No description available.

Broadband wireless access systems can be used to deliver a variety of high data rate applications and services. Many of the channels being considered for such applications exhibit multipath propagation coupled with large delay spreads. Cur- rently, orthogonal frequency division multiplexing is employed in these channels to compensate the effect of dispersion. Single carrier (SC) modulation in conjunc- tion with frequency-domain equalization (FDE) at the receiver has been shown to be a practical alternate solution as it has lower peak to average power ratio and is less sensitive to frequency offsets and phase noise compared to OFDM. The effect of multipath propagation increases with increasing data rate for SC systems. This leads to larger inter-symbol-interference (ISI) spans. In addition the achievable ca- pacity of SC-broadband systems depends on their ability to accommodate multiple signal transmissions in the same frequency band, which results in co-channel inter- ference (CCI) when detecting the desired data stream. The effects of CCI and ISI are more pronounced at high data rates. The objective of this research is to investi- gate and a develop low-complexity frequency domain receiver architectures capable of suppressing both CCI and ISI and employing practical channel estimation. In this thesis, a linear and a non-linear receiver architecture are developed in the frequency domain for use in highly dispersive channels employing multiple input multiple output (MIMO) antennas. The linear receiver consists of parallel branches each corresponding to a transmit data stream and implements linear equalization and demodulation. Frequency domain joint CCI mitigation and ISI equalization is implemented based on estimated channel parameters and is called space-frequency Broadband wireless access systems can be used to deliver a variety of high data rate applications and services. Many of the channels being considered for such applications exhibit multipath propagation coupled with large delay spreads. Cur- rently, orthogonal frequency division multiplexing is employed in these channels to compensate the effect of dispersion. Single carrier (SC) modulation in conjunc- tion with frequency-domain equalization (FDE) at the receiver has been shown to be a practical alternate solution as it has lower peak to average power ratio and is less sensitive to frequency offsets and phase noise compared to OFDM. The effect of multipath propagation increases with increasing data rate for SC systems. This leads to larger inter-symbol-interference (ISI) spans. In addition the achievable ca- pacity of SC-broadband systems depends on their ability to accommodate multiple signal transmissions in the same frequency band, which results in co-channel inter- ference (CCI) when detecting the desired data stream. The effects of CCI and ISI are more pronounced at high data rates. The objective of this research is to investi- gate and a develop low-complexity frequency domain receiver architectures capable of suppressing both CCI and ISI and employing practical channel estimation. In this thesis, a linear and a non-linear receiver architecture are developed in the frequency domain for use in highly dispersive channels employing multiple input multiple output (MIMO) antennas. The linear receiver consists of parallel branches each corresponding to a transmit data stream and implements linear equalization and demodulation. Frequency domain joint CCI mitigation and ISI equalization is implemented based on estimated channel parameters and is called space-frequency

With the increasing demand for more wireless multimedia applications, it is desired to design a wireless system with higher data rate. Furthermore, the frequency spectrum has become a limited and valuable resource, making it necessary to utilize the available spectrum efficiently and coexist with other wireless systems. Orthogonal frequency division multiplexing (OFDM) modulation is widely used in communication systems to meet the demand for ever increasing data rates. The major advantage of OFDM over single-carrier transmission is its ability to deal with severe channel conditions without complex equalization. However, OFDM systems suffer from a high peak to average power ratio, and they are sensitive to carrier frequency offset and Doppler spread. This dissertation first focuses on the development of intercarrier interference (ICI) reduction and signal detection algorithms for OFDM systems over time-varying channels. Several ICI reduction algorithms are proposed for OFDM systems over doubly-selective channels. The OFDM ICI reduction problem over time-varying channels is formulated as a combinatorial optimization problem based on the maximum likelihood (ML) criterion. First, two relaxation methods are utilized to convert the ICI reduction problem into convex quadratic programming (QP) problems. Next, a low complexity ICI reduction algorithm applicable to $M$-QAM signal constellations for OFDM systems is proposed. This formulates the ICI reduction problem as a QP problem with non-convex constraints. A successive method is then utilized to deduce a sequence of reduced-size QP problems. For the proposed algorithms, the QP problems are solved by limiting the search in the 2-dimensional subspace spanned by its steepest-descent and Newton directions to reduce the computational complexity. Furthermore, a low-bit descent search (LBDS) is employed to improve the system performance. Performance results are given to demonstrate that the proposed ICI reduction algorithms provide excellent performance with reasonable computational complexity. A low complexity joint semiblind detection algorithm based on the channel correlation and noise variance is proposed which does not require channel state information. The detection problem is relaxed to a continuous non-convex quadratic programming problem. Then an iterative method is utilized to deduce a sequence of reduced-size quadratic programming problems. A LBDS method is also employed to improve the solution of the derived QP problems. Results are given which demonstrate that the proposed algorithm provides similar performance with lower computational complexity compared to that of a sphere decoder. A major challenge to OFDM systems is how to obtain accurate channel state information for coherent detection of the transmitted signals. Thus several channel estimation algorithms are proposed for OFDM systems over time-invariant channels. A channel estimation method is developed to utilize the noncircularity of the input signals to obtain an estimate of the channel coefficients. It takes advantage of the nonzero cyclostationary statistics of the transmitted signals, which in turn allows blind polynomial channel estimation using second-order statistics of the OFDM symbol. A set of polynomial equations are formulated based on the correlation of the received signal which can be used to obtain an estimate of the time domain channel coefficients. Performance results are presented which show that the proposed algorithm provides better performance than the least minimum mean-square error (LMMSE) algorithm at high signal to noise ratios (SNRs), with low computational complexity. Near-optimal performance can be achieved with large OFDM systems. Finally, a CS-based time-domain channel estimation method is presented for OFDM systems over sparse channels. The channel estimation problem under consideration is formulated as a small-scale $l_1$-minimization problem which is convex and admits fast and reliable solvers for the globally optimal solution. It is demonstrated that the magnitudes as well as delays of the significant taps of a sparse channel model can be estimated with satisfactory accuracy by using fewer pilot tones than the channel length. Moreover, it is shown that a fast Fourier transform (FFT) matrix of extended size can be used as a set of appropriate basis vectors to enhance the channel sparsity. This technique allows the proposed method to be applicable to less-sparse OFDM channels. In addition, a total-variation (TV) minimization based method is introduced to provide an alternative way to solve the original sparse channel estimation problem. The performance of the proposed method is compared to several established channel estimation algorithms. / Graduate

The use of multiple antennas at base stations and user devices is a key component in the design of cellular communication systems that can meet the capacity demands of tomorrow. The downlink transmission from base stations to users is particularly limiting, both from a theoretical and a practical perspective, since user devices should be simple and power-efficient, and because many applications primarily create downlink traffic (e.g., video streaming). The potential gain of employing multiple antennas for downlink transmission is well recognized: the total data throughput increases linearly with the number of transmit antennas if the spatial dimension is exploited for simultaneous transmission to multiple users. In the design of practical cellular systems, the actual benefit of multiuser multiantenna transmission is limited by a variety of factors, including acquisition and accuracy of channel information, transmit power, channel conditions, cell density, user mobility, computational complexity, and the level of cooperation between base stations in the transmission design. The thesis considers three main components of downlink communications: 1) estimation of current channel conditions using training signaling; 2) efficient feedback of channel estimates; and 3) allocation of transmit resources (e.g., power, time and spatial dimensions) to users. In each area, the thesis seeks to provide a greater understanding of the interplay between different system properties. This is achieved by generalizing the underlying assumptions in prior work and providing both extensions of previous outcomes and entirely new mathematical results, along with supporting numerical examples. Some of the main thesis contributions can be summarized as follows. A framework is proposed for estimation of different channel quantities using a common optimized training sequence. Furthermore, it is proved that each user should only be allocated one data stream and utilize its antennas for receive combining and interference rejection, instead of using the antennas for reception of multiple data streams. This fundamental result is proved under both exact channel acquisition and under imperfections from channel estimation and limited feedback. This also has positive implications on the hardware and system design. Next, a general mathematical model is proposed for joint analysis of cellular systems with different levels of base station cooperation. The optimal multicell resource allocation can in general only be found with exponential computational complexity, but a systematic algorithm is proposed to find the optimal solution for the purpose of offline benchmarking. A parametrization of the optimal solution is also derived, creating a foundation for heuristic low-complexity algorithms that can provide close-to-optimal performance. This is exemplified by proposing centralized and distributed multicell transmission strategies and by evaluating these using multicell channel measurements. / In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of KTH Royal Institute of Technology's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.QC 20111026

Multiple Input Multiple Output with Orthogonal Frequency Division Multiplexing (MIMOOFDM) has been widely adopted as one of the most promising air interface solutions for future broadband wireless communication systems due to its high rate transmission capability and robustness against multipath fading. However, these MIMO-OFDM advantages cannot be achieved unless the channel state information (CSI) can be obtained accurately and promptly at the receiver to assist coherent detection of data symbols. Channel estimation and training sequence design are, therefore, still open challenges of great interest. In this work, we investigate the Linear Minimum Mean Square Error (LMMSE) channel estimation and design nearly optimal training sequences for MIMO-OFDM systems in spatially correlated fading. We, first, review the LMMSE channel estimation model for MIMO-OFDM in spatially correlated fading channels. We, then, derive a tight theoretical lower bound of the channel estimation Mean Square Error (MSE). By exploiting the information on channel correlation matrices which is available at the transmitter, we design a practical and nearly optimal training sequence for MIMO-OFDM systems . The optimal transmit power allocation for training sequences is found using the Iterative Bisection Procedure (IBP). We also propose an approximate transmit power allocation algorithm which is computationally more efficient than the IBP while maintaining a similar MSE performance. The proposed training sequence design method is also applied to MIMO-OFDM systems where Cyclic Prefixing OFDM (CP-OFDM) is replaced by Zero Padding OFDM - OverLap-Add method (ZP-OFDM-OLA). The simulation results show that the performance of the proposed training sequence is superior to that of all existing training sequences and almost achieves the MSE theoretical lower bound.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / In this dissertation the problem of channel estimation in cooperative MIMO systems is investigated. More specifically, channel estimation techniques have been developed for a communication system assisted by relays with amplify-and-forward (AF) processing system in a three-hop scenario. The techniques developed use training sequences and enable, at the receiving node, the estimation of all the channels involved in the communication process. In an initial scenario, we consider a communication system with N transmit antennas and M receive antennas and between these nodes we have two relay groups with R1 and R2 antennas each. We propose protocols based on temporal multiplexing to coordinate the retransmission of the signals. At the end of the training phase, the receiving node estimates the channel matrices by combining the received data. By exploiting the multilinear (tensorial) structure of the sets of signals, we can model the received data through tensor models, such as PARAFAC and PARATUCK2 . This work proposes the combined use of these models and algebraic techniques to explore the spatial diversity. Secondly, we consider that the number of transmit and receive antennas at the relays may be different and that the data can travel in a bidirectional scheme (two-way). In order to validate the algorithms we use Monte-Carlo simulations in which we compare our proposed models with competing channel estimation algorithms, such as, the PARAFAC and Khatri-Rao factorization based algorithms in terms of NMSE and bit error rate. / Nesta dissertaÃÃo o problema de estimaÃÃo de canal em sistemas MIMO cooperativos à investigado. Mais especificamente, foram desenvolvidas tÃcnicas para estimaÃÃo de canal em um sistema de comunicaÃÃo assistida por relays com processamento do tipo amplifica-e-encaminha (do inglÃs, amplify-and-forward) em um cenÃrio de 3 saltos. As tÃcnicas desenvolvidas utilizam sequÃncia de treinamento e habilitam, no nà receptor, a estimaÃÃo de todos os canais envolvidos no processo de comunicaÃÃo. Em um cenÃrio inicial, consideramos um sistema de comunicaÃÃo com N antenas transmissoras e M antenas receptoras e entre esses nÃs temos dois grupos de relays com R1 e R2 antenas cada um. Foram desenvolvidos protocolos de transmissÃo baseado em multiplexaÃÃo temporal para coordenar as retransmissÃes dos sinais. Ao final da fase de treinamento, o nà receptor faz a estimaÃÃo das matrizes de canal atravÃs da combinaÃÃo dos dados recebidos. Explorando a estrutura multilinear (tensorial) dos diversos conjuntos de sinais, podemos modelar os dados recebidos atravÃs de modelos tensoriais, tais como: PARAFAC e PARATUCK2. Este trabalho propÃe a utilizaÃÃo combinada desses modelos e de tÃcnicas algÃbricas para explorar a diversidade espacial. Em um segundo momento, consideramos que o nÃmero de antenas transmissoras e receptoras dos relays podem ser diferentes e ainda que os dados podem trafegar em um esquema bidirecional (do inglÃs, two-way). Para fins de validaÃÃo dos algoritmos utilizamos simulaÃÃes de Monte-Carlo em que comparamos os modelos propostos com outros algoritmos de estimaÃÃo de canal, tais como os algoritmos baseados em PARAFAC e FatoraÃÃo de Khatri-Rao em termos de NMSE e taxa de erro de bit.

