Deep Learning Techniques for Hybrid Beamforming in Massive MIMO Systems

Hybrid beamforming combines analogue and digital signal processing to steer multiple antenna arrays simultaneously, reducing hardware cost and energy consumption in massive multiple-input multiple-output (MIMO) systems. The high dimensionality and nonconvex constraints of hybrid beamforming present formidable challenges, including constant-modulus hardware limitations and imperfect channel knowledge. Recent advances integrate deep learning to learn beamforming weights directly from channel observations, enabling end-to-end optimisation, rapid adaptation to changing propagation environments and substantial improvements in spectral efficiency. These techniques leverage neural-network architectures—such as convolutional, recurrent and deep reinforcement learning models—to map raw channel measurements to optimal hybrid precoder and combiner matrices, achieving near fully digital performance with fewer radio-frequency chains. Applications span millimetre-wave and terahertz bands, supporting 5G and beyond by delivering high data rates, low latency and robust interference suppression.

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Recent work has proposed a semi-supervised incremental learning scheme for time-varying channels, in which a broad-network structure jointly trains analogue and digital beamformers on both labelled and vast unlabelled transmission data. This approach leverages graph-based clustering to update models chunk by chunk, improving spectral efficiency over single-shot updates while reducing computational complexity under realistic feedback constraints.

Another study introduced a convolutional neural network-based hybrid precoding method for cell-free massive MIMO operating at terahertz frequencies. By predicting optimal precoding weights from spatiotemporal channel snapshots, the model mitigates pilot contamination and captures dynamic channel behaviour. Simulations demonstrate superior energy efficiency and reduced system complexity compared with classical hybrid schemes, achieving over one bit per joule at high signal-to-noise ratios.

A foundational framework employed a hybrid analog–digital deep neural network architecture that embeds unit-modulus constraints within an extended network design. This architecture approximates fully digital beamforming mappings with arbitrarily high precision, yielding hybrid beamformers that match digital performance while cutting the number of radio-frequency chains. The method attains robustness to channel estimation errors and diverse channel models, signalling a practical route to real-time deployment.

Deep Learning Techniques for Hybrid Beamforming in Massive MIMO Systems publication trend

The graph below shows the total number of articles in deep learning techniques for hybrid beamforming in massive mimo systems across all publications each year (not limited to Nature Index journals).

Technical terms

Massive MIMO: A system employing large antenna arrays to serve many users simultaneously.

Hybrid Beamforming: A technique combining analogue phase-shifters and digital precoders for efficient beam steering.

Deep Learning: A subset of machine learning using layered neural networks to model complex patterns.

Convolutional Neural Network (CNN): A neural-network architecture that extracts spatial features via convolutional filters.

Channel State Information (CSI): Knowledge of the propagation environment used to design beamforming weights.

Spectral Efficiency: The rate of data transmission per unit bandwidth, measured in bits per second per hertz.

References

1. Semi-supervised learning based hybrid beamforming under time-varying propagation environments. Digital Communications and Networks (2024).
2. Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication. Frontiers in Communications and Networks (2024).
3. Deep Learning-Based Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems. IEEE Access (2022).