Managing interference is the main technical challenge in wireless networks. Multiple input multiple output (MIMO) methods are key components to overcome the interference bottleneck and deliver higher data rates. The most efficient MIMO techniques require channel state information (CSI). In practice, this information is inaccurate due to errors in CSI acquisition, as well as mobility and delay. CSI inaccuracy reduces the performance gains provided by MIMO. When compounded with uncoordinated intercell interference, the degradation in MIMO performance is accentuated. This dissertation investigates the impact of CSI inaccuracy on the performance of increasingly complex interference limited networks, starting with a single interferer scenario, continuing to a heterogeneous network with a femtocell overlay, and finishing with a clustered multicell coordination model for randomly deployed transmitting nodes. First, this dissertation analyzes limited feedback beamforming and precoded spatial multiplexing over temporally correlated channels. Assuming uncoordinated interference from one dominant interferer, using Markov chain convergence theory, the gain in the average successful throughput at the mobile user is shown to decrease exponentially with the feedback delay. The decay rate is amplified when the user is interference limited. Interference cancellation methods at the receiver are shown to mitigate the effect of interference. This work motivates the need for practical MIMO designs to overcome the adverse effects of interference. Second, limited feedback beamforming is analyzed on the downlink of a more realistic heterogeneous cellular network. Future generation cellular networks are expected to be heterogeneous, consisting of a mixture of macro base stations and low power nodes, to support the increasing user traffic capacity and reliability demand. Interference in heterogeneous environments cannot be coordinated using traditional interference mitigation techniques due to the on demand and random deployment of low power nodes such as femtocells. Using tools from stochastic geometry, the outage and average achievable rate of limited feedback MIMO is computed with same-tier and cross-tier interference, and feedback delay. A hybrid fixed and random network deployment model is used to analyze the performance in a fixed cell of interest. The maximum density of transmitting femtocells is derived as a function of the feedback rate and delay. The detrimental effect of same-tier interference is quantified, as the mobile user moves from the cell-center to the cell-edge. The third part of this dissertation considers limited coordination between randomly deployed transmitters. Building on the established degrading effect of uncoordinated interference on practical MIMO methods, and the analytical tractability of random deployment models, interference coordination is analyzed. Using multiple antennas at the transmitter for interference nulling in ad hoc networks is first shown to achieve MIMO gains using single antenna receivers. Clustered coordination is then investigated for cellular systems with randomly deployed base stations. As full coordination in the network is not feasible, a random clustering model is proposed where base stations located in the same cluster coordinate. The average achievable rate can be optimized as a function of the number of antennas to maximize the coordination gains. For multicell limited feedback, adaptive partitioning of feedback bits as a function of the signal and interference strength is proposed to minimize the loss in rate due to finite rate feedback. / text

Los sistemas de comunicaciones con múltiples antenas o sistemas MIMO (multiple-input multiple-output) se presentan como una de las tecnologías más prometedoras en el campo de las comunicaciones inalámbricas, ya que permiten aprovechar la dimensión espacial además de las dimensiones de frecuencia y tiempo. De esta forma, se pueden obtener tasas de transmisión más elevadas usando el mismo ancho de banda, que es un recurso escaso, y manteniendo una potencia de transmisión baja, lo cual es crucial para dispositivos alimentados por baterías. Por estas razones, la tecnología MIMO ha sido adoptada en muchos estándares como Long-Term Evolution (LTE), LTE Advanced y Worldwide Interoperability for Microwave Access (WiMAX). Las técnicas MIMO también pueden se pueden emplear en un escenario multiusuario, donde varios usuarios comparten la dimensión espacial provocando una interferencia multiusuario. A través de la precodificación y del uso de múltiples antenas en el transmisor, la señal de los diferentes usuarios puede ser multiplexada espacialmente