M ulticast utility based scheduling for uwb networks
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M ulticast Utility-Based Scheduling for UWB Networks. Kuang-Hao Liu et al Presented by Xin Che 11/18/09. Introduction . IEEE 802.15.3 For WPANs Piconet Controller Peer-to-Peer mode It is proposed for narrowband wireless communications It is not suitable for UWB

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M ulticast Utility-Based Scheduling for UWB Networks

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M ulticast utility based scheduling for uwb networks

Multicast Utility-Based Scheduling for UWB Networks

Kuang-Hao Liu et al

Presented by

Xin Che

11/18/09


Introduction

Introduction

  • IEEE 802.15.3

    • For WPANs

    • Piconet Controller

    • Peer-to-Peer mode

    • It is proposed for narrowband wireless communications

    • It is not suitable for UWB

      • Concurrent transmissions: Multiple user interference

      • Ranging capability


Introduction1

Introduction

  • UWB-based WPANs

formulate the optimal scheduling problem as a utility maximization problem !


Introduction2

Introduction

  • Pro:

    • A utility-based scheduling algorithm aiming at multiclass QoS provisioning with fairness consideration

  • Cons:

    • an efficient scheduling algorithm requires feedback information from the network to appropriately make scheduling decisions

    • it is very difficult, if not impossible, for the PNC to acquire instantaneous channel information of each flow.(Due to peer-to-peer communications)


Introduction3

Introduction

  • To estimate the achievable data rate of a flow

    • PNC can make use of the ranging capability featured by UWB communications [14], [15].

    • But, distance information obtained may be noisy due to multipath fading !

    • the utility estimation may be biased, and thus affects the scheduling decisions !


Introduction4

Introduction

  • Solution in this paper

    • resort to metaheuristic methods and choose to use the global search algorithm (GSA) [17].

      • its convergence to the global optimum can be proved,

      • the tradeoff between computational complexity and efficiency is tunable.

  • the exclusive-region-based GSA (ER-GSA)

    • a desired convergence with reasonable computational complexity for practical implementations


Introduction5

Introduction

  • Contributions of this paper

    • The scheduling algorithm for concurrent UWB transmissions maximizes the weighted utility is formulated (NP-Hard)

    • a utility-based scheduling scheme is proposed to support multiclass traffic with fairness constraint

    • The assumption of perfect distance information for measuring flow throughput is relaxed by factoring estimation errors into the objective function.

    • The stochastic optimization problem is solved by the proposed ER-GSA, and its convergence property and computational complexity are studied


System model

System Model

  • Network Structure


System model1

System Model


System model2

System Model

  • Simplified channel model

    • Assume that a UWB receiver can adapt its transmission rate to an arbitrary SINR level

    • the achievable data rate r_iof flow i is upper bounded by

    • neglect the multipath fast fading when we estimate the average data rate ri


System model3

System Model

  • Utility Function

    • Utility is defined as the satisfaction level of a user with respect to the amount of allocated bandwidth.

      • For heterogeneous traffic, general nondecreasing functions with values within [0, 1]

  • Traffic types are classified into three classes


System model4

System Model

  • Class 1

    • constant bit-rate app. E.g. audio streams

  • Class 2

    • Can adapt to the allocated bandwidth to a certain extent : video stream


System model5

System Model

  • Class 3

    • Can adapt to the allocated bandwidth to a certain extent : video stream


Optimal scheduling with conccurent transmission

Optimal Scheduling With Conccurent Transmission


System model6

System Model


Optimal scheduling

Optimal Scheduling


Optimal scheduling1

Optimal Scheduling


Optimal scheduling2

Optimal Scheduling


Optimal schedulng

Optimal Schedulng


Optimal scheduling3

Optimal Scheduling

  • Deriving

    • very difficult, if not impossible, as U(k) is combinatiorial : dependent on the element in κ.

  • Use discrete approximation

    • Let be


Proposed algorithm

Proposed Algorithm

  • ER-GSA

    • the optimal flow set κ* can be found by evaluating the utility value for each member in K to locate the maximal member

      • Simple, but has exponential complexity.

      • Cannot deal with estimation errors.

    • the GSA is selected as the base to solve (13) since its convergence to a global optimum can be theoretically proved under certain conditions.


Proposed algorithm1

Proposed Algorithm

  • GSA

    • relies on a random sequence generated during the algorithm iterations to efficiently find the optimum.

    • The resulting random sequence is a Markov chain, where each state represents a point in the solution space that has been visited by the algorithm

    • In each iteration, the transition of the Markov chain is determined by comparing the objective value of the current state and that of a randomly chosen point from the solution space


Proposed algorithm2

Proposed Algorithm


Proposed algorithm3

Proposed Algorithm


Proposed algorithm4

Proposed Algorithm

  • The convergence of ER-GSA


Proposed algorithm5

Proposed Algorithm


Proposed algorithm6

Proposed Algorithm


Proposed algorithm7

Proposed Algorithm

  • Utility Update


Proposed algorithm8

Proposed Algorithm

  • The scheduling policy has the followoing properties :


Performance evaluation

Performance Evaluation

  • Experiment Setting


Performance evaluation1

Performance Evaluation

  • Experiment Setting

    • Each superframe contains ten slots.

    • The size of exclusive region, which is denoted as dER, is set to 2 m,

    • in Section V-C, we vary the size of exclusive region to study its impact on the aforementioned three performance metrics.


Performance evaluation2

Performance Evaluation

  • Traffic


Performance evaluation3

Performance Evaluation

  • Utility-Based Scheduling


Performance evaluation4

Performance Evaluation

  • Utility-Based Scheduling


Performance evaluation5

Performance Evaluation

  • Utility Vs. Fariness

    • Total Utility Vs. Fairness


Performance evaluation6

Performance Evaluation

  • Utility Vs. Fariness


Performance evaluation7

Performance Evaluation

  • Minimum Utility


Performance evaluation8

Performance Evaluation

  • Algorithm Efficiency and Stability


Performance evaluation9

Performance Evaluation

  • Algorithm Efficiency and Stability


Performance evaluation10

Performance Evaluation

  • Algorithm Efficiency and Stability

    • Stability factor


Performance evaluation11

Performance Evaluation

  • Stability


Conclusion

Conclusion

  • a utility-based optimal scheduling for concurrent UWB transmissions supporting heterogeneous traffic has been proposed

  • it is found that the size of the exclusive region in UWB networks is independent of the transceiver distance, which, on the contrary, is a dependent parameter in narrowband wireless systems.

  • The proposed algorithm can also maintain a good balance between the computation complexity and the robustness against measurement and estimation errors, and thus, it suits UWB network schedulers with limited computation power.


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