近幾十年來隨著行動通信技術的快速發展，其中最熱門也最常被探討的研究議題之一是基地台細胞規劃的問題(Cell Planning Problem, CPP)，而其中位置管理(Location Management, LM)除了是影響細胞規劃的成本因素外，更是行動通信網路中，最重要的行動計算(Mobile Computing)探討議題。本論文研究的重點著重在應用啟發式方法作細胞網路最佳化的佈署，使得位置管理方式更有效率，並達成信號成本最小化。當基地台網路是由N個細胞構成時，如何在2N個組合中找到細胞網路的最佳佈署，是屬於組合最佳化的問題(Combinatorial Optimization Problem, COP)，已被證明是屬於NP-Complete的問題，許多學者也嚐試以各種啟發式演算法應用在解決該類問題上。
本研究主要是採用靜態式的匯報細胞(Reporting Cell, RC)位置管理方式，搭配John Holland所提的基因演算法(Genetic Algorithm, GA)以及Fred Glover所提的禁忌搜尋法(Tabu Search, TS)為基礎提出兩項新的架構。首先應用多重改善基因演算法(Multiple Improved Genetic Algorithm, MIGA)架構以解決基地台細包規劃的問題，其中提出幾項策略，包括初始化(Initialization)、本地搜尋(Local Search, LS)、區域交配(Area Crossover, AC)和鄰居交配(Neighbor Crossover, NC)等，除了探討個別策略的影響外，並評估整合所有策略的成效，經由實驗結果顯示MIGA架構有令人滿意的效果，比傳統及其他學者所提之基因演算法更有效率。
其次提出多重改善禁忌搜尋法(Multiple Improved Tabu Search, MITS)架構，在MITS的架構下，本研究提出兩項新的策略，分別是集中搜尋(Intensification)及分散搜尋(Diversification)，其中集中搜尋採用細胞信號成本法(Cell Signaling Cost, CSC)，而分散搜尋採用交換鄰居法(Neighbor Swap, NS)，經由實驗結果顯示，在解決基地台細胞規劃的問題上，本研究所提出之MITS架構優於傳統基因演算法、傳統禁忌搜尋法、以及其他學者所提出之基因演算法、禁忌搜尋法及螞蟻族群演算法(Ant Colony Algorithm, ACA)。

During the past decades, mobile communication is in the vigorous development, where the cell planning problem (CPP) is one of impressive mobile computing research issues. Among many factors to affect cell planning, the major one is the signaling cost, where the location management is critical to the signaling cost consideration. The CPP objective is to find the best cell deployment in mobile communication network, so that the signaling cost can be minimized. When a network has N cells, we have 2N possible deployments in the cell network, and how to find out the best one is a combinatorial optimization problem (COP), which has been proved to be NP-Complete. As a result, many researchers develop meta-heuristic search strategies for solving it.
In this dissertation, we consider static reporting cell (RC) location management scheme, and adopt genetic algorithm (GA) and tabu search (TS) to resolve cell planning problem. First, we propose a novel cell planning architecture for base stations using Multiple Improved Genetic Algorithm (MIGA) with initialization, local search, and particular mechanisms of area and neighbor crossovers. Several simulations are conducted on various network cells, including previous, random and real configurations. The simulation results reveal that our schemes are superior to the considered algorithms.
Second, we adopt a meta-heuristic local search algorithm of Tabu Search (TS) to deal with the cell planning issue for the base station, and propose novel designs using Multiple Improved Tabu Search (MITS) to improve the TS capability, including cell signaling cost (CSC) and strategy of neighbor swap (NS). The simulation results reveal that our improved TS clearly outperform traditional TS and genetic algorithms in attacking CPP.

ACKNOWLEDGEMENTS IV
摘要 VI
ABSTRACT VIII
CONTENT X
LIST OF FIGURES XIII
LIST OF TABLES XV
CHAPTER 1. INTRODUCTION 1
1.1 MOTIVATION AND OBJECTIVES 1
1.2 SUMMARY OF THE DISSERTATION 3
1.3 ORGANIZATION OF THE DISSERTATION 5
CHAPTER 2. RELATED WORKS 8
2.1 LOCATION MANAGEMENT SCHEMES 9
2.2 CELL PLANNING PROBLEM 17
2.2.1. Scope and Limitations 17
2.2.2. Problem Formulation 19
2.3 SIGNALING COST OF LM 20
2.4 APPROACHES TO CPP 25
2.4.1 Non Heuristic Approaches 25
2.4.2 Simulated Annealing and Scatter Search 26
2.4.3 Genetic Algorithm 27
2.4.4 Tabu Search 32
CHAPTER 3. IMPROVED GENETIC ALGORITHMS TO CPP 35
3.1 REPRESENTATION OF GENETIC ALGORITHM IN CPP 35
3.2 PROPOSED SCHEMES 36
3.2.1. Initialization 36
3.2.2. Local Search 38
3.2.3. Area Crossover 39
3.2.4. Neighbor Crossover 41
3.2.5. MIGA Architecture 42
3.3 SIMULATIONS AND ANALYSIS 44
3.3.1. Test Networks and Experimental Setting 44
3.3.2. Case 1: Initialization and Local Search 46
3.3.3. Case 2: Area Crossover 48
3.3.4. Case 3: Neighbor Crossover 51
3.3.5. Case 4: Comprehensive comparison of the TGA, AGA and NGA 53
3.4 DISCUSSION 54
CHAPTER 4. IMPROVED TABU SEARCH TO CPP 56
4.1 REPRESENTATION OF TABU SEARCH IN CPP 56
4.2 PROPOSED SCHEMES 56
4.2.1 Intensification Type 1-CMR 57
4.2.2 Intensification Type 2-CSC 62
4.2.3 Diversification-Neighbor Swap 70
4.2.4 MITS Architecture 73
4.3 SIMULATIONS AND ANALYSIS 74
4.3.1 Test Networks and Experimental Setting 75
4.3.2 Combined Intensification and Diversification 77
4.3.3 Comprehensive comparison of various strategies 79
4.4 DISCUSSION 83
CHAPTER 5. CONCLUSIONS AND FUTURE WORK 86
5.1 CONCLUSIONS 86
5.2 FUTURE WORK 88
REFERENCES 90
APPENDIX 96
A. ABBREVIATIONS 96
B. BENCHMARKS 99
B.1. Subrata's 4x4, 6x6 and 8x8 Test Networks 99
B.2. CHT's 15x15 and 20x20 Test Networks 100

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