Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.

Wireless Network Virtualization: A Survey, Some Research Issues and Challenges

This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS’s phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS’s phase control and the transceivers’ transmission control in different network design problems, e.g., rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications.

Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

https://biblio.ugent.be/publication/8585223/file/8585225.pdf

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Cognitive radio (CR) has emerged as a promising technology to exploit the unused portions of spectrum in an opportunistic manner. The fixed spectrum allocation of governmental agencies results in unused portions of spectrum, which are called "spectrum holes" or "white spaces". CR technology overcomes this issue, allowing devices to sense the spectrum for unused portions and use the most suitable ones, according to some pre-defined criteria. Spectrum assignment is a key mechanism that limits the interference between CR devices and licensed users, enabling a more efficient usage of the wireless spectrum. Interference is a key factor that limits the performance in wireless networks. The scope of this work is to give an overview of the problem of spectrum assignment in cognitive radio networks, presenting the state-of-the-art proposals that have appeared in the literature, analyzing the criteria for selecting the most suitable portion of the spectrum and showing the most common approaches and techniques used to solve the spectrum assignment problem. Finally, an analysis of the techniques and approaches is presented, discussing also the open issues for future research in this area.

Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey

Increase in system capacity and data rates can be achieved efficiently in a wireless system by getting the transmitter and receiver closer to each other. Femtocells deployed in the macrocell significantly improve the indoor coverage and provide better user experience. The femtocell base station called as Femtocell Access Point (FAP) is fully user deployed and hence reduces the infrastructure, maintenance and operational cost of the operator while at the same time providing good Quality of Service (QoS) to the end user and high network capacity gains. However, the mass deployment of femtocell faces a number of challenges, among which interference management is of much importance, as the fundamental limits of capacity and achievable data rates mainly depends on the interference faced by the femtocell network. To cope with the technical challenges including interference management faced by the femtocells, researchers have suggested a variety of solutions. These solutions vary depending on the physical layer technology and the specific scenarios considered. Furthermore, the cognitive capabilities, as a functionality of femtocell have also been discussed in this survey. This article summarises the main concepts of femtocells that are covered in literature and the major challenges faced in its large scale deployment. The main challenge of interference management is discussed in detail with its types in femtocells and the solutions proposed over the years to manage interference have been summarised. In addition an overview of the current femtocell standardisation and the future research direction of femtocells have also been provided.

Interference Management in Femtocells

Small cell base stations (SBSs) and multiple antennas are seen as fundamental technologies in the emergence of the next generation [i.e., 5th generation (5G)] of cellular wireless technology. This paper provides a comprehensive survey of literature relating to the applications and challenges associated with using multiple antennas in SBSs. The use of multiple antenna techniques in conventional wireless base stations has undergone much study and is widespread. With heterogeneity in current networks and a furthering of this theme together with greater densification expected in 5G systems, their use in SBSs is at an evolutionary stage. In this paper, unique design challenges associated with size, cost, and performance in SBSs are presented. We present a clear understanding of this increasingly important research area, identifying a clear classification of use and design guidelines. We present a state-of-the-art review of the literature to show how researchers are using and considering the use of multiple antennas in small cells. Attention is given to current generation networks, and with SBSs being a dominant technology necessary for 5G, we also provide insights into the design challenges in such possible future networks.

A Survey of the Challenges, Opportunities and Use of Multiple Antennas in Current and Future 5G Small Cell Base Stations

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for the future healthcare and biomedical applications. It is aimed toward the development of modern smart healthcare systems enabled with P4, i.e., predictive, preventive, personalized, and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in the future applications. Finally, the open challenges, the future directions for researchers in the evolving healthcare domain, are presented.

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.

/pdf/collaborative-spectrum-sensing-optimisation-algorithms-for-3pe72jf6x7.pdf

Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks

Spectrum sensing for cognitive radio is challenging. In this letter, a spectrum sensing method based on quintiles of Order-Statistics is proposed. We derive the test statistic and evaluate the performance of the proposed method by Monte Carlo simulations. Simulation results show that order statistics based sensing considerably outperforms both energy detection and anderson darling based sensing in an Additive White Gaussian Noise (AWGN) channel; especially in a lower signal to noise ratio region.

Kamran Arshad

Papers

Interference Management in Femtocells

A Survey of the Challenges, Opportunities and Use of Multiple Antennas in Current and Future 5G Small Cell Base Stations

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks

Order-Statistic Based Spectrum Sensing for Cognitive Radio