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Distributed Satellite-Terrestrial Cooperative Routing Strategy Based on Minimum Hop-Count Analysis in Mega LEO Satellite Constellation

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第11期23卷 10678-10693页

作者： Feng, Xin'ao Sun, Yaohua Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Mega low earth orbit (LEO) satellite constellation is promising in achieving global coverage with high capacity. However, forwarding packets in mega constellation faces long end-to-end delay caused by multi-hop routing and high-complexity routing table construction, which will detrimentally impair the network transmission efficiency. To overcome this issue, a distributed low-complexity satellite-terrestrial cooperative routing approach is proposed in this paper, and its core idea is that each node forwards packets to next-hop node under the constraints of minimum end-to-end hop-count and queuing delay. Particularly, to achieve an accurate and low-complexity minimum end-to-end hop-count estimation in satellite-terrestrial cooperative routing scenario, we first introduce a satellite real-time position based graph (RTPG) to simplify the description of three-dimensional constellation, and further abstract RTPG into a key node based graph (KNBG). Considering the frequent regeneration of KNBG due to satellite movement, a low complexity generation method of KNBG is studied as well. Finally, utilizing KNBG as input, we design the minimum end-to-end hop-count estimation method (KNBG-MHCE). Meanwhile, the computational complexity, routing path survival probability and practical implementation of our proposal are all deeply discussed. Extensive simulations are also conducted in systems with Ka and laser band inter-satellite links to verify the superiority of our proposal.

关键词： Routing Satellites Orbits Space-air-ground integrated networks Complexity theory Proposals Delays End-to-end routing low earth orbit (LEO) satellite network mega constellation minimum hop-count analysis

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Inter-Symbol Interferences Deteriorated Ultraviolet Communications Using Photon-Counting Receivers

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第5期23卷 4097-4113页

作者： Wang, Zhifeng Yuan, Renzhi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The strong scattering effects of ultraviolet (UV) signals in the atmosphere enables the non-line-of-sight (NLOS) ability of UV communications, but introduces large time dispersion of the channel impulse response (CIR), which inevitably results in serious inter-symbol interferences (ISIs). Because a larger transmitting rate can introduce a larger ISI, it is meaningful to study the impact of CIR on the ISI and further quantify the achievable information rate (AIR) of NLOS UV communications. In this paper, we analyzed the influence of both coplanar and non-coplanar geometries on the CIR for multiple scattering effects and proposed a modified Gamma function (MGF) to precisely characterize the CIR. Based on the MGF model, we quantified the ISIs and derived the bit-error rate (BER) and the AIR under thermal noise for both on-off keying (OOK) modulation and 2-pulse-position modulation (2-PPM) using photon-counting receivers. The simulation results showed that the proposed MGF model can achieve higher fitting precision of both CIRs and BERs compared with the existing Gamma function model. Besides, we demonstrated that when the thermal noise photons are less than 10, the OOK and 2-PPM can achieve an AIR of 15 Mbit/s and 13 Mbit/s, respectively, under typical system geometries.

关键词： Channel impulse response inter-symbol interference thermal noise ultraviolet communication

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Graph neural networks-based preference learning method for object ranking

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2024年 167卷

作者： Meng, Zhenhua Lin, Rongheng Wu, Budan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Preference learning refers to the task of predicting the ranking of a collection of alternatives based on observed or revealed preference information. Object ranking is a critical problem within the domain of preference learning, which can be described as learning a ranking function based on training data in a ranked form. Some existing parametric preference learning methods are difficult to balance the relationship between expressive power and training cost. To deal with this issue, we introduce the concept of graph neural networks (GNNs) into preference learning, and propose a GNNs-based preference learning method. Our method is composed of three stages to cope with preference learning, i.e. preference relation graph construction, preference relation prediction, and object preference ranking. In the first stage, we map preference information onto the graph structure, and construct a directed graph with objects as nodes and preference relations between objects as edges. In the second stage, we formulate relation prediction as an edge classification problem on the graph, design a model consisting of multi -layer perceptron (MLP) and GNNs to extract edge features. In the third stage, we build a comparator neural network structure, which takes pairwise preference information as input and the score of each object as output, and the ranking of object scores is the objects' preference order. Experiments on preference learning datasets demonstrate that, compared to baselines, our method can achieve marked performance gains in terms of object ranking scenario.

关键词： Preference learning Graph neural networks Pairwise preference Learning to rank Object ranking

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Near-Field Beam Training With Hierarchical Codebook: Two-Stage Learning-Based Approach

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IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2024年第9期73卷 14003-14008页

作者： Weng, Caihao Guo, Xufeng Wang, Ying Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is envisioned as a key enabler for future six-generation (6G) networks. Nevertheless, the pronounced expansion in both antenna aperture and operational frequency mandates the consideration of near-field conditions. This shift complicates traditional far-field beam training designs, which struggle with near-field spherical-wave-based modeling, thereby compromising channel capacity. Specifically, in near-field systems, unfavorable computational complexity and pilot overheads become almost inevitable, given that beam training codebooks are coupled with both angles and distances of scatterers in significant propagation paths. Moreover, the power leakage effect in the near-field system can erode beamforming gains if not addressed. To counter these challenges, we introduce a novel two-stage learning-based beam training protocol that independently manages angles and distances. Building on this foundation, we present a hierarchical codebook design with modular narrow codeword units termed hierarchical codebook design via accumulation (HCD-A) to alleviate the power leakage problem. Our simulation results underscore the superior performance of our proposed solutions at different signal-to-noise ratio (SNR).

关键词： Training Design methodology Protocols Aperture antennas Vectors Signal to noise ratio Radio frequency XL-MIMO beam training near-field deep learning hierarchical codebook

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Multiparticipant Double Auction for Resource Allocation and Pricing in Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第8期11卷 14007-14016页

作者： Huang, Jie Li, Sisi Yang, Lipei Si, Jianing Ma, Xiao Wang, Shangguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Edge computing serves as a critical solution for latency-sensitive services on mobile and IoT devices. However, the high cost and limited edge resources present significant challenges for service and infrastructure providers in establishing efficient collaborations, particularly with conflicting profit objectives. Inspired by the pseudo elbow formation of octopuses, we propose a multiparticipant double auction for resource allocation and pricing between service and infrastructure providers. We introduce a neutral third-party auctioneer to eliminate direct bargaining among participants, leading to an improved amount of allocated resources and matching efficiency. The presence of heterogeneous participants, many-to-many mapping and an advisable payment strategy that satisfies economic properties exacerbate the difficulty. To address these challenges, we propose a matching and pricing resource allocation algorithm for a long-term steady market, and a truthful resource allocation algorithm for a short-term market. Simulation results demonstrate that the proposed algorithms exhibit superior performance not only in maximizing social welfare and utility of both service and infrastructure providers but also in improving resource utilization.

关键词： Double auction edge computing resource allocation

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Cognition Guided Video Anomaly Detection Framework for Surveillance Services

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2109-2123页

作者： Zhang, Menghao Wang, Jingyu Qi, Qi Zhuang, Zirui Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The aim of surveillance services is to detect anomalous events that occur in given surveillance videos. Most existing video anomaly detection methods rely on minimizing reconstruction or prediction errors due to the lack of abnormal data, which results in poor generalization and overfitting. In fact, cognitions for anomalies in surveillance videos mainly relies on crucial relationships, including ones between objects and ones between objects and scenes. Focusing on this property of anomaly detection, a Cognition Guided Video Anomaly Detection framework based on prior knowledge is proposed, called CG-VAD. CG-VAD introduces both explicit and implicit prior knowledge into the frame prediction network to let the model exploit crucial relationships. Explicit knowledge containing crucial relationships related to anomaly is introduced into the anomaly detection model through a proposed embedding network based on multi-layer Graph Convolutional Networks. Implicit knowledge in the form of learnable parameters enhances the ability of the model to learn crucial relationships through prompt tuning. By integrating prior knowledge to focus the model on the relationships associated with the anomaly, we find that CG-VAD is not only quick to adapt to new real-world scenarios, but it is also able to recognize the type of anomaly. We have conducted extensive experiments on four benchmark datasets and the results indicate that the proposed method outperforms previous methods. Specifically, CG-VAD achieves an AUROC score of 87.2$\%$% on the Shanghaitech dataset.

关键词： Anomaly detection Surveillance Knowledge engineering Task analysis Visualization Explosions Data models Multi-layer GCN prior knowledge prompt tuning video anomaly detection surveillance service

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IRS-Aided Location Sensing and Beamforming in ISAC Systems: Distributed or Not?

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IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2024年第9期73卷 13219-13233页

作者： Lyu, Shuyang Hu, Xiaoling Liu, Chenxi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Integrated sensing and communication (ISAC) has been envisioned as one of the key features of the sixth-generation wireless networks. However, the coverage and sensing-communication performance are limited due to the uncontrollable environments, especially when operating in the high-frequency bands. To address this issue, we utilize the benefits of intelligent reflecting surface (IRS) in reshaping the propagation environments and propose a distributed IRS-aided ISAC framework, in which the impacts of IRS deployment on both sensing and communication performances are investigated. Specifically, in the proposed framework, multiple IRSs including both passive IRSs (PIRSs) and semi-passive IRSs (SIRSs) are deployed to realize communication and sensing simultaneously. We first design a distributed IRS-aided location sensing scheme, by invoking communication signals. Using the sensed location information, a distributed-IRS beamforming scheme is designed to enhance the transmission rate with low overhead. Numerical results show that a high positioning accuracy of millimeter-level can be achieved. By using the sensed location information, the proposed distributed-IRS beamforming scheme achieves comparable performance to the benchmark with perfect channel state information. We also show that, with a fixed total number of IRS elements, a distributed deployment with 2 SIRSs is preferable in terms of location sensing performance and a distributed deployment with more PIRSs is preferable in terms of communication performance.

