波束賦形(Beamforming)是第五代行動通訊(5G)系統中的一項關鍵技術。然而傳統的優化算法通常需要大量且複雜的迭代計算量導致在實時通訊中難以執行。近年來已有些許研究證明在通訊領域的研究中，使用基於深度學習(Deep Learning，簡稱DL)的算法可以有效地快速提高系統效能，同時降低運算時間複雜度。因此本論文提出一種採用非監督式學習(Unsupervised-learning)的DL模型算法，其整體架構的設計概念是受殘餘神經網絡(Residual Network，簡稱ResNet)的工作流程所啟發，我們命名為：受殘餘神經網路啟發式波束賦形(ResNet-Inspired Beamforming，簡稱RI-BF)算法。
在本論文所設計的RI-BF模型中，我們引入了深度展開(Deep Unfolding)技術來設計深度神經網路(Deep Neural Network，簡稱DNN)的架構，使得系統效能能參考基於梯度之波束賦形算法進行優化。該技術的引入使得我們設計的RI-BF能同時繼承迭代優化算法和基於DL的波束賦形設計算法兩者的優勢。
此外，本論文所提出的RI-BF具有三個特徵。首先，與現有基於DL的波束賦形設計算法不同，在訓練RI-BF時不需要在損失函數(Loss function)後加上正則項(Regularization term)或是在模型輸出時對其波束賦形進行功率縮放機制來滿足系統總傳送功率約束。其次，受到ResNet的模型啟發，我們搭建了一個深度展開模塊(Deep unfolding module，簡稱DUM)來模仿ResNet模型的殘差塊(Residual block)，從而進一步提高RI-BF中初始波束賦形的效能。第三，整個RI-BF是採用非監督式學習訓練的方式。因此不需要對訓練資料(Training data)進行額外的標記(Label)工作。最後，模擬結果顯示，本論文所提出之RI-BF其系統效能和運算時間複雜度都明顯優於現有DL的波束賦形設計算法和迭代優化算法。

Beamforming is a key technology in communication systems of the fifth generation and beyond. However, traditional optimization-based algorithms are often computationally prohibited from performing in a real-time manner. On the other hand, the performance of existing deep learning (DL)-based algorithms can be further improved. As an alternative, we propose an unsupervised ResNet-inspired beamforming (RI-BF) algorithm in this paper that inherits the advantages of both pure optimization-based and DL-based beamforming for better efficiency.
In particular, a deep unfolding technique is introduced to implement the optimization process of the gradient ascent beamforming algorithm for the design of our deep neural network (DNN) architecture.
Moreover, the proposed RI-BF has three features. First, unlike the existing DL-based beamforming method, which employs a regularization term for the loss function or an output scaling mechanism to satisfy system power constraints, a novel DNN architecture is introduced in RI-BF to generate initial beamforming with a promising performance. Second, inspired by the success of residual neural network (ResNet)-based DL models, a deep unfolding module is constructed to mimic the residual block of the ResNet-based model, which further improves the performance of RI-BF based on the initial beamforming. Third, the entire RI-BF is trained in an unsupervised manner; as a result, labelling efforts can be avoided. The simulation results demonstrate that the performance and computational complexity of our RI-BF improves significantly compared to the existing DL-based and optimization-based algorithms.

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