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.     

基于压缩感知技术的双向中继信道估计

设计了一种基于压缩感知（compressive sensing, CS）技术的双向中继信道（two-way relay channels, TWRC）估计方法,并具体采用正交匹配追踪算法（orthogonal matching pursuit, OMP）对OFDM系统下的信道脉冲响应进行估计。双向中继信道往往呈现出稀疏多径结构,这种结构会随着信号空间维数的增大而越加明显。传统的线性估计方法没有考虑到TWRC的潜在稀疏性,因而导致了对关键通信资源的过度使用。而基于CS的TWRC估计方法能够很好地利用这种传输信道的稀疏多径结构,与传统线性估计方法相比,在获得同样估计性能的前提下,需要的训练序列长度大大减少,有效地提高了频谱、能量等资源利用率。同时,所采用的OMP算法的时间复杂度主要依赖于信道稀疏度,因此计算效率往往比传统的方法高。仿真也证实了基于CS的TWRC估计算法的优越性。      

Iterative channel estimation and pilot design rules for high‐mobility comb‐pilot OFDM system

With wireless communications in high‐mobility environment becoming popular, this poses a big challenge for communication systems based on the comb‐pilot OFDM, such as IEEE 802.11p, since it has not the enough pilots to estimate the time‐ and frequency‐selective channel accurately. In this paper, several comb‐pilot schemes and three comb‐pilot design rules are proposed to meet the Nyquist criterion for sampling the vehicle‐to‐vehicle (V2V) channel and the requirements of second‐order statistic of V2V channel. Based on the proposed pilot schemes, an iterative channel estimation method from the CE‐BEM model is proposed, together with three ICI cancellation methods. After thorough simulation, the effectiveness of the comb‐pilot design rules, the proposed channel estimation method, and intercarrier interference (ICI) cancellation methods is verified. Compared with other channel estimation methods, the proposed method performs better. The simulation results also reveal that the channel order L+1 has a great impact on the performance of the comb‐pilot OFDM system.     

Compressive sensing for ultra‐wideband channel estimation: on the sparsity assumption of ultra‐wideband channels

Due to the sparse structure of ultra‐wideband (UWB) multipath channels, there has been a considerable amount of interest in applying the compressive sensing (CS) theory to UWB channel estimation. The main consideration of the related studies is to propose different implementations of the CS theory for the estimation of UWB channels, which are assumed to be sparse. In this study, we investigate the suitability of standardized UWB channel models to be used with the CS theory. In other words, we question the sparsity assumption of realistic UWB multipath channels. For that, we particularly investigate the effects of IEEE 802.15.4a UWB channel models and the selection of channel resolution both on channel estimation and system performances from a practical implementation point of view. In addition, we compare the channel estimation performance with the Cramer‐Rao lower bound for various channel models and number of measurements. The study shows that although UWB channel models for residential environments (e.g., channel models CM1 and CM2) exhibit a sparse structure yielding a reasonable channel estimation performance, channel models for industrial environments (e.g., CM8) may not be treated as having a sparse structure due to multipaths arriving densely. Furthermore, it is shown that the sparsity increased by channel resolution can improve the channel estimation performance significantly at the expense of increased receiver processing. Copyright © 2013 John Wiley & Sons, Ltd.     

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...     

压缩采样中模拟信息转换器的性能仿真

受奈李斯特采样定理的约束,传统通信设备在提高分辨率和满足实时性要求时,面临高采样率、快处理速度等问题的挑战.而根据压缩采样(Compressive Sensing,CS)理论构建的模拟信息转换器(Analog to Information Converter,AIC)只需进行远低于奈奎斯特采样率采样信号,即可实现对原信...     

An Improved DFT-Based Channel Estimation Algorithm for OFDM System in Non-sample-Spaced Multipath Channels

It is well known that channel impairment caused by multipath reflections can deeply degrade the transmission efficiency in wireless communication systems. The conventional DFT-based channel estimation methods improve the performance by neglecting nonsignificant channel taps. However, in multipath channels with non-sample-spaced time delays, this will cause power leakage and result in an error floor. In order to overcome this problem, based on the property of Channel impulse response, we describe a utility of channel estimation technology for OFDM systems using discriminant analysis. Usually, significant channel taps are detected on the basis of a predetermined threshold, so the optimal threshold value becomes a crucial factor. But it is difficult to decide channel taps that are approximately equal to the threshold value. To solve this disadvantage, the proposed algorithm improves performance by using Mahalanobis distance discriminant analysis for channel taps. This approach can mitigate the aliasing effect in the DFT-based channel estimator and reduce the leakage power efficiently when there is non-sample-spaced path delay. Simulation results show that the proposed channel estimators can eliminate the error floor substantially and its performance is better than the LS and conventional DFT estimators.     

