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Browsing Staff publications (AIRS) by Publisher "IEEE"

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    Blockchain and distributed digital watermarking effort on federated learning: innovating intellectual property protection
    (IEEE, 2024-12-02) Chao, Kailin; Li, JunJie; Jiang, Yirui; Xiao, Jianmao; Cao, Yuanlong
    Federated Learning with Digital Watermarks (FLDW) have been recognized as a promising solution for property protection. However, the existing FLDW-related technologies neglect the requirements of decentralized settings, leading to recurrent issues such as discrepancies in distributed client data. This paper introduces a Blockchain Federated Learning Intellectual Property Protection Framework (BFLIPR), to address the data security and model validation challenges in decentralized federated learning environments. BFLIPR merges blockchain, digital watermarking, and federated learning technologies. By harnessing the blockchain’s tamper-proof properties, digital watermarking’s concealment capabilities, and federated learning’s distributed feature, the framework offers a solution that aligns with intellectual property protection mechanism, to bolster data security and property safeguarding. Experimental findings demonstrate its high feasibility and robust for data privacy and model security in the federated learning.
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    Cascade network stability of synchronized traffic load balancing with heterogeneous energy efficiency policies
    (IEEE, 2024-12-08) Zou, Mengbang; Guo, Weisi
    Cascade stability of load balancing is critical for ensuring high efficiency service delivery and preventing undesirable handovers. In energy efficient networks that employ diverse sleep mode operations, handing over traffic to neighbouring cells' expanded coverage must be done with minimal side effects. Current research is largely concerned with designing distributed and centralized efficient load balancing policies that are locally stable. There is a major research gap in identifying largescale cascade stability for networks with heterogeneous load balancing policies arising from diverse plug-and-play sleep mode policies in ORAN, which will cause heterogeneity in the network stability behaviour. Here, we investigate whether cells arbitrarily connected for load balancing and having an arbitrary number undergoing sleep mode can: (i) synchronize to a desirable load-balancing state, and (ii) maintain stability. For the first time, we establish the criterion for stability and prove its validity for any general load dynamics and random network topology. Whilst its general form allows all load balancing and sleep mode dynamics to be incorporated, we propose an ORAN architecture where the network service management and orchestration (SMO) must monitor new load balancing policies to ensure overall network cascade stability.
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    Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
    (IEEE, 2024-12-08) Li, Jiawei; Wang, Dawei; Zhang, Jiankang; Alfarraj, Osama; He, Yixin; Al-Rubaye, Saba; Yu, Keping; Mumtaz, Shahid
    This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.
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    Distributed Spaceborne SAR: a review of systems, applications, and the road ahead
    (IEEE, 2025-12-31) Hu, Cheng; Li, Yuanhao; Chen, Zhiyang; Liu, Feifeng; Zhang, Qingjun; Monti-Guarnieri, Andrea V.; Hobbs, Stephen E.; Anghel, Andrei; Datcu, Mihai
    As a crucial sensor for wide-area Earth observation, spaceborne synthetic aperture radar (SAR) plays a pivotal role in large-scale terrain mapping, ocean observation, disaster monitoring, and so forth. Driven by the increasing demands for diverse applications, enhanced performance, and the continuous advancement of satellite and radar technologies, the distributed configuration has emerged as a key developmental trend for spaceborne SAR. This review comprehensively summarizes the systems and typical applications of distributed spaceborne SAR. The system configurations encompass homogenous distributed SAR, formed by multiple identical or similar platforms, and heterogeneous distributed SAR, characterized by significant differences between the transmitting and receiving platforms. Typical applications of distributed SAR include intelligent target recognition, terrain mapping, deformation retrieval, atmosphere measurement, and ocean observation, among others. Finally, the review offers a prospective outlook on the future development of distributed spaceborne SAR.
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    Enhancing performance and interpretability of multivariate time-series model through sparse saliency
    (IEEE, 2024-09-24) Kong, Xiangqi; Xing, Yang; Liu, Zeyu; Tsourdos, Antonios; Wikander, Andreas
    Explainable time-series modelling is an essential task for modern intelligent transportation systems (ITS). How-ever, balancing accuracy and interpretability in multivariate time series forecasting presents significant challenges. These challenges arise from the necessity to understand the significance of features and their temporal variations. Factors such as autocorrelation in time series and data processing techniques like sliding windows expand feature sets, thereby complicating pattern recognition using traditional post-hoc explanation methods and making the issue even more complex. To overcome these challenges, in this study, we propose a flexible post-process approach which generates sparse and normalized saliency values based on existing saliency generation methods such as GradientSHAP. Additionally, an optional window aggregation and alignment strategy is introduced to align with the original time series dataset, enhancing the intuitive understanding of feature importance. Furthermore, the potential use of sparse saliency for data augmentation to improve the model is explored. Lastly, we utilize naturalistic data from San Francisco airport to demonstrate our approach for ITS time-series prediction and explanation. The evaluation results indicate that integrating sparse saliency from high-performing models not only boosts the performance of XGBoost models by 10.92% but also simplifies model complexity, facilitating easier interpretation.
