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Cognitive radio had been proposed as a methodology for overcoming the inefficiency of the conventional static allocation of the available spectrum in wireless communication networks. The majority of opportunistic spectrum access schemes in cognitive radio networks (CRNs) rely on the Listen-Before-Talk (LBT) model due to the half-duplex nature of conventional wireless radios. However, LBT su ers from the problem of high collision rates and low secondary user throughput if time is misaligned among the secondary users (SUs) and the primary users (PUs). This problem can be mitigated by leveraging full-duplex (FD) communications that facilitate concurrent sensing and transmission. This thesis considers the problem of optimal opportunistic multi-channel spectrum sensing and access using FD radios in the presence of uncertain primary user (PU) activity statistics. A joint learningand spectrum access scheme is proposed. To optimize its throughput, the SU sensing period has to be carefully tuned. However, in absence of exact knowledge of the PU activity statistics, the PU's performance may be adversely a ected. To address this problem, a robust optimization problem is formulated. Analysis shows that under some non-restrictive simplifying assumptions, the robust optimization problem is convex. The impact of sensing periods on the PU collision probability and the SU throughput are analyzed, and the optimal sensing period is found via convex optimization. An "\epsilon-greedy algorithm is proposed for use by the SU to learn the PUs' activity statistics in multichannel networks. It is shown that sublinear regrets can be attained by the proposed estimation and robust optimization strategy. Simulation studies demonstrate that the resulting robust solution achieves a good trade-o between optimizing the SU's throughput and protecting the PU. / Thesis / Master of Applied Science (MASc)

No / Abstract: The scarcity of frequency spectrum used for wireless communication systems has attracted a considerable amount of attention in recent years. The cognitive radio (CR) terminology has been widely accepted as a smart platform mainly aimed at the efficient interrogation and utilization of permitted spectrum. Non-parametric spectrum sensing, or estimation, represents one of the prominent tools that can be proposed when CR works under an undetermined environment. As such, the periodogram, filter bank, and multi-taper methods are well considered in many studies without relying on the transmission channel's characteristics. A unified approach to all these non-parametric spectrum sensing techniques is presented in this paper with analytical and performance comparison using simulation methods. Results show that the multi-taper method outperforms the others.

No / Eigenvalue based spectrum sensing schemes such as Maximum Minimum Eigenvalue (MME), Maximum Energy Detection (MED) and Energy with Minimum Eigenvalue (EME) have higher spectrum sensing performance without requiring any prior knowledge of Primary User (PU) signal but the decision hypothesis used in these eigenvalue based sensing schemes depends on the calculation of maximum eigenvalue from covariance matrix of measured signal. Calculation of the covariance matrix followed by eigenspace analysis of the covariance matrix is a resource intensive operation and takes overhead time during critical process of spectrum sensing. In this paper we propose a new blind spectrum sensing scheme based on the approximation of the maximum eigenvalue using state of the art results from Random Matrix Theory (RMT). The proposed sensing scheme has been evaluated through extensive simulations on wireless microphone signals and the proposed scheme shows higher probability of detection (Pd) performance. The proposed spectrum sensing also shows higher detection performance as compared to energy detection scheme and RMT based sensing schemes such as MME and EME.

This paper describes the implementation of a scanning signal detector and automatic modulation classification system. The classification technique is a completely blind method, with no prior knowledge of the signal's center frequency, bandwidth, or symbol rate. An energy detector forms the initial approximations of the signal parameters. The energy detector used in the wideband sweep is reused to obtain fine estimates of the center frequency and bandwidth of the signal. The subsequent steps reduce the effect of frequency offset and sample timing error, resulting in a constellation of the modulation of interest. The cumulant of the constellation is compared to a set of known ideal cumulant values, forming the classification estimate. The algorithm uses two platforms that together provide high speed parallel processing and flexible run-time operation. High-rate spectral scanning using an energy detector is run in parallel with a variable down sampling path; both are highly pipelined structures, which allows for high data throughput. A pair of processing cores is used to record spectral usage and signal characteristics as well as perform the actual classification. The resulting classification system can accurately identify modulations below 5 dB of signal-to-noise ratio (SNR) for some cases of the phase shift keying family of modulations but requires a much higher SNR to accurately classify higher-order modulations. These estimates tend toward classifying all signals as binary phase shift keying because of limits of the noise power estimation part of the cumulant normalization process. Other effects due to frequency offset and synchronization timing are discussed. / Master of Science

Development of Cognitive Radio (CR) applications, which rely on a radio's ability to adapt intelligently to it's spectral surroundings will soon make the all important technological jump from research interest to systems integration, as demand for highly adaptive wireless applications expand. VT-CORNET (Virginia Tech – Cognitive Radio Network Testbed) is a unique testbed concept, designed to facilitate this technology leap by offering researchers — both local and remote — the opportunity to conduct CR experiments on an installed infrastructure of highly flexible radio nodes. These nodes — 48 in total — are distributed throughout four floors of a building on the Virginia Tech campus, and provide researchers with diverse options in terms of channel conditions and deployment scenarios. The radios themselves consist of the widely used USRP2 Software Defined Radio (SDR) platform, coupled to a centrally located cluster of rack servers — which provide a high performance GPP environment for real-time software based signal processing. VT-CORNET is specially licensed to operate our low-power nodes over a broad range of frequencies, which provides researcher the opportunity to conduct experiments on live spectrum — in the presence of real primary users. Testbeds are a widely used tool in the wireless and networking fields, and VT-CORNET expands the concept through a focus on CR research and education. This thesis describes the construction and deployment of the CORNET testbed in detail. Specific contributions made to the testbed include the design and implementation of the management network, as well as the initial deployment of the SDR nodes in the ceiling. In addition, this thesis describes the administration and management of the CORNET GPP cluster, and provides a instructions for the basic usage of CORNET from an administrative and user perspective. Finally, this thesis describes a number of custom SDR waveforms implemented on CORNET which demonstrate the utility of the testbed for cognitive radio applications. / Master of Science

The task of spectrum sensing, defined here to consist of signal detection, signal parameter estimation, and signal identification, is a critically important task in a wide-variety of wireless communication applications. For example, in recent years, government and research initiatives have proposed the idea of communication systems that could gain access to spectrum opportunistically when being unused by primary licensed spectrum users. In order for these opportunistic systems to be realizable, methods by which secondary spectrum users can detect and classify these primary users will be necessary. Furthermore, detection and classification among the secondary users themselves will be important for efficient spectrum usage in these systems. As another example, spectrum sensing is also of critical importance in many military applications. This is due to the inherent expectation that a priori information of hostile wireless systems will be minimal or unavailable. The goal of this dissertation is to provide both insight and solutions in the critical area of spectrum sensing. More specifically, the research contained within this dissertation deals with the development and analysis of spectrum sensing algorithms that address key issues related to channel and radio impairments that are at present underdeveloped in the literature. First, research is presented on a method-of-moments based signal parameter estimation and likelihood-based modulation classification approach for linear digital amplitude-phase modulated signals (PAM, PSK, QAM, ...) in slowly-varying flat-fading channels. Based on this work, research is then presented on a feature-based modulation classification approach which relaxes the requirements of perfect frequency synchronization and knowledge of the phase information of the received signal that the likelihood-based approach requires. Finally, research is presented on the impact that both sensor reliability and sensor correlation information have on collaborative signal detection and intelligent sensor selection. / Ph. D.

Opportunistic use of unused spectrum could efficiently be carried out using the paradigm of Cognitive Radio (CR). A spectrum remains idle when the primary user (licensee) is not using it. The secondary nodes detect this spectral hole quickly and make use of it for data transmission during this interval and stop transmitting once the primary starts transmitting. Detection of spectral holes by the secondary is called spectrum sensing in the CR scenario. Spectrum Sensing is formulated as a hypothesis testing problem wherein under H0 the spectrum is free and under H1, occupied. The samples will have different probability distributions, P0 and P1, under H0 and H1 respectively. In the first part of the thesis, a new algorithm - entropy test is presented, which performs better than the available algorithms when P0 is known but not P1. This is extended to a distributed setting as well, in which different secondary nodes collect samples independently and send their decisions to a Fusion Centre (FC) over a noisy MAC which then makes the final decision. The asymptotic optimality of the algorithm is also shown. In the second part, the spectrum sensing problem under impediments such as fading, electromagnetic interference and outliers is tackled. Here the detector does not possess full knowledge of either P0 or P1. This is a more general and practically relevant setting. It is found that a recently developed algorithm (which we call random walk test) under suitable modifications works well. The performance of the algorithm theoretically and via simulations is shown. The same algorithm is extended to the distributed setting as above.

