The DC component suppressing method, called Guided Scrambling (GS), has been proposed, where a source bit stream within a data block is scrambled into many kinds of random bit streams as candidates for the next channel bit stream, and one of them having least DC component is selected. The convolutional GS and the GF (Galois field) multiplicative GS are known as typical techniques in some GSs. The convolutional GS has high performance in the DC component suppressing capability, but has low performance in the symbol error rate because a bit error propagates an adjacent codeword before RS decoding. On the GF multicative GS, the error rate is lower, but performance in DC component suppression is worse, especially when easing the constitution of the hardware. In this paper, we propose the GF additive GS method which generates the better channel bit stream in both characteristics. We also analyze the spectrum and the average symbol error rate of this method with computer simulation, and show its good characteristics by comparing with other GSs.
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