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Frequency domain techniques have been widely used in indoor radio propagation measurements and modeling for telecommunication applications. This work addresses measurement of the time of arrival (TOA) of the first path for geolocation applications using results of frequency domain channel measurements. First, we analyze the effect upon TOA measurement accuracy due to: sampling period of the radio channel in the frequency domain, sampling period in the time domain used for detection of the TOA and the windowing filter used before transformation to the time domain. Then, we provide some results of measurement made in line of sight (LOS) and Obstructed LOS (OLOS) indoor environments in order to compare the characteristics of the measured TOA in these two important scenarios for indoor geolocation applications. Finally, we compare the measurement results with the ray tracing based model that had been developed previously for indoor geolocation applications.

Malgré leur réussite remarquable, les premières versions des normes de réseaux locaux sans fil IEEE 802.11, IEEE 802. 11 a/b/g WLAN, sont caractérisées par une efficacité spectrale faible qui est devenue insuffisante pour satisfaire la croissance explosive de la demande de capacité et de couverture. Grâce aux progrès considérables dans le domaine des communications sans fil et l'utilisation de la bande de fréquence autour de 5 gigahertz le standard IEEE 802.11n et plus récemment 1'IEEE 802.11ac ont amélioré les débits offerts par la couche physique. Cela été possible grâce principalement à l'introduction des techniques multi-antennaires (MIMO, pour Multiple-Input) et des techniques avancées de modulation et de codage. Aujourd'hui, deux décennies après sa première apparition, le Wi-Fi est présenté comme une technologie WLAN permettant des débits supérieurs à 1 gigabit par seconde. Cependant, dans la plupart des scénarios de déploiement du monde réel, il n'est pas possible d'atteindre la pleine capacité offerte par la couche physique. Avec la croissance rapide de la densité des déploiements des WLANs et l'énorme popularité des équipements Wi-Fi, la réutilisation spatiale doit être optimisée. D'autre part, des nouveaux cas d’utilisation sont prévus pour décharger les réseaux cellulaires et pour couvrir des grandes surfaces (stades, gares, etc.). Ces environnements de haute densité représentent un vrai défi pour les générations actuelles de Wi-Fi qui doivent offrir une meilleure qualité à moindre coût. C'est dans ce contexte que s’inscrit l'objectif de cette thèse qui porte sur l'amélioration de l'efficacité des protocoles de la couche MAC des réseaux WLAN de haute densité. Notamment, un des buts de cette thèse est de contribuer à la préparation de la prochaine génération du standard Wi-Fi : IEEE 802.11ax High Efficiency WLAN (HEW). Plutôt que de continuer à cibler l'augmentation des débits maximums théoriques, nous nous concentrons dans le contexte de HEW sur l'amélioration du débit réel des utilisateurs. Pour cela, on prend en compte tous les autres équipements associés à des WLANs voisins, qui essayent d'accéder au même canal de transmission d’une manière simultanée. Pour améliorer la performance du Wi-Fi dans ces environnements denses, nous proposons une adaptation dynamique du mécanisme de détection de signal. Comparé au contrôle de la puissance de transmission, le mécanisme proposé est plus incitatif parce que l'utilisateur concerné bénéficie directement de son application. Les résultats de nos simulations montrent des gains importants en termes de débit atteint dans les scénarios de haute densité. Ensuite, nous étudions l’impact de la nouvelle adaptation sur les mécanismes de sélection de débit actuellement utilisés. D'après les résultats obtenus, 1'adaptation proposée peut être appliquée sans avoir besoin de modifications substantielles des algorithmes de sélection de débit. Pour améliorer l'équité entre les différents utilisateurs, nous élaborons une nouvelle approche distribuée pour adapter conjointement le mécanisme de détection de signal et le contrôle de la puissance de transmission. Cette approche est évaluée ensuite dans différents scénarios de simulation de haute densité où elle prouve sa capacité à résoudre les problèmes d'équité en particulier en présence de nœuds d'anciennes générations dans le réseau, cela tout en améliorant le débit moyen d'un facteur 4 par rapport à la performance conventionnelle du standard. Enfin, nous concevons et mettons en œuvre une solution centralisée basée sur l'apprentissage à base de réseaux de neurones. Cette approche repose sur l'adaptation conjointe de puissance de transmission et du mécanisme de détection du signal. [...] / Despite their remarkable success, the first widely spread versions of the Institute of Electrical and Electronics Engineers (IEEE) 802.11 Wireless Local Area Network (WLAN) standard, IEEE 802. 11 a/b/g, featured low spectral efficiencies that are becoming insufficient to satisfy the explosive growth in capacity and coverage demands. Thanks to the advances in the communication theory and the use of the 5 GHz frequency band, the IEEE 802.11n and recently the IEEE 802.1lac amendments improved the Physical Layer (PHY) data rates by introducing Multiple-Input Multiple Output (MIMO) techniques, higher Modulation and Coding Scheme (MCS), etc. Today, after almost two decades of its first appearance, Wi-Fi is presented as a gigabit wireless technology. However, the full potential of the latest PHY layer advances cannot be enabled in all real world deployment scenarios. With the rapidly increasing density of WLAN deployments and the huge popularity of Wi-Fi enabled devices, spatial reuse must be optimized. On another hand, the new challenging use case environments and the integration of mobile networks mainly for cellular offloading are limiting the opportunity of the current Wi-Fi generations to provide better quality at lower cost.In this thesis, we contribute to the current standardization efforts aiming to leverage the Wi-Fi efficiency in high density environments. At the time of writing this document, the IEEE 802.11ax Task Group (TG) is developing the specification for the High Efficiency WLAN (HEW) standard (next Wi-Fi evolution). Rather than continuing to target increased theoretical peak throughputs, we focus in the context of HEW on improving the throughput experienced by users in real life conditions where many other devices, belonging to neighboring overlapping networks, simultaneously contend to gain access. To enhance this performance, we propose a dynamic adaptation of the carrier sensing mechanism. Compare to controlling the transmission power, the proposed mechanism has more incentives because it benefits directly the concerned user. Extensive simulation results show impor1ant throughput gains in dense scenarios. Then, we study the impact of the new adaptation on the current rate control algorithms. We find that our adaptation mechanism operates efficiently without substantially modifying these algorithms that are widely used in today's operating WLANs. Furthermore, after analyzing the fairness performance of the proposed adaptation, we devise a new approach to jointly adapt the carrier sensing and the transmission power in order to preserve higher fairness degrees while improving the spatial reuse. This approach is evaluated in different dense deployment scenarios where it proves its capability to resolve the unfairness issues especially in the presence of legacy nodes in the network, while improving the achieved throughput by 4 times compared to the standard performance. Finally, we design and implement centralized learning-based solution that uses also an approach based on joint adaptation of transmission power and carrier sensing. This new solution takes benefit from the capability of artificial neural networks to model complex nonlinear functions to optimize the spatial reuse in dense WLANs while preserving fairness among contending nodes. The different contributions of this work have helped bring efficient solutions for future WiFi networks. We have presented these solutions to the IEEE 802.11ax TG where they were identified as important potential technical improvements for the next WLAN standard.

One of the local area network medium access control standards created by the Institute of Electrical and Electronic Engineers in IEEE Project 802 is the carrier sense multiple access with collision detection (CSMA/CD) medium access control. Numerous studies have been reported in the literature on the performance of CSMA/CD. These studies show that CSMA/CD performs well under light network load but not well at heavy load. To improve the performance of CSMA/CD under heavy load, a new concept called network partitioning is presented. Network partitioning allows the network to be partitioned into segments when under heavy load. Partition stations then act as bridges between the segments. The impact of network partitioning on network performance was tested using a simulation model of an Ethernet local area network (an implementation of the CSMA/CD medium access control). The simulation results show that network partitioning can improve the performance of CSMA/CD under heavy load.

Network research experiments have traditionally been conducted in emulated or simulated environments. Emulators are frequently deployed on physical networks. Network simulators provide a self-contained and simple environment that can be hosted on one host. Simulators provide a synthetic environment that is only an approximation of the real world and therefore the results might not be a true re ection of reality. Recent progress in virtualisation technologies enable the deployment of multiple interconnected, virtual hosts on one machine. Virtual hosts run real network protocol stacks and therefore provide an emulated environment on a single host. The rst objective of this dissertation is to build a network emulator (viNEX) using a virtualisation platform (XEN). The second objective is to evaluate whether viNEX can be used to conduct some network research experiments. Thirdly, some limitations of this approach are identified / Computing / M. Sc. (Computer Science)

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This thesis is concerned with the design of a high-speed backbone network which provides a high bandwidth interconnection for various Personal Computer Networks (PC-Networks) with an integrated service of voice and data. With the advanced technology of optical fiber as the transmission medium, several different existing topologies and protocols are discussed for the backbone network design. The token ring protocol is simulated and evaluated to find out a suitable buffer size and the length of voice and data packet for backbone network. The Network II.5 simulation tool is applied to simulate the token ring simulation model with different parameters. The Network Interface Unit (NIU) is designed from the simulation results with a cost-effective consideration.

No description available.

by Au Yuk Van, Maria Assumpta, Yung, Thomas. / Bibliography: leaves 116-118 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1986

by Evan, Fung-yu Young. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 161-166). / List of Figures --- p.vi / List of Tables --- p.vii / List of Graphs --- p.viii / Abstract --- p.ix / Chapter Part I --- Learning Algorithms / Chapter 1 --- Representing Time in Connectionist Models --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Temporal Sequences --- p.2 / Chapter 1.2.1 --- Recognition Tasks --- p.2 / Chapter 1.2.2 --- Reproduction Tasks --- p.3 / Chapter 1.2.3 --- Generation Tasks --- p.4 / Chapter 1.3 --- Discrete Time v.s. Continuous Time --- p.4 / Chapter 1.4 --- Time Delay Neural Network (TDNN) --- p.4 / Chapter 1.4.1 --- Delay Elements in the Connections --- p.5 / Chapter 1.4.2 --- NETtalk: An Application of TDNN --- p.7 / Chapter 1.4.3 --- Drawbacks of TDNN --- p.8 / Chapter 1.5 --- Networks with Context Units --- p.8 / Chapter 1.5.1 --- Jordan's Network --- p.9 / Chapter 1.5.2 --- Elman's Network --- p.10 / Chapter 1.5.3 --- Other Architectures --- p.14 / Chapter 1.5.4 --- Drawbacks of Using Context Units --- p.15 / Chapter 1.6 --- Recurrent Neural Networks --- p.16 / Chapter 1.6.1 --- Hopfield Models --- p.17 / Chapter 1.6.2 --- Fully Recurrent Neural Networks --- p.20 / Chapter A. --- EXAMPLES OF USING RECURRENT NETWORKS --- p.22 / Chapter 1.7 --- Our Objective --- p.25 / Chapter 2 --- Learning Algorithms for Recurrent Neural Networks --- p.27 / Chapter 2.1 --- Introduction --- p.27 / Chapter 2.2 --- Gradient Descent Methods --- p.29 / Chapter 2.2.1 --- Backpropagation Through Time (BPTT) --- p.29 / Chapter 2.2.2 --- Real Time Recurrent Learning Rule (RTRL) --- p.30 / Chapter A. --- RTRL WITH TEACHER FORCING --- p.32 / Chapter B. --- TERMINAL TEACHER FORCING --- p.33 / Chapter C. --- CONTINUOUS TIME RTRL --- p.33 / Chapter 2.2.3 --- Variants of RTRL --- p.34 / Chapter A. --- SUB GROUPED RTRL --- p.34 / Chapter B. --- A FIXED SIZE STORAGE 0(n3) TIME COMPLEXITY LEARNGING RULE --- p.35 / Chapter 2.3 --- Non-Gradient Descent Methods --- p.37 / Chapter 2.3.1 --- Neural Bucket Brigade (NBB) --- p.37 / Chapter 2.3.2 --- Temporal Driven Method (TO) --- p.38 / Chapter 2.4 --- Comparison between Different Approaches --- p.39 / Chapter 2.5 --- Conclusion --- p.41 / Chapter 3 --- Locally Connected Recurrent Networks --- p.43 / Chapter 3.1 --- Introduction --- p.43 / Chapter 3.2 --- Locally Connected Recurrent Networks --- p.44 / Chapter 3.2.1 --- Network Topology --- p.44 / Chapter 3.2.2 --- Subgrouping --- p.46 / Chapter 3.2.3 --- Learning Algorithm --- p.47 / Chapter 3.2.4 --- Continuous Time Learning Algorithm --- p.50 / Chapter 3.3 --- Analysis --- p.51 / Chapter 3.3.1 --- Time Complexity --- p.51 / Chapter 3.3.2 --- Space Complexity --- p.51 / Chapter 3.3.3 --- Local Computations in Time and Space --- p.51 / Chapter 3.4 --- Running on Parallel Architectures --- p.52 / Chapter 3.4.1 --- Mapping the Algorithm to Parallel Architectures --- p.52 / Chapter 3.4.2 --- Parallel Learning Algorithm --- p.53 / Chapter 3.4.3 --- Analysis --- p.54 / Chapter 3.5 --- Ring-Structured Recurrent Network (RRN) --- p.55 / Chapter 3.6 --- Comparison between RRN and RTRL in Sequence Recognition --- p.55 / Chapter 3.6.1 --- Training Sets and Testing Sequences --- p.56 / Chapter 3.6.2 --- Comparison in Training Speed --- p.58 / Chapter 3.6.3 --- Comparison in Recalling Power --- p.59 / Chapter 3.7 --- Comparison between RRN and RTRL in Time Series Prediction --- p.59 / Chapter 3.7.1 --- Comparison in Training Speed --- p.62 / Chapter 3.7.2 --- Comparison in Predictive Power --- p.63 / Chapter 3.8 --- Conclusion --- p.65 / Chapter Part II --- Applications / Chapter 4 --- Sequence Recognition by Ring-Structured Recurrent Networks --- p.67 / Chapter 4.1 --- Introduction --- p.67 / Chapter 4.2 --- Related Works --- p.68 / Chapter 4.2.1 --- Feedback Multilayer Perceptron (FMLP) --- p.68 / Chapter 4.2.2 --- Back Propagation Unfolded Recurrent Rule (BURR) --- p.69 / Chapter 4.3 --- Experimental Details --- p.71 / Chapter 4.3.1 --- Network Architecture --- p.71 / Chapter 4.3.2 --- Input/Output Representations --- p.72 / Chapter 4.3.3 --- Training Phase --- p.73 / Chapter 4.3.4 --- Recalling Phase --- p.73 / Chapter 4.4 --- Experimental Results --- p.74 / Chapter 4.4.1 --- Temporal Memorizing Power --- p.74 / Chapter 4.4.2 --- Time Warping Performance --- p.80 / Chapter 4.4.3 --- Fault Tolerance --- p.85 / Chapter 4.4.4 --- Learning Rate --- p.87 / Chapter 4.5 --- Time Delay --- p.88 / Chapter 4.6 --- Conclusion --- p.91 / Chapter 5 --- Time Series Prediction --- p.92 / Chapter 5.1 --- Introduction --- p.92 / Chapter 5.2 --- Modelling in Feedforward Networks --- p.93 / Chapter 5.3 --- Methodology with Recurrent Networks --- p.94 / Chapter 5.3.1 --- Network Structure --- p.94 / Chapter 5.3.2 --- Model Building - Training --- p.95 / Chapter 5.3.3 --- Model Diagnosis - Testing --- p.95 / Chapter 5.4 --- Training Paradigms --- p.96 / Chapter 5.4.1 --- A Quasiperiodic Series with White Noise --- p.96 / Chapter 5.4.2 --- A Chaotic Series --- p.97 / Chapter 5.4.3 --- Sunspots Numbers --- p.98 / Chapter 5.4.4 --- Hang Seng Index --- p.99 / Chapter 5.5 --- Experimental Results and Discussions --- p.99 / Chapter 5.5.1 --- A Quasiperiodic Series with White Noise --- p.101 / Chapter 5.5.2 --- Logistic Map --- p.103 / Chapter 5.5.3 --- Sunspots Numbers --- p.105 / Chapter 5.5.4 --- Hang Seng Index --- p.109 / Chapter 5.6 --- Conclusion --- p.112 / Chapter 6 --- Chaos in Recurrent Networks --- p.114 / Chapter 6.1 --- Introduction --- p.114 / Chapter 6.2 --- Important Features of Chaos --- p.115 / Chapter 6.2.1 --- First Return Map --- p.115 / Chapter 6.2.2 --- Long Term Unpredictability --- p.117 / Chapter 6.2.3 --- Sensitivity to Initial Conditions (SIC) --- p.118 / Chapter 6.2.4 --- Strange Attractor --- p.119 / Chapter 6.3 --- Chaotic Behaviour in Recurrent Networks --- p.120 / Chapter 6.3.1 --- Network Structure --- p.121 / Chapter 6.3.2 --- Dynamics in Training --- p.121 / Chapter 6.3.3 --- Dynamics in Testing --- p.122 / Chapter 6.4 --- Experiments and Discussions --- p.123 / Chapter 6.4.1 --- Henon Model --- p.123 / Chapter 6.4.2 --- Lorenz Model --- p.127 / Chapter 6.5 --- Conclusion --- p.134 / Chapter 7 --- Conclusion --- p.135 / Appendix A Series 1 Sine Function with White Noise --- p.137 / Appendix B Series 2 Logistic Map --- p.138 / Appendix C Series 3 Sunspots Numbers from 1700 to 1979 --- p.139 / Appendix D A Quasiperiodic Series with White Noise --- p.141 / Appendix E Hang Seng Daily Closing Index in 1991 --- p.142 / Appendix F Network Model for the Quasiperiodic Series with White Noise --- p.143 / Appendix G Network Model for the Logistic Map --- p.144 / Appendix H Network Model for the Sunspots Numbers --- p.145 / Appendix I Network Model for the Hang Seng Index --- p.146 / Appendix J Henon Model --- p.147 / Appendix K Network Model for the Henon Map --- p.150 / Appendix L Lorenz Model --- p.151 / Appendix M Network Model for the Lorenz Map --- p.159 / Bibliography --- p.161