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MILCOM, Military Communications Conference

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

The 6G mobile wireless networks are expected to support massive ultra-reliable and low-latency communications (mURLLC) with stringent requirements on transmission-delay and error-rate bounded quality-of-service (QoS). Correspondingly, the finite blocklength coding (FBC) has developed as the effective technique to implement small-packet communications for the statistical QoS provisioning over mURLLC-based 6G mobile networks. On the other hand, guaranteeing the security for 6G networks services imposes another new dimension of statistical QoS requirements. However, how to efficiently upper-bound the statistical delay and the error probability over time-varying wireless fading channels while guaranteeing the security QoS using FBC remain a challenging problem. To overcome these difficulties, in this paper we propose and develop the new concept of secrecy ϵ-effective capacity as a statistical security QoS scheme to guarantee the performance of statistical delay-bounded QoS, error-rate bounded QoS, and the secrecy-information leak-probability bounded QoS by using the FBC technique. First, we establish the wireless channel model of massive multiple-input-multiple-output (MIMO) for supporting the communications between the base station and multiple mobile users with the existence of an eavesdropper. Second, we define and derive the eavesdropping rate and secrecy rate under the massive MIMO communication. Third, we derive the secrecy-information leak probability and integrate it with statistical delay and error-rate bounded QoS to define the new wireless network communications performance metrics of secrecy ϵ-effective capacity. Finally, we use the numerical analyses to validate and evaluate our developed schemes for supporting statistical delay and error-rate bounded QoS provisioning while upper-bounding the secrecy-information leak probability over 6G mobile networks using massive-MIMO/FBC.

关键词： 6G mobile communication Military communication Measurement Numerical analysis Wireless networks Quality of service Delays Security Communication system security Capacity planning

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Demystifying Device-specific Compatibility Issues in Android Apps

arXiv

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arXiv 2024年

作者： Chen, Junfeng Li, Kevin Chen, Yifei Wei, Lili Liu, Yepang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Computer Engineering McGill University Montreal Canada

The Android ecosystem is profoundly fragmented due to the frequent updates of the Android system and the prevalent customizations by mobile device manufacturers. Previous research primarily focused on identifying and repairing evolution-induced API compatibility issues, with limited consideration of devicespecific compatibility issues (DSC issues). To fill this gap, we conduct an empirical study of 197 DSC issues collected from 94 open-source repositories on GitHub. We introduce a new perspective for comprehending these issues by categorizing them into two principal groups, Functionality Breaks, and OEM Features, based on their manifestations and root causes. The functionality break issues disrupt standard Android system behaviors, lead to crashes or unexpected behaviors on specific devices, and require developers to implement workarounds to preserve the original functionality. The OEM feature issues involve the introduction of device-specific functionalities or features beyond the basic Android system. The different nature of functionality break issues and OEM feature issues lead to unique challenges in addressing them. For example, our examination of issue fixing practices highlights significant differences in addressing the two categories of DSC issues. Common solutions for functionality break issues involve calling additional APIs, substituting problematic ones, or using specific parameters, while resolving OEM feature issues often relies on Android inter-component communication methods and reflection, with additional unconventional strategies. Such observations highlight the distinctive challenges in addressing DSC issues in Android apps and will facilitate the future development of testing and analysis tools targeting these issues. Our study demonstrates that Functionality break and OEM feature issues have different characteristics, and future research may need to investigate them separately. © 2024, CC BY-SA.

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Dual Frequency Improvements to the Time-Series Ratio Algorithm for Soil Moisture Retrievals in the SMEX02 Campaign

Dual Frequency Improvements to the Time-Series Ratio Algorit...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Horton, Dustin Johnson, Joel T. Al-Khaldi, Mohammad Park, Jeonghwan Bindlish, Rajat Department of Electrical and Computer Engineering ElectroScience Laboratory The Ohio State University ColumbusOH43212 United States NASA Goddard Space Flight Center GreenbeltMD United States Global Science & Technology Inc. GreenbeltMD20770 United States

ISBN: (纸本)9781946815200

Surface soil moisture is a key variable in Earth's hydrological cycle that describes the exchange of moisture between land, air, and sea. Soil moisture plays an integral role in balancing the exchange of energy between the atmosphere and land surface (evapotranspiration), ecology, infiltration, and runoff. Soil moisture information also influences applications such as agriculture and food security by supporting of crop yield prediction, drought forecasting, and irrigation, water resource management and flood dynamics, and weather forecasting. © 2025 USNC-URSI.

