The concepts of a digital twin and extended reality have recently emerged, which require a massive amount of sensor data to be transmitted with low latency and high reliability. For low-latency communications, joint source-channel coding (JSCC) is an attractive method for error correction coding and compared to highly complex digital systems that are currently in use. I propose the use of complex-valued and quaternionic neural networks (QNN) to decode JSCC codes, where the complex-valued neural networks show a significant improvement over real-valued networks and the QNNs have an exceptionally high performance. Furthermore, I propose mapping encoded JSCC code words to the baseband of the frequency domain in order to enable time/frequency synchronization as well as to mitigate fading using robust estimation theory. Additionally, I perform robust statistical signal processing on the high-dimensional JSCC code showing significant noise immunity with drastic performance improvements at low signal-to-noise ratio (SNR) levels. The performance of the proposed JSCC codes is within 5 dB of the optimal performance theoretically achievable and outperforms the maximum likelihood decoder at low SNR while exhibiting the smallest possible latency. I designed a Bayesian minimum mean square error estimator for decoding high-dimensional JSCC codes achieving 99.96% accuracy. With the recent introduction of electromagnetic reconfigurable intelligent surfaces (RIS), a paradigm shift is currently taking place in the world of wireless communications. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. In order to decode polarization-space modulated RIS reflections, robust polarization state decoders are proposed using the Weiszfeld algorithm and an generalized Huber M-estimator. Additionally, QNNs are trained and evaluated for the recovery of the polarization state. Furthermore, I propose a novel 64-ary signal constellation based on scaled and shifted Eisenstein integers and generated using media-based modulation with a RIS. The waveform is received using an antenna array and decoded with complex-valued convolutional neural networks. I employ the circular cross-correlation function and a-priori knowledge of the phase angle distribution of the constellation to blindly resolve phase offsets between the transmitter and the receiver without the need for pilots or reference signals. Furthermore, the channel attenuation is determined using statistical methods exploiting that the constellation has a particular distribution of magnitudes. After resolving the phase and magnitude ambiguities, the noise power of the channel can also be estimated. Finally, I tune an Sq-estimator to robustly decode the Eisenstein waveform. / Doctor of Philosophy / This dissertation covers three novel wireless communications methods; analog coding, communications using the electromagnetic polarization and communications with a novel signal constellation. The concepts of a digital twin and extended reality have recently emerged, which require a massive amount of sensor data to be transmitted with low latency and high reliability. Contemporary digital communication systems are highly complex with high reliability at the expense of high latency. In order to reduce the complexity and hence latency, I propose to use an analog coding scheme that directly maps the sensor data to the wireless channel. Furthermore, I propose the use of neural networks for decoding at the receiver, hence using the name neural receiver. I employ various data types in the neural receivers hence leveraging the mathematical structure of the data in order to achieve exceptionally high performance. Another key contribution here is the mapping of the analog codes to the frequency domain enabling time and frequency synchronization. I also utilize robust estimation theory to significantly improve the performance and reliability of the coding scheme. With the recent introduction of electromagnetic reconfigurable intelligent surfaces (RIS), a paradigm shift is currently taking place in the world of wireless communications. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. Therefore, I propose to use the polarization state of the electromagnetic wave to convey information over the channel, where the polarization is determined using a RIS. As with the analog codes, I also extensively employ various methods of robust estimation to improve the performance of the recovery of the polarization at the receiver. Finally, I propose a novel communications signal constellation generated by a RIS that allows for equal probability of error at the receiver. Traditional communication systems utilize reference symbols for synchronization. In this work, I utilize statistical methods and the known distributions of the properties of the transmitted signal to synchronize without reference symbols. This is referred to as blind channel estimation. The reliability of the third communications method is enhanced using a state-of-the-art robust estimation method.

