Adaptive multichannel decision feedback equalization using subarray processing

Abstract

The adaptive multichannel Decision Feedback Equalizer (DFE) has been shown to be an effective algorithm for enabling reliable high rate acoustic communications in complex and time-varying underwater environments. The choice of the number of channels used in an equalizer presents a performance trade-off. The minimal achievable error that can be realized by the equalizer decreases as the number of channels increases. However, an increase in the number of channels increases the number of filter weights that need to be adapted. Thus, the computational complexity of least squares and Kalman type adaptation algorithms is proportional to the number of channels squared. In addition, the averaging interval required by an adaptation algorithm in order to achieve good performance grows linearly with the number of parameters. Thus, an increase in the number of M-DFE channels can reduce the rate of channel fluctuation that can successfully tracked by the M-DFE. The partitioning of an array into sub-arrays which are each independently equalized before combining their outputs can both improve performance and reduce complexity in processing real-world signals. The choice of the size and sensor locations for the subarrays is analyzed. The resulting adaptive subarray multichannel DFE algorithm is compared to other multichannel equalization algorithms.