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Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

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Defence Technology（防务技术） 2024年第2期32卷 271-284页

作者： Yiwen Zhang Wei Zheng Zongqiang Liu Qian Xuesen laboratory of space Technology China Academy of Space TechnologyBeijing 100094China China Academy of Aerospace Science and Innovation Beijing 100176China state key laboratory of space-Ground Integrated Information Technology Beijing Institute of Satellite Information EngineeringBeijing 100194China

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater *** present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically ***,GNSS-R satellite constellations that meet the global altimetry needs to be ***,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric ***,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is ***,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is ***,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

关键词： GNSS-R satellite constellations Sea surface altimetric precision Underwater navigation Multilayer feedforward neural network

Experimental demonstration of a quantum downstream access network in continuous variable quantum key distribution with a local local oscillator

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Photonics Research 2024年第6期12卷 1262-1273页

作者： DENGKE QI XIANGYU WANG ZHENGHUA LI JIAYU MA ZIYANG CHEN YUEMING LU SONG YU state key laboratory of Information Photonics and Optical communications Beijing University of Posts and TelecommunicationsBeijing 100876China key laboratory of Trustworthy Distributed Computing and Service(MoE) Beijing University of Posts and TelecommunicationsBeijing 100876China state key laboratory of Advanced Optical Communication Systems and networks School of Electronicsand Center for Quantum Information TechnologyPeking UniversityBeijing 100871China

Quantum networks provide opportunities and challenges across a range of intellectual and technical frontiers,including quantum computation,communication,and *** traditional communication networks,quantum networks utilize quantum bits rather than classical bits to store and transmit *** key distribution(QKD)relying on the principles of quantum mechanics is a key component in quantum networks and enables two parties to produce a shared random secret key,thereby ensuring the security of data *** this work,we propose a cost-effective quantum downstream access network structure in which each user can get their corresponding key information through terminal *** on this structure,we demonstrate the first four-end-users quantum downstream access network in continuous variable QKD with a local local *** contrast to point-to-point continuous variable QKD,the network architecture reevaluates the security of each user and accounts for it accordingly,and each user has a lower tolerance for excess noise as the overall network expands with more ***,the feasibility of the experiment is based on the analysis of the theoretical model,noise analysis,and multiple techniques such as the particle filter and adaptive equalization algorithm used to suppress excess *** results show that each user can get a low level of excess noise and can achieve secret key rates of 546 kbps,535 kbps,522.5 kbps,and 512.5 kbps under a transmission distance of 10 km,respectively,with the finite-size block of 1×10~*** not only verifies the good performance but also provides the foundation for the future multi-user quantum downstream access networks.

关键词： quantum oscillator local

Automatic Generation of Artificial space Weather Forecast Product Based on Sequence-to-sequence Model

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空间科学学报 2024年第1期44卷 80-94页

作者：罗冠霆 ZOU Yenan CAI Yanxia state key laboratory of space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190 University of Chinese Academy of Sciences Beijing 100049 key laboratory of Science and Technology on Environment space Situation Awareness Chinese Academy of SciencesBeijing 100190

Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast *** the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically *** conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is ***,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two *** model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are *** comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,*** results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.

关键词： space weather Deep learning Data-to-text Natural language generation

Resource Allocation in Multi-User Cellular networks:A Transformer-Based Deep Reinforcement Learning Approach

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China communications 2024年第5期21卷 77-96页

作者： Zhao Di Zheng Zhong Qin Pengfei Qin Hao Song Bin state key laboratory of Integrated Services networks Xidian University710071China China Academy of space Technology Beijing 100081China

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing *** this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user *** first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)***,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource *** results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

关键词： dynamic resource allocation multi-user cellular network spectrum efficiency user fairness

A Recursive DRL-Based Resource Allocation Method for Multibeam Satellite Communication Systems

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Chinese Journal of Electronics 2024年第5期33卷 1286-1295页

作者： Haowei MENG Ning XIN Hao QIN Di ZHAO state key laboratory of Integrated Services networks Xidian University China Academy of space Technology Institute of Telecommunication Satellite

Optimization-based radio resource management(RRM) has shown significant performance gains on high-throughput satellites(HTSs). However, as the number of allocable on-board resources increases, traditional RRM is difficult to apply in real satellite systems due to its intense computational complexity. Deep reinforcement learning(DRL) is a promising solution for the resource allocation problem due to its model-free advantages. Nevertheless, the action space faced by DRL increases exponentially with the increase of communication scale, which leads to an excessive exploration cost of the algorithm. In this paper, we propose a recursive frequency resource allocation algorithm based on long-short term memory(LSTM) and proximal policy optimization(PPO), called PPO-RA-LOOP,where RA means resource allocation and LOOP means the algorithm outputs actions in a recursive manner. Specifically, the PPO algorithm uses LSTM network to recursively generate sub-actions about frequency resource allocation for each beam, which significantly cuts down the action space. In addition, the LSTM-based recursive architecture allows PPO to better allocate the next frequency resource by using the generated sub-actions information as a prior knowledge, which reduces the complexity of the neural network. The simulation results show that PPO-RA-LOOP achieved higher spectral efficiency and system satisfaction compared with other frequency allocation algorithms.

