基於深度學習技術於無細胞大規模多輸入多輸出系統做最小化功率分配

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：56

、訪客IP：18.117.168.40

姓名

陳柄富(Bing-Fu Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於深度學習技術於無細胞大規模多輸入多輸出系統做最小化功率分配
(Minimizing Power Allocation in Cell-Free Massive MIMO Systems Using Deep Learning Techniques)

相關論文

★ 利用手持式手機工具優化行動網路系統於特殊型活動環境	★ 穿戴裝置動態軌跡曲線演算法設計
★ 石英諧振器之電極面設計對振盪頻率擾動之溫度相依性研究	★ 股票開盤價漲跌預測
★ 感知無線電異質網路下以不完美頻譜偵測進行資源配置之探討	★ 大數量且有限天線之多輸入多輸出系統效能分析
★ 具有元學習分類權重轉移網路生成遮罩於少樣本圖像分割技術	★ 具有注意力機制之隱式表示於影像重建三維人體模型
★ 使用對抗式圖形神經網路之物件偵測張榮	★ 基於弱監督式學習可變形模型之三維人臉重建
★ 以非監督式表徵分離學習之邊緣運算裝置低延遲樂曲中人聲轉換架構	★ 基於序列至序列模型之 FMCW雷達估計人體姿勢
★ 基於多層次注意力機制之單目相機語意場景補全技術	★ 應用於3GPP WCDMA-FDD上傳鏈路系統的遞迴最小平方波束合成犛耙式接收機
★ 調適性遠時程瑞雷衰退通道預測演算法設計與性能比較	★ 智慧型天線之複合式到達方位-時間延遲估測演算法及Geo-location應用

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-11-16以後開放)

摘要(中)

無細胞大規模MIMO系統被視為5G和6G的重要技術之一，具有相當潛力。該系統不同於傳統蜂巢式網路架構，其中包含一個中央控制器和眾多的無線存取點(AP)，分佈在覆蓋的範圍內。每個無線存取點皆搭載多根的服務天線，形成獨特的結構，通過相干聯合傳輸同時為覆蓋範圍內的所有用戶進行服務。一個實際的挑戰是如何在受限於限制功率下，選擇用戶的設備並進行適當的功率分配，以便可以在所有用戶設備獲得適當的傳輸功率下並且不超過所限制的功率。
本文考慮了在無細胞大規模多輸入多輸出(MIMO)系統下行鏈路中的功率分配問題。我們基於最大比(MR)預編碼技術下完成最小化問題，並使用基於大規模衰落(LSF)參數的啟發式功率分配作為DNN的預處理輸入。我們使用隨機分布的存取位置和使用者設備用作訓練數據去訓練DNN。將訓練後的神經網路做比較後可以證明此方法運行時間上的優勢。

摘要(英)

The concept of a cell-free massive MIMO system is a promising technology, marked as a pivotal element for 5G and 6G advancements. Contrary to the conventional cellular arrangement, the cell-free massive MIMO system involves a central control unit and an array of wireless access points (APs) dispersed across the coverage zone. Each of these APs is equipped with an extensive set of service antennas. This architecture facilitates coherent joint transmission, capable of concurrently servicing all user devices within the coverage area. However, a practical hurdle lies in efficiently selecting user devices and distributing power within predetermined limits to ensure optimal transmission without breaching stipulated power constraints.
This study tackles the power allocation predicament within the downlink facet of a cell-free massive MIMO system. We tackle this problem using the maximum ratio (MR) precoding technique and formulate the minimization problem. We utilize a heuristic power allocation technique relying on large-scale fading (LSF) coefficients as an initial input for the deep neural network (DNN). Training data for the DNN is generated using a random distribution of access points and user
iii
devices. After training, we compare the performance of the trained neural network to demonstrate its advantages in terms of runtime.

關鍵字(中)

★ 無細胞大規模多輸入多輸出
★ 深度學習
★ 功率分配

關鍵字(英)

★ cell free massive multiple-input multiple-output
★ deep learning
★ power allocation

論文目次

論文摘要 ..................................................................................................... i
Abstract ..................................................................................................... ii
致謝 ........................................................................................................... iv
Contents ..................................................................................................... v
List of Figures ......................................................................................... vii
List of Tables ......................................................................................... viii
Chpater 1. Introduction ....................................................................... 1
1.1 Cell Free Massive MIMO ............................................................. 1
1.2 Power Allocation ........................................................................... 3
1.3 Deep Learning ............................................................................... 4
1.4 Related Work ................................................................................ 5
1.5 Contributions ................................................................................. 6
1.6 Organization .................................................................................. 7
1.7 Abbreviations ................................................................................ 8
Chpater 2. System model ..................................................................... 9
2.1 Channel Model .............................................................................. 9
2.2 Channel Estimation ..................................................................... 10
vi
2.3 Pilot assignment .......................................................................... 12
2.4 Downlink data transmission ........................................................ 13
2.5 Problem Formulation .................................................................. 15
Chpater 3. Propose Scheme .............................................................. 16
3.1 Using sparsity to disable APs ..................................................... 17
3.2 Deep Neural Network ................................................................. 25
3.3 Gradient Descent ......................................................................... 26
3.4 Distributed DNN – Based Power Allocation .............................. 27
3.5 Complexity Analysis ................................................................... 31
Chpater 4. Simulation Results .......................................................... 33
4.1 Simulation parameter setup ......................................................... 33
4.2 Results ......................................................................................... 35
Chpater 5. Conclusion ....................................................................... 43
Reference ................................................................................................. 44

參考文獻

[1] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515.
[2] H. Q. Ngo, L.-N. Tran, T. Q. Duong, M. Matthaiou, and E. G. Larsson, “Energy efficiency optimization for cell-free massive MIMO,” in Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC), Hokkaido, Japan, Jul. 2017
[3] Özlem Tugfe Demir; Emil Björnson; Luca Sanguinetti, Foundations of User-Centric Cell-Free Massive MIMO , now, 2021.
[4] P.Mangayarkarasi, M.Ramya, and S.Jayashri, "Analysis of various power allocation algorithms for wireless networks," International Conference on Communication and Signal Processing, 2012.
[5] E. Bjornson, J. Hoydis, and L. Sanguinetti, “Massive MIMO networks: ¨ Spectral, energy, and hardware efficiency,” Foundations and Trends in Signal Processing, vol. 11, no. 3-4, pp. 154–655, 2017.
[6] H. Q. Ngo, L. -N. Tran, T. Q. Duong, M. Matthaiou and E. G. Larsson, "On the Total Energy Efficiency of Cell-Free Massive MIMO," in IEEE Transactions on Green Communications and Networking, vol. 2, no. 1, pp. 25-39, March 2018, doi: 10.1109/TGCN.2017.2770215.
[7] M. Zaher, Ö. T. Demir, E. Björnson and M. Petrova, "Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 174-188, Jan. 2023, doi: 10.1109/TWC.2022.3192203.
[8] T. Van Chien, E. Björnson and E. G. Larsson, "Joint Power Allocation and Load Balancing Optimization for Energy-Efficient Cell-Free Massive MIMO Networks," in IEEE Transactions on Wireless Communications, vol. 19, no. 10, pp. 6798-6812, Oct. 2020, doi: 10.1109/TWC.2020.3006083.
[9] T. L. Marzetta, ‘‘Noncooperative cellular wireless with unlimited numbers of base station antennas,’’ IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590–3600, Nov. 2010.
[10] R. Irmer, H. Droste, P. Marsch, M. Grieger, G. Fettweis, S. Brueck, H.-P. Mayer, L. Thiele, and V. Jungnickel, ‘‘Coordinated multipoint: Concepts, performance, and field trial results,’’ IEEE Commun. Mag., vol. 49, no. 2, pp. 102–111, Feb. 2011.
[11] H. Q. Ngo, L.-N. Tran, T. Q. Duong, M. Matthaiou, and E. G. Larsson, “On the total energy efficiency of cell-free massive MIMO,” IEEE Trans. Green Commun. Netw., vol. 2, no. 1, pp. 25–39, Mar. 2018.
[12] R. Chartrand and W. Yin, “Iteratively reweighted algorithms for compressive sensing,” in Proc. IEEE ICASSP, Mar. 2008, pp. 3869–3872.
[13] D. Ba, B. Babadi, P. L. Purdon, and E. N. Brown, “Convergence and stability of iteratively re-weighted least squares algorithms,” IEEE Trans. Signal Process., vol. 62, no. 1, pp. 183–195, Jan. 2014.
[14] G. Interdonato, P. Frenger, and E. G. Larsson, “Scalability aspects of cell-free massive MIMO,” in IEEE International Conference on Communications (ICC), pp. 1–6, IEEE, 2019
[15] Y. Zhao, I. G. Niemegeers, and S. H. De Groot, “Power allocation in cell-free
massive MIMO: A deep learning method,” IEEE Access, vol. 8, pp. 87185–87200, 2020.
[16] E. Bjornson and L. Sanguinetti, “Making cell-free massive MIMO ¨ competitive
with MMSE processing and centralized implementation,” IEEE Transactions on
Wireless Communications, vol. 19, no. 1, pp. 77– 90, 2019.
[17] H. Q. Ngo, L.-N. Tran, T. Q. Duong, M. Matthaiou, and E. G. Larsson, “On the
total energy efficiency of cell-free massive MIMO,” IEEE Trans. Green Commun. Netw., vol. 2, no. 1, pp. 25–39, Mar. 2018.
[18] Y. Zhao, I. G. Niemegeers, and S. H. De Groot, “Power allocation in cell-free massive MIMO: A deep learning method,” IEEE Access, vol. 8, pp. 87185–87200, 2020.
[19] I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning, vol. 1. MIT press Cambridge, 2016.

指導教授

陳永芳(Yung-Fang Chen)

審核日期

2023-11-16

推文