Estimation of Primary Traffic Statistics Based on Spectrum Sensing

Cognitive Radio (CR) systems can benefit from the knowledge of the activity statistics of primary channels, which can use this information to intelligently adapt their spectrum use to the operating environment and work more efficiently and reduce interference on primary users. Particularly relevant statistics are the minimum, mean and variance of the on/off period durations, the channel duty cycle and the governing distribution. The main aim of this thesis is to improve the estimation of the primary user statistics under different environments. At the beginning of operation, the CR does not have any information about the primary traffic statistics. Spectrum sensing is one of the key methods to obtain this knowledge. Unfortunately, the estimation of primary traffic statistics based on spectrum sensing suffers from some flaws, which are investigated in detail in this thesis. In general, two main working environments for the CRs can be identified based on the primary signal power, namely low and high signal-to-noise ratio (SNR) at the secondary users. For the high SNR scenario, an analytical model to link the sensing period with the observed spectrum occupancy and quantify its impact is proposed. Simulation results show that the proposed model captures with reasonable accuracy the spectrum occupancy observed at the CR. Moreover, the effect of the sample size (number of on/off periods) on the estimated accuracy is studied as well. Closed form expressions to estimate the statistics of the primary channel to a certain desired level of accuracy are derived to link such sample size with the accuracy of the observed primary activity statistics. The accuracy of the obtained analytical results is validated and corroborated with both simulation and experimental results, showing a perfect agreement. For the low SNR scenario, both local and cooperative estimation are considered based on the number of SUs performing the estimation. For the single estimation scenario, three novel algorithms are proposed to enhance the estimation of primary user activity statistics under imperfect spectrum sensing given the knowledge of minimum transmission time. Simulation results show that the proposed methods enable an accurate estimation for the primary user statistics. For the cooperative estimation scenario, a new reporting mechanism is proposed in order to increase the spectrum and energy efficiency of the cooperative network and improve resilience under Byzantine attacks. The proposed method is compared in terms of efficiency with methods proposed in the literature and the default periodic reporting method. Simulation results show that the proposed scheme not only reduces significantly the signalling overhead, but with a minor modification it can estimate the primary user distribution under Byzantine attacks with high accuracy. In summary, this thesis contributes a holistic set of mathematical models and novel methods for an accurate estimation of the primary traffic statistics in CR networks based solely on spectrum sensing

Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks

Outdoor user equipment (UE) localisation has attracted a significant amount of attention due to its importance in many location-based services. Typically, in rural and open areas, global navigation satellite systems (GNSS) can provide an accurate and reliable localisation performance. However, in urban areas GNSS localisation accuracy is significantly reduced due to shadowing, scattering and signal blockages. In this work, the UE positioning assisted by deep learning in 5G and beyond networks is investigated in an urban area environment. We study the impact of utilising the spatial correlation in the received signal strengths (RSSs) on the UE positioning accuracy and how to utilise such correlation with deep learning algorithms to improve the localisation accuracy. Numerical results showed the importance of utilising the spatial correlation in the RSS to improve the prediction accuracy for all of the considered models. In addition, the impact of varying the number of access points (APs) transmitters on the localisation accuracy is also investigated. Numerical results showed that a lower number of APs may be sufficient when not considering uncertainties in RSS measurements. Moreover, we study how much the degrading effect of RSS uncertainty can be compensated for by increasing the number of APs.Peer reviewe

Research Issues, Challenges, and Opportunities of Wireless Power Transfer-Aided Full-Duplex Relay Systems

We present a comprehensive review for wireless power transfer (WPT)-aided full-duplex (FD) relay systems. Two critical challenges in implementing WPT-aided FD relay systems are presented, that is, pseudo FD realization and high power consumption. Existing time-splitting or power-splitting structure based-WPT-aided FD relay systems can only realize FD operation in one of the time slots or only forward part of the received signal to the destination, belonging to pseudo FD realization. Besides, self-interference is treated as noise and self-interference cancellation (SIC) operation incurs high power consumption at the FD relay node. To this end, a promising solution is outlined to address the two challenges, which realizes consecutive FD realization at all times and forwards all the desired signal to the destination for decoding. Also, active SIC, that is, analog/digital cancellation, is not required by the proposed solution, which effectively reduces the circuit complexity and releases high power consumption at the FD relay node. Specific classifications and performance metrics of WPT-aided FD relay systems are summarized. Some future research is also envisaged for WPT-aided FD systems

