Search Results for author: Arumugam Nallanathan

Found 33 papers, 1 papers with code

Active Inference for Sum Rate Maximization in UAV-Assisted Cognitive NOMA Networks

no code implementations20 Sep 2023 Felix Obite, Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni

Given the surge in wireless data traffic driven by the emerging Internet of Things (IoT), unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access (NOMA) have been recognized as promising techniques to overcome massive connectivity issues.

Adaptive Federated Pruning in Hierarchical Wireless Networks

no code implementations15 May 2023 Xiaonan Liu, Shiqiang Wang, Yansha Deng, Arumugam Nallanathan

We present the convergence analysis of an upper on the l2 norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation.

Federated Learning Privacy Preserving

Computation and Privacy Protection for Satellite-Ground Digital Twin Networks

no code implementations16 Feb 2023 Yongkang Gong, Haipeng Yao Xiaonan Liu, Mehdi Bennis, Arumugam Nallanathan, Zhu Han

Satellite-ground integrated digital twin networks (SGIDTNs) are regarded as innovative network architectures for reducing network congestion, enabling nearly-instant data mapping from the physical world to digital systems, and offering ubiquitous intelligence services to terrestrial users.


Federated Learning and Meta Learning: Approaches, Applications, and Directions

no code implementations24 Oct 2022 Xiaonan Liu, Yansha Deng, Arumugam Nallanathan, Mehdi Bennis

Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.

Decision Making Federated Learning +2

Goal-Oriented Semantic Communications for 6G Networks

no code implementations17 Oct 2022 Hui Zhou, Yansha Deng, Xiaonan Liu, Nikolaos Pappas, Arumugam Nallanathan

In this article, we propose a generic goal-oriented semantic communication framework for various tasks with diverse data types, which incorporates both semantic level information and effectiveness-aware performance metrics.

Knowledge-aided Federated Learning for Energy-limited Wireless Networks

no code implementations25 Sep 2022 Zhixiong Chen, Wenqiang Yi, Yuanwei Liu, Arumugam Nallanathan

Inspired by this, we define a new objective function, i. e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control.

Federated Learning Scheduling

Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing

no code implementations15 Aug 2022 Zhixiong Chen, Wenqiang Yi, Atm S. Alam, Arumugam Nallanathan

To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution.


A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach

no code implementations10 Aug 2022 Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study.

Bayesian Inference Q-Learning

Efficient Wireless Federated Learning with Partial Model Aggregation

no code implementations20 Apr 2022 Zhixiong Chen, Wenqiang Yi, Arumugam Nallanathan, Geoffrey Ye Li

On this basis, we maximize the scheduled data size to minimize the global loss function through jointly optimize the device scheduling, bandwidth allocation, computation and communication time division policies with the assistance of Lyapunov optimization.

Federated Learning Scheduling

Towards Explainable Meta-Learning for DDoS Detection

no code implementations5 Apr 2022 Qianru Zhou, Rongzhen Li, Lei Xu, Arumugam Nallanathan, Jian Yang, Anmin Fu

With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more.

Intrusion Detection Meta-Learning

Optimization of Grant-Free NOMA with Multiple Configured-Grants for mURLLC

no code implementations17 Nov 2021 Yan Liu, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, George K. Karagiannidis

To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs.

Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT

no code implementations28 Oct 2021 Shunpu Tang, Lunyuan Chen, Ke HeJunjuan Xia, Lisheng Fan, Arumugam Nallanathan

In this paper, we investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks.

AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

no code implementations25 Mar 2021 Yasaman Omid, Seyed MohammadReza Hosseini, Seyyed MohammadMahdi Shahabi, Mohammad Shikh-Bahaei, Arumugam Nallanathan

Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.

reinforcement-learning Reinforcement Learning (RL)

Machine Learning for Massive Industrial Internet of Things

no code implementations10 Mar 2021 Hui Zhou, Changyang She, Yansha Deng, Mischa Dohler, Arumugam Nallanathan

With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements.

BIG-bench Machine Learning

Learning based signal detection for MIMO systems with unknown noise statistics

no code implementations21 Jan 2021 Ke He, Le He, Lisheng Fan, Yansha Deng, George K. Karagiannidis, Arumugam Nallanathan

Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics.

Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems

no code implementations7 Jan 2021 Le He, Ke He, Lisheng Fan, Xianfu Lei, Arumugam Nallanathan, George K. Karagiannidis

This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems.

RACH in Self-Powered NB-IoT Networks: Energy Availability and Performance Evaluation

no code implementations23 Nov 2020 Yan Liu, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, Jinhong Yuan, Ranjan K. Mallik

In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission.

Analysis of Random Access in NB-IoT Networks with Three Coverage Enhancement Groups: A Stochastic Geometry Approach

no code implementations14 Sep 2020 Yan Liu, Yansha Deng, Nan Jiang, Maged Elkashlan, Arumugam Nallanathan

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks.

Low-Complexity Robust Beamforming Design for IRS-Aided MISO Systems with Imperfect Channels

no code implementations24 Aug 2020 Yasaman Omid, Seyyed MohammadMahdi Shahabi, Cunhua Pan, Yansha Deng, Arumugam Nallanathan

In this paper, large-scale intelligent reflecting sur-face (IRS)-assisted multiple-input single-output (MISO) system is considered in the presence of channel uncertainty.

Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks

no code implementations12 Aug 2020 Tiankui Zhang, Ziduan Wang, Yuanwei Liu, Wenjun Xu, Arumugam Nallanathan

In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance.

Q-Learning Scheduling

Cache-enabling UAV Communications: Network Deployment and Resource Allocation

no code implementations22 Jul 2020 Tiankui Zhang, Yi Wang, Yuanwei Liu, Wenjun Xu, Arumugam Nallanathan

We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS).

Joint Transmit Power and Placement Optimization for URLLC-enabled UAV Relay Systems

no code implementations20 Jul 2020 Hong Ren, Cunhua Pan, Kezhi Wang, Wei Xu, Maged Elkashlan, Arumugam Nallanathan

This letter considers an unmanned aerial vehicle (UAV)-enabled relay communication system for delivering latency-critical messages with ultra-high reliability, where the relay is operating under amplifier-and-forward (AF) mode.

Robust Transmission Design for Intelligent Reflecting Surface Aided Secure Communication Systems with Imperfect Cascaded CSI

no code implementations24 Apr 2020 Sheng Hong, Cunhua Pan, Hong Ren, Kezhi Wang, Kok Keong Chai, Arumugam Nallanathan

To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model that usually models the channel estimation error.

Analyzing Grant-Free Access for URLLC Service

no code implementations18 Feb 2020 Yan Liu, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, George K. Karagiannidis

Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances.

Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface

no code implementations17 Feb 2020 Sheng Hong, Cunhua Pan, Hong Ren, Kezhi Wang, Arumugam Nallanathan

To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the TPC matrix, AN covariance matrix, and phase shifts while keeping SR non-decreasing.

A Framework of Robust Transmission Design for IRS-aided MISO Communications with Imperfect Cascaded Channels

no code implementations20 Jan 2020 Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Arumugam Nallanathan

Specifically, the transmit power minimization problems are formulated subject to the worst-case rate constraints under the bounded CSI error model and the rate outage probability constraints under the statistical CSI error model, respectively.

Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems

no code implementations14 Nov 2019 Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Marco Di Renzo, Arumugam Nallanathan

In this paper, we study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI.

Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing

no code implementations10 Nov 2019 Liang Wang, Kezhi Wang, Cunhua Pan, Wei Xu, Nauman Aslam, Arumugam Nallanathan

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE).

Edge-computing reinforcement-learning +1

Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing

no code implementations17 Oct 2019 Tong Bai, Cunhua Pan, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, Lajos Hanzo

In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS.


Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge

no code implementations11 Oct 2019 Zhiyong Du, Yansha Deng, Weisi Guo, Arumugam Nallanathan, Qihui Wu

To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations.

Decision Making Management +3

Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

no code implementations10 Sep 2019 Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Arumugam Nallanathan

We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint.

A Machine Learning Approach for Power Allocation in HetNets Considering QoS

1 code implementation18 Mar 2018 Roohollah Amiri, Hani Mehrpouyan, Lex Fridman, Ranjan K. Mallik, Arumugam Nallanathan, David Matolak

However, as the density of the network increases so does the complexity of such resource allocation methods.

Information Theory Information Theory

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