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As the date rates and bandwidths of communication systems scale up, the cost and power consumption of high-precision (e.g., 8-12 bits) analog-to- digital converters (ADCs) become prohibitive. One possible approach to relieve this bottleneck is to redesign communication systems with the starting assumption that the receiver employs ADCs with drastically reduced precision (e.g., 1-4 bits). Encouraging results from information- theoretic analysis in idealized settings prompt a detailed investigation of receiver signal processing algorithms when ADC precision is reduced. In this paper, we investigate the problem of automatic gain control (AGC) for pulse amplitude modulation (PAM) signaling over the AWGN channel, with the goal being to align the ADC thresholds with the maximum likelihood (ML) decision regions. The approach is to apply a variable gain to the ADC input, fixing the ADC thresholds, with the gain being determined by estimating the signal amplitude from the quantized ADC output. We consider a blind approach in which the ML estimate for the signal amplitude is obtained based on the quantized samples corresponding to an unknown symbol sequence. We obtain good performance, in terms of both channel capacity and uncoded bit error rate, at low to moderate SNR, but the performance can actually degrade as SNR increases due to the increased sensitivity of the ML estimator in this regime. However, we demonstrate that the addition of a random Gaussian dither, with power optimized to minimize the normalized mean squared error of the ML estimate, yields performance close to that of ideal AGC over the entire range of SNR of interest.
Date of Conference: 6-10 Dec. 2010