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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2410.03891 |
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| _version_ | 1866915501539590144 |
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| author | Nguyen, Toan-Van Nassirpour, Sajjad Atzeni, Italo Tolli, Antti Swindlehurst, A. Lee Nguyen, Duy H. N. |
| author_facet | Nguyen, Toan-Van Nassirpour, Sajjad Atzeni, Italo Tolli, Antti Swindlehurst, A. Lee Nguyen, Duy H. N. |
| contents | The spatial Sigma-Delta ($ΣΔ$) 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 $ΣΔ$ 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 $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order $ΣΔ$ 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. We also study the impact of mutual coupling on the performance of the proposed SD-VB algorithms when the antenna spacing is reduced. Simulation results show that the proposed $2^{\mathrm{nd}}$-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 $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-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 mutual coupling, the steering angle of the $ΣΔ$ architecture, the number of ADC resolution bits, and the number of antennas and users are also extensively analyzed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03891 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MIMO Detection with Spatial Sigma-Delta ADCs: A Variational Bayesian Approach Nguyen, Toan-Van Nassirpour, Sajjad Atzeni, Italo Tolli, Antti Swindlehurst, A. Lee Nguyen, Duy H. N. Signal Processing The spatial Sigma-Delta ($ΣΔ$) 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 $ΣΔ$ 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 $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order $ΣΔ$ 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. We also study the impact of mutual coupling on the performance of the proposed SD-VB algorithms when the antenna spacing is reduced. Simulation results show that the proposed $2^{\mathrm{nd}}$-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 $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-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 mutual coupling, the steering angle of the $ΣΔ$ architecture, the number of ADC resolution bits, and the number of antennas and users are also extensively analyzed. |
| title | MIMO Detection with Spatial Sigma-Delta ADCs: A Variational Bayesian Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2410.03891 |