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13th International Conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Cao, Yongxu He, Xinxin Yu, Congqing Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (纸本)9781665407854

The millimeter wave(mmWave) communication is a viable approach to support the Gbps data rates that would be required for raw sensor data exchange among autonomous vehicles. However, there is a main challenges resulting from the use of high-frequency bands: the mmWave blockage effect. In this paper, we propose a blockage avoidance based mmWave relay scheme for the Internet of Vehicles. Specifically, we first propose a mmWave link availability identification method based on the vehicle geometric relationship and the spatial correlation between sub-6 GHz signals and mmWave signals. Secondly, in order to improve the reliability of all vehicle sensor data transmission and reduce the overall transmission delay, a resource allocation scheme based on the real-time blocking relationship graph among multilane vehicles is proposed. The simulation show that compared with the baseline, the packet loss rate of proposed scheme is increased by 20%, and the transmission delay is reduced by 80%, which can realize stable, reliable and efficient transmission. © 2021 IEEE.

关键词： Millimeter waves

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Metric Learning for Semantic Metric Learning for Semantic⁃Based Clothes Retrieval

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ZTE Communications 2022年第1期20卷 76-82页

作者： YANG Bo GUO Caili LI Zheng Beijing Laboratory of Advanced Information Networks Beijing Uni-versity of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Network System Architecture and Conver-gence Beijing University of Posts and TelecommunicationsBeijing 100876China

Existing clothes retrieval methods mostly adopt binary supervision in metric *** each iteration,only the clothes belonging to the same instance are positive samples,and all other clothes are“indistinguishable”negative samples,which causes the following *** relevance between the query and candidates is only treated as relevant or irrelevant,which makes the model difficult to learn the continu-ous semantic similarities between *** that do not belong to the same instance are completely considered irrelevant and are uni-formly pushed away from the query by an equal margin in the embedding space,which is not consistent with the ideal retrieval ***-vated by this,we propose a novel method called semantic-based clothes retrieval(SCR).In SCR,we measure the semantic similarities be-tween clothes and design a new adaptive loss based on these *** margin in the proposed adaptive loss can vary with different se-mantic similarities between the anchor and negative *** this way,more coherent embedding space can be learned,where candidates with higher semantic similarities are mapped closer to the query than those with lower *** use Recall@K and normalized Discounted Cu-mulative Gain(nDCG)as evaluation metrics to conduct experiments on the DeepFashion dataset and have achieved better performance.

关键词： clothes retrieval metric learning semantic-based retrieval

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Location-Based Multi-site Coordination Beam Tracking for Vehicle mmWave Communications 15th

Location-Based Multi-site Coordination Beam Tracking for Veh...

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15th EAI International Conference on Communications and networking, ChinaCom 2020

作者： He, Xingwen Liu, Danpu Zhang, Zhilong Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783030677190

Millimeter wave (mmWave) system with massive multiple input multiple output (mMIMO) meets increasing data traffic requirements. However, fast beam tracking for vehicles with high mobility causes enormous overhead, especially in an ultra-dense network (UDN) with frequently base station (BS) handover. In this paper, we proposed a multi-site coordination beam tracking scheme utilizing the spatial correlation of channel state information (CSI) among different sites to reduce the signaling overhead for beam training and handover. The scenario is a hyper-cellular network (HCN) with one control-BS (CBS) and multiple traffic-BSs (TBSs). The proposed scheme consists of two stages. In the first stage, more accurate position measurement of the moving user equipment (UE) can be achieved by using uniform planar array (UPA), and Extended Kalman Filter (EKF) is exploited in CBS to predict the UE’s location in the next slot. In the second stage, the relationship between multi-site and UE’s location is used by the CBS to remotely infer the candidate beam between each TBS and the UE, and make a TBS handover decision when necessary. Given that it is the CBS in charge of beam tracking between all the TBSs and the UE centrally, the overhead for beam training and handover are both efficiently reduced. Simulation results based on realistic 3D scenario show that the proposed scheme can achieve 99% of the optimal spectral efficiency with fewer overhead for beam sweeping and handover signaling. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Extended Kalman filters

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Arrival Angle Estimation Based on Transfer Learning in Complex Environments

Arrival Angle Estimation Based on Transfer Learning in Compl...

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International Conference on Computer and Communications (ICCC)

作者： Xingjian Li Xinghe Chu Luhan Wang Zhaoming Lu Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Infrmation Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

Signal Angle-of-Arrival estimation has been widely studied as an important sensing technology in Integrated Sensing and Communications, but its performance is limited by complex multipath environments and the scale of antenna arrays. In recent years, learning-based signal angle-of-arrival estimation methods have made breakthroughs, but the effectiveness of learning methods is closely related to the richness of datasets. In the new generation of communication systems, the large-scale deployment of base stations for sensing needs, the data set is difficult to cover a large number of complex and changeable scenarios, and there are problems such as excessive collection scale and difficult labeling. In order to achieve effective estimation of signal angle-of-arrival in complex environments, this paper uses the method of transfer learning to reduce the cost of data set collection and improve the accuracy of estimation. The transfer learning in this paper uses a domain adversarial model to achieve unlabeled transfer from simple to complex scenes. The experiments set up multiple scenarios such as multi-source, multi-path, and array defects. The migration results show that the method can effectively improve the performance of angle-of-arrival estimation in most scenarios, and verify the adaptability of the proposed method to complex environments.

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Joint Base Station Sleeping and Functional Split Orchestration in Crosshaul-Based V-RAN

Joint Base Station Sleeping and Functional Split Orchestrati...

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IEEE Conference on Wireless Communications and networking

作者： Zhenghe Zhu Hang Li Yawen Chen Xiangming Wen Zhaoming Lu Luhan Wang Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

The intensive deployment of base stations (BSs) in the radio access network (RAN) incurs huge energy consumption and operating overheads. Although the centralized RAN (C-RAN) architecture can significantly relieve the BS overheads by centralizing BS functions, the strict front-haul requirements of C-RAN make it challenging to realize a fully centralized RAN. Recently, the virtualized RAN (V-RAN) architecture has been proposed, allowing flexible function split (FS) and crosshaul to balance the centralization and mid-haul requirements. On the other hand, considering the tidal effect of traffic, sleep some BSs with low loads and migrating their traffic to other BSs is an effective way to further reduce energy consumption. In this paper, we investigate joint BS sleeping and FS orchestration in crosshaul-based V-RAN that jointly optimizes BS working mode, FS, traffic migration, and routing selection. The joint optimization model is formulated as a mixed-integer nonlinear programming (MINLP) that minimizes total expenditure, including RAN energy consumption and operating overheads. Considering the complexity of the problem, we propose a heuristic algorithm to solve it in polynomial time. Simulation results validate that our algorithm can save significant expenditure compared to baselines, and the results can reach within 1.13 times the optimum in a relatively short time.

关键词： Energy consumption Base stations Costs Simulation Heuristic algorithms Programming Routing

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Cooperative double-IRS Assisted Integrated Sensing and Communication Under NLoS Conditions

Cooperative double-IRS Assisted Integrated Sensing and Commu...

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IEEE International Conference on Communications (ICC)

作者： Wan Xiang Yawen Chen Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

We study an integrated sensing and communication (ISAC) system assisted by cooperative double-intelligent reflecting surface (IRS). The IRSs are deployed near the base station (BS) and the users respectively. The BS uses its nearby IRS to sense the potential target in the blocked area and uses the double-reflection and single-reflection links provided by the IRS to realize the communication between the BS and the multi-users. We consider an orthogonal transmission signal, aiming to maximize the weighted sum of the minimum signal-to-interference-plus-noise ratio (SINR) of the sensing and the minimum communication SINR among all users, jointly optimizing the active beamforming of the BS and the passive beamforming of two distributed IRSs. We propose an efficient algorithm for obtaining high quality solutions using alternating optimization and semidefinite relaxation techniques. Simulation results show that, compared with the benchmark algorithm, the proposed joint beamforming design achieves the balance between communication and sensing performance, and shows that the use of cooperative double-IRS is beneficial to improve the performance of ISAC system.

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Joint Latency and Energy Consumption Optimization with Deep Reinforcement Learning for Proximity Detection in Road networks 7

Joint Latency and Energy Consumption Optimization with Deep ...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Zhao, Tongyu Liu, Yaqiong Shou, Guochu Hu, Yihong Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781665409506

Artificial intelligence and the automobile business have grown significantly in recent years, and autonomous driving has progressively been the industry's focus. The problem of proximity detection in road networks refers to determining if two moving objects are close to one other in real time. However, in the real world, mobile devices' battery life and computing capabilities are restricted, resulting in excessive latency and energy usage. As a result, determining the proximity relationship between mobile users with low latency and energy usage is a difficult task. We formalize the joint latency and energy consumption optimization problem for proximity detection in road networks into a constrained optimization problem (COP) and solve it using a deep Q network (DQN). The simulation results show that DQN can ensure the desired sum cost under a variety of important factors, effectively reducing latency and energy usage. © 2021 IEEE.

关键词： Reinforcement learning

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Time Error Compensation with Neural network for Time Synchronization in TSN 7

Time Error Compensation with Neural Network for Time Synchro...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Yu, Haoze Shou, Guochu Zhang, Xiaodong Hu, Ledong Liu, Yaqiong Hu, Yihong Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665409506

Time-sensitive networking featured with precise time synchronization, is significant in modern industrial systems for real-time guarantee. However, some industrial scenarios, such as smart grids and 5G fronthaul networks, have much higher requirements on time synchronization. This paper proposes a method to improve the synchronization performance of TSN with a neural network model. A time error compensation algorithm including timestamp data processing, neural network training, and error compensation is designed. Comparative experiment results show that this algorithm improves the stability and accuracy of time synchronization. © 2021 IEEE.

关键词： Synchronization

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Multi-domain Time Synchronization Model and Performance Evaluation in TSN 7

Multi-domain Time Synchronization Model and Performance Eval...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Hu, Ledong Shou, Guochu Zhang, Xiaodong Liu, Yaqiong Hu, Yihong Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781665409506

Time-Sensitive networking (TSN) has precise time synchronization capabilities and can provide reliable time-deterministic services for industrial communications and other fields. IEEE 802.1AS is used as the time synchronization protocol in TSN and has support for multiple gPTP domains, which further expands the application range of TSN and enhances the synchronization capabilities of TSN. Industrial automation and other use cases also have multi-domain time synchronization requirements of TSN, but there is still a lack of research in this field. Therefore, this paper studies the multi-domain synchronization mechanism in TSN and proposes the multi-domain time synchronization model according to actual application requirements. Based on the multi-domain model, this paper analyzes the time errors in multi-domain time synchronization, introduces the SDN paradigm, and proposes a software-defined time compensation method to compensate the time errors centrally. Further, the time synchronization experiment environment is built to analyze synchronization performance. The results verify the effectiveness of the multi-domain time synchronization model, demonstrating that the multi-domain TSN network can provide high-precision time synchronization services. © 2021 IEEE.

关键词： Errors

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Research of Epidemic Dynamics Model Considering Individual Movements and Urban Areas 7

Research of Epidemic Dynamics Model Considering Individual M...

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7th IEEE International Conference on network Intelligence and Digital Content, IC-NIDC 2021

作者： Pan, Boxu Yang, Jie Wang, Yu Wang, Zehao Zhu, Yuangeng Zhang, Zhiqi Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China Aisino Corporation Beijing100195 China

ISBN: (纸本)9781665405829

With the rapid spread of COVID-19, hundreds of millions of people worldwide have been infected. In order to cope with the epidemic, experts from various countries have carried out a lot of research works. Most of these works chose to use the traditional SEIR model, but the traditional model doesn't consider the individual's movement in the city. Based on the transmission characteristics of COVID-19, this paper optimized the traditional SEIR model by combining the in-depth mining and processed multiple data, such as the real epidemic data published by some official organizations, as well as data with certain credibility obtained from reference papers, journals or newspapers. Compared with the traditional SEIR model, the proposed model takes into account the impact of individuals' movement and the division of urban functional areas. The outcomes can play a certain role in the prediction and analysis of the spread of the epidemic in cities with regular individuals' movements and functions of urban areas. © 2021 IEEE.

关键词： COVID-19

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