This paper presents a 5G-enabled private network solution designed to support industrial digital transformation within the aerospace sector. Building upon prior research that identified 5G adoption challenges in the Ayrshire region of Scotland, this work introduces a practical use case demonstrating an integrated 5G and edge computing system for autonomous aerial surveillance. The proposed system employs a 5G private network with network slicing to guarantee ultra-reliable low-latency communication (URLLC) even under network congestion. A fleet of 5G-enabled Unmanned Aerial Vehicles (UAVs) form a resilient mesh network using the Better Approach To Mobile Adhoc Networking (BATMAN) protocol to maintain continuous connectivity. At the network edge, a Human Detector AI module performs real-time intruder detection using a custom deep learning model, UWS-YOLO. Experimental evaluations demonstrate that network slicing ensures service continuity for mission-critical applications, highlighting the potential of 5G MPNs as a key enabler of digital transformation for the aerospace sector and the wider industrial landscape.

Multi-Resident activity recognition refers to the task of recognizing activities performed by multiple individuals living in the same residence. It involves using sensors or other monitoring devices to capture data about the activities taking place in the living space, and then using Machine Learning (ML) or Deep Learning (DL) algorithms to analyze and classify these activities. Federated Learning (FL) is a technique that enables multiple devices to collaboratively train a model without sharing their data with each other, while Genetic Mutation (GM) is a technique used in evolutionary algorithms to introduce random changes to the genetic code of individuals in a population. Our proposed framework involves the use FL and GM for Human Activity Recognition (HAR). The approach was evaluated on the ARAS dataset, collected from two houses with different activity patterns. Two Recurrent Neural Network (RNN) models, Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM), were employed for the activity classification task and a genetic mutation operator was applied to the weights of the models before federated averaging. The results indicate that FL is suitable for privacy preserving activity recognition, it can help with early deployment and even improve the performance of the models in some cases.

Free space optical (FSO) communication offers an excellent opportunity to develop energy-efficient, secure, and ultrafast data links for 5G and beyond applications, including heterogeneous networks with massive connectivity and wireless backhauls for cellular systems. However, the effect of an optical beam's pointing inaccuracy combined with the impact of climate factors must be considered in the FSO communication system. In this paper, we first evaluate the performance reliability and availability of NRZ-based mode division multiplexing (MDM)-FSO backhaul. In particular, a single wavelength laser is used to transmit four different optical beams, using four different wavelengths. It also explores and classifies four beams used for capacity enhancement in mode division multiplexed MDM-FSO backhaul links. Several Machine Learning (ML) models are used to classify the four optical modes. Results indicate successful transmission of 80 Gbps. Furthermore, the primary findings indicate that the ML model exhibits an impressive accuracy rate of approximately 97% in classifying four distinct beams.

Physiological health indicators can provide valuable insights into the general health and well-being of a person. However, acquiring these indicators implies being physically connected to a medical device or using wearable sensors. Moreover, the aforementioned devices only measure the indicators but provide no information on what influences them. This study proposes an approach for estimating such indicators from ambient data, enabling simultaneously non-invasive monitoring and providing details on how the environment affects one’s health. A system based on Internet of Things (IoT) sensors is used for data collection and Machine Learning (ML) algorithms are employed for data analysis. The study focused on two health signals, Heart Rate (HR) and Skin Resistance (SR). Out of the three tested algorithms, Random Forest (RF) yielded the best results in terms of Mean Absolute Error (MAE) for both indicators. The results obtained proved that physiological signals estimation exclusively from ambient data is possible and identified which environmental factors are most important.

The exponential growth of traffic across the mobile networks called for exploitation of new spectrum bands of 5G networks; whose deployment still rely on support from underlying 4G long term evolution (LTE) networks in both stand-alone (SA) and non-stand-alone (NSA) architectures. This scenario poses challenges on the choice of Radio Access Technology (RAT) selection between 4G LTE and 5G new radio (NR) networks to these ever increasing mobile users with respect to their geographical location, mobility and network coverage. Hence, this study investigates joint user requirements and network constraints for appropriate RAT selection between 4G LTE and 5G NR by recording live radio measurements over a distance of 300 meters between a pedestrian user and 5G NSA base-station. The problem (RAT selection) was formulated as a classification process, hence implemented with classification machine learning (ML) algorithms: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost) to select an appropriate RAT. Evaluation of results with standard classification metrics, show measure of accuracy of algorithms: DT at 91.82%, RF at 87.64%, XTREE at 86.75%, GB at 91.86%, and XGBoost at 93.86%, where XGBoost showed highest performance value, therefore proposed as ML model for RAT selection to achieve effective and efficient resource allocation.

Intelligent vehicular communication is fundamental to manage vehicle-to-grid (V2G) interaction, where electric vehicles (EVs) provide energy to balance demand of critical loads (CLs). We propose resource efficiency (RE) to exploit the tradeoff between spectral efficiency (SE) and cost efficiency (CE) of EVs in a V2G communication network. The CE is the data rate of the V2G channel between EVs and base station (BS) over the operating cost of EVs to supply energy to CLs. We consider maximizing the RE in the downlink of a V2G communication network, where EVs are served by a BS and associated with CLs, while satisfying energy demand and charging station constraints. As the proposed RE problem is inherently non-convex and known to be NP-hard, we develop a suboptimal scheme based on a two-phase algorithm. Phase 1 derives optimum EV-CL association using a heuristic approach, while phase 2 finds optimum power allocation using geometric programming. We then derive upper and lower bounds to the optimal RE as a benchmark to study the performance gap of the suboptimal scheme. Simulation results demonstrate that the proposed suboptimal scheme is close to the optimal solution, while its complexity is relatively low, making it promising for V2G applications.

This special issue (SI) aims to present recent advances in the design and analysis of communication interfaces for Industry 4.0. The Industry 4.0 paradigm aims to integrate advanced manufacturing techniques with Industrial Internet-of-Things (IIoT) to create an agile digital manufacturing ecosystem. The main goal is to instrument production processes by embedding sensors, actuators and other control devices which autonomously communicate with each other throughout the value-chain[1].

Ultrareliable and low-latency communications (uRLLC) is one of the most significant requirements for future wireless networks. The conventional terrestrial base stations cannot always provide the required uRLLC for emerging applications and scenarios, e.g., Tactile Internet services or when a large number of users get connected during an event. Therefore, multilayer airborne networks with low/medium/high altitude platforms can be deployed as an effective solution to offer capacity and coverage along with required latency and reliability for wireless networks. In this article, we propose three layers of the airborne network to support the uRLLC requirement in wireless networks. Optimized link selection schemes have been provided based on polychromatic sets (PSets) theory to focus on the uRLLC. With the optimized link selection algorithm, multiple properties of the airborne platforms are exploited, and the links are selected based on the multiconstrained requirements to support the desired performance of the airborne network. Moreover, two links distribution schemes have been proposed as distributed greedy scheme and centralized greedy scheme to demonstrate the deployment of the proposed airborne network. Numerical results show that both PSets-based links distribution schemes outperform the general distribution schemes on average latency and overall reliability also known as the unassociated ratio, which strongly supports the uRLLC in considered airborne networks.

The smart learning environment is a technology-supported learning environment that is enriched with digital resources, context-aware and adaptive devices, and can provide appropriate support to meet the learning style and abilities of diverse students to promote better and enhances learning process in higher education. It is a student-centered learning environment that has the capacity to engage students and offers an effective learning process. It is characterized by the ability to provide interactions between students and facilitators, and personalized and inclusive learning experiences to anyone, anytime and anywhere using smart mobile devices. However, despite the increasing use of smart learning environment in higher education, there is no well-defined model with a set of educational requirements for developing and evaluating self-regulated smart learning environment which considers both instructional and evaluation design. Therefore, this article explores instructional design and learning evaluation process to propose a new set of educational requirements referred to as smart learning environment pedagogical and educational requirements model (SLE-PERM) for developing and evaluating self-regulated smart learning environment. Next, the article presents the application of the educational requirements on three common learning management system (LMS) namely: Moodle, Blackboard and Schoology which can be used to evaluate the suitability of LMS to guide stakeholders on the critical issues that require pedagogical and educational requirements for developing a self-regulated smart learning environment in higher education.

In this study, the authors present and evaluate the performance of two mode selection schemes for device‐to‐device (D2D) enabled unmanned aerial vehicle‐based wireless networks. The proposed schemes are based on a threshold received signal strength and an average threshold D2D distance to select the D2D mode. The focus of the two schemes is either to enhance the quality of the signal or the connectivity in case of emergency situations. To evaluate the performances of the schemes, they derive the corresponding expressions of the probability of using D2D mode and ergodic capacity. Numerical results show the advantage of the presented schemes in off‐loading traffic from aerial platforms and shed lights on the effect of the environment on the performance of D2D enabled aerial networks.

Networked flying platforms can be used to provide cellular coverage and capacity. Given that 5G and beyond networks are expected to be always available and highly reliable, resilience and reliability of these networks must be investigated. This paper introduces the specific features of airborne networks that influence their resilience. We then discuss how machine learning and blockchain technologies can enhance the resilience of networked flying platforms.

The potential of telemedicine in respiratory health care has not been completely unveiled in part due to the inexistence of reliable objective measurements of symptoms such as cough. Currently available cough detectors are uncomfortable and expensive at a time when generic smartphones can perform this task. However, two major challenges preclude smartphone-based cough detectors from effective deployment namely, the need to deal with noisy environments and computational cost. This paper focuses on the latter, since complex machine learning algorithms are too slow for real-time use and kill the battery in a few hours unless specific actions are taken. In this paper, we present a robust and efficient implementation of a smartphone-based cough detector. The audio signal acquired from the device's microphone is processed by computing local Hu moments as a robust feature set in the presence of background noise. We previously demonstrated that pairing Hu moments and a standard k-NN classifier achieved accurate cough detection at the expense of computation time. To speed-up k-NN search, many tree structures have been proposed. Our cough detector uses an improved vantage point (vp)-tree with optimized construction methods and a distance function that results in faster searches. We achieve 18× speed-up over classic vp-trees, and 560× over standard implementations of k-NN in state-of-the-art machine learning libraries, with classification accuracies over 93%, enabling real-time performance on low-end smartphones.

https://doi.org/10.1155/2018/1907506

The densification of small cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small cell base stations. This involves efficient association and placement of the backhaul hubs that connects these small-cells with the core network. Terrestrial hubs suffer from an inefficient non line of sight link limitations and unavailability of a proper infrastructure in an urban area. Realizing the popularity of flying platforms, we employ here an idea of using networked flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned balloons flying at different altitudes, as aerial backhaul hubs. The association problem of these NFP-hubs and small- cell base stations is formulated considering backhaul link and NFP related limitations such as maximum number of supported links and bandwidth. We then present an efficient and distributed solution of the designed problem, which performs a greedy search in order to maximize the sum rate of the overall network. A favorable performance is observed via a numerical comparison of our proposed method with optimal exhaustive search algorithm in terms of sum rate and run-time speed.

