Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Techniques for broadband power line communications: impulsive noise mitigation and adaptive modulation

The development of power line communication systems for broadband multimedia applications requires a comprehensive knowledge of the channel characteristics and the main peculiarities that may influence the communication over this channel. PLC has the potential to become the preferred connectivity solution to homes and offices. Additionally, indoor power line networks can serve as local area networks offering high-speed data, audio, video and multimedia applications. The PLC technology eliminates the need for new wires by using an already-existing infrastructure that is much more pervasive than any other wired system. Power line networks, however, present a hostile channel for communication signals. Noise, multipath, selective fading and attenuation are well-known peculiarities of power line grids and. Particularly, random impulsive noise characterized with short durations and very high amplitudes is identified as one of the major impairments that degrade the performance of PLC systems. Orthogonal frequency division multiplexing (OFDM) is the technique of choice for broadband PLC systems. OFDM minimizes the effects of multipath and provides high robustness against selective fading. It is also powerful in impulsive noise environments and performs better than single-carrier modulation methods. If an OFDM symbol is affected by impulsive noise, the effect is spread over multiple subcarriers due to the discrete Fourier transform at the receiver. In order to achieve reliable outcomes, suitable channel and noise models must be used in the investigations. In this thesis, the power line channel transfer function is modelled using a multipath model that was proposed by Zimmermann and Dostert [1], [2]. This model describes the signal propagation scenario and attenuation effects in power line networks. To represent the actual noise scenario in power networks, the noise is classified into two main classes: background noise and impulsive noise. To reduce the effect of impulsive noise, conventional time domain nonlinearities are examined in this thesis under PLC environments. An adaptive-threshold selection method based on minimum bit-error rate (BER) is proposed. At the cost of additional complexity, the effect of impulsive noise is further mitigated using a novel joint time-domain/frequency-domain suppression technique. Since channel coding is essential for most telecommunication systems, we examine convolutional codes combined with interleaving in a PLC channel impaired with AWGN and impulsive noise. The results show substantial performance gains especially in heavily-disturbed environments, where signal-to-noise ratio (SNR) gains of more than 15 dB can be achieved with a code rate of 1/3. Bit-interleaved convolutionally-coded OFDM completely eliminates the effect of impulsive noise in weakly-disturbed noise environments, while a negligible effect may remain in medium-disturbed environments. A new power-loading algorithm that minimizes the transmission power for target BER and data rate constraints is introduced in later chapters of the thesis. Results indicate that the algorithm achieves performance gains of more than 4 dB SNR over conventional OFDM systems. Furthermore, a novel minimum-complexity bit-loading algorithm that maximizes the data rate given BER and power level constraints is proposed in chapter 6. Results show that this bit-loading algorithm achieves almost identical performance as the incremental algorithm but with much lower complexity

Contributions to Improve Cognitive Strategies with Respect to Wireless Coexistence

Cognitive radio (CR) can identify temporarily available opportunities in a shared radio environment to improve spectral efficiency and coexistence behavior of radio systems. It operates as a secondary user (SU) and accommodates itself in detected opportunities with an intention to avoid harmful collisions with coexisting primary user (PU) systems. Such opportunistic operation of a CR system requires efficient situational awareness and reliable decision making for radio resource allocation. Situational awareness includes sensing the environment followed by a hypothesis testing for detection of available opportunities in the coexisting environment. This process is often known as spectral hole detection. Situational knowledge can be further enriched by forecasting the primary activities in the radio environment using predictive modeling based approaches. Improved knowledge about the coexisting environment essentially means better decision making for secondary resource allocation. This dissertation identifies limitations of existing predictive modeling and spectral hole detection based resource allocation strategies and suggest improvements. Firstly, accurate and efficient estimation of statistical parameters of the radio environment is identified as a fundamental challenge to realize predictive modeling based cognitive approaches. Lots of useful training data which are essential to learn the system parameters are not available either because of environmental effects such as noise, interference and fading or because of limited system resources particularly sensor bandwidth. While handling environmental effects to improve signal reception in radio systems has already gained much attention, this dissertation addresses the problem of data losses caused by limited sensor bandwidth as it is totally ignored so far and presents bandwidth independent parameter estimation methods. Where, bandwidth independent means achieving the same level of estimation accuracy for any sensor bandwidth. Secondly, this dissertation argues that the existing hole detection strategies are dumb because they provide very little information about the coexisting environment. Decision making for resource allocation based on this dumb hole detection approach cannot optimally exploit the opportunities available in the coexisting environment. As a solution, an intelligent hole detection scheme is proposed which suggests classifying the primary systems and using the documented knowledge of identified radio technologies to fully understand their coexistence behavior. Finally, this dissertation presents a neuro-fuzzy signal classifier (NFSC) that uses bandwidth, operating frequency, pulse shape, hopping behavior and time behavior of signals as distinct features in order to xii identify the PU signals in coexisting environments. This classifier provides the foundation for bandwidth independent parameter estimation and intelligent hole detection. MATLAB/Simulink based simulations are used to support the arguments throughout in this dissertation. A proof-of-concept demonstrator using microcontroller and hardware defined radio (HDR) based transceiver is also presented at the end.</p

Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth is examined. The benefit of using an additional AUV motion model in the navigation is surveyed. Underwater navigation requires acoustic positioning sensors. In this work, so-called Ultra-Short-Baseline (USBL) devices were used allowing the AUV to localize itself relative to an opposite device attached to a (surface) vehicle. Despite their easy use, the devices\u27 absolute positioning accuracy decreases proportional to range. This makes underwater navigation a sophisticated estimation task requiring integration of multiple sensors for inertial, orientation, velocity and position measurements. First, error models for the necessary sensors are derived. The emphasis is on the USBL devices due to their key role in navigation - besides a velocity sensor based on the Doppler effect. The USBL model is based on theoretical considerations and conclusions from experimental data. The error models and the navigation algorithms are evaluated on real-world data collected during field experiments in shallow sea. The results of this evaluation are used to parametrize an AUV motion model. Usually, such a model is used only for model-based motion control and planning. In this work, however, besides serving as a simulation reference model, it is used as a tool to improve navigation accuracy by providing virtual measurements to the navigation algorithm (model-aided navigation). The benefit of model-aided navigation is evaluated through Monte Carlo simulation in a deep-sea exploration mission. The final and main contributions of this work are twofold. First, the basic expected navigation accuracy for a typical deep-sea mission with USBL and an ensemble of high-quality navigation sensors is evaluated. Secondly, the same setting is examined using model-aided navigation. The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor is close to the ground (e.g. 100m or closer). The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15m w.r.t. the expected root-mean-square error. This also depends on the reference vehicle\u27s position at the surface. In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12%

18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings

Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I Table of Contents ........................ Page III Symposium Committees .............. Page IV Special Thanks ............................. Page V Conference program (incl. page numbers of papers) ................... Page VI Conference papers Invited talks ................................ Page 1 Regular Papers ........................... Page 14 Wednesday, May 26th, 2010 ......... Page 15 Thursday, May 27th, 2010 .......... Page 110 Friday, May 28th, 2010 ............... Page 210 Author index ............................... Page XII

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot