基于SA-BP神经网络算法的光接入网络通道质量评估方法

doi:10.11959/j.issn.1000-0801.2018124

Abstract

Abstract:

So far,existing works related to quality evaluation of channel mainly focused on equipment layer and network layer in optical access network.Most of researches lack synthesis evaluation methods covering physical layer,network layer and business layer.As a result,it is hard for administrators to judge the actual quality of channel by network monitoring completely and accurately.To solve this problem,key influence factors of channel quality in optical access network was analyzed firstly,and a comprehensive multi-layer-multi-index evaluation model of channel quality was proposed.Then a SA-BP neural network algorithm was designed to train the parameters of the proposed evaluation model to make the result more accurate.The simulation results show that the proposed method has higher accuracy and stability,and can improve the quality and efficiency of network operation and maintenance.

Key words: optical access network, channel quality, evaluation model, simulated annealing-back propagation neural network algorithm

CLC Number:

G304

Bingqing CAI,Siya XU,Feng QI,Weichun GE,Guiping ZHOU,Botao YU,Yueyue LI. A SA-BP neural network algorithm based channel quality evaluation method in optical access network[J]. Telecommunications Science, 2018, 34(4): 162-172.

Figures/Tables 15

二级指标	三级指标	参数及计算方法
健康度	光缆使用未超过10年比率	$\frac{使用未超过 10 年光缆数 f}{光缆总数 f_{N}}$
	光缆交接箱使用未超过10年比率	$\frac{使用未超过 10 年光缆交接箱数 b}{光缆交接箱总数 b_{N}}$
	设备使用未超过10年比率	$\frac{使用未超过 10 年设备数 e}{设备总数 e_{N}}$
故障率	严重缺陷次数	s_d
	危急缺陷次数	c_d
	一般缺陷次数	g_d
	重大故障次数	m_f
	严重故障次数	s_f
	一般故障次数	g_f
	平均故障恢复时间	$\frac{故障总时间 (t_{m} + t_{s} + t_{g})}{故障总次数 (m_{f} + s_{f} + g_{f})}$
生存性	设备维护等级	EML
	应急保障等级	EPL
	设备冗余系数	备用设备数 s_e

二级指标	三级指标	参数及计算方法
上/下行网络性能	时延抖动次数	D
	分组丢失率	$\frac{P_{loss}}{P_{N}}$
	信噪比	$10 \lg (\frac{信号有效功率 P_{s}}{噪声有效功率 P_{n}})$
	误码率	$\frac{误码数 B_{err}}{总码数 B_{N}}$
	带宽利用率	$\frac{总字节数 B_{a}}{理论带宽 {BW}_{t} \cdot 时间 T}$
	阻塞率	$\frac{阻塞时间 T_{b}}{总时间 T_{N}}$
上/下行流量状态	吞吐量	总比特数TH_N
	流量平均速率	$\frac{总比特数 {TH}_{N}}{时间 T}$
	流量峰值速率	F_max
	最大突发长度	$\frac{F_{\max}}{C}$

二级指标	三级指标	参数及计算方法
完整性	配电自动化业务中断率	$\frac{配电自动化业务中断次数 I_{PDA}}{配电自动化业务总数 {BUS}_{PDA}}$
	用电信息采集系统业务中断率	$\frac{用电信息采集系统业务中断次数 I_{EIAS}}{用电信息采集系统业务总数 {BUS}_{EIAS}}$
	智能用电业务中断率	$\frac{智能用电业务中断次数 I_{SE}}{智能用电业务总数 {BUS}_{SE}}$
	平均中断时间	$\frac{业务中断总时间 (T_{PDA} + T_{EIAS} + T_{SE})}{业务中断总次数 (I_{PDA} + I_{EIAS} + I_{SE})}$
可用性	配电自动化业务无错率	$\frac{配电自动化业务无错次数 {NM}_{PDA}}{配电自动化业务总数 {BUS}_{PDA}}$
	用电信息采集系统业务无错率	$\frac{用电信息采集系统业务无错次数 {NM}_{EIAS}}{用电信息采集系统业务总数 {BUS}_{EIAS}}$
	智能用电业务无错率	$\frac{智能用电业务无错次数 {NM}_{SE}}{智能用电业务总数 {BUS}_{SE}}$
支撑性	业务响应超时率	$\frac{业务响应超时次数 R_{out}}{业务总次数 BUS}$
	平均配电自动化业务服务时间	$\frac{配电自动化业务服务总时间 S_{PDA}}{配电自动化业务总数 {BUS}_{PDA}}$
	平均用电信息采集系统业务服务时间	$\frac{用电信息采集系统业务服务总时间 S_{EIAS}}{用电信息采集系统业务总数 {BUS}_{EIAS}}$
	平均智能用电业务服务时间	$\frac{智能用电业务服务总时间 S_{SE}}{智能用电业务总数 {BUS}_{SE}}$
安全性	安全保护等级	SL
	恶意篡改次数	MT
	错操作次数	WO

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事故原因	比例
设备老化	32.4%
超期检修	26.5%
设备缺陷	14.6%
其他原因	11.8%
超负荷运作	8.8%
不可控外因	5.9%

地区编号	5条通道有效样本数	每组有效样本总数
1	44　 43　 44　 49　 31	211
2	41　 43　 47　 48　 43	222
3	50　 38　 44　 49　 43	224

参数名称	取值范围
输入层节点数m	37
隐含层节点数n	详见第4.2节
输出层节点数p	1
连接权值w	-1,1]的均匀分布
误差阈值ε	0.000 1
初始温度T₀	100
最低温度t_min	0.001
每个温度扰动次数上限M	10
最大迭代次数f	8

地区编号	预测样本数	标准BP神经网络		SA-BP神经网络
地区编号	预测样本数	拟合样本数	拟合率	拟合样本数	拟合率
1	53	41	77.36%	49	92.45%
2	55	43	78.18%	50	90.91%
3	56	43	76.79%	51	91.07%

A SA-BP neural network algorithm based channel quality evaluation method in optical access network

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