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Available Bandwidth Estimation in IEEE 802.11-Based Wireless Networks

Learn about bandwidth estimation in IEEE 802.11 networks, including factors affecting available bandwidth, MAC layer schemes, and channel capacity calculations. Discover how to normalize estimates for different packet sizes and apply bandwidth management strategies in wireless environments. Explore simulation results and dynamic bandwidth control techniques.

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Available Bandwidth Estimation in IEEE 802.11-Based Wireless Networks

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  1. Available Bandwidth Estimation in IEEE 802.11-Based Wireless Networks Samarth Shah, Kai Chen, Klara Nahrstedt Department of Computer Science University of Illinois at Urbana-Champaign {shshah,kaichen,klara}@cs.uiuc.edu http://cairo.cs.uiuc.edu/adhoc This work was funded by the DoD ONR MURI N00014-00-1-0564 and NSF EIA 99-72884 grants

  2. Introduction • Theoretical channel capacity depends on the physical layer • 1, 2, 5.5 or 11 Mbps for IEEE 802.11 • Bandwidth actually available to the application is less due to: • Protocol overhead • MAC layer contention • Location-dependent in multi-hop or multi-cell environments • Location-dependent channel errors • Signal fading, bit-errors due to physical objects such as walls, doors, etc.

  3. IEEE 802.11 MAC and Our Scheme • IEEE 802.11 MAC: • Carrier sense: • Medium idle? Send RTS • Medium busy? Wait until medium idle, backoff for collision avoidance, send RTS • RTS-CTS-DATA-ACK • Collision? Increase backoff interval exponentially • Our scheme: • Does not modify IEEE 802.11 in any way • Uses data transmissions for bandwidth estimation • No separate probing packets, etc. • Performed in the device driver of the wireless interface • Device driver loaded as a module under Linux

  4. Bandwidth Estimation channel busy, backoff, contention • Measured BW = S/(tr – ts) • Running average with decay/Average over an interval • More contention? More time channel sensed as busy, more RTS/CTS collisions, higher backoffs => BW estimate smaller • More channel errors? Bit-errors in RTS/DATA cause RTS/DATA retransmission => BW estimate smaller • Only successfully transmitted MAC frames used in estimate RTS CTS DATA (size S) ACK time ts packet ready tr packet recvd

  5. Packet Size • Packet size affects BW estimate • Low channel bit-error rate (BER)? Larger packet size => higher throughput • High BER? Larger packet size => Larger probability of bit-error => lower throughput • Indexed table of BW estimates for different packet size ranges • Separate estimation for data and acks (i.e., higher-layer acks) at source and destination respectively Higher BER T’put Lower BER size

  6. Normalization • For low BERs • Scenarios used in our simulation and testbed experiments • Linear part of BER-packet size-throughput curve • Packet size from 64B to 640B • We can have a single estimate normalized to a reference packet size (512B) • Key observations for normalization: • Channel busy + backoff + RTS/CTS + ACK overhead same for packets of all sizes • Once channel captured, DATA is transmitted at physical channel rate, for all packet sizes • Normalization enables estimation at source for both data and acks

  7. Simulation Results • CBR Contention experiments: • Running average with decay • Variable contention experiments (for predictability): • %age difference between successive 2 sec. intervals • 1 contending TCP flow: >97% of the time <= 20% difference • 7 contending TCP flows: >80% of the time <= 20% difference

  8. Application • Channel Time Proportion (CTP) • A link has bandwidth estimate k bps, a flow over it requires j bps => it requires a fraction j/k of the channel shared by nodes in its neighborhood • Use this in admission control for both single- and multi-hop IEEE 802.11 networks • Admission control inaccurate • Admitting new traffic increases contention in the shared channel • Changes bandwidth estimate of flows • Dynamic bandwidth management • For more see: • S. Shah, K. Chen and K. Nahrstedt, Dynamic Bandwidth Management in Single-hop Ad hoc Wireless Networks, MONET journal special issue on Algorithmic Solutions for Wireless, Mobile, Ad Hoc and Sensor Networks (eds. Bar-Noy, Bertossi, Pinotti and Raghavendra), 2004. • K. Chen and K. Nahrstedt, EXACT: An Explicit Rate-based Flow Control Framework in MANET, Technical Report UIUCDCS-R-2002-2286/UILU-ENG-2002-1730, Department of Computer Science, University of Illinois at Urbana-Champaign, updated December, 2002.

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