Orthogonal Frequency Division Multiplexing (OFDM) plays an important role in the implementation of high data rate communication. In this thesis, the problems of data detection and channel and carrier frequency offset estimation in OFDM systems are studied. <p>Multi-symbol non-coherent data detection is studied which performs data detection by processing multiple symbols without the knowledge of the channel impulse response (CIR). <p>For coherent data detection, the CIR needs to be estimated. Our objective in this thesis is to work on blind channel estimators which can extract the CIR using just one block of received OFDM data. A blind channel estimator for (Single Input Multi Output) SIMO OFDM systems is derived. The conditions under which the estimator is identifiable is studied and solutions to resolve the phase ambiguity of the proposed estimator are given.<p>A channel estimator for superimposed OFDM systems is proposed and its CRB is derived. The idea of simultaneous transmission of pilot and data symbols on each subcarrier, the so called superimposed technique, introduces the efficient use of bandwidth in OFDM context. Pilot symbols can be added to data symbols to enable CIR estimation without sacrificing the data rate. Despite the many advantages of OFDM, it suffers from sensitivity to carrier frequency offset (CFO). CFO destroys the orthogonality between the subcarriers. Thus, it is necessary for the receiver to estimate and compensate for the frequency offset. Several high accuracy estimators are derived. These include CFO estimators, as well as a joint iterative channel/CFO estimator/data detector for superimposed OFDM. The objective is to achieve CFO estimation with using just one OFDM block of received data and without the knowledge of CIR.

The main objective of this dissertation is to present the structural design, performance evaluation, and complexity reduction of iterative joint channel estimation and data detection receivers. One of the main technical challenges in advanced wireless communications stems from the characteristics of a wireless channel, e.g., time selectivity of a channel, mobility of users, and multipath propagation. Channel estimation is essential for achieving reliable information transmission for practical wireless communication applications. Numerous channel estimation structures have been developed for different underlying channels using pilot-symbol assisted modulation (PSAM) approaches. However, since pilot symbols carry no data information, the time and the power spent on pilot symbols degrades the efficiency and the throughput of the system. Therefore, it is necessary to minimize the pilot insertion ratio without degrading the error performance. This motivates our research on iterative joint channel estimation and data detection receivers with full- and reduced- or low-complexity. In this thesis, we first propose an iterative channel estimator (ICE), based on a maximum a posteriori (MAP) algorithm, for single-carrier systems with PSAM structures. In contrast to existing MAP channel estimators, the proposed channel estimator has a lower computational complexity, which increases linearly with the modulation alphabet size. The computational complexity is reduced by exploiting a survivor in an efficient manner, while achieving comparable error performance to a full complexity receiver. For orthogonal frequency division multiplexing (OFDM) systems, we also propose novel signal constellations to facilitate channel estimation without pilot symbol transmission, and analyze the bit error rate for the proposed constellations. We also develop a suitable joint channel estimation and data detector with full- and low-complexity for the proposed constellations. This low-complexity ICE achieves an error performance comparable to the ICE with full-complexity. Finally, for vertical Bell Laboratories layered space-time OFDM systems, we propose an ICE based on a PSAM structure for time-varying multipath fading channels. By exploiting the statistical properties of a wireless channel, we also develop a method to suppress intercarrier interference due to the channel time selectivity, and propose a low-complexity ICE that exploits a priori information in an efficient manner.

Recently, the difficulty of placing multiple antennas onto a mobile terminal to exploit more diversity has been solved by using the cooperative communication technique, in which several relay nodes with a single antenna partner with each other to serve as virtual multiple antennas for providing the spatial diversity. Many existing researches in cooperative communication focuses on designing relay strategies to achieve better communication performance. However, most of their designs require the channel state information (CSI) being perfectly known. Unfortunately, CSI is generally unknown in practice. Therefore, before getting benefits brought by the relay-assisted network, it is necessary to obtain accurate channel state information (CSI) at the destination or relays. In this thesis, we also consider the training design for channel estimation in the AF relay network. The involvement of multiple relay nodes to exploit space diversity in cooperative communications requires sophisticated and complicated protocols, which poses a difficulty in avoiding all possible misbehaving relay nodes. Therefore, the channel estimation scheme in cooperative communication network needs to be robust against the possible relay misbehaviors. However, most prior works focused on developing channel estimation schemes by assuming perfect relayassisted communication protocol. By contrast, this work focuses on designing robust channel estimation schemes to combat the possible presence of the relay misbehaviors. Besides considering the robust design against relay misbehaviors, this work also considers more general channel model when designing the training sequence and channel estimation scheme. Specifically, in contrast to assume independent channels across relays, this thesis considers the correlated channels in both phases and the correlated noises in the first phase. Overall, the main problem of this work is to design robust channel estimation and training sequences against relay misbehaviors when the communication channels within the cooperative network are not restricted to be independent.

In this thesis, we study the effect of channel estimation on the performance of distributed space-time coding (DSTC) in amplify-and-forward (AF) cooperative networks. The relay based transmission takes two phase. In phase I, the source transmits a block of symbols, which include training symbols and data to destination. After receiving signals at relay, the DSTC is adopted to re-encode signals in order to achieve diversity gain at relay nodes. At destination, the signals received in two phase are combined and used to detected data symbols. In the thesis, for AF cooperative networks, the signal received at destination is effected the multiplication of channel coefficients on the source to relay and relay to destination links. Before detection, channel coefficients of all links need to be estimated. We propose a semiblind method to estimate the channel coefficients of direct link and the relay links. The semi-blind channel estimation scheme, exploits a small number of training symbols and second-order statistics of received signals. To improve the detection quality, the channel estimation is modified by treating the detected symbols as extra training symbols. Through simulation, it shows that the proposed channel estimation and the modification leads to obvious performance improvement.

