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IEEE OPEN JOURNAL OF VEHICULAR technology 2024年 5卷 108-124页

作者： Xu, Huixin Zhang, Jianhua Tang, Pan Tian, Lei Wang, Qixing Liu, Guangyi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China

The 6 GHz band plays a crucial role in the development of the 6G. A profound comprehension of channel reciprocity is essential for designing time division duplexing/frequency division duplexing (TDD/FDD) systems within this band. Firstly, in an indoor corridor scenario, precise and impartial measurements are conducted for both the uplink (UL) and downlink (DL) channels in the 6 GHz band;A denoising algorithm is proposed to extract multipath components (MPCs) from the measurement data, enabling a more equitable assessment of channel reciprocity;Then, a comprehensive analysis of channel reciprocity has been conducted, focusing on four aspects: path loss, delay spread, cluster-based correlation coefficient (CBCC), and multipath power dissimilarity (MPD). The findings indicate that TDD systems demonstrate nearly perfect reciprocity, whereas FDD systems exhibit partial reciprocity in indoor scenarios. Specifically, in TDD systems, the CBCCs between UL and DL exceed 95%, while in FDD systems, they fluctuate between 80% and 90%. Additionally, a model has been provided to depict the relationship between MPD and center frequency, as well as frequency interval;Finally, a comparative analysis of measured and ray-tracing simulated results reveals the presence of numerous public MPCs, which share the same propagation delay and spatial angle between the UL and DL in FDD systems, as well as private MPCs that exist exclusively in either the UL or DL. They collectively influence the channel reciprocity.

关键词： 6G 6 GHz band channel measurement channel reciprocity frequency division duplexing (FDD) time division duplexing (TDD)

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CTGAN-assisted CNN for high-resolution wireless channel delay estimation 24

CTGAN-assisted CNN for high-resolution wireless channel dela...

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IEEE 24th International Conference on High Performance switching and Routing, (HPSR)

作者： Xu, Liyan Feng, Lei Li, Wenjing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781665476409

The estimation accuracy of first-arrival-path (FAP) delay plays a vital role in positioning performance. We investigate the limitations of traditional cross-correlation (CC) algorithms in delay estimation. Our work proposes a FAP delay estimation mechanism using conditional tabular generative adversarial network (CTGAN) assisted convolutional neural network (CNN). The mechanism uses the CC algorithm to extract the delay feature in the wireless signal as input and finally outputs the FAP delay. For communication scenarios where it is difficult to obtain a large amount of training data, we use CTGAN to assist CNN training to improve the accuracy of FAP delay estimation. A series of simulation experiments were presented to evaluate the performance of CTGAN-assisted CNN and compare it with traditional high-resolution delay estimation algorithms. The results show that CNN performs well in weak LOS signals and dense multipath situations. It can still maintain high precision in the case of insufficient data.

关键词： FAP delay estimation cross-correlation multipath environment high resolution CNN CTGAN

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Controlled node dialogue in IoT networks based on nonlocal orthogonal product states

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JOURNAL OF OPTICAL COMMUNICATIONS AND networking 2024年第3期16卷 317-327页

作者： Che, Bichen Zheng, Shuntian Dou, Zhao Chen, Xiubo Li, Lixiang Li, Jian Yang, Yixian Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr Beijing 100876 Peoples R China

The rapid expansion of the Internet of Things (IoT) and advancements in quantum computing pose security challenges for IoT systems, encompassing classical attacks and quantum attacks. In this work, we concentrate on secure information exchange in the quantum IoT, mainly addressing the problem of establishing direct and secure quantum dialogue between two authorized IoT nodes located at a distance. The nonlocal quantum orthogonal product basis (OPB) is adopted for the first time, to our best knowledge, in a controlled quantum dialogue protocol, which eliminates the need for pre-key sharing or key storage. Through uniquely corresponding operations, private information is encrypted onto the nonlocal OPB, which is transmitted in one way. Compared with entangled states, the OPB is easier to prepare, thus reducing the quantum capability required for IoT nodes. Our approach achieves high transmission efficiency (57.1%) and qubit efficiency (100%) while providing comprehensive security measures that withstand various attacks and effectively prevent information leakage. Furthermore, an OPB-based self-error-correction quantum repeater is proposed to mitigate noise in the communication channel between distant IoT nodes. This repeater requires fewer physical resources compared with repeaters based on entangled states.

