The need for underwater wireless communications exists in many applications such as unmanned underwater vehicles, speech transmission between divers, defense and collection of data recorded at ocean-bottom stations.
The project goal is to implement an underwater acoustic channel estimator based on sparsity assumption. We compare the performance of this estimator to the performance of other channel estimators in terms of estimation error, error robustness, bitrate and computational complexity.
Underwater acoustic channels pose grand challenges for effective communications due to their multipath, significant Doppler shifts and rapidly changing channel. Underwater acoustic channels are characterized by a small number of paths. Each path is characterized by Doppler shift, delay and amplitude attenuation. This kind of channel has a sparse transfer function and we want to exploit this character.
The existing solution uses the least squares method. The disadvantages of this method are: the addition of delays that doesn't exist, no differentiation between close paths, Doppler compensation that does no differentiate between paths and the sparsity characteristic is not used. All those factors led us to investigate and explore other methods.
We analyze the performance of three channel estimators: the least squares one tap estimator versus two sparse estimation methods. Both methods are based on the orthogonal matching pursuit algorithm and its extension to narrow search grids. We then suggest a modification on the latter by adding a second step, where the channel attenuations are calculated by performing iterations of the least squares algorithm to reduce the inter-carrier-Interference effects. The performance of all four algorithms is tested in simulation. Comparison to the LS and OMP algorithms indicates an improvement in terms of mean squared error and bit error rate.
RAFAEL Dr. Alon Amar