TY - GEN

T1 - A general framework for MIMO receivers with low-resolution quantization

AU - Rini, Stefano

AU - Barletta, Luca

AU - Eldar, Yonina C.

AU - Erkip, Elza

N1 - Funding Information:
The work of S. Rini is funded by the Ministry of Science and Technology, (MOST) 105-2221-E-009-029. The work of E. Erkip is funded by NSF EARS: #1547332 and NSF NeTS: #1302336. The work of Y. C. Eldar is funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 646804-ERCCOG-BNYQ and by the Ollendorf Foundation.
Publisher Copyright:
© 2017 IEEE.

PY - 2018/1/31

Y1 - 2018/1/31

N2 - 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.

AB - 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.

KW - Analog-to-digital conversion

KW - Channel output quantization

KW - MIMO channel

KW - One-bit quantization

UR - http://www.scopus.com/inward/record.url?scp=85046358609&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046358609&partnerID=8YFLogxK

U2 - 10.1109/ITW.2017.8277991

DO - 10.1109/ITW.2017.8277991

M3 - Conference contribution

AN - SCOPUS:85046358609

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 599

EP - 603

BT - 2017 IEEE Information Theory Workshop, ITW 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Information Theory Workshop, ITW 2017

Y2 - 6 November 2017 through 10 November 2017

ER -