关键词： Internet of Things Quantum entanglement Particle measurements Atmospheric measurements Protocols Qubit Security

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Semi-Federated Learning for Connected Intelligence With Computing-Heterogeneous Devices

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IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 34078-34092页

作者： Han, Jiachen Ni, Wanli Li, Li Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Federated learning (FL) is a promising distributed learning approach which enables multiple devices to collaboratively train deep neural networks in a privacy-preserving fashion. However, computing-limited devices in Internet of Things (IoT) networks are difficult to perform local model training, which is an obstacle to implementing connected intelligence at the network edge. In this article, we propose a semi-federated learning (SemiFL) framework to support computing-heterogeneous devices for collaborative model training, even if some local devices do not have computing capabilities. Specifically, in SemiFL, computing-limited devices upload raw data to the base station (BS), while computing-powerful devices upload model parameters to the BS. To characterize the achievable learning performance of SemiFL in practical IoT networks, we derive the convergence upper bound with nonindependent and identically distributed (non-IID) data. Furthermore, we formulate an optimization problem to minimize the computation and communication latency by jointly design the transmit power of IoT devices and the receive factor at the BS. Accordingly, we propose an two-stage optimization algorithm to solve the formulated nonconvex problem efficiently. Extensive simulation results show that our SemiFL framework outperforms traditional FL in both IID and non-IID settings. In addition, it is shown that the proposed algorithm achieves lower latency than benchmarks under different settings.

关键词： Computational modeling Atmospheric modeling Performance evaluation Internet of Things Training Data models Convergence Connected intelligence convergence analysis heterogeneous devices latency minimization over-the-air computation (AirComp) semi-federated learning (SemiFL)

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An optimal bound for factoring unbalanced RSA moduli by solving Generalized Implicit Factorization Problem

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-24页

作者： Zhang, Ran Bi, Jingguo Li, Lixiang Peng, Haipeng Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China State Key Lab Cryptol POB 5159 Beijing 100878 Peoples R China

The integer factorization problem, concerning the product of two primes N = pq, has been a focal point for researchers since its rise to prominence with the RSA algorithm, a renowned public key scheme. It is well known that the integer factorization problem can be solved by the Number Field Sieve (NFS) algorithm in sub-exponential time, this challenge may also find resolution in polynomial time under certain conditions for the primes. In this paper, we revisit the problem of factoring RSA moduli with the implicit hint, where the implicit hint is the shared bits of primes. As for k RSA moduli, we present a novel implicit factorization approach applicable when the unknown multiples ap of the prime factors p share a certain number of most significant bits (MSBs) or least significant bits (LSBs). By drawing inspiration from addressing the Approximate Common Divisor Problem (ACDP), we transform this problem into solving the small roots of k-1 trivariate polynomials. To compute these small roots, we employ Coppersmith's method to construct a lattice basis for the modular polynomials. We modify the lattice basis by introducing new variables, achieving an optimal bound in the MSB and LSB cases. Our attack breaks the RSA system and improves upon all previous attacks on such variants when the amount of the shared bits is sufficiently large. Concluding our paper, we meticulously validate the theoretical analysis results, with experimental data confirming the full practical feasibility of our attack on such an RSA variant system.

关键词： RSA system Generalized Implicit Factorization Problem Coppersmith's method LLL algorithm

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Multiservice Reliability Evaluation Algorithm Considering Network Congestion and Regional Failure Based on Petri Net

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第2期15卷 684-697页

作者： Rui, Lanlan Chen, Xushan Wang, Xiaomei Gao, Zhipeng Qiu, Xuesong Wang, Shangguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the development of complex networks and with increasing service demands, service use is becoming more complex and the composition of services is becoming more complicated. In the XaaS (X as a Service) environment, users only care about the QoE of a service and do not care about the composition process of the service. Therefore, it is important to evaluate the reliability of the entire service. In this article, we use Petri Net as a basis for modeling the composition of services. In addition, we consider the problems of shared resources and common cause faults. Both of these problems can cause network congestion and regional failures. We use distance to assess the effects of regional faults and queuing theory to simulate the network congestion process. Moreover, in the simulation, we verify the impacts of regional failures and network congestion on service reliability. We choose the Tree-Based Search algorithm and the Semi-Markov Model as comparison algorithms. The results of our algorithm are related to service time. Our algorithm can timely reflect the impact of regional failure or network congestion, and it can feedback different evaluation results according to environmental changes. Therefore, our algorithm is more comprehensive and has better performance.

