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

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Presentation transcript:

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

1 Wireless Link = AWGN Channel1 Wireless Link = ON/OFF Channel “information theory”“queueing theory” + Symbols Noise C = log(1 + SNR) Packet Arrivals Pr[ON]=p C = p packets/slot Capacity: Mathematical Models for a Wireless System (two meaningful perspectives) -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)

Mathematical Models for a Wireless System (two meaningful perspectives) N-User Gauss. Broadcast DownlinkN-User Downlink (Fading Channels) “information theory”“queueing theory” bits    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

Mathematical Models for a Wireless System (two meaningful perspectives) N-User Gauss. Broadcast DownlinkN-User Downlink (Fading Channels) “information theory”“queueing theory” bits    ON/OFF Capacity Region:all ( 1,…, N ) s.t. for all subsets K of users. [Tassiulas & Ephremides 93] (degraded Gauss. BC)

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

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

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

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

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

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

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

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

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

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

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

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

Capacity Region  The Theory: Generalized Max-Weight Matches, Backpressure Georgiadis, Neely, Tassiulas, Foundations and Trends in Networking, General Interference Models Multi-hop Max: [W l (t)C( I (t), S (t)) - VCost l (t)] Control ActionTopology State

Capacity Region  The Theory: Generalized Max-Weight Matches, Backpressure Georgiadis, Neely, Tassiulas, Foundations and Trends in Networking, General Interference Models Multi-hop Max: [W l (t)C( I (t), S (t)) - VCost l (t)] Control ActionTopology State

Capacity Region  The Theory: Generalized Max-Weight Matches, Backpressure Georgiadis, Neely, Tassiulas, Foundations and Trends in Networking, Multi-hop General Interference Models Max: [W l (t)C( I (t), S (t)) - VCost l (t)] Control ActionTopology State

Capacity Region  The Theory: Generalized Max-Weight Matches, Backpressure *Max: W l (t)C( I (t), S (t)) Control ActionTopology State *[Neely Thesis 03] *[Georgiadis, Neely, Tassiulas, NOW F&T 2006] *Maximizing to within a factor  yields  -factor throughput region!  Multi-hop General Interference Models

The Issues: (A comparison to info theory) “info theory” “queueing theory” -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! -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.

Final Slide: Two Suggested Approaches: 1)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] broadcasting error