Vishaka Basnayake

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Email: vishakabasnayake@gmail.com
Work Address: Faculty of Postgraduate Studies and Research, Sri Lanka Technological Campus, Sri Lanka

About Me

Vishaka Basnayake earned her Ph.D. in Computer Science from the Universite Bourgogne Franche Comte (UBFC), France, in June 2023. Prior to that, she completed her M.Sc. in Wireless Communications Engineering at the University of Oulu, Finland, in 2019, and her B.Sc. in Electrical and Electronics Engineering at the University of Peradeniya, Sri Lanka, in 2017. Her research interests encompass a range of topics, including Signal Processing, Federated Learning, Internet of Things, Network Protocol Design, Mobile App Development, Optimization, Short range D2D Communications, Cellular Wireless networks, Non orthogonal Multiple Access Schemes, and Localization.

Education and Training

Work Experience

University Lecturer, Wireless Communication Systems, Faculty of Postgraduate Studies and Research, Sri Lanka Technological Campus, Sri Lanka (September 2019 - Present)

Doctoral Researcher, Universite Bourgogne Franche Comte, France (January 2020 - June 2023)

Research Assistant and Master Thesis Student, University of Oulu, Finland (September, 2018 - July 2019)

Instructor, Computing Center, University of Peradeniya, Sri Lanka (November 2017 - August 2018)

Industrial Trainee, ZTE Corporation (Telecommuication Services), Sri Lanka (September 2015 - January 2016)

Digital Skills

Projects and Publications

Continuous and Responsive D2D Victim Localization for Post-Disaster Emergencies

One of the most challenging tasks in a disaster scenario is the detection and localization of victims with high accuracy and minimum delay, especially in out-of-coverage areas. In the event of a disaster that disrupts the cellular network infrastructure, emergency calls can be relayed to the core network via multi-hop D2D communications. In this paper, a localization system is proposed that uses radio measurements obtained through such D2D multi-hop assisted emergency calls to localize in-coverage and out-of-coverage devices. To address the uncertainty and gradual reception of data in real-time in this scenario, a dynamic constraint satisfaction-based Multi Victim Localization Algorithm (MVLA) is proposed. This algorithm locates multi-hop devices in a progressive propagation manner to provide fast and accurate updates on victim locations. Additionally , three modes of MVLA, namely MVLArecent, MVLAseq, and MVLA all are proposed. Simulation results demonstrate that MVLA all has a lower localization error compared to MVLArecent and MVLAseq. Moreover, MVLA all , is compared with an existing particle filtering-based localization algorithm called RSSI Monte-Carlo Boxed Localization (RSSI-MCL) under an increasing number of emergency user devices and functional gNodeBs. Results show that MVLA all significantly outperforms the RSSI-MCL method in terms of localization accuracy and computational delay.

Post-Disaster Victim Localization via D2D Communications

Ph.D Thesis: Reliable Emergency Service for 5G Networks

During large-scale disasters, emergency communication systems that are reliable, responsive, and energy-efficient are crucial. This thesis focuses on designing reliable emergency communication systems for disaster scenarios in out-of-coverage areas. The proposed systems are designed to work seamlessly across the data link, network, and application layers. At the data link layer, a new decoding scheme named Cyclic Triangular Successive Interference Cancellation (Cyclic T-SIC) is proposed to enhance the reliability in Asynchronous NOMA-assisted D2D communications. Moreover, at the network layer, new multi-hop protocols namely Multi-Hop Emergency caLl Protocol (M-HELP) and 5G Standalone Service (5G-SOS) that comply with 3GPP standards are introduced to reduce control traffic and improve emergency information transfer reliability. Moreover, a new Multi Victim Localization Algorithm (MVLA) is proposed at the application layer to locate victim devices during emergencies. This scheme uses radio data from outband D2D-assisted multi-hop emergency calls and applies constraint satisfaction methods to locate victims in a progressive propagation manner. Additionally, an emergency service architecture is also proposed comprising an optimized machine learning model to locate population-congested areas during pandemics. By comparing and evaluating the proposed methods and schemes with conventional state-of-the-art approaches, valuable insights are obtained into the design of efficient and optimal emergency communication systems for areas with limited network coverage.

