### Abstract

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 language | English (US) |
---|---|

Title of host publication | 2017 IEEE Information Theory Workshop, ITW 2017 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 599-603 |

Number of pages | 5 |

Volume | 2018-January |

ISBN (Electronic) | 9781509030972 |

DOIs | |

State | Published - Jan 31 2018 |

Event | 2017 IEEE Information Theory Workshop, ITW 2017 - Kaohsiung, Taiwan, Province of China Duration: Nov 6 2017 → Nov 10 2017 |

### Other

Other | 2017 IEEE Information Theory Workshop, ITW 2017 |
---|---|

Country | Taiwan, Province of China |

City | Kaohsiung |

Period | 11/6/17 → 11/10/17 |

### Fingerprint

### Keywords

- 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

### Cite this

*2017 IEEE Information Theory Workshop, ITW 2017*(Vol. 2018-January, pp. 599-603). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITW.2017.8277991

**A general framework for MIMO receivers with low-resolution quantization.** / Rini, Stefano; Barletta, Luca; Eldar, Yonina C.; Erkip, Elza.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2017 IEEE Information Theory Workshop, ITW 2017.*vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 599-603, 2017 IEEE Information Theory Workshop, ITW 2017, Kaohsiung, Taiwan, Province of China, 11/6/17. https://doi.org/10.1109/ITW.2017.8277991

}

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

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

VL - 2018-January

SP - 599

EP - 603

BT - 2017 IEEE Information Theory Workshop, ITW 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -