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IEEE Conference on Wireless Communications and Networking

作者： Yubo Wan An Liu Tony Q. S. Quek College of Information Science and Electronic Engineering Zhejiang University Information Systems Technology and Design Pillar Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Pilot pattern optimization in orthogonal frequency division multiplexing (OFDM) systems has been widely investigated due to its positive impact on channel estimation. In this paper, we consider the problem of multi-user pilot pattern optimization in OFDM systems. In particular, the goal is to enhance channel extrapolation performance for 5G NR systems by optimizing multi-user pilot patterns in frequency-domain. We formulate a novel pilot pattern optimization problem with the objective of minimizing the maximum integrated side-lobe level (ISL) among all users, subject to a statistical resolution limit (SRL) constraint. Unlike existing literature that only utilizes ISL for controlling side-lobe levels of the ambiguity function, we also leverage ISL to mitigate multi-user interference in code-domain. Additionally, the introduced SRL constraint ensures sufficient delay resolution of the system to resolve multipath, thereby improving channel extrapolation performance. Then, we employ the estimation of distribution algorithm (EDA) to solve the formulated problem in an offline manner. Simulation results demonstrate that the optimized pilot pattern yields significant performance gains in channel extrapolation over the conventional pilot patterns.

关键词： Extrapolation 5G mobile communication OFDM Simulation Frequency-domain analysis Estimation Interference Performance gain Delays Optimization

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Competitive Multi-armed Bandit Games for Resource Sharing

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Hongbo Duan, Lingjie The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States Singapore University of Technology and Design Pillar of Engineering Systems and Design 487372 Singapore

In modern resource-sharing systems, multiple agents access limited resources with unknown stochastic conditions to perform tasks. When multiple agents access the same resource (arm) simultaneously, they compete for successful usage, leading to contention and reduced rewards. This motivates our theoretical study of competitive multi-armed bandit (CMAB) games. In this paper, we study a new N-player K-arm competitive MAB game, where non-myopic players (agents) compete with each other to form diverse private estimations of unknown arms over time. Their possible collisions on the same arms and the time-varying nature of arm rewards make the policy analysis here more involved than the existing studies for myopic players. We explicitly analyze the threshold-based structures of the social optimum and the existing selfish policy, showing that the latter causes prolonged convergence times (Formula presented), while the socially optimal policy with coordinated communication reduces it to (Formula presented). Based on the policy comparison, we prove that the competition among selfish players for the best arm can result in an infinite price of anarchy (PoA), indicating an arbitrarily large efficiency loss compared to the social optimum. We further prove that no informational (non-monetary) mechanism (including Bayesian persuasion) can reduce the infinite PoA, as strategic misreporting by non-myopic players undermines such approaches. To address this, we propose a Combined Informational and Side-Payment (CISP) mechanism, which provides socially optimal arm recommendations with proper informational and monetary incentives to players according to their diverse and time-varying private beliefs. Our CISP mechanism keeps ex-post budget balanced for the social planner and ensures truthful reporting from players, thereby achieving the minimum PoA = 1 and the same convergence time as the social optimum. © 2002-2012 IEEE.

关键词： Budget control

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EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Liu, Chunsheng Chang, Faliang Wang, Wenqian Hao, Penghui Huang, Yiming Yang, Zhiqiang Shandong University School of Control Science and Engineering Jinan250061 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore

Associating driver attention with driving scene across two fields of view is a challenging cross-domain perception problem, which requires comprehensive consideration of cross-view mapping, dynamic driving scene analysis and driver status tracking. Previous methods typically analyze a single view or map attention to the scene through a two-step projection, failing to exploit their implicit connections and establish accurate associations. Moreover, simple fusion modules are inadequate for modeling the complex relationships between the two views, making information integration complicated. To address these issues, we propose EraW-Net, a novel end-to-end framework for scene-associated driver attention estimation by aggregating information from dual views. This method enhances the most discriminative dynamic cues, refines feature representations, and facilitates semantically aligned cross-domain integration through a W-shaped architecture, termed W-Net. Specifically, a Dynamic Adaptive Filter Module (DAF-Module) is proposed to address the challenges of frequently changing driving environments by extracting vital regions. It suppresses the indiscriminately recorded dynamics and highlights crucial ones by innovative joint frequency-spatial analysis, enhancing the model's ability to parse complex dynamics. Additionally, to track driver states during non-fixed facial poses, we propose a Global Context Sharing Module (GCS-Module) to construct refined feature representations by capturing hierarchical features that adapt to various scales of head and eye movements. Finally, W-Net achieves systematic cross-view information integration through its unique two-stage decoding strategy, addressing semantic misalignment in heterogeneous data integration. Experiments demonstrate that the proposed method robustly and accurately estimates scene-associated driver attention on large public datasets. © 1999-2012 IEEE.

