Pour supporter l’évolution constante des standards de communication numérique, du GSM vers la 5G, les équipements de communication doivent continuellement s’adapter. Face à l’utilisation croissante de l’internet, on assiste à une explosion du trafic de données, ce qui augmente la consommation d'énergie des appareils de communication sans fil et conduit donc à un impact significatif sur les émissions mondiales de CO2. De plus en plus de recherches se sont concentrées sur l'efficacité énergétique de la communication sans fil. La radio Intelligente, ou Cognitive Radio (CR), est considérée comme une technologie pertinente pour les communications radio vertes en raison de sa capacité à adapter son comportement à son environnement. Sur la base de métriques fournissant suffisamment d'informations sur l'état de fonctionnement du système, une décision optimale peut être effectuée en vue d'une action de reconfiguration, dans le but de réduire au minimum la dissipation d'énergie tout en ne compromettant pas les performances. Par conséquent, tout équipement intelligent doit disposer d’une architecture de gestion de la reconfiguration. Nous avons retenu l’architecture HDCRAM (Hierarchical and Distributed Cognitive Radio Architecture Management), développée dans notre équipe, et nous l’avons déployée sur des plates-formes hétérogènes. L'un des objectifs est d'améliorer l'efficacité énergétique par la mise en œuvre de l’architecture HDCRAM. Nous l’avons appliquée à un système OFDM simplifié pour illustrer comment HDCRAM permet de gérer efficacement le système et son adaptation à un environnement évolutif. / As the digital communication systems evolve from GSM and now toward 5G, the supported standards are also growing. The desired communication equipments are required to support different standards in a single device at the same time. And more and more wireless Internet services have been being provided resulting in the explosive growth in data traffic, which increase the energy consumption of the communication devices thus leads to significant impact on global CO2 emission. More and more researches have focused on the energy efficiency of wireless communication. Cognitive Radio (CR) has been considered as an enabling technology for green radio communications due to its ability to adapt its behavior to the changing environment. In order to efficiently manage the sensing information and the reconfiguration of a cognitive equipment, it is essential, first of all, to gather the necessary metrics so as to provide enough information about the operating condition thus helping decision making. Then, on the basis of the metrics obtained, an optimal decision can be made and is followed by a reconfiguration action, whose aim is to minimize the power dissipation while not compromising on performance. Therefore, a management architecture is necessary to be added into the cognitive equipment acting as a glue to realize the CR capabilities. We introduce a management architecture, namely Hierarchical and Distributed Cognitive Radio Architecture Management (HDCRAM), which has been proposed for CR management by our team. This work focuses on the implementation of HDCRAM on heterogeneous platforms. One of the objectives is to improve the energy efficiency by the management of HDCRAM. And an example of a simplified OFDM system is used to explain how HDCRAM works to efficiently manage the system to adapt to the changing environment.

This thesis investigates the design and implementation of turbo codes over the GNU radio. The turbo codes is a class of iterative channel codes which demonstrates strong capability for error correction. A software defined radio (SDR) is a communication system which can implement different modulation schemes and tune to any frequency band by means of software that can control the programmable hardware. SDR utilizes the general purpose computer to perform certain signal processing techniques. We implement a turbo coding system using the Universal Software Radio Peripheral (USRP), a widely used SDR platform from Ettus. Detail configuration and performance comparison are also provided in this research.

This thesis describes design of Simulator FM-CW range finders. It is choosing the optimal method of realization and inform about basic properties of continuous working radar. The work includes an analysis of errors in real rangefinder and a numerical estimate of the maximum error in real devices. Contains detailed block diagram of simulator FM-CW range-finder and computer simulation of function generator frequency modulated signal, demodulator. Further work includes the complete construction documents for the preparation and implementation of basic functional verification

Cognitive radio (CR) technology introduces a revolutionary wireless communication mechanism in terminals and network segments, so that they are able to learn their environment and adapt intelligently to the most appropriate way of providing the service for the user's exact need. By supporting multi-band, mode-mode cognitive applications, the cognitive radio addresses an interactive way of managing the spectrum that harmonizes technology, market and regulation. This dissertation gives a complete story of building a cognitive radio. It goes through concept clarification, architecture definition, functional block building, system integration, and finally to the implementation of a fully-functional cognitive radio node prototype that can be directly packaged for application use. This dissertation starts with a comprehensive review of CR research from its origin to today. Several fundamental research issues are then addressed to let the reader know what makes CR a challenging and interesting research area. Then the CR system solution is introduced with the details of its hierarchical functional architecture called the Egg Model, modular software system called the cognitive engine, and the kernel machine learning mechanism called the cognition cycle. Next, this dissertation discusses the design of specific functional building blocks which incorporate environment awareness, solution making, and adaptation. These building blocks are designed to focus on the radio domain that mainly concerns the radio environment and the radio platform. Awareness of the radio environment is achieved by extracting the key environmental features and applying statistical pattern recognition methods including artificial neural networks and k-nearest neighbor clustering. Solutions for the radio behavior are made according to the recognized environment and the previous knowledge through case based reasoning, and further adapted or optimized through genetic algorithm solution search. New experiences are gained through the practice of the new solution, and thus the CR's knowledge evolves for future use; therefore, the CR's performance continues improving with this reinforcement learning approach. To deploy the solved solution in terms of the radio's parameters, a platform independent radio interface is designed. With this general radio interface, the algorithms in the cognitive engine software system can be applied to various radio hardware platforms. To support and verify designed cognitive algorithms and cognitive functionalities, a complete reconfigurable SDR platform, called the CWT2 waveform framework, is designed in this dissertation. In this waveform framework, a hierarchical configuration and control system is constructed to support flexible, real-time waveform reconfigurability. Integrating all the building blocks described above allows a complete CR node system. Based on this general CR node structure, a fully-functional Public Safety Cognitive Radio (PSCR) node is prototyped to provide the universal interoperability for public safety communications. Although the complete PSCR node software system has been packaged to an official release including installation guide and user/developer manuals, the process of building a cognitive radio from concept to a functional prototype is not the end of the CR research; on-going and future research issues are addressed in the last chapter of the dissertation. / Ph. D.

The goal of software-defined radio (SDR) is to move the processing of radio signals from the analog domain to the digital domain — to use digital microchips instead of analog circuit components. Until faster, higher-precision analog-to-digital (ADCs) and digital-to-analog converters (DACs) become affordable, however, some analog signal processing will be necessary. We still need to convert high-radio frequency (RF) signals that we receive to low intermediate-frequency (IF) or baseband (centered on zero Hz) signals in order for ADCs to sample them and feed them into microchips for processing. The reverse is true when we transmit. Amplification is also needed on the receive side to fully utilize the dynamic range of the ADC and power amplification is needed on the transmit side to increase the power output from the DAC for transmission. Analog filtering is also needed to avoid saturating the ADC or to filter out interference when receiving and to avoid transmitting spurs. The analog frequency conversion, amplification and filtering section of a radio is called the RF front end. This thesis describes work on a new RF front end daughterboard for the Universal Software Radio Peripheral, or USRP. The USRP is a software-radio hardware platform designed to be used with the GNU Radio software radio software package. Using the Motorola RFIC4 chip, the new daughterboard receives RF signals, converts them to baseband and does analog filtering and amplification before feeding the signal into the USRP for processing. The chip also takes transmit signals from the USRP, converts them from baseband to RF and amplifies and filters them. The board was designed and laid out by Randall Nealy. I wrote the software driver for GNU Radio. The driver defines the interface between the USRP and the RFIC chip, controls the physical settings, and calculates and sets the hundreds of variables necessary to operate this extremely complex chip correctly. It allows plug-and-play compatibility with the current USRP daughterboards and supplies additional functions not available in any other daughterboard. / Master of Science

No description available.

Algorithm design methods for radio communications systems are poised to undergo a massive disruption over the next several years. Today, such algorithms are typically designed manually using compact analytic problem models. However, they are shifting increasingly to machine learning based methods using approximate models with high degrees of freedom, jointly optimized over multiple subsystems, and using real-world data to drive design which may have no simple compact probabilistic analytic form. Over the past five years, this change has already begun occurring at a rapid pace in several fields. Computer vision tasks led deep learning, demonstrating that low level features and entire end-to-end systems could be learned directly from complex imagery datasets, when a powerful collection of optimization methods, regularization methods, architecture strategies, and efficient implementations were used to train large models with high degrees of freedom. Within this work, we demonstrate that this same class of end-to-end deep neural network based learning can be adapted effectively for physical layer radio systems in order to optimize for sensing, estimation, and waveform synthesis systems to achieve state of the art levels of performance in numerous applications. First, we discuss the background and fundamental tools used, then discuss effective strategies and approaches to model design and optimization. Finally, we explore a series of applications across estimation, sensing, and waveform synthesis where we apply this approach to reformulate classical problems and illustrate the value and impact this approach can have on several key radio algorithm design problems. / Ph. D.

