UE-to-Network relay自從被引入第四代行動網路(LTE)以後，已被廣泛用作提高能源效率的有效方法。由於能量消耗的急劇增加，UE-to-Network relay在第五代行動網路(5G)中顯得更為重要。但是，UE-to-Network relay在5G網路中的應用尚未被深入的研究。本文考慮在一個5G網路中，一些通訊品質不佳的行動裝置(UE)，可以將其資料轉傳到附近具有良好通訊頻道的行動裝置，以此降低系統耗能。經由非時隙調度(non-slot-based scheduling)，行動裝置間可以分享資源，以保證協助轉傳的行動裝置不需要消耗額外的能量，從而確保其轉傳意願。我們將問題表述為混合整數非線性規劃問題，並提出一個三階段的中繼選擇與資源和傳輸功率分配 (JRRP) 演算法。該演算法考慮混合參數集(mixed-numerology)和迷你時隙(mini-slot)。我們分別在彈性和固定的子載波間隔設定下，對所提出的解決方案進行效能評估，模擬結果證明 我們提出的方法JRRP優於前人的研究成果。

UE-to-Network relay has been widely used as a promising approach to improve energy efficiency since its introduction in the LTE network. Its importance could be emphasized again with the emerging 5G due to the dramatic increase in energy consumption. However, it is not well-explored under the 5G network. This paper considers a scenario where some UEs suffer from bad channel conditions, while relaying their data to other UEs in proximity can reduce system energy consumption. By leveraging the benefit of non-slot-based scheduling, UEs share their resources to guarantee the relaying UEs are not charged additional energy, which ensuring their willingness. We formulate the problem as a Mixed-Integer Nonlinear Programming problem (MINLP) and propose a three-stage joint relay selection with resource and power allocation (JRRP) algorithm, which takes mixed-numerology and mini-slots into account. The proposed solution is evaluated with flexible and fixed sub-carrier spacing, and simulation results show that JRRP outperforms existing works in both scenarios.

Contents

Abstract

List of Tables

List of Figures

Chapter 1. Introduction 1

Chapter 2. Related Work 5

Chapter 3. System Model and Problem Formulation 8

Chapter 4. The Proposed Three-Stage Scheme: JRPP 19
4.1 Greedy Algorithm to Assign DUEs to RUEs 19
4.2 Resource Partition 25
4.3 Mini-slot and Power Allocation for DUE set and RUE 31
4.3.1 Mini-slot and Power Allocation for DUE set 32
4.3.2 Mini-slot and Power Allocation for RUE 35

Chapter 5. Simulation Results 37
5.1 The impact of BWP partition 38
5.2 The impact of the granularity for reallocation 42
5.3 The impact of the maximum number∆ 45

Chapter 6. Conclusion 49

References 50

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