Plug-And-Play mMIMO Precoding Achieves Generalization across Sites and Power Levels

Researchers are tackling the significant challenge of deploying massive MIMO (mMIMO) precoding in real-world wireless networks, where computationally intensive algorithms hinder practical implementation. Ali Hasanzadeh Karkan, Ahmed Ibrahim, Jean-François Frigon, and François Leduc-Primeau from Polytechnique Montréal and Ericsson Canada’s R&D present a novel ‘plug-and-play’ deep learning framework, termed PaPP, designed to overcome these limitations. Their work demonstrates a model trainable for both fully digital and hybrid beamforming, crucially allowing reuse across different deployment sites and conditions without requiring extensive retraining. By combining teacher-student learning with domain-generalisation techniques, PaPP not only outperforms existing methods on ray-tracing data from unseen locations but also offers a substantial reduction in computational energy, paving the way for more energy-efficient mMIMO systems.

Adaptable mMIMO precoding using Deep learning transfer

Scientists have developed a novel deep learning framework, termed the plug-and-play precoder (PaPP), to significantly improve the efficiency and adaptability of massive multiple-input multiple-output (mMIMO) downlink precoding. This breakthrough addresses the limitations of existing methods, which are often computationally expensive, sensitive to signal-to-noise ratio (SNR) and channel estimation quality, or require extensive retraining for new deployment sites. The research team achieved a system capable of being trained for either fully digital (FDP) or hybrid beamforming (HBF) precoding and then reused across different sites, transmit-power levels, and with varying degrees of channel estimation error. PaPP combines a high-capacity teacher network with a compact student network, leveraging a self-supervised loss function that balances teacher imitation and normalized sum-rate.

The study unveils a training process utilizing meta-learning domain-generalization and transmit-power-aware input normalization, enabling the model to generalize effectively to unseen environments. Experiments conducted using ray-tracing data from three distinct, previously unseen sites demonstrate that the PaPP models, both FDP and HBF, consistently outperform conventional and other deep learning-based baselines after fine-tuning with a limited amount of local, unlabeled data. This approach circumvents the need for complete retraining at each new deployment location, offering a substantial practical advantage. The researchers proved that PaPP achieves more than a 21-fold reduction in modeled computation energy while maintaining robust performance even when faced with inaccuracies in channel estimation.

This innovation directly tackles the computational burden associated with near-optimal algorithms like the weighted minimum mean squared error (WMMSE) method, which suffers from cubic complexity with the number of antennas. By learning a direct mapping from channel state information (CSI) to beamforming weights, PaPP drastically reduces inference time, making it suitable for real-time applications in dense and power-constrained deployments. The work opens possibilities for energy-efficient mMIMO precoding, paving the way for more sustainable and scalable wireless communication networks. The framework’s ability to adapt to varying SNR levels and imperfect CSI further enhances its practicality in real-world scenarios, where conditions are rarely ideal.

Deep learning for adaptable mMIMO precoding offers significant

Scientists developed a plug-and-play precoder (PaPP), a deep learning framework designed for both fully digital (FDP) and hybrid beamforming (HBF) precoding, to address computational challenges in massive multiple-input multiple-output (mMIMO) downlink systems. The research team trained a high-capacity teacher network and a compact student network using a self-supervised loss function, carefully balancing teacher imitation with normalized sum-rate optimisation. This training employed domain-generalization techniques and transmit-power-aware input normalization, enabling the model to function across diverse deployment sites and power levels without requiring retraining from scratch. The study pioneered a method for reusing a single trained model, significantly reducing the computational burden associated with near-optimal algorithms like weighted minimum mean squared error (WMMSE).

Experiments utilised a large-scale ray-tracing dataset generated from detailed three-dimensional maps of Montreal, encompassing industrial campuses and dense urban streets, to rigorously benchmark the generalisation capabilities of the learned precoding methods. Researchers engineered the system to evaluate performance across three key generalisation goals: site generalisation, SNR independence, and robustness to channel-estimation errors. The team then fine-tuned the PaPP models with a small set of local, unlabeled samples collected from three unseen sites, demonstrating adaptability to new environments. This approach enabled the PaPP FDP and HBF models to outperform conventional and existing deep learning baselines in spectral efficiency.

The study quantified a greater than 21% reduction in modeled computation energy achieved by PaPP, alongside sustained performance even under realistic channel-estimation errors. Scientists harnessed this energy efficiency through a novel combination of network architecture and training strategy, addressing the limitations of iterative algorithms like WMMSE, which exhibit cubic complexity with the number of antennas. The technique reveals a practical solution for energy-efficient mMIMO precoding, offering a substantial improvement over existing methods in both performance and resource utilisation, and paving the way for real-time applications in dense and power-constrained deployments.

Plug-and-play precoder cuts mMIMO energy use by up

Scientists have developed a plug-and-play precoder (PaPP), a deep learning framework designed for both fully digital (FDP) and hybrid beamforming (HBF) precoding, achieving significant advancements in mMIMO downlink performance. The team measured substantial improvements in spectral efficiency and computational energy reduction, demonstrating a more than 21-fold decrease in modeled computation energy across both FDP and HBF architectures. Experiments revealed that PaPP can be trained once and then reused across different deployment sites, transmit-power levels, and even with varying degrees of channel estimation error, eliminating the need for site-specific retraining. Results demonstrate that the PaPP models, after fine-tuning with a small set of local unlabeled samples, consistently outperform conventional and other deep learning baselines on ray-tracing data from three unseen sites.

The research focused on achieving site generalization, enabling the precoder to maintain high spectral efficiency when transferred to new urban, suburban, or industrial landscapes. Scientists recorded performance independent of signal-to-noise ratio (SNR), allowing the precoder to adapt gracefully across a broad SNR range without retraining, crucial for varying base station transmit power. Measurements confirm that PaPP maintains robust performance even under realistic channel estimation impairments, caused by noise, quantization, and pilot contamination. The framework combines a high-capacity teacher network with a compact student network, utilizing a self-supervised loss function that balances teacher imitation and normalized sum-rate.

This training process leverages meta-learning domain-generalization and transmit-power-aware input normalization, enhancing the model’s adaptability. Data shows that this approach delivers a practical solution for energy-efficient mMIMO precoding, addressing the computational challenges of iterative algorithms like WMMSE, which scale cubically with the number of antennas. The breakthrough delivers a significant reduction in computational complexity, making real-time applications feasible in dense or power-constrained deployments. Tests prove that PaPP’s ability to generalize across diverse conditions and hardware constraints represents a substantial step towards practical mMIMO implementation. Researchers utilized a large-scale ray-tracing dataset generated from detailed three-dimensional maps of Montreal to rigorously benchmark the generalization capabilities of the learned precoding methods, ensuring the robustness of the findings across varied propagation conditions.

Generalizable precoder adapts to diverse mMIMO channels

Scientists have developed a new deep learning framework, termed plug-and-play precoder (PaPP), designed to improve the spectral efficiency of massive multiple-input multiple-output (mMIMO) downlink precoding. This framework addresses the computational expense and sensitivity to channel conditions that plague existing algorithms, while also overcoming the limitations of prior deep learning solutions which often require extensive retraining for new deployment sites. PaPP utilises a teacher-student architecture, trained with domain-generalization and transmit-power-aware input normalization, enabling it to be reused across different sites, transmit power levels, and with varying degrees of channel estimation error. Numerical results, utilising ray-tracing data from three independent sites, demonstrate that PaPP outperforms both conventional and existing deep learning approaches, even after fine-tuning with a limited amount of local data.

The research indicates a reduction of over 21% in modelled computation energy, alongside maintained performance despite channel estimation errors, suggesting a practical solution for energy-efficient mMIMO precoding. The key achievement of this work is a computationally efficient deep learning approach that generalises well across diverse deployment scenarios. PaPP achieves near-optimal spectral efficiency across a wide range of transmit power levels and exhibits robustness to channel estimation errors, delivering sum-rate gains of up to 94% compared to conventional methods under severe error conditions. The authors acknowledge that the benefits of adaptation diminish with increasingly severe channel estimation errors, with performance converging to the baseline when errors exceed a certain threshold. Future research could explore methods to further enhance performance under extremely poor channel conditions or investigate the application of this framework to even more complex wireless communication scenarios.