5G+ systems expect enhancement in data rate and coverage area under limited power constraint. Such requirements can be fulfilled by the densification of small cells (SCs). However, a major challenge is the management of fronthaul links connected to an ultra dense network of SCs. A cost effective and scalable idea of using network flying platforms (NFPs) is employed here, where the NFPs are used as fronthaul hubs that connect the SCs to the core network. The association problem of NFPs and SCs is formulated considering a number of practical constraints such as backhaul data rate limit, maximum supported links and bandwidth by NFPs and quality of service requirement of the system. The network centric case of the system is considered that aims to maximize the number of associated SCs without any biasing, i.e., no preference for high priority SCs. Then, two new efficient greedy algorithms are designed to solve the presented association problem. Numerical results show a favorable performance of our proposed methods in comparison to exhaustive search.

Network Flying Platforms (NFPs) such as unmanned aerial vehicles, unmanned balloons or drones flying at low/medium/high altitude can be employed to enhance network coverage and capacity by deploying a swarm of flying platforms that implement novel radio resource management techniques. In this paper, we propose a novel layered architecture where NFPs, of various types and flying at low/medium/high layers in a swarm of flying platforms, are considered as an integrated part of the future cellular networks to inject additional capacity and expand the coverage for exceptional scenarios (sports events, concerts, etc.) and hard-to-reach areas (rural or sparsely populated areas). Successful roll-out of the proposed architecture depends on several factors including, but are not limited to: network optimisation for NFP placement and association, safety operations of NFP for network/equipment security, and reliability for NFP transport and control/signaling mechanisms. In this work, we formulate the optimum placement of NFP at a Lower Layer (LL) by exploiting the airborne Self-organising Network (SON) features. Our initial simulations show the NFP- LL can serve more User Equipment (UE)s using this placement technique.

Spreading across many parts of the world and presently hard striking California, extended droughts could even potentially threaten reliable electricity production and local water supplies, both of which are critical for data center operation. While numerous efforts have been dedicated to reducing data centers' energy consumption, the enormity of data centers' water footprints is largely neglected and, if still left unchecked, may handicap service availability during droughts. In this paper, we propose a water-aware workload management algorithm, called WATCH (WATer-constrained workload sCHeduling in data centers), which caps data centers' long-term water consumption by exploiting spatio-temporal diversities of water efficiency and dynamically dispatching workloads among distributed data centers. We demonstrate the effectiveness of WATCH both analytically and empirically using simulations: based on only online information, WATCH can result in a provably-low operational cost while successfully capping water consumption under a desired level. Our results also show that WATCH can cut water consumption by 20 percent while only incurring a negligible cost increase even compared to state-of-the-art cost-minimizing but water-oblivious solution. Sensitivity studies are conducted to validate WATCH under various settings.

In this paper, a novel devices-to-device (Ds2D) communication paradigm is proposed to enable green (energy efficient) wireless networks. Different from the conventional D2D communications, the sink device establishes simultaneous associations with multiple source devices for file download using its multiple radio interfaces and the multi-homing technique. In such a networking setting, we propose a network-controlled algorithm for optimal selection of source devices and their respective radio interfaces to support green Ds2D communications. Simulation results demonstrate that the proposed Ds2D communication paradigm under optimal selection of source devices and radio interfaces presents an improved energy efficiency performance compared with the conventional D2D communications, and leads to a lower energy consumption per source device.

