Guided Scrambling Based on Galois Field Addition Operation and Assessment of its Performance

Abstract

A DC component suppressing method, called Guided Scrambling (GS), has been proposed. GS constructs a selection set of channel bit streams by subjecting a source bit stream within a data block to several kinds of scrambling and to RLL (Run Length Limited) coding. The one which has the least DC component is then selected. Typically, this technique uses a convolutional operation or GF (Galois field) multiplication. The convolutional GS is good at suppressing the DC component, but has poor performance in the symbol error rate because a bit error propagates to an adjacent codeword before RS decoding. In the GF multiplicative GS, the error rate is lower, but performance in DC component suppression is worse, especially when a simple hardware configuration is used or when a source bit stream is not random. In this paper, we propose the GF additive GS method which generates a better channel bit stream with both these characteristics. We also analyze the spectrum and the average symbol error rate of this method with computer simulations, and show its advantages over other GSs.