Reducing normalized mean square error during channel estimation using minimum pilot symbols in massive MIMO network.

Area of Science:

• Wireless Communications
• Deep Learning Applications
• Signal Processing

Background:

• Deep learning (DL) presents significant advancements in wireless communications.
• Accurate channel state information (CSI) estimation is crucial for system performance.
• Traditional methods like least square (LS) and minimal mean square estimation (MMSE) have limitations.

Purpose of the Study:

• To employ deep learning models for efficient channel state information estimation.
• To assess the performance of deep learning in estimating channel coefficients using minimal pilot symbols.
• To evaluate the impact of pilot symbol length and cross-cell interference on estimation accuracy.

Main Methods:

• Utilized fully connected neural network based on deep learning (DL-FCNN) and convolutional neural network (CNN) models.
• Focused on estimating channel coefficients with a minimal number of pilot symbols.
• Analyzed performance based on normalized mean square errors (NMSE) under varying interference conditions.

Main Results:

• Deep learning methods significantly outperform traditional LS and MMSE techniques in NMSE.
• The proposed DL approach demonstrates superior performance in the presence of pilot contamination.
• Effective channel estimation was achieved even with varying levels of cross-cell interference.

Conclusions:

• Deep learning models are highly effective for channel estimation in wireless systems.
• DL offers a robust solution for physical layer signal processing in Massive MIMO networks.
• The proposed DL methods provide enhanced accuracy and resilience against interference.