The increasing demand for any-time any-where wireless connectivity has posed a formidable ‘1000 × data challenge’ for service providers. With the envisioned 1000 × explosion in mobile data traffic by the end of year 2020, wireless network architecture needs to rapidly evolve. In particular, the evolution trajectory should be charted such that exponential gains can be realised in network wide resource efficiency. This requires a clean slate design for future 5G wireless networks while provisioning interoperability with the legacy deployment. Both operators and technology providers realise that 5G will not merely be a newer version of 4G simply provisioning faster data transfers. These 5G networks are expected to be more dynamic due to heterogeneity in terms of devices, technologies, spectral bands and deployment models. Heterogeneity is indeed the intrinsic and central feature of the evolving networking paradigm. Now several potential solutions have recently been proposed to meet the aforementioned challenges and all address both network architecture and technologies. On the architectural front, concepts such as (i) cloudification & softwarisation of radio access networks; (ii) split-plane deployment; (iii) licensed shared access; (iv) decoupled uplink and downlink transmissions; and (v) information/content centric networking are all being considered as the enabling candidates. In terms of new technologies: (i) mmWave communications; (ii) massive MIMO; (iii) D2D communications; (iv) small cell deployment; and (v) low power IoT communication technologies (such as Bluetooth Low Energy, 802.11.ah WiFi, LoRA, SIGFOX) are all vital design tools for future 5G HetNets. In addition, the so-called concept of ‘tactile internet’, which has a wide spectrum of requirements ranging from ultra-low latency to ultra-high throughput via deployment of HetNets, cannot be realised without significant advances in signal processing algorithms. Thus, the main objective of this Special Section is to provide a platform for the dissemination of important results in those signal processing techniques necessary for enabling large scale, heterogeneous, 5G wireless networks. The first part of this Special Section presents four contributions. In the first paper, Mumtaz et al. present an energy efficient algorithm for D2D users in the presence of other cellular users (CUs). The authors employ Lagrangian duality theory for optimising both the power and rate of the D2D users while guaranteeing an acceptable quality-of-service (QoS) for the CUs. Finally, the solution of the proposed algorithm is then employed to achieve proportional fairness between the D2D and the CU users. The second paper (Butt et al.) reflects a growing interest in the area of green communication. It has recently been accepted that opportunistic exploitation of ambient energy sources is going to be the cornerstone of future wireless networks. The authors discuss relay selection schemes with the objective of minimising outage probability for a network consisting of a single source, multiple relays and a single destination. The relays are powered by radio frequency (RF) signals from the source and the authors present an optimal relay selection strategy to minimise outage probability for the system. Finally, a numerical solution is developed to determine the optimal number of relays. In the third paper, Gurjar et al. examine the significance of wireless channel estimation error on the performance of an analogue network coding (ANC)-based MIMO two-way relay system employing zero-forcing (ZF) transceivers in a Rayleigh fading environment. An analytical framework has been developed to study the overall outage analysis and some interesting (exact) expressions have been derived for special cases such as when the relay is equipped with less than two antennas. Some of the important contributions of this work are: a) exact expressions for the overall outage probability and the ergodic sum-rate have been derived within the context of channel estimation error; b) the authors have shown that system diversity may reduce to zero in the presence of channel estimation error due to imperfect self-interference cancellation; c) they conclude that a low complexity solution can be further derived by exploiting channel estimation error with ZF transmission/reception for an ANC based MIMO two-way relay system. In the final paper, Li et al. propose a hierarchical precoding approach for multi-cell, multi-user systems with any number of base stations and users, which is suitable for any number of data streams. The key feature of this approach is to align the inter-user interferences within the same cell to the room spanned by the inter-cell interferences, by which both the inter-cell and inter-user interferences are cancelled simultaneously. The effectiveness of this proposed method is demonstrated with an extensive set of simulations. In summary, this Special Section presents some important recent advances in D2D and relay assisted communication networks with a special focus on energy efficiency. Moreover, some of the state-of-the-art methods in multiuser MIMO systems have also been studied. For those interested in future 5G wireless networks, these articles will serve as a good springboard to appreciate further developments in this important topic. Finally, we would like to thank (i) all the submitting authors for considering this Special Section as a potential journal in which to publicise their research work; (ii) the reviewers for their high quality evaluations; and (iii) the Editorial team of the IET Signal Processing journal for their professional support. Syed Ali Raza Zaidi is currently University Academic Fellow (Assistant Professor) at the University of Leeds, UK. Prior to this, he was a Research Fellow in SPCOM Research Group at Leeds. He received his B. Eng. degree in information and communication system engineering from the School of Electronics and Electrical Engineering, NUST, Pakistan in 2008. He was awarded the NUST's most prestigious Rector's gold medal for his final year project. From September 2007 till August 2008, he served as a Research Assistant in Wireless Sensor Network Lab on a collaborative research project between NUST, Pakistan and Ajou University, South Korea. In 2008, he was awarded overseas research student scholarship along with Tetley Lupton and Excellence Scholarships to pursue his PhD at the School of Electronics and Electrical Engineering, the University of Leeds, U.K. He was also awarded with COST IC0902, DAAD and Royal Academy of Engineering grants to promote his research. In 2013, he was conferred with the prestigious F.W. Carter Prize for outstanding Doctoral thesis by the University of Leeds. Dr. Ali was a visiting Research Scientist at Qatar Innovations and Mobility Centre from October to December 2013. He has served as an invited reviewer for IEEE flagship journals and conferences. Dr. Ali is also UK Liaison for the European Association for Signal Processing (EURASIP). He is currently serving as an editor for IEEE Communication Letters and Lead Guest Editor for IET Signal Processing Special Section on 5G Wireless Networks. He is also the general secretary for IEEE Technical Committee on 5G Networks. He has published more than 60 papers in leading IEEE journals and conferences and has chaired several IEEE workshops/conferences. His current research interests are in the area of design and implementation of large scale networks for machine-to-machine communication (including robotics and autonomous systems). Des McLernon received his B.Sc in electronic and electrical engineering and his MSc in electronics, both from the Queen's University of Belfast, N. Ireland. He then worked in industry on radar systems research and development with Ferranti Ltd in Edinburgh, Scotland and later joined Imperial College, University of London, where he took his PhD in signal processing. After first lecturing at South Bank University, London, UK, he moved to the School of Electronic and Electrical Engineering, at the University of Leeds, UK, where he is a Reader in Signal Processing. His research interests are broadly within the domain of signal processing for wireless communications (in which area he has published over 285 journal and conference papers). He has supervised over 35 PhD students, given many invited talks in the UK and abroad and is Associate Editor of the IET Signal Processing journal. He has been a member of various international conference TPC's and conference organisation committees - recent conference organisation includes IEEE SPAWC 2010, European Signal Processing Conference (EUSIPCO) 2013, IET Conference on Intelligent Signal Processing (London, 2013/2015) and IEEE Globecom 2014/2015 (2nd /3rd Workshops on Trusted Communications with Physical Layer Security). His current research projects include distributed sensing, PHY layer security, caching and energy efficiency in heterogeneous networks, energy harvesting, robotic and drone communications, intrusion detection in software defined networks, compressive sensing and time-frequency analysis. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in Communications in the Institute for Communication Systems (ICS - formerly known as CCSR) at the University of Surrey, UK and an adjunct Associate Professor at the University of Oklahoma, USA. He has lead a number of multimillion-funded international research projects encompassing the areas of energy efficiency, fundamental performance limits, sensor networks and self-organising cellular networks. He is also leading the new physical layer work area for 5G innovation centre at Surrey. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has supervised 21 successful PhD graduates and published over 200 peer-reviewed research papers including more than 20 IEEE Transaction papers. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as WCNC, PIMRC and ICC. Recently, he has been appointed as an area Chair for IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He secured first rank in his B.Sc. and a distinction in his M.Sc. degree along with an award of excellence in recognition of his academic achievements conferred by the President of Pakistan. He has been awarded IEEE ComSoc's Fred Ellersick award 2014 and FEPS Learning and Teaching award 2014 and twice nominated for Tony Jean's Inspirational Teaching award. He is a shortlisted finalist for The Wharton-QS Stars Awards 2014 for innovative teaching and VC's learning and teaching award in University of Surrey. He is a senior member of IEEE and a Senior Fellow of Higher Education Academy (SFHEA), UK. Muhammad Zeeshan Shakir is a Senior Research Fellow at Carleton University, Canada. In recent years, he has been involved in several joint R&D initiatives with Telus, DragonWave, University of Surrey, KAUST, and TAMUQ. His research interests include design and deployment of diverse wireless communication systems, including hyper-dense heterogeneous networks and related 5G technologies. He has published more than 75 technical journal and conference papers and has contributed to seven books, all in reputable venues. He is an author of three research monographs including one authored book. He earned his PhD degree in electronic and electrical engineering from University of Strathclyde, Glasgow, UK in 2010. He is an Associate Technical Editor of IEEE Communications Magazine and has served as a Lead Guest Editor for IEEE Communications and IEEE Wireless Communications Magazines. He has been serving as Chair/Co-chair of several workshops/symposia in IEEE flagship conferences, such as ICC and GlobalSIP. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as Globecom, ICUWB, PIMRC and ICC. Recently, he has been appointed as a Chair to IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He is an active member of IEEE, IEEE ComSoc and IEEE Standard Association. Mounir Ghogho received his MSc degree in 1993 and PhD degree in 1997 from the National Polytechnic Institute of Toulouse, France. He was an EPSRC Research Fellow with the University of Strathclyde, Glasgow (Scotland), from September 1997 to November 2001. Since December 2001, he has been a faculty member with the school of Electronic and Electrical Engineering at the University of Leeds, UK, where he currently holds a Chair in Signal Processing and Communications. Since 2010, he has also been a Research Director at the International University of Rabat (Morocco). He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is one of the recipients of the 2013 IBM Faculty award. He is currently an Associate Editor of the IEEE Signal Processing magazine. He served as an Associate Editor of the IEEE Transactions on Signal Processing from 2005 to 2008, the IEEE Signal Processing Letters from 2001 to 2004, and the Elsevier's Digital Signal Processing journal from 2011 to 2012. He is currently a member of the IEEE Signal Processing Society SAM Technical Committee. He served as a member of the IEEE Signal Processing Society SPCOM Technical Committee from 2005 to 2010 and a member of IEEE Signal Processing Society SPTM Technical Committee from 2006 to 2011. He was the General Chair of the 11th IEEE workshop on Signal Processing for Advanced Wireless Communications (SPAWC2010) and the 21st edition of the European Signal Processing Conference (EUSIPCO 2013), and the Technical co-Chair of the MIMO symposium of IWCMC 2007 and IWCMC 2008. His research interests are in signal processing and communication networks. He has published over 260 journal and conferences papers. He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is also one of the recipients of the 2013 IBM Faculty award and is the EURASIP Liaison in Morocco.

