Semi-blind Channel Equalization Research Articles

Estimation and equalization for shallow water communication channel using geometric encoding of channel multipath

The shallow water acoustic channel is well-known to exhibit rapid fluctuations in its impulse response, which is challenging to estimate and compensate for in real time. In particular, the moving ocean surface and static sea bottom reflect the acoustic signal in unpredictable ways to create rapidly time-varying multipath arrivals. The talk will focus on the nexus of shallow water acoustic propagation paths and how multipath can be discovered and exploited to improve shallow water acoustic communications. In particular, the talk will review sampling strategies for the time-varying shallow water acoustic channel and propose novel geometric encoding techniques that exploit non-uniform channel sampling strategies. We will also demonstrate how geometric encoding can be harnessed for real-time channel estimation as well as semi-blind channel equalization. Results based on experimental field data in recent work as well simulation results will be presented.

Semi-blind identification of ARMA systems using a dynamic-based approach

A novel dynamic-based semi-blind approach is proposed to identify an autoregressive and moving average (ARMA) system in this paper. By using a chaotic driving signal, an ARMA system can be identified accurately by a dynamic-based estimation method called the ergodic-based minimum phase space volume (EMPSV). A maximum-likelihood formulation of EMPSV is provided to certify its unbiasedness and asymptotical efficiency. Monte Carlo simulations show that the EMPSV approach has a smaller mean-square error performance than the minimum phase space volume method and the conventional identification approach based on least-squares estimation with white Gaussian probing signals. The proposed approach is then applied to blind deconvolution of real audio signals and semi-blind channel equalization for chaos communications. It is shown that the EMPSV approach has improved deconvolution and equalization performances compared to conventional techniques in both applications.