Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Wideband Autonomous Cognitive Radios: Spectrum Awareness and PHY/MAC Decision Making

The cognitive radios (CRs) have opened up new ways of better utilizing the scarce wireless spectrum resources. The CRs have been made feasible by recent advances in software-defined radios (SDRs), smart antennas, reconfigurable radio frequency (RF) front-ends, and full-duplex RF front-end architectures, to name a few. Generally, a CR is considered as a dynamically reconfigurable radio capable of adapting its operating parameters to the surrounding environment. Recent developments in spectrum policy and regulatory domains also allow more flexible and efficient utilization of wider RF spectrum range in the future. In line with the future directions of CRs, a new vision of a future autonomous CR device, called Radiobots, was previously proposed. The goals of the proposed Radiobot surpass the dynamic spectrum access (DSA) to achieve wideband operability and the main features of cognition. In order to ensure the practicality and robust operation of the Radiobot structure, the research focus of this dissertation includes the following aspects: 1) robust spectrum sensing and operability in a centralized CR network setup; 2) robust multivariate non-parametric quickest detection for dynamic spectrum usage tracking in an alien RF environment; 3) joint physical layer and medium access control layer (PHY/MAC) decision-making for wideband bandwidth aggregation (simultaneous operation over multiple modes/networks); and 4) autonomous spectrum sensing scheduling solutions in an alien ultra wideband RF environment. The major contribution of this dissertation is to investigate the feasibility of the autonomous CR operation in heterogeneous RF environments, and to provide novel solutions to the fundamental and crucial problems/challenges, including spectrum sensing, spectrum awareness, wideband operability, and autonomous PHY/MAC protocols, thus bringing the autonomous Radiobot one step closer to reality

Sensing UMTS bands using cyclostationary features and cooperation between opportunistic terminals

The Opportunistic Radio (OR) concept relies on the cognitive features of the OR terminals, namely the ability to adapt its transmitter parameters, based upon interaction with the RF environment in which it operates. An OR system operates in licensed frequency bands, exploiting opportunities and operating with a lower priority regarding the licensed system, implementing a spectrum pool mechanism. The most important constraint is that the OR network should always avoid harmful interference with the licensed system, therefore it should reliably detect licensed signals in the used band in order to avoid interfering with the licensed owner of that band. Given the importance of UMTS systems in current wireless communications, this paper is focused on 3G bands and addresses the problem of sensing weak UMTS signals. The proposed sensing algorithm exploits the cyclostationary features of UMTS signals and the cooperation between multiple OR terminals clustered in the OR network

Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically

Sensing opportunities in UMTS spectrum

The UMTS radio frequency spectrum is a highly expensive commodity. While the UMTS standards make very efficient use of the allocated bands there is however opportunity for further advancements. This paper focuses on opportunistic use of the UMTS spectrum as a means of ensuring that the maximum possible use of this valuable resource is made. In particular we focus on the local detection of UMTS TDD signals through the use of a cyclostationary feature detector. Simulation results for the use of this detector in the presence of multipath propagation and shadowing effects are presented

Cyclostationarity Based Sonar Signal Processing

AbstractThis paper presents a reliable method for target vessel identification in passive sonar by exploiting the underlying periodicity of propeller noise signal, using the principles of cyclostationarity. In conventional signal processing methods, random signals are treated as statistically stationary and the parameters of the underlying physical mechanism that generates the signal would not vary in time. However, for most manmade signals, some parameters vary periodically with time and this requires that random signals be modeled as cyclostationary. In the field of sonar, the propeller noise signal generated by underwater vessels is cyclostationary. As a ship propagates in the sea, noise generated during the collapse of cavitation-induced bubbles are modulated by the rotating propeller shaft and this results in characteristic amplitude modulated random noise signal, which can be detected using passive sonar. Processing these signals, the number of blades and the shaft frequency of the propeller can be identified. In this work, cyclostationary processing technique is introduced for processing propeller noise signal and it is observed to provide better noise immunity. A detailed comparison with the conventional DEMON processing is also presented

