Non-Pilot-Aided Sequential Monte Carlo Method to Joint Signal, Phase Noise, and Frequency Offset Estimation in Multicarrier Systems

Abstract

We address the problem of phase noise (PHN) and carrier frequency offset (CFO) mitigation in multicarrier receivers. In multicarrier systems, phase distortions cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the symbol detection stage. Here, we propose a non-pilot-aided scheme to jointly estimate PHN, CFO, and multicarrier signal in time domain. Unlike existing methods, non-pilot-based estimation is performed without any decision-directed scheme. Our approach to the problem is based on Bayesian estimation using sequential Monte Carlo filtering commonly referred to as particle filtering. The particle filter is efficiently implemented by combining the principles of the Rao-Blackwellization technique and an approximate optimal importance function for phase distortion sampling. Moreover, in order to fully benefit from time-domain processing, we propose a multicarrier signal model which includes the redundancy information induced by the cyclic prefix, thus leading to a significant performance improvement. Simulation results are provided in terms of bit error rate (BER) and mean square error (MSE) to illustrate the efficiency and the robustness of the proposed algorithm.

Impulsive interference mitigation in ad hoc networks based on alpha-stable modeling and particle filtering

“…In this paper, we consider a dynamic state space model, widely used in communication, to represent OFDM systems [8].…”

On the accuracy of phase noise bandwidth estimation in OFDM systems

“…In [13], [17], [18], the authors propose an insightful approach to the joint estimation problem, by taking into account the statistical properties of PHN 2 . Considering PHN in a freerunning oscillator, PHN is modelled by a continuous Brownian motion process:…”

“…Assuming perfect synchronization at the beginning of the OFDM symbol, the initial condition of the respective Brownian Motion is φ −(Ng+1) = 0 [8], [13], [17], [18], and hence the PHN can be expressed as:…”

“…This allows one to express the system (after CP removal) in the form of a dynamic state-space model [17], [18]:…”

“…Once CFO has been compensated, CIR and PHN are jointly estimated by employing the maximum a posteriori (MAP) criterion, considering all the unknowns as parameters instead of random variables. In [17], it is assumed that the parameters satisfy a random walk, and particle filtering is used for a non-pilot-aided joint estimation scheme of PHN, CFO and the transmitted signal. In [18], joint CIR, CFO, PHN and noise variance estimation is proposed, based on Monte-Carlo methods and the EM algorithm, with different strategies for online and offline estimation.…”