This paper studies the achievable rate for three-node discrete memoryless relay channel. Specifically in this mode, we explore two generalized feedbacks simultaneously： the source node actively collects feedback sign...This paper studies the achievable rate for three-node discrete memoryless relay channel. Specifically in this mode, we explore two generalized feedbacks simultaneously： the source node actively collects feedback signals from the channel; and at the same time, the destination node actively transmits feedback signals to the relay node. These two feedback signals, which are called generalized feedback overheard from the channel that is likely to be noisy, induce that all the three nodes are in full duplex mode. The basic coding strategies of Cover and El Gamal are applied to the relay-source feedback transmission by the source forwarding the compressions of the channel output sequences at the relay node to the destination, and are also applied to the destination-relay feedback transmission to improve the decoding ability at the relay. Based on Cover and El Gamal coding, a new coding scheme adopting rate splitting and four-block Markov superposition encoding is proposed and the corresponding achievable rate is achieved. The proposed scheme is able to exploit two feedbacks simultaneously which can effectively eliminate underlying transmission bottlenecks for the channels. The derived achievable rate result generalizes several previously known results by including them as special cases.展开更多
In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acq...In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acquiring and reconstructing a signal completely from reduced number of measurements, by exploiting its compressibility. The measurements are not point samples but more general linear functions of the signal. CS can capture and represent sparse signals at a rate significantly lower than ordinarily used in the Shannon’s sampling theorem. It is interesting to notice that most signals in reality are sparse;especially when they are represented in some domain (such as the wavelet domain) where many coefficients are close to or equal to zero. A signal is called K-sparse, if it can be exactly represented by a basis, , and a set of coefficients , where only K coefficients are nonzero. A signal is called approximately K-sparse, if it can be represented up to a certain accuracy using K non-zero coefficients. As an example, a K-sparse signal is the class of signals that are the sum of K sinusoids chosen from the N harmonics of the observed time interval. Taking the DFT of any such signal would render only K non-zero values . An example of approximately sparse signals is when the coefficients , sorted by magnitude, decrease following a power law. In this case the sparse approximation constructed by choosing the K largest coefficients is guaranteed to have an approximation error that decreases with the same power law as the coefficients. The main limitation of CS-based systems is that they are employing iterative algorithms to recover the signal. The sealgorithms are slow and the hardware solution has become crucial for higher performance and speed. This technique enables fewer data samples than traditionally required when capturing a signal with relatively high bandwidth, but a low information rate. As a main feature of CS, efficient algorithms such as -minimization can be used for recovery. This paper gives a su展开更多
高分辨率POLSAR图像的机场感兴趣区域(Region of interest,ROI)的自动提取是自动目标识别(AutomaticTarget Recognition,ATR)系统的任务之一,也是准确识别分类飞机等小目标的基础。针对全极化合成孔径雷达(POLSAR)图像极化相干的特点,...高分辨率POLSAR图像的机场感兴趣区域(Region of interest,ROI)的自动提取是自动目标识别(AutomaticTarget Recognition,ATR)系统的任务之一,也是准确识别分类飞机等小目标的基础。针对全极化合成孔径雷达(POLSAR)图像极化相干的特点,提出一种融合提取方法:先使用J.S.Lee Sigma filter滤波,再利用Shannon-Entropy理论提高ROI和背景对比度,采用基于CV模型的方法分割图像,然后对分割得到的图像进行形态学等图像处理,最终得到机场ROI。实验结果表明,该方法具有分割界限清晰、定位准确的优点。展开更多
基金supported by the National Natural Science Foundation of China (60972045)the Cultivation and Innovation Project for Jiangsu Provincial Postgraduate (CX10B_192Z)
文摘This paper studies the achievable rate for three-node discrete memoryless relay channel. Specifically in this mode, we explore two generalized feedbacks simultaneously： the source node actively collects feedback signals from the channel; and at the same time, the destination node actively transmits feedback signals to the relay node. These two feedback signals, which are called generalized feedback overheard from the channel that is likely to be noisy, induce that all the three nodes are in full duplex mode. The basic coding strategies of Cover and El Gamal are applied to the relay-source feedback transmission by the source forwarding the compressions of the channel output sequences at the relay node to the destination, and are also applied to the destination-relay feedback transmission to improve the decoding ability at the relay. Based on Cover and El Gamal coding, a new coding scheme adopting rate splitting and four-block Markov superposition encoding is proposed and the corresponding achievable rate is achieved. The proposed scheme is able to exploit two feedbacks simultaneously which can effectively eliminate underlying transmission bottlenecks for the channels. The derived achievable rate result generalizes several previously known results by including them as special cases.
文摘In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acquiring and reconstructing a signal completely from reduced number of measurements, by exploiting its compressibility. The measurements are not point samples but more general linear functions of the signal. CS can capture and represent sparse signals at a rate significantly lower than ordinarily used in the Shannon’s sampling theorem. It is interesting to notice that most signals in reality are sparse;especially when they are represented in some domain (such as the wavelet domain) where many coefficients are close to or equal to zero. A signal is called K-sparse, if it can be exactly represented by a basis, , and a set of coefficients , where only K coefficients are nonzero. A signal is called approximately K-sparse, if it can be represented up to a certain accuracy using K non-zero coefficients. As an example, a K-sparse signal is the class of signals that are the sum of K sinusoids chosen from the N harmonics of the observed time interval. Taking the DFT of any such signal would render only K non-zero values . An example of approximately sparse signals is when the coefficients , sorted by magnitude, decrease following a power law. In this case the sparse approximation constructed by choosing the K largest coefficients is guaranteed to have an approximation error that decreases with the same power law as the coefficients. The main limitation of CS-based systems is that they are employing iterative algorithms to recover the signal. The sealgorithms are slow and the hardware solution has become crucial for higher performance and speed. This technique enables fewer data samples than traditionally required when capturing a signal with relatively high bandwidth, but a low information rate. As a main feature of CS, efficient algorithms such as -minimization can be used for recovery. This paper gives a su
