Efficient Trainable Least Squares Approach for PAPR Reduction in OFDM-based Hybrid Beamforming Systems

Core Concepts

A trainable least squares approach is proposed to efficiently reduce the peak-to-average power ratio (PAPR) of OFDM signals in hybrid beamforming systems, which have limited bandwidth and digital subspace compared to fully digital beamforming.

Abstract

The paper proposes a trainable least squares (LS) approach to reduce the peak-to-average power ratio (PAPR) of OFDM signals in hybrid beamforming (HBF) systems.
Key highlights:

In HBF, the number of antennas exceeds the number of digital ports, which restricts PAPR reduction capabilities due to limited bandwidth and digital subspace.
The proposed approach divides the problem into two steps:

Generating band-limited PAPR reduction vectors for each antenna.
Fitting the band-limited PAPR reduction matrix into the limited digital subspace using trainable LS.

A genetic algorithm is used for training, and the optimal performance bound is calculated using convex optimization.
Complexity analysis confirms the feasibility of the proposed algorithm for future generation systems.

Stats

High PAPR causes high power amplifiers to operate nonlinearly, producing additive non-linear noise.
Using digital-to-analog converters with better resolution is required when the PAPR value is large.

Quotes

"The problem is to meet both conditions. Moreover, the major HBF advantage is a reduced system complexity, thus the complexity of the PAPR reduction algorithm is expected to be low."
"To justify the performance of the proposed trainable LS, we provide a performance bound achieved by convex optimization using the CVX Matlab package."

Key Insights Distilled From

Trainable Least Squares to Reduce PAPR in OFDM-based Hybrid Beamforming Systems

by Andrey Ivano... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02160.pdf

Deeper Inquiries

How can the proposed trainable LS approach be extended to handle dynamic channel conditions and user mobility in HBF systems

To extend the proposed trainable LS approach to handle dynamic channel conditions and user mobility in HBF systems, several adaptations can be made. Firstly, incorporating feedback mechanisms from the channel state information (CSI) can enable real-time adjustments to the PAPR reduction algorithm based on the changing channel conditions. By continuously updating the PAPR reduction vectors using the latest CSI, the system can adapt to dynamic channel variations.
Moreover, for user mobility scenarios, the trainable LS technique can be enhanced by integrating predictive algorithms that anticipate user movements. By predicting user locations or beamforming requirements, the system can proactively adjust the PAPR reduction strategy to optimize performance for mobile users. This predictive capability can help mitigate the impact of user mobility on PAPR reduction efficiency.
Additionally, the trainable LS approach can leverage machine learning models trained on historical channel data to predict future channel conditions and user behavior. By utilizing these predictive models, the system can preemptively adjust PAPR reduction parameters to accommodate anticipated changes in the channel environment, thereby enhancing performance in dynamic scenarios.

What are the potential trade-offs between PAPR reduction performance and computational complexity in the context of 5G and 6G wireless networks

In the context of 5G and 6G wireless networks, there exist potential trade-offs between PAPR reduction performance and computational complexity. As the demand for higher data rates and lower latency increases, the need for efficient PAPR reduction techniques becomes crucial. However, achieving high PAPR reduction performance often comes at the cost of increased computational complexity.
One trade-off is between the level of PAPR reduction achieved and the computational resources required to implement the algorithm. More sophisticated PAPR reduction methods, such as deep learning-based approaches, may offer superior performance but at the expense of higher computational demands. Balancing the trade-off between performance and complexity is essential in designing efficient PAPR reduction solutions for 5G and 6G networks.
Furthermore, the trade-off between PAPR reduction performance and computational complexity can impact system scalability and energy efficiency. Complex PAPR reduction algorithms may consume more power and resources, limiting their scalability in large-scale network deployments. Therefore, optimizing the trade-off between performance and complexity is crucial for ensuring the practical feasibility of PAPR reduction techniques in advanced wireless networks.

Could the trainable LS technique be combined with other PAPR reduction methods, such as deep learning-based approaches, to further improve efficiency and robustness

The trainable LS technique can be effectively combined with other PAPR reduction methods, such as deep learning-based approaches, to further enhance efficiency and robustness. By integrating trainable LS with deep learning models, the system can benefit from the strengths of both techniques.
Deep learning algorithms can provide advanced pattern recognition capabilities and adaptability to complex signal environments, complementing the trainable LS approach's optimization capabilities. The deep learning model can learn intricate patterns in the channel data and optimize the PAPR reduction process based on these learned patterns.
Moreover, the combination of trainable LS and deep learning can offer a more adaptive and intelligent PAPR reduction solution that can dynamically adjust to changing channel conditions and user requirements. This hybrid approach can leverage the strengths of both techniques to achieve superior PAPR reduction performance while maintaining manageable computational complexity.
By integrating trainable LS with deep learning-based methods, the system can achieve a more robust and efficient PAPR reduction solution that is well-suited for the demanding requirements of 5G and 6G wireless networks.