This thesis investigates the application of an iterative leakage-based precoding algorithm to practical multiuser-MIMO systems. We consider the effect of practical impairments including imperfect channel state information, transmit antenna correlation, and time-varying channels. Solutions are derived which improve performance of the algorithm with imperfect channel state information at the transmitter by leveraging knowledge of the second-order statistics of the error. From this work we draw a number of conclusions on how imperfect channel state information may impact the system design including the importance of interference suppression at the receiver and the selection of the number of co-scheduled users. We also demonstrate an efficient approach to improve the convergence of the algorithm when using interference-rejection-combining receivers. Finally, we conduct simulations of an LTE-A system employing the improved algorithm to show its utility for modern communication systems. / Master of Science

Wireless communication links with multiple antennas at both the transmitter and the receiver sides, so-called multiple-input-multiple-output (MIMO)systems, are attracting much interest since they can significantly increase the capacity of band-limited wireless channels to meet the requirements of the future high data rate wireless communications. The treatment of channel state information (CSI) is critical in the design of MIMO systems. Accurate CSI at the transmitter is often not possible or may require high feedback rates, especially in multi-user scenarios. Herein, we consider the robust design of linear transceivers with imperfect CSI either at the transmitter or at both sides of the link. The framework considers the design problem where the imperfect CSI consists of a channel mean and an channel covariance matrix or, equivalently, a channel estimate and an estimation error covariance matrix. For single-user systems, the proposed robust transceiver designs are based on a general cost function of the average mean square errors. Under different CSI conditions, our robust designs exhibit a similar structure to the transceiver designs for perfect CSI, but with a different equivalent channel and/or noise covariance matrix. Utilizing majorization theory, the robust linear transceiver design can be readily solved by convex optimization approaches in practice. For multi-user systems, we consider both the communication link from the users to the access point (up-link) as well as the reverse link from the access point to the users (down-link). For the up-link channel, it is possible to optimally design robust linear transceivers minimizing the average sum mean square errors of all the data streams for the users. Our robust linear transceivers are designed either by reformulating the optimization problem as a semidefinite program or by extending the design of a single-user system in an iterative manner. Under certain channel conditions, we show that the up-link design problem can even be solved partly in a distributed fashion. For the down-link channel, a system with one receive antenna per user is considered. A robust system design is obtained by reducing the feedback load from all users to allow only a few selected users to feed back accurate CSI to the access point. We study the properties of four typical user selection algorithms in conjunction with beamforming that guarantee certain signal-to-interference-plus-noise ratio (SINR) requirements under transmit power minimization. Specifically, we show that norm-based user selection is asymptotically optimal in the number of transmitter antennas and close-to-optimal in the number of users. Rooted in the practical significance of this result, a simpler down-link system design with reduced feedback requirements is proposed. / QC 20100922

The high deployment and maintenance costs of last mile wireline networks (i.e., DSL and cable networks) have urged service providers to search for new cost-effective solutions to provide broadband connectivity. Broadband wireless access (BWA) networks, which offer a wide coverage area and high transmission rates in addition to their fast and low-cost deployment, have emerged as an alternative to last mile wireline networks. Therefore, BWA networks are expected to be deployed in areas with different terrain profiles (e.g., urban, suburban, rural) where wireless communication faces different channel impairments. This fact necessitates the adoption of various transmission technologies that combat the channel impairments of each profile. Implementation scenarios of BWA networks considered in this thesis are multicarrier-based direct transmission and single carrier-based cooperative transmission scenarios. The performance of these transmission technologies highly depends on how resources are allocated. In this thesis, we focus on the development of practical resource allocation schemes for the mentioned BWA networks implementation scenarios. In order to develop practical schemes, the imperfection of channel state information (CSI) and computational power limitations are among considered practical implementation issues. The design of efficient resource allocation schemes at the MAC layer heavily relies on the CSI reported from the PHY layer as a measure of the wireless channel condition. The channel estimation error and feedback delay renders the reported CSI erroneous. The inaccuracy in CSI propagates to higher layers, resulting in performance degradation. Although this effect is intuitive, a quantitative measure of this degradation is necessary for the design of practical resource allocation schemes. An approach to the evaluation of the ergodic mutual information that reflects this degradation is developed for single carrier, multicarrier, direct, and cooperative scenarios with inaccurate CSI. Given the CSI estimates and estimation error statistics, the presented