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A multi band orthogonal frequency division multiplexing (MB-OFDM) compatible ultra wideband (UWB) receiver with narrowband interference (NBI) suppression capability is presented. The average transmit power of UWB system is limited to -41.3 dBm/MHz in order to not interfere existing narrowband systems. Moreover, it must operate even in the presence of unintentional radiation of FCC Class-B compatible devices. If this unintentional radiation resides in the UWB band, it can jam the communication. Since removing the interference in digital domain requires higher dynamic range of analog front-end than removing it in analog domain, a programmable analog notch filter is used to relax the receiver requirements in the presence of NBI. The baseband filter is placed before the variable gain amplifier (VGA) in order to reduce the signal swing at the VGA input. The frequency hopping period of MB-OFDM puts a lower limit on the settling time of the filter, which is inverse proportional to notch bandwidth. However, notch bandwidth should be low enough not to attenuate the adjacent OFDM tones. Since these requirements are contradictory, optimization is needed to maximize overall performance. Two different NBI suppression schemes are tested. In the first scheme, the notch filter is operating for all sub-bands. In the second scheme, the notch filter is turned on during the sub-band affected by NBI. Simulation results indicate that the UWB system with the first and the second suppression schemes can handle up to 6 dB and 14 dB more NBI power, respectively. The results of this work are not limited to MB-OFDM UWB system, and can be applied to other frequency hopping systems.

Abstract Spread spectrum communication systems may be affected by other types of signals called outliers. These coexisting signals are typically narrow (or concentrated) in the considered domain. This thesis considers two areas of outlier detection, namely the concentrated interference suppression (IS) and concentrated signal detection. The focus is on concentrated signal extraction using blind, iterative and low-complex consecutive mean excision (CME) -based algorithms that can be applied to both IS and detection. A summary of results obtained from studying the performance of the existing IS methods, namely the CME, the forward CME (FCME) and the transform selective IS algorithms (TSISA), is presented. Accurate threshold parameter values for the FCME algorithm are defined. These accurate values are able to control the false alarm rate. The signal detection capability of the CME algorithms is studied and analyzed. It is noticed that the CME algorithms are able to detect signals, but they are not able to estimate signal parameters such as the bandwidth. The presented generic shape-based analysis leads to the limits of detection in which the concentrated signals can be detected. These limits enable checking fast whether the signal is detectable or not without time consuming computer simulations. The performance of the TSISA method is evaluated. Simulation results demonstrate that the TSISA method is able to suppress several types of concentrated interfering signals with a reasonable computational complexity. Finally, new CME-based methods are proposed and evaluated. The proposed methods are the extended TSISA method for IS and the localization algorithm based on double-thresholding (LAD), LAD with normalized thresholds (LAD NT), LAD with adjacent cluster combining (LAD ACC) and two-dimensional (2-D) LAD methods for detection. The simulations indicate that the extended TSISA method has a good performance against several types of concentrated interfering signals. The narrowband signal detection capability of the LAD methods is studied. Numerical results show that the proposed LAD methods are able to detect and localize signals in their domain, and they are able to estimate the number of narrowband signals and their parameters, including, for example, bandwidths and signal-to-noise ratio (SNR) values. The simulations show that the LAD methods outperform the CME algorithms, and ACC and 2-D LAD methods outperform the original LAD method. The LAD methods are also proposed to be used for spectrum sensing purposes in cognitive radios.

