Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Wideband cyclostationary spectrum sensing and characterization for cognitive radios

Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or absence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptability, and recongurability, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques

On the feasibility of the communications in the TVWS spectrum analysis and coexistence issue

In the last decade, the enormous growth in the wireless industry has come from using only a small part of the wireless spectrum, nominally less than 10% under 3 GHz. Nowadays, the vast majority of the available spectral resources have already been licensed. Measurements made by the Federal Communication Commission (FCC) have shown that a great part of the spectrum, although allocated, is virtually unused. For all this reasons, in the last years, several countries have already (USA) or are in the process (EU, China, Japan, South Korea) of switching off analog TV broadcasting in favor of Digital Terrestrial Television (DTT) broadcasting systems and digital switchover plans have driven a thorough review of TV spectrum exploitation. The resulting unused channels within this band are called “TV white spaces” (TVWS). Even after the redistribution of the digital TV channels, the problem of an efficient utilization of the allocated frequencies is still far from being solved. For example, there are still large territorial areas on which, although allocated, the TV channels result unused, due to coverage problems. New spectrum allocation approaches such as the dynamic spectrum access method have been studied. This new concept implies that the radio terminals have the capacity to monitor their own radio environment and consequently adapt to the transmission conditions on whatever frequency band are available (adaptive radio). If this concept is supplemented with the capacity of analyzing the surrounding radio environment in search of white spaces, the term adaptive radio is extended to Cognitive Radio (CR). The spectrum management rule of CR is that all new users for the spectrum are secondary (cognitive) users (SU) and requires that they must detect and avoid the primary (licensed) users (PU) in terms of used frequencies, transmission power and modulation scheme. In the TV bands specifically, the presence of PUs (e.g. TV broadcasters) can be revealed both performing a spectrum sensing operation and considering the information provided by the external databases called “geo-location databases” (GL-DB). The database provides, for a certain location, the list of the free TV channels and the allowable maximum effective isotropic radiated power (EIRP) for transmitting without harmful interference to incumbent users. Decision thresholds are still a critical parameter for protecting services in a scenario where cognitive devices would be operating. There are cases where the approach based on GL Spectrum Occupancy DB might not be available, either because the database does not exist for that area (for example in non densely populated areas) or in the case that access to the database is not possible (deep indoor operation, low populated areas etc.). Several studies have suggested that radio noise has increased significantly over the last decades and consequently the assumptions about decision thresholds and interference protection ratios might be outdated. The Hidden Node Margin (HNM) is a parameter that quantifies the difference between the potential interfered signal values at the location where it is measured or estimated by the cognitive device, and the actual value at the location where the receiving antenna for this signal is located. HNM is a key parameter to define the protection requirements that cognitive devices must comply in order not to create any harmful interference to broadcast receiving systems. In this context, this thesis goes in a precise direction, with four main topics related to the feasibility of communication cognitive systems operating in the TVWS, considering coexistence as the main operational issue. The first topic studies new spectrum sensing approaches in order to improve the more critical functionality of CRs. In the second topic an unlicensed indoor short-range distribution system for the wireless retransmission in the DTT band of High definition TV (HDTV) contents with immediate implementations as home entertainment systems has been carried out. The third topic of this thesis is about a particular database developed in order to provide information to easily calculate HNM values and associated statistics, TV Channel Occupancy and Man Made Noise Upper Limits. The empirical data for this work has been recorded in different locations of Spain and Italy during 2011 and 2012 thanks to the partnership between the Department of Electrical and Electronic Engineering (D.I.E.E.) of the University of Cagliari and the Department of Electronics and Telecommunications of the University of Bilbao (UPV/EHU). Finally in the last topic we focus on the IEEE 802.22 WRAN standard evaluating, thanks to extended measurements, the performance of an 802.22 system operating into the same coverage range of a DTT receiver

Blind Estimation of OFDM System Parameters for Automatic Signal Identification

Orthogonal frequency division multiplexing (OFDM) has gained worldwide popular­ ity in broadband wireless communications recently due to its high spectral efficiency and robust performance in multipath fading channels. A growing trend of smart receivers which can support and adapt to multiple OFDM based standards auto­ matically brings the necessity of identifying different standards by estimating OFDM system parameters without a priori information. Consequently, blind estimation and identification of OFDM system parameters has received considerable research atten­ tions. Many techniques have been developed for blind estimation of various OFDM parameters, whereas estimation of the sampling frequency is often ignored. Further­ more, the estimated sampling frequency of an OFDM signal has to be very accurate for data recovery due to the high sensitivity of OFDM signals to sampling clock offset. To address the aforementioned problems, we propose a two-step cyclostation- arity based algorithm with low computational complexity to precisely estimate the sampling frequency of a received oversampled OFDM signal. With this estimated sampling frequency and oversampling ratio, other OFDM system parameters, i.e., the number of subcarriers, symbol duration and cyclic prefix (CP) length can be es­ timated based on the cyclic property from CP sequentially. In addition, modulation scheme used in the OFDM can be classified based on the higher-order statistics (HOS) of the frequency domain OFDM signal. All the proposed algorithms are verified by a lab testing system including a vec­ tor signal generator, a spectrum analyzer and a high speed digitizer. The evaluation results confirm the high precision and efficacy of the proposed algorithm in realistic scenarios

Pattern recognition using genetic programming for classification of diabetes and modulation data

The field of science whose goal is to assign each input object to one of the given set of categories is called pattern recognition. A standard pattern recognition system can be divided into two main components, feature extraction and pattern classification. During the process of feature extraction, the information relevant to the problem is extracted from raw data, prepared as features and passed to a classifier for assignment of a label. Generally, the extracted feature vector has fairly large number of dimensions, from the order of hundreds to thousands, increasing the computational complexity significantly. Feature generation is introduced to handle this problem which filters out the unwanted features. The functionality of feature generation has become very important in modern pattern recognition systems as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic programming (GP) based framework has been utilised in this thesis for feature generation. GP is a process based on the biological evolution of features in which combination of original features are evolved. The stronger features propagate in this evolution while weaker features are discarded. The process of evolution is optimised in a way to improve the discriminatory power of features in every new generation. The final features generated have more discriminatory power than the original features, making the job of classifier easier. One of the main problems in GP is a tendency towards suboptimal-convergence. In this thesis, the response of features for each input instance which gives insight into strengths and weaknesses of features is used to avoid suboptimal-convergence. The strengths and weaknesses are utilised to find the right partners during crossover operation which not only helps to avoid suboptimal-convergence but also makes the evolution more effective. In order to thoroughly examine the capabilities of GP for feature generation and to cover different scenarios, different combinations of GP are designed. Each combination of GP differs in the way, the capability of the features to solve the problem (the fitness function) is evaluated. In this research Fisher criterion, Support Vector Machine and Artificial Neural Network have been used to evaluate the fitness function for binary classification problems while K-nearest neighbour classifier has been used for fitness evaluation of multi-class classification problems. Two Real world classification problems (diabetes detection and modulation classification) are used to evaluate the performance of GP for feature generation. These two problems belong to two different categories; diabetes detection is a binary classification problem while modulation classification is a multi-class classification problem. The application of GP for both the problems helps to evaluate the performance of GP for both categories. A series of experiments are conducted to evaluate and compare the results obtained using GP. The results demonstrate the superiority of GP generated features compared to features generated by conventional methods

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

Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements