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
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Introduction
Model Description and Problem
Formulation
Proposed QoS Scheduling Framework
Simulation Result
Conclusions
2
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
3
Paper Goal
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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
4
Model Description (1/2)
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Paper denote by S the set of all subchannels 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
5
Model Description (2/2)
Only Consider Downlink
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In the system, at each slot,
the proposed scheduler
determines the subchannel assignment based
on each flow’s current
channel quality, minimum
average throughput and
individual packet deadline
6
Problem Formulation
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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
7
Problem Formulation (1/3)
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the derivative of utility function of flow i
U’i(・) is used as a weight
μij(t) is the achievable channel capacity when subchannel j is assigned to flow i at time slot t
8
Problem Formulation (2/3)
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is the long-term
throughput for flow i up to time slot t
δij(τ) is the 0-1 indicator of allocating the subchannel j to the flow i or not
OFDMA constraint :
9
Problem Formulation (3/3)
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θi(t) is the actual amount of data allocated to realtime flow i at time slot t and πi(t) is given by :
Mi is the minimum required average traffic rate of realtime 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
10
Beta Deadline Parameter (1/2)
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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
11
Beta Deadline Parameter (2/2)
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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
12
Real-time QoS Scheduling
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Paper can formulate the following maximum weighted
bipartite matching (MWBM) problem to find the subchannel 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
13
Unweighted Bipartite Matching
Definitions
Matching
Free Vertex
Definitions
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Maximum Matching: matching with the
largest number of edges
Definition
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Note that maximum matching is not unique.
Alternating Path
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Alternating between matching and non-matching edges.
a
b
c
d
e
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
Idea
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“Flip” augmenting path to get better matching

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Note: After flipping, the number of matched edges
will increase by 1!
Idea of Algorithm
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Start with an arbitrary matching
While we still can find an augmenting path
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Find the augmenting path P
Flip the edges in P
Labelling Algorithm
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Start with arbitrary matching
Labelling Algorithm
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Pick a free vertex in the bottom
Labelling Algorithm
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Run Breadth-first search (BFS)
Labelling Algorithm
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Alternate unmatched/matched edges
Labelling Algorithm
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Until a augmenting path is found
Augmenting Tree
Flip!
Repeat
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Pick another free vertex in the bottom
Repeat
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Run BFS
Repeat
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Flip
Answer
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Since we cannot find any augmenting path,
stop!
Weighted Bipartite Graph
3
6
6
4
Weighted Matching
Score: 6+3+1=10
3
6
6
4
Maximum Weighted Matching
Score: 6+1+1+1+4=13
3
6
6
4
Augmenting Path (change of
definition)
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Any alternating path such that total score of unmatched
edges > that of matched edges
The score of the augmenting path is
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Score of unmatched edges – that of matched edges
3
6
6
4
Note: augmenting path need not start and end at free vertices!
Detailed Procedure
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the result of MWBM
algorithm using
average sub-channel
capacity cannot give
exact number of subchannels to the flows
36
Non-Real-time QoS Scheduling
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general utility function is defined for α ≥ 0
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α = 0 : maximum throughput
α = 1 : proportional fairness
α = ∞ : max-min fairness
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the minimum data rate that a dataflow achieves is maximized;
secondly, the second lowest data rate that a dataflow achieves
is maximized, etc
37
Simulation Environment
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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
38
Beta deadline parameter
characteristics of VoIP traffic
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beta = 0 : strict priority
beta = inf : urgent scheduling
39
Traffic class prioritization
performance
40
Burst traffic response
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beta = 0 : strict priority
beta = inf : urgent scheduling
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%
41
Conclusions
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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
42