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Part III – Wireline Multiuser Basics

Part III – Wireline Multiuser Basics. Prof. John M. Cioffi Dept of EE Stanford University cioffi@stanford.edu. September 9, 2001. March 31, 2001. April 26 2001. Parts 3 and 4: Outline/Schedule. 2:00-2:45 MU Theory 2:45-3:30 channels for wireline 3:30-4:00 Coffee

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Part III – Wireline Multiuser Basics

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  1. Part III – Wireline Multiuser Basics Prof. John M. Cioffi Dept of EE Stanford University cioffi@stanford.edu September 9, 2001 March 31, 2001 April 26 2001

  2. Parts 3 and 4: Outline/Schedule • 2:00-2:45 MU Theory • 2:45-3:30 channels for wireline • 3:30-4:00 Coffee • 4:00-4:30 DSL and Ethernet arch • 4:30-5:15 Multiuser improvements • 5:15-5:30 Zeke

  3. Part 3 Outline • Levels of Coordination among Multi Users • GDFE Theory • Solutions • MUD – no coordination • Iterative Waterfilling – Interference Chan • Vectoring • Channels

  4. User 1 Router DSLAMS User 2 User K Controller ? Wireline Multiuser Basics • Goal: Best PHY signals for user sharing of channel • Set spectra/signals, optimization via controller . . .

  5. Ultimate Goal: use of Rate Regions Rlong • Plot of all possible rates of users • Any point in region is possible, but each with different spectra • Varies for each channel Spectral pair 1 Spectral pair 2 Rshort

  6. Wireline Coordination ? • How much coordination among lines is allowed? • None • Spectra, all or some • Signals • Answer: it depends on application (DSL, ethernet) and evolves with time

  7. Shared channel User 1 User 1 FEXT NEXT User 2 User 2 User L User L No Coordination • Multiuser Detectors only (MUD) • Different users could be competitive service providers (different DSLAMS, different modulation) • Unbundled state of art . . . Controller ?

  8. Coordinated Spectra (only) Shared channel User 1 User 1 • “interference problem” in Information Theory • Good, but not optimum, solution known • Iterative waterfilling User 2 User 2 . . . User L User L Controller

  9. Coordinated 1-sided Signals User 1 Shared channel • Multiple access and Broadcast problems • Monopoly Service Provider Router DSLAMS User 2 . . . User L Controller

  10. Coordinated 2-sided Signals • Full Vectoring Problem – private networks (cat 5) • Highest data rates Shared channel Router DSLAMS Router DSLAMS Controller Controller

  11. Part 3 Outline • Coordination Levels • GDFE Theory • Solutions • MUD – no coordination • Iterative Waterfilling – Interference Chan • Vectoring • Channels

  12. n data 1 H Y X + rcvr data 2 data L Block/Packet Transmission channel • Assumes NO NEXT (FDM used to separate up/down • X is input vector of • One or up to L users’ data samples • Coordinated or not, L users x mN dimensions • Y is output vector of • One or more receivers output packets • Coordinated or not, L users x nN dimensions • H is linear coupling, Noise vector is n

  13. SNR decision sequence for packet W X + Y Z B Generalized DFE • W and B are matrix operations on the packets Y and X • Traditional structures become matrices that do not necessarily correspond to convolution • Applies to all single-user and multiple-user situations

  14. Finding the Equivalent Channel X H “good part” - gets through channel Input components best are green “null space” - blocked by channel “null space” - zeroed by design

  15. GDFE Solutions • Always can be combined with good AWGN transmit codes and “green” signal optimization to get best performance • b=c=log(1 +SNRGDFE) • Use good (turbo, LDPC) code on green components • fundamental structure used to analyze (not implement) • Useful one way or another in all the multiple user problems • Introduced Cioffi/Forney, 1996 – see [5]

  16. Part 3 Outline • Coordination Levels • GDFE Theory • Solutions • MUD – no coordination • Iterative Waterfilling – Interference Chan • Vectoring • Channels

  17. No coordination - MUD • Similar to wireless case • Various interference cancellation strategies • Linear • Decision-aided • Each receiver learns or estimates channel from all users • Each receiver attempts to reduce/eliminate signals from all other users while estimating the signal it wants • Other signals may not be orthogonal for many reasons • Intersymbol interference • Interchannel interference (crosstalk) • Wireline case differences (from wireless) • Crosstalkers may be very large or very small, and still significant in all cases • Channel is relatively stationary (usually)

