Wireless device development requires extensive testing of the hardware and software that is being developed. Existing technologies used to test Bluetooth systems are limited by both design constraints and high hardware costs, restricting their usefulness. This project developed and demonstrated a Bluetooth testbed addressing limitations with existing systems by taking a unique approach to data collection. The three parts of this project are: a throughput test with data logging, a firmware loading utility for the hardware used in this project, and an interface with a commercially available Software-Defined Radio.

Répondre aux contraintes des architectures reconfigurables pour des systèmes véhiculaires n'est pas toujours une tâche aisée. Des solutions existent dans le domaine de la radio logicielle où des plateformes flexibles qui prennent en charge un large éventail de différentes standards de communication sans fil peuvent être conçus. Une de ces architectures est la plateforme ExpressMIMO qui est développée par Eurecom et Télécom ParisTech. Les objectifs principales de cette thèse sont de proposer le premier prototype d'un récepteur pour ExpressMIMO, d'évaluer le potentiel de la plateforme pour les standards ayant des latences critiques, d'identifier des goulots d'étranglement et de proposer des solutions pour surmonter ces limitations. Comme standard, IEEE 802.11p a été choisi qui spécifie la communication entre des véhicules. Au-delà, nous étudions une possible exécution multimodal du 802.11p et du DAB sur ExpressMIMO. Notre analyse lors des expérimentations sur une cible FPGA révèle que le Front-End Processor DSP est lourdement chargé et que le temps de configuration requis dépasse le temps d'exécution. Pour relever ce défi nous proposons un Application Specific Instruction-Set Processor (ASIP) comme solution lorsque les contraintes de latence sont fortes. Pour compléter la chaîne de réception, nous présentons enfin un premier prototype de Préprocesseur qui connecte les convertisseurs A/D et D/A avec les autres composants de la bande de base. Dans ce contexte nous présentons un convertisseur générique et flexible pour le ré-échantillonnage qui travaille sur des rapports fractionnaires de fréquence d'échantillonnage. / Dealing with the requirements of reconfigurable radio architectures in the vehicular domain is a very challenging task. Solutions can be found in the context of Software Defined Radio (SDR). Under its umbrella, flexible hardware platforms that support a wide range of different wireless communication standards are designed. One of them is the OpenAirInterface ExpressMIMO platform that is developed by Eurecom and Télécom ParisTech. Main objectives of this thesis are to propose the first receiver chain prototype for ExpressMIMO, to assess the applicability of the platform for latency critical standards, to identify design bottlenecks and to propose and implement solutions to overcome the identified limitations. Standard of interest in this context is IEEE 802.11p which is required for the Car-to-Car communication. Our analysis reveals that the Front-End Processing (FEP) DSP engine is heavily charged and that the required configuration time outreaches the pure execution time for short vectors. To meet this challenge we introduce an Application Specific Instruction-Set Processor (ASIP) as the solution of choice when dealing with strong latency requirements. To complete the receiver chain we further present a first Preprocessor prototype which connects the external A/D and D/A converters with the remaining baseband engine. In this context we focus on a generic, flexible and hardware optimized Sample Rate Converter (SRC) that is operating on fractional ratios. As the combination of Car-to-Car and Car-to-Infrastructure communications within only device enables various new applications for future cars we finally investigate on a possible multimodal execution of 802.11p and DAB on the chosen target platform.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This design adopts the software radio and DBPSK（Differential Binary Phase Shift Keying）modulation-demodulation, which detects the telecommand receiving by the guided-missile system correctly. The DBPSK modulation module in Altera FPGA chip converts the binary telecommand into DBPSK signal, which will be frequency modulated after D/A conversion. In the receiver, the FM signal is demodulated and A/D converted before sending to the FPGA. The DBPSK demodulation module in FPGA finally gets the telecommand which will be tally with the telecommand from transmitter. At last, the whole DBPSK modulation-demodulation module is embedded into the remote control test system. The design is working properly and meeting the requirements of the test system.

