完整後設資料紀錄
DC 欄位語言
dc.contributor.author陳義昇en_US
dc.contributor.authorYih-Shen Chenen_US
dc.contributor.author張仲儒en_US
dc.contributor.authorDr. Chung-Ju Changen_US
dc.date.accessioned2014-12-12T02:11:58Z-
dc.date.available2014-12-12T02:11:58Z-
dc.date.issued2003en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT008813811en_US
dc.identifier.urihttp://hdl.handle.net/11536/57890-
dc.description.abstract為了支援多媒體服務之叢集性傳輸和異質性服務品質之要求,精確設計無線資源配置機制是必備的,藉以有效提升系統資源使用效率。現有文獻已指出,多媒體服務之特性為動態傳輸和多樣化傳輸需求,因此有必要應用智慧型技術來解決傳統無線資源配置問題。在本論文中,我們探討行動通訊網路中,應用類神經乏晰技術之無線資源配置機制。 我們首先探討分時多工行動通訊網路之無線資源配置機制。我們採用適應性網路式乏晰推論系統技術,提出一『乏晰資源配置控制器』。該乏晰控制器為雙層式架構,並挑選新連線需求之頻寬需求、未來換手連線之頻寬保留,以及空中介面效能為輸入語意變數,所以,此控制器可最大化行動多媒體網路之統計多工特點所衍生之頻寬增益。模擬結果顯示,乏晰資源配置控制器可有效降低換手失敗機率,但不會大幅增加塞機率。同時,與傳統機制相比,此乏晰資源配置控制器可確保服務品質,並且提升系統效能。 接著我們探討WCDMA系統之多速率傳輸控制機制。該多速率傳輸控制問題可模型化為一馬可夫決策鍊過程,而為了滿足服務品質並提升資源使用率之目的,傳輸成本採用服務品質為參數。我們應用『Q-learning』學習演算法來精確估算傳輸成本,因此設計了一『Q-learning式多速率傳輸控制』機制。此外,我們也採用特徵擷取法和RBFN類神經網路來解決Q函數近似問題,可以降低此機制之狀態空間和記體體需求,並且增進Q-learning之收斂特性。模擬結果顯示,Q-learning式多速率傳輸控制機制可提升WCDMA系統之系統容量、用戶滿意度,同時保證服務品質。 最後我們探討多細胞WCDMA通訊系統之封包接取控制機制,並且提出一『乏晰Q-learning式封包接取控制器』。該控制器包含一『乏晰Q-learning式剩餘資源預估器』,和一『封包速率排程器』。該剩餘資源預估器可依據負載狀態,精確預估系統剩餘資源;它選用自身細胞和相鄰細胞之接收干擾量為輸入語意變數,並採用感知式協調機制來將多細胞環境簡化為單細胞環境。該排程器則採用改良式指數型排程原則,有效地為非及時性用戶配置系統資源。模擬結果顯示,在同質性和非同質性多細胞WCDMA環境中,該乏晰Q-learning式封包接取控制器可有效降低封包錯誤率,並提升非及時性訊務之傳輸速率。zh_TW
dc.description.abstractTo support bursty-transmission and heterogeneous quality of services (QoS) requirements for multimedia services, a well-designed sophisticated radio resource allocation scheme is required to effectively enhance the system utilization. Research has shown that the non-stationarity of work-loads, together with heterogeneous traffic characteristics and QoS constraints of multimedia services, constitute the necessity for applying intelligent techniques in future mobile multimedia networks. In this dissertation, the radio resource allocation schemes by using neural/fuzzy techniques for mobile communication networks are studied. Firstly, the radio resource allocation scheme for TDMA-based mobile communication networks is investigated. The adaptive-network-based fuzzy inference system (ANFIS) is applied to propose a fuzzy resource allocation controller (FRAC). The FRAC is designed in a two-layer architecture and properly selects the capacity requirement of new call request, the capacity reservation for future handoffs, and the air interface performance as input linguistic variables. Therefore, the statistical multiplexing gain of mobile multimedia networks can be maximized in FRAC. Simulation results indicate that FRAC can keep the handoff call blocking rate low without jeopardizing the new call blocking rate. Also, compared to the conventional schemes, FRAC can indeed guarantee QoS contracts and achieve higher system performance. And then, the multi-rate transmission control scheme for WCDMA communication systems is studied. The multi-rate transmission control problem is modelled as a Markov decision process (MDP), where the transmission cost is defined in terms of the QoS parameters for enhancing spectrum utilization subject to QoS constraints. The Q-learning reinforcement algorithm is adopted to accurately estimate the transmission cost and propose a Q-learning-based multi-rate transmission control (Q-MRTC) scheme. In the meanwhile, the feature extraction method and RBFN network are successfully employed for the $Q$-function approximation. The state space and memory storage requirement are then reduced, and the convergence property of $Q$-learning algorithm is improved. Simulation results show that, for a multimedia WCDMA system, the Q-MRTC can achieve higher system throughput and better users' satisfaction while the QoS requirements are guaranteed. Finally, the data access control scheme for multi-cell WCDMA systems is investigated. By using fuzzy Q-learning technique, a novel situation-aware data access manager (FQ-SDAM) is proposed. The FQ-SDAM contains a fuzzy Q-learning-based residual capacity estimator (FQ-RCE) and a data rate scheduler (DRS). The FQ-RCE can accurately estimate the situation-dependent residual system capacity; it appropriately chooses the received interferences from home-cell and adjacent-cell as input linguistic variables and simplifies the multi-cell environment into a single-cell one by applying a perceptual coordination mechanism. Also, the DRS can effectively allocate the resource for non-real-time terminals by adopting a modified exponential rule which takes the interference influence on adjacent cells into consideration. Simulation results show that the FQ-SDAM can effectively reduce the packet error probability and improve aggregate throughput of the non-real-time services in both the homogeneous and non-homogeneous multi-cell WCDMA environment.en_US
dc.language.isoen_USen_US
dc.subject無線資源配置zh_TW
dc.subject行動通訊網路zh_TW
dc.subject類神經乏晰網路zh_TW
dc.subjectradio resource allocationen_US
dc.subjectmobile communication networken_US
dc.subjectneural fuzzy techniqueen_US
dc.title行動通訊網路之類神經乏晰無線資源配置機制zh_TW
dc.titleRadio Resource Allocation Schemes for Mobile Communication Networks Using Neural/Fuzzy Techniquesen_US
dc.typeThesisen_US
dc.contributor.department電信工程研究所zh_TW
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