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
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
• Everymans
• 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
Solutions

• 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

7
The Communication Channel
Source coding
Channel coding
Adaptive transmission
Signal processing
Unknown channel
Transmitter
Receiver
Com- pression

• Channel Dispersion
• (Low pass) filter effect (wireline filters,
  frequency selective fading)
• Intersymbol Interference (ISI)
• Non-linear distortions (power amplifiers)

• 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
DS/CDMA

• 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

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
Long versus Short Codes
Long Codes
Short Codes

• 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

12
Multi-user Detection

• 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

13
History of Multi-user Detection
Optimum Multi-user Detector
Linear Multi-user Detector
Decorrelating Detector
Blind Decorrelating Detector
Blind MMSE Detector
Minimum Mean Squared Error (MMSE) Detector
Group-Blind MMSE
Subtractive Interference Cancellation Detector
Successive IC
Parallel IC
14
Synchronous CDMA

• K users with no ISI.
• Sufficient to consider signal in single symbol
  interval, i.e., 0,T
• Received signal

• where
• bk Î -1,1 is the kth users transmitted bit.
• Ak is the kth users amplitude
• sk(t) is the kth users waveform (code or PN
  sequence)
• n(t) is additive, white Gaussian noise.

15
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
  kth 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
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 yy1, y2,...,yKT is sufficient
  statistic for bb1, b2,...,bKT

23
Optimum Multi-user Detector
output
Viterbi algorithm
...
...

• Too complex 2K Comparison
• Impractical
• S. Verdú, Optimum multiuser signal detection, PhD
  thesis, University of Illinois at
  Urbana-Champaign, Aug. 1984.

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
Blind Multiuser Detection

• 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

27
System Model - Synchroneous CDMA

• 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

28
Linear Detectors

• Conventional detector
• General linear detector

29
The Decorrelating Detector

• Choose w1 so that
• 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
The MMSE Detector

• Choose w1 to satisfy
• Solution

33
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 r0 r1 ... 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
Group-Blind MUD

• 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

37
Blind Multi-User Detection

• 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

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

• K7 users with known codes
• Variable number (4 or 10) of users with unknown
  codes
• Purely random codes of length M31
• SNR20 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
Simulation Results

• 7 Known users
• 4 Unknown users
• All same power

43
Simulation Results

• 7 Known users
• 10 Unknown users
• 4 Unknown users with power 0dB
• 6 unknown users with power -6dB

44
Simulation Results, BER

• 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

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