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Mobile Networking - Part I (Physical Layer)

Mobile Networking - Part I (Physical Layer). EE206A (Spring 2004): Lecture #2. This + Next Few Lectures. What are the problems in communications caused by mobility of network elements? How do various system layers cope with these problems in traditional mobile networks? We will look at:

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Mobile Networking - Part I (Physical Layer)

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  1. Mobile Networking - Part I(Physical Layer) EE206A (Spring 2004): Lecture #2

  2. This + Next Few Lectures • What are the problems in communications caused by mobility of network elements? • How do various system layers cope with these problems in traditional mobile networks? • We will look at: • Physical layer THIS LECTURE • Link/Network layers • Transport layer • OS/Application layer

  3. Reading List for This Lecture • [REQUIRED] The Mistaken Axioms of Wireless-Network Research. David Kotz, Calvin Newport, Bob Gray, Jason Liu, Yougu Yuan, and Chip Elliot. Technical Report TR2003-467, Dept. of Computer Science, Dartmouth College, July, 2003. http://nesl.ee.ucla.edu/courses/ee206a/sharedp/papers/Kotz04_axioms_mh.pdf (preferred version to read - password protected [see class mailing list]) http://www.cs.dartmouth.edu/~dfk/papers/kotz:axioms-tr.pdf

  4. Shades of Mobility of Network Elements • Terminal moves • Discrete: unplug from one network port, moves elsewhere while disconnected, re-plug at a new network port • Continuous: move terminal with applications unaffected as topology changes • User moves • User moves from terminal to terminal • Infrastructure elements move • Moving basestations - e.g. Iridium satellites, UAVs • Chunks of network moves • Airplane with 802.11 network • Platoon of soldiers

  5. Some Mobility Issues • Terminal speed impacts wireless channel quality • Fading • Terminal and user location • Become system variables of interest • Change dynamically • Constants become variable • Location, environment, connectivity,topology, b/w, I/O devices, security domain • Spoofing becomes easier • Need more of: protocol processing, signal processing, and signaling traffic

  6. Example: Disconnections • Planned vs. unplanned • Choices? • engineer to prevent disconnections • gracefully cope (adapt) to disconnections • Mask disconnections and round-trip latencies • decouple communication from data production/consumption • asynchronous operation (multiple REQs before ACKs), prefetching, delayed write-back etc. • Tolerate by autonomous operation, caching/hoarding, local applications etc. • disconnected filesystems, e.g. CMU’s CODA • Good user interfaces to give feedback about disconnection

  7. Example: Address Migration due to Mobility • Dynamically changing network access point • Network addresses usually correspond to the point of attachment to n/w • applications/calls connect to a fixed address • active connections cannot be moved to new address • How to support changing network access point? • How to find the current address? • How to do rerouting? • How to do route optimization? • How to do multicast?

  8. Example: Location-dependent Information • Location affects configuration parameters • DNS, timezone, printer etc. • Location affects answer to user queries • e.g. where is the nearest printer • More complex location-dependent queries • e.g. where is the nearest taxi • Privacy concerns due to location tracking • Changing context • small movements may cause large changes • caching may become ineffective • dynamic transfer to nearest server for a service • Localization

  9. Mobility (Location, Speed) andthe Wireless Physical Layer

  10. Mobility and the Physical Layer • Key factor is the wireless link which is impacted by terminal mobility • For network designers, wireless is quite “confusing”! • Does not quite “fit” the wireline paradigm • Breaks down traditional concepts of topology and link quality From Keynote Talk by A. Ephemeredes @ MobiCom 2002

  11. Consequences of being Wireless • Clearly no fixed topology (even without mobility) • Cross-layer coupling • power  energy consumption  higher/lower layers • other users  MAC • rate throughput  higher/lower layers • BER other QoS measure  application layer • Different world • Rich theory of communications, Complex details • Ignoring the physical layer limits the meaningfulness of upper layers • E.g. disk models of radio range • The notion of “wireless links” is very slippery… From Keynote Talk by A. Ephemeredes @ MobiCom 2002

  12. E.g. of Fluid Notion of Wireless Links: Connectivity & Rate • Lowering the rate permits the packaging of more energy per symbol (SINR > ) • So a faltering link can become more reliable (elasticity) • A previously non-existent link can be created • Rate reduction lowers throughput or increases delay or distorts the signal • Preferable to power because it does not affect interference (non-invasive)

  13. Understanding the Wireless Physical Layer

  14. Wireless Link Behavior • Large-scale variations due to T-R distance, obstructions • Small-scale variations due to fading • Three effects: mean path loss, slow variation about the mean, rapid variation • All affected by location and speed!

  15. Path Loss • Path loss is inversely proportional to dn

  16. Path Loss vs. Distance in Free Space • Clear, unobstructed, line of sight • Received power at distance d from sender • where Gt and Gr are antenna gains, L ≥ 1 is a system loss factor due to filter losses, hardware • Path loss = signal attenuation in dB • Model valid only for large d in the “far field” (Fraunhofer Region) of the antenna • D is the largest physical linear dimension of the antenna

  17. Free Space Path Loss in Practice • Use a receive power reference point = d0 • Path loss too expressed in a relative manner

  18. Space with Objects • Reflection (with transmittance and absorbtion) • Objects >> wavelength (e.g. earth, building, atmosperic layers) • Diffraction • Waves bend around sharp edges of impenetrable objects (Huygen’s principle) • A.k.a “shadowing” • Scattering • Objects < wavelength (e.g. foliage , street signs, lamp posts etc.) • Received signal sum of contributions of signals along many different paths

  19. Example: Ground Reflection • For large d >> sqrt (hthr), • Much more rapid path loss with increasing T-R separation

  20. Log-distance Path Loss Model • Path loss exponent n depends on propagation environment and obtained by measurements • Problem: “environment clutter” may differ at two locations at the same d • Measured PL(d) may differ from mean PL(d) • In indoor environments, a floor attenuation factor also added

  21. Log-normal Shadowing Model • Assumes path loss at a given d has a normal distribution • X is a zero-mean gaussian r.v. •  says how good the model is • n ~ 2-4,  ~ 2-5 dB • Obtained by measurements

  22. Example

  23. More Sophisticated Empirical Models • Log-distance and Log-normal models lump everything into d and  • Sophisticated models take into account other factors such as terrain, antenna height, urban clutter etc. • Many outdoor models from cellular industry: Longley-Rice, Durkin, Okumura, Hata etc. • Example: Okumura model for 150-1920 MHz, 1-100 km distances, and 30-1000 m antenna heights

  24. Non-empirical Models • Empirical models provide exact match for system under consideration, but are: • Expensive (due to measurement process) • Hard to generalize (change in frequency, environment) • Non-empirical approaches • Analytic models • e.g. [Xia97] • Ray-tracing models

  25. Link Budget using Path Loss Models • Bit-error-rate is a function of SNR (signal-to-noise ratio), or equivalently CIR (carrier-to-interference ratio), at the receiver • the “function” itself depends on the modulation scheme! • Link budget calculations allow one to compute SNR or CIR • requires estimate of power received from transmitter at a receiver • Tx antenna, Rx antenna, Rx amplifier, path loss (free space, shadowing) • also, estimate of noise and power received from “interferers” • SNR (dB) = Pr(d) dBm - N dBm • Where • N = KT0BF, or N dBm = -174 dBm + 10 log B (in Hz) + F (dB) • where K is the Boltzmann’s constant, and F is the noise figure of the receiver • Pr(d) is calculated using large scale path loss models

  26. Example Link Budget Calculation • Maximum separation distance vs. transmitted power (with fixed BW) • Given: • cellular phone with 0.6W transmit power • unity gain antenna, 900 MHz carrier frequency • SNR must be at least 25 dB for proper reception • receiver BW is B = 30 KHz, and noise figure F = 10 dB • What will be the maximum distance between mobile and basestation? • Solution: • N = -174 dBm + 10 log 30000 + 10 dB = -119 dBm • For SNR > 25 dB, we must have Pr > (-119+25) = -94 dBm • Pt = 0.6W = 27.78 dBm • This allows path loss PL(d) = Pt - Pr < 122 dB • l = c/f = 1/3 m • Assuming d0 = 1 km, PL(d0) = 91.5 dB • For free space, n = 2, so that: 122 > 91.5 + 10*2*log(d/(1 km)) • or, d < 33.5 km • Similarly, for shadowed urban with n = 4, 122 > 91.5 + 10*2*log(d/(1 km)) • or, d < 5.8 km

  27. Small-scale Fading Effects(over small Dt and Dx) • Fading manifests itself in three ways • Time dispersion caused by different delays limits transmission rates due to inter-symbol interference • Rapid changes in signal strengths (up to 30-40 dB) over small Dt and Dx (< l) • Random frequency modulation due to varying Doppler shifts on each multipath component • Mobility if the terminal and of objects in the environment impacts all three • Mobile terminal may stop in a deep fade (signal null) • Moving surrounding objects cause time varying fading

  28. Multipath Channel • Multipath channel impulse response • Four important parameters of interest

  29. Types of Fading

  30. Raleigh and Rician Fading

  31. Multipath Fading Example

  32. Low-level Multipath Fading Model

  33. Higher-level Models: Raleigh Fading • Received signal a sum of contributions from different directions • Random phases make the sum behave as noise (Rayleigh Fading) • “Fades”: interval of increased BER, or reduced channel capacity • Function of the speed of mobile as well as other objects, e.g. • A 50 km/hr car in 900 MHz band: 1 ms long > 20 dB fades every 100 ms • A 2 km/hr pedestrian in 900 MHz band: 25 ms > 20 dB fades every 2.5 s • Also a function of frequency, depth fade etc. • Burst of errors and losses: need special radio processing, and higher layer protocol support

  34. Dependence of Raleigh Fading on Mobility

  35. Mapping Raleigh Fading to2-State Markov Model

  36. Generalized Finite-State Markov Channel [Wang95] • Each state corresponds to an interval of SNR • Assumes slow fading • Received SNR remains at a certain level for the duration of a symbol • States associated with consecutive symbols are neighboring states • i.e. no more than three outgoing transitions from a state • Analysis similar to previous slide shows that

  37. Frequency Selective Fading • Data rate limitations due to inter-symbol interference (ISI) in frequency selective fading and receivers with no “equalizaters”: • Maximum data rate without significant errors = max(Rb) = d/st • where d depends on specific channel, modulation type etc. • Thumb rule for unequalized radio channels: max(Rb) = 0.1/st • Data rate can be improved by “equalization” • equalizer is a signal processing function (filter) • cancels the ISI, usually implemented at baseband or IF in a receiver • Mobility makes channel time varying  adptive equalizer • training, tracking, and re-training during data transmission • equalizer needs to be “updated” at a rate described by Doppler spread • GSM example: • GSM has a bit period of 3.69 ms, or a rate of 270 kbps • with its equalizer, GSM can tolerate up to 15 ms of delay spread • otherwise, with 15 ms of delay spread, GSM would be limited to 7 kbps • Data rate = f(multipath delays, mobility, receiver complexity)

  38. Combating Channel Impairments in the Presence of Mobility • Countermeasures for wireless impairments • Increase Tx power, Equalization, Error correction, Modulation, Spreading, ARQ protocols • Mobility introduces time variations • Two approaches to cope with these time variations: • Adaptivity • E.g. adaptive equalizer, adaptive coding, adaptive packet length, adaptive modulation • Diversity • Send same information on multiple independent channels (time, frequency, space, polarization etc.) • Combine at the receiver Adaptivity Diversity

  39. Example: Smart Antenna • An M-element antenna array provides • Antenna gain (reduction in required RF power for given SNR) • Diversity gain to combat fading • Interference suppression gain • Angle reuse for spatial multiplexing • Two strategies: • Beam can be “steered” towards a mobile • Beam can be “selected” from a set of fixed beams

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