A general framework for MIMO receivers with low-resolution quantization

Stefano Rini, Luca Barletta, Yonina C. Eldar, Elza Erkip

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The capacity of a discrete-time, multi-input multi-output (MIMO) channel with output quantization is investigated for different receiver architectures. A general framework for low-resolution quantization is proposed in which the antenna outputs are processed by analog combiners and sign quantizers are used for analog-to-digital conversion. The configuration of the analog combiners is chosen as a function of the channel realization so that the transmission rate can be maximized over the set of available configurations. To exemplify the proposed approach, four analog receiver architectures are considered: (a) sign quantization of the antenna outputs, (b) single antenna selection, (c) multiple antenna selection, and (d) linear processing of the antenna outputs. In each scenario, capacity is investigated as a function of the transmit power, the number of transmit/receive antennas and sign quantizers. In particular, it is shown that architecture (a) is sufficient to approach the optimal high signal-to-noise ratio (SNR)performance for a MIMO receiver in which the number of receive antennas is larger than the number of sign quantizers. Numerical evaluations of the average performance are presented for the case in which the channel gains are i.i.d. Gaussian distributed.

Original languageEnglish (US)
Title of host publication2017 IEEE Information Theory Workshop, ITW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509030972
StatePublished - Jul 2 2017
Event2017 IEEE Information Theory Workshop, ITW 2017 - Kaohsiung, Taiwan, Province of China
Duration: Nov 6 2017Nov 10 2017

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Other2017 IEEE Information Theory Workshop, ITW 2017
Country/TerritoryTaiwan, Province of China


  • Analog-to-digital conversion
  • Channel output quantization
  • MIMO channel
  • One-bit quantization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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