O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.
Traffic-aware Resource allocation with
aggregation in Heterogeneous Networks
with WLANs
Thursday, 5 July 2018 1
Haeyoung L...
Overview
Thursday, 5 July 2018 2
q Increase of mobile data traffic
Ø 45% annual growth & ten-fold increase in total traffi...
Introduction
Thursday, 5 July 2018 3
q The use of multiple spectrum bands by aggregation
Ø Carrier aggregation in LTE-A (u...
Ø Multiple carriers in licensed bands are available
Freq.F1 F2 F3
… … …
Ø Carriers can be utilized through carrier aggrega...
Objective of proposed approach
Thursday, 5 July 2018 5
q To maximize guaranteed QoS
while guaranteeing the fairness betwee...
Utility function
Thursday, 5 July 2018 6
q Introduction of utility [14]
Ø It represents the degree of UE’s satisfaction wh...
Application Utility Functions
Thursday, 5 July 2018 7
q Inelastic traffic
Ø Normalized Sigmoidal-like function
q Elastic t...
Flowchart of prop. approach
Thursday, 5 July 2018 8
U1 U2 U3 U4
r2req
Allocation of primary carrier in licensed bands
{αij...
Exploitation of the WLAN
Thursday, 5 July 2018 10
q Selection of best suited UEs for WLAN with access index
Ø Access index...
Simulation Results
Thursday, 5 July 2018 11
q Throughput and utility for UEs with different traffic types
Ø With different...
Simulation Results
Thursday, 5 July 2018 12
q For different UE’s mobility,
Ø The reference algorithm only consider the Wi-...
Conclusions
Thursday, 5 July 2018 13
q Resource allocation in Heterogeneous networks inc. WLANs
Ø Utilizes carrier aggrega...
Próximos SlideShares
Carregando em…5
×

Traffic aware resource allocation with aggregation in heterogeneous networks with wla ns (hlee-2018_eucnc)

161 visualizações

Publicada em

Presentation given at EUCNC 2018, Resource allocation with aggregation, using LWA in heterogeneous access networks

Publicada em: Engenharia
  • Seja o primeiro a comentar

  • Seja a primeira pessoa a gostar disto

Traffic aware resource allocation with aggregation in heterogeneous networks with wla ns (hlee-2018_eucnc)

