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BER performance analysis of coarsely quantized uplink massive MIMO
Razi Univ, Iran.
Univ Kurdistan, Iran.
Razi Univ, Iran.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5954-434X
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 161, p. 259-267Article in journal (Refereed) Published
Abstract [en]

Having lower quantization resolution, has been introduced in the literature, to reduce the power consumption of massive MIMO and millimeter wave MIMO systems. Here, we analyze the bit error rate (BER) performance of quantized uplink massive MIMO employing few-bit resolution ADCs. Considering ZF detection, we derive a signal-to-interference, quantization and noise ratio (SIQNR) to achieve an analytical BER approximation for coarsely quantized M-QAM massive MIMO systems, by using a linear quantization model. The proposed expression is a function of the quantization resolution in bits. We further numerically investigate the effects of different quantization levels, from 1-bit to 4-bits, on the BER of three modulation types QPSK, 16-QAM, and 64-QAM. The uniform and non-uniform quantizers are employed in our simulation. Monte Carlo simulation results reveal that our approximate formula gives a tight upper bound on the BER performance of b-bit resolution quantized systems using non-uniform quantizers, whereas the use of uniform quantizers cause a lower performance. We also found a small BER performance degradation in coarsely quantized systems, for example 2-3 bits QPSK and 3-4 bits 16-QAM, compared to the full-precision (unquantized) case. However, this performance degradation can be compensated by increasing the number of antennas at the BS. (C) 2019 Published by Elsevier B.V.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2019. Vol. 161, p. 259-267
Keywords [en]
Bit Error Rate (BER); Low resolution ADC; Coarse quantization; Massive MIMO
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-157510DOI: 10.1016/j.sigpro.2019.03.005ISI: 000466258200021OAI: oai:DiVA.org:liu-157510DiVA, id: diva2:1328797
Available from: 2019-06-23 Created: 2019-06-23 Last updated: 2019-06-28

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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