关键词： Sensors Array signal processing Location awareness Vectors Uplink Signal to noise ratio Radar Intelligent reflecting surface (IRS) integrated sensing and communication (ISAC) distributed IRS-aided location sensing sensing-aided beamforming design

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 1065-1076页

作者： Zhang, Qiuyang Wang, Ying Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The industrial Internet of Things (IIoT) has led to the emergence of various information-based industrial applications. Due to the uninterrupted and complex nature of production processes in industrial systems, these applications often run continuously and rely on information from multiple sensors. As a result, a single sensor can support multiple applications simultaneously, leading to complex correlations in the system. To address this challenge, we introduce the concept of the age of correlated information (AoCI) and formulate the scheduling problem as a Markov game problem to optimize the information freshness of industrial applications. To solve the problem, we propose a multi-heterogeneous-agent-reinforcement-learning (MHARL) scheme, which uses neural networks with different structures to represent agents participating in the game. Our numerical results demonstrate that the proposed MHARL scheme outperforms typical baselines, such as Qmix and VDN, in terms of AoCI and energy efficiency.

关键词： Sensors Job shop scheduling Industrial Internet of Things Quality of service Correlation Sensor systems and applications Sensor systems Industrial Internet of Things (IIoT) Age of Correlated Information (AoCI) multi-agent reinforcement learning

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Convergence Analysis and Latency Minimization for Semi-Federated Learning in Massive IoT Networks

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND networking 2024年第1期8卷 413-426页

作者： Ren, Jianyang Ni, Wanli Tian, Hui Nie, Gaofeng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

As the number of sensors becomes massive in Internet of Things (IoT) networks, the amount of data is humongous. To process data in real-time while protecting user privacy, federated learning (FL) has been regarded as an enabling technique to push edge intelligence into IoT networks with massive devices. However, FL latency increases dramatically due to the increase of the number of parameters in deep neural network and the limited computation and communication capabilities of IoT devices. To address this issue, we propose a semi-federated learning (SemiFL) paradigm in which network pruning and over-the-air computation are efficiently applied. To be specific, each small base station collects the raw data from its served sensors and trains its local pruned model. After that, the global aggregation of local gradients is achieved through over-the-air computation. We first analyze the performance of the proposed SemiFL by deriving its convergence upper bound. To reduce latency, a convergence-constrained SemiFL latency minimization problem is formulated. By decoupling the original problem into several sub-problems, iterative algorithms are designed to solve them efficiently. Finally, numerical simulations are conducted to verify the effectiveness of our proposed scheme in reducing latency and guaranteeing the identification accuracy.

关键词： Federated learning over-the-air computation network pruning convergence analysis latency minimization

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Intelligent Reflecting Surface Aided Wireless Clock Synchronization for Indoor Substation

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IEEE TRANSACTIONS ON SMART GRID 2024年第2期15卷 2362-2378页

作者： Xie, Kunyi Feng, Lei Yang, Yang Zhou, Fanqin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The intelligent revolution in substations desires wireless networks to provide high-precision timing services while ensuring clock synchronization among intelligent electronic devices (IEDs) to support the collaboration of time-sensitive grid applications, such as phasor measurement unit (PMU), saving the cost on disposing communication elements and timing elements for the IED separately. However, the unreliable nature of the wireless network in the indoor substation may give rise to the loss of timestamp packets, which plays a negative role in the clock synchronization accuracy. This paper constructs a novel intelligent reflecting surface (IRS)-aided wireless clock synchronization framework to solve the aforementioned problem. First, an IRS-assisted incomplete observation clock synchronization model is established based on the two-way timestamp exchanging mechanism. Then quantitative analyses of the relationship between the reliability of the wireless channel and the steady-state clock synchronization estimation error covariance are given. In addition, we formulate a clock synchronization optimization problem to balance the system clock synchronization accuracy and fairness among IEDs. In the case of obtaining the Pareto fronts, the proposed clock synchronization framework outperforms the other representative methods, since IRS brings out more reliable transmission of timestamp packets when serving multiple IEDs.

关键词： Indoor substation wireless clock synchronization intelligent reflecting surface Pareto front

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