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    Explainable reinforcement and causal learning for improving trust to 6G stakeholders
    (IEEE, 2025-06-01) Arana-Catania, Miguel; Sonee, Amir; Khan, Abdul-Manan; Fatehi, Kavan; Tang, Yun; Jin, Bailu; Soligo, Anna; Boyle, David; Calinescu, Radu; Yadav, Poonam; Ahmadi, Hamed; Tsourdos, Antonios; Guo, Weisi; Russo, Alessandra
    Future telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent's behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field.
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    Interaction-aware and driving style-aware trajectory prediction for heterogeneous vehicles in mixed traffic environment
    (IEEE, 2025) Zhang, Qixiang; Xing, Yang; Wang, Jinxiang; Fang, Zhenwu; Liu, Yahui; Yin, Guodong
    Trajectory prediction (TP) of surrounding vehicles (SVs) is crucial for autonomous vehicles (AVs) to understand traffic situations and achieve safe-efficient decision-making and motion planning. However, different drivers’ personalized driving preferences will bring uncertainties for long-term TP in the mixed traffic environment. To this end, this paper proposes a TP model with interaction awareness and driving style awareness for long-term TP of heterogeneous SVs. Firstly, the driving conditions in the highD dataset are distinguished, and three different driving styles of the vehicle in the car-following condition are obtained based on an unsupervised clustering algorithm. Then, an encoder-decoder architecture based on novel lane attention and multi-head attention mechanisms is proposed, where the encoder analyzes historical trajectory patterns and the decoder generates future trajectory sequences. The lane attention mechanism enhances the spatial perception capability of vehicles towards the target lane, and the multi-head attention mechanism extracts high-dimensional global interaction information about the heterogeneous vehicle group (HVG) surrounding the target vehicle (TV). Experimental results show that the proposed model outperforms state-of-the-art models in root-mean-square-error (RMSE) for long-term TP and exhibits excellent adaptability to diverse driving tasks. Moreover, this paper verifies that the driving style topology within the HVG has multiple impacts on the TP accuracy of the TV.
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    Mitigating no fault found phenomena through ensemble learning: a mixture of experts approach
    (IEEE, 2024-09-24) Liu, Zeyu; Kong, Xiangqi; Chen, Yang; Wang, Ziyue; Jia, Huamin; Al-Rubaye, Saba
    In the aviation industry, the reliance on precise fault diagnostic decision-making is critical for equipment maintenance. A significant challenge encountered is the erroneous categorization of components under 'No Fault Found' (NFF), which subjects these components to unwarranted repairs or further testing. Such misclassifications not only trap on airlines through costly cycles of unnecessary maintenance but also exacerbate degeneration and potential safety hazards. Consequently, there is a heightened demand for the development of effective fault diagnosis models that are adapting to the aircraft complex systems and adeptly addressing issues related to the NFF phenomenon. In this study, we draw inspiration from ensemble learning and propose a multiple Naive Bayes experts (MNBMoEs) approach based on a mixture of experts (MoEs) model. This method leverages the predictive advantages of each sub-model on specific features, allowing the hybrid expert decision to outperform any single expert. It also includes a quantitative analysis method for the NFF issue, derived from the confusion matrix according to the industrial definition of NFF. Experiments evaluated on public datasets results show that the ensemble learning approach, based on Mixture of Multiple Naive-Bayes expert models, can effectively utilize the strengths of different models, improving fault diagnosis accuracy to 96.96%, with a maximum reduction in NFF occurrence rates of up to 94.17% and 84.2% model performance improvement.
