Adaptive and efficient nonlinear channel equalization for underwater acoustic communication

Abstract

We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance. Due to the high complexity of nonlinear equalizers and poor performance of linear ones, to equalize highly difficult underwater acoustic channels, we employ piecewise linear equalizers. However, in order to achieve the performance of the best piecewise linear model, we use a tree structure to hierarchically partition the space of the received signal. Furthermore, the equalization algorithm should be completely adaptive, since due to the highly non-stationary nature of the underwater medium, the optimal MSE equalizer as well as the best piecewise linear equalizer changes in time. To this end, we introduce an adaptive piecewise linear equalization algorithm that not only adapts the linear equalizer at each region but also learns the complete hierarchical structure with a computational complexity only polynomial in the number of nodes of the tree. Furthermore, our algorithm is constructed to directly minimize the final squared error without introducing any ad-hoc parameters. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.

Online Nonlinear Modeling via Self-Organizing Trees

“…In this section, we illustrate the performance of SOTs under different scenarios with respect to stateof-the-art methods. The proposed method has a wide variety of application areas, such as channel equalization [26], underwater communications [27], nonlinear modeling in big data [28], speech and texture analysis [29,Chapter 7] and health monitoring [30]. Yet, in this section, we consider nonlinear modeling for fundamental regression and classification problems.…”

Improved Channel Equalization using Deep Reinforcement Learning and Optimization

“…In such circumstances, non-linear equalizers are employed to attain the least Bit Error Rate (BER) and Mean Squared Error (MSE) which was not possible with the traditional linear equalization technique. In this context, Kari et al had evaluated the effectiveness of the nonlinear channel equalization technique that proved to be highly efficient for acoustic communication with improved MSE [6]. Following this, Katwal et al had taken advantage of adaptive filers for channel equalization that were improved based on Cuckoo Search (CS) optimization strategies followed by neural network architecture [7].…”

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

“…The parameters of this receiver are adaptively adjusted (Stojanovic et al, 1994). An adaptive nonlinearity (piecewise linear) was introduced into the channel equalization algorithm and its effectiveness was demonstrated through highly realistic experiments conducted on real-field data as well as accurate simulations of UWA channels (Kari et al, 2017). In recent years, in order to alleviate propagation errors, expedite convergence speed, and further enhance receiver performance, there has been growing research on adaptive turbo equalization (He et al, 2019;Xi et al, 2019;Qin et al, 2020).…”