Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

中文翻译：

---

具有上行信道信息的通用 MIMO 下行波束成形的模型驱动学习

准确的下行信道信息对波束赋形设计至关重要，但在实践中很难获得。本文研究了一种基于深度学习的下行波束成形优化方法，以在只有上行信道信息可用时最大化系统总速率。我们的主要贡献是提出了一种模型驱动的学习技术，该技术利用最佳下行链路波束成形的结构来设计有效的混合学习策略，旨在最大限度地提高总速率性能。这是通过在训练阶段联合考虑下行信道的学习性能、功率和总速率来实现的。建议的方法适用于上行链路信道信息可用的一般情况，但它与下行信道的关系是未知的，不需要明确的下行信道估计。我们进一步将开发的技术扩展到大规模多输入多输出场景，并在没有小区间信令开销的情况下实现了多小区系统的分布式学习策略。仿真结果验证了我们提出的方法提供的性能接近最先进的数值算法，具有完美的下行链路信道信息，并且在总速率方面明显优于现有的数据驱动方法。