異構(gòu)無線網(wǎng)絡(luò)中基于人工神經(jīng)網(wǎng)絡(luò)的自適應(yīng)垂直切換算法

馬彬; 李尚儒; 謝顯中

doi:10.11999/JEIT180534

異構(gòu)無線網(wǎng)絡(luò)中基于人工神經(jīng)網(wǎng)絡(luò)的自適應(yīng)垂直切換算法

doi: 10.11999/JEIT180534

1.
重慶郵電大學(xué)計算機科學(xué)與技術(shù)學(xué)院 ??重慶 ??400065
2.
重慶郵電大學(xué)重慶市計算機網(wǎng)絡(luò)與通信技術(shù)重點實驗室 ??重慶 ??400065

基金項目: 國家自然科學(xué)基金(61471076, 61601070)，重慶市基礎(chǔ)與前沿研究計劃(cstc2016jcyjA0455)，重慶市基礎(chǔ)研究與前沿探索項目(cstc2018jcyjAX0432)，重慶郵電大學(xué)博士啟動基金(A2015-16)

詳細(xì)信息

作者簡介:
馬彬：男，1978年生，教授，研究方向為異構(gòu)無線網(wǎng)絡(luò)、認(rèn)知無線電網(wǎng)絡(luò)等

李尚儒：男，1993年生，碩士生，研究方向為異構(gòu)無線網(wǎng)絡(luò)

謝顯中：男，1966年生，教授，博士生導(dǎo)師，研究方向為無線和移動通信技術(shù)

通訊作者:
李尚儒　lishangru93@163.com

中圖分類號: TN915
計量
- 文章訪問數(shù): 1536
- HTML全文瀏覽量: 712
- PDF下載量: 62
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-05-30
- 修回日期: 2019-01-30
- 網(wǎng)絡(luò)出版日期: 2019-02-25
- 刊出日期: 2019-05-01

An Adaptive Vertical Handover Algorithm Based on Artificial Neural Network in Heterogeneous Wireless Networks

1.
Institute of Computer Science and Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Computer Network and Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61471076, 61601070), The Foundation and Advanced Research Program of Chongqing (cstc2016jcyjA0455), The Foundation Research and Advanced Exploration Project of Chongqing (cstc2018jcyjAX0432), The Doctoral Start-up Fund of Chongqing University of Posts and Telecommunications (A2015-16)

摘要

摘要: 針對當(dāng)前基于人工神經(jīng)網(wǎng)絡(luò)的垂直切換算法(ANN-VHO)，存在業(yè)務(wù)自適應(yīng)性差和計算復(fù)雜度高的問題，該文提出一種基于人工神經(jīng)網(wǎng)絡(luò)的自適應(yīng)垂直切換算法。首先，根據(jù)終端獲取到的接收信號強度(RSS)，采用閾值判斷的方法，遴選出候選網(wǎng)絡(luò)集；其次，根據(jù)該文劃分的不同業(yè)務(wù)類型，對參數(shù)進(jìn)行自適應(yīng)選擇和歸一化；再次，把選擇的參數(shù)輸入人工神經(jīng)網(wǎng)絡(luò)，判決出候選網(wǎng)絡(luò)集中最佳的接入網(wǎng)絡(luò)。最后，實驗結(jié)果表明，該算法能根據(jù)用戶的業(yè)務(wù)類型合理地選擇切換網(wǎng)絡(luò)，降低切換阻塞率，同時降低算法的時間復(fù)雜度。
- 異構(gòu)無線網(wǎng)絡(luò) /
- 業(yè)務(wù)類型 /
- 自適應(yīng)選擇 /
- 神經(jīng)網(wǎng)絡(luò)
Abstract: Current research on Vertical HandOver algorithm based on Artificial Neural Network (ANN-VHO) has a poor service adaptability and high computational complexity. Considering this problem, an adaptive vertical handover algorithm based on artificial neural network is proposed. Firstly, according to the Received Signal Strength (RSS) obtained by the terminal, a method of thresholding is used to select a candidate network set. Secondly, in terms of the different types of services classified in this paper, the parameters are normalized and adaptively selected; Thirdly, the selected parameters are input into the artificial neural network to choose the best access network from the candidate network. Finally, the experimental results show that the algorithm can reasonably select the handover network according to the user's service type, reduce the handover blocking rate and lower the time complexity of the algorithm.
- Heterogeneous wireless networks /
- Type of service /
- Adaptive selection /
- Neural network

HTML全文

圖 1 判決算法流程圖

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圖 2 參數(shù)自適應(yīng)選擇

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圖 3 神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)

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圖 4 異構(gòu)無線網(wǎng)絡(luò)系統(tǒng)模型

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圖 5 算法時間開銷

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圖 6 會話類業(yè)務(wù)得分值

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圖 7 3種類業(yè)務(wù)得分值

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圖 8 切換阻塞率

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圖 9 網(wǎng)絡(luò)總吞吐量

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表 1 各參數(shù)的范圍值

業(yè)務(wù)	帶寬(kbps)	時延(ms)	抖動(ms)	丟包率(×10^–6)
會話類		30～100	5～40
交互類		50～270		1～100
流類	50～10000		10～50
后臺類	10～1000			10～1000

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表 2 候選網(wǎng)絡(luò)的參數(shù)值

網(wǎng)絡(luò)	帶寬(kbps)	時延(ms)	抖動(ms)	丟包率(×10^–6)
LTE	310	48	9	28
WLAN1	4100	105	38	9
WLAN2	6900	180	67	1
WLAN3	3400	50	17	1
WLAN4	2300	60	11	20
WLAN5	5600	91	12	16

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參考文獻(xiàn)(16)

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