Asymptotically Efficient Recursive Identification Under One-Bit Communications Achieving Original CRLB

TL;DR

This paper introduces an asymptotically efficient recursive identification algorithm for autoregressive systems with exogenous inputs using one-bit data, achieving the original CRLB and outperforming existing methods.

Contribution

The paper proposes a novel quantization method and analysis framework that enable the estimator to asymptotically reach the original CRLB under one-bit communication constraints.

Findings

Achieves asymptotic normality and efficiency matching the original CRLB.

Reduces asymptotic mean squared error by at least 36%.

Effectiveness demonstrated through numerical simulations.

Abstract

This paper develops an asymptotically efficient recursive identification algorithm for autoregressive systems with exogenous inputs under one-bit communications. In particular, the proposed method asymptotically achieves the Cramer-Rao lower bound (CRLB) based on the original data before quantization (original CRLB), whereas existing approaches typically attain only the CRLB corresponding to the quantized observations. The primary reason is that the existing methods quantize only the current system output, resulting in non-negligible information loss under one-bit quantization. To overcome this challenge, we present a novel quantization method that integrates both current and historical system outputs and inputs to provide richer parameter information in one-bit data, allowing the information loss caused by quantization to become a minor term relative to the original CRLB. Based on this technique, a corresponding remote estimation algorithm is further proposed. To address the convergence analysis challenge posed by the non-independence of the one-bit data, we establish a new framework that analyzes the tail probability of integrated data formed by combining current and historical system outputs and inputs before quantization, thereby eliminating the need for the traditional independence assumption on the quantized data. It is proven that the remote estimate achieves asymptotic normality, and the error covariance matrix converges to the original CRLB, confirming its asymptotic efficiency. Compared to existing identification algorithms under one-bit data, this method reduces the asymptotic mean squared error by at least 36%. Several numerical examples are simulated to show the effectiveness of the proposed algorithm.