Multicast recipient maximization in ieee 802 16j wimax relay networks
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Multicast Recipient Maximization in IEEE 802.16j WiMAX Relay Networks. Wen-Hsing Kuo † ( 郭文興 ) & Jeng-Farn Lee ‡ ( 李正帆 ) † Department of Electrical Engineering, Yuan Ze University, Taiwan ‡ Department of CSIE, National Chung Cheng University, Taiwan

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Multicast Recipient Maximization in IEEE 802.16j WiMAX Relay Networks

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Multicast recipient maximization in ieee 802 16j wimax relay networks

Multicast Recipient Maximization in IEEE 802.16j WiMAX Relay Networks

Wen-Hsing Kuo †(郭文興) & Jeng-Farn Lee ‡(李正帆)

† Department of Electrical Engineering, Yuan Ze University, Taiwan

‡ Department of CSIE, National Chung Cheng University, Taiwan

IEEE Transactions on Vehicular Technology, vol. 59, no. 1, Jan. 2010


Outline

Outline

  • Introduction

  • Problem & Goal

  • System model

  • Challenge

  • Proposed Resource

    • Greedy Approach (GD)

    • Dynamic Station Selection (DSS)

  • Performance

  • Conclusion


Introduction

Introduction

  • WiMAX 802.16 networks

    • better coverage

    • higher throughput

  • Wireless resources available for each wireless service is inevitably limited.

  • As the capacity of wireless devices improves, the multicast applications, including Video conferencing, have been developed.


Introduction1

Introduction

  • Resource-management policy

    • limit the maximum time slots of a single multicast,e.g., 10% of a TDD super-frame

    • to maintain the quality of different services

  • With the given resource budget, a BS should serve as many recipients, i.e., SSs, as possible

    • to maximize user satisfaction

    • to maximize resource utilization


Problem goal

Problem & Goal

  • How to address the multicast recipient maximization (MRM) problem in the WiMAX 802.16j network ?

  • To propose a resource-allocation scheme for multicast service in downlink transmission

    • To maximize the total number of recipients

    • with the given budget (maximal usable resource)

  • To the best of authors’ knowledge, this is the first work to study the problem.


System model

System model

  • Resource can be distributed to different transmissions

    • time slots

  • This budget is to be distributed among the BS and RSs

    • since they are in the same interference range

    • only one of them can transmit at the same time

  • Routing of each SS is assumed to be decided beforehand

    • SS accesses the BS either directly or through an RS

    • it is impractical that the whole multicast tree can dynamically be formed and adjusted as the channel condition of any recipient changes.


System model1

System model

M RSs & N SSs

Let not only the SSs but also RSsdirectly served by the BS be classified as group 0.

The SSs that receive data via the mth RS be placed in group m, where m > 0.

Group 0

Group m

Group 1

Group 2


System model2

System model

  • Nm : number of nodes in group m

  • S(m,n) : the nth node in group m

  • r(m,n) : the resource requirement of S(m,n)

  • Since SSs have different bit error rates due to heterogeneous channel conditions, they may require different amounts of resource for receiving the same data from the BS.

Group 2


System model3

System model

Group 0

  • im : RS’s order in group 0

    • RSm = S(0,im) = S(m,0)

  • r(0,im) =0= RS’s resource requirement

RS2 = S(0,2)= S(2,0)

Group 2


System model4

System model

Nodes in each group are placed in increasing order of r(m,n), i.e., r(m,1) ≤ r(m,2) ≤ · · · ≤ r(m, Nm)


System model5

System model

Δrm(n) represents the additionally required resource of S(m,n) when the last node S(m, n−1) is served.

Δrm(n) = r(m,n) – r(m, n – 1)


System model6

System model

Binary function Dm(n)

RSm can receive from the BS when n nodes are served in group 0. Dm(n) is equal to 1 if im ≤ n and 0 otherwise.

Um(n) is the number of served SSs when serving S(m,n), starting from the BS.


Challenge

Challenge

MRM Problem is NP-Complete

The goal of MRM is to maximize the total number of served SSs; however, the total resource consumed by the RSs and the BS should not be greater than rbudget.

Likes the integral knapsack problem (NP-hard)

(1) Object’s price and its weight, (2) the weight limitation

(1) Group’s nodal amount and the resource requirements,(2) the budget limitation


Challenge1

Challenge

  • MRM Problem is NP-Complete

  • MRM is also NP, because the a solution (i.e., {n0, n1, ... , nM }) can be validated by calculating

  • MRM problem is NP-hard and NP, so that MRM problem is NP-Complete


Proposed algorithm

Proposed Algorithm

Greedy approach (GD)

um(n): allocation utility of including S(m,n)

Um(n), ( um(n) )


Proposed algorithm1

Proposed Algorithm

Greedy approach (GD)

um(n): allocation utility of including S(m,n)

Um(n), ( um(n) )


Proposed algorithm2

Proposed Algorithm

Greedy approach (GD)

um(n): allocation utility of including S(m,n)

Um(n), ( um(n) )


Proposed algorithm3

Proposed Algorithm

Greedy approach (GD)

um(n): allocation utility of including S(m,n)

If rbudget = 2


Proposed algorithm4

Proposed Algorithm

Dynamic Station Selection (DSS)

Um*(n) : the envelope function of Um(n)

um*(n) : the optimal allocation utility of including S(m,n)

U0*(0) = U0(0) = 0


Proposed algorithm5

Proposed Algorithm

Dynamic Station Selection (DSS)

Um*(n), ( um*(n) )

If rbudget = 2


Performance

Performance

  • BS at (0,0), RS uniformly distributed, SS random deployed

  • Required resource for each node = (1/da)

    • d : distance between sender and receiver

    • a : channel attenuation factor, 2  a  4


Simulation i

Simulation I

a = 2

a = 3

  • DSS: Dynamic station selection

  • OP: Optimal solution

  • GD: Greedy algorithm

5 RSs and 100 SSs


Simulation ii

Simulation II

  • DSS: Dynamic station selection

  • OP: Optimal solution

  • GD: Greedy algorithm

Resource budget = 20000

a = 2


Conclusion future work

Conclusion & Future Work

  • This paper have considered a resource-allocation problem called MRM for multicast over WiMAX relay networks.

  • It proposes a dynamic station selection (DSS) to solve the problem based on the proposed envelope function.

  • The future research can be extended in (1) relay networks with more than two hops(2) the distributed approach to solve MRM problem

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Thanks for your attention !


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