Abstract In future heterogeneous cellular networks, cognitive radio compatible with device to device communication technique can be an aid to further enhance system spectral and energy efficiency. The unlicensed smart devices (SDs) are allowed to detect the available licensed spectrum and utilise the spectrum resource which is detected as not being used by the licensed users. In this work, we propose such a system and provide comprehensive analysis of the effect of selection of SDs' frame structure on the energy efficiency, throughput and interference. Moreover, uplink power control strategy is also considered where the licensed users and SDs adapt the transmit power based on the distance from their reference receivers. The optimal frame structure with power control is investigated under high‐signal‐to‐noise ratio (SNR) and low‐SNR network environments. The impact of power control and optimal sensing time and frame length, on the achievable energy efficiency, throughput and interference are illustrated and analysed by simulation results. It has been also shown that the optimal sensing time and frame length which maximizes the energy efficiency of SDs strictly depends on the power control factor employed in the underlying network such that the considered power control strategy may decrease the energy efficiency of SDs under very low‐SNR regime. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents an energy efficient and quality of service aware dynamic cell zooming algorithm for dense heterogeneous networks. The exponential growth of mobile data traffic would lead to dense deployment of small base stations and eventually higher energy consumption in Fifth Generation (5G) wireless networks. We formulate a dynamic cell zooming and base stations sleep optimization algorithm for dense heterogeneous networks as a Linear Programming (LP) problem in order to not only minimize the system power consumption but also to guarantee the quality of service to end user. This is possible by optimally zooming the coverage area of macro base stations and small cells based upon real time traffic conditions. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. The extensive performance evaluation of our proposed dynamic cell zooming algorithm shows that our proposed algorithm can significantly decrease both system energy consumption and outage probability.

