Analog equalization and low resolution quantization in strong line-of-sight MIMO communication

TitleAnalog equalization and low resolution quantization in strong line-of-sight MIMO communication
Publication TypeConference Paper
Year of Publication2016
AuthorsSong, X., Hälsig T., Rave W., Lankl B., & Fettweis G.
Published in2016 IEEE International Conference on Communications (ICC)
Page(s)1-7
Date Published05/2016
Keywordsanalog equalization, analog equalizing networks design, analog-to-digital converters, energy efficiency, Energy resolution, equalisers, line-of-sight MIMO communication, low resolution quantization, MIMO, MIMO communication, Multiplexing, quantisation (signal), Quantization (signal), Signal resolution, Transmitting antennas, Wireless communication
Abstract

In this work we show that the analog equalizing networks are suitable for low resolution quantization with limited mutual information loss in strong line-of-sight MIMO communication. More specifically, a simplified analog equalizing network design in comparison with state-of-the-art works is proposed in this work. Additionally, the new network design works equally good for larger displacement ranges. Furthermore, by using analog equalizing networks in line-of-sight MIMO systems, it is shown that the drawbacks of low resolution quantization can be minimized. This increases the energy efficiency of the analog-to-digital converters via minimizing the required magnitude resolution. Generally, low resolution quantization causes low entropy on the receive vectors which will effectively reduce the mutual information of the desired transmission. The analog equalizing network reshapes the distribution and reduces the dynamics of the received signals in the complex constellation plane before quantization. Therefore, the entropy loss after low resolution quantization is reduced and the system performs essentially as good as a system with higher resolutions. Finally, an algorithm is proposed to remove ambiguities which arise after analog-to-digital conversion with a given low resolution.

URLhttp://www.icsi.berkeley.edu/pubs/initiatives/analogequalizationandlowres16.pdf
DOI10.1109/ICC.2016.7511627
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Research Initiatives