CYCLOSTATIONARY FEATURES OF PAL TV AND WIRELESS MICROPHONE FOR COGNITIVE RADIO APPLICATIONS

Frequency spectrum being a scarce resource in communication system design, spectrum sharing seems to be the solution to an optimal utilization of frequency spectrum. The traditional fixed frequency allocation is not suitable for futuristic networks that demand more and more spectrum for new wireless services. Cognitive radio is a new emerging technology based on spectrum sharing concept. Spectrum sensing is a vital task in this emerging technology by which it is able to scan the frequency spectrum to identify the unused spectrum bands and utilize them. In this thesis, we discuss spectrum sensing in the context of IEEE 802.22 Wireless Regional Area Network (WRAN). In order to do so, we develop the co-existence scenario with three cases according to geographical positions of primary services and secondary service. In WRAN application, the SUs utilize the unused channel in TV spectrum, which means that the primary users are TV service and other FCC part 74 low power licensed devices. We focus on special case of Analog TV-PAL service and wireless microphone service as part 74 devices. Before discussing the spectrum sensing technique, we propose architecture for sensing receiver. The concept of noise uncertainty is also introduced in this context. The cyclostationarity theory is introduced and we explain the motivation behind using the theory for spectrum sensing and the reason that makes the cyclostationary features detector a powerful detection technique in cognitive radio. We obtain the cyclostationary features of these primary signals using spectral correlation function. Based on these features, we develop two algorithms for spectrum sensing and their performances are evaluated in comparison with energy detector which is considered as the standard simple detector. Given that the cyclostationary features are unique for a particular signal; these features can be used for signals classification. In our case, we use those features to decide if the licensed channel is used by TV service or wireless microphone service. This provides additional information for spectrum management and power control. Implementation issue is very important in cognitive radio generally and spectrum sensing specially, hence we discuss the implementation of cyclostationary features detector and compare its complexity with that of energy detector

Study of the cyclostationarity properties of various signals of opportunity

Global Navigation Satellite Systems (GNSS) offer precise position estimation and navigation services outdoor but they are rarely accessible in strong multipath environments, such as indoor environments. Fortunately, several Signals of Opportunity (SoO), (such as RFID, Wi-Fi, Bluetooth, digital TV signals, etc.) are readily available in these environments, creating an opportunity for seamless positioning. Performance evolution of positioning can be achieved through contextual exploitation of SoO. The detection and identification of available SoO signals or of the signals which are most relevant to localization and the signal selection in an optimum way, according to designer defined optimality criteria, are important stages to enter such contextual awareness domain. Man-made modulated signals have certain properties which vary periodically in time and this time-varying periodical characteristics trigger what is known as cyclostationarity. Cyclostationarity analysis can be used, among others, as a tool for signal detection. Detected signals through cyclostationary features can be exploited as SoO. The main purpose of this thesis is to study and analyze the cyclostationarity properties of various SoO. An additional goal is to investigate whether such cyclostationarity properties can be used to detect, identify and distinguish the signals which are present in a certain frequency band. The thesis is divided into two parts. In the literature review part, the physical layer study of several signals is given, by emphasizing the potential of SoO in positioning. In the implementation part, the possibility of signals detection through cyclostationary features is investigated through MATLAB simulations. Cyclostationary properties obtained through FFT accumulation Method (FAM) and statistical performance of detection are studied in the presence of stationary additive white Gaussian noise (AWGN). Besides that, the performance in signal detection using cyclostationary-based detector is also compared to the performance with the energy-based detectors, used as benchmarks. The simulated result suggest that cyclostationary features can certainly detect the presence of signals in noise, but simple cases, such as one type of signal only and AWGN noise, are better addressed via traditional energy-based detection. However, cyclostationary features can exhibit advantages in other types of noises and in the presence of signal mixtures which in fact may fulfil one of the preliminary requirements of cognitive positioning

Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model