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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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RIS-Aided Hotspot Capacity Enhancement for Multibeam Satellite systems

RIS-Aided Hotspot Capacity Enhancement for Multibeam Satelli...

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IEEE International Conference on Communications (ICC)

作者： Ziyuan Zheng Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Precoding techniques combined with aggressive full frequency reuse (FFR) are a promising solution to meet the evergrowing capacity demand for multibeam satellite systems (MSSs). However, LOS-dominant satellite-ground propagation environments will cause strong channel correlation among the geographically clustered hotspot users (HUs), and the consequent degradation of spatial multiplexing gain severely restricts multiuser multiple-input multiple-output (MU-MIMO) capacity. In this paper, we propose a reconfigurable intelligent surface (RIS)-aided scheme for MSSs to enhance the hotspot capacity of HUs via RIS-improved spatial-multiplexed transmission. Specifically, we propose to employ a RIS in the MSS's hotspot area formed by HUs for channel decorrelation and then jointly optimize the RIS passive beamforming and satellite precoding to reap the benefits of the spatial multiplexing. In this context, we formulate a novel HUs' sum rate maximization problem, subject to the transmit power constraint, the quality-of-service constraints for non-hotspot users, and the unit-modulus constraint for the RIS. The formulated problem is non-convex, and we propose an efficient iterative algorithm based on quadratic transform, semi-definite relaxation, and alternating optimization methods to solve it. Simulation results show that in our proposed RIS-aided scheme with optimized precoding and passive beamforming, significant sum rate enhancement is achieved for HUs in the MSS, owing to channel reconfigurable capability brought by the RIS.

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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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Refine Reinforcement Learning for Safety Training of Autonomous Driving

Refine Reinforcement Learning for Safety Training of Autonom...

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International Conference on Intelligent Transportation

作者： Fukun Yang Zhiqun Hu Yuanming Zhang Hao Huang Guixin Wang Beijing Key Laboratory for Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The random exploration nature of reinforcement learning (RL) impedes the way to achieve human-like autonomous driving, owing to the prohibitively high safety requirement. In this paper, we propose a deep refine reinforcement learning (DR2L) approach to removing non-safety-critical actions and reconstructing critical ones, which effectively enhance the efficiency of exploration. The core is to design an action filter based on two-stage vehicle motion model to calculate the critical value of dangerous actions and reconstructe the action space by filtering out the obvious incorrect actions. Besides, we propose to use the beta distribution as the stochastic policy, which eliminates the bias of the Gaussian policy and provides faster convergence. Finally, we design spacial-temporal attention network to extract hidden information of environment as the state to enhance the performance of RL. The simulation shows that DR2L can effectively improve the safety of agent during training process. Our resutls show that the beta policy provides significantly faster convergence over the Gaussian policy when both are used with proximal policy optimization (PPO).

关键词： Training Space vehicles Filtering Stochastic processes Reinforcement learning Safety Autonomous vehicles Optimization Intelligent transportation systems convergence

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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Slice-Based Service Function Chain Embedding with Multiple Factors

Slice-Based Service Function Chain Embedding with Multiple F...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Li, Hang Kong, Zixuan Xiang, Wan Wen, Xiangming Lu, Zhaoming Wang, Luhan Chen, Yawen Jing, Wenpeng 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

ISBN: (纸本)9781665423908

Network slicing is a promising technology to provide various services on a common physical network infrastructure. A network slice is a logical virtual network composed of a group of service function chains (SFCs) and the network operator performs SFC embedding (SFCE) to create and orchestrate network slices. Compared to conventional SFCE, implementing slice-based SFCE (SBSFCE) faces some new challenges like diversified requirements of SFCs, inter-slice and intra-slice VNF sharing, and slice priority-based admission control due to the introduction of slice dimension. In this paper, we study the SBSFCE problem jointly considering the above challenges. We formulate the problem and devise a dynamic programming-based heuristic, SBSEA to solve it. The simulation results demonstrate that SBSEA outperforms the existing solution and achieves near-optimal performance. © 2021 IEEE.