Abstract The performance of conventional receivers for wireless communications may severely deteriorate in the presence of unaccounted interference. The effectiveness of methods for mitigating these effects greatly depends on the knowledge that is available about the interference and signal-of-interest (SOI), therefore making the design of robust receivers a great challenge. This thesis focuses on receiver structures for channel coded systems that exploit different levels of knowledge about the SOI and interference in an iterative fashion. This achieves both robustness and overall performance improvement compared to non-iterative receivers. Code division multiple access (CDMA) and spatial division multiple access (SDMA) systems are considered. The overlay of a turbo coded direct-sequence spread-spectrum (DS-SS) system and strong digitally modulated tone interference is studied. An iterative receiver, which is capable of blind cancellation of both wideband and narrowband interference is proposed based on the adaptive self-reconfigurable -filter scheme. Asymptotic performance analysis of the iterative receiver shows that significant iteration gains are possible if the signal-to-interference-plus-noise-ratio (SINR) is relatively large and the processing gain (PG) of the SOI is relatively small. Robust diversity detection in turbo-coded DS-SS system with statistically modeled interference is studied. A non-parametric type-based iterative receiver that estimates the probability density function (PDF) of interference-plus-noise is proposed. Its performance is shown to be rather robust to the number of interferers and their distances from the victim receiver and very similar to the performance of a clairvoyant receiver. Amazingly, this is achievable with no prior knowledge about the interference parameters. Furthermore, iteration gain is shown to significantly reduce the length of the pilot sequence needed for the PDF estimation. A family of iterative minimum-mean-squared-error (MMSE) and maximum-likelihood (ML) receivers for convolutionally and space-time coded SDMA systems is proposed. Joint iterative multiuser-detection (MUD), equalization and interference suppression are proposed to jointly combat co-channel interference (CCI), inter-symbol-interference (ISI) and unknown CCI (UCCI) in broadband single-carrier systems. It is shown that both in convolutional and space-time coded systems the ISI and CCI interference can be completely eliminated if UCCI is absent. This is achievable with a number of receive antennas equal to the number of users of interest and not to the total number of transmit antennas. In case UCCI is present, the effectiveness of CCI and ISI cancellation and UCCI suppression depends on the effective degrees of freedom of the receiver. Receiver robustness can be significantly preserved by using hybrid MMSE/ML detection for the signals of interest, or by using estimation of the PDF of the UCCI-plus-noise. A low complexity hybrid MMSE/ML iterative receiver for SDMA is proposed. It is shown that its performance is not significantly degraded compared to the optimal ML receiver. Its sensitivity to spatial correlation and a timing offset is assessed by using field measurement data. It was shown that the hybrid MMSE/ML receiver is robust against spatial correlation. The sensitivity to the timing offset is significantly reduced if the receiver performs UCCI suppression.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Despite the inherent resistance to narrowband signal interference afforded by GPS spread spectrum modulation, the low level of GPS signals makes them susceptible to narrowband interference. This paper discusses the application of a pre-correlation adaptive temporal filter for stationary and nonstationary narrowband interference suppression. Various adaptive algorithms are studied and implemented. Comparison of the convergence and tracking behavior of various algorithms is made.

The wireless spectrum is limited and the demand for its use is increasing due to technological advancements in wireless communication, resulting in persistent interference issues. Despite progress in addressing interference, it remains a challenge for effective spectrum usage, particularly in the use of license-free and managed shared bands and other opportunistic spectrum access solutions. Therefore, efficient and interference-resistant spectrum usage schemes are critical. In the past, most interference solutions have relied on avoidance techniques and expert system-based mitigation approaches. Recently, researchers have utilized artificial intelligence/machine learning techniques at the physical (PHY) layer, particularly deep learning, which suppress or compensate for the interfering signal rather than simply avoiding it. In addition, deep learning has been utilized by researchers in recent years to address various difficult problems in wireless communications such as, transmitter classification, interference classification and modulation recognition, amongst others. To this end, this dissertation presents a thorough analysis of deep learning techniques for interference classification and suppression, and it thoroughly examines complexity (sample and model) issues that arise from using deep learning. First, we address the knowledge gap in the literature with respect to the state-of-the-art in deep learning-based interference suppression. To account for the limitations of deep learning-based interference suppression techniques, we discuss several challenges, including lack of interpretability, the stochastic nature of the wireless channel, issues with open set recognition (OSR) and challenges with implementation. We also provide a technical discussion of the prominent deep learning algorithms proposed in the literature and also offer guidelines for their successful implementation. Next, we investigate convolutional neural network (CNN) architectures for interference and transmitter classification tasks. In particular, we utilize a CNN architecture to classify interference, investigate model complexity of CNN architectures for classifying homogeneous and heterogeneous devices and then examine their impact on test accuracy. Next, we explore the issues with sample size and sample quality with regards to the training data in deep learning. In doing this, we also propose a rule-of-thumb for transmitter classification using CNN based on the findings from our sample complexity study. Finally, in cases where interference cannot be avoided, it is important to suppress such interference. To achieve this, we build upon autoencoder work from other fields to design a convolutional neural network (CNN)-based autoencoder model to suppress interference thereby ensuring coexistence of different wireless technologies in both licensed and unlicensed bands. / Doctor of Philosophy / Wireless communication has advanced a lot in recent years, but it is still hard to use the limited amount of available spectrum without interference from other devices. In the past, researchers tried to avoid interference using expert systems. Now, researchers are using artificial intelligence and machine learning, particularly deep learning, to mitigate interference in a different way. Deep learning has also been used to solve other tough problems in wireless communication, such as classifying the type of device transmitting a signal, classifying the signal itself or avoiding it. This dissertation presents a comprehensive review of deep learning techniques for reducing interference in wireless communication. It also leverages a deep learning model called convolutional neural network (CNN) to classify interference and investigates how the complexity of the CNN effects its performance. It also looks at the relationship between model performance and dataset size (i.e., sample complexity) in wireless communication. Finally, it discusses a CNN-based autoencoder technique to suppress interference in digital amplitude-phase modulation system. All of these techniques are important for making sure different wireless technologies can work together in both licensed and unlicensed bands.

