在無線多媒體網路中利用時間空間序列模型 來動態預測資源保留機制之研究

Predictive Dynamic Resource Reservation Scheme with Time-Space Series Model in Multimedia Wireless Networks

陳亭芳(Ting-Fung Chen)

指導教授 ： 賴國華

元智大學/資訊學院/資訊工程學系/碩士(2006年)

摘要

在無線多媒體網路中，多個微細胞服務範圍劃分及使用者移動特性造成行動台的換手機率頻繁，產生不穩定、非帕松分布且多時變的通訊模型。並且，細胞無足夠的資源可供使用時，將造成阻塞，影響系統效能。多種服務型態在下一代無線通訊系統是主要的潮流，因此，適當的資源預留機制以及配置方式是一項非常重要的議題。
本論文提出一個動態預測資源保留機制，利用時間空間序列模型，預估在多種服務類別考量下，換手以及新使用者要求服務的資源需求量，利用時間相關的歷史資料及網路拓墣等空間因素來作通訊量預測。首先，將過去歷史紀錄，利用時間序列模型(ARIMA)模擬未來狀況及趨勢，再結合細胞與細胞之間換手的空間相關性調整權重，利用類神經網路修正預測值，以接近真實通訊量。此模型結合線上預測機制於資源預留標準的決定，使得更有效率來預測、控制、配置系統資源。最後，再以我們所提出的方法與幾個資源預留機制做比較。
為了評估動態預測資源保留機制的效能，同時考量即時及非即時性服務，以換手使用者阻塞率、新使用者丟失率和頻寬使用率表示實驗結果。實驗結果顯示，與其他機制相比，高通訊量於動態預測資源保留機制有較為良好的表現。整體阻塞率減少7~26%，丟失率可維持在5%以下。在大多數環境下，系統頻寬使用率增加11%。然而，此預測方法如利用非適當的更新間隔時，亦造成效果不佳或系統負荷過大的問題。

關鍵字

無線多媒體網路 ； 預測資源保留 ； 時間空間序列模型 ； 換手優先權

並列摘要

Due to a large number of picocells and different user mobility in multimedia wireless networks, the probability of handover becomes higher and the traffic pattern can be arbitrary with time and following a non-Poisson and non-stationary process. As a result, without enough available resource for request call arrival, it may cause much higher blocking rate. Hence, how to reserve and control the resource allocation is one of most important issues in next generation wireless networks.
This thesis proposed a predictive resource reservation method with time-space series model for estimating resource requirement of handover call and new call from different service classes. Using local information and recent history from a spatial neighborhood of observed cells, a time-space series model is utilized to perform a traffic prediction. First, Seasonal-ARIMA(SARIMA) model is adopted to fit the actual traffic data measured in the reference cells. Then, spatial dependencies between the target cells and its neighbor cells are incorporated into this model for actual network environment. Due to spatial and temporal dependencies are modeled simultaneously for approximating and forecasting traffic, the prediction model is solved using artificial neural network. Based on time-space series prediction model, we estimate the resource requirement with online traffic predictor, it could be more efficient for supporting resource forecast, control, and allocation in the variable wireless networks, and the system performance could be increased by using the precise reservation levels.
In order to demonstrate the difference performance of our mechanism between those of several reservation methods, a service-dependent system model is adopted to integrate real-time and non-real-time services for call admission control (CAC). The performance indicators of our evaluations in terms of handover request call blocking probability (HDP) and the new call dropping probability (NBP) are represented. The results show that it performs well at heavy traffic load. The total reduction in call blocking rate is about 7~26%. It can also keep the total call dropping rate below 5%. At various call arrival rates, the bandwidth utilization can be increase 11%. However, using an unsuitable update interval may cause worst performance for fixed PDC scheme, even if in PDC scheme, it may causes heavy system overhead for repeated prediction.

並列關鍵字

Multimedia Wireless Network ； Predictive Resource Reservation ； Time-Space Series Model ； Handover Prioritization

參考文獻

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