基于有限新息率采样理论的超宽带信道估计

提出利用有限新息率采样理论解决超宽带信道估计问题,建立了可用于超宽带信道估计的框架,此框架的抽样率低于奈奎斯特速率.通过实例展示了科用奈奎斯特速率抽样而工作在低于奈奎斯特抽样率的算法性能,减少了功耗,也降低了硬件的复杂性.     

压缩感知在稀疏信道估计中的应用

压缩感知( CS,Compressive Sensing)理论指出可以用低于奈奎斯特抽样定理的速率对稀疏信号进行采样并在收端以很高的概率重建信号,它是目前信号处理领域的研究热点.基于CS理论的信道估计会降低导频辅助信道估计的导频数量且估计性能好,介绍了CS的基本原理和信道估计相关内容,以及正交频分复用(OFDM,Ort...     

基于压缩感知的加权宽带谱重构算法

压缩感知利用宽带无线信号的频域稀疏特性,能够在低于奈奎斯特速率的采样下利用少量观测数据实现宽带频谱估计和空穴检测。但相关频谱压缩感知算法的性能并不理想,为了实现宽带信道的快速准确感知,本文基于宽带信道的时频统计特性,在去噪基追踪算法(BPDN)的基础上提出了一种优化的加权去噪算法(WBPDN)。该算法利用子频段历史平均功率密度水平来构建各子频段权重以优化目标函数,改善算法性能。实验结果表明:该算法能通过少量观测数据准确重构宽带信道的谱估计,且比传统的BPDN和OMP算法具有更好的压缩性能及更小的重构误差;另外加权后的算法收敛速度更快,显著减少了算法所需的运行时间。      

基于窄带信号抽样的信号高效传输

奈奎斯特抽样定理是对频率宽度有限长的模拟信号进行数字化处理的重要定理之一,而对于这类频宽有限信号中的特殊信号,即窄带信号而言,依据奈奎斯特抽样定理对信号进行抽样时会出现一些新问题。在指出窄带信号存在原因的基础上,以抽样信号的频谱不混叠为根本对窄带信号进行抽样,并使其与按照奈奎斯特定理抽样后的信号进行比较分析。借助通信原理相关知识予以在信道传输中进行分析,得出信号经窄带信号抽样定理处理后的优势所在。     

基于压缩感知的OFDM稀疏信道估计应用

压缩感知理论可通过远低于那奎斯特准则的方式进行采样数据,仍能够精确恢复出原始信号,基于CS技术的信道估计可减少OFDM系统中导频的数量,同时可获得较好的估计性能,本文通过介绍CS理论和OFDM信道估计方法,将CS理论应用到信道估计中,重点介绍通过ROMP算法估计信道冲击响应函数。     

基于欠奈奎斯特采样的超宽带信号总体最小二乘重建算法

该文针对超宽带无线通信中需要设计高速模数转换器的问题,提出了一种欠奈奎斯特采样方法,该方法所要求的采样率仅与信号新息率相关,低于奈奎斯特率1个数量级。基于欠采样得到的离散时间超宽带信号,从理论上推导出信号的傅里叶频谱表达式,由此给出了一种总体最小二乘参数估计算法,能够准确地估计出冲激串信号的幅度和时移;通过将估计出的冲激串信号与高斯单脉冲波形卷积,完成超宽带信号的波形重建。仿真和实验结果表明,该文算法能够准确地重建原始超宽带信号,且算法性能优于现有的零化滤波重建算法。     

针对块稀疏信道的估计算法

无线多径信道中存在着块稀疏结构。针对块稀疏信道中分块信息是否已知的不同场景,分别提出了两种基于块稀疏贝叶斯学习(BSBL)框架的OFDM系统信道估计算法。这两种算法根据边界最优(BO)方法估计信道分块的稀疏度参数,提升算法运算速率。为进一步提升信道估计性能,在基于BSBL框架算法仅利用导频信号估计信道的基础上,又提出了基于联合块稀疏贝叶斯学习(JBSBL)的信道估计新算法,该算法利用导频与数据子载波实现信道的联合估计。仿真结果表明,与传统的最小二乘算法比较,本文提出的算法均可获得很好的信道估计性能,且基于JBSBL的信道估计算法性能更佳。      

一种并行数据传输系统的性能分析

并行正交调幅数据传输系统利用很多相互重叠的子信道,能够使得总的信号速率非常接近给定频带的奈奎斯特速率。文中首先对并行数据传输系统进行了描述,然后,在存在相位偏移和延迟失真的情况下,对系统的性能进行了分析。结果表明总失真与子信道数目的平方成反比,从而说明了并行传输系统具有抵抗延迟失真的作用。此外,通过改变解调相位,还可大大改善并行系统的性能。     