de forma que se mitigue la interferencia multiusuario incluso con usuarios de una sola antena. Estos sistemas, conocidos como sistemas MU-MISO (multiuser multiple-input single-output), han atraído mucho la atención en los últimos años ya que permiten el desarrollo de terminales pequeños y baratos, manteniendo así el equipamiento más caro en el transmisor. Sin embargo, estos beneficios conllevan un sistema más complejo. Por una parte, el multiplexado espacial requiere una carga de procesado considerable, que depende del tamaño del sistema: número de antenas transmisoras, número de receptores y ancho de banda. Por otra parte, las técnicas MIMO requieren un conocimiento del canal en transmisión o CSIT (channel state information at the transmitter) preciso. En sistemas con duplexación por división en frecuencia o FDD (frequency-division duplex), la información de canal o CSI (channel state information) debe ser estimada en el receptor y proporcionada al transmisor a través del enlace de realimentación, reduciendo así la eficiencia del sistema. Por lo tanto, esta tesis se centra en la mejora de la eficiencia de las implementaciones de precodificación y en el rendimiento de los esquemas de realimentación de canal en sistemas MU-MISO. El problema de la precodificación se aborda en primer lugar. Se ha llevado a cabo un análisis de algunas de las técnicas de precodificación más usadas, prestando especial atención a su rendimiento y a su complejidad. Este análisis revela que aquellas técnicas que hacen uso de lattice reduction (LR) obtienen un mejor rendimiento. Sin embargo, la complejidad computacional de la técnica LR dificulta su implementación en la práctica. El análisis también revela que las técnicas zero-forcing (ZF), Tomlinson-Harashima precoding (THP) y LR-THP son las técnicas más adecuadas para cubrir todo el rango de rendimiento y complejidad computacional. Asimismo, se ha llevado a cabo un análisis de estas técnicas bajo CSIT imperfecto. Dicho análisis ha demostrado que LR es una técnica muy importante también para el caso de CSIT imperfecto. A continuación, se han presentado implementaciones paralelas de técnicas de precodificación sobre unidades de procesamiento gráfico o GPUs (graphic processing unit), comparándose con implementaciones en unidades de procesamiento central o CPU (central processing unit). Dado que las implementaciones de THP y LR-THP han demostrado ser las que mejor se adaptan a la arquitectura de la GPU y ya que tienen muchas operaciones comunes, se ha propuesto una implementación sobre GPU de un esquema THP reconfigurable combinado con LR. La reconfigurabilidad de las GPUs permite desactivar la etapa de LR cuando los requisitos de los usuarios están garantizados por el esquema THP, combinando complejidad computacional con rendimiento. Aunque esta implementación consigue una mejora significativa respecto a la implementación sobre CPU, su paralelismo viene limitado por la naturaleza secuencial del problema LR. Por ello, se han propuesto varias estrategias para la paralelización del problema LR que han sido evaluadas en distintas plataformas: CPU multi-núcleo, GPU y plataforma heterogénea que consiste en CPU+GPU. Los resultados revelan que la arquitectura GPU permite reducir considerablemente el tiempo de computación del problema LR, especialmente en la plataforma heterogénea. La segunda parte de la tesis trata el problema de la realimentación de canal en sistemas FDD. En estos sistemas, los receptores normalmente proporcionan una versión cuantificada del canal a través del canal de realimentación. Con el objetivo de mantener una eficiencia alta, el canal debe ser cuantificado con los mínimos bits posibles. En primer lugar, se explora el uso de la correlación en frecuencia para reducir el volumen de información de realimentación. Se han presentado dos esquemas diferentes basados en cuantificación vectorial o VQ (vector quantization) y en la transformación Karhunen-Loève, respectivamente, y se han comparado con esquemas existentes en términos de rendimiento y complejidad computacional. Los resultados muestran que ambas técnicas son capaces de reducir significativamente el volumen de información de realimentación aprovechando la correlación en frecuencia. Finalmente, la correlación espacial también se aprovecha para reducir la información de realimentación. Se ha presentado una caracterización espacial estadística del modelo de canal SCM (spatial channel model) del 3GPP (3rd Generation Partnership Project) para un entorno de alta correlación. Basado en esta caracterización, se propone un esquema de cuantificación de canal para entornos de alta correlación. Con el objetivo de obtener