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Low-cost 802.11 Wireless Infrastructure Networks . Stefan Savage and John Bellardo Department of Computer Science and Engineering University of California, San Diego. Motivation. Large-scale 802.11 deployments are expensive

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low cost 802 11 wireless infrastructure networks

Low-cost 802.11 Wireless Infrastructure Networks

Stefan Savage and John Bellardo

Department of Computer Science and Engineering

University of California, San Diego

  • Large-scale 802.11 deployments are expensive
    • Capital expenditures typically < 35% (and hardware is on commodity price curve)
    • Operational expenditures
      • Site-survey
      • Test and tuning
      • Network wiring and provisioning
      • Ongoing management (software update, rebalance, etc)
  • Our goal: make it cheap and trivial to provide building or campus-wide 802.11 APs (OpEx -> 0)
  • Radio hardware is cheap
    • Multiple independent radios in a package is reasonable
  • Antenna technology is not
    • Omni antennas (low gain/directional separation)
  • Intra/Internet access usage model
    • Not point to point
  • Largely homogenous administrative domain
    • Not dealing with apartment building problem (initially)
  • Indoor focus
    • 3D, dense deployment, complex RF domain, significant spatial and temporal load shifts
  • Must not require 802.11 client modifications
    • Ok as optimization
aside why use 802 11
Aside: Why use 802.11?
  • Bad experience with simulation
    • Our wireless immigration project (USENIX Sec ’03)
    • Send CTS with large duration to freeze channel (devestating in simulation, then we built it)
    • Have tried three wireless simulators (including $$$) – can’t find any that predict our measurements
      • Multi-path, fading, variable noise, people (i.e. moving bags of water)
      • Variable xmit pwr, receive sensitivity, power spectrum, MAC behavior on client NICs
  • We want experience with real traffic driven by real users, hence we need to build real systems
  • Not equipped/funded to build a lot of radios
    • Although we do have some (CalRadio – at end of talk)
two elements of our work
Two elements of our work
  • Radio Tomography and Frequency Management(RTFM)
    • Measurement-based inference of RF domain capacity and contention
    • Auto AP configuration to maximize system goodput
      • Frequency, transmit power, CCA, coding
    • Goal: no site survey, no tuning, no manual configuration
  • LessWire
    • Simplified multi-hop routing (3 hop max)
    • Best-exit routing wrt RF domain impact
    • Goal: opportunistic use of wiring, expand coverage/density
radio tomography
Radio Tomography
  • Key questions
    • If I send pkt x at rate r with power t on channel z, what is the distribution of delivery delay times?
    • Why?
      • Background interference
      • Co-channel interference
      • Client<->AP propagation (fading, multipath, etc)
radio tomography first try




Radio Tomography: first try
  • Naïve approach
    • Model nodes as point transmitters with set xmit range r and channel z
    • If two sphere’s overlap, delay is proportional to the sum over load
    • Re-color, re-size to minimize delay
  • Why this doesn’t even vaguely work
    • Non-uniform propagation
    • Channel not exclusive
    • Coding matters
    • Channel conditions and clients change
radio tomography 2 nd 3rd attempts
Radio Tomography: 2nd & 3rd attempts
  • Next idea:
    • Observe visible MACs and share among APs
    • If two nodes share the same node then assumethey interfere
  • Problems:
    • Incredibly conservative (ignores attenuation and RF capture)
  • Next idea:
    • Measure RSSI and infer impact on xput
    • Fine grained “ground-truth” measurements(sample over every 3x3 feet by hand)
  • Problems
    • RSSI is very very noisy and highly variable
      • Hard to infer “ground truth” from few samples
    • Very poor predictor of pkt delivery
      • Happy to learn about any non-brittle modelshere that work
radio tomography current approach
Radio Tomography: current approach
  • Synchronized Co-Channel Interference Inference
    • Idea: create interference and see impact (analogies to slow start)
    • APs send short burst on channel x at time t and rate r
    • Other APs measure change in re-transmission probability and back-to-back xmit timing at same time (CCA indication)
    • Infer same from client based on retry bit in header & CRC failures
  • Findings
    • Rate sensitivity
      • Both for data (makes sense)and interferer (unsure)
      • Discontinuities (fastest rates -> practically slower)
    • Strong bimodality
    • Good at characterizing interference
      • ~85% for sub-second samples
      • Gotchas: low S/N
rf management
RF Management
  • RF Parameter optimization (work in progress)
    • Minimize xmit power to maximally split offered load across APs
    • Color frequency and set CCA to minimize interference effects
  • Research questions
    • NP-hard, Heuristic challenge – ordering of power/frequency opt
    • How often to re-optimize?
      • Don’t want to react to short workload dynamics (ftp transfer) or RF dynamics (jiffy pop time-scale)
      • Client delay on reassociation
        • Some NICs very bad
        • Our APs support fast handoff (SyncScan, UCSD-TR) but requires client mods to take advantage
      • Want to react quickly to AP failure
    • Centralized vs distributed control?
    • Impact if some nodes don’t play?(e.g. static frequency inholding)
  • Idea: use additional radios to provide multi-hop backhaul
  • Research challenges
    • Point-to-multipoint route optimization over RF domain (not ad hoc routing)
    • Interaction with RF management
      • Backhaul-only channels vs joint assignment
      • Optimize freq/power assignment over “opportunity cost” of a route
    • Simplicities from being short hop (2-3 hops max) network (very low state)
where we are
Where we are
  • RTFM prototype limping along at UCSD
    • Interference inference
      • Background channel quality
      • Co-channel impact on predicted delay on given frequency
      • Extrapolate rate impact based on empirical curves
    • Greedy channel assignment based on static threshold
    • Lots of work left… (CCA validation, TX power, better assignment, more features to classifier)
  • LessWire
    • In algorithmic stage – no results to report today
    • We are assuming that wired bandwidth is infinite
ucsd cse infrastructure
UCSD CSE Infrastructure
  • 266Mhz Soekris w/40GB trace store
    • Dual-radio Atheros 5212 miniPCIs
      • Driver hacks for CCA adjust, per-packet TPC&rate control
      • Global time synced packet scheduling
    • 5Ghz deployment on two floors-12APs(soon 2 more + indoor/outdoor-40APs)
ucsd infrastructure calradio i








512K x 16









+2.8, +1.8

Power Supplies

RF serial bus



























32K x 16

16K x 16


UCSD Infrastructure: CalRadio I
  • Joint project of UCSD CalIT2, ECE and CSE
  • Intersil baseband, 2.4Ghz RF, DSP-based MAC (TI ‘C5471/ARM7, Symbol derived IP, about 4”x4”)
  • Designed to allow L2 experimentation/innovation
ucsd infrastructure calradio ii

LVDS Serial Interfaces

UCSD Infrastructure: CalRadio II
  • More aggressive: physical layer innovation
    • Several RF modules being constructed (2.4, 5Ghz WiFi, 2x2 MIMO 900Mhz, 3-10Ghz UWB)
    • Modulation all in FPGA, Matlab/Simulink compatible