A particle filter for frequency synchronization in MIMO-OFDM systems

Authors:
Zhang Jing;Luo Han-wen;Jin Rong-hong;Zhang Jing
Affiliations:
Dept. Electronic Engineering, Shanghai Jiao Tong University, Shanghai, PRC;Dept. Electronic Engineering, Shanghai Jiao Tong University, Shanghai, PRC;Dept. Electronic Engineering, Shanghai Jiao Tong University, Shanghai, PRC;Dept. Communication Engineering, Shanghai Normal University, Shanghai, PRC
Venue:
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Year:
2009

Citing 9
Cited 0

On sequential Monte Carlo sampling methods for Bayesian filtering

Statistics and Computing
A sequential Monte Carlo technique for blind synchronization and detection in frequency-flat Rayleigh fading wireless channels

Signal Processing - Signal processing in communications
Joint Time-Domain Tracking of Channel and Frequency Offsets for MIMO OFDM Systems

Wireless Personal Communications: An International Journal
Adaptive blind multiuser detection over flat fast fading channels using particle filtering

EURASIP Journal on Wireless Communications and Networking - Special issue on advanced signal processing algorithms for wireless communications
Particle filtering for joint symbol and code delay estimation in DS spread spectrum systems in multipath environment

EURASIP Journal on Applied Signal Processing
A blind particle filtering detector of signals transmitted over flat fading channels

IEEE Transactions on Signal Processing
Blind estimation of OFDM carrier frequency offset via oversampling

IEEE Transactions on Signal Processing
A sequential Monte Carlo method for adaptive blind timing estimation and data detection

IEEE Transactions on Signal Processing - Part I
ML estimation of time and frequency offset in OFDM systems

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A particle filter algorithm is investigated to perform joint estimation of carrier frequency offset (CFO) and channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless communication systems. The filter marginalizes out the channel parameters from the sampling space in sequential importance sampling, and propagates them with the Kalman filter. Then the importance weights of CFO particles are evaluated according to the imaginary part of error between measurement and estimate. Moreover, the varieties of particles are maintained by sequential importance resampling. Simulations demonstrate the algorithm can estimate the CFO and channel with high accuracy, as well as has some robustness to the channel model variation.