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

Novel algorithms in wireless CDMA systems for estimation and kernel based equalization

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A powerful technique is presented for joint blind channel estimation and carrier offset method for code- division multiple access (CDMA) communication systems. The new technique combines singular value decomposition (SVD) analysis with carrier offset parameter. Current blind methods sustain a high computational complexity as they require the computation of a large SVD twice, and they are sensitive to accurate knowledge of the noise subspace rank. The proposed method overcomes both problems by computing the SVD only once. Extensive simulations using MatLab demonstrate the robustness of the proposed scheme and its performance is comparable to other existing SVD techniques with significant lower computational as much as 70% cost because it does not require knowledge of the rank of the noise sub-space. Also a kernel based equalization for CDMA communication systems is proposed, designed and simulated using MatLab. The proposed method in CDMA systems overcomes all other methods

Low Complexity AOFDM System for Time-varying Wireless Channels

Signal transmitted through a wireless channel undergoes distortion due to the pres­ ence of reflectors in the environment between a transmitter and a receiver as well as due to the Doppler shift caused by the relative movement of the receiver with respect to the transmitter. Distorted signal is recovered at the receiver side by means of predicting the channel responses and performing inverse operation that the channel introduced on the transmitted signal. Prediction of channel responses becomes more complex when the receiver moves with a varying speed since it directly affects the auto-correlation of the channel responses. First part of this thesis provides a solution for recovering the transmitted data when the receiver is moving with varying speed. The system first tracks the receiver speed variations using the number of deep fadings (nulls) in the received signal enve­ lope of one sub-carrier during a fixed time period. If there is a significant change in receiver speed then the Kalman filter parameters are calculated and updated. Future channel responses are predicted using the updated Kalman filter parameters and used in equalizer to recover the distorted signal. The performance and computational effi­ ciency of the proposed system outperforms the conventional system which calculates predictor parameters at a fixed interval. Second part of the thesis presents an adaptive modulation technique based on the signal-to-noise ratio and the receiver speed. Modulation schemes for different combinations of signal-to-noise ratio and receiver speeds are obtained by selecting the higher modulation scheme with the bit error rate less than target bit error rate. Boundaries of the selected modulation schemes are found using support vector ma­ chine classifiers. The receiver uses the designed system to select appropriate modula­ tion scheme by mapping the current modulation scheme and the channel conditions. The proposed system outperforms conventional adaptive modulation technique that uses instantaneous signa-to-noise ratio by a margin of 5 dB

Space-time-frequency block codes for MIMO-OFDM in next generation wireless systems

In this thesis the use of space-frequency block codes (SFBC) and space-time-frequency block codes (STFBC) in wireless systems are investigated. A variety of SFBC and STFBC schemes are proposed for particular propagation scenarios and system settings where each has its own advantages and disadvantages. The objective is to pro-pose coding strategies with improved flexibility, feasibility and spectral efficiency,and reduce the decoding complexity in an MIMO-OFDM system. Firstly an efficient SFBC with improved system performance is proposed for MIMO-OFDM systems. The proposed SFBC incorporates the concept of matched rotation precoding (MRP) to achieve full transmit diversity and optimal system performance foran arbitrary numberoftransmitantennas,subcarrierinterval andsubcarriergrouping. The MRP is proposed to exploit the inherent rotation and repetition properties of SFBC, arising from the channel power delay profile, in order to fully capture both space and frequency diversity of SFBC in a MIMO-OFDM system. It is able to relax restrictions on subcarrier interval and subcarrier grouping, making it ideal for adaptive/time-varying systems or multiuser systems. The SFBC without an optimization process is unstable in terms of achievable system performance and diversity order, and also risks diversity loss within a specific propagation scenario. Such loss or risk is prominent while wireless propagation channel has a limited number of dominant paths, e.g. relatively close to transmitters or relatively flat topography. Hence in orderto improve the feasibility of SFBC in dynamic scenarios, the lower bound of the coding gain for MRP is derived. The SFBC with MRP is proposed for more practical scenarios when only partial channel power delay profile information is known at the transmit end, for example the wireless channel has dominant propagation paths. The proposed rate one MRP has a relatively simple optimization process that can be transformed into an explicit diagram and hence an optimal result can be derived intuitively without calculations. Next, a multi-rate transmission strategy is proposed for both SFBCand STFBC to balance the system performance and transmission rate. A variety of rate adaptive coding matrices are obtained by a simple truncation of the coding matrix, or by parameter optimization for coding matrices for a given transmission rate and constellation. Pro-posed strategy can easily and gradually adjust the achievable diversity order. As a result it is capable of achieving a relatively smooth balance between system performance and transmission rate in both SFBC and STFBC, without a significant change of coding structure or constellation size. Such tradeoff would be useful to maintain stable Quality of Service (QoS) for users by providing more scalability of achievable performance in a time-varying channel. Finally the decoding procedure of space-time block code (STBC), SFBCand STFBC is discussed. The decoding of all existing STBC/SFBC/STFBC is unified at first, in order to show a concise procedure and make fair comparisons. Then maximum likelihood decoding (MLD) and arbitrary sphere decoding (SD) can be adopted. To reduce the complexity of decoding further, a novel decoding method called compensation de-coding (CD) is presented for a given space-time-frequency coding scheme. By taking advantage of the simplicity of zero-forcing decoding (ZFD) we are able to calculate a compensation vector for the output of ZFD. After modification by utilizing the com-pensation vector, the BER performance can be improved significantly. The decoding procedure is relatively simple and is independent of the constellation size. The per-formance of the proposed decoding method is close to maximum-likelihood decoding for low to medium SNR. A low complexity detection scheme, classifier based decoding (CBD), is further proposed for MIMO systems incorporating spatial multiplexing. The CBD is a hybrid of an equalizer-based technique and an algorithmic search stage. Based on an error matrix and its probability density functions for different classes of error, a particular search region is selected for the algorithmic stage. As the probability of occurrence of error classes with larger search regions is small, overall complexity of the proposed technique remains low, whilst providing a significant improvement in the bit error rate performance

On Efficient Signal Processing Algorithms for Signal Detection and PAPR Reduction in OFDM Systems

The driving force of the study is susceptibility of LS algorithm to noise. As LS algorithm is simple to implement, hence it’s performance improvement can contribute a lot to the wireless technology that are especially deals with high computation. Cascading of AdaBoost algorithm with LS greatly influences the OFDM system performance. Performance of Adaptive Boosting based symbol recovery was investigated on the performance of LS, MMSE, BLUE were also compared with the performance of AdaBoost algorithm and MMSE has been found the higher computational complexity. Furthermore, MMSE also requires apriori channel statistics and computational complexity O(5N3) of the MMSE increases exponentially as the number of carrier increases. For the Adaboost case the computational complexity calculation is little different.Therefore, in the training stage of the AdaBoost algorithm, the computational complexity is only O(nT M) Furthermore, as it is a classification algorithm so in the receiver side we will require a separate de-mapper (or decoder) to get the desired data bits, i.e., a. SAS aided DCT based PAPR reduction 1326 and b. SAS aided DCT based PAPR reduction. A successive addition subtraction preprocessed DCT based PAPR reduction technique was proposed. Here, the performance of proposed method was compared with other preexisting techniques like SLM and PTS and the performance of the proposed method was seen to outperform specially in low PAPR region. In the proposed PAPR reduction method, the receiver is aware of the transmitted signal processing, this enables a reverse operation at the receiver to extract the transmit data. Hence the requirement of sending extra information through extra subcarrier is eliminated. The proposed method is also seen to be spectrally efficient. In the case of PTS and SLM it is inevitable to send the side information to retrieve the transmit signal. Hence, these two methods are spectrally inefficient. Successive addition subtraction based PAPR reduction method was also applied to MIMO systems. The performance of the SAS based PAPR reduction method also showed better performance as compared to other technique. An extensive simulation of MIMO OFDM PAPR reduction was carried out by varying the number of subcarriers and number of transmitter antennas. A detailed computational complexity analysis was also carried out. BATE aided SDMA multi user detection. A detailed study of SDMA system was carried out with it’s mathematical analysis.Many linear and non linear detectors like ML, MMSE, PIC, SIC have been proposed in literature for multiuser detection of SDMA system. However, except MMSE every receivers other are computational extensive. So as to enhance the performance of the MMSE MUD a meta heuristic Bat algorithm was incorporated in cascade with MMSE

