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

Abstract

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.