With the rapid migration of physical layer design of radio towards software, it becomes necessary to select or develop the platform and tools that help in achieving rapid design and development along with flexibility and reconfigurability. The availability of field programmable gate arrays (FPGAs) has promoted the concept of reconfigurable hardware for software defined radio (SDR). It enables the designer to create high speed radios with flexibility, low latency and high throughput. Generally, the traditional method of designing FPGA based radios limits productivity. Productivity can be improved using Model based design (MBD) tools. These tools encourage a modular way of developing waveforms for radios. The tools based on MBD have been the focus of recent research exploring the concept of the platform independent model (PIM) and portability across platforms by the platform specific model (PSM). The thesis presented here explores the tools based on MBD to achieve prototyping for wireless standards like IEEE 802.11a and IEEE 802.16e on reconfigurable hardware. It also describes the interfacing of the universal software radio peripheral (USRP2), acting as a radio frequency (RF) front end, with an additional FPGA board for baseband processing. / Master of Science

Dynamic spectrum access (DSA) wireless networks focus on using RF spectrum more efficiently and dynamically. Significant progress has been made during the past few years. For example, many measurements of current spectrum utilization are available. Theoretical analyses and computational simulations of DSA networks also abound. In sharp contrast, few network systems, particularly those with a decentralized structure, have been built even at a small scale to investigate the performance, behavior, and dynamics of DSA networks under different scenarios. This dissertation provides the theory, design, and implementation of a software radio-based decentralized DSA network prototype, and its enabling technologies: software radio, signal detection and classification, and distributed cooperative spectrum sensing. By moving physical layer functions into the software domain, software radio offers an unprecedented level of flexibility in radio development and operation, which can facilitate research and development of cognitive radio (CR) and DSA networks. However, state-of-the-art software radio systems still have serious performance limitations. Therefore, a performance study of software radio is needed before applying it in any development. This dissertation investigates three practical issues governing software radio performance that are critical in DSA network development: RF front end nonlinearity, dynamic computing resource allocation, and execution latency. It provides detailed explanations and quantitative results on SDR performance. Signal detection is the most popular method used in DSA networks to guarantee non-interference to primary users. Quickly and accurately detecting signals under all possible conditions is challenging. The cyclostationary feature detection method is attractive for detecting primary users because of its ability to distinguish between modulated signals, interference, and noise at a low signal-to-noise ratio (SNR). However, a key issue of cyclostationary signal analysis is the high computational cost. To tackle this challenge, parallel computing is applied to develop a cyclostationary feature based signal detection method. This dissertation presents the method's performance on multiple signal types in noisy and multi-path fading environments. Distributed cooperative spectrum sensing is widely endorsed to monitor the radio environment so as to guarantee non-interference to incumbent users even at a low SNR and under hostile conditions like shadowing, fading, interference, and multi-path. However, such networks impose strict performance requirements on data latency and reliability. Delayed or faulty data may cause secondary users to interfere with incumbent users because secondary users could not be informed quickly or reliably. To support such network performance, this dissertation presents a set of data process and management schemes in both sensors and data fusion nodes. Further, a distributed cooperative sensor network is built from multiple sensors; together, the network compiles a coherent semantic radio environment map for DSA networks to exploit available frequencies opportunistically. Finally, this dissertation presents the complete design of a decentralized and asynchronous DSA network across the PHY layer, MAC layer, network layer, and application layer. A ten-node prototype is built based on software radio technologies, signal detection and classification methods, distributed cooperative spectrum sensing systems, dynamic wireless protocols, and a multi-channel allocation algorithm. Systematic experiments are carried out to identify several performance determining factors for decentralized DSA networks. / Ph. D.

Most times people are unprepared to face natural disasters resulting in chaos, increased number of deaths, etc.Emergency responders need an efficiently working communication network to get in touch with the emergency services like hospitals, police, fire and rescue as well as people who are stranded. Such a network is also the need of the hour for survivors to contact their near and dear ones. One of the major barriers of communication during an emergency is the destruction of network elements. In case the communication devices survive the calamity, odds of the network getting congested are certainly high because almost everyone will be trying to use the same network resources. An important factor when dealing with emergency situations is the calls for an immediate response and an efficient Emergency Communication Systems (ECS). Currently there is a capability gap between existing ECS solutions and what we dream of achieving. Most current solutions do not meet cost or mobility constraints. An inexpensive, portable and mobile system will fulfill this capability gap. The main purpose of this research is to optimize the altitude and received signal strength of an aerial base station to provide maximum radio coverage on the ground as well as propose the best fit radio propagation channel model to carry out the experiment for the current scenario.