FM subcarrier fiber optical transmission system design and itsapplication in next-generation wireless access

This paper presents a design method of optical frequency modulation (FM) subcarrier (with super carrier) transmission modem. The nonideal link characteristics, including laser chirping, fiber dispersion, voltage controlled oscillator (VCO) phase noise, relative intensity noise (RIN), and equivalent network model of laser diode of such a system that may bring about signal distortion are discussed first. We then propose a hierarchical methods to establish the system equivalent model. Finally this FM modem is applied to a GSM wireless system, in which multichannel signals are transmitted over fiber between radio port and basestation, and system performance is appraised by its dynamic range. It is found that optical FM subcarrier (with super carrier) transmission technique can improve the system dynamic range, compared with the intensity modulated direct detection (IMDD) method, and this is a economical and efficient method     

利用期望-最大化算法实现基于动态词典的压缩感知

在现有压缩感知(CS)理论中,重构信号需要预设其稀疏表示词典。对于以参数化模型表示的信号,只能预知该词典为某种形式的参数化词典,参数的具体取值难以确定。若将参数设定为取值空间的均匀离散格点,预设词典与真实词典之间的失配将使传统CS重构方法的性能严重恶化。为解决这一问题,该文提出一种基于动态词典的CS重构方法。通过迭代地优化词典参数,该方法在信号重构过程中对词典进行动态调整。为同时实现稀疏恢复与词典调整,该方法利用变分期望-最大化(EM)算法交替执行信号系数估计与词典参数优化。实验结果表明所提方法是有效的。     

Is FM a RF-Based Positioning Solution in a Metropolitan-Scale Environment? A Probabilistic Approach With Radio Measurements Analysis

Recent mobile devices have already contained a low-cost FM receiving function due to the continuing improvements in the device manufacturing. This paper shows that positioning based on FM signal is an alternative radio option while meeting the FCC requirement. We present a probabilistic location system using a wider-covered and longer-lived FM infrastructure. The performance is evaluated in two different metropolitan-scale environments including National Taiwan University (NTU) and Wen-Shan rural area. Both results show that the FM based location system not only satisfies the FCC requirement but also provides a comparable or even better performance to GSM based solution. Moreover, we completely analyze the realistic radio measurements of FM and GSM from four perspectives including temporal variation, spatial separation, measurement correlation and spectrum allocation. Most FM measurements are observed to provide a lower temporal variation but a weaker spatial separation than GSM. Fortunately, we discover that the lack of spatial separation can be compensated by adding additional sensed channels. This property is useful especially for a rural area where the available GSM base stations are limited and distant. Furthermore, we point out that the spatial separation of GSM signal decreases when the signal level is weaker than $-$ 90 dBm. At such a condition, FM reports a better accuracy than GSM even with the fewer channels.      

利用基字典构造的直扩测控信号稀疏性分析

压缩感知理论为直扩测控信号降低采集成本、缓解同步解调处理压力提供了新的思路,其中稀疏性是压缩感知理论的重要应用前提,但目前该类信号稀疏性的相关研究鲜有报道。该文从信号稀疏基字典构造着手,对直扩测控信号稀疏性进行了深入研究,提出了双阶段字典学习方法,对直扩测控信号数学模型进行了深入分析,通过两种方式分别获得了学习基字典和延时-多普勒基字典,并对两种基字典的稀疏表示性能进行了仿真验证。仿真结果表明,直扩测控信号在所构造两种基字典上表现出了很强的稀疏性,这为基于压缩感知的扩频测控信号处理奠定了理论基础。      

A New Microwave Amplifier for Multichannel FM Signals Using a Synchronized Oscillator

Because of the excellent results of the noise-loading test on an IMPATT oscillator that is phase-locked to an external FM driving signal, a new microwave amplifier for multichannel FM signals using a synchronized oscillator is proposed. An IMPATT oscillator, injection-synchronized with an FM signal, is regarded as an amplifier and its noise characteristics for multichannel signals are evaluated with the noise-loading method as recommended by the International Radio Consultative Committee. The results show that when this oscillator satisfies the noise characteristics for 960 telephone message channels, it assures a gain of approximately 15 dB at 11 GHz, and in this sense, this oscillator can be regarded as an amplifier for multimessage channels. The degradation of SNR of this amplifier is less than 2 dB. This excellent result is obtained because of the following. In the video band, the modulation suppression of an FM driving signal in the injection-synchronized oscillator, such as the IMPATT, is very small, while the oscillator noise, which dominates the entire output characteristic, is considerably reduced by the synchronizing action of the oscillator. This amplification continues up to the high-frequency range in which this principle of operation applies.     

Compressed Sensing via Dimension Spread in Dimension-Restricted Systems

Compressed sensing (CS) is applied to capture signals at sub-Nyquist rate when the sensing matrix satisfies the restricted isometry property (RIP). When in a dimension-restricted system which has small row dimension and not so good coherence, the RIP and measurement bound will not be satisfied, and compressed sensing can not be applied directly. In this letter, we propose the dimension spread CS to the dimension-restricted system by directed dimension spread and diversity dimension spread, which make the compressed sensing applicable. The spread dimension bounds for the proposed algorithms are deduced to guarantee exact recovery which are also proved by simulations. Meanwhile, the experimental comparisons for the directed dimension spread CS and diversity dimension spread CS are given and different CS recovery algorithms are carried out to show the effectiveness of the proposed algorithms in the dimension-restricted system. The diversity dimension spread CS outperforms the directed dimension spread CS for its effective dimension spread and diversity. The proposed algorithms can be directly applied in channel estimation and multiuser detection in overload system.     

Interference into Angle-Modulated Systems Carrying Multichannel Telephony Signals

An investigation of interference between angle-modulated systems carrying multichannel telephony, which is directly applicable to existing satellite and terrestrial radio-relay systems, has been undertaken. This study includes a general solution for arbitrary narrow-band interference into an angle-modulated system with arbitrary modulation. The algorithm has been implemented on a digital computer to provide calculations of interference between two FDM/FM systems. The analysis is valid for all modulation indices, and may be applied to any PM, FM, or preemphasized FM baseband. A comparison of calculated results and experimental measurements shows good agreement. An extensive investigation, covering a wide range of modulation indices and basebands, has been conducted for the particular case of preemphasized FDM/FM signals. Wherever possible, a generalized presentation of interference results is given. Interleaving criteria are also presented for certain cases of interference involving two identical signals.     

基于压缩感知与结构自相似性的遥感图像超分辨率方法

本文提出了一种基于压缩感知、结构自相似性和字典学习的遥感图像超分辨率方法,其基本思路是建立能够稀疏表示原始高分辨率图像块的字典。实现超分辨率所必需的附加信息来源于遥感图像中广泛存在的自相似结构,该信息可在压缩感知框架下通过字典学习而得到。这里,本文采用K-SVD方法构建字典、并采用OMP方法获取用于稀疏表达的相关系数。与现有基于样本的超分辨率方法的最大不同在于,本文方法仅使用了低分辨率图像及其插值图像,而不需要使用其它高分辨率图像。另外,为了评价方法的效果,本文还引入了一个衡量图像结构自相似性程度的新型指标SSSIM。对比实验结果表明,本文方法具有更好的超分辨率重构效果和运算效率,并且SSSIM指标与超分辨率重构效果具有较强的相关性。      

Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization

Sparse signal representation, analysis, and sensing have received a lot of attention in recent years from the signal processing, optimization, and learning communities. On one hand, learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. On the other hand, the framework of compressed sensing (CS) has shown that sparse signals can be recovered from far less samples than those required by the classical Shannon-Nyquist Theorem. The samples used in CS correspond to linear projections obtained by a sensing projection matrix. It has been shown that, for example, a nonadaptive random sampling matrix satisfies the fundamental theoretical requirements of CS, enjoying the additional benefit of universality. On the other hand, a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy or further reduce the necessary number of samples. In this paper, we introduce a framework for the joint design and optimization, from a set of training images, of the nonparametric dictionary and the sensing matrix. We show that this joint optimization outperforms both the use of random sensing matrices and those matrices that are optimized independently of the learning of the dictionary. Particular cases of the proposed framework include the optimization of the sensing matrix for a given dictionary as well as the optimization of the dictionary for a predefined sensing environment. The presentation of the framework and its efficient numerical optimization is complemented with numerous examples on classical image datasets.     

