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

Automatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.

This thesis is concerned with channel estimation and data detection of MIMO-OFDM communication systems using Space-Time Block Coding (STBC) and Space-Frequency Block Coding (SFBC) under frequency selective channels. A new iterative joint channel estimation and signal detection technique for both STBC-OFDM and SFBC-OFDM systems is proposed. The proposed algorithm is based on a processive sequence of events for space time and space frequency coding schemes where pilot subcarriers are used for channel estimation in the first time instant, and then in the second time instant, the estimated channel is used to decode the data symbols in the adjacent data subcarriers. Once data symbols are recovered, the system recursively performs a new channel estimation using the decoded data symbols as pilots. The iterative process is repeated until all MIMO-OFDM symbols are recovered. In addition, the proposed channel estimation technique is based on the maximum likelihood (ML) approach which offers linearity and simplicity of implementation. Due to the orthogonality of STBC and SFBC, high computation efficiency is achieved since the method does not require any matrix inversion for estimation and detection at the receiver. Another major novel contribution of the thesis is the proposal of a new group decoding method that reduces the processing time significantly via the use of sub-carrier grouping for transmitted data recovery. The OFDM symbols are divided into groups to which a set of pilot subcarriers are assigned and used to initiate the channel estimation process. Designated data symbols contained within each group of the OFDM symbols are decoded simultaneously in order to improve the decoding duration. Finally, a new mixed STBC and SFBC channel estimation and data detection technique with a joint iterative scheme and a group decoding method is proposed. In this technique, STBC and SFBC are used for pilot and data subcarriers alternatively, forming the different combinations of STBC/SFBC and SFBC/STBC. All channel estimation and data detection methods for different MIMO-OFDM systems proposed in the thesis have been simulated extensively in many different scenarios and their performances have been verified fully.

[pt] Os sistemas de transmissão em bloco, permitem a transmissão de N símbolos de forma simultânea, seja em modulação de portadora única ou multiportadora. A recepção ótima, no sentido de máxima verossimilhança em canais com multipercursos apresenta um custo computacional elevado de AN, onde A é a ordem da constelação (A igual a 2 para BPSK). Para evitar este alto custo computacional é usual fazer a detecção símbolo a símbolo após a equalização. Nesta dissertação é proposto um receptor com detecção por grupos que apresenta uma complexidade intermediária entre o receptor ótimo e os receptores que utilizam detecção símbolo-a-símbolo em sistemas com transmissão em blocos. O tipo de estrutura idealizada agrupa as componentes do bloco equalizado em grupos e realiza detecção conjunta ótima dos símbolos em cada grupo. Com relação possíveis estratégias de agrupamento foram propostos três métodos, o primeiro método faz uma busca exaustiva pelo agrupamento ótimo e tem como consequência um custo computacional elevado para um número grande de símbolos por bloco. Na procura por algoritmos que evitem uma busca exaustiva pelo agrupamento ótimo, mas que resultem em bons ganhos de desempenho, e a sua aplicação em sistemas com um número elevado de símbolos por bloco, foram propostos dois métodos de agrupamento sub-ótimos e eficientes, cujos receptores apresentaram ganhos de desempenho apreciáveis quando comparados ao receptor convencional. / [en] Block transmission systems allow transmissions of N symbols simultaneously, with single carrier or multi-carrier modulation. Maximum likelihood optimal reception in multipath channels have a high computational cost of AN, where A is the constellation order (A iqual 2 for BPSK). To avoid this cost is usual to make symbol-by-symbol detection after equalization. In this work we propose a receiver with group detection that has a good tradeof between computation complexity and bit error rate performance. The idealized structure groups the components of the equalized block in sub-blocks and does optimal joint detection of the symbols in each sub-block. With relation to possible grouping strategies three methods were proposed. The first one searchs for an optimal grouping and has, as a consequence, a high computational cost for block with a large number of symbols. Sub-optimal efficient algorithms that avoid the exhaustive search for the optimal grouping but show good performance gains and feasible application in systems with large number of symbols per block were proposed. The resulted receivers achieved substantial performance gain in comparison with the conventional symbol-by-symbol receiver.

