This project presents a deep learning–based 1-bit precoding framework for downlink massive MIMO systems. The proposed method learns an efficient mapping from Channel State Information (CSI) to 1-bit quantized precoding vectors, enabling low-power and hardware-efficient transmission suitable for next-generation 5G and 6G wireless systems.
- Deep learning–based 1-bit precoding for massive MIMO
- Unsupervised training using a constructive interference–based loss function
- Lightweight residual architecture with depth-wise separable convolutions
- Robust performance under imperfect CSI and varying SNR conditions
- Suitable for real-time and energy-efficient wireless communication systems
The architecture consists of complex-aware preprocessing followed by efficient convolutional and attention-based residual blocks. A power-aware quantization layer ensures compatibility with 1-bit DAC constraints.
Main components:
- Real–imaginary CSI preprocessing
- Depth-wise separable convolution layers
- Lightweight channel attention mechanism
- Optimized residual blocks
- 1-bit power-normalized quantization module
The dataset simulates a realistic massive MIMO-OFDM downlink system.
- Base station antennas: 32
- Users: 4
- Subcarriers: 64
- Total samples: 120,000
Hardware impairments modeled:
- 1-bit DAC quantization
- Phase noise
- IQ imbalance
- Power amplifier nonlinearity
- Optimizer: AdamW
- Learning rate: 3e-4
- Batch size: 16
- Epochs: 40
- Loss function: Constructive interference–based unsupervised loss
- Training strategy: Chunked data loading for memory efficiency
- Bit Error Rate (BER)
- Signal-to-Noise Ratio (SNR)
- Cosine Similarity
- Mean Squared Error (MSE)
| Metric | Value |
|---|---|
| SNR | ~18.2 dB |
| BER | ~5.4% |
| Cosine Similarity | ~0.87 |
- Programming Language: Python
- Deep Learning Framework: PyTorch
- Scientific Libraries: NumPy, SciPy
- Tools: Git, Linux
- Massive MIMO wireless systems
- Low-resolution DAC transmitters
- Energy-efficient 5G and 6G base stations
- Hardware-constrained communication systems
Gokulprasanth M
Bachelor of Engineering in Electronics and Communication Engineering
This project is based on recent research in one-bit precoding, constructive interference, and deep learning–based signal processing for massive MIMO systems.