Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications.

Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications

In this survey paper, we characterize the learning problem in cognitive radios (CRs) and state the importance of artificial intelligence in achieving real cognitive communications systems. We review various learning problems that have been studied in the context of CRs classifying them under two main categories: Decision-making and feature classification. Decision-making is responsible for determining policies and decision rules for CRs while feature classification permits identifying and classifying different observation models. The learning algorithms encountered are categorized as either supervised or unsupervised algorithms. We describe in detail several challenging learning issues that arise in cognitive radio networks (CRNs), in particular in non-Markovian environments and decentralized networks, and present possible solution methods to address them. We discuss similarities and differences among the presented algorithms and identify the conditions under which each of the techniques may be applied.

A Survey on Machine-Learning Techniques in Cognitive Radios

Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

https://ir.nctu.edu.tw:443/bitstream/11536/145531/1/3ecec2a2f6e38b0824ccca63f66259e9.pdf

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

Dynamic spectrum policies combined with software defined radio are powerful means to improve the overall spectral efficiency allowing the development of new wireless services and technologies. Medium Access Control (MAC) protocols exploit sensing stimuli to build up a spectrum opportunity map (cognitive sensing). Available resources are scheduled (dynamic spectrum allocation), improving coexistence between users that belong to heterogeneous systems (dynamic spectrum sharing). Furthermore, MAC protocols may allow cognitive users to vacate selected channels when their quality becomes unacceptable (dynamic spectrum mobility). The contribution of this survey is threefold. First, we show the fundamental role of the MAC layer and identify its functionalities in a cognitive radio (CR) network. Second, a classification of cognitive MAC protocols is proposed. Third, advantages, drawbacks, and further design challenges of cognitive MAC protocols are discussed.

A Survey on MAC Strategies for Cognitive Radio Networks

This paper addresses two fundamental and interrelated issues in device-to-device (D2D) enhanced cellular networks. The first issue is how D2D users should access spectrum, and we consider two choices: overlay (orthogonal spectrum between D2D and cellular UEs) and underlay (non-orthogonal). The second issue is how D2D users should choose between communicating directly or via the base station, a choice that depends on distance between the potential D2D transmitter and receiver. We propose a tractable hybrid network model where the positions of mobiles are modeled by random spatial Poisson point process, with which we present a general analytical approach that allows a unified performance evaluation for these questions. Then, we derive analytical rate expressions and apply them to optimize the two D2D spectrum sharing scenarios under a weighted proportional fair utility function. We find that as the proportion of potential D2D mobiles increases, the optimal spectrum partition in the overlay is almost invariant (when D2D mode selection threshold is large) while the optimal spectrum access factor in the underlay decreases. Further, from a coverage perspective, we reveal a tradeoff between the spectrum access factor and the D2D mode selection threshold in the underlay: as more D2D links are allowed (due to a more relaxed mode selection threshold), the network should actually make less spectrum available to them to limit their interference.

Spectrum Sharing for Device-to-Device Communication in Cellular 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.

/pdf/a-survey-on-spectrum-management-in-cognitive-radio-networks-p8ekergqia.pdf

A survey on spectrum management in cognitive radio networks

Rapid progress in research and development of wireless networking and communication technologies have created different types of wireless systems (e.g., Bluetooth, IEEE 802.11, UMTS, and satellite networks). These systems are envisioned to coordinate with each other to provide ubiquitous high-data-rate services to mobile users. In this article, the architecture for ubiquitous mobile communications (AMC) is introduced that integrates these heterogeneous wireless systems. AMC eliminates the need for direct service level agreements among service providers by using a third party, a network interoperating agent. Instead of deploying a totally new infrastructure, AMC extends the existing infrastructure to integrate heterogeneous wireless systems. It uses IP as the interconnection protocol. By using IP as the gluing protocol, transparency to the heterogeneities of the individual systems is achieved in AMC. Third-party-based authentication and billing algorithms are designed for AMC. New mobility management protocols are also developed to support seamless roaming between different wireless systems.

A ubiquitous mobile communication architecture for next-generation heterogeneous wireless systems

Information about a mobile user's velocity is important for efficient resource management and quality of service (QoS) provisioning in next generation (NG) wireless systems. A mobile receiver's velocity spreads the received signal envelope in the frequency domain. This spreading is directly proportional to its velocity. A novel algorithm called VEPSD (velocity estimation using the power spectral density of the received signal envelope) is introduced in this paper that uses the amount of Doppler spread in the received signal envelope to estimate the velocity of a mobile user. The Doppler spread is estimated using the slope of the power spectral density (PSD) of the received signal envelope. The performance of the proposed algorithm is evaluated in both Rayleigh and Rician fading environments. The sensitivity of the estimation error to additive white Gaussian noise (AWGN), the estimation interval (effect of finite sample size), the sampling period, Rice factor (K), and the angle of arrival of the line of sight (LOS) component is analyzed and compared with the level crossing rate (LCR) and co-variance based velocity estimators. Also, it is shown that the proposed algorithm, VEPSD, can be used for velocity estimation under non-isotropic scattering and frequency selective fading and is well suited for NG wireless systems.

S. Mohanty

Papers

A survey on spectrum management in cognitive radio networks

A ubiquitous mobile communication architecture for next-generation heterogeneous wireless systems

An accurate velocity estimation algorithm for resource management in next generation wireless systems