Sparse Channel Estimation Based on Compressive Sensing with Overcomplete Dictionaries in OFDM Communication Systems

2018 ◽  
pp. 99-111
Author(s):  
Yi Zhang ◽  
Ramachandran Venkatesan ◽  
Octavia A. Dobre ◽  
Cheng Li
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Sparse Channel Estimation ◽  
Sparse Channel

scholarly journals Compressive Sensing Based Bayesian Sparse Channel Estimation for OFDM Communication Systems: High Performance and Low Complexity

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guan Gui ◽  
Li Xu ◽  
Lin Shan ◽  
Fumiyuki Adachi
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Channel Model ◽  
Primary Objective ◽  
Channel Uncertainty ◽  
Multiple Channel ◽  
Sparse Channel Estimation ◽  
Estimation Performance ◽  
Sparse Channel

In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods.

Application of compressive sensing to sparse channel estimation

2010 ◽  
Vol 48 (11) ◽  
pp. 164-174 ◽  
Author(s):  
Christian R. Berger ◽  
Zhaohui Wang ◽  
Jianzhong Huang ◽  
Shengli Zhou
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Sparse Channel Estimation ◽  
Sparse Channel

scholarly journals Improved Sparse Channel Estimation for Cooperative Communication Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Guan Gui ◽  
Wei Peng ◽  
Ling Wang
Keyword(s):  
Channel Estimation ◽  
Cooperative Communication ◽  
Communication Systems ◽  
Channel Model ◽  
Linear Method ◽  
Estimation Method ◽  
Estimation Methods ◽  
Sparse Constraint ◽  
Sparse Channel Estimation ◽  
Sparse Channel

Accurate channel state information (CSI) is necessary at receiver for coherent detection in amplify-and-forward (AF) cooperative communication systems. To estimate the channel, traditional methods, that is, least squares (LS) and least absolute shrinkage and selection operator (LASSO), are based on assumptions of either dense channel or global sparse channel. However, LS-based linear method neglects the inherent sparse structure information while LASSO-based sparse channel method cannot take full advantage of the prior information. Based on the partial sparse assumption of the cooperative channel model, we propose an improved channel estimation method with partial sparse constraint. At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over global sparse channel estimation methods.

Compressive Sensing-Based Pilot Design for Sparse Channel Estimation in OFDM Systems

2017 ◽  
Vol 21 (1) ◽  
pp. 4-7 ◽  
Author(s):  
Roozbeh Mohammadian ◽  
Arash Amini ◽  
Babak Hossein Khalaj
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Ofdm Systems ◽  
Sparse Channel Estimation ◽  
Pilot Design ◽  
Sparse Channel

scholarly journals Pilot Design for Sparse Channel Estimation in Orthogonal Frequency Division Multiplexing Systems

2018 ◽  
Vol 2 ◽  
pp. 60-68 ◽  
Author(s):  
P. Vimala ◽  
G. Yamuna
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Fading Channel ◽  
Orthogonal Frequency Division Multiplexing ◽  
Wide Band ◽  
Frequency Division Multiplexing ◽  
Frequency Division ◽  
Ofdm Systems ◽  
Sparse Channel Estimation ◽  
Sparse Channel

Orthogonal Frequency Division Multiplexing (OFDM) is a well-known technique used in modern wide band wireless communication systems. Coherent OFDM systems achieve its advantages over a multipath fading channel, if channel impulse response is estimated precisely at the receiver. Pilot-aided channel estimation in wide band OFDM systems adopts the recently explored compressive sensing technique to decrease the transmission overhead of pilot subcarriers, since it exploits the inherent sparsity of the wireless fading channel. The accuracy of compressive sensing techniques in sparse channel estimation is based on the location of pilots among OFDM subcarriers. A sufficient condition for the optimal pilot selection from Sylow subgroups is derived. A Sylow subgroup does not exist for most practical OFDM systems. Therefore, a deterministic pilot search algorithm is described to select pilot locations based on minimizing coherence, along with minimum variance. Simulation results reveal the effectiveness of the proposed algorithm in terms of bit error rate, compared to the existing solutions.

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