A Nearest Neighbor Regression Based Channel Estimation Algorithm for Acoustic Channel-Aware Routing

The underwater acoustic channel is one of the most challenging communication channels. Due to periodical tidal and daily climatic variation, underwater noise is periodically fluctuating, which result in the periodical changing of acoustic channel quality in long-term. Also, time variant channel quality leads to routing failure. Routing protocols with acoustic channel estimation, namely underwater channel-aware routing protocols are recently proposed to maintain the routing performance. However, channel estimation algorithms for these routing protocols are mostly linear and rarely consider periodicity of acoustic channels. In this paper, we introduce nearest neighbor regression based acoustic channel estimation for underwater acoustic networks. We extend nearest neighbor regression for SNR time series prediction, providing an outstanding prediction accuracy for intricately periodical and fluctuating received SNR time series. Moreover, we propose a quick search algorithm and use statistical storage compression to optimize time and space complexity of the algorithm. In contrast with linear methods, this algorithm significantly improved channel prediction accuracy (over three times at most) on both simulation and sea trial data sets.