<p>Broadcasting correlated Gaussians is one of the cases where separate source-channel coding is suboptimal. In this dissertation, we will study the distortion region of sending correlated Gaussian sources over an AWGN-BC using hybrid digital-analog coding approach, where each receiver wishes to reconstruct one source component subject to the mean squared error distortion constraint.</p> <p>First of all, the problem of transmitting m independent Gaussian source components over an AWGN-BC is studied. We show this problem setup is closely related to broadcasting correlated Gaussian sources with genie-aided receivers. Moreover, the separate source-channel coding approach is proven to be optimal in these setups.</p> <p>Second, we consider two new scenarios and find the achievable distortion regions for both cases, where three Gaussian source components are sent to three receivers. The difference is that for the first scenario, the first two source components are correlated and they are independent of the third one while for the second scenario, the last two source components are correlated and they are independent of the first one. Inner bounds based on hybrid analog-digital coding and outer bounds based on genie-aided arguments are proposed for both cases and the optimality is proven.</p> <p>Finally, we study two cases where side information is presented at one receiver. Hybrid analog-digital coding schemes are used and the optimality is proven.</p> / Master of Applied Science (MASc)

Viele Anwendungen zur satellitenbasierten Erdbeobachtung sind auf hochauflösende Daten angewiesen. Infolgedessen hat die Menge der Daten stark zugenommen, was aufgrund von begrenzten Kommunikationsmöglichkeiten zwischen Satelliten und der Erde ein ernsthaftes Problem darstellt. Insbesondere sind Kleinsatelliten davon betroffen. Sie ermöglichen zwar schnell umzusetzende und kostengünstige Weltraummissionen, aber schränken aufgrund ihrer Größe die Download-Raten ein. Dadurch entsteht eine grundlegende Diskrepanz zwischen den schnell wachsenden Datenerzeugungsraten und den deutlich langsamer wachsenden Downlink-Kapazitäten. In dieser Arbeit werden systematisch Lösungen untersucht, die dieses Problem adressieren. Der Lösungsraum lässt sich in zwei Kategorien unterteilen. Die erste Kategorie umfasst Techniken, die das Problem durch Verteilung angehen. So können mehrere Satelliten eingesetzt werden, um die Einschränkungen von einzelnen Satelliten zu überwinden. Die zweite Kategorie umfasst Lösungen, die den Kommunikationsbedarf durch die Verarbeitung der Daten an Bord der Satelliten verringern. Die Beiträge zur ersten Kategorie liegen im Bereich von mathematischer Optimierung, die einen effizienten Umgang mit Satellitenmobilität ermöglicht. Die Berechnungskomplexität ist hierbei ein wichtiger limitierender Faktor. Daher wird ein Dekompositionsansatz vorgeschlagen, um die Optimierungsprobleme effizient zu lösen. Zur zweiten Kategorie leistet diese Arbeit zwei Beiträge. Erstens wird ein Mechanismus zur Datenkompression und Kommunikation betrachtet. Zu diesem Zweck wird ein neuronales Netz trainiert, das es erlaubt, Quell- und Kanal-Kodierung für Kommunikation gemeinsam zu optimieren. Zweitens, um effiziente Aktualisierung von neuronalen Netzen an Bord von Satelliten zu ermöglichen, werden Kommunikationsprotokolle entwickelt, die eine inkrementelle Übertragung neuronaler Netze erlauben und so deren Nutzung bei teilweise fehlenden Parametern ermöglichen. / The importance of timely analysis of Earth observation data can hardly be overestimated. The ever-growing demand for it in many areas, such as climate monitoring and disaster management, has encouraged rapid advances of satellite technologies. As a result, the amount of satellite data has grown significantly. Meanwhile, small satellites have gained popularity in the space industry. Their use constrains the download rates due to energy restrictions as well as limited communication opportunities with Earth. This creates a fundamental gap between growing data generation rates and downlink capacities, which increase at a much slower pace. We cannot expect this gap to be soon eliminated by the advances in communication technologies. In this thesis, we systematically explore the scope of solutions that allow to mitigate this problem, which we divide into two categories. The first category includes techniques approaching the problem in a distributed manner. For instance, multiple satellites can be introduced to deal with intermittent connectivity. The second category comprises solutions that aim to reduce the communication demand by processing the data onboard. To contribute to the first category, we formulate an optimization program that models communication in a satellite constellation. To reduce the computation complexity, we propose a decomposition approach that allows to efficiently solve such optimization problems. Our contribution to the second category is twofold. First, to dynamically address the challenges arising in small satellite networks, we combine data compression and communication. To this end, we introduce joint source-channel coding using neural networks for satellite communication. Second, we identify the limited opportunities for updating neural networks from Earth as an important issue. To allow for early deployment of updated neural network models, we develop communication protocols, enabling incremental updates of their parameters.