Microphone arrays have seen wide usage in a variety of fields; especially in sonar, acoustic source monitoring and localization, telecommunications, and diagnostic medicine. The goal of most of these applications is to detect or extract a signal of interest. This task is complicated by the presence of interferers and noise, which corrupts the recorded array signals. This dissertation explores two new techniques that increase signal clarity: interferer suppression and emergent signal detection. Spatial processing is often used to suppress interferers that are spatially distinct from the signal of interest. If the signal of interest and the interferer are statistically independent, blind source separation can be used to statistically extract the signal of interest. The first new method to improve signal clarity presented in this work combines spatial processing with blind source separation to suppress interferers. This technique allows for the separation of independent sources that are not necessarily simultaneously mixed or spatially distinct. Simulations and experiments are used to show the capability of the new algorithm for a variety of conditions. The major contributions in this dissertation under this topic are to use independent component analysis to extract the signal of interest from a set of array signals, and to improve existing independent component analysis algorithms to allow for time delayed mixing. This dissertation presents a novel method of improving signal clarity through emergent signal detection. By determining which time frames contain the signal of interest, frames that contain only interferers and noise can be eliminated. When a new signal of interest emerges in a measurement of a mixed set of sources, the principal component subspace is altered. By examining the change in the subspace, the emergent signal can be robustly detected. This technique is highly effective for signals that have a near constant sample variance, but is successful at detecting a wide variety of signals, including voice signals. To improve performance, the algorithm uses a feed-forward processing technique. This is helpful for the VAD application because voice does not have a constant sample variance. Experiments and simulations are used to demonstrate the performance of the new technique. / Ph. D.

With usable spectrum becoming increasingly more congested, the need for robust, adaptive communications to take advantage of spatially-separated signal sources is apparent. Traditional phased array beamforming techniques used for interference removal rely on perfect calibration between elements and precise knowledge of the array configuration; however, if the exact array configuration is not known (unknown or imperfect assumption of element locations, unknown mutual coupling between elements, etc.), these traditional beamforming techniques are not viable, so a blind beamforming approach is required. A novel blind beamforming approach is proposed to address complex narrow-band interference environments where the precise array configuration is unknown. The received signal is decomposed into orthogonal narrow-band partitions using a polyphase filter-bank channelizer, and a rank-reduced version of the received matrix on each sub-channel is computed through reconstruction by retaining a subset of its singular values. The wideband spectrum is synthesized through a near-perfect polyphase reconstruction filter, and a composite wideband spectrum is obtained from the maximum eigenvector of the resulting covariance matrix.The resulting process is shown to suppress numerous interference sources (in special cases even with more than the degrees of freedom of the array), all without any knowledge of the primary signal of interest. Results are validated with both simulation and wireless laboratory over-the-air experimentation. / M.S. / As the number of devices using wireless communications increase, the amount of usable radio frequency spectrum becomes increasingly congested. As a result, the need for robust, adaptive communications to improve spectral efficiency and ensure reliable communication in the presence of interference is apparent. One solution is using beamforming techniques on digital phased array receivers to maximize the energy in a desired direction and steer nulls to remove interference. However, traditional phased array beamforming techniques used for interference removal rely on perfect calibration between antenna elements and precise knowledge of the array configuration. Consequently, if the exact array configuration is not known (unknown or imperfect assumption of element locations, unknown mutual coupling between elements, etc.), these traditional beamforming techniques are not viable, so a beamforming approach with relaxed requirements (blind beamforming) is required. This thesis proposes a novel blind beamforming approach to address complex narrow-band interference in spectrally congested environments where the precise array configuration is unknown. The resulting process is shown to suppress numerous interference sources, all without any knowledge of the primary signal of interest. Results are validated with both simulation and wireless laboratory experimentation conducted with a two-element array, verifying that proposed beamforming approach achieves a similar performance to the theoretical performance bound of receiving packets in AWGN with no interference present.

