References & Further Reading

References

S. Jacobsson, G. Durisi, M. Coldrey, U. Gustavsson, and C. Studer, with G. Caire, Throughput Analysis of Massive MIMO Uplink With Low-Resolution ADCs, 2017
The CommIT contribution of this chapter. Bussgang-based rate analysis of the low-resolution massive-MIMO uplink for arbitrary $b$, closed-form SINR expressions for MRC and ZF, and the mixed-ADC extension underpinning Sections 19.2 and 19.3. Sections III-V of the paper map onto Sections 19.2, 19.3, and 19.5 of this chapter.
J. Mo and R. W. Heath Jr., Capacity Analysis of One-Bit Quantized MIMO Systems With Transmitter Channel State Information, 2015
The canonical capacity analysis for 1-bit MIMO with CSIT. Proves tight bounds on the mutual information and characterizes the optimal input distribution. Section II of the paper defines the 1-bit quantizer used in our Definition <a href="#def-1bit-quantizer" class="ferkans-ref" title="Definition: 1-Bit (Sign) Quantizer" data-ref-type="definition">D1-Bit (Sign) Quantizer</a>.
J. Singh, O. Dabeer, and U. Madhow, On the Limits of Communication with Low-Precision Analog-to-Digital Conversion at the Receiver, 2009
The paper that proved the exact $2/\pi$ (1.96 dB) low-SNR capacity loss of 1-bit quantization. Theorem 1 is the basis of Theorem <a href="#thm-1bit-low-snr-loss" class="ferkans-ref" title="Theorem: Low-SNR Capacity Loss of the 1-Bit Receiver" data-ref-type="theorem">TLow-SNR Capacity Loss of the 1-Bit Receiver</a> in Section 19.1.
A. Mezghani and J. A. Nossek, Capacity Lower Bound of MIMO Channels with Output Quantization and Correlated Noise, 2012
Introduced the Bussgang decomposition as the primary analytical tool for quantized MIMO receivers. The MIMO Bussgang matrix and the distortion covariance formulas of Definition <a href="#def-bussgang-matrix" class="ferkans-ref" title="Definition: Bussgang Matrix and Distortion Covariance (MIMO)" data-ref-type="definition">DBussgang Matrix and Distortion Covariance (MIMO)</a> follow this reference.
J. J. Bussgang, Crosscorrelation Functions of Amplitude-Distorted Gaussian Signals, 1952
The original paper on the Bussgang theorem for memoryless nonlinearities applied to Gaussian inputs. The scalar form of Theorem <a href="#thm-bussgang-scalar" class="ferkans-ref" title="Theorem: Bussgang's Theorem (Scalar Gaussian Input)" data-ref-type="theorem">TBussgang's Theorem (Scalar Gaussian Input)</a> is due to Bussgang.
J. H. Van Vleck and D. Middleton, The Spectrum of Clipped Noise, 1966
The arcsine law $\mathbb{E}[\operatorname{sign}(Y_1) \operatorname{sign}(Y_2)] = (2/\pi)\arcsin(\rho)$ comes from this paper. The identity is the backbone of the 1-bit receiver analysis used throughout Section 19.2.
R. H. Walden, Analog-to-Digital Converter Survey and Analysis, 1999
The Walden figure-of-merit $P_{\text{ADC}} \propto 2^b f_s$ that motivates the energy-efficiency analysis of Section 19.5 comes from this seminal ADC survey.
B. Murmann, ADC Performance Survey 1997-2015, 2015. [Link]
The ongoing empirical ADC survey that updates Walden's original analysis. Used in Section 19.1 for the per-conversion energy figures and the FoM trend lines.
N. Liang and W. Zhang, Mixed-ADC Massive MIMO, 2016
The original mixed-ADC paper. Derives the achievable rate of a receiver combining a fraction of high-resolution ADCs with 1-bit ones and proves the linear gain-interpolation result used in Theorem <a href="#thm-mixed-rate" class="ferkans-ref" title="Theorem: Achievable Rate of the Mixed-ADC Uplink" data-ref-type="theorem">TAchievable Rate of the Mixed-ADC Uplink</a>.
