NDLI: Study of Adaptive Detection and Channel Estimation for MIMO

Content Provider	Springer Nature Link
Author	Suikkanen, Essi Juntti, Markku
Copyright Year	2014
Abstract	Requirements for higher data rates and lower power consumption set new challenges for hardware implementation of multiple-input multiple-output orthogonal frequency division multiplexing receivers. Simple detectors have the advantage of low complexity and power consumption, but they cannot offer as good performance as more complex detectors. Therefore, it would be beneficial to be able to adapt the detector algorithm to suit the channel conditions to minimize the receiver processing power consumption while satisfying the quality of service requirements. At low signal-to-noise ratio and/or low rank channel, more power and computation resources could be used for detection in order to guarantee reliable communication, while in good conditions, a simple and less power consuming detector could be used. In this paper, we compare the performance of different detection algorithms. The performance results are based on simulations in a long term evolution system where precoding and hybrid automatic repeat request are used. The effect of channel estimation on the performance is shown. Theoretical complexities of the detectors as numbers of arithmetic operations are presented. Also hardware implementation results based on the existing literature are included in the comparison. We discuss when it would be beneficial to use a complex detector and when a simple one would be sufficient and what kind of impact the channel estimation has on the choice of the detector.
Starting Page	811
Ending Page	831
Page Count	21
File Format	PDF
ISSN	09296212
Journal	Wireless Personal Communications
Volume Number	93
Issue Number	3
e-ISSN	1572834X
Language	English
Publisher	Springer US
Publisher Date	2014-12-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	LTE MIMO–OFDM Detection Channel estimation Communications Engineering, Networks Signal,Image and Speech Processing Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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