Channel estimation of data reuse proportional RLS for single-carrier UWA communication

To enhance the channel estimation accuracy in single-carrier underwater acoustic (UWA) communication, we propose integrating data reuse (DR) and proportional updating into the recursive least squares (RLS) algorithm, designing the DR proportional RLS (DRPRLS) algorithm. By using system sparsity, the convergence speed of the DRPRLS algorithm is accelerated. Additionally, the gain factor is incorporated into the weight update of the DRPRLS algorithm, computed from the reused input and reference signals, further improving the estimation accuracy. We perform numerical simulations of the proposed DRPRLS algorithm and compared it with least mean squares (LMS), improved normalized LMS, RLS, proportional RLS, and DRRLS algorithms, considering computational complexity and tracking performance. In the at-sea experiments, we obtain the bit error rates (BER) driven by different algorithms through the minimum mean square error equalizer. Real data indicates that the DRPRLS algorithm improves the BER performance.