High mobility in OFDM based wireless communication systems

Orthogonal Frequency Division Multiplexing (OFDM) has been adopted as the transmission scheme in most of the wireless systems we use on a daily basis. It brings with it several inherent advantages that make it an ideal waveform candidate in the physical layer. However, OFDM based wireless systems are severely affected in High Mobility scenarios. In this thesis, we investigate the effects of mobility on OFDM based wireless systems and develop novel techniques to estimate the channel and compensate its effects at the receiver. Compressed Sensing (CS) based channel estimation techniques like the Rake Matching Pursuit (RMP) and the Gradient Rake Matching Pursuit (GRMP) are developed to estimate the channel in a precise, robust and computationally efficient manner. In addition to this, a Cognitive Framework that can detect the mobility in the channel and configure an optimal estimation scheme is also developed and tested. The Cognitive Framework ensures a computationally optimal channel estimation scheme in all channel conditions. We also demonstrate that the proposed schemes can be adapted to other wireless standards easily. Accordingly, evaluation is done for three current broadcast, broadband and cellular standards. The results show the clear benefit of the proposed schemes in enabling high mobility in OFDM based wireless communication systems.Orthogonal Frequency Division Multiplexing (OFDM) wurde als Übertragungsschema in die meisten drahtlosen Systemen, die wir täglich verwenden, übernommen. Es bringt mehrere inhärente Vorteile mit sich, die es zu einem idealen Waveform-Kandidaten in der Bitübertragungsschicht (Physical Layer) machen. Allerdings sind OFDM-basierte drahtlose Systeme in Szenarien mit hoher Mobilität stark beeinträchtigt. In dieser Arbeit untersuchen wir die Auswirkungen der Mobilität auf OFDM-basierte drahtlose Systeme und entwickeln neuartige Techniken, um das Verhalten des Kanals abzuschätzen und seine Auswirkungen am Empfänger zu kompensieren. Auf Compressed Sensing (CS) basierende Kanalschätzverfahren wie das Rake Matching Pursuit (RMP) und das Gradient Rake Matching Pursuit (GRMP) werden entwickelt, um den Kanal präzise, robust und rechnerisch effizient abzuschätzen. Darüber hinaus wird ein Cognitive Framework entwickelt und getestet, das die Mobilität im Kanal erkennt und ein optimales Schätzungsschema konfiguriert. Das Cognitive Framework gewährleistet ein rechnerisch optimales Kanalschätzungsschema für alle möglichen Kanalbedingungen. Wir zeigen außerdem, dass die vorgeschlagenen Schemata auch leicht an andere Funkstandards angepasst werden können. Dementsprechend wird eine Evaluierung für drei aktuelle Rundfunk-, Breitband- und Mobilfunkstandards durchgeführt. Die Ergebnisse zeigen den klaren Vorteil der vorgeschlagenen Schemata bei der Ermöglichung hoher Mobilität in OFDM-basierten drahtlosen Kommunikationssystemen

Fractional fourier based sparse channel estimation for multicarrier underwater acoustic communication system

This paper presents a hybrid sparse channel estimation based on Fractional Fourier Transform (FrFT) for orthogonal frequency division multiplex (OFDM) scenario to exploit channel sparsity of underwater acoustic (UWA) channel. A novel channel dictionary matrix based on chirp signals is constructed and mutual coherence is adopted to evaluate its preservation of sparse information. In addition, Compressive Sampling Matching Pursuit (CoSaMP) is implemented to estimate the sparse channel coefficients. Simulation results demonstrate a significant Normalized Mean Square Error (NMSE) improvement of 10dB over Basis Expansion Model (BEM) with less complexity

