Studies in Software-Defined Radio System Implementation

Over the past decade, software-defined radios (SDRs) have an increasingly prevalent aspect of wireless communication systems. Different than traditional hardware radios which implement radio protocols using static electrical circuit, SDRs implement significant aspects of physical radio protocol using software programs running on a host processor. Because they use software to implement most of the radio functionality, SDRs are much more easily modified, edited, and upgraded than their hardware-defined counterparts. Consequently, researchers and developers have been developing previously hardware-defined radio systems within software. Thus, communication standards can be tested under different conditions or swapped out entirely by simply changing some code. Additionally, developers hope to implement more advanced functionality with SDRs such as cognitive radios that can sense the conditions of the environment and change parameters or protocol accordingly. This paper will outline the major aspects of SDRs including their explanation, advantages, and architecture. As SDRs have become more commonplace, many companies and organizations have developed hardware front-ends and software packages to help develop software radios. The most prominent hardware front-ends to date have been the USRP hardware boards. Additionally, many software packages exist for SDR development, including the open source GNU Radio and OSSIE and the closed source Simulink and Labview SDR packages. Using these development tools, researchers have developed many of the most relevant radio standards. This paper will explain the major hardware and software development tools for creating SDRs, and it will explain some of the most important SDR projects that have been implemented to date

Aerial Networking: Creating a Resilient Wireless Network for Multiple Unmanned Aerial Vehicles

The goal of this report is to design the groundwork of a wireless communications system between several Unmanned Aerial Vehicles (UAVs) that will help conduct Search and Rescue (SAR) missions. UAVs could help with these missions because they can provide aerial reconnaissance at low cost and risk. To maximize efficiency, the architecture of our ad hoc network includes several UAVs with cameras (drones) relaying their data through a central UAV called a mothership. Our specific objectives, which we successfully met, were to demonstrate the feasibility of such a network in the laboratory and to lay the groundwork for the physical implementation of the system, including the assembly of a motherboard and Wi-Fi transmitters that will perform the communication between the user and UAVs

Analysis of radio frequency spectrum usage using cognitive radio

This paper presents the analysis of radio frequency (RF) spectrum usage using cognitive radio. The aim was to determine the unused spectrum frequency bands for efficiently utilization. A program was written to reuse a range of vacant frequency with different model element working together to produce a spectrum sensing in MATLAB/Simulink environment. The developed Simulink model was interfaced with a register transfer level - software defined radio, which measures the estimated noise power of the received signal over a given time and bandwidth. The threshold estimation performed generates a 1\0 output for decision and prediction. It was observed that some spectrum, identified as vacant frequency, were underutilized in FM station in Benin City. The result showed that when cognitive radio displays “1” output, which is decision H1, the channel is occupied and cannot be used by the cognitive radio for communication. Conversely, when “0” output (decision H0) is displayed, the channel is unoccupied. There is a gradual decrease in the probability of detection (Pd), when the probability of false alarm (Pfa) is increased from 1% to 5%. In the presence of higher Pfa, the Pd of the receiver maintains a high stability. Hence, the analysis finds the spectrum hole and identifies how it can be reuse

Design methodology addressing static/reconfigurable partitioning optimizing software defined radio (SDR) implementation through FPGA dynamic partial reconfiguration and rapid prototyping tools

The characteristics people request for communication devices become more and more demanding every day. And not only in those aspects dealing with communication speed, but also in such different characteristics as different communication standards compatibility, battery life, device size or price. Moreover, when this communication need is addressed by the industrial world, new characteristics such as reliability, robustness or time-to-market appear. In this context, Software Defined Radios (SDR) and evolutions such as Cognitive Radios or Intelligent Radios seem to be the technological answer that will satisfy all these requirements in a short and mid-term. Consequently, this PhD dissertation deals with the implementation of this type of communication system. Taking into account that there is no limitation neither in the implementation architecture nor in the target device, a novel framework for SDR implementation is proposed. This framework is made up of FPGAs, using dynamic partial reconfiguration, as target device and rapid prototyping tools as designing tool. Despite the benefits that this framework generates, there are also certain drawbacks that need to be analyzed and minimized to the extent possible. On this purpose, a SDR design methodology has been designed and tested. This methodology addresses the static/reconfigurable partitioning of the SDRs in order to optimize their implementation in the aforementioned framework. In order to verify the feasibility of both the design framework and the design methodology, several implementations have been carried out making use of them. A multi-standard modulator implementing WiFi, WiMAX and UMTS, a small-form-factor cognitive video transmission system and the implementation of several data coding functions over R3TOS, a hardware operating system developed by the University of Edinburgh, are these implementations.Las características que la gente exige a los dispositivos de comunicaciones son cada día más exigentes. Y no solo en los aspectos relacionados con la velocidad de comunicación, sino que también en diferentes características como la compatibilidad con diferentes estándares de comunicación, autonomía, tamaño o precio. Es más, cuando esta necesidad de comunicación se traslada al mundo industrial, aparecen nuevas características como fiabilidad, robustez o plazo de comercialización que también es necesario cubrir. En este contexto, las Radios Definidas por Software (SDR) y evoluciones como las Radios Cognitivas o Radios Inteligentes parecen la respuesta tecnológica que va a satisfacer estas necesidades a corto y medio plazo. Por ello, esta tesis doctoral aborda la implementación de este tipo de sistemas de comunicaciones. Teniendo en cuenta que no existe una limitación, ni en la arquitectura de implementación, ni en el tipo de dispositivo a usar, se propone un nuevo entrono de diseño formado por las FPGAs, haciendo uso de la reconfiguración parcial dinámica, y por las herramientas de prototipado rápido. A pesar de que este entorno de diseño ofrece varios beneficios, también genera algunos inconvenientes que es necesario analizar y minimizar en la medida de lo posible. Con este objetivo, se ha diseñado y verificado una metodología de diseño de SDRs. Esta metodología se encarga del particionado estático/reconfigurable de las SDRs para optimizar su implementación sobre el entrono de diseño antes comentado. Para verificar la viabilidad tanto del entorno, como de la metodología de diseño propuesta, se han realizado varias implementaciones que hacen uso de ambas cosas. Estas implementaciones son: un modulador multi-estándar que implementa WiFi, WiMAX y UMTS, un sistema cognitivo y compacto de transmisión de video y la implementación de varias funciones de codificación de datos sobre R3TOS, un sistema operativo hardware desarrollado por la Universidad de Edimburgo

Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing

Filter Bank Multicarrier Modulation for Spectrally Agile Waveform Design

In recent years the demand for spectrum has been steadily growing. With the limited amount of spectrum available, Spectrum Pooling has gained immense popularity. As a result of various studies, it has been established that most of the licensed spectrum remains underutilized. Spectrum Pooling or spectrum sharing concentrates on making the most of these whitespaces in the licensed spectrum. These unused parts of the spectrum are usually available in chunks. A secondary user looking to utilize these chunks needs a device capable of transmitting over distributed frequencies, while not interfering with the primary user. Such a process is known as Dynamic Spectrum Access (DSA) and a device capable of it is known as Cognitive Radio. In such a scenario, multicarrier communication that transmits data across the channel in several frequency subcarriers at a lower data rate has gained prominence. Its appeal lies in the fact that it combats frequency selective fading. Two methods for implementing multicarrier modulation are non-contiguous orthogonal frequency division multiplexing (NCOFDM)and filter bank multicarrier modulation (FBMC). This thesis aims to implement a novel FBMC transmitter using software defined radio (SDR) with modulated filters based on a lowpass prototype. FBMCs employ two sets of bandpass filters called analysis and synthesis filters, one at the transmitter and the other at the receiver, in order to filter the collection of subcarriers being transmitted simultaneously in parallel frequencies. The novel aspect of this research is that a wireless transmitter based on non-contiguous FBMC is being used to design spectrally agile waveforms for dynamic spectrum access as opposed to the more popular NC-OFDM. Better spectral containment and bandwidth efficiency, combined with lack of cyclic prefix processing, makes it a viable alternative for NC-OFDM. The main aim of this thesis is to prove that FBMC can be practically implemented for wireless communications. The practicality of the method is tested by transmitting the FBMC signals real time by using the Simulink environment and USRP2 hardware modules

