Multiuser Detection for CDMA

1. Multiuser Detection for CDMA Anders Høst-Madsen (with contributions from Yu Jaechon, Ph.D student) TRLabs & University of Calgary

2. Overview • Introduction • Communications Signal Processing • CDMA • 3G CDMA • Multiuser Detection (MUD) • Basics • Blind MUD • Group-blind MUD • Performance

3. Even babies in Korea have mobile phones! Some Impression ofa Changing Korea • Compared with 2 years ago • A lot has changed, fast • Internet • 90% of subway ads about internet • All ads have internet address • Cell phones • Everyman’s • Fashion item • Small!

4. The Demands • “The future of the internet is wireless,” Steve Balmer, CEO Microsoft • Now • Internet through telephone • Wireless voice phones • Emerging • High-speed internet (ADSL, cable, satellite, fixed wireless) • Some wireless terminals (Nokia 9000, Palm VII, RIM Blackberry) • Web on wireless phones • Future • Wireless everything • Internet terminals • LAN, home networks • Devices (Bluetooth) • Wireless video phones? • More webphones than wired internet connections in 2004 (Ericsson, Nokia, Motorola) • All wireless phones web enabled from 2001 (Nokia)

5. The Constraints • Limited spectrum • Limited power • Complex channels • Multipath, shading • Interference: Other users, other electronics

6. Efficient compression Coding Channel signal processing Efficient, cost-controlled media access Software radio New standards for mobile communications 3rd generation systems W-CDMA cdma2000 4th generation by year 2010 Solutions

7. Channel Dispersion (Low pass) filter effect (wireline filters, frequency selective fading) Intersymbol Interference (ISI) Non-linear distortions (power amplifiers) Video Speech Data The Communication Channel Source coding Channel coding Adaptive transmission Signal processing Unknown channel Transmitter Receiver Com- pression • Multipath • Slow fading • Time selective fading • Space-selective fading • Interference • External Interference (other electronics, communications, cars) • Multiple Access Interference (MAI) (other users using the same channel) • Echo (line hybrids, room microphones, hands-free mobiles)

8. The Wireless Channel Frequency-selective fading: ISI Doppler spread: Time-varying channel Path loss Space-selective fading: Beamforming

9. Applications US IS-95 standard Korean cellular system IMT-2000 (wide band (WB) CDMA) Part of future European Frames standards Principle Users share frequency and time Distinguished by unique code Separated by correlation with code DS/CDMA† †Direct Sequence Code Division Multiple Access

10. 3G CDMA • cdma2000 • North America, Korea? • Compatible with IS-95 • Promoted by Qualcomm • Long codes, synchronous • Wideband CDMA (WCDMA) • Europe, Japan • Compatible with GSM • Promoted by Nokia, Ericsson • Long/short codes, asynchronous • FDD and TDD modes

11. Principle Code “infinite” Applications IS-95 cdma2000 Advantages Interference averaged out Disadvantages Limited signal processing options Principle Code repeats on every symbol Applications W-CDMA (FDD)? W-CDMA (TDD) Advantages More signal processing options Higher capacity Disadvantages Without advanced processing, high interference Long versus Short Codes Long Codes Short Codes

12. Multiple-Access Interference (MAI) Due to non-orthogonality of codes Caused by channel dispersion Multiuser detection reduction of MAI through interference cancellation 2-4 times capacity increase of cellular systems Probably part of future wireless systems (cellular, satellite, WLAN) Included in WCDMA TDD standard Several companies involved: Siemens, Nokia, Nortel Some field trials [Siemens] Multi-user Detection

13. History of Multi-user Detection Optimum Multi-user Detector Linear Multi-user Detector Decorrelating Detector Blind Decorrelating Detector Minimum Mean Squared Error (MMSE) Detector Blind MMSE Detector Group-Blind MMSE Subtractive Interference Cancellation Detector Successive IC Parallel IC

14. K users with no ISI. Sufficient to consider signal in single symbol interval, i.e., [0,T] Received signal where bkÎ {-1,+1} is the k’th user’s transmitted bit. Ak is the k’th user’s amplitude sk(t) is the k’th user’s waveform (code or PN sequence) n(t) is additive, white Gaussian noise. Synchronous CDMA

15. Decision Decision Decision Conventional detector y1 t = i T s1(t) r(t) y2 t = i T s2(t) ......... ......... yK Matched filter bank t = i T sK(t)

16. Detection of CDMA signals • The signal is processed by cross correlation (or matched filtering): • In the conventional detector, the estimate of the k’th bit is • If the MAI term is not small, the error probability will be large • MAI can be kept small by • small cross correlation between codes ( small) • Power control (all Ai same value) Desired signal Multiple Access Interference (MAI) noise

17. Signals on Vector Form • The signal is processed by cross correlation (or matched filtering):

18. Signals on Vector Form • The signal is processed by cross correlation (or matched filtering):

19. Signals on Vector Form • The signal is processed by cross correlation (or matched filtering):

20. Signals on Vector Form • The signal is processed by cross correlation (or matched filtering):

21. =r12 =n2 =1 R A b n Signals on Vector Form • The signal is processed by cross correlation (or matched filtering): =r12 =n1 =1

22. Detection of CDMA signals 2 • The output y=[y1, y2,...,yK]T is sufficient statistic for b=[b1, b2,...,bK]T

23. correlator correlator correlator Optimum Multi-user Detector output • Too complex : 2K Comparison • Impractical • S. Verdú, Optimum multiuser signal detection, PhD thesis, University of Illinois at Urbana-Champaign, Aug. 1984. Viterbi algorithm ... ...

