Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing

Enhanced Spectrum Sensing for Cognitive Cellular Systems

This dissertation aims at improving spectrum sensing algorithms in order to effectively apply them to cellular systems. In wireless communications, cellular systems occupy a significant part of the spectrum. The spectrum usage for cellular systems are rapidly expanding due to the increasing demand for wireless services in our society. This results in radio frequency spectrum scarcity. Cellular systems can effectively handle this issue through cognitive mechanisms for spectrum utilization. Spectrum sensing plays the first stage of cognitive cycles for the adaptation to radio environments. This dissertation focuses on maximizing the reliability of spectrum sensing to satisfy regulation requirements with respect to high spectrum sensing performance and an acceptable error rate. To overcome these challenges, characteristics of noise and manmade signals are exploited for spectrum sensing. Moreover, this dissertation considers system constraints, the compatibility with the current and the trends of future generations. Newly proposed and existing algorithms were evaluated in simulations in the context of cellular systems. Based on a prototype of cognitive cellular systems (CCSs), the proposed algorithms were assessed in realistic scenarios. These algorithms can be applied to CCSs for the awareness of desired signals in licensed and unlicensed bands. For orthogonal frequency-division multiplexing (OFDM) signals, this dissertation exploits the characteristics of pilot patterns and preambles for new algorithms. The new algorithms outperform the existing ones, which also utilize pilot patterns. Additionally, the new algorithms can work with short observation durations, which is not possible with the existing algorithms. The Digital Video Broadcasting - Terrestrial (DVB-T) standard is taken as an example application for the algorithms. The algorithms can also be developed for filter bank multicarrier (FBMC) signals, which are a potential candidate for multiplexing techniques in the next cellular generations. The experimental results give insights for the reliability of the algorithms, taking system constraints v into account. Another new sensing algorithm, based on a preamble, is proposed for the DVBT2 standard, which is the second generation of of DVB system. DVB-T2 systems have been deployed in worldwide regions. This algorithm can detect DVB-T2 signals in a very short observation interval, which is helpful for the in-band sensing mode, to protect primary users (in nearly real-time) from the secondary transmission. An enhanced spectrum sensing algorithm based on cyclostationary signatures is proposed to detect desired signals in very low signal-to-noise ratios (SNRs). This algorithm can be developed to detect the single-carrier frequency division multiple access (SC-FDMA) signal, which is adopted for the uplink of long-term evolution (LTE) systems. This detector substantially outperforms the existing detection algorithms with the marginal complexity of some scalar multiplications. The test statistics are explicitly formulated in mathematical formulas, which were not presented in the previous work. The formulas and simulation results provide a useful strategy for cyclostationarity-based detection with different modulation types. For multiband spectrum sensing, an effective scheme is proposed not only to detect but also to classify LTE signals in multiple channels in a wide frequency range. To the best of our knowledge, no scheme had previously been described to perform the sensing tasks. The scheme is reliable and flexible for implementation, and there is almost no performance degradation caused by the scheme compared to single-channel spectrum sensing. The multiband sensing scheme was experimentally assessed in scenarios where the existing infrastructures are interrupted to provide mobile communications. The proposed algorithms and scheme facilitate cognitive capabilities to be applied to real cellular communications. This enables the significantly improved spectrum utilization of CCSs

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

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

Performance analysis of FBMC over OFDM in Cognitive Radio Network

Cognitive Radio (CR) system is an adaptive, reconfigurable communication system that can intuitively adjust its parameters to meet users or network demands. The major objective of CR is to provide a platform for the Secondary User (SU) to fully utilize the available spectrum resource by sensing the existence of spectrum holes without causing interference to the Primary User (PU). However, PU detection has been one of the main challenges in CR technology. In comparison to traditional wireless communication systems, due to the Cross-Channel Interference (CCI) from the adjacent channels used by SU to PU, CR system now poses new challenges to Resource Allocation (RA) problems. Past efforts have been focussed on Orthogonal Frequency Division Multiplexing (OFDM) based CR systems. However, OFDM technique show various limitations in CR application due to its enormous spectrum leakage. Filter Bank based Multicarrier (FBMC) has been proposed as a promising Multicarrier Modulation (MCM) candidate that has numerous advantages over OFDM. In this dissertation, a critical analysis of the performance of FBMC over OFDM was studied, and CR system was used as the testing platform. Firstly, the problem of spectrum sensing of OFDM based CR systems in contrast to FBMC based were surveyed from literature point of view, then the performance of the two schemes was analysed and compared from the spectral efficiency point of view. A resource allocation algorithm was proposed where much attention was focused on interference and power constraint. The proposed algorithms have been verified using MATLAB simulations, however, numerical results show that FBMC can attain higher spectrum efficiency and attractive benefit in terms of spectrum sensing as opposed to OFDM. The contributions of this dissertation have heightened the interest in more research and findings on how FBMC can be improved for future application CR systems

Spectrum Optimisation in Wireless Communication Systems: Technology Evaluation, System Design and Practical Implementation

Two key technology enablers for next generation networks are examined in this thesis, namely Cognitive Radio (CR) and Spectrally Efficient Frequency Division Multiplexing (SEFDM). The first part proposes the use of traffic prediction in CR systems to improve the Quality of Service (QoS) for CR users. A framework is presented which allows CR users to capture a frequency slot in an idle licensed channel occupied by primary users. This is achieved by using CR to sense and select target spectrum bands combined with traffic prediction to determine the optimum channel-sensing order. The latter part of this thesis considers the design, practical implementation and performance evaluation of SEFDM. The key challenge that arises in SEFDM is the self-created interference which complicates the design of receiver architectures. Previous work has focused on the development of sophisticated detection algorithms, however, these suffer from an impractical computational complexity. Consequently, the aim of this work is two-fold; first, to reduce the complexity of existing algorithms to make them better-suited for application in the real world; second, to develop hardware prototypes to assess the feasibility of employing SEFDM in practical systems. The impact of oversampling and fixed-point effects on the performance of SEFDM is initially determined, followed by the design and implementation of linear detection techniques using Field Programmable Gate Arrays (FPGAs). The performance of these FPGA based linear receivers is evaluated in terms of throughput, resource utilisation and Bit Error Rate (BER). Finally, variants of the Sphere Decoding (SD) algorithm are investigated to ameliorate the error performance of SEFDM systems with targeted reduction in complexity. The Fixed SD (FSD) algorithm is implemented on a Digital Signal Processor (DSP) to measure its computational complexity. Modified sorting and decomposition strategies are then applied to this FSD algorithm offering trade-offs between execution speed and BER

Multi-standard context-aware cognitive radio : sensing and classification mechanisms

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Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems