A Utility-based Mechanism for Broadcast Recipient
Maximization in WiMAX Multi-level Relay Networks
Cheng-Hsien Lin, Jeng-Farn Lee, Jia-Hui Wan
Department of Computer Science and Information Engineering,
National Chung Cheng University, Taiwan
IEEE Transactions on Vehicular Technology (IEEE TVT 2012)
Outline
 Introduction
 Goal
 Network Model and Assumption
 Problem specification
 Multi-Level Utility-based Resource Allocation (ML-URA)
 Simulations
 Conclusions
2
Introduction
 The emergence of IEEE 802.16 WiMAX and advances in
video coding technologies have made real-time applications
possible.
 The granted applications (e.g., real-time IPTV Broadcast)
 Allocated limited time-slots (Resource Budget).
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Problem
 This paper studies the resource allocation problem
 Broadcast receipt maximization in IEEE 802.16j
 IEEE 802.16j
 Multihop Relay Base Station(MR-BS)
 multiple Relay Stations(RSs)
 Mobile Stations(MSs)
 Broadcast data is sent by the MR-BS to a set of receivers
 How to allocate the given resource budget to maximize the
number of MSs is a challenging issue.
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Problem
 The broadcast receipt maximization problem
5
Problem
 The broadcast receipt maximization problem
6
Problem
 The broadcast receipt maximization problem
7
Problem
 The broadcast receipt maximization problem
8
Related works
 Existing researches
 heuristic resource allocation strategies
 single-level relay networks (two-hop relay networks)
 This paper models the resource allocation problem in IEEE
802.16j WiMAX multi-level relay networks (multi-hop)
 Multi-Level Broadcast Receipt Maximization (ML-BRM) problem
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Goal
 To propose multi-level resource allocation mechanism
 Consider the multi-level relay paths and the required resource
 Maximize resource utilization in WiMAX multi-level relay networks
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Network Model and Assumption
 In a WiMAX relay network,
 one MR-BS RS0
Each RS y (1 ≤ y ≤ Y) is denoted by RSy
 Y RSs
 N MSs that subscribe to a certain real-time program
Each MS n (1 ≤ n ≤ N) is denoted by MSn
 This paper assumes that the real-time program, whose
streaming data size is M
 Resource budget: rbudget
 total time slots in a TDD super frame
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Network Model and Assumption
 The number of time slots required to transmit a broadcast
stream varies
 MSs and RSs have different channel conditions
 MSs and RSs have different modulation schemes
 the transmission rates required for RSs to successfully send data also
vary
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Network Model and Assumption
 The transmission rate bx,y between sender x and receiver y
 based on one of the channel conditions, such as the SNR value
 sender x: MR-BS or RS
 receiver y: RS or MS
 The resource required by the receiver y: M/bx,y
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Network Model and Assumption
 RAx: a node x with the allocated resource RAx
 all nodes whose required resource is not larger than RAx can receive the
downlink data successfully through one downlink transmission from
node x.
MS
RAx
x
MS
MS
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Network Model and Assumption
 For all RSs, the channel conditions are represented by
R RS   R1RS , R2RS ,..., RYRS  where RyRS   RRes y ,0 , RRes y ,1 ,..., RRes y ,Y 
records the resource required by RSy to receive streaming data
from other RSs.
 RResy,y= 0: RSy doesn’t demand any resource from itself.
R1RS   RRes1,0 , RRes1,1 ,..., RRes1,8 
RS5
R2RS   RRes 2,0 , RRes 2,1 ,..., RRes 2,8 
RS1
...
RS6
RS4
R8RS   RRes8,0 , RRes8,1 ,..., RRes8,8 
RS0
RS8
RS2
RS3
RS7
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Network Model and Assumption
 Similarly, the matrix R MS   R1MS , R2MS ,..., RNMS  portrays the
resource requirement of all MSs, where RnMS  MResn,0 , MResn,1 ,..., MResn,Y 
records the resource that MSn requires to receive data from all
RSs.
R1MS   MRes1,0 , MRes1,1 ,..., MRes1,8 
MS1
MS2
R2MS   MRes 2,0 , MRes 2,1 ,..., MRes 2,8 
RS5
RS1
RS6
RS4
RS0
RS8
RS2
RS3
RS7
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Network Model and Assumption
 Finally, the resource allocation vector is denoted by
RA = [RA0, RA1, RA2, …, RAY ], where RAy represents the
amount of the resource allocated to RSy.
