A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge

Analysis of Ultra Wide Band (UWB) Technology for an Indoor Geolocation and Physiological Monitoring System

The goal of this research is to analyze the utility of UWB for indoor geolocation and to evaluate a prototype system, which will send information detailing a person’s position and physiological status to a command center. In a real world environment, geolocation and physiological status information needs to be sent to a command and control center that may be located several miles away from the operational environment. This research analyzes and characterizes the UWB signal in the various operational environments associated with indoor geolocation. Additionally, typical usage scenarios for the interaction between UWB and other devices are also tested and evaluated

Multilevel Coding and Unequal Error Protection for Multiple-Access Communications and Ultra-Wideband Communications in the Presence of Interference.

Interference is one of the major factors that degrade the performance of a communication system. Various types of interference cause di Kerent impact on the system performance. In this thesis, we consider interference management at the physical layer. In order to enhance the performance, the receiver needs to have the knowledge about the interference. By exploiting the knowledge about interference, such as statistical properties, it can be suppressed to enhance the link quality. This thesis contains two main topics: multilevel coding (MLC) for unequal error protection (UEP) and receiver design for ultra-wideband (UWB) communications to suppress interference. Both topics deal with interference in di Kerent ways, and face di Kerent design challenges. MLC is a way to provide UEP for different streams of information with different levels of importance in a communication system. It combines coding and modulation schemes to optimize the system performance. The idea is to protect each bit in the modulation constellation point by an individual binary code. We designed and analyzed a DS-CDMA system with asymmetric PSK modulation and MLC using BCH codes in an AWGN channel. The analysis includes probability of bit error of the system, and the capacity and throughput of the MLC scheme combined with 8-PSK modulation. The results show that the MLC scheme can have a higher throughput than the regular coding scheme in the low SNR region in the AWGN channel. We also analyzed the performance of UWB communications in the presence of MAI and jamming interference. We considered a nonlinear interference suppression technique for impulse radio based UWB systems in the AWGN channel. The technique is based on the locally optimum Bayes detection (LOBD) algorithm, which utilizes the interference probability density function (PDF) for receiver design. This type of receiver has low complexity, and numerical results show that its performance asymptotically approaches that of the optimum receiver. Lastly, we discussed the implementation of the proposed receiver by adaptively monitor and update the interference PDF. The adaptive LOBD algorithm makes the proposed receiver implementation practical to deal with different types of interference.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75955/1/wangcw_1.pd

A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

Ultra Wide Band Signal Modeling for Radar Receiver Characterization

Results for modeling, simulation, and analysis of interference effects that modern wideband signals have on existing narrowband radar system performance are presented. In particular, radar detection performance is characterized using a basic radar receiver model and operational parameters consistent with those of the ARSR-4 air route surveillance radar. Two modern wideband signals (interferers) are addressed in this work, including the GPS military signal (M-Code signal) and a direct sequence ultra wideband (DS-UWB) waveform meeting outdoor emission restrictions imposed by the Federal Communications Commission (FCC). Interference effects are characterized for an unmodulated sinusoidal pulse as well as linear frequency modulated (LFM) and bi-phase Barker coded pulse compression waveforms. Finally, coherent pulse integration is addressed and interference mitigation demonstrated via improved detection performance. Worst case detection scenarios from the radar\u27s perspective are considered for all cases. M-Code interference results indicate that at proposed received power levels of -160 to -130 dBW, radar detection performance is severely degraded with expected improvement occurring when pulse integration is employed. DS-UWB interference results indicate that at maximum transmit power levels specified by the FCC, the DS-UWB waveform has minimal impact on detection performance for radar receiver/UWB transmitter separation distances beyond 0.5 meters. This separation distance is reduced further when pulse integration is employed. (8 tables, 42 figures, 25 refs.

Performance of Bit Error Rate and Power Spectral Density of Ultra Wideband with Time Hopping Sequences.

This thesis focuses on several modulation methods for an ultra wideband (UWB) signal. These methods are pulse position modulation (PPM), binary phase shift keying (BPSK), on/off key shifting (OOK), and pulse amplitude modulation (PAM). In addition, time hopping is considered for these modulation schemes, where the capacity per time frame of time hopping PPM is studied using different spreading ratios. This thesis proves that with the addition of time hopping to all types of modulated UWB signals, the performance of power spectral density improves in all aspects, despite the increase of data per time frame. Note that despite the increase of data per frame, the bit error rate remains the same as standard non-time hopping UWB modulated signals

A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception

Unmanned aerial vehicles (UAVs) can be used for a variety of illegal activities (e.g., industrial espionage, smuggling, terrorism). Given their growing popularity and availability, and advances in communications technology, more sophisticated ways to disable these vehicles must be sought. Various forms of jamming are used to disable drones, but more advanced techniques such as deception and UAV takeover are considerably difficult to implement, and there is a large research gap in this area., Currently, machine and deep learning techniques are popular and are also used in various drone-related applications. HOwever, no detailed research has been conducted so far on the use of these techniques for jamming and deception of UAVs. This paper focuses on exploring the current techniques in the are of jamming and deception. A survey on the use of machine or deep learning specifically in UAV-related applicsations is also conducted. The paper provides insight into the issues described and encourages more detailed research in this area

Adaptive Suppression of Interfering Signals in Communication Systems

The growth in the number of wireless devices and applications underscores the need for characterizing and mitigating interference induced problems such as distortion and blocking. A typical interference scenario involves the detection of a small amplitude signal of interest (SOI) in the presence of a large amplitude interfering signal; it is desirable to attenuate the interfering signal while preserving the integrity of SOI and an appropriate dynamic range. If the frequency of the interfering signal varies or is unknown, an adaptive notch function must be applied in order to maintain adequate attenuation. This work explores the performance space of a phase cancellation technique used in implementing the desired notch function for communication systems in the 1-3 GHz frequency range. A system level model constructed with MATLAB and related simulation results assist in building the theoretical foundation for setting performance bounds on the implemented solution and deriving hardware specifications for the RF notch subsystem devices. Simulations and measurements are presented for a Low Noise Amplifer (LNA), voltage variable attenuators, bandpass filters and phase shifters. Ultimately, full system tests provide a measure of merit for this work as well as invaluable lessons learned. The emphasis of this project is the on-wafer LNA measurements, dependence of IC system performance on mismatches and overall system performance tests. Where possible, predictions are plotted alongside measured data. The reasonable match between the two validates system and component models and more than compensates for the painstaking modeling efforts. Most importantly, using the signal to interferer ratio (SIR) as a figure of merit, experimental results demonstrate up to 58 dB of SIR improvement. This number represents a remarkable advancement in interference rejection at RF or microwave frequencies

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks