混和無線電與可見光通訊之異質網路能源效率最佳化設計

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研究生：	蕭奕群 Hsiao, Yi-Chun
論文名稱：	混和無線電與可見光通訊之異質網路能源效率最佳化設計 Energy Efficiency Maximization in Multi-user MISO Mixed RF/VLC Heterogeneous Wireless Network
指導教授：	林澤 Lin, Che
口試委員:	洪樂文 Hong, Yao-Win 吳卓諭 Wu, Jwo-Yuh 蘇炫榮 Su, Hsuan-Jung
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2018
畢業學年度：	106
語文別：	中文
論文頁數：	58
中文關鍵詞：	異質網路、協調式波束成型、可見光通訊、能源效率、凸函數最佳化
外文關鍵詞：	heterogeneous wireless network, coordinated beamforming, visible light communication, energy efficiency, convex optimization
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本研究中，我們探討一個有別於傳統的異質網路架構，其中包含一個無線電 (radio frequency) 存取站 (access point) 與一個可見光通訊 (visible light communication) 存取站。此網路中存在數個使用者，且每個使用者皆同時接收來自無線電存取站與可見光通訊存取站的訊息。我們考慮以下兩種情況：基地台知道與接收端間的完整通道資訊 (perfect channel state information) 與基地台只知道與接收端間的不完整通道資訊 (imperfect channel state information)。在此兩種環境下，我們分別針對系統的能源效率 (energy efficiency) 進行最佳化波束成型 (beamforming) 設計。然而，建構出的最佳化問題相當複雜且難以處理。因此我們提出了兩套演算法：dinkelbach combined with successive convex approximation method 與 low-complexity successive convex approximation method。透過這兩套演算法，我們可將原本繁雜的數學問題近似為容易處理的凸函數問題 (convex problem)，並利用不斷地疊代運算來進一步提升得出的近似解。在模擬結果中，我們首先驗證所提出演算法確實能達到接近最佳 (near-optimal) 解，接著比較這兩套演算法間的效能差異。第一套演算法雖然能得到高於第二套演算法 0.1% 的效能，然而花費的運算時間卻是第二套演算法的三倍以上。除此之外，在完整通道資訊的情況下我們可以觀察到當可見光通訊存取站與無線電存取站的功率限制在 0.1 Watts 時，本論文提出的混合無線電與可見光通訊之架構相較於傳統異質無線電網路所能達到的能源效率高出 42% 與 35%。而我們所設計的演算法相較於最大比率傳輸 (maximum ratio transmission) 效能高出 48% 與 64%。另一方面，在不完整通道資訊的模擬結果中，我們首先說明設計穩健波束成型 (robust beamforming) 優於非強健波束成型 (non-robust beamforming) 的原因。接著觀察當可見光通訊與無線電通訊之存取站的功率限制在 0.1 Watts 時，混合無線電與可見光通訊之架構所得到的最糟情況能源效率 (worst-case EE) 相較於傳統異質無線電網路有 50% 與 51% 的提升。而當可見光通訊與無線電通訊之存取站的電路消耗功率固定在 1 Watts 時，混合無線電與可見光通訊之架構的最糟情況能源效率比傳統異質無線電網路還要高出 80% 與 92%。此結果再一次印證我們提出的混合無線電與可見光通訊之架構相較於傳統異質網路能有更突出的能源效率表現。此外，我們也將所設計的演算法與最大比率傳輸方案比較，可以發現可見光通訊與無線電通訊之存取站的功率同樣限制在 0.1 Watts 時，我們設計的演算法能勝過最大比率傳輸方案 90% 與 110%。而當可見光通訊與無線電通訊之存取站的電路消耗功率固定在 1 Watts 時，我們設計的演算法比最大比率傳輸方案提升了 120% 與 196%。這說明在通道有誤差的情況下，我們所設計的演算法能有更亮眼的表現，更加適合應用於實際無線通訊系統。

In this work, we investigated coordinated beamforming design in a heterogeneous wireless network consisting of a radio frequency (RF) and a visible light communication (VLC) access point (AP). There are several users receive informations from the RF and the VLC AP simultaneously. Our goal is to maximize energy efficiency (EE) of the entire system under the perfect channel state information (CSI) case and the imperfect CSI case, respectively. We constructed the optimization problem as a fractional programming problem, which is complicated to solve due to the non-convex objective function and constraints. To tackle this problem, we develop four successive convex approximation methods. Based on extensive numerical experiments, we demonstrated that the proposed algorithms are able to achieve near-optimal performance. Furthermore, in the perfect CSI case, we can observe that the EE performance of our proposed algorithm improves by as much as 64% over the maximum ratio transmission (MRT) scheme and the EE of the proposed RF/VLC network structure improves by as much as 42% over the traditional RF/RF heterogeneous wireless network. Besides, in the imperfect CSI case, we first investigated the importance for designing robust beamforming when channel error occurs. Moreover, we can see that the proposed algorithm outperforms the MRT scheme by as much as 196% and the proposed network architecture outperforms the traditional heterogeneous wireless network by as much as 92%. In summary, we demonstrated that a careful design of an energy efficient beamforming scheme is essential and the proposed mixed RF and VLC network architecture is much more efficient than those without VLC.

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 ProblemFormulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Convex Approximation Method  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1 Dinkelbach Combined with Successive Convex Approximation Method  . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Convergence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Low-complexity Successive Convex Approximation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Extension to the Imperfect CSI Case  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1 Worst-Case Problem Formulation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2 Robust Dinkelbach Combined with Successive Convex Approximation Method  . . . . . . . . . . . . . . . . . . 28
3 Robust Low-complexity Successive Convex Approximation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
1 Performance for the Perfect CSI Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2 Performance for the Imperfect CSI Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
                                

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