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Mitigation of Optical Multipath Interference Algorithms in IM-DD Transmission System

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China communications 2024年第4期21卷 1-9页

作者： Huo Jiahao Zhu Jin Zhu Zuqing Zhang Xiaoying Liu Shaonan Tao Jianlong Wei Huangfu School of Computer and communication Engineering University of Science and Technology BeijingBeijing 100083China School of Information Science and technology University of Science and Technology of ChinaHefei 230026China

This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulation(PAM4)***-M reduced the fluctuation by averaging the signal in blocks,RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block,and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting *** paper firstly proposed to separate the signal before decision-making into multiple blocks,which significantly reduced the complexity of DA-M and *** results showed that the MPI noise of 28 GBaud IMDD system under the linewidths of 1e5 Hz,1e6 Hz and 10e6 Hz can be effectively alleviated.

关键词： IM-DD multipath interference PAM4

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 Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

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Computers, Materials & Continua 2024年第7期80卷 897-912页

作者： Xu Zhang Yi He Haiwen Yi Yulu Zhang Yuan Li Shuai Ma Gui Li Zhiyuan Zhao Yue Liu Junyang Liu Guangjun Wen Jian Li School of Information and communication Engineering/Yibin Institute University of Electronic Science and Technology of ChinaChengdu611731China department of IoT technology Research China Mobile Research InstituteBeijing100053China The 10th Research Institute of China Electronics technology Group Corporation Chengdu611731China College of Information and communication National University of Defense TechnologyWuhan430019China

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to *** on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is *** algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame *** grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID *** outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

关键词： Radio frequency identification anti-collision tags grouping dynamic framed slotted Aloha

Passive IoT Localization technology Based on SD-PDOA in NLOS and Multi-Path Environments

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Computers, Materials & Continua 2024年第7期80卷 913-930页

作者： Junyang Liu Yuan Li Yulu Zhang Shuai Ma Gui Li Yi He Haiwen Yi Yue Liu Xiaotao Xu Xu Zhang Jinyao He Guangjun Wen Jian Li School of Information and communication Engineering/Yibin Institute University of Electronic Science and Technology of ChinaChengdu611731China department of IoT technology Research China Mobile Research InstituteBeijing100053China The 10th Research Institute of China Electronics technology Group Corporation Chengdu611731China College of Information and communication National University of Defense TechnologyWuhan430019China

Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID *** efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 *** findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 *** complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 *** compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath *** research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.

关键词： Indoor localization multipath and NLoS environments combined SD-PDOA and RSSI technology

Simultaneous transmission of time-frequency and data with co-amplification over urban fiber links

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Chinese Optics Letters 2024年第1期22卷 28-33页

作者：操前童周刘雷王家亮应康庞拂飞桂有珍 Key Laboratory of Specialty Fiber Optics and Optical Access networks School of Communication and Information EngineeringShanghai UniversityShanghai 200444China Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Key Laboratory of Space Laser communication and Detection technology Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

We demonstrate a simultaneous transmission of time-frequency and data over a 160-km urban business network in *** signals are transmitted through a cascaded optical link consisting of 48 km and 32 km,which are connected by an optical *** metrological signals are inserted into the communication network using dense wavelength division *** influence of the interference between different signals has been *** experimental results demonstrate that the radio frequency(RF)instability can reach 2.1×10^(-14)at 1 s and 2.3×10^(-17)at 10,000 s,and the time interval transfer of one pulse per second(1 PPS)signal with less than 10 ps at 1 s is *** work paves the way for the widespread dissemination of ultra-stable time and frequency signals over the communication networks.

