MIMO-OFDM系统中基于变分贝叶斯EM算法的联合符号检测与信道估计

基于变分贝叶斯期望最大化(VBEM, variational Bayes expectation maximization)算法和Turbo原理,提出了时变信道条件下MIMO-OFDM系统中的联合符号检测与信道估计算法.设计的软入软出空时检测器在采用列表球形译码避免穷尽搜索的同时,考虑了信道估计误差方差矩阵的影响;利用空时检测获得的发送信号后验概率分布估计,推出了新的Kalman前向后向递归信道估计器.仿真结果表明,在时变多径信道条件下,提出的算法比传统EM算法和面向判决算法更加具有顽健性.     

一种频选衰落信道下的Turbo多用户检测算法

联合MAP多用户检测与信道解码的迭代多用户检测(MUD)技术可显著提高宽带移动CDMA系统的容量和性能.在多径时变衰落的编码信道下,提出一种迭代实现干扰抑制、符号估计、信道解码的Turbo多用户检测算法.在每次迭代中,MUD自适应地实现干扰抑制并输出符号估计的软信息,软输入软输出的信道解码器使用LOG MAP方法实现信道解码并反馈符号估计的软信息作为下一次TurboMUD迭代的先验信息.仿真结果证实了该算法在频选衰落信道下经两次迭代就能逼近单用户编码CDMA系统的接收性能.     

时变条件下MIMO-OFDM系统中的信道估计算法

本文设计了时变多径衰落条件下MIMO-OFDM系统中一种新的信道估计算法.该算法结合递归EM算法和Kalman预测对时变信道进行跟踪.借助软球形译码器(List Sphere Decoder,LSD)产生的搜索列表,递归EM算法序贯遍历搜索列表中可能的符号组合来估计各个子载波上的信道频率响应;基于获得的信道频率响应估计,Kalman预测器利用衰落信道的时域二阶统计特性进一步跟踪信道时变.仿真结果表明:本文设计的算法可以有效跟踪信道时变,性能优于传统的软输入Kalman滤波算法.     

LDPC编码慢跳频系统中的一种信道状态估计算法

采用信道编码技术能提高跳频系统的抗干扰能力,而精确的信道状态信息有利于提高系统误码率性能。基于和积译码过程,提出一种新的迭代信道估计译码算法,把译码后验信息反馈给信道节点并重新计算信道状态概率,生成新的对数似然比作为下一次迭代的先验消息,而且信道估计与编码信息在迭代中交替更新。仿真结果表明在部分频带干扰的LDPC编码慢跳频系统中,该算法性能优于传统的门限检测法,而且每个跳频时隙只需要较少符号就能够接近有精确信道状态信息的情况。     

基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡

针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法。基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计。基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能。进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性。     

一种有效的MIMO-OFDM系统MAP信道估计

对于MIMO-OFDM系统,最大后验概率(MAP)信道估计算法可通过期望最大化(EM)算法降低计算复杂度,但将产生误差平底(error floor)现象。并且,系统的数据传输效率受限于发送端天线的数目。针对这些问题,该文提出了一种有效的MAP信道估计算法,并分析了算法的性能。所提算法在利用EM算法减小MAP 算法计算复杂度的基础上,利用角域内信道间的独立性降低估计误差。为改善系统数据传输效率及估计性能,通过多个OFDM符号进行联合的信道估计。仿真实验验证了所提算法拥有更好的估计性能和数据传输效率。     

基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡

针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法.基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计.基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能.进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性.     

一种LDPC编码慢跳频系统迭代信道估计译码算法

低密度奇偶校验码(LDPC, Low Density Parity Check)的和积译码算法(SPA, Sum-product Algorithm)在加性高斯白噪声信道中具有很好的译码性能,但需要已知信道状态信息。提出了一种在部分频带干扰条件下LDPC编码慢跳频(SFH, Slow Frequency Hopping)系统的迭代信道估计译码算法。该算法利用迭代译码过程中产生的比特后验信息作为信道估计器的先验信息辅助信道估计,进而更新下一次译码的初始信息。仿真结果显示,该算法性能逼近已知信道信息时的性能,而且每跳所含的符号数很少,不需要插入导频,增大了传输功率效率。另外,相对于SPA算法运算量增加不大,实现简单。     

一种OFDM导频的信道估计算法的研究

提出了一种基于编码的OFDM系统的导频符号迭代辅助信道估计。利用信道解码器中的APP符号来形成虚拟的导频。与原有的信道估计算法相比,此种算法不仅在一般的信道条件下具有良好的性能,而且更加适合快变信道条件下的OFDM系统。仿真结果表明:提出的OFDM信道估计算法不仅可以给出精度较高的信道信息,而且近似达到EM信道估计的性能。     

多径信道下的软序列检测

本文提出一种基于软信息的序列检测算法,建立了状态转移条件下软信息的递推方程,并给出了计算不同时刻符号软信息的关系表达式.这种软序列检测算法与多径信道估计器配合,可以有效地解调发送符号序列.与传统的MLSE解调算法相比,软信息检测需要两个级连解调器,通过前后迭代来为每个发送符号计算边信息,因此误码率会得到很大的改善.此外,前端的信道估计可以由RLS算法及Kalman滤波器联合实时估计.仿真结果显示:软序列检测算法尽管在复杂度上增加了,但是误码性能远优于ML最佳准则下的解调算法.     

Code-aided ML joint synchronization and channel estimation for downlink MC-CDMA

In this paper, we present a novel code-aided joint synchronization and channel estimation algorithm for downlink multicarrier code-division multiple access. The expectation-maximization algorithm is used to locate the maximum-likelihood estimate of the channel impulse response, propagation delay, and carrier frequency offset. The estimator accepts soft information from the decoder in the form of a posteriori probabilities of the coded symbols, and can be interpreted as performing joint estimation and data detection. The performance of the proposed algorithm is verified through computer simulations. Impressive performance gains are visible as compared with a conventional data-aided estimation scheme.     

Turbo Equalization with Prediction and Iterative Estimation of Time-Varying Frequency-Selective Channels

Turbo equalization that cooperates with channel prediction and iterative channel estimation is investigated for mobile wireless communications. Frames of information bits are encoded, interleaved, and mapped to symbols for transmission over time-varying frequency-selective fading channels. At the receiver, the Turbo equalizer consists of a maximum a posteriori probability equalizer/demapper and a soft-input soft-output maximum a posteriori probability decoder. With initial channel estimates and sparse pilot insertion across a number of frames, the receiver predicts the channel of the current frame. The effect of error propagation of channel prediction is mitigated by the de-interleaver that is embedded in the Turbo equalizer. The predicted and interpolated channel is refined through a channel estimator that uses the soft estimates of data symbols at each Turbo iteration. Due to the bandlimiting feature of channel variation, the channel estimation error can be smoothed by low-pass filters that follow the channel estimator. Simulation results show that incorporating Turbo equalization with channel prediction and iterative channel estimation can combat time- and frequency-selective fading and improve reception performance.     

Soft Decoding Assisted SNR Estimation Under Block Fading Channels for Orthogonal Modulations

The performance of the coded orthogonal modulation (OM) system under slow fading channels heavily depends on the estimation of the signal-to-noise ratio (SNR), including the fading amplitude and the noise spectral density. However, a relatively long packet of pilot symbols is often required to guarantee the accuracy of the SNR estimation, which makes it impractical in some situations. To address this problem, this paper proposes an iterative SNR estimation algorithm using the soft decoding information based on the expectation-maximization algorithm. In the proposed method, a joint iterative loop between the SNR estimator and decoder is performed, where the extrinsic information generated by the soft decoder is employed to enhance the estimation accuracy and the SNR estimated by the estimator is used to generate the soft information to the decoder. Also, no pilot symbols are needed to estimate the SNR in the proposed estimator. The Cramer–Rao lower bound (CRLB) of fully data-aided (FDA) estimation is derived to works as the final benchmark. The performance of the proposed algorithm is evaluated in terms of the normalized mean square errors (NMSEs) and the bit error rates (BERs) under block fading channels. Simulation results indicate that the NMSE of the proposed estimator reaches the CRLB of the FDA estimator and outperforms that of the approximate ML (ML-A) estimator proposed by Hassan et al. by 4.1 dB. The BER performance of coded OM system with the proposed estimation algorithm is close to the ideal case where the channel fading and the noise spectral density are known at the receiver.     

An iterative receiver for turbo-coded pilot-assisted modulation infading channels

We introduce an iterative joint channel and data estimation receiver that exploits both the power of pilot-symbol assisted modulation (PSAM) and turbo coding for fading channels. The key innovation is a low-complexity soft channel estimator which divides a processing block into overlapped cells and performs maximum a posteriori (MAP) sequence estimation and MMSE filtering based on the received signal and extrinsic information delivered by the soft channel decoder. Simulation results show that for turbo-coded PSAM systems under time-variant fading the proposed receiver offers significant performance gains over a non-iterative receiver and two other cancellation schemes     

On channel parameter estimation in a space-time bit-interleaved-coded modulation system for multipath DS-CDMA uplink with receive diversity

In this paper, we consider iterative space-time multiuser detection and channel parameter estimation in a bit-interleaved coded modulation scheme for asynchronous direct-sequence code division multiple access (DS-CDMA) transmission over frequency selective, slowly fading channels. Accurate estimation of the channel parameters is critical as it has great impact on the overall BER performance. We present an iterative space-time multiuser (STMU) turbo detection and estimation scheme, based on space alternating generalized expectation maximization (SAGE) algorithm. This algorithm operates on the coded symbols by exchanging soft information between the detector and the estimator. We show through computer simulations that the proposed low complexity STMU receiver considerably outperforms conventional estimation schemes and achieves excellent performance, both in terms of BER and estimation error variance. Finally, we will consider different mapping strategies and investigate their impact on the performance and complexity of the estimator.     

EM‐based iterative receiver for coded MIMO systems in unknown spatially correlated noise

We present iterative channel estimation and decoding schemes for multi‐input multi‐output (MIMO) Rayleigh block fading channels in spatially correlated noise. An expectation‐maximization (EM) algorithm is utilized to find the maximum likelihood (ML) estimates of the channel and spatial noise covariance matrices, and to compute soft information of coded symbols which is sent to an error‐control decoder. The extrinsic information produced by the decoder is then used to refine channel estimation. Several iterations are performed between the above channel estimation and decoding steps. We derive modified Cramer–Rao Bound (MCRB) for the unknown channel and noise parameters, and show that the proposed EM‐based channel estimation scheme achieves the MCRB at medium and high SNRs. For a bit error rate of 10⁻⁶ and long frame length, there is negligible performance difference between the proposed scheme and the ideal coherent detector that utilizes the true channel and noise covariance matrices. Copyright © 2006 John Wiley & Sons, Ltd.     

A new approach based on “soft statistics” to thenonlinear blind-deconvolution of unknown data channels

In this paper, we present a new nonlinear receiver for the blind deconvolution of intersymbol interference (ISI) impaired data. The proposed receiver achieves fast identification of an unknown transmission channel using only one channel estimator and requiring the computation of only the second-order conditional statistics of the baud-rate sampled received signal and the knowledge of the transmitted constellation. The main novelty of the proposed approach is that the receiver accomplishes fast channel-identification by using soft-statistics. In particular, the receiver consists of a symbol-by-symbol maximum a posteriori (SbS-MAP) detector that feeds a nonlinear Kalman-like channel estimator with the soft statistics constituted by the a posteriori probabilities (APPs) of the state sequence of the ISI channel. Several numerical results confirm that the proposed blind detector achieves the identification of nonminimum phase channels with deep spectral notches within 300 symbols, even at low signal-to-noise ratios (SNRs). Furthermore, an attractive feature of the proposed blind channel estimator is that it directly estimates the discrete-time impulse response of the unknown channel so that, in principle, any equalization technique for known channels may be performed after channel identification has been achieved     

Differential space-time turbo codes

Serial concatenation of simple error control codes and differential space-time modulation is considered. Decoding is performed iteratively by passing symbol-wise a posteriori probability values between the decoders of the inner space-time code and the outer code. An extrinsic information transfer analysis is used to predict thresholds for outer convolutional codes of various memory orders and a simple outer parity-check code. This parity-check code is well matched to the inner differential space-time code and achieves a bit-error rate (BER) of 10/sup -6/ less than 2 dB from the Shannon capacity of the fast fading multiple antenna channel. The differential space-time code can also be used to generate a priori information in the absence of channel knowledge. This information can be exploited by a channel estimator inserted into the decoding iteration. It is demonstrated that the inner space-time code provides soft training symbols from periodically inserted training symbols. The reliability of these soft training symbols does not depend on the speed of the channel variations, but on the structure of the inner code and the signal-to-noise ratio (SNR). Simulation studies confirm these findings and show that the proposed system with no initial channel knowledge achieves a performance very close to that of the system with perfect channel knowledge.     

Multiple differential detection of parallel concatenatedconvolutional (turbo) codes in correlated fast Rayleigh fading

A new technique for iterative decoding of parallel concatenated convolutional (turbo) codes (PCCCs) for the correlated fast Rayleigh fading channel is proposed and evaluated. This technique is based upon the use of a multiple differential detector (MDD) receiver structure which exploits the statistical characteristics of the fading process to overcome the effects of the rapid phase and amplitude variations. Since traditional MDD receivers cannot be used with PCCCs because they do not produce soft output and are not compatible with channel interleaving, a novel MDD receiver structure is derived which overcomes these shortfalls. In addition, with careful use of extrinsic information related to the a posteriori probability distribution function of the transmitted symbols, the receiver is designed in such a fashion as to allow channel estimation to improve with each iteration. Evaluation of the proposed receiver by means of computer simulation has shown dramatic performance improvements in fast Rayleigh fading channels as compared to long constraint-length conventional convolutional codes using both single and traditional MDD receiver structures