Optimizing Channel Estimation Overhead for OTFS with Prior Channel Statistics

2019 ◽

Vol 8 (10) ◽

pp. 4516-4520

Keyword(s):

Kalman Filter ◽

Channel Estimation ◽

Communication Systems ◽

Impulse Noise ◽

Wireless Communication Systems ◽

Delay Spread ◽

Rate Performance ◽

Ofdm System ◽

Estimation Scheme ◽

Channel Statistics

Due to its robustness to multipath delay spread and impulse noise, and also elevated spectral efficiency like characteristics, OFDM technology has been commonly adopted in contemporary wireless communication systems like LTE. The heart of an OFDM system is Channel estimator and the efficiency of channel estimation (CE) has a direct impact on the bit error rate performance of the system also the CE performance itself depends on effective estimation of fading channel hence the channel estimation problem has to be addressed. The popular LS based CE method is accepted for its simplicity but is found to be vulnerable to noise whereas the simple MMSE based CE that provides fine-tuned estimation still suffers from complexity as it depends on the knowledge of channel statistics (KCS). Hence a two level channel estimation technique that is a combination of kalman filter and thresholding scheme which denoises the LS initial estimates is proposed. The kalman filter tracks the estimates with initial data as LS estimates and threshold helps in selecting the significant data thus denoising the estimates and also filters the redundancy in estimated data. On comparing the existed CE techniques with that of the proposed scheme, the MSE performance of proposed scheme is found to be optimal and simple to that of the LMMSE technique.

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SPC04-6: Channel Estimation for MIMO-OFDM Systems in Rapid Time Variant Environments Based On Channel Statistics Estimation

IEEE Globecom 2006 ◽

10.1109/glocom.2006.555 ◽

2006 ◽

Cited By ~ 1

Author(s):

Igor V. Kaioukov ◽

Vladimir B. Manelis ◽

Joseph R. Cleveland

Keyword(s):

Channel Estimation ◽

Ofdm Systems ◽

Channel Statistics ◽

Mimo Ofdm

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Channel Estimation and Signal Detection in OFDM Systems using Deep Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l1052.10812s19 ◽

2019 ◽

Vol 8 (12S) ◽

pp. 176-179

Keyword(s):

Deep Learning ◽

Channel Estimation ◽

Signal Detection ◽

Orthogonal Frequency Division Multiplexing ◽

Minimum Mean Square Error ◽

Ofdm Systems ◽

Simulation Based ◽

Channel Statistics ◽

Channel Distortion ◽

Deep Learning Model

this article presents “channel estimation and signal detection in OFDM systems by using deep learning”. OFDM stands for “Orthogonal Frequency Division Multiplexing”. This paper exploits end to end handling of wireless OFDM channels by deep learning. It is different from the existing OFDM receivers as it estimates the channel state information (CSI) explicitly and then estimated CSI is used to recover the transmitted symbols, thee proposed approach of deep learning implicitly estimates CSI and the transmitted symbols are recovered directly. The online transmitted data is directly recovered by the offline training a deep learning model using simulation based channel statistics generated data for addressing channel distortion. The performance comparable to “minimum mean square error” (MSME) estimator with transmitted symbols is detected by using deep learning based channel distortion. Using fewer number of pilots, omitting cyclic prefix and in the existence of nonlinear clipping noise, the approach of deep learning is more robust as compared to traditional methods.

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