Satellite Cognitive Communications and Spectrum Regulation

Due to rapid increase of wireless users and the popularity of multimedia applications, the demand for wireless spectrum is increasing rapidly. However, due to current static spectrum policy, the available usable spectrum is becoming scarce while a significant amount of spectrum remains underutilized. In this aspect, cognitive communications can be considered as a promising technology to enhance spectrum usage efficiency by allowing the coexistence of heterogeneous networks within the same spectrum. In this paper, starting with the rationale of cognitive communication, we present two different coexistence scenarios in the context of satellite cognitive communication. We then present the current status of spectrum regulation in the context of Cognitive Radio (CR) and the relevant decisions of World Radio Conference 2012 (WRC-12). Finally, we present the technical aspects and regulatory challenges of this technology and provide some suggestions from research, industrial and regulatory perspectives

Cognitive-Based Solutions to Spectrum Issues in Future Satellite Communication Systems

With particular attention to Satellite Communications (SatComs), cognitive-based solutions are investigated. With cognitive-based solutions we refer to all those techniques that aim at improving spectrum utilization of the available spectrum and rely on the knowledge of the environment in which the systems operate. As a matter of fact, an improved spectrum utilization enables higher throughput capacities that will satisfy the future markets and demands of an increasingly connected world. Throughout the thesis, several techniques are proposed, developed, and assessed with respect to specific scenarios of interest. Particular focus has been put on spectrum awareness techniques for system coexistence, and on spectrum exploitation techniques for an improved efficiency in terms of resource utilization

New Generation Cooperative and Cognitive Dual Satellite Systems: Performance Evaluation

Investigating innovative satellite architectures with enhanced system through- put is one of the most important challenges towards realizing the next generation of satellite communication systems. In this context, we study two advanced architectures, namely cooperative and cognitive satellite systems. These designs allow the spectral coexistence of two multibeam satellites over a common coverage area with the overlapping beam patterns. In the cooperative dual satellite system, we consider coordination between two coexisting transmitters in order to reduce the intersatellite interference. This is achieved by employing adequate user scheduling, based on the channel state information of each user. To this end, a semi-orthogonal interference aware scheduling algorithm is applied. Further, in the cognitive dual satellite system, we employ a cognitive beamhopping technique assuming that the secondary gateway is aware of the primary's beamhopping pattern. Moreover, we compare the performances of these schemes with those of the conventional multi- beam and overlapping dual satellite systems in terms of spectral efficiency, power efficiency and user fairness. Finally, we provide several insights on the performance of these schemes and provide interesting future works in these domains

Eigenvalue based SNR Estimation for Cognitive Radio in Presence of Channel Correlation

In addition to spectrum sensing capability required by a Cognitive Radio (CR), Signal to Noise Ratio (SNR) estimation of the primary signals is crucial in order to adapt its coverage area dynamically using underlay techniques. Furthermore, in practical scenarios, the fading channel may be correlated due to various causes such as insufficient scattering in the propagation path and antenna mutual coupling. In this context, we consider the SNR estimation problem for a CR in the presence of channel correlation. We study an eigenvaluebased SNR estimation technique for large-scale CR networks using asymptotic Random Matrix Theory (RMT). We carry out detailed theoretical analysis of the signal plus noise hypothesis to derive the asymptotic eigenvalue probability distribution function (a.e.p.d.f.) of the received signal’s covariance matrix in the presence of the correlated channel. Then an SNR estimation technique based on the derived a.e.p.d.f. is proposed for PU SNR in the presence of channel correlation and its performance is evaluated in terms of normalized Mean Square Error (MSE). It is shown that the PU SNR can be accurately estimated in the presence of channel correlation using the proposed technique even in low SNR region

System Modelling and Design Aspects of Next Generation High Throughput Satellites

Future generation wireless networks are targeting the convergence of fixed, mobile and broadcasting systems with the integration of satellite and terrestrial systems towards utilizing their mutual benefits. Satellite Communications (Sat- Com) is envisioned to play a vital role to provide integrated services seamlessly over heterogeneous networks. As compared to terrestrial systems, the design of SatCom systems require a different approach due to differences in terms of wave propagation, operating frequency, antenna structures, interfering sources, limitations of onboard processing, power limitations and transceiver impairments. In this regard, this letter aims to identify and discuss important modeling and design aspects of the next generation High Throughput Satellite (HTS) systems. First, communication models of HTSs including the ones for multibeam and multicarrier satellites, multiple antenna techniques, and for SatCom payloads and antennas are highlighted and discussed. Subsequently, various design aspects of SatCom transceivers including impairments related to the transceiver, payload and channel, and traffic-based coverage adaptation are presented. Finally, some open topics for the design of next generation HTSs are identified and discussed.Comment: submitted to IEEE Journa

Spectrum Monitoring Algorithms for Wireless and Satellite Communications

Nowadays, there is an increasing demand for more efficient utilization of the radio frequency spectrum as new terrestrial and space services are deployed resulting in the congestion of the already crowded frequency bands. In this context, spectrum monitoring is a necessity. Spectrum monitoring techniques can be applied in a cognitive radio network, exploiting the spectrum holes and allowing the secondary users to have access in an unlicensed frequency band for them, when it is not occupied by the primary user. Furthermore, spectrum monitoring techniques can be used for interference detection in wireless and satellite communications. These two topics are addressed in this thesis. In the beginning, a detailed survey of the existing spectrum monitoring techniques according to the way that cognitive radio users 1) can detect the presence or absence of the primary user; and 2) can access the licensed spectrum is provided. Subsequently, an overview of the problem of satellite interference and existing methods for its detection are discussed, while the contributions of this thesis are presented as well. Moreover, this thesis discusses some issues in a cognitive radio system such as the reduction of the secondary user's throughput of the conventional \listen before talk" access method in the spectrum. Then, the idea of simultaneous spectrum sensing and data transmission through the collaboration of the secondary transmitter with receiver is proposed to address these concerns. First, the secondary receiver decodes the signal from the secondary transmitter, then, removes it from the total received signal and finally, applies spectrum sensing in the remaining signal in order to decide if the primary user is active or idle. The effects of the imperfect signal cancellation due to decoding errors, which are ignored in the existing literature, are considered in our analysis. The analytical expressions for the probabilities of false alarm and detection are derived and numerical results through simulations are also presented to validate the proposed study. Furthermore, the threat of interference for the satellite communications services is studied in this thesis. It proposes the detection of interference on-board the satellite by introducing a spectrum monitoring unit within the satellite transponder. This development will bring several benefits such as faster reaction time and simplification of the ground stations in multi-beam satellite systems. Then, two algorithms for the detection of interference are provided. The first detection scheme is based on energy detector with signal cancellation exploiting the pilot symbols. The second detection scheme considers a two-stage detector, where first, the energy detector with signal cancellation in the pilot domain is performed, and if required, an energy detector with signal cancellation in the data domain is carried out in the second stage. Moreover, the analytical expressions for the probabilities of false alarm and detection are derived and numerical results through simulations are provided to verify the accuracy of the proposed analysis. Finally, this thesis goes one step further and the developed algorithms are evaluated experimentally using software defined radios, particularly universal software radio peripherals (USRPs), while it concludes discussing some open research topics

Spectrum sensing and occupancy prediction for cognitive machine-to-machine wireless networks

A thesis submitted to the University of Bedfordshire, in partial fulfil ment of the requirements for the degree of Doctor of Philosophy (PhD)The rapid growth of the Internet of Things (IoT) introduces an additional challenge to the existing spectrum under-utilisation problem as large scale deployments of thousands devices are expected to require wireless connectivity. Dynamic Spectrum Access (DSA) has been proposed as a means of improving the spectrum utilisation of wireless systems. Based on the Cognitive Radio (CR) paradigm, DSA enables unlicensed spectrum users to sense their spectral environment and adapt their operational parameters to opportunistically access any temporally unoccupied bands without causing interference to the primary spectrum users. In the same context, CR inspired Machine-to-Machine (M2M) communications have recently been proposed as a potential solution to the spectrum utilisation problem, which has been driven by the ever increasing number of interconnected devices. M2M communications introduce new challenges for CR in terms of operational environments and design requirements. With spectrum sensing being the key function for CR, this thesis investigates the performance of spectrum sensing and proposes novel sensing approaches and models to address the sensing problem for cognitive M2M deployments. In this thesis, the behaviour of Energy Detection (ED) spectrum sensing for cognitive M2M nodes is modelled using the two-wave with dffi use power fading model. This channel model can describe a variety of realistic fading conditions including worse than Rayleigh scenarios that are expected to occur within the operational environments of cognitive M2M communication systems. The results suggest that ED based spectrum sensing fails to meet the sensing requirements over worse than Rayleigh conditions and consequently requires the signal-to-noise ratio (SNR) to be increased by up to 137%. However, by employing appropriate diversity and node cooperation techniques, the sensing performance can be improved by up to 11.5dB in terms of the required SNR. These results are particularly useful in analysing the eff ects of severe fading in cognitive M2M systems and thus they can be used to design effi cient CR transceivers and to quantify the trade-o s between detection performance and energy e fficiency. A novel predictive spectrum sensing scheme that exploits historical data of past sensing events to predict channel occupancy is proposed and analysed. This approach allows CR terminals to sense only the channels that are predicted to be unoccupied rather than the whole band of interest. Based on this approach, a spectrum occupancy predictor is developed and experimentally validated. The proposed scheme achieves a prediction accuracy of up to 93% which in turn can lead to up to 84% reduction of the spectrum sensing cost. Furthermore, a novel probabilistic model for describing the channel availability in both the vertical and horizontal polarisations is developed. The proposed model is validated based on a measurement campaign for operational scenarios where CR terminals may change their polarisation during their operation. A Gaussian approximation is used to model the empirical channel availability data with more than 95% confi dence bounds. The proposed model can be used as a means of improving spectrum sensing performance by using statistical knowledge on the primary users occupancy pattern

