Abstract
The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and it increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the optimal maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm is of much higher complexity, especially at lower signal-to-noise ratio (SNR). In this paper, we propose an efficient ML detection algorithm for the MIMO system based on differential metrics. We first define differential metrics of different orders and derive the associated the recursive calculation. We then give the indicative functions, which can be used to determine some ML bits of the initial sequence. The differential metrics and indicative functions are then used to implement an efficient tree search for the ML detection. The proposed algorithm does not need QR decomposition and matrix inversion, and the tree search process needs only the operation of addition while there are constant numbers of multiplication before the tree search. Unlike the SD algorithm whose complexity increases rapidly with the decreasing SNR, the proposed algorithm has nearly constant complexity for all SNR and the average complexity is lower than the SD algorithm, especially at lower SNR.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 603-608 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479984060 |
| DOIs | |
| Publication status | Published - 2015 Jun 17 |
| Event | 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015 - New Orleans, United States Duration: 2015 Mar 9 → 2015 Mar 12 |
Publication series
| Name | 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015 |
|---|
Other
| Other | 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015 |
|---|---|
| Country | United States |
| City | New Orleans |
| Period | 15-03-09 → 15-03-12 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Computer Science Applications
- Signal Processing
Cite this
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Efficient maximum-likelihood detection for the MIMO system based on differential metrics. / Chang, Ming-Xian; Chang, Wang Yueh.
2015 IEEE Wireless Communications and Networking Conference, WCNC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 603-608 7127538 (2015 IEEE Wireless Communications and Networking Conference, WCNC 2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Efficient maximum-likelihood detection for the MIMO system based on differential metrics
AU - Chang, Ming-Xian
AU - Chang, Wang Yueh
PY - 2015/6/17
Y1 - 2015/6/17
N2 - The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and it increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the optimal maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm is of much higher complexity, especially at lower signal-to-noise ratio (SNR). In this paper, we propose an efficient ML detection algorithm for the MIMO system based on differential metrics. We first define differential metrics of different orders and derive the associated the recursive calculation. We then give the indicative functions, which can be used to determine some ML bits of the initial sequence. The differential metrics and indicative functions are then used to implement an efficient tree search for the ML detection. The proposed algorithm does not need QR decomposition and matrix inversion, and the tree search process needs only the operation of addition while there are constant numbers of multiplication before the tree search. Unlike the SD algorithm whose complexity increases rapidly with the decreasing SNR, the proposed algorithm has nearly constant complexity for all SNR and the average complexity is lower than the SD algorithm, especially at lower SNR.
AB - The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and it increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the optimal maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm is of much higher complexity, especially at lower signal-to-noise ratio (SNR). In this paper, we propose an efficient ML detection algorithm for the MIMO system based on differential metrics. We first define differential metrics of different orders and derive the associated the recursive calculation. We then give the indicative functions, which can be used to determine some ML bits of the initial sequence. The differential metrics and indicative functions are then used to implement an efficient tree search for the ML detection. The proposed algorithm does not need QR decomposition and matrix inversion, and the tree search process needs only the operation of addition while there are constant numbers of multiplication before the tree search. Unlike the SD algorithm whose complexity increases rapidly with the decreasing SNR, the proposed algorithm has nearly constant complexity for all SNR and the average complexity is lower than the SD algorithm, especially at lower SNR.
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UR - http://www.scopus.com/inward/citedby.url?scp=84938701112&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2015.7127538
DO - 10.1109/WCNC.2015.7127538
M3 - Conference contribution
AN - SCOPUS:84938701112
T3 - 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015
SP - 603
EP - 608
BT - 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -