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.     

Effect of multipath channel models to the recovery algorithms on compressed sensing in UWB channel estimation

Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely the signal sparsity of the impulse response of the UWB channel that is exploited in this work aiming at UWB channel estimation based on Compressed Sensing (CS). However, these multipath arrivals mainly depend on the channel environments that generate different sparse levels (low-sparse or high-sparse) of the UWB channels. According to this basis, we have analyzed the two most basic recovery algorithms, one based on linear programming Basis Pursuit (BP), another using greedy method Orthogonal Matching Pursuit (OMP), and chosen the best recovery algorithm which are suitable to the sparse level for each type of channel environment. Besides, the results of this work is an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.     

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。      

Multiple access interference and multipath interference analysis of orthogonal complementary code‐based ultra‐wideband systems over multipath channels

In order to alleviate multiple access interference and multipath interference of ultra‐wideband (UWB) system, we propose the orthogonal complementary code (OCC)‐based direct‐sequence UWB system and offset‐stacking (OS)‐UWB system. OCC has perfect partial autocorrelation and cross‐correlation characteristics. With the application of OCC in UWB system, we can obtain better performance in multiple access interference and multipath interference. The proposed OS‐UWB structure can also achieve variable data rate transmission because of its innovative OS spreading technique. Theoretical analysis and simulation results show that the proposed UWB system can achieve excellent performance and outperform the unitary code‐based direct‐sequence UWB system. Copyright © 2013 John Wiley & Sons, Ltd.     

MIMO-OFDM快衰落信道的稀疏自适应感知估计

在多输入多输出（MIMO）-正交频分复用（OFDM） 系统中,怎样在较高频谱利用率的情况下对快时变信道进行较为准确的估计是一个具有挑战性的课题。该文在利用压缩感知理论可提高系统频谱利用率的基础上,提出了一种适合于快时变环境下MIMO-OFDM 系统的稀疏自适应信道估计方法。该方法不再受到奈奎斯特采样频率条件约束,避免了传统导频辅助信道估计方法频谱利用率低的缺点。该文方法通过构建多天线群时频结构特征稀疏基,利用多天线间和群时变OFDM符号内信道冲激响应具有更强稀疏性的特点,对MIMO-OFDM快衰落信道进行稀疏变换。由于实际MIMO-OFDM快衰落信道往往处于频率选择性、时变性和多种干扰并存的复杂环境,受到干扰的信道参数对系统而言是未知,采用该方法克服了现有基于压缩感知理论的信道估计方法需要预先知道信道冲激响应稀疏度才能重构信道参数的不足,在信道稀疏度未知道的情况下,运用稀疏自适应的方法来对不同时频结构特征的信道参数进行估计。仿真结果表明所提估计方法具有对快时变信道参数估计的鲁棒性和较高频谱利用率,且均方误差小。      

长时延扩展水声信道的联合稀疏恢复估计

对具有长时延扩展的水声信道,传统的信道估计算法如最小二乘法将在大量零值抽头产生严重的估计噪声,导致估计性能下降,同时信道估计时所需的较高估计器阶数大大提高了运算复杂度。压缩感知信道估计方法可有效利用多径稀疏特性改善性能,但需采用较大的训练序列长度以保证稀疏恢复精度,由此导致额外的系统开销。利用水声信道多径稀疏结构在数据块间存在的相关性,建立基于分布式压缩感知的长时延水声信道联合稀疏模型,从而可利用同步正交匹配追踪算法进行联合重构,以进一步减小系统的训练序列开销,提高估计性能。最后通过仿真和海上实验验证了所提方法的有效性。     

托普利兹矩阵在压缩多径信道估计中的应用

可靠的无线通信需要准确地知道下层信道的信息,因此需要进行信道估计。而许多真实信道表现为仅有一些相对较少的非零信道系数的稀疏多径信道。对于稀疏多径信道的估计,传统方法例如最小二乘法,没有利用稀疏信道本身的低维度特性,所需训练序列的长度较长,因此估计代价较大。基于压缩感知的信道估计方法,利用稀疏先验信息,能较大地缩短所需训练序列的长度,获得较好的估计效果。该文结合压缩感知观测矩阵的特点,证明了当训练序列的长度不长于信道冲激响应的长度,且托普利兹观测矩阵的行数小于列数时,观测矩阵仍然满足有限等距性质;明确提出了稀疏多径信道估计中所使用的观测矩阵的构造条件。实验结果验证了这种优化了的托普利兹观测矩阵的可行性和实用性。      

