Now showing items 161-180 of 3490

    • Robust Secure Precoding for UAV-Aided Multi-beam Satellite NOMA Communications 

      Huang, M; Li, G; Gong, F; Zhang, N; Yin, Z; Li, X; Nallanathan, A; Ding, Z (2024-01-01)
      The wide coverage and broadcasting characteristics of satellite communications lead to multi-beam downlinks being vulnerable to security threats, such as eavesdropping, hacking and illegal access. This paper takes into ...
    • Hardness in Markov Decision Processes: Theory and Practice 

      Conserva, M; Rauber, P (2022-01-01)
      Meticulously analysing the empirical strengths and weaknesses of reinforcement learning methods in hard (challenging) environments is essential to inspire innovations and assess progress in the field. In tabular reinforcement ...
    • QoS-Aware Resource Allocation of RIS-Aided Multi-User MISO Wireless Communications 

      Gao, Y; Lu, C; Lian, Y; Li, X; Chen, G; Da Costa, DB; Nallanathan, A (2024-02-01)
      In this article, it is investigated a multi-user multiple-input single-output wireless communication scenario (MISO) in which a base station (BS) serves multiple users with the assistance of a reconfigurable intelligent ...
    • Rate-Splitting Multiple Access-Based Cognitive Radio Network with ipSIC and CEEs 

      Gao, X; Li, X; Han, C; Zeng, M; Liu, H; Mumtaz, S; Nallanathan, A (2024-01-01)
      In this paper, we study the outage and ergodic rate performance of a rate-splitting multiple access (RSMA)-based cognitive radio (CR) system, where the secondary transmitter aims to communicate with two RSMA users. Imperfect ...
    • Performance Analysis of Fingerprint-Based Indoor Localization 

      Yang, L; Wu, N; Xiong, Y; Yuan, W; Li, B; Li, Y; Nallanathan, A (2024-01-01)
      Fingerprint-based indoor localization holds great potential for the Internet of Things. Despite numerous studies focusing on its algorithmic and practical aspects, a notable gap exists in theoretical performance analysis ...
    • RIS-aided Near-Field MIMO Communications: Codebook and Beam Training Design 

      Lv, S; Liu, Y; Xu, X; Nallanathan, A; Swindlehurst, AL (2024-01-01)
      Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information ...
    • Offline-Online Design for Energy-Efficient IRS-Aided UAV Communications 

      Wang, T; Pang, X; Liu, M; Zhao, N; Nallanathan, A; Wang, X (2024-02-01)
      In this correspondence, we consider the intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) uplink transmission, where a UAV collects data from ground users via an IRS. The objective is to maximize ...
    • Short-Packet Edge Computing Networks With Execution Uncertainty 

      Lai, X; Wu, T; Pan, C; Mai, L; Nallanathan, A (2024-01-01)
      Low-latency computational tasks in Internet-of-Things (IoT) networks require short-packet communications. In this paper, we consider a mobile edge computing (MEC) network under time division multiple access (TDMA)-based ...
    • Sum Rate of Extremely Large STAR-RIS Aided Uplink NOMA 

      Qureshi, HA; Oh, S; Khan, N; Kim, YH; Nallanathan, A (2024-01-01)
      This letter investigates the effect of an extremely large (XL) simultaneously transmitting and reflecting intelligent reconfigurable surface (STAR-RIS) on the sum rate of the uplink nonorthogonal multiple access (NOMA). ...
    • Robust Federated Learning for Unreliable and Resource-limited Wireless Networks 

      Chen, Z; Yi, W; Liu, Y; Nallanathan, A (2024-01-01)
      Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm that enables massive edge devices to train machine learning models collaboratively. Although various communication schemes have ...
    • Resource Allocation for Secure URLLC in Mission-Critical IoT Scenarios 

      Ren, H; Pan, C; Deng, Y; Elkashlan, M; Nallanathan, A (2020)
    • Measurement challenges for cyber cyber digital twins: Experiences from the deployment of facebooksww simulation system 

      Bojarczuk, K; Dvortsova, I; George, J; Gucevska, N; Harman, M; Lomeli, M; Lucas, S; Meijer, E; Rojas, R; Sapora, S (Association for Computing Machinery, 2021-10-11)
      A cyber cyber digital twin is a deployed software model that executes in tandem with the system it simulates, contributing to, and drawing from, the systems behaviour. This paper outlines Facebooks cyber cyber digital twin, ...
    • Posterior Sampling for Deep Reinforcement Learning 

      Sasso, R; Conserva, M; Rauber, P (2023-01-01)
      Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-based algorithms promise sample efficiency by ...
    • DeviceRadar: Online IoT Device Fingerprinting in ISPs Using Programmable Switches 

      Li, R; Li, Q; Lin, T; Zou, Q; Zhao, D; Huang, Y; Tyson, G; Xie, G; Jiang, Y (IEEE, 2024-05-17)
      Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some important ...
    • When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective 

      Chen, Y; Lu, H; Qin, L; Deng, Y; Nallanathan, A (IEEE, 2023-12-12)
      The advent of next-generation ultra-reliable and low-latency communications (xURLLC) presents stringent and unprecedented requirements for key performance indicators (KPls). As a disruptive technology, non-orthogonal ...
    • Choosing Representation, Mutation, and Crossover in Genetic Algorithms 

      Dockhorn, A; Lucas, S (IEEE, 2022-11-01)
      This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically ...
    • Adaptive Federated Pruning in Hierarchical Wireless Networks 

      Liu, X; Wang, S; Deng, Y; Nallanathan, A (IEEE, 2023-11-08)
      Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a ...
    • Deep Learning-Based CFO Estimation for Multi-User Massive MIMO With One-Bit ADCs 

      Feng, Y; Zhou, K; Han, H; Lu, W; Tang, J; Zhao, N; Nallanathan, A (IEEE, 2024-02-26)
      Low-resolution architectures represent a compelling and power-efficient approach for high-bandwidth communication in massive multiple-input multiple-output (MIMO) systems. In this letter, we present a novel residual ...
    • Multiuser Beamforming for Partially-Connected Millimeter Wave Massive MIMO 

      Qi, C; Hu, J; Du, Y; Nallanathan, A (IEEE, 2023-11-10)
      Multiuser beamforming is considered for partially-connected millimeter wave massive MIMO systems. Based on perfect channel state information (CSI), a low-complexity hybrid beamforming scheme that decouples the analog ...
    • Resource and Trajectory Optimization for UAV-Relay-Assisted Secure Maritime MEC 

      Lu, F; Liu, G; Lu, W; Gao, Y; Cao, J; Zhao, N; Nallanathan, A (IEEE, 2023-11-07)
      With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able ...