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    Mixed-precision federated learning via multi-precision over-the-air aggregation
    (IEEE, 2025-03-24) Yuan, Jinsheng; Wei, Zhuangkun; Guo, Weisi
    Over-the-Air Federated Learning (OTA-FL) is a privacy-preserving distributed learning mechanism, by aggregating updates in the electromagnetic channel rather than at the server. A critical research gap in existing OTA - FL research is the assumption of homogeneous client computational bit precision. While in real world application, clients with varying hardware resources may exploit approximate computing (AxC) to operate at different bit precisions optimized for energy and computational efficiency. Model updates with varying precisions among clients present a significant challenge for OTA - FL, as they are incompatible with the wireless modulation superposition process. Here, we propose an mixed-precision OTA-FL framework of clients with multiple bit precisions, demonstrating the following innovations: (i) the superior trade-off for both server and clients within the constraints of varying edge computing capabilities, energy efficiency, and learning accuracy requirements compared to homogeneous client bit precision, and (ii) a multi-precision gradient modulation scheme to ensure compatibility with OTA aggregation and eliminate the overheads of precision conversion. Through case study with real world data, we validate our modulation scheme that enables AxC based mixed-precision OTA-FL. In comparison to homogeneous standard precision of 32-bit and 16-bit, our framework presents more than 10% in 4-bit ultra low precision client performance and over 65% and 13% of energy savings respectively. This demonstrates the great potential of our mixed-precision OTA-FL approach in heterogeneous edge computing environments.
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    Personalizing driver agent using large language models for driving safety and smarter human–machine interactions
    (IEEE, 2025-12-31) Xu, Zixuan; Chen, Tiantian; Huang, Zilin; Xing, Yang; Chen, Sikai
    Driver assistance systems have been shown to reduce crashes by providing real-time warnings or assistance, with their effectiveness depending on communication with driver. Due to their unique characteristics, human drivers possess varying hazard perception skills and interaction preferences, making personalized assistance crucial to improving the user experience and system acceptance. However, how to leverage multimodal interfaces that dynamically adapt to warning contents and driver characteristics remains an open question. At the same time, large language models (LLMs) have demonstrated advanced capabilities in knowledge acquisition, planning, and human–machine collaboration, offering potential solutions for existing warning systems. Thus, we develop an LLM-based personalized driver agent (PDA), which provides personalized warnings through multimodal interactions (visual, voice, and tactile). The agent’s architecture mimics human cognitive processes via four core modules: memory, perception, control, and action. Results from our experiments indicate that the LLM-PDA effectively customizes warning contents for different drivers in various situations, providing enhanced safety and driver support. This article pioneers the integration of LLMs into automotive human–vehicle interaction and offers novel insights into personalized human–machine interaction in intelligent vehicles.
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    Signal-to-interference-noise-ratio density distribution for UAV-carried IRS-to-6G ground communication
    (IEEE, 2025) Nnamani, Christantus Obinna; Anioke, Chidera Linda; Al-Rubaye, Saba; Tsourdos, Antonios
    This paper investigates the probability distribution of the signal-to-interference noise ratio (SINR) for a 6G communication system comprising a multi-antenna transmitter, an intelligent reflecting surface (IRS) and a remote receiver station. A common assumption in the literature is that the density distribution function for SINR and signal-to-noise ratio (SNR) of an IRS-to-ground communication follows a Rayleigh and Rician distribution. This assumption is essential as it influences the derivation of the properties of the communication system such as the physical layer security models and the designs of IRS controller units. Therefore, in this paper, we present an analytical derivation for the density distribution functions of the SINR for an IRS-to-6G ground communication ameliorating the typical assumptions in the literature. We demonstrated that the SINR density function of an IRS-to-6G ground communication contains a hypergeometric function. We further applied the derived density distribution function to determine the average secrecy rate for passive eavesdropping.
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    Sustainable 6G-NTN for seamless air mobility: exploring channel propagation characteristics
    (IEEE, 2025-04-08) Chen, Yang; Bocciarelli, Hugo; Al-Rubaye, Saba; Tsourdos, Antonios
    The air transportation vision for sustainable sixthgeneration (6 G) wireless communications networks revolves around ensuring ubiquitous coverage and spectral efficiency with enhanced network intelligence in the diverse communication scenarios. This vision extends beyond terrestrial networks to include non-terrestrial networks (NTN) by incorporating GX Inmarsat satellites and aircraft networks. In the context of 6G GX satellite scenarios, aircraft seamless transportation plays a crucial role as a densely populated intermediate network layer between ground networks and space-based ones. The paper proposes a new sustainable mechanism with mathematical model to improve channel propagation, which has been validated by crucial analysis of propagation channel modeling within the framework of 6 G technology. It highlights the significance of such modeling with the guarantee of dependable communications, maximizing availability, and establishing system parameters like antenna layout and relay deployment. It explores industry trends and ongoing field trial initiatives, offering valuable insights into the progress and outcomes that will shape the future of 6G NTN.