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

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={p j,f j,u ij,g hj,(x j,y j )} where p j is the power level of access point j f j is the frequency channel of access point j u ij is the binary variable that indicated where user i associates with access point j or not g hj is the binary variable that indicated whether grid point h can receive signal from access point j or not (x j,y j ) indicates the location of access points

Cap-WLAN CST formulation(3)  D={D p,D f,D u,D g,D (xj,yj) } where D p is the doamin of p j variable D f is the domain channel of access point j D u is the domain of u ij variable={0,1} D g is the domain of g hj variable={0,1} D (xj,yj) the domain of (x j,y j ) variable ={x min <x j <x max and y min <y j <y max } Ex: In b 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 check Access point initialization INPUT: -User location -Traffic demand -Structure of service area i=1 i=i+1 i=N 0 Check constraint i Try other power Level in D p Try other frequency Channel in D r Move AP to other Location in D(x,y) Output: -#Access Point -Parameter( location, power level … etc) Add access point 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(f j,( x i,y i ),( x j,y j ))=L(d 0 ) +10n 0 log[ d ij /d 0 ]+k σ where L(d 0 )=10 log [ [4лd 0 f j /3x10 8 ] 2 ] D 0 the reference distance D ij the distance between user I and ap j n 0 the path loss exponent K σ the shadow fading margin f j the frequency channel of access point j C ap access point capacity P R the received signal strength threshold d i 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

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