Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

A Survey on Spectrum Management in Cognitive Radio Networks

Cognitive radio networks will provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques. However, CR networks impose challenges due to the fluctuating nature of the available spectrum, as well as the diverse QoS requirements of various applications. Spectrum management functions can address these challenges for the realization of this new network paradigm. To provide a better understanding of CR networks, this article presents recent developments and open research issues in spectrum management in CR networks. More specifically, the discussion is focused on the development of CR networks that require no modification of existing networks. First, a brief overview of cognitive radio and the CR network architecture is provided. Then four main challenges of spectrum management are discussed: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility

ETSI reconfigurable radio systems: status and future directions on software defined radio and cognitive radio standards

This article details the current work status of the ETSI Reconfigurable Radio Systems Technical Committee, positions the ETSI work with respect to other standards efforts (IEEE 802, IEEE SCC41) as well as the European Regulatory Framework, and gives an outlook on the future evolution. In particular, software defined radio related study results are presented with a focus on SDR architectures for mobile devices such as mobile phones. For MDs, a novel architecture and inherent interfaces are presented enabling the usage of SDR principles in a mass market context. Cognitive radio principles within ETSI RRS are concentrated on two topics, a cognitive pilot channel proposal and a Functional Architecture for Management and control of reconfigurable radio systems, including dynamic self-organizing planning and management, dynamic spectrum management, joint radio resource management. Finally, study results are indicated that are targeting a SDR/CR security framework.Postprint (published version

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Spectrum Sensing Techniques for Cognitive Radio Sensor Networks (CRSN)

Cognitive radio sensor network (CRSN) is a recently emerging paradigm that aims to utilize the unique features provided by CR concept to incorporate additional capabilities to Wireless Sensor Network (WSN). A CRSN is a distributed network of wireless cognitive radio sensor nodes, which perform sensing operation on event signals and collaboratively communicate their readings over dynamically available spectrum bands in a multi-hop manner ultimately to satisfy the application-specific requirements. The realization of CRSN depends on addressing many difficult challenges, posed by the unique characteristics of both cognitive radio and sensor networks, and further amplified by their union. Spectrum sensing technique plays an important role in the design of a CRSN. The first phase of this thesis work is concentrated in identifying the suitable spectrum sensing strategy for a CRSN by analysing different spectrum sensing strategies and comparing together. The second phase involves a search for an optimum spectrum sensing scheme suitable for the resource constrained nature of CRSN by combining two or more sensing schemes together i.e. Hybrid Spectrum Sensing. The thesis concludes with a remark that hybrid spectrum sensing schemes are the most appropriate sensing schemes for CRSN under its unique constraints

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

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

ETSI Reconfigurable Radio Systems – Status and Future Directions on Software Defined Radio and Cognitive Radio Standards

This article details the current work status of the ETSI Reconfigurable Radio Systems Technical Committee, positions the ETSI work with respect to other standards efforts (IEEE 802, IEEE SCC41) as well as the European Regulatory Framework, and gives an outlook on the future evolution. In particular, software defined radio related study results are presented with a focus on SDR architectures for mobile devices such as mobile phones. For MDs, a novel architecture and inherent interfaces are presented enabling the usage of SDR principles in a mass market context. Cognitive radio principles within ETSI RRS are concentrated on two topics, a cognitive pilot channel proposal and a Functional Architecture for Management and control of reconfigurable radio systems, including dynamic self-organizing planning and management, dynamic spectrum management, joint radio resource management. Finally, study results are indicated that are targeting a SDR/CR security framework