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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Qi, Yonghao Xu, Siya Qi, Feng Yu, Peng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9798350310900

With the advancement and widespread adoption of Artificial Intelligence (AI) and Internet of Things (IoT) technology, machine learning (ML) can be leveraged in automated factories to enable intelligent robot fault detection and recognition. As an emerging distributed machine learning framework, federated learning (FL) enables collaborative model training while safeguarding the privacy of user data. However, FL encounters various challenges, including the lack of stable energy supply to maintain continuous training and the existence of malicious terminals in the system. These situations could result in issues such as free-riding attacks and robots running out of energy during the training process. Hence, this paper presents a reliability and energy balanced computation resource allocation mechanism in FL system for IoT businesses. Firstly, a training latency and model contribution based reputation evaluation model is proposed to reveal the reliability of IoT terminals. Then, a training client reliable selection and delayed admission strategy is proposed, which takes into account both the reputation and energy consumption, aiming at enabling terminals with highquality data and low energy capacity to keep contributing to the model during the later training stage. Especially, we integrate reputation and computing capability of IoT terminals to decide the freshness level of the allocated FL global model, which can effectively defend against free-riding attacks. The simulation results verify that the proposed mechanism outperforms non-delayed/partial-delayed admission mechanisms in terms of model quality and system stability.

关键词： federated learning reputation delayed admission client selection model freshness

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Diffuse Scattering Analysis of Indoor Propagation Channel at Terahertz Frequency 25

Diffuse Scattering Analysis of Indoor Propagation Channel at...

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IEEE Wireless Communications and networking Conference (IEEE WCNC)

作者： Guo, Gan Yang, Xiaoli Zhou, Yi Ma, Nan BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China China Telecommun Technol Lab CAICT Beijing Peoples R China

ISBN: (纸本)9798350303582;9798350303599

To derive reliable channel models for future terahertz (THz) indoor communication systems, accurate propagation mechanisms of electromagnetic waves are essential. In this paper, we focus on the diffuse scattering in the THz bands, which has often been overlooked in previous work. We present a parameterized model for the diffuse scattering of different materials at 180GHz by measuring the diffuse scattering power spectrum in indoor propagation environment. New contributions representing diffuse scattering are added to the traditional specular rays in ray tracing simulation. The impact of this enrichment on the simulated indoor propagation channel is analyzed by comparing simulations taking into account or not the diffuse scattering. The simulation results demonstrate that the small-scale parameters of the channel are significantly modified when taking into account the diffuse scattering phenomenon. This means that considering the diffuse scattering component is particularly important for establishing a more realistic THz channel model.

关键词： THz Diffuse scattering Channel characteristics Channel model

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Multi-level Intelligent Collaborative Computing Mechanism Based on DDQN for Smart Highways

Multi-level Intelligent Collaborative Computing Mechanism Ba...

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

作者： Shao, Sujie Su, Lili Chai, Ruijun Guo, Shaoyong Xu, Siya Zheng, Juntao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350321524

The rapid development of 5G has made mobile edge computing technology the first choice for smart highways, but the exponentially growing number of vehicles has also brought great challenges, that is, how to provide vehicle users with low-latency and reliable services has become a key issue. To this end, a multi-level intelligent collaborative computing mechanism based on Double Deep Q-learning(DDQN) for smart highways was proposed. First, a multi-level intelligent collaborative computing model based on RSUs, mobile edge computing (MEC) servers and clouds was established. Further, aiming at the goals of task delay minimization and network load balancing, a multi-level intelligent collaborative computing algorithm based on deep reinforcement learning was proposed to perform collaborative computing on massive tasks generated by high-speed moving vehicles. Simulation results show that the proposed mechanism can reduce service delay and maintain network load balancing while completing service requirements. © 2023 IEEE.

关键词： Reinforcement learning

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Seamless Cross-Edge Service Migration for Real-Time Rendering Applications

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第6期23卷 7084-7098页

作者： Li, Yuanzhe Wang, Shangguang Li, Yuanchun Zhou, Ao Xu, Mengwei Ma, Xiao Liu, Yunxin Tsinghua Univ Inst AI Ind Res AIR Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Seamless cross-edge migration for real-time rendering applications is challenging. The strong interactive nature of real-time rendering applications demands a downtime lower than 15 ms to achieve an imperceptible migration. Existing methods based on virtual machine migration and container migration suffer from unpleasant downtime brought by dirty page retransmission-induced repeated memory data copy and the shared storage failure-induced extensive disk data copy. In this article, we propose Cloud-assisted Service Migration (CSM) which leverages cloud-edge collaboration to achieve seamless service migration for real-time rendering applications. CSM improves service migration user experience in three folds: First, it introduces a dual rendering mechanism to bypass the peer-to-peer data copy and compresses the freezing stage. Second, a user equipment-centric session switch mechanism is proposed to save time by well coordinating application session switches and 5G user plane session switches. Third, a smooth switching mechanism is leveraged to prevent unpleasant frame flickers during session switching. We implement CSM in edge-rendering multiplayer games and deploy it on a 5G test bed with a full-stack user plane protocol stack. The evaluation results show that CSM can reduce downtime to < 14 ms and the service migration process is user imperceptible.

关键词： 5G core network downtime mobile edge computing service migration

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PsyQoE: Improving Quality-of-Experience Assessment With Psychological Effects in Video Streaming

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第6期17卷 4138-4150页

作者： Sheng, Daoxu Qi, Qi Wang, Jingyu Li, Lianyuan Yu, Wei Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Mobile Res Inst Dept Serv Technol Beijing 100053 Peoples R China

As media streaming grows, service providers have to focus more on users' quality of experience (QoE). Among the metrics that influence QoE, network-oriented and media-oriented factors have been the primary concern of most existing work. Apart from the two factors, however, another crucial aspect of QoE is the user. Though the analysis and modeling of users is a turning point in the transition from QoS to QoE, the impact of users' memory and cognitive-psychological effects have not been fully explored. In this work, we analyze, validate and quantify the relationship between multiple cognitive effects and the QoE of users in media streaming. We propose PsyQoE, a new QoE assessment framework that uniquely focuses on user perceptions during media sessions. Besides providing real-time QoE assessments through leveraging machine learning, PsyQoE also predicts the overall QoE by taking into account user biases and long-term effects. Additionally, we have designed a further modified version to cater to the needs of hardware and energy-restricted scenarios. When compared with other existing methods, our approach demonstrates enhanced performance by boosting accuracy by 7% to 16%. It also provides better interpretability and broader applicability.

关键词： Quality of experience Streaming media Psychology Media Bit rate Quality of service Real-time systems Multimedia adaptive video streaming quality-of-experience assessment

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Quantum Secret Reconstruction

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ADVANCED QUANTUM TECHNOLOGIES 2024年第2期7卷

作者： Ma, Rui-Hai Gao, Fei Cai, Bin-Bin Lin, Song Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In addition to secret splitting, secret reconstruction is another important component of secret sharing. In this paper, the first quantum secret reconstruction protocol is proposed to protect the reconstruction process. Before the protocol, a classical secret is divided into multiple shares that are distributed to the shareholds, respectively. In the presented protocol, the dealer utilizes the secret to encrypt a privacy quantum state, and sends the encrypted state to a combiner who is chosen by the dealer within the shareholders. With the help of other shareholders, the combiner utilizes the properties of cluster states to recover the privacy quantum state. It is shown that the proposed protocol is secure against several common attacks, including external and internal attacks. Compared with classical secret reconstruction protocols, this protocol not only achieves information-theoretic security of all shares, but is also more efficient due to the reduced distribution and computation cost. To demonstrate the feasibility of the protocol, a corresponding simulation quantum experiment is conducted on the IBM Q. Furthermore, in conjunction with quantum fingerprinting, it can be directly applied to achieve the task of quantum multiple secrets sharing, since the classical shares can be reused in the proposed protocol. The work proposes the first quantum secret reconstruction protocol to protect the secret reconstruction process. Compared with classical secret reconstruction protocols, the presented protocol not only achieves information-theoretic security of all shares but is also more efficient due to the reduced distribution and computation cost. The corresponding simulation quantum experiment conducted on the IBM Q demonstrates the feasibility of the ***

关键词： cluster state secret reconstruction quantum experiment quantum multiple secrets sharing

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Energy-Efficient Multiple Access Scheme with Power Control for mMTC Networks 23

Energy-Efficient Multiple Access Scheme with Power Control f...

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23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Meng, Yue Peng, keyi Wu, Di Zhang, Zhi Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350325959

The grant-free non-orthogonal multiple access (GF-NOMA) is especially conducive to support massive machine-type communication (mMTC) service. Owing to the limited energy resources and the reliability requirements for MTC devices, how to design an energy-efficient multiple access scheme is an urgent problem. In this paper, we investigate a comprehensive learning framework for GF-NOMA with power control scheme in mMTC networks, focusing on the power level design and the collision resolution. We formulate an optimization problem aiming to minimize the long-term system power consumption with satisfying the constraint of packet error rate. A scheme based on a deep reinforcement learning approach is further applied, comprising two interrelated subalgorithm, to optimize the power levels and the contention-transmission unit (CTU) allocation. The numerical results demonstrate the superiority of our proposed scheme over other resource allocation schemes, particularly in terms of enhancing the network energy efficiency. © 2023 IEEE.

