Machine Learning based RF Transmitter Characterization in the Presence of Adversaries

The advances in wireless technologies have led to autonomous deployments of various wireless networks. As these networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can learn and adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize the RF environment. In this dissertation, we address some of the fundamental challenges on how to effectively apply different learning techniques in the RF domain. In the presence of adversaries, malicious activities such as jamming, and spoofing are inevitable which render most machine learning techniques ineffective. To facilitate learning in such settings, we propose an adversarial learning-based approach to detect unauthorized exploitation of RF spectrum. First, we show the applicability of existing machine learning algorithms in the RF domain. We design and implement three recurrent neural networks using different types of cell models for fingerprinting RF transmitters. Next, we focus on securing transmissions on dynamic spectrum access network where primary user emulation (PUE) attacks can pose a significant threat. We present a generative adversarial net (GAN) based solution to counter such PUE attacks. Ultimately, we propose recurrent neural network models which are able to accurately predict the primary users\u27 activities in DSA networks so that the secondary users can opportunistically access the shared spectrum. We implement the proposed learning models on testbeds consisting of Universal Software Radio Peripherals (USRPs) working as Software Defined Radios (SDRs). Results reveal significant accuracy gains in accurately characterizing RF transmitters- thereby demonstrating the potential of our models for real world deployments

Cross-Reference Ewod Driving Scheme And Cross-Contamination Aware Net Placement Technique For Meda Based Dmfbs

Droplet based digital microfluidics is a popular emerging technology for laboratory experiments. However, certain limitations exist in specific cases for implementation that require further enhancement. Pin-count minimization and cross-contamination avoidance between droplets of different bio-molecules during droplet routing are primary design challenges for biochips. A competent architecture namely Microelectrode Dot Array (MEDA) has recently been introduced as a new highly scalable, field programmable and reconfigurable dot array architecture which allows dynamic configuration. This work considers the cross contamination problems in pin constrained biochips based on MEDA architecture. In order to reduce the cross-contamination problem, in this work we present a MEDA architecture based cross-reference driving scheme that allows simultaneous driving of multiple droplets and thereby propose a suitable net placement technique applicable for MEDA architecture. The objectives of this proposed technique include reducing the crossovers with intelligent collision avoidance, minimizing the overall routing time and increasing grouping number to reduce the total pin-count. Simulation results thus presented in this paper indicate the efficiency of our algorithm for practical bioassays

PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization

In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a machine learning (ML)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse CFO estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations

Strong infrared radiation through passive dispersive wave generation and its control

We observe strong infrared (IR) radiation as a result of passive dispersive wave generation for a realistic microstructured fiber having two zero-dispersion wavelengths. The IR radiation frequency can be suitably controlled by varying the operational wavelength, which falls in the first normal dispersion regime. The amplitude of the radiation can be significantly increased by introducing a suitable amount of chirp in the input pulse. This strong phase-matching radiation can be considered as an alternative solution for the IR laser for different applications. (C) 2011 Optical Society of Americ

Video Quality Assessment For Inter-Vehicular Streaming With Ieee 802.11P, Lte, And Lte Direct Networks Over Fading Channels

Availability of real time video streams capturing surrounding road conditions can not only aid automobile drivers and autonomous vehicles, they can also enhance road safety and improve traffic efficiency. However, provisioning of such services is challenging due to the harsh operational environments and stringent resource requirements of the applications. Nevertheless, radio access technologies like IEEE 802.11p, LTE, and LTE Direct have provisions for supporting real time streams for inter-vehicular communications. In this paper, we present an emulation-based study to demonstrate to what extent these networks are able to sustain real-time video streaming for vehicle-to-vehicle communication. We model highways and congested urban road scenarios using Ricean and Rayleigh fading channel models respectively, that closely mimic the real-life environment. We use H.264 codec for transcoding 360p and 480p videos at the sender vehicle, and then stream the transport segments over a real-time transport protocol. The quality of the video received at the target vehicle is assessed using two metrics: PSNR and SSIM. The emulation is done for varying encoding rates, relative speeds between vehicles, and inter-vehicular distances. We also find the link layer throughput that these networks can support. The results reveal that LTE Direct performs better than 802.11p, which in turn performs better than LTE. This study also provides insights into how to configure the radio and network parameters for delivering streaming services to vehicular networks

Determination of modal effective indices and dispersion of microstructured fibers with different configurations: a variational approach

A simple semi-analytical model based on the variational method is developed for determining the effective indices of the fundamental modes and consecutively the dispersion properties of microstructured optical fibers (MOFs) with different lattice geometries without resorting to any numerical tool. We consider an equivalent step-index (ESI) profile of the MOF and the fundamental mode shape is approximated as a simple Gaussian function in the core. Effective index data and dispersion obtained from the proposed variational method offer reasonable agreement with numerically derived data using the Finite Element Method (FEM). The proposed model offers an alternative and swift way for reasonably precise determination of the effective index of the fundamental mode and dispersion properties of MOF designs with different lattice geometries. Finally, as a major application, the dispersion property of a fabricated MOF, derived through the proposed variational method, is directly used in order to model experimental supercontinuum (SC) spectra with satisfactory agreement

Supercontinuum Generation in Microstructured Silica Optical Fibers : The Formation of Artificial White Light

The inherent feature of nonlinear optics is the modification of the optical properties of the medium due to the interaction of the propagating high intensity light with the medium ultimately leading to the generation of new frequencies. An ultra-short optical pulse experiences spectral broadening when it is passed through a nonlinear medium such as high purity silica fiber and eventually generates artificial white light with unique spectral properties, controlled time duration and high spectral brightness. Owing to its wide and continuous spectra, such phenomenon is generally called supercontinuum (SC) generation. A variety of nonlinear processes governed by the associated pulse duration are involved in such spectral broadening. For femtosecond pump pulses, soliton dynamics plays a pivotal role whereas self-phase modulation (SPM), four wave mixing (FWM) etc are important for wider pump pulses. The generation of white light is an interesting physical phenomenon and it opens up new possibilities in the field of optical communication, optical metrology, nonlinear spectroscopy, microscopy and laser biomedicine. In the present review particular attention is paid to the description of the formation of white light in different operational conditions in highly nonlinear waveguides like photonic crystal fiber (PCF)

Efficient supercontinuum sources based on suspended core microstructured fibers

We fabricate uniform silica microstructured optical fibers (MOFs) having very simple geometry with only three rings of air holes in order to generate efficient supercontinuum (SC). The fabricated MOFs possess suspended core with comparatively larger pitch and are the most active component in a SC source. We use the suspension factor as a design parameter which significantly influences the nonlinear and dispersion properties of the MOFs. It is experimentally shown that our fabricated MOFs generate efficient SC both in femtosecond and picosecond pumping domain. We also numerically model the nonlinear dynamics for SC sources in order to identify the nonlinear processes and illustrate the spectral broadening mechanisms

Two zero dispersion points and enhanced nonlinearity in microstructured fibers: role of core structure

Microstructured fibers with high nonlinearity and tailored dispersion are designed, optimized and fabricated. Two zero dispersion points are achieved by varying and optimizing the core shape that can be critically controlled during fiber drawing process