ML-Optimized Beam-based Radio Coverage Processing in IEEE 802.11 WLAN Networks

Dynamic Radio Resource Management (RRM) is a major building block of Wireless LAN Controllers (WLC) function in WLAN networks. In a dense and frequently changing WLANs, it maximizes Wireless Devices (WD) opportunity to transmit and guarantees conformance to the design Service Level Agreement (SLA). To achieve this performance, a WLC processes and applies a network-wide optimized radio plan based on data from access points (AP) and upper-layer application services. This coverage processing requires a "realistic" modelization approach of the radio environment and a quick adaptation to frequent changes. In this paper, we build on our Beam-based approach to radio coverage modelization. We propose a new Machine Learning Regression (MLR)-based optimization and compare it to our NURBS-based solution performance, as an alternative. We show that both solutions have very comparable processing times. Nevertheless, our MLR-based solution represents a more significant prediction accuracy enhancement than its alternative

Design of 10 to 12 GHz Low Noise Amplifier for Ultrawideband (UWB) Syste

Balanced amplifier is the structure proposed in this article, it provides better performance. In fact, the single amplifier meets the specification for noise figure and gain but fails to meet the return loss specification due to the large mis-matches on the input & outputs. To overcome this problem one solution is to use balanced amplifier topography. In this paper, a wide-band and highgain microwave balanced amplifier constituted with branch line coupler circuit is proposed. The amplifier is unconditionally stable in the band [9-13] GHz where the gain is about 20dB. The input reflection (S11) and output return loss (S22) at 11 GHz are -33.4dB and -33.5dB respectively

Study of the PIFA Antenna for RFID Applications

In this chapter, we did an introduction to radio frequency identification (RFID) technology, to define the different components of this system, then the frequencies of utilization for this application, and finally the advantages and disadvantages of this technology. Then we presented the design and simulation of a planar inverted-F antenna (PIFA) with a T-shaped slot. We studied the effect of changing the type of feed supply, the type of substrate, and the position of the connecting line between the ground plane and the radiating element. We chose the frequency of resonance of the antenna for the RFID applications at 5.8 GHz. The results obtained by the HFSS software are very satisfactory with a very minimal return loss

Microruban Dipole Antenna for RFID Applications at 2.45 GHz

Radio Frequency Identification (RFID) is a technology used mainly to identify tagged items or to track their locations. The most used antennas for RFID application are planar dipoles. For antenna design, it is necessary that the antenna has an impedance value equal to the conjugate of the impedance of the integrated circuit CI. To have a good adaptation allowing the maximum power transfer, there are several techniques. In this work we focus to the adaptation technical T-match which is based on the insertion of a second folded dipole in the center of the first dipole. This technique is modeled by an equivalent circuit to calculate the size of the folded dipole to have new input impedance of the antenna equal to the conjugate of the impedance of the IC. We also look to present a conceptual and technological approach of new topologies of linear dipoles. We proceeded to fold at right angles of the radiating strands in order to explore other topologiesof type  L and Z. The interest of this microstrip folded dipole is their effectiveness to achieve coverage of Blind directions. The results obtained by the platform Ansoft HFSS, allowed us to obtain a quasi-uniform radiation patterns and the reflection coefficients that exceed -37 dB

A NURBS-optimized dRRM solution in a mono-channel condition for IEEE 802.11 entreprise Wlan networks

Dynamic radio resource management, RRM, is an essential design block in the functional architecture of any Wifi controller in IEEE 802.11 indoor dense enterprise Wlans. In a mono-channel condition, it helps tackle co-channel interference problem and enrich end-to-end Wifi clients experience. In this work, we present our dRRM solution: WLCx, and demonstrate its performance over related-work and vendor approaches. Our solution is built on a novel and realistic per-Beam coverage representation approach. Unlike the other RRM solutions, WLCx is dynamic: even the calculation system parameters are processed. This processing comes at price in terms of processing time. To overcome this limitation, we constructed and implemented a NURBS surface-based optimization to our RRM solution. Our NURBS optimized WLCx, N-WLCx, solution achieves almost 92.58% time reduction in comparison with basic WLCx. Furthermore, our optimization could easily be extended to enhance others, vendors and research, RRM solutions

Performance Analysis of Low noise amplifier using Combline Bandpass Filter for X Band Applications

This paper describes a procedure for designing broadband low noise amplifier for X-Band applications. The design and implementation is based on HEMT transistors AFP02N2-00 of Alpha Industries®. The matching circuit used for modeling the microwave amplifier is the quarter-wave transformers impedance matching technique associated to combline bandpass filter.  The proposed amplifier is implemented on a substrate of epoxy FR4 with a central frequency of 11GHz and a fractional bandwidth of 0.18% and is designed to be used in radar reception systems. The results show that the proposed LNA is unconditionally stable with a simulated gain of 20dB over the working frequency range of [9.5−12.5] GHz

