Reference-free amplitude-based WiFi passive sensing

The parasitic exploitation of WiFi signals for passive sensing purposes is a topic that is attracting considerable interest in the scientific community. In an attempt at meeting the requirements for sensor compactness, easy deployment, and low cost, we resort to a non-coherent signal processing scheme that does not rely on the availability of a reference signal and relaxes the constraints on the sensor hardware implementation. Specifically, with the proposed strategy, the presence of a moving target echo is determined by detecting the amplitude modulation that it produces on the direct signal transmitted from the WiFi access point. We investigate the target discrimination capability of the resulting sensor against the competing interference background and we theoretically characterize the impact of undesired amplitude fluctuations in the received signal that are determined by causes other than the superposition of the target echo, thereby including the waveform properties. Hence, we propose different solutions to address the limitations identified, characterized by different complexities, and we investigate their advantages and drawbacks. The conceived signal processing schemes are thoroughly validated on both simulated and experimental data, collected in different operational scenarios

Adaptive Bit Allocation With Reduced Feedback for Wireless Multicarrier Transceivers

With the increasing demand in the wireless mobile applications came a growing need to transmit information quickly and accurately, while consuming more and more bandwidth. To address this need, communication engineers started employing multicarrier modulation in their designs, which is suitable for high data rate transmission. Multicarrier modulation reduces the system's susceptibility to the frequency-selective fading channel, by transforming it into a collection of approximately flat subchannels. As a result, this makes it easier to compensate for the distortion introduced by the channel. This thesis concentrates on techniques for saving bandwidth usage when employing adaptive multicarrier modulation, where subcarrier parameters (bit and energy allocations) are modulated based on the channel state information feedback obtained from previous burst. Although bit and energy allocations can substantially increase error robustness and throughput of the system, the feedback information required at both ends of the transceiver can be large. The objective of this work is to compare different feedback compression techniques that could reduce the amount of feedback information required to perform adaptive bit and energy allocation in multicarrier transceivers. This thesis employs an approach for reducing the number of feedback transmissions by exploiting the time-correlation properties of a wireless channel and placing a threshold check on bit error rate (BER) values. Using quantization and source coding techniques, such as Huffman coding, Run length encoding and LZWalgorithms, the amount of feedback information has been compressed. These calculations have been done for different quantization levels to understand the relationship between quantization levels and system performance. These techniques have been applied to both OFDM and MIMO-OFDM systems

Applications of Lattice Codes in Communication Systems

In the last decade, there has been an explosive growth in different applications of wireless technology, due to users' increasing expectations for multi-media services. With the current trend, the present systems will not be able to handle the required data traffic. Lattice codes have attracted considerable attention in recent years, because they provide high data rate constellations. In this thesis, the applications of implementing lattice codes in different communication systems are investigated. The thesis is divided into two major parts. Focus of the first part is on constellation shaping and the problem of lattice labeling. The second part is devoted to the lattice decoding problem. In constellation shaping technique, conventional constellations are replaced by lattice codes that satisfy some geometrical properties. However, a simple algorithm, called lattice labeling, is required to map the input data to the lattice code points. In the first part of this thesis, the application of lattice codes for constellation shaping in Orthogonal Frequency Division Multiplexing (OFDM) and Multi-Input Multi-Output (MIMO) broadcast systems are considered. In an OFDM system a lattice code with low Peak to Average Power Ratio (PAPR) is desired. Here, a new lattice code with considerable PAPR reduction for OFDM systems is proposed. Due to the recursive structure of this lattice code, a simple lattice labeling method based on Smith normal decomposition of an integer matrix is obtained. A selective mapping method in conjunction with the proposed lattice code is also presented to further reduce the PAPR. MIMO broadcast systems are also considered in the thesis. In a multiple antenna broadcast system, the lattice labeling algorithm should be such that different users can decode their data independently. Moreover, the implemented lattice code should result in a low average transmit energy. Here, a selective mapping technique provides such a lattice code. Lattice decoding is the focus of the second part of the thesis, which concerns the operation of finding the closest point of the lattice code to any point in N-dimensional real space. In digital communication applications, this problem is known as the integer least-square problem, which can be seen in many areas, e.g. the detection of symbols transmitted over the multiple antenna wireless channel, the multiuser detection problem in Code Division Multiple Access (CDMA) systems, and the simultaneous detection of multiple users in a Digital Subscriber Line (DSL) system affected by crosstalk. Here, an efficient lattice decoding algorithm based on using Semi-Definite Programming (SDP) is introduced. The proposed algorithm is capable of handling any form of lattice constellation for an arbitrary labeling of points. In the proposed methods, the distance minimization problem is expressed in terms of a binary quadratic minimization problem, which is solved by introducing several matrix and vector lifting SDP relaxation models. The new SDP models provide a wealth of trade-off between the complexity and the performance of the decoding problem

