TAPs: An Architecture and Protocols for a High-Performance Multi-hop Wireless Infrastructure

1. TAPs: An Architecture and Protocols for a High-Performance Multi-hop Wireless Infrastructure Ed Knightly ECE/CS Departments Rice University http://www.ece.rice.edu/~knightly Joint work with V. Kanodia, A. Sabharwal, and B. Sadeghi

2. The Killer App is the Service • High bandwidth • High availability • Large-scale deployment • High reliability • Nomadicity • Economic viability • Why? • Broadband to the home and public places • Enable new applications

3. WiFi Hot Spots? • 11 Mb/sec, free spectrum, inexpensive APs/NICs • Why? poor economics • High costs of wired infrastructure ($10k + $500/month) • Pricing: U.S. $3 for 15 minutes; CH: 0.90 CHF/minute • Dismal coverage averaging 0.6 km2 per 50 metro areas projected by 2005 Carrier’s Backbone/Internet T1 Medium bandwidth (wire), sparse, and expensive

4. High availability, but slow and expensive Cellular? • Cellular towers are indeed ubiquitous • Coverage, mobility, … • High bandwidth is elusive • Aggregate bandwidths in Mb/sec range, per-user bandwidths at dial-up speeds • Expensive: spectral fees and high infrastructure costs

5. Ad Hoc Networks? • Availability • Problems: intermediate nodes can move, power off, fade, DoS attack  routes break, packets are dropped, TCP collapses, … • Low bandwidth • Poor capacity scaling • Unlike cellular, users consume wireless resources at remote locations “Free” but low availability and low bandwidth

6. TAPs: Multihop Wireless Infrastructure • Transit Access Points (TAPs) are APs with • beam forming antennas • multiple air interfaces • enhanced MAC/scheduling/routing protocols • Form wireless backbone with limited wired gateways

7. Multihop Wireless Infrastructure • Transit Access Points (TAPs) are APs with • beam forming antennas • multiple air interfaces • enhanced MAC/scheduling/routing protocols • Form wireless backbone with limited wired gateways • High bandwidth • High spatial reuse and capacity scaling • Opportunistic protocols • High availability • Redundant paths and non-mobile infrastructure • Deployability • Good economics • Unlicensed spectrum, few wires, exploit WiFi components

8. Prototype and Testbed Deployment • FPGA implementation of enhanced opportunistic, beamforming, multi-channel, QoS MAC • Build prototypes and deploy on Rice campus and nearby neighborhoods • Measurement study from channel conditions to traffic patterns

9. Outline • TAP architecture • OAR: an opportunistic auto-rate MAC • MOAR: multi-channel OAR • Open problems

10. Received signal: superposition of different reflections, with different delays and attenuations channel gain  • Coherence time time  Motivation • Wireless channel is variable

11. user 1 channel gain  user 2 time  user 1 user 2 Opportunistic MAC Goal • Exploit the variations inherent in wireless channel to increase throughput • Maintain fair temporal shares for different flows • Constraint: distributed random access protocol

12. IEEE 802.11 Multi-rate • Support of higher transmission rates in better channel condition • 802.11b available rates: 2, 5.5, 11 Mbps • 802.11a available rates: up to 54 Mbps • Auto Rate Fallback (ARF) • [Monteban et al. 97] • Use history of previous transmissions to adaptively select future rates

13. user 1 user 3 access point user 2 Temporal vs. Throughput Fairness • Equivalent in single-rate networks • Throughput fairness results in significant inefficiency in multi-rate networks Example

14. Throughput Fair user 1 DATA user 2 DATA user 3 DATA Temporal vs. Throughput Fairness • Equivalent in single-rate networks • Throughput fairness results in significant inefficiency in multi-rate networks Example user 1 user 3 access point user 2 Even 1 user with low transmission rate results in a very low network throughput

15. Temporal Fair user 1 DATA user 2 DATA DATA DATA DATA DATA DATA user 3 DATA DATA DATA DATA DATA DATA Temporal vs. Throughput Fairness • Equivalent in single-rate networks • Throughput fairness results in significant inefficiency in multi-rate networks Example user 1 user 3 access point user 2 Same time-shares of the channel for different flows, also higher throughput

16. Opportunistic MAC Goal • Exploit short-time-scale variations inherent in wireless channel to increase throughput in wireless ad hoc networks Issue • Maintaining temporal share of each node Challenge • Channel info available only upon transmission

17. Opportunistic Auto Rate (OAR) • Observation: coherence time on order of multiple packet transmission times • measure channel quality on RTS/CTS handshake • hold goodchannels for multiple transmissions • Ensure fairness by scaling number of packets transmitted to channel quality • # packets = Current rate / Base rate • with random access, all flows equally likely to access channel OAR: High throughput, while maintaining temporal fairness properties of single rate IEEE 802.11

18. CTS RTS DATA ACK RBAR Protocol Receiver Based AutoRate (RBAR) • Receiver controls the sender’s transmission rate • Control messages sent at Base Rate Reservation Sub-Header destination source

19. CTS RTS DATA DATA ACK ACK OAR Protocol OAR Reservation Sub-Header • Once access granted, it is possible to send multiple packets if the channel is good destination source

20. RBAR R D1 R D1 Transmitter C A C A Receiver OAR Observation II OAR contends for the same total time as singe-rate IEEE 802.11 but transmits more data Observation I Time spent in contention per packet is the same for RBAR and single-rate IEEE802.11 R D1 D2 D3 Transmitter C A A A Receiver Performance Comparison IEEE 802.11 R D1 Transmitter C A Receiver

21. RBAR R D1 R D1 Transmitter C A C A Receiver OAR Observation III OAR holds high-quality channels for multiple transmissions R D1 D2 D3 Transmitter C A A A Receiver Performance Comparison IEEE 802.11 R D1 Transmitter C A Receiver

22. Analytical Model • Challenge: MAC and channel are random processes with memory • Model relates physical-layer characteristics to MAC throughput: • Time spent in contention • Markov model of modified IEEE 802.11 • Average transmission rate • Due to channel distribution • Comparative model b/t multi-rate OAR and tractable systems • TIME: OAR contends as often as single-rate IEEE 802.11 with increased data per contention • PACKETS: OAR reduces packet transmission time via per-contention rate adaptation

23. Nodes Simulation Results Under Ricean Fading • OAR has 42% to 56% gain over RBAR • Increase in gain as number of flows increases • Model predicts OAR & RBAR throughput to within 7% accuracy

24. Outline • TAP architecture • OAR: an opportunistic auto-rate MAC • MOAR: multi-channel OAR • Open problems

25. Example: at 2.4 GHz WiFi, 5 vs. 1-3 MHz • Figure for Ricean, K=4 Multi-Channel Problem Formulation • Observe: for two MUs, quality of different channels can have low correlation if channel separation >> coherence bandwidth

26. Challenge • Ideal protocol is simple: select the best channel at the instant of transmission • In practice, channel qualities are unknown a priori • Must first transmit and measure • Cost of measuring channels must be balanced with benefits of finding good ones

27. MOAR Protocol Sketch • Measure channel SNR at RTS/CTS handshake • If channel quality is high (above an SNR threshold), transmit via OAR • If channel quality is poor, skip to a new channel • next channel piggybacked in CTS • Design optimal stopping rule for skipping • stop when throughput gain of skipping to a better channel is outweighed by overhead • Ensure fairness

28. Optimal Stopping Rule Formulation • Let Xn denote the SNR of the nth measured channel • Let c denote the cost (in time) of measuring the channel • After observing Xn transmit or measure again? • cannot go back to previous channel (coherence time) • The reward for the nth selection is Xn-nc • after scaling SNR to rate and then to time • Objective: maximize the expected reward • In a class of stopping rule problems (without recall)

29. Optimal Stopping Time • Let V* denote the expected return from the optimal stopping rule • Suppose pay c and observe X1= x1 • If continue, x1 is lost and c is paid • continuing, can obtain return V*, but not more • start afresh • Optimal rule is threshold based • If xn < V*, continue; if xn > V* stop • N* = min{n 1: Xn  V*}

30. Calculating the Stopping Threshold V* • V* = E max(X1,V*) – c • F(x) represents the SNR distribution • Compute V* • channel model and parameters (ex. K, d) • system’s rate-SNR thresholds (ex. 1, 2, 5.5, 11)

31. MOAR Throughput Gains Ricean parameter K = 0 is no line-of-sight signal • Gains of 40%-60% increasing with K and SNR variance

32. Effect of Node Distance • Greatest help when far away • Non-monotonic due to rate-SNR thresholds

33. Random Topologies • Nodes are uniform-randomly placed in a 250m circle • “Optimal Skipping” cheats: looks at all channels (with no cost) and jumps to the best • Observe • MOAR extracts most available gain • close-by nodes detract from average gain

34. Outline • TAP architecture • OAR: an opportunistic auto-rate MAC • MOAR: multi-channel OAR • Open problems

35. DoS Resilience and Security • Old methodology • Design a network protocol • Optimize for performance • Discover DoS/Security holes • Ex. Route query floods • Patch one-by-one • Challenge • DoS-resilience and security as the foundation of network protocols • Recognize these issues are as important as performance

36. TAP Media Access and Scheduling • Challenge: distributed scheduling • Others’ channel states, priority, & backlog condition unknown • Ex. TAP A’s best recv’r may be transmitting elsewhere • Ex. Traffic to be recv’d may be higher priority than that to be sent • Traffic and system dynamics preclude scheduled cycles • Modulate aggressiveness according to overheard information

37. Multi-Destination Routing/Scheduling • Most data sources or sinks at a wire • Routing protocols for any wire abstraction • Scheduling • At fast time scales, which path is best (channels, contention, …) now? • Can delay/throughput gains be realized despite TCP?

38. Distributed Traffic Control • Distributed resource management: how to throttle flows to their system-wide fair rate? • Throttle traffic “near-the-wire” to ensure fairness and high spatial reuse • TCP cannot achieve it (too slow and RTT biased) • Incorporate channel conditions as well as traffic demands

39. Capacity Driven Protocol DesignProtocol Driven Capacity Analysis • Traditional view of network capacity assumes zero protocol overhead (no routing overhead, contention, etc.) • Protocols themselves require capacity • A new holistic system view: “the network is the channel” • Incorporate overhead in discovering/measuring the resource • Explore capacity limits under real-world protocols

40. Problem: Multiple APs/TAPs/…within Radio Range • PHY Interference has disproportionate throughput degradation at MAC layer • Interference can lead to severe scaling limitations and starvation (worse than zero-sum game)

41. Summary • Transit Access Points • WiFi “footprint” is dismal • Removing wires is the key for economic viability • Opportunistic Scheduling (OAR/MOAR) • Exploit time and frequency diversity • Challenges • Multi-hop wireless architectures • Distributed control • Scalable protocols