自适应无线网络QoS评估：增强参数调整算法的应用

60 浏览量更新于2024-08-26 收藏 1.12MB PDF 举报

"这篇研究论文探讨了如何通过增强的参数调整算法(Enhanced Parameters Tuning Algorithm, EPT)来优化自适应无线网络的QoS(服务质量)评估。该算法主要应用于802.11e标准，即针对仲裁间隔时间(AIFS)、最小竞争窗口(CWmin)和最大竞争窗口(CWmax)进行调整。论文作者包括Ben-Bin Chen、Hua-Ching Chen和Dong-Hui Guo，分别来自厦门大学电子工程系和国立金门大学计算机科学与信息工程系。在无线网络中，QoS是衡量网络性能和服务质量的重要指标，它涉及到数据传输的延迟、丢包率、带宽利用率等多个方面。802.11e是一种用于无线局域网(WLAN)的QoS增强标准，旨在提供更稳定的多媒体应用服务。论文提出的EPT算法以简单而有效的方式调整优先级组合策略，以实现高QoS。内部业务竞争分析方法被用于识别通道忙碌的概率。EPT算法通过动态设置AIFS、CWmin和CWmax来调整冲突概率，从而在流量业务退化到空闲或零状态时进行性能分析。这种策略有助于减少网络拥塞，提高网络资源的有效利用。论文进行了三次模拟实验，以验证EPT算法在不同网络条件下的效果。这些实验可能包括不同流量负载、用户数量以及多种应用类型的情况。通过对比分析，EPT算法的表现优于传统的静态参数配置，能够更好地适应网络环境的变化，提供更加自适应的QoS保障。此外，论文还可能探讨了算法的实现细节，如参数调整的策略和频率，以及算法对网络性能的影响，包括吞吐量、延迟和公平性等关键指标。这将为无线网络的QoS管理和优化提供理论支持和技术参考。这篇研究论文深入研究了如何通过增强的参数调整算法改善自适应无线网络的QoS评估，为无线网络的性能优化提供了新的视角和解决方案。"

Here m is the maximal retransmission number

and m

’

is the maximal backoff time number. In

addition,

0 min

[]

W CW i

and

max min

[ ] 2 [ ]

CW i CW i

Let



be the probability of i-th class to send data

frames within a given time slot.



is the

probability to send information in the website within

the time interval.

is the detection probability of

busy channel for the i-th traffic class during the

backoff time cycle. The

indicates as the conflict

probability in sending data when the backoff timer

is countering to 0.

The next step transition probability in the

Markov chain mode is formulated as following:

,0 ,0

{ , , , , 1} 1 , [0, 2], [0, ]

{ , , , , } , [0, 1], [0, ]

{ , , , 1,0} / , [0, 1], [1, ]

{ ,0, , ,0} (1 ) / , [0, 1], [0,

it i j

io i j i j

io i i

p i j k i j k p k W j m

p i j k i j p W k W j m

p i k i j p W k W j

     

   

    

    

,0 ,0

{ ,0, , ,0} 1/ , [0, 1]

p i k i m W k W













  





(3)

This equation displays five possible conditions.

denotes the probability of the countdown timer

in the current traffic state. It keeps in certain level of

countdown probability while not detecting the data

flow in the network channel.

is the original

probability of countdown timer, which begins to

gradually decrease in the uniform interval of idle

time. From 3-subequation, the original probability

of the i+1-th countdown timer is randomly selected

in the range of

[0, 1]

W 

when the collision is

happen at the i-th transition state. It presents in the

above description of 4-subequation. After the data

frame is successfully transformed, the probability of

countdown timer for new frame is randomly

selected in the range of

[0, 1]

W 

. If the network

channel reaches the maximum count downing

number, all traffic flames will be withdrawn. New

frames retransmit in the transmission probability of

W

. Based on the internal scheduling algorithm

of the EDCA machine in the traffic channel, the

delivering probability of the packets is described as

formulas:

0 1 0 1

(1 ) ... (1 )

     





     



(4)

The probability formulas of Markov chain model

is discussed in the previous formulas. It is shown

that the traffic flow in the countdown is idle. The

is the probability that the other different sites

of networks does not send to the traffic flow in this

website. The probability

presents that

1 (1 ) (1 )

h h i





   





(5)

A primary traffic service successfully transmits

the network packet when the timer is counting down

to 0. The unsuccessful transmission probability is

1 (1 ) (1 )

io h







   



(6)

Suppose that

lim { ( , ) , ( , ) }

i j k

b P s i t j b i t k



  

, it denotes the

distributed steady state of the Markov chain model.

Thus,

, ,0 ,0,0

, [0, ]

i j io i

b p b j m

(7)

And

, [1, 1]

, ,0

,0,0

b k W

i j it

i j k

p b k



















(8)

The stationary condition is satisfied by

i j k









(9)

Therefore, the calculation of

,0,0i

denotes as

formulas (10),

,0,0

1 ' 1

' 1 1

2(1 2 )(1 )(1 )

2(1 )(1 2 )(1 ) (1 (2 ) )(1 ) (1 )(1 2 )

2(1 2 )(1 )(1 )

2(1 )(

2(1 2 )(1 )(1 )

(1 2 )(2 1)( )

io io it

io it i io io io

io io

io io it

io io

ppp

p p W p p p

ppp









  

        



  



  

1 2 )(1 ) (1 (2 ) )(1 )

2(1 2 )(1 )(1 )

, '

(1 )(1 2 )

io it i io io

io io it

p p W p p

ppp



    

  















　－

(10)

Let the i-th class of traffic sending data for a

given time slot of the probability (



) presents in the

formula (11)

, ,0 ,0,0

, [0, ]

i j io i

b p b j m

(11)

, , ,0,0

i i j k i















(12)

WSEAS TRANSACTIONS on COMMUNICATIONS

Ben-Bin Chen, Hua-Ching Chen, Dong-Hui Guo

E-ISSN: 2224-2864

219

Volume 13, 2014

剩余11页未读，继续阅读

weixin_38577551

粉丝: 6
资源: 939

自适应无线网络QoS评估：增强参数调整算法的应用

网络游戏-无线网络中的自适应探测.zip

异构无线网络中基于人工神经网络的自适应垂直切换算法.pdf

自适应遗传算法在认知无线电参数优化中的应用

电信设备-使用自适应信用重新分布算法的无线通信装置和方法.zip

网络游戏-无线宽带网络中数据包自适应调度方法和系统.zip

网络游戏-在网络中自适应地执行无线链路控制的方法及其装置.zip

基于模糊神经网络的自适应认知网络接入选择算法

OFDMA下行自适应资源分配算法仿真对比分析

无线宽带网络数据包自适应调度系统研究

自适应粒子群优化算法在Ad Hoc网络多播路由中的应用

最新资源