关键词： Common cause fault composite service petri net reliability assessment shared resources

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MS-Transformer: Masked and Sparse Transformer for Point Cloud Registration

MS-Transformer: Masked and Sparse Transformer for Point Clou...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Jia, Qingyuan Luo, Guiyang Yuan, Quan Li, Jinglin Shao, Congzhang Chen, Ziyue Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350337020

In this paper, we propose a masked and sparse transformer to address the problem of point cloud registration with low overlap. The mask mechanism reduces the overall data, increasing the corresponding point ratio in the overlap region, while also reducing the computational cost to accelerate the algorithm's execution speed. Moreover, we combine spatial position encoding and sparse self-attention to establish relationships within the source point cloud, as well as the relationships and attention scores between the source and target point clouds. This approach is specifically designed for the task of point cloud registration. Finally, we search for the maximum overlap area by matching the spatial consistency between points and calculate the 3D transformation matrix to complete the registration process. Our method achieves an improvement in the inlier ratio and performs well on the 3DMatch and 3DLoMatch datasets, demonstrating high registration efficiency. © 2023 IEEE.

关键词： Mask mechanism Point cloud registration Self-attention

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Improved MAPPO-based Collaborative Computing Offloading Algorithm in Broadband Mobile Internet

Improved MAPPO-based Collaborative Computing Offloading Algo...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Hu, Hao Yang, Yang Guo, Yanpeng Gao, Zhipeng Rui, Lanlan Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350321524

Collaborative computation offloading technology can offload computational tasks from mobile devices to mobile edge servers and idle devices, thus breakthrough the performance bottleneck of MEC computation offloading systems and providing a new idea to meet the needs of computation-intensive mobile applications. In collaborative computation offloading systems, variations in the number of mobile devices cause changes of their state space and action space, and traditional deep reinforcement learning-based computation offloading decision algorithms (e.g., MADDPG) are usually unavailable at this case. A computational offloading algorithm based on improved MAPPO is proposed to transform variable dimensional input information into fixed dimensional aggregated information through the attention mechanism, ensuring that the input dimensions of the policy network and value network do not change with the number of smart devices, which meet the demand for flexibility and scalability of computational offloading decision algorithms in collaborative computational offloading systems. © 2023 IEEE.

关键词： Deep learning

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Overcoming catastrophic forgetting with classifier expander 15

Overcoming catastrophic forgetting with classifier expander

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15th Asian Conference on Machine Learning (ACML)

作者： Liu, Xinchen Wang, Hongbo Tian, Yingjian Xie, Linyao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

It is essential for models to gradually adapt to the world's increasing complexity, and we can use models more effectively if they keep up with the times. However, the technique of continuous learning (CL) has an issue with catastrophic forgetting, and effective continuous learning methods can only be attained by effectively limiting forgetting and learning new tasks. In this study, we offer the Classifier Expander(CE) method, which combines the regularization-based and replay-based approaches. By undergoing two stages of training, it fulfills the aforementioned standards. The training content for the new task is limited to the portion of the network relevant to that task in the first stage, which uses the replay approach to reduce the forgetting problem. This strategy minimizes disruption to the old task while facilitating efficient learning of the new one. Utilizing all of the data available, the second stage retrains the network and sufficiently trains the classifier to balance the learning performance of the old and new tasks. Our method regularly outperforms previous CL methods on the CIFAR-100 and CUB-200 datasets, obtaining an average improvement of 2.94% on the class-incremental learning and 1.16% on the task-incremental learning compared to the best method currently available. Our code is available at https://***/EmbraceTomorrow/CE.

关键词： catastrophic forgetting continuous learning classifier expander

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Deep Reinforcement Learning for Resource Allocation With Mixed Traffic in NOMA System

Deep Reinforcement Learning for Resource Allocation With Mix...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Miao Meng, Yue Zhang, Zhi Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350345384

The non-orthogonal multiple access (NOMA) system with mixed traffic has emerged as a promising solution to satisfy the requirements of various services while mitigating the spectrum scarcity issue. In this paper, we investigate the resource allocation problem with mixed traffic in an uplink NOMA system, which consists of real-time (RT) users with strict quality-of-service (QoS) requirements on rate and latency, and best-effort (BE) users that aim to achieve a balance between throughput and fairness. In order to account for the diverse requirements of different users, we propose a utility function and formulate the resource allocation problem. To address this mixed integer non-convex problem, a deep reinforcement learning (DRL) algorithm is proposed which consists of two parts. Specifically, we design a deep Q network (DQN) to generate subcarrier assignment policies and a deep deterministic policy gradient (DDPG) network to generate power allocation policies. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in the proposed utility function. © 2023 IEEE.

关键词： Reinforcement learning

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