Performance evaluation of non‐Gaussian channel estimation techniques on ultrawideband impulse radio channels

We study the problem of adaptive channel estimation for a multipath channel on an ultrawideband (UWB) impulse radio (IR) system. The purpose of the work is to demonstrate that the statistical characteristics of the UWB‐IR environment are strongly non‐Gaussian and that as a result, non‐Gaussian signal processing techniques are both efficient and powerful in a UWB‐IR environment. The multipath channel estimation problem has been chosen as an illustrative example because UWB‐IR systems often operate in complex multipath environments. We study both blind and training‐based estimation techniques and demonstrate that even simple non‐Gaussian strategies can achieve significant performance improvement when compared to more common second‐order estimation techniques. Copyright © 2006 John Wiley & Sons, Ltd.     

Compressed sensing based deconvolution algorithm for time-domain UWB channel modeling

Extracting the parameters of the multipath with high accuracy can be achieved by using high-resolution algorithm for time-domain ultra wideband (UWB) channel modeling.The CLEAN algorithm has been used ...     

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.     

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。      

超宽带传输参考接收机的性能研究

分析了超宽带传输参考(TR)接收机的原理和优势,针对UWB通信应用在多个比特传输中信道基本不变的特点,将TR信号集合扩展为一帧几个参考信号和多个数据信号,提出了一种改进TR接收机。文中对基本TR接收机和改进TR接收机的性能进行了理论分析,并采用IEEE的CM1、CM2多径信道模型进行了性能仿真,结果表明,综合考虑性能、传输效率和成本,改进TR接收机优于基本TR接收机。     

基于毫米波通信的混合MIMO系统的信道估计

作为5G的一项关键技术,毫米波通信要求提出专有的信道估计和预编码算法,为此,针对毫米波MIMO系统的窄带平坦衰落信道研究下行信道估计方案.由于毫米波系统的稀疏特性,将稀疏多径信道的信道估计转化为稀疏信号的重建,基于压缩感知设计适用于毫米波通信系统的信道估计方案,深入研究了正交匹配追踪算法.仿真结果显示,它可以高概率地恢复信号,与传统的最小二乘法比较,能获得更好的信道估计性能.     

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.     

Support vector machine‐based equalisation for direct‐sequence ultra wideband systems

We proposed the support vector machine (SVM)‐based equalisation schemes for direct‐sequence ultra wideband (UWB) systems. The severe intersymbol interference caused by the UWB channel was formulated as a pattern classification problem in the SVM‐based equaliser, which operates in two main modes: training and detection. We also applied the least squares support vector classifiers (LS‐SVCs) to reduce the training complexity and sparse LS‐SVCs to reduce the detection complexity, with little performance loss compared to SVCs. Simulation results confirm the outperformance of the proposed equalisers over the conventional rake receiver with the same order of complexity for detection, especially when no channel information is known at the receiver. Also, the SVM‐based equalisers in the line‐of‐sight scenario provide a performance close to the case with additive white Gaussian noise only. Copyright © 2011 John Wiley & Sons, Ltd.     

一种稀疏增强的压缩感知MIMO-OFDM信道估计算法

 基于压缩感知(Compressed Sensing, CS)的信道估计可以达到减少导频的目的,但在频-时域信道矩阵到时延-多普勒域的稀疏变换中存在谱泄漏现象,影响了信道矩阵的稀疏性和估计的均方误差(MSE)性能。为此该文对信道的稀疏性进行研究,提出一种时域加窗的稀疏优化CS信道估计算法。通过对时域加窗,所提算法抑制了由离散截断导致的多普勒域泄漏,再据此设计出观测矩阵,以此方式增强信道在时延-多普勒域的稀疏性,并实现对稀疏的信道矩阵更为准确的重构,达到改善信道估计MSE性能的目的。仿真结果表明随信噪比的增大,加窗CS算法相比无窗CS算法有效改善了信道估计的性能。     

