淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


下載電子全文限經由淡江IP使用) 
系統識別號 U0002-1308202002290700
中文論文名稱 OFDMA下行鏈路系統中結合波束成型機制之資源分配方法
英文論文名稱 Joint Beamforming And Resource Allocation In OFDMA Downlink Systems
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生中文姓名 卓傳衛
研究生英文姓名 Chuan-Wei Cho
學號 607410148
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2020-07-10
論文頁數 67頁
口試委員 指導教授-潘孟鉉
委員-鄭建富
委員-曾學文
中文關鍵字 類比波束成型  波束排程  資源分配  QoS  低延遲  行動網路  OFDMA  5G 
英文關鍵字 Analog beamforming  Beam scheduling  Resource allocation  QoS  low-latency communication  Cellular network  OFDMA  5G 
學科別分類 學科別應用科學資訊工程
中文摘要 為了支援高頻寬低延遲的語音服務,近年來巨量天線系統與波束成形 (Beamforming) 的技術已經被廣泛地探討可以強化特定方向的訊號強度並降低對其他方向的訊號干擾以提升系統整體的訊號品質,此論文考慮基地台配置有巨量天線系統並且有大量波束可供基地台選擇的網路情境,我們發現到下行鍊路(Downlink)傳輸時所採用的波束的選擇會直接影響基地台與使用者裝置間的通訊品質,也因此在進行無線資源分配排程(Scheduling)時應同時考慮傳輸時所採用波束的選擇。在本論文中,我們提出兩個波束選擇以及無線資源分配之策略,首先我們定義此網路的最大化吞吐量問題,並且提出可以達到最佳吞吐量之解決方案,接下來為了同時保證最大化吞吐量並滿足服務品質保證的需求我們提出了基於累加懲罰值與封包掉落感知的波束選擇與資源分配方法,所提出的方法在滿足最大化吞吐量的同時亦能夠同時盡可能地使每個 UE 都有較為公平的傳輸機會。模擬結果顯示所提出的的方法確實可以獲得接近於最大化吞吐量方法的吞吐量並在多數場景中都能明確的降低封包被丟棄的機率。
英文摘要 In order to support high-bandwidth and low-latency voice services, in recent years, massive antenna systems and beamforming technologies have been extensively explored to enhance the signal strength in specific directions and reduce signal interference in other directions to improve the overall signal quality of the system. This paper considers the network scenario where the base station is equipped with a huge number of antenna systems and there are a large number of beams for the base station to choose. We found that the choice of the beam used in downlink transmission will directly affect the communication quality between the base station and the user's device. Therefore, the selection of beams used during transmission should be considered when scheduling radio resource allocation. In this paper, we propose two strategies for beam selection and wireless resource allocation. First, we define the maximum throughput problem of this network and propose a solution that can achieve the best throughput. Next, in order to simultaneously ensure maximum throughput and meet the requirements of service quality assurance, we propose a beam selection and resource allocation method based on accumulated penalty value and packet drop perception. The proposed method can maximize the throughput while making every UE have a fairer transmission opportunity as much as possible. The simulation results show that the proposed method can indeed achieve a throughput close to the maximum throughput method and can clearly reduce the probability of packet being discarded in most scenarios.
論文目次 目錄
第一 章、緒論 1
第二 章、相關文獻 6
2.1 無線資源排程方法 6
2.2 波束選擇方法 7
第三 章、系統模型 9
第四 章、最大吞吐量策略 13
第五 章、機會公平策略 17
第六 章、模擬結果 27
6.1 模擬環境與參數 27
6.2 模擬結果比較對象 29
6.3 系統吞吐量的模擬結果 30
6.4 封包掉落率的模擬結果 31
6.5 傳輸次數的模擬結果 35
6.6 流量負載變化的模擬結果 36
6.7 低延遲封包掉落率的模擬結果 37
第七 章、總結 38
參考文獻 40
附錄­英文論文 43

圖目錄
Fig. 1 系統架構 9
Fig. 2 選擇波束與資源分配之計算流程 12
Fig. 3 在 Case 1 場景中的隨使用者裝置人數變化的吞吐量模擬結果 30
Fig. 4 在 Case 2 的場景中隨使用者裝置人數變化的吞吐量模擬結果 30
Fig. 5 mˆ = 120 時於 Scenes 1 環境下 Case 2 的使用者裝置吞吐量由與傳輸等待時間分布圖 31
Fig. 6 在 Case 1 場景中的封包掉落率模擬結果 32
Fig. 7 在 Case 2 場景中的封包掉落率模擬結果 32
Fig. 8 CBR 主導負載量的 Case 2 場景中 mˆ = 120 時使用者裝置的吞吐量分布 33
Fig. 9 CBR 主導負載量的 Case 2 場景中 mˆ = 120 時使用者裝置的封包傳輸成功率分布 33
Fig. 10 CBR 主導負載量的 Case 2 場景中 mˆ = 120 時使用者裝置的傳輸等待時間由小到大分布 34
Fig. 11 video 主導負載的場景下 mˆ = 120 時使用者裝置傳輸次數分布 35
Fig. 12 Case 3 video 主導負載場景下 mˆ = 100 時 CBR 負載提升引起吞吐量分布集中的變化 36
Fig. 13 Case 4 低延遲封包掉落率 37

表目錄
Table. 1 建立每個使用者裝置對每個波束所回報 CQI 的關係表 10
Table. 2 Table 5.2.2.1­2: 4­bit CQI Table[4] 11
Table. 3 QoS Flow 設置參數 27
Table. 4 QoS Flow 對應封包大小 27
Table. 5 模擬不同流量模式 Case 的參數 28
參考文獻 [1] 3GPP. Physical Layer Aspects. Technical Report (TR) 38.802, 3rd Generation Partnership Project (3GPP), 09 2017. version 14.2.0.