Modeling and Compensation of Polarization Effects in Fiber-Optic Communication Systems

Optical communication systems that exploit the orthogonality between two polarizations of light convey information over optical fibers by modulating data over the two polarizations. In an idealized scenario, the two polarizations propagate through the fiber without interfering. However, this is not the case for practical fibers, which suffer from various imperfections that lead to polarization-related interference between the two polarizations. This thesis is concerned with polarization effects that arise in communication systems over optical fibers. In particular, we consider modeling and compensation of such effects, and their impact on and improvement of nonlinearity mitigation algorithms.The impact of an impairment on the performance of a transmission system can be understood via a channel model, which should describe the behavior of the channel as accurately as possible. A theoretical framework is introduced to model the stochastic nature of the state of polarization during transmission. The model generalizes the one-dimensional carrier phase noise random walk to higher dimensions, modeling the phase noise and state of polarization drift jointly as rotations of the electric field and it has been successfully verified using experimental data. Thereafter, the model is extended to account for polarization-mode dispersion and its temporal random fluctuations. Such models will be increasingly important in simulating and optimizing future systems, where sophisticated digital signal processing will be natural parts.The typical digital signal processing solution to mitigate phase noise and drift of the state of polarization consists of two separate blocks that track each phenomenon independently and have been developed without taking into account mathematical models describing the impairments. Based on the proposed model for the state of polarization, we study a blind tracking algorithm to compensate for these impairments. The algorithm dynamically recovers the carrier phase and state of polarization jointly for an arbitrary modulation format. Simulation results show the effectiveness of the proposed algorithm, having a fast convergence rate and an excellent tolerance to phase and polarization noise.The optical fiber is a nonlinear medium with respect to the intensity of the incident light. This effect leads to nonlinear interference as the intensity of light increases, which made nonlinear interference mitigation techniques to be an intensively studied topic. Typically, these techniques do not take into account polarization-mode dispersion, which becomes detrimental as the nonlinear effects interact with polarization-mode dispersion. We study digital-domain nonlinear interference mitigation algorithms that take into account polarization-mode dispersion by i) reversing the polarization effects concurrently with reversing the nonlinear effects and by ii) mitigating only the polarization-insensitive nonlinear contributions. These algorithms will be increasingly important in future optical systems capable of performing large bandwidth nonlinear interference mitigation, where even small amounts of polarization-mode dispersion become a limiting factor

Communications over fading channels with partial channel information : performance and design criteria

The effects of system parameters upon the performance are quantified under the assumption that some statistical information of the wireless fading channels is available. These results are useful in determining the optimal design of system parameters. Suboptimal receivers are designed for systems that are constrained in terms of implementation complexity. The achievable rates are investigated for a wireless communication system when neither the transmitter nor the receiver has prior knowledge of the channel state information (CSI). Quantitative results are provided for independent and identically distributed (i.i.d.) Gaussian signals. A simple, low-duty-cycle signaling scheme is proposed to improve the information rates for low signal-to-noise ratio (SNR), and the optimal duty cycle is expressed as a function of the fading rate and SNR. It is demonstrated that the resource allocations and duty cycles developed for Gaussian signals can also be applied to systems using other signaling formats. The average SNR and outage probabilities are examined for amplify-and-forward cooperative relaying schemes in Rayleigh fading channels. Simple power allocation strategies are determined by using knowledge of the mean strengths of the channels. Suboptimal algorithms are proposed for cases that optimal receivers are difficult to implement. For systems with multiple transmit antennas, an iterative method is used to avoid the inversion of a data-dependent matrix in decision-directed channel estimation. When CSI is not available, two noncoherent detection algorithms are formulated based on the generalized likelihood ratio test (GLRT). Numerical results are presented to demonstrate the use of GLRT-based detectors in systems with cooperative diversity

