1. COST 289 15-16 March 2004, Zurich Traffic Hotspots in UMTS Networks : influence on RRM strategies Ferran Adelantado i Freixer (ferran-adelantado@tsc.upc.es)

2. COST 289 15-16 March 2004, Zurich Outline Introduction Simulation environment Results  Path loss analysis  CAC performance Conclusions and future work

3. COST 289 15-16 March 2004, Zurich Introduction The main goal of the study is to analyse non-uniformly traffic distributed scenarios. It is important to be able to maintain the target QoS. All alternatives should be taken into account before deploying hotspot WLAN networks. Assessment of RRM strategies becomes necessary to deal with high traffic density areas (hotspots). Is it possible to dynamically react to environment changes?

4. COST 289 15-16 March 2004, Zurich Simulation Environment A single isolated cell (radius R). A traffic hotspot with radius r and placed D meters from base station. T total =T HS +T No HS T HS =αT total T No HS =(1-α)T total Only videophone users considered Propagation model: L p (d)=L o +  log(d)  D R where

5. COST 289 15-16 March 2004, Zurich Results Simulation Parameters (1/2) BS parameters Cell typeOmnidirectional Thermal Noise-103 dBm Pilot and common control channel power 32 dBm Shadowing deviation3 dB Shadowing decorrelation length 20 m UE parameters Maximum transmitted power21 dBm Minimum transmitted power-44 dBm Mobile speed10 km/h Cell radius1000 m Hotspot radius50 m

6. COST 289 15-16 March 2004, Zurich Results Traffic model Call duration120 seg Offered bit rate64 kb/seg (CBR) Activity factor1 Call rate15 calls/h/user QoS parameters BLER target1 % Eb/No target2.95 dB Simulation Parameters (2/2) Propagation model LoLo 128.1  37.6

7. COST 289 15-16 March 2004, Zurich Results Impact of traffic distribution (1/5) Path loss distribution variation BLER variation Path loss pdf : where no hotspot users path loss pdf : hotspot users path loss pdf : Non-uniformly distributed traffic scenario

8. COST 289 15-16 March 2004, Zurich Results Impact of traffic distribution (2/5) No hotspot users path loss :

9. COST 289 15-16 March 2004, Zurich Results Impact of traffic distribution (3/5) Hotspot users path loss:

10. COST 289 15-16 March 2004, Zurich Results Impact of traffic distribution (4/5) Hotspot close to the base stationHotspot far from the base station Variation of hotspot location

11. COST 289 15-16 March 2004, Zurich Results Impact of traffic distribution (5/5)  = 0.0  =0.3  =0.5 BLER1.531.862.04 HS BLERN/A2.632.60 No HS BLER1.53 D=150mD=550mD=950m BLER1.461.482.04 HS BLER1.001.072.60 No HS BLER1.931.891.53 No hotspot users BLER is maintained when increasing  Total BLER grows as  is increased. As D increases, total BLER increases. Hotspot users BLER grows for large D. No hotspot users BLER is lower for high D.

12. COST 289 15-16 March 2004, Zurich Results Call Admission Control design (1/3) Transmitted power for mobile terminal Outage probability in UL Maximum admission threshold for a certain L p

13. COST 289 15-16 March 2004, Zurich Results Call Admission Control design (2/3) Outage probability = 0.5 % BLER ≈ 1.3 % Admission threshold may be determined with Path Loss statistics (Cumulative density function) :   max 0.00.77 0.50.68 BLER can be maintained by adjusting  max

14. COST 289 15-16 March 2004, Zurich Results Call Admission Control design (3/3) Maintaining low BLER with hotspots leads to an admission probability decrease.

15. COST 289 15-16 March 2004, Zurich Conclusions and Future Work In non-uniformly distributed traffic scenarios, without applying CAC, hotspots with high D and  cause a QoS degradation. Suitable admission control threshold (  max ) can be determined if path loss statistics are known. Maintaining low BLER implies an admission probability decrease. Future work will be focused on dynamic hotspot detection. Design and assessment of adapted RRM strategies will determine if it is necessary to include a hotspot WLAN.