With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an i...With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.展开更多
Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper ...Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper investigated the unique features of deep space communica-tions in detail, discussed the key technologies and its development trends for deep space communica-tions.展开更多
This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata d...This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidden layers to enhance the predictive performance of CGMKL significantly. The experimental results onvarious data sets demonstrate the superior performance of the E-CGMKLalgorithm compared to other competing methods including the benchmarkCGMKL.展开更多
正交时频空(Orthogonal Time Frequency Space, OTFS)调制技术凭借对多普勒频移的优良抗性,保证了高动态场景下的可靠性通信。与大多数OTFS信号检测方案相比,基于深度学习(Deep Learning, DL)的OTFS检测器不需要耗费高额的导频能量,以...正交时频空(Orthogonal Time Frequency Space, OTFS)调制技术凭借对多普勒频移的优良抗性,保证了高动态场景下的可靠性通信。与大多数OTFS信号检测方案相比,基于深度学习(Deep Learning, DL)的OTFS检测器不需要耗费高额的导频能量,以此获得精确的信道状态信息。基于多维输入的卷积神经网络(Convolutional Neural Networks, CNN)和一维输入的深度神经网络(Deep Neural Networks, DNN),搭建了OTFS信号检测模型,并结合OTFS的输入输出关系,以模型驱动,提出一种部分输入方法。与数据驱动DL相比,该方法沿时延轴截断输入数据,仅向网络输入与待检测信号相关性强的部分接收信号。该方法不仅减小了数据驱动CNN和DNN的训练参数量,降低了训练复杂度,而且检测性能也不弱于传统的线性最小均方误差(Linear Minimum Mean Square Error, LMMSE)算法。展开更多
时空视频超分辨率(space-time video super-resolution,STVSR)通过时间和空间2个尺度提升视频的质量,从而实现在视频采集设备、传输或者存储有限的情况下依然能实时地呈现高分辨率和高帧率的视频,满足人们对超高清画质的追求。相比两阶...时空视频超分辨率(space-time video super-resolution,STVSR)通过时间和空间2个尺度提升视频的质量,从而实现在视频采集设备、传输或者存储有限的情况下依然能实时地呈现高分辨率和高帧率的视频,满足人们对超高清画质的追求。相比两阶段方法,一阶段方法实现的是特征层面而非像素层面的帧插值,其在推理速度和计算复杂度上都明显更胜一筹。一些现有的一阶段STVSR方法采用基于像素幻觉的特征插值,这幻化了像素,因此很难应对帧间快速运动物体的预测。为此,提出一种基于光流法的金字塔编码器-解码器网络来进行时间特征插值,实现快速的双向光流估计和更真实自然的纹理合成,在使得网络结构更高效的同时弥补了大运动对光流估计带来的不稳定性。另外,空间模块采用基于滑动窗口的局部传播和基于循环网络的双向传播来强化帧对齐,整个网络称为时间特征细化网络(temporal feature refinement netowrk,TFRnet)。为了进一步挖掘TFRnet的潜力,将空间超分辨率先于时间超分辨率(space-first),在几种广泛使用的数据基准和评估指标上的实验证明了所提出方法TFRnet-sf的出色性能,在总体峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)提升的同时,插入中间帧的PSNR和SSIM也得到提升,在一定程度上缓和了插入的中间帧与原有帧之间PSNR和SSIM差距过大的问题。展开更多
One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this pa...One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this paper presents a class of code-domain nonorthogonal multiple accesses(NOMAs)for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system(JTIDS).In the considered scenario,a satellite equipped with Nr antennas servers K devices including vehicles,drones,ships,sensors,handset radios,etc.Nonorthogonal coded modulation,a special form of multiple input multiple output(MIMO)-NOMA is proposed.The discussion starts with evaluating the output signal to interference-plus-noise(SINR)of receiver filter,leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered.The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved.The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying(MSK)and Gaussian MSK(GMSK),two pivotal modulation formats in IoMT standards such as Link-16 and JITDS.Numerical results show that near capacity performance is offered.Fortunately,the performance is obtained using simple forward error corrections(FECs)of higher coding rate than existing schemes do,while the transmit power is reduced by 6 dB.The proposed design finds wide applications not only in IoMT but also in deep space communications,where ultra reliability and massive connectivity is a keen concern.展开更多
基金supported by National Natural Science Foundation of China (61471109, 61501104 and 91438110)Fundamental Research Funds for the Central Universities ( N140405005 , N150401002 and N150404002)Open Fund of IPOC (BUPT, IPOC2015B006)
文摘With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.
基金Supported by the National Natural Science Foundation of China (No. 60972061,60972062,and 61032004)the National High Technology Research and Development Program of China ("863" Program) (No. 2008AA12A204)
文摘Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper investigated the unique features of deep space communica-tions in detail, discussed the key technologies and its development trends for deep space communica-tions.
文摘This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidden layers to enhance the predictive performance of CGMKL significantly. The experimental results onvarious data sets demonstrate the superior performance of the E-CGMKLalgorithm compared to other competing methods including the benchmarkCGMKL.
文摘时空视频超分辨率(space-time video super-resolution,STVSR)通过时间和空间2个尺度提升视频的质量,从而实现在视频采集设备、传输或者存储有限的情况下依然能实时地呈现高分辨率和高帧率的视频,满足人们对超高清画质的追求。相比两阶段方法,一阶段方法实现的是特征层面而非像素层面的帧插值,其在推理速度和计算复杂度上都明显更胜一筹。一些现有的一阶段STVSR方法采用基于像素幻觉的特征插值,这幻化了像素,因此很难应对帧间快速运动物体的预测。为此,提出一种基于光流法的金字塔编码器-解码器网络来进行时间特征插值,实现快速的双向光流估计和更真实自然的纹理合成,在使得网络结构更高效的同时弥补了大运动对光流估计带来的不稳定性。另外,空间模块采用基于滑动窗口的局部传播和基于循环网络的双向传播来强化帧对齐,整个网络称为时间特征细化网络(temporal feature refinement netowrk,TFRnet)。为了进一步挖掘TFRnet的潜力,将空间超分辨率先于时间超分辨率(space-first),在几种广泛使用的数据基准和评估指标上的实验证明了所提出方法TFRnet-sf的出色性能,在总体峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)提升的同时,插入中间帧的PSNR和SSIM也得到提升,在一定程度上缓和了插入的中间帧与原有帧之间PSNR和SSIM差距过大的问题。
基金supported in part by the National Natural Science Foundation of China(Grant Nos.61601346 and 62377039)the Natural Science Basic Research Plan in Shaanxi Province of China(Grant No.2018JQ6044)+2 种基金the Ministry of Industry and Information Technology of the People's Republic of China(Grant No.2023-276-1-1)the Fundamental Research Funds for the Central Universities,Northwestern Polytechnical University(Grant No.31020180QD089)the Aeronautical Science Foundation of China(Grant Nos.20200043053004 and 20200043053005)。
文摘One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this paper presents a class of code-domain nonorthogonal multiple accesses(NOMAs)for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system(JTIDS).In the considered scenario,a satellite equipped with Nr antennas servers K devices including vehicles,drones,ships,sensors,handset radios,etc.Nonorthogonal coded modulation,a special form of multiple input multiple output(MIMO)-NOMA is proposed.The discussion starts with evaluating the output signal to interference-plus-noise(SINR)of receiver filter,leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered.The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved.The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying(MSK)and Gaussian MSK(GMSK),two pivotal modulation formats in IoMT standards such as Link-16 and JITDS.Numerical results show that near capacity performance is offered.Fortunately,the performance is obtained using simple forward error corrections(FECs)of higher coding rate than existing schemes do,while the transmit power is reduced by 6 dB.The proposed design finds wide applications not only in IoMT but also in deep space communications,where ultra reliability and massive connectivity is a keen concern.
