An Adaptive Multiple LS Channel Estimation Method for MIMO System

2012 ◽  
Vol 239-240 ◽  
pp. 1084-1088
Author(s):  
Jin Lun Chen
Keyword(s):  
Numerical Simulation ◽  
Channel Estimation ◽  
Communication Systems ◽  
Mimo System ◽  
Estimation Method ◽  
Linear Least Squares ◽  
Mimo Communication ◽  
Channel Estimate ◽  
Wide Range ◽  
Multiple Input

Accurate channel estimation plays a key role in multiple-input and multiple-output (MIMO) communication systems. In this paper, we firstly discuss the popular linear least squares (LS) channel estimation and the multiple LS (MLS) channel estimation. Then an adaptive multiple LS (AMLS) channel estimate approach is proposed. Using numerical simulation, it is found that the proposed estimation method outperforms LS and MLS for a wide range of training SNRs.

scholarly journals Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 711 ◽  
Author(s):  
Naveed Ur Rehman Junejo ◽  
Hamada Esmaiel ◽  
Haixin Sun ◽  
Zeyad A. H. Qasem ◽  
Junfeng Wang
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Estimation Method ◽  
Spatial Modulation ◽  
Mimo Channel ◽  
Underwater Acoustic ◽  
Mimo Communication ◽  
Adaptive Channel Estimation ◽  
Perfect Channel ◽  
Ber Performance

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.

scholarly journals Downlink Channel Estimation in Massive Multiple-Input Multiple-Output with Correlated Sparsity by Overcomplete Dictionary and Bayesian Inference

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 473 ◽  
Author(s):  
Wei Lu ◽  
Yongliang Wang ◽  
Xiaoqiao Wen ◽  
Shixin Peng ◽  
Liang Zhong
Keyword(s):  
Bayesian Inference ◽  
Channel Estimation ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Temporal Correlation ◽  
Angular Domain ◽  
Multiple Input ◽  
Overcomplete Dictionary ◽  
Input Multiple Output

We exploited the temporal correlation of channels in the angular domain for the downlink channel estimation in a massive multiple-input multiple-output (MIMO) system. Based on the slow time-varying channel supports in the angular domain, we combined the channel support information of the downlink angular channel in the previous timeslot into the channel estimation in the current timeslot. A downlink channel estimation method based on variational Bayesian inference (VBI) and overcomplete dictionary was proposed, in which the support prior information of the previous timeslot was merged into the VBI for the channel estimation in the current timeslot. Meanwhile the VBI was discussed for a complex value in our system model, and the structural sparsity was utilized in the Bayesian inference. The Bayesian Cramér–Rao bound for the channel estimation mean square error (MSE) was also given out. Compared with other algorithms, the proposed algorithm with overcomplete dictionary achieved a better performance in terms of channel estimation MSE in simulations.

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

scholarly journals Improved Pilot Selection for Channel Estimation of Polar Codes in Wireless 5G

2019 ◽  
Vol 9 (2) ◽  
pp. 4978-4983
Keyword(s):  
Channel Estimation ◽  
Fading Channels ◽  
Communication Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Poor Performance ◽  
Polar Codes ◽  
Selection Scheme ◽  
Polar Code ◽  
Pilot Selection

Fading channels learning about polar codes is great prominence. Knowledge of polar codes is very important while they are applied to the wireless communication systems. In fading Channels the communication through channel estimation which is an essential step for communication. The structure is constructed by a set of information bits for both systematic polar code and non-systematic polar code and the information set recognized frozen bits. In fading channels uneven pilot selection scheme and even pilot selection scheme are two pilot selection schemes are considered for polar codes. There is an improvement in decoding performance of polar codes using these selection schemes. In this choosing of coded symbols treated as pilots is a replacement of insertion of pilots. Polar codes have poor performance in fixed domain. So the EPS selection scheme can be active for tracing or channel estimation. The structure of polar code encoding is acapable structure and pilot selection is grave since whole selections cannot use the existing structure again. By conjoining the above advantages, pilot signals are selected without any addition from outside and insertion of pilot symbols impartial to estimation of the channel. Leveraging this, the DM-BS scheme is applyto multiple input multiple output (MIMO) system in frequency selective fading channel.

Joint Channel Estimation and Data Detection for STBC MIMO-OFDM Wireless Communication System

2015 ◽  
Vol 24 (04) ◽  
pp. 1550059 ◽  
Author(s):  
Gajanan R. Patil ◽  
Vishwanath K. Kokate
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Data Detection ◽  
Channel Estimate ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm ◽  
The Cost ◽  
Joint Channel

This paper presents a joint channel estimation and data detection technique for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Initial estimate of the channel is obtained using semi-blind channel estimation (SBCE). The whitening rotation (WR)-based orthogonal pilot maximum likelihood (OPML) method is used to obtain the channel estimate. The estimate is further enhanced by extracting information through the received data symbols. The performance of the proposed estimator is studied under various channel models. The simulation study shows that this approach gives better performance over training-based channel estimation (TBCE) and OPML SBCE methods but at the cost of higher computational complexity.

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