動態頻譜共享(dynamic spectrum sharing) 允許不具有執照的無線電用戶(坎級用戶)擇機使用具有執照的無線電用戶(主用戶)的頻譜，因此被認為是一種有效解決頻譜低效利用問題的方案。本論文研究次級用戶如何智能地實現高效率的動態頻譜共享。我們考慮兩種智能共享模式:社交智能(social intelligence) 以及個體智能(individual intelligence) 。 / 對於社交智能，次級用戶基於社交互動(social interactions) 來協作地共享頻譜。受到電子商務工業的推薦系統(recommendation sYstem) 的啟發，我們提出了一種基於推薦的社交頻譜共享機制。其中，次級用戶相互協作，彼此推薦良好的信道， 并動態接入信道。我們設計了種基於馬爾科夫決策過程( Markovdecision process) 的自適應信道推薦算法。該算法可突現良好的系統通信性能。同時，我們也提出種基於模仿(imitation) 的社交頻譜分享機制。其中，次級用戶根據自身觀察來估計自己的期望通信速率并彼此分享。如果鄰近用戶的期望通信速率更高，該用戶則模仿鄰近用戶的信道接入。我們證明該機制能夠有效地收斂到模仿均衡。如果次級用戶的數目較多，收斂的模仿均衡即是納什均衡(Nashequilibrium) 。該均衡是個次級用戶相互滿意的頻譜共享結果。 / 對於個體智能，次級用戶基於策略互動(strategic interactions) 來競爭地共享頻譜。對於基於空間複用(spatial reuse) 的競爭性頻譜共享，我們提出了種新穎的空間頻譜接入博弈框架。我們研究了不同的干擾圖形結構對於納什均衡的存在性的影響。同時，我們設計了種基於用戶自身觀察的分佈式學習算法。該算法適用於所有空間頻譜接入博弈，并能夠有效地收斂到近似納什均衡(approximateNash equilibrium) 。對於基於數據庫的電視頻譜(white-space spectrum) 無線AP(access point)網絡，我們運用博弈理論方法為分佈式AP 信道選擇問題以及分佈式次級用戶AP 連接問題建立理論模型。我們證明了分佈式AP信道選擇博奔以及分佈式次級用戶AP 連接博弈屬於勢博弈(potential game) 的範疇。基於勢博莽的有限改進性質(finite improvement property) ，我們設計了分佈式算法能夠有效地收斂到納什均衡。 / Dynamic spectrum sharing enables unlicensed secondary wireless users to opportunistically share the spectrum with licensed primary users, and thus is envisioned as a promising solution to address the spectrum under-utilization problem. This thesis explores the intelligence of secondary users for achieving efficient distributed spectrum sharing. We consider two types of intelligences: social intelligence and individual intelligence. / For the social intelligence, secondary users share the spectrum collaboratively based on social interactions. Inspired by the recommendation system in the electronic commerce industry, we propose a recommendation-based social spectrum sharing mechanism, where secondary users collaboratively recommend "good" channels to each other and access accordingly. We devise an adaptive channel recommendation algorithm based on Markov decision process, which achieves a good system communication performance. We then propose an imitation-based social spectrum sharing mechanism, where each secondary user estimates its expected throughput based on local observations, and imitates another neighboring user’s channel selection if neighbor’s estimated throughput is higher. We show that the mechanism can converge to an imitation equilibrium. When the number of users is large, the convergent imitation equilibrium corresponds to a Nash equilibrium, which is a mutually satisfactory spectrum sharing solution. / For the individual intelligence, secondary users share the spectrum competitively based on strategic interactions. To formulate the competitive spectrum sharing with spatial reuse, we propose a framework of spatial spectrum access game on general directed interference graphs. We investigate the impact of the underlying interference graph structure on the existence of a Nash equilibrium. We also design a distributed learning algorithm based on local observations that can converge to an approximate Nash equilibrium for any spatial spectrum access games. We then apply the game theoretic approach for modeling the distributed channel selection problem among the APs and distributed AP association problem among the secondary users in database-assisted white-space AP networks. We show that both the distributed AP channel selection game and the distributed AP association game are potential games. We then design distributed algorithms for achieving Nash equilibria by utilizing the finite improvement property of potential game. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Chen, Xu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 180-188). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Overview --- p.1 / Chapter 1.2 --- Thesis Outline --- p.5 / Chapter I --- Social Intelligence For Distributed Spectrum Sharing --- p.7 / Chapter 2 --- Recommendation-based Social Spectrum Sharing --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- System Model --- p.12 / Chapter 2.3 --- Introduction To Channel Recommendation --- p.13 / Chapter 2.3.1 --- Review of Static Channel Recommendation --- p.14 / Chapter 2.3.2 --- Motivations For Adaptive Channel Recommendation --- p.16 / Chapter 2.4 --- Adaptive Channel Recommendation With Channel Homogeneity --- p.18 / Chapter 2.4.1 --- MDP Formulation For Adaptive Channel Recommendation --- p.19 / Chapter 2.4.2 --- Existence of Optimal Stationary Policy --- p.21 / Chapter 2.5 --- Model Reference Adaptive Search For Optimal Spectrum Access Policy --- p.22 / Chapter 2.5.1 --- Model Reference Adaptive Search Method --- p.23 / Chapter 2.5.2 --- Model Reference Adaptive Search For Optimal Spectrum Access Policy --- p.24 / Chapter 2.5.3 --- Convergence of Model Reference Adaptive Search --- p.29 / Chapter 2.6 --- Adaptive Channel Recommendation With Channel Heterogeneity --- p.30 / Chapter 2.7 --- Numerical Results --- p.33 / Chapter 2.7.1 --- Simulation Setup --- p.33 / Chapter 2.7.2 --- Homogeneous Channel Recommendation --- p.34 / Chapter 2.7.3 --- Heterogenous Channel Recommendation --- p.35 / Chapter 2.8 --- Chapter Summary --- p.38 / Chapter 2.9 --- Appendix --- p.39 / Chapter 2.9.1 --- Proof of Lemma 2.1 --- p.39 / Chapter 2.9.2 --- Derivation of Transition Probability --- p.40 / Chapter 2.9.3 --- Proof of Theorem 2.1 --- p.41 / Chapter 2.9.4 --- Proof of Theorem 2.2 --- p.42 / Chapter 2.9.5 --- Proof of Theorem 2.3 --- p.47 / Chapter 2.9.6 --- Proof of Theorem 2.4 --- p.50 / Chapter 3 --- Imitation-based Social Spectrum Sharing --- p.52 / Chapter 3.1 --- Introduction --- p.52 / Chapter 3.2 --- Spectrum Sharing System Model --- p.55 / Chapter 3.3 --- Imitative Spectrum Access Mechanism --- p.58 / Chapter 3.3.1 --- Expected Throughput Estimation --- p.59 / Chapter 3.3.2 --- Information Sharing Graph --- p.63 / Chapter 3.3.3 --- Imitative Spectrum Access --- p.63 / Chapter 3.4 --- Convergence of Imitative Spectrum Access --- p.65 / Chapter 3.4.1 --- Cluster-based Representation of Information Sharing Graph --- p.65 / Chapter 3.4.2 --- Dynamics of Imitative Spectrum Access --- p.67 / Chapter 3.4.3 --- Convergence of Imitative Spectrum Access --- p.71 / Chapter 3.5 --- Imitative Spectrum Access with Innovation --- p.73 / Chapter 3.6 --- Imitative Spectrum Access With User Heterogeneity --- p.75 / Chapter 3.7 --- Simulation Results --- p.77 / Chapter 3.7.1 --- Large User Population --- p.78 / Chapter 3.7.2 --- Small User Population --- p.82 / Chapter 3.7.3 --- Markovian Channel Environment --- p.85 / Chapter 3.7.4 --- Imitative Spectrum Access With User Heterogeneity --- p.88 / Chapter 3.8 --- Chapter Summary --- p.88 / Chapter 3.9 --- Appendix --- p.89 / Chapter 3.9.1 --- Proof of Theorem 3.1 --- p.89 / Chapter 3.9.2 --- Proof of Theorem 3.2 --- p.91 / Chapter II --- Individual Intelligence For Distributed Spectrum Sharing --- p.93 / Chapter 4 --- Spatial Spectrum Access Game --- p.94 / Chapter 4.1 --- Introduction --- p.94 / Chapter 4.2 --- System Model --- p.97 / Chapter 4.3 --- Spatial Spectrum Access Game --- p.101 / Chapter 4.4 --- Existence of Nash Equilibria --- p.102 / Chapter 4.4.1 --- Existence of Pure Nash Equilibria on Directed Interference Graphs --- p.103 / Chapter 4.4.2 --- Existence of Pure Nash Equilibria on Undirected Interference Graphs --- p.108 / Chapter 4.5 --- Distributed Learning For Spatial Spectrum Access --- p.113 / Chapter 4.5.1 --- Expected Throughput Estimation --- p.114 / Chapter 4.5.2 --- Distributed Learning Algorithm --- p.115 / Chapter 4.5.3 --- Convergence of Distributed Learning Algorithm --- p.117 / Chapter 4.6 --- Numerical Results --- p.121 / Chapter 4.7 --- Chapter Summary --- p.126 / Chapter 4.8 --- Appendix --- p.127 / Chapter 4.8.1 --- Proof of Theorem 4.2 --- p.127 / Chapter 4.8.2 --- Proof of Theorem 4.3 --- p.129 / Chapter 4.8.3 --- Proof of Lemma 4.4 --- p.131 / Chapter 4.8.4 --- Proof of Lemma 4.5 --- p.133 / Chapter 4.8.5 --- Proof of Theorem 4.5 --- p.136 / Chapter 4.8.6 --- Proof of Theorem 4.6 --- p.139 / Chapter 5 --- Distributed AP Channel Selection Game --- p.141 / Chapter 5.1 --- Introduction --- p.141 / Chapter 5.2 --- Distributed AP Channel Selection --- p.144 / Chapter 5.2.1 --- Problem Formulation --- p.144 / Chapter 5.2.2 --- Distributed AP Channel Selection Game --- p.146 / Chapter 5.3 --- Distributed AP Channel Selection Algorithms --- p.149 / Chapter 5.3.1 --- Distributed AP Channel Selection Algorithm With Information Exchange --- p.149 / Chapter 5.3.2 --- Distributed AP Channel Selection Algorithm Without Information Exchange --- p.151 / Chapter 5.4 --- Numerical Results --- p.157 / Chapter 5.4.1 --- Distributed AP Channel Selection With Information Exchange --- p.157 / Chapter 5.4.2 --- Distributed AP Channel Selection Without Information Exchange --- p.159 / Chapter 5.5 --- Chapter Summary --- p.161 / Chapter 5.6 --- Appendix --- p.162 / Chapter 5.6.1 --- Proof of Theorem 5.2 --- p.162 / Chapter 6 --- Distributed AP Association Game --- p.165 / Chapter 6.1 --- Introduction --- p.165 / Chapter 6.2 --- Distributed AP Association --- p.166 / Chapter 6.2.1 --- Channel Contention Within an AP --- p.167 / Chapter 6.2.2 --- Distributed AP Association Game --- p.168 / Chapter 6.2.3 --- Distributed AP Association Algorithm --- p.170 / Chapter 6.3 --- Numerical Results --- p.172 / Chapter 6.4 --- Chapter Summary --- p.175 / Chapter 7 --- Conclusions and Future Work --- p.176 / Bibliography --- p.180
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328105 |
| Date | January 2012 |
| Contributors | Chen, Xu, Chinese University of Hong Kong Graduate School. Division of Information Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | electronic resource, electronic resource, remote, 1 online resource (xvi, 188 leaves) : ill. (chiefly col.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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