Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying
Self-Interference Channel
Abstract
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.