Analyze the Performance of Massive MIMO System Utilizing One Bit ADCs: Deep Learning-Based Approach with Varying SNR and Limited Pilot Resources
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 11 |
| Year of Publication : 2024 |
| Authors : Raksha Thakur, Vineeta Saxena Nigam |
10.14445/23488379/IJEEE-V11I11P127
How to Cite?
Raksha Thakur, Vineeta Saxena Nigam, "Analyze the Performance of Massive MIMO System Utilizing One Bit ADCs: Deep Learning-Based Approach with Varying SNR and Limited Pilot Resources," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 11, pp. 284-294, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P127
Abstract:
One of the essential technologies announced for 5G wireless networks is “Massive Multiple-Input Multiple-Output (MIMO)”. This technology incorporates many antennas at the base station, enabling more information signals to be transmitted and received simultaneously on a common radio channel. Massive-MIMO technology empowers deployments such as signal detection, beamforming, channel estimation, etc. Using conventional methods, the requirement of very long pilot sequences is a prevalent issue in obtaining accurate channel estimation. The main objective of this work is to investigate Deep Learning (DL)-centered hybrid Convolution Neural Network with Multi-Layer Perceptron (CNN-MLP) based channel estimation facilitated with one-bit “Analog-to-Digital Converters” (ADCs) in an uplink Massive MIMO scenario. Initially, preprocessing and data preparation are done to evaluate the model. This involves gathering, normalizing, and dividing considered data into training, validation, and test sets. Then, a CNN-MLP architecture will be designed using a deep learning framework with an input layer, several hidden layers, and an output layer for NMSE prediction. By proper training of the proposed model, efficient channel parameters are calculated with fewer pilot lengths to reduce overhead and increase spectral efficiency. By implementing the proposed model, channel estimation accuracy is enhanced efficiently. Simulation achieves better results according to performance metrics such as NMSE and attainable SNR per antenna. Results showed that “NMSE” performance approach as low as -22.2dB as the number of antennas increases while “SNR per antenna” achieves 99.5% gain for varying received SNRs of 0dB, 10dB, and 20 dB even at smaller pilot length.
Keywords:
Massive MIMO, One-bit ADCs, Channel estimation, Deep Learning (DL), CNN-MLP.
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