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The Optimality of Two Prices: Maximizing Revenue in a Stochastic Network

Slides from conference paper at Allerton 2007: L. Huang and M. J. Neely, The Optimality of Two Prices: Maximizing Revenue in a Stochastic Network," Proc. of45th Annual Allerton Conference on Communication, Control,and Computing (invited paper), Sept. 2007.PDF of paper available on:http://ww

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The Optimality of Two Prices: Maximizing Revenue in a Stochastic Network

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    1. The Optimality of Two Prices: Maximizing Revenue in a Stochastic Network Longbo Huang Michael J. Neely University of Southern California

    3. The Revenue Maximization Problem for an Access Point (AP)

    4. Motivation: We want to solve a general stochastic Max Revenue problem for wireless service providers

    5. Motivation: We want to solve a general stochastic Max Revenue problem for wireless service providers

    6. Motivation: We want to solve a general stochastic Max Revenue problem for wireless service providers

    7. Network Model:

    11. If the AP wants to admit new data, it gives an “OPEN MARKET” signal (Z(t) = 1). It chooses a price p(t) and informs the users. Users react by sending packets.

    12. If the AP doesn’t want to admit new data, it sends a “CLOSED” signal (Z(t)=0). Users will NOT send any packets.

    13. -The AP queues the data (Q(t) = backlog). -Observes channel state S(t). -Makes Resource Allocation decision cost(t) C. -Transmission rate = m(t) = F(cost(t), S(t))

    14. The optimum avg. profit can be obtained with exact steady state distribution information for M(t) and S(t)

    15. The Optimality of Two Prices Theorem

    21. This example shows that using a single price does NOT always lead to optimal average profit, thus the number Two is tight

    22. This example shows that using a single price does NOT always lead to optimal average profit, thus the number Two is tight

    23. This example shows that using a single price does NOT always lead to optimal average profit, thus the number Two is tight

    24. Our dynamic Pricing and Transmission Scheduling Algorithm PTSA achieves the optimum average profit

    25. Our dynamic Pricing and Transmission Scheduling Algorithm PTSA achieves the optimum average profit

    26. Performance of the joint Pricing and Transmission Scheduling Algorithm (PTSA)

    27. Proof Technique: Use General Framework for Stochastic Network Optimization developed in:

    28. Simulation setup of PTSA

    29. Simulation results of PTSA

    30. Simulation results of PTSA

    31. More simulation results of PTSA

    32. More simulation results of PTSA

    33. More simulation results of PTSA

    34. This work characterizes the max avg. profit of APs in wireless mesh nets and offers a revenue maximizing algorithm

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