evaluation of ergodic mutual information can be used in resource allocation and in assessing the severity of estimation error on performance degradation. A point-to-multipoint (PMP) network that employs orthogonal frequency division multiple access (OFDMA) is considered as one of the most common implementation scenarios of BWA networks. Replacing wireline networks requires not only providing the last mile connectivity to subscribers but also supporting their diverse services with stringent quality of service (QoS) requirements. Therefore, the resource allocation problem (i.e., subcarriers, rate and power allocation) is modeled as a network utility maximization (NUM) one that captures the characteristics of this implementation scenario. A dual decomposition-based resource allocation scheme that takes into consideration the diversity of service requirements and inaccuracy of the CSI estimation is developed. Numerical evaluations and simulations are conducted to validate our theoretical claims that the scheme maximizes resource utilization, coordinates with the call admission controller to guarantee QoS, and accounts for CSI inaccuracy. Cooperation has recently received great attention from the research community and industry because of its low cost and fast deployment in addition to the performance improvement it brings to BWA networks. In cooperative scenarios, subscribers cooperate to relay each other's signals. For this implementation scenario of BWA networks, a robust and constrained Kalman filter-based power allocation scheme is proposed to minimize power consumption and guarantee bit error probability (BEP) requirements. The proposed scheme is robust to CSI inaccuracy, responsive to changes in BEP requirements, and optimal in allocating resources. In summary, research results presented in this thesis contribute to the development of practical resource allocation schemes for BWA networks.

The high deployment and maintenance costs of last mile wireline networks (i.e., DSL and cable networks) have urged service providers to search for new cost-effective solutions to provide broadband connectivity. Broadband wireless access (BWA) networks, which offer a wide coverage area and high transmission rates in addition to their fast and low-cost deployment, have emerged as an alternative to last mile wireline networks. Therefore, BWA networks are expected to be deployed in areas with different terrain profiles (e.g., urban, suburban, rural) where wireless communication faces different channel impairments. This fact necessitates the adoption of various transmission technologies that combat the channel impairments of each profile. Implementation scenarios of BWA networks considered in this thesis are multicarrier-based direct transmission and single carrier-based cooperative transmission scenarios. The performance of these transmission technologies highly depends on how resources are allocated. In this thesis, we focus on the development of practical resource allocation schemes for the mentioned BWA networks implementation scenarios. In order to develop practical schemes, the imperfection of channel state information (CSI) and computational power limitations are among considered practical implementation issues. The design of efficient resource allocation schemes at the MAC layer heavily relies on the CSI reported from the PHY layer as a measure of the wireless channel condition. The channel estimation error and feedback delay renders the reported CSI erroneous. The inaccuracy in CSI propagates to higher layers, resulting in performance degradation. Although this effect is intuitive, a quantitative measure of this degradation is necessary for the design of practical resource allocation schemes. An approach to the evaluation of the ergodic mutual information that reflects this degradation is developed for single carrier, multicarrier, direct, and cooperative scenarios with inaccurate CSI. Given the CSI estimates and estimation error statistics, the presented evaluation of ergodic mutual information can be used in resource allocation and in assessing the severity of estimation error on performance degradation. A point-to-multipoint (PMP) network that employs orthogonal frequency division multiple access (OFDMA) is considered as one of the most common implementation scenarios of BWA networks. Replacing wireline networks requires not only providing the last mile connectivity to subscribers but also supporting their diverse services with stringent quality of service (QoS) requirements. Therefore, the resource allocation problem (i.e., subcarriers, rate and power allocation) is modeled as a network utility maximization (NUM) one that captures the characteristics of this implementation scenario. A dual decomposition-based resource allocation scheme that takes into consideration the diversity of service requirements and inaccuracy of the CSI estimation is developed. Numerical evaluations and simulations are conducted to validate our theoretical claims that the scheme maximizes resource utilization, coordinates with the call admission controller to guarantee QoS, and accounts for CSI inaccuracy. Cooperation has recently received great attention from the research community and industry because of its low cost and fast deployment in addition to the performance improvement it brings to BWA networks. In cooperative scenarios, subscribers cooperate to relay each other's signals. For this implementation scenario of BWA networks, a robust and constrained Kalman filter-based power allocation scheme is proposed to minimize power consumption and guarantee bit error probability (BEP) requirements. The proposed scheme is robust to CSI inaccuracy, responsive to changes in BEP requirements, and optimal in allocating resources. In summary, research results presented in this thesis contribute to the development of practical resource allocation schemes for BWA networks.