Cognitive radios require spectral occupancy information in a given location, to avoid any interference with the existing licensed users. This is achieved by spectrum sensing. Existing narrowband, serial spectrum sensors are spectrally inefficient and power hungry. Wideband spectrum sensing increases the number of probable fre-quency candidates for cognitive radio. Wideband RF systems cannot use analog to digital converters (ADCs) for spectrum sensing without increasing the sampling rate and power consumption. The use of ADCs is limited because of the dynamic range of the signals that need to be sampled and the frequency of operation. In this work, we have presented a CMOS based area efficient, dedicated and scalable wideband parallel/serial spectrum sensor for cognitive radio. The key contributions of the thesis are: 1. An injection locked oscillator cascade (ILOC) for parallel LO synthesis. An area-efficient, wideband RF frequency synthesizer, which simultaneously gen-erates multiple local oscillator (LO) signals, is designed. It is suitable for parallel wideband RF spectrum sensing in cognitive radios. The frequency synthesizer consists of an injection locked oscillator cascade where all the LO signals are derived from a single reference oscillator. The ILOC is implemented in a 130-nm technology with an active area of 0.017 mm2. It generates 4 uni-formly spaced LO carrier frequencies from 500 MHz to 2 GHz. 2. A wideband, parallel RF spectrum sensor for cognitive radios has been de-signed. This spectrum sensor is designed to detect RF occupancy from 250 MHz to 5.25 GHz by using an array of CMOS receivers with envelope detec-tors. A parallel LO synthesizer is implemented as an ILOC. The simulated sensitivity is around -25 dBm for 250 MHz wide bandwidth. 3. A mitigation technique for harmonic downconversion in wideband spectrum sensors. The downconversion of radio frequency (RF) components around the harmonics of the local oscillator (LO), and its impact on the accuracy of white space detection using integrated spectrum sensors, is (are) studied. We propose an algorithm to mitigate the impact of harmonic Down conversion by utilizing multiple parallel downconverters in the system architecture. The proposed algorithm is validated on a test-board using commercially avail-able integrated circuits (IC) and a test-chip implemented in a 130-nm CMOS technology. The measured data shows that the impact of the harmonic down-conversion is closely related to the LO characteristics, and that much of it can be mitigated by the proposed technique. 4. A wideband spectrum sensor for narrowband energy detection. A wideband spectrum sensing system for cognitive radio is designed and implemented in a 130-nm RF mixed-mode CMOS technology. The system employs an I-Q downconverter, a pair of complex filters and a pair of envelope detectors for energy detection. The spectrum sensor works from 250 MHz to 3.25 GHz. The design makes use of the band pass nature of the complex filter to achieve two objectives : i) Separation of upper sideband (USB) and lower sideband (LSB) around the local oscillator (LO) signal and ii) Resolution of smaller bands within a large detection bandwidth. The measured sensitivity is close to -45 dBm for a single tone test over a bandwidth of 40 MHz. The measured Image reject ratio (IRR) is close to 30 dB. The overall sensing bandwidth is 3.5 GHz and the overall wideband detection bandwidth is 250 MHz which is partitioned into 40 MHz narrowband chunks with 8 such overlapping chunks.

To explore spectral opportunities in wideband regime for cognitive radio we need a wideband spectrum sensing receiver. Current wideband receiver architectures need wideband analog to digital converter (ADC) to sample wideband signal. As current state-of-art ADC has limitation in terms of power and sampling rate, we need to explore some alternative solutions. Compressive sampling (CS) data acquisition method is one of the solutions. Cognitive Radio signal, which is sparse in frequency domain can be sampled at Sub-Nyquist rate using low rate ADC. To relax the receiver complexity in terms of performance requirement we can use Modulated Wideband Converter (MWC) architecture, a Sub-Nyquist sampling method. In this thesis circuit design of this architecture covers signal within a frequency range of 500 MHz to 2.1 GHz, with a channel bandwidth of 1600 MHz. By using 8 parallel lines with channel trading factor of 11, effective sampling rate of 550 MHz is achieved for successful support recovery of multi-band input signal of size N=12.