Device-to-device (D2D) communication is being considered an important traffic offloading mechanism for future cellular networks. Coupled with pro-active device caching, it offers huge potential for capacity and coverage enhancements. In order to ensure maximum capacity enhancement, number of nodes for direct communication needs to be identified. In this paper, we derive analytic expression that relates number of D2D nodes (i.e., D2D user density) and average coverage probability of reference D2D receiver. Using stochastic geometry and poisson point process, we introduce retention probability within cooperation region and shortest distance based selection criterion to precisely quantify interference due to D2D pairs in coverage area. The simulation setup and numerical evaluation validates the closed-form expression.

3D visualization has become one of the most sort after tool to display hidden information to the students utilizing their sense of vision. This tool has been successful in aiding education, more specifically in teaching engineering subjects. Intricate concepts and phenomenons can easily be visualized in motion to the emerging engineers. This gives great insight of otherwise hard to understand principles and constructs. Wireless communication subjects are a good example of the use of visualization tool in teaching engineering. Recent prolific growth in wireless devices such as smart phones, tablet computers and other easy to carry devices made the use of radio frequency omnipresent. The widespread use of these devices has also raised health concerns among the masses due to the possible malign effects of electromagnetic radiations on the human body, especially on the brain due to its proximity with the hand-held radio devices. These radiations are absorbed in the head while making phone calls, and thereby increasing the direct and indirect health risks. These risks include, the rise of temperature in human body tissues resulting in adverse physiological problems [1]. As a matter of fact, even a small change in temperature in brain can be detrimental; a few degrees rise in temperature in the hypothalamus may cause thermoregulatory behavior [2]. The most important aspect of these effects is the fact that they largely go un-noticed. There are also no means to verify or observe the RF absorption, termed as Specific Absorption Rate (SAR) on the human head and brain. This paper presents the significance of 3D visualization for engineering students and help elucidate the intricate and challenging scientific engineering concepts. A useful case study has been developed at Texas A&M University at Qatar's Immersive Visualization Facility (IVF) to visualize the impact of RF signals on human head and brain. We have presented our initial efforts to visualize and observe the effects of SAR on a human head model. The series of 3D visualizations are built by using the mathematical model of SAR to understand the distribution of RF over the layers of considered human head model.

Enquiry-based student-centered learning activities in engineering classrooms may lose its focus and success if the activities in classroom are biased due to the failure in forming mixed-ability oriented teams or groups of students. This paper proposes a technology driven team-building methodology to enhance enquiry-based learning in the conventional engineering classrooms, which is referred to as Heterogeneous Ability-Centered Team Building (H-ACT-B) method. The H-ACT-B method guarantees mixed-ability based team or group formation in classroom to promote effective communication, collaboration and critical thinking based on the programmed evaluation of individual student's aptitude. The proposed team building method is expected to aid the individual student and the team or group to ensure their progress toward achieving the common team task efficiently. Moreover, the study is strongly formulated by considering the useful insights about the current team building practices and methods by conducting survey among the engineering faculty and students.

Heterogeneous networks (HetNets) represent a promising solution for the next generation wireless networks (NGWNs), where many low power, low cost small-cells (e.g., fem-tocells) are planned to support the existing macrocell networks to reduce the over the air signaling and uplink power consumption, and thereby enhance the spectral efficiency compared to the macro-only network. In this context, the massive deployment of many lightly loaded small-cells is anticipated to increase the downlink power consumption of the HetNets. This paper investigates the end-to-end downlink power consumption of the HetNets, which consists of the power consumed by the macrocell and small-cell base stations and the backhaul to carry the traffic from the access to the core network. The downlink power consumption depends probabilistically on the population of the active mobile users in both the macrocell and small-cell networks such that the regulating factor is referred to as active user population factor (AUPF). A mathematical framework is presented to derive AUPF by assuming that the total population of active mobile users is a random variable and has a Binomial probability distribution. The number of active users and small-cells are calculated by the proposed probabilistic traffic model which assures the reduction in downlink power consumption since it now consists of the power consumption due to the base stations and backhaul for only the active population of mobile users. This model helps to evaluate the power consumption of HetNets. The simulations results indicate that AUPF and traffic load have significant impact on the downlink power consumption of HetNets.

With the cell coverage area of current and future mobile networks becoming smaller, heterogeneous small-cell networks (HetSNets), where multiple low-power, low-cost base stations (BSs) complement the existing macrocell infrastructure, are considered constitutive elements of future mobile networks. In this paper, we propose a K-tier HetSNet, where multiple tiers of small-cells are padded between macrocells which in turn expand the network coverage and significantly increase in capacity without compromising the frequency reuse factor. In this context, we derive analytical capacity bounds of the K-tier HetSNets based on the distance of the desired user from its serving BS and all other interfering BSs. It was observed that the upper bound of the capacity becomes tighter as the number of small-cell tiers increases due to the increase in the number of small-cells. Simulation results show the performance of the proposed K-tier HetSNets against the macro-only network in terms of frequency reuse factor, capacity and area spectral efficiency.

