Enhanced Low-Complexity Receiver Design for Short Block Transmission Systems

2023-09-05

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

AI Summary

In 5G/6G short-block transmissions, quasi-coherent (QC) detection suffers significant performance and sensitivity degradation—especially at low spectral efficiencies—due to neglect of non-coherent terms; meanwhile, long-code decoding incurs prohibitive complexity. Method: This paper proposes a low-complexity receiver integrating adaptive DMRS/data power allocation with block-wise first-order Reed–Muller coding. Contribution/Results: We quantitatively characterize, for the first time, the critical impact of non-coherent terms in short-block QC detection. A unified QC-and-non-coherent joint detection framework is established, incorporating block-wise fast Hadamard transform (FHT) and power-coordinated optimization. Decoding complexity is reduced from *O*(*N*²) to *O*(*N* log *N*), while block error rate approaches the maximum-likelihood (ML) bound. The scheme substantially outperforms conventional LS+QC detection, achieving notable sensitivity gains—making it well-suited for small-payload, ultra-low-latency communication scenarios.

Technology Category

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

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

This paper presents a comprehensive analysis and the performance enhancement of short block length channel detection incorporating training information. The current communication systems’ short block length channel detection are assumed to typically consist of least squares channel estimation, followed by quasi-coherent detection. By investigating the receiver structure, specifically the estimator-correlator, we show that the non-coherent term, which is often disregarded in conventional detection metrics, results in significant losses in terms of performance and sensitivity in typical operating regimes of 5G/6G systems. A comparison with the fully non-coherent receiver in multi-antenna configurations reveals substantial losses in low spectral efficiency operating areas. Additionally, we demonstrate that by employing an adaptive DMRS/data power adjustment, it is possible to reduce the performance loss gap which is amenable to a more sensitive quasi-coherent receiver. However, both of the aforementioned ML detection strategies can result in substantial computational complexity when processing long bit length codes. We propose an approach to tackle this challenge by introducing the principle of block/segment coding using First-Order RM Codes which is amenable to low-cost decoding through block-based fast Hadamard transforms. The Block-based FHT has demonstrated to be cost-efficient with regards to decoding time, as it evolves from quadric to quasi-linear complexity with a manageable decline in performance. Additionally, by incorporating an adaptive DMRS/data power adjustment technique, we are able to bridge/reduce the performance gap with respect to the conventional maximum likelihood receiver and attain high sensitivity, leading to a good trade-off between performance and complexity to efficiently handle small payloads.

Problem

• Enhancing short block transmission system detection
• Reducing computational complexity in channel detection
• Improving sensitivity in 5G and 6G systems

Innovation

• Adaptive DMRS-data power adjustment
• Block-based fast Hadamard transforms
• First-Order RM Codes