We consider the transmission of correlated sources over a multiple access channel(MAC). Multiple access channels are important building blocks in many practical communication systems, e.g., local area networks(LAN), cellular systems, wireless multi-hop networks. Thus this topic has been studied for last several decades. One recent motivation is estimating a random field via wireless sensor networks. Often the sensor nodes are densely deployed resulting in correlated observations. These sensor nodes need to transmit their correlated observations to a fusion center which uses this data to estimate the sensed random field. Sensor nodes have limited computational and storage capabilities and very limited energy. Since transmission is very energy intensive, it is important to minimize it. This motivates our problem of energy efficient transmission of correlated sources over a sensor network. Sensor networks are often arranged in a hierarchical fashion. Neighboring nodes can first transmit their data to a cluster head which can further compress information before transmission to the fusion center. The transmission of data from sensor nodes to their cluster-head is usually through a MAC. At the fusion center the underlying physical process is estimated. The main trade-off possible is between the rates at which the sensors send their observations and the distortion incurred in estimation at the fusion center. The availability of side information at the encoders and/or the decoder can reduce the rate of transmission. In this thesis, the above scenario is modeled as an information theoretic problem. Efficient joint source-channel codes are discussed under various assumptions on side information and distortion criteria. Sufficient conditions for transmission of discrete/continuous alphabet sources with a given distortion over a discrete/continuous alphabet MAC are given. We recover various previous results as special cases from our results. Furthermore, we study the practically important case of the Gaussian MAC(GMAC) in detail and propose new joint source-channel coding schemes for discrete and continuous sources. Optimal schemes are identified in different scenarios. The protocols like TDMA, FDMA and CDMA are widely used across systems and standards. When these protocols are used the MAC becomes a system of orthogonal channels. Our general conditions can be specialized to obtain sufficient conditions for lossy transmission over this system. Using this conditions, we identify an optimal scheme for transmission of Gaussian sources over orthogonal Gaussian channels and show that the Amplify and Forward(AF) scheme performs close to the optimal scheme even at high SNR. Next we investigate transmission of correlated sources over a fast fading MAC with perfect or partial channel state information available at both the encoders and the decoder. We provide sufficient conditions for transmission with given distortions. We also provide power allocation policies for efficient transmission. Next, we use MAC with side information as a building block of a hierarchical sensor network. For Gaussian sources over Gaussian MACs, we show that AF performs well in such sensor network scenarios where the battery power is at a premium. We then extend this result to the hierarchical network scenario and show that it can perform favourably to the Slepian-Wolf based source coding and independent channel coding scheme. In a hierarchical sensor network the cluster heads often need to send only a function of the sensor observations to the fusion center. In such a setup the sensor nodes can compress the data sent to the cluster head exploiting the correlation in the data and also the structure of the function to be computed at the cluster head. Depending upon the function, exploiting the structure of the function can substantially reduce the data rate for transmission. We provide efficient joint source-channel codes for transmitting a general class of functions of the sources over the MAC.

Ce travail de thèse présente une stratégie de transmission exploitant la diversité spatiale pour la transmission d'images sur canal sans fil. On propose ainsi une approche originale mettant en correspondance la hiérarchie de la source avec celle des sous-canauxSISO issus de la décomposition d'un canal MIMO. On évalue les performances des précodeurs usuels dans le cadre de cette stratégie via une couche physique réaliste, respectant la norme IEEE802.11n, et associé à un canal de transmission basé sur un modèle de propagation à tracé de rayons 3D. On montre ainsi que les précodeurs usuels sont mal adaptés pour la transmission d'un contenu hiérarchisé. On propose alors un algorithme de précodage allouant successivement la puissance sur les sous-canaux SISO afin de maximiser la qualité des images reçues. Le précodeur proposé permet d'atteindre un TEB cible compte tenu ducodage canal, de la modulation et du SNR des sous-canaux SISO. A partir de cet algorithme de précodage, on propose une solution d'adaptation de lien permettant de régler dynamiquement les paramètres de la chaîne en fonction des variations sur le canal de transmission. Cette solution détermine la configuration de codage/transmission maximisant la qualité de l'image en réception. Enfin, on présente une étude sur la prise en compte de contraintes psychovisuelles dans l'appréciation de la qualité des images reçues. On propose ainsi l'intégration d'une métrique à référence réduite basée sur des contraintes psychovisuelles permettant d'assister le décodeur vers la configuration de décodage offrant la meilleure qualité d'expérience. Des tests subjectifs confirment l'intérêt de l'approche proposée. / This thesis presents a transmission strategy for exploiting the spatial diversity for image transmission over wireless channel. We propose an original approach based on the matching between the source hierarchy and the SISO sub-channels hierarchy, resulting from the MIMO channel decomposition. We evaluate common precoder performance in the context of this strategy via a realistic physical layer respecting the IEEE802.11n standard and associated with a transmission channel based on a 3D-ray tracer propagation model. It is shown that common precoders are not adapted for the transmission of a hierarchical content. Then, we propose a precoding algorithm which successively allocates power over SISO subchannels in order to maximize the received images quality. The proposed precoder achieves a target BER according to the channel coding, the modulation and the SISO subchannels SNR. From this precoding algorithm, we propose a link adaptation scheme to dynamically adjust the system parameters depending on the variations of the transmission channel. This solution determines the optimal coding/transmission configuration maximizing the image quality in reception. Finally, we present a study for take into account some psychovisual constraints in the assessment of the received images quality. We propose the insertion of a reduced reference metric based on psychovisual constraints, to assist the decoder in order to determine the decoding configuration providing the highest quality of experience. Subjective tests confirm the interest of the proposed approach.

