Olutayo O. Oyerinde

Review of Channel Estimation for Wireless Communication Systems

Abstract Wireless communication systems have evolved over the ages. However, there are some undesirable effects of a wireless communication channel on the signals transmitted through it which are caused by the physical properties of the channel. These in turn always result in attenuation, distortion, delays, and phase shift of the signals arriving at the receiver end of the communication system. Consequently, there is a need to provide perfect and up-to-date estimate of the channel, in order to compensate for these effects, and for accurate signal demodulation, equalization, decoding, and a host of other baseband processing applications at the receiver end of the systems. In this article, a review of the various channel estimation techniques is provided. This review will be of assistance in reaching a compromised decision regarding channel estimation techniques to be employed in any wireless communication systems being developed.

Decision Directed Channel Estimation for OFDM Systems Employing Fast Data Projection Method Algorithm

In this paper we propose a Fast Data Projection Method (FDPM)-based Channel Impulse Response (CIR) estimator of a Decision Directed Channel Estimation (DDCE) scheme for OFDM system. The proposed algorithm is employed in the context of a more realistic channel condition, the Fractionally Spaced (FS)-CIR based channel model. The performance of the proposed algorithm is compared with an earlier proposed deflated Projection Approximation Subspace Tracking (PASTd) algorithm. It is found from the simulation results that the FDPM-based DDCE outperformed the PASTd-based DDCE.

Combined channel estimation and adaptive prediction for MC-IDMA systems

Combination of Orthogonal Frequency Division Multplexing (OFDM) and Interleave Division Multiple Access (IDMA) to form OFDM-IDMA scheme has been shown as a promising approach to resolving the dual problems of intersymbol interference (ISI) and multiple access interference (MAI). For the optimum performance of this scheme, efficient method of obtaining channel state information (CSI) to be used by the multi-user detector for accurate signal detection is required. This paper proposes channel estimation scheme that employs combination of linear estimation of the frequency domain channel transfer function CTF) and the adaptive prediction of the time domain channel impulse response (CIR). Regularized adaptive algorithm is employed for the CIR prediction in order to exploit the sparsity characteristics of the OFDM channel. The achievable performance of the proposed channel estimation scheme is documented in the context of OFDM-IDMA system operating in both very slow and very fast mobile speed scenarios.

Subspace Tracking-Based Decision Directed CIR Estimator and Adaptive CIR Prediction

One of the essential prerequisites for coherent detection in Orthogonal Frequency Division Multiplexing (OFDM)-based communication systems is the availability of an accurate estimate of the channel state information (CSI). In the absence of symbol errors, the decision directed channel estimation (DDCE) scheme has been proved to provide better and more accurate CSI than the channel estimation scheme based purely on pilot symbols. In this paper a subspace algorithm, the fast data projection method (FDPM), is employed for estimation of the channel impulse response (CIR) of the OFDM channel in a decision directed mode. A variable step size normalized least mean square (VSSNLMS)-based predictor is derived for the implementation of the CIR prediction stage of the estimation scheme. The whole channel estimation scheme based on the two algorithms is simulated in the context of fractionally spaced (FS)-CIR based channel model, also known as non-sample spaced CIR model. The performance of the FDPM-based decision directed channel estimator is compared through computer simulation with another subspace algorithm, deflated projection approximation subspace tracking (PASTd)-based channel estimator, while employing normalized least mean square (NLMS) and the proposed VSSNLMS predictors, respectively. It was discovered from the simulation results that the proposed scheme performed better than the PASTd-based channel estimator. The channel estimation scheme employing VSSNLMS-predictor also shows enhanced performance in comparison with the scheme using NLMS-based predictor.

Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels

In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.

Adaptive CIR prediction of time-varying channels for OFDM systems

Channel Impulse Response (CIR) prediction is important because it makes possible the provision of up-to-date channel state information which is essential for coherent detection of transmitted message symbols. Different prediction techniques have been proposed for OFDM systems. These range from the Minimum Mean Square Error (MMSE) techniques to Adaptive techniques. However, it has been confirmed that the adaptive predictors present better performance than its MMSE counterpart. Besides, the computational complexity of the MMSE class of predictors is more costly than the adaptive predictors. In this paper we propose an improved version of an adaptive Normalized Least Mean Square (NLMS) predictor named Variable Step Size Normalized Least Mean Square (VSSNLMS) predictor. The proposed VSSNLMS predictor is employed for the implementation of Decision Directed Channel Estimation (DDCE) for OFDM systems. Simulation results demonstrate that the proposed VSSNLMS predictor outperforms the NLMS predictor at a cost of a negligible high complexity, and its performance is very close to that of the Recursive Least Square (RLS) predictor that exhibits an enormous computational complexity.

