In many communications systems, spread spectrum techniques are used to spread the original message data over a large bandwidth in order to improve system performance in the presence of narrowband interference. The extent to which such interference can be tolerated depends on the system's processing gain and may be augmented using adaptive filtering techniques. In order to mitigate narrowband interference, spread spectrum receivers can incorporate filtering techniques that suppress interference and make bit decisions on the remaining signal energy. In this paper, block transforms and multirate filterbanks based on hierarchical subband trees are used to transform the received data signal to the transform domain wherein adaptive filtering is performed. Pre-and post-correlation transform domain Least-Mean-Squared (LMS) algorithms are employed on a block-by-block basis to suppress the narrowband interference while simultaneously minimizing the mean-squared error between the received signal and the original data message. Convergence and misadjustment noise are evaluated as functions of the underlying subband transform and various system parameters. Subsequent performance analysis of these algorithms is presented in terms of the overall system bit-error-rate. Analytical and simulation results obtained in the presence of single-tone interference sources are presented. Although not considered, the analytical expressions discussed herein can be easily extended to handle other narrowband sources such as multitone jammers and narrowband Gaussian interference.
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