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M ulticast Utility-Based Scheduling for UWB NetworksPowerPoint Presentation

M ulticast Utility-Based Scheduling for UWB Networks

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### Multicast 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
- Concurrent transmissions: Multiple user interference
- Ranging capability

Introduction

- UWB-based WPANs

formulate the optimal scheduling problem as a utility maximization problem !

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)

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 !

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.

- resort to metaheuristic methods and choose to use the global search algorithm (GSA) [17].
- the exclusive-region-based GSA (ER-GSA)
- a desired convergence with reasonable computational complexity for practical implementations

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

- Network Structure

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 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]

- Utility is defined as the satisfaction level of a user with respect to the amount of allocated bandwidth.
- Traffic types are classified into three classes

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 Model

- Class 3
- Can adapt to the allocated bandwidth to a certain extent : video stream

Optimal Scheduling With Conccurent Transmission

Optimal Schedulng

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

- 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.

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

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 Algorithm

- The convergence of ER-GSA

Proposed Algorithm

- Utility Update

Proposed Algorithm

- The scheduling policy has the followoing properties :

Performance Evaluation

- Experiment Setting

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 Evaluation

- Traffic

Performance Evaluation

- Utility-Based Scheduling

Performance Evaluation

- Utility-Based Scheduling

Performance Evaluation

- Utility Vs. Fariness
- Total Utility Vs. Fairness

Performance Evaluation

- Utility Vs. Fariness

Performance Evaluation

- Minimum Utility

Performance Evaluation

- Algorithm Efficiency and Stability

Performance Evaluation

- Algorithm Efficiency and Stability

Performance Evaluation

- Algorithm Efficiency and Stability
- Stability factor

Performance Evaluation

- Stability

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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