  18. s s n Noise, , 2 n Noise, 2 , H H y y Xmit 1 Xmit 1 Line channel, H + DSL rcvr Line channel, H + DSL rcvr ( ) x ( ) x 1 1 1 1 Xmit 2 Xmit 2 Xtalk filtering, H Xtalk filtering, H + + ( ) 2 x 2 ( ) x 2 2 . . . . . . Xmit L Xmit L Xtalk filtering, H Xtalk filtering, H ( ) x ( ) x L L L L MUD Channel Model • y=Hx + n = H1x1 + H2 x2 …+ n • H=[H1 H2 … HL ]

  19. SNR decision sequence for packet W X + Y Z B (L users) x (1N dimensions) –Generalized DFE • Tries to estimate all users, even if we don’t want them all • Helps estimate the user of interest in no-coordination problem • Best that can be done, given any input spectra • Error propagation can be enormous degradation

  20. Error Prop Fix: Iterative Decoding h1,1 Channel output Decoder 1 (prob of x1 symbol) • Compute probabilities, rather than hard decisions • when done iterating, then do hard decision • Effectively achieves level of performance of no-error GDFE/capacity p1 Hard Decison x1 User 1 + + x2 n User 2 noise h1,2 Hard Decison Decoder 2 (prob of x2 symbol) p2 (typically not implemented)

  21. Soft vs Hard Canceller [18] Soft symbols • ciis average value of xi, computed from p.d. Hard decisions Soft or hard x3 +

  22. 3 step iteration • Compute new soft output • compute probability distribution from soft outputs for each output dimension • compute new soft symbol and variance • Do it again and again, cycling through estimates of all users’ signals x

  23. Example for MUD: HPNA into VDSL CO • No signal necessarily much larger than another • Error propagation would destroy GDFE alone • Iterative decoding with GDFE works at near optimum levels (I.e., as if there were no error prop.) vdsl hpna vdsl telco hpna home

  24. Optimum Detector • 6 tones of 256 zeroed in 5-10 MHz band 8 Mbps] HLAN 26 Mbps] VDSL

  25. Example: DSL and HPNA • VDSL and HPNA both share 5-10 MHz on twisted pair • Use GDFE concept and soft-cancellation at rcvr for VDSL • Works like HPNA wasn’t there (mutliuser capacity on phone line is 200 Mbps vs 20 Mbps when other user is Gaussian noise)

  26. Part 3 Outline • Coordination Levels • GDFE Theory • Solutions • MUD – no coordination • Iterative Waterfilling – Interference Chan • Vectoring • Channels

  27. Interference Channel – Spectral Balancing • No transmit or receive signal coordination • Only spectra can be designed jointly • Only cases for which opt. solution is known are • 1N dimensions by L users - broadcast • L by 1N – multiple access • 1N by 1N – single user = “Water Filling” • General case, a good (not nec opt) solution is known as “iterative waterfilling”

  28. Sub optimal Solution Shared channel GDFE 1 GDFE 1 • GDFE on each receiver for all L users • Best for any given spectra of all users • Don’t know best spectra for set of users • Try to optimize anyway using iterative waterfilling GDFE 2 GDFE 2 . . . GDFE 3 GDFE L Controller

  29. Waterfilling • Waterfilling is known optimum on single-user channel NSR(f) S(f) /g(f)=  |N(f)/H(f)|2

  30. Iterative Waterfilling [15],[16] • Each channel considers all others to have fixed spectra • Can start with flat on all • Waterfilling executed for user 1 • New spectrum for user 1 replaces old • Waterfilling executed for user 2 with new spectra for 1 • New spectra for user 2 replaces old • … user N • Recycle a few times • Converges – close to optimum solution for Inteference channel – nearly maximizes sum of all rates

  31. Generation of Rate Regions Rlong • Each user has power limit • For each user • Lower the power limit in IterWater and get increased rates on others • Sketch N-dimensional rate region by running IterWater for many different power combinations • Check if desired rate is in region Spectral pair 1 Spectral pair 2 Rshort

  32. Part 3 Outline • Coordination Levels • GDFE Theory • Solutions • MUD – no coordination • Iterative Waterfilling – Interference Chan • Vectoring • Channels

