Next Generation Wireless LAN System Design

姓 名 : 謝興健學 號 : 937472

Outline

WLANs Design Introduction Cap-WLAN CST versus Coverage based WLAN design Cap-WLAN CST formulation Cap-WLAN CST Algorithm Path loss model Formulations of Constraint Brute- Force Search Algorithm Design Example Conclusion

WLANs Design Introduction(1)

WLANs Design manually Place APs in buildings at opportunistic locations, measures the received

signal strength and adjusts the AP locations, power levels, frequency channel etc….

Time consuming when deploying large numbers of WLAN APs

Coverage based WLANs Design Formulate optimal access point/base station placement problems for

Ensuring that an adequate received signal strength and signal-to-interference ratio(SIR) are maintained

When the number of WLAN users and applications increases,network capacity becomes issue

WLANs Design Introduction(2)

Cap-WLAN CSP (capacity base WLAN constraint satisfaction problem)

Still satisfying signal coverage and interference level requirement Providing the access point locations ,the frequency channel allocation, power level

required for the WLAN to meet expected user demands.

Cap-WLAN CST versus Coverage based WLAN design

Coverage based WLAN design Coverage based WLAN Design aim to minimize the number of APs Optimize the locations of the access points

Cap-WLAN CST It is unnecessary to minimize the number of APs because CSP focus on improving capacity of

WLAN Avoid serious co-channel interference caused by over-provisioning service area

Cap-WLAN CST formulation(1)

Defined By (V,D,C) V=the set of variables D=the set of finite domains associated with the variables C=the set of constraints

Cap-WLAN CST formulation(2)

V={pj,fj,uij,ghj,(xj,yj)}

where

pj is the power level of access point j

fj is the frequency channel of access point j

uij is the binary variable that indicated where user i associates

with access point j or not

ghj is the binary variable that indicated whether grid point h can

receive signal from access point j or not

(xj,yj) indicates the location of access points

Cap-WLAN CST formulation(3)

D={Dp,Df,Du,Dg,D(xj,yj)}

where

Dp is the doamin of pj variable

Df is the domain channel of access point j

Du is the domain of uij variable={0,1}

Dg is the domain of ghj variable={0,1}

D(xj,yj) the domain of (xj,yj) variable

={xmin <xj<xmax and ymin<yj<ymax}Ex:

In 802.11b pratice

Dp={15,20,24} in dBm

Df ={2.412,2.437,2.462} in GHz

Cap-WLAN CST formulation(4)

C={C1,C2,C3,C4,C5,C6}

where

C1: each wireless terminal is associated to one access point

C2: the signal received at each wireless terminal must be

greater than the receiver threshold sensitivity

C3: the traffic demand of wireless terminals assigned to a

particular AP does not exceed the data rate capacity of the AP

C4: Specifies the interference threshold of the wireless teminal

C5: a portion of mean data rate from all wireless users in a service

area is served by available Aps

C6: the radio signal will be available across the specified coverage

space

Cap-WLAN CST Algorithm

Feasibility checkAccess point initialization

INPUT:-User location-Traffic demand-Structure of service area

i=1

i=i+1

i=N0

Check constraint iTry other power

Level in Dp

Try other frequencyChannel in Dr

Move AP to otherLocation in D(x,y)

Output:-#Access Point-Parameter( location, power level…etc)

Add access point

No solution found

No solution found

PASS

NO YES

PART 1: Determining #Access Point

PART 2: CSP Module

Path loss models

No solution found

Brute- Force Search

Path loss model

L(fj ,( xi ,yi ),( xj ,yj ))=L(d0 ) +10n0 log[ dij /d0 ]+kσ

where

L(d0 )=10 log [ [4лd0 fj /3x108]2]D0 the reference distance

Dij the distance between user I and ap jn0 the path loss exponent

Kσ the shadow fading margin

fj the frequency channel of access point j

Cap access point capacity

PR the received signal strength threshold

di traffice demand from user i

α portion of traffice demand guaranteed to be served

β access point effective capacity coefficient

Formulations of Constraint

Brute- Force Search Algorithm

Brute- Force Search Algorithm

Design Sample

Small service area using coverage based design

Design Sample

Small service area using capacity based design

Design Sample

Large service area light load using capacity based design

Design Sample

Large service area , heavy load using capacity based design

Conclusion

The experiments that illustrate the benefits of capacity-based approach over coverage based design Guarantee raido coverage Provide specified data rate capacity to carry the traffic demand from user

By limiting the search space even a brute-search technique succeeds in resonable time