una caracterización para alta y baja correlación, se considera un modelo de correlación más sencillo como el modelo de Kronecker. Basado en esta caracterización, se proponen dos esquemas de cuantificación y se evalúan con un modelo de canal realista como el SCM. Los resultados muestran que ambos esquemas son capaces de reducir la información de realimentación en ambientes con correlación alta y moderada. / Multiple-input multiple-output (MIMO) communication systems have emerged as one of the most promising technologies in the field of wireless communications, allowing to exploit the spatial dimension as well as the time and frequency dimensions. Thus, higher rates can be obtained by using the same bandwidth, which is a scarce resource, and keeping a low transmit power, which is crucial in battery-operated devices. For these reasons, MIMO technologies have been adopted by many standards such as Long-Term Evolution (LTE), LTE advanced (LTE-A) and Worldwide Interoperability for Microwave Access (WiMAX). MIMO techniques can also be used in a multiuser scenario, where several usersshare the spatial dimension causing multiuser interference. By means of precoding and the use of multiple antennas at the transmitter, the signal of the different users can be spatially multiplexed so that multiuser interference is mitigated even for single-antenna users. These systems, known as multiuser multiple-input singular-output (MU-MISO) systems, have attracted much attention in recent years since they allow the development of small and inexpensive terminals, keeping the most expensive hardware at the transmitter. However, these benefits come at the cost of having a more complex system. On the one hand, spatial multiplexing requires a considerable processing load that depends on the size of the system: number of transmit antennas, number of receivers and bandwidth. On the other hand, MIMO techniques require accurate channel state information at the transmitter (CSIT). In frequency-division duplex (FDD) systems, channel state information (CSI) has to be estimated at the receiver and provided to the transmitter through the feedback link, hence reducing the efficiency of the system. Therefore, this thesis is primarily focused on improving the efficiency of precoding implementations and the performance of feedback schemes in MU-MISO systems. First, the problem of precoding is addressed. An analysis of some of the most utilized precoding techniques is conducted, paying special attention to their performance and computational complexity. The analysis reveals that those techniques that make use of lattice reduction (LR) achieve the best performance. However, the computational complexity of LR makes its implementation difficult for practical systems. The analysis reveals that zero-forcing (ZF), Tomlinson-Harashima precoding (THP) and lattice reduction Tomlinson-Harashima precoding (LR-THP) are the most suitable techniques for covering the entire range of performance and computational complexity. An analysis of these techniques with imperfect CSIT has also been performed. In this analysis, LR has proven to be a key technique also when imperfect CSIT is available. Next, parallel implementations of the precoding techniques on a graphic processing unit (GPU) are presented and compared to implementations that use a central processing unit (CPU). Since the implementations of THP and LR-THP have shown to best fit the GPU architecture and since they also share many operations, a GPU implementation of a reconfigurable THP scheme combined with LR has been proposed. The reconfigurable nature of GPUs allows gating the LR stage off when the user requirements are sufficiently guaranteed by the THP scheme, trading computational cost and performance. Although this implementation achieves a significant speed-up compared to its CPU implementation, its parallelism is limited by the sequential nature of LR. Therefore, several strategies for the parallelization of the LR problem are proposed and evaluated on different platforms: multicore CPU, GPU and a heterogeneous platform consisting of CPU+GPU. Results reveal that a GPU architecture allows a considerable reduction of the computational time of the LR problem, especially in the heterogeneous platform. The second part of this thesis addresses the problem of feedback in FDD systems. In these systems, a quantized version of the channel is usually provided by the receivers through the feedback link. In order to keep a high efficiency, the channel must be quantized using as few bits as possible. First, the use of the frequency correlation to reduce the feedback information is explored. Two different schemes