SRML: Space Radio Machine Learning

Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft\u27s operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector\u27s electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference

Advanced RFI detection, RFI excision, and spectrum sensing : algorithms and performance analyses

Because of intentional and unintentional man-made interference, radio frequency interference (RFI) is causing performance loss in various radio frequency operating systems such as microwave radiometry, radio astronomy, satellite communications, ultra-wideband communications, radar, and cognitive radio. To overcome the impact of RFI, a robust RFI detection coupled with an efficient RFI excision are, thus, needed. Amongst their limitations, the existing techniques tend to be computationally complex and render inefficient RFI excision. On the other hand, the state-of-the-art on cognitive radio (CR) encompasses numerous spectrum sensing techniques. However, most of the existing techniques either rely on the availability of the channel state information (CSI) or the primary signal characteristics. Motivated by the highlighted limitations, this Ph.D. dissertation presents research investigations and results grouped into three themes: advanced RFI detection, advanced RFI excision, and advanced spectrum sensing. Regarding advanced RFI detection, this dissertation presents five RFI detectors: a power detector (PD), an energy detector (ED), an eigenvalue detector (EvD), a matrix-based detector, and a tensor-based detector. First, a computationally simple PD is investigated to detect a brodband RFI. By assuming Nakagami-m fading channels, exact closed-form expressions for the probabilities of RFI detection and of false alarm are derived and validated via simulations. Simulations also demonstrate that PD outperforms kurtosis detector (KD). Second, an ED is investigated for RFI detection in wireless communication systems. Its average probability of RFI detection is studied and approximated, and asymptotic closed-form expressions are derived. Besides, an exact closed-form expression for its average probability of false alarm is derived. Monte-Carlo simulations validate the derived analytical expressions and corroborate that the investigated ED outperforms KD and a generalized likelihood ratio test (GLRT) detector. The performance of ED is also assessed using real-world RFI contaminated data. Third, a blind EvD is proposed for single-input multiple-output (SIMO) systems that may suffer from RFI. To characterize the performance of EvD, performance closed-form expressions valid for infinitely huge samples are derived and validated through simulations. Simulations also corroborate that EvD manifests, even under sample starved settings, a comparable detection performance with a GLRT detector fed with the knowledge of the signal of interest (SOI) channel and a matched subspace detector fed with the SOI and RFI channels. At last, for a robust detection of RFI received through a multi-path fading channel, this dissertation presents matrix-based and tensor-based multi-antenna RFI detectors while introducing a tensor-based hypothesis testing framework. To characterize the performance of these detectors, performance analyses have been pursued. Simulations assess the performance of the proposed detectors and validate the derived asymptotic characterizations. Concerning advanced RFI excision, this dissertation introduces a multi-linear algebra framework to the multi-interferer RFI (MI-RFI) excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for SIMO systems. Having employed smoothed observation windows, a smoothed MLSEP (s-MLSEP) algorithm is also proposed. MLSEP and s-MLSEP require the knowledge of the number of interferers and their respective channel order. Accordingly, a novel smoothed matrix-based joint number of interferers and channel order enumerator is proposed. Performance analyses corroborate that both MLSEP and s-MLSEP can excise all interferers when the perturbations get infinitesimally small. For such perturbations, the analyses also attest that s-MLSEP exhibits a faster convergence to a zero excision error than MLSEP which, in turn, converges faster than a subspace projection algorithm. Despite its slight complexity, simulations and performance assessment on real-world data demonstrate that MLSEP outperforms projection-based RFI excision algorithms. Simulations also corroborate that s-MLSEP outperforms MLSEP as the smoothing factor gets smaller. With regard to advanced spectrum sensing, having been inspired by an F–test detector with a simple analytical false alarm threshold expression considered an alternative to the existing blind detectors, this dissertation presents and evaluates simple F–test based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna CRs. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are also robust against noise uncertainty