关键词： Heuristic algorithms

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RIS-Assisted Coverage Extension for LEO Satellite Communication in Blockage Scenarios

RIS-Assisted Coverage Extension for LEO Satellite Communicat...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Ning Cao Ziyuan Zheng Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

Low Earth Orbit (LEO) satellite communication (SatCom) is considered a promising solution to supplement terrestrial networks. However, line-of-sight (LoS) links between the satellite and terrestrial terminals are possibly blocked by objects on Earth (e.g., mountains, tall buildings, etc.), leading to communication interruptions. This paper introduces Reconfigurable Intelligent Surface (RIS) into the LEO SatCom system to extend satellite coverage in blockage scenarios. Specifically, in contrast to the signal transmission model under the far-field assumption in usual studies, we present a model combining the near-field and far-field of the RIS, which can characterize the operational differences of each reflecting element more accurately. Next, a user-received power maximization problem is formulated and we solve it by jointly optimizing the RIS phase shifts and orientation. Simulation results demonstrate the effectiveness of the proposed optimization algorithm. Remarkably, it is shown that the scheme based on the proposed algorithm can assist the satellite in extending its coverage and providing higher power than that before blockage occurs compared to the baseline schemes.

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Atmospheric Ducting Effect in Wireless Communications:Challenges and Opportunities

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Journal of Communications and Information networks 2021年第2期6卷 101-109页

作者： Fangfang Liu Jiaxi Pan Xiangwei Zhou Geoffrey Ye Li Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing 100876China Louisiana State University Baton Rouge LA 70803USA Imperial College London London SW72AZU.K.

Atmospheric ducting has a significant impact on electromagnetic wave *** signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the *** ducting brings both challenges and opportunities to wireless *** one hand,the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel *** the other hand,a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight *** this article,the formation of the atmospheric duct and its effects on radio wave propagation are first *** solutions and standardization activities in the 3rd Generation Partnership Project(3GPP)to mitigate atmospheric duct induced remote interference are ***,the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested.

关键词： atmospheric duct ducting channel modeling beyond line-of-sight remote interference management

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An Energy-Efficient Beam Hopping Strategy in Interference Coexistence Satellite systems

An Energy-Efficient Beam Hopping Strategy in Interference Co...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Hao Sun Wenpeng Jing Zhaoming Lu Xiangming Wen Wei Li Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Satellite Frequency and Orbit Resource Research Center The State Radio Monitoring Center Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

While beam hopping (BH) has revolutionized satel-lite communication by addressing non-uniform traffic demands and minimizing beam interference, existing research on multi-satellite BH scenarios often overlooks energy efficiency (EE) optimization. This can lead to increased operational costs and reduced satellite lifespans due to higher onboard energy consumption. In this paper, we propose a novel energy-efficient beam hopping strategy in the coexistence scenario of geosynchronous orbit (GSO) and non-geosynchronous orbit (NGSO) satellite systems. Specifically, the proposed strategy can both balance traffic load among NGSO satellites and avoid interference to GSO ground stations. A joint multi-satellite BH optimization problem is formulated to optimize a trade-off function between quality of service (QoS) and EE for the NGSO system. Then, the original problem is divided into the satellite-cell assignment, BH pattern design, and beam power allocation sub-problems and solved sequentially. The numerical results demonstrate the superiority of the proposed strategy in enhancing the QoS and EE of the NGSO system while effectively mitigating interference with the GSO system.

关键词： Energy consumption Satellites Interference Quality of service Telecommunication traffic Energy efficiency Orbits Resource management Optimization Satellite communications

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Fast Beam Switching Based on Machine Learning for MmWave Massive MIMO systems 3rd

Fast Beam Switching Based on Machine Learning for MmWave Mas...

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3rd EAI International Conference on Artificial Intelligence for Communications and networks, AICON 2021

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

ISBN: (纸本)9783030901950

Millimeter wave (mmWave) and massive multiple-input-multiple-output (MIMO) systems are two key technologies for 5G. Beamforming based on massive MIMO can produce high directional beams with array gain, and thus effectively compensate for the high path loss of mmWave. As the number of antennas increases, the beams become increasingly narrow, resulting in large overhead and high latency in the initial access and handover of the beams. For high-speed mobile scenarios, beam switching becomes more challenging since the traversal search among a large number of beams cannot be completed in a short period of time. To address this problem, this paper proposes a method based on machine learning to predict the optimal Base Station (BS) and the optimal beam pair at the successive instant for the User Equipment (UE) in motion. More specifically, a Random Forest (RF) classification model is trained to learn the channel’s features in a multi-cell scenario, and complete the nonlinear modeling of the propagation environment. Furthermore, this model is used to predict the future optimal BS and the optimal beam pair for a moving UE based on the present UE’s location, BS beam index and RSRP value. The simulation results show that the prediction accuracy is greater than 90% in most situations, thus the latency and the consumption of signaling resources for beam switch is reduced significantly while the loss in spectral efficiency is little. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Machine learning

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