The evolution of fifth generation (5G) wireless communication networks has led to an increased need for wireless resource management solutions that provide higher data rates, wide coverage, low latency, and power efficiency. Yet, many of existing traditional approaches remain non-practical due to computational limitations, and unrealistic presumptions of static network conditions and algorithm initialization dependencies. This creates an important gap between theoretical analysis and real-time processing of algorithms. To bridge this gap, deep learning based techniques offer promising solutions with their representational capabilities for universal function approximation. We propose a novel unsupervised deep learning based joint power allocation and beamforming design for multi-user multiple-input single-output (MU-MISO) system. The objective is to enhance the spectral efficiency by maximizing the sum-rate with the proposed joint design framework, NNBF-P while also offering computationally efficient solution in contrast to conventional approaches. We conduct experiments for diverse settings to compare the performance of NNBF-P with zero-forcing beamforming (ZFBF), minimum mean square error (MMSE) beamforming, and NNBF, which is also our deep learning based beamforming design without joint power allocation scheme. Experiment results demonstrate the superiority of NNBF-P compared to ZFBF, and MMSE while NNBF can have lower performances than MMSE and ZFBF in some experiment settings. It can also demonstrate the effectiveness of joint design framework with respect to NNBF.

翻译：第五代（5G）无线通信网络的发展对无线资源管理方案提出了更高要求，需要实现更高的数据速率、更广的覆盖范围、更低延迟与更高功率效率。然而，由于计算能力的限制，以及对静态网络条件和算法初始化依赖性的不切实际假设，许多现有传统方法仍难以实际应用。这在理论分析与算法实时处理之间形成了重要鸿沟。为弥合这一差距，基于深度学习的技术凭借其通用函数逼近的表征能力提供了具有前景的解决方案。本文针对多用户多输入单输出（MU-MISO）系统，提出了一种新颖的无监督深度学习联合功率分配与波束成形设计方案。该方案旨在通过所提出的联合设计框架NNBF-P最大化总速率以提升频谱效率，同时相较于传统方法提供计算高效的解决方案。我们在多种场景下进行实验，将NNBF-P与迫零波束成形（ZFBF）、最小均方误差（MMSE）波束成形以及NNBF（即我们未联合功率分配的深度学习波束成形方案）进行性能对比。实验结果表明，NNBF-P相较于ZFBF和MMSE具有优越性，而NNBF在某些实验场景中性能可能低于MMSE和ZFBF。这也验证了联合设计框架相对于NNBF的有效性。