1 research outputs found
Constellation Optimization in the Presence of Strong Phase Noise
In this paper, we address the problem of optimizing signal constellations for
strong phase noise. The problem is investigated by considering three
optimization formulations, which provide an analytical framework for
constellation design. In the first formulation, we seek to design
constellations that minimize the symbol error probability (SEP) for an
approximate ML detector in the presence of phase noise. In the second
formulation, we optimize constellations in terms of mutual information (MI) for
the effective discrete channel consisting of phase noise, additive white
Gaussian noise, and the approximate ML detector. To this end, we derive the MI
of this discrete channel. Finally, we optimize constellations in terms of the
MI for the phase noise channel. We give two analytical characterizations of the
MI of this channel, which are shown to be accurate for a wide range of
signal-to-noise ratios and phase noise variances. For each formulation, we
present a detailed analysis of the optimal constellations and their performance
in the presence of strong phase noise. We show that the optimal constellations
significantly outperform conventional constellations and those proposed in the
literature in terms of SEP, error floors, and MI.Comment: 10 page, 10 figures, Accepted to IEEE Trans. Commu