Nowadays, several studies are launched for the design of reliable and safe communications systems that introduce Unmanned Aerial Vehicle (UAV), this paves the way for UAV communication systems to play an important role in a lot of applications for non-segregated military and civil airspaces. Until today, rules for integrating commercial UAVs in airspace still need to be defined, the design of secure, highly reliable and cost effective communications systems still a challenging task. This thesis is part of this communication context. Motivated by the rapid growth of UAV quantities and by the new generations of UAVs controlled by satellite, the thesis aims to study the various possible UAV links which connect UAV/aircraft to other communication system components (satellite, terrestrial networks, etc.). Three main links are considered: the Forward link, the Return link and the Mission link. Due to spectrum scarcity and higher concentration in aircraft density, spectral efficiency becomes a crucial parameter for largescale deployment of UAVs. In order to set up a spectrally efficient UAV communication system, a good understanding of transmission channel for each link is indispensable, as well as a judicious choice of the waveform. This thesis begins to study propagation channels for each link: a mutipath channels through radio Line-of-Sight (LOS) links, in a context of using Meduim Altitude Long drones Endurance (MALE) UAVs. The objective of this thesis is to maximize the solutions and the algorithms used for signal reception such as channel estimation and channel equalization. These algorithms will be used to estimate and to equalize the existing muti-path propagation channels. Furthermore, the proposed methods depend on the choosen waveform. Because of the presence of satellite link, in this thesis, we consider two low-papr linear waveforms: classical Single-Carrier (SC) waveform and Extented Weighted Single-Carrier Orthogonal Frequency-Division Multiplexing (EW-SC-OFDM) waveform. channel estimation and channel equalization are performed in the time-domain (SC) or in the frequency-domain (EW-SC-OFDM). UAV architecture envisages the implantation of two antennas placed at wings. These two antennas can be used to increase diversity gain (channel matrix gain). In order to reduce channel equalization complexity, the EWSC- OFDM waveform is proposed and studied in a muti-antennas context, also for the purpose of enhancing UAV endurance and also increasing spectral efficiency, a new modulation technique is considered: Spatial Modulation (SM). In SM, transmit antennas are activated in an alternating manner. The use of EW-SC-OFDM waveform combined to SM technique allows us to propose new modified structures which exploit exces bandwidth to improve antenna bit protection and thus enhancing system performances.

