毫米波通信的低复杂度时变信道估计算法

当毫米波通信系统工作于时变信道场景中时,为解决通信用户快速位移导致的方位角计算误差和信道估计算法精度下降等问题,通常要消耗大量计算资源。因此,基于用户在快时变信道场景下的信号传输特征,首先在确定毫米波传输信号方位时采用对角加载方法,根据高斯白噪声先验信息得到对角加载方法的参数;然后采用对角带矩阵分解方法对所得到的信道干扰矩阵模型进行处理,从而降低信道估计与均衡算法的复杂度。仿真结果表明,所提算法能够使工作在时变信道条件下的通信系统以低于常规算法的运算复杂度处理系统中的干扰,且不降低传输性能。     

引入加权系数消除ICI的二次快时变信道估计算法

针对高速移动环境下由多普勒频移引起的子载波间干扰（inter-carrier interference,ICI）,提出一种引入加权系数消除ICI的二次快时变信道估计算法。该算法进行两次信道估计,第一次估计用于提供信道状态信息,在保证符号率的前提下消除ICI,第二次估计在ICI消除的情况下进行,可以提高信道估计精度;并对简化的并行干扰消除（parallel interference cancellation,PIC）算法进行改进,基于最小均方误差准则引入加权系数,使得ICI消除后的残余误差最小。仿真结果和理论分析达到很好的一致性,当信道信噪比为0 dB时,归一化均方误差（normalized mean square error,NMSE）性能增益最大约为0.0714,提高了快时变信道估计的精度。     

LTE-A系统中基于小区参考信号的信道估计算法

增强型长期演进（LTE-A）系统中,通常利用插值算法估计出所有数据位置的信道频率响应值。针对传统的线性最小均方误差（LMMSE）算法需要预先获取信道统计特性,矩阵实时求逆运算量大的问题,提出了一种改进的频域LMMSE信道估计插值算法。首先通过导频插值来增加虚拟导频,以此提升算法性能;然后借助时域内的信道能量比较集中的优势,给出了自相关矩阵和信噪比的近似估计方法;最后采用滑动窗方法进一步简化算法复杂度,从而完成频域LMMSE插值。仿真结果表明,所提算法总体性能优于线性插值以及基于离散傅里叶变换（DFT）的插值方法,且与传统LMMSE插值算法具有相近的误码率（BER）和均方误差（MSE）;但与传统算法相比,运算次数降低了98.67%,实现了算法性能与复杂度的较好折中,适用于实际的工程应用。     

一种改进的快速递推干扰抵消多用户检测算法

由于传统的MIMO-OFDM系统干扰消除多用户检测算法对错误传播和干扰消除顺序考虑的较少,提出一种采用逆向排列顺序的快速递推干扰消除多用户检测算法;该算法采用逆向检测顺序,选择用户数据相关程度最大的数据进行最后检测,得到信噪比最优的串行干扰消除用户排列,避免了错误传播;采用递推的方法实现最小均方误差(MMSE)检测器与排序的求取,避免了传统检测算法中的直接矩阵求逆运算,降低了算法的计算量;理论分析和实验结果表明,该算法在有效降低复杂度的同时,具有更好的系统容量性能和误码性能。     

基于改进OMP算法的毫米波大规模MIMO混合预编码设计

针对全连接单用户毫米波大规模MIMO系统,以最大化系统可达和速率为目标,提出一种基于改进的正交匹配追踪（orthogonal matching pursuit,OMP）算法的混合预编码方案。在既有的基于OMP算法的混合预编码基础上,首先,针对其迭代次数过多的问题,受多步长思想的启发,从阵列响应集合中选取与射频链路数目相等的最优的前多列矢量,从而求得模拟预编码矩阵;其次,针对其求逆运算复杂度高的问题,利用Hlder不等式及Schatten范数来逼近待优化的目标函数,从而求得最优的数字预编码矩阵。仿真结果表明,所提基于改进的OMP算法的混合预编码方案有效降低了运算复杂度,且在数据流数目与射频链数目相差较小时,其系统性能更优。     

