53 research outputs found
Machine Learning in Communication Systems and Networks
No full textRecent advances in machine learning, coupled with the availability of powerful computing platforms, have garnered significant attention from academic, research, and industry communities. Machine learning is considered a promising tool to tackle the challenge posed by increasingly complex, heterogeneous, and dynamic communication environments. It holds the potential to contribute to the intelligent management and optimization of communication systems and networks by enabling us to predict changes, find patterns of uncertainties in the communication environment, and make data-driven decisions. This Topic seeks to explore the intersection of machine learning and communication research, showcasing a compilation of cutting-edge contributions which underscore the transformative potential of machine learning as a driving force behind adaptive and intelligent communication
Optimal Cooperative Spectrum Sensing for Cognitive Radio
No full textThe rapid increasing interest in wireless communication has led to the continuous development of wireless devices and technologies. The modern convergence and interoperability of wireless technologies has further increased the amount of services that can be provided, leading to the substantial demand for access to the radio frequency spectrum in an efficient manner. Cognitive radio (CR) an innovative concept of reusing licensed spectrum in an opportunistic manner promises to overcome the evident spectrum underutilization caused by the inflexible spectrum allocation. Spectrum sensing in an unswerving and proficient manner is essential to CR. Cooperation amongst spectrum sensing devices are vital when CR systems are experiencing deep shadowing and in a fading environment. In this thesis, cooperative spectrum sensing (CSS) schemes have been designed to optimize detection performance in an efficient and implementable manner taking into consideration: diversity performance, detection accuracy, low complexity, and reporting channel bandwidth reduction. The thesis first investigates state of the art spectrums sensing algorithms in CR. Comparative analysis and simulation results highlights the different pros, cons and performance criteria of a practical CSS scheme leading to the problem formulation of the thesis. Motivated by the problem of diversity performance in a CR network, the thesis then focuses on designing a novel relay based CSS architecture for CR. A major cooperative transmission protocol with low complexity and overhead - Amplify and Forward (AF) cooperative protocol and an improved double energy detection scheme in a single relay and multiple cognitive relay networks are designed. Simulation results demonstrated that the developed algorithm is capable of reducing the error of missed detection and improving detection probability of a primary user (PU). To improve spectrum sensing reliability while increasing agility, a CSS scheme based on evidence theory is next considered in this thesis. This focuses on a data fusion combination rule. The combination of conflicting evidences from secondary users (SUs) with the classical Dempster Shafter (DS) theory rule may produce counter-intuitive results when combining SUs sensing data leading to poor CSS performance. In order to overcome and minimise the effect of the counter-intuitive results, and to enhance performance of the CSS system, a novel state of the art evidence based decision fusion scheme is developed. The proposed approach is based on the credibility of evidence and a dissociability degree measure of the SUs sensing data evidence. Simulation results illustrate the proposed scheme improves detection performance and reduces error probability when compared to other related evidence based schemes under robust practcial scenarios. Finally, motivated by the need for a low complexity and minmum bandwidth reporting channels which can be significant in high data rate applications, novel CSS quantization schemes are proposed. Quantization methods are considered for a maximum likelihood estimation (MLE) and an evidence based CSS scheme. For the MLE based CSS, a novel uniform and optimal output entropy quantization scheme is proposed to provide fewer overhead complexities and improved throughput. While for the Evidence based CSS scheme, a scheme that quantizes the basic probability Assignment (BPA) data at each SU before being sent to the FC is designed. The proposed scheme takes into consideration the characteristics of the hypothesis distribution under diverse signal-to-noise ratio (SNR) of the PU signal based on the optimal output entropy. Simulation results demonstrate that the proposed quantization CSS scheme improves sensing performance with minimum number of quantized bits when compared to other related approaches
Efficient evidence‐based decision fusion scheme for cooperative spectrum sensing in cognitive radio networks
