基于贝叶斯压缩感知的子空间拟合离格DOA估计

针对传统的基于稀疏贝叶斯学习（Sparse Bayesian Learning,SBL）的波达方向估计算法对噪声鲁棒性不高的问题,提出了一种基于SBL的子空间拟合离格波达方向(Direction of Arrival,DOA)估计方法。首先对接收数据的协方差矩阵进行特征分解,获得信号的加权子空间,构造等价信号的稀疏表示模型并利用贝叶斯学习算法进行参数求解。同时对于网格划分带来的建模误差问题,采用了离格贝叶斯推导（Sparse Bayesian Inference,SBI）算法进行求解,利用期望最大化算法迭代更新相应的参数。仿真结果表明,相对于传统的DOA方法,该方法具有更好的估计精度。     

Peak‐to‐average power ratio reduction of a hidden training sequence‐aided precoding scheme for MIMO‐OFDM

We analyze a peak‐to‐average power ratio (PAPR) reduction property based on a hidden training sequence‐aided precoding scheme for MIMO‐OFDM systems. In addition to the benefits of a hidden training sequence‐aided precoding scheme such as improvement in bandwidth efficiency and frequency diversity gain, we address that power amplifier efficiency can be improved without any additional complexity burden. By mathematically analyzing PAPR of the precoded MIMO‐OFDM signal with a hidden training sequence, we demonstrate that PAPR reduction can be obtained by varying the allocated power to the hidden training sequence. Because of the low PAPR property of this scheme, it is possible to utilize a low‐cost power amplifier, resulting in the reduction in the total cost for hardware implementation. Copyright © 2014 John Wiley & Sons, Ltd.     

Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning

A rapid off-grid DOA estimating method of RV-OGSBL was raised based on unitary transformation,against the problem of traditional sparse Bayesian learning (SBL) algorithm in solving effectiveness of signal’s DOA estimation under condition of lower signal noise ratio (SNR).Actual received signal of uniform linear array was generated through constructing augment matrix as the processing signal used by DOA estimation.Then,estimation model was transformed from complex value to real value by using unitary transformation.In the next step,off-grid model and sparse Bayesian learning algorithm were combined together to process the realization of DOA estimation iteratively.The accuracy of estimation could made relatively high.The simulation result demonstrates that the RV-OGSBL method not only maintains the performance of traditional SBL algorithm,but also reduces the computational complexity significantly.Under the situation of lower signal noise ratio (SNR) and low number of snapshots,the running time of algorithm is reduced about 50%.This shows the RV-OGSBL method is a rapid DOA estimation algorithm.     

ICI problem and compensation in MIMO SC‐FDMA system with SC‐SFBC scheme

In this paper, we address the ICI (intercarrier interference) problem and compensation in MIMO (multiple input multiple output) SC‐FDMA (single carrier frequency division multiple access) system that exploits SC‐SFBC (single carrier‐space frequency block coding) scheme. Recently, SC‐FDMA technique has received more attention due to the low PAPR (peak to average power ratio) property. However, SC‐FDMA system is sensitive to phase noise and CFO (carrier frequency offset) which is unavoidable in wireless communication systems. Phase noise and CFO introduce CPE (common phase error) as well as ICI into the received signal and seriously degrade the system performance. Therefore, analysis and suppression of these interferences are of great importance. In this paper, we analyze the interferences in MIMO SC‐FDMA system with SC‐SFBC. Then a new ICI estimation and suppression method is proposed to suppress the interferences. Instead of directly estimating the CFO and phase noise, which is pretty difficult and complex, this algorithm exploits block‐type pilots, which is a common pilot pattern in wireless communication systems, such as LTE standard, to estimate the interferences. After that the interferences are suppressed by the inverse matrix method. Simulation results show that the system performance is significantly improved. Copyright © 2010 John Wiley & Sons, Ltd.     

Peak‐to‐average power ratio reduction in space–time coded MIMO‐OFDM via preprocessing

In this paper, we propose a trellis exploration algorithm based preprocessing strategy to lower the peak‐to‐average power ratio (PAPR) of precoded MIMO‐OFDM. We first illustrate the degradation in PAPR due to optimal linear precoding in MIMO‐OFDM systems. Then we propose two forms of multi‐layer precoding (MLP) schemes to reduce PAPR. In both schemes, the inner‐layer precoder is designed to optimize system capacity/BER performance. In the first MLP scheme (MLP‐I), a common outer‐layer polyphase precoding matrix is employed. In the second MLP scheme (MLP‐II), data stream corresponding to every transmit antenna is precoded with a different outer‐layer polyphase precoding matrix. Both outer‐layer precoders are custom designed using the trellis exploration algorithm by applying the aperiodic autocorrelation of OFDM data symbols as the metric to minimize. Simulation results indicate that both MLP schemes show superior PAPR performance over conventional MIMO‐OFDM with and without precoding. In addition, MLP better exploits frequency diversity resulting in BER performance gains in multi‐path environments. Copyright © 2010 John Wiley & Sons, Ltd.     

