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.

基于深度学习的无监督式深度学习的联合功率分配和波束形成设计方案可以提高频谱效率，并且相较于传统方法具有较高的计算效率。

基于深度学习的联合多用户 MISO 功率分配与波束形成设计

Deep Learning Based Joint Multi-User MISO Power Allocation and  Beamforming Design

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.

通过提出一种名为 NNBF 的无监督深度学习框架，该研究旨在设计上行接收多用户单输入多输出（MU-SIMO）波束成形，以增强吞吐量并提供计算效率高于传统方法的解决方案。实验结果表明，NNBF 相对于零强制波束成形（ZFBF）和最小均方误差（MMSE）均衡器这两种基准方法在性能上表现出更好的表现，并且能够根据单天线用户设备（UEs）的数量进行扩展而无需进行矩阵伪逆运算，而基准方法则具有显著的计算负担。

基于深度学习的上行多用户 SIMO 波束赋型设计

Deep Learning Based Uplink Multi-User SIMO Beamforming Design

For the past couple of decades, numerical optimization has played a central
role in addressing wireless resource management problems such as power control
and beamformer design. However, optimization algorithms often entail
considerable complexity, which creates a serious gap between theoretical
design/analysis and real-time processing. To address this challenge, we propose
a new learning-based approach. The key idea is to treat the input and output of
a resource allocation algorithm as an unknown non-linear mapping and use a deep
neural network (DNN) to approximate it. If the non-linear mapping can be
learned accurately by a DNN of moderate size, then resource allocation can be
done in almost real time -- since passing the input through a DNN only requires
a small number of simple operations.
In this work, we address both the thereotical and practical aspects of
DNN-based algorithm approximation with applications to wireless resource
management. We first pin down a class of optimization algorithms that are
`learnable' in theory by a fully connected DNN. Then, we focus on DNN-based
approximation to a popular power allocation algorithm named WMMSE (Shi {\it et
al} 2011). We show that using a DNN to approximate WMMSE can be fairly accurate
-- the approximation error $\epsilon$ depends mildly [in the order of
$\log(1/\epsilon)$] on the numbers of neurons and layers of the DNN. On the
implementation side, we use extensive numerical simulations to demonstrate that
DNNs can achieve orders of magnitude speedup in computational time compared to
state-of-the-art power allocation algorithms based on optimization.

本文提出了一种基于深度神经网络的学习算法来近似数值优化算法，在无线资源管理的应用中，此算法可以在几乎实时完成资源分配，大大提高了计算效率。同时，本文也在理论和实践两方面讨论了基于深度神经网络的算法逼近，并在实现方面用大量数值模拟来证明该算法相对于基于优化的最先进的功率分配算法可以实现几个数量级的加速。