The aim of the thesis is to find out the most suitable channel estimation algorithms for the existing MIMO-OFDM SDR platform. Starting with the analysis of several prevalent channel estimation algorithms, MSE performance are compared under different scenarios. As a result of the hardware independent analysis, the complexvalued matrix computations involved in the algorithms are decomposed to real FLoating-point OPerations (FLOPs). Four feasible algorithms are selected for hardware dependent discussion based on the proposed hardware architecture. The computational latency is exposed as a manner of case study.

Next generation wireless systems will support higher data rates, improved spectral efficiency, and less latency. Massive multiple-input multiple-output (MIMO) is proposed to satisfy these demands. In massive MIMO, many benefits come from employing hundreds of antennas at the base station (BS) and serving dozens of user terminals (UTs) per cell. As the number of antennas increases at the BS, the channel becomes sparse. By exploiting sparse channel in massive MIMO, compressive sensing (CS) methods can be implemented to estimate the channel. In CS methods, the length of pilot sequences can be shortened compared to pilot-based methods. In this paper, a novel channel estimation algorithm based on a CS method called elastic net is proposed. Channel estimation accuracy of pilot-based, lasso, and elastic-net based methods in massive MIMO are compared. It is shown that the elastic-net based method gives the best performance in terms of error for the less pilot symbols and SNR values.

Abstract Advanced receiver structures capable of suppressing multiple-access interference in code-division multiple-access (CDMA) systems operating in frequency-selective fading channels are considered in this thesis. The aim of the thesis is to develop and validate novel receiver concepts suitable for future wideband cellular CDMA systems. Data detection and synchronization both for downlink and uplink receivers are studied. The linear minimum mean squared error (LMMSE) receivers are derived and analyzed in frequency-selective fading channels. Different versions of the LMMSE receivers are shown to be suitable for different data rates. The precombining LMMSE receiver, whichis also suitable for relatively fast fading channels, is shown to improve the performance of the conventional RAKE receivers signicantly in the FRAMES wideband CDMA concept. It is observed that the performance of the conventional RAKE receivers is degraded signicantly with highest data rates due to multiple-access interference (MAI) as well as due to inter-path interference. Based on a general convergence analysis, it is observed that the postcombining LMMSE receivers are mainly suited to the high data rate indoor systems. The blind adaptive LMMSE-RAKE receiverdeveloped for relatively fast fading frequency-selective channels gives superior rate of convergence and bit error rate (BER) performance in comparison to other blind adaptive receivers based on least mean squares algorithms. The minimum variance method based delay estimation in blind adaptive receivers is shown to result in improved delay acquisition performance in comparison to the conventional matched filter and subspace based acquisition schemes. A novel delay tracking algorithm suitable to blind least squares receivers is also proposed. The analysis shows improved tracking performance in comparison to the standard delay-locked loops. Parallel interference cancellation (PIC) receivers are developed for the uplink. Data detection, channel estimation, delay acquisition, delay tracking, inter-cell interference suppression, and array processing in PIC receivers are considered. A multistage data detector with the tentative data decision and the channel estimate feedback from the last stage is developed. Adaptive channel estimation filters are used to improve the channel estimation accuracy. The PIC method is also applied to the timing synchronization of the receiver. It is shown that the PIC based delay acquisition and tracking methods can be used to improve the performance of the conventional synchronization schemes. Although the overall performance of the PIC receiver is relatively good in the single-cell case, its performance is signicantly degraded in a multi-cell environment due to unknown signal components which degrade the MAI estimates and subsequently the cancellation efficiency. The blind receiver concepts developed for the downlink are integrated into the PIC receivers for inter-cell interference suppression. The resulting LMMSE-PIC receiver is capable of suppressing residual interference and results in good BER performance in the presence of unknown signal components.