基于自适应压缩感知的信道估计算法

传统方法压缩感知算法截取训练序列最后未被数据干扰固定部分作为观测矩阵,该方法为了抵抗最差的信道而浪费了大量的可用观测数据。在此基础上提出了一种自适应压缩感知的信道估计算法,首先对训练序列进行自适应检测,得到整个未受干扰的观测矩阵,再用压缩感知算法计算信道估计。仿真结果表明,这种基于自适应压缩感知的信道估计算法大幅提高了信道估计的准确性。     

PN sequence blind estimation in multiuser DS‐CDMA systems with multipath channels based on successive subspace scheme

In this paper, we proposed a new method based on expanding subspace algorithm and finite alphabet characteristics, for blind estimation of the users' spreading sequences in the multiuser direct sequence code division multiple access system in the presence of the multipath channels. In the proposed scheme, we show that the estimation of the users' overall channels in the direct sequence code division multiple access system is equivalent to the impulse response estimation of the multi‐input multi‐output finite impulse response channels. Our proposed approach is based on the successive estimation of the columns of the equivalent multi‐input multi‐output finite impulse response channels from the lowest degree columns to the highest degree ones. Accordingly, each user's overall channel that is the convolution of the original multipath channel and the spreading sequence is estimated. Then we extract PN sequences from the overall channel using finite alphabet characteristics of the spreading sequence chips for each user. According to simulation results, our proposed scheme outperforms the conventional methods in that it does not require symbol synchronization and does not have channel constraints (for example, AWGN and single user system) in the multipath channels.     

Performance Analysis of UWB Channels for Wireless Personal Area Networks

Ultra-wide band (UWB) communication is one of the most promising technology for high data rate networks over short-range communication. The ultra-wide bandwidth offers pulses with very short duration that provides frequency diversity and multipath resolution. Ultra-wide band (UWB) channels raise new effects in the receiver, the amplitude fading statistics being different compared to the conventional narrow band wireless channels. This review paper focuses on modeling of ultra-wide band channels, especially for simulation of personal area networks and also discusses the benefits, application potential and technical challenges in wideband communication. The concept of Orthogonal Frequency Division Multiplexing (OFDM) has recently been applied in wireless communication systems due to its high data rate transmission capability with high bandwidth efficiency and its robustness to multi-path delay. UWB OFDM communication was proposed for physical layer in the IEEE 802.15.3a standard which covers wideband communication in wireless personal area networks. Since the channel model for multicarrier UWB communication is different from that of plain ultra-wide band channel, a novel modification method in UWB channel model is proposed with specific center frequency and multipath resolution. Moreover, dynamic channel estimation is necessary before demodulation of UWB OFDM signals since the radio channel is time varying and frequency selective for wideband systems. The performance of the proposed method is statistically analyzed using LS and MMSE based channel estimation methods.     

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...     

Efficient time domain threshold for sparse channel estimation in OFDM system

A novel efficient time domain threshold based sparse channel estimation technique is proposed for orthogonal frequency division multiplexing (OFDM) systems. The proposed method aims to realize effective channel estimation without prior knowledge of channel statistics and noise standard deviation within a comparatively wide range of sparsity. Firstly, classical least squares (LS) method is used to get an initial channel impulse response (CIR) estimate. Then, an effective threshold, estimated from the noise coefficients of the initial estimated CIR, is proposed. Finally, the obtained threshold is used to select the most significant taps. Theoretical analysis and simulation results show that the proposed method achieves better performance in both BER (bit error rate) and NMSE (normalized mean square error) than the compared methods has good spectral efficiency and moderate computational complexity.     

A novel compressed sensing ultra‐wideband channel estimation method based on non‐convex optimization

Due to the low power spectral density and complicated transfer propagation of ultra‐wideband (UWB) signal, it is important to estimate UWB channel accurately. But it is difficult to sample UWB signals directly due to their wider band width. However, compressed sensing (CS) theory provides a feasible way through lower sampling speed. Common CS‐UWB channel estimation methods adopt convex optimization, non‐sparse or non‐restricted form. In order to strengthen the restriction on sparsity of the reconstructed channel vector, a non‐convex optimization method is proposed in this paper to estimate UWB channel. Proposed method sets the objective function as a non‐convex optimization model using l_p–norm. This model is combined as a convex function to approximate the objective function and reconstruct the UWB channel vector iteratively. Because l_p–norm is closer to l₀–norm than l₁ and l₂–norm, its restriction on sparsity of objective vector is stricter. The simulation results show that this method can enhance reconstruction performance compared with existing CS‐UWB channel estimation methods. Copyright © 2013 John Wiley & Sons, Ltd.     