Cognitive radio systems are a promising solution to the spectrum scarcity problem but accurate modeling of both primary and secondary user activity, spectrum utilization, and system throughput are vital to achieve good performance in such systems. In this paper, we consider a set of primary users that are distributed in space based on a Poisson point process and demand for the available spectrum. We assess the effect of these demands on primary user activity, spectrum occupancy, and total system throughput under various subcarrier-request distributions and fading environments. The asymptotic mean number of active primary users and occupied subcarriers are analytically derived and evaluated further considering the primary network traffic and the average probability of miss-detection of an occupied subcarrier by a single sensing secondary user. It is seen that the average probability of miss-detection is a function of system traffic and the subcarrier-request distribution of primary users and that for extreme primary user traffic, the applied subcarrier-request distribution and total number of provided subcarriers cannot improve further the asymptotic sensing accuracy of the secondary user. Finally, the primary network and the secondary user throughputs in the presence of mutual interference due to imperfect detection of secondary users are investigated and their asymptotic values for large primary network traffic, primary user transmit power, and secondary user transmit power are analytically derived. The results are critically investigated, formulated as theorems and compared with simulations. It is observed that analytical and simulation results are in perfect agreement. It is shown that increasing the primary network transmit power benefits both the primary network and the secondary user throughputs.

Ubiquitous availability of wireless and electronic devices has raised some health alarms among the masses. There exist possible malign effects of electromagnetic radiations on the human body, and especially on the brain due to existence of radio frequency (RF) signals close to the human head. The increased usage of contemporary wireless technologies around us has raised major concerns about their harmful impact on human brain's communication signals (neural oscillations). Thorough investigations are required to answer whether or not these wireless devises and RF signals interfere with human brain's signals and are directly responsible for physiological and behavioral aspects. This answer is mutually valuable for health care providers and technology experts. This paper investigates the effects of RF radiations emitted by electronic devices on the cognition ability of the brain and its behavioral impingement. In this context, this study utilized the Emotiv EEG signal recording tool to study the behavior of the brain's EEG signals both under normal conditions and under the influence of RF signals. The findings are interesting and the experiments have indicated some possible effects and abnormalities due to the RF radiations on the normal conditions and cognition of the brain's signal. Further investigations is anticipated to open a plethora of useful applications and investigations.

Heterogeneous networks (HetNets) are considered as a standard part of the future generation of wireless networks where masses of low power, low cost smallcells (e.g., femtocells) are anticipated to support the existing macrocell networks. While HetNets are increasing the spectral efficiency and decreasing the over-the-air signaling and uplink power consumption compared to macro networks, the large scale deployment of many lightly loaded smallcells is expected to increase the downlink power consumption of the HetNets. This paper studies the impact of smallcell population on the downlink power consumption of the HetNets. In this context, we propose that the population of smallcells is strictly depending on the traffic load due to active mobile users, which is a random variable and time-varying. We derive the mathematical framework to calculate the required population of smallcells based on the probabilistic traffic models where the number of total mobile users and number of active mobile users have different probabilistic distributions such as different combinations of Binomial and Poisson distributions. The proposed method guarantees the reduction in downlink power consumption of HetNets by forcing the smallcells to turn on the sleeping mode under low and medium traffic load conditions. Several simulation results are included to illustrate the impact of traffic load dependent population of smallcells on the downlink power consumption of HetNets. Moreover, it is shown that the mathematical and simulation results are in perfect agreement.

In this paper, a unified generalized eigenvalue based spectrum sensing framework referred to as Generalized mean detector (GMD) has been introduced. The generalization of the detectors namely (i) the eigenvalue ratio detector (ERD) involving the ratio of the largest and the smallest eigenvalues; (ii) the Geometric mean detector (GEMD) involving the ratio of the largest eigenvalue and the geometric mean of the eigenvalues and (iii) the Arithmetic mean detector (ARMD) involving the ratio of the largest and the arithmetic mean of the eigenvalues is explored. The foundation of the proposed unified framework is based on the calculation of exact analytical moments of the random variables of test statistics of the respective detectors. In this context, we approximate the probability density function (PDF) of the test statistics of the respective detectors by Gaussian/Gamma PDF using the moment matching method. Finally, we derive closed-form expressions to calculate the decision threshold of the eigenvalue based detectors by exchanging the derived exact moments of the random variables of test statistics with the moments of the Gaussian/Gamma distribution function. The performance of the eigenvalue based detectors is compared with the traditional detectors such as energy detector (ED) and cyclostationary detector (CSD) and validate the importance of the eigenvalue based detectors particularly over realistic wireless cognitive environments. Analytical and simulation results show that the GEMD and the ARMD yields considerable performance advantage in realistic spectrum sensing scenarios. Moreover, our results based on proposed simple and tractable approximation approaches are in perfect agreement with the empirical results.

