Least-squares Channel Estimation Method Research Articles

An improved least squares (LS) channel estimation method based on CNN for OFDM systems

<abstract><p>Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods.</p></abstract>

Peak-to-average power ratio reduction for DCO-OFDM underwater optical wireless communication system based on an interleaving technique

In underwater direct current-biased optical orthogonal frequency-division multiplexing (DCO-OFDM) system, high peak-to-average power ratio (PAPR) brings in-band distortion and out-of-band power. It also decreases the signal-to-quantization noise ratio of the analog-to-digital converter and the digital-to-analog converter. A time–frequency-domain interleaved subbanding scheme is proposed to reduce the PAPR of underwater DCO-OFDM system with low computation complexity and bit error rate (BER). The system BER is evaluated by the distances of the underwater optical wireless communication (UOWC), as well as by the signal attenuation of the UOWC channel. A least-square channel estimation method is adopted for adaptive power amplification at the receiver side, to further decrease the system BER.

Genetic Grey Wolf Optimizer Based Channel Estimation in Wireless Communication System

Various methods are available for channel estimation in the orthogonal frequency division multiplexing and orthogonal frequency and code division multiplexing (OFCDM) based wireless communication schemes. Along with this, the most utilized techniques are namely the minimum mean square error (MMSE) and least square (LS). The process of LS channel estimation method is simple but it occupies a very high mean square error. On the other hand, the performance of MMSE is better than LS in terms of SNR, though it shows high computational complexity. Compared to MMSE and LS based techniques, the combination of MMSE and LS techniques using evolutionary programming reduces the error significantly to receive exact signal. In this study, we propose a hybrid method namely GGWO that includes grey wolf optimization (GWO) and genetic algorithms (GA) for estimate the channel in MIMO–OFCDM schemes. At first, the best channel is estimated using GWO and afterwards, the MMSE and LS are hybridized through GA for calculating the best channel to decrease error. Overall, the GWO and GA contribute in fine tuning the obtained channel scheme so that the channel model is derived further to correlate with the ideal scheme. Our results demonstrate that the proposed scheme is superior to conventional MMSE and LS in terms of BER and SNR.

Improvement of Complexity and Performance of Least Square Based Channel Estimation in MIMO System

<p>Multiple-input multiple-output (MIMO) systems play a vital role in fourth generation wireless systems to provide advanced data rate. In this paper, a better performance and reduced complexity channel estimation method is proposed for MIMO systems based on matrix factorization. This technique is applied on training based least squares (LS) channel estimation using STBC for performance improvement. Simulation results indicate that the proposed method not only alleviates the performance of MIMO channel estimation but also significantly reduces the complexity caused by matrix inversion. The performance evaluations are validated through computer simulations using MATLAB in terms of bit error rate (BER) for modified LS with LS and MMSE channel estimation techniques. Simulation results show that the BER performance and complexity of the proposed method clearly outperforms the conventional LS channel estimation method.</p><p><em>Journal of Advanced College of Engineering and Management, Vol. 1, 2015</em>, pp. 11-24</p>

A Modified LS Channel Estimation Algorithm for OFDM System in Mountain Wireless Environment

Orthogonal frequency division multiplexing (OFDM) is a transmission technique that is based on many orthogonal carriers that are transmitted simultaneously. OFDM effectively mitigates inter symbol interference (ISI) and has high-efficient frequency utilization. OFDM channel estimation is one of the key technologies in the OFDM system. In this paper, channel estimation techniques for OFDM systems, based on pilot-aided channel estimation algorithm, over mountain wireless environment are investigated. It is well known that least square (LS) and linear minimum mean square error (LMMSE) algorithms are effectual channel estimation methods for OFDM. We propose a novel modified LS channel estimation method, which is based on noise reduction procedure to improve the channel estimation accuracy. The simulation results demonstrate that comparing to LS channel estimation algorithm, LMMSE algorithm and the proposed modified LS algorithm provide better performances.

PERFORMANCE AND COMPLEXITY IMPROVEMENT OF TRAINING BASED CHANNEL ESTIMATION IN MIMO SYSTEMS

Multiple-input multiple-output (MIMO) systems play a vital role in fourth generation wireless systems to provide advanced data rate. In this paper, a better performance and reduced complexity channel estimation method is proposed for MIMO systems based on matrix factorization. This technique is applied on training based least squares (LS) channel estimation for performance improvement. Experimentation results indicate that the proposed method not only alleviates the performance of MIMO channel estimation but also signiflcantly reduces the complexity caused by matrix inversion. The performance evaluations are validated through computer simulations using MATLAB R ∞ 7.0 in terms of bit error rate (BER). Simulation results show that the BER performance and complexity of the proposed method clearly outperforms the conventional LS channel estimation method.

Channel estimation assisted by postfixed pseudo-noise sequences padded with zero samples for mobile orthogonal-frequency-division-multiplexing communications

A channel estimation (CE) technique that uses a postfixed pseudo-random sequence padded with zero samples is investigated to extend pseudo-random-postfix orthogonal-frequency-division-multiplexing (PRP-OFDM) communications onto mobile applications. The proposed technique takes advantage of pseudo-noise sequences periodically inserted after each OFDM block within the original guard interval (GI) in conventional zero-padded OFDM (ZP-OFDM) or within the original cyclic prefix (CP) in conventional CP-OFDM. This allows accurate estimation of the channel impulse response. In addition, the proposed technique takes advantage of the null samples padded after the PN sequences for reducing inter-symbol interference occurring with the CEs and with the overlap-add information detection in the conventional PRP-OFDM. The proposed technique successfully applies either (1) a least-squares (LS) CE with assistance from decision-directed data or (2) an approximate LS CE method onto PRP-OFDM-like communications for the purpose of improving CE performance on rapid time-varying channels. Computer simulations in conjunction with statistical analysis assess the performance evaluation of the proposed technique and show that it can achieve lower mean-square estimation errors and lower error probabilities in mobile environments.