Next Generation Wireless LAN System Design

1. Next Generation Wireless LAN System Design 姓 名: 謝興健 學 號: 937472

2. 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

3. 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

4. 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.

5. 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

6. 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

7. Cap-WLAN CST formulation(2) • V={pj,fj,uij,ghj,(xj,yj)} where pj is the power level of access point j fjis 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

8. 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

9. 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 particularAP 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

10. Cap-WLAN CST Algorithm PART 1: Determining #Access Point Feasibility check Access point initialization INPUT: -User location -Traffic demand -Structure of service area Add access point i=1 Move AP to other Location in D(x,y) Path loss models Try other frequency Channel in Dr PASS No solution found Check constraint i No solution found Try other power Level in Dp No solution found i=i+1 Brute- Force Search i=N0 PART 2: CSP Module NO YES Output: -#Access Point -Parameter( location, power level…etc)

11. 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 j n0 the path loss exponent Kσthe shadow fading margin fjthe frequency channel of access point j Cap access point capacity PR the received signal strength threshold di traffice demand from user i αportion of traffice demand guaranteed to be served βaccess point effective capacity coefficient

12. Formulations of Constraint

13. Brute- Force Search Algorithm

14. Brute- Force Search Algorithm

15. Design Sample Small service area using coverage based design

16. Design Sample Small service area using capacity based design

17. Design Sample Large service area light load using capacity based design

18. Design Sample Large service area , heavy load using capacity based design

19. 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