Efficient equalizer update algorithms for acoustic communication channels of varying complexity

Underwater acoustic communication channels vary from stationary with sparse arrivals to rapidly varying and fully reverberant. A receiver structure capable of operating over this wide range of characteristics has evolved and consists of a fractionally-spaced multi-channel combiner, a sparse DFE, and an embedded PLL. At the computational heart of this receiver is an adaptation algorithm, used to update the various equalizer parameters, The adaptation algorithm may use one of the standard strategies such as adaptive step-size LMS, RLS, adaptive memory RLS, or a hybrid of these. The choice of algorithm involves trade-offs between training time, tracking-rate, operating SNR, and computational requirements of the receiver. To maximize the receiver throughput, we wish to select the least computational algorithm for which the error-rate is still acceptable and, given the volatility of the underwater channel, this choice should be made for each received packet, In this paper, we compare the performance of several LMS and RLS adaptation algorithms over a range of real acoustic data. We show that the computationally-efficient adaptive step-size LMS performs well for many channel classes, but that the faster tracking of RLS is essential for some complex non-stationary channels. The results provide a first step towards the automatic selection of adaptation algorithm, as would be required in a fully autonomous receiver