A Cooperative Spectrum Sensing Network with Signal Classification Capabilities

This report describes the design and implementation of the spectrum sensing and signal classification sub-systems of a cooperative network. A sensor blindly receives and calculates the cyclic statistics of a signal decides whether or not the signal represents information or noise. If the signal\u27s statistics indicate the presence of data, the system attempts to classify its modulation scheme. Finally, the decisions of several independent sensors are combined to provide a reliable estimate of the contents of the spectrum of interest. Independently, sensors correctly classify a signal about 60-70% of the time in a low SNR environment. The data fusion module improves this number significantly - especially as the number of sensors increases

An evaluation of low cost fpga-based software defined radios for education and research

The purpose of this study is to evaluate a low-cost Software Defined Radio (SDR) platform for educational and research purposes. An evaluation of existing SDR platforms and design techniques was performed, identifying low cost hardware and software suitable for a laboratory environment. The idea behind the project is to provide undergraduate students with a generic hardware platform so that they can perform simple radio communication experiments. This paper compares and evaluates the existing research projects and educational lab experiments done for SDR. Basic AM and FM radios are created and simulated on the hardware. The detailed procedure to create a design and download the design onto the hardware has been documented, and tutorials are created for step-by-step procedures to perform the experiments. With their ease of use and low cost, Spartan3E FPGA board and Simulink are the best choices for conducting low frequency radio communication experiments

Digital Spectrum Sensing for the Localization of Public Safety Responders

This project has developed a modular sensor network to localize two-way radio transmitters without transmitter cooperation. The sensor network is capable of detecting the spectral location of signals, as well as the transmitting radioÂ’s modulation scheme through the use of a matched filter and autocorrelation spectrum sensing scheme. Each receiving node in the sensor network is capable of identifying a signal as an analog FM or Public Safety P25 transmission. After a signal has been identified, the control center attempts to localize the signal based on the received signal strength (RSS). The sensor network collects information about the transmitters in its environment and displays the transmitters center frequency, modulation scheme, and position as outputs on the central controller

RF channel characterization for cognitive radio using support vector machines

Cognitive Radio promises to revolutionize the ways in which a user interfaces with a communications device. In addition to connecting a user with the rest of the world, a Cognitive Radio will know how the user wants to connect to the rest of the world as well as how to best take advantage of unused spectrum, commonly called white space\u27. Through the concept of Dynamic Spectrum Acccess a Cognitive Radio will be able to take advantage of the white space in the spectrum by first identifying where the white space is located and designing a transmit plan for a particular white space. In general a Cognitive Radio melds the capabilities of a Software Defined Radio and a Cognition Engine. The Cognition Engine is responsible for learning how the user interfaces with the device and how to use the available radio resources while the SDR is the interface to the RF world. At the heart of a Cognition Engine are Machine Learning Algorithms that decide how best to use the available radio resources and can learn how the user interfaces to the CR. To decide how best to use the available radio resources, we can group Machine Learning Algorithms into three general categories which are, in order of computational cost: 1.) Linear Least Squares Type Algorithms, e.g. Discrete Fourier Transform (DFT) and their kernel versions, 2.) Linear Support Vector Machines (SVMs) and their kernel versions, and 3.) Neural Networks and/or Genetic Algorithms. Before deciding on what to transmit, a Cognitive Radio must decide where the white space is located. This research is focused on the task of identifying where the white space resides in the spectrum, herein called RF Channel Characterization. Since previous research into the use of Machine Learning Algorithms for this task has focused on Neural Networks and Genetic Algorithms, this research will focus on the use of Machine Learning Algorithms that follow the Support Vector optimization criterion for this task. These Machine Learning Algorithms are commonly called Support Vector Machines. Results obtained using Support Vector Machines for this task are compared with results obtained from using Least Squares Algorithms, most notably, implementations of the Fast Fourier Transform. After a thorough theoretical investigation of the ability of Support Vector Machines to perform the RF Channel Characterization task, we present results of using Support Vector Machines for this task on experimental data collected at the University of New Mexico.\u2