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

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 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






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 algorithm4
Proposed Algorithm

  • The convergence of ER-GSA




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 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



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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