矩阵求逆及其在北斗双星定位系统上的应用

矩阵理论不但是经典数学的基础,同时又是很有实用价值的数学理论,它是工程技术以及经济管理等领域的不可缺少的数学工具,其中逆矩阵又是矩阵理论中一个非常重要的概念,矩阵求逆运算在线性预测,误差控制码,图像处理及3D运算中很常见。计算机的广泛应用为矩阵理论的应用开辟了广阔的应用前景。为了提高运算速度,增强其性能,选择一种好的算法显得尤为重要。本文简单介绍了几种矩阵求逆方法,其中详细介绍了全选主元Gauss-Jordan快速求逆算法,进行了算法分析,并编程实现了用此种方法对矩阵进行求逆运算。在此基础之上介绍了矩阵求逆在北斗双星定位系统上的应用。基于双星导航定位系统存在的缺点介绍了利用3颗卫星的导航定位系统,它可以消除双星导航定位系统存在的两大缺点—用户位置易暴露和系统用户数量容易饱和。因此,为我国发展卫星导航定位系统提供了一种新思路。     

OFDM移动接收中的迭代干扰消除

针对正交频分复用系统在高速移动接收中存在快变衰落信道难以估计、子载波间干扰(ICI)过大的问题,提出基于迭代结构的信道估计算法和ICI消除算法。利用信道解码器输出判决信息,通过离散傅里叶变换实现信道估计,并对时变信道进行线性近似,以较低的计算复杂度消除子载波间的干扰。仿真结果表明,该方法可以对抗10%~15%的归一化多普勒频移。     

一种新型的动态矩阵控制算法及仿真研究

动态矩阵控制(DMC)中顺序开环控制增量的计算涉及到矩阵的求逆运算,计算量很大,不利于微处理机的在线应用。为减少运算量,使微机编程实现方便,并保持系统优良的控制性能和较强的鲁棒性,在深入理解动态矩阵控制算法的基础上引入了控制增量的期望衰减因子,以期望控制增量在控制时域长度内逐步接近于零。这一思想使得矩阵求逆运算成为数的求逆(倒数)运算,运算量大为减少,运算速度大大提高。基于这一思想提出两种设计方案,并通过计算机仿真研究,证实了算法的有效性。     

MIMO-OFDM系统自适应信道估计算法分析

在MIMO-OFDM系统中,信道参数估计对相干检测至关重要.现有的信道估计算法大多需要对大矩阵做求逆运算,计算复杂度高,或者需要预知信道统计信息.提出一种MIMO-OFDM系统的自适应信道估计算法,无需任何先验信道统计信息,就能够通过导频辅助和迭代的方法对时变信道状态参数进行即时跟踪与估计.仿真结果表明在MIMO-OFDM系统中,提出的基于LMS或RLS的自适应信道估计算法相比于传统的不考虑噪声的LS信道估计算法,MSE和BER等性能均有大幅提高,能够很好地抵御无线信道引起的多径效应的影响,适宜应用于MIMO-OFDM系统.     

一种MIMO-OTFS低复杂度信号检测算法

由于高速多径环境下频率色散现象严重破坏正交频分复用（OFDM）系统子载波正交性,为解决高速多径环境下的频率色散问题,正交时频空调制技术技术（OTFS）应运而生;而多输入多输出技术（MIMO）具有可以有效提升信道容量和系统可靠性的特点,文章采用了MIMO技术与OTFS技术结合的方法,推导了MIMO-OTFS系统中最大比合并信号检测算法（MRC）理论并加以仿真;针对MIMO-OTFS系统中MRC信号检测算法复杂度过高的情况,提出了一种低复杂度信号检测算法;该算法的基本思想是利用最大比合并方法对时延—多普勒网格中传输符号的接收多径分量进行提取和相干合并,以提高合并信号的信噪比,同时利用Cholesky矩阵分解理论,对信道增益矩阵进行求逆运算的优化,降低了算法复杂度,为MIMO-OTFS系统的实际应用做出贡献;     

Equalization and carrier frequency offset compensation for UWA-OFDM communication systems based on the discrete sine transform