基于SystemView的奈奎斯特第一准则的验证与分析

本文阐述了奈奎斯特第一准则的基本原理,并使用SystemView软件对数字基带系统进行仿真设计。我们修改信道噪声和基带信号的码元传播速率,并观察比较接收波形和发送波形,直观看到噪声和码间干扰对数字基带系统的影响,同时验证了奈奎斯特第一准则。     

基于压缩感知的多发多收高分辨SAR成像算法研究

压缩感知理论指出,稀疏信号可以通过以低于奈奎斯特采样的测量数据重建出原始信号。针对高分辨率SAR成像在奈奎斯特理论下所面临的高速A／D采样、大数据量存储、传输等问题挑战。本文提出了一种基于压缩感知理论的多发多收高分辨率SAR二维成像算法。该算法减轻了高分辨率SAR成像的压力,采用压缩感知处理降低了A／D采样速率、数据量...     

Sub‐Nyquist rate ADC sampling‐based compressive channel estimation

To realize high‐speed communication, broadband transmission has become an indispensable technique in the next‐generation wireless communication systems. Broadband channel is often characterized by the sparse multipath channel model, and significant taps are widely separated in time, and thereby, a large delay spread exists. Accurate channel state information is required for coherent detection. Traditionally, accurate channel estimation can be achieved by sampling the received signal with large delay spread by analog‐to‐digital converter (ADC) at Nyquist rate and then estimate all of channel taps. However, as the transmission bandwidth increases, the demands of the Nyquist sampling rate already exceed the capabilities of current ADC. In addition, the high‐speed ADC is very expensive for ordinary wireless communication. In this paper, we present a novel receiver, which utilizes a sub‐Nyquist ADC that samples at much lower rate than the Nyquist one. On the basis of the sampling scheme, we propose a compressive channel estimation method using Dantzig selector algorithm. By comparing with the traditional least square channel estimation, our proposed method not only achieves robust channel estimation but also reduces the cost because low‐speed ADC is much cheaper than high‐speed one. Computer simulations confirm the effectiveness of our proposed method. Copyright © 2013 John Wiley & Sons, Ltd.     

High‐resolution compressive channel estimation for broadband wireless communication systems

Broadband channel is often characterized by a sparse multipath channel where dominant multipath taps are widely separated in time, thereby resulting in a large delay spread. Accurate channel estimation can be done by sampling received signal with analog‐to‐digital converter (ADC) at Nyquist rate and then estimating all channel taps with high resolution. However, these Nyquist sampling‐based methods have two main disadvantages: (i) demand of the high‐speed ADC, which already exceeds the capability of current ADC, and (ii) low spectral efficiency. To solve these challenges, compressive channel estimation methods have been proposed. Unfortunately, those channel estimators are vulnerable to low resolution in low‐speed ADC sampling systems. In this paper, we propose a high‐resolution compressive channel estimation method, which is based on sampling by using multiple low‐speed ADCs. Unlike the traditional methods on compressive channel estimation, our proposed method can approximately achieve the performance of lower bound. At the same time, the proposed method can reduce communication cost and improve spectral efficiency. Numerical simulations confirm our proposed method by using low‐speed ADC sampling. Copyright © 2012 John Wiley & Sons, Ltd.     

Performance bounds for polynomial phase parameter estimation withnonuniform and random sampling schemes

Estimating the parameters of a cisoid with an unknown amplitude and polynomial phase using uniformly spaced samples can result in ambiguous estimates due to Nyquist sampling limitations. It has been shown previously that nonuniform sampling has the advantage of unambiguous estimates beyond the Nyquist frequency; however, the effect of sampling on the Cramer-Rao bounds is not well known. This paper first derives the maximum likelihood estimators and Cramer-Rao bounds for the parameters with known, arbitrary sampling times. It then outlines two methods for incorporating random sampling times into the lower variance bounds, describing one in detail. It is then shown that for a signal with additive white Gaussian noise the bounds for the estimation with nonuniform sampling tend toward those of uniform sampling. Thus, nonuniform sampling overcomes the ambiguity problems of uniform sampling without incurring the penalty of an increased variance in parameter estimation     

Sampling, data transmission, and the Nyquist rate

The sampling theorem for bandlimited signals of finite energy can be interpreted in two ways, associated with the names of Nyquist and Shannon. 1) Every signal of finite energy and bandwidth W Hz may be completely recovered, in a simple way, from a knowledge of its samples taken at the rate of 2W per second (Nyquist rate). Moreover, the recovery is stable, in the sense that a small error in reading sample values produces only a correspondingly small error in the recovered signal. 2) Every square-summable sequence of numbers may be transmitted at the rate of 2W per second over an ideal channel of bandwidth W Hz, by being represented as the samples of an easily constructed band-limited signal of finite energy. The practical importance of these results, together with the restrictions implicit in the sampling theorem, make it natural to ask whether the above rates cannot be improved, by passing to differently chosen sampling instants, or to bandpass or multiband (rather than bandlimited) signals, or to more elaborate computations. In this paper we draw a distinction between reconstructing a signal from its samples, and doing so in a stable way, and we argue that only stable sampling is meaningful in practice. We then prove that: 1) stable sampling cannot be performed at a rate lower than the Nyquist, 2) data cannot be transmitted as samples at a rate higher than the Nyquist, regardless of the location of sampling instants, the nature of the set of frequencies which the signals occupy, or the method of construction. These conclusions apply not merely to finite-energy, but also to bounded, signals.     

基于压缩感知的定位新型UWB信号抽样方法

Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirement of high sampling rate which leads to complicated signal processing and expensive hardware. In this paper, we present a novel UWB signal sampling method called UWB signal sampling via temporal sparsity (USSTS). Its sampling rate is much lower than Nyquist rate. Moreover, it is implemented in one step and no extra processing unit is needed. Simulation results show that USSTS can not recover the signal precisely, but for the use in localization, the accuracy of TOA estimation is the same as that in traditional methods. Therefore, USSTS gives a novel and effective solution for the use of UWB signals in localization.     

DFT-based optical offset-QAM OFDM: analytical,numerical, and experimental studies

We investigate discrete Fourier transform-based offset quadrature amplitude modulation (offset-QAM) orthogonal frequency division multiplexing (OFDM) technology. We derive a closed-form expression for the de-multiplexed signal and analyze the influence of crosstalk on implementation algorithms and system performance. It is found that channel estimation in offset-QAM OFDM is different from that in conventional OFDM (C-OFDM) due to the residual crosstalk terms and requires particular study. We propose simple and efficient channel estimation algorithms and show, in a 38-Gbit/s offset-16QAM OFDM experiment with 840-km single-mode fiber, that these algorithms can enable the system performance close to the theoretical limit. By using these algorithms, we compare this technology with C-OFDM and Nyquist FDM (N-FDM) and numerically and experimentally show that DFT-based offset-QAM OFDM can greatly enhance the net data rate for fiber transmissions compared to C-OFDM and exhibit lower complexity than N-FDM. These advantages together with the successfully developed implementation algorithms make this technology very promising for optical communication systems.     

Sequence estimation and synchronization from nonsynchronizedsamples

The authors study the problem of maximum-likelihood sequence estimation and synchronization from samples of the output of a matched filter taken at integer multiples of the symbol rate, which is assumed perfectly known by the receiver. A general analysis is presented of sampled receivers that handle arbitrary baseband pulse shapes and arbitrary sampling rates. It is observed that the optimal processing of the matched-filter samples consists of digital interpolation, followed by symbol-by-symbol decoding when sampling is at (or above) the Nyquist rate or Viterbi decoding when sampling is below the Nyquist rate. Performance is studied through the Cramer-Rao bound on mean-square estimation error and a lower-bound on error-probability     

A sampling function appropriate for deconvolution (Corresp.)

We present a sampling theory, in one and two dimensions, which is appropriate for deconvolution problems. Our concern is with the possibility of sampling at a rate lower than the Nyquist rate. A new criterion for choosing sampling rates is introduced.     

A translinear SiGe BiCMOS current-controlled oscillator with 80 Hz–800 MHz tuning range

A novel identification and digital predistortion scheme of weakly nonlinear passband systems such as RF power amplifiers (PA) is presented. It is well known that for the identification of weakly nonlinear systems, despite the spectral regrowth, it suffices to sample the input-output (I/O) data of the system at the Nyquist rate of the input signal. Many applications such as linearization (digital predistortion) and mixed signal simulations require system models at a higher sampling rate than Nyquist. Up to now the construction of such high-rate predistorters has been done by oversampling the corresponding I/O data. This leads to high computational complexity, ill-posedness of the estimation, and high demand on the analog-to-digital converter (ADC) sampling rate for the implementation. This paper discusses an efficient way to obtain high-rate predistorters from low-rate system models and shows the validity of the proposed scheme for a 5th-order complex baseband PA model, where adjacent channel power suppression of 20 dB is achieved.