based on vector quantization (VQ) and the Karhunen-Loève (KL) transform, respectively, are presented and compared with existing schemes in terms of performance and complexity. Results show that both techniques are able to significantly reduce the feedback overhead by taking advantage of the frequency correlation. Finally, the spatial correlation is leveraged to reduce the feedback information. A spatial statistical characterization of the spatial channel model (SCM) from the 3rd Generation Partnership Project (3GPP) for a highly correlated environment is presented. Based on this characterization, a channel quantization scheme for highly correlated environments is proposed. In order to obtain a statistical characterization for both high and low correlations, a simpler model such as the Kronecker correlation model is considered. Based on this characterization, two quantization schemes have been presented and evaluated using a realistic channel model such as the SCM. Results show that both schemes are able to reduce the feedback overhead in highly and moderately correlated scenarios. / Domene Oltra, F. (2015). Evaluation of precoding and feedback quantization schemes for multiuser communications systems [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/46971 / Alfresco

Les communications sans fils sont devenues un outil fondamental pour les sociétés modernes. Les réseaux cellulaires sont le moyen préféré pour l’accès à Internet. L’augmentation de la capacité du réseau est étroitement liée au problème des interférences. Les réseaux coopératifs ont été largement étudiés dans les années récentes. Cette thèse porte sur deux techniques de coopération dans la voie descendante :La première partie étudie les effets de quantification et délais sur les informations de retour nécessaires pour la mise en opération des différentes techniques d’émission coordonnée, connues sous le nom de CoMP (Coordinated Multipoint Transmission). Cette technique qui promet des augmentations importantes sur la capacité du réseau en conditions idéales, or ses vrais résultats sous le feedback limité doivent être encore décrits de manière analytique. En particulier, pour les modes d’émission connus comme JT (Joint Transmission) et CBF (Coordinated Beamforming), des expressions analytiques ont été déduites pour calculer la capacité du réseau et la probabilité de succès de transmission.Finalement une nouvelle technique de coopération de réseau pour les récepteurs avancés du type SIC (Successive Interference Cancellation) est présentée. La condition mathématique qui garantit des gains de capacité grâce à l’utilisation des récepteurs SIC est obtenue. Pour en profiter, une méthode de coopération est nécessaire pour assurer une adaptation de lien adéquate pour que l’interférence soit décodable et le débit somme soit supérieur à celui atteint avec des récepteurs traditionnels. Cette technique montre des gains importants de capacité pour des utilisateurs en bordure de cellule. / Wireless communications have become a fundamental feature of any modern society. In particular, cellular networks are essential for societal welfare but the increasing demand for data traffic set enormous scientific challenges. Increasing the network capacity is closely related to the problem of interference mitigation. In this regard, network cooperation has been studied in recent years and several different techniques have been proposed. In the first part, different transmission techniques commonly referred to as Coordinated Multi-Point Transmission (CoMP), are studied under the effect of feedback quantization and delay, unequal pathloss and other-cell interference (OCI). An analytical framework is provided, which yields closed-form expressions to calculate the ergodic throughput and outage probabilities of Coordinated Beamforming (CBF) and Joint Transmission (JT). The results indicate the optimal configuration for a system using CoMP and provide guidelines and answers to key questions, such as how many transmitters to coordinate, how many antennas to use, how many users to serve, which SNR regime is more convenient, whether to apply CBF or prefer a more complex JT, etc. Second, a new coordination technique at the receiver side is proposed to obtain sum-rate gains by means of Successive Interference Cancellation (SIC). The conditions that guarantee network capacity gains by means of SIC at the receiver are provided. To take advantage of these conditions, network coordination is needed to adapt the rates to be properly decoded at the different users involved. This technique is named Cooperative SIC and is shown to provide significant throughput gains for cell-edge users.

