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| 1 | Tensor-Based Low-Complexity Channel Estimation for mmWave Massive MIMO-OTFS Systems显示文摘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. | Xianda Wu Shaodan Ma Xi Yang | 2020 | Journal of Communications and Information Networks2020，5，3： | 5 |
| 2 | Organelle-inspired supramolecular nanomedicine to precisely abolish liver tumor growth and metastasis显示文摘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. | Jie Zhan Yuhan Wang Shaodan Ma Qin Qin Ling Wang Yanbin Cai Zhimou Yang | 2022 | Bioactive Materials2022，7，3： | 1 |
| 3 | On performance of cooperative communication systems with spatial random relays显示文摘 | Wang Hongzheng Ma Shaodan Ng TungSang | 2011 | IEEETrans on Communications2011，59，4： | 1 |
| 4 | From frequency domain to time domain:performance analysis on cyclic prefixed multi-user single-carrier transmission显示文摘This paper analyzes the performance of cyclic prefixed single-carrier transmission under multiuser interference(MUI). For multi-user receivers using single carrier frequency domain adaptive antenna array,we derive the signal to interference plus noise ratio by analyzing the time domain estimated signal after the transformation from frequency domain to time domain. By assuming the MUI follows the Gaussian distribution,we can derive the analytical expression for the bit error rate(BER) performance. The analytical result is verified by the numerical results generated by Monte Carlo method. It is concluded from the analytical results that if the number of receive antennas goes to infinity as assumed in massive multiple input multiple output(MIMO)system, the BER performance will be determined by two ratios: signal to noise ratio as well as the Nr to U ratio, where Nr is the number of receive antennas and U is the number of users. | Wei PENG Qing F. ZHOU An HUANG Shaodan MA | 2016 | Science China(Information Sciences)2016，59，8： | 1 |
| 5 | Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function显示文摘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. | Yunxuan Dong Shaodan Ma Hongcai Zhang Guanghua Yang | 2022 | Journal of Modern Power Systems and Clean Energy2022，10，5： | 1 |
| 6 | Joint CFO and channel estimation for muhi-user MIMO-OFDM systems with optimal training sequences显示文摘 | Chen Jian-wu Wu Yik-Chung Ma Shaodan | 2008 | IEEE Transactions on Signal Processing2008，56，8： | 1 |
| 7 | Matrix-monotonic optimization for MIMO sys?tems显示文摘 | Chengwen Xing Shaodan Ma and Yiqing Zhou | 2015 | IEEE Transactions on Signal Processing2015，63，2： | 1 |
| 8 | Pilot-aided IQ Imbalance Compensation for OFDM Systems Operating Over Doubly Selective Channels显示文摘 | He Lanlan Ma Shaodan Wu Yik-Chung | 2010 | IEEE Transactions on Signal Processing2010，59，5： | 1 |
| 9 | TOA-based Passive Localization in Quasi-Synchronous Networks 显示文摘 | Wang Yuan Ma Shaodan Philip C L Chen | 2014 | IEEE Communi- cations Letters2014，18，4： | 1 |
| 10 | Blind Symbol Synchronization Based on Cyclic Prefix for OFDM Systems显示文摘 | Ma Shaodan Pan Xinyue Yang Guanghua | | 0，，04： | 1 |
| 11 | Blind Symbol Synchronization Based on Cyclic Prefix for OFDM Systems 显示文摘 | Ma Shaodan | 2009 | IEEE Transactions on Vehicular Techno- logy2009，58，4： | 1 |
| 12 | Matrix-Monotonic optimization for MIMO systems 显示文摘 | Xing Chengwen Ma Shaodan Zhou Yiqing | 2015 | IEEE Transactions on Signal Processing2015，63，2： | 1 |
| 13 | TOA-Based Passive Localization in Quasi-Synchronous Networks 显示文摘 | Wang Yuan Ma Shaodan Philip Chen C L | 2014 | IEEE Communi- cations Letters2014，18，4： | 1 |
| 14 | Blind symbol synchronization based on cyclic prefix for OFDM systems显示文摘 | Ma Shaodan Pan Xinyue Yang Guanghua | 2009 | IEEE Transactions on Vehicular Tech- nology2009，58，4： | 1 |
| 15 | Cooperative Beamforming for Dual-hop Amplify-and-forward Multi- antenna Relaying Cellular Networks显示文摘 | Xing Chengwen Ma Shaodan Xia Minghua | 2012 | Signal Processing2012，92，11： | 1 |
| 16 | Blind symbol synchronization based on cyclic prefix for OFDM systems显示文摘 | Ma Shaodan Pan Xinyue Yang Guanghua | 2009 | IEEE Transactions on Vehicular Technology2009，58，4： | 1 |
| 17 | Time-Domain Signal Detection Based on-Second-Order Statistics for MIMO-OFDM Systems Signal Processing显示文摘 | Shaodan Ma Tung-Sang Ng | 2007 | IEEE Transactions2007，55，3： | 1 |
| 18 | Blind Symbol Synchronization Based on Cyclic Prefix for OFDM Systems显示文摘 | Ma Shaodan Pan Xinyue Yang Guanghua | 2009 | IEEE Transactions on Vehicular Technology2009，，4： | 1 |
| 19 | Outage Performance and Optimal Design of MIMO-NOMA Enhanced Small Cell Networks with Imperfect Channel-State Information显示文摘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. | Zheng Shi Hong Wang Yaru Fu Guanghua Yang Shaodan Ma Xinrong Ye | 2021 | China Communications2021，18，10： | 0 |
| 20 | Application of UAV-Based Imaging and Deep Learning in Assessment of Rice Blast Resistance显示文摘Rice blast is regarded as one of the major diseases of rice.Screening rice genotypes with high resistance to rice blast is a key strategy for ensuring global food security.Unmanned aerial vehicles(UAV)-based imaging,coupled with deep learning,can acquire high-throughput imagery related to rice blast infection.In this study,we developed a segmented detection model(called RiceblastSegMask)for rice blast detection and resistance evaluation.The feasibility of different backbones and target detection models was further investigated.RiceblastSegMask is a two-stage instance segmentation model,comprising an image-denoising backbone network,a feature pyramid,a trinomial tree fine-grained feature extraction combination network,and an image pixel codec module.The results showed that the model combining the image-denoising and fine-grained feature extraction based on the Swin Transformer and the feature pixel matching feature labels with the trinomial tree recursive algorithm performed the best.The overall accuracy for instance segmentation of RiceblastSegMask reached 97.56%,and it demonstrated a satisfactory accuracy of 90.29%for grading unique resistance to rice blast.These results indicated that low-altitude remote sensing using UAV,in conjunction with the proposed RiceblastSegMask model,can efficiently calculate the extent of rice blast infection,offering a new phenotypic tool for evaluating rice blast resistance on a field scale in rice breeding programs. | LIN Shaodan YAO Yue LI Jiayi LI Xiaobin MA Jie WENG Haiyong CHENG Zuxin YE Dapeng | 2023 | Rice science2023，30，6： | 0 |
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