Soft Metrics and Their Performance Analysis for Optimal Data Detection in the Presence of Strong Oscillator Phase Noise

Abstract

In this paper, we address the classical problem of maximum-likelihood (ML) detection of data in the presence of random phase noise. We consider a system, where the random phase noise affecting the received signal is first compensated by a tracker/estimator. Then the phase error and its statistics are used for deriving the ML detector. Specifically, we derive an ML detector based on a Gaussian assumption for the phase error probability density function (PDF). Further without making any assumptions on the phase error PDF, we show that the actual ML detector can be reformulated as a weighted sum of central moments of the phase error PDF. We present a simple approximation of this new ML rule assuming that the phase error distribution is unknown. The ML detectors derived are also the aposteriori probabilities of the transmitted symbols, and are referred to as soft metrics. Then, using the detector developed based on Gaussian phase error assumption, we derive the symbol error probability (SEP) performance and error floor analytically for arbitrary constellations. Finally we compare SEP performance of the various detectors/metrics in this work and those from literature for different signal constellations, phase noise scenarios and SNR values.

Receiver Algorithm Based on Differential Signaling for SIMO Phase Noise Channels with Common and Separate Oscillator Configurations

“…over receive antennas. 3 In order to detect φ k we perform a maximum likelihood detection as follows:…”

“…These effects are more severe when high-order modulation schemes are used in order to attain high spectral efficiency [3]. It is also known that phase noise in RF oscillators increases with frequency [4], [5].…”

“…Phase synchronization methods in the case of the CLO configuration is similar to that in the case of a single-antenna system [1]- [3], which is followed by data detection that is generally used for MIMO systems [19]. However, designing receiver algorithms for joint phase noise estimation and data detection in the SLO case is more challenging.…”

“…For this scenario, we develop an algorithm that consists of differential transmission, followed by a two-stage amplitude and phase detector for detection of the transmitted signal (see [3] for two-stage detection). Differential transmission can be performed without the aid of pilot symbols, and no additional phase noise estimator/trackers are required at the receiver in order to track the random phase noise.…”

Improving MIMO detection performance in presence of phase noise using norm difference criterion

“…Receiver algorithms for single-antenna systems have been studied in [9]- [11] and the references within. Iterative decoding algorithms in the presence of strong phase noise has been studied in [9].…”