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Traffic Hotspots in UMTS Networks : influence on RRM strategies

Traffic Hotspots in UMTS Networks : influence on RRM strategies. Ferran Adelantado i Freixer (ferran-adelantado@tsc.upc.es). Outline. Introduction Simulation environment Results Path loss analysis CAC performance Conclusions and future work. Introduction.

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Traffic Hotspots in UMTS Networks : influence on RRM strategies

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  1. Traffic Hotspots in UMTS Networks : influence on RRM strategies Ferran Adelantado i Freixer (ferran-adelantado@tsc.upc.es)

  2. Outline • Introduction • Simulation environment • Results • Path loss analysis • CAC performance • Conclusions and future work

  3. 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. a R D Simulation Environment • A single isolated cell (radius R). • A traffic hotspot with radius r and placed D meters from base station. • Ttotal=THS+TNo HS • THS=αTtotal • TNo HS=(1-α)Ttotal • Only videophone users considered • Propagation model: • Lp(d)=Lo+ log(d) where

  5. Results Simulation Parameters (1/2)

  6. Results Simulation Parameters (2/2)

  7. no hotspot users path loss pdf : hotspot users path loss pdf : Results Impact of traffic distribution (1/5) Path loss distribution variation Non-uniformly distributed traffic scenario BLER variation Path loss pdf : where

  8. Results Impact of traffic distribution (2/5) No hotspot users path loss :

  9. Results Impact of traffic distribution (3/5) Hotspot users path loss:

  10. Hotspot close to the base station Hotspot far from the base station Variation of hotspot location Results Impact of traffic distribution (4/5)

  11. Results Impact of traffic distribution (5/5) • 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. Transmitted power for mobile terminal Outage probability in UL Maximum admission threshold for a certain Lp Results Call Admission Control design (1/3)

  13. Results Call Admission Control design (2/3) Admission threshold may be determined with Path Loss statistics (Cumulative density function) : Outage probability = 0.5 % BLER ≈ 1.3 % BLER can be maintained by adjusting max

  14. Results Call Admission Control design (3/3) Maintaining low BLER with hotspots leads to an admission probability decrease.

  15. 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 .

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