Low-complexity detection for MIMO visible light communication system using generalized spatial modulation
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| Publicado en: | EURASIP Journal on Advances in Signal Processing vol. 2025, no. 1 (Dec 2025), p. 5 |
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| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 245 | 1 | |a Low-complexity detection for MIMO visible light communication system using generalized spatial modulation | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In this paper, the detection algorithms of two generalized spatial modulation (GSM) schemes in a multiple-input multiple-output (MIMO) visible light communication (VLC) system are developed and analyzed. The considered optical GSM schemes are GSM with Transmit Diversity (GSM-TD) and GSM with Multi-Stream (GSM-MS). By allowing each activated LED to transmit independent temporal symbols simultaneously, GSM-MS benefits from higher bits per channel use (bpcu) than the GSM-TD scheme. Existing maximum likelihood (ML) detection algorithm is computationally prohibitive; hence, we develop a two-stage (identification followed by detection) receiver that decodes the spatial and temporal symbols in a sequential manner. In the first stage, we propose two algorithms, namely subspace tracking and spatial matched filtering (SMF), to identify the indices of activated LEDs, while in the second stage, effective matched filter (EMF) and decorrelating detector are employed to extract temporal symbols for GSM-TD and GSM-MS scheme, respectively. The performance assessment, including the computation load, correct identification probability and the average symbol error rate (SER), is comprehensively evaluated by computation simulation. It is shown that the complexity of the proposed two detection algorithms is extensively reduced compared with the ML algorithm. It is also demonstrated that ML detector offers the best SER performance; nevertheless, SER of the proposed algorithms approaches ML detector in high signal-to-noise ratio (SNR) or large receiver array size scenarios. | |
| 653 | |a MIMO communication | ||
| 653 | |a Computation | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Performance assessment | ||
| 653 | |a Maximum likelihood decoding | ||
| 653 | |a Modulation | ||
| 653 | |a Error correction | ||
| 653 | |a Matched filters | ||
| 653 | |a Sensors | ||
| 653 | |a Communications systems | ||
| 653 | |a Algorithms | ||
| 653 | |a Symbols | ||
| 653 | |a Complexity | ||
| 653 | |a Optical communication | ||
| 653 | |a Error detection | ||
| 653 | |a Signal to noise ratio | ||
| 653 | |a Simulation | ||
| 773 | 0 | |t EURASIP Journal on Advances in Signal Processing |g vol. 2025, no. 1 (Dec 2025), p. 5 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3167826461/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3167826461/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |