This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state informat...This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state information(CSI).The estimation error and the spatial randomness of base stations(BSs)are characterized by using Kronecker model and Poisson point process(PPP),respectively.The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies,including random grouping and distance-based grouping.It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime,while the outage performance deteriorates if the intensity is sufficiently low.Besides,as the channel uncertainty lessens,the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference.Moreover,highly correlated estimation error ameliorates the outage performance under a low quality of CSI,otherwise it behaves oppositely.Afterwards,the goodput is maximized by choosing appropriate precoding matrix,receiver filters and transmission rates.In the end,the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.展开更多
Orthogonal time frequency space(OTFS)modulation,collaborated with millimeter-wave(mmWave)massive multiple-input-multiple-output(MIMO),is a promising technology for next generation wireless communications in high mobil...Orthogonal time frequency space(OTFS)modulation,collaborated with millimeter-wave(mmWave)massive multiple-input-multiple-output(MIMO),is a promising technology for next generation wireless communications in high mobility scenarios.However,one of the main challenges for mmWave massive MIMO-OTFS systems is the enormous computational complexity of channel estimation incurred by the huge OTFS symbol size and the large number of antennas.To address this issue,in this paper,a tensor-based orthogonal matching pursuit(OMP)channel estimation algorithm is proposed by exploiting the channel sparsity in the delay-Doppler-angle domain.In particular,we firstly propose a novel pilot design for the OTFS symbol structure in the frequency-time domain.Then,based on the proposed pilot structure,we formulate the channel estimation as a sparse signal recovery problem,and the tensor decomposition and parallel support detection are introduced into the tensor-based OMP algorithm to reduce the signal processing dimension significantly.Numerical simulations are performed to verify the superiority and the robustness of the proposed tensor-based OMP algorithm.展开更多
The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispens...The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.展开更多
Organelles are responsible for the efficient storage and transport of substances in living systems.A myriad of extracellular vesicles(EVs)acts as a bridge to exchange signaling molecules in cell-cell communication,and...Organelles are responsible for the efficient storage and transport of substances in living systems.A myriad of extracellular vesicles(EVs)acts as a bridge to exchange signaling molecules in cell-cell communication,and the highly dynamic tubulins and actins contribute to efficient intracellular substance transport.The inexhaustible cues of natural cargo delivery by organelles inspire researchers to explore the construction of biomimetic architectures for“smart”delivery carriers.Herein,we report a 10-hydroxycamptothecin(HCPT)-peptide conjugate HpYss that simulates the artificial EV-to-filament transformation process for precise liver cancer therapy.Under the sequential stimulus of extracellular alkaline phosphatase(ALP)and intracellular glutathione(GSH),HpYss proceeds via tandem self-assembly with a morphological transformation from nanoparticles to nanofibers.The experimental phase diagram elucidates the influence of ALP and GSH contents on the self-assembled nanostructures.In addition,the dynamic transformation of organelle-mimetic architectures that are formed by HpYss in HepG2 cells enables the efficient delivery of the anticancer drug HCPT to the nucleus,and the size-shape change from extracellular nanoparticles(50-100 nm)to intracellular nanofibers(4-9 nm)is verified to be of key importance for nuclear delivery.Nuclear targeting of HpYss amplifies apoptosis,thus significantly enhancing the inhibitory effect of HCPT(>10-fold)to HepG2 cells.Benefitting from the spatiotemporally controlled nanostructures,HpYss exhibited deep penetration,enhanced accumulation,and long-term retention in multicellular spheroid and xenograft models,potently abolishing liver tumor growth and preventing lung metastasis.We envision that our organelle-mimicking delivery strategy provides a novel paradigm for designing nanomedicine to cancer therapy.展开更多
基金supported in part by the National Key Research and Development Program of China under Grant 2017YFE0120600in part by National Natural Science Foundation of China under Grants 61801192,62171200,and 61801246+7 种基金in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515012136in part by Natural Science Foundation of Anhui Province under Grant 1808085MF164in part by the Science and Technology Planning Project of Guangdong Province under Grants 2018B010114002 and 2019B010137006in part by the Science and Technology Development Fund,Macao SAR(File no.0036/2019/A1 and File no.SKL-IOTSC2021-2023)in part by the Hong Kong Presidents Advisory Committee on Research and Development(PACRD)under Project No.2020/1.6in part by Qinglan Project of University of Jiangsu Provincein part by the Research Committee of University of Macao under Grant MYRG2018-00156-FSTin part by 2018 Guangzhou Leading Innovation Team Program(China)under Grant 201909010006。
文摘This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state information(CSI).The estimation error and the spatial randomness of base stations(BSs)are characterized by using Kronecker model and Poisson point process(PPP),respectively.The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies,including random grouping and distance-based grouping.It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime,while the outage performance deteriorates if the intensity is sufficiently low.Besides,as the channel uncertainty lessens,the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference.Moreover,highly correlated estimation error ameliorates the outage performance under a low quality of CSI,otherwise it behaves oppositely.Afterwards,the goodput is maximized by choosing appropriate precoding matrix,receiver filters and transmission rates.In the end,the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.
