Exploiting the source-to-relay channel phase information at the relays can increase the rate upper-bound of distributed orthogonal space-time block codes(STBC)from 2/K to 1/2,where Kis the number of relays.This techni...Exploiting the source-to-relay channel phase information at the relays can increase the rate upper-bound of distributed orthogonal space-time block codes(STBC)from 2/K to 1/2,where Kis the number of relays.This technique is known as distributed orthogonal space-time block codes with channel phase information(DOSTBC-CPI).However,the decoding delay of existing DOSTBC-CPIs is not optimal.Therefore,based on the rate of 1/2 balanced complex orthogonal design(COD),an algorithm is provided to construct a maximal rate DOSTBC-CPI with only half the decoding delay of existing DOSTBC-CPI.Simulation results show that the proposed method exhibits lower symbol error rate than the existing DOSTBC-CPIs.展开更多
In material science and engineering,obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult.In this work,we propose a machine learning strate...In material science and engineering,obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult.In this work,we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system,i.e.,(Ba_(1−x−y)Ca_(x)Sr_(y))(Ti_(1−u−v−w)Zr_(u)Sn_(v)Hf_(w))O_(3).The deep neural network model uses physical features as inputs and directly outputs the full spectrum,in addition to yielding the octahedral factor,Matyonov–Batsanov electronegativity,ratio of valence electron to nuclear charge,and core electron distance(Schubert)as four key descriptors.Owing to the physically meaningful features,our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid solutions.And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature.Furthermore,the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information.The prediction is also experimentally validated by typical samples of(Ba_(0.85)Ca_(0.15))(Ti_(0.98–x)Zr_(x)Hf_(0.02))O_(3).This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.61271230,61472190)the National Mobile Communications Research Laboratory,Southeast University(No.2013D02)
文摘Exploiting the source-to-relay channel phase information at the relays can increase the rate upper-bound of distributed orthogonal space-time block codes(STBC)from 2/K to 1/2,where Kis the number of relays.This technique is known as distributed orthogonal space-time block codes with channel phase information(DOSTBC-CPI).However,the decoding delay of existing DOSTBC-CPIs is not optimal.Therefore,based on the rate of 1/2 balanced complex orthogonal design(COD),an algorithm is provided to construct a maximal rate DOSTBC-CPI with only half the decoding delay of existing DOSTBC-CPI.Simulation results show that the proposed method exhibits lower symbol error rate than the existing DOSTBC-CPIs.
基金supported by the National Key R&D Program of China(2022YFB3807401)National Natural Science Foundation of China(52173217)111 project(B170003).
文摘In material science and engineering,obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult.In this work,we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system,i.e.,(Ba_(1−x−y)Ca_(x)Sr_(y))(Ti_(1−u−v−w)Zr_(u)Sn_(v)Hf_(w))O_(3).The deep neural network model uses physical features as inputs and directly outputs the full spectrum,in addition to yielding the octahedral factor,Matyonov–Batsanov electronegativity,ratio of valence electron to nuclear charge,and core electron distance(Schubert)as four key descriptors.Owing to the physically meaningful features,our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid solutions.And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature.Furthermore,the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information.The prediction is also experimentally validated by typical samples of(Ba_(0.85)Ca_(0.15))(Ti_(0.98–x)Zr_(x)Hf_(0.02))O_(3).This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.