Ericsson Brasil / The International Telecommunications Union (ITU) established through the International Mobile Telecommunications (IMT)-Advanced a set of requirements for high performance of 4th Generation (4G) communication systems and, with the aim of meeting such requirements, 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is considering a set of enhancements, referred to as LTE-Advanced. In the LTE-Advanced context, Coordinated Multi-Point (CoMP) communication appears as a promising technology to boost system throughput and to allow for an efficient Radio Resource Allocation (RRA). CoMP systems promise very high performance in terms of spectral efficiency and coverage benefits when perfect Channel State Information (CSI) is available at the transmitter. However, perfect CSI is difficult to obtain in CoMP systems due to an increased number of channel parameters to be estimated at the receiver and to be fed back to the transmitter. So, the performance of such systems is compromised when the CSI is not perfectly known during CoMP processing, which is an important problem to be addressed. Space Division Multiple Access (SDMA) grouping algorithms are usually employed in order to find a suitable set of users for spatial multiplexing. The largest SDMA group is not always the best group in a given data transmission such that higher gains might be achieved by dynamically adjusting the SDMA group size. Besides, algorithms that balance the Signal to Interference-plus-Noise Ratio (SINR) among different links might ensure a certain level of link quality and so provide a more reliable communication for the scheduled users. This master thesis provides system-level analyses for RRA algorithms that exploit coordination in the downlink of CoMP systems to implement adaptive resource reuse and so improve system throughput. Herein, RRA strategies which consider dynamic SDMA grouping, joint precoding and power allocation for SINR balancing are studied in CoMP systems assuming imperfect CSI in order to obtain a better approximation with regard to the real-world implementations. It is shown through system-level analyses that quite high throughput gains are achieved through intelligent RRA. In conclusion, the results show that Sequential Removal Algorithms (SRAs) and SINR balancing provide system spectral efficiency gains. However, a critical degradation on the performance of these RRA strategies due to imperfect CSI is also shown. / A UniÃo Internacional para TelecomunicaÃÃes (ITU) estabeleceu atravÃs da iniciativa para o Sistema AvanÃado Internacional de TelecomunicaÃÃes MÃveis (IMT-Advanced), um conjunto de requisitos de alto desempenho para os sistemas de comunicaÃÃo de quarta geraÃÃo (4G) e, com o objetivo de atender tais requisitos, a EvoluÃÃo de Longo Prazo (LTE) do Projeto de Parceria para a Terceira GeraÃÃo (3GPP) està considerando um conjunto de melhorias, referidas como LTE-AvanÃado. No contexto do LTE-AvanÃado, a comunicaÃÃo multi-ponto coordenada (CoMP) aparece como uma tecnologia promissora para aumentar a vazÃo do sistema e permitir uma AlocaÃÃo de Recursos de RÃdio (RRA) eficiente. Os sistemas CoMP prometem alto desempenho em termos de eficiÃncia espectral e benefÃcios de cobertura quando a InformaÃÃo do Estado do Canal (CSI) perfeita està disponÃvel no transmissor. No entanto, CSI perfeita à difÃcil de se obter em sistemas CoMP devido a um alto nÃmero de parÃmetros de canal a serem estimados no receptor e enviados para o transmissor. Assim, o desempenho de tais sistemas à comprometido quando a CSI nÃo à perfeitamente conhecida durante o processamento CoMP tal que esse à um problema importante a ser abordado. Algoritmos de agrupamento para MÃltiplo Acesso por DivisÃo no EspaÃo (SDMA) geralmente sÃo utilizados a fim de encontrar um conjunto adequado de usuÃrios para multiplexaÃÃo espacial. O maior grupo SDMA nem sempre à o melhor grupo em uma transmissÃo de dados tal que maiores ganhos podem ser obtidos ajustando dinamicamente o tamanho do grupo SDMA. AlÃm disso, os algoritmos que balanceiam a RazÃo Sinal-InterferÃncia mais RuÃdo (SINR) entre diferentes canais podem garantir um certo nÃvel de qualidade de canal e assim proporcionar uma comunicaÃÃo mais confiÃvel para os usuÃrios agrupados. Esta dissertaÃÃo de mestrado fornece anÃlises em nÃvel sistÃmico para algoritmos de RRA que exploram