AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
Abstract
:1. Introduction
- Develops an ensemble framework integrating multiple ML methods, leveraging their complementary strengths for optimal pilot placement and channel estimation;
- Proposes a hybrid beamforming system that combines NN-predicted SVD for adaptive optimization of beamforming matrices, reducing complexity and enhancing spectral efficiency;
- Extends the evaluation to include realistic channel conditions, advanced modulation schemes, and scalable antenna configurations, providing a more holistic and practical analysis;
- Demonstrates a significant reduction in pilot overhead (up to 82%) while maintaining or improving BER and SE compared to traditional and existing AI-based methods.
2. System Model
2.1. Hybrid Beamforming and Pilot Placement
2.1.1. Hybrid Beamforming System
2.1.2. Pilot Placement
2.1.3. Singular Value Decomposition
2.2. AI-Driven Pilot Placement
2.2.1. Unsupervised K-Clustering ML
- Feature selection: Input features for clustering include the temporal positions of pilot symbols, their spatial allocation, and inter-pilot spacing. These features reflect the structure of the pilot signals rather than channel metrics or user-specific parameters;
- Clustering process: Using k-means clustering, the algorithm partitions the pilot signals into k clusters. Each cluster represents a group of pilot signals with similar temporal or spatial properties;
- Pilot Allocation: A representative pilot signal is selected for each cluster, which reduces the number of total pilots needed while maintaining sufficient information for channel estimation.
2.2.2. Supervised ML
Linear Regression
Random Forest Regression
Neural Networks SVD Enhancement
3. Results
3.1. Classical Pilot Placement Technique
3.2. K-Means Clustering for Pilot Overhead Reduction
3.3. Linear Regression for Pilot Overhead Reduction
3.4. Random Forest Regression for Pilot Overhead Reduction
3.5. Random Forest Regression NNSVD for Pilot Overhead Reduction
3.6. Random Forest Regression NNSVD with 64 Tx/16 Rx Antennas
3.7. Random Forest Regression NNSVD with QPSK Moduation
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Parameters | Values |
---|---|
Carrier frequency | 28 × 109 Hz |
Sampling frequency | 100 × 106 Hz |
Modulation technique | BPSK |
Transmitter antennas | 16 |
Receiver antennas | 4 |
Number of streams | 4 |
Number of symbols | 1000 |
SNR | −10 dB to 30 dB |
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Abou Yassin, M.R.; Abou Chahine, S.; Issa, H. AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems. Appl. Syst. Innov. 2025, 8, 24. https://doi.org/10.3390/asi8010024
Abou Yassin MR, Abou Chahine S, Issa H. AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems. Applied System Innovation. 2025; 8(1):24. https://doi.org/10.3390/asi8010024
Chicago/Turabian StyleAbou Yassin, Mohammad Riad, Soubhi Abou Chahine, and Hamza Issa. 2025. "AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems" Applied System Innovation 8, no. 1: 24. https://doi.org/10.3390/asi8010024
APA StyleAbou Yassin, M. R., Abou Chahine, S., & Issa, H. (2025). AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems. Applied System Innovation, 8(1), 24. https://doi.org/10.3390/asi8010024