Blind channel estimation and equalization of multiple-input multiple-output channels

Blind channel estimation and blind minimum mean square error (MMSE) equalization of multiple-input multiple-output (MIMO) communications channels arising in multiuser systems is considered, using primarily the second-order statistics of the data. The basis of the approach is the design of multiple zero-forcing equalizers that whiten the noise-free data at multiple delays. In the past such an approach has been considered using just one zero-forcing equalizer at zero-delay. Infinite impulse response (IIR) channels are allowed. Moreover, the multichannel transfer function need not be column-reduced. The proposed approach also works when the "subchannel" transfer functions have common zeros so long as the common zeros are minimum-phase zeros. The channel length or model orders need not be known. Using second-order statistics, the sources are recovered up to a unitary mixing matrix, and are further "unmixed" using higher order statistics of the data. Two illustrative simulation examples are provided where the proposed method is compared with its predecessors and an existing method to show its efficacy.

Blind channel estimation and blind equalization of SIMO (single-input multiple-output) communications channels is considered using only the second-order statistics of the data. Estimation of (partial) channel impulse response and design of finite-length MMSE (minimum mean-square error) blind equalizers is investigated. The basis of the approach is the design of multiple zero-forcing equalizers that whiten the noise-free data at multiple delays. In the past such an approach has been considered using just one zero-forcing equalizer at zero-delay. Infinite impulse response (IIR) channels are allowed. The proposed approach also works when the subchannel transfer functions have common zeros so long as the common zeros are minimum-phase zeros. The channel length of model orders need not be known. An illustrative simulation example is provided.

The problem of blind equalization of single-input multiple-output (SIMO) communications channels is considered using only the second-order statistics of the data. Such models arise when a single receiver data is fractionally sampled (assuming that there is excess bandwidth) or when an antenna array is used with or without fractional sampling. We focus on direct design of finite-length minimum mean-square error (MMSE) blind equalizers. Unlike the past work on this problem, we allow infinite impulse response (IIR) channels. Our approaches also work when the "subchannel" transfer functions have common zeros as long as the common zeros are minimum-phase zeros. Illustrative simulation examples are provided.

ABSTRACTWhile inter-symbol interference is not of concern, inter-carrier interference that is caused by carrier frequency offset (CFO) is more concerned in orthogonal frequency division multiplexing (OFDM) systems. The estimation of channel impulse response (CIR) is another important parameter of interest in the system. Recently, a number of approaches for the joint estimation of CFO and CIR in OFDM systems have been proposed in the literature, and most of them are traditional maximum-likelihood-based approaches. In this paper, we propose a number of mixture cost-reference particle filtering (CRPF) approaches for joint estimation of CIR and CFO, and a couple of them can be applied without the knowledge of both state and measurement noise statistics. Conventional least mean squares algorithm or recursive least squares algorithm is jointly employed with recently developed CRPF for nonlinear and multiple parameters estimation in OFDM systems. The computer simulation results show that mixture CRPFs are more robust against particle impoverishment (PI) problem than standard mixture particle filtering (PF) and mixture regularized PF (RPF) approaches although RPF was particularly developed to tackle the PI problem. In this paper, we investigate a number of mixture PF approaches including ones that do not need the information of noise statistics for the joint estimation of CFO and channel in OFDM systems.

This paper introduces novel hybrid pilot-aided channel estimation with decision feedback equalizer(DFE) for OFDM systems and its corresponding hardware co-simulation platform. This pilot-aided channel estimation algorithm consists of two parts: coarse estimation and fine estimation. In the coarse estimation, the combined classical channel estimation methods including carrier frequency offset (CFO) and channel impulse response (CIR) estimation are used. Based on the received training sequence and pilot tones in the frequency domain, the major CFO, sampling clock frequency offset (SFO) and CIR effect coefficients are derived. In the fine estimation, the pilot-aided polynomial interpolation estimation combined with a new decision feedback equalizer scheme based on minimum mean squared error (MMSE) criteria is proposed to reduce the residual effect caused by imperfect CIR equalizer, SFO and CFO. At the same time, for the purpose of speeding up the whole development and verification process, a new architecture of co-simulation platform which combines software and hardware is introduced. The simulation results on the co-simulation platform indicate that the proposed hybrid channel estimation scheme can enhance the receiver performance by 6 dB in terms of error vector magnitude (EVM) over large ranges of CFO and SFO and BER performance by 7 dB for SNR range over 15 dB.

A computationally efficient ranging scheme that estimates time of arrival (TOA) by a low complexity minimum mean square error (MMSE) and a matrix pencil techniques is proposed for IEEE 802.15.4a chirp spread spectrum system. In TOA estimation, it is known that accurate channel delay can be estimated by the MMSE technique. However, the major drawback of the MMSE is its high computational complexity, which grows with number of signal samples. In this paper, thus, a computational efficient MMSE technique based on the chirp spectrum characteristic is proposed for a low complexity ranging algorithm. The proposed TOA estimation method consists of two-step signal processing: proposed low complexity MMSE-based channel impulse response (CIR) estimation and the channel delay tracking by matrix pencil algorithm. The proposed TOA estimation method not only has low computational complexity, but also achieves small ranging error. The effectiveness of the proposed algorithm is demonstrated by simulation results.

A improved algorithm to perform channel estimation and equalization for multiple input multiple output(MIMO) systems in frequency and time-selective fading environments is put forward. lt contains two steps: training and tracking. In training phase, improved Kalman filtering, namely robust Kalman filtering(RKF), is exploited to estimate channel impulse response(CIR). After that, in tracking stage,the RKF and minimum mean-square error feedback decision equalizer (MMSE-DFE) cooperate to track the time-varying channel. The RKF recursions is presented and a closed-form solution for baud rate MIMO MMSE-DFE under perfect knowledge of CIR and correct past decisions conditions is derived. In addition, it regards unknown dc-offset due to zero intermediate frequency(IF) at the receiver as the mean of measurement noise, which is estimated as a byproduct through robust Kalman filters. Finally, it is compared with well-known ones,such as least mean-square (LMS), recursive least square (RLS), Kalman filtering (KF). All these show that the proposal exhibits better performance.

We introduce a new structured channel impulse response (CIR) estimation method for sparse multipath channels where we demonstrate a robust way of restoring the pulse shape into the composite CIR. We call this novel CIR estimation method time-of-arrival based blended least squares (TOA-BLS) which uses symbol rate sampled signals, and it is based on blending correlation processing followed by TOA estimation in the frequency domain by the least squares based channel estimation. TOA estimation in the frequency domain is accomplished by estimating the AR model parameters by solving the forward and forward-backward linear prediction equations in the least squares sense. Simulation examples are drawn from the ATSC digital TV 8-VSB system (ATSC Digital Television Standard, A/53, 1995). The delay spread for digital TV systems can be as long as several hundred times the symbol duration; however, digital TV channels are sparse where there are only a few dominant multipaths.

In this paper, a new joint timing and channel estimation scheme is proposed for orthogonal frequency division multiplexing and single carrier block transmission ultra-wideband (UWB) systems. In particular, a new preamble pattern is used for both coarse timing and channel estimation. Despite of the presence of coarse timing error, the estimated channel impulse response (CIR) is simply the cyclic shifted version of the real CIR, thanks to the unique structure of the preamble. Then a new method is proposed to search for the first channel tap in the CIR vector, which can be employed to fine tune the timing position and adjust the channel estimation for later data block detection. The proposed scheme has low complexity, saves preamble overhead by performing joint synchronization and channel estimation, and outperforms existing timing acquisition methods in the literature by a large margin in dense multipath UWB channels. Moreover, this paper presents an analysis of the effect of residual timing error on the performance of single carrier systems with frequency domain equalization.

This paper addresses the effective sparse channel estimation based on the most significant taps (MST) selection with high spectral efficiency and moderate computational complexity for orthogonal frequency division multiplexing (OFDM) systems. The main goal of this paper is to obtain an efficient time domain threshold and achieve the channel estimation performance approaching the least squares (LS) method with exact sparsity (exact number of non-zero channel taps) without using prior knowledge of both channel statistics and noise standard deviation. Firstly, classical LS method is used to get an initial channel impulse response (CIR) estimate. Then a novel effective threshold, estimated from the noise coefficients of the initial estimated CIR, is proposed. Finally, the obtained threshold is used to select the most significant taps. Simulations show that the proposed method is able to achieve good channel estimation performances for sparse channel within a comparatively wide range of sparsity with small number of pilots and moderate computational complexity.

Accurate channel estimation is essential for achieving the performance gains offered by reconfigurable intelligent surface (RIS)-aided wireless communications. A variety of channel estimation methods have been proposed for such systems; however, none of the existing methods takes into account the effect of synchronization errors such as carrier frequency offset (CFO). In general, CFO can significantly degrade the channel estimation performance of orthogonal frequency division multiplexing (OFDM) systems. Motivated by this, we investigate the effect of CFO on channel estimation for RIS-aided OFDM systems. Furthermore, we propose a joint CFO and channel impulse response (CIR) estimation method for these systems. Simulation results demonstrate the effectiveness of our proposed method, and also demonstrate that the use of time-domain rather than frequency-domain estimation in this context results in an improvement in the mean-squared error (MSE) performance of channel estimation as well as in a significantly lower overall computational complexity.

A pilot-aided scheme is proposed for the joint estimation and tracking of channel impulse response (CIR), carrier frequency offset (CFO) and sampling frequency offset (SFO) in burst mode, multiple-input-multiple-output (MIMO), and orthogonal frequency division multiplexing(OFDM) systems. First, a received signal model that takes into account nonlinear CFO/SFO effects is formulated. Then, based on the derived signal model and the received multicarrier signal samples containing the pilot tones in the frequency domain, an objective function that includes MIMO CIR, CFO, and SFO is introduced to develop a vector recursive-least-square (RLS)-based joint channel estimation and synchronization algorithm. The analysis and simulation results show that, over the large ranges of the CFO and SFO values, the proposed estimation and tracking scheme offers fast convergence, high stability, and a near-optimum receiver performance that is remarkably close to the ideal performance in the case of perfect channel estimation and synchronization over quasi-static Rayleigh multipath fading channels.

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1−l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.

Massive multi input multi output (MIMO) systems incorporate orthogonal frequency division multiplexing (OFDM) technology to render high data rate services for future wireless communication applications. The channel estimator (CE) employed by a reliable massive MIMO-OFDM system requires huge amount of overhead in the form of known and null data transmissions, hence limiting the system spectral efficiency (SE). Often, CE design is a tradeoff between SE and system reliability. In this paper, CE with three different overhead arrangements, namely time domain synchronous (TDS), comb type with cyclic prefix (CTCP), 2D grid type with cyclic prefix (GTCP) are investigated and a GTCP based CE is proposed which offers both high SE and improved system reliability. The proposed CE uses autocorrelation based denoising threshold for channel impulse response (CIR) estimation and does not require any knowledge of channel statistics (KCS). A 4 x 16 MIMO-OFDM system is simulated in a rayleigh fading channel environment with U-shaped doppler spectrum. From the bit error rate (BER) performance results inWiMax SUI-4, Advanced Television Technology Center (ATTC) and Brazil A channel environments, it is verified that the proposed CE with GTCP overhead and proposed denoising scheme, indeed improves both SE and system reliability. Hence it is suitable for application in all massive MIMO-OFDM systems.

A novel efficient algorithm for the estimation of sparse channel impulse response (CIR) is addressed for OFDM systems. The innovation of this algorithm comes from the fact that it equivalently sees the CIR estimation problem as a decoding one. To do so, it exploits first the channel sparsity through the modelling of the sparse CIR as a Bernoulli-Gaussian process. Then, using the relationship between the Reed-Solomon codes and the OFDM modulator it efficiently estimates the sparse CIR using directly the decoding of the OFDM received signal. The obtained simulation results highlight that using the proposed algorithm gives good estimation performance in terms of mean squares error on the sparse CIR estimates.