Sampling theorems for bandlimited random signals in the offset linear canonical transform domain

Sampling theorems for deterministic signals in the offset linear canonical transform (OLCT) domain have been widely proposed in the literature. In this paper, we consider the sampling and reconstruction schemes for random signals in the OLCT domain. We first define the power spectral density and correlation function for random signals in the OLCT domain. By utilizing the definitions, the power spectral density relations for the OLCT filter are derived. Subsequently, the multichannel sampling theorem for bandlimited random signals in the OLCT domain is derived. In addition, some sampling theorems for bandlimited random signals associated with the OLCT are also presented by designing different OLCT filters. Finally, the simulation results are presented to show the advantage and effectiveness of the theorems.     

基于字典尺度自适应学习的欠定盲语音重构算法

针对欠定盲语音分离传统字典学习算法不能优化字典尺寸的问题,提出了一种尺度自适应同步码字优化(Scale Adaptive Simultaneous Codeword Optimization, SASimCO)算法。设计了一种迭代调整字典尺寸的自适应字典学习策略,将训练的字典用于语音盲分离中,以提高语音源信号的恢复性能。所提算法依据设计的候选矩阵,计算候选矩阵中的原子重要性,按照原子重要性准则对字典进行添加与删除原子操作,最后迭代训练得到一个稀疏表示误差最优的字典,用于语音源信号的恢复。使用SiSEC(Signal Separation Evaluation Campaign)数据集对所提算法进行的仿真实验表明,相较于传统字典学习算法,所提算法提高了1～3 dB语音源分离性能,证明了该算法的优势。     

LTE‐Based Passive Bistatic Radar System for Detection of Ground‐Moving Targets

Use of a passive bistatic radar (PBR) system in the surveillance or monitoring of an area has its advantages. For example, a PBR system is able to utilize any available signal of opportunity (for example, broadcasting, communication, or radio navigation signals) for the purposes of surveillance. With this in mind, there are potentially many research areas to be explored; in particular, the capability of signals from existing and future communication systems, such as 4G and 5G. Long‐Term Evolution (LTE) is the world's most current communication system. Given this fact, this paper presents the latest feasibility studies and experimental results from using LTE signals in PBR applications. Details are provided about aspects such as signal characteristics, experimental configurations, and SNR studies. Six experimental scenarios are carried out to investigate the detection performance of our proposed system on ground‐moving targets. The ability to detect is demonstrated through use of the cross‐ambiguity function. The detection results suggest that LTE signals are suitable as a source signal for PBR.     

Compressed Sensing and Redundant Dictionaries

This paper extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic dictionary, has small restricted isometry constants. Thus, signals that are sparse with respect to the dictionary can be recovered via basis pursuit (BP) from a small number of random measurements. Further, thresholding is investigated as recovery algorithm for compressed sensing, and conditions are provided that guarantee reconstruction with high probability. The different schemes are compared by numerical experiments.     

An efficient lattice predictor algorithm for instantaneous frequency estimation

An efficient method for spectral analysis of non-stationary signals is proposed. The signals are modelled as time-varying autoregressive (AR) processes and the relevant AR parameters are obtained from the adaptive lattice predictor coefficients minimizing the forward/backward prediction errors. In this paper it is proposed to use the sign algorithm, which computes the pertinent coefficients using only the signs of the forward/backward prediction errors at each stage. This method requires less number of multiplications than other adaptive lattice predictor algorithms. The performance of this method is compared with that of the adaptive lattice predictor using the least mean square algorithm for the problems of estimating instantaneous frequencies of FM signals via computer simulations.     

一种新的基于稀疏表示的宽带信号DOA估计方法

该文提出一种基于稀疏表示的宽带信号波达方向(DOA)估计方法,解决稀疏表示方法在宽带信号DOA估计中由于基矩阵维数过大而使算法存储量和重构计算量大的问题。用单一频点的基矩阵代替频率和角度联合构建的基矩阵,使基矩阵的列数仅相当于一个频点处冗余基矩阵的列数,大大降低了稀疏重构方法的存储量和计算量。该方法首先对各频点的频域数据进行聚焦处理,将不同频率的数据堆叠到参考频率上并建立参考频率处的基矩阵,然后建立聚焦后的稀疏表示模型进行DOA估计,并采用奇异值分解进一步降低算法的运算量,最后给出残差门限的选择方法。该算法不仅适用于非相关信号,也可直接处理相关信号而不需要任何的去相关运算,且具有高的检测概率和估计精度,仿真实验和分析验证了该方法的有效性。     

Range resolution improvement in FM-based passive radars using deconvolution

FM-based passive bistatic radar (PBR) systems suffer from low range resolution because of the low baseband bandwidth of commercial FM broadcasts. In this paper, we propose a range resolution improvement method using deconvolution. The output of the PBR matched filter is processed using a deconvolution algorithm which assumes that targets are isolated, i.e., sparse in the range domain. The deconvolution algorithm is iterative and was implemented by performing successive orthogonal projections onto supporting hyperplanes of the epigraph set of a convex cost function. Simulation examples are presented.     

基于优化字典学习的遥感图像融合方法

为提升全色图像和多光谱图像的融合效果,该文提出基于优化字典学习的遥感图像融合方法。首先将经典图像库中的图像分块作为训练样本,对其进行K均值聚类,根据聚类结果适度裁减数量较多且相似度较高的图像块,减少训练样本个数。接着对裁减后的训练样本进行训练,得到通用性字典,并标记出相似字典原子和较少使用的字典原子。然后用与原稀疏模型差异最大的全色图像块规范化后替换相似字典原子和较少使用的字典原子,得到自适应字典。使用自适应字典对多光谱图像经IHS变换后获取的亮度分量和源全色图像进行稀疏表示,把每一个图像块稀疏系数中的模极大值系数分离,得到极大值稀疏系数,将剩下的稀疏系数称为剩余稀疏系数。针对极大值稀疏系数和剩余稀疏系数分别选择不同的融合规则进行融合,以保留更多的光谱信息和空间细节信息,最后进行IHS逆变换获得融合图像。实验结果表明,与传统方法相比所提方法得到的融合图像主观视觉效果较好,且客观评价指标更优。     

基于光谱稀疏模型的高光谱压缩感知重构

提出了一种基于光谱稀疏化的压缩感知采样与重构模型,通过从训练样本中构建光谱稀疏字典提升光谱稀疏化效果,同时在重构时兼顾空间图像的全变分约束进一步提升重构精度.对200波段AVIRIS高光谱场景进行压缩感知重构的实验表明,利用构建的光谱稀疏字典与传统的DCT字典和Haar小波字典相比光谱稀疏化效果明显提升,同时在25%采样下基于光谱稀疏字典几乎无差别重构出了高光谱图像,同样条件下在空间和光谱的精度与现有常用方法相比有较大的提升.     

Separation of cochannel GSM signals using an adaptive array

The Global System for Mobile communications (GSM) is a digital cellular radio network that employs time division multiple access (TDMA). In such a cellular system, frequencies are reused in different regions for spectral efficiency, and thus, the transmissions in a given cell can interfere with those in distant cells. This cochannel interference can be a major impairment to the signal of interest. In this paper, we describe a beamformer and equalizer system that is capable of separating and demodulating several cochannel GSM signals. The signal model includes intersymbol interference (ISI) induced by the Gaussian transmit filter, and the channel model incorporates multipath propagation and additive white Gaussian noise. The GSM synchronization sequences are used to compute the beamformer weights and achieve frame synchronization simultaneously. Decision-feedback equalization is employed to compensate for the transmit filter ISI and to demodulate the data     

基于用户信号的多径信号分组的DOA估计

在移动通信中,多径信号的分组一直是研究的重点。本文提出一种多径环境下的宿主用户和多径信号的分组和DOA估计算法。该算法不需要采用扩频技术确定多径信号的归属问题,因此不但可以应用于CDMA系统,而且可以应用于GSM系统。