Eigenvalue Ratio (ER) detector based on the two extreme eigenvalues of the received signal covariance matrix is currently one of the most effective solution for spectrum sensing. However, the analytical results of such scheme often depend on asymptotic assumptions since the distribution of the ratio of two extreme eigenvalues is exceptionally complex to compute. In this paper, a non-asymptotic spectrum sensing approach for ER detector is introduced to approximate the marginal and joint distributions of the two extreme eigenvalues. The two extreme eigenvalues are considered as dependent Gaussian random variables such that their joint probability density function (PDF) is approximated by a bivariate Gaussian distribution function for any number of cooperating secondary users and received samples. The PDF approximation approach is based on the moment matching method where we calculate the exact analytical moments of joint and marginal distributions of the two extreme eigenvalues. The decision threshold is calculated by exploiting the statistical mean and the variance of each of the two extreme eigenvalues and the correlation coefficient between them. The performance analysis of our newly proposed approximation approach is compared with the already published asymptotic Tracy-Widom approximation approach. It has been shown that our results are in perfect agreement with the simulation results for any number of secondary users and received samples.

Heterogeneous networks are an attractive means of expanding mobile network capacity. A heterogeneous network is typically composed of multiple radio access technologies (RATs) where the base stations are transmitting with variable power. In this paper, we consider a Heterogeneous network where we complement the macrocell network with low-power low-cost user deployed nodes, such as femtocell base stations to increase the mean achievable capacity of the system. In this context, we integrate macro-femto cellular networks and derive the area spectral efficiency of the proposed two tier Heterogeneous network. We consider the deployment of femtocell base stations around the edge of the macrocell such that this configuration is referred to as femto-on-edge (FOE) configuration. Moreover, FOE configuration mandates reduction in intercell interference due to the mobile users which are located around the edge of the macrocell since these femtocell base stations are low-power nodes which has significantly lower transmission power than macrocell base stations. We present a mathematical analysis to calculate the instantaneous carrier to interference ratio (CIR) of the desired mobile user in macro and femto cellular networks and determine the total area spectral efficiency of the Heterogeneous network. Details of the simulation processes are included to support the analysis and show the efficacy of the proposed deployment. It has been shown that the proposed setup of the Heterogeneous network offers higher area spectral efficiency which aims to fulfill the expected demand of the future mobile users.

Femtocells provide an efficient solution to overcome the indoor coverage problems and also to deal with the traffic within Macro cells. The possibility of localizing femtocell subscriber stations based on the timing ranging advance parameter (TRAP), obtained from E-UTRAN (Evolved UMTS Terrestrial Radio Access Network), within the network signal internals is challenging and is studied throughout in this paper. The principle approach to localization based on Euclidean distances from multiple base stations is outlined.We investigate the specifications of the timing parameters or TRAP used for air interface of 4G network as they relate to calculating the subscriber distances. Computer simulation is used to demonstrate the localization accuracy using multiple base station networks when estimating likely locations of femtocell subscribers stations on a twodimensional coordinate mapping system. However, we further extend our simulations to demonstrate expected location accuracy of subscriber stations, for multiple base station networks, on a three dimensional coordinate mapping scheme. The possibility of of error-fixes shows eight times greater accuracy than in previous results is expected to achieve by applying timing advance techniques to Global System for Mobile communications networks, by using a two-dimensional coordinate mapping scheme. We later compare our study with the effect of global positioning system (GPS) by using a three-dimensional coordinate mapping scheme, which is predicted to give an 72.4 cms accuracy of subscriber station location.

In cognitive radio networks, the licensed frequency bands of the primary users (PUs) are available to the secondary user (SU) provided that they do not cause significant interference to the PUs. In this study, the authors analysed the normalised throughput of the SU with multiple PUs coexisting under any frequency division multiple access communication protocol. The authors consider a cognitive radio transmission where the frame structure consists of sensing and data transmission slots. In order to achieve the maximum normalised throughput of the SU and control the interference level to the legal PUs, the optimal frame length of the SU is found via simulation. In this context, a new analytical formula has been expressed for the achievable normalised throughput of SU with multiple PUs under prefect and imperfect spectrum sensing scenarios. Moreover, the impact of imperfect sensing, variable frame length of SU and the variable PU traffic loads, on the normalised throughput has been critically investigated. It has been shown that the analytical and simulation results are in perfect agreement. The authors analytical results are much useful to determine how to select the frame duration length subject to the parameters of cognitive radio network, such as network traffic load, achievable sensing accuracy and number of coexisting PUs.

Seaweb is an acoustic communication technology that enables communication between sensor nodes. Seaweb interconnects the underwater nodes through digital signal processing (DSP)-based modem by using acoustic links between the neighbouring sensors. In this paper, we design and investigate a global positioning system (GPS)-free passive localization protocol using seaweb technology. This protocol uses the range data and planar trigonometry to estimate the positions of the discovered nodes. We take into consideration the small displacement of sensor nodes due to watch circles and placement of sensor nodes on non-uniform underwater surface, for precise localization. Once the nodes are localized, we divide the whole network field into circular levels that minimizes the traffic complexity and thereby increases the lifetime of the sensor network field. We then form the mesh network inside each of the circular levels that increases the reliability. The algorithm is designed in such a way that it overcomes the ambiguous nodes errata and reflected paths and makes the algorithm more robust. The synthetic network geometries are so designed which can evaluate the algorithm in the presence of perfect or imperfect ranges or in case of incomplete data. A comparative study is made with the existing algorithms which proves our newly proposed algorithm to be more effective.