通用滤波多载波通信系统中干扰抑制均衡算法

作为5G多载波技术强有力的候选对象,通用滤波多载波利用子带滤波技术抑制带外功率泄露,进而降低同步要求和获得更高的频谱效率。本文首先针对通用滤波多载波在慢时变多径信道下的性能进行了分析和研究;其次为消除多径信道所带来的干扰,提出了适用于该多载波系统的信道估计方案,该方案设计了具有重复样式的导频结构进行信道估计,复杂度低;最后针对通用滤波多载波在多径信道下容易遭受符号间干扰的问题,提出了基于干扰消除的Zero-Forcing均衡算法和基于迭代干扰消除的均衡算法,两种算法均能够在消除ISI的基础上进一步地消除ICI和IBI。仿真结果表明,本文提出的信道估计和均衡算法能有效消除通用滤波多载波技术在多径信道下所经受的ISI、ICI和IBI。      

Jointly multi‐user detection and channel estimation with genetic algorithm

This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multi‐user channel estimation (MuChE) and detection problems at its maximum‐likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi‐user detection (MuD) show that the proposed genetic algorithm multi‐user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi‐user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near‐optimum multi‐user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi‐user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE–GAMuD scheme can be regarded as a promising alternative for implementing third‐generation (3G) and fourth‐generation (4G) wireless systems in the near future. Copyright © 2010 John Wiley & Sons, Ltd.     

Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems

A sparsity-adaptive channel estimation algorithm based on compressive sensing was proposed for massive MIMO systems when the number of channel multi-paths was unknown.By exploiting the joint sparsity characteristics of the sub-channels，the proposed block sparsity adaptive matching pursuit (BSAMP) algorithm first selected atoms by setting a threshold and finding the position of the maximum backward difference，which reduces the energy dispersion caused by the non-orthogonality of the observation matrix and improves the performance of the algorithm.Then a regularization method was utilized to improve the stability of the algorithm.Simulation results demonstrate that the proposed algorithm recovers the channel state information accurately and shows a high computational efficiency.     

应用加权高斯模型的非理想稀疏信道估计

在高速通信系统中,由于多径信道通常存在一些小的散射体,使得抽头向量不满足理想的稀疏特性,导致经典的稀疏估计算法存在一定的性能损失.针对上述非理想稀疏特性问题,提出了一种基于酉变换近似消息传递(Unitary Transform Approximate Message Passing,UT-AMP)和加权高斯(Weigh...     

Joint channel estimation and data detection in MIMO-OFDM using distributed compressive sensing

Channel impulse response of a multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) channel contains a smaller number of nonzero components. In addition, locations of nonzero taps coincide in delay domain. So channel impulse responses can be modeled into an approximately group sparse signals. In this work we use extended sparse Bayesian learning (ESBL), a new method for multichannel compressive sensing for channel estimation in MIMO-OFDM. In joint extended sparse Bayesian learning (JESBL), both pilot and data subcarriers are utilized for channel estimation. These methods can reduce the number of pilot subcarriers in OFDM and improve the spectral efficiency of the MIMO-OFDM system.     

多径信道下基于压缩扩张的OFDM系统PAPR抑制算法

OFDM系统中存在较大的峰均比(Peak-to-Average Power Ratio,PAPR),特别在多径信道中接收的信号严重失真,星座图恶化,严重影响了系统传输性能。针对该问题,对多径信道下OFDM系统PAPR抑制进行研究,采用频域均衡技术,提出了一种多径信道下基于压缩扩张的OFDM系统PAPR抑制算法。仿真结果表明,所提算法在抑制PAPR的同时能消除多径效应影响,避免信号失真和星座图恶化。对比传统算法,所提算法星座图EVM最大降低0.19,抑制PAPR能力提升1 dB。     

A Bayesian recovery technique with Toeplitz matrix for compressive spectrum sensing in cognitive radio networks

Compressive sensing has been proposed as a low‐cost solution for dynamic wideband spectrum sensing in cognitive radio networks. It aims to accelerate the acquisition process and minimize the hardware cost. It consists of directly acquiring a sparse signal in its compressed form that includes the maximum information using a minimum number of measurements and then recovering the original signal at the receiver. Over the last decade, a number of compressive sensing techniques have been proposed to enable scanning the wideband radio spectrum at or below the Nyquist rate. However, these techniques suffer from uncertainty due to random measurements, which degrades their performances. To enhance the compressive sensing efficiency, reduce the level of randomness, and handle uncertainty, signal sampling requires a fast, structured, and robust sampling matrix; and signal recovery requires an accurate and efficient reconstruction algorithm. In this paper, we proposed a method that addresses the previously mentioned problems by exploiting the Bayesian model strengths and the Toeplitz matrix structure. The proposed method was implemented and extensively tested. The simulation results were analyzed and compared to those of the 2 techniques: basis pursuit and orthogonal matching pursuit algorithms with Toeplitz and random matrix. To evaluate the efficiency of the proposed method, several metrics were used, namely, sampling time, sparsity, required number of measurements, recovery time, processing time, recovery error, signal‐to‐noise ratio, and mean square error. The results demonstrate the superiority of our proposed method over the 2 other techniques in speed, robustness, recovery success, and handling uncertainty.