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6th IEEE International Conference on Energy Internet, ICEI 2022

作者： Zhang, Jingyi Wang, Yueting Jing, Wenpeng Lu, Zhaoming Wen, Xiangming Liu, Yong Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665493277

Widespread employment of renewable energy such as wind and solar pushes power grids to move towards comprehensive data and predictive analysis. At present, a large number of researches have been conducted especially on machine learning methods to achieve load forecast. However, premature convergence and redundant iteration are two major defects of existing machine learning-based load forecasting methods, resulting in poor prediction effect and high time consumption. In this paper, a novel combined intelligent optimization algorithm based on genetic algorithm (GA), artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO) is proposed for optimizing machine learning-based load forecasting models. By replacing GA's mutation process with AFSA operator and PSO operator, the proposed algorithm named GA-AFSA-PSO Algorithm (GAPA) enhances both global search ability and local search ability, leading to its high prediction accuracy and fast convergence speed. To validate its effectiveness, GAPA is applied to the optimization of support vector machine (SVM) and artificial neural network (ANN) to predict one-day ahead load data. Moreover, two different sets of comparative tests are carried out to confirm the advantages of GAPA. The simulation results illustrate that, compared with GA, AFSA, PSO, AFSA-GA and GA-PSO, GAPA brings forth advancement in prediction accuracy, convergence rate and global search ability. © 2022 IEEE.

关键词： Genetic algorithms

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Secure Data Storage system Based on Consortium Blockchain and Hybrid Consensus Mechanism in Smart Grid 8

Secure Data Storage System Based on Consortium Blockchain an...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hao, Wanyu Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665450515

In order to realize safe, reliable, and efficient smart grids, Wireless Sensor networks (WSNs) are widely deployed to collect and analyze grid data to monitor power grids. However, in the existing smart grids, sensing data in the WSNs are needed to be uploaded to a trusted central node. This centralized storage method is prone to security problems such as single point failure, data leakage and data tampering due to malicious attacks on the central node. To deal with these problems, this paper proposes a smart grid data security storage system. Use distributed storage blockchain technology to change the security issues of traditional centralized storage systems. In order to reduce network energy consumption, several data acquisition base stations are pre-selected in the network as nodes in the distributed network using the consortium blockchain technology. In order to improve consensus efficiency and ensure consensus security, the optimal grouping algorithm and randomly grouping algorithm are used to group nodes, the raft consensus mechanism is used within the group, and the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism is used between groups. The security analysis shows that compared with the traditional centralized storage system, the storage system proposed in this paper has the advantages of safety, reliability and low energy consumption. © 2022 IEEE.

关键词： Blockchain

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Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

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China Communications 2024年第1期21卷 88-101页

作者： Shanzhi Chen Xinxin He Rui Zhao Jinling Hu Xin Zhang China Information and Communication Technology Group Co. Ltd.(CICT)the State Key Laboratory of Wireless Mobile CommunicationsBeijing 100083China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing 100876China CICT Connected and Intelligent Technologies Co. LtdChina Academy of Telecommunication TechnologyBeijing 100191China CICT Mobile Communications Technology Co.Ltd.

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and *** millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary *** this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage *** vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

关键词： C-V2X enabling technologies mmWave NR-V2X

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MIMO Radar Aided Beam Selection in Millimeter Wave V2I Communications 8

MIMO Radar Aided Beam Selection in Millimeter Wave V2I Commu...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Li, Dianang He, Xinxin Lv, Jie Wang, Tao 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)9781665450515

The multiple-in-multiple-out (MIMO) radar is deployed to assist the millimeter wave (mmWave) communication with low beam training overhead in vehicle-to-infrastructure (V2I), which has attracted increasing attention. This paper proposes a structured weighted radar aided beam selection. To be specific, since the spatial information provided by radar involves inevitable inaccuracy due to the angle deviation, we calculate the probability distribution of the codewords based on the deviation angle and use it for designing the training codebook. Furthermore, beam selection is formulated as prior weighted sparse signal recovery problem, where the prior structured-weight for communication is determined by the spatial congruence with radar channel, and the difference between two frequency bands is derived to correct the weight. The resulting prior structured weight is further deployed to the orthogonal matching pursuit algorithm to enhance its performance. Simulation results demonstrate that the proposed method provides the efficient and reliable beam selection performance of the mobility mmWave channel with fewer training overhead. © 2022 IEEE.

关键词： Millimeter waves

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Semantic-driven Computation Offloading and Resource Allocation for UAV-assisted Monitoring system in Vehicular networks 48

Semantic-driven Computation Offloading and Resource Allocati...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Sun, Xin Chen, Jiujiu Guo, Caili Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In the vehicular networks monitoring scenario where the unmanned aerial vehicle (UAV) assists the vehicle data collection and the edge-cloud server cooperates to complete the visual intelligent task (e.g. object detection), a large amount of video data needs to be transmitted. For task-oriented communication systems, traditional resource allocation schemes mainly focus on the network performance or user experience of video transmission, without considering the impact of semantic content on task performance. In this paper, we propose a semantic-driven computation offloading and resource allocation scheme, namely semantic-driven CO&RA. In the offloading decision, we propose a convolutional neural network (CNN) segmentation scheme, which makes full use of computing resources and better completes intelligent tasks. At the same time, facing the complex resource allocation problem, we design a multi-agent deep Q-network (DQN) algorithm. Last, the experimental results show that our proposed scheme has more advantages in the optimization of multiple objectives of latency, energy consumption and task performance. © 2022 IEEE.