Y. Li, C. Tao, G. Seco-Granados, A. Mezghani, A. L. Swindlehurst, and L. Liu, Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems, 2017
Channel estimation and mixed-ADC performance analysis. Provides the LMMSE channel estimator for 1-bit systems and numerical benchmarks used in Section 19.3. Propositions 1-2 are the basis of the mixed-ADC rate expressions in Theorem <a href="#thm-mixed-rate" class="ferkans-ref" title="Theorem: Achievable Rate of the Mixed-ADC Uplink" data-ref-type="theorem">TAchievable Rate of the Mixed-ADC Uplink</a>.
C. Mollén, J. Choi, E. G. Larsson, and R. W. Heath Jr., Uplink Performance of Wideband Massive MIMO with One-Bit ADCs, 2017
Extends 1-bit massive MIMO to wideband OFDM. The constellation design discussion of Section 19.1 (PSK vs QAM for 1-bit) follows Section V of this paper.
J. Choi, J. Mo, and R. W. Heath Jr., Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems With One-Bit ADCs, 2016
Bit allocation and near-ML detection for 1-bit massive MIMO. Provides an early formulation of the bit-allocation problem used in Section 19.4.
K. Roth and J. A. Nossek, Achievable Rate and Energy Efficiency of Hybrid and Digital Beamforming Receivers with Low Resolution ADCs, 2018
Provides the water-filling formulation for optimal per-antenna bit allocation (Theorem <a href="#thm-bit-alloc-waterfilling" class="ferkans-ref" title="Theorem: Continuous Bit Allocation is Water-Filling on Log Gains" data-ref-type="theorem">TContinuous Bit Allocation is Water-Filling on Log Gains</a>). Sections III and IV map onto Section 19.4 of this chapter and provide the greedy-integer rounding algorithm.
E. Björnson, L. Sanguinetti, and M. Kountouris, Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO, 2016
The canonical energy-efficiency analysis for massive MIMO. Includes the $\eta(b)$ formulation of Definition <a href="#def-energy-efficiency" class="ferkans-ref" title="Definition: Receiver Energy Efficiency" data-ref-type="definition">DReceiver Energy Efficiency</a> and the low-SNR optimum $b^\star = 1$ of Theorem <a href="#thm-optimal-b-lowsnr" class="ferkans-ref" title="Theorem: Optimal Resolution at Low Per-Antenna SNR" data-ref-type="theorem">TOptimal Resolution at Low Per-Antenna SNR</a>.
C. Studer and G. Durisi, Quantized Massive MU-MIMO-OFDM Uplink, 2016
Practical quantized MU-MIMO-OFDM uplink analysis. Contains the baseband-processing cost model referenced in Section 19.5 and demonstrates 1-bit OFDM at hardware level.
Q. Zhang, S. Jin, K.-K. Wong, H. Zhu, and M. Matthaiou, Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means and Mixed-ADC Architecture, 2018
Power-scaling analysis of mixed-ADC massive MIMO with Rician channels. Numerical results complement Section 19.3's analysis and motivate the mmWave engineering notes of Section 19.5.
J. Max, Quantizing for Minimum Distortion, 1960
Max-Lloyd algorithm for designing minimum-distortion quantizers. Provides the optimized $\rho_b$ table used in Definition <a href="#def-distortion-factor" class="ferkans-ref" title="Definition: Bussgang Distortion Factor" data-ref-type="definition">DBussgang Distortion Factor</a>. Lloyd's 1957 manuscript was published separately in 1982 and is an equivalent source.
B. Widrow, I. Kollár, and M.-C. Liu, Statistical Theory of Quantization, 1996
Comprehensive treatment of quantization as a statistical operation. The historical note of Section 19.4 on bit-allocation methods in signal processing follows this reference.