Electronic Engineering and Computer Science: Recent submissions

Now showing items 141-160 of 3490

    • Distributed Digital Twin Migration in Multi-Tier Computing Systems 

      Chen, Z; Yi, W; Nallanathan, A; Chambers, JA (IEEE, 2024-01-26)
      At the network edges, the multi-tier computing framework provides mobile users with efficient cloud-like computing and signal processing capabilities. Deploying digital twins in the multi-tier computing system helps to ...

    • E2E Network Slicing Optimization for Control- and User-Plane Separation-based SAGINs with DRL 

      Wang, Y; Zhao, L; Chu, X; Song, S; Deng, Y; Nallanathan, A; Zhou, G (IEEE, 2024-06-18)
      Existing works on space-air-ground integrated networks (SAGINs) have not sufficiently explored the synergy between control- and user-plane separation (CUPS) and end-to-end (E2E) network slicing. In this paper, we present ...

    • Computation Efficient Task Offloading and Bandwidth Allocation in VEC Networks 

      Zhang, N; Liang, S; Wang, K; Wu, Q; Nallanathan, A (IEEE, 2024-05-30)
      With the development of intelligent connected vehicles, vehicular edge computing (VEC) systems can provide them with low latency and low energy consumption of task computation and data processing. In this paper, we propose ...

    • Contrastive Learning based Semantic Communications 

      Tang, S; Yang, Q; Fan, L; Lei, X; Nallanathan, A; Karagiannidis, GK (IEEE, 2024-05-14)
      Recently, there has been a growing interest in learning-based semantic communication because it can prioritize the preservation of meaningful semantic information over the accuracy of the transmitted symbols, resulting in ...

    • Cost-efficient Cooperative Video Caching Over Edge Networks 

      Zhu, B; Zhao, L; Yi, W; Chen, Z; Nallanathan, A (IEEE, 2024-04-12)
      Cooperative caching has emerged as an efficient way to alleviate backhaul traffic and enhance user experience by proactively prefetching popular videos at the network edge. However, it is challenging to achieve the optimal ...

    • Covert Communication of STAR-RIS Aided NOMA Networks 

      Li, X; Tian, Z; He, W; Chen, G; Gursoy, MC; Mumtaz, S; Nallanathan, A (IEEE, 2024-01-04)
      This paper investigates covert communication in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) systems, assuming for the sake of being ...

    • Hybrid NOMA Offloading With Semi-Dynamic IRS Beamforming in Wireless Powered MEC Systems 

      Zhang, X; Lv, L; Yang, L; Chu, X; Nallanathan, A; Chen, J (Institute of Electrical and Electronics Engineers (IEEE), 2024-06-24)
      To address the power and computation limitations of the Internet-of-Things (IoT) devices, this letter investigates integrating wireless power transfer (WPT) and mobile edge computing (MEC) with intelligent reflective ...

    • Joint Active and Passive Beamforming in RIS-Assisted Covert Symbiotic Radio Based on Deep Unfolding 

      He, X; Xu, H; Wang, J; Xie, W; Li, X; Nallanathan, A (IEEE, 2024-04-25)
      In this paper, we consider an reconfigurable intelligent surface (RIS)-assisted multiple input single output (MISO) covert symbiotic radio (SR) communication system. RIS, as a secondary transmitter (STx), can enhance primary ...

    • Iterative Joint Frequency Synchronization and Channel Estimation for Uplink Massive MIMO 

      Feng, Y; Shen, H; Lu, W; Zhao, N; Nallanathan, A (IEEE, 2024-05-29)
      As the number of users connected to communication networks such as cellular networks and Internet of Things (IoT) networks increases, massive multiple-input multiple-output (MIMO) technique has been widely adopted to improve ...

    • Efficient Wireless Federated Learning with Partial Model Aggregation 

      Chen, Z; Yi, W; Shin, H; Nallanathan, A; Li, GY (IEEE, 2024-05-03)
      The data heterogeneity across clients and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To tackle these challenges, we first devise ...

    • Exploring Representativity in Device Scheduling for Wireless Federated Learning 

      Chen, Z; Yi, W; Nallanathan, A (IEEE, 2023-06-06)
      Existing device scheduling works in wireless federated learning (FL) mainly focused on selecting the devices with maximum gradient norm or loss function and require all devices to perform local training in each round. This ...

    • Fast Multibeam Training for RIS-Assisted Millimeter Wave Massive MIMO 

      Zhang, C; Qi, C; Nallanathan, A (IEEE, 2023-11-16)
      For reconfigurable intelligent surface (RIS)-assisted mmWave massive MIMO, we propose a fast multibeam training (FMT) scheme with two stages. In the first stage, we find a multibeam together with the RIS reflection ...

    • Federated Contrastive Learning for Personalized Semantic Communication 

      Wang, Y; Ni, W; Yi, W; Xu, X; Zhang, P; Nallanathan, A (IEEE, 2024-06-17)
      In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple ...

    • Finite SNR Diversity-Multiplexing Trade-off in Hybrid ABCom/RCom-Assisted NOMA Networks 

      Li, X; Zheng, Y; Zhang, J; Dang, S; Nallanathan, A; Mumtaz, S (IEEE, 2024-01-23)
      The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we ...

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

      Liu, X; Deng, Y; Nallanathan, A; Bennis, M (IEEE, 2023-11-07)
      Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ...

    • Joint Optimization for Covert Communications in UAV-Assisted NOMA Networks 

      Deng, D; Dang, S; Li, X; Ng, DWK; Nallanathan, A (IEEE, 2023-08-25)
      In this article, we investigate the design of the trajectory of an unmanned aerial vehicle (UAV) and the transmit power of ground users to improve covert communications against a flying warden in UAV-assisted non-orthogonal ...

    • Mixture of Normalizing Flows for European Option Pricing 

      Yang, Y; Hospedales, TM; UAI (2023-01-01)
      We present a mixture of normalizing flows (MoNF) approach to European option pricing with guarantees that its estimations are free from static arbitrage. In contrast to many existing methods that meet economic rationality ...

    • Radar-Aided Beam Selection in MIMO Communication Systems: A Federated Transfer Learning Approach 

      Zhou, Q; Gong, Y; Nallanathan, A (IEEE, 2024-03-12)
      By leveraging massive available data and hidden communication patterns, deep learning (DL) has enabled diverse applications in wireless network operations. In this paper, we consider radar-aided beam prediction in multi-input ...

    • Open-Source Edge AI for 6G Wireless Networks 

      Zhao, L; Wang, Y; Chu, X; Song, S; Deng, Y; Nallanathan, A; Karagiannidis, GK (IEEE, 2024-06-10)
      Multi-access Edge Computing (MEC) has been recognized as a key enabler for next-generation networks in supporting a large variety of compelling applications with challenging requirements. With its widely proved strength ...

    • Two-Way Satellite-HAP-Terrestrial Networks with Non-Orthogonal Multiple Access 

      Guo, K; Shuai, H; Li, X; Yang, L; Tsiftsis, TA; Nallanathan, A; Wu, M (2024-01-01)
      Satellite-high altitude platform (HAP)-terrestrial networks have been considered as an indispensable infrastructure of next-generation networks because they can offer massive access service with high throughput and broad ...