基于方差成分扩张压缩的稀疏贝叶斯ISAR成像方法

基于贝叶斯框架下的稀疏重构方法,由于考虑了稀疏信号的先验信息以及测量过程中的加性噪声,因而能够更好地重建目标系数,然而传统的稀疏贝叶斯学习(SBL)算法参数多,时效性差。该文考虑一种新的稀疏贝叶斯学习方法方差成分扩张压缩(ExCoV),其不同于SBL中赋予所有的信号元素各自的方差分量参数,ExCoV方法仅仅赋予有重要意义的信号元素不同的方差分量,并拥有比SBL方法更少的参数。基于计算机层析成像技术框架下的ISAR成像模型,该文将ExCoV方法结合压缩感知(CS)理论将其进行ISAR成像,并从适用性和成像效果等方面与常用的极坐标格式算法(PFA),卷积逆投影算法(CBPA)和传统的稀疏重构算法进行比较,点目标仿真结果表明基于ExCoV的方法得到的ISAR像具有低旁瓣,高分辨率的特点,真实数据的成像结果表明该方法是一种比SBL更有效的ISAR成像算法。     

基于稀疏贝叶斯学习的多跳频信号DOA估计方法

针对多跳频信号空域参数估计问题,该文在稀疏贝叶斯学习(SBL)的基础上,利用跳频信号的空域稀疏性实现了波达方向(DOA)的估计。首先构造空域离散网格,将实际DOA与网格点之间的偏移量建模进离散网格中,建立多跳频信号均匀线阵接收数据模型;然后通过SBL理论得到行稀疏信号矩阵的后验概率分布,用超参数控制偏移量和信号矩阵的行稀疏程度;最后利用期望最大化(EM)算法对超参数进行迭代,得到信号矩阵的最大后验估计以完成DOA的估计。理论分析与仿真实验表明该方法具有良好的估计性能并能适应较少快拍数的情况。     

降低MIMO-OFDM系统峰均比的改进PTS算法

多输入多输出频分复用技术是将OFDM与MIMO技术相结合的无线通信系统,也具有OFDM系统存在的峰均比较高的问题。分析了MIMO-OFDM系统模型及PAPR问题,在次优PTS算法的基础上,提出了迭代限幅PTS联合算法。该算法先将MIMO-OFDM信号经过迭代PTS算法进行处理,然后再将其经过限幅处理进一步降低系统的PAPR。最后在自适应PTS算法的基础上进行了改进,仿真结果表明系统复杂度和PAPR性能有所降低,但可以通过调整门限来提高性能。     

基于稀疏贝叶斯方法的脉间捷变频ISAR成像技术研究

传统捷变频成像方法具有高旁瓣、低分辨率的缺点。鉴于捷变频ISAR回波信号的稀疏性,该文基于原始数据的2维压缩感知方案,在贝叶斯原理框架下,用稀疏贝叶斯算法方差成分扩张压缩方法(ExCoV)实现捷变频ISAR像的重建。贝叶斯框架下的稀疏重构算法考虑了稀疏信号的先验信息以及测量过程中的加性噪声,因而能够更好地重建目标系数。作为一种新的稀疏贝叶斯算法,ExCoV不同于稀疏贝叶斯学习(SBL)算法中赋予所有的信号元素各自的方差分量参数,ExCoV方法仅仅赋予有重要意义的信号元素不同的方差分量,并拥有比SBL方法更少的参数,克服了SBL算法参数多时效性差的缺点。仿真结果表明,该方法能克服传统捷变频成像缺点,并能够实现低信噪比条件下的2维高精度成像。     

Compressed sensing channel estimation for STBC‐SM based hybrid MIMO‐OFDM system for visible light communication