Quality of service in WiMAX networks

Mestrado em Engenharia Electrónica e TelecomunicaçõesO acesso à banda larga é um requisito importante na actualidade para satisfazer os utilizadores em termos de novas aplicações e serviços em tempo real. O WiMAX, como tecnologia sem fios para áreas metropolitanas, prometendo cobrir uma maior superfície e com maior débito, é uma tecnologia promissora para as redes de próxima geração. No entanto um requisito importante para a instalação e massificação desta tecnologia é o seu comportamento a nível de qualidade de serviços e garantia aos utilizadores do cumprimento eficiente dos requisitos de QoS. Esta tese aborda e estuda o suporte de qualidade de serviços para redes WiMAX presente em diferentes modelos de simulação, implementados na ferramenta de simulação ns-2. Para além da validação e comparação entre os modelos existentes, também é efectuada a especificação e implementação de uma solução de QoS composta por um classificador e escalonador, e é proposto e avaliado um algoritmo de escalonamento que utiliza prioritização de classes de serviço e informação física dinâmica “cross layer” para decisões de escalonamento no simulador. Para validar e avaliar as soluções propostas e desenvolvidas, um conjunto de cenários orientados para a utilização de vários serviços e aferição de métricas de QoS foram simulados. Os resultados obtidos mostram a diferenciação entre distintas classes de tráfego. O mecanismo proposto apresenta um pequeno ganho em débito e latência comparativamente às soluções previamente analisadas/implementadas. ABSTRACT: Broadband access is an important requirement to satisfy user demands and support a new set of real time services and applications. WiMAX, as a Broadband Wireless Access solution for Wireless Metropolitan Area Networks, covering large distances with high throughputs, is a promising technology for Next Generation Networks. Nevertheless, for the successful deployment and massification of WiMAX based solutions, Quality of Service (QoS) is a mandatory feature that must be supported. In this thesis , the QoS support for WiMAX in ns-2 simulation software is addressed. A QoS framework, composed by a packet classification mechanism and a scheduler, has been specified and implemented on the simulator, providing service differentiation over WiMAX networks. Furthermore, validation and comparison of different IEEE 802.16 simulation models is provided. Finally a scheduling solution is proposed and evaluated that uses prioritization and dynamic cross layer information for schedulling decisions in WiMAX networks. In order to validate the developed solutions, a set of QoS oriented scenarios have been simulated and the obtained results show that the implemented schedullers are able to efficiently differentiate between the different traffic classes and achieve gains in throughput and delay

Doctor of Philosophy

dissertationThis dissertation deals with blind modulation identification of quadrature amplitude modulations (QAM) and phase-shift keying (PSK) signals in dual-polarized channels in digital communication systems. The problems addressed in this dissertation are as follows: First, blind modulation identification of QAM and PSK signals in single noisy channels and multipath channels are explored. Second, methods for blind separation of two information streams in a dual-polarized channel and identification of the modulation types of the two information streams are developed. A likelihood-based blind modulation identification for QAM and PSK signals in a single channel with additive white Gaussian noise (AWGN) is developed first. This algorithm selects the modulation type that maximizes a log-likelihood function based on the known probability distribution associated with the phase or amplitude of the received signals for the candidate modulation types. The approach of this paper does not need prior knowledge of carrier frequency or baud rate. Comparisons of theory and simulation demonstrate good agreement in the probability of successful modulation identification under different signal-to-noise ratios (SNRs). Simulation results show that for the signals in AWGN channels containing 10000 symbols and 20 samples per symbol, the system can identify BPSK, QPSK, 8PSK and QAMs of order 16, 32, 64, 128 and 256 with better than 99% accuracy at 4 dB SNR. Under the same condition, the simulation results indicate the two competing methods available in the literature can only reach at most 85% accuracy even at 20 dB SNR for all the modulation types. The simulation results also suggest that when the symbol length decreases, the system needs higher SNRs in order to get accurate identification results. Simulations using different noisy environments indicate that the algorithm is robust to variations of noise environments from the models assumed for derivation of the algorithm. In addition, the combination of a constant modulus amplitude (CMA) equalizer and the likelihood-based modulation identification algorithm is able to identify the QAM signals in multipath channels in a wide range of SNRs. When compared with the results for the signals in AWGN channels, the combination of the CMA equalizer and the likelihood-based modulation identification algorithm needs higher SNRs and longer signal lengths in order to obtain accurate identification results. The second contribution of this dissertation is a new method for blindly identifying PSK and QAM signals in dual-polarized channels. The system combines a likelihood-based adaptive blind source separation (BSS) method and the likelihood-based blind modulation identification method. The BSS algorithm is based on the likelihood functions of the amplitude of the transmitted signals. This system tracks the time-varying polarization coefficients and recovers the input signals to the two channels. The simulation results presented in this paper demonstrate that the likelihood-based adaptive BSS method is able to recover the source signals of different modulation types for a wide range of input SNRs. Comparisons with a natural gradient-based BSS algorithm indicate that the likelihood-based method results in smaller symbol error rates. When a modulation identification algorithm is applied to the separated signals, the overall system is able to identify different PSK and QAM signals with high accuracy at sufficiently high SNRs. For example, with 20,000 symbols, the system identified BPSK and 16-QAM signals with better than 99% accuracy when the input SNR was 8dB and the polarization coefficients rotated with a rate of 1.3 ms. Higher SNRs are needed to obtain similar levels of accuracy when the polarization changes faster or when the number of input symbols is shorter. When compared with the identification results for signals in AWGN channels, the system needs higher SNRs and longer signal length to obtain accurate results for signals in dual-polarized channels