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研究生:張逸倫
研究生(外文):Yi-Luen Chang
論文名稱:針對感知無線電系統之多目標限制滿足問題給予有效率解法且耐用解
論文名稱(外文):An Efficient and Robust for Multi-Objective Constraint-Satisfaction Problem in Cognitive Radio Systems
指導教授:熊博安熊博安引用關係
指導教授(外文):Pao-Ann Hsiung
口試委員:嚴茂旭李宗演陳鵬升熊博安
口試委員(外文):Mao-Hsu YenTrong-Yen LeePeng-Sheng ChenPao-Ann Hsiung
口試日期:100/6/30
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
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:2011
畢業學年度:99
語文別:英文
論文頁數:65
中文關鍵詞:感知無線電重構類神經網路蒙地卡羅法耐用性
外文關鍵詞:Cognitive RadioReconfigurationArtificial Neural NetworkMonte CarloRobustness
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隨著無線裝置的快速部署及通訊技術的快速發展,使用者需要特定的通訊設備以便使用不同的通訊協定,這對於使用者帶來諸多不便。為了讓一個通訊設備可以達到使用不同的無線通訊協定,Mitola等人提出了感知無線電(Cognitive Radio)的概念。而感知無線電能做到改變無線電參數以便適應環境的變化和試圖滿足用戶的需求。但調整無線電參數非常耗費時間,因為需要協調不同的網絡層的通訊參數,這也代表若調整參數無法滿足使用者需求,會付出相當大的時間代價。所以感知無線電需要一個模擬環境的方法,並依據模擬出的環境模型搭配可產生耐用解的方法去調整參數,以讓感知無線電因環境變動而要再重新調整參數的機率最低。本篇論文使用類神經網絡(Artificial Neural Network)去動態模擬環境,並利用我們提出的方法Robust Light-weight Reasoning for Cognitive Radio (RoLR)作為解決使用者限制下的多目標問題。實驗部份利用基因演算法(genetic algorithm)當作比較對象,RoLR的解在準確度上比起基因演算法準兩倍以上,且只需約1/20的基因演算法執行時間。在解的耐用性上,當環境不斷變動下,RoLR最後仍有42%的解可以符合限制而基因演算法僅存2%。此外,使用RoLR只需約10筆的訓練資料訓練類神經網路建構網路模型,就可以讓產出的解有90%以上可滿足所有限制。
With rapid deployment of wireless applications and the development of new communication technologies, users need specific communication devices for different communication protocols, which causes great inconvenience to the users. To support different wireless communication protocols, Mitola et al. proposed the concept of cognitive radio (CR). CR adapts to wireless environment changes and tries to satisfy the demand of users by tuning radio parameters. However, the process of tuning the radio parameters is quite time-consuming because it needs to coordinate varying communication parameters across networking layers, thus its failure leads to high overhead. In order to allow a CR system to make accurate decisions, the wireless environment must be precisely modelled by reliable methods. A CR system also needs a method for tuning the radio parameters in a robust way so as to decrease the probability of doing system reconfiguration with each and every time of environment change. This Thesis uses artificial neural network (ANN) to dynamically model the environment, and use the Robust Light-weight Reasoning for Cognitive Radio (RoLR) to solve the multi-objective problem of satisfying user given constraints. Comparing RoLR with a method based on the genetic algorithm (GA), it is found that RoLR is more than two times accurate than the GA method, takes about 1/20 of the execution time of the GA method, and RoLR is more robust than GA became 42% of the RoLR solutions are feasible, while only 2% of the GA solution are feasible after the environment changes 12 times. Further, RoLR needs very few amount of training data around 10 for training the ANN model such that 90% of the candidate solutions are feasible.
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Related Work 11
3 Preliminaries 20
3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3 Basic Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 Robust Light-weight Reasoning for Cognitive Radio 29
4.1 Awareness Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Reasoning Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5 Experiments 44
5.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.2.1 Evaluation on training data for ANN . . . . . . . . . . . . . . 47
5.2.2 Number of MC points . . . . . . . . . . . . . . . . . . . . . . 48
5.2.3 Evaluation on Accuracy and Execution Time . . . . . . . . . . 51
5.2.4 Scenario of the High definition Video Streaming . . . . . . . . 54
5.2.5 Evaluation on Robustness . . . . . . . . . . . . . . . . . . . . 55
6 Conclusions and Future Work 58
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