This dissertation addresses multi-layer optimization aspects of multiple input multiple output (MIMO) and deep learning-based communication systems. The initial focus is on the rate optimization for multi-user MIMO (MU-MIMO) configurations; specifically, multiple access channel (MAC) and interference channel (IC). First, the ergodic sum rates of MIMO MAC and IC configurations are determined by jointly integrating the error and overhead effects due to channel estimation (training) and feedback into the rate optimization. Then, we investigated methods that will increase the achievable rate for parallel Gaussian IC (PGIC) which is a special case of MIMO IC where there is no interference between multiple antenna elements. We derive a generalized iterative waterfilling algorithm for power allocation that maximizes the ergodic achievable rate. We verified the sum rate improvement with our proposed scheme through extensive simulation tests. Next, we introduce a novel physical layer scheme for single user MIMO spatial multiplexing systems based on unsupervised deep learning using an autoencoder. Both transmitter and receiver are designed as feedforward neural networks (FNN) and constellation diagrams are optimized to minimize the symbol error rate (SER) based on the channel characteristics. We first evaluate the SER in the presence of a constant Rayleigh-fading channel as a performance upper bound. Then, we quantize the Gaussian distribution and train the autoencoder with multiple quantized channel matrices. The channel is provided as an input to both the transmitter and the receiver. The performance exceeds that of conventional communication systems both when the autoencoder is trained and tested with single and multiple channels and the performance gain is sustained after accounting for the channel estimation error. Moreover, we evaluate the performance with increasing number of quantization points and when there is a difference between training and test channels. We show that the performance loss is minimal when training is performed with sufficiently large number of quantization points and number of channels. Finally, we develop a distributed and decentralized MU-MIMO link selection and activation protocol that enables MU-MIMO operation in wireless networks. We verified the performance gains with the proposed protocol in terms of average network throughput. / Doctor of Philosophy / Multiple Input Multiple Output (MIMO) wireless systems include multiple antennas both at the transmitter and receiver and they are widely used today in cellular and wireless local area network systems to increase robustness, reliability and data rate. Multi-user MIMO (MU-MIMO) configurations include multiple access channel (MAC) where multiple transmitters communicate simultaneously with a single receiver; interference channel (IC) where multiple transmitters communicate simultaneously with their intended receivers; and broadcast channel (BC) where a single transmitter communicates simultaneously with multiple receivers. Channel state information (CSI) is required at the transmitter to precode the signal and mitigate interference effects. This requires CSI to be estimated at the receiver and transmitted back to the transmitter in a feedback loop. Errors occur during both channel estimation and feedback processes. We initially analyze the achievable rate of MAC and IC configurations when both channel estimation and feedback errors are taken into account in the capacity formulations. We treat the errors associated with channel estimation and feedback as additional noise. Next, we develop methods to maximize the achievable rate for IC by using interference cancellation techniques at the receivers when the interference is very strong. We consider parallel Gaussian IC (PGIC) which is a special case of MIMO IC where there is no interference between multiple antenna elements. We develop a power allocation scheme which maximizes the ergodic achievable rate of the communication systems. We verify the performance improvement with our proposed scheme through simulation tests. Standard optimization techniques are used to determine the fundamental limits of MIMO communications systems. However, there is still a gap between current operational systems and these limits due to complexity of these solutions and limitations in their assumptions. Next, we introduce a novel physical layer scheme for MIMO systems based on machine learning; specifically, unsupervised deep learning using an autoencoder. An autoencoder consists of an encoder and a decoder that compresses and decompresses data, respectively. We designed both the encoder and the decoder as feedforward neural networks (FNNs). In our case, encoder performs transmitter functionalities such as modulation and error correction coding and decoder performs receiver functionalities such as demodulation and decoding as part of the communication system. Channel is included as an additional layer between the encoder and decoder. By incorporating the channel effects in the design process of the autoencoder and jointly optimizing the transmitter and receiver, we demonstrate the performance gains over conventional MIMO communication schemes. Finally, we develop a distributed and decentralized MU-MIMO link selection and activation protocol that enables MU-MIMO operation in wireless networks. We verified the performance gains with the proposed protocol in terms of average network throughput.