Les schémas de Codage Vidéo Linéaire (CVL) inspirés de SoftCast ont émergé dans la dernière décennie comme une alternative aux schémas de codage vidéo classiques. Ces schémas de codage source-canal conjoint exploitent des résultats théoriques montrant qu’une transmission (quasi-)analogique est plus performante dans des situations de multicast que des schémas numériques lorsque les rapports signal-à-bruit des canaux (C-SNR) diffèrent d’un récepteur à l’autre. Dans ce contexte, les schémas de CVL permettent d’obtenir une qualité de vidéo décodée proportionnelle au C-SNR du récepteur.Une première contribution de cette thèse concerne l’optimisation de la matrice de précodage de canal pour une transmission de type OFDM de flux générés par un CVL lorsque les contraintes de puissance diffèrent d’un sous-canal à l’autre. Ce type de contrainte apparait en sur des canaux DSL, ou dans des dispositifs de transmission sur courant porteur en ligne (CPL). Cette thèse propose une solution optimale à ce problème de type multi-level water filling et nécessitant la solution d’un problème de type Structured Hermitian Inverse Eigenvalue. Trois algorithmes sous-optimaux de complexité réduite sont également proposés. Des nombreux résultats de simulation montrent que les algorithmes sous-optimaux ont des performances très proches de l’optimum et réduisent significativement le temps de codage. Le calcul de la matrice de précodage dans une situation de multicast est également abordé. Une seconde contribution principale consiste en la réduction de l’impact du bruit impulsif dans les CVL. Le problème de correction du bruit impulsif est formulé comme un problème d’estimation d’un vecteur creux. Un algorithme de type Fast Bayesian Matching Pursuit (FBMP) est adapté au contexte CVL. Cette approche nécessite de réserver des sous-canaux pour la correction du bruit impulsif, entrainant une diminution de la qualité vidéo en l'absence de bruit impulsif. Un modèle phénoménologique (MP) est proposé pour décrire l’erreur résiduelle après correction du bruit impulsif. Ce modèle permet de d’optimiser le nombre de sous-canaux à réserver en fonction des caractéristiques du bruit impulsif. Les résultats de simulation montrent que le schéma proposé améliore considérablement les performances lorsque le flux CVL est transmis sur un canal sujet à du bruit impulsif. / SoftCast based Linear Video Coding (LVC) schemes have been emerged in the last decade as a quasi analog joint-source-channel alternative to classical video coding schemes. Theoretical analyses have shown that analog coding is better than digital coding in a multicast scenario when the channel signal-to-noise ratios (C-SNR) differ among receivers. LVC schemes provide in such context a decoded video quality at different receivers proportional to their C-SNR.This thesis considers first the channel precoding and decoding matrix design problem for LVC schemes under a per-subchannel power constraint. Such constraint is found, e.g., on Power Line Telecommunication (PLT) channels and is similar to per-antenna power constraints in multi-antenna transmission system. An optimal design approach is proposed, involving a multi-level water filling algorithm and the solution of a structured Hermitian Inverse Eigenvalue problem. Three lower-complexity alternative suboptimal algorithms are also proposed. Extensive experiments show that the suboptimal algorithms perform closely to the optimal one and can reduce significantly the complexity. The precoding matrix design in multicast situations also has been considered.A second main contribution consists in an impulse noise mitigation approach for LVC schemes. Impulse noise identification and correction can be formulated as a sparse vector recovery problem. A Fast Bayesian Matching Pursuit (FBMP) algorithm is adapted to LVC schemes. Subchannels provisioning for impulse noise mitigation is necessary, leading to a nominal video quality decrease in absence of impulse noise. A phenomenological model (PM) is proposed to describe the impulse noise correction residual. Using the PM model, an algorithm to evaluate the optimal number of subchannels to provision is proposed. Simulation results show that the proposed algorithms significantly improve the video quality when transmitted over channels prone to impulse noise.

