Tree-Based Adaptive Spatial Detection for Adaptive Modulated MIMO Systems

Chengwei Zheng, Yun Wu, John McAllister, Roger Woods

Research output: Contribution to conferencePaper

Abstract

Adaptive Multiple-Input Multiple-Output (MIMO) systems achieve a much higher information rate than conventional fixed schemes due to their ability to adapt their configurations according to the wireless communications environment. However, current adaptive MIMO detection schemes exhibit either low performance (and hence low spectral efficiency) or huge computational
complexity. In particular, whilst deterministic Sphere Decoder (SD) detection schemes are well established for static MIMO systems, exhibiting deterministic parallel structure, low computational complexity and quasi-ML detection performance, there are no corresponding adaptive schemes. This paper solves
this problem, describing a hybrid tree based adaptive modulation detection scheme. Fixed Complexity Sphere Decoding (FSD) and Real-Values FSD (RFSD) are modified and combined into a hybrid scheme exploited at low and medium SNR to provide the highest possible information rate with quasi-ML Bit Error
Rate (BER) performance, while Reduced Complexity RFSD, BChase and Decision Feedback (DFE) schemes are exploited in the high SNR regions. This algorithm provides the facility to balance the detection complexity with BER performance with compatible information rate in dynamic, adaptive MIMO communications
environments.
Original languageEnglish
Pages107-113
Number of pages5
Publication statusPublished - Nov 2011
EventSDR'11 Technical Conference and Product Exposition - Washington D.C., United States
Duration: 29 Nov 201101 Dec 2011

Conference

ConferenceSDR'11 Technical Conference and Product Exposition
CountryUnited States
CityWashington D.C.
Period29/11/201101/12/2011

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  • Cite this

    Zheng, C., Wu, Y., McAllister, J., & Woods, R. (2011). Tree-Based Adaptive Spatial Detection for Adaptive Modulated MIMO Systems. 107-113. Paper presented at SDR'11 Technical Conference and Product Exposition, Washington D.C., United States.