1 / 42

QoS Scheduling for Heterogeneous Traffic in OFDMA-based Wireless Systems

QoS Scheduling for Heterogeneous Traffic in OFDMA-based Wireless Systems. Youngki Kim Mobile R&D Laboratory KT, Korea Kyuho Son and Song Chong School of EECS KAIST, Korea IEEE GLOBECOM 2009 proceedings. Speaker ： Tsung-Yin Lee. Outline. Introduction

tamah
Download Presentation

QoS Scheduling for Heterogeneous Traffic in OFDMA-based Wireless Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. QoS Scheduling for Heterogeneous Traffic in OFDMA-based Wireless Systems Youngki Kim Mobile R&D Laboratory KT, Korea Kyuho Son and Song Chong School of EECS KAIST, Korea IEEE GLOBECOM 2009 proceedings. Speaker：Tsung-Yin Lee

  2. Outline • Introduction • Model Description and Problem Formulation • Proposed QoS Scheduling Framework • Simulation Result • Conclusions

  3. Introduction • the key access technologies in current and next generation wireless systems is OFDMA • Packet scheduling plays an important role in QoS provisioning by providing mechanisms for the resource allocation

  4. Paper Goal • provide QoS guarantee to the real-time traffic in multi-carrier wireless systems • utility maximization of the non real-time traffic while providing QoS guarantee to the real-time traffic • balance between QoS guarantee and utility maximization in a simple and organized manner

  5. Model Description (1/2) • Paper denote by S the set of all sub-channels in the system, NRTand NNRT, the set of all real-time (RT) and non real-time (NRT) flows • RT flow, VoIP or MPEG, has its own QoS parameters such as maximum latency • NRT flow has no explicit QoS parameters

  6. Model Description (2/2) • In the system, at each slot, the proposed scheduler determines the sub-channel assignment based on each flow’s current channel quality, minimum average throughput and individual packet deadline Only Consider Downlink

  7. Problem Formulation • Proposed scheduling framework that maximizes the weighted sum rate of non real-time flows while maintaining QoS constraints of real-time flows in each time slot with the equal power allocation assumption

  8. Problem Formulation (1/3) • the derivative of utility function of flow i • U’i(・) is used as a weight • μij(t) is the achievable channel capacity when sub-channel j is assigned to flow i at time slot t

  9. Problem Formulation (2/3) • is the long-term throughput for flow i up to time slot t • δij(τ) is the 0-1 indicator of allocating the sub-channel j to the flow i or not • OFDMA constraint :

  10. Problem Formulation (3/3) • θi(t) is the actual amount of data allocated to real-time flow i at time slot t andπi(t) is given by : • Mi is the minimum required average traffic rate of real-time flow i • is the maximum possible data rate of real-time flow i at time slot t • properly based on the newly introduced beta deadline parameter

  11. Beta Deadline Parameter (1/2) • urgent scheduling : which only considers the most urgent packets (required data rate is 6) • strict priority scheduling : provide higher priority to the real-time traffic than non real-time traffic (required data rate is 18) • paper may take a policy somewhere between these two extreme cases

  12. Beta Deadline Parameter (2/2) • lik is the length of the k-th packet of flow i • eik is the time to expire value of the k-th packet of flow i • Qi is the total number of packets of real-time flow i at time slot t

  13. Real-time QoS Scheduling • Paper can formulate the following maximum weighted bipartite matching (MWBM) problem to find the sub-channel allocation matrix • : the number of sub-channels to be assigned to flow i at time slot t • : the average sub-channel capacity of the flow i

  14. Unweighted Bipartite Matching

  15. Definitions Matching Free Vertex

  16. Definitions • Maximum Matching: matching with the largest number of edges

  17. Definition • Note that maximum matching is not unique.

  18. Alternating Path • Alternating between matching and non-matching edges. a c d e b f h i j g d-h-e: alternating path a-f-b-h-d-i: alternating path starts and ends with free vertices f-b-h-e: not alternating path e-j: alternating path starts and ends with free vertices

  19. Idea • “Flip” augmenting path to get better matching • Note: After flipping, the number of matched edges will increase by 1! 

  20. Idea of Algorithm • Start with an arbitrary matching • While we still can find an augmenting path • Find the augmenting path P • Flip the edges in P

  21. Labelling Algorithm • Start with arbitrary matching

  22. Labelling Algorithm • Pick a free vertex in the bottom

  23. Labelling Algorithm • Run Breadth-first search (BFS)

  24. Labelling Algorithm • Alternate unmatched/matched edges

  25. Labelling Algorithm • Until a augmenting path is found

  26. Augmenting Tree

  27. Flip!

  28. Repeat • Pick another free vertex in the bottom

  29. Repeat • Run BFS

  30. Repeat • Flip

  31. Answer • Since we cannot find any augmenting path, stop!

  32. Weighted Bipartite Graph 3 4 6 6

  33. Weighted Matching Score: 6+3+1=10 3 4 6 6

  34. Maximum Weighted Matching Score: 6+1+1+1+4=13 3 4 6 6

  35. Augmenting Path (change of definition) • Any alternating path such that total score of unmatched edges > that of matched edges • The score of the augmenting path is • Score of unmatched edges – that of matched edges 3 4 6 6 Note: augmenting path need not start and end at free vertices!

  36. Detailed Procedure • the result of MWBM algorithm using average sub-channel capacity cannot give exact number of sub-channels to the flows

  37. Non-Real-time QoS Scheduling • general utility function is defined for α ≥ 0 • α = 0 : maximum throughput • α = 1 : proportional fairness • α = ∞ : max-min fairness • the minimum data rate that a dataflow achieves is maximized; secondly, the second lowest data rate that a dataflow achieves is maximized, etc

  38. Simulation Environment • VoIP traffic is based on G.711 codec standard and generates each VoIP packet every 20 ms, with 160-byte data • Video streaming traffic has more bursty nature because packet size can be different according to the codec rate such as MPEG-FGS

  39. Beta deadline parameter characteristics of VoIP traffic • beta = 0 : strict priority • beta = inf : urgent scheduling

  40. Traffic class prioritization performance

  41. Burst traffic response • During the 2000 time slot and 3000 time slot, offered traffic rate increases up to 150% of the average traffic rate. • During the 7000 time slot and 8000 time slot, offered traffic rate increases to 300% • beta = 0 : strict priority • beta = inf : urgent scheduling

  42. Conclusions • The proposed scheduling algorithm (beta deadline parameter)satisfies the QoS requirements of the real-time traffic and maximizes the utility of the non real-time traffic while utilizing the system resources efficiently

More Related