請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73659完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡志宏 | |
| dc.contributor.author | Yu-Hsin Lin | en |
| dc.contributor.author | 林禹欣 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:07:34Z | - |
| dc.date.available | 2020-08-20 | |
| dc.date.copyright | 2019-08-20 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-19 | |
| dc.identifier.citation | [1] Kittipiyakul, S., Elia, P., & Javidi, T. (2009). High-SNR analysis of outage-limited communications with bursty and delay-limited information. IEEE Transactions on Information Theory, 55(2), 746-763.
[2] Zheng, L., & Tse, D. N. C. (2003). Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels. IEEE Transactions on information theory, 49(5), 1073-1096. [3] Tse, D. N. C., Viswanath, P., & Zheng, L. (2004). Diversity-multiplexing tradeoff in multiple-access channels. IEEE Transactions on Information Theory, 50(9), 1859-1874. [4] Yang, J., & Ulukus, S. (2009, June). Delay minimization in multiple access channels. In Information Theory, 2009. ISIT 2009. IEEE International Symposium on (pp. 2366-2370). IEEE. [5] Ross, S. M. (2014). Introduction to stochastic dynamic programming. Academic press. [6] Marshall, A. W., Olkin, I., & Arnold, B. C. (1979). Inequalities: theory of majorization and its applications (Vol. 143, pp. xx+-569). New York: Academic press. [7] Tse, D. N. C., & Hanly, S. V. (1998). Multiaccess fading channels. I. Polymatroid structure, optimal resource allocation and throughput capacities. IEEE Transactions on Information Theory, 44(7), 2796-2815. [8] Shortle, J. F., Thompson, J. M., Gross, D., & Harris, C. M. (2018). Fundamentals of queueing theory (Vol. 399). John Wiley & Sons. [9] Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press. [10] Berry, R. A., & Gallager, R. G. (2002). Communication over fading channels with delay constraints. IEEE Transactions on Information Theory, 48(5), 1135-1149. [11] Berry, R. A. (2013). Optimal power-delay tradeoffs in fading channels—Small-delay asymptotics. IEEE Transactions on Information Theory, 59(6), 3939-3952. [12] Yang, J., & Ulukus, S. (2011). Trading rate for balanced queue lengths for network delay minimization. IEEE Journal on Selected Areas in Communications, 29(5), 988-996. [13] Ehsan, N., & Javidi, T. (2008). Delay optimal transmission policy in a wireless multiaccess channel. IEEE Transactions on Information Theory, 54(8), 3745-3751. [14] Negi, R., & Goel, S. (2004, November). An information-theoretic approach to queuing in wireless channels with large delay bounds. In Global Telecommunications Conference, 2004. GLOBECOM’04. IEEE (Vol. 1, pp. 116-122). IEEE. [15] Gallager, R. G. (1968). Information theory and reliable communication (Vol. 588). New York: Wiley. [16] Polyanskiy, Y., Poor, H. V., & Verdú, S. (2010). Channel coding rate in the finite blocklength regime. IEEE Transactions on Information Theory, 56(5), 2307. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73659 | - |
| dc.description.abstract | 可靠度，延遲與大量的使用者形成物聯網之中的黃金三角。在這篇論文，我們探討了它們在多使用者系統慢衰減多輸入多輸出通道下，彼此的關係。在跨階層系統模型下，整體的延遲包括物理層的傳輸時間以及網路層的佇列延遲需要被考慮，我們探討這個問題的兩種情境，首先我們聚焦在靜態的傳輸策略及延遲界線的指標上，我們的目標是在給定系統錯誤率低於預設門檻下，找出可以最小化延遲界線之最佳策略，藉由高訊雜比的分析方法，原本的問題可以公式化成一個非凸函數的最佳化問題，進而使用KKT條件找出最佳解。結果顯示到達分佈的參數、錯誤率的預設門檻、使用者的數量決定了最佳策略的選擇，此外整體延遲的成長與使用者數量的之間的關係超越線性。接著，我們考慮動態的策略以及平均延遲的指標，並透過馬可夫決定程序解出此問題，藉由值函數的非凸和對稱特性，我們找出了最佳策略，此外我們也提出了兩種找出最佳策略的演算法。 | zh_TW |