关键词： Soil moisture

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Programmable photonic time circuits for highly scalable universal unitaries

arXiv

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arXiv 2023年

作者： Piao, Xianji Yu, Sunkyu Park, Namkyoo Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Programmable photonic circuits (PPCs) have garnered substantial interest in achieving deep learning accelerations and universal quantum computations. Although photonic computation using PPCs offers critical advantages, including ultrafast operation, energy-efficient matrix calculation and room-temperature quantum states, its poor scalability impedes the integration required for industrial applications. This challenge arises from the temporally one-shot operation using propagating light in conventional PPCs, which leads to the light-speed increase of device footprints. Here we propose a concept of programmable photonic time circuits, which employ time-cycle-based computations analogous to the gate cycling in the von Neumann architecture and quantum computation. As a building block, we develop a reconfigurable SU(2) time gate composed of two resonators, which have tunable resonances and are coupled through time-coded dual-channel gauge fields. We demonstrate universal U(N) operations with high fidelity using the systematic assembly of the SU(2) time gates, achieving improved scalability from O(N2) to O(N) in both the footprint and gate number. This result opens a pathway to industrial-level PPC implementation in very large-scale integration. © 2023, CC BY.

关键词： Scalability

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Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks 57

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-B...

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57th Annual Conference on Information sciences and systems, CISS 2023

作者： Wang, Fei Zhang, Xi Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

ISBN: (纸本)9781665451819

We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Federated Learning Based Integrated Sensing, Communications, and Powering Over 6G Massive-MIMO Mobile Networks

Federated Learning Based Integrated Sensing, Communications,...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

To sense mobile users' positions and channel states while simultaneously conveying information for supporting the 6G traffic, the integrated sensing and communication (ISAC) has been proposed as a key technique for sensing and communicating using the same radio frequency and hardware. Moreover, to sup-port energy-constrained and battery-limited wireless networks, simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. To integrate these two techniques, integrated sensing, communications, and powering (ISACP) has been developed to simultaneously sense the targeted mobile users, transmit the information, and deliver the power. However, how to efficiently optimize the power allocation in ISACP schemes to support the 6G traffic has imposed many new challenges not encountered before. To overcome these challenges, in this paper we propose a federated-learning (FL) enabled ISACP scheme to maximize the energy-efficiency over 6G massive MIMO wireless networks. First, we establish the system models for our proposed ISACP and FL schemes. Second, we design the training data set collection in the FL algorithm by sending the pilot signal from mobile users to the massive MIMO base station, and formulate an energy-efficiency maximization problem for the developed ISACP scheme. Third, To solve the formulated energy-efficiency maxi-mization problem, we propose an actorcritic enabled multi-agent based FL mechanism. Finally, we use the numerical analyses to validate and evaluate our developed schemes.

关键词： 6G mobile communication Wireless sensor networks Federated learning Wireless networks Training data Massive MIMO Integrated sensing and communication

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ISAC-Driven Channel-State-Estimation for Statistical-QoS Based MURLLC Over 6G M-MIMO Mobile Networks Using FBC

ISAC-Driven Channel-State-Estimation for Statistical-QoS Bas...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xi Zhang Qixuan Zhu Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support communications among massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements in 6G. Correspondingly, the integrated sensing and communications (ISAC) technique can improve various QoS performances through spectrum/hardware sharing between target-sensing and data-communications. In addition, the massive multiple-input multiple-output (massive MIMO) and finite blocklength coding (FBC) have been also widely recognized as the powerful 6G techniques to efficiently support mURLLC. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information from the base station to MUs to support mURLLC using massive MIMO and FBC has imposed many new difficulties not encountered before. To conquer these challenges, in this paper we propose the new ISAC performance metrics for statistical-QoS guaranteed mURLLC over 6G massive MIMO mobile networks using FBC. First, we establish system models for ISAC-based architectures. Second, we detect targeted MUs and estimate the channel state using the radar sensing echo. Third, we develop a new ISAC performance metric as a function of the data-communication rate and channel state estimation distortion. Finally, we use the numerical analyses to validate and evaluate our developed ISAC performances in supporting mURLLC-oriented QoS requirements.

