Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)

Iterative synchronisation and DC-offset estimation using superimposed training

In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DC-offset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in [2],[4] and [5], due to interference from the data they performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were estimated in previous papers in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by iteratively removing the effect of the data “noise”. The result is a BER performance superior to all other ST algorithms that have not assumed a-priori synchronisation

Block synchronisation for joint channel and DC-offset estimation using data-dependent superimposed training

In this paper, we propose a new (single-step) block synchronisation algorithm for joint channel and DC-offset estimation for data-dependent superimposed training (DDST). While a (two-step) block synchronisation algorithm for DDST has previously been proposed in [5], due to interference from the information-bearing data it performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were then estimated in [5] in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by exploiting the special structure of the channel output’s cyclic mean vector. The result is a BER performance superior to the DDST synchronisation algorithm first published in [5]

Exploiting hidden pilots for carrier frequency offset estimation for generalized MC-CDMA systems

This paper proposes a novel carrier frequency offset (CFO) estimation method for generalized MC-CDMA systems in unknown frequency-selective channels utilizing hidden pi- lots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear re-gression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator

MIMO channel identification using joint white noise statistics estimation and Kalman filtering

提出了一种新的时间选择性衰落环境下MIMO信道辨识算法。为了提高信息传输效率,训练序列被直接叠加于信息序列之上。算法将信息符号输出、接收端AWGN和由于采用零中频接收技术而产生的直流偏移当做虚拟的观测噪声,其均值和自协方差均未知。通过联合的递推白噪声统计估计器和卡尔曼滤波器对时变信道进行跟踪,推导了一种计算简单的次优无偏时变白噪声统计估计器。以简单有效的方法抑制直流偏移对辨识精度的影响。仿真结果表明了算法具有良好的性能。A novel scheme to perform multiple input multiple output (MIMO) channel identification in time -selective fading environments is suggested. In order to improve transmission efficiency, training sequences are arithmetically added to the information symbols. The output of information symbols ,additive white noises and dc -offsets at the receiver are regarded as fiction measurement noise, whose mean and autocovariance are both unknown.A time -varying measurement noise recursive estimator and Kalman filters cooperate to track the time - varying MIMO channel impulse response (CIR).A low complexity,sub -optimal and unbiased time-varying white noise statistics estimator is derived. Moreover, the influence of dc-offset is restrained in a simple way. Finally,it is compared with some existing methods, and all these indicate that the proposal exhibits good performance.国家高技术研究发展(863计划)项目(2006AA09Z108);; 国家自然科学基金资助项目(05C777

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel’s sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for BER=10 −1.9 BER=10−1.9, the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity