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A Perspective on Network Interference and Multiple Access Control

A Perspective on Network Interference and Multiple Access Control. Capacity Region L. Michael J. Neely University of Southern California May 2008. Mathematical Models for a Wireless System (two meaningful perspectives). “ information theory ”. “ queueing theory ”.

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A Perspective on Network Interference and Multiple Access Control

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  1. A Perspective on Network Interference and Multiple Access Control Capacity Region L Michael J. Neely University of Southern California May 2008

  2. Mathematical Models for a Wireless System (two meaningful perspectives) “information theory” “queueing theory” 1 Wireless Link = AWGN Channel 1 Wireless Link = ON/OFF Channel Symbols Packet Arrivals Pr[ON]=p + Noise C = p packets/slot Capacity: C = log(1 + SNR) Capacity: -Symbol-by-symbol transmission -Capacity optimizes bit rate over all coding of symbols (Shannon Theory) -Slot-by-slot packet transmission -Capacity is obvious (Basic Queueing Theory)

  3. Mathematical Models for a Wireless System (two meaningful perspectives) “information theory” “queueing theory” N-User Gauss. Broadcast Downlink N-User Downlink (Fading Channels) l1 ON/OFF bits l2 bits ON/OFF bits lN ON/OFF -Symbol-by-symbol transmission -Capacity is a REGION of achievable bit rates -Optimizes coding of symbols -Opportunistic scheduling -Observe ON/OFF channels, decide which queue to serve (“collision free” = easy) -Capacity is a REGION of achievable rates

  4. Mathematical Models for a Wireless System (two meaningful perspectives) “information theory” “queueing theory” N-User Gauss. Broadcast Downlink N-User Downlink (Fading Channels) l1 ON/OFF bits l2 bits ON/OFF bits lN ON/OFF Capacity Region:all (l1,…, lN) s.t. Capacity Region: all (l1,…, lN) s.t. for all subsets K of users. (degraded Gauss. BC) [Tassiulas & Ephremides 93]

  5. Mathematical Models for a Wireless System (two meaningful perspectives) “information theory” “queueing theory” N-Node Static Multi-Hop Network (multiple sources and destinations) N-Node Static Multi-Hop Network (multiple sources and destinations) Capacity = Known Exactly (Multi-Commodity Flow Subject to “Graph Family” Link Constraints) Capacity = ??? -Symbol-by-Symbol Transmissions -Optimize the coding • Optimize Scheduling/Routing • -General Interference Sets [Backpressure, Tassiulas, Ephremides 92]

  6. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  7. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  8. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  9. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  10. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  11. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  12. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  13. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  14. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  15. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  16. Mathematical Models for a Wireless System (two meaningful perspectives) “info theory” “queueing theory” N-Node MANET N-Node MANET Capacity = Known Exactly -Ergodic Mobility -Optimize the Scheduling/Routing -General Channel Interference Models (SINR, Collision Sets, etc.) Capacity = ??? [Neely, Modiano, et. al. JSAC 05, IT 05]

  17. The Theory: Generalized Max-Weight Matches, Backpressure Capacity Region L General Interference Models Multi-hop [Wl(t)C(I(t), S(t)) - VCostl(t)] Max: Control Action Topology State Georgiadis, Neely, Tassiulas, Foundations and Trends in Networking, 2006. http://www-rcf.usc.edu/~mjneely/pdf_papers/NOW_stochastic_nets.pdf

  18. The Theory: Generalized Max-Weight Matches, Backpressure Capacity Region L General Interference Models Multi-hop [Wl(t)C(I(t), S(t)) - VCostl(t)] Max: Control Action Topology State Georgiadis, Neely, Tassiulas, Foundations and Trends in Networking, 2006. http://www-rcf.usc.edu/~mjneely/pdf_papers/NOW_stochastic_nets.pdf

  19. The Theory: Generalized Max-Weight Matches, Backpressure Capacity Region L General Interference Models Multi-hop [Wl(t)C(I(t), S(t)) - VCostl(t)] Max: Control Action Topology State Georgiadis, Neely, Tassiulas, Foundations and Trends in Networking, 2006. http://www-rcf.usc.edu/~mjneely/pdf_papers/NOW_stochastic_nets.pdf

  20. The Theory: Generalized Max-Weight Matches, Backpressure Capacity Region L General Interference Models gL Multi-hop Wl(t)C(I(t), S(t)) *Max: Control Action Topology State *Maximizing to within a factor g yields g-factor throughput region! *[Neely Thesis 03] *[Georgiadis, Neely, Tassiulas, NOW F&T 2006] http://www-rcf.usc.edu/~mjneely/pdf_papers/NOW_stochastic_nets.pdf

  21. The Issues: (A comparison to info theory) “info theory” “queueing theory” -Capacity Region characterized exactly (in terms of optimization) -Randomized Scheduling can achieve full Capacity… [Tassiulas 98] [Modiano, Shah, Zussman 2006] [Erylimaz, Ozdaglar, Modiano 07] [Shakkottai 08] [Shah 08] [Jiang, Walrand 08], etc. -But Complexity and Delay is the Challenge! [Neely et al. 02], [Shah, Kopikare 02], etc. -Capacity log(1+SNR) known exactly -Randomized Coding can achieve capacity but… …Complexity and Delay! -Shannon Created the Challenge: Prompted years of research in the design of efficient, low complexity Codes that perform near capacity (analytically or experimentally) was the research. Turbo-codes work well experimentally!

  22. Final Slide: Two Suggested Approaches: The Analogy: Information Theory ==> Design of Codes to work well in practice, Turbo Codes Network Queue Theory ==> Design of practical MAC Scheduling Protocols, Implementation, “Turbo” Multiple Access Eg: *[Bayati, Shah, Sharma 05] (uses iterative detection theory) [Modiano, Shah, Zussman 2006], [Erylimaz, Ozdaglar, Modiano 07] [Shakkottai 08], [Shah 08], [Jiang, Walrand 08],etc. 2) “Beyond Links”: Combine PHY layer and Networking MIMO [Kobayashi, Caire 05] Cooperative Comms [Yeh, Berry 05] Network Coding [Ho, Viswanathan 05], [Lun, Medard 05] Multi-Receiver Diversity [Neely 06] error broadcasting

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