This thesis presents novel algorithms and a revised modeling framework to evaluate residential aggregate electrical demand response performance under scenarios with limited device-state feedback. These algorithms permit the provision of balancing reserves, or the smoothing of variable renewable energy generation, via an externally supplied target trajectory. The responsive load populations utilized were home heat pumps and deferred electric vehicle charging. As fewer devices in a responsive population report their state information, the error of the demand response program increases moderately but remains below 8%. The associated error of the demand response program is minimized with responsive load populations of approximately 4500 devices; the available capacity of the demand response system scales proportionally with population size. The results indicate that demand response programs with limited device-state feedback may provide a viable option to reduce overall system costs and address privacy concerns of individuals wishing to participate in a demand response program. / Graduate

In this thesis we investigate how to improve the performance of MU-MIMO wireless system in terms of achieving Shannon capacity limit and efficient use of precious resource of radio spectrum in wireless communication. First a new suboptimal volume-based scheduling algorithm is presented, which can be applied in MU-MIMO downlink system to transmit signals concurrently to multiple users under the assumption of perfect channel information at transmitter and receiver. The volume-based scheduling algorithm utilises Block Diagonalisation precoding and Householder reduction procedure of QR factorisation. In comparison with capacity-based suboptimal scheduling algorithm, the volume-based algorithm has much reduced computational complexity with only a fraction of sum-rate capacity penalty from the upper bound of system capacity limit. In comparison with semi-orthogonal user selection suboptimal scheduling algorithm, the volume-based scheduling algorithm can be implemented with less computational complexity. Furthermore, the sum-rate capacity achieved via volume-based scheduling algorithm is higher than that achieved by SUS scheduling algorithm in the MIMO case. Then, a two-step scheduling algorithm is proposed, which can be used in the MU-MIMO system and under the assumption that channel state information is known to the receiver, but it is not known to the transmitter and the system under the feedback resource constraint. Assume that low bits codebook and high bits codebook are stored at the transmitter and receiver. The users are selected by using the low bits codebook; subsequently the BD precoding vectors for selected users are designed by employing high bits codebook. The first step of the algorithm can alleviate the load on feedback uplink channel in the MU-MIMO wireless system while the second step can aid precoding design to improve system sum-rate capacity. Next, a MU-MIMO cognitive radio (CR) wireless system has been studied. In such system, a primary wireless network and secondary wireless network coexist and the transmitters and receivers are equipped with multiple antennas. Spectrum sensing methods by which a portion of spectrum can be utilised by a secondary user when the spectrum is detected not in use by a primary user were investigated. A Free Probability Theory (FPT) spectrum sensing method that is a blind spectrum sensing method is proposed. By utilizing the asymptotic behaviour of random matrix based on FPT, the covariance matrix of transmitted signals can be estimated through a large number of observations of the received signals. The method performs better than traditional energy spectrum sensing method. We also consider cooperative spectrum sensing by using the FPT method in MU-MIMO CR system. Cooperative spectrum sensing can improve the performance of signal detection. Furthermore, with the selective cooperative spectrum sensing approach, high probability of detection can be achieved when the system is under false alarm constraint. Finally, spectrum sensing method based on the bispectrum of high-order statistics (HOS) and receive diversity in SIMO CR system is proposed. Multiple antennas on the receiver can improve received SNR value and therefore enhance spectrum sensing performance in terms of increase of system-level probability of detection. Discussions on cooperative spectrum sensing by using the spectrum sensing method based on HOS and receive diversity are presented.

With the ever-growing demand for higher data rate in next generation communication systems, researchers are pushing the limits of the existing architecture. Due to the stochastic nature of communication channels, most systems use some form of adaptive methods to adjust the transmitting parameters and allocation of resources in order to overcome channel variations and achieve optimal throughput. We will study four cases of adaptive transmission and dynamic resource allocation in collaborative systems that are practically significant. Firstly, we study hybrid automatic repeat request (HARQ) techniques that are widely used to handle transmission failures. We propose HARQ policies that improve system throughput and are suitable for point-to-point, two-hop relay, and multi-user broadcast systems. Secondly, we study the effect of having bits of mixed SNR qualities in finite length codewords. We prove that by grouping bits according to their reliability so that each codeword contains homogeneous bit qualities, the finite blocklength capacity of the system is increased. Thirdly, we study the routing and resource allocation problem in multiple collaborative networks. We propose an algorithm that enables collaboration between networks which needs little to no side information shared across networks, but rather infers necessary information from the transmissions. The collaboration between networks provides a significant gain in overall throughput compared to selfish networks. Lastly, we present an algorithm that allocates disjoint transmission channels for our cognitive radio network in the DARPA Spectrum Collaboration Challenge (SC2). This algorithm uses the real-time spectrogram knowledge perceived by the radios and allocates channels adaptively in a crowded spectrum shared with other collaborative networks.