International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California / Legacy telemetry systems, although widely deployed, are being severely taxed to support the high data rate requirements of advanced aircraft and missile platforms. Increasing data rates, in conjunction with loss of spectrum have created a need to use available spectrum more efficiently. In response to this, new modulation techniques have been developed which offer more data capacity in the same operating bandwidth. Demodulation of these new waveforms is a computationally challenging task, especially at high data rates. This paper describes the design, implementation and performance of a high-speed, multi-mode demodulator for the Advanced Range Telemetry (ARTM) program which meets these challenges.

Time-compression overlap-add (TC-OLA) is presented as a novel method of communications over a (wireless) channel, which is shown to have benefits over other methods in some applications. TC-OLA is initially explored in an experimental context using a custom wideband software-defined radio (SDR) to gain insight into some of the possibilities of this method. Basic analysis is developed showing the processing gain, transmitted spectrum, and behaviour in fading channels. The method is considered as a candidate for low power wide area network (LPWAN) applications, highlighting the equivalent channel property, channel averaging, and ability to handle more simultaneous users in the uplink than other schemes in this application area.The method is then considered as an alternative to single carrier frequency domain equalization (SC-FDE) for ultrawideband (UWB) applications, where the ability to reduce or eliminate the cyclic prefix (CP) overhead while still using frequency domain equalization (FDE) techniques is highlighted. Additional application areas for this technology are briefly considered, including cognitive radio and radar. The process of patenting this technology is outlined in an appendix. The issued patent can be found through the United States Patent and Trademark Office (USPTO) as U.S. Patent 9,479,216. / Graduate

Digital communications devices designed with application-specific integrated circuit (ASIC) technology suffer from one very significant limitation�the integrated circuits are not programmable. Therefore, deploying a new algorithm or an updated standard requires new hardware. Field-programmable gate arrays (FPGAs) solve this problem by introducing what is essentially reconfigurable hardware. Thus, digital communications devices designed on FPGAs are capable of accommodating multiple communications protocols without the need to deploy new hardware, and can support new protocols in a matter of seconds. In addition, FPGAs provide a means to update systems that are physical difficult to access. For these reasons, FPGAs provide us with an ideal platform for implementing adaptive communications algorithms. This thesis focuses on using FPGAs to implement an adaptive digital communications system. Using the Universal Software Radio Peripheral (USRP) as a base, this thesis aims to create a highly-adaptive, plug and play software-defined radio (SDR) that fits CubeSat form-factor satellites. Such a radio platform would enable CubeSat engineers to develop new satellites faster and with lower costs. This thesis presents a new system, the COSMIAC CubeSat SDR, that adapts the USRP platform to better suit the space and power limitations of a CubeSat.

Adaptive modulation has been actively researched as a means to increase spectral efficiency of wireless communications systems. In general, analytic closed form models have been derived for the performance of the communications system as a function of the control parameters. However, in systems where general error correction coding is employed, it may be difficult to derive closed form performance functions of the communications systems. In addition, in closed form optimization, real time adaptation is not possible. Systems designed with deterministic state optimization are developed offline for a certain set of parameters and hardwired into mobile devices. In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of the system is measured and stochastic approximation techniques are used to learn the optimal transmission parameters of the system. The technique is applied to Software Defined Radio (SDR) platforms, an emerging wireless technology which is currently being researched as a means of designing intelligent communications devices. The fundamental property that sets SDR apart from traditional radios is that the communications parameters are controlled in software, allowing for real-time control of physical layer communications. Our treatment begins by modeling the time evolution of the adaptive modulation process as a general state space Markov chain. We show the existence and uniqueness of the invariant measure and model performance functions as expectations with respect to the invariant measure. We consider constrained and unconstrained throughput optimization. We show that the cost functions considered are convex. Next we present stochastic approximation algorithms that are used to estimate the gradient of the cost function given only noisy estimates. We conclude by presenting simulation results obtained by the presented method. The learning based method is able to achieve the maximum throughput as dictated by exhaustive Monte Carlo simulation of the communications system, which provide an upper bound on performance. In addition, the learning algorithm is able to optimize communications under various error correction schemes. The tracking abilities of the algorithm are also demonstrated. We see that the proposed method is able to track optimal throughput settings as constraints are changed in time.

