Blind channel estimation for FBMC‐based PMR transmission

Abstract

The paper presents a blind channel estimation algorithm for an filter bank multicarriers waveform in a fast varying channel context for professional mobile radio transmissions. A fourth‐order power elevation algorithm, followed by an auto‐deconvolution step, directly leading to the channel impulse response estimation, is developed. Simulation results are adapted to the Tetra enhanced data services standard context for hilly terrain propagation model. Copyright © 2016 John Wiley & Sons, Ltd.

Performance Comparison of High Order Moments Blind Channel Identification

“…Furthermore, obtained results show that 4 th , 8 th order moment estimation algorithms exhibit outperformance of the 4 th order moments performance in term of BER for the 4-QAM and better performance at high Eb/N0 for the 8 th order moments and 8-QAM configuration. These results comfirm the theritical expression of the estimation error given by (20). A slightly different performance on NMSE is observed.…”

“…Finally and using equation ( 18), (19) and Table I, estimation error can be written as equation (20) (at the end of the sixth page of this article). .…”

“…The simulation results are obtained for a 16-QAM constellation, and using N = 64 OFDM symbols for channel estimation. The BER is steel following the estimated error power given by the equation (20) and illustrated by the figure 2 at high Eb/N0. This joined the hypothesis token in section III of the high number of the averaged OFDM symbols to justify the validity of the Taylor linearization.…”

“…In term of overall error probability of the blind estimation of the propagation channel performed with the presented algorithm, can be given by the product of the error probability estimated from | ℎ | 2 obtained in the equation (20) and the power probability of the inter subcarrier ambiguity resolving derivated from the quantity | − ′| 2 .…”

“…Figure 5. Error probability of π 2 ambiguity resolving versus NMSE.The results showed by figures 6 and 7 are obtained by simulating the 4 th order moment algorithm, the subspace based algorithm[6] and the autocorrelation matrix based algorithm for 4-QAM and 16-QAM modulation consecutively in the propagation conditions described by Hilly terrain channel[20]. The generation of the propagation channel is done according to the Jakes Model[21].…”