524 citations · 624 across the 27 of their papers we have counts for
28 papers
Beamforming and RIS-Aided Ambient Backscatter Communications with Residual-Feature SVM Detection
Aobakwe Makgarapa, Burhan Wafai, Sangarapillai Lambotharan +1
Ambient backscatter communication (AmBC) enables battery-free connectivity by modulating data onto existing radio-frequency (RF) signals, eliminating the need for dedicated power s…
Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA
Yogachandran Rahulamathavan, Nasir Iqbal, Juncheng Hu +1
Continual machine unlearning aims to remove the influence of data that should no longer be retained, while preserving the usefulness of the model on everything else. This setting b…
QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model Quantisation
Charuka Herath, Yogachandran Rahulamathavan, Varuna De Silva +1
Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequen…
Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems
Marios Aristodemou, Yasaman Omid, Sangarapillai Lambotharan +2
The integration of satellite communication networks with next-generation (NG) technologies is a promising approach towards global connectivity. However, the quality of services is…
DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation
Charuka Herath, Yogachandran Rahulamathavan, Varuna De Silva +1
Federated Learning (FL) enables decentralized model training without sharing raw data, offering strong privacy guarantees. However, existing FL protocols struggle to defend against…
LLM-Enhanced Space-Air-Ground-Sea Integrated Networks
Halvin Yang, Sangarapillai Lambotharan, Mahsa Derakhshani +1
The space-air-ground-sea integrated networking (SAGSIN) concept promises seamless global multimedia connectivity, yet two obstacles still limit its practical deployment. Firstly, h…
Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +4
The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulatio…
A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +1
Advantages of deep learning over traditional methods have been demonstrated for radio signal classification in the recent years. However, various researchers have discovered that e…
Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +3
Due to great success of transformers in many applications such as natural language processing and computer vision, transformers have been successfully applied in automatic modulati…
FAS-LLM: Large Language Model-Based Channel Prediction for OTFS-Enabled Satellite-FAS Links
Halvin Yang, Sangarapillai Lambotharan, Mahsa Derakhshani
This paper proposes FAS-LLM, a novel large language model (LLM)-based architecture for predicting future channel states in Orthogonal Time Frequency Space (OTFS)-enabled satellite…
Performance Analysis of Fluid Antenna System Aided OTFS Satellite Communications
Halvin Yang, Mahsa Derakhshani, Sangarapillai Lambotharan +1
Internet-of-Things (IoT) networks typically rely on satellite communications to provide coverage in rural areas. However, high-mobility satellite links introduce severe Doppler and…
RIS-Empowered Integrated Location Sensing and Communication with Superimposed Pilots
Wenchao Xia, Ben Zhao, Wankai Tang +4
In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-a…
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning
Marios Aristodemou, Xiaolan Liu, Yuan Wang +3
As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine…
A General Sensing-assisted Channel Estimation Framework in Distributed MIMO Network
Hui Zhou, Xiaolan Liu, Sangarapillai Lambotharan
In 6G communications, it is envisioned to equip the traditional access point (AP) with sensing capability to fully benefit the existing wireless communication infrastructures. Thus…
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection
Charuka Herath, Xiaolan Liu, Sangarapillai Lambotharan +1
Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, securi…
Reinforcement Learning-Based Downlink Transmit Precoding for Mitigating the Impact of Delayed CSI in Satellite Systems
Yasaman Omid, Marios Aristodemou, Sangarapillai Lambotharan +2
The integration of low earth orbit (LEO) satellites with terrestrial communication networks holds the promise of seamless global connectivity. The efficiency of this connection, ho…
A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +3
Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applyi…
Countermeasures Against Adversarial Examples in Radio Signal Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +2
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vuln…
Tackling Delayed CSI in a Distributed Multi-Satellite MIMO Communication System
Yasaman Omid, Sangarapillai Lambotharan, Mahsa Derakhshani
In this study, we explore the integration of satellites with ground-based communication networks. Specifically, we analyze downlink data transmission from a constellation of satell…
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
Yogachandran Rahulamathavan, Charuka Herath, Xiaolan Liu +2
The federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm. While FL ensures that a user's data always remain…
Distributed Intelligence in Wireless Networks
Xiaolan Liu, Jiadong Yu, Yuanwei Liu +4
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wire…
Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines
Tiep M. Hoang, Trung Q. Duong, Hoang Duong Tuan +3
This paper presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdroppin…
Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration
Juping Zhang, Wenchao Xia, Minglei You +3
This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has it…
Edge Caching in Dense Heterogeneous Cellular Networks with Massive MIMO Aided Self-backhaul
Lifeng Wang, Kai-Kit Wong, Sangarapillai Lambotharan +2
This paper focuses on edge caching in dense heterogeneous cellular networks (HetNets), in which small base stations (SBSs) with limited cache size store the popular contents, and m…