The design and development of a software defined radio (SDR) receiver prototype has been completed. The goal is to replace the existing automatic identification system (AIS) manufactured by Vesper Marine with a software driven system that reduces costs and provides a high degree of reconfigurability. One of the key concepts of the SDR is the consideration of directly digitizing the radio frequency (RF) signal using subsampling. This idea arises from the ambition to implement an analog-to-digital converter (ADC) as close to the antenna interface as practically possible. Thus, majority of the RF processing is encapsulated within in the digital domain. Evaluation of a frequency planning strategy that utilizes a combination of subsampling and oversampling will illustrate how the maritime bandwidth is aliased to a lower frequency. An analog front-end (AFE) board was constructed to implement the frequency planning strategy so that the digitized bandwidth can be streamed into a field programmable gate array (FPGA) for real-time processing. Research is shown on digital front-end (DFE) techniques that condition the digitized maritime signal for baseband processing. The process of a digital down converter (DDC) is conducted by an FPGA, which acquired the in-phase and quadrature signals. By implementing a digital signal processor (DSP) for baseband processing, demodulation on an AIS test signal is evaluated. The SDR prototype achieved a receiver sensitivity of -113dBm, outperforming the required sensitivity of -107dBm specified in the International Electrotechnical Commission (IEC) 62287-1 standard for AIS applications [1].

Adaptive modulation has been actively researched as a means to increase spectral efficiency of wireless communications systems. In general, analytic closed form models have been derived for the performance of the communications system as a function of the control parameters. However, in systems where general error correction coding is employed, it may be difficult to derive closed form performance functions of the communications systems. In addition, in closed form optimization, real time adaptation is not possible. Systems designed with deterministic state optimization are developed offline for a certain set of parameters and hardwired into mobile devices. In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of the system is measured and stochastic approximation techniques are used to learn the optimal transmission parameters of the system. The technique is applied to Software Defined Radio (SDR) platforms, an emerging wireless technology which is currently being researched as a means of designing intelligent communications devices. The fundamental property that sets SDR apart from traditional radios is that the communications parameters are controlled in software, allowing for real-time control of physical layer communications. Our treatment begins by modeling the time evolution of the adaptive modulation process as a general state space Markov chain. We show the existence and uniqueness of the invariant measure and model performance functions as expectations with respect to the invariant measure. We consider constrained and unconstrained throughput optimization. We show that the cost functions considered are convex. Next we present stochastic approximation algorithms that are used to estimate the gradient of the cost function given only noisy estimates. We conclude by presenting simulation results obtained by the presented method. The learning based method is able to achieve the maximum throughput as dictated by exhaustive Monte Carlo simulation of the communications system, which provide an upper bound on performance. In addition, the learning algorithm is able to optimize communications under various error correction schemes. The tracking abilities of the algorithm are also demonstrated. We see that the proposed method is able to track optimal throughput settings as constraints are changed in time.

Adaptive modulation has been actively researched as a means to increase spectral efficiency of wireless communications systems. In general, analytic closed form models have been derived for the performance of the communications system as a function of the control parameters. However, in systems where general error correction coding is employed, it may be difficult to derive closed form performance functions of the communications systems. In addition, in closed form optimization, real time adaptation is not possible. Systems designed with deterministic state optimization are developed offline for a certain set of parameters and hardwired into mobile devices. In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of the system is measured and stochastic approximation techniques are used to learn the optimal transmission parameters of the system. The technique is applied to Software Defined Radio (SDR) platforms, an emerging wireless technology which is currently being researched as a means of designing intelligent communications devices. The fundamental property that sets SDR apart from traditional radios is that the communications parameters are controlled in software, allowing for real-time control of physical layer communications. Our treatment begins by modeling the time evolution of the adaptive modulation process as a general state space Markov chain. We show the existence and uniqueness of the invariant measure and model performance functions as expectations with respect to the invariant measure. We consider constrained and unconstrained throughput optimization. We show that the cost functions considered are convex. Next we present stochastic approximation algorithms that are used to estimate the gradient of the cost function given only noisy estimates. We conclude by presenting simulation results obtained by the presented method. The learning based method is able to achieve the maximum throughput as dictated by exhaustive Monte Carlo simulation of the communications system, which provide an upper bound on performance. In addition, the learning algorithm is able to optimize communications under various error correction schemes. The tracking abilities of the algorithm are also demonstrated. We see that the proposed method is able to track optimal throughput settings as constraints are changed in time. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate