Cheng- Hsien Lin, Jeng-Farn Lee, Jia-Hui Wan

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