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

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  • Slide 1
  • 1 Multiuser Detection for CDMA Anders Hst-Madsen (with contributions from Yu Jaechon, Ph.D student) TRLabs & University of Calgary
  • Slide 2
  • Overview l Introduction n Communications Signal Processing l CDMA n 3G CDMA l Multiuser Detection (MUD) n Basics n Blind MUD n Group-blind MUD n Performance
  • Slide 3
  • Some Impression of a Changing Korea l Compared with 2 years ago n A lot has changed, fast l Internet n 90% of subway ads about internet n All ads have internet address l Cell phones n Everymans n Fashion item n Small! Even babies in Korea have mobile phones!
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  • The Demands l The future of the internet is wireless, Steve Balmer, CEO Microsoft l Now n Internet through telephone n Wireless voice phones l Emerging n High-speed internet (ADSL, cable, satellite, fixed wireless) n Some wireless terminals (Nokia 9000, Palm VII, RIM Blackberry) n Web on wireless phones l Future n Wireless everything Internet terminals LAN, home networks Devices (Bluetooth) n Wireless video phones? n More webphones than wired internet connections in 2004 (Ericsson, Nokia, Motorola) n All wireless phones web enabled from 2001 (Nokia)
  • Slide 5
  • The Constraints l Limited spectrum l Limited power l Complex channels n Multipath, shading n Interference: Other users, other electronics
  • Slide 6
  • Solutions l Efficient compression l Coding l Channel signal processing l Efficient, cost-controlled media access l Software radio l New standards for mobile communications 3rd generation systems n W-CDMA n cdma2000 l 4th generation by year 2010
  • Slide 7
  • The Communication Channel Com- pression TransmitterReceiver Speech Data Video Source coding Source coding Channel coding Channel coding Adaptive transmission Adaptive transmission Signal processing Signal processing l Channel Dispersion n (Low pass) filter effect (wireline filters, frequency selective fading) n Intersymbol Interference (ISI) n Non-linear distortions (power amplifiers) l Multipath n Slow fading n Time selective fading n Space-selective fading l Interference n External Interference (other electronics, communications, cars) n Multiple Access Interference (MAI) (other users using the same channel) n Echo (line hybrids, room microphones, hands-free mobiles)
  • Slide 8
  • The Wireless Channel Frequency-selective fading: ISI Doppler spread: Time-varying channel Space-selective fading: Beamforming Path loss
  • Slide 9
  • DS/CDMA l Applications n US IS-95 standard n Korean cellular system n IMT-2000 (wide band (WB) CDMA) n Part of future European Frames standards l Principle n Users share frequency and time n Distinguished by unique code n Separated by correlation with code Direct Sequence Code Division Multiple Access
  • Slide 10
  • 3G CDMA l cdma2000 n North America, Korea? n Compatible with IS-95 n Promoted by Qualcomm n Long codes, synchronous l Wideband CDMA (WCDMA) n Europe, Japan n Compatible with GSM n Promoted by Nokia, Ericsson n Long/short codes, asynchronous n FDD and TDD modes
  • Slide 11
  • Long versus Short Codes l Principle n Code infinite l Applications n IS-95 n cdma2000 l Advantages n Interference averaged out l Disadvantages n Limited signal processing options l Principle n Code repeats on every symbol l Applications n W-CDMA (FDD)? n W-CDMA (TDD) l Advantages n More signal processing options n Higher capacity l Disadvantages n Without advanced processing, high interference Long CodesShort Codes
  • Slide 12
  • Multi-user Detection l Multiple-Access Interference (MAI) n Due to non-orthogonality of codes n Caused by channel dispersion l Multiuser detection n reduction of MAI through interference cancellation n 2-4 times capacity increase of cellular systems n Probably part of future wireless systems (cellular, satellite, WLAN) Included in WCDMA TDD standard Several companies involved: Siemens, Nokia, Nortel Some field trials [Siemens]
  • Slide 13
  • History of Multi-user Detection Optimum Multi-user Detector Linear Multi-user Detector Subtractive Interference Cancellation Detector Decorrelating Detector Parallel IC Successive IC Blind MMSE Detector Blind Decorrelating Detector Minimum Mean Squared Error (MMSE) Detector Group-Blind MMSE
  • Slide 14
  • Synchronous CDMA l K users with no ISI. l Sufficient to consider signal in single symbol interval, i.e., [0,T] l Received signal l where b k {-1,+1} is the kth users transmitted bit. n A k is the kth users amplitude n s k (t) is the kth users waveform (code or PN sequence) n n(t) is additive, white Gaussian noise.
  • Slide 15
  • Conventional detector Matched filter bank s 1 (t) s 2 (t) s K (t) t = i T y1y1 y2y2 yKyK Decision......... r(t)
  • Slide 16
  • Detection of CDMA signals l The signal is processed by cross correlation (or matched filtering): l In the conventional detector, the estimate of the kth bit is l If the MAI term is not small, the error probability will be large l MAI can be kept small by n small cross correlation between codes ( small) n Power control (all A i same value) Desired signalMultiple Access Interference (MAI)noise
  • Slide 17
  • Signals on Vector Form l The signal is processed by cross correlation (or matched filtering):
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  • Signals on Vector Form l The signal is processed by cross correlation (or matched filtering):
  • Slide 19
  • Signals on Vector Form l The signal is processed by cross correlation (or matched filtering):
  • Slide 20
  • Signals on Vector Form l The signal is processed by cross correlation (or matched filtering):
  • Slide 21
  • Signals on Vector Form l The signal is processed by cross correlation (or matched filtering): =1 = 12 =n 1 =n 2 RAbn
  • Slide 22
  • Detection of CDMA signals 2 l The output y=[y 1, y 2,...,y K ] T is sufficient statistic for b=[b 1, b 2,...,b K ] T
  • Slide 23
  • Optimum Multi-user Detector l Too complex : 2 K Comparison l Impractical l S. Verd, Optimum multiuser signal detection, PhD thesis, University of Illinois at Urbana-Champaign, Aug. 1984. Viterbi algorithm... output correlator
  • Slide 24
  • Linear Multi-User Detectors l Decorrelating detector l General linear detector l Linear MMSE detector n Minimizes n Gives n Lower bit error rate (BER) than decorrelating
  • Slide 25
  • Parallel Interference Canceller (PIC) l Received signal l Suppose b known: l Use initial estimate of b l Advantages n works for long codes n Each stage simple (no matrix inversion) l Problems n If bit wrong, magnifies MAI n Many stages needed
  • Slide 26
  • Blind Multiuser Detection l Traditional, non-blind MUD n Codes of all users known n Sufficient statistics l Blind MUD n Only code of desired user known n Similar to beam forming in antenna arrays n Works only for short codes n Mobile station
  • Slide 27
  • System Model - Synchroneous CDMA l Signal is sampled at chip rate (from matched filter) l Received signal on vector form b k ( 1): transmitted bits l A k : received amplitude l s k : code waveforms l n: white, additive noise
  • Slide 28
  • Linear Detectors l Conventional detector l General linear detector:
  • Slide 29
  • The Decorrelating Detector l Choose w 1 so that l Detector:
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  • l Choose w 1 to satisfy l Solution The MMSE Detector l Choose w 1 to satisfy
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  • The MMSE Detector l Choose w 1 to satisfy l Solution =1 =0
  • Slide 32
  • The MMSE Detector l Choose w 1 to satisfy l Solution R
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  • The Blind MMSE Detector l Choose w 1 to satisfy l Solution Bit 1Bit 2Bit 3Bit 4Bit 5Bit 6Bit... r1r1 r2r2 r3r3 r4r4 r5r5 r6r6 r... Chip rate sampling
  • Slide 34
  • Subspace Methods l Correlation matrix of received data l The correlation matrix for CDMA has EVD l The MMSE detector is given by:
  • Slide 35
  • Subspace Tracking l Computation of l Direct EVD n Estimate R: n Calculate EVD of R Find U s and s from K largest eigenvalues l Singular Value Decomposition n Calculate SVD of [r 0 r 1... r n-1 ] Find U s and s from K largest singular values l Subspace tracking n Low complexity methods of dynamically updating EVD/SVD n complexity O(MK 2 ) (e.g., F2) n or O(MK) (e.g., PASTd)
  • Slide 36
  • Group-Blind MUD l Multiple-Access Interference (MAI) n Intra-cell interference: users in same cell as desired user n Inter-cell interference: users from other cells n Inter-cell interference 1/3 of total interference
  • Slide 37
  • Blind Multi-User Detection l Non-Blind multi-user detection n Codes of all users known n Cancels only intracell interference l Blind multi-user detection n Only code of desired user known n Cancels both intra- and inter-cell interference
  • Slide 38
  • Group-blind MUD l Codes of some, but not all, users known l Cancels both intra- and inter-cell interference l Uses all information available to receiver l Decreases estimation error n Decreases BER l Potentially less computationally complex n Only one adaptive IC common to all users. n Adaptive IC can have lower complexity than pure blind IC