The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance. Nonetheless, traditional approaches have shortcomings when it comes to computational complexity and their ability to adapt to dynamic conditions, creating a gap between theoretical analysis and the practical execution of algorithmic solutions for managing wireless resources. Deep learning-based techniques offer promising solutions for bridging this gap with their substantial representation capabilities. We propose a novel unsupervised deep learning framework, which is called NNBF, for the design of uplink receive multi-user single input multiple output (MU-SIMO) beamforming. The primary objective is to enhance the throughput by focusing on maximizing the sum-rate while also offering computationally efficient solution, in contrast to established conventional methods. We conduct experiments for several antenna configurations. Our experimental results demonstrate that NNBF exhibits superior performance compared to our baseline methods, namely, zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) equalizer. Additionally, NNBF is scalable to the number of single-antenna user equipments (UEs) while baseline methods have significant computational burden due to matrix pseudo-inverse operation.

翻译：第五代（5G）无线通信网络的发展对无线资源管理解决方案提出了更高要求，这些方案需具备高数据速率、广覆盖范围、低时延及能效优化性能。然而，传统方法在计算复杂度与动态环境适应能力方面存在缺陷，导致无线资源管理算法在理论分析与实际部署之间产生脱节。基于深度学习的技术凭借其强大的表征能力，为弥合这一差距提供了可行方案。我们提出一种名为NNBF的新型无监督深度学习框架，用于上行接收端多用户单输入多输出（MU-SIMO）波束赋形设计。其主要目标是在提升系统吞吐量的同时，通过最大化总速率实现计算高效性，这与现有传统方法形成鲜明对比。我们在多种天线配置下开展实验。实验结果表明，与基线方法——迫零波束赋形（ZFBF）和最小均方误差（MMSE）均衡器相比，NNBF展现出更优性能。此外，当基线方法因矩阵伪逆运算面临显著计算负担时，NNBF对单天线用户设备（UEs）数量具有可扩展性。