关键词： Mapping

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Mechanism design for Blockchain Order Books against Selfish Miners

arXiv

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arXiv 2025年

作者： Liu, Yunshu Duan, Lingjie The School of Cybersecurity Northwestern Polytechnical University Xi’an710129 China The Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore

In blockchain-based order book systems, buyers and sellers trade assets, while it is miners to match them and include their transactions in the blockchain. It is found that many miners behave selfishly and myopically, prioritizing transactions with high fees and ignoring many desirable matches that could enhance social welfare. Existing blockchain mechanisms fail to address this issue by overlooking miners' selfish behaviors. To our best knowledge, this work presents the first analytical study to quantify and understand buyer and seller transaction fee choices and selfish miners' transaction matching strategies, proving an infinitely large price of anarchy (PoA) for social welfare loss. To mitigate this, we propose an adjustable block size mechanism that is easy to implement without altering the existing decentralized protocols and still allows buyers and sellers to freely decide transaction fees and miners to selfishly match. The analysis is challenging, as pure strategy Nash equilibria do not always exist, requiring the analysis of many buyers' or sellers' interactive mixed-strategy distributions. Moreover, the system designer may even lack information about each buyer's or seller's bid/ask prices and trading quantities. Nevertheless, our mechanism achieves a well-bounded PoA, and under the homogeneous-quantity trading for non-fungible tokens (NFT), it attains a PoA of 1 with no social welfare loss. We implement our mechanism on a local instance of Ethereum to demonstrate the feasibility of our approach. Experiments based on the realistic dataset demonstrate that our mechanism achieves social optimum for homogeneous-quantity trading like NFT. It can enhance social welfare up to 3.7 times compared to the existing order book benchmarks for heterogeneous-quantity trading of Bitcoin tokens. It exhibits robustness against random variations in buyers and sellers. © 2025, CC BY-SA.

关键词： Sales

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Deep Learning for Spectrum Prediction in Cognitive Radio Networks: State-of-the-Art, New Opportunities, and Challenges

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IEEE Network 2025年

作者： Pan, Guangliang Yau, David K.Y. Zhou, Bo Wu, Qihui Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore

Spectrum prediction is considered to be a promising technology that enhances spectrum efficiency by assisting dynamic spectrum access (DSA) in cognitive radio networks (CRN). Nonetheless, the highly nonlinear nature of spectrum data across time, frequency, and space domains, coupled with the intricate spectrum usage patterns, poses challenges for accurate spectrum prediction. Deep learning (DL), recognized for its capacity to extract nonlinear features, has been applied to solve these challenges. This paper first shows the advantages of applying DL by comparing with traditional prediction methods. Then, the current state-of-the-art DL-based spectrum prediction techniques are reviewed and summarized in terms of intra-band and cross-band prediction. Notably, this paper uses a real-world spectrum dataset to prove the advancements of DL-based methods. Then, this paper proposes a novel intra-band spatiotemporal spectrum prediction framework named ViTransLSTM. This framework integrates visual self-attention and long short-term memory to capture both local and global long-term spatiotemporal dependencies of spectrum usage patterns. Similarly, the effectiveness of the proposed framework is validated on the aforementioned real-world dataset. Finally, the paper presents new related challenges and potential opportunities for future research. © 2025 IEEE.