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2017-11-13T21:02:54Z No. of bitstreams: 1 Felipe_Gohring_de_Magalh?es_TES.pdf: 7728697 bytes, checksum: f2b34275e49f32253d8c38848a3d9258 (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-11-21T12:14:27Z (GMT) No. of bitstreams: 1 Felipe_Gohring_de_Magalh?es_TES.pdf: 7728697 bytes, checksum: f2b34275e49f32253d8c38848a3d9258 (MD5) / Made available in DSpace on 2017-11-21T12:26:58Z (GMT). No. of bitstreams: 1 Felipe_Gohring_de_Magalh?es_TES.pdf: 7728697 bytes, checksum: f2b34275e49f32253d8c38848a3d9258 (MD5) Previous issue date: 2017-05-25 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / As tend?ncias de design para os sistemas multiprocessadores da pr?xima gera??o apontam para a integra??o de um grande n?mero de n?cleos de processamento, exigindo interconex?es de alto desempenho. Uma solu??o a ser aplicada para melhorar a infraestrutura de comunica??o em tais sistemas ? o uso de redes on-chip, pois estas apresentam uma melhoria consider?vel na largura de banda e escalabilidade. Ainda assim, o n?mero de n?cleos integrados continua a aumentar ao mesmo tempo em que o sistema cresce, dessa maneira as interconex?es met?licas em redes on-chip podem tornar-se um gargalo no desempenho. Como resultado, uma nova estrat?gia deve ser adotada para que essas quest?es sejam solucionadas. As Redes ?pticas Integradas (do ingl?s Optical Integrated Networks - OINs) s?o atualmente consideradas como um dos paradigmas mais promissores neste contexto de design: elas apresentam maior largura de banda, menor consumo de energia e baixa lat?ncia para transmitir informa??es. Al?m disso, trabalho recentes demonstram a viabilidade de OINs com suas tecnologias de fabrica??o dispon?veis e compat?veis com CMOS. No entanto, os designers de OINs enfrentam v?rios desafios: ? Atualmente, os controladores representam o principal gargalo na comunica??o e s?o um dos fatores que limitam o uso de OINs. Portanto, novas solu??es de controle de baixa lat?ncia s?o necess?rias. ? Designers n?o possuem ferramentas para modelar e validar OINs. A maioria das pesquisas atualmente est? focada em projetar dispositivos e melhorar os componentes b?sicos, deixando o sistema sem melhorias. Neste contexto, para facilitar a implanta??o de sistemas baseados em OIN, este projeto de doutorado concentra-se em tr?s contribui??es principais: (1) o desenvolvimento da plataforma de simula??o a n?vel de sistema; (2) a defini??o e o desenvolvimento de uma abordagem de controle eficiente para sistemas baseados em OIN e; (3) a avalia??o, a n?vel do sistema, da abordagem de controle proposta usando a modelagem definida. / Design trends for next-generation Multi-Processor Systems point to the integration of a large number of processing cores, requiring high-performance interconnects. One solution being applied to improve the communication infrastructure in such systems is the usage of Networkson- Chip as they present considerable improvement in the bandwidth and scaleability. Still as the number of integrated cores continues to increase and the system scales, the metallic interconnects in Networks-on-Chip can become a performance bottleneck. As a result, a new strategy must be adopted in order for those issues to be remedied. Optical Integrated Networks (OINs) are currently considered to be one of the most promising paradigm in this design context: they present higher bandwidth, lower power consumption and lower latency to broadcast information. Also, the latest work demonstrates the feasibility of OINs with their fabrication technologies being available and CMOS compatible. However, OINs? designers face several challenges: ? Currently, controllers represent the main communication bottleneck and are one of the factors limiting the usage of OINs. Therefore, new controlling solutions with low latency are required. ? Designers lack tools to model and validate OINs. Most research nowadays is focused on designing devices and improving basic components performance, leaving system unattended. In this context, in order to ease the deployment of OIN-based systems, this PhD project focuses on three main contributions: (1) the development of accurate system-level modelling study to realize a system-level simulation platform; (2) the definition and development of an efficient control approach for OIN-based systems, and; (3) the system-level evaluation of the proposed control approach using the defined modelling.