Approach for the Design of a Broadband Microwave Power Amplifier in Microstrip Technology for Mobile Communications Systems

This work presents a broadband power amplifier in S-band in microstrip technology. The proposed power amplifier is modeled with a single-stage architecture based on a field effect transistor ATF13786 of Agilent Technologies (hp)®. The used transistor has been polarized with transmission lines and it has been adapted with matching networks in the form of λ/4 transformers filters at the input and at the output. This amplifier has been studied and optimized using the Advanced Design System (ADS®) software. The simulation results of the output power and S parameters show excellent characteristics with a satisfactory gain greater than 10.9 dB, low reflections, a saturated output power of 16.4 dBm with a 1 dB compression point at an input power level of about 5 dBm, a maximum PAE of 25.3% and unconditional stability in the desired frequency band. The modeled amplifier can be integrated into mobile communications systems namely LTE mobile networks (2500 to 2690 MHz) and wireless networks using Wi-Fi protocol (2400 to 2485 MHz)

Modeling of a Microwave Amplifier Operating around 11 GHz for Radar Applications

The low noise amplifier is one of the basic functional blocks in communication systems. The main interest of the LNA at the input of the analog processing chain is to amplify the signal without adding significant noise. In this work, we have modeled a LNA for radar reception systems operating around 11 GHz, using the technique of impedance transformations with Smith chart utility. The type of transistor used is: the transistor HEMT AFP02N2-00 of Alpha Industries®. The results show that the modeled amplifier has a gain greater than 20 dB, a noise figure less than 2 dB, input and output reflection coefficients lower than -20 dB and unconditional stability

POS tagging in Amazigh using support vector machines and conditional random fields

The aim of this paper is to present the first Amazighe POS tagger. Very few linguistic resources have been developed so far for Amazighe and we believe that the development of a POS tagger tool is the first step needed for automatic text processing. The used data have been manually collected and annotated. We have used state-of-art supervised machine learning approaches to build our POS-tagging models. The obtained accuracy achieved 92.58% and we have used the 10-fold technique to further validate our results. © Springer-Verlag Berlin Heidelberg 2011We would like to thank all IRCAM researchers for their valuable assistance. The work of the third author was funded by the MICINN research project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i).Outahajala, M.; Benajiba, Y.; Rosso, P.; Zenkouar, L. (2011). POS tagging in Amazigh using support vector machines and conditional random fields. En Natural Language Processing and Information Systems. Springer Verlag (Germany). 6716:238-241. https://doi.org/10.1007/978-3-642-22327-3_28S238241671

L'étiquetage grammatical de l'amazighe en utilisant les propriétés n-grammes et un prétraitement de segmentation

[FR] L’objectif de cet article est de présenter le premier étiqueteur grammatical amazighe. Très peu de ressources ont été développées pour l’amazighe et nous croyons que le développement d’un outil d’étiquetage grammatical est une étape préalable au traitement automatique de textes. Afin d'atteindre cet objectif, nous avons formé deux modèles de classification de séquences en utilisant les SVMs, séparateurs à vaste marge (Support Vector Machines) et les CRFs, champs markoviens conditionnels (Conditional Random Fields) en utilisant une phase de segmentation. Nous avons utilisé la technique de 10 fois la validation croisée pour évaluer notre approche. Les résultats montrent que les performances des SVMs et des CRFs sont très comparables. Dans l'ensemble, les SVMs ont légèrement dépassé les CRFs au niveau des échantillons (92,58% contre 92,14%) et la moyenne de précision des CRFs dépasse celle des SVMs (89,48% contre 89,29%). Ces résultats sont très prometteurs étant donné que nous avons utilisé un corpus de seulement ~ 20k mots.[EN] The aim of this paper is to present the first amazigh POS tagger. Very few linguistic resources have been developed so far for amazigh and we believe that the development of a POS tagger tool is the first step needed for automatic text processing. In order to achieve this endeavor, we have trained two sequence classification models using Support Vector Machines (SVMs) and Conditional Random Fields (CRFs) after using a tokenization step. We have used the 10- fold technique to evaluate our approach. Results show that the performance of SVMs and CRFs are very comparable. Across the board, SVMs outperformed CRFs on the fold level (92.58% vs. 92.14%) and CRFs outperformed SVMs on the 10 folds average level (89.48% vs. 89.29%). These results are very promising considering that we have used a corpus of only ~20k tokens.Les travaux du troisième auteur ont été financés par le projet de recherche EU FP7 Marie Curie PEOPLE-IRSES 269180 WiQ-Ei, MICINN TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i), VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Outahajala, M.; Benajiba, Y.; Rosso, P.; Zenkouar, L. (2012). L'étiquetage grammatical de l'amazighe en utilisant les propriétés n-grammes et un prétraitement de segmentation. E-TI : la revue électronique des technologies de l'information. 6:48-61. http://hdl.handle.net/10251/47570S4861