Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel E...Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.展开更多
At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in w...At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability.But the BFO-based algorithm is easy to fall into local optimum.Therefore,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local optimization.Firstly,the binary matrix is constructed according to whether atoms are selected or not.And the support set of the sparse signal is recovered according to the BOMP-based algorithm.Then,the QBFO-based algorithm is used to obtain the estimated channel matrix.The optimization function of the least squares method is taken as the fitness function.Based on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria position.Simulation results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(MIMO)systems.Meanwhile,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.展开更多
Catalytic hydrogenolysis of end-of-life polyolefins can produce value-added liquid fuels and therefore holds great promises in plastic waste reuse and environmental remediation.The major challenge limiting the recycli...Catalytic hydrogenolysis of end-of-life polyolefins can produce value-added liquid fuels and therefore holds great promises in plastic waste reuse and environmental remediation.The major challenge limiting the recycling economic benefit is the severe methanation(usually>20%)induced by terminal C–C cleavage and fragmentation in polyolefin chains.Here,we overcome this challenge by demonstrating that Ru single-atom catalyst can effectively suppress methanation by inhibiting terminal C–C cleavage and preventing chain fragmentation that typically occurs on multi-Ru sites.The Ru single-atom catalyst supported on CeO_(2)shows an ultralow CH_(4)yield of 2.2%and a liquid fuel yield of over 94.5%with a production rate of 314.93 gfuels gRu^(−1)h^(−1)at 250℃for 6 h.Such remarkable catalytic activity and selectivity of Ru single-atom catalyst in polyolefin hydrogenolysis offer immense opportunities for plastic upcycling.展开更多
基金supported by National Natural Science Foundation of China under Grant Nos.61901409 and 61961013Jiangxi Provincial Natural Science Foundation under Grant No.20202BABL212001Open Project of State Key Laboratory of Marine Resources Utilization in South China Sea under Grant No.MRUKF2021034.
文摘Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.
基金supported by the National Natural Science Foundation of China(Nos.61861015,62061013 and 61961013)Key Research and Development Program of Hainan Province(No.ZDYF2019011)+3 种基金National Key Research and Development Program of China(No.2019CXTD400)Young Elite Scientists Sponsorship Program by CAST(No.2018QNRC001)Scientific Research Setup Fund of Hainan University(No.KYQD(ZR)1731)the Natural Science Foundation High-Level Talent Project of Hainan Province(No.622RC619).
文摘At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability.But the BFO-based algorithm is easy to fall into local optimum.Therefore,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local optimization.Firstly,the binary matrix is constructed according to whether atoms are selected or not.And the support set of the sparse signal is recovered according to the BOMP-based algorithm.Then,the QBFO-based algorithm is used to obtain the estimated channel matrix.The optimization function of the least squares method is taken as the fitness function.Based on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria position.Simulation results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(MIMO)systems.Meanwhile,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.
基金the National Natural Science Foundation of China(51901147)J.C.thanks the support from the Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices(ZZ2103)+2 种基金Gusu Innovation and Entrepreneurship Leading Talent Program(ZXL2022492)the Suzhou Key Laboratory of Functional Nano&Soft Materials,Collaborative Innovation Center of Suzhou Nano Science&Technology,the 111 Project.the Advanced Photon Source,an office of Science User Facility operated for the U.S.Department of Energy Office of Science by Argonne National Laboratory and was supported by the U.S.Department of Energy under contract no.DE-AC02-06CH11357.
文摘Catalytic hydrogenolysis of end-of-life polyolefins can produce value-added liquid fuels and therefore holds great promises in plastic waste reuse and environmental remediation.The major challenge limiting the recycling economic benefit is the severe methanation(usually>20%)induced by terminal C–C cleavage and fragmentation in polyolefin chains.Here,we overcome this challenge by demonstrating that Ru single-atom catalyst can effectively suppress methanation by inhibiting terminal C–C cleavage and preventing chain fragmentation that typically occurs on multi-Ru sites.The Ru single-atom catalyst supported on CeO_(2)shows an ultralow CH_(4)yield of 2.2%and a liquid fuel yield of over 94.5%with a production rate of 314.93 gfuels gRu^(−1)h^(−1)at 250℃for 6 h.Such remarkable catalytic activity and selectivity of Ru single-atom catalyst in polyolefin hydrogenolysis offer immense opportunities for plastic upcycling.