基金This work was supported by the Science and Technology Development Fund,Macao S.A.R.,China(File No.0036/2019/A1 and File No.SKL-IOTSC2018-2020).
文摘Orthogonal time frequency space(OTFS)modulation,collaborated with millimeter-wave(mmWave)massive multiple-input-multiple-output(MIMO),is a promising technology for next generation wireless communications in high mobility scenarios.However,one of the main challenges for mmWave massive MIMO-OTFS systems is the enormous computational complexity of channel estimation incurred by the huge OTFS symbol size and the large number of antennas.To address this issue,in this paper,a tensor-based orthogonal matching pursuit(OMP)channel estimation algorithm is proposed by exploiting the channel sparsity in the delay-Doppler-angle domain.In particular,we firstly propose a novel pilot design for the OTFS symbol structure in the frequency-time domain.Then,based on the proposed pilot structure,we formulate the channel estimation as a sparse signal recovery problem,and the tensor decomposition and parallel support detection are introduced into the tensor-based OMP algorithm to reduce the signal processing dimension significantly.Numerical simulations are performed to verify the superiority and the robustness of the proposed tensor-based OMP algorithm.
基金supported by the Guangdong-Macao Joint Funding Project(No. 2021A0505080015)Science and Technology Planning Project of Guangdong Province (No. 2019B010137006)Science and Technology Development Fund,Macao SAR (No. SKL-IOTSC(UM)-2021-2023)。
文摘The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.
基金We thank Prof.Chihua Fang from Zhujiang hospital of Southern Medical University for sharing the HepG2-luci cells and the c(RGDfC)peptide.We acknowledge the financial support from the National Science Fund for Distinguished Young Scholars(31825012)National Natural Science Foundation of China(21875116,31961143004,81921004,31900952,51973090)+2 种基金Tianjin Science Fund for Distinguished Young Scholars(17JCJQJC44900)Guangdong Basic and Applied Basic Research Foundation(2018A030313446,2019A1515011706,2019A1515110638)and the China Postdoctoral Science Foundation(BX20190149,2019M662972).
文摘Organelles are responsible for the efficient storage and transport of substances in living systems.A myriad of extracellular vesicles(EVs)acts as a bridge to exchange signaling molecules in cell-cell communication,and the highly dynamic tubulins and actins contribute to efficient intracellular substance transport.The inexhaustible cues of natural cargo delivery by organelles inspire researchers to explore the construction of biomimetic architectures for“smart”delivery carriers.Herein,we report a 10-hydroxycamptothecin(HCPT)-peptide conjugate HpYss that simulates the artificial EV-to-filament transformation process for precise liver cancer therapy.Under the sequential stimulus of extracellular alkaline phosphatase(ALP)and intracellular glutathione(GSH),HpYss proceeds via tandem self-assembly with a morphological transformation from nanoparticles to nanofibers.The experimental phase diagram elucidates the influence of ALP and GSH contents on the self-assembled nanostructures.In addition,the dynamic transformation of organelle-mimetic architectures that are formed by HpYss in HepG2 cells enables the efficient delivery of the anticancer drug HCPT to the nucleus,and the size-shape change from extracellular nanoparticles(50-100 nm)to intracellular nanofibers(4-9 nm)is verified to be of key importance for nuclear delivery.Nuclear targeting of HpYss amplifies apoptosis,thus significantly enhancing the inhibitory effect of HCPT(>10-fold)to HepG2 cells.Benefitting from the spatiotemporally controlled nanostructures,HpYss exhibited deep penetration,enhanced accumulation,and long-term retention in multicellular spheroid and xenograft models,potently abolishing liver tumor growth and preventing lung metastasis.We envision that our organelle-mimicking delivery strategy provides a novel paradigm for designing nanomedicine to cancer therapy.
