本論文旨在建立包含多個能量採集蜂巢式系統的異質蜂巢式網路中之分析架構，並透過此分析架構來評估網路傳輸量和能源效率。此文中探討之網路結構包含一層的傳統接電基地台以及多層的能量採集小型細胞存取點。首先利用離散時間馬可夫鏈對任意傳輸功率的小型細胞存取點來建立電池能量動態模型，推導出速率覆蓋量。接著藉由考量能量採集的能力、細胞聯接偏差和能量採集小細胞的傳輸功率可以推導出網路傳輸量以及能源效率。透過分析網路傳輸量和能源效率，可以找到最佳的傳輸功率以及細胞聯結偏差。透過分析模擬結果，我們指出當所有使用者流量都被卸載到傳輸功率小的能量採集小細胞存取點時，將能夠增強網路傳輸量和能源效率。此外，使用低傳輸功率的小細胞存取點將可達到更高的網路傳輸量，而在能源效率方面，最佳的傳輸功率將會隨著能量採集小細胞密度增加而提高。本論文中考慮包含細胞聯接偏差、小細胞存取點密度和傳輸功率對傳輸效率及能源效率的影響，並提供一個在異質網路中提升頻譜效率以及能源效率的設計架構。

關鍵字: 能量採集、流量卸載、網路傳輸量、能量效率、隨機幾何

In this work, we develop an analytical framework to evaluate the network throughput and energy efficiency in heterogeneous cellular networks (HCNs) with multiple energy harvesting cells. The network consists of a tier of power-grid connected base stations (BSs) and multiple tiers of energy harvesting small cell access points (EH-SAPs). Specifically, we derive the rate coverage after modeling the battery energy dynamic using discrete time Markov chain for energy harvesting (EH)-small cell access points (SAPs) according to an arbitrary transmission power. We then derive the network throughput and the energy efficiency by taking account for the energy harvesting probability, cell association bias, and the transmission power of EH-SAPs. We also explore the optimal transmission power of EH-SAPs and cell association bias in terms of the throughput and the energy efficiency. We show that offloading all traffic of the network to EH-SAPs can enhance the network throughput also the energy efficiency, especially when the transmission power of EH-SAPs is low. Moreover, lower transmission power of EH-SAPs can achieve higher network throughput, but the optimal transmission power of EH-SAPs in terms of network energy efficiency generally increases with the density of EH-SAPs. This research offers insights on the design of spectral- and energy-efficient heterogeneous network including the effects of cell association bias, SAP density, and transmission power of EH-SAP on the network performance.

Abstract i
Contents ii
1 Introduction 1
1.1 Motivation and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 System Model 6
2.1 Network Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Energy Harvesting and Battery Model . . . . . . . . . . . . . . . . . . . . . 8
2.3 Cell Association . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Rate Coverage 14
4 Performance Analysis 19
4.1 Network Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Energy Efficiency Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Insights on Network Throughput and Energy Efficiency . . . . . . . . . . . . 22
5 Numerical Results 26
5.1 Effect of Traffic Offloading . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.2 Effect of Spatial Density of EH-SAPs . . . . . . . . . . . . . . . . . . . . . . 28
6 Conclusion 34
7 Appendix 35
7.1 Proof of Lemma 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
7.2 Proof of Lemma 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
7.3 Proof of Lemma 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
7.4 Proof of Lemma 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

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