Scalable Transceiver Design for Multi-User Communication in FDD Massive MIMO Systems via Deep Learning (2504.11162v1)

Abstract: This paper addresses the joint transceiver design, including pilot transmission, channel feature extraction and feedback, as well as precoding, for low-overhead downlink massive multiple-input multiple-output (MIMO) communication in frequency-division duplex (FDD) systems. Although deep learning (DL) has shown great potential in tackling this problem, existing methods often suffer from poor scalability in practical systems, as the solution obtained in the training phase merely works for a fixed feedback capacity and a fixed number of users in the deployment phase. To address this limitation, we propose a novel DL-based framework comprised of choreographed neural networks, which can utilize one training phase to generate all the transceiver solutions used in the deployment phase with varying sizes of feedback codebooks and numbers of users. The proposed framework includes a residual vector-quantized variational autoencoder (RVQ-VAE) for efficient channel feedback and an edge graph attention network (EGAT) for robust multiuser precoding. It can adapt to different feedback capacities by flexibly adjusting the RVQ codebook sizes using the hierarchical codebook structure, and scale with the number of users through a feedback module sharing scheme and the inherent scalability of EGAT. Moreover, a progressive training strategy is proposed to further enhance data transmission performance and generalization capability. Numerical results on a real-world dataset demonstrate the superior scalability and performance of our approach over existing methods.

• The paper proposes a flexible deep learning framework integrating pilot training, CSI feedback, and multi-user precoding for scalable FDD massive MIMO transceiver design.
• It introduces a Residual Vector-Quantized Variational Autoencoder (RVQ-VAE) for scalable CSI feedback adaptable to varying capacities with minimal retraining.
• An Edge Graph Attention Network (EGAT) is used for multi-user precoding, demonstrating sum-rate gains of 18.9% to 90.3% over traditional methods.

Scalable Transceiver Design for Multi-User Communication in FDD Massive MIMO Systems via Deep Learning

The research paper "Scalable Transceiver Design for Multi-User Communication in FDD Massive MIMO Systems via Deep Learning" addresses key challenges in designing flexible, efficient transceiver systems for downlink communications in FDD massive MIMO environments. The paper focuses on reducing overhead associated with channel state information (CSI) feedback, a critical bottleneck in massive MIMO systems, by leveraging deep learning techniques to design scalable solutions that adapt to varying system conditions.

The primary contributions of the paper can be summarized as follows:

1. Flexible Deep Learning Framework: The authors propose a deep learning-based transceiver framework that integrates three core modules: pilot training, CSI feedback, and multi-user precoding. This integration enables the system to adapt to varying numbers of users and feedback capacities, addressing a common limitation of existing deep learning methods that often fall short in dynamic operating environments.
2. Residual Vector-Quantized Variational Autoencoder (RVQ-VAE): To surmount issues arising from varying feedback capacities, the paper presents an RVQ-VAE for CSI feedback. This approach effectively uses a hierarchical codebook structure, which allows for scalable quantization and dequantization. By enabling multiple levels of codebooks, the system dynamically adjusts feedback quantities with minimal retraining requirements, representing an efficient way to handle diverse feedback scenarios.
3. Edge Graph Attention Network (EGAT): The paper introduces an EGAT for the multi-user precoding task to process the quantized CSI at the base station. The EGAT efficiently captures network topologies and interference patterns through an attention mechanism, enabling robust precoding strategies that adapt to fluctuating user numbers. This precoding method leverages GNNs to foster improved scalability and reduced computational complexity in real-time operations.
4. Progressive Training Strategy: The researchers employ a progressive training strategy for the RVQ codebooks, updating the quantization process incrementally to avoid overfitting and enhance generalization. By progressively adjusting codebook layers, the framework can better accommodate varying training environments, leading to superior system performance.

The framework was tested on a real-world dataset, demonstrating a significant improvement in scalability and performance over existing methods. The numerical results show that the proposed framework can achieve sum-rate gains ranging from 18.9% to 90.3% compared to traditional schemes. This highlights the effectiveness of the deep learning approach in handling uncertainties and dynamics intrinsic to FDD massive MIMO systems.

Implications and Future Directions

Practically, the design promises an adaptable strategy for network operators to manage downlink CSI acquisition more efficiently in an environment constrained by bandwidth and computational resources. The theoretical implications suggest advancements in how deep learning architectures, particularly GNNs, can be integrated with wireless communication protocols to effectively handle scalability challenges.

Future work could explore the application of similar deep learning frameworks in other duplex systems or adapt the RVQ-VAE model for enhanced adaptations to even more drastic changes in network configurations. Moreover, as wireless networks continue to evolve, future research could investigate the integration of this scalable framework in emerging paradigms like beyond 5G and 6G networks, potentially incorporating more granular user behavior models and environmental factors during transceiver design.

In conclusion, the paper makes significant strides in addressing the scalability issues in transceiver design for massive MIMO systems. By incorporating innovative deep learning techniques and introducing new elements like RVQ-VAE and EGAT, the research provides a robust foundation for future technological advancements in efficient and flexible wireless communication systems.