Optimal and Approximate Methods for Detection of Uncoded Data with Carrier Phase Noise
Paper in proceeding, 2011

Previous results in the literature have shown that derivation of the optimum \textit{maximum-likelihood} (ML) receiver for \textit{symbol-by-symbol} (SBS) detection of an uncoded data sequence in the presence of \textit{random phase noise} is an intractable problem, since it involves the computation of the conditional \textit{probability distribution function} (PDF) of the phase noise process. In this paper, we seek to minimize \textit{symbol error probability} (SEP), which is achieved by SBS detection of the sequence based on all received signals. We show that the ML detector for this problem can be formulated as a weighted sum of central moments of the conditional PDF of phase noise. Given that the central moments of the conditional PDF of phase noise can be estimated, this new optimal structure is tractable with respect to the previously known optimal ML receiver. Furthermore, based on the new receiver structure, we propose a simple approximate method for SBS detection and investigate its scope and applicability. Simulation results demonstrate that SEP performance close to optimality can be obtained through the proposed method for scenarios of low phase noise variance and low \textit{signal-to-noise ratio} (SNR).

coding

Modulation

and diversity techniques for wireless

Detection and estimation

Physical-layer aspects of cellular networks

Author

Rajet Krishnan

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

Hani Mehrpouyan

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

Thomas Eriksson

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

Tommy Svensson

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

IEEE Globecom 2011

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

More information

Created

10/7/2017