<p>This dissertation considers packet-switched wireless networks for transmission of variable-rate layered hybrid video streams. Target applications are video streaming and broadcasting services. The work can be divided into two main parts.</p><p>In the first part, a novel quality-scalable scheme based on coefficient refinement and encoder quality constraints is developed as a possible extension to the video coding standard H.264. After a technical introduction to the coding tools of H.264 with the main focus on error resilience features, various quality scalability schemes in previous research are reviewed. Based on this discussion, an encoder decoder framework is designed for an arbitrary number of quality layers, hereby also enabling region-of-interest coding. After that, the performance of the new system is exhaustively tested, showing that the bit rate increase typically encountered with scalable hybrid coding schemes is, for certain coding parameters, only small to moderate. The double- and triple-layer constellations of the framework are shown to perform superior to other systems.</p><p>The second part considers layered code streams as generated by the scheme of the first part. Various error propagation issues in hybrid streams are discussed, which leads to the definition of a decoder quality constraint and a segmentation of the code stream to transmit. A packetization scheme based on successive source rate consumption is drafted, followed by the formulation of the channel code rate optimization problem for an optimum assignment of available codes to the channel packets. Proper MSE-based error metrics are derived, incorporating the properties of the source signal, a terminate-on-error decoding strategy, error concealment, inter-packet dependencies, and the channel conditions. The Viterbi algorithm is presented as a low-complexity solution to the optimization problem, showing a great adaptivity of the joint source channel coding scheme to the channel conditions. An almost constant image qualiity is achieved, also in mismatch situations, while the overall channel code rate decreases only as little as necessary as the channel quality deteriorates. It is further shown that the variance of code distributions is only small, and that the codes are assigned irregularly to all channel packets.</p><p>A double-layer constellation of the framework clearly outperforms other schemes with a substantial margin. </p><p>Keywords — Digital lossy video compression, visual communication, variable bit rate (VBR), SNR scalability, layered image processing, quality layer, hybrid code stream, predictive coding, progressive bit stream, joint source channel coding, fidelity constraint, channel error robustness, resilience, concealment, packet-switched, mobile and wireless ATM, noisy transmission, packet loss, binary symmetric channel, streaming, broadcasting, satellite and radio links, H.264, MPEG-4 AVC, Viterbi, trellis, unequal error protection</p>

This dissertation considers packet-switched wireless networks for transmission of variable-rate layered hybrid video streams. Target applications are video streaming and broadcasting services. The work can be divided into two main parts. In the first part, a novel quality-scalable scheme based on coefficient refinement and encoder quality constraints is developed as a possible extension to the video coding standard H.264. After a technical introduction to the coding tools of H.264 with the main focus on error resilience features, various quality scalability schemes in previous research are reviewed. Based on this discussion, an encoder decoder framework is designed for an arbitrary number of quality layers, hereby also enabling region-of-interest coding. After that, the performance of the new system is exhaustively tested, showing that the bit rate increase typically encountered with scalable hybrid coding schemes is, for certain coding parameters, only small to moderate. The double- and triple-layer constellations of the framework are shown to perform superior to other systems. The second part considers layered code streams as generated by the scheme of the first part. Various error propagation issues in hybrid streams are discussed, which leads to the definition of a decoder quality constraint and a segmentation of the code stream to transmit. A packetization scheme based on successive source rate consumption is drafted, followed by the formulation of the channel code rate optimization problem for an optimum assignment of available codes to the channel packets. Proper MSE-based error metrics are derived, incorporating the properties of the source signal, a terminate-on-error decoding strategy, error concealment, inter-packet dependencies, and the channel conditions. The Viterbi algorithm is presented as a low-complexity solution to the optimization problem, showing a great adaptivity of the joint source channel coding scheme to the channel conditions. An almost constant image qualiity is achieved, also in mismatch situations, while the overall channel code rate decreases only as little as necessary as the channel quality deteriorates. It is further shown that the variance of code distributions is only small, and that the codes are assigned irregularly to all channel packets. A double-layer constellation of the framework clearly outperforms other schemes with a substantial margin. Keywords — Digital lossy video compression, visual communication, variable bit rate (VBR), SNR scalability, layered image processing, quality layer, hybrid code stream, predictive coding, progressive bit stream, joint source channel coding, fidelity constraint, channel error robustness, resilience, concealment, packet-switched, mobile and wireless ATM, noisy transmission, packet loss, binary symmetric channel, streaming, broadcasting, satellite and radio links, H.264, MPEG-4 AVC, Viterbi, trellis, unequal error protection