The power usage of the communication technology industry and the consistent energy-related pollution are becoming major societal and economic concerns. These concern stimulated academia and industry to an intense activity in the new research area of green cellular networks. Bandwidth Efficiency (BE) is one of the most important metrics to select candidate technologies for next-generation wireless communications systems. Nevertheless, the important goal is to design new innovative network architecture and technologies needed to encounter the explosive development in cellular data demand without increasing the power consumption. As a result, Energy Efficiently (EE) has become another significant metric for evaluating the performance of wireless communications systems. MIMO technology has drawn lots of attention in wireless communication, as it gives substantial increases in link range and throughput without an additional increase in bandwidth or transmits power. Multi-user MIMO (MU-MIMO) regarded when evolved Base Station equipped with multiple antennas communicates with several User Terminal (UEs) at the same time. MU-MIMO is capable of improving either the reliability or the BE by improving either the multiplexing gains or diversity gains. A proposed new idea in MU-MIMO refers to the system that uses hundreds of antennas to serve dozens of UEs simultaneously. This so-called, Large Scale-MIMO (LS MIMO) regarded as a candidate technique for future wireless communication systems. An analysis is conducted to investigate the performance of the proposed uplink and downlink of LS MIMO systems with different linear processing techniques at the base station. The most common precoding and receive combining are considered: minimum mean squared error (MMSE), maximum ratio transmission/combining (MRT/MRC), and zero-forcing (ZF)processing. The fundamental problems answered on how to select the number of (BS) antennas M, number of active (UEs) K, and the transmit power to cover a given area with maximal EE. The EE is defined as the number of bits transferred per Joule of energy. A new power consumption model is proposed to emphasise that the real power scales faster with M and K than scaling linearly. The new power consumption model is utilised for deriving closed-form EE maximising values of the number of BS antennas, number of active UEs and transmit power under the assumption that ZF processing is deployed in the uplink and downlink transmissions for analytic convenience. This analysis is then extended to the imperfect CSI case and to symmetric multi-cell scenarios. These expressions provide valuable design understandings on the interaction between systems parameters, propagation environment, and different components of the power consumption model. Analytical results are assumed only for ZF with perfect channel state information (CSI) to compute closed-form expression for the optimal number of UEs, number of BS antennas, and transmit power. Numerical results are provided (a) for all the investigated schemes with perfect CSI and in a single-cell scenario; (b) for ZF with imperfect CSI, and in a multi-cell scenario. The simulation results show that (a) an LS MIMO with 100 - 200 BS antennas are the correct number of antennas for energy efficiency maximisation; (b) these number of BS antennas should serve number of active UEs of the same size; (c) since the circuit power increases the transmit power should increase with number of BS antennas; (d) the radiated power antenna is in the range of 10-100 mW and decreases with number of BS antennas; (e) ZF processing provides the highest EE in all the scenarios due to active interference-suppression at affordable complexity. Therefore, these are highly relevant results that prove LS MIMO is the technique to achieve high EE in future cellular networks.

Aeronautical telemetry suffers from multipath interference, which can be resolved through the use of equalizers at the receiver. The coefficients of data-aided equalizers are computed from a channel estimate. Most channels seen in aeronautical telemetry are sparse, meaning that most of the coefficients of the channel are zero or nearly zero. The maximum likelihood (ML) estimate does not always produce a sparse channel estimate. This thesis surveys a number of sparse estimation algorithms that produce a sparse channel estimate and compares the post-equalizer bit error rates (BER) using these sparse estimates with the post-equalizer BER using the ML estimate. I show that the generalized Orthogonal Matching Pursuit (GOMP) performs the best followed by the Sparse Estimation based on Validation Re-estimated Least Squares (SPARSEVA-RE) and the Least Absolute Shrinkage and Selection Operator (LASSO).

New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these parsimonious channel models, low complexity receivers such as RAKE receivers are considered to exploit these propagation effects and enhance the system performance. We consider the scenario where multipath is frequency selective slowly fading and where the channel components including delays and attenuation coefficients are assumed to be constant over one or few signalling intervals. We model the channel as a long FIR-like filter (or a tapped delay line filter) with the number of taps related to the ratio between the channel delay-spread and the chip duration. Due to the high data rate of new CDMA systems, the channel length in terms of the chip duration will be very large. With classical channel estimation techniques this will result in poor estimates of many of the channel parameters where most of them are zero leading to a reduction in the system performance. Unlike classical techniques which estimate directly the channel response given the number of taps or given an estimate of the channel length, the proposed techniques in this work will firstly identify the significant multipath parameters using model selection techniques, then estimate these identified parameters. Statistical tests are proposed to determine whether or not each individual parameter is significant. A low complexity RAKE receiver is then considered based on estimates of these identified parameters only. The level of significance with which we will make this assertion will be controlled based on statistical tests such as multiple hypothesis tests. Frequency and time domain based approaches and model selection techniques are proposed to achieve the above proposed objectives. / The frequency domain approach for parsimonious channel estimation results in an efficient implementation of RAKE receivers in DS-CDMA systems. In this approach, we consider a training based strategy and estimate the channel delays and attenuation using the averaged periodogram and modified time delay estimation techniques. We then use model selection techniques such as the sphericity test and multiple hypotheses tests based on F-Statistics to identify the model order and select the significant channel paths. Simulations show that for a pre-defined level of significance, the proposed technique correctly identifies the significant channel parameters and the parsimonious RAKE receiver shows improved statistical as well as computational performance over classical methods. The time domain approach is based on the Bootstrap which is appropriate for the case when the distribution of the test statistics required by the multiple hypothesis tests is unknown. In this approach we also use short training data and model the channel response as an FIR filter with unknown length. Model parameters are then estimated using low complexity algorithms in the time domain. Based on these estimates, bootstrap based multiple hypotheses tests are applied to identify the non-zero coefficients of the FIR filter. Simulation results demonstrate the power of this technique for RAKE receivers in unknown noise environments. Finally we propose adaptive blind channel estimation algorithms for CDMA systems. Using only the spreading code of the user of interest and the received data sequence, four different adaptive blind estimation algorithms are proposed to estimate the impulse response of frequency selective and frequency non-selective fading channels. Also the idea is based on minimum variance receiver techniques. Tracking of a frequency selective varying fading channel is also considered. / A blind based hierarchical MDL model selection method is also proposed to select non-zero parameters of the channel response. Simulation results show that the proposed algorithms perform better than previously proposed algorithms. They have lower complexity and have a faster convergence rate. The proposed algorithms can also be applied to the design of adaptive blind channel estimation based RAKE receivers.

