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研究生:詹震浩
研究生(外文):Chan, Jen-Hau
論文名稱:基於A2C結合LSTM之D2D通訊於蜂巢式網路功率調整演算法
論文名稱(外文):Power Adjustment for D2D Communication Underlying LTE Cellular Networks Based on Advantage Actor-Critic with LSTM
指導教授:王嘉斌王嘉斌引用關係
指導教授(外文):Wang, Chia-Pin
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:58
中文關鍵詞:裝置對裝置之通訊系統容量功率調整強化學習機器學習
外文關鍵詞:Device-to-Device (D2D) communicationCapacityPower adjustmentReinforce learningMachine learning
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  • 被引用被引用:0
  • 點閱點閱:15
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  • 下載下載:4
  • 收藏至我的研究室書目清單書目收藏:0
我們考慮到裝置對裝置之通訊(Device-to-Device communication, D2D)有可能在非理想情況下被建立,我們提出一個下行鏈路干擾緩解機制,在保障蜂巢式用戶的鏈路品質前提下,提升整體系統容量,我們模擬多個D2D用戶時採用抑制干擾範圍與正交資源分配,接著使用本實驗室學長先前所撰寫之位置推薦的結果,並稱之為傳統方法,本篇研究接續著傳統方法,搭配人工智慧的方法針對D2D提出功率調整演算法(Location Recommendation and Power Adjustment using A2C with LSTM, LR&PA_A2C+LSTM),管理D2D複用蜂巢鏈路的資源時,基地台對D2D的干擾及D2D對MUE的干擾。我們比較LR的兩種策略,並且證明以最短移動距離作為唯一考量的策略較為實際,因此在我們的演算法中將採取此策略。模擬結果證明, 我們所提出的方法在提升D2D容量方面優於傳統方法,而蜂巢式用戶容量的減少相較於D2D容量的提升是非常小的,因此整體系統容量可以明顯地有所改善。
We consider the Device-to-Device (D2D) communication is likely to be established in non-ideal condition. The new downlink interference mitigation mechanism is proposed to enhance the overall system capacity on the premise of guaranteeing the links quality of macrocell user equipments (MUEs). We simulate the result of interference-suppression-area (ISA) and orthogonal resources allocation scheme with multiple D2D user equipment (DUE), using the Location Recommendation method the senior in our laboratory proposed before, and that is called conventional scheme. This paper proposes a power adjustment using A2C with LSTM (PA_A2C+LSTM) algorithm for DUEs to manage the interference from base station (BS) to DUEs and DUEs to MUEs when sharing same resources. We compared the two strategy about LR scheme and showed the strategy of LR by considering distance is more practical for users and hence this strategy is adopted by our proposed system. Numerical results show that our proposed algorithm is better than the conventional scheme in terms of DUE capacity. The loss of MUEs capacity is small compared to the gain of DUE capacity. The system capacity can thus be obviously improved.
中文摘要 i
英文摘要 ii
誌謝 iii
圖目錄 vi
表目錄 ix

第一章 緒論 1
1.1 研究動機與背景 1
1.2 研究目的 5
1.3 其他相關研究 6
1.4 論文架構 9

第二章 相關知識介紹 10
2.1 無線網路的未來趨勢 10
2.2 Device-to-Device Communication (D2D) 12
2.3 長短期記憶網路(Long Short-Term Memory,LSTM) 14
2.4 強化學習(Reinforce Learning) 16
2.5 Advantage Actor-Critic架構 18

第三章 本論文提出之演算法 21
3.1 研究作法之動機 21
3.2 系統模型之建立 22
3.3 功率調整演算法(Power Adjustment using Advantage Actor-Critic with LSTM, PA_A2C+LSTM)設計 29

第四章 數值分析與模擬結果 34
4.1 模擬環境與參數設定 34
4.2 模擬結果與討論 35

第五章 結論 52

參考文獻 53
自傳 57
學術成就 58
參 考 文 獻

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[2] Qualcomm Technologies, https://www.qualcomm.com/invention/1000x
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[8] P. Janis, V. Koivunen, C. Ribeiro, J. Korhonen, K. Doppler, and K. Hugl, "Interference-Aware Resource Allocation for Device-to-Device Radio Underlaying Cellular Networks," in Vehicular Technology Conference, 2009. VTC Spring 2009. IEEE 69th, 2009, pp. 1-5.
[9] B. Guo, S. Sun, and Q. Gao, "Downlink interference management for D2D communication underlying cellular networks," in Communications in China - Workshops (CIC/ICCC), 2013 IEEE/CIC International Conference on, 2013, pp. 193-196.
[10] 3GPP,3rd generation partnership project; technical specification group SA;feasibility study for proximity services (ProSe) (Release 12),”TR 22.803 V1.0.0,August 2012.
[11] 4G Americas’ Recommendations on 5G Requirements and Solutions, http://www.4gamericas.org/files/2714/1471/2645/4G_Americas_Recommendations_on_5G_Requirements_and_Solutions_10_14_2014-FINALx.pdf
[12] 蔡孟原, " D2D通訊用於蜂巢式網路之位置推薦及功率調整演算法" 碩士, 電機工程學系, 國立臺灣師範大學, 台北市, 2016.
[13] W. Li, M. Zhang and B. Bai, “Power Threshold Based Interference Alignment in Hybrid D2D & Cellular Uplink Transmissions,” 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, 2017, pp. 1-5.
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[15] Asheralieva, Alia, and Y. Miyanaga. “QoS-Oriented Mode, Spectrum, and Power Allocation for D2D Communication Underlaying LTE-A Network.” IEEE Transactions on Vehicular Technology 65.12(2016):9787-9800.
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[17] H. Saad, A. Mohamed, and T. ElBatt, “A cooperative q-learning approach for online power allocation in femtocell networks,” in Vehicular Technology Conference (VTC Fall), 2013 IEEE 78th, pp. 1-6, IEEE, 2013.
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[22] Zhiqiang Fan, Xinyu Gu, Shiwen Nie,Ming Chen.“D2D power control based on supervised and unsupervised learning.”ICCC 2017, in press.
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