| dc.description.abstract | Reliability, latency, and the number of connected users form the golden triangle of Internet of Things. In this thesis, the fundamental trade-off among them are investigated in the multi-user system with quasi-static MIMO channel. By a cross-layer system model, the overall latency including transmission time at the physical layer as well as queuing delay at the network layer is considered. We study two different scenarios of this problem. First, we focus on the static transmission policy and delay-bound metric. Our goal is to find the optimal policy that can minimize the delay-bound, given that the system error probability is below a prescribed threshold. By taking the problem to the high SNR asymptotic regime, it can be formulated into a convex optimization problem. Hence Karush-Kuhn-Tucker (KKT) conditions can be used to find the optimal solution. The result shows that the parameters of arrival distribution, the prescribed threshold of the error probability, and the number of users determine the choice of the optimal policy. In addition, the overall latency grows super-linearly with the number of users. Then we turn to the dynamic policy and average delay metric. We solve this problem based on Markov decision process. By convexity and symmetry of the value function, we can find the optimal policy. In addition, two algorithms for finding the optimal policy are proposed. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:07:34Z (GMT). No. of bitstreams: 1 ntu-108-R05942119-1.pdf: 1001968 bytes, checksum: 6e5373b4998901f01dc6ef6ed24191a7 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 iii
摘要 v Abstract vii 1 Introduction 1 1.1 Contribution of the thesis 3 1.2 Organization 4 2 Background 5 2.1 The Trade-off between Diversity and Delay in point-to-point system 5 2.2 Uplink System with Dynamic Policy 10 3 Problem Formulation and Main Results for Static Transmission Policy 17 3.1 Physical and Network Layer Models 18 3.2 2-User Asymmetric Case with Delay Bound Metric 20 3.3 K-User Symmetric Case with Delay Bound Metric 25 4 Problem Formulation and Main Results for Dynamic Transmission Policy 29 4.1 Physical and Network Layer Models 29 4.2 The Optimal Dynamic Policy in K-User Symmetric Case with Average Delay Metric 31 4.3 The Algorithms of the Optimal Dynamic Policy 34 4.4 K-User Symmetric Broadcast Channel 35 4.5 Overflow Probability 40 5 Conclusion 43 5.1 Summary 43 5.2 Future Research Direction 44 A Proof of Dynamic policy 45 A.1 Proof of lemma 4.2.1 45 A.2 Proof of lemma 4.2.2 46 A.3 Proof of lemma 4.2.3 46 A.4 Proof of lemma 4.2.4 48 A.5 Proof of theorem 4.2.1 49 Bibliography 51 | |
| dc.language.iso | en | |
| dc.subject | 慢衰退 | zh_TW |
| dc.subject | 分集多工取捨 | zh_TW |
| dc.subject | 延遲 | zh_TW |
| dc.subject | 多輸入多輸出 | zh_TW |
| dc.subject | 馬可夫決定程序 | zh_TW |
| dc.subject | 動態傳輸策略 | zh_TW |
| dc.subject | dynamic transmission policy | en |
| dc.subject | slow fading | en |
| dc.subject | diversity-multiplexing tradeoff | en |
| dc.subject | Markov decision process | en |
| dc.subject | delay | en |
| dc.subject | MIMO | en |
| dc.title | 延遲與可靠度於多使用者系統之靜態與動態傳輸策略分析 | zh_TW |
| dc.title | On the Trade-off between Diversity and Delay in Multi-user System: Static and Dynamic Transmission Policies | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 王奕翔 | |
| dc.contributor.oralexamcommittee | 林士駿,黃昱智 | |
| dc.subject.keyword | 多輸入多輸出,延遲,分集多工取捨,慢衰退,馬可夫決定程序,動態傳輸策略, | zh_TW |
| dc.subject.keyword | MIMO,delay,diversity-multiplexing tradeoff,slow fading,Markov decision process,dynamic transmission policy, | en |
| dc.relation.page | 52 | |
| dc.identifier.doi | 10.6342/NTU201901235 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-108-1.pdf 未授權公開取用 | 978.48 kB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。