关键词： Semantics

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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A multipath-assisted localization algorithm under 5G-based opensource testbed 9

A multipath-assisted localization algorithm under 5G-based o...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Lu, Yuye Zheng, Wei Chu, Xinghe Lu, Zhaoming Hu, Ruoqian Li, Meiling Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350317251

Indoor localization services are playing an important role in a variety of emerging industries. Traditional positioning methods suffer from insufficient accuracy or high deployment costs in indoor environments, and with the rapid development of cellular mobile communication systems, positioning under cellular networks has been more widely used due to its low cost and easy deployment. In the indoor environment, the number of available base stations is limited, and positioning by direct radius information provided by only one base station suffers from insufficient accuracy. In this paper, we designed a method for indoor localization based on cellular networks, which considering the localization information carried by the multi-path, to verify the performance of this algorithm in practical scene. We implement this algorithm in a 5G-based testbed based on software-defined radio (SDR) technologies. The results show that our algorithm can meet the requirements of indoor cases defined in 3GPP 38.855. © 2023 IEEE.

关键词： Testbeds

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Demo: A Hybrid Semantic RAN Protocol Stack Design for 6G system and its Implementation

Demo: A Hybrid Semantic RAN Protocol Stack Design for 6G Sys...

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IEEE Conference on Wireless Communications and Networking

作者： Luhan Wang Haiwen Niu Zhaoming Lu Xiangming Wen Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recently, Semantic Communication (SC) has been recognized as a crucial new paradigm in 6G, significantly improving information transmission efficiency. However, the diverse range of service types in 6G networks, such as high-data-volume services like AR/VR/MR and low-data-volume applications requiring high accuracy, such as industrial control and data collection, presents significant challenges to fully replacing the fundamental technologies with SC. Therefore, we design a Hybrid Semantic Communication Ratio Access Network (HSC-RAN) protocol stack demo for 6G systems to achieve compatibility and smooth transition between SC and non-SC. Specifically, we take the Physical Downlink Shared Channel (PDSCH) as an example, to efficiently integrate SC with Orthogonal Frequency Division Multiplexing (OFDM). Furthermore, we introduce a novel Downlink Control Information (DCI) format that jointly supports SC and non-SC, enabling real-time video transmission via SC and text transmission through non-SC. Experimental results demonstrate that our approach allows simultaneous transmission of semantic and non-semantic information while maintaining high-quality reconstruction at the receiver.

关键词： 6G mobile communication Wireless communication Protocols OFDM Receivers Streaming media Downlink Semantic communication Real-time systems Next generation networking

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A Neural-Based OTFS Channel Estimator

A Neural-Based OTFS Channel Estimator

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

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

ISBN: (纸本)9798350368369

orthogonal time frequency space (OTFS) systems are considered as the reliable solution for addressing the challenges of high mobility in sixth generation (6G) scenarios. By modulating data across both the delay and Doppler dimensions, OTFS efficiently handles the double spread problems caused by high mobility. Accurate and efficient channel estimation is essential for ensuring reliable data reception in OTFS systems. In this paper, we propose a convolutional neural network (CNN) based channel estimator within the standard OTFS transceiver framework to estimate the transmitted channel from the received OTFS signal. Simulation results demonstrate that our neural channel estimator achieves superior channel reconstruction and outperforms existing neural-based methods. Moreover, we analyze its performance with higher-order modulation and show that it effectively supports such modulation under good channel conditions, improving spectral efficiency and throughput. © 2025 IEEE.

关键词： Channel estimation

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DRLRA: A Deep Reinforcement Learning architecture Based on Resource Allocation for Mining Key Players in networks

DRLRA: A Deep Reinforcement Learning Architecture Based on R...

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IEEE Conference on Wireless Communications and Networking

作者： Wang Kaili Wu Muqing Zhao Min Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Network key nodes are very important in a network, after identifying them, network optimization can be carried out for these nodes. For example, increasing bandwidth or resource allocation, enhancing fault tolerance, protecting and strengthening key nodes. Or destroy key nodes to quickly break down the network, just like spreading rumors and preventing diseases. Different network optimization measures may vary in different network scenarios, but the core is to enhance or break key nodes, thereby affecting the overall network performance and effectiveness. However, most current network key node identification is based on fixed mathematical models or formulas, limiting the possibility of deep information mining in the network. To address this issue, this paper proposes a deep learning network architecture based on resource allocation to identify key nodes in the network. This allows for more in-depth feature extraction of network semantics and structures while combining the resource allocation situation of each node. In the process of gradually disintegrating the network through deep reinforcement learning, finding the strategy with the fastest decrease in cumulative connectivity rate is considered optimal. Finally, this paper compares the performance of six classical algorithms and the optimal algorithm on six real networks. The effectiveness improvement is 8.11% without sacrificing performance, validating the effectiveness of the method.

关键词： Limiting Semantics Fault tolerant systems Network architecture Deep reinforcement learning Feature extraction Mathematical models Graph neural networks Resource management Optimization

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