以資訊年齡為指標之下世代無線網絡資料新鮮度：專注於節能及多播系統

Abstract

在下一代無線網絡迅疾發展之際，確保數據新鮮度已成為一項關鍵挑戰。本文提出了針對使用資訊年齡（AoI）作為主要指標以提升先進網絡中數據新鮮度的深入分析。我們的研究分為兩個不同但同等重要之網路模型：通過不連續接收（DRX）機制的節能系統和基於無碼率編碼的多播網絡，每一領域對下一代無線技術的功能和效率皆至關重要。

此研究之第一部分，我們深入探討了作為下一代無線網絡用戶設備（UE）節能策略的DRX機制。我們嚴謹地分析了這些機制對資訊年齡的影響，探討了DRX如何平衡能源效率與即時訊息更新的需求。當電力資源有限但對最新訊息的需求高時，此一平衡尤為關鍵。我們的分析包括了對時間平均資訊年齡（TAoI）和峰值平均資訊年齡（PAoI）的影響，求解不同DRX參數所能得到的封閉解，並進一步找出在功耗限制下優化這些參數以最小化資訊年齡之最優策略。

此外，本研究亦擴展至下一代無線應用中新興的無碼率編碼多播啟用感測器網絡領域，例如5G的多播廣播服務（MBS）。此處，我們專注於無碼率編碼策略在多播設置中對資訊年齡的影響，特別是在多樣化的通道條件下。我們將問題轉化為馬可夫決策過程（MDP），以找到最優和具有低複雜度之次優策略，並提出了基於無碼率編碼的無線多播系統的首個最小資訊年齡演算法。我們將提出的最優和次優策略與其他基線策略進行比較，結果顯示我們的策略在資訊年齡方面一致優於其他策略。

通過並置此二相異之研究領域，我們的論文凸顯了在下一代無線網絡中管理資訊年齡的多面性。本文提供了一個全面的視角，展示了如何藉由策略的進步達到於DRX系統和多播網絡維持高數據新鮮度的目標，並為不同網絡場景中資訊年齡的動態提供了更深入的理解。

In the rapidly evolving landscape of next-generation wireless networks, ensuring data freshness has emerged as a critical challenge. This thesis presents an in-depth analysis employing Age of Information (AoI) as the principal metric to enhance data freshness in these advanced networks. Our study is divided into two distinct yet equally vital network models: energy-saving systems by the Discontinuous Reception (DRX) mechanism and rateless coding-based multicast networks, each pivotal for the functionality and efficiency of next-generation wireless technologies.

In the first part of our investigation, we delve into the DRX mechanism implemented as an energy-saving strategy in user equipment (UE) for next-generation wireless networks. We rigorously analyze the impact of the mechanisms on AoI, exploring how DRX can balance the trade-off between energy efficiency and the need for timely information updates. This is particularly crucial when power resources are limited, yet the demand for up-to-date information is high. Our analysis includes the impact on both time-average AoI (TAoI) and peak-average AoI (PAoI), obtaining closed-form solutions with various DRX parameters and optimizing DRX parameters to minimize the AoI with a constraint power consumption.

Additionally, the study extends to the realm of rateless coding in multicast-enabled sensor networks, an emerging area in next-generation wireless applications. For instance, the 5G multicast broadcast service (MBS). Here, we focus on how rateless coding strategies impact the AoI in a multicast setting, particularly under diverse channel conditions. We transform the problem into a Markov Decision Process (MDP) to locate optimal and low-complexity suboptimal policies and present the first age-minimum scheme for rateless code-based wireless multicast systems. The proposed optimal and suboptimal policies are compared with other baseline strategies, and it has shown that our strategies consistently outperform the others in terms of AoI.

By juxtaposing these two distinct research areas, our thesis highlights the multifaceted nature of AoI management in next-generation wireless networks. It presents a holistic view, showing how improvements in strategies can contribute to the goal of maintaining high data freshness in both DRX systems and multicast networks, and provides a deeper understanding of the dynamics of AoI in different network scenarios.