{'fraction_non_alphanumeric': 0.01092896174863388, 'fraction_numerical': 0.0, 'mean_word_length': 5.318965517241379, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Distributed estimation over fading channels using one-bit quantization

The problem of distributed estimation of an unknown parameter in noise is considered. Sensor observations are compressed using a one-bit quantizer and then transmitted to a fusion center over fading channels. We propose a mean estimator which requires only the knowledge of the mean of the channel gain and a sign estimator where the signs of the received signals are used for parameter estimation. Our analysis shows that these two estimators are not only computationally efficient, but also achieve comparable performance of maximum likelihood estimation. A robust iterative algorithm is proposed to address the design issue of local quantization thresholds.