Chee Yen Leow

Bio: Chee Yen Leow is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Relay & Beamforming. The author has an hindex of 16, co-authored 105 publications receiving 1879 citations. Previous affiliations of Chee Yen Leow include Imperial College London.

An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

Abstract: The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.

A Comprehensive Survey of Pilot Contamination in Massive MIMO—5G System

Abstract: Massive MIMO has been recognized as a promising technology to meet the demand for higher data capacity for mobile networks in 2020 and beyond. Although promising, each base station needs accurate estimation of the channel state information (CSI), either through feedback or channel reciprocity schemes in order to achieve the benefits of massive MIMO in practice. Time division duplex (TDD) has been suggested as a better mode to acquire timely CSI in massive MIMO systems. The use of non-orthogonal pilot schemes, proposed for channel estimation in multi-cell TDD networks, is considered as a major source of pilot contamination in the literature due to the limitations of coherence time. Given the importance of pilot contamination in massive MIMO systems, we provide an extensive survey on pilot contamination, and identify other possible sources of pilot contamination, which include hardware impairment and non-reciprocal transceivers. We review established theories that have analyzed the effect of pilot contamination on the performance of massive MIMO systems, particularly on achievable rates. Next, we categorize the different proposed mitigation techniques for pilot contamination using the following taxonomy: pilot-based approach and subspace-based approach. Finally, we highlight the open issues, such as training overhead, deployment scenario, computational complexity, use of channel reciprocity, and conclude with broader perspective and a look at future trends in pilot contamination in massive MIMO systems.

Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Assisted Communication

Abstract: The future wireless networks promise to provide ubiquitous connectivity to a multitude of devices with diversified traffic patterns wherever and whenever needed. For the sake of boosting resilience against faults, natural disasters, and unexpected traffic, the unmanned aerial vehicle (UAV)-assisted wireless communication systems can provide a unique opportunity to cater for such demands in a timely fashion without relying on the overly engineered cellular network. However, for UAV-assisted communication, issues of capacity, coverage, and energy efficiency are considered of paramount importance. The case of non-orthogonal multiple access (NOMA) is investigated for aerial base station (BS). NOMA’s viability is established by formulating the sum-rate problem constituting a function of power allocation and UAV altitude. The optimization problem is constrained to meet individual user-rates arisen by orthogonal multiple access (OMA) bringing it at par with NOMA. The relationship between energy efficiency and altitude of a UAV inspires the solution to the aforementioned problem considering two cases, namely, altitude fixed NOMA and altitude optimized NOMA. The latter allows exploiting the extra degrees of freedom of UAV-BS mobility to enhance the spectral efficiency and the energy efficiency. Hence, it saves joules in the operational cost of the UAV. Finally, a constrained coverage expansion methodology, facilitated by NOMA user rate gain is also proposed. Results are presented for various environment settings to conclude NOMA manifesting better performance in terms of sum-rate, coverage, and energy efficiency.

Beamforming in Wireless Energy Harvesting Communications Systems: A Survey

Abstract: Wireless energy harvesting (EH) is a promising solution to prolong lifetime of power-constrained networks such as military and sensor networks. The high sensitivity of energy transfer to signal decay due to path loss and fading, promotes multi-antenna techniques like beamforming as the candidate transmission scheme for EH networks. Exploiting beamforming in EH networks has gained overwhelming interest, and lot of literature has appeared recently regarding this topic. The objective of this paper is to point out the state-of-the-art research activity on beamforming implementation in EH wireless networks. We first review the basic concepts and architecture of EH wireless networks. In addition, we also discuss the effects of beamforming transmission scheme on system performance in EH wireless communication. Furthermore, we present a comprehensive survey of multi-antenna EH communications. We cover the supporting network architectures like broadcasting, relay, and cognitive radio networks with the various beamforming deployment within the network architecture. We classify the different beamforming approaches in each network topology according to its design objective such as increasing the throughput, enhancing the energy transfer efficiency, and minimizing the total transmit power, with paying special attention to exploiting the physical layer security. We also survey major advances as well as open issues, challenges, and future research directions in multi-antenna EH communications.

Distributed and Collaborative Beamforming in Wireless Sensor Networks: Classifications, Trends, and Research Directions

Abstract: Distributed and collaborative beamforming (DCBF) scheme in wireless sensor networks (WSNs) is receiving new-found interest in recent times due to the rapid advancements in wireless technology and embedded systems. Although studies on distributed and collaborative beamforming have been carried out for more than ten years, the DCBF was initially considered impractical due to high complexity and hardly achievable requirements. It gained prominence only in the past few years as small wireless communication electronic sensors with high processing capability became easily available. Recent works showcasing distributed and collaborative beamforming as a suitable solution for 5G communication systems such as mm-wave communication and machine to machine communications has further ignited the interest in this research field. Motivated by these factors, this paper presents a survey on the research trends of distributed and collaborative beamforming in WSNs. We provide classifications of the DCBF research areas and conduct an extensive review of the various proposals which have appeared in the literature for each classification. This survey uncovered that majority of existing research can be broadly divided into four major research trends: beampattern analysis, power and lifetime optimization, synchronization, and finally, prototype design. The inherent features, constraints and challenges of each research category in the distributed and collaborative beamforming are presented and the lessons learned from the shortcomings of previous research are summarized. Finally, this paper has unveiled open research directions in the field of distributed and collaborative beamforming in WSNs.

Table Of Integrals Series And Products

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Deep Learning in Mobile and Wireless Networking: A Survey

Abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Ultra-Dense Networks: A Survey

Abstract: The exponential growth and availability of data in all forms is the main booster to the continuing evolution in the communications industry. The popularization of traffic-intensive applications including high definition video, 3-D visualization, augmented reality, wearable devices, and cloud computing defines a new era of mobile communications. The immense amount of traffic generated by today’s customers requires a paradigm shift in all aspects of mobile networks. Ultradense network (UDN) is one of the leading ideas in this racetrack. In UDNs, the access nodes and/or the number of communication links per unit area are densified. In this paper, we provide a survey-style introduction to dense small cell networks. Moreover, we summarize and compare some of the recent achievements and research findings. We discuss the modeling techniques and the performance metrics widely used to model problems in UDN. Also, we present the enabling technologies for network densification in order to understand the state-of-the-art. We consider many research directions in this survey, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment. Finally, we discuss the challenges and open problems to the researchers in the field or newcomers who aim to conduct research in this interesting and active area of research.

An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

Abstract: The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.

Digital Twin: Enabling Technologies, Challenges and Open Research

Abstract: Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.