Parameter Estimation of Convolutional and Helical Interleavers in a Noisy Environment

Abstract

Forward error correction (FEC) codes followed by an interleaver play a significant role in improving the error performance of the digital systems by counteracting random and burst errors. In most of the applications, interleaver and FEC code parameters are known at the receiver to successfully de-interleave and decode the information bits. However, in certain non-cooperative applications, only partial information about the code and interleaver parameters is known. Furthermore, in cognitive radio applications, an intelligent receiver should adapt itself to the transmission parameters. Hence, there is a need to blindly estimate the FEC code and interleaver parameters in the mentioned applications from the received data stream with the availability of partial knowledge about the transmission parameters at the receiver. In this paper, a blind recognition of convolutional and helical interleaver parameters is carried out using innovative algorithms for unsynchronized, convolutionally encoded data in the presence of bit errors. In addition, the proposed algorithms also estimate the starting bit position for achieving proper synchronization. In a nutshell, it has been observed from the numerical results that the interleaver parameters have been estimated successfully over erroneous channel conditions from the proposed algorithms. Finally, the performances of the proposed algorithms for both the interleavers considering various bit error rate values have also been analyzed.