No full textIn this article, an evidence based decision fusion cooperative spectrum sensing (CSS) schemes has been considered for overcoming the hidden terminal problem, improving reliability, and increasing SU agility. Under practical conditions, the combination of conflicting evidences with the classical Dempster‐Shafer theory (DS theory) rule may produce counter‐intuitive results when combining the secondary users (SUs) sensing data evidence leading to poor CSS performance. In order to overcome and minimize the effect of conflicting data, and to enhance performance of the CSS system, a novel efficient evidence‐based decision fusion scheme CSS is proposed. The approach is based on the credibility of evidence from the SUs sensing decision, which represents the similarity or the relation among the different SUs sensing data evidence, and a dissociability degree measure that indicates the quality or clarity of the SUs sensing data evidence. Furthermore, a weighted averaging factor determined by the credibility and dissociability of the SU sensing data evidence is proposed. Simulation results presented show that under practical conditions the proposed scheme enhances the performance of the CSS system when compared to traditional fusion rules that do not take into account the difference in local sensing reliability between the SUs
Robust Statistics Evidence Based Secure Cooperative Spectrum Sensing for Cognitive Radio Networks
No full textCognitive radio networks (CRNs), an assemble of smart schemes intended for permitting secondary users (SUs) to opportunistically access spectral bands vacant by primary user (PU), has been deliberated as a solution to improve spectrum utilization. Cooperative spectrum sensing (CSS) is a vital technology of CRN systems used to enhance the PU detection performance by exploiting SUs' spatial diversity, however CSS leads to spectrum sensing data falsification (SSDF), a new security threat in CR system. The SSDF by malicious users can lead to a decrease in CSS performance. In this work, we propose a CSS scheme in which the presence and absence hypotheses distribution of PU signal is estimated based on past sensing received energy data incorporating robust statistics, and the data fusion are performed according to an evidence based approach. Simulation results show that the proposed scheme can achieve a significant malicious user reduction due to theabnormality of the distribution of malicious users compared with that of other legitimate users. Furthermore, the performance of our data fusion scheme is improved by supplemented nodes' credibility weight
LTE RSRP, RSRQ, RSSNR and local topography profile data for RF propagation planning and network optimization in an urban propagation environment
No full textIn the design of 5 G cellular communication to guarantee quality signal reception at every point within a coverage area, fundamental knowledge of the channel propagation characteristics is vital. A correct knowledge of electromagnetic wave propagation is required for efficient radio network planning and optimization. Propagation data are used extensively in network planning, particularly for conducting feasibility studies. Hence, measurement of accurate propagation models that predict how the channel varies as people move about is crucial. However, these measured data are often not widely available for channel characterization and propagation model development. In this data article, the Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) and Reference Signal Signal to Noise Ratio (RSSNR) at various points in space which is covered by a Long-Term Evolution (LTE) marco base station operating at 2100 MHz located in Hatfield, Hertfordshire, United Kingdom were measured. Further, local topography profile data of the study area were extracted from a digital elevation model (DEM) to account for the features of the propagation environment. Correlation matrix and descriptive statistics of the measured LTE data along different routes are analyzed. The RSRP, RSRQ and RSSNR variation with transmitter (Tx) – receiver (Rx) separation distance along the routes are presented. The probability distribution and the DEM of LTE data measurement are likewise presented. The data provided in this article will facilitate research advancement in wireless channel characterization that accounts for local topography features in an urban propagation environment. Moreover, the data sets provided in this article can be extended using simulation-based analysis to extract spatial and temporal channel model parameters in urban cellular environments in the development of 5 G channel propagation models. Keywords: LTE, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Reference Signal Signal to Noise Ratio (RSSNR), RF propagation planning, RF network optimizatio
A Stochastic based Physical Layer Security in Cognitive Radio Networks: Cognitive Relay to Fusion Center
No full textCognitive radio networks (CRNs) are found to be, without difficulty wide-open to external malicious threats. Secure communication is an important prerequisite for forthcoming fifth-generation (5G) systems, and CRs are not exempt. A framework for developing the accomplishable benefits of physical layer security (PLS) in an amplify-andforward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN the spectrum sensing data from secondary users (SU) are collected by a fusion center (FC) with the assistance of access points (AP) as cognitive relays, and when malicious eavesdropping SU are listening. In this paper we focus on the secure transmission of active APs relaying their spectrum sensing data to the FC. Closed expressions for the average secrecy rate are presented. Analytical formulations and results substantiate our analysis and demonstrate that multiple antennas at the APs is capable of improving the security of an AF-CSSCRN. The obtained numerical results also show that increasing the number of FCs, leads to an increase in the secrecy rate between the AP and its correlated FC
