下世代行動通訊系統將可提供更高傳輸速率、更低通訊延遲、高速移動使用者之通訊、高能量使用效率及高頻譜使用效率等。為達到這些通訊需求，網路架構將變得遠比傳統行動通訊架構複雜。而高速移動使用者的通訊環境及異質性網路架構，將使得通道環境變得更加複雜且不穩定。加上在基地台和裝置端使用再生能源，使得資源分配策略面臨額外的不確定性。為了提供穩定的無線傳輸品質，我們必須配合快速變化的環境設計動態通訊策略。在此論文中，我們將在三個情境下探討動態多天線傳輸機制的設計，分別為裝置與裝置間通訊系統，無人機通訊系統與可使用再生能源之通訊系統。
在裝置與裝置間通訊系統中，我們考慮可相互合作之多對裝置與裝置間通訊系統，並提出動態多天線傳輸機制。合作式裝置間通訊包含兩個傳輸階段：資料分享階段(階段一)和協同傳輸階段(階段二)。於階段一我們使用多播預編碼(multicast precoding)傳輸並於階段二使用區塊對角化預編碼(block-diagonalization precoding)傳輸。在考慮長期能量限制、長期傳輸率限制和即時之干擾上限後，我們協同設計所有預編碼矩陣以最大化裝置間通訊之長期效用，藉由Lyapunov最佳化方式，長期效用最大化問題將可被拆解為一系列短期權重傳輸率減能量懲罰最佳化問題，並由理論分析得到得到長期效用的傳輸率保證。
在無人機基地台系統中，我們考慮多架無人機基地台的使用，並提出可動態連結之系統。我們同時考慮波束成形訊號設計及能量分配，並搭配無人機飛行軌跡學習及群聚機制設計。在此系統中，我們假設無人機基地台可動態相互群聚連結，並透過實體連線形成同域天線陣列，且連結之無人機基地台可相互分享能量並協同傳輸波束成形訊號。群聚基地台依據總和訊號對干擾洩漏加雜訊比(SLNR)設計之波束成形向量及能量分配，我們提出交替最佳化(alternating optimization)技術疊代設計出波束成形向量及能量分配。依照各時槽之傳輸機制，我們進一步提出無人機飛行軌跡學習及群聚結合之策略，無人機位置和群聚將根據隨機梯度(stochastic gradient)調整以最大化期望之系統總和傳輸速率。
在使用再生能源的細胞通訊系統中，我們考慮多時槽的傳輸環境並設計動態波束成形傳輸矩陣及能量分配機制。我們考慮基地台同時使用再生能源及傳統電網作為能量來源，並設計區塊對角化預編碼(block-diagonalization precoding)傳輸矩陣及能量分配機制，以期望最大化於傳輸時間內之無線資料傳輸率。我們在設計時假設通道及能量來源已知，並設計預編碼矩陣以符合能量因果(energy causality)限制、電池上限(battery capacity)限制和電網消耗(grid energy cost)限制。其中能量因果限制確定傳輸當下需有足夠能量且不可使用未來知能量、電池上限限制以確保無會有能量浪費、電網消耗限制以限制傳統電網之能量消耗量。此問題可寫為一凸優化問題，並可推導最佳之傳輸條件，我們可證明最佳之預編碼矩陣及能量分配問題可以被拆解為獨立的兩子問題，並求得最佳之預編碼矩陣方向。而根據最佳化之條件，我們提出一個疊代式(iterative)能量分配演算法。

Next generation mobile communication systems are expected to support high mobility, high data rate, low latency, high spectral efficiency, and high energy efficiency. To support these service demands, the network structure is becoming significantly more complicated than that of conventional systems. The channel may be more unstable due to high user mobility and complex interference environments stemming from the heterogeneous network structure. The use of renewable energy at both cellular base-stations (BSs) and devices are also being considered to increase energy efficiency but introduces additional uncertainty in the resource allocation policy. To provide stable wireless services over time, dynamic transmission policies must be devised to adapt to rapidly varying environments. In this dissertation, we examine dynamic multi-antenna techniques for three scenarios, namely, device-to-device (D2D) communication systems, and unmanned aerial vehicle (UAV) communication systems and renewable energy empowered cellular systems.

For D2D systems, we consider a multi-pair cooperative D2D system and propose a dynamic MIMO transmission policy. The cooperative transmission consists of two phases: a data-sharing phase (i.e., phase 1) and a joint transmission phase (i.e, phase 2), where multicast precoders are used in phase 1 and coordinated block-diagonalization precoders are considered in phase 2. The precoders are jointly designed to maximize the long-term utility of the D2D users subject to long-term individual power and rate-gain constraints, and an instantaneous interference constraint at the BS. By adopting the Lyapunov optimization framework , the long-term optimization problem can be decoupled into a series of short-term weighted-rate-minus-energy-penalty (WRMEP) maximization problems that can be solved efficiently, and theoretical performance guarantees are derived.

For UAV-BS systems, a multi-UAV system is studied and we examine joint beamforming and power allocation schemes as well as trajectory learning and clustering mechanisms for dynamically connectable UAV-BSs. Here, UAV-BSs are allowed to join together dynamically and form physically-connected collocated antenna arrays that enable joint transmission and cooperative energy sharing among the connected UAVs. A joint design of the transmit powers and beamformers in each time slot is proposed based on the maximization of the sum signal-to-leakage-plus-noise-ratio (SLNR) of all users. Based on the per-time-slot design, a dynamic UAV trajectory learning and clustering policy is proposed where the expected sum rate of the system is maximized while adapting to changes in the users’ locations and environment.

For renewable energy enabled cellular systems, we propose a dynamic precoding and power allocation scheme for multiple time slot transmission. By considering BSs that are supported by both renewable energy and grid energy, we aim to find the optimal design of the block diagonalization (BD) precoders and the energy allocation policy. The main objective is maximizing the sum throughput within a transmission period. The precoders are derived subject to energy causality and grid energy cost constraints, i.e., the constraint that energy cannot be used before it arrives and the constraint that limits the amount of the grid energy usage. The problem can be formulated as a convex problem and optimality conditions are derived. According to the optimality conditions, we propose an iterative algorithm and demonstrate the performance of the algorithm through numerical simulations.

中文摘要
Abstract . . . i
Contents . . . iii
1 Introduction . . . 1
2 Related Works . . . 8
3 Dynamic MIMO Transmission Policies for Cooperative Device-to-Device Communication . . . 13
4 Dynamical MIMO Technique for Connectable UAV Base-Stations with
Cooperative Energy Sharing . . . 44
5 Dynamic Precoding and Power Allocation for Renewable Energy Empowered Base-Station . . . 59
6 Conclusion . . . 71
Appendices . . . 74
A Proof of Theorem 1 . . . 75
B Proof of Theorem 2 . . . 79
C Proof of Lemma 1 . . . 80

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