关键词： 6G mobile communication Measurement Wireless sensor networks Wireless networks Channel estimation Quality of service Massive MIMO Integrated sensing and communication Distortion Testing

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Wireless Semantic Communication Based on Probability Distribution: An Initial Work

Wireless Semantic Communication Based on Probability Distrib...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Luo, Qingxiang Guo, Yashuang Zheng, Aoran Ni, Zhitong Yu, F. Richard Leung, Victor C.M. School of Computer Science and Technology Beijing Jiaotong University China Big Data Technologies Centre University of Technology Sydney Australia Carleton University Department of Systems and Computer Engineering Canada The University of British Columbia Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350368369

In the paper, we consider the general semantic transmission in wireless networks based on probability distribution. Firstly, we extract a multidimensional semantic probability distribution function, independent of any a specific wireless channel model, by using the variational inference technique. Secondly, we propose a new semantic similarity metric for measuring the difference between the received semantics and the expected semantics based on Kullback-Leibler divergence. Then, we formulate the semantic transmission problem as an optimization problem of transmission symbol adjustment with the aim to maximize the semantic similarity. Finally, we develop an optimal semantic transformation and transmission (STT) algorithm to obtain the optimal transmission symbol adjustment decision. This decision makes the closed-form expression of semantic transmission symbol available, which can realize lossless semantic transmission with energy constraint. Simulation results verify the effectiveness and robustness of the proposed STT algorithm. © 2025 IEEE.

关键词： Normal distribution

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Attentive Hawkes Process Application for Sequential Recommendation 28th

Attentive Hawkes Process Application for Sequential Recomme...

引用

28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Yu, Shuodian Ma, Li Gao, Xiaofeng Guo, Jianxiong Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China

ISBN: (纸本)9783031306716

Sequential recommendation systems which aim to predict the users’ future clicking items are essential in both research and industry services. Based on the contextual characteristics of users, a large number of existing methods exploit abundant information from user behavior sequences and gain users’ interests through expressive sequential models like recurrent neural networks and self-attention mechanism to predict the next item. However, the temporal generative process of behavior sequences, which is crucial to the complex relationships and distributions of users’ dynamic preferences, is rarely considered. In this paper, we propose HawRec (Hawkes process based sequential recommendation), which is a new representation learning approach to model the interacted sequences of users from a temporal point process perspective. In particular, we leverage temporal self-attention mechanism to make an analogy to the self-exciting mechanism of Hawkes process. We bring expressive superiority of neural network into the Hawkes process with non-parametric conditional intensity kernel, resulting in more accurate recommendation and inheriting robustness from the temporal point process. Furthermore, extensive experiments conducted on four public benchmarks demonstrate that our HawRec outperforms various baselines of state-of-the-art sequential recommendation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning

arXiv

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arXiv 2024年

作者： Yue, Sheng Hua, Xingyuan Ren, Ju Lin, Sen Zhang, Junshan Zhang, Yaoxue Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston TX United States Department of Electrical and Computer Engineering University of California Davis United States

In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the naïve combination of existing offline IL and online IL methods tends to behave poorly in this context, because the initial discriminator (often used in online IL) operates randomly and discordantly against the policy initialization, leading to misguided policy optimization and unlearning of pretraining knowledge. To overcome this challenge, we propose a principled offline-to-online IL method, named OLLIE, that simultaneously learns a near-expert policy initialization along with an aligned discriminator initialization, which can be seamlessly integrated into online IL, achieving smooth and fast finetuning. Empirically, OLLIE consistently and significantly outperforms the baseline methods in 20 challenging tasks, from continuous control to vision-based domains, in terms of performance, demonstration efficiency, and convergence speed. This work may serve as a foundation for further exploration of pretraining and finetuning in the context of IL. Copyright © 2024, The Authors. All rights reserved.

关键词： E-learning

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