Planar High-Gain WLAN PCB Antenna

A simple design for a low-profile high-gain planar antenna has been presented in the letter. The antenna has the realized gain between 9 and 11 dBi and the return loss better than 10 dB over the 5.6-6.3-GHz frequency band, i.e. 11% bandwidth. A numerical study highlighting effects of key geometrical parameters on the gain and return loss of the antenna has been performed. It has been shown as well that the presented antenna occupies area 20% smaller than a conventional microstrip patch antenna array with a similar gain.

Liquid crystal based Rotman lens antenna with switchable monopulse patterns

An electronically reconfigurable Rotman lens is described which generates multiple beams that can be switched from monopulse sum to difference radiation patterns when used in conjunction with a six element Vivaldi antenna array. This is achieved by exploiting the voltage-dependent dielectric anisotropy property of nematic state liquid crystals to provide switched 0 degrees and 180 degrees phase shifts in the array feed lines. The viability of the concept is demonstrated by designing an antenna which exhibits dynamically reconfigurable monopulse radiation patterns over the frequency band 6-10 GHz. Measured and simulated results are shown to be in good agreement. (c) 2013 Wiley Periodicals, Inc.

Multipair Full-Duplex Relaying with Massive Arrays and Linear Processing

We consider a multipair decode-and-forward relay channel, where multiple sources transmit simultaneously their signals to multiple destinations with the help of a full-duplex relay station. We assume that the relay station is equipped with massive arrays, while all sources and destinations have a single antenna. The relay station uses channel estimates obtained from received pilots and zero-forcing (ZF) or maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To reduce significantly the loop interference effect, we propose two techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the relay station. We derive an exact achievable rate in closed-form for MRC/MRT processing and an analytical approximation of the achievable rate for ZF processing. This approximation is very tight, especially for large number of relay station antennas. These closed-form expressions enable us to determine the regions where the full-duplex mode outperforms the half-duplex mode, as well as, to design an optimal power allocation scheme. This optimal power allocation scheme aims to maximize the energy efficiency for a given sum spectral efficiency and under peak power constraints at the relay station and sources. Numerical results verify the effectiveness of the optimal power allocation scheme. Furthermore, we show that, by doubling the number of transmit/receive antennas at the relay station, the transmit power of each source and of the relay station can be reduced by 1.5dB if the pilot power is equal to the signal power, and by 3dB if the pilot power is kept fixed, while maintaining a given quality-of-service.

Secure D2D Communication in Large-Scale Cognitive Cellular Networks with Wireless Power Transfer

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multi-antenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the cellular base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, we propose a new power transfer policy, namely, best power beacon (BPB) power transfer. To characterize the power transfer reliability of the proposed policy, we derive new closed-form expressions for the exact power outage probability and the asymptotic power outage probability with large antenna arrays at PBs. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), and 2) nearest receiver selection (NRS). To assess the secrecy performance, we derive new expressions for the secrecy throughput considering the two receiver selection schemes using the BPB power transfer policies. We show that secrecy performance improves with increasing densities of PBs and D2D receivers because of a larger multiuser diversity gain. A pivotal conclusion is reached that BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead.

Modulation Insensitive PLL for Tracking Antenna Applications

This article shows practical results of a self-tracking receiving antenna array using a new phase locked loop (PLL) tracking configuration. The PLL configuration differs from other architectures, as it has the new feature of being able to directly track phase modulated signals without requiring an additional unmodulated pilot carrier to be present. The PLLs are used within the antenna array to produce a constant phase intermediate frequency (IF) for each antenna element. These IF's can then be combined in phase, regardless of the angle of arrival of the signal, thus utilizing the antennas array factor. The article's main focus is on the phase jitter performance of the modulation insensitive PLL carrier recovery when tracking phase modulated signals of low signal to noise ratio. From this analysis, it is concluded that the new architecture, when optimally designed, can produce phase jitter performance close to that of a conventional tracking PLL.

