Loading in 5 sec....

M ulticast Utility-Based Scheduling for UWB NetworksPowerPoint Presentation

M ulticast Utility-Based Scheduling for UWB Networks

- 87 Views
- Uploaded on
- Presentation posted in: General

M ulticast Utility-Based Scheduling for UWB Networks

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Multicast Utility-Based Scheduling for UWB Networks

Kuang-Hao Liu et al

Presented by

Xin Che

11/18/09

- 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

- UWB-based WPANs

formulate the optimal scheduling problem as a utility maximization problem !

- 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)

- 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 !

- 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

- 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

- Network Structure

- 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

- 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

- Class 1
- constant bit-rate app. E.g. audio streams

- Class 2
- Can adapt to the allocated bandwidth to a certain extent : video stream

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

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

- Use discrete approximation
- Let be

- 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

- 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

- The convergence of ER-GSA

- Utility Update

- The scheduling policy has the followoing properties :

- Experiment Setting

- 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.

- Traffic

- Utility-Based Scheduling

- Utility-Based Scheduling

- Utility Vs. Fariness
- Total Utility Vs. Fairness

- Utility Vs. Fariness

- Minimum Utility

- Algorithm Efficiency and Stability

- Algorithm Efficiency and Stability

- Algorithm Efficiency and Stability
- Stability factor

- Stability

- 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.