高速移动通信系统中OTFS信道估计算法研究

针对高速移动环境中双色散信道会出现信道估计可靠性下降的问题,该文在正交时频空(OTFS)调制系统的输入-输出模型中提出一种基于压缩感知的信道估计算法。该算法利用信道中最大多普勒频移和最大时延确定导频发送矩阵的大小,相比传统的正交匹配追踪(OMP)信道估计算法,能够在保证相似信道估计准确度的情况下节省导频资源;并在此基础上,对OTFS调制符号做相位旋转,增加差分矩阵的秩,理论分析和仿真结果表明,该方案能够提升OTFS系统的分集阶数进而降低噪声的干扰。     

高速移动通信系统中OTFS信道估计算法研究

针对高速移动环境中双色散信道会出现信道估计可靠性下降的问题,该文在正交时频空(OTFS)调制系统的输入-输出模型中提出一种基于压缩感知的信道估计算法.该算法利用信道中最大多普勒频移和最大时延确定导频发送矩阵的大小,相比传统的正交匹配追踪(OMP)信道估计算法,能够在保证相似信道估计准确度的情况下节省导频资源;并在此基础上,对OTFS调制符号做相位旋转,增加差分矩阵的秩,理论分析和仿真结果表明,该方案能够提升OTFS系统的分集阶数进而降低噪声的干扰.     

OTFS系统中基于最小多普勒间干扰的导频设计

正交时频空(OTFS)调制系统能够改进时变频选信道的错误概率,但是目前只是简单地采用矩形导频。文中在OTFS系统中一种基于冲激的信道估计模型的基础上,优化其导频图案。在固定导频开销的约束下,最小化多普勒间干扰,也即是最大化被估计的信道平均功率。经论证,该优化问题可以转化为一个经典的背包问题,并可以通过贪心算法求解。仿真结果表明,文中经过优化的导频图案与原矩形导频图案相比,相同的导频开销下,具有更低的均方误差和误码率。     

基于Kalman滤波跟踪的MIMO-OFDM半盲信道估计

为克服常用的最小均方(LS)估计算法误差平底较高,且需要占用大量导频资源跟踪时变信道的缺点,提出一种基于Kalman滤波跟踪的MIMO-OFDM半盲的信道估计算法,该算法在采用少量导频的前提下,建立参数化信道模型,采用自适应Kalman滤波技术跟踪时变信道状态信息。仿真表明:该算法能很好地跟踪时变信道状态,显著降低多普勒频移条件下估计的均方误差。     

基于扩展路径识别算法的水声OFDM系统低复杂度迭代稀疏信道估计

移动OFDM水声通信系统中,基于压缩感知的稀疏信道估计方法计算量较大,不适用于实时通信.针对这一问题,该文基于一致多普勒信道模型提出一种扩展路径识别(GPI)算法.该方法首先使用信道多普勒扩展矩阵构造等效发射序列,将多普勒信道转化为等效线性时不变信道.然后使用GPI算法估计信道多普勒及各路径的时延及幅度参数,实现低复杂度稀疏信道估计.此外,该文将GPI算法扩展到Turbo接收机中,通过利用信道译码器反馈的数据符号先验信息迭代提高信道估计精度.仿真结果表明,所提方法的性能优于传统的路径识别算法,且与OMP算法接近,而其计算量远低于后者.     

基于扩展路径识别算法的水声OFDM系统低复杂度迭代稀疏信道估计

移动OFDM水声通信系统中,基于压缩感知的稀疏信道估计方法计算量较大,不适用于实时通信。针对这一问题,该文基于一致多普勒信道模型提出一种扩展路径识别(GPI)算法。该方法首先使用信道多普勒扩展矩阵构造等效发射序列,将多普勒信道转化为等效线性时不变信道。然后使用GPI算法估计信道多普勒及各路径的时延及幅度参数,实现低复杂度稀疏信道估计。此外,该文将GPI算法扩展到Turbo接收机中,通过利用信道译码器反馈的数据符号先验信息迭代提高信道估计精度。仿真结果表明,所提方法的性能优于传统的路径识别算法,且与OMP算法接近,而其计算量远低于后者。     

OFDM系统中一种新的导频辅助信道估计算法

无线OFDM系统中基于导频辅助的信道估计必须折衷考虑算法性能与算法复杂度。该文提出了一种新的信道估计算法。新算法利用维纳滤波与内插滤波相结合以降低信道估计的运算复杂度。分析和仿真结果显示,新算法在保持获得良好估计性能情况下,有效地降低了信道估计算法的复杂度。     

LTE系统中有关维纳滤波信道估计的性能分析

基于LTE系统下行信道中离散分布的导频符号,本文提出了一种时频维纳滤波信道估计方法。分别在频域和时域使用维纳滤波方法获得信道响应。这种方法有两种实现形式：一种是利用自相关矩阵计算导频部分的信道响应,再利用插值算法获得数据部分的信道响应,另一种是利用互相关矩阵直接计算导频和数据部分的信道响应。在瑞利衰落信道环境下进行MATLAB仿真,维纳滤波相比LS算法性能有很大提高,时频维纳滤波信道估计算法的性能优于LMMSE算法,自相关时频维纳滤波信道估计算法性能优于互相关时频维纳滤波信道估计算法。     

分布式多入多出正交频分复用快衰落感知估计

针对分布式多输入多输出正交频分复用(MIMO-OFDM)快衰落系统,为了消除快衰落信道的干扰,提出一种分布式MIMO-OFDM系统快衰落信道估计方法.该方法利用分布式快衰落信道间的感知信息在空间、时延和多普勒频移上具有的互相关性,求取三维互相关联合稀疏信道模型.推导预相关随机导频测量矩阵,结合分布式压缩感知算法,以能量有效的方式,对快衰落稀疏信道参数进行联合压缩测量和重构,得到各分布式MIMO接收端任意的稀疏信道系数.理论分析与仿真结果都表明,该方法与传统的信道估计方法相比,可以明显提高系统频谱效率和误码率,减少计算复杂度.     

基于模型驱动深度学习的OTFS信道估计

针对单输入单输出(SISO)的正交时频空间(OTFS)调制系统,该文利用一种模型驱动深度学习算法进行OTFS信道估计。该方案首先将去噪近似消息传递(DAMP)算法进行深度展开,利用去噪卷积神经网络代替传统的去噪器,对含噪的时延多普勒信道进行去噪估计,然后提供了状态演化方程来预测可学习去噪近似消息传递(LDAMP)算法的理论归一化均方误差性能。仿真结果表明,相比于其他估计方案,该方案不仅在低信噪比条件下具有优越的性能表现,而且还具有非常好的鲁棒性,在信道路径总数不变时,增加OTFS 2维网格点数量,可以有效提升信道估计精确度。     

Wiener-based smoother and predictor for massive-MIMO downlink system under pilot contamination

One of the challenges in massive-MIMO system is pilot contamination during the channel estimation process. Pilot contamination can cause error or inaccurate channel estimation process for future fifth generation (5G) downlink transmissions. This paper considers using a Wiener-based filter to smooth and predict the channel estimation to reduce the pilot contamination for more accurate CSI during channel estimation. The simulation results show that the Wiener-based smoothing and predicting technique reduces the effect of pilot contamination and increases the accuracy of CSI during channel estimation process. Wiener smoother (WS) is implemented based on Wiener-based filtering technique. The previous estimated CSI and weight coefficient vector are used to smooth the current estimated CSI by using block data formulation to reduce the effect of pilot contamination. However, WS technique suffers from pilot contamination due to pilot training. This motivates the development of two Wiener predictors (WP), known as WP1 and WP2. The WP1 and WP2 run a prediction technique for CSI and number of pilot training during the prediction period, which is missing from the original WS. Comparison results show that the proposed WS and WP outperforms the conventional minimum mean square error and least square, in terms of channel estimation error and per-cell rate. WP2 perform better than WS and WP1 because of the algorithm complexity that required more information to be updated, stored and processed for prediction. Thus, WP2 requires large computation and matrix operation compared to WS and WP1. The results indicate that the channel estimation error due to pilot contamination can be reduced by using the Wiener-based approaches.     

Multiple-Antenna Signaling Over Fading Channels With Estimated Channel State Information: Capacity Analysis

Multiple-antenna concepts for wireless communication systems promise high spectral efficiencies and improved error rate performance by proper exploitation of the randomness in multipath propagation. In this paper, we investigate the impact of channel uncertainty caused by channel estimation errors on the capacity of Rayleigh and Ricean block-fading channels. We consider a training-based multiple-antenna system that reserves a portion of time to sound the channel. The training symbols are used to estimate the channel state information (CSI) at the receiver by means of an arbitrary linear estimation filter. No CSI is assumed at the transmitter. Our analysis is based on an equivalent system model for training-based multiple-antenna systems which specifies the channel by the estimated (and hence, known) channel coefficients and an uncorrelated, data-dependent, multiplicative noise. This model includes the special cases of perfect CSI and no CSI. We present new upper and lower bounds on the maximum instantaneous mutual information to compute ergodic and outage capacities, and extend previous results to arbitrary (and possibly mismatched) linear channel estimators and to correlated Ricean fading. Several numerical results for single- and multiple-antenna systems with estimated CSI are included as illustration.     

Joint channel estimation and data detection for high rate orthogonal time frequency space systems

This paper proposes a new pilot pattern in the delay-Doppler (DD) domain for the orthogonal time frequency space (OTFS) system. In contrast to the embedded-pilot schemes, guard intervals are not used so as increase the spectral efficiency. Also, compared to the superimposed design where data symbols and pilots are arranged on the entire DD grid, in the proposed rearrangement, the number of pilots used is only spread over a subgrid of the DD grid. Hence, the interference of pilots with data symbols is reduced. Afterwards, an algorithm for channel estimation (CE) and symbol detection in the DD domain benefiting from the sparsity of the DD channel is designed. The sparse CE step is formulated as a specific marginalization of the maximum a posteriori (MAP) criterion by providing a Bayesian approach via the mean-field approximation that involves the variational Bayesian expectation maximization (VB-EM) algorithm. Detection of data symbols is done using a low complexity MP algorithm. We also propose an interference cancellation (IC) scheme to mitigate contamination of data by pilots that is run after each CE step. To achieve a high CE accuracy, based on the mean mutual incoherence property (MIP), a pilot optimization problem for OTFS is formulated and develop a simulated annealing-based algorithm to solve it. Finally, simulation results show that the proposed scheme achieves a good compromise between spectral efficiency, complexity, and performance in terms of bit error rate (BER) and normalized mean square error (NMSE) when compared to literature benchmarks.     

Efficient Rayleigh-Quotient Detector for Multi-layered Orthogonal STBC System

This paper investigates the detecting algorithm for MIMO system, in which the Multi-layered orthogonal space-time block code (OSTBC) is employed to transmit the data symbols. First, the matched filter design problem based on maximizing the SINR of each layer is investigated, which is characterized as a generalized eigen-value problem that can be efficiently solved by the Generalized Rayleigh-Quotient (R-Q) Theorem. Then a new efficient space time detecting algorithm is proposed for Multi-layered OSTBC with this R-Q matched filter, in which first a linear recombination strategy is applied on the received signal to construct a high-dimensional equivalent channel derived from the OSTBC encoding pattern, as a joint characterization of channel fading and OSTBC encoding. Then using the high-dimensional equivalent channel matrix, a group of R-Q matched filters are designed to effectively extract the receive signal of the desired layer and suppress the interference from other layers. Finally, linear decoding is used to detect the transmitted symbols by exploiting the OSTBC-induced structure in the equivalent channel matrix of each layer after interference cancellation. Furthermore, the proposed algorithm in presence of the channel estimation error is also analyzed. Theory analysis and simulation results reveal that the proposed algorithm has the benefits of good robustness, better BER and capacity characteristics.     

Rake接收机中信道最大多普勒频移估计的一种新方法及其在信道估计中的应用

Ｒａｋｅ接收机是ＣＤＭＡ系统中实现多径分集接收的核心部件,而信道估计对其接收性能有很大影响。传统连续导频符号的信道估计是采用对固定区间的信道参数估计值进行平均的方法以抑制接收噪声。     

大频偏卫星扩频信号的基带处理算法的FPGA实现

多普勒频偏一直是卫星通信中不可忽视的消极因素。针对多路混叠的卫星扩频信号下变频以后所产生的大多普勒频偏,在匹配滤波结构中采取改进的部分相关算法,在基于并行导频的信道估计中根据信道状况改变估计长度,实现了大频偏下卫星扩频信号的基带处理。     

Multiple-Antenna Signaling Over Fading Channels With Estimated Channel State Information: Performance Analysis

Multiple-antenna concepts for wireless communication systems promise high spectral efficiency and low error rates by proper exploitation of the randomness in multipath propagation. In this paper, we investigate the impact of channel uncertainty caused by channel estimation errors on the error rate performance. We consider a training-based multiple-antenna system that reserves a portion of time to sound the channel. Training symbols are used to estimate the channel by means of an arbitrary linear filter at the receiver. No channel state information (CSI) is assumed at the transmitter. We present a new framework to analyze training-based multiple-antenna systems by introducing an equivalent system model that specifies the channel by the estimated (and hence, known) channel coefficients and an uncorrelated, data-dependent, multiplicative noise. We derive the maximum-likelihood (ML) detector and highlight its behavior in the limiting cases of perfect CSI and no CSI, and its relation to several mismatched detectors. We deduce new exact expressions and Chernoff bounds of the pairwise error probability (PEP) used to assess word-error and bit-error rate bounds for ML and mismatched detection. Finally, we review the code design guidelines in terms of the deleterious effect of channel uncertainty for coherent and noncoherent signaling schemes, and present numerical results.