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, NRT and 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. Only Consider Downlink
Model Description (2/2)
Only Consider Downlink
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

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 and πi(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.f g h i j
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 Graph3 4 6 6

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

34. Maximum Weighted Matching
Score: =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