The Zero Forcing (ZF) equalizer suffers from noise enhancement and high complexity due to direct matrix inversion. On the other hand, the Minimum Mean Square Error (MMSE) equalizer suffers from high complexity, and requires Signal-to-Noise Ratio (SNR) estimation to work properly. In this paper, we use the Discrete Sine Transform (DST) for Multiple-Input–Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) instead of the Discrete Fourier Transform (DFT). Moreover, we present a Joint Low-Complexity Regularized ZF (JLRZF) equalizer to perform both equalization and Carrier Frequency Offset (CFO) compensation, jointly, in Underwater Acoustic (UWA)-OFDM systems. This equalizer mitigates the noise enhancement problem by using a constant regularization parameter. It has low complexity as it is based on banded-matrix approximation. The whole proposed system is compared with that based on the DFT. Simulation results prove the superiority of the proposed system compared to the traditional one.     

Blind equalization using parallel Bayesian decision feedback equalizer

The purpose of this paper is to propose a new method for blind equalization using parallel Bayesian decision feedback equalizer (DFE). Blind equalization based on decision-directed algorithm, including the previous proposed Chen’s blind Bayesian DFE, cannot give the correct convergence without the suitable initialization corresponding to the small inter-symbol interference. How to find the suitable initialization becomes the key for the correct convergence. Here, the “start” vector with several states is used to obtain several channel estimates which are the initial channel estimates in proposed method. In these initial channel estimates, the best one which has converged toward the correct result in some degree must exist. The decision-directed algorithm for parallel blind Bayesian DFE is purchased from these initial channel estimates respectively. Evaluating the Bayesian likelihood which is defined as the accumulation of the natural logarithm of the Bayesian decision variable, the correct channel estimates corresponding to the maximum Bayesian likelihood can be found. Compared with Chen’s blind Bayesian DFE, the proposed method presents better convergence performance with less computational complexity. Furthermore, the proposed algorithm works satisfactorily even for channel with severe ISI and in-band spectral null, while Chen’s blind Bayesian DFE fails.     

Complex-bilinear recurrent neural network for equalization of adigital satellite channel

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values in the paper. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C-BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex-MLPNN equalizer. The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex-MLPNN equalizer.     

Iterative decision feedback equalizer with cyclic detection for DFT-S OFDM system

One of the most challenging problems in high-data-rate wireless transmission is to reduce inter-symbol interference (ISI) resulted from the time dispersion caused by multi-path propagation. To solve this problem, equalization is introduced. Generally, efficient decision feedback equalizer (DFE) holds better performance than linear equalizer for the cancellation of ISI without noise enhancement. However, its inherent error propagation has some limitation for further improvement. In fact, as shown later in simulation results, conventional frequency domain DFE (FD-DFE) almost cannot be used in Discrete Fourier Transform-Spread Orthogonal Frequency Division Multiplex (DFT-S OFDM) system in Evolved-UMTS (Universal Mobile Telecommunications System) Terrestrial Radio Access (E-UTRA) uplink transmission [3GPP, TR 25.814 V1.2.3. Physical layer aspects for evolved UTRA. May 2006 [release 7]]. Unique word (UW) is proposed in the literatures, as a known symbol sequence, can make error propagation beyond one FFT-block impossible since the last symbols of every block are always decided correctly. But the UW-based scheme is not appropriate for DFT-S OFDM, so it must be modified. This paper proposes a simple DFE-based equalization algorithm for DFT-S OFDM. Through cyclic detection and iteration, the new algorithm obtains a noticeable gain than conventional DFE, and is almost as effective as the modified UW-based methods but with much higher bandwidth efficiency. Furthermore, it does not need to change the frame structure of existing protocol.     

Accurate signal detection for BPSK-OFDM systems in time-varying channels

Binary phase shift keying (BPSK) orthogonal frequency division multiplexing (OFDM) has been adopted in the fourth-generation wireless communications. However, the real-valued property of BPSK-OFDM signals has not yet been utilized for channel equalization. In this work, this property is employed to develop a new equalizer based on the minimum mean square error (MMSE) criterion, leading to considerable enhancement in signal detection. In particular, the proposed equalizer is able to achieve 3 dB improvement over the existing MMSE equalizer in large signal-to-noise ratio region, and is more robust against channel estimation errors. Moreover, successive detection technique employed in the devised method is able to make use of diversity in time-varying channels, resulting in further performance enhancement. Additionally, the computational complexity of the proposed approach is analyzed. Simulation results are included to illustrate its performance superiority and computational attractiveness.     

循环保护前缀不足的OFDM通信系统的频域均衡

当循环保护前缀(CP)长度小于信道冲激响应长度时,正交频分复用(OFDM)通信系统的子载波间的正交性遭到破坏,接收信号存在符号间干扰(ISI)和子载波间干扰(ICI),频域单抽头均衡器不再适用。为解决这个问题,通常的方法是对接收信号先进行时域均衡,减少信道的有效冲激响应长度和恢复子载波间的正交性。但时域均衡器结构复杂,且其收敛性不能保证。为此,作者提出了一种频域均衡器,用来解决循环保护前缀不足的OFDM通信系统的频域均衡问题。频域均衡器的设计利用了OFDM系统的空闲子载波信息。该均衡器具有稀疏矩阵结构,因此计算量小。理论分析和计算机仿真表明:它能有效消除由于循环前缀不足引起的符号间干扰和子载波间干扰,从而较好地恢复传输信号。     

基于深度神经网络的高速信道自适应均衡器

高速串行接口是提高高性能互连网络带宽的关键技术,而信道均衡器则是提高信号完整性的核心部件。利用现代数字信号处理(DSP)结构,提出了基于深度神经网络(DNN)的高速信道均衡研究方法,此方法在面向未来50 GB以上的高速信道时,克服了传统判决反馈均衡器(DFE)的判决速度受限于反馈回路的固有缺陷问题。仿真结果表明,在采用PAM4编码方式,高速信道波特率为28 GB,信道损耗为15 dB,或者波特率为56 GB,信道损耗为30 dB时,与传统的15阶FFE组合2阶DFE的均衡器结构相比,本文所提出的3层DNN结构,具有更好的均衡效果,以及更快的均衡收敛速度。     

ST OFDM载波间干扰线性均衡技术

分析了频率非选择性衰落信道下STOFDM系统载波间干扰的具体内容,利用连续发射已知ST-OFDM符号来估计ST-OFDM中各个子载波对其他子载波的干扰,然后通过特定的线性均衡方法达到STOFDM载波间干扰的抑制。在莱斯衰落信道下的仿真结果表明,选择6个相邻子载波12个干扰系数的均衡技术ST-OFDM系统可获得至少10dB的信干比增益,可以使STOFDM系统在性能和运算量上获得最佳。     

Equalization of nonlinear time-varying channels using type-2 fuzzyadaptive filters

Presents a kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Two structures are used for the equalizer: transversal equalizer (TE) and decision feedback equalizer (DFE). A decision tree structure is used to implement the decision feedback equalizer, in which each leaf of the tree is a type-2 FAF. This DFE vastly reduces computational complexity as compared to a TE. Simulation results show that equalizers based on type-2 FAFs perform much better than nearest neighbor classifiers (NNC) or equalizers based on type-1 FAFs     

Unified low-complexity decision feedback equalizer with adjustable double radius constraint

The maximum likelihood sequence estimation (MLSE) is an optimal equalization method to suppress Inter-Symbol Interference (ISI) in communication and storage systems. The Viterbi Algorithm (VA) provides an exact solution of MLSE. To reduce the complexity of VA, MLSE-DFE, which combines the VA within a decision feedback equalizer (DFE), is widely used in practical designs; however, the computing complexity is still too high. In this paper, we propose the SDVA-DFE, a unified DFE combining the concept of sphere detector (SD) and VA. The computing complexity of the SDVA-DFE can be reduced by proposed double radius constraints, upper radius (UR) and lower radius (LR). By adjusting the values of the two radiuses, the SDVA-DFE also provides a trade-off between performance and complexity. Simulation shows that this method is suitable for high-order modulation and long-length channel impulse response. When applied to a Lorentzian channel and channels of different eigenvalue spread, the SDVA-algorithm can reduce the complexity by over 90% at high SNR compared with MLSE-DFE.