Divergence scaling of fixed-length, binary-output, one-to-one distribution matching
In this work, arithmetic distribution matching (ADM) is presented. ADM invertibly transforms a discrete memoryless source (DMS) into a target DMS. ADM can be used for probabilistic shaping and for rate adaption. Opposed to existing algorithms for distribution matching, ADM works online and can transform arbitrarily long input sequences. It is shown analytically that as the input length tends to infinity, the ADM output perfectly emulates the target DMS with respect to the normalized informational divergence and the entropy rate. Numerical results are presented that confirm the analytical bounds.