EXTRINSIC CHANNEL-LIKE FINGERPRINT EMBEDDING FOR TRANSMITTER AUTHENTICATION IN WIRELESS SYSTEMS

We present a physical-layer fingerprint-embedding scheme for wireless signals, focusing on multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) transmissions, where the fingerprint signal conveys a low capacity communication suitable for authenticating the transmission and further facilitating secure communications. Our system strives to embed the fingerprint message into the noise subspace of the channel estimates obtained by the receiver, using a number of signal spreading techniques. When side information of channel state is known and leveraged by the transmitter, the performance of the fingerprint embedding can be improved. When channel state information is not known, blind spreading techniques are applied. The fingerprint message is only visible to aware receivers who explicitly preform detection of the signal, but is invisible to receivers employing typical channel equalization. A taxonomy of overlay designs is discussed and these designs are explored through experiment using time-varying channel-state information (CSI) recorded from IEEE802.16e Mobile WiMax base stations. The performance of the fingerprint signal as received by a WiMax subscriber is demonstrated using CSI measurements derived from the downlink signal. Detection performance for the digital fingerprint message in time-varying channel conditions is also presented via simulation

Broadband wireless communication systems: Channel modeling and system performance analysis

Wideband channel modeling, which can accurately describe the most important characteristics of wideband mobile fading channels, is essential for the design, evaluation, and optimization of broadband wireless communication systems. In the field of wideband channel modeling, the tradeoff between the prediction accuracy and simulation efficiency has to be taken into account. On one hand, channel models should be as accurate as possible. On the other hand, channel models are supposed to be simple and easy to put into use. There are several commonly used approaches to channel modeling, e.g., measurement-based channel modeling and deterministic channel modeling. Both methods are efficient in capturing the fading behavior of real-world wireless channels. However, the resulting channel models are only valid for the specific environments as those where the measurements were carried out or the ray-tracing scenario was considered. Moreover, these methods are quite time consuming with high computational cost. Alternatively, the geometry-based stochastic channel modeling approach can be employed to model wideband mobile fading channels. The most attractive feature of this method is that the derived channel models are able to predict fading behavior for various propagation environments, and meanwhile they can be easily implemented. Thus, the dissertation will complete the wideband channel modeling task by adopt the geometry-based stochastic approach. In the dissertation, several geometry-based channel models are proposed for both outdoor and indoor propagation scenarios. The significance of the work lies in the fact that it develops channel models under more realistic propagation conditions which have seldom been considered, such as for non-isotropic scattering environxi ments and mobile-to-mobile (M2M) fading channels. In addition, the proposed channel models remove the scarcity that proper geometry-based channel models are missing for indoor environments. The most important statistical properties of the developed channel models including their temporal autocorrelation function (ACF), the two-dimensional (2D) space cross-correlation function (CCF), and the frequency correlation function (FCF) are analyzed. Furthermore, efficient channel simulators with low realization expenditure are obtained. Finally, the validity of the proposed channel models is demonstrated by comparing their analytical channel statistics with the empirical ones measured from real world channels. Besides the work in the field of wideband channel modeling, another part of the dissertation is dedicated to investigate the performance of SISO1 orthogonal frequency division multiplexing (OFDM) broadband communication systems and space-time (ST) coded MIMO2 OFDM broadband communication systems. This work provides a deep insight into the performance of a broadband mobile radio communication system over realistic wideband fading channels. Analytical expressions are derived for bit error probability (BEP) or symbol error rate (SER) of systems. In order to confirm the correctness of the theoretical results as well as to show the usefulness of the wideband channel models in the testing and analysis of a broadband communication system, SISO OFDM systems and space-time coded MIMO OFDM systems are simulated in the dissertation. In order to improve the reliability of digital transmission over broadband wireless radio channels, a differential super-orthogonal space-time trellis code (SOSTTC) is designed for noncoherent communications, where neither the transmitter nor the receiver needs the channel state information (CSI) for decoding. In addition, a new decoding algorithm is proposed. The new algorithm has exactly the same decoding performance as the traditional one. However, it is superior from the standpoint of overall computing complexity