Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
Source abstract
Abstract
In this paper, a linearly approximate signal detection scheme is proposed in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. The huge MIMO-OFDM system, which uses many transmit antennas and high order modulation, requires a detection scheme at the receiver with very low complexity for practical implementation. In the proposed detection scheme, one N × N MIMO-OFDM system is divided into N / 2 2 × 2 MIMO-OFDM systems for linear increase of complexity. After the partial zero-forcing (ZF), decision feedback equalizer (DFE) and QR decomposition-M algorithm (QRD-M) are applied to each 2 × 2 MIMO-OFDM system. Despite nonlinear detection schemes, the overall complexity of the proposed algorithm increases almost linearly because the DFE and the QRD-M are applied to 2 × 2 MIMO-OFDM systems. Also, the value of M in the QRD-M is fixed according to position of the center point in constellation for efficient signal detection. In simulation results, the proposed detection scheme has higher error performance and lower complexity than the conventional ZF. Also, the proposed detection scheme has very lower complexity than the conventional DFE, with slight loss of error performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Wireless Communication Techniques · Advanced MIMO Systems Optimization · Cooperative Communication and Network Coding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
uT Communication Research Institute, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, Korea
References (22)
Show source-provided references
1. Wolniansky, P.W., Foschini, G.J., Golden, G.D., and Valenzuela, R.A. (October, January 29). V-BLAST: An Architecture for Realizing Very High Data Rates over the Rich-Scattering Wireless Channel. Proceedings of the 1998 URSI International Symposium on Signals, Systems, and Electronics (ISSSE 98), Pisa, Italy.
2. Detection Algorithm and Initial Laboratory Results Using V-BLAST Space-Time Communication Architecture10.1049/el:19990058 ↗
3. Low Complexity ML Detection Technique for V-BLAST Systems with DFE Decoding10.1093/ietcom/e90-b.5.1261 ↗
4. You, W., Yi, L., and Hu, W. (2012, January 21–23). Reduced Complexity Maximum-Likelihood Detection for MIMO-OFDM Systems. Proceedings of the 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Shanghai, China.10.1109/WiCOM.2012.6478638 ↗
5. Bohnke, R., Wubben, D., Kuhn, V., and Kammeyer, K.D. (2003, January 1–5). Reduced Complexity MMSE Detection for BLAST Architectures. Proceedings of the 2003 IEEE Global Telecommunications Conference (GLOBECOM ‘03), San Francisco, CA, USA.
6. Wubben, D., Bohnke, R., Kuhn, V., and Kammeyer, K.D. (2003, January 6–9). MMSE Extension of V-BLAST Based on Sorted QR Decomposition. Proceedings of the 2003 IEEE 58th Vehicular Technology Conference (VTC 2003-Fall), Orlando, FL, USA.10.1109/VETECF.2003.1285069 ↗
7. Wang, Z., and Zhang, S. (2010, January 24–25). Group Iterative Linear ZF Receiver for MIMO-OFDM Systems. Proceedings of the 2010 Second International Conference on Networks Security Wireless Communications and Trusted Computing (NSWCTC), Wuhan, China.10.1109/NSWCTC.2010.65 ↗
8. Wang, Z., and Chen, F. (2009, January 24–26). A Hybrid ZF and QR Receiver for MIMO-OFDM Systems. Proceedings of the 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCom’09), Beijing, China.10.1109/WICOM.2009.5302393 ↗
9. Massive MIMO Channel-Aware Decision Fusion10.1109/TSP.2014.2376886 ↗
10. Ciuonzo, D., Rossi, P.S., and Dey, S. (2014, January 22–25). Massive MIMO Meets Decision Fusion: Decode-and-Fuse vs. Decode-then-Fuse. Proceedings of the 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), A Coruña, Spain.10.1109/SAM.2014.6882391 ↗
11. Efficient Algorithm for Decoding Layered Space-Time Codes10.1049/el:20010899 ↗
12. Peng, W., Ma, S., Ng, T.S., and Wang, J.Z. (2007, January 17–19). Adaptive QRD-M Detection with Variable Number of Surviving Paths for MIMO Systems. Proceedings of the International Symposium on Communications and Information Technologies (ISCIT’07), Sydney, Australia.
13. Kim, B.S., and Choi, K.H. (2008, January 11–14). A Very Low Complexity QRD-M Algorithm Based on Limited Tree Search for MIMO Systems. Proceedings of the IEEE Vehicular Technology Conference (VTC Spring 2008), Singapore.10.1109/VETECS.2008.263 ↗
14. Duan, X., Ren, G., Yang, L., and Yu, G. (2010, January 11–14). Low Complexity QRM-MLD Algorithm for MIMO Systems with 256 QAM Modulations. Proceedings of the 12th IEEE International Conference on Communication Technology (ICCT), Nanjing, China.
15. Liu, S., and Qian, S. (2010, January 4–6). A Lower Complexity QRD-M Algorithm Based on VBLAST for MIMO-OFDM Systems. Proceedings of the 2nd International Conference on Information Science and Engineering (ICISE), Hangzhou, China.10.1109/ICISE.2010.5691569 ↗
16. You, W., Yi, L., and Hu, W. (2012, January 21–23). A Hybrid Dynamic QRDM and ZF Detection Algorithm for MIMO-OFDM Systems. Proceedings of the 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Shanghai, China.10.1109/WiCOM.2012.6478292 ↗
17. Lim, H.Y., Jang, Y.S., Li, T., and Yoon, D.W. (2014, January 6–10). Improved QRD-M Algorithm Based on Adaptive Threshold for MIMO Systems. Proceedings of the 6th International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India.
18. Interference Cancellation and Signal Detection Technique Based on QRD-M Algorithm for FTN Signaling10.1049/el.2016.3377 ↗
19. Algorithm and Implementation of the K-best Sphere Decoding for MIMO Detection10.1109/JSAC.2005.862402 ↗
20. Low-Complexity Dominance-Based Sphere Decoder for MIMO Systems10.1016/j.sigpro.2013.02.011 ↗
21. A Dominance-Based Soft-Input Soft-Output MIMO Detector with Near-Optimal Performance10.1109/TCOMM.2014.2367013 ↗
22. A Semidefinite Relaxation Approach to MIMO Detection for High-Order QAM Constellations10.1109/LSP.2006.874443 ↗