Le thème principal de ce mémoire est la commande sous contraintes pour des systèmes à retard, en tenant compte de la problématique d’échantillonnage (où les informations concernant le système en temps continu sont, par exemple, envoyées par un réseau de communication) et de la présence de contraintes sur les trajectoires du système et sur l’entrée de commande. Pendant le processus d’échantillonnage, le retard variable dans le temps peut être traité comme une incertitude, le but étant de confiner cette variation dans un polytope, de façon à couvrir toutes les variations possibles du retard. Pour stabiliser des systèmes à retard, nous nous sommes basés sur la théorie de Lyapunov. En utilisant cette méthode, nous pouvons trouver un retour d’état qui stabilise le système malgré la présence du retard variable dans la boucle. Une autre possibilité est l’utilisation des candidates de Lyapunov-Krasovskii. La théorie des ensembles invariants est largement utilisée dans ce manuscrit, car il est souhaitable d’obtenir une région de ≪ sûreté ≫, ou le comportement du système est connu, en dépit de la présence du retard (variable) et des contraintes sur les trajectoires du système. Lorsqu’ils sont obtenus dans l’espace d’état augmenté, les ensembles invariants sont très complexes, car la dimension de l’espace Euclidien sera proportionnelle à la taille du système mais aussi à la taille du retard. Le concept de D-invariance est ainsi proposé. La commande prédictive (en anglais MPC) est présentée, pour tenir compte des contraintes sur les trajectoires et appliquer une commande optimale à l’entrée du système. / The main interest of the present thesis is the constrained control of time-delay system, more specifically taking into consideration the discretization problem (due to, for example, a communication network) and the presence of constraints in the system’s trajectories and control inputs. The effects of data-sampling and modeling problem are studied in detail, where an uncertainty is added into the system due to additional effect of the discretization and delay. The delay variation with respect to the sampling instants is characterized by a polytopic supra-approximation of the discretization/delay induced uncertainty. Some stabilizing techniques, based on Lyapunov’s theory, are then derived for the unconstrained case. Lyapunov-Krasovskii candidates were also used to obtain LMI conditions for a state feedback, in the ``original” state-space of the system. For the constrained control purposes, the set invariance theory is used intensively, in order to obtain a region where the system is ``well-behaviored”, despite the presence of constraints and (time-varying) delay. Due to the high complexity of the maximal delayed state admissible set obtained in the augmented state-space approach, in the present manuscript we proposed the concept of set invariance in the ``original” state-space of the system, called D-invariance. Finally, in the las part of the thesis, the MPC scheme is presented, in order to take into account the constraints and the optimality of the control solution.

Sensor networks are being used in several emerging applications not even imagined some years ago due to advances in sensing, computing, and communication techniques. However, these advances also pose various challenges that must be faced. One important challenge is related to the autonomous capability needed to setup and adapt the networks, which decentralizes the control of the network, saving communication and energy resources. Middleware technology helps in addressing this kind of problem, but there is still a need for additional solutions, particularly considering dynamic changes in users' requirements and operation conditions. This paper presents an agent-based framework acting as an integral part of a middleware to support autonomous setup and adaptation of sensor networks. It adds interoperability among heterogeneous nodes in the network, by means of autonomous behavior and reasoning. These features also address the needs for system setup and adaptations in the network, reducing the communication overhead and decentralizing the decision making mechanism. Additionally, preliminary results are also presented.

No description available.

The main fokus of this D thesis consists in the creating of horizontal points in Lešná village that is closed to Valašské Meziříčí, part Vysoká. Sixty five points of horizontal control (PPBP) were located and set in Vysoká. The network adjustment was done with the help of the method of lest squares and coordinates were counted with the G-NET/Mini programme.

No description available.