Mobile Networking - Part I (Physical Layer)

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