Adaptive averaging channel estimation for DVB-T2 systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn modern communication systems, the rate of transmitted data is growing rapidly. This leads to the need for more sophisticated methods and techniques of implementation in every block of the transmitter-receiver chain. The weakest link in radio communications is the transmission channel. The signal, which is passed through it, suffers from many degrading factors like noise, attenuation, diffraction, scattering etc. In the receiver side, the modulated signal has to be restored to its initial state in order to extract the useful information. Assuming that the channel acts like a filter with finite impulse, one has to know its coefficients in order to apply the inverse function, which will restore the signal back to its initial state. The techniques which deal with this problem are called channel estimation. Noise is one of the causes that degrade the quality of the received signal. If it could be discarded, then the process of channel estimation would be easier. Transmitting special symbols, called pilots with known amplitude, phase and position to the receiver and assuming that the noise has zero mean, an averaging process could reduce the noise impact to the pilot amplitudes and thus simplify the channel estimation process. In this thesis, a novel channel estimation method based on noise rejection is introduced. The estimator takes into account the time variations of the channel and adapts its buffer size in order to achieve the best performance. Many configurations of the estimator were tested and at the beginning of the research fixed size estimators were tested. The fixed estimator has a very good performance for channels which could be considered as stationary in the time domain, like Additive White Gaussian Noise (AWGN) channels or slowly time-varying channels. AWGN channel is a channel model where the only distorting factor is the noise, where noise is every unwanted signal interfering with the useful signal. The properties of the noise are that it is additive, which means that the noise is superimposed on the transmitted signal, it is white so the power density is constant for all frequencies, and it has a Gaussian distribution in the time domain with zero mean and variance σ2=N. A slowly time varying channel refers to channel with coherence time larger than the transmitted symbol duration. The performance of a fixed size averaging estimator in case of fast time-varying channels is subject to the buffering time. When the buffering time is smaller or equal to a portion of the coherence time the averaging process offers better performance than the conventional estimation, but when the buffering time exceeds this portion of the coherence time the performance of the averaging process degrades fast. So, an extension has been made to the averaging estimator that estimates the Doppler shift and thus the coherence time, where the channel could be assumed as stationary. The improved estimator called Adaptive Averaging Channel Estimator (AACE) is capable to adjust its buffer size and thus to average only successive Orthogonal Frequency Division Multiplexing (OFDM) symbols that have the same channel distortions. The OFDM is a transmission method where instead of transmitting the data stream using only on carrier, the stream is divided into parallel sub-streams where the subcarriers conveying the sub-streams are orthogonal to each other. The use of the OFDM increases the symbol duration making it more robust against Inter-Symbol Interference (ISI), which the interference among successive transmitted symbols, and also divides the channel bandwidth into small sub-bandwidths preventing frequency selectivity because of the multipath nature of the radio channel. Simulations using the Rayleigh channel model were performed and the results clearly demonstrate the benefits of the AACE in the channel estimation process. The performance of the combination of AACE with Least Square estimation (AACE-LS) is superior to the conventional Least Square estimation especially for low Doppler shifts and it is close to the Linear Minimum Mean Square Error (LMMSE) estimation performance. Consequently, if the receiver has low computational resources and/or the channel statistics are unknown, then the AACE-LS estimator is a valid choice for modern radio receivers. Moreover, the proposed adaptive averaging process could be used in any OFDM system based on pilot aided channel estimation. In order to verify the superiority of the AACE algorithm, quantitative results are provided in terms of BER vs SNR. It is demonstrated that AACE-LS is 7dB more sensitive than the LS estimator

Adaptive Averaging Channel Estimation for DVB-T2 using Doppler Shift information

The problem of channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems in fast timevarying and frequency selective radio channels has attracted the attention of scientists and receiver manufacturers. The wireless communication suffers from Additive White Gaussian Noise (AWGN), fading due to the multipath nature of the channel and spectral dispersion caused by the relative movement of the receiver with respect to the transmitter. In this paper a novel channel estimator for the Next Generation Terrestrial Digital Video Broadcasting (DVB-T2) is proposed. It is based on the estimation of the Doppler Shift (DS) from the variations of the envelope of the edge pilots (EP), in order to estimate the time interval where the channel could be considered as flat and then adaptively adjusting a buffer where the scattered pilots (ScP) carried within the received OFDM symbols during this time interval, are stored and averaged. Because the noise is considered AWGN with zero mean, the averaging process discards the noise and by interpolating the pilots an estimation of the channel is obtained

Performance Comparison of LS, LMMSE and Adaptive Averaging Channel Estimation (AACE) for DVB-T2

In this paper the performance of the Adaptive Averaging Channel Estimator (AACE-LS) which is a modified Least Square (LS) estimator and the AACE-LMMSE which is a modified Linear Minimum Mean Error (LMMSE) estimator, are compared with respect to the conventional LS and the LMMSE estimators. The AACE is an estimator which is based on the averaging of the last N Scattered Pilots (SP) from the DVB-T2 model carried in the received OFDM symbols. The proposed method could in general be applied to any pilot based estimator. The noise introduced by the channel is considered as Additive White Gaussian Noise (AWGN) with zero mean and thus an averaging process is used to eliminate it. The estimator adaptively follows the fluctuations of the amplitude envelope in the time domain and adapts the size of the buffer N, with respect to the coherence time (Tc). Finally, based on the averaged estimated channel, the LS or the LMMSE equalizer is applied to the received signal in the frequency domain. Simulations clearly show that the performance of the AACE-LS is superior to the conventional LS estimator and is near to the performance of the LMMSE with no need of a prior knowledge of the statistics and the noise of the channel and thus if the channel is unknown to the receiver, the AACE is a good choice

Longley-Rice model prediction inaccuracies in the UHF and VHF TV bands in mountainous terrain

Coverage prediction is of prime importance for TV broadcasting. A classic model used for TV coverage prediction is the Longley-Rice ITM (Irregular Terrain Model). Other well-known multiple knife-edge diffraction models are the Epstein-Peterson, Deygout, and Giovaneli methods. In this paper, comparisons are presented between accurate field-strength measurements, taken by a Rohde & Schwarz FSH-3 portable spectrum analyzer using precision calibrated antennas and calculated results from the Longley-Rice model, and the multiple knife-edge models in conjunction with the 3-arc-second SRTM (Satellite Radar Topography Mission) terrain data. Calculations are limited to the main 2 knife-edges of the propagation path. The Longley-Rice model predicts received field strength accurately in most cases even in mountainous terrain with multiple diffracting obstacles in the VHF and UHF TV Bands. However, in some long distance fringe reception areas field-strength is underestimated by the Longley-Rice model, while it is accurately calculated by the multiple knife-edge diffraction models