SPARC-LDPC Coding for MIMO Massive Unsourced Random Access
| Autor: | Wenjun Zhang, Yongpeng Wu, Mengfan Zheng, Tianya Li, Dongming Wang |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Interleaving Computer science Computer Science - Information Theory Information Theory (cs.IT) MIMO 020206 networking & telecommunications 02 engineering and technology Spectral efficiency Data_CODINGANDINFORMATIONTHEORY Belief propagation Compressed sensing 0202 electrical engineering electronic engineering information engineering Low-density parity-check code Algorithm Decoding methods Random access Computer Science::Information Theory |
| Zdroj: | GLOBECOM (Workshops) |
| DOI: | 10.48550/arxiv.2009.10912 |
| Popis: | A joint sparse-regression-code (SPARC) and low-density-parity-check (LDPC) coding scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) is proposed in this paper. Different from the state-of-the-art covariance-based maximum likelihood (CB-ML) detection scheme, we first split users' messages into two parts. The former part is encoded by SPARCs and tasked to recover part of the messages, the corresponding channel coefficients as well as the interleaving patterns by compressed sensing. The latter part is coded by LDPC codes and then interleaved by the interleave-division multiple access (IDMA) scheme. The decoding of the latter part is based on belief propagation (BP) joint with successive interference cancellation (SIC). Numerical results show our scheme outperforms the CB-ML scheme when the number of antennas at the base station is smaller than that of active users. The complexity of our scheme is with the order $\mathcal{O}\left(2^{B_p}ML+\widehat{K}ML\right)$ and lower than the CB-ML scheme. Moreover, our scheme has higher spectral efficiency (nearly $15$ times larger) than CB-ML as we only split messages into two parts. Comment: accepted by Globecom 2020 Workshop on AT5G+ |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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