关键词： deep reinforcement learning energy-efficient GF-NOMA multiple access Power control

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DRL and Main-Side Blockchain Empowered Edge Computing Framework for Assistant Driving 36

DRL and Main-Side Blockchain Empowered Edge Computing Framew...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Zhong, Yuxuan Xu, Siya Li, Zihan Yu, Peng Wang, Ying Zhou, Fanqin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9781665477161

To provide intelligent and accurate assistant driving services in smart city, as well as ensure the security and tamper proof of vehicle data, this paper build a deep reinforcement learning (DRL) and main-side blockchain empowered service framework. By storing driving data and vehicle information on the sidechain, while deploying index information on the mainchain, the main-side blockchain structure can enhance the scalability of blockchain, decrease the communication overhead, improve consensus efficiency, and avoid the leakage of data between different sidechains. However, the resource limited vehicles on sidechain cannot process numerous computation-intensive mining tasks in time, resulting in high service delays. Thus, this paper integrate mobile edge computing with blockchain system to design a double-layer mining service offloading mechanism, allowing the edge nodes and neighboring vehicles to form a cooperative mining network and collaboratively participate in mining process with specific offloading rates. The first layer uses Asynchronous Advantage Actor-critic (A3C) algorithm to efficiently offload partial mining task from the task vehicle to the road side unit (RSU), and the second layer applies double auction to specifically obtain the offloading rates from RSU to multiple service vehicles. Simulation results demonstrate that, our proposed mechanism outperforms other compared algorithms in the average profit and consensus delay. © 2023 IEEE.

关键词： Blockchain

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FRACTAL: Data-aware Clustering and Communication Optimization for Decentralized Federated Learning

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IEEE Transactions on Big Data 2024年 1-16页

作者： Ma, Qianpiao Liu, Jianchun Xu, Hongli Jia, Qingmin Xie, Renchao Purple Mountain Laboratories Nanjing Jiangsu China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Decentralized federated learning (DFL) is a promising technique to enable distributed machine learning over edge nodes without relying on a centralized parameter server. However, existing DFL network topologies, such as fully connected, partially connected, or lower-tier hierarchical topology often struggle to effectively address the unique challenges presented by edge networks, including edge heterogeneity, communication resource constraint, and data Non-IID. In order to tackle these challenges, we propose a data-aware clustering algorithm, called FRACTAL, to construct a multi-tier hierarchical topology in a bottomup manner taking into consideration both data distribution and communication efficiency for DFL. We theoretically explore the quantitative relationship between the convergence bound of multi-tier FL and the data distribution among each-tier servers. To further improve communication efficiency and address edge heterogeneity, we deploy a time-sharing communication scheduling algorithm within each fractal unit (the basic structure in FRACTAL consisting of multiple nodes and an aggregator), called magic mirror method (MMM), to determine the optimal order of model distributing and uploading for nodes. We conduct extensive experiments on the classical models and datasets to evaluate the performance of FRACTAL, and the results show that FRACTAL can significantly accelerate the DFL model training by 48.6%- 72.3% compared with the state-of-the-art solutions. IEEE

关键词： Network topology

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Maximizing Energy Efficiency for LEO Satellite Systems With Massive MIMO-NOMA 9

Maximizing Energy Efficiency for LEO Satellite Systems With ...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Li, Lihua Liu, Xin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350317251

In this paper, we consider the application of Non-Orthogonal Multiple Access (NOMA) in Low Earth Orbit (LEO) satellite communication systems (SCS) with hybrid beamforming structure to support Massive Machine Communication (mMTC). Satellites are typically powered by solar energy, leading to significant energy costs, therefore, we propose a resource allocation scheme aimed at maximizing the energy efficiency of the system. Specifically, we firstly group users based on their channel correlations using the agglomerative nesting (AGNES) algorithm. Subsequently, a joint hybrid beamforming and power allocation problem is formulated to maximize the energy efficiency of the system. To address this non-convex problem with high-dimensional variables, we first obtain the power allocation solution under fixed hybrid beamforming using fractional Programming (FP) and Successive Convex Approximation (SCA) methods. Then, given fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming. Finally, a Boundary Compressed-Particle Swarm Optimization (BC-PCO) algorithm is used to obtain the analog beamforming. The simulation results demonstrate that the proposed algorithm exhibits significant superiority in terms of energy efficiency. © 2023 IEEE.

关键词： Energy efficiency

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