a coordenaÃÃo no enlace direto de sistemas CoMP para implementar reuso adaptativo de recursos e assim melhorar o desempenho do sistema. SÃo estudadas aqui estratÃgias de RRA em sistemas CoMP que consideram agrupamento SDMA dinÃmico, precodificaÃÃo e alocaÃÃo de potÃncia conjuntas para balanceamento de SINR, sendo assumida CSI imperfeita a fim de conseguir maior aproximaÃÃo com relaÃÃo Ãs implementaÃÃs em cenÃrios reais. à mostrado atravÃs de anÃlises em nÃvel sistÃmico que ganhos de vazÃo bastante altos sÃo alcanÃados atravÃs de RRA inteligente. Em conclusÃo, os resultados mostram que Algoritmos de RemoÃÃo Sequencial (SRAs) e de balanceamento de SINR proporcionam ganhos de eficiÃncia espectral do sistema. No entanto, à tambÃm mostrada uma degradaÃÃo crÃtica no desempenho dessas estratÃgias de RRA devido à CSI imperfeita.

Depuis plusieurs années, les opérateurs de téléphonie mobile sont confrontés à une croissance considérable du trafic de données mobiles. Dans un tel contexte, la technologie Cloud Radio Access Network (CRAN) qui intègre les solutions de Cloud Computing aux réseaux d’accès radio est considérée comme une nouvelle architecture pour les futures générations de réseaux 5G. L’approche CRAN permet une optimisation globale des fonctions de traitement en bande de base du signal et de la gestion des ressources radio pour l’ensemble des RRH et des utilisateurs. Parallèlement, les réseaux hétérogènes (HetNets) ont été proposés pour augmenter efficacement la capacité et la couverture du réseau 5G tout en réduisant la consommation énergétique. En combinant les avantages du Cloud avec ceux des réseaux HetNets, le concept de réseaux H-CRAN (Heterogeneous Cloud Radio Access Networks) est né et est considéré comme l’une des architectures les plus prometteuses pour répondre aux exigences des futurs systèmes. Plus particulièrement, nous abordons le problème important de l’optimisation jointe de l’association utilisateur-RRH et de la solution de beamforming sur la liaison descendante d’un système H-CRAN. Nous formulons un problème de maximisation du débit total du système sous des contraintes de mobilité et d’imperfection de CSI (Channel State Information). Notre principal défi consiste à concevoir une solution capable de maximiser le débit tout en permettant, contrairement aux autres solutions de référence, de réduire la complexité de calcul, et les coûts de signalisation et de feedback CSI dans divers environnements. Notre étude commence par proposer un algorithme Hybride, qui active périodiquement des schémas de clustering dynamiques et statiques pour aboutir à un compromis satisfaisant entre optimalité et le coût en complexité et signalisation CSI et réassociation. L’originalité de l’algorithme Hybride réside aussi dans sa prise en compte de la dimension temporelle du processus d’allocation sur plusieurs trames successives plutôt que son optimalité (ou sous-optimalité) pour la seule trame d’ordonnancement courante. De plus, nous développons une analyse des coûts de l’algorithme en fonction de plusieurs critères afin de mieux appréhender le compromis entre les nombreux paramètres impliqués. La deuxième contribution de la thèse s’intéresse au problème sous la perspective de la mobilité utilisateur. Deux variantes améliorées de l’algorithme Hybride sont proposées : ABUC (Adaptive Beamforming et User Clustering), une version adaptée à la mobilité des utilisateurs et aux variations du canal radio, et MABUC (Mobility-Aware Beamforming et User Clustering), une version améliorée qui règle dynamiquement les paramètres de feedback du CSI (périodicité et type de CSI) en fonction de la vitesse de l’utilisateur. L’algorithme MABUC offre de très bonnes performances en termes de débit cible tout en réduisant efficacement la complexité et les coûts de signalisation CSI. Dans la dernière contribution de la thèse, nous approfondissons l’étude en explorant l’optimisation automatique des paramètres d’ordonnancement du CSI. Pour ce faire, nous exploitons l’outil de l’apprentissage par renforcement afin d’optimiser les paramètres de feedback CSI en fonction du profil de mobilité individuelle des utilisateurs. Plus spécifiquement, nous proposons deux modèles d’apprentissage. Le premier modèle basé sur un algorithme de type Q-learning a permis de démontrer l’efficacité de l’approche dans un scénario à taille