Secure D2D Communication in Large-Scale Cognitive Cellular Networks: A Wireless Power Transfer Model

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multiantenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the primary base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, three wireless power transfer (WPT) policies are proposed: 1) co-operative power beacons (CPB) power transfer, 2) best power beacon (BPB) power transfer, and 3) nearest power beacon (NPB) power transfer. To characterize the power transfer reliability of the proposed three policies, we derive new expressions for the exact power outage probability. Moreover, the analysis of the power outage probability is extended to the case when PBs are equipped with large antenna arrays. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), where the receiver with the strongest channel is selected; and 2) nearest receiver selection (NRS), where the nearest receiver is selected. To assess the secrecy performance, we derive new analytical expressions for the secrecy outage probability and the secrecy throughput considering the two receiver selection schemes using the proposed WPT policies. We presented Monte carlo simulation results to corroborate our analysis and show: 1) secrecy performance improves with increasing densities of PBs and D2D receivers due to larger multiuser diversity gain; 2) CPB achieves better secrecy performance than BPB and NPB but consumes more power; and 3) BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead. A pivotal conclusion- is reached that with increasing number of antennas at PBs, NPB offers a comparable secrecy performance to that of BPB but with a lower complexity.

SATCOM Retrodirective Array

We present an in depth look at the challenges involved in using analogue retrodirective arrays for satellite communications. The main technical issues surrounding the development of a retrodirective (self-steering) Satellite Communications (SATCOM) system are given and techniques for mitigating these issues provided. Detailed results are given for a prototype high performance circularly polarized retrodirective array architecture suitable for mounting on an un-stabilized mobile platform. The paper concludes with practical retrodirective L-band array results with the array used to acquire actual broadband satellite data signals from a commercial L-band satellite system. Received satellite signals as low as -130dBm at the antenna elements are tracked. Accurate self-tracking occurs over the azimuth range of up to +/- 40 degrees.

HIGH PERFORMANCE, LOW COST SUBSPACE DECOMPOSITION AND POLYNOMIAL ROOTING FOR REAL TIME DIRECTION OF ARRIVAL ESTIMATION: ANALYSIS AND IMPLEMENTATION

This thesis develops high performance real-time signal processing modules for direction of arrival (DOA) estimation for localization systems. It proposes highly parallel algorithms for performing subspace decomposition and polynomial rooting, which are otherwise traditionally implemented using sequential algorithms. The proposed algorithms address the emerging need for real-time localization for a wide range of applications. As the antenna array size increases, the complexity of signal processing algorithms increases, making it increasingly difficult to satisfy the real-time constraints. This thesis addresses real-time implementation by proposing parallel algorithms, that maintain considerable improvement over traditional algorithms, especially for systems with larger number of antenna array elements. Singular value decomposition (SVD) and polynomial rooting are two computationally complex steps and act as the bottleneck to achieving real-time performance. The proposed algorithms are suitable for implementation on field programmable gated arrays (FPGAs), single instruction multiple data (SIMD) hardware or application specific integrated chips (ASICs), which offer large number of processing elements that can be exploited for parallel processing. The designs proposed in this thesis are modular, easily expandable and easy to implement. Firstly, this thesis proposes a fast converging SVD algorithm. The proposed method reduces the number of iterations it takes to converge to correct singular values, thus achieving closer to real-time performance. A general algorithm and a modular system design are provided making it easy for designers to replicate and extend the design to larger matrix sizes. Moreover, the method is highly parallel, which can be exploited in various hardware platforms mentioned earlier. A fixed point implementation of proposed SVD algorithm is presented. The FPGA design is pipelined to the maximum extent to increase the maximum achievable frequency of operation. The system was developed with the objective of achieving high throughput. Various modern cores available in FPGAs were used to maximize the performance and details of these modules are presented in detail. Finally, a parallel polynomial rooting technique based on Newton’s method applicable exclusively to root-MUSIC polynomials is proposed. Unique characteristics of root-MUSIC polynomial’s complex dynamics were exploited to derive this polynomial rooting method. The technique exhibits parallelism and converges to the desired root within fixed number of iterations, making this suitable for polynomial rooting of large degree polynomials. We believe this is the first time that complex dynamics of root-MUSIC polynomial were analyzed to propose an algorithm. In all, the thesis addresses two major bottlenecks in a direction of arrival estimation system, by providing simple, high throughput, parallel algorithms.