  33. Multiple Access – Up Link User 1 Shared Channel H LN x LN • GDFE at one side with H=[H1 H2 … HL] • Vector receiver, with synch-DMT, LxL GDFE • Best Solution now known – [15] Yu, Rhee, Cioffi (only FDM when there is only one receiver [14] – more complicated than that here) • May have all L users on each tone • GDFE separates them at DSLAM • May have error propagation, so iterative decoding necessary GDFE DSLAM User 2 . . . User L Controller

  34. Input Spectra • Can again be computed by iterative waterfilling across N tones • known optimum in this multiple-access case • Each tone is L x L GDFE receiver with DMT modulation on channel • Can approximately compute a lower bound on rate region using iterative waterfilling and varying powers on each channel as in spectral balancing

  35. Broadcast Downlink simple 1 Shared Channel H LN x LN • Optimum known (solved after 30 yrs in 2001) • More complicated version of IterWater • See Wei Yu recent work [17] , iterative solution of Ricatti eqn • Achieves nearly same performance as multiple access for most wireline cases, but optimization occurs with precoder at transmit side to leave each receiver independent – GDFE at each receiver is diagonal (no feedback) and is slicer for each user. Vector DSLAM Vector precoder simple 2 . . . simple L Controller

  36. Ginis/Negi QR Simplification of Vectored GDFE SNR • ZF-GDFE close to MMSE-GDFE on wireline channels (max rate sum) • FEXT from any source is less than on-line signal from that source • H=QR (orthogonal, triangular) • Applies directly to Multiple Access Uplink Problem for each tone decision sequence for packet W=Q’ X + Y Z B=R

  37. Vectored Transmitter - downlink • Vector version of Tomlinson precoder, done for each tone independently • Prewarps transmitted signal to avert FEXT X x Q’ + X’ mod I-R

  38. Full Vectoring Solution – SVD Y • H=M’LF (M,F orthogonal, L diagonal) • Singular value decomposition • Vectored VDSL or VDMT • Always gets max rate-sum capacity • QR is close on DSL channels, but not in all situations • Easy to implement on per-tone basis • MIMO Echo cancellation possible (so full band) decision sequence for packet X X M H W=F’

  39. Part 3 Outline • Coordination Levels • GDFE Theory • Solutions • MUD – no coordination • Iterative Waterfilling – Interference Chan • Vectoring – MA, Broadcast • Full Vectoring solution • Channels

  40. ADSL Loops d d 3-5 mile loops loops with bridge taps

  41. Crosstalk phone line 1 NEXT FEXT phone line 2 Dominant noises, increased coupling at higher frequencies - must be mitigated in design NEXT - 10-13 f1.5 ; FEXT - 10-19 d |H(f)|2 f2

  42. Other Noises • Radio Noise, AM, HAM • 1 mW differential into rcvr • must reject HAM by 70-90 dB (VDSL) and AM by 20-40 dB (ADSL) • Impulse Noise • 10’s millivolts • 100’s microseconds • narrowband (high amplitude) • broadband (low amplitude)

  43. no signal allowed 2 MHz 3.5 MHz 7 MHz 10 MHz -60 dBm/Hz signal allowed -80 dBm/Hz frequency Radio Emissions • like crosstalk, except into radio receivers • VDSL amateur (HAM) bands Public Safety bands • transmit in discontinuous bands

  44. MIMO Line Quantities [3] • Matrix Channel Xfer • Individual lines i=j • Magnitude profiles (i.e., no phase information) • Virtual Binder Group ] [ ( ) ) ( = H f H f ij = = 1 ,..., ; 1 ,..., i K j K

  45. Noises [3],[4] • Noises are “unknown” crosstalkers and thermal/radio • Psd N(f) • Frequency bandwidth of measurement • Time interval for measurement • Requisite accuracy

  46. Source Information [3],[5] • Clock offsets – can be determined at various points for virtual binder lines • Transmit power level – needs reporting

  47. Channel ID 1 nk • Estimate gains at several frequencies • Estimate noise variances at same freqs + Size-N IFFT (with prefix) + pk + Xn Size-N FFT + + En $ P - n

  48. Gain Estimation • Divide/average channel-out by known in • Need about L=40 symbols of training to reduce gain estimation error to .1 dB Y L 1 å ˆ = , l n P n L X = 1 l , l n

  49. Noise Estimation • Use Errors from Gain estimation • Need L=4000 for .1 dB error • SNR is then gain-squared/noise estimate L 1 å 2 s × 2 = ˆ E 1 , n n L = 1 l

  50. MIMO Complications • Training may not be available • Use actual data • Different systems may not have same clock • Interpolation problem

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