In this paper a soft-input soft-output (SISO) QAM demodulator with robust performance on imperfect channel
information are proposed for bit-interleaved OFDM systems. A full Bayesian approach is proposed to the channel
estimation and demodulation problem. The frequency-selective fading channel impulse response and AWGN
variance encountered in OFDM systems are jointly modeled as complex Gaussian-gamma random variables.
The uncertainty of the channels are naturally encoded in the posterior distribution. Robust demodulators for
known and unknown AWGN variance are derived basing on Bayesian posterior predictive distribution. It's
performance combined with the bit-interleaved coded modulation (BICM) is demonstrated. And schemes with
reduced complexity are also discussed. Simulation results show an improved BER (more than 0:5dB in most
cases) comparing to that of the conventional demodulators ignorant of the channel estimation errors.
KEYWORDS: Orthogonal frequency division multiplexing, Doppler effect, Compressed sensing, Space based lasers, Reconstruction algorithms, Telecommunications, Detection and tracking algorithms, Data communications, Computer simulations, Systems modeling
In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the
time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that
wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order
to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality,
the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed
channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian
learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large
time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be
avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.
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