臺灣博碩士論文加值系統

Wi-Fi無線通訊已經成為一項在公共區域的基本服務。然而，由於以下幾個原因，造成服務品質不良：其一，可用非干擾的頻道數有限；其二，因隨意佈署存取點的情形將造成更大的干擾；其三，使用者的變動分佈，造成服務區域內傳輸情形不均勻。再者，若存取點發生故障時，造成服務中斷，目前尚未有一套完善自我修復的機制。故本文的研究問題是如何在網路存取點發生故障時，藉由自我修復的機制進行修復，同時考量干擾抑制以提供可靠且穩定的服務。目標是最大化最小的可容忍延遲與實際延遲差異以滿足使用者的需求。但是為求達到其他使用者的公平性，本文提出了一個帶有可變動調整傳輸功率的演算法，藉由調整功率的大小，達成小區呼吸(cell breathing)的概念用以當存取點故障時可自我修復。達成兼具自我修復及使用者公平性的目的。

Wi-Fi wireless communication has become a basic service in public areas. But the quality would not be good due to the influential factors: 1) limited numbers of non-overlapping channels, 2) un-planned deployment will cause interference, and 3) the distribution of customers is changed rapidly. It results the traffic distribution is not uniformed. Moreover, the services may not be available due to the failure of APs(Access Points). There is not a well-defined healing mechanism for solving this problem. In this paper, the problems of AP failure and interference are concerned. We propose a Self-Healing algorithm to provide seamless and reliable service based on scenarios of AP failure. The aim of this work is to maximize the minimum gap between actual delay and tolerable delay, and to satisfy each user''s demands simultaneously. Variable transmission power range is adjusted to achieve the optimal solution. Cell breathing is also adopted to solve the problem of AP is out of service. So that user fairness of resource allocation and user''s Quality of Service(QoS) can be satisfied.

口試委員審定書 I
謝誌 II
論文摘要: III
Abstract: IV
Table of Contents V
List of Figures VII
List of Tables VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 5
1.3 Objective 6
1.4 Research Scope 7
Chapter 2 Network Model & Problem Description 9
2.1 Network Model 9
2.2 Channel Assignment 10
2.3 Tolerable Delay Model 11
2.4 Problem Description 13
2.4.1 Assumptions 15
2.4.2 Parameters 16
2.4.3 Objective Function & Constraints 18
Chapter 3 Solutions Approach 24
3.1 Lagrangean Relaxation method 24
3.2 Solution Approach 24
3.2.1 Lagrangean Relaxation 24
3.2.2 The Dual Problem and the Subgradient Method 37
3.2.3 Getting Primal Feasible Solution 1: 38
3.2.4 Getting Primal Feasible solution 2: 39
Chapter 4 Computational Experiments 40
4.1 Experiment Environments 40
4.2 Experiment Results 43
Chapter 5 Conclusions And Future Works 47
5.1 Conclusions 47
5.2 Future Works 48
References 50

[1]K. Lee ,H. Lee, and D.-H. Cho, “Fairness-Aware Cooperative Resource Allocation for Self-Healing in SON-based Indoor System,” IEEE Communications Letters, Vol. 16, No. 7, pp. 1030 – 1033, May 2012.
[2]K. Lee, H. Lee, and D.-H. Cho, “CoBRA: Cooperative Beamforming-Based Resource Allocation for Self-Healing in SON-Based Indoor Mobile Communication System,” IEEE Wireless Communications, Vol. 12, No. 11, pp. 5520-5528, Sept. 2013.
[3]H. Lee, H. Kim, and K. Lee, “Collaborative Self-Healing With Opportunistic IBS Selection in Indoor Wireless Communication Systems,” IEEE Communications Letters, Vol. 18, No. 12, pp. 2209-2212,Oct. 2014.
[4]H. Lee, H. Kim, and K. Lee, “A Survey of 5G Network: Architecture and Emerging Technologies,” IEEE Access, Vol. 3, pp. 1206-1232, July 2015.
[5]Managing IP Video in the Enterprise. (G.Audin). Retrieved Dec. 28, 2015, from http://www.webtorials.com/content/2013/06/managing-ip-video-in-the-enterprise.html
[6]What’s This 802.11ac I Keep Hearing About? (G. Kamer). Retrieved Nov. 12, 2015, from http://www.mirazon.com/whats-802-11ac-keep-hearing/
[7]Lu Wei ,Zhu Yian ,Ma Chunyan, and Zhang Longmei, “A Model Driven Approach for Self-healing Computing System,” Computational Intelligence and Security, 2011 Seventh International Conference on, pp. 185 – 189, Dec 2011.
[8]Ye Ning, Li Zhe, and Liu Jun, “A Topology Control Algorithm for Self-Organizing Spatial Networks,” 2009 Chinese Control and Decision Conference, pp. 1758-1763, Jun. 2009.
[9]S. Ramamoorthy, S. P. Rajagopalan, and S. Sathyalakshmi, “Process for Security in Self-healing Systems'' Architecture,” Sustainable Energy and Intelligent Systems(SEISCON 2011), International Conference on, pp. 839-843, July. 2011.
[10]Haoxun Chen, Chengbin Chu, and J. -M. Proth, “An Improvement of the Lagrangean Relaxation Approach for Job Shop Scheduling: A Dynamic Programming Method,” IEEE Transactions on Robotics and Automation, Vol. 14, No. 5, pp. 786-795,Oct. 1998.
[11]M. Guignard, “On Solving Structured Integer Programming Problems with Lagrangean Relaxation and/or Decomposition,” Decision and Control, 1989., Proceedings of the 28th IEEE Conference on, Vol. 2, pp. 1136-1141,Dec. 1989.
[12]Z. H. Kamal, A. Al-Fuqaha, and A. Gupta, “Using Lagrangean Relaxation for Service Location Planning with QoS Constraints in Large-Scale Networks,” 2008 IEEE Internal Conference on Communications, pp. 424-428, May. 2008.