Enhanced data-aided frequency estimation by collaboration in a distributed receiver

In this paper, a communication system with digital burst-mode transmission and distributed reception in the presence of carrier frequency offset and Additive White Gaussian Noise (AWGN) is considered. The distributed receiver consists of distributed nodes and a fusion center. Data-aided frequency estimation at a receiving node can be performed using a preamble. However, accurate frequency estimation may not be achievable at nodes with low signal-to-noise ratio (SNR). The problem can be alleviated by collaboration between nodes. Low-SNR nodes can improve their data-aided frequency estimation by fetching already decoded symbols from the fusion center. This paper investigates deterministic and data dependent criteria for selecting and fetching of additional symbols. The mean-square error (MSE) of frequency estimation errors achieved by different criteria are numerically compared via Monte-Carlo simulations. The Cramer-Rao Lower Bounds (CRLB) for frequency estimation under the considered criteria are presented. The bit-error-rates (BER) of the distributed receiver across different symbol fetching schemes are numerically compared.

Automated summary

This paper investigates a communication system with digital burst-mode transmission and distributed reception in the presence of carrier frequency offset and Additive White Gaussian Noise (AWGN). The collaboration between nodes is explored to improve the frequency estimation accuracy of low-SNR nodes, and the criteria for selecting and fetching additional symbols are studied. Numerical comparisons and analysis are conducted to evaluate the performance of different schemes.