Beeshanga Abewardana Jayawickrama

Bio: Beeshanga Abewardana Jayawickrama is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Cognitive radio & Base station. The author has an hindex of 10, co-authored 41 publications receiving 412 citations. Previous affiliations of Beeshanga Abewardana Jayawickrama include Apple Inc. & Macquarie University.

Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

Abstract: Future 5th generation networks are expected to enable three key services—enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements.

Comprehensive Energy Consumption Model for Unmanned Aerial Vehicles, Based on Empirical Studies of Battery Performance

Abstract: Unmanned aerial vehicles (UAVs) are fast gaining popularity in a wide variety of areas and are already being used for a range of tasks. Despite their many desirable features, a number of drawbacks hinder the potential of UAV applications. As typical UAVs are powered by on-board batteries, limited battery lifetime is identified as a key limitation in UAV applications. Thus, in order to preserve the available energy, planning UAV missions in an energy efficient manner is of utmost importance. For energy efficient UAV mission planning, it is necessary to predict the energy consumption of specific UAV manoeuvring actions. Accurate energy prediction requires a reliable and realistic energy consumption model. In this paper, we present a consistent and complete energy consumption model for UAVs based on empirical studies of battery usage for various UAV activities. We considered the impact of different flight scenarios and conditions on UAV energy consumption when developing the proposed model. The energy consumption model presented in this paper can be readily used for energy efficient UAV mission planning.

Empirical Power Consumption Model for UAVs

Abstract: Unmanned Aerial Vehicles (UAV) are gaining popularity in a range of areas and are already being used for a wide variety of purposes. While UAVs have many desirable features, limited battery lifetime is identified as a key restriction in UAV applications. Typical UAVs being electric devices, powered by on-board batteries, this constrain has limited their capabilities to a considerable extent. Thus planning UAV missions in an energy efficient manner is of utmost importance. To achieve this, for prediction of power consumption, it is necessary to have a reliable power consumption model. In this paper, we present a consistent and complete power consumption model for UAVs based on empirical studies of battery usage for various UAV activities. The power consumption model presented in this paper can be readily used for energy efficient UAV mission planning.

Improved performance of spectrum cartography based on compressive sensing in cognitive radio networks

Abstract: Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. Multiple research groups have recently proposed to use spectrum cartography in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. In our novel approach, we exploit the sparsity of primary users in space to formulate the cartography process as a compressive sensing problem. Further, we present a novel algorithm for solving the cartography problem that builds on the well-known Orthogonal Matching Pursuit algorithm. We evaluate the performance of our approach by simulating a cognitive radio network where primary users are low power wireless microphones. Our simulation results show a significant improvement in reconstruction error, in comparison to two existing compressive sensing based methods.

Enabling Ultra-Reliable and Low-Latency Communications through Unlicensed Spectrum

Abstract: In this article, we aim to address the question of how to exploit the unlicensed spectrum to achieve URLLC. Potential URLLC PHY mechanisms are reviewed and then compared via simulations to demonstrate their potential benefits to URLLC. Although a number of important PHY techniques help with URLLC, the PHY layer exhibits an intrinsic trade-off between latency and reliability, posed by limited and unstable wireless channels. We then explore MAC mechanisms and discuss multi-channel strategies for achieving low-latency LTE unlicensed band access. We demonstrate, via simulations, that the periods without access to the unlicensed band can be substantially reduced by maintaining channel access processes on multiple unlicensed channels, choosing the channels intelligently, and implementing RTS/CTS.

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

Abstract: Path planning is one of the most important problems to be explored in unmanned aerial vehicles (UAVs) for finding an optimal path between source and destination. Although, in literature, a lot of research proposals exist on the path planning problems of UAVs but still issues of target location and identification persist keeping in view of the high mobility of UAVs. To solve these issues in UAVs path planning, optimal decisions need to be taken for various mission-critical operations performed by UAVs. These decisions require a map or graph of the mission environment so that UAVs are aware of their locations with respect to the map or graph. Keeping focus on the aforementioned points, this paper analyzes various UAVs path planning techniques used over the past many years. The aim of path planning techniques is not only to find an optimal and shortest path but also to provide the collision-free environment to the UAVs. It is important to have path planning techniques to compute a safe path in the shortest possible time to the final destination. In this paper, various path planning techniques for UAVs are classified into three broad categories, i.e., representative techniques, cooperative techniques, and non-cooperative techniques. With these techniques, coverage and connectivity of the UAVs network communication are discussed and analyzed. Based on each category of UAVs path planning, a critical analysis of the existing proposals has also been done. For better understanding, various comparison tables using parameters such as-path length, optimality, completeness, cost-efficiency, time efficiency, energy-efficiency, robustness and collision avoidance are also included in the text. In addition, a number of open research problems based on UAVs path planning and UAVs network communication are explored to provide deep insights to the readers.

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Abstract: Many network applications, eg, industrial control, demand ultra-low latency (ULL) However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds The IEEE 8021 time sensitive networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF deterministic networking (DetNet) standards seek to provide the complementary network layer ULL support This paper provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management Throughout, we identify the pitfalls and limitations of the existing standards and research studies This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations

Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

Abstract: The ever-increasing number of resource-constrained machine-type communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultra-reliable and low latency communications (URLLCs), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include quality of service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead, and radio access network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy random access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem, and then identify potential advantages, challenges, and use cases for the applications of emerging machine learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity $Q$ -learning approach in the mMTC scenario along with the recent advances toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.

A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects

Abstract: The interest in electric unmanned aerial vehicles (UAVs) is rapidly growing in recent years. The reason is that UAVs have abilities to perform some difficult or dangerous tasks, with high mobility, safety, and low cost. It should be noted that UAVs are revolutionizing many public services including real time monitoring, search and rescue, wildlife surveys, delivery services, wireless coverage, and precision agriculture. To increase endurance and achieve good performance, UAVs generally use a hybrid power supply system architecture. A hybrid power architecture may combine several power sources such as fuel cell, battery, solar cells, and supercapacitor. The choice of a suitable power source hybridization architecture with an optimal energy management system are therefore crucial to enable an efficient operation of advanced UAVs. In the context of battery-powered UAV platforms, including new technologies such as swapping laser-beam inflight recharging and tethering, this paper proposes a comprehensive and critical state of the art review on power supply configurations and energy management systems to find out gaps and to provide insights and recommendations for future research.

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Abstract: Compressive sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the under-utilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency, and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CS-related works applied to different categories such as wideband sensing, signal parameter estimation and radio environment map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.