Due to its significant advantage in spectral efficiency, multiple-antenna communication technology will undoubtedly be a major component in future wireless system implementations. However, the full exploitation of this technology also requires perfect feedback of channel state information (CSI) to the transmitter-- something that is not practically feasible. This motivates the study of limited feedback systems, where CSI feedback is rate limited. This thesis focuses on the optimal design of limited feedback systems for three types of communication channels: the relay channel, the single-user point-to-point channel, and the multiuser broadcast channel. For the relay channel, we prove the efficiency of the Grassmannian codebooks as the source and relay beamforming codebooks, and propose a method for CSI exchange between the relay and the destination when global CSI is not available at destination. For the single-user point-to-point channel, we study the joint power control and beamforming problem and address the channel magnitude and direction quantization codebook design problem. It is shown that uniform quantization of the channel magnitude (in dB scale) is asymptotically optimal regardless of the channel distribution. The analysis further derives the optimal split of feedback bandwidth between the magnitude and direction quantization codebooks. For the multiuser broadcast channel, we first prove the sufficiency of a product magnitude-direction quantization codebook for managing the multiuser interference. We then derive the optimal split of feedback bandwidth across the users and their magnitude and direction codebooks. The optimization results reveal an inherent structural difference between the single-user and multiuser quantization codebooks: a multiuser codebook should have a finer direction quantization resolution as compared to a single-user codebook. It is further shown that the users expecting higher rates and requiring more reliable communication should provide a finer quantization of their CSI. Finally, we determine the minimum required total feedback rate based on users' quality-of-service constraints and derive the scaling of the system performance with the total feedback rate.

Due to its significant advantage in spectral efficiency, multiple-antenna communication technology will undoubtedly be a major component in future wireless system implementations. However, the full exploitation of this technology also requires perfect feedback of channel state information (CSI) to the transmitter-- something that is not practically feasible. This motivates the study of limited feedback systems, where CSI feedback is rate limited. This thesis focuses on the optimal design of limited feedback systems for three types of communication channels: the relay channel, the single-user point-to-point channel, and the multiuser broadcast channel. For the relay channel, we prove the efficiency of the Grassmannian codebooks as the source and relay beamforming codebooks, and propose a method for CSI exchange between the relay and the destination when global CSI is not available at destination. For the single-user point-to-point channel, we study the joint power control and beamforming problem and address the channel magnitude and direction quantization codebook design problem. It is shown that uniform quantization of the channel magnitude (in dB scale) is asymptotically optimal regardless of the channel distribution. The analysis further derives the optimal split of feedback bandwidth between the magnitude and direction quantization codebooks. For the multiuser broadcast channel, we first prove the sufficiency of a product magnitude-direction quantization codebook for managing the multiuser interference. We then derive the optimal split of feedback bandwidth across the users and their magnitude and direction codebooks. The optimization results reveal an inherent structural difference between the single-user and multiuser quantization codebooks: a multiuser codebook should have a finer direction quantization resolution as compared to a single-user codebook. It is further shown that the users expecting higher rates and requiring more reliable communication should provide a finer quantization of their CSI. Finally, we determine the minimum required total feedback rate based on users' quality-of-service constraints and derive the scaling of the system performance with the total feedback rate.

