A Survey on Spectral Handoff Mechanisms for the Cognitive Radio Network

In a cognitive radio network, the cognitive radio (CR) devices also called as secondary users (SU's) need to change their operating frequency due to the inclusion of primary user (PU) in that frequency band. Thus when a PU arrives in a frequency band and asks for a channel in that band, it gets that band and occupies the channel which may be occupied by a SU. In this situation, the SU needs to find another channel in a different frequency band which leads to the spectral handoff. Thus in addition to the location based handoffs for the SU, spectral handoff also occur. This spectral handoff may be done several times for the SU. Thus this situation leads to the study of handoff mechanism. This paper carries out a survey of the handoff types and their mechanisms which have been already conceptualized. DOI: 10.17762/ijritcc2321-8169.15012

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

Efficient spectrum-handoff schemes for cognitive radio networks

Radio spectrum access is important for terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations. The services offered by terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations have evolved due to technological advances. They are expected to meet increasing users' demands which will require more spectrum. The increasing demand for high throughput by users necessitates allocating additional spectrum to terrestrial wireless networks. Terrestrial radio astronomy observations s require additional bandwidth to observe more spectral windows. Commercial earth observation requires more spectrum for enhanced transmission of earth observation data. The evolution of terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations leads to the emergence of new interference scenarios. For instance, terrestrial wireless networks pose interference risks to mobile ground stations; while inter-satellite links can interfere with terrestrial radio astronomy observations. Terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations also require mechanisms that will enhance the performance of their users. This thesis proposes a framework that prevents interference between terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations when they co-exist; and enhance the performance of their users. The framework uses the cognitive radio; because it is capable of multi-context operation. In the thesis, two interference avoidance mechanisms are presented. The first mechanism prevents interference between terrestrial radio astronomy observations and inter-satellite links. The second mechanism prevent interference between terrestrial wireless networks and the commercial earth observation ground segment. The first interference reductionmechanism determines the inter-satellite link transmission duration. Analysis shows that interference-free inter-satellite links transmission is achievable during terrestrial radio astronomy observation switching for up to 50.7 seconds. The second mechanism enables the mobile ground station, with a trained neural network, to predict the terrestrial wireless network channel idle state. The prediction of the TWN channel idle state prevents interference between the terrestrial wireless network and the mobile ground station. Simulation shows that incorporating prediction in the mobile ground station enhances uplink throughput by 40.6% and reduces latency by 18.6%. In addition, the thesis also presents mechanisms to enhance the performance of the users in terrestrial wireless network, commercial earth observations and terrestrial radio astronomy observations. The thesis presents mechanisms that enhance user performance in homogeneous and heterogeneous terrestrial wireless networks. Mechanisms that enhance the performance of LTE-Advanced users with learning diversity are also presented. Furthermore, a future commercial earth observation network model that increases the accessible earth climatic data is presented. The performance of terrestrial radio astronomy observation users is enhanced by presenting mechanisms that improve angular resolution, power efficiency and reduce infrastructure costs

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table

Protocolo Cross-Layer Proactivo Basado en Técnicas de Inteligencia Artificial para Handover sin Fisuras en Ambientes Móviles WLAN

En este documento se presenta una nueva propuesta de protocolo predictivo basado en técnicas de inteligencia artificial para pronosticar la siguiente red a conectarse, este marco de referencia está basado en un protocolo de handover Cross-Layer y un pronosticador de siguiente red basado en cinco clasificadores: regresión logística, Bayes ingenuo, máquina de soporte vectorial, arboles de decisión y k vecinos más cercanos, obteniendo hasta un 92 % de exactitud en el pronóstico de red. Basado en este marco de referencia se obtiene un traspaso sin fisuras en ambientes móviles WLAN

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

20 Years of Evolution from Cognitive to Intelligent Communications

It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AI-enabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.Comment: The paper has been accepted by IEEE Transactions on Cognitive Communications and Networkin