Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE Transactions on Information Forensics and Securit

Master of Science

thesisChannel estimation techniques are crucial for reliable communications. This thesis is concerned with channel estimation in a #12;lter bank multicarrier spread spectrum (FBMC-SS) system. We explore two channel estimator options: (i) a method that makes use of a periodic preamble and mimics the channel estimation techniques that are widely used in OFDM-based systems; and (ii) a method that stays within the traditional realm of #12;lter bank signal processing. For the case where the channel noise is white, both methods are analyzed in detail and their performance is compared against their respective Cramer-Rao Lower Bounds (CRLB). Advantages and disadvantages of the two methods under di#11;erent channel conditions are also discussed to provide insight to the reader as to when one will outperform the other. After the theoretical exercise of deriving these channel estimation algorithms, we examine some practical considerations for the traditional channel estimation approach such as the channel delay spread and the e#11;ects of signal interference. First, a set of guidelines about designing the subcarrier spacing of FMBC-SS vs. the channel coherence bandwidth are provided to ensure channel estimates are su#14;ciently unbiased. Next, we provide a method for detecting the channel delay spread and rejecting in-band interference that results in nearly unbiased channel estimation scheme that can achieve a performance close to the CRLB in low SNR environments

Periodic Preamble-Based Frequency Recovery in OFDM Receivers Plagued by I/Q Imbalance

The direct conversion receiver (DCR) architecture has received much attention in the last few years as an effective means to obtain user terminals with reduced cost, size, and power consumption. A major drawback of a DCR device is the possible insertion of I/Q imbalances in the demodulated signal, which can seriously degrade the performance of conventional synchronization algorithms. In this paper, we investigate the problem of carrier frequency offset (CFO) recovery in an OFDM receiver equipped with a DCR front-end. Our approach is based on maximum likelihood (ML) arguments and aims at jointly estimating the CFO, the useful signal component, and its mirror image. In doing so, we exploit knowledge of the pilot symbols transmitted within a conventional repeated training preamble appended in front of each data packet. Since the exact ML solution turns out to be too complex for practical purposes, we propose two alternative schemes which can provide nearly optimal performance with substantial computational saving. One of them provides the CFO in closed-form, thereby avoiding any grid-search procedure. The accuracy of the proposed methods is assessed in a scenario compliant with the 802.11a WLAN standard. Compared with existing solutions, the novel schemes achieve improved performance at the price of a tolerable increase of the processing load

Waveform Advancements and Synchronization Techniques for Generalized Frequency Division Multiplexing

To enable a new level of connectivity among machines as well as between people and machines, future wireless applications will demand higher requirements on data rates, response time, and reliability from the communication system. This will lead to a different system design, comprising a wide range of deployment scenarios. One important aspect is the evolution of physical layer (PHY), specifically the waveform modulation. The novel generalized frequency division multiplexing (GFDM) technique is a prominent proposal for a flexible block filtered multicarrier modulation. This thesis introduces an advanced GFDM concept that enables the emulation of other prominent waveform candidates in scenarios where they perform best. Hence, a unique modulation framework is presented that is capable of addressing a wide range of scenarios and to upgrade the PHY for 5G networks. In particular, for a subset of system parameters of the modulation framework, the problem of symbol time offset (STO) and carrier frequency offset (CFO) estimation is investigated and synchronization approaches, which can operate in burst and continuous transmissions, are designed. The first part of this work presents the modulation principles of prominent 5G candidate waveforms and then focuses on the GFDM basic and advanced attributes. The GFDM concept is extended towards the use of OQAM, introducing the novel frequency-shift OQAM-GFDM, and a new low complexity model based on signal processing carried out in the time domain. A new prototype filter proposal highlights the benefits obtained in terms of a reduced out-of-band (OOB) radiation and more attractive hardware implementation cost. With proper parameterization of the advanced GFDM, the achieved gains are applicable to other filtered OFDM waveforms. In the second part, a search approach for estimating STO and CFO in GFDM is evaluated. A self-interference metric is proposed to quantify the effective SNR penalty caused by the residual time and frequency misalignment or intrinsic inter-symbol interference (ISI) and inter-carrier interference (ICI) for arbitrary pulse shape design in GFDM. In particular, the ICI can be used as a non-data aided approach for frequency estimation. Then, GFDM training sequences, defined either as an isolated preamble or embedded as a midamble or pseudo-circular pre/post-amble, are designed. Simulations show better OOB emission and good estimation results, either comparable or superior, to state-of-the-art OFDM system in wireless channels

