IEEE Access (Jan 2024)
A Universal Approximation-Centered Deep Learning Framework for the Massive 5G MIMO-OFDM Channel Estimation
Abstract
In the era of wireless communication, the MIMO technology contributes the communication among users with high data rate and spectral efficiency. The interference of data transmission along the multipath channel is tackled by the OFDM of the MIMO system. However, the existing works didn’t focus on efficient signal modulation, thus resulting in sub-optimal beam forming and inaccurate channel estimation in the MIMO system. Hence, in this paper, an efficient CE using the Universal Approximation Layer-based P-ReLU DNN (UAL-P-DNN), Line Sweep-Multi-Variate Kernel Density estimation (LS-MVKD), and two-dimensional Gray Codes Quadratic Amplitude Modulation (GCQAM) is proposed. Here, in this paper, the UAL-P-DNN is mainly used for the CE, LS-MVKD is used for the prior estimation of the channel, and GCQAM is adopted for the signal modulation process. In the proposed methodology, the process begins with the transmission. In the transmission phase, the data bits of input text, audio, and video are randomly generated and encoded using Montgomery Curve-based Elliptic Curve Cryptography (MC-ECC). Then, the exact data bits to be transmitted are extracted by the Rate matching. Further, the extracted bits are mapped using GCQAM for modulating the signal amplitude. After that, the modulated signal along with the pilot symbol is given to the IFrFT for converting signal characteristics to the time domain. Subsequently, the message bits are transmitted along the Rayleigh fading channel, and the prior channel is estimated using LS-MVKD. Based on the obtained signal matrix, input data, and target data, the channel is estimated using the UAL-P-DNN model. In the receiver phase, the original data is retrieved by inversely performing the operations, which are done on the transmitter. The executed results show that the proposed system estimated a channel with 54.485db PSNR and 0.29% BER. Also, the input data is encoded by the proposed method within 2130 ms and 98.46% security level.
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