Abstract The multiple–input multiple–output (MIMO) technique, applying several transmit and receive antennas in wireless communications, has emerged as one of the most prominent technical breakthroughs of the last decade. Wideband MIMO parameter estimation and its applications to the MIMO orthogonal frequency division multiplexing (MIMO–OFDM) channel estimation and mobile positioning are studied in this thesis. Two practical MIMO channel models, i.e., correlated-receive independent-transmit channel and correlated-transmit-receive channel, and associated space-time parameter estimation algorithms are considered. Thanks to the specified structure of the proposed training signals for multiple transmit antennas, the iterative quadrature maximum likelihood (IQML) algorithm is applied to estimate the time delay and spatial signature for the correlated-receive independent-transmit MIMO channels. For the correlated-transmit-receive MIMO channels, the spatial signature matrix corresponding to a time delay can be further decomposed in such a way that the angle of arrival (AOA) and the angle of departure (AOD) can be estimated simultaneously by the 2-D unitary ESPRIT algorithm. Therefore, the combination of the IQML algorithm and the 2-D unitary ESPRIT algorithm provides a novel solution to jointly estimate the time delay, the AOA and the AOD for the correlated-transmit-receive MIMO channels. It is demonstrated from the numerical examples that the proposed algorithms can obtain good performance at a reasonable cost. Considering the correlated-receive independent-transmit MIMO channels, channel coefficient estimation for the MIMO–OFDM system is studied. Based on the parameters of the correlated-receive independent-transmit MIMO channels, the channel statistics in terms of the correlation matrix are developed. By virtue of the derived channel statistics, a joint spatial-temporal (JST) filtering based MMSE channel estimator is proposed which takes full advantage of the channel correlation properties. The mean square error (MSE) of the proposed channel estimator is analyzed, and its performance is also demonstrated by Monte Carlo computer simulations. It is shown that the proposed JST minimum mean square error (MMSE) channel estimator outperforms the more conventional temporal MMSE channel estimator in terms of the MSE when the signals in the receive antenna array elements are significantly correlated. The closed form bit error probability of the space-time block coded OFDM system with correlation at the receiver is also developed by taking the channel estimation errors and channel statistics, i.e., correlation at the receiver, into account. Mobile positioning in the non-line of sight (NLOS) scenarios is studied. With the knowledge of the time delay, the AOA and the AOD associated with each NLOS propagation path, a novel geometric approach is proposed to calculate the MS's position by only exploiting two NLOS paths. On top of this, the least squares and the maximum likelihood (ML) algorithms are developed to utilize multiple NLOS paths to improve the positioning accuracy. Moreover, the ML algorithm is able to estimate the scatterers' positions as well as those of the MSs. The Cramer-Rao lower bound related to the position estimation in the NLOS scenarios is derived. It is shown both analytically and through computer simulations that the proposed algorithms are able to estimate the mobile position only by employing the NLOS paths.