La technologie OFDMA (Orthogonal frequency division multiple access) a été adoptée par les systèmes de télécommunications de 4ème génération (4G) comme technique de transmission et d'accès multiple pour ses performances supérieures en termes d'efficacité spectrale. Dans ce type de systèmes, l'adaptation dynamique du débit en fonction de la qualité du canal CQI (Channel Quality Indicator) constitue une problématique de recherche d'actualité qui attire l'attention de plusieurs acteurs académiques et industriels. Ce problème d'adaptation dynamique est encore plus complexe à gérer dans des environnements multi-utilisateurs hétérogènes et à ressources limitées tels que les systèmes OFDMA comme WiMAX Mobile et Long-term Evolution (LTE). Dans cette thèse, nous nous intéressons au problème d'allocation de ressources de l'information de feedback relative au CQI dans le cadre de systèmes OFDMA multi-porteuses multi-utilisateurs. Dans le but de réduire la charge (overhead) du feedback, nous proposons une méthode de prédiction du CQI basée sur l'exploitation de la corrélation temporelle de ce dernier et d'une solution inter-couches. L'objectif est de trouver des schémas d'allocation de ressources adaptatifs respectant les contraintes de qualité de service (QoS) applicatives.Nous proposons en premier lieu un algorithme de réduction de feedback PBF (Prediction Based Feedack) qui permet à la station de base (BS) à prédire certaines occurrences du CQI en se basant sur l'algorithme des moindres carrés récursif RLS (Recursive least-square). Les résultats de simulation montrent que l'outil de prédiction du CQI réduit sensiblement l'overhead du feedback et améliore par conséquent le débit de la liaison montante. Nous proposons, par la suite, une version opportuniste de PBF pour atténuer les éventuels effets de sur et sous estimations liées à l'algorithme de prédiction. Dans ce mécanisme, nous exploitons les informations inter-couches pour améliorer les performances des mécanismes de feedbacks périodiques dont PBF fait partie. L'approche opportuniste améliore sensiblement les performances du système pour les cas de mobilité élevée comparés aux cas de faible mobilité.Dans un second temps, nous proposons une plateforme (FEREP : feedback resource allocation and prediction) basée sur une approche inter-couches. Implémentée au niveau de la station BS, FEREP intègre les fonctionnalités de prédiction, d'adaptation dynamique du CQI et d'ordonnancement des demandes de feedback. Elle comporte trois modules. Le module FWA (feedback window adaptation) gère dynamiquement la fenêtre de feedbacks de chaque station mobile (MS) en se basant sur les messages ARQ (Automatic Repeat Request) reçus qui reflètent l'état actuel des canaux respectifs. Le module PBFS (priority-based feedback scheduling) effectue ensuite l'ordonnancement des feedbacks en tenant compte de la taille de la fenêtre de feedback, du profil de l'utilisateur sous la contrainte de la limitation des ressources globales du systèmes réservées au feedback. Afin de choisir les paramètres de transmission MCS (modulation and coding schemes), le module PBF (prediction based feedback) est utilisé pour les utilisateurs dont le feedabck n'a pas pu être ordonnancé dans la trame courante. Les résultats de simulation ont montré un gain significatif des performances de FREREP en comparaison à un mécanisme de référence, en particulier, sous de fortes contraintes de limitation des ressources du feedback.Le protocole ARQ génère un accusé de réception uniquement si l'utilisateur est sélectionné par l'ordonnanceur pour envoyer des données sur la liaison descendante. Dans le cas où la fréquence d'ordonnancement des utilisateurs sur le lien descendant est réduite, les messages ARQ s'en trouvent également réduits, dégradant par conséquent les performances de la plateforme FEREP proposée ci-dessus. En effet, dans ce cas la signalisation ARQ devient insuffisante pour adapter efficacement la fenêtre de feedback de chaque utilisateur. Pour pallier à ce problème, nous proposons l'algorithme DCRA (dynamic CQI resource allocation) qui utilise deux modes d'estimation de la fenêtre de feedback. Le premier est un mode hors-ligne basé sur des études empiriques permettant d'estimer la fenêtre moyenne optimale de feedback en utilisant les profils applicatif et de mobilité de l'utilisateur. Notre analyse de performance par simulation montre que la fenêtre de feedback peut être estimée en fonction de la classe de service des utilisateurs et de leurs profils de mobilité pour un environnement cellulaire donné. Le second mode de fonctionnement