关键词： Spectrum efficiency

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AIGC-Enhanced Hybrid Content Caching in Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Ding Duan, Lingjie Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Singapore University of Technology and Design Pillar of Engineering Systems and Design 487372 Singapore

Content caching is a promising solution to overcome the backhaul traffic delay issue by caching content at the base station (BS). However, the performance of content caching is restricted by the limited BS cache storage. In this paper, we are the first to employ artificial intelligence generated content (AIGC) to enhance the content caching performance by empowering the BS’s intelligence capability. Besides caching a popular file, our AIGC-enhanced BS can alternatively cache its prompt of smaller size to reconstruct the file whenever needed by mobile terminals. We reveal the fundamental tradeoff between caching storage saving and computation delay for AIGC to decide whether to cache a file or its prompt. In this regard, this paper investigates the new problem of joint hybrid caching, computation and communication resource allocation optimization to minimize the average system latency to serve mobile terminals. As the problem is a non-convex and involves mixed integer non-linear programming, we develop a divide-and-conquer algorithm to decompose the problem into two timescale levels. We theoretically prove that our AIGC-enhanced hybrid content caching outperforms the conventional content caching once the computation capacity is non-trivial. Extensive simulation results also validate the superiority of the proposed hybrid content caching. © 2002-2012 IEEE.

关键词： artificial intelligence generated content Content caching hybrid caching policy resource allocation

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Hybrid Content Caching Empowered By AIGC in Wireless Networks

Hybrid Content Caching Empowered By AIGC in Wireless Network...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ding Xu Lingjie Duan Hongbo Zhu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Content caching at base stations (BS) can reduce backhaul traffic delays to deliver the requested files to users, but its effectiveness is limited by BS storage capacity. We propose a novel approach that integrates artificial intelligence-generated content (AIGC) into the BS operations. Instead of caching entire files, our AIGC-enhanced BS can cache smaller prompts, allowing files to be reconstructed on demand. We explore the challenge of jointly optimizing hybrid caching, AIGC computation, and communication resource allocation with the goal of minimizing average system latency. Given the non-convex nature and the complexity of mixed integer non-linear programming involved, we propose a divide-and-conquer algorithm that breaks down the problem into two timescale levels. Theoretical analysis and simulations confirms that our AIGC-enhanced hybrid content caching outperforms the conventional content caching.

关键词： Wireless networks Simulation Computational modeling Signal processing algorithms Signal processing Programming Delays Complexity theory Resource management Speech processing

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Competitive Multi-armed Bandit Games for Resource Sharing

arXiv

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arXiv 2025年

作者： Li, Hongbo Duan, Lingjie Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore487372 Singapore

关键词： Budget control

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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ScNeuGM: Scalable Neural Graph Modeling for Coloring-Based Contention and Interference Management in Wi-Fi 7

arXiv

引用

arXiv 2025年

作者： Gu, Zhouyou Park, Jihong Choi, Jinho Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore School of Electrical and Mechanical Engineering the University of Adelaide AdelaideSA5005 Australia

Carrier-sense multiple access with collision avoidance in Wi-Fi often leads to contention and interference, thereby increasing packet losses. These challenges have traditionally been modeled as a graph, with stations (STAs) represented as vertices and contention or interference as edges. Graph coloring assigns orthogonal transmission slots to STAs, managing contention and interference, e.g., using the restricted target wake time (RTWT) mechanism introduced in Wi-Fi 7 standards. However, legacy graph models lack flexibility in optimizing these assignments, often failing to minimize slot usage while maintaining reliable transmissions. To address this issue, we propose ScNeuGM, a neural graph modeling (NGM) framework that flexibly trains a neural network (NN) to construct optimal graph models whose coloring corresponds to optimal slot assignments. ScNeuGM is highly scalable to large Wi-Fi networks with massive STA pairs: 1) it utilizes an evolution strategy (ES) to directly optimize the NN parameters based on one network-wise reward signal, avoiding exhaustive edge-wise feedback estimations in all STA pairs;2) ScNeuGM also leverages a deep hashing function (DHF) to group contending or interfering STA pairs and restricts NGM NN training and inference to pairs within these groups, significantly reducing complexity. Simulations show that the ES-trained NN in ScNeuGM returns near-optimal graphs 4–10 times more often than algorithms requiring edge-wise feedback and reduces 25% slots than legacy graph constructions. Furthermore, the DHF in ScNeuGM reduces the training and the inference time of NGM by 4 and 8 times, respectively, and the online slot assignment time by 3 times in large networks, and up to 30% fewer packet losses in dynamic scenarios due to the timely assignments. © 2025, CC BY.

关键词： Wireless local area networks (WLAN)

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