no code implementations • 6 Apr 2025 • Yuquan Xiao, Qinghe Du, Wenchi Cheng, George K. Karagiannidis, Arumugam Nallanathan, Mohsen Guizani
Generative Artificial Intelligence (GenAI) is playing an increasingly important role in enriching and facilitating human life by generating various useful information, of which real-time GenAI is a significant part and has great potential in applications such as real-time robot control, automated driving, augmented reality, etc.
no code implementations • 12 Feb 2025 • Xiaoxia Xu, Xidong Mu, Yuanwei Liu, Arumugam Nallanathan
To solve this highly coupled and nonconvex problem, both optimization-based and learning-based methods are proposed.
no code implementations • 3 Feb 2025 • Jia Guo, Yuanwei Liu, Arumugam Nallanathan
The GPASS is with a staged architecture, where the positions of pinching antennas are first learned by a sub-GNN.
no code implementations • 3 Feb 2025 • Jia Guo, Xiaoxia Xu, Yuanwei Liu, Arumugam Nallanathan
The potential of applying diffusion models (DMs) for multiple antenna communications is discussed.
no code implementations • 26 Jan 2025 • Ni Zhang, Kunlun Wang, Wen Chen, Jing Xu, Yonghui Li, Arumugam Nallanathan
We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation, subchannel-vehicle matching and power allocation.
no code implementations • 11 Jan 2025 • Yuang Chen, Hancheng Lu, Langtin Qin, Yansha Deng, Arumugam Nallanathan
In this article, we develop a NOMA-assisted uplink xURLLC network architecture that incorporates an SNC-based SQP theoretical framework (SNC-SQP) to support tail distribution analysis in terms of delay, age-of-information (AoI), and reliability.
no code implementations • 19 Dec 2024 • Hao Jiang, Zhaolin Wang, Yuanwei Liu, Arumugam Nallanathan
A Cram\'er-Rao bound (CRB) optimization framework for near-field sensing (NISE) with continuous-aperture arrays (CAPAs) is proposed.
no code implementations • 14 Nov 2024 • Jia Guo, Yuanwei Liu, Hyundong Shin, Arumugam Nallanathan
A DeepCAPA (Deep Learning for Continuous Aperture Array (CAPA)) framework is proposed to learn beamforming in CAPA systems.
1 code implementation • 8 Oct 2024 • Xiaoxia Xu, Xidong Mu, Yuanwei Liu, Hong Xing, Yue Liu, Arumugam Nallanathan
First, a DM-driven communication architecture is proposed, which introduces two key paradigms, i. e., conditional DM and DM-driven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively.
no code implementations • 20 Aug 2024 • Jia Guo, Yuanwei Liu, Arumugam Nallanathan
The continuous aperture array (CAPA) can provide higher degree-of-freedom and spatial resolution than the spatially discrete array (SDPA), where optimizing multi-user current distributions in CAPA systems is crucial but challenging.
no code implementations • 9 Jul 2024 • Xiaoxia Xu, Yuanwei Liu, Xidong Mu, Hong Xing, Arumugam Nallanathan
A novel accelerated mobile edge generation (MEG) framework is proposed for generating high-resolution images on mobile devices.
no code implementations • 13 Jun 2024 • Yining Wang, Wanli Ni, Wenqiang Yi, Xiaodong Xu, Ping Zhang, Arumugam Nallanathan
Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios.
no code implementations • 17 Oct 2023 • Felix Obite, Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni
This paper investigates the design of joint subchannel and power allocation in an uplink UAV-based cognitive NOMA network.
no code implementations • 20 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.
no code implementations • 15 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.
no code implementations • 16 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.
no code implementations • 24 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.
no code implementations • 17 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.
no code implementations • 25 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.
no code implementations • 15 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.
no code implementations • 10 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.
no code implementations • 20 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.
no code implementations • 5 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.
no code implementations • 17 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.
no code implementations • 28 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.
no code implementations • 25 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.
no code implementations • 10 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.
1 code implementation • 21 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.
1 code implementation • 7 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.
no code implementations • 23 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.
no code implementations • 14 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.
no code implementations • 24 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.
no code implementations • 12 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.
no code implementations • 22 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).
no code implementations • 20 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.
no code implementations • 24 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.
no code implementations • 18 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.
no code implementations • 17 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.
no code implementations • 20 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.
no code implementations • 14 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.
no code implementations • 10 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).
no code implementations • 17 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.
no code implementations • 11 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.
no code implementations • 10 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.
1 code implementation • 18 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.
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