARQ-ACK Payload Authors:Akash Doshi, Pinar Sen, Kirill Ivanov, Wei Yang, June Namgoong, Runxin Wang, Rachel Wang, Taesang Yoo, Jing Jiang, Tingfang Ji View a PDF of the paper titled AI/ML based Joint Source and Channel Coding for HARQ-ACK Payload, by Akash Doshi and 8 other authors View PDF HTML (experimental) Abstract:Channel coding from 2G to 5G has assumed the inputs bits at the physical layer to be uniformly distributed. However, hybrid automatic repeat request acknowledgement (HARQ-ACK) bits transmitted in the uplink are inherently non-uniformly distributed. For such sources, significant performance gains could be obtained by employing joint source channel coding, aided by deep learning-based techniques. In this paper, we learn a transformer-based encoder using a novel "free-lunch" training algorithm and propose per-codeword power shaping to exploit the source prior at the encoder whilst being robust to small changes in the HARQ-ACK distribution. Furthermore, any HARQ-ACK decoder has to achieve a low negative acknowledgement (NACK) error rate to avoid radio link failures resulting from multiple NACK errors. We develop an extension of the Neyman-Pearson test to a coded bit system with multiple information bits to achieve Unequal Error Protection of NACK ...