Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

• Published in: IEEE transactions on signal processing 2018-05, Vol.66 (10), p.2557-2571
• Main Authors: Stein, Manuel S., Bar, Shahar, Nossek, Josef A., Tabrikian, Joseph
• Format: Article
• Language: English
• Subjects: 1-bit ADC; Analog-digital conversion; Channel estimation; Cramér–Rao bounds; Estimation; hard-limiting loss; intersymbol interference; nuisance parameter; Quantization (signal); quantization threshold; Receivers; Signal processing algorithms; Signal to noise ratio; wireless communication
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2018.2815022

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with 1-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultrahigh sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by 2/π (-1.96 dB) when comparing to an ideal ∞-bit converter. Due to hardware imperfections, low-complexity 1-bit ADCs will, in practice, exhibit an unknown threshold different from zero. Therefore, we study the accuracy that can be obtained with received data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with intersymbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.