Implementação de conversor de polarização linear-circular para antenas de microfita usando metasuperfícies

This work aims to propose a new model of metasurface with simplified basic cell, able to convert linearly polarized signals generated by planar antenna array in circularly polarized signals, for the ISM frequency band (2.45 GHz), with good bandwidth of return loss and axial ratio. To study the behavior of the proposed structure, the metasurface is coupled to three different structures. First, initial tests are made with the metasurface coupled to a microstrip antenna in its simple configuration. Then the metasurface is coupled to an array with two elements of patch type. And later it is coupled to an optimized array, that uses a stub in its main feed, to get a better impedance matching. The structures are analyzed numerically through Ansoft HFSS™, and to validate these results, the structures are characterized experimentally. The characteristics of transmissions simulated and measures are presented. A good agreement between simulated and measured results was obtained. The structure proposed here has the advantage of meeting the desired characteristics, with a simple geometry to be built using a low-cost substrate (FR-4).

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

Multipair massive MIMO full-duplex relaying with MRC/MRT processing

We consider a multipair relay channel, where multiple sources communicate with multiple destinations with the help of a full-duplex (FD) relay station (RS). All sources and destinations have a single antenna, while the RS is equipped with massive arrays. We assume that the RS estimates the channels by using training sequences transmitted from sources and destinations. Then, it uses maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To significantly reduce the loop interference (LI) effect, we propose two massive MIMO processing techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the RS. We derive an exact achievable rate in closed-form and evaluate the system spectral efficiency. We show that, by doubling the number of antennas at the RS, the transmit power of each source and of the RS can be reduced by 1.5 dB if the pilot power is equal to the signal power and by 3 dB if the pilot power is kept fixed, while maintaining a given quality-of-service. Furthermore, we compare FD and half-duplex (HD) modes and show that FD improves significantly the performance when the LI level is low.

Sistema de rádio comunicações para UAV

Os veículos aéreos não tripulados, mais conhecidos por drones, têm tomado atualmente uma posição importante na sociedade. Para além da sua importância no meio militar, têm sido cada vez mais utilizados para meios comerciais uma vez que o seu custo é relativamente baixo e podem ser utilizados para inúmeras aplicações. Devido à sua importância em missões de salvamento, reconhecimento de terreno e até mesmo de ataque, é fundamental uma boa comunicação entre a aeronave e a estação terrestre. Sendo a antena um dos principais elementos do sistema de comunicação, esta dissertação centrou-se no desenvolvimento de uma agregado de antenas a operar à frequência de 2.45GHz. Pretende-se que este agregado apresente polarização circular direita bem como um ganho e largura de banda elevados. Com o objetivo de se obter uma comunicação mais eficiente entre a aeronave e a estação terrestre, o agregado permitirá o redirecionamento do feixe principal do diagrama de radiação. Para tal, serão analisadas três abordagens distintas recorrendo a linhas de atraso e switches, permitindo que seja efetuado beamforming.

Implémentation sur FPGA de l’algorithme MUSIC sur antenne-réseau expérimentale à 10 GHz

Les techniques des directions d’arrivée (DOA) sont une voie prometteuse pour accroitre la capacité des systèmes et les services de télécommunications en permettant de mieux estimer le canal radio-mobile. Elles permettent aussi de suivre précisément des usagers cellulaires pour orienter les faisceaux d’antennes dans leur direction. S’inscrivant dans ce contexte, ce présent mémoire décrit étape par étape l’implémentation de l’algorithme de haut niveau MUSIC (MUltiple SIgnal Classification) sur une plateforme FPGA afin de déterminer en temps réel l’angle d’arrivée d’une ou des sources incidentes à un réseau d’antennes. Le concept du prototypage rapide des lois de commande (RCP) avec les outils de XilinxTM System generator (XSG) et du MBDK (Model Based Design Kit) de NutaqTM est le concept de développement utilisé. Ce concept se base sur une programmation de code haut niveau à travers des modèles, pour générer automatiquement un code de bas niveau. Une attention particulière est portée sur la méthode choisie pour résoudre le problème de la décomposition en valeurs et vecteurs propres de la matrice complexe de covariance par l’algorithme de Jacobi. L’architecture mise en place implémentant cette dernière dans le FPGA (Field Programmable Gate Array) est détaillée. Par ailleurs, il est prouvé que MUSIC ne peut effectuer une estimation intéressante de la position des sources sans une calibration préalable du réseau d’antennes. Ainsi, la technique de calibration par matrice G utilisée dans ce projet est présentée, en plus de son modèle d’implémentation. Enfin, les résultats expérimentaux du système mis à l’épreuve dans un environnement réel en présence d’une source puis de deux sources fortement corrélées sont illustrés et analysés.

