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×published date:2023-Jul-14
FULL TEXT in - | page 276 -283
Abstract
Channel estimation is an essential part of millimeter wave architectures design. Least Square (LS) and Orthogonal Matching Pursuit (OMP) have largely been employed for the task. OMP is susceptible to noise and performs poorly at low signal to noise ratio while LS method is unable to exploit the sparse property of millimeter wave channel and therefore exhibits poorer performance than OMP. This work therefore considered Deep Learning (DL) for millimeter wave channel estimation in downlink millimeter wave communication scenario. By using normalized mean square errors , spectral efficiency and bit rate as performance metrics, it was shown for all the cases considered that DL outperforms OMP and LS over signal to noise ratio regimes of -30 to -10dB and hence an alternative and better choice for obtaining information concerning a channel characterized by noise and the model can also accomodate a number of channel paths in a manner better than adaptive method
Keywords: Channel estimation, Deep learning, Multiple Input-Multiple Output ,,
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