Blind Demodulation of Pass Band OFDMA Signals and Jamming Battle Damage Assessment Utilizing Link Adaptation

This research focuses on blind demodulation of a pass band OFDMA signal so that jamming effectiveness can be assessed; referred to in this research as BDA. The research extends, modifies and collates work within literature to perform a new method of blindly demodulating of a passband OFDMA signal, which exhibits properties of the 802.16 Wireless MAN OFDMA standard, and presents a novel method for performing BDA via observation of SC LA. Blind demodulation is achieved by estimating the carrier frequency, sampling rate, pulse shaping filter roll off factor, synchronization parameters and CFO. The blind demodulator\u27s performance in AWGN and a perfect channel is evaluated where it improves using a greater number OFDMA DL symbols and increased CP length. Performance in a channel with a single multi-path interferer is also evaluated where the blind demodulator\u27s performance is degraded. BDA is achieved via observing SC LA modulation behavior of the blindly demodulated signal between successive OFDMA DL sub frames in two scenarios. The first is where modulation signaling can be used to observe change of SC modulation. The second assumes modulation signaling is not available and the SC\u27s modulation must be classified. Classification of SC modulation is performed using sixth-order cumulants where performance increases with the number of OFDMA symbols. The SC modulation classi er is susceptible to the CFO caused by blind demodulation. In a perfect channel it is shown that SC modulation can be classified using a variety of OFDMA DL sub frame lengths in symbols. The SC modulation classifier experienced degraded performance in a multi-path channel and it is recommended that it is extended to perform channel equalization in future work

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

A Comparative Study Of Spectrum Sensing Methods For Cognitive Radio Systems

With the increase of portable devices utilization and ever-growing demand for greater data rates in wireless transmission, an increasing demand for spectrum channels was observed since last decade. Conventionally, licensed spectrum channels are assigned for comparatively long time spans to the license holders who may not over time continuously use these channels, which creates an under-utilized spectrum. The inefficient utilization of inadequate wireless spectrum resources has motivated researchers to look for advanced and innovative technologies that enable an efficient use of the spectrum resources in a smart and efficient manner. The notion of Cognitive Radio technology was proposed to address the problem of spectrum inefficiency by using underutilized frequency bands in an opportunistic method. A cognitive radio system (CRS) is aware of its operational and geographical surroundings and is capable of dynamically and independently adjust its functioning. Thus, CRS functionality has to be addressed with smart sensing and intelligent decision making techniques. Therefore, spectrum sensing is one of the most essential CRS components. The few sensing techniques that have been proposed are complicated and come with the price of false detection under heavy noise and jamming scenarios. Other techniques that ensure better detection performance are very sophisticated and costly in terms of both processing and hardware. The objective of the thesis is to study and understand the three of the most basic spectrum sensing techniques i.e. energy detection, correlation based sensing, and matched filter sensing. Simulation platforms were developed for each of the three methods using GNU radio and python interpreted language. The simulated performances of the three methods have been analyzed through several test matrices and also were compared to observe and understand the corresponding strengths and weaknesses. These simulation results provide the understanding and base for the hardware implementation of spectrum sensing techniques and work towards a combined sensing approach with improved sensing performance with less complexity

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Security-aware Cooperation in Dynamic Spectrum Access

We have witnessed a massive growth in wireless data, which almost doubles every year. The wireless data is expected to skyrocket further in the future due to the proliferation of devices and the emerging data-hungry applications. To accommodate the explosive growth in mobile traffic, a large amount of wireless spectrum is needed. With the limited spectrum resource, the current static spectrum allocation policy cannot serve well for future wireless systems. Moreover, it exacerbates the spectrum scarcity by resulting in severe spectrum underutilization. As a promising solution, dynamic spectrum access (DSA) is envisaged to increase spectrum efficiency by dynamic sharing all the spectrum. DSA can be enabled by cognitive radio technologies, which allow the unlicensed users (the secondary users, i.e., SUs) to dynamically access the unused spectrum (i.e., spectrum holes) owned by the licensed users (the primary users i.e., PUs). In order to identify the unused spectrum (spectrum holes), unlicensed users need to conduct spectrum sensing. While spectrum sensing might be inaccurate due to multipath fading and shadowing. To address this problem, user cooperation can be leveraged, with two main forms: cooperative spectrum sensing and cooperative cognitive radio networking (CCRN). For the former, SUs cooperate with each other in spectrum sensing to better detect the spectrum holes. For the latter, SUs cooperate with the PUs to gain access opportunities from the PUs by improving the transmission performance of the PUs. Whereas cooperation can also incur security issues, e.g., malicious users might participate into cooperation, corrupting or disrupting the communication of legitimate users, selfish users might refuse to contribute to cooperation for self-interests, etc. Those security issues are of great importance and need to be considered for cooperation in DSA. In this thesis, we study security-aware cooperation in DSA. First, we investigate cooperative spectrum sensing in multi-channel scenario such that a user can be scheduled for spectrum sensing and spectrum sharing. The cooperative framework can achieve a higher average throughput per user, which provides the incentive for selfish users to participate in cooperative spectrum sensing. Second, secure communication in CCRN is studied, where the SUs cooperate with the PU to enhance the latter’s communication security and then gain transmission opportunities. Partner selection, spectrum access time allocation, and power allocation are investigated. Third, we study risk aware cooperation based DSA for the multiple channel scenario, where multiple SUs cooperate with multiple PUs for spectrum access opportunities, considering the trustworthiness of SUs. Lastly, we propose an incentive mechanism to stimulate SUs to cooperate with PUs when they have no traffic. The cooperating SUs are motivated to cooperate with PUs to enhance the security of the PUs by accumulating credits and then consume the earned credits for spectrum trading when they have traffic in the future

