A Deep Learning Framework for Optimization of MISO Downlink Beamforming (1901.00354v2)

Abstract: Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

• The paper introduces CNN-based beamforming neural networks that reduce computational complexity by leveraging uplink-downlink duality.
• It designs problem-specific BNNs for SINR balancing, power minimization, and sum rate maximization that outperform conventional methods.
• Simulations confirm significant latency reduction, making the framework suitable for real-time deployment in advanced wireless systems.

Overview of Deep Learning Framework for MISO Downlink Beamforming Optimization

The paper "A Deep Learning Framework for Optimization of MISO Downlink Beamforming" addresses the critical issue of optimizing beamforming in multiple-input-single-output (MISO) systems. Traditional iterative algorithms for beamforming optimization are computationally expensive, making them unsuitable for real-time applications. This research proposes a deep learning (DL) framework that aims to significantly reduce computational complexity while achieving near-optimal beamforming solutions.

Beamforming in MISO systems can be formulated into three primary optimization problems: SINR balancing, power minimization, and sum rate maximization. Each of these problems typically requires complex iterative solutions which are computationally intensive. The proposed framework utilizes convolutional neural networks (CNNs) and expert domain knowledge, such as exploiting the uplink-downlink duality, to construct beamforming neural networks (BNNs) tailored for each problem type. The results indicate significant improvements in computational speed while maintaining performance comparable to traditional algorithms.

Key Contributions

1. Framework Design: The paper introduces a CNN-based BNN framework that leverages expert knowledge of solution structures and the uplink-downlink duality to simplify the beamforming optimization process. This design reduces the number of parameters the neural networks need to learn, which decreases complexity without sacrificing performance.
2. Problem-Specific BNNs: Specific BNNs are proposed for SINR balancing, power minimization, and sum rate maximization:
   • SINR Balancing: Utilizes supervised learning to predict virtual uplink power allocations, which are then used to infer downlink beamforming matrices. The BNN achieves near-optimal performance with a significant reduction in computational time.
   • Power Minimization: This network also employs supervised learning, predicting uplink power allocations to determine the downlink beamforming matrices while effectively managing computational delay.
   • Sum Rate Maximization: Given the non-convex nature of this problem, the authors employ a hybrid learning strategy combining supervised and unsupervised learning. The BNN achieves a performance comparable to the Weighted Minimum Mean Squared Error (WMMSE) algorithm but with less computational overhead.
3. Simulation Verification: Extensive simulations are conducted to validate the proposed BNNs. The results show that the BNNs provide a close approximation to optimal solutions with significantly reduced latency, rendering them suitable for real-time implementations. For the SINR balancing and power minimization, the BNNs outperform the Zero-Forcing (ZF) and Regularized ZF (RZF) beamforming methods.

Implications and Future Directions

This research presents a pivotal step towards integrating deep learning for real-time optimization in wireless communications. The proposed framework demonstrates how domain-specific knowledge can be embedded into neural network architectures, thereby achieving efficient and effective solutions for complex optimization problems in MISO systems.

Practical implications include deployment in next-generation wireless communication systems such as 5G, where low-latency applications are critical. The approach provides a scalable solution that could extend to various MISO configurations and adaptive network conditions.

Future work could explore further generalizing this framework to multi-antenna systems with more complex configurations and channel conditions. Additionally, improvements in neural network models, such as incorporating reinforcement learning techniques, might enhance the adaptability and robustness of beamforming solutions in dynamic environments.

In conclusion, this paper lays the groundwork for efficient deep learning-based beamforming optimization in MISO downlink systems and opens avenues for further research in AI-driven wireless communications.