以多重解析度分析為基礎的可調式智慧型移動偵測演算法

Multi-Resolution Based Adaptive Motion Detection Algorithm in Heterogeneous Networks

翁嘉宏(Jia-Hung Weng)

指導教授 ： 鍾添曜

元智大學/資訊學院/資訊工程學系/碩士(2008年)

摘要

下一代無線網路技術將會提供無縫通訊且有效率的服務。為了達到這樣的服
務內容，學者們都在研究如何能夠更有效率的換手。在過去，有兩個為了提早驅動換手程序的演算法：NDMD (Network Discovery Motion Detection)和 MRSS(Momentum of Received Signal Strength) 。這兩種演算法都是計算EMA(Exponential Weighted Moving Average)來偵測行動裝置的移動情形。然而，這兩種方法都沒有一個理論基礎能夠支持他們的想法。所以，本篇論文藉由小波理論和機率神經網路的理論基礎，提出一個偵測行動裝置移動的演算法—MRMD(Multi-Resolution based Motion Detection)。這個演算法具有一般化的架構，並且可以有效且準確的偵測出使用者的移動情形。我們在模擬中比較了這三種演算法的效能。透過模擬結果，不管使用者是在同質性網路環境或者是異質性網路環境中，我們可以得知MRMD 偵測到移動的準確度是最高的，並且有最少的能源消耗、最低的換手失敗次數以及連線中斷次數是三者之中表現最好的。

關鍵字

換手 ； 網路探索 ； 異質性網路 ； 動態偵測 ； 接收訊號強度 ； 乒乓效應 ； 機率神經網路 ； 小波理論

並列摘要

The next generation wireless networks integrate several network technologies to provide an intelligent and ubiquitous communication. The success of next generation
mobile networks relies on the degree of seamless mobility support among heterogeneous technologies. In the past, two mobile-controlled handoff mechanisms, Network Discovery with User Motion Detection (NDMD) and Momentum of Received Signal Strength (MRSS) have been proposed to trigger handoff procedure earlier to reduce handoff delay. NDMD and MRSS use different smooth factors to calculate Exponential Weighted Moving Averages (EMA) of Received Signal Strength (RSS) to determine the motion of the MN. However, both of NDMD and MRSS have not mention a general analytic framework of the motion prediction. Moreover, the adjusting mechanism of different smoothing factor is not provided. This research
proposes a general analytic framework and a Multi-Resolution based Motion Detection algorithm (MRMD). In the framework, the smoothing factor assignment and RSS analysis are modeled by the Discrete Wavelet Transform (DWT) theory. Additional, a probability neural network based learning model is applied to learn the adjusted smoothing factors that trained by the movement of mobile nodes. Based on the multi-resolution theory and the probability neural network, the proposed MRMD
algorithm can detect user motion efficiently. In the simulation, we compare the performance of MRMD with NDMD, MRSS and traditional Dwell time based handoff approach. The simulation results confirm that MRMD has the highest motion
detection success rate, the lowest power consumption rate, lowest number of failure handoff and lowest number of connection loss.

並列關鍵字

Handoff ； heterogeneous network ； MRSS ； multiresolution ； motion detection ； NDMD ； neuron network ； received signal strength ； RSS ； wavelet

參考文獻

[1] V. V. Veeravalli and O. E. Kelly, “A locally optimal handoff algorithmfor cellular communications,” IEEE Trans. Vehicular Technology, vol 46, no. 3, pp. 603–609, Aug. 1997

[2] G.P. Pollini, “Trends in Handover design”, IEEE Commun. Mag., vol. 34, no. 3, pp. 82-90, Mar.1996

[3] Q. Guo, J. Zhu and X. Xu, “An Adaptive Multi-criteria Vertical Handoff Decision Algorithm for Radio Heterogeneous Network,” IEEE ICC Proc., Vol. 4, pp. 2769-2773, May. 2005

[4] N. Zhang and J.M. Holtzman, “Analysis of handoff algorithms using both absolute and relative measurements,” IEEE Trans. Vehicular Technology, vol. 45, no.1 pp.174–179, Feb. 1996.

[5] A. Zahran, B. Liang, and A. Saleh, “Signal threshold adaptation for vertical handoff in heterogeneous wireless networks,” Mobile Networks and Applications, vol. 11, no. 4, 2006, pp. 625-640