User scheduling for low earth orbit multi-user multiple-input-multiple-output non-terrestrial network systems

In rural and isolated areas without cellular coverage, Satellite Communication (SatCom) is the best candidate to complement terrestrial coverage. However, the main challenge for future generations of wireless networks will be to meet the growing demand for new services while dealing with the scarcity of frequency spectrum. As a result, it is critical to investigate more efficient methods of utilizing the limited bandwidth; and resource sharing is likely the only choice. The research community’s focus has recently shifted towards the interference management and exploitation paradigm to meet the increasing data traffic demands. In the Downlink (DL) and Feedspace (FS), LEO satellites with an on-board antenna array can offer service to numerous User Terminals (UTs) (VSAT or Handhelds) on-ground in FFR schemes by using cutting-edge digital beamforming techniques. Considering this setup, the adoption of an effective user scheduling approach is a critical aspect given the unusually high density of User terminals on the ground as compared to the on-board available satellite antennas. In this context, one possibility is that of exploiting clustering algorithms for scheduling in LEO MU-MIMO systems in which several users within the same group are simultaneously served by the satellite via Space Division Multiplexing (SDM), and then these different user groups are served in different time slots via Time Division Multiplexing (TDM). This thesis addresses this problem by defining a user scheduling problem as an optimization problem and discusses several algorithms to solve it. In particular, focusing on the FS and user service link (i.e., DL) of a single MB-LEO satellite operating below 6 GHz, the user scheduling problem in the Frequency Division Duplex (FDD) mode is addressed. The proposed State-of-the-Art scheduling approaches are based on graph theory. The proposed solution offers high performance in terms of per-user capacity, Sum-rate capacity, SINR, and Spectral Efficiency.

Non-linear modelling of microwave devices embedding nano-ferroelectric materials for real-time reconfigurable transmitarray

In this thesis work a nonlinear model for Interdigitated Capacitors (IDCs) based on ferroelectric materials, is proposed. Through the properties of materials such as Hafnium-Zirconium Oxide (HfZrO2), it is possible to realize tunable radiofrequency (RF) circuits. In particular, the model proposed in this thesis describes the use of an IDC, realized on a High-Resistivity silicon substrate, as a phase shifter for beam-steering applications. The model is obtained starting from already present experimental measurements, through which it is possible to identify a circuit model. The model is tested for both low power values and other power values using Harmonic Balance simulations, which show an excellent convergence of the model up to 40 dBm of input power. Furthermore, an array composed by two patches operating both at 2.55 GHz, which exploits the tunable properties of the HfZrO-based IDC is proposed. At 0dBm the model shows a 47° phase shift with polarization -1 V and 1 V which leads to a 11° steering of the main lobe of the array.

Graph-based User Scheduling for MIMO LEO-based Satellite Communication Systems

The study of the user scheduling problem in a Low Earth Orbit (LEO) Multi-User MIMO system is the objective of this thesis. With the application of cutting-edge digital beamforming algorithms, a LEO satellite with an antenna array and a large number of antenna elements can provide service to many user terminals (UTs) in full frequency reuse (FFR) schemes. Since the number of UTs on-ground are many more than the transmit antennas on the satellite, user scheduling is necessary. Scheduling can be accomplished by grouping users into different clusters: users within the same cluster are multiplexed and served together via Space Division Multiple Access (SDMA), i.e., digital beamforming or Multi-User MIMO techniques; the different clusters of users are then served on different time slots via Time Division Multiple Access (TDMA). The design of an optimal user grouping strategy is known to be an NP-complete problem which can be solved only through exhaustive search. In this thesis, we provide a graph-based user scheduling and feed space beamforming architecture for the downlink with the aim of reducing user inter-beam interference. The main idea is based on clustering users whose pairwise great-circle distance is as large as possible. First, we create a graph where the users represent the vertices, whereas an edge in the graph between 2 users exists if their great-circle distance is above a certain threshold. In the second step, we develop a low complex greedy user clustering technique and we iteratively search for the maximum clique in the graph, i.e., the largest fully connected subgraph in the graph. Finally, by using the 3 aforementioned power normalization techniques, a Minimum Mean Square Error (MMSE) beamforming matrix is deployed on a cluster basis. The suggested scheduling system is compared with a position-based scheduler, which generates a beam lattice on the ground and randomly selects one user per beam to form a cluster.

GEODA-SARAS: Multi-Phased array planar antenna for satellite communications

Nowadays, earth stations have as a common feature the use of large reflector antenna for downloading data from satellites. Large reflectors have impairments such as mechanical complexity, low flexibility and high cost. Thus, the feasibility of other antenna technologies must be evaluated, such as conformal adaptive antennas based on multiple planar active arrays. In the scenery under study, the capability to track several satellites simultaneously, higher flexibility, lower production and maintenance cost, modularity and a more efficient use of the spectrum; are the most important advantage to boost up active antenna arrays over large dishes.

Analysis of the error performance of adaptive array antennas for CDMA with noncoherent M-ary orthogonal modulation in Nakagami fading

This letter presents an analytical model for evaluating the Bit Error Rate (BER) of a Direct Sequence Code Division Multiple Access (DS-CDMA) system, with M-ary orthogonal modulation and noncoherent detection, employing an array antenna operating in a Nakagami fading environment. An expression of the Signal to Interference plus Noise Ratio (SINR) at the output of the receiver is derived, which allows the BER to be evaluated using a closed form expression. The analytical model is validated by comparing the obtained results with simulation results.

A beam-forming network for a circular switched-beam phased array antenna

This paper reports on the design and development of a dividing/phasing network for a compact switched-beam array antenna for Land-vehicle mobile satellite communications, The device is formed by a switched radial divider/combiner and 1-bit phase shifters and generates a sufficient number of beams for the proper satellite tracking.