Improving ADC figure-of-merit in wideband antenna array receivers using multidimensional space-time delta-sigma multiport circuits

Madanayake, Arjuna; Akram, Najath; Mandal, Soumyajit; Liang, Jifu; Belostotski, Leonid

doi:10.1109/nds.2017.8070633

Cited by 13 publications

(9 citation statements)

References 6 publications

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“…Whereas the achievable rate bounds derived in [44] assume perfect knowledge of the CSI, our result takes into account the channel estimation error. In our derivation of the worst-case bound, we assume that the channel estimateĝ is Gaussian with covariance matrix Cĝ given by (47). Similarly, q d is also assumed to be Gaussian and its covariance matrix is obtained as described earlier in this section.…”

Section: Uplink Achievable Rate Analysismentioning

confidence: 99%

“…Only recently has the noise shaping characteristics of first and second-order spatial and cascaded space-time Σ∆ architectures been studied for a few array processing applications. In particular, applications have been considered for massive MIMO [40], [41], [42], [43], [44], phased arrays [45], [46], interference cancellation [35], and spatio-temporal Σ∆ circuit implementations [47], [48]. With the exception of our preliminary studies in [42], [49], there has been no prior work focused on channel estimation for spatial Σ∆ massive MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

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Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs

et al. 2021

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We consider channel estimation for an uplink massive multiple-input multiple-output (MIMO) system where the base station (BS) uses an array with low-resolution (1-2 bit) analog-to-digital converters and a spatial Sigma-Delta (Σ∆) architecture to shape the quantization noise away from users in some angular sector. We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. We also analyze the uplink achievable rate with maximal ratio combining (MRC), zero-forcing (ZF) and LMMSE receivers and provide a lower bound for the achievable rate with the MRC receiver. Numerical results show superior channel estimation and sum spectral efficiency performance using the Σ∆ architecture compared to conventional 1-or 2-bit quantized massive MIMO systems.

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Section: Uplink Achievable Rate Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs

et al. 2021

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“…Algorithm 1: Channel estimation using Σ∆ array 1) Set β = 1.05 for one-bit operation. For m = 1 to M N , repeat: (i) Update the diagonal elements of C r , σ 2 rm , using (35), and σ 2 ym using (20) for one-bit ADCs and (29) for two-bit ADCs. (ii) The elements of C q , σ 2 qm , are updated using…”

Section: (38)mentioning

confidence: 99%

“…Let σ 2 r dm , σ 2 y dm and σ 2 q dm denote the powers of the mth components of r d , y d and q d , respectively. Then, (35) is modified for the data transmission stage as…”

Section: Uplink Achievable Rate Analysismentioning

confidence: 99%

Massive Mimo Channel Estimation with 1-Bit Spatial Sigma-delta ADCS

Rao

Swindlehurst

Pirzadeh

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider channel estimation for an uplink massive multiple input multiple output (MIMO) system where the base station (BS) uses an array with low-resolution (1-2 bit) analog-to-digital converters and a spatial Sigma-Delta (Σ∆) architecture to shape the quantization noise away from users in some angular sector. We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. We also analyze the uplink achievable rate with maximal ratio combining (MRC) and zero-forcing (ZF) receivers and provide a closed-form expression for the achievable rate with the MRC receiver. Numerical results show superior channel estimation and sum spectral efficiency performance using the Σ∆ architecture compared to conventional 1-or 2-bit quantized massive MIMO systems.

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“…Relatively little research has focused on the spatial Σ∆ architecture. Prior related work has dealt with phased-array beamforming [31], [32], generalized structures for interference cancellation [33], and circuit implementations [34], [35]. Applications of the idea to massive MIMO were first presented in [36], [37], and more recently algorithms have been developed for channel estimation [38] and transmit precoding using Σ∆ DACs [39].…”

Section: Introductionmentioning

confidence: 99%

Spectral Efficiency of Mixed-ADC Massive MIMO

Pirzadeh

Swindlehurst

2018

IEEE Trans. Signal Process.

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We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Σ∆ sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the Σ∆ approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the Σ∆ array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the Σ∆ approach for both MRC and zero-forcing receivers.

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Improving ADC figure-of-merit in wideband antenna array receivers using multidimensional space-time delta-sigma multiport circuits

Cited by 13 publications

References 6 publications

Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs

Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs

Massive Mimo Channel Estimation with 1-Bit Spatial Sigma-delta ADCS

Spectral Efficiency of Mixed-ADC Massive MIMO

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