Problem: Designing the “best” channel for an analog cellular system

1. Problem: Designing the “best” channel for an analog cellular system • A specific example of a generic radio system problem, in which both signal and interference are controlled. • A seemingly simple problem that turns out to be complex and interesting • Goals: Good voice quality; “spectrum efficiency” Critique the approach! Does it apply to “unregulated” designs Like 802.11? How much complexity needs to be included? R. Frenkiel 9/18/03

2. What’s this about “spectrum efficiency?” • Politically important in the 60’s and 70’s • Why?

3. What’s this about “spectrum efficiency?” • Politically important in the 60’s and 70’s • Free spectrum to the “best” system • “efficiency” implied serving more people • BH Erlangs/MHz of spectrum/square mile • But… cell size accomplishes the same thing • So… what’s the real reason to be “efficient?”

4. What’s this about “spectrum efficiency?” • But… cell size accomplishes the same thing • So… what’s the real reason to be “efficient?” • COST!! (want to maximize channels/cell) • Base station radios are a minor cost. The cost of a cell is almost entirely fixed (land, building, etc.), and the cost of a system (investment/customer) is almost entirely in the cells, so doubling the calls handled by a cell cuts the system investment almost in half.

5. What’s this about “spectrum efficiency?” • Today– you “buy” the spectrum, and efficiency is seldom mentioned • But… the problem of being “efficient” remains– but only to minimize investment (not as a “political objective”)

6. Returning to the problem: Designing the “best” channel for an analog cellular system 2 4 7 1 2 6 1 5 3 6 1 5 4 3 5 2 4 7 7 1 6 1 6 D/R=4.6; N=7 FM Deviation vs. Reuse Distance

7. Problem: Designing the “best” channel for analog cellular system • Underlying logic (circa 1970) • Channel reuse distance determined by interference • Greater FM deviation (wider channels) rejects interference better • Thus, wider channels can be used in nearer cells • Fewer Channels, but more channels per cell? • Systems engineers love an optimum

8. We need an objective • Radio systems used a 5 level quality scale-- excellent, good, fair, poor or unintelligible • Tentative Objective: 90% of calls good or excellent • But cells (and therefore calls) are variable • Re-statement: quality is “good” at 90th %-ile s/i of the cell; i.e., the quality is good 90% of the time • Is that the same? • How do we apply this objective to the problem?

9. A two-phase approach • Model voice quality vs. s/i– Use subjective listening tests (recorded “Harvard sentences”, recorded at different s/i), to determine (for each FM deviation) the minimum s/i that yields “good” quality. (Example result: 12 KHz deviation requires 17 dB s/I for “good” quality) • Model the s/i distribution for each reuse distance to determine 90th %-ile. Propagation studies yield mean path loss and variance. Do we need simulations? (Example result: a 7-cell pattern yields 17 dB s/i at the 90th %-ile) Thus, combining (1) and (2), achieving “good” quality at the 90th %-ile with 12 KHz deviation requires a 7-cell pattern

10. A two-phase approach (continued) • Repeat for other deviations. Example result; using 5KHz deviation, we need a 16-cell pattern • Calculate channel spacing for each deviation (how many total channels per MHz of spectrum) • Calculate spectrum efficiency (channels/cell/Mhz of allocated spectrum) • If 12 KHz deviation requires a 40 KHz channel spacing, this example yields 106/(4x104)(7) = 3.6 channels/cell/MHz • If 5 KHz deviation requires a 25 KHz channel and a 16-cell pattern, we get 106/(2.5x104)(16) = 2.5 channels/cell/MHz • 12 KHz is more “efficient”

11. We have skipped over some significant problems What channel conditions are we actually recording for these tests? Just s/i = x dB?

12. Problem: Rayleigh Fading • How does fading get included in this method? • Not realistic to use recordings at constant s/i • Sounds too good • How does fading get included in this method?

13. Problem: Rayleigh Fading • How does fading get included in this method? • Include fading in the recording • Create s/i distributions based on “local mean” of fading signal and interference signals • What fading rate? • What other questions does this suggest?

14. Problem: Receiver diversity? • Cost effective? (at base? at mobile? what type?)

15. Problem: Other radio parameters(radio is non-linear device; we need not just a radio– we need the radio) • What does “12 KHz” deviation really mean?

16. Problem: Radio parameters(radio is non-linear device) • What does “12 KHz” deviation mean? • What if I shout? (need peak limiter) • How do we set limiter (hard vs. soft)? • Relationship of mean to peak? (deviation vs. distortion)

17. Problem: Radio parameters(radio is non-linear device) • What if I whisper (need AGC)? • parameters of AGC? • How do we maximize deviation without distortion?

18. Problem: Radio parameters(radio is non-linear device) • What if I whisper (need AGC)? • parameters of AGC? • How do we maximize deviation without distortion? • Compandor (a real breakthrough!) • What limits the compression rule (2:1, 4:1, etc)

19. Problem: Channel Spacing • 12 KHz deviation generally meant 40 KHz channel spacing (Carson’s Rule) • Why did cellular use only 30 KHz channel spacing?

20. Problem: Channel Spacing • 12 KHz deviation generally meant 40 KHz channel spacing (Carson’s Rule) • Why did cellular use only 30 KHz channel spacing? • Spacing must tolerate near/far problem • Filter must reject adjacent channel at much higher level • Cellular can use adjacent channel at different cell

21. Problem: Politics Motorola vs. the Bell System “Political Science” Bell Labs: Wider Channels reject interference so reuse distances are reduced- more channels per cell Motorola: Narrow Channels means less spectrum for Cellular – more channels for “fleets” Conclusion: Using complex technical arguments with non-technical people for political purposes yields major delays

22. Problem: Non-uniform Cell grids • How do we account for irregular grids and variable terrain? (Good and bad cells?)

23. Cells in the Real world Tolerances and Propagation

24. Problem: Non-uniform Cell grids • Non-uniform grids increase the spread in s/I (more potential dead spots) • How do we account for irregular grids and variable terrain? • Study actually said N=4 would work • N=7 was based on concerns about irregularity • Further fueled political debate (was it a ploy to get more spectrum?)

25. Comparing deviation vs. quality over the whole cell (not just the 90th %-ile) E 12 KHz channels G Q U A L I T Y 5 KHz channels F P U 17 22 12 S/I

26. Equal at 90 percentile Wide- 7-cells Narrow- 16 cells 0 50 90 %-ile Wide is “more efficient”, but different

27. Equal Efficiency Wide with 7-cell pattern Narrow with 12 cell pattern Which performance is “better?”

28. What have we ignored?

29. What have we ignored? • Variability during call • Pedestrians vs. cars • Harvard sentences vs conversation • Quiet listening booths vs environmental noise

30. What have we ignored? • Variability during call • Pedestrians vs. cars • Harvard sentences vs conversation • Quiet listening booths vs environmental noise • NEW TECHNOLOGY

31. And now-- the new world of“digital quality” • Digital voice compression (more channels) • Dramatic cost reduction (more than 50%) • Error coding to allow “good” performance at 12 dB (3-4 cell reuse patterns) • Any Concerns?

32. Digital Processing for more reuse? 4-cell w. processing? Wide- 7-cells Narrow- 12 cells

33. So what do we conclude? • Is such a process worthwhile? • Is it so complex that conclusions are meaningless? • Does it lead to improvements in subsystems (like companding)? • Is it applicable to “unregulated” systems?