Adaptive Channel Estimation Method Research Articles

Boundaryless Visible Light Communication System Without Attocell for Industrial Internet of Things

Recently, visible light communication (VLC) in the industrial Internet of Things has been widely concerned. In these systems, multi-access technologies are always used to eliminate interference between adjacent transmitters, significantly decreasing transmission efficiency. Meanwhile, the attocell boundaries caused by multi-access technologies can also limit communication coverage and lead to frequent receiving switches. To overcome these weaknesses caused by the division of attocell, we design a boundaryless none-attocell VLC system in this paper. In the proposed system, each transmitter uses all available communication resources, and a cubic receiver is adopted to receive in both non-overlapping and overlapping areas. Specifically, we propose a scheme for adaptive signal selection, enabling the receiver to select the recoverable transmitting signals. After that, we introduce an adaptive channel estimation and signal recovery method, which adaptively achieves signal combining and overlapping signal recovery. Our simulations verify the proposed scheme and demonstrate that the proposed system with the adaptive receiving scheme has better error performance and broader communication coverage than the traditional attocell system.

OTFS underwater acoustic communications based on passive time reversal

Orthogonal time-frequency-space (OTFS) is a new two-dimensional (2D) modulation technique that offers reliable communications over time-varying channels. Due to the characteristics of underwater acoustic (UWA) channels, the computational complexity of traditional OTFS receivers is too high to implement in practice. In this paper, a 2D passive time reversal (PTR) structure receiver is designed in the Delay-Doppler domain, achieving spatial, temporal, and frequency focusing. Compared with existing methods, the proposed method could achieve similar performance with a lower complexity. Meanwhile, we also propose an adaptive channel estimation method based on the improved proportionate normalized least mean squares (IPNLMS) algorithm to reduce the complexity of channel estimation for the OTFS UWA communications system. The performance is evaluated under statistical simulated channels and measured channels from real-world experiments. The results verify the proposed 2D PTR receiver and demonstrate it could achieve good BER performance with a lower complexity.

Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC

In order to track the changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is prior to estimate channel impulse response adaptively. In this paper, we proposed an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weight the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of third-order tensor which consists of the weighted OFDM data symbols. To reduce the computational load, we adopt a recursive singular value decomposition method for tensor decomposition; then, the channel parameters can be estimated adaptively. Simulation results validate the effectiveness of the proposed algorithm under diverse signalling conditions.

Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems

In order to track a changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is a priority to estimate channel impulse response adaptively. In this paper, we propose an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weigh the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of a third-order tensor, which consists of the weighted OFDM data symbols. By preserving the Khatri-Rao product, we used a recursive least squares solution to update the estimated subspace at each time instant, then the channel parameters can be estimated adaptively, and the algorithm achieves superior convergence performance. Simulation results validate the effectiveness of the proposed algorithm.

A new combination of adaptive channel estimation methods and TORC equalizer in MC‐CDMA systems

SummaryAdaptive systems identification has been widely studied, but most studies have focused on the convergence of these methods. Applications of equalization systems have also received much attention. This paper presents a new combination of adaptive Broadband Radio Access Networks (BRAN) channel identification algorithms for multicarrier code division multiple access (MC‐CDMA) systems downlink equalization. In fifth‐generation (5G) wireless communications, MC‐CDMA is expected to support the associated networks. The BRAN E channel parameters, representing an outdoor scenario normalized for MC‐CDMA systems, are identified using a recursive least mean pth power algorithm with logarithmic transformation (RlogLMP). For validity and test aim, this algorithm is compared with the existing recursive least square (RLS) and least mean square (LMS) algorithms. Moreover, we use the estimated coefficients in the adaptive equalization problem. We give a review of the threshold orthogonality restoring combining (TORC) equalizer, which is coupled with the presented algorithms to counteract channel fading, as evaluated by the bit error rate (BER). Our performance results show that the RlogLMP algorithm can estimate the measured BRAN E channel with good efficiency for various values of the signal‐to‐noise ratio (SNR), as compared with the classical algorithms RLS and LMS. In adaptive equalization problems, the achieved results demonstrate that two thresholds ρTH in the TORC equalizer minimize the performance degradation, in terms of the BER, of the MC‐CDMA system under multipath channel fading with very good accuracy, especially if the coefficients are estimated with the specific case of the power p in the RlogLMP algorithm.

Adaptive Channel Estimation and Tracking for URA-Based Massive MIMO Systems

In this paper, an adaptive channel estimation and tracking method is proposed for the uniform rectangular array (URA) based massive multiple-input and multiple-output (M-MIMO) systems. To decrease the computational complexity while tracking the change on the channel state information (CSI) in time, a principal subspace analysis (PSA) based scheme is proposed to estimate and track the signal subspace based on the received signal covariance matrix. Based on the estimated signal subspace, the channel parameters including the direction of arrival (DoA) angle and the channel gain corresponding to each resolvable path of the channel, can be recovered via the classic multiple signal classification (MUSIC) algorithm. Then, the Cramer-Rao lower bound (CRLB) is derived to evaluate the performance of the proposed scheme. Simulation results are provided to verify the accuracy of the proposed scheme on the channel estimation.

An Adaptive Channel Estimation Based on Fixed-Point Generalized Maximum Correntropy Criterion

Many conventional adaptive channel estimation methods are based on minimum mean square error (MMSE) criterion, maximum correntropy criterion (MCC) or least p-norm criterion. However, these criteria are not always desirable in the presence of the various kinds of noises in different wireless channels. To further enhance the adaptability of the channel estimation to different noises, this paper introduces a new criterion named generalized maximum correntropy criterion (GMCC), and calculates the estimated channel parameters using the fixed-point iteration. The convergence analysis of the fixed-point iteration based on GMCC is discussed theoretically and is verified by experiments. More experiments are implemented to show the steady-state performance of the algorithm under different noise environments and the guide of parameter selection is provided.

Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies

Spatial Modulation Technologies (SMTs) are schemes that reduce inter-carrier interference (ICI), inter-channel interference, inter-antenna synchronization (IAS), and system complexity for multiple-input multiple-output (MIMO) communication systems. Moreover, high spectral and energy efficiency have rendered SMTs attractive to underwater acoustic (UWA) MIMO communication systems. Consequently, this paper focuses on SMTs such as spatial modulation (SM), generalized spatial modulation (GSM), and fully generalized spatial modulation (FGSM) in which one constant number and one multiple number of antennas are active to transmit data symbols in any time interval for underwater acoustic communication (UWAC). In SMTs, the receiver requires perfect channel state information (P-CSI) for accurate data detection. However, it is impractical that the perfect channel knowledge is available at the receiver. Therefore, channel estimation is of critical importance to obtain the CSI. This paper proposes the pilot-based recursive least-square (RLS) adaptive channel estimation method over the underwater time-varying MIMO channel. Furthermore, maximum likelihood (ML) decoder is used to detect the transmitted data and antennas indices from the received signal and the estimated UWA-MIMO channel. The numerical computation of mean square error (MSE) and bit error rate (BER) performance are computed for different SMTs like SM, GSM and FSGM using Monte Carlo iterations. Simulation results demonstrate that the RLS channel estimation method achieves the nearly same BER performance as P-CSI.

Stable adaptive channel estimation method under impulsive noise environments

SummaryChannel estimation is one of the key technologies for ensuring reliable wireless communications under impulsive noise environments. This paper studies robust adaptive channel estimation methods for mitigating harmful impulsive noises, which are described as alpha‐stable (α‐stable) distribution models. Traditional adaptive channel estimation using the second‐order statistics based least mean square (SOS‐LMS) algorithm does not perform well under α‐stable noise environments, even though it was considered one of attractive approaches for estimating channels in the case of Gaussian noises. Unlike the traditional SOS‐LMS algorithm, in this research, we propose a stable sign‐function‐based LMS algorithm, which can mitigate the impulsive noises. Specifically, we first construct the cost function with minimum ℓ1‐norm error criterion and then derive the updating equation of the proposed algorithm. Compared with the traditional SOS‐LMS, the effectiveness of the proposed algorithm is validated via Monte Carlo simulations in various α‐stable noise scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

Space–Frequency Block Coding for Underwater Acoustic Communications

In this paper, Alamouti space-frequency block coding, applied over the carriers of an orthogonal frequency-division multiplexing (OFDM) system, is considered for obtaining transmit diversity in an underwater acoustic channel. This technique relies on the assumptions that there is sufficient spatial diversity between the channels of the two transmitters, and that each channel changes slowly over the carriers, thus satisfying the basic Alamouti coherence requirement and allowing simple data detection. We propose an adaptive channel estimation method based on Doppler prediction and time smoothing, whose decision-directed operation allows for reduction in the pilot overhead. System performance is demonstrated using real data transmitted in the 10-15-kHz acoustic band from a vehicle moving at 0.5-2 m/s and received over a shallow-water channel, using quadrature phase-shift keying (QPSK) and a varying number of carriers ranging from 64 to 1024. Results demonstrate an average mean squared error gain of about 2 dB as compared to the single-transmitter case and an order of magnitude decrease in the bit error rate when the number of carriers is chosen optimally.

Low overhead and nonlinear-tolerant adaptive zero-guard-interval CO-OFDM.

We propose an adaptive channel estimation (CE) method for zero-guard-interval (ZGI) coherent optical (CO)-OFDM systems, and demonstrate its performance in a single channel 28 Gbaud polarization-division multiplexed ZGI CO-OFDM experiment with only 1% OFDM processing overhead. We systematically investigate its robustness against various transmission impairments including residual chromatic dispersion, polarization-mode dispersion, state of polarization rotation, sampling frequency offset and fiber nonlinearity. Both experimental and numerical results show that the adaptive CE-aided ZGI CO-OFDM is highly robust against these transmission impairments in fiber optical transmission systems.

LTE-Advanced 상향 링크 시스템을 위한 적응적 채널 추정을 통한 고속 ICI 제거 방법 연구

In this paper, we propose a fast ICI suppression algorithm with adaptive channel estimation for the LTE-Advanced uplink system. In order to effectively remove phase noise and carrier frequency offset at time varying channel, we use the comb type pilot. The purpose is to improve performance by reducing computational complexity than conventional PNFS(Phase Noise & Frequency offset Suppression) algorithm. We reduce computational complexity by decreasing overlapping computation or unnecessary computation at conventional PNFS algorithm. Also, we propose an effective channel estimation method. We estimate and compensate multipath channel through the proposed adaptive channel estimation method. The BER performance of the proposed method is better about 0.5 dB than the conventional method at the Vehicular A channel.