Multiuser Detection in CDMA

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Multiuser Detection in CDMA

• A. Chockalingam
• Assistant Professor
• Indian Institute of Science, Bangalore-12
• achockal_at_ece.iisc.ernet.in
• http//ece.iisc.ernet.in/achockal

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Outline

• Near-Far Effect in CDMA
• CDMA System Model
• Conventional MF Detector
• Optimum Multiuser Detector
• Sub-optimum Multiuser Detectors
• Linear Detectors
• MMSE, Decorrelator
• Nonlinear Detectors
• Subtractive Interference cancellers (SIC, PIC)
• Decision Feedback Detectors

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DS-CDMA

• Efficient means of sharing a given RF spectrum
• by different users
• User data is spread by a PN code before
• transmission
• Base station Rx distinguishes different users
• based on different PN codes assigned to them
• All CDMA users simultaneously can occupy
• the entire spectrum
• So system is Interference limited

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DS-SS

• DS-SS signal is obtained by multiplying the
  information bits with a wideband PN signal

Information Bits
Carrier Modulation
Tb
PN Signal
Information Bits
t
Tb N Tc
Tc
N Processing Gain
PN Signal
t
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Processing Gain

• Ratio of RF BW (W) to information rate (R)
• (e.g., In IS-95A, W
  1.25 MHz, R 9.6 Kbps
• i.e.,
  )
• System Capacity (K) proportional to
• 
  (voice activity gain)
• 
  (sectorization gain)
• 
  (other cell interference loss)
• 
  (typically required)
  

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Near-Far Effect in DS-CDMA

• Assume users in the system.
• Let be the average Rx power of each signal.
• Model interference from users as AWGN.
• SNR at the desired user is
• Let one user is near to BS establishes a stronger
• Rx signal equal to
• SNR then becomes
• When is large, SNR degrades drastically.
• To maintain same SNR, has to be reduced
• 
  i.e., loss in capacity.

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Near-Far Effect

• Factors causing near-far effect (unequal Rx
  Signal powers from different users) in cellular
  CDMA
• Distance loss
• Shadow loss
• Multipath fading (Most detrimental. Dynamic range
  of fade power variations about 60 dB)
• Two common approaches to combat near-far effect
• Transmit Power Control
• Near-far Resistant Multiuser Detectors

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CDMA System Model
Data of User 1
Chip shaping filter
Spreading Sequence of user 1
AWGN
Data of User 1
Chip shaping filter
To Demod/ Detector
Spreading Sequence of user 2
Data of User 1
Chip shaping filter
Spreading Sequence of user K
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Matched Filter Detector (MFD)
MF User 1
MF User 2
MF User K
Correlation Matrix
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MFD Performance Near-Far Scenario
2-User system
0.4
NFR 20 dB
0.1
NFR 10 dB
Bit Error Rate
NFR 5 dB
NFR 0 dB
E/b/No (dB)

• Problem with MF Detector Treats other user
  interference
• 
  (MAI) as merely noise
• But MAI has a structure which can be exploited
  in the
• 
  detection process

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Optimum Multiuser Detector

• Jointly detect all users data bits
• Optimum Multiuser Detector
• Maximum Likelihood Sequence Detector
• Selects the mostly likely sequences of data bits
  given the observations
• Needs knowledge of side information such as
• received powers of all users
• relative delays of all