M-Ary QAM Asynchronous-NOMA D2D Network With Cyclic Triangular-SIC Decoding Scheme

During large-scale disasters, emergency communication systems that are reliable, responsive, and energy-efficient are crucial. This thesis focuses on designing reliable emergency communication systems for disaster scenarios in out-of-coverage areas. The proposed systems are designed to work seamlessly across the data link, network, and application layers. At the data link layer, a new decoding scheme named Cyclic Triangular Successive Interference Cancellation (Cyclic T-SIC) is proposed to enhance the reliability in Asynchronous NOMA-assisted D2D communications. Moreover, at the network layer, new multi-hop protocols namely Multi-Hop Emergency caLl Protocol (M-HELP) and 5G Standalone Service (5G-SOS) that comply with 3GPP standards are introduced to reduce control traffic and improve emergency information transfer reliability. Moreover, a new Multi Victim Localization Algorithm (MVLA) is proposed at the application layer to locate victim devices during emergencies. This scheme uses radio data from outband D2D-assisted multi-hop emergency calls and applies constraint satisfaction methods to locate victims in a progressive propagation manner. Additionally, an emergency service architecture is also proposed comprising an optimized machine learning model to locate population-congested areas during pandemics. By comparing and evaluating the proposed methods and schemes with conventional state-of-the-art approaches, valuable insights are obtained into the design of efficient and optimal emergency communication systems for areas with limited network coverage.

Adaptive Emergency Call Service for Disaster Management

Reliable and efficient transmission of emergency calls during a massive network failure is both an indispensable and challenging task. In this paper, we propose a novel fully 3GPP and 5G compatible emergency call protocol named 5G StandalOne Service (5G-SOS). A 5G-SOS-enabled emergency service provides potential out-of-coverage victims’ devices with a way to contact the 4G/5G core network through D2D multi-hop relaying protocol. The objective of 5G-SOS is to maintain this connection even when a large fraction of the network infrastructure is destroyed. 5G-SOS is a fully distributed protocol designed to generate zero additional control traffic and to adapt its parameters based on the local emergency call congestion. Therefore, devices behave as an ad-hoc network with the common purpose to ensure the best chances for emergency call transfer within a reasonable delay. A densely populated Traverse city of Michigan, USA, with a 15,000 population, is used to evaluate 5G-SOS under extreme emergency scenarios. The performance of 5G-SOS is shown to be significant when compared with existing protocols, namely, M-HELP and FINDER, in terms of transmission success rate, end-to-end latency, network traffic control, and energy management. 5G-SOS provides satisfactory performance (success rate of 50%) even when the number of simultaneous emergency calls is very high (5000 calls over 10 min). On average, 5G-SOS performs 24.9% better than M-HELP and 73.9% than FINDER in terms of success rate. Additionally, 5G-SOS reduces the average end-end latency of the emergency calls transfer by 20.8% compared to M-HELP and 61.7% compared to FINDER.

Optimization of Secure Emergency Call Services in Asynchronous-NOMA D2D Network

This paper investigates the issue of improving secrecy capacity of device-to-device (D2D) communications in disaster scenarios under the presence of jammers in close proximity. Furthermore, an asynchronous-non orthogonal multiple access (A-NOMA) assisted transmission scheme is considered due to the resource limitations and the asynchrony in signal receptions in out-of-coverage D2D scenarios. A binary optimization problem is proposed to select the optimal data which enhances the sum secrecy capacity of the transmissions. The results show that the proposed optimized scheme outperforms the conventional secrecy capacity.

