Abstract:

To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.