A Study on High-Efficiency Energy Detection-Based Spectrum Measurements

Statistical information in terms of spectrum occupancy is useful for the efficient and smart dynamic spectrum sharing, and it can be obtained by long-term, broadband, and wide-area spectrum measurements. In this paper, we investigate an energy detection (ED)-based spectrum measurements, in which the noise floor (NF) estimation is a key functionality for the appropriate ED threshold setting. Typically, the NF has the slowly time- varying property and frequency-dependency, and several NF estimation algorithms, including forward consecutive mean excision (FCME) algorithm-based method, have been proposed. However, these methods did not deeply consider the slowly time varying property of the NF and is computationally inefficient. Accordingly, we propose a computational complexity reduction algorithm based on NF level change detection. This algorithm is computationally efficient, since it skips the NF estimation process when the NF does not change. In numerical evaluations, we show the efficiency and the validity of the proposed algorithm

Analytical Study on the Estimation of Primary Activity Distribution Based on Spectrum Sensing

International audienceWe investigate the inside structure of one-dimensional reaction-diffusion traveling fronts. The reaction terms are of the monostable, bistable or ignition types. Assuming that the fronts are made of several components with identical diffusion and growth rates, we analyze the spreading properties of each component. In the monostable case, the fronts are classified as pulled or pushed ones, depending on the propagation speed. We prove that any localized component of a pulled front converges locally to $0$ at large times in the moving frame of the front, while any component of a pushed front converges to a well determined positive proportion of the front in the moving frame. These results give a new and more complete interpretation of the pulled/pushed terminology which extends the previous definitions to the case of general transition waves. In particular, in the bistable and ignition cases, the fronts are proved to be pushed as they share the same inside structure as the pushed monostable critical fronts. Uniform convergence results and precise estimates of the left and right spreading speeds of the components of pulled and pushed fronts are also established

PECAS:a low-cost prototype for the estimation of channel activity statistics in cognitive radio

Abstract Dynamic spectrum access/cognitive radio (DSA/CR) systems can benefit from an accurate knowledge of the primary channel ON/OFF activity statistics. This information can be readily obtained from spectrum sensing observations. However, given the practical limitations of spectrum sensing, an accurate and realistic evaluation of estimation methods is required before they can be implemented in real systems. This paper presents the design and implementation of a low-cost Prototype for the Estimation of Channel Activity Statistics (PECAS) in DSA/CR systems. An in-depth description of the hardware design (based on common and inexpensive components) and software implementation (based on free open source code) is provided along with an illustrative applicability example. The developed platform provides researchers and engineers with a low-cost fully functional tool for proof-of- concept, validation and optimisation of algorithms and designs

Analytical study on the estimation of primary activity distribution based on spectrum sensing

Abstract Cognitive Radio (CR) systems utilize spectrum sensing to decide transmission time in an opportunistic manner. Spectrum sensing can also be used not only to determine the instantaneous on/off state of the channel but also to monitor the statistics of primary user to gain information on occupancy pattern. This knowledge can be exploited in many ways to improve CR systems. In this paper, we propose an analytical model to link the sensing period with the observed spectrum occupancy. Moreover, the effect of spectrum sensing periods on the estimated primary activity pattern is analysed. Simulation results show that the proposed model captures with reasonable accuracy the spectrum occupancy observed at the CR

Investigating the estimation of primary occupancy patterns under imperfect spectrum sensing

Abstract Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, noninterfering manner with the primary network. As a result, CR performance depends on the primary channel occupancy pattern. The occupancy pattern of primary network is affected by multiple factors including time, location and frequency band. This work focuses on the time domain of spectrum sharing. The objective of this work is to study how the primary user activity pattern in the time domain (i.e., statistical distribution of the durations of idle/busy periods) affects the ability of the CR system to obtain accurate statistical information based on spectrum sensing observations. In this research, we model the primary activity pattern as a Continuous-Time Semi-Markov Chain (CTSMC). Different distributions to imitate occupancy patterns of primary network are tested by means of simulation, first when having a perfect spectrum sensing, then in the presence of imperfect spectrum sensing. It is shown that every occupancy pattern (i.e., distribution) actually leads to different levels of accuracy in the estimated statistics. A new algorithm to palliate the degrading effects of spectrum sensing errors is proposed and evaluated. The new and considered algorithms can improve the prediction of primary network statistics, however with different levels of effectiveness depending on the primary activity pattern

Distance estimation based on molecular absorption at THz frequencies

Abstract One of the main approaches for distance estimation is the received signal strength (RSS) based techniques. Their drawbacks include the requirement of accurate knowledge of the transmit power and antenna gains. Also, the traditional RSS-based techniques do not take into account the molecular absorption that occurs at terahertz frequencies. In this paper, we propose a distance estimation method for the line-of-sight case that actually takes advantage of the molecular absorption. The proposed method measures the RSS at two frequencies and does not require known transmit powers and antenna gains, which are assumed to be almost the same at the two frequencies. Therefore, the two frequencies have to be relatively close to each other. The proposed distance estimation method calculates the difference of the received powers (in the dB domain) at the two frequencies and finds the link distance based on it and the humidity level