关键词： simultaneous transmission radio frequency transfer communication network wavelength multiplexing

Controlling a peristaltic robot inspired by inchworms

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Biomimetic Intelligence & Robotics 2024年第1期4卷 88-96页

作者： Yanhong Peng Hiroyuki Nabae Yuki Funabora Koichi Suzumori department of Information and communication Engineering Graduate School of EngineeringNagoya UniversityNagoya 464-8601Japan department of Mechanical Engineering Tokyo Institute of TechnologyTokyo 152-8550Japan

This study presents an innovative approach in soft robotics,focusing on an inchworm-inspired robot designed for enhanced transport *** explore the impact of various parameters on the robot’s performance,including the number of activated sections,object size and material,supplied air pressure,and command execution *** a series of controlled experiments,we demonstrate that the robot can achieve a maximum transportation speed of 8.54 mm/s and handle loads exceeding 100 g,significantly outperforming existing models in both speed and load *** findings provide valuable insights into the optimization of soft robotic design for improved efficiency and adaptability in transport *** research not only contributes to the advancement of soft robotics but also opens new avenues for practical applications in areas requiring precise and efficient object *** study underscores the potential of biomimetic designs in robotics and sets a new benchmark for future developments in the field.

关键词： McKibben artificial muscles Bionics Soft robotics Soft actuator Bio-inspired robotics

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

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Journal of Systems Engineering and Electronics 2024年第3期35卷 509-531,F0002页

作者： OU Xinwei CHEN Zhangxin ZHU Ce LIU Yipeng School of Information and communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Deep neural networks(DNNs)have achieved great success in many data processing ***,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power *** this paper,we focus on low-rank optimization for efficient deep learning *** the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network *** the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast *** model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,*** a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and *** addition to summary of recent technical advances,we have two findings for motivating future *** is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network *** other is a spatial and temporal balance for tensorized neural *** accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

关键词： model compression subspace training effective rank low rank tensor optimization efficient deep learning

Transformer in reinforcement learning for decision-making:a survey

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Frontiers of Information technology & Electronic Engineering 2024年第6期25卷 763-790页

作者： Weilin YUAN Jiaxing CHEN Shaofei CHEN Dawei FENG Zhenzhen HU Peng LI Weiwei ZHAO College of Information and communication National University of Defense TechnologyWuhan 430014China College of Intelligence Science and technology National University of Defense TechnologyChangsha 410072China Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410072China

Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world ***,deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making *** by current major success of Transformer in natural language processing and computer vision,numerous bottlenecks have been overcome by combining Transformer with RL for *** paper presents a multiangle systematic survey of various Transformer-based RL(TransRL)models applied in decision-making tasks,including basic models,advanced algorithms,representative implementation instances,typical applications,and known *** work aims to provide insights into problems that inherently arise with the current RL approaches,and examines how we can address them with better TransRL *** our knowledge,we are the first to present a comprehensive review of the recent Transformer research developments in RL for *** hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future *** keep track of the rapid TransRL developments in the decision-making domains,we summarize the latest papers and their open-source implementations at https://***/williamyuanv0/Transformer-in-Reinforcement-Learning-for-Decision-Making-A-Survey.

关键词： Transformer Reinforcement learning(RL) Decision-making(DM) Deep neural network(DNN) Multi-agent reinforcement learning(MARL) Meta-reinforcement learning(Meta-RL)

Aspect-Level Sentiment Analysis Based on Deep Learning

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Computers, Materials & Continua 2024年第3期78卷 3743-3762页

作者： Mengqi Zhang Jiazhao Chai Jianxiang Cao Jialing Ji Tong Yi department of communication University of New South WalesSydney2052Australia Higher Vocational Education Publishing Division Higher Education PressBeijing100029China State Key Laboratory of Media Convergence and communication Communication University of ChinaBeijing100020China Key Lab of Education Blockchain and Intelligent technology Ministry of EducationGuangxi Normal UniversityGuilin541004China

In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language *** the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of ***,previous studies did not take into account the relationship between user feature extraction and contextual *** address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis *** be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature ***,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency ***,three embedding methods are devised to embed the user feature vector into the ASGCN *** empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.

关键词： Aspect-level sentiment analysis deep learning graph convolutional neural network user features syntactic dependency tree