시변 스파스 수중 음향 통신 채널 매개변수 추정

학위논문 (박사)-- 서울대학교 대학원 : 조선해양공학과, 2012. 8. 성우제.This dissertation addresses the problem of estimating the channel parameters of time-varying sparse underwater acoustic communication channels. A new method to estimate the channel parameters including arrival time delay, incidence angle, Doppler frequency, and complex amplitude of impinging wave components is presented. The new method exploits the sparse structure of the wideband underwater acoustic communication channel and is based on the matching pursuit which iteratively identifies multipath components by projecting the target signal on the columns of dictionary which are hypothesized by the channel parameters. Because of the large dimension of the parameter space, the size of dictionary can be prohibitively large especially when the parameter range is oversampled for effective sparse approximation. In order to prevent the dictionary from being too large, the parameter estimation is achieved in two stages which are the identification and the iterative estimation stages. In the identification stage, the initial parameter values are identified using a pre-computed dictionary of low coherence. In the next estimation stage, a coherent and redundant dictionary of the oversampled parameter range is constructed from the identified parameter values, and the channel parameters are estimated by projecting the residual signal onto the redundant dictionary. To reduce memory requirement and computational complexity caused by using the redundant dictionary, a space-alternating estimation scheme is introduced to separate the parameter search space. The space-alternating scheme limits the size of the redundant dictionary within practical extent and accordingly reduces the computational burden of the matrix-vector product required in the iteration. The performance of the new method is evaluated via Monte Carlo simulation and real channel measurement data analysis. The Monte Carlo simulation evaluates the resolution performance by resolving two paths of small parameter differences, and its result shows that the new method successfully decomposes multipath components whose parameter differences are merely a subfraction of the resolution limit of the classical correlation-based method. It is also applied to the experimental data obtained in the large scale water tank which is capable of making a surface gravity wave with designated wave parameter. The channel parameters under the time-varying regular surface wave condition is analytically derived from a simple reflection constraint, and the channel parameters by the new channel estimation method are compared with those analytic solution showing that the estimation results are consistent with theoretical expectation. Finally, it is applied to the real channel data of shallow water which were acquired at various transmitter-receiver ranges. The performance of the estimated channel parameters is evaluated indirectly via comparison of the channel characteristic functions which are the delay-Doppler-spread function, the angle-Doppler-spread function, and the power delay and angle profiles. The comparison result shows that the estimated channel parameters coincide well with the channel characteristic functions obtained by the matched filter and accordingly proves that the presented method gives consistent estimation result for the estimation of real channel parameters.ABSTRACT I TABLE OF CONTENTS III LIST OF TABLES V LIST OF FIGURES VI CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 DEFINITIONS 3 1.3 PRIOR WORK 4 1.4 THESIS OUTLINE 8 CHAPTER 2 SPARSE CHANNEL ESTIMATION 9 2.1 ANGLE-DELAY-DOPPLER-SPREAD FUNCTION 9 2.2 ORTHOGONAL MATCHING PURSUIT 17 2.3 SPACE-ALTERNATING MATCHING PURSUIT 23 2.4 COMPUTATIONAL COMPLEXITY 27 CHAPTER 3 PARAMETER ESTIMATION OF SYNTHETIC CHANNEL 33 3.1 PERFORMANCE EVALUATION METHOD 33 3.2 MEAN SQUARE ERROR PERFORMANCE 36 CHAPTER 4 PARAMETER ESTIMATION OF REAL CHANNELS 42 4.1 WATER TANK CHANNEL EXPERIMENT 42 4.1.1 Surface Reflected Signal Model 48 4.1.2 Comparison between Data and Model 51 4.2 SHALLOW WATER CHANNEL EXPERIMENT 55 4.2.1 Estimation of Incidence Angle 58 4.2.2 Estimation of Doppler Shift 62 4.2.3 Delay and Angle Profiles 67 CHAPTER 5 CONCLUSIONS 76 REFERENCES 78 초 록 82 감사의 글 85Docto

Sliding-MOMP Based Channel Estimation Scheme for ISDB-T Systems

Compressive sensing based channel estimation has shown its advantage of accurate reconstruction for sparse signal with less pilots for OFDM systems. However, high computational cost requirement of CS method, due to linear programming, significantly restricts its implementation in practical applications. In this paper, we propose a reduced complexity channel estimation scheme of modified orthogonal matching pursuit with sliding windows for ISDB-T (Integrated Services Digital Broadcasting for Terrestrial) system. The proposed scheme can reduce the computational cost by limiting the searching region as well as making effective use of the last estimation result. In addition, adaptive tracking strategy with sliding sampling window can improve the robustness of CS based methods to guarantee its accuracy of channel matrix reconstruction, even for fast time-variant channels. The computer simulation demonstrates its impact on improving bit error rate and computational complexity for ISDB-T system

Identification of Matrices Having a Sparse Representation

We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary. Connections with sparse signal recovery allows for the use of efficient reconstruction techniques such as Basis Pursuit (BP). Of particular interest is the dictionary of time-frequency shift matrices and its role for channel estimation and identification in communications engineering. We present recovery results for BP with the time-frequency shift dictionary and various dictionaries of random matrices

Structured Compressed Sensing: From Theory to Applications

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.Comment: To appear as an overview paper in IEEE Transactions on Signal Processin

Orthogonal Time Frequency Space for Integrated Sensing and Communication: A Survey

Sixth-generation (6G) wireless communication systems, as stated in the European 6G flagship project Hexa-X, are anticipated to feature the integration of intelligence, communication, sensing, positioning, and computation. An important aspect of this integration is integrated sensing and communication (ISAC), in which the same waveform is used for both systems both sensing and communication, to address the challenge of spectrum scarcity. Recently, the orthogonal time frequency space (OTFS) waveform has been proposed to address OFDM's limitations due to the high Doppler spread in some future wireless communication systems. In this paper, we review existing OTFS waveforms for ISAC systems and provide some insights into future research. Firstly, we introduce the basic principles and a system model of OTFS and provide a foundational understanding of this innovative technology's core concepts and architecture. Subsequently, we present an overview of OTFS-based ISAC system frameworks. We provide a comprehensive review of recent research developments and the current state of the art in the field of OTFS-assisted ISAC systems to gain a thorough understanding of the current landscape and advancements. Furthermore, we perform a thorough comparison between OTFS-enabled ISAC operations and traditional OFDM, highlighting the distinctive advantages of OTFS, especially in high Doppler spread scenarios. Subsequently, we address the primary challenges facing OTFS-based ISAC systems, identifying potential limitations and drawbacks. Then, finally, we suggest future research directions, aiming to inspire further innovation in the 6G wireless communication landscape