Adaptive algorithms based-time domain iterative channel estimation for MC-IDMA systems

Multicarrier-Interleave Division Multiple Access (MC-IDMA) scheme is an attractive Multiuser technique that has generated a large interest in the research community. However, availability of accurate estimate of channel state information (CSI) at the receiver end of the system remains an essential factor in the optimum performance of the whole system. In this paper, two families of Least Mean Square (LMS) Algorithm, namely, Normalized Least Mean Square (NLMS) and Variable Step Size Normalized Least Mean Square (VSSNLMS) algorithms are proposed for implementation of time domain iterative channel estimation for MC-IDMA systems. Channel estimation in time domain is considered in this paper because it has earlier been confirmed that if the number of OFDM subcarriers exceed the number of channel taps, as the case is in most practical OFDM based systems, the time domain channel estimate is more accurate than the channel estimation in frequency domain. The performance of the proposed adaptive algorithms based-time domain iterative channel estimators are validated in comparison with the LMS-based channel estimator proposed earlier in literature.

Regularized Adaptive Algorithms-Based CIR Predictors for Time-Varying Channels in OFDM Systems

In a bid to improve the performance of adaptive channel impulse response (CIR) prediction using the decision directed channel estimation (DDCE) scheme for OFDM systems, regularized adaptive algorithms are proposed. These are obtained by incorporating sparsity inducing penalty terms in the cost functions to be minimized by the algorithms. Computer simulation results comparing the performance of the regularized adaptive algorithms-based CIR predictors with the conventional predictors are presented. The results show improved performance of the modified predictors over the conventional ones. The improved performance is apparently due to the exploitation of the inherent sparsity in the system.

Soft Iterative Decision Directed Channel Estimation for OFDM systems employing adaptive predictor

Soft Iterative Decision Directed Channel Estimation (DDCE) employing Fast Data Projection Method (FDPM) subspace tracking algorithm and Variable Step Size Normalized Least Mean Square-based predictor is proposed for OFDM system in this paper. A more realistic Fractionally Spaced Channel Impulse Response (FS-CIR) model is considered. The performance of the soft iterative DDCE employing FDPM subspace tracking algorithm is validated in comparison with DDCE employing deflated version of Projection Approximation Subspace Tracking (PASTd) algorithm through computer simulations. The simulation results show that the soft iterative DDCE scheme based on FDPM algorithm outperformed its counterpart based on PASTd algorithm under both slow and fast fading channel scenarios. The proposed VSSNLMS-based CIR predictor for the iterative DDCE scheme also brings about an improved performance in comparison with the iterative scheme employing NLMS-based predictor.

Regularized adaptive algorithms based-time domain iterative channel estimation for MC-IDMA systems

The importance of near to optimum channel estimation scheme cannot be over-emphasized in wireless communication systems, especially in a Multicarrier-Interleave Division Multiple Access (MC-IDMA)-based wireless communication systems. Availability of accurate estimate of channel state information (CSI) at the receiver end of the system remains an essential factor for its optimum performance. In this paper, two families of regularized recursive least square algorithm, namely, Regularized Rounding square Error based variable forgetting factor-recursive least square (ℓ1-REVFF-RLS)-based channel impulse response (CIR) Estimator and Regularized Mean square error Gradient-based variable forgetting factor-recursive least square (ℓ1-MGVFF-RLS)-based CIR Estimator are developed and proposed for implementation of time domain iterative channel estimation for MC-IDMA systems. The performances of the proposed regularized adaptive algorithms based-time domain iterative channel estimators are validated in comparison with the least mean square (LMS)-based channel estimator and combined channel transfer function (CTF)-estimator and CIR predictor that were earlier proposed in literature for MC-IDMA systems.