Cheng- Hsien Lin, Jeng-Farn Lee, Jia-Hui Wan
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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)
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Outline Introduction Goal Network Model and Assumption Problem specification Multi-Level Utility-based Resource Allocation (ML-URA) Simulations Conclusions
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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
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Problem The broadcast receipt maximization problem
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Problem The broadcast receipt maximization problem
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Problem The broadcast receipt maximization problem
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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 Y RSs N MSs that subscribe to a certain real-time program
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
RS0
Each RS y (1 ≤ y ≤ Y) is denoted by RSy
Each MS n (1 ≤ n ≤ N) is denoted by MSn
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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.
x
MS
MS
MS
RAx
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Network Model and Assumption For all RSs, the channel conditions are represented by
where
records the resource required by RSy to receive streaming data
from other RSs. RResy,y= 0: RSy doesn’t demand any resource from itself.
1 2, ,...,RS RS RS RSYR R R R ,0 ,1 ,RRes ,RRes ,...,RResRS
y y y y YR
RS2RS0
RS4
RS8
RS5
RS3
RS1RS6
RS7
1 1,0 1,1 1,8RRes ,RRes ,...,RResRSR
2 2,0 2,1 2,8RRes ,RRes ,...,RResRSR
8 8,0 8,1 8,8RRes ,RRes ,...,RResRSR
...
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Network Model and Assumption Similarly, the matrix portrays the
resource requirement of all MSs, where
records the resource that MSn requires to receive data from all
RSs.
1 2, ,...,MS MS MS MSNR R R R
,0 ,1 ,MRes ,MRes ,...,MResMSn n n n YR
MS1 MS2
1 1,0 1,1 1,8MRes ,MRes ,...,MResMSR
2 2,0 2,1 2,8MRes ,MRes ,...,MResMSR
RS2RS0
RS4
RS8
RS5
RS3
RS1RS6
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
RS2RS0
RS4
RS8
RS5
RS3
RS1RS6
RS7
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Network Model and Assumption U(): whether the MSn can receive data from RSy successfully.
,
,
1, if MRes 0( MRes )
0, otherwise
y n y
y n y
RAU RA
MS1
RS0RS1
RA1 = 5
MRes1,1 = 3
MS2
MRes2,1 = 7
U(RA1-MRes1,1) = U(5-3) = 1 U(RA1-MRes2,1) = U(5-7) = 0
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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.
D0(RA) = 1: MR-BS is the source node of the real-time stream.
,1, if RRes and ( ) 1( )
0, otherwise
x y x x
y
RA DD
RARA
RS0RS1
RA1 = 5
RRes2,1 = 3
RRes3,1 = 7RS3
RS2
D2(RA) = D2(5-3) = 1 D3(RA) = D3(5-7) = 0
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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)
RS2MR-BS
RS4
RS8
RS5
RS3
RS1RS6
RS7
SP1
SP6 ѱ(SP6) = 2Γ(SP6, 1) = 1Γ(SP6, 2) = 6
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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
RS0RSk RSk+1
MSj
check if MSj can be served by SPy
……...RSy
……...
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
MSj
MSi
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
the additional number of MSs that can be served
whether MSj has been served in previous rounds of the resource allocation process
, , , ,
1, if the above condition met
0, otherwisej y j y j y j ySP RS SP RSF F F F
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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
RS0RSk RSk+1
MSi
……...RSy
……...
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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 Find-Most-MS-Path procedure
(ui,y)
(number of MSs)
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ML-URA_Greedy procedure
Greedy procedure
stop conditions exists: (i) the entire resource budget has been allocated (ii) all MSs have been served.
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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
5/52/2
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Find-Most-MS-Path procedure
ML-URA_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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SimulationsOPTMLRA => computes the optimal solution in a brute-force
manner
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Simulations
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Simulations
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Conclusions The proposed ML-URA mechanism improve
Resource utilization Performance
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