One of the fundamental limitations of mobile radio communications is their time-varying fading channel. This thesis addresses the efficient use of channel state information to improve the communication systems, with a particular emphasis on practical issues such as compatibility with the existing wireless systems and low complexity implementation. The closed-loop transmit diversity technique is used to improve the performance of the downlink channel in MIMO communication systems. For example, the WCDMA standard endorsed by 3GPP adopts a mode of downlink closed-loop scheme based on partial channel state information known as mode 1 of 3GPP. Channel state information is fed back from the mobile unit to the base station through a low-rate uncoded feedback bit stream. In these closed-loop systems, feedback error and feedback delay, as well as the sub-optimum reconstruction of the quantized feedback data, are the usual sources of deficiency. In this thesis, we address the efficient reconstruction of the beamforming weights in the presence of the feedback imperfections, by exploiting the residual redundancies in the feedback stream. We propose a number of algorithms for reconstruction of beamforming weights at the base-station, with the constraint of a constant transmit power. The issue of the decoding at the receiver is also addressed. In one of the proposed algorithms, channel fading prediction is utilized to combat the feedback delay. We introduce the concept of Blind Antenna Verification which can substitute the conventional Antenna Weight Verification process without the need for any training data. The closed-loop mode 1 of 3GPP is used as a benchmark, and the performance is examined within a WCDMA simulation framework. It is demonstrated that the proposed algorithms have substantial gain over the conventional method at all mobile speeds, and are suitable for the implementation in practice. The proposed approach is applicable to other closed-loop schemes as well. The problem of (long-range) prediction of the fading channel is also considered, which is a key element for many fading-compensation techniques. A linear approach, usually used to model the time evolution of the fading process, does not perform well for long-range prediction applications. We propose an adaptive algorithm using a state-space approach for the fading process based on the sum-sinusoidal model. Also to enhance the widely-used linear approach, we propose a tracking method for a multi-step linear predictor. Comparing the two methods in our simulations shows that the proposed algorithm significantly outperforms the linear method, for both stationary and non-stationary fading processes, especially for long-range predictions. The robust structure, as well as the reasonable computational complexity, makes the proposed algorithm appealing for practical applications.

One of the fundamental limitations of mobile radio communications is their time-varying fading channel. This thesis addresses the efficient use of channel state information to improve the communication systems, with a particular emphasis on practical issues such as compatibility with the existing wireless systems and low complexity implementation. The closed-loop transmit diversity technique is used to improve the performance of the downlink channel in MIMO communication systems. For example, the WCDMA standard endorsed by 3GPP adopts a mode of downlink closed-loop scheme based on partial channel state information known as mode 1 of 3GPP. Channel state information is fed back from the mobile unit to the base station through a low-rate uncoded feedback bit stream. In these closed-loop systems, feedback error and feedback delay, as well as the sub-optimum reconstruction of the quantized feedback data, are the usual sources of deficiency. In this thesis, we address the efficient reconstruction of the beamforming weights in the presence of the feedback imperfections, by exploiting the residual redundancies in the feedback stream. We propose a number of algorithms for reconstruction of beamforming weights at the base-station, with the constraint of a constant transmit power. The issue of the decoding at the receiver is also addressed. In one of the proposed algorithms, channel fading prediction is utilized to combat the feedback delay. We introduce the concept of Blind Antenna Verification which can substitute the conventional Antenna Weight Verification process without the need for any training data. The closed-loop mode 1 of 3GPP is used as a benchmark, and the performance is examined within a WCDMA simulation framework. It is demonstrated that the proposed algorithms have substantial gain over the conventional method at all mobile speeds, and are suitable for the implementation in practice. The proposed approach is applicable to other closed-loop schemes as well. The problem of (long-range) prediction of the fading channel is also considered, which is a key element for many fading-compensation techniques. A linear approach, usually used to model the time evolution of the fading process, does not perform well for long-range prediction applications. We propose an adaptive algorithm using a state-space approach for the fading process based on the sum-sinusoidal model. Also to enhance the widely-used linear approach, we propose a tracking method for a multi-step linear predictor. Comparing the two methods in our simulations shows that the proposed algorithm significantly outperforms the linear method, for both stationary and non-stationary fading processes, especially for long-range predictions. The robust structure, as well as the reasonable computational complexity, makes the proposed algorithm appealing for practical applications.