{'fraction_non_alphanumeric': 0.029154518950437316, 'fraction_numerical': 0.0, 'mean_word_length': 5.776315789473684, '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}}
Distortion-Rate for Non-Distributed and Distributed Estimation Problems

Wireless sensor networks are characterized by strict energy and bandwidth constraints, which motivate efficient compression schemes. The distortion incurred due to compression is an important metric for determining performance of reconstruction or estimation algorithms implemented at the fusion center based on observations collected at individual sensors. For the single-sensor link with the fusion center (non-distributed case), the distortion-rate (DR) function when reconstructing Gaussian observations has been fully characterized. But this is not true for signal estimation problems with either single-sensor, or, multi-sensor observations (distributed case). In this work, we derive novel D-R results pertaining to signal estimation in a centralized point-to-point link, and we offer an interesting extension of an iterative procedure for numerically determining strict (achievable) upper bounds on the D-R region in a distributed estimation setup.