Work

Quantized Sparse Regression Codes with Optimized OAMP Decoding

Year: 2026

Type: conference-paper

Abstract: Sparse regression (SR) codes are designed to enable efficient communication. In this paper, we consider SR codes with nonlinear functions, which we call nonlinear SR codes. Previous studies have shown that incorporating clippers with appropriately chosen thresholds can significantly improve finite-length performance under additive white Gaussian noise (AWGN) channels. In this work, we extend the nonlinear framework by introducing quantization functions, which may bring benefits for hardware implementation. We show that regularly quantized SR codes (SR codes with a single quantizer) are comparable to irregularly clipped SR codes at code rate 1. Further, we show that a gain of nearly 0.5 dB over irregular clipping with 4–10 clippers can be achieved by employing only two irregular quantization functions. The design of the irregular quantization functions is obtained by optimizing the state evolution (SE) of an orthogonal approximate message passing (OAMP) decoder.

Source: IEEE International Symposium on Information Theory (ISIT)

DOI: 10.1109/isit62367.2026.11653983

Authors: Chengpin Luo, Brian M. Kurkoski, Lei Liu

Institutions: Japan Advanced Institute of Science and Technology, Zhejiang University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7204178626

FWCI:

Cites: 24

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Related to: 10