MS1
MS2
RS5
RS1
RS6
RS4
RS0
RS8
RS2
RS3
RS7
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Network Model and Assumption
 U(): whether the MSn can receive data from RSy successfully.
1, if RAy  MRes n , y  0
U ( RAy  MRes n , y )  
0, otherwise
RA1 = 5
MS2
RS0
 U(RA1-MRes1,1) = U(5-3) = 1
 U(RA1-MRes2,1) = U(5-7) = 0
MRes2,1 = 7
RS1
MS1
MRes1,1 = 3
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Network Model and Assumption
 D(): whether RSy is eligible to receive real-time streaming
data from the MR-BS when the current resource allocation RA
is given.
1, if RAx  RRes y , x and Dx ( RA)  1
Dy ( RA)  
0, otherwise
 D0(RA) = 1: MR-BS is the source node of the real-time stream.
RA1 = 5
RS3
RS0
 D2(RA) = D2(5-3) = 1
 D3(RA) = D3(5-7) = 0
RRes3,1 = 7
RS1
RS2
RRes2,1 = 3
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Problem specification
 We now define the Multi-Level Broadcast Recipient
Maximization (ML-BRM) problem.
 resource budget (rbudget)
 channel conditions of the wireless relay network (RMS and RRS )
 ML-BRM searches for an allocation RA vector that will
maximize the number of MSs receiving the real-time program.
The ML-BRM problem is NP-complete
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ML-URA
 Multi-Level Utility-based Resource Allocation
 Definition of Utility
 ui,y: the number of additional MSs divided by the extra resource that
the network must allocate to the RSs on the relay path
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ML-URA
 Construct single-source shortest path tree that is rooted at the
MR-BS and connects all RSs. (SPy)
 ѱ(SPy) counts the number of RSs on SPy
 Γ(SPy, k) obtains the ID of the kth RS on SPy, 1 ≤ k ≤ ѱ(SPy)
RS5
SP1
RS1
SP6
RS6
ѱ(SP
= 2= 1
Γ(SP66), 1)
Γ(SP6, 2) = 6
RS4
MR-BS
RS8
RS2
RS3
RS7
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ML-URA
 To derive the utility of a relay path ui,y
 count the number of additional MSs
 calculate the amount of extra resource required
check if MSj can be served by SPy
……...
……...
RSk
RSk+1
RSy
RS0
MSj
 Because of the broadcast nature of the wireless medium,
MSj can receive data of the real-time program
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ML-URA
 To derive the utility of a relay path ui,y
 count the number of additional MSs
 calculate the amount of extra resource required
RSy is allocated MResi,y to serve MSi
check if MSj can be served by RSy
RSy
MSi
MSj
 Because of the broadcast nature of the wireless medium,
MSj can receive data of the real-time program
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ML-URA
 the union operation
1, if the above condition met
FSP j , y  FRS j , y  FSP j , y FRS j , y  
0, otherwise
whether MSj has been served in previous rounds of
the resource allocation process
the additional number of MSs that can be served
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ML-URA
 To derive the utility of a relay path ui,y
 count the number of additional MSs
 calculate the amount of extra resource required
……...
……...
RSk
RSk+1
RSy
RS0
MSi
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ML-URA
 To derive the utility of a relay path ui,y
 count the number of additional MSs
 calculate the amount of extra resource required
RSk
MSi
Rsk+1
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ML-URA
 The expression of the utility of a relay path ui,y is defined as
follows:
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ML-URA
 The ML-URA Mechanism
 Greedy procedure (ui,y)
 Find-Most-MS-Path procedure
(number of MSs)
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ML-URA_Greedy procedure
stop conditions exists:
(i) the entire resource budget has been allocated
(ii) all MSs have been served.
Greedy procedure
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ML-URA_Greedy procedure
 Resource-Recycle procedure
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ML-URA_Greedy procedure
 Two distinct paths that have the same utility value
2/2
5/5
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ML-URA_Find-Most-MS-Path procedure
Find-Most-MS-Path
procedure
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ML-URA_Find-Most-MS-Path procedure
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Simulations
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Simulations
OPT
=> computes the optimal solution in a brute-force manner
RAML
36
Simulations
37
Simulations
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Conclusions
 The proposed ML-URA mechanism improve
 Resource utilization
 Performance
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