Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
item: Thesis-Full-text
Implementation of deep neural networks on embedded systems for real-time hand gesture recognition using a 24GHz Doppler radar
(2025) Ijaz, MAM; Edussooriya, C; Samarasinghe, K
The rapid proliferation of 5G networks has led to increased demand for efficient resource allocation at the edge cloud to support latency-sensitive and compute-intensive applications. This thesis presents an optimal resource allocation framework for 5G edge cloud environments, leveraging a closed-loop architecture that integrates demand prediction, intelligent scheduling, and load balancing. The proposed framework employs machine learning-based traffic prediction models, such as Prophet, to forecast real-time demand and optimize resource provisioning dynamically. The framework consists of key components, including a central cloud resource orchestrator, edge cloud nodes, a traffic prediction module, and a resource orchestrator for cluster-wide task scheduling and load balancing. A feedback mechanism ensures continuous updates on resource utilization, enabling adaptive scaling and efficient workload distribution. Additionally, an optimization engine is incorporated to balance energy efficiency and privacy constraints while maintaining service quality. This research is implemented and evaluated using CloudSim Plus, a simulation framework for cloud computing environments and NS3, a discrete-event network simulator for internet systems. The effectiveness of the proposed approach is assessed based on metrics such as resource utilization, energy consumption, and service latency. Experimental results demonstrate that the closed-loop framework enhances the adaptability of 5G edge clouds, reducing underutilization and overprovisioning while improving overall system efficiency. The findings of this study contribute to the advancement of intelligent resource management in 5G edge computing, paving the way for scalable and autonomous cloud-native network operations. Future work may explore the integration of reinforcement learning-based optimization techniques to further enhance decision-making in dynamic edge cloud environments.
item: Thesis-Full-text
Novel resource allocation technique for demand aware 5G edge cloud dimensioning
(2025) Sivanujan, Y; Sumanasena, A; Hemachandra, K
The rapid proliferation of 5G networks has led to increased demand for efficient resource allocation at the edge cloud to support latency-sensitive and compute-intensive applications. This thesis presents an optimal resource allocation framework for 5G edge cloud environments, leveraging a closed-loop architecture that integrates demand prediction, intelligent scheduling, and load balancing. The proposed framework employs machine learning-based traffic prediction models, such as Prophet, to forecast real-time demand and optimize resource provisioning dynamically. The framework consists of key components, including a central cloud resource orchestrator, edge cloud nodes, a traffic prediction module, and a resource orchestrator for cluster-wide task scheduling and load balancing. A feedback mechanism ensures continuous updates on resource utilization, enabling adaptive scaling and efficient workload distribution. Additionally, an optimization engine is incorporated to balance energy efficiency and privacy constraints while maintaining service quality. This research is implemented and evaluated using CloudSim Plus, a simulation framework for cloud computing environments and NS3, a discrete-event network simulator for internet systems. The effectiveness of the proposed approach is assessed based on metrics such as resource utilization, energy consumption, and service latency. Experimental results demonstrate that the closed-loop framework enhances the adaptability of 5G edge clouds, reducing underutilization and overprovisioning while improving overall system efficiency. The findings of this study contribute to the advancement of intelligent resource management in 5G edge computing, paving the way for scalable and autonomous cloud-native network operations. Future work may explore the integration of reinforcement learning-based optimization techniques to further enhance decision-making in dynamic edge cloud environments.
item: Thesis-Abstract
Service-based 5G network dimensioning framework
(2025) Keeragala, KAVCK; Sumanasena, A; Hemachandra, K
The fifth generation (5G) mobile networks are designed to deliver multiple services with varying requirements and are mostly delivered through a single New Radio (NR) carrier in an early 5G network. However, designing a network to serve multiple services will not be simple and will drastically vary depending on the user demand and the business requirements of the operator. This thesis introduces a service-based 5G network dimensioning framework designed to address the multifaceted requirements of such 5G networks that deliver multiple services through the same NR carrier. Unlike previous studies that rely solely on theoretical models and are based on population census data which do not provide an accurate distribution of users, this research uses real user traffic data and Minimization of Drive Test (MDT) based serving locations to create an accurate and very granular traffic map with user geolocation to service areas of dimension 100m x 100m, offering a practical approach to accurate network dimensioning. The thesis clearly differentiates between the Mobile and Fixed service throughput requirements and how network operators should consider both services in dimensioning a network considering the each service users separately, while previous studies mainly focused on coverage and capacity dimensioning for a common cell edge throughput. The thesis elaborates how the increase in user density due to demographic variation or capture rate impacts the utilization of 5G NR resources and impacts dimensioning parameters contributing to the increase of required Next Generation Node B (gNodeB) to cover certain areas such as hot-spots. Further, the thesis briefly goes through pre-dimensioning process before dimensioning of gNodeB cells, where crucial decision for spectrum selection is done giving a holistic view of the dimensioning process, which are not commonly addressed in existing literature. This work is particularly beneficial for operators in emerging markets, providing a practical guide to address challenges in early stage 5G deployments with mixed services on the same carrier.
item: Thesis-Abstract
Valuation approach for 5G spectrum in Sri Lanka
(2023) Jayasinghe, SL; Sumanasena , A; Hemachandra, K
The radio spectrum is a scarce and vital resource. Every country in the world has sovereign control over its spectrum. Demand for the spectrum in terms of mobile communication-related applications is increasing continuously. As a result, demand for additional spectrum acquisition is also increased. 5G technology, as the latest generation of mobile telephony, having many advanced features have opened many doors to other industries such as virtual reality, the Internet of Things(IoT), smart cities, smart health care, etc., and demand for them is trending. As a result of its technological advancement of 5G technology, network operators are keen enough to migrate to 5G considering its economic benefit. Due to the excessive demand for the spectrum, the true value of the spectrum has gone up drastically. Therefore, the understanding of the true value of the spectrum has become a deem requirement to acquire them at the right price. The main objective of this research is to identify a suitable method to estimate the value of the 5G spectrum of Sri Lanka. A literature survey compares techniques used for estimating the value of the spectrum. More specifically, this research tests the suitability of multivariate regression analysis to estimate the value of 5G spectrum. This study estimates the value of 5G spectrum in Sri Lanka using multivariate regression analysis and compares its accuracy with the historical auction prices.
item: Thesis-Full-text
Compressed sensing-based receivers for spatial modulation-MIMO NOMA systems
(2023) Thuvarakan, M; Wavegedara, C
Spatial modulation (SM) offers a promising solution to the demands of up-and-coming wireless networks. In this thesis, the Generalized Orthogonal Matching Pursuit (gOMP) algorithm is introduced into the receiver of NOMA aided SM-MIMO system to improve processing speed and reduce computational complexity. Furthermore, we present comprehensive analysis of the latest research contributions for NOMA based SM-MIMO system which has considerable attention recently. In addition, we focused our attention on the CS based receivers for NOMA based SM and compared the complexity which contributes performance degradation, with typical ML detectors. Detection of SM can be thought of as a process of sparse reconstruction, due to its an inherent property of sparsity. The sparse signal can be detected by Compressed sensing (CS) algorithm which became competitive alternatives. Some modified CS detection algorithms which showed improved performance compared to the conventional CS based once, have not been adopted in NOMA based SM-MIMO system. We formulate the recovery problem by exploiting the SM-MIMO communication system as downlink power domain NOMA where the base station supports more users in downlink scenario. Also, we deployed a low complexity pairing in this system to increase sum throughput. In addition, we analyzed the behavior of gOMP algorithm at this system. Simulations show that the gOMP detector outperforms conventional CS detection schemes