关键词： High-throughput satellites Proximal policy optimization Deep reinforcement learning Long-short term memory High-throughput satellites Proximal policy optimization Deep reinforcement learning Long-short term memory OPTIMIZATION POWER

Multi-stream signals separation based on space-time-isomeric (SPATIO) array using metasurface antennas

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Science China(Information Sciences) 2024年第2期67卷 181-200页

作者： Yangming LOU Liang JIN Huiming WANG Zhou ZHONG Junyan DAI PLA Strategic Support Force Information Engineering University School of Information and communications Engineering Xi'an Jiaotong University 3. State Key Laboratory of Millimeter WavesSoutheast University

In spatial domain signal processing, it is necessary to equip more antennas at the receiver to improve spatial demultiplexing capability. However, increasing the number of antennas under restricted space will reduce antenna spacing and raise the channel correlation, making the number of signal streams spatially demultiplexed much smaller than that of antennas. This paper proposes a method to design a space-timeisomeric(SPATIO) array based on metasurface antennas under wireless multipath conditions. Each antenna in this array has a different pattern and varies independently with time, reducing the channel correlation by superposing multipath at distinct positions and moments. Based on the SPATIO array, we present an array parameter design scheme based on infinity norm minimization, which can maximize the received energy of each stream while separating multi-stream received signals. Simulation results illustrate the performance of the SPATIO array for multi-stream signal reception. Compared with conventional multiple-input multipleoutput arrays, the proposed array can reduce the bit error rate by one order of magnitude under the same simulation conditions.

关键词： multiple-input multiple-output metasurface antenna space-time-isomeric array multipath channel degree of freedom

A survey on blockchain-enabled federated learning and its prospects with digital twin

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Digital communications and networks 2024年第2期10卷 248-264页

作者： Kangde Liu Zheng Yan Xueqin Liang Raimo Kantola Chuangyue Hu state key Lab on Integrated Services network School of Cyber EngineeringXidian UniversityXi'anChina Department of communications and networking Aalto UniversityEspooFinland Yangtze Delta Region Blockchain Technology Research Institute JiaxingChina

Digital Twin(DT)supports real time analysis and provides a reliable simulation platform in the Internet of Things(IoT).The creation and application of DT hinges on amounts of data,which poses pressure on the application of Artificial Intelligence(AI)for DT descriptions and intelligent *** Learning(FL)is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model ***,DT can benefit by combining with FL,successfully solving the"data island"problem in traditional ***,FL still faces serious challenges,such as enduring single-point failures,suffering from poison attacks,lacking effective incentive *** the successful deployment of DT,we should tackle the issues caused by *** from industry and academia have recognized the potential of introducing Blockchain Technology(BT)into FL to overcome the challenges faced by FL,where BT acting as a distributed and immutable ledger,can store data in a secure,traceable,and trusted ***,to the best of our knowledge,a comprehensive literature review on this topic is still *** this paper,we review existing works about blockchain-enabled FL and visualize their prospects with *** this end,we first propose evaluation requirements with respect to security,faulttolerance,fairness,efficiency,cost-saving,profitability,and support for ***,we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation ***,we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL,based on which we further propose some directions for future research.

关键词： Digital twin Artificial intelligence Federated learning Blockchain

Service Function Chain Migration in LEO Satellite networks

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China communications 2024年第3期21卷 247-259页

作者： Geng Yuhui Wang Niwei Chen Xi Xu Xiaofan Zhou Changsheng Yang Junyi Xiao Zhenyu Cao Xianbin School of Electronic and Information Engineering Beihang UniversityBeijing 100191China key laboratory of Advanced Technology of Near space Information System Ministry of Industry and Information Technology of ChinaBeijing 100191China Shanghai key laboratory of Satellite network Shanghai 201204China Shanghai Satellite network Research Institute Co. Ltd.Shanghai 201204China

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite *** due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service *** this paper,we consider migration and establish an online migration model,especially considering the dynamic *** the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as *** to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are *** results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

关键词： network function virtualization(NFV) resource allocation satellite networks service function chain(SFC) SFC migration SFC placement soft-ware defined network(SDN)

Cross-Modal Graph Semantic Communication Assisted by Generative AI in the Metaverse for 6G

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Research 2024年第2期2024卷 19-27页

作者： Mingkai Chen Minghao Liu Congyan Wang Xingnuo Song Zhe Zhang Yannan Xie Lei Wang key laboratory of Broadband Wireless Communication and Sensor network Technology Nanjing University of Posts and TelecommunicationsNanjng 210003China state key laboratory of Organic Electronics and Information Displays&Institute of Advanced Materials(IAM) Nanjing University of Posts and TelecommunicationsNanjing 210023China

Recently, the development of the Metaverse has become a frontier spotlight, which is an important demonstration of the integration innovation of advanced technologies in the Internet. Moreover, artificial intelligence (AI) and 6G communications will be widely used in our daily lives. However, the effective interactions with the representations of multimodal data among users via 6G communications is the main challenge in the Metaverse. In this work, we introduce an intelligent cross-modal graph semantic communication approach based on generative AI and 3-dimensional (3D) point clouds to improve the diversity of multimodal representations in the Metaverse. Using a graph neural network, multimodal data can be recorded by key semantic features related to the real scenarios. Then, we compress the semantic features using a graph transformer encoder at the transmitter, which can extract the semantic representations through the cross-modal attention mechanisms. Next, we leverage a graph semantic validation mechanism to guarantee the exactness of the overall data at the receiver. Furthermore, we adopt generative AI to regenerate multimodal data in virtual scenarios. Simultaneously, a novel 3D generative reconstruction network is constructed from the 3D point clouds, which can transfer the data from images to 3D models, and we infer the multimodal data into the 3D models to increase realism in virtual scenarios. Finally, the experiment results demonstrate that cross-modal graph semantic communication, assisted by generative AI, has substantial potential for enhancing user interactions in the 6G communications and Metaverse.

关键词： modal frontier guarantee