Sparse least mean fourth algorithm for adaptive channel estimation in low signal‐to‐noise ratio region

Both least mean square (LMS) and least mean fourth (LMF) are popular adaptive algorithms with application to adaptive channel estimation. Because the wireless channel vector is often sparse, sparse LMS‐based approaches have been proposed with different sparse penalties, for example, zero‐attracting LMS and L_p‐norm LMS. However, these proposed methods lead to suboptimal solutions in low signal‐to‐noise ratio (SNR) region, and the suboptimal solutions are caused by LMS‐based algorithms that are sensitive to the scaling of input signal and strong noise. Comparatively, LMF can achieve better solution in low SNR region. However, LMF cannot exploit the sparse information because the algorithm depends only on its adaptive updating error but neglects the inherent sparse channel structure. In this paper, we propose several sparse LMF algorithms with different sparse penalties to achieve better solution in low SNR region and take the advantage of channel sparsity at the same time. The contribution of this paper is briefly summarized as follows: (1) construct the cost functions of the LMF algorithm with different sparse penalties; (2) derive their lower bounds; and (3) provide experiment results to show the performance advantage of the propose method in low SNR region. Copyright © 2013 John Wiley & Sons, Ltd.     

Ultra‐wideband multiple‐access relay channel with correlated noises and its diversity analysis

In this study, we investigate a new and practical model, ultra‐wideband Slepian–Wolf multiple‐access relay channel (UWB‐SW‐MARC) with correlated noises at the relay and receiver, which includes two important models, that is, UWB‐SW multiple‐access channel with correlated noises and UWB relay channel with correlated noises, as its special cases. We derive a general rate region for UWB‐SW‐MARC and obtain an outer bound for this model and, thereby, prove two certain capacity theorems for two important and different classes of MARC. Also, we analyze outage probability and diversity gain as the practically important concepts in wireless communications and prove that adding a relay to the multiple‐access channel improves the diversity gain. Finally, we evaluate some results numerically and illustrate that the noise correlation coefficient plays an important role in determining the relay position, and show that the channel performance depends on the noise variances. Copyright © 2014 John Wiley & Sons, Ltd.     

Ranging With Ultrawide Bandwidth Signals in Multipath Environments

Over the coming decades, high-definition situationally-aware networks have the potential to create revolutionary applications in the social, scientific, commercial, and military sectors. Ultrawide bandwidth (UWB) technology is a viable candidate for enabling accurate localization capabilities through time-of-arrival (TOA)-based ranging techniques. These techniques exploit the fine delay resolution property of UWB signals by estimating the TOA of the first signal path. Exploiting the full capabilities of UWB TOA estimation can be challenging, especially when operating in harsh propagation environments, since the direct path may not exist or it may not be the strongest. In this paper, we first give an overview of ranging techniques together with the primary sources of TOA error (including propagation effects, clock drift, and interference). We then describe fundamental TOA bounds (such as the CramÉr–Rao bound and the tighter Ziv–Zakai bound) in both ideal and multipath environments. These bounds serve as useful benchmarks in assessing the performance of TOA estimation techniques. We also explore practical low-complexity TOA estimation techniques and analyze their performance in the presence of multipath and interference using IEEE 802.15.4a channel models as well as experimental data measured in indoor residential environments.      

智能反射面辅助MU-MISO系统宽带信道估计算法

为适应移动通信系统的宽带化应用,针对智能反射面（Intelligent Reflecting Surface,IRS）辅助多用户多输入单输出系统信道估计问题,提出了一种基于稀疏矩阵分解的改进正交匹配追踪（Orthogonal Matching Pursuit,OMP）算法。首先,考虑基站导频约束及IRS相移约束,建立一个压缩感知模型。然后,通过自适应地细化变换矩阵的分辨率,避免稀疏信号的细节丢失;再利用信道在不同子载波处共享的公共稀疏性,对多个子频带进行联合估计。最后利用级联信道的双结构稀疏性,将信道估计扩展至多用户场景。仿真结果表明,与传统压缩感知算法相比,所提算法能以较小的导频开销获得更高的准确性。