Abstract
This paper presents our initial results in assessing the efficiency of deep learning-based channel estimation compared to the conventional Pilot-Assisted Channel Estimation (PACE) techniques, such as least-square (LS) and minimum mean-square error (MMSE) estimators. A simulation environment to evaluate OFDM performance at different channel models has been used. A DL process that estimates the channel from training data is also employed to get estimated channel impulse response. Two channel models have been used in the comparison: Clustered Delay Line (CDL) and Nakagami-m fading channel models. The performance is evaluated under different parameters including number of pilots, number of subcarriers, the length of cyclic prefix and carrier frequency through computer simulation using MATLAB. From the simulation results, the trained DL estimator provides better results in estimating the channel and detecting the transmitted symbols compared to LS and MMSE estimators at a remarkably less complexity. Furthermore, the DL estimator also demonstrates its effectiveness with various pilot densities and with different cyclic prefix periods.
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Mousa, A.S., Taman, A.I., Hassan, A.M., Zekry, A.A. (2022). Deep Learning Channel Estimation for OFDM 5G Systems with Different Channel Models. In: S. Shmaliy, Y., Abdelnaby Zekry, A. (eds) 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021). CCIE 2021. Lecture Notes in Electrical Engineering, vol 920. Springer, Singapore. https://doi.org/10.1007/978-981-19-3927-3_23
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