A Stochastic Method to Physical Layer Security of an Amplify-and-Forward Spectrum Sensing in Cognitive Radio Networks: Secondary User to Relay
No full textIn this paper, a framework for capitalizing on the potential benefits of physical layer security in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN network the sensing data from secondary users (SUs) are collected by a fusion center (FC) with the help of access points (AP) as relays, and when malicious eavesdropping secondary users (SUs) are listening. We focus on the secure transmission of active SUs transmitting their sensing data to the AP. Closed expressions for the average secrecy rate are presented. Numerical results corroborate our analysis and show that multiple antennas at the APs can enhance the security of the AF-CSS-CRN. The obtained numerical results show that average secrecy rate between the AP and its correlated FC decreases when the number of AP is increased. Nevertheless, we find that an increase in the number of AP initially increases the overall average secrecy rate, with a perilous value at which the overall average secrecy rate then decreases. While increasing the number of active SUs, there is a decrease in the secrecy rate between the sensor and its correlated AP
Information-seeking behavior of social sciences scholars: a nigerian case study
No full textInformation-seeking behavior is one of the most important areas of user studies and a concept affected by many factors. Previous researches in these areas indicate that the information-seeking practices of scholars are dependent on their field of research, and vary from one discipline to another. This paper examines the information-seeking behavior of scholars in the social sciences, based on the premise that information-seeking behavior follows universally applicable stages and patterns worldwide. The study was conducted at the Nigerian Institute of Social and Economic Research (NISER). Fifty eight active social sciences scholars were interviewed via a questionnaire about their information sources for research and consultancy purposes, their preference for electronic or printed formats, their use of electronic or Internet resources, and how they meet or satisfy their information needs, among others. Results show that journals and books were the most preferred information sources, and a large majority of scholars “regularly” used electronic information resources for their research and consultancy needs. The findings of the study also demonstrate diverse usage patterns for electronic information resources among users of different academic ranks and age range. Based on the research findings, the author provides suggestions on how current information services and products can be improved to better serve the users. The author also makes recommendations for improving library services and technologies to better meet the information needs of social sciences scholars in general
A novel resonant ZVS power converter with self‐driven synchronous rectifier for low‐voltage high‐current applications
No full textAbstract This paper presents a novel isolated resonant zero‐voltage switching converter with a self‐driven synchronous rectifier for low‐voltage high‐current applications. The active resonant tank comprises of a transformer leakage inductance, a capacitor and a diode‐connected MOSFET which provides zero‐voltage switching conditions for all switches. Due to the use of leakage inductance of the transformer in both the primary and secondary sides, the resonant tank and the output section require no external inductor, resulting in a major size reduction of the circuit. The proposed converter has the advantages of high efficiency; low switching, conducting, and thermal losses; high switching frequency range and isolation; and small size. To verify the proposed converter, a laboratory prototype was manufactured with satisfactory performance. The practical results show that the power efficiencies of the converter including self‐driven synchronous rectifier for the light load and the full load are 91.9% and 94.95% at output power Pout = 20 W and Pout = 100 W, respectively
An Isolated ZVS DC/DC Converter with Diode-connected MOSFET in Rectifier
No full textThis paper presents an isolated resonant zero voltage switching ZVS converter with a diode-connected MOSFET in a rectifier. The active resonant network is the composition of a resonant capacitor, a transformer leakage inductance and a diode-connected MOSFET. The output capacitor of the main switches together with their reverse recovery diodes provide zero-voltage switching condition for all switches. The input voltage/current of the proposed circuit is 0.35V/500uA while the output voltage/current is 1.5V/75uA. The simulated circuit in PSIM is presented to verify the proposed converter performance
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