The demands of multimedia services from mobile user equipment (UE) for achieving high data rate, high capacity and reliable communication in modern mobile wireless systems are continually ever-growing. As a consequence, several technologies, such as the Universal Mobile Telecommunications System (UMTS) and the 3rd Generation Partnership Project (3GPP), have been used to meet these challenges. However, due to the channel fading and the Doppler shifts caused by user mobility, a common problem in wireless systems, additional technologies are needed to combat multipath propagation fading and Doppler shifts. Time-variant channel estimation is one such crucial technique used to improve the performance of the modern wireless systems with Doppler spread and multipath spread. One of vital parts of the mobile wireless channel is channel estimation, which is a method used to significantly improve the performance of the system, especially for 4G and Long Term Evolution (LTE) systems. Channel estimation is done by estimating the time-varying channel frequency response for the OFDM symbols. Time-variant channel estimation using Discrete Prolate Spheroidal Sequences (DPSS) technique is a useful channel estimation technique in mobile wireless communication for accurately estimating transmitted information. The main advantage of DPSS or Slepian basis expansion is allowing more accurate representation of high mobility mobile wireless channels with low complexity. Systems such as the fourth generation cellular wireless standards (4G), which was recently introduced in Sweden and other countries together with the Long Term Evolution, can use channel estimation techniques for providing the high data rate in modern mobile wireless communication systems. The main goal of this thesis is to test the recently proposed method, time-variant channel estimation using Discrete Prolate Spheroidal Sequences (DPSS) to model the WINNER phase II channel model. The time-variant sub-carrier coefficients are expanded in terms of orthogonal DPS sequences, referred to as Slepian basis expansions. Both Slepian basis expansions and DPS sequences span the low-dimensional subspace of time-limited and band-limited sequences as Slepian showed. Testing is done by using just two system parameters, the maximum Doppler frequency Dmax v and K, the number of basis functions of length N = 256. The main focus of this thesis is to investigate the Power spectrum and channel gain caused by Doppler spread of the WINNER II channel model together with linear fitting of curves for both the Slepian and Fourier basis expansion models. In addition, it investigates the Mean Square Error (MSE) using the Least Squares (LS) method. The investigation was carried out by simulation in Matlab, which shows that the spectrum of the maximum velocity of the user in mobile wireless channel is upper bounded by the maximum normalized one-sided Doppler frequency. Matlab simulations support the values of the results. The value of maximum Doppler bandwidth vDmax  of the WINNER model is exactly the same value as DPS sequences. In addition to the Power spectrum of the WINNER model, the fitting of Slepian basis expansion performs better in the WINNER model than that of the Fourier basis expansion.

During processing of data received by an airborne radar one of the issues is that the typical signal echo from the ground produces a large perturbation. Due to this perturbation it can be difficult to detect targets with low velocity or a low signal-to-noise ratio. Therefore, a filtering process is needed to separate the large perturbation from the target signal. The traditional method include a tapered Fourier transform that operates in parallel with a MTI filter to suppress the main spectral peak in order to produce a smoother spectral output. The difference between a typical signal echo produced from an object in the environment and the signal echo from the ground can be of a magnitude corresponding to more than a 60 dB difference. This thesis presents research of how the multitaper approach can be utilized in concurrence with the minimum variance estimation technique, to produce a spectral estimation that strives for a more effective clutter suppression. A simulation model of the ground clutter was constructed and also a number of simulations for the multitaper, minimum variance estimation technique was made. Compared to the traditional method defined in this thesis, there was a slight improvement of the improvement factor when using the multitaper approach. An analysis of how variations of the multitaper parameters influence the results with respect to minimum detectable velocity and improvement factor have been carried out. The analysis showed that a large number of time samples, a large number of tapers and a narrow bandwidth provided the best result. The analysis is based on a full factorial simulation that provides insight of how to choose the DPSS parameters if the method is to be implemented in a real radar system.

In this dissertation, channel estimation and channel prediction are studied for wireless communication systems. Wireless communication for time-variant channels becomes more important by the fast development of intelligent transportation systems which motivates us to propose a reduced rank channel estimator for time-variant frequency-selective high-speed railway (HSR) systems and a reduced rank channel predictor for fast time-variant flat fading channels. Moreover, the potential availability of large bandwidth channels at mm-wave frequencies and the small wavelength of the mm-waves, offer the mm-wave massive multiple-input multiple-output (MIMO) communication as a promising technology for 5G cellular networks. The high fabrication cost and power consumption of the radio frequency (RF) units at mm-wave frequencies motivates us to propose a low-power hybrid channel estimator for mm-wave MIMO orthogonal frequency-division multiplexing (OFDM) systems. The work on HSR channel estimation takes advantage of the channel's restriction to low dimensional subspaces due to the time, frequency and spatial correlation of the channel and presents a low complexity linear minimum mean square error (LMMSE) estimator for MIMO-OFDM HSR channels. The channel estimator utilizes a four-dimensional (4D) basis expansion channel model obtained from band-limited generalized discrete prolate spheroidal (GDPS) sequences. Exploiting the channel's band-limitation property, the proposed channel estimator outperforms the conventional interpolation based least square (LS) and MMSE estimators in terms of estimation accuracy and computational complexity, respectively. Simulation results demonstrate the robust performance of the proposed estimator for different delay, Doppler and angular spreads. Channel state information (CSI) is required at the transmitter for improving the performance gain of the spatial multiplexing MIMO systems through linear precoding. In order to avoid the high data rate feedback lines, which are required in fast time-variant channels for updating the transmitter with the rapidly changing CSI, a subframe-wise channel tracking scheme is presented. The proposed channel predictor is based on an assumed DPS basis expansion model (DPS-BEM) for exploiting the variation of the channel coefficients inside each sub-frame and an autoregressive (AR) model of the basis coefficients over each transmitted frame. The proposed predictor properly exploits the channel's restriction to low dimensional subspaces for reducing the prediction error and the computational complexity. Simulation results demonstrate that the proposed channel predictor out-performs the DPS based minimum energy (ME) predictor for different ranges of normalized Doppler frequencies and has better performance than the conventional Wiener predictor for slower time-variant channels and almost the similar performance to it for very fast time-variant channels with the reduced amount of computational complexity. The work on the hybrid mm-wave channel estimator considers the sparse nature of the mm-wave channel in angular domain and leverages the compressed sensing (CS) tools for recovering the angular support of the MIMO-OFDM mm-wave channel. The angular channel is treated in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS based channel estimators. The power leakage problem is also addressed by modeling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage. The RF combiner is designed to be implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the effectiveness of the proposed hybrid channel estimator both in terms of the estimation accuracy and the RF power consumption. / Graduate