Blind Identification of Sparse Systems Using Symbolic Dynamics Encoding

The unique properties of chaotic signals have led to their application in improving blind system identification performance. However, the role of chaos in blind identification of a sparse system has not been investigated. In this letter, we apply symbolic dynamics to encode a random signal to reap the benefits of chaos in improving blind identification of a sparse Moving Average (MA) system. We derive an estimation technique using the encoded signal by training a machine learning model that mimics a chaotic map. The novelty of our work is to exploit the merits of chaos in improving blind estimation performance of sparse systems at low signal-to-noise (SNR) ratio. The estimation error of our method is close to the minimum mean square error of the nonblind method for sparse system estimation and works well for a short data sequence.