In this thesis, the multi-carrier (MC) code division multiple access (CDMA) system is considered in Rayleigh fading channel. The main concern of this thesis is to devise a new direct linearly constrained constant modulus (LCCM) inverse QRD-RLS algorithm for multiple access interference (MAI) cancellation and the problem due to the mismatch of the channel estimator. In the conventional approach, two significant detectors are applied to the system for multiuser interference suppression, one is the blind adaptation algorithm and the other is adaptive linearly constrained PLIC approach. However, the mirror effect may occur when the blind adaptation algorithm is employed. It might affect the performance in terms of bit error rate (BER), although the desired signal to interference (due to other users) improvement is still acceptable. Moreover, in case that the channel coefficients could not be estimated perfectly, the mismatch problem may occur to degrade the performance of the adaptive linearly constrained PLIC approach with the LMS or RLS algorithm. To overcome the mismatch problem, the conventional approach is to use the LCCM criterion with gradient algorithm. However, the convergence rate of the gradient algorithm is too slow to be implemented in real-time wireless communication system. In this thesis, to have fast convergence rate and to circumvent the mismatch problem, the robust LCCM-IQRD algorithm is devised and applied to the MC-CDMA system in Rayleigh fading channel. The proposed robust LCCM-IQRD algorithm has shown to be more effective in terms of MAI cancellation and the mismatch due to imperfect channel estimator. The performance, in terms of BER, of the proposed algorithm is superior to that of the conventional PLIC based algorithms, the blind adaptation algorithm, and the conventional LCCM gradient algorithm.

Effective antenna selection and deployment strategies are important for reducing co-channel interference in indoor wireless systems. Low-cost solutions are essential, and strategies that utilise simple antennas (such as directional patches) are advantageous from this perspective. However, performance is always an issue and the improvements achievable through clever antenna deployment need to be quantified. In this thesis, an experimental investigation of indoor propagation comparing the performance of directional antennas and multiple-element arrays (MEAs) with omni-directional antennas is reported. Estimation of the performance of a direct sequence code division multiple access (DS-CDMA) system operating in a variety of deployment scenarios allows the identification of a range of performance-limiting factors and the optimal deployment strategies. It is shown that the orientation of single-element directional antennas can significantly impact on system performance compared to omni-directional antennas in traditional systems. The deployment of MEAs with an active diversity combining scheme can further improve system performance by more than one order of magnitude. From the perspective of system planning, the choice of antenna selection and deployment options depends on the current and future demand for system performance and the financial resources available. An evolutionary path has been proposed to provide a smooth transition from conventional (low-cost) to high-performance (high-cost) antenna systems as demand dictates. Other performance-limiting factors in indoor wireless systems include the physical environment and external interference. It is also shown that electromagnetically-opaque obstacles in the environment can amplify the effectiveness of the antenna deployment by acting as physical zone boundaries that restrict interference. External interference has been shown to cause a significant degradation to the performance of an indoor system when the carrier-to-external-interference ratio (CEIR) is below 30 dB. This performance degradation can be minimised by appropriate antenna deployment, although the optimum antenna orientations depends on the strength of the external interference.