Enhanced Convex Hull based Clustering for High Population Density Avoidance under D2D Enabled Network

Global pandemics such as Covid-19 have led to massive loss of human lives and strict lockdown measures worldwide. To return to a certain level of normalcy, community awareness on avoiding high population density areas is significantly important for infection prevention and control. With the availability of new telecommunication technologies, it is possible to provide highly informative population clustering data back to people using wireless aerial agents (WAAs) placed in a local area. Hence, a service architecture that allows users to access the localization of population clusters is proposed. Further, a convex hull-based clustering method, enhanced population clustering (E-PC), is proposed. This method refined the result of conventional clustering methods such as K-means and Gaussian mixture model (GMM). Moreover, the potential in E-PC to achieve the same or higher results compared to the original K-means and GMM, while consuming lesser data points, is demonstrated. On average, E-PC improved the cluster detection performance in both K-means and GMM by 18.93% under different environments such as remote, rural, suburban, and urban in terms of silhouette score. Further, E-PC allows a 15% data reduction which results in decreasing the computational cost and energy consumption of the WAAs.

M-HELP - Multi-Hop Emergency Call Protocol in 5G

Wireless mobile networks are widely used during large catastrophes such as earthquakes and floods where robust networking systems are indispensable to protect human lives. The objective of this paper is to present a self-adaptive emergency call protocol that allows keeping potential victims connected to the core network through the available functional stations, called gNBs in 5G, when a fraction of gNBs in a network area are fully destructed with no access to other gNBs or the core network due to the disaster. Nowadays, the density of mobile devices and progress in outband device to device (D2D) communication provide the framework for the extension of both mobile and network coverage. We propose a novel, 3GPP compatible and completely distributed protocol called M-HELP for emergency call service for 4G/5G enabled mobile networks. We assess M-HELP efficiency under various scenarios representing different degrees of network destruction and different emergency call conditions. The tests demonstrate the significant performance of M-HELP in terms of transmission success rate, energy management, latency and control traffic load.

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

Energy efficiency is a major concern in the emerging mobile cellular wireless networks since massive connectivity is to be expected with high energy requirements from the network operators. Non-orthogonal multiple access (NOMA) being the frontier multiple access scheme for 5G, there exists numerous research attempts on enhancing the energy efficiency of NOMA enabled wireless networks while maintaining its outstanding performance metrics such as high throughput, data rates and capacity maximized optimally.The concept of green NOMA is introduced in a generalized manner to identify the energy efficient NOMA schemes. These schemes will result in an optimal scenario in which the energy generated for communication is managed sustainably. Hence, the effect on the environment, economy, living beings, etc is minimized. The recent research developments are classified for a better understanding of areas which are lacking attention and needs further improvement. Also, the performance comparison of energy efficient, NOMA schemes against conventional NOMA is presented. Finally, challenges and emerging research trends, for energy efficient NOMA are discussed.

Master Thesis: Federated Learning For Enhanced Sensor Reliability Of Automated Wireless Networks

Autonomous mobile robots working in-proximity humans and objects are becoming frequent and thus, avoiding collisions becomes important to increase the safety of the working environment. This thesis develops a mechanism to improve the reliability of sensor measurements in a mobile robot network taking into the account of inter-robot communication and costs of faulty sensor replacements. In this view, first, we develop a sensor fault prediction method utilizing sensor characteristics. Then, network-wide cost capturing sensor replacements and wireless communication is minimized subject to a sensor measurement reliability constraint. Tools from convex optimization are used to develop an algorithm that yields the optimal sensor selection and wireless information communication policy for aforementioned problem. Under the absence of prior knowledge on sensor characteristics, we utilize observations of sensor failures to estimate their characteristics in a distributed manner using federated learning. Finally, extensive simulations are carried out to highlight the performance of the proposed mechanism compared to several state-of-the-art methods.

Honours and Awards

First Runner-Up in the Best Paper Award | SLTC International Research Conference (IRC) 2022 at Sri Lanka Technological Campus, Sri Lanka | (30/09/2022)

Licenses and Certifications

SQL for Data Science

Wireless Communications for Everybody

How Google Does Machine Learning

Programming for Everybody (Getting Started with Python)

Management and Leadership Skills

Lead in MSc in Electronics and Communications Engineering

Lead in Editorial Committee | SLTC International Research Conference 2023

Lectures & Talks

Undergraduate Lecture Series

Youtube

Presentations on Research Findings

Youtube

References

Dr. Hakim Mabed

Dr. Philippe Canalda

Dr. Himal Suraweera

Last updated: 28th Nov 2023