A Consistent OFDM Carrier Frequency Offset Estimator Based on Distinctively Spaced Pilot Tones

A pilot-tone-based maximum likelihood estimator (PBMLE) for carrier frequency offset (CFO) in orthogonal frequency-division multiplexing (OFDM) systems is proposed. To obtain a consistent estimate of the CFO over a frequency-selective fading channel, the proposed method employs a preamble comprising distinctively spaced pilot tones. As a result of this preamble configuration, a large estimation range equal to the bandwidth of the OFDM signal can be achieved. Different from previous ad hoc pilot-tone-based CFO estimators, the PBMLE exploits the relationship between the CFO and the periodogram of the preamble. Analysis shows that the proposed PBMLE is asymptotically unbiased and efficient. To realize this PBMLE in practice, a suboptimal estimator is also introduced, in which a zero-padded fast Fourier transform is invoked and the CFO estimation is split into two phases: coarse and fine estimation. Coarse estimation is obtained through the correlation between the received preamble and its original pattern, whereas fine estimation is obtained by exploiting the magnitude attenuation in the vicinities of those CFO-shifted pilot tones. Both analytical investigations and computer simulations indicate that the accuracy of this simplified suboptimal estimator is proportional to the oversize ratio of zero padding. When the oversize ratio is sufficiently high, the performance of the suboptimal estimator approaches that of the proposed PBMLE.published_or_final_versio

Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach

Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is a key technology component in the evolution towards cognitive radio (CR) in next-generation communication in which the accuracy of timing and frequency synchronization significantly impacts the overall system performance. In this paper, we propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. Specifically, exploiting the preamble signals with synchronization offsets, two ELMs are incorporated into a traditional MIMO-OFDM system to estimate both the residual symbol timing offset (RSTO) and the residual carrier frequency offset (RCFO). The simulation results show that the performance of the proposed ELM-based synchronization scheme is superior to the traditional method under both additive white Gaussian noise (AWGN) and frequency selective fading channels. Furthermore, comparing with the existing machine learning based techniques, the proposed method shows outstanding performance without the requirement of perfect channel state information (CSI) and prohibitive computational complexity. Finally, the proposed method is robust in terms of the choice of channel parameters (e.g., number of paths) and also in terms of "generalization ability" from a machine learning standpoint.Comment: 13 pages, 12 figures, has been accepted for publication in IEEE Transactions on Cognitive Communications and Networkin

Scattered Pilots and Virtual Carriers Based Frequency Offset Tracking for OFDM Systems: Algorithms, Identifiability, and Performance Analysis

In this paper, we propose a novel carrier frequency offset (CFO) tracking algorithm for orthogonal frequency division multiplexing (OFDM) systems by exploiting scattered pilot carriers and virtual carriers embedded in the existing OFDM standards. Assuming that the channel remains constant during two consecutive OFDM blocks and perfect timing, a CFO tracking algorithm is proposed using the limited number of pilot carriers in each OFDM block. Identifiability of this pilot based algorithm is fully discussed under the noise free environment, and a constellation rotation strategy is proposed to eliminate the c-ambiguity for arbitrary constellations. A weighted algorithm is then proposed by considering both scattered pilots and virtual carriers. We find that, the pilots increase the performance accuracy of the algorithm, while the virtual carriers reduce the chance of CFO outlier. Therefore, the proposed tracking algorithm is able to achieve full range CFO estimation, can be used before channel estimation, and could provide improved performance compared to existing algorithms. The asymptotic mean square error (MSE) of the proposed algorithm is derived and simulation results agree with the theoretical analysis

Carrier Frequency Offset Estimation for OFDM Systems using Repetitive Patterns

This paper deals with Carrier Frequency Offset (CFO) estimation for OFDM systems using repetitive patterns in the training symbol. A theoretical comparison based on Cramer Rao Bounds (CRB) for two kinds of CFO estimation methods has been presented in this paper. Through the comparison, it is shown that the performance of CFO estimation can be improved by exploiting the repetition property and the exact training symbol rather than exploiting the repetition property only. The selection of Q (number of repetition patterns) is discussed for both situations as well. Moreover, for exploiting the repetition and the exact training symbol, a new numerical procedure for the Maximum-Likelihood (ML) estimation is designed in this paper to save computational complexity. Analysis and numerical result are also given, demonstrating the conclusions in this paper