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Impact of Traffic Hotspots on RRM Strategies in UMTS Networks

This study explores the influence of non-uniformly distributed traffic across UMTS networks, focusing on the interaction between traffic hotspots and resource management strategies. It emphasizes the need for effective call admission control (CAC) to maintain quality of service (QoS) in high-density traffic scenarios. Through simulation environments, the research analyzes path loss, user distribution effects, and CAC performance, ultimately providing insights into optimal resource allocation and future work involving dynamic hotspot detection.

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Impact of Traffic Hotspots on RRM Strategies in UMTS Networks

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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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