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Cross layer design is wireless multi-hop network

Cross layer design is wireless multi-hop network. 歐永俊. Outline. Introduction Problem formulation System Model Simulation Referen ce. Introduction.

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Cross layer design is wireless multi-hop network

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  1. Cross layer design is wireless multi-hop network 歐永俊 20090611

  2. Outline • Introduction • Problem formulation • System Model • Simulation • Reference 20090611

  3. Introduction • The overall communication network is modeled by a generalized network utility maximization problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as functions of the optimization variables coordinating the subproblems. • Vertical decomposition into functional modules such as congestion control, routing, scheduling and power control. • Horizontal decomposition into distributed computation. 20090611

  4. Introduction Primal problem: Dual problem: dual decomposition 20090611

  5. Introduction Dual problem: Primal problem: dual decomposition 20090611

  6. Problem formulation • In traditional framework, each link provides a fixed coding and modulation scheme in the physical layer, and that each user’s utility is only a function of local source rate. • In many practical systems, utility for each user depends on both transmission rate and signal quality • A higher throughput can be obtained on a link at the expense of lower decoding reliability, which in turn lowers the end-to-end signal quality for sources traversing the link and reduces users’ utilities, thus leading to an intrinsic tradeoff between rate and reliability • the Pareto optimal tradeoff curves between rate and reliability. 20090611

  7. System Model • End-to-end reliability constraint is the end-to-end error probability is the error probability of source s at link l is the code rate of source s at link l is an increasing convex function reflecting the rate-reliability trade-off is the set of links used by source s 20090611

  8. System Model • Capacity constraint is the information data rate of source s is the transmission data rate of source s at link l is the set of sources using link l is the capacity of link l We assumes 20090611

  9. System Model maximize subject to source problems link problems :“congestion price”, the price per unit rate to use link l : “reliability price”, the price per unit reliability that the source s must pay to the network :with an interpretation of “end-to-end congestion price” on source s : with an interpretation of “aggregate reliability price” paid by sources using link l 20090611

  10. System Model • at each source s The Lagrange dual function is maximize subject to separable parallel on each link l maximize subject to The dual problem is minimize subject to 20090611

  11. System Model Local information exchange 20090611

  12. System Model • Note that when each link can provide different reliabilities(code rates) for the incoming traffic of different sources, such as there exist two class of users (primary users and secondary users),the problem becomes a GP. By change of variables , decomposition method still applies. maximize subject to posynomials 20090611

  13. Simulation • a linear topology consisting of four links and eight users accounts for scalarization ,changes form 0 to 1 in step size 0.1 20090611

  14. Simulation a=0 a=1 Figure 1. rate reliability trade-off among users, each source has same code rate for a link 20090611

  15. Simulation a=0 a=1 Figure 2. rate reliability trade-off among users, each source can have different code rate for a link 20090611

  16. Reference • [1] Lee, J.-W.; Mung Chiang; Calderbank, A.R., "Price-based distributed algorithms for rate-reliability tradeoff in network utility maximization," Selected Areas in Communications, IEEE Journal on , vol.24, no.5, pp. 962-976, May 2006 • [2] M. Chiang “Balancing transport and physical layer in wireless multihop networks: Jointly optimal congestion control and power control,” IEEE J. Sel. Areas Commun., vol. 23, pp. 104, Jan. 2005. • [3] Mung Chiang; Low, S.H.; Calderbank, A.R.; Doyle, J.C., "Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures," Proceedings of the IEEE , vol.95, no.1, pp.255-312, Jan. 2007 • [4] Palomar, D.P.; Mung Chiang, "A tutorial on decomposition methods for network utility maximization," Selected Areas in Communications, IEEE Journal on , vol.24, no.8, pp.1439-1451, Aug. 2006 • [5] Lee Jang-Won; Tang Ao; Huang Jianwei; Mung Chiang; Robert, A., "Reverse-Engineering MAC: A Non-Cooperative Game Model," Selected Areas in Communications, IEEE Journal on , vol.25, no.6, pp.1135-1147, August 2007 • [6] Jang-Won Lee; Mung Chiang; Calderbank, A.R., "Utility-Optimal Random-Access Control," Wireless Communications, IEEE Transactions on , vol.6, no.7, pp.2741-2751, July 2007 • [7] Jang-Won Lee; Chiang, M.; Calderbank, R.A., "Jointly optimal congestion and contention control based on network utility maximization," Communications Letters, IEEE , vol.10, no.3, pp. 216-218, Mar 2006 20090611

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