Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Published byAshlynn Boyd Modified over 8 years ago

Similar presentations

Presentation on theme: "A Perspective on Network Interference and Multiple Access Control Michael J. Neely University of Southern California May 2008 Capacity Region "— Presentation transcript:

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

2 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)

3 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

4 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)

5 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]

6 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.)

7 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.)

8 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.)

9 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.)

10 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.)

11 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.)

12 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.)

13 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.)

14 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.)

15 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.)

16 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.)

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

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

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

20 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] http://www-rcf.usc.edu/~mjneely/pdf_papers/NOW_stochastic_nets.pdf *Maximizing to within a factor  yields  -factor throughput region!  Multi-hop General Interference Models

21 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.

22 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

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

Similar presentations

A DISTRIBUTED CSMA ALGORITHM FOR THROUGHPUT AND UTILITY MAXIMIZATION IN WIRELESS NETWORKS.

A DISTRIBUTED CSMA ALGORITHM FOR THROUGHPUT AND UTILITY MAXIMIZATION IN WIRELESS NETWORKS.
Optimal Pricing in a Free Market Wireless Network Michael J. Neely University of Southern California *Sponsored in part.

Optimal Pricing in a Free Market Wireless Network Michael J. Neely University of Southern California *Sponsored in part.
Chorus: Collision Resolution for Efficient Wireless Broadcast Xinyu Zhang, Kang G. Shin University of Michigan 1.

Chorus: Collision Resolution for Efficient Wireless Broadcast Xinyu Zhang, Kang G. Shin University of Michigan 1.
Network Utility Maximization over Partially Observable Markov Channels 1 1 Channel State 1 = ? Channel State 2 = ? Channel State 3 = ? 2 2 3 3 Restless.

Network Utility Maximization over Partially Observable Markov Channels 1 1 Channel State 1 = ? Channel State 2 = ? Channel State 3 = ? Restless.
Tradeoffs between performance guarantee and complexity for distributed scheduling in wireless networks Saswati Sarkar University of Pennsylvania Communication.

Tradeoffs between performance guarantee and complexity for distributed scheduling in wireless networks Saswati Sarkar University of Pennsylvania Communication.
DYNAMIC POWER ALLOCATION AND ROUTING FOR TIME-VARYING WIRELESS NETWORKS Michael J. Neely, Eytan Modiano and Charles E.Rohrs Presented by Ruogu Li Department.

DYNAMIC POWER ALLOCATION AND ROUTING FOR TIME-VARYING WIRELESS NETWORKS Michael J. Neely, Eytan Modiano and Charles E.Rohrs Presented by Ruogu Li Department.
XPRESS: A Cross-Layer Backpressure Architecture for Wireless Multi-Hop Networks Rafael Laufer, Theodoros Salonidis, Henrik Lundgren, Pascal Le Guyadec.

XPRESS: A Cross-Layer Backpressure Architecture for Wireless Multi-Hop Networks Rafael Laufer, Theodoros Salonidis, Henrik Lundgren, Pascal Le Guyadec.
Successive Interference Cancellation: A Back of the Envelope Perspective Souvik Sen, Naveen Santhapuri, Romit Roy Choudhury, Srihari Nelakuditi I have.

Successive Interference Cancellation: A Back of the Envelope Perspective Souvik Sen, Naveen Santhapuri, Romit Roy Choudhury, Srihari Nelakuditi I have.
1 ENERGY: THE ROOT OF ALL PERVASIVENESS Anthony Ephremides University of Maryland April 29, 2004.

1 ENERGY: THE ROOT OF ALL PERVASIVENESS Anthony Ephremides University of Maryland April 29, 2004.
Dynamic Index Coding Broadcast Station 1 1 2 2 N N Michael J. Neely, Arash Saber Tehrani, Zhen Zhang University of Southern California Paper available.

Dynamic Index Coding Broadcast Station N N Michael J. Neely, Arash Saber Tehrani, Zhen Zhang University of Southern California Paper available.
Universal Scheduling for Networks with Arbitrary Traffic, Channels, and Mobility Michael J. Neely, University of Southern California Proc. IEEE Conf. on.

Universal Scheduling for Networks with Arbitrary Traffic, Channels, and Mobility Michael J. Neely, University of Southern California Proc. IEEE Conf. on.
Delay-Based Network Utility Maximization Michael J. Neely University of Southern California IEEE INFOCOM, San Diego, March.

Delay-Based Network Utility Maximization Michael J. Neely University of Southern California IEEE INFOCOM, San Diego, March.
Dynamic Index Coding 12312345 User set N Packet set P Broadcast Station 1 1 2 2 N N p p p Michael J. Neely, Arash Saber Tehrani, Zhen Zhang University.

Dynamic Index Coding User set N Packet set P Broadcast Station N N p p p Michael J. Neely, Arash Saber Tehrani, Zhen Zhang University.
Max Weight Learning Algorithms with Application to Scheduling in Unknown Environments Michael J. Neely University of Southern California

Max Weight Learning Algorithms with Application to Scheduling in Unknown Environments Michael J. Neely University of Southern California
Dynamic Data Compression for Wireless Transmission over a Fading Channel Michael J. Neely University of Southern California CISS 2008 *Sponsored in part.

Dynamic Data Compression for Wireless Transmission over a Fading Channel Michael J. Neely University of Southern California CISS 2008 *Sponsored in part.
*Sponsored in part by the DARPA IT-MANET Program, NSF OCE-0520324 Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks Rahul.

*Sponsored in part by the DARPA IT-MANET Program, NSF OCE Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks Rahul.
1 Cross-Layer Design for Wireless Communication Networks Ness B. Shroff Center for Wireless Systems and Applications (CWSA) School of Electrical and Computer.

1 Cross-Layer Design for Wireless Communication Networks Ness B. Shroff Center for Wireless Systems and Applications (CWSA) School of Electrical and Computer.
FUNDAMENTAL CROSS-LAYER APPROACH TO WIRELESS NETWORKS Prof. Edmund Yeh Jian Cao Yufang Xi Nov. 18, 2003.

FUNDAMENTAL CROSS-LAYER APPROACH TO WIRELESS NETWORKS Prof. Edmund Yeh Jian Cao Yufang Xi Nov. 18, 2003.
Multi-Hop Networking with Hard Delay Constraints Michael J. Neely, University of Southern California DARPA IT-MANET Presentation, January 2011 PDF of paper.

Multi-Hop Networking with Hard Delay Constraints Michael J. Neely, University of Southern California DARPA IT-MANET Presentation, January 2011 PDF of paper.
Cross Layer Adaptive Control for Wireless Mesh Networks (and a theory of instantaneous capacity regions) Michael J. Neely, Rahul Urgaonkar University of.

Cross Layer Adaptive Control for Wireless Mesh Networks (and a theory of instantaneous capacity regions) Michael J. Neely, Rahul Urgaonkar University of.

Similar presentations

Ads by Google