Optimal detection in MIMO systems using spatial Sigma-Delta ADCs
Toan-Van Nguyen; Sajjad Nassirpour; Italo Atzeni; Antti Tölli; A. Lee Swindlehurst; Duy H. N. Nguyen (2025-04-04)
IEEE
04.04.2025
T. -V. Nguyen, S. Nassirpour, I. Atzeni, A. Tölli, A. L. Swindlehurst and D. H. N. Nguyen, "Optimal Detection in MIMO Systems Using Spatial Sigma-Delta ADCs," 2024 58th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2024, pp. 1625-1629, doi: 10.1109/IEEECONF60004.2024.10942859
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© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202501241332
https://urn.fi/URN:NBN:fi:oulu-202501241332
Tiivistelmä
Abstract
The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed.
The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed.
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