本論文是以變動長度基因演算法為基礎，解決異質性無線傳輸設備佈建之問題。在目前的無線傳輸技術，包括無線區域網路、3G 技術的蓬勃發展之下，如何正確的將一個空間佈滿指定的基地台，並且符合流量等的各種需求，是一個相當大的難題。目前的研究相關論文雖然能考慮相當多的因素，但是在演算法上面，卻因為演算法目前發展的限制，而必須先由使用者給予一個欲使用的基地台的上限值，其演算法才能夠找尋相對應的佈建方式。本論文提出以一個變動長度的基因演算法為基礎的演算法，跳脫以往佈建基地台必須先給予一個指定的基地台個數值的方式，能夠自動找尋所需的基地台數，並且能夠同時搜尋基地台所放置的位置，並且能夠同時考量佈建面積以及佈建所需的成本，以達到最佳化基地台佈建。
本論文最主要的貢獻如下：
一、 以往在佈建基地台前必須給予一個地圖以及使用基地台數的上限值，本論文所提出的方法可以自動找尋所需的基地台數。
二、 過去的方法必須先大略估計所需的基地台數後，再利用這個數字找尋基地台的放置位置，本論文所提出的方法可以同時找到所需的基地台數以及其基地台所放置的位置。
三、 本論文利用變動長度基因演算法為基礎，發展適合無線傳輸設備佈建問題之演算法。
透過我們所設計的一系列標準檢查程式，驗證本論文所提出的方法的正確性。本論文所提出的方法可以在使用者輸入一個地圖之後，自動找尋所需的基地台個數，並且找尋基地台的位置，且可達到覆蓋率95%以上。

Wireless network placement of transmitters, such as base stations for 2G
and 3G, access points for WLAN, is a NP-hard problem, since many factors
have to be considered, like QoS, coverage, cost, etc. In wireless network
placement problem, the goal is to find a set of transmitters which achieves the
widest coverage on a given map and spends the minimal cost. In this thesis,
we propose a novel variable-length genetic algorithm for solving this problem.
Most of existing methods for solving wireless network placement problem, to
our best knowledge, users must assign an upper bound or a total number of
transmitters for placement. Unlike these existing methods, the proposed
algorithm can search the optimal number of transmitters automatically. In
addition, the proposed algorithm can find near optimal solutions even in
heterogeneous transmitters placement problem, i.e., transmitters with different
power radius or cost. The results on several benchmarks are very close to the
optimal solutions, which validate the capability of the proposed method in
finding the numbers, the types, are the positions of transmitters in
heterogeneous wireless network environment.

Contents
1. INTRODUCTION 1
2. PROBLEM STATEMENT 6
2.1. PLANNING MODEL 6
2.1.1. Map 6
2.1.2. Receiver 6
2.1.3. Transmitter 7
2.2. PROPAGATION MODEL 7
2.3. OBJECTIVES 8
2.3.1. Coverage 8
2.3.2. Cost 8
2.3.3. Objective Functions 9
3. PROPOSED ALGORITHM 10
3.1. REPRESENTATION 11
3.2. INITIALIZATION 12
3.3. FITNESS EVALUATION 13
3.4. SELECTION 13
3.5. CROSSOVER 13
3.5.1. Overall crossover 13
3.5.2. Uniform crossover for genes 14
3.5.3. Scheme for choosing genes 14
3.5.4. One-point crossover for chromosomes 15
3.6. MUTATION 16
4. SIMULATION AND RESULTS 17
4.1. BENCHMARK 1 17
4.2. BENCHMARK 2 20
4.3. BENCHMARK 3 22
4.4. BENCHMARK 4 23
5. CONCLUSION 26
REFERENCES 27

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