Master of Engineering (Research) / Multiple antenna systems are an appealing candidate for emerging fourth-generation wireless networks due to its potential to exploit space diversity for increasing conveyed throughput without wasting bandwidth and power resources. Particularly, layered space-time architecture (LST) proposed by Foschini, is a technique to achieve a significant fraction of the theoretical capacity with a reasonable implementation complexity. There has been a great deal of challenges in the detection of space-time signal; especially to design a low-complexity detector, which can efficiently remove multi-layer interference and approach the interference free bound. The application of iterative principle to joint detection and decoding has been a promising approach. It has been shown that, the iterative receiver with parallel interference canceller (PIC) has a low linear complexity and near interference free performance. Furthermore, it is widely accepted that the performance of digital communication systems can be considerably improved once the channel state information (CSI) is used to optimize the transmit signal. In this thesis, the problem of the design of a power allocation strategy in LST architecture to simultaneously optimize coding, diversity and weighting gains is addressed. A more practical scenario is also considered by assuming imperfect CSI at the receiver. The effect of channel estimation errors in LST architecture with an iterative PIC receiver is investigated. It is shown that imperfect channel estimation at an LST receiver results in erroneous decision statistics at the very first iteration and this error propagates to the subsequent iterations, which ultimately leads to severe degradation of the overall performance. We design a transmit power allocation policy to take into account the imperfection in the channel estimation process. The transmit power of various layers is optimized through minimization of the average bit error rate (BER) of the LST architecture with a low complexity iterative PIC detector. At the receiver, the PIC detector performs both interference regeneration and cancellation simultaneously for all layers. A convolutional code is used as the constituent code. The iterative decoding principle is applied to pass the a posteriori probability estimates between the detector and decoders. The decoder is based on the maximum a posteriori (MAP) algorithms. A closed-form optimal solution for power allocation in terms of the minimum BER is obtained. In order to validate the effectiveness of the proposed schemes, substantial simulation results are provided.

<p>In this thesis, a single carrier ATSC DTV baseband transmitter, part of the receiver(including channel estimator and channel equalizer), were modeled. Since multi-pathinduced ISI (inter symbol interference) is the most significant impact on theperformance of single carrier DTV reception, modeling and implementation of singlecarrier channel estimator and channel equalizer have been the focus of the thesis. Westarted with the investigation of channel estimation methods. Afterwards, severalchannel estimators and equalizers were modeled and the performance of each channelequalization methods in different scenarios was evaluated. Our results show that thefrequency domain equalizer can achieve low computing cost and handle long delaypaths. Another important issue to be considered in block equalization is Inter-BlockInterference (IBI). The impact of IBI was investigated via behavior modeling. In lastpart of our thesis, two methods for IBI cancellation are compared and the proposal forhardware implementation was given.</p>