réduite. Le second modèle, plus scalable car basé sur une approche Deep Q-learning, a été formulé sous la forme d’un processus de type POMDP (Partially observable Markov decision process). Les résultats montrent l’efficacité des solutions qui permettent de sélectionner les paramètres de feedback les plus adaptés à chaque profil de mobilité, même dans le cas complexe où chaque utilisateur possède un profil de mobilité différent et variable dans le temps. / Recently, mobile operators have been challenged by a tremendous growth in mobile data traffic. In such a context, Cloud Radio Access Network (CRAN) has been considered as a novel architecture for future wireless networks. The radio frequency signals from geographically distributed antennas are collected by Remote Radio Heads (RRHs) and transmitted to the cloud-centralized Baseband Units (BBUs) pool through fronthaul links. This centralized architecture enables a global optimization of joint baseband signal processing and radio resource management functions for all RRHs and users. At the same time, Heterogeneous Networks (HetNets) have emerged as another core feature for 5G network to enhance the capacity/coverage while saving energy consumption. Small cells deployment helps to shorten the wireless links to end-users and thereby improving the link quality in terms of spectrum efficiency (SE) as well as energy efficiency (EE). Therefore, combining both cloud computing and HetNet advantages results in the so-called Heterogeneous-Cloud Radio Access Networks (H-CRAN) which is regarded as one of the most promising network architectures to meet 5G and beyond system requirements. In this context, we address the crucial issue of beamforming and user-to-RRH association (user clustering) in the downlink of H-CRANs. We formulate this problem as a sum-rate maximization problem under the assumption of mobility and CSI (Channel State Information) imperfectness. Our main challenge is to design a framework that can achieve sum-rate maximization while, unlike other traditional reference solutions, being able to alleviate the computational complexity, CSI feedback and reassociation signaling costs under various mobility environments. Such gain helps in reducing the control and feedback overhead and in turn improve the uplink throughput. Our study begins by proposing a simple yet effective algorithm baptized Hybrid algorithm that periodically activates dynamic and static clustering schemes to balance between the optimality of the beamforming and association solutions while being aware of practical system constraints (complexity and signaling overhead). Hybrid algorithm considers time dimension of the allocation and scheduling process rather than its optimality (or suboptimality) for the sole current scheduling frame. Moreover, we provide a cost analysis of the algorithm in terms of several parameters to better comprehend the trade-off among the numerous dimensions involved in the allocation process. The second key contribution of our thesis is to tackle the beamforming and clustering problem from a mobility perspective. Two enhanced variants of the Hybrid algorithm are proposed: ABUC (Adaptve Beamforming and User Clustering), a mobility-aware version that is fit to the distinctive features of channel variations, and MABUC (Mobility-Aware Beamforming and User Clustering), an advanced version of the algorithm that tunes dynamically the feedback scheduling parameters (CSI feedback type and periodicity) in accordance with individual user velocity. MABUC algorithm achieves a targeted sum-rate performance while supporting the complexity and CSI signaling costs to a minimum. In our last contribution, we propose to go further in the optimization of the CSI feedback scheduling parameters. To do so, we take leverage of reinforcement learning (RL) tool to optimize on-the-fly the feedback scheduling parameters according to each user mobility profile. More specifically, we propose two RL models, one based on Q-learning and a second based on Deep Q-learning algorithm formulated as a POMDP (Partially observable Markov decision process). Simulation results show the effectiveness of our proposed framework, as it enables to select the best feedback parameters tailored to each user mobility profile, even in the difficult case where each user has a different mobility profile.