In this thesis, we study channel estimation in time-varying cooperative network. Since channels vary with time, we insert training blocks periodically to trace channel variation. In this work, we adopt Kalman filter to trace channel variation due to its low complexity. By storing previous channel estimate, Kalman filter simply requires to process next received vectors to update current channel estimate. We use all past observations to estimate current channel state to avoid wasting information. In content of cooperation, we directly estimate effective channel from source through relay to the destination. The reason is that, we separately estimate the source-relay and relay-destination links, relays need extra efforts to estimate the channel and feedback estimates to the destination. It will increase the computational loading on relays, and the feedback channel may suffer channel fading, resulting in more distortion of estimates. Therefore, the destination directly estimate effective channel, using Kalman filter to trace variation. Furthermore, we design pre-coding scheme on relays for forwarding training symbols in order to reduce channel estimation errors and obtain more accurate channel information. To detect data symbols, we need to channel state information over each data block as well. Therefore, estimates over previous training blocks are interpolated to estimate channel over data blocks based on LMMSE criterion. Since estimates over training blocks are obtained from Kalman filter, it consequently improves estimation quality of the channel over the data blocks. The main contributions of the thesis are optimal training design to reduce the estimation error, the estimation based on Kalman filter, and linearly combing the estimates to provide more accurate estimates of the channels over data blocks.

Bandwidth efficiency is a critical concern in wireless communications. To fully utilize the available bandwidth, the superimposed training (ST) scheme is adopted in this thesis for orthogonal frequency division multiplexing (OFDM) systems without using the cyclic prefix (CP) and the guard interval (GI). It is shown that the performance of the channel estimation using the ST scheme is the same for both the proposed architecture, denoted as OFDM-ST, and the conventional OFDM system with CP, denoted as CP-OFDM-ST. In addition, since the CP is not added in the proposed system, leading to substantial increase in both the inter-symbol interference (ISI) and the inter-carrier interference (ICI), an interference cancellation scheme is derived. To further improve the performance of channel estimation using ST scheme, the joint ML data detection and channel estimation method is investigated. The simulation results illustrate that the proposed algorithm enhances the systems performance significantly. Finally, it is demonstrated that the proposed structure has a much better effective data rate than the CP-OFDM-ST system.

In this thesis, a new scheme based on the concept of compress-and-forward (CF) technique has been proposed. And expectation maximization (EM) algorithm is utilized to attain the aim of converging to a local optimum solution. According to the characteristic of EM algorithm, destination node would feed back a better decision to the relay node to be the next initial value. After the iteration, relay node would obtain a better detection result which would converge to a local optimum performance. At last the destination node would receive the optimum detection result from each relay and make a final decision. In the new structure, channel estimation can also be made at the relay node by EM algorithm, which is the reason why it is called joint distributed detection and estimation. Simulation shows that the proposed scheme would acquire an iteration gain at both the relay and destination node.

<p>Orthogonal Frequency Division Multiplexing (OFDM) is well known as an efficient technology for wireless communications and is widely used in many of the current and upcoming wireless and wireline communication standards. However, it has some intrinsic drawbacks, e.g., sensitivity to the inter-carrier interference (ICI) and high peak-to-average power ratio (PAPR). Additionally, the cyclic prefix (CP) is not spectrum efficient and fails when the channel delay spread exceeds the length of CP, which will result in inter-symbol interference (ISI). In order to combat or alleviate these drawbacks various techniques have been proposed, which can be categorised into two main classes: techniques that keep the structure of OFDM and meanwhile increase the system robustness or re-organise the symbol streams on each sub-carrier, and techniques that increase the ISI/ICI immunity by adopting well designed pulse shapes and/or resorting to general system lattices. The latter class are coined as Generalised FDM (GFDM) throughout this thesis to distinguish with the former class.</p><p>To enable seamless handover and efficient usage of spectrum and energy, GFDM is expected to dynamically adopt pulse shapes that are optimal in doubly (time and frequency) dispersive fading channels. This is however not an easy task as the method of optimal pulse shape adaptation is still unclear, let alone efficient implementationmethods. Besides, performance of GFDM highly depends on the channel estimation quality, which is not straightforward in GFDM systems.</p><p>This thesis addresses, among many other aspects of GFDM systems, measures of the time frequency localisation (TFL) property, pulse shape adaptation strategy, performance evaluation and channel estimation.  We first provide a comparative study of state-of-the-art GFDM technologies and a brief overview of the TFL functions and parameters which will be used frequently in later analysis and discussion. A framework for GFDM pulse shape optimisation is formulated targeting at minimising the combined ISI/ICI over doubly dispersive channels. We also propose a practical adaptation strategy utilising the extended Gaussian functions (EGF) and discuss the trade-off between performance and complexity.  One realisation under the umbrella of GFDM, namely OFDM/OQAM, is intensively studied and an efficient implementation method by direct discretisation of the continuous time model has been proposed.  Besides, a theoretical framework for a novel preamble-based channel estimation method has been presented and a new preamble sequence with higher gain is identified. Under the framework, an optimal pulse shape dependent preamble structure together with a suboptimal but pulse shape independent preamble structure have been proposed and evaluated in the context of OFDM/OQAM.</p>

<p>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.</p>

This paper presents the investigations into the performance of a multiple-input multiple-output (MIMO) system with its transmitters on a spinning vehicle and no available channel state information (CSI) at the transmitter or the receiver. The linear least squares approach is used to estimate the channel and the estimation error is measured. Spinning gives rise to a periodic component in the channel which can be estimated based on the spin rate relative to the data rate of the system. It is also determined that spinning causes the bit error rate of the system to degrade by a few dB.