de DCRA effectue une adaptation dynamique de la fenêtre en temps réel dans le cas où la signalisation ARQ est suffisante. Une étude comparative avec les mécanismes DFS (deterministic feedback scheduling) et OFS (opportunistic feedback scheduling), a montré que DCRA arrive à réaliser un meilleur gain en ressources montantes grâce à la réduction de l'overhead des feedbacks, sans pour autant trop dégrader le débit descendant des utilisateurs. Du point de vue des utilisateurs, DCRA améliore les contraintes de QoS tels que le taux de perte de paquets et réduit la consommation énergétique des terminaux grâce à la réduction de feedback. / Orthogonal frequency division multiple access (OFDMA) technology has been adopted by 4th generation (a.k.a. 4G) telecommunication systems to achieve high system spectral efficiency. A crucial research issue is how to design adaptive channel quality indicator (CQI) feedback mechanisms so that the base station can use adaptive modulation and coding (AMC) techniques to adjust its data rate based on the channel condition. This problem is even more challenging in resource-limited and heterogeneous multiuser environments such as Mobile WiMAX, Long-term Evolution (LTE) networks. In this thesis, we consider CQI feedback resource allocation issue for multiuser multicarrier OFDMA systems. We exploit time-domain correlation for CQI prediction and cross-layer information to reduce feedback overhead for OFDMA systems. Our aim is find resource allocation schemes respecting the users QoS constraints.Our study begins with proposing prediction based feedback (PBF) which allows the base station to predict the CQI feedbacks based on recursive least-square (RLS) algorithm. We showed that it is useful to use channel prediction as a tool to reduce the feedback overhead and improve the uplink throughput. Then, we propose an opportunistic periodic feedback mechanism to mitigate the possible under and over estimation effects of CQI prediction. In this mechanism, we exploited the cross-layer information to enhance the performance of periodic feedback mechanisms. The opportunistic mechanism improves the system performance for high mobility cases compared to low mobility cases.For OFDMA systems with limited feedback resource, we propose an integrated cross-layer framework of feedback resource allocation and prediction (FEREP). The proposed framework, implemented at the BS side, is composed of three modules. The feedback window adaptation (FWA) module dynamically tunes the feedback window size for each mobile station based on the received ARQ (Automatic Repeat Request) messages that reflect the current channel condition. The priority-based feedback scheduling (PBFS) module then performs feedback allocation by taking into account the feedback window size, the user profile and the total system feedback budget. To choose adapted modulation and coding schemes (MCS), the prediction based feedback (PBF) module performs channel prediction by using recursive least square (RLS) algorithm for the user whose channel feedback has not been granted for schedule in current frame. Through extensive simulations, the proposed framework shows significant performance gain especially under stringent feedback budget constraint.ARQ protocol receives users acknowledgement only if the user is scheduled in the downlink. The reduction in users scheduling frequency also reduces the rate of ARQ hints and degrades the performance of above contributions. In this case, it is difficult to exploit the ARQ signal to adapt the feedback window for that user. To address this issue, we propose a cross-layer dynamic CQI resource allocation (DCRA) algorithm for multiuser multicarrier OFDMA systems. DCRA uses two modes for feedback window estimation. The first one is an off-line mode based on empirical studies to derive optimal average feedback window based on user application and mobility profile. Our experimental analysis shows that the feedback window can be averaged according to users service class and their mobility profile for a given cell environment. DCRA performs a realtime dynamic window adaptation if sufficient cross-layer hints are available from ARQ signaling. DCRA increases uplink resource by reducing feedback overhead without degrading downlink throughout significantly compared to deterministic feedback scheduling (DFS) and opportunistic feedback scheduling (OFS). From the users perspective, DCRA improves QoS constraints like packet loss rate and saves users power due to feedback reduction.

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm.