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研究生:王聖杰
研究生(外文):Wang, Sheng-Jie
論文名稱:噪聲限制網路下以模擬退火演算法優化拉格朗日乘數最佳化機率性快取策略
論文名稱(外文):Simulated-Annealing-Enhanced Lagrange Multiplier Optimization of the Probabilistic Caching Policy in Noise-Limited Network
指導教授:陳伯寧
指導教授(外文):Chen, Po-Ning
口試委員:黃昱智謝欣霖
口試委員(外文):Huang, Yu-ChihShieh, Shin-Lin
口試日期:2020-06-19
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:41
中文關鍵詞:無線快取機率性快取策略拉格朗日乘數法模擬退火演算法
外文關鍵詞:wireless cachingprobabilistic content placement policyLagrange multiplierssimulated annealing
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快取是一項實用的技術,透過在網路使用量離峰時段,將受歡迎的檔案預先存取在一些快取幫助者中,用以降低尖峰時段的網路壅塞情形。在這篇研究裡,我們重新回顧了於躁聲限制網路架構下機率性檔案快取的問題,用戶會根據一個給定的機率分布模型,連續地送出多個檔案需求。我們提出一個基於拉格朗日乘數的演算法,在最大化各類用戶中最小的檔案傳送成功機率的目標下,保證所求得的解至少為區域最佳解。而基於這一個非凸優化的問題,由拉格朗日乘數演算法所得到的解可能會陷入很小的區域最大值,我們便更進一步結合模擬退火演算法的概念,使得我們原先基於拉格朗日乘數的演算法能夠跳離微小的區域最大值,成為更有力度的求解演算法。

由大量的實驗結果驗證得出,我們提出的演算法求得的解在效能上勝過目前其他現有的算法,並且解的收斂情形更不易受到初始值的影響。再者,為了增加系統整體的傳送流通量,我們提出最大化用戶的加權平均檔案傳輸成功機率作為另一個效能指標,取代原先最大化各類用戶中最小的檔案傳送成功機率。在新的效能指標下,透過調整原先的演算法使其能套用在新問題中,亦得出一些與前述相似的結論。
Caching is a powerful technique that reduces the peak traffic loading by pre-storing popular contents in caching helpers during off-peak hours. In this work, the problem of probabilistic content caching in noise-limited network is revisited in which users may sequentially request multiple contents according to a probability distribution. A novel algorithm based on the method of Lagrange multiplier is proposed to produce policy that guarantees (locally) maximal content delivery success probability (CDSP) of the worst user. Due to the non-convex nature of the problem, it is likely that this algorithm would be trapped into an insignificant local maximum. We further propose an enhanced version of the algorithm based on the idea of simulated annealing, which enables the algorithm to escape local maxima. Extensive simulations are conducted and the results show that the proposed enhanced algorithm provides policies that outperform the state-of-the-art and is significantly less sensitive to initial values. Moreover, to increase the overall system throughput, we propose another metric which maximizes the weighted average CDSP, instead of the worst user's CDSP. For this new metric, an algorithm adapted from the proposed algorithm is introduced, for which similar conclusions can be drawn.
摘要 . . . i
Abstract . . . ii
Acknowledgement . . . iii
Table of Contents . . . iv
List of Figure . . . vi
1 Introductions . . . 1
2 System Model and Problem Formulation . . . 4
2.1 Wireless Caching Network with Categorized Contents . . . 4
2.2 Content Popularity . . . 5
2.3 Probabilistic Content Placement (PCP) Policy in Noise-Limited Network . . . 7
2.4 Problem Formulation . . . 9
3 Lagrange Multiplier Optimization of Probabilistic Caching Policy . . . 11
3.1 Proposed Lagrange Multiplier Maximization . . . 11
3.2 Proposed Simulated-Annealing-Enhanced LMM . . . 15
3.2.1 Cooling process . . . 16
3.2.2 Generation of P′ . . . 17
3.2.3 Statistical acceptance between state transition . . . 19
3.3 Maximization of the Weighted Average CDSP . . . 20
3.4 Complexity Analysis . . . 20
4 Numerical Results . . . 23
4.1 Maximization of the CDSP of the Type-L Typical User . . . 24
4.1.1 Initialization with Uniform P(0) . . . 24
4.1.2 Random initialization of P(0) . . . 28
4.2 The Weighted Average CDSP . . . 30
5 Conclusion . . . 33
References . . . 34
Appendix A . . . 36
Appendix B . . . 39
[1] “Cisco visual networking index: Global mobile data traffic forecast
update, 2017-2022 white paper,” Available at http://www.cisco.com.
[2] X. Wang, A. V. Vasilakos, M. Chen, Y. Liu, and T. T. Kwon, “A survey of
green mobile networks: Opportunities and challenges,” Mobile Networks
and Applications, vol. 17, no. 1, pp. 4–20, 2011.
[3] Z. Zhao, M. Xu, Y. Li, and M. Peng, “A non-orthogonal multiple access
(noma)-based multicast scheme in wireless content caching networks,”
IEEE J. Sel. Areas Commun., vol. 35, no. 12, pp. 2723–2735, Dec 2017.
[4] Z. Ding, P. Fan, G. K. Karagiannidis, R. Schober, and H. V. Poor, “Noma
assisted wireless caching: Strategies and performance analysis,” IEEE
J. Sel. Areas Commun., vol. 66, no. 10, pp. 4854–4876, May 2018.
[5] B. Blaszczyszyn and A. Giovanidis, “Optimal geographic caching in
cellular networks,” IEEE International Conference on Communications
(ICC), June 2015.
[6] S. H. Chae and W. Choi, “Caching placement in stochastic wireless
caching helper networks: Channel selection diversity via caching,” IEEE
Transactions on Wireless Communications, vol. 15, no. 10, pp. 6626–
6637, October 2016.
[7] M. Choi, D. Kim, D. Han, J. Kim, and J. Moon, “Probabilistic caching
policy for categorized contents and consecutive user demands,” IEEE
International Conference on Communications (ICC), May 2019.
[8] X. Zhang and J. Wang, “Heterogeneous statistical qos-driven power
allocation for collaborative d2d caching over edge-computing networks,”
2019 IEEE 39th International Conference on Distributed Computing
Systems (ICDCS), July 2019.
[9] D. Wang, T. Z. Y. Lan, Z. Yin, and X. Wang, “On the design of
computation offloading in cache-aided d2d multicast networks,” IEEE
Access, vol. 6, pp. 63 426–63 441, October 2018.
[10] Y. Li, Z. Chen, M. C. Gursoy, and S. Velipasalar, “Learning-based
delayaware caching in wireless d2d caching networks,” IEEE Access
,
vol. 6, pp. 77 250–77 264, 2018.
[11] H. J. Kang and C. G. Kang, “Mobile device-to-device (d2d) content
delivery networking: A design and optimization framework,” J. Commun. Netw., vol. 16, no. 5, pp. 568–577, October 2014.
[12] J. Rao, H. Feng, C. Yang, Z. Chen, and B. Xia, “Optimal caching placement for d2d assisted wireless caching networks,” IEEE International
Conference on Communications (ICC), 2016.
[13] S. N. Chiu, D. Stoyan, W. S. Kendall, and J. Mecke, Stochastic Geometry
and Its Applications, 3rd ed. Wiley, 2013.
[14] F. Alajaji and P.-N. Chen, An Introduction to Single-User Information
Theory. Springer, July 2018.
[15] P. van Laarhoven and E. Aarts, Simulated Annealing: Theory and
Applications. Springer Science and Business Media, 1987.
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