  1. 1. Traffic-aware Resource allocation with aggregation in Heterogeneous Networks with WLANs Thursday, 5 July 2018 1 Haeyoung Lee, Seiamak Vahid, and Klaus Moessner EuCNC, 18-21 June, 2018 Institute for Communication Systems Faculty of Engineering and Physical Sciences University of Surrey
  2. 2. Overview Thursday, 5 July 2018 2 q Increase of mobile data traffic Ø 45% annual growth & ten-fold increase in total traffic by 2021, compared to 2015 <Mobile data traffic by application type (ExaBytes/Month)> Ø Especially, the growth of real-time traffic (i.e., video) will be very large [Ref] Ericsson, Ericsson Mobility Report, Nov. 2015
  3. 3. Introduction Thursday, 5 July 2018 3 q The use of multiple spectrum bands by aggregation Ø Carrier aggregation in LTE-A (up to 32 CCs in Rel-13) Ø Exploitation of unlicensed bands (LAA/LWA) How to utilize radio resource in heterogeneous networks (inc. WLANs) to support the QoS of different traffic types (Inelastic vs. elastic)?
  4. 4. Ø Multiple carriers in licensed bands are available Freq.F1 F2 F3 … … … Ø Carriers can be utilized through carrier aggregation SC SC PC System model Thursday, 5 July 2018 4 q DL with aggregation for multiple-users in a single cell § carrier aggregation, ü Primary carrier (PC) • Robust connection setup ü Secondary carriers (SCs) • Support of high data rates BS UE 1 UE 2 UE K UE i WiFi AP Ø WLAN links are also available by aggregation. http VoIP video streaming Ø Multiple users requiring heterogeneous traffic types (Inelastic vs. Elastic)
  5. 5. Objective of proposed approach Thursday, 5 July 2018 5 q To maximize guaranteed QoS while guaranteeing the fairness between UEs SC SC PC BS UE 1 UE 2 UE K UE i WiFi AP http VoIP video streaming Ø Different characteristics of heterogeneous traffic (Elastic vs. Inelastic traffic) Ø Utilization of heterogeneous networks Ø Cellular network having multiple carriers in different licensed bands (PC & SC) Ø WLAN in unlicensed bands (SC only)
  6. 6. Utility function Thursday, 5 July 2018 6 q Introduction of utility [14] Ø It represents the degree of UE’s satisfaction when it acquires a certain amount of the resource (i.e. meeting the QoS requirements/data rate) Ø Services with different QoS requirements can be modelled with different utility functions q Properties of util. functions Ø U(0)=0 & U(∞) = 1, U(r) >0 Ø U(r) is an increasing function of achievable data rate r Ø Twice differential function with r Ø U’(r)>0, U’’(r) <0 à U(r) is a concave function <Example of Utility function for Inelastic traffic> rmin
  7. 7. Application Utility Functions Thursday, 5 July 2018 7 q Inelastic traffic Ø Normalized Sigmoidal-like function q Elastic traffic Ø Normalized Logarithmic function <Utility functions for heterogeneous traffics> a, b, k – utility parameters, α – carrier allocation indicator, I – user index, j – carrier index
  8. 8. Flowchart of prop. approach Thursday, 5 July 2018 8 U1 U2 U3 U4 r2req Allocation of primary carrier in licensed bands {αij, ri pc } Choose UEs requiring more data rates, I = { i | ripc <rireq} Estimate the data rate achievable from WLAN, restWLAN restWLAN => rthWLAN Select the appropriate UEs for WLAN (w/ access index) YES UEs selected for WLAN ? WLAN YES {αij, ri } NO Allocation of secondary carriers in licensed bands NO r4req r1req r3req
  9. 9. Exploitation of the WLAN Thursday, 5 July 2018 10 q Selection of best suited UEs for WLAN with access index Ø Access index = F(mobility index, signal strength, traffic index) Ø preferred UEs with low mobility, strong WLAN signal, & elastic traffic where is the maxim value of rx. signal power where Ø Calculation of Mobility/Signal/Traffic index
  10. 10. Simulation Results Thursday, 5 July 2018 11 q Throughput and utility for UEs with different traffic types Ø With different utility function, inelastic UEs tends to be prioritized (two solid lines) Ø With the WLANs access, the system throughput is significantly improved (63.0%) at the high traffic load Ø While elastic traffic is considered more suitable for the WLANs, the QoS of elastic UEs could gain higher than one of inelastic UEs from the use of WLANs (19.5% vs. 6.7%)
  11. 11. Simulation Results Thursday, 5 July 2018 12 q For different UE’s mobility, Ø The reference algorithm only consider the Wi-Fi signal strength Ø The proposed algorithm gains 39.4% improvement in the average utility and 52% improvement in fairness when UEs move at high speed (avg. 32 km/h) Ø UEs of high mobility tend to be allocated to licensed bands considering limited coverage of WLANs
  12. 12. Conclusions Thursday, 5 July 2018 13 q Resource allocation in Heterogeneous networks inc. WLANs Ø Utilizes carrier aggregation with two-step allocation considering the characteristics of PC and SCs Ø The WLAN link is used as supplemental carrier as it q Efficient exploitation of unlicensed spectrumof WLANs Ø Need to consider the different characteristics of licensed bands and WLAN links of unlicensed bands. Ø Traffic types, the estimated user mobility speed, and channel quality for each UEs is considered for efficient traffic steering. q Application utility function for Heterogeneous traffic Ø Different utility functions for different traffic types Ø Achieved good QoS even for Inelastic traffics q Further work Ø Need to consider the aspects of different network latency

×