Quantized Uplink Massive MIMO Systems With Linear Receivers

Kolomvakis, Nikolaos; Eriksson, Thomas; Coldrey, Mikael; Viberg, Mats

doi:10.1109/icc40277.2020.9149088

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…We first revisit the Bussgang-based linear channel estimators including BMMSE and BWZF for low-resolution massive MIMO systems [3], [22]. The system model in (7) can be linearized by the Bussang decomposition as follows:…”

Section: A Bussang-based Linear Channel Estimatorsmentioning

confidence: 99%

“…The distortion factor η b depending on the number of quantization bits b is given in Table I. A BWZF channel estimator was also proposed in [22] as follows:…”

Section: A Bussang-based Linear Channel Estimatorsmentioning

confidence: 99%

“…Since the majority of work in the literature focused on one-bit systems, there were limited results on few-bit massive MIMO channel estimation [22]- [25]. Specifically, the Bussgang decomposition was exploited in [22] to derive two linear channel estimators including BMMSE and Bussgang-based weighted zero-forcing (BWZF). A DNN-based joint pilot signal and channel estimator design is proposed in [23].…”

Section: Introductionmentioning

confidence: 99%

“…Data detection in few-bit massive MIMO systems has been studied in recent papers [22], [36]- [39]. While generalized approximate message passing (GAMP) and Bayes inference are exploited in [36], the work in [37] employed variational Bayesian (VB) inference and belief propagation (BP) for soft symbol decoding.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the blind detection method in [27] which was developed for one-bit systems, the learning-based blind detection methods in [38], [39] are applicable for few-bit systems, but they are also restricted to MIMO systems with a small number of transmit antennas and only low-dimensional constellations. The BMMSE and BWZF detection methods in [22] are linear detectors and thus simple and applicable for large-scale MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems

Nguyen¹,

Nguyen²,

Swindlehurst³

2021

Preprint

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Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the lowresolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of the domain knowledge in the few-bit quantization process. While the first data detection network, namely B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, namely FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the vanishing gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into its weight matrices of the channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gain in estimation accuracy by the proposed deep learning framework.

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Section: A Bussang-based Linear Channel Estimatorsmentioning

confidence: 99%

“…The distortion factor η b depending on the number of quantization bits b is given in Table I. A BWZF channel estimator was also proposed in [22] as follows:…”

Section: A Bussang-based Linear Channel Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems

Nguyen¹,

Nguyen²,

Swindlehurst³

2021

Preprint

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A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs

Nguyen

Swindlehurst

Nguyen

2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

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The spatial Sigma-Delta (Σ∆) architecture can be leveraged to reduce the quantization noise and enhance the effective resolution of few-bit analog-to-digital converters (ADCs) at certain spatial frequencies of interest. Utilizing the variational Bayesian (VB) inference framework, this paper develops novel data detection algorithms tailored for massive multiple-input multiple-output (MIMO) systems with few-bit Σ∆ ADCs and angular channel models, where uplink signals are confined to a specific angular sector. We start by modeling the corresponding Bayesian networks for the 1 st -and 2 nd -order Σ∆ receivers. Next, we propose an iterative algorithm, referred to as Sigma-Delta variational Bayes (SD-VB), for MIMO detection, offering low-complexity updates through closed-form expressions of the variational densities of the latent variables. Simulation results show that the proposed 2 nd -order SD-VB algorithm delivers the best symbol error rate (SER) performance while maintaining the same computational complexity as in unquantized systems, matched-filtering VB with conventional quantization, and linear minimum mean-squared error (LMMSE) methods. Moreover, the 1 st -and 2 nd -order SD-VB algorithms achieve their lowest SER at an antenna separation of one-fourth wavelength for a fixed number of antenna elements. The effects of the steering angle of the Σ∆ architecture, the number of ADC resolution bits, and the number of antennas and users are also extensively analyzed.

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