[2] 3GPP. Radio Interface Protocol Aspects. Technical Report (TR) 38.804, 3rd Generation Partnership Project (3GPP), 03 2017. version 14.0.0.
[3] 3GPP. 5G;NR;Physical channels and modulation. Technical Specification (TS) 38.211, 3rd Generation Partnership Project (3GPP), 07 2018. version 15.2.0.
[4] 3GPP. 5G;NR;Physical layer procedures for data. Technical Specification (TS) 38.214, 3rd Generation Partnership Project (3GPP), 10 2018. version 15.3.0.
[5] 3GPP. 5G;NR;System Architecture for the 5G System. Technical Specification (TS) 23.501, 3rd Generation Partnership Project (3GPP), 06 2018. version 15.2.0.
[6] D. E. Berraki, S. M. D. Armour, and A. R. Nix. Codebook based beamforming and multiuser scheduling scheme for mmwave outdoor cellular systems in the 28, 38 and 60ghz bands. In 2014 IEEE Globecom Workshops (GC Wkshps), pages 382–387, 2014.
[7] E. Bjornson, L. Van der Perre, S. Buzzi, and E. G. Larsson. Massive mimo in sub­6 ghz and mmwave: Physical, practical, and use­case differences. IEEE Wireless Communications, 26(2):100–108, 2019.
[8] A. A. Esswie and K. I. Pedersen. Multi­user preemptive scheduling for critical low latency communications in 5G networks. In Proc. of IEEE Symposium on Computers and Communications (ISCC), 2018.
[9] G. Femenias, F. Riera­Palou, X. Mestre, and J. J. Olmos. Downlink scheduling and resource allocation for 5G MIMO­multicarrier: OFDM vs FBMC/OQAM. IEEE Access, 5:13770–13786, 2017.
[10] J. Gante, G. Falciao, and L. Sousa. Data­aided fast beamforming selection for 5G. In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
[11] M. Giordani, M. Polese, A. Roy, D. Castor, and M. Zorzi. A tutorial on beam management for 3gpp nr at mmwave frequencies. IEEE Communications Surveys Tutorials, 21(1):173– 196, 2019.
[12] I. Hadar, L.­o. Raviv, and A. Leshem. Scheduling for 5G cellular networks with priority and deadline constraints. In Proc. of IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE), 2018.
[13] S. Han, C. I, Z. Xu, and C. Rowell. Large­scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5g. IEEE Communications Magazine, 53(1):186–194, 2015.
[14] S. He, Y. Wu, D. W. K. Ng, and Y. Huang. Joint optimization of analog beam and user scheduling for millimeter wave communications. IEEE Communications Letters, 21(12):2638–2641, 2017.
[15] M. Huang and X. Zhang. Enhanced automatic neighbor relation function for 5G cellular systems with massive MIMO. In Proc. of IEEE International Conference on Communications (ICC), 2017.
[16] M. Huang and X. Zhang. Big data analysis on beam spectrum for handover optimization in massive­MIMO cellular systems. In Proc. of IEEE Wireless Communications and Networking Conference (WCNC), 2018.
[17] H. Ji, S. Park, J. Yeo, Y. Kim, J. Lee, and B. Shim. Ultra­reliable and low­latency communications in 5g downlink: Physical layer aspects. IEEE Wireless Communications, 25(3):124–130, 2018.
[18] Z. Jiang, S. Chen, S. Zhou, and Z. Niu. Joint user scheduling and beam selection optimization for beam­based massive mimo downlinks. IEEE Transactions on Wireless Communications, 17(4):2190–2204, 2018.
[19] P. Kela, X. Gelabert, J. Turkka, M. Costa, K. Heiska, K. Leppänen, and C. Qvarfordt. Supporting mobility in 5g: A comparison between massive mimo and continuous ultra dense networks. In 2016 IEEE International Conference on Communications (ICC), pages 1–6, 2016.
[20] P. Kela, J. Puttonen, N. Kolehmainen, T. Ristaniemi, T. Henttonen, and M. Moisio. Dynamic packet scheduling performance in utra long term evolution downlink. In Proc. of IEEE International Symposium on Wireless Pervasive Computing, 2008.
[21] H. J. Kushner and P. A. Whiting. Convergence of proportional­fair sharing algorithms under general conditions. IEEE Transactions on Wireless Communications, 3(4):1250– 1259, 2004.
[22] S. Kutty and D. Sen. Beamforming for millimeter wave communications: An inclusive survey. IEEE Communications Surveys Tutorials, 18(2):949–973, 2016.
[23] G. Kwon and H. Park. Joint user association and beamforming design for millimeter wave udn with wireless backhaul. IEEE Journal on Selected Areas in Communications, 37(12):2653–2668, 2019.
[24] Q. Li, G. Li, W. Lee, M. Lee, D. Mazzarese, B. Clerckx, and Z. Li. Mimo techniques in wimax and lte: a feature overview. IEEE Communications Magazine, 48(5):86–92, 2010.
[25] Y. R. Li, B. Gao, X. Zhang, and K. Huang. Beam management in millimeter­wave communications for 5g and beyond. IEEE Access, 8:13282–13293, 2020.
[26] M. Mezzavilla, M. Polese, A. Zanella, A. Dhananjay, S. Rangan, C. Kessler, T. S. Rappaport, and M. Zorzi. Public safety communications above 6 ghz: Challenges and opportunities. IEEE Access, 6:316–329, 2018.
[27] H. Miao, M. Faerber, M. Fresia, and V. Frascolla. Joint beam­frequency multiuser scheduling for millimeter­wave downlink multiplexing. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), pages 1–5, 2016.
[28] M. Nicolaou, A. Doufexi, S. Armour, and Y. Sun. Performance analysis for partial feedback downlink MIMO with unitary codebook beamforming for LTE. In Proc. of IEEE International Conference on Communications Workshops, 2009.
[29] M. Polese, M. Giordani, M. Mezzavilla, S. Rangan, and M. Zorzi. Improved handover through dual connectivity in 5G mmWave mobile networks. IEEE Journal on Selected Areas in Communications, 35(9):2069–2084, 2017.
[30] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson. Scaling up mimo: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30(1):40–60, 2013.
[31] Q. H. Spencer, C. B. Peel, A. L. Swindlehurst, and M. Haardt. An introduction to the multi­user mimo downlink. IEEE Communications Magazine, 42(10):60–67, 2004.
[32] P. Viswanath, D. N. C. Tse, and R. Laroia. Opportunistic beamforming using dumb antennas. In Proc. of IEEE International Symposium on Information Theory (ISIT), 2002.
[33] A. Vora and K.­D. Kang. Downlink scheduling and resource allocation for 5G MIMO multicarrier systems. In Proc. of IEEE 5G World Forum (5GWF), 2018.
[34] J. Wang, Z. Lan, C.­W. Pyo, T. Baykas, C.­S. Sum, M. A. Rahman, R. Funada, F. Kojima, I. Lakkis, H. Harada, et al. Beam codebook based beamforming protocol for multi­gbps millimeter­wave WPAN systems. In Proc. of IEEE Global Communications Conference (Globecom), 2009.
[35] Y.­C. Wang and S.­Y. Hsieh. Service­differentiated downlink flow scheduling to support QoS in long term evolution. Computer Networks, 94:344–359, 2016.
[36] Z. Xie and W. Chen. A joint channel and queue aware scheduling method for multi­user massive MIMO systems. In Proc. of IEEE International Conference on Communications (ICC), 2019.
[37] Youtube. Choose live encoder settings, bitrates, and resolutions. https://support. google.com/youtube/answer/2853702, 2020. [Online; accessed 27­May­2020].
[38] H. Zhang and W. Huang. Tractable mobility model for multi­connectivity in 5g user­centric ultra­dense networks. IEEE Access, 6:43100–43112, 2018.
論文使用權限
  • 同意紙本無償授權給館內讀者為學術之目的重製使用,於2020-08-19公開。
  • 同意授權瀏覽/列印電子全文服務,於2020-08-19起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2487 或 來信