Low-Complexity Vector-by-Vector Detector for AFDM-IM Systems by Reconstructing Sparse Channel Matrix

In this letter, a low-complexity vector-by-vector aided expectation propagation (VV-EP) detector is proposed for affine frequency division multiplexing (AFDM) with index modulation (IM) through reconstructing the sparse effective channel matrix. Specifically, the low-correlation elements of the effective channel matrix are set to zero by a preset threshold. Based on the reconstructed sparse matrix, approximate inverse operation can be implemented by Cholesky decomposition to reduce computational complexity. Moreover, the lower triangular matrix is constructed iteratively to improve detection performance. Simulation results indicate that the designed VV-EP algorithm is capable of reducing complexity by about 90% with a negligible performance loss compared to the orthogonal approximate message passing (OAMP) detector.

Authors

Institutions

Publication Details

Journal
IEEE Communications Letters
Published
2025-06-06
DOI
https://doi.org/10.1109/lcomm.2025.3577262
Citations
6
Primary Topic
Blind Source Separation Techniques
Type
article
Field-Weighted Citation Impact
8.91
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Low-Complexity Vector-by-Vector Detector for AFDM-IM Systems by Reconstructing Sparse Channel Matrix

Jiaxi Zhou, Lixia Xiao, Miaowen Wen, Qu Luo et al.
6 citations
IEEE Communications Letters
Blind Source Separation Techniques
8.91
article

Low-Complexity Vector-by-Vector Detector for AFDM-IM Systems by Reconstructing Sparse Channel Matrix

Jiaxi Zhou, Lixia Xiao, Miaowen Wen, Qu Luo, Xiaoshan Wang, Tao Jiang
article en
6 citations

Abstract

In this letter, a low-complexity vector-by-vector aided expectation propagation (VV-EP) detector is proposed for affine frequency division multiplexing (AFDM) with index modulation (IM) through reconstructing the sparse effective channel matrix. Specifically, the low-correlation elements of the effective channel matrix are set to zero by a preset threshold. Based on the reconstructed sparse matrix, approximate inverse operation can be implemented by Cholesky decomposition to reduce computational complexity. Moreover, the lower triangular matrix is constructed iteratively to improve detection performance. Simulation results indicate that the designed VV-EP algorithm is capable of reducing complexity by about 90% with a negligible performance loss compared to the orthogonal approximate message passing (OAMP) detector.

IEEE Communications LettersVol. 29(8)
University of Surrey (GB), Huazhong University of Science and Technology (CN), South China University of Technology (CN)
Openalex Percentile: Top 2%
Blind Source Separation Techniques
8.91
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.