Spectrum Sensing Security in Cognitive Radio Networks

This thesis explores the use of unsupervised machine learning for spectrum sensing in cognitive radio (CR) networks from a security perspective. CR is an enabling technology for dynamic spectrum access (DSA) because of a CR's ability to reconfigure itself in a smart way. CR can adapt and use unoccupied spectrum with the help of spectrum sensing and DSA. DSA is an efficient way to dynamically allocate white spaces (unutilized spectrum) to other CR users in order to tackle the spectrum scarcity problem and improve spectral efficiency. So far various techniques have been developed to efficiently detect and classify signals in a DSA environment. Neural network techniques, especially those using unsupervised learning have some key advantages over other methods mainly because of the fact that minimal preconfiguration is required to sense the spectrum. However, recent results have shown some possible security vulnerabilities, which can be exploited by adversarial users to gain unrestricted access to spectrum by fooling signal classifiers. It is very important to address these new classes of security threats and challenges in order to make CR a long-term commercially viable concept. This thesis identifies some key security vulnerabilities when unsupervised machine learning is used for spectrum sensing and also proposes mitigation techniques to counter the security threats. The simulation work demonstrates the ability of malicious user to manipulate signals in such a way to confuse signal classifier. The signal classifier is forced by the malicious user to draw incorrect decision boundaries by presenting signal features which are akin to a primary user. Hence, a malicious user is able to classify itself as a primary user and thus gains unrivaled access to the spectrum. First, performance of various classification algorithms are evaluated. K-means and weighted classification algorithms are selected because of their robustness against proposed attacks as compared to other classification algorithm. Second, connection attack, point cluster attack, and random noise attack are shown to have an adverse effect on classification algorithms. In the end, some mitigation techniques are proposed to counter the effect of these attacks

Evaluation of Overlay/underlay Waveform via SD-SMSE Framework for Enhancing Spectrum Efficiency

Recent studies have suggested that spectrum congestion is mainly due to the inefficient use of spectrum rather than its unavailability. Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) are two terminologies which are used in the context of improved spectrum efficiency and usage. The DSA concept has been around for quite some time while the advent of CR has created a paradigm shift in wireless communications and instigated a change in FCC policy towards spectrum regulations. DSA can be broadly categorized as using a 1) Dynamic Exclusive Use Model, 2) Spectrum Commons or Open sharing model or 3) Hierarchical Access model. The hierarchical access model envisions primary licensed bands, to be opened up for secondary users, while inducing a minimum acceptable interference to primary users. Spectrum overlay and spectrum underlay technologies fall within the hierarchical model, and allow primary and secondary users to coexist while improving spectrum efficiency. Spectrum overlay in conjunction with the present CR model considers only the unused (white) spectral regions while in spectrum underlay the underused (gray) spectral regions are utilized. The underlay approach is similar to ultra wide band (UWB) and spread spectrum (SS) techniques utilize much wider spectrum and operate below the noise floor of primary users. Software defined radio (SDR) is considered a key CR enabling technology. Spectrally modulated, Spectrally encoded (SMSE) multi-carrier signals such as Orthogonal Frequency Domain Multiplexing (OFDM) and Multi-carrier Code Division Multiple Access (MCCDMA) are hailed as candidate CR waveforms. The SMSE structure supports and is well-suited for SDR based CR applications. This work began by developing a general soft decision (SD) CR framework, based on a previously developed SMSE framework that combines benefits of both the overlay and underlay techniques to improve spectrum efficiency and maximizing the channel capacity. The resultant SD-SMSE framework provides a user with considerable flexibility to choose overlay, underlay or hybrid overlay/underlay waveform depending on the scenario, situation or need. Overlay/Underlay SD-SMSE framework flexibility is demonstrated by applying it to a family of SMSE modulated signals such as OFDM, MCCDMA, Carrier Interferometry (CI) MCCDMA and Transform Domain Communication System (TDCS). Based on simulation results, a performance analysis of Overlay, Underlay and hybrid Overlay/Underlay waveforms are presented. Finally, the benefits of combining overlay/underlay techniques to improve spectrum efficiency and maximize channel capacity are addressed