24. Linear Multi-User Detectors • Decorrelating detector • General linear detector • Linear MMSE detector • Minimizes • Gives • Lower bit error rate (BER) than decorrelating

25. Parallel Interference Canceller (PIC) • Received signal • Suppose b known: • Use initial estimate of b • Advantages • works for long codes • Each stage simple (no matrix inversion) • Problems • If bit wrong, magnifies MAI • Many stages needed

26. Traditional, non-blind MUD Codes of all users known Sufficient statistics Blind MUD Only code of desired user known Similar to beam forming in antenna arrays Works only for short codes Mobile station Blind Multiuser Detection

27. Signal is sampled at chip rate (from matched filter) Received signal on vector form bk (±1): transmitted bits Ak: received amplitude sk: code waveforms n: white, additive noise System Model - Synchroneous CDMA

28. Conventional detector General linear detector: Linear Detectors

29. Choose w1 so that Detector: The Decorrelating Detector

30. The MMSE Detector • Choose w1 to satisfy • Solution • Choose w1 to satisfy

31. The MMSE Detector • Choose w1 to satisfy • Solution =1 =0 =0

32. R The MMSE Detector • Choose w1 to satisfy • Solution

33. Bit 1 Bit 2 Bit 3 Bit 4 Bit 5 Bit 6 Bit ... Chip rate sampling r1 r2 r3 r4 r5 r6 r... The Blind MMSE Detector • Choose w1 to satisfy • Solution

34. Subspace Methods • Correlation matrix of received data • The correlation matrix for CDMA has EVD • The MMSE detector is given by:

35. Subspace Tracking • Computation of • Direct EVD • Estimate R: • Calculate EVD of R • Find Us and Ls from K largest eigenvalues • Singular Value Decomposition • Calculate SVD of [r0r1 ... rn-1] • Find Us and Ls from K largest singular values • Subspace tracking • Low complexity methods of dynamically updating EVD/SVD • complexity O(MK2) (e.g., F2) • or O(MK) (e.g., PASTd)

36. Multiple-Access Interference (MAI) Intra-cell interference: users in same cell as desired user Inter-cell interference: users from other cells Inter-cell interference 1/3 of total interference Group-Blind MUD

37. Non-Blind multi-user detection Codes of all users known Cancels only intracell interference Blind multi-user detection Only code of desired user known Cancels both intra- and inter-cell interference Blind Multi-User Detection

38. Group-blind MUD • Codes of some, but not all, users known • Cancels both intra- and inter-cell interference • Uses all information available to receiver • Decreases estimation error • Decreases BER • Potentially less computationally complex • Only one adaptive IC common to all users. • Adaptive IC can have lower complexity than pure blind IC

39. Group-Blind Hybrid Detector • Hybrid detector • Decorrelating among known users • MMSE with respect to unknown users • Has convenient, simple expression • Algorithm • Projection onto subspace of known codes • Orthogonal Projection • EVD • Detector

40. Group-Blind Detector

41. Performance Simulations • K=7 users with known codes • Variable number (4 or 10) of users with unknown codes • Purely random codes of length M=31 • SNR=20 dB • Ensemble of 50 different random code assignments is generated • Median signal to inference and noise ratio (SINR) • Over all code choices and known users • total ensemble of 350

42. 7 Known users 4 Unknown users All same power 20 18 Full 16 Group-blind 14 Blind 1 12 2 10 Direct SINR(dB) Non-blind 8 6 Single user 4 2 0 -2 50 100 150 200 250 300 350 400 Bits Simulation Results

43. 7 Known users 10 Unknown users 4 Unknown users with power 0dB 6 unknown users with power -6dB 20 18 Full 16 14 Group-blind 12 Blind 10 Direct SINR(dB) 8 Non-blind 2 1 6 Single user 4 2 0 -2 50 100 150 200 250 300 350 400 Bits Simulation Results

44. 7 Known users 4 Unknown users Blocksize fixed at 200 20 different code matrices Ensemble of 140 for each SNR value Upper curve: 90-percentile Lower curve: median 0 10 -1 10 -2 10 Non-blind -3 10 Blind BER -4 10 Group-blind -5 10 -6 10 0 2 4 6 8 10 12 14 16 18 20 SNR (dB) Simulation Results, BER

45. Summary • Multiuser Detection • Gives considerably performance improvement • Most useful for short codes • PIC also useful for long codes • (Group) blind MUD • For short code MUD • More useful in real environments • Future Developments • Further development of PIC • Practical, real-time implementation of MUD • Complexity reduction of (group-) blind MUD