Generalized soft decision metric generation for MPSK/MQAM without noise variance knowledge

In this paper, a new simplified soft decision metric, without noise variance knowledge, is derived. By recognizing that it is simply a distance metric in Euclidian space, we further generalize it to p-norm. Performance comparisons are presented for LDPC coded 8PSK/16QAM with different parameters. Simulation results show that, when compared with the simplified soft decision metric reported in [F. Tosato et al. 2002], there is no performance loss with our proposed generalized soft decision metric. Moreover, it is unnecessary to estimate noise variance at the channel output, which greatly facilitates practical implementation. Further simulation indicates that, at high signal to noise ratio, a small performance gain can be obtained with p a bit less than 2.