GEODA-GRUA: Adaptive Multibeam Conformal Antenna for Satellites Communications

The GEODA-GRUA is one conformal adaptive antenna array designed for satellite communications. Operating at 1.7 GHz with circular polarization, it is possible to track and communicate with several satellites at once being able to receive signals in full azimuth and within the range of 5° to broadside elevation thanks to its adaptive beam. The complex structure of the antenna array has 2700 radiating elements based on a set of 60 similar triangular arrays that are divided in 15 subarrays of 3 radiating elements. A control module governs each transmission/receiver (T/R) module associated to each cell in order to manage beam steering by shifting phases.

Red Multihaz de Desfase Escalonado para el Control de un Array de Antenas.

This paper presents a general systems that can be taken into account to control between elements in an antenna array. Because the digital phase shifter devices have become a strategic element and also some steps have been taken for their export by U.S. Government, this element has increased its price to the low supply in the market. Therefore, it is necessary to adopt some solutions that allow us to deal with the design and construction of antenna arrays. system based on a group of a staggered phase shift with external switching is shown, which is extrapolated array.

Analytical evaluation of uncertainty on active antenna arrays

An analytical method for evaluating the uncertainty of the performance of active antenna arrays in the whole spatial spectrum is presented. Since array processing algorithms based on spatial reference are widely used to track moving targets, it is essential to be aware of the impact of the uncertainty sources on the antenna response. Furthermore, the estimation of the direction of arrival (DOA) depends on the array uncertainty. The aim of the uncertainties analysis is to provide an exhaustive characterization of the behavior of the active antenna array associated with its main uncertainty sources. The result of this analysis helps to select the proper calibration technique to be implemented. An illustrative example for a triangular antenna array used for satellite tracking is presented showing the suitability of the proposed method to carry out an efficient characterization of an active antenna array.

Study of an electronic steering antenna with a staggered phase shifter configuration

This paper presents a study of three possible solutions that can be taken into account to control the phase shift between elements in an antenna array. Because commercial digital phase shifters have become a strategic element by U.S. Government, these elements have increased their price. For this reason, it is necessary to adopt some solutions that allow us to deal with the design and construction of antenna arrays.

Comparison of different MIMO antenna arrays and user's effect on their performances

Multiple-input multiple-output (MIMO) systems have entailed a great enhancement in wireless communications performances. The use of multiple antennas at each side of the radio link has been included in recent drafts and standards such as WLAN, WIMAX, or DVB-T2. The MIMO performances depend on the antenna array characteristics and thus several aspects have to be taken into account to design MIMO antennas. In the literature, many articles can be found in terms of capacity or antenna design, but in this article, different types of antenna arrays for MIMO systems are measured in a reverberation chamber with and without a phantom as a user's head. As a result, the MIMO performances are degraded by the user in terms of efficiency, diversity gain, and capacity. Omnidirectional antennas such as monopoles with high radiation efficiency offer the highest performance for a rich scattering nonline of sight indoor environment.

Broadband active differential array for the mid-frequency SKA band

This paper presents the design and characterization process of an active array demonstrator for the mid-frequency range (i.e., 300 MHz-1000 MHz) of the future Square Kilometre Array (SKA) radio telescope. This demonstrator, called FIDA3 (FG-IGN: Fundación General Instituto Geográfico Nacional - Differential Active Antenna Array), is part of the Spanish contribution for the SKA project. The main advantages provided by this design include the use of a dielectric-free structure, and the use of a fully-differential receiver in which differential low-noise amplifiers (LNAs) are directly connected to the balanced tapered-slot antennas (TSAs). First, the radiating structure and the differential low-noise amplifiers were separately designed and measured, obtaining good results (antenna elements with low voltage standing-wave ratios, array scanning capabilities up to 45°, and noise temperatures better than 52 K with low-noise amplifiers at room temperature). The potential problems due to the differential nature of the proposed solution are discussed, so some effective methods to overcome such limitations are proposed. Second, the complete active antenna array receiving system was assembled, and a 1 m2 active antenna array tile was characterized.

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.