臺灣博碩士論文加值系統

3GPP 已經定義了一種新的無線電接入技術 5G 新無線電(NR)，以增強 5G 網絡 的靈活性、可擴展性和效率。通過利用天線陣列自適應地形成多個定向波束並同時服 務於地理分佈的用戶設備，可以提高數據速率。但是，天線陣列不完善的波束型可能 會產生旁瓣，從而導致用戶之間的干擾。儘管最近的研究著重在減輕用戶間干擾並使 總速率最大化的波束選擇上，但是，我們注意到所選波束可能沒有得到充分利用。根 本原因在於，一次只能配置一組固定的波束來為一個寬頻帶服務，但是由於流量的限 制，某些資源塊(即子載波)可能無法分配給任何用戶設備。為了解決這種效率低下的 問題，本文提出了已知流量的聯合波束配置和資源分配，該配置明確考慮了用戶設備 的流量需求，並配置了可在所有資源塊中最佳利用的波束。我們的模擬結果顯示，我 們的已知流量分配可以更好地利用波束，且比傳統的最大容量配置達到更高的有效吞 吐量。

3GPP has been defining 5G New Radio (NR), a new radio access technology, to enhance the flexibility, scalability, and efficiency of 5G networks. An increasing data rate can be achieved by leveraging antenna arrays to adaptively form multiple directional beams and serve geo­ distributed user equipments (UEs) concurrently. However, the imperfect beam pattern of an antenna array may create side lobes, leading to inter­user interference. While the most recent re­ search focuses on beam selection that mitigates inter­user interference and maximizes the sum rate, we, however, notice that the selected beams may not be fully utilized. The root cause is that only a fixed set of beams can be configured at a time to serve a wide frequency band but some resource blocks (i.e., subcarriers) may not be able to be allocated to any UEs due to lim­ited traffic demands. To address such inefficiency, this thesis presents traffic­aware joint beam configuration and resource allocation, which explicitly considers UEs’ traffic demands and con­ figures beams that can be optimally utilized in all the RBs (i.e., the operational frequency band). Our simulation results show that our traffic­aware allocation configures beams with better uti­lization and achieve an effective throughput much higher than conventional maximal capacity configuration.

摘要 i
Abstract ii
Acknowledgement iii
Table of Contents iv
List of Figures vi
1 Introduction 1
2 Related Work 6
2.1 Beam Management 6
2.2 Beam Selection for Lower Complexity 6
2.3 Beam Selection and Power Allocation 6
2.4 Beam Management for Mobility 7
2.5 Beam Selection in D2D Communications 7
2.6 Beam Selection for Capacity Maximization 7
2.7 Traffic ­Aware Resource Allocation 8
3 Problem Formulation 9
3.1 System Model 9
3.2 Model Relaxation 10
4 Traffic­ Aware Beam and Resource Allocation 14
4.1 UE­-Beam Association 14
4.2 Beam Selection and Resource Allocation 15
4.2.1 Max­-Demand Allocation 17
4.2.2 Top­-K Demand Allocation 18
4.3 Improvement with Interference Avoidance 20
5 CQIFeedback 24
6 Performance Evaluation 25
6.1 Benchmark 26
6.2 Comparison with the near-optimal allocation 26
6.3 Large­-scale simulation 30
6.4 Fairness evaluation 39
6.5 Overhead analysis 41
7 Conclusion 45
References 46

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