This paper presents the idea of sparse channel estimation using compressed sensing (CS) method for space–time block coding (STBC), and spatially multiplexing (SM) derived hybrid multiple‐input multiple‐output (MIMO) Asymmetrically clipped optical‐orthogonal frequency division multiplexing (ACO‐OFDM) optical wireless communication system. This hybrid system accounts multiplexing gain of SM and diversity gain of STBC technique. We present a new variant of sparsity adaptive matching pursuit (SaMP) algorithm called dynamic step‐size SaMP (DSS‐SaMP) algorithm. It makes use of the inherent and implicit structure of SaMP, along with dynamic adaptivity of step‐size feature which is compatible with the energy of the input signal, thus the name dynamic step size. Existing CS‐based recovery algorithms like orthogonal matching pursuit, SaMP, adaptive step‐size SaMP, and proposed DSS‐SaMP were compared for hybrid MIMO‐ACO‐OFDM visible light communication system. The performance analysis is demonstrated through simulation results with respect to bit error rate, symbol error rate, mean square error, computational complexity, and peak‐to‐average power ratio. Simulation results show that the proposed technique gives improved performance and lesser computational complexity in comparison with conventional estimation algorithms.     

基于稀疏贝叶斯学习的稀疏信号 表示ISAR成像方法

作为一种新的稀疏信号表示算法,SBL(稀疏贝叶斯学习)方法没有BP方法的结构错误,也比FOCUSS方法具有少的多的局部最小点.ISAR成像问题可以转化为稀疏信号表示的问题,因此本文首次将SBL用于ISAR成像.真实数据的成像结果表明SBL是一种比BP和FOCUSS更有效的ISAR成像算法.     

Blind coding classification in the presence of interference in MIMO systems using ML algorithm

This paper presents a framework for coding classification in multiple‐input multiple‐output (MIMO) systems in the presence of inter‐user interference (IUI). This framework is performed at the receiver beginning with a signal separation step. The signal separation is implemented with a multi‐user kurtosis (MUK) algorithm. The classification step estimates the code parameter (CP) using the maximum‐likelihood (ML) method applied to the covariance matrix of the received signal without a priori knowledge about the transmitted signal. Experimental results show that the proposed coding classifier is easy to implement and efficient for the classification of the CP over Rayleigh fading channels in the presence of time and frequency offsets. Furthermore, the success rate of code classification is high at low signal‐to‐noise ratios (SNRs). The signal separation increases the probability of true classification.     

The p -Sphere Encoder: Peak-Power Reduction by Lattice Precoding for the MIMO Gaussian Broadcast Channel

A vector precoding scheme has been recently proposed for the multiple-input multiple-output (MIMO) Gaussian broadcast channel. This technique minimizes the instantaneous transmitted power of linearly precoded signals, but does not take into account the peak-to-average-power ratio (PAPR). In this paper, we present a modified vector precoding scheme, where a perturbation vector is chosen in order to take into account both the instantaneous power and the instantaneous peak power of the transmitted signal. This is obtained by minimizing the p-norm of the transmitted signal for pges2. For this reason, the scheme is referred to as the p-sphere encoder. We demonstrate the performance of the proposed scheme applied to a MIMO independent, identically distributed Rayleigh fading downlink channel with simple channel inversion. We show that by choosing pges2, the proposed p-sphere encoder yields a tradeoff between power efficiency, PAPR reduction, and complexity. In particular, with eight antennas, the proposed algorithm significantly reduces the tail of the PAPR distribution, with very small degradation in terms of bit-error rate with respect to standard vector precoding     

基于空域稀疏性的自适应频谱检测算法

现有的频谱检测算法没有充分利用信号在角度维的稀疏性质。该文根据角度维的稀疏特性建立信号模型,通过稀疏贝叶斯学习(Sparse Bayesian Learning, SBL)算法解决稀疏信号的重构问题,并在迭代过程中引入二元假设检验思想,推导出一种自适应门限的选取策略,把传统的重构算法转化为一个针对不同来波方向的信号检测问题。该算法能够在恒虚警概率下对多信号进行全盲检测,同时实现信号来波方向的精确估计。实验结果证明,自适应判决方法能够有效地提高稀疏重构算法的重构精度,降低运算复杂度,参数估计精度和信号检测性能相比于现有算法得到明显的提升。     

Direction-of-Arrival Method Based on Randomize-Then-Optimize Approach

The direction-of-arrival(DOA) estimation problem can be solved by the methods based on sparse Bayesian learning(SBL). To assure the accuracy, SBL needs massive amounts of snapshots which may lead to a huge computational workload. In order to reduce the snapshot number and computational complexity, a randomizethen-optimize(RTO) algorithm based DOA estimation method is proposed. The “learning” process for updating hyperparameters in SBL can be avoided by using the optimization and Metropolis-Hasti...     

A partial transmit sequence technique with error correction capability and low computation

Orthogonal frequency division multiplexing (OFDM) is a popular transmission technique in wireless communication. Although already widely addressed in many studies, OFDM still has flaws, one of which is the occurrence of high peak‐to‐average power ratio (PAPR) in the transmission signal. The partial transmit sequence (PTS) technique is one method adopted to reduce high PAPR in OFDM systems. However, as PTS utilizes phase factors to generate multiple candidate signals, large amounts of calculation and time are required to search the candidate signal with the minimal PAPR, which will then be adopted as the final transmission signal. This paper proposes a novel PAPR reduction method, which can be applied in OFDM systems with M‐ary phase‐shift keying modulation. It not only requires less computation but also possesses error correction capabilities. More precisely, the proposed method is to divide a block‐coded modulation code into the direct sum of a correcting subcode for encoding information bits and a scrambling subcode for generating phase factors. Our proposed method is a suboptimal technique with low computation, because it uses a genetic algorithm with a partheno‐crossover operator as the transmitted signal selection mechanism. Simulation results show our proposed method has better PAPR performance than the GA‐PTS scheme. Based on the simulation results in Figures 5 and 6, it is evident that our proposed method can be employed in any OFDM system by using M‐PSK modulation.Copyright © 2013 John Wiley & Sons, Ltd.     

The$p$-Sphere Encoder: Peak-Power Reduction by Lattice Precoding for the MIMO Gaussian Broadcast Channel

A vector precoding scheme has been recently proposed for the multiple-input multiple-output (MIMO) Gaussian broadcast channel. This technique minimizes the instantaneous transmitted power of linearly precoded signals, but does not take into account the peak-to-average-power ratio (PAPR). In this paper, we present a modified vector precoding scheme where a perturbation vector is chosen in order to take into account both the instantaneous power and the instantaneous peak power of the transmitted signal. This is obtained by minimizing the$rho$-norm of the transmitted signal, for$rhogeq 2$. For this reason, the scheme is referred to as the$rho$-sphere encoder. We demonstrate the performance of the proposed scheme applied to a MIMO i.i.d. Rayleigh fading downlink channel with simple channel inversion. We show that by choosing$rhogeq 2$, the proposed$rho$-sphere encoder yields a tradeoff between power efficiency, PAPR reduction, and complexity. In particular, with eight antennas, the proposed algorithm significantly reduces the tail of the PAPR distribution with very small degradation terms of bit-error rate with respect to standard vector precoding.     

A novel simplified tone reservation combined with cross antenna rotation and inversion to reduce PAPR for MIMO‐OFDM system

In this paper, we first propose a simplified tone reservation (STR) method with low computational complexity which is based on the Fourier series expansion. Then, we analyze how to combine the STR method with the cross antenna rotation and inversion method to reduce the peak‐to‐average power ratio (PAPR) for multi‐input multi‐output orthogonal frequency division multiplexing (MIMO‐OFDM) system. To validate the analytical results, extensive simulations are conducted and the numerical results show the efficiency of the proposed schemes including the PAPR reduction and low computational complexity for MIMO‐OFDM system. Copyright © 2010 John Wiley & Sons, Ltd.     

用于SFBC MIMO-OFDM系统的改进SLM算法

在空频编码(SFBC)多输入多输出正交频分复用(MIMO-OFDM)系统中传输符号存在较高峰均功率比(PAPR)问题,采用SLM算法能够有效降低系统峰均功率比,但随着发射天线数的增加,较多的快速傅里叶反变换(IFFT)会增加系统的计算复杂度,因此,构造F矩阵并提出了一种基于F矩阵SFBC MIMO-OFDM系统的改进SLM算法。采用F矩阵作为相位序列组对空频编码信号进行独立处理,获得最优相位序列取共轭,将共轭序列中每两个旋转因子为一个单位交换位置,并扰码SFBC后各天线的信号,以此减少了每根发射天线上的IFFT次数。理论和MATLAB仿真分析表明,该算法获得了良好的峰均比性能,同时也降低了系统的计算复杂度。     

基于快速SBL的双基地ISAR成像

针对稀疏孔径条件下双基地ISAR成像分辨率低、运算时间长等问题,提出了一种基于快速稀疏贝叶斯学习的高分辨成像算法。首先,建立基于压缩感知的双基地ISAR稀疏孔径回波模型,然后将整个二维回波数据进行分块处理,并假设目标图像各像元服从高斯先验,建立稀疏贝叶斯模型,再利用快速边缘似然函数最大化方法求解得到高质量目标图像,最后将所求的每块回波对应的目标图像合成整个二维图像。由于采取了分块处理,在每块图像重构时减少了数据存储量和计算量。另外,相比于传统的稀疏贝叶斯学习求解方法,本文所提快速算法在保证重构质量的同时进一步缩短了运算时间,仿真实验验证了算法的有效性和优越性。