Amplifier-enhanced memristive massive MIMO linear detector circuit: an ultra-energy-efficient and robust-to-conductance-error design
Amplifier-enhanced memristive massive MIMO linear detector circuit: an ultra-energy-efficient and robust-to-conductance-error design
The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of massive MIMO channel matrix. This oversight makes their computational accuracy highly sensitive to conductance errors of memristive devices, which is unacceptable for massive MIMO receivers. In this paper, we propose an MCA-based circuit design for massive MIMO zero forcing and minimum mean-square error detectors. Unlike the existing MCA-based detectors, we decompose the channel matrix into the product of small-scale and large-scale fading coefficient matrices, thus employing an MCA-based matrix computing module and amplifier circuits to process the two matrices separately. We present two conductance mapping schemes which are crucial but have been overlooked in all prior studies on MCA-based detector circuits. The proposed detector circuit exhibits significantly superior performance to the conventional MCA-based detector circuit, while only incurring negligible additional power consumption. Our proposed detector circuit maintains its advantage in energy efficiency over traditional digital approach by tens to hundreds of times.
Bi, Jia-Hui
97a5186b-ef45-4db0-981e-85563c99e6a1
Yang, Shaoshi
23650ec4-bcc8-4a2c-b1e7-a30893f52e52
Zhang, Ping
2def4374-679d-41d1-bf3a-483028a73275
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
8 December 2024
Bi, Jia-Hui
97a5186b-ef45-4db0-981e-85563c99e6a1
Yang, Shaoshi
23650ec4-bcc8-4a2c-b1e7-a30893f52e52
Zhang, Ping
2def4374-679d-41d1-bf3a-483028a73275
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Bi, Jia-Hui, Yang, Shaoshi, Zhang, Ping and Chen, Sheng
(2024)
Amplifier-enhanced memristive massive MIMO linear detector circuit: an ultra-energy-efficient and robust-to-conductance-error design.
In Proceedings of IEEE Global Communications Conference (GLOBECOM 2024).
IEEE.
6 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of massive MIMO channel matrix. This oversight makes their computational accuracy highly sensitive to conductance errors of memristive devices, which is unacceptable for massive MIMO receivers. In this paper, we propose an MCA-based circuit design for massive MIMO zero forcing and minimum mean-square error detectors. Unlike the existing MCA-based detectors, we decompose the channel matrix into the product of small-scale and large-scale fading coefficient matrices, thus employing an MCA-based matrix computing module and amplifier circuits to process the two matrices separately. We present two conductance mapping schemes which are crucial but have been overlooked in all prior studies on MCA-based detector circuits. The proposed detector circuit exhibits significantly superior performance to the conventional MCA-based detector circuit, while only incurring negligible additional power consumption. Our proposed detector circuit maintains its advantage in energy efficiency over traditional digital approach by tens to hundreds of times.
Text
globcom2024-p1
- Other
More information
Published date: 8 December 2024
Venue - Dates:
IEEE Global Communications Conference (GLOBECOM 2024), , Cape Town, South Africa, 2024-12-08 - 2024-12-12
Identifiers
Local EPrints ID: 497078
URI: http://eprints.soton.ac.uk/id/eprint/497078
PURE UUID: 47698515-6706-4993-bf3b-128f19bd5c84
Catalogue record
Date deposited: 14 Jan 2025 16:04
Last modified: 14 Jan 2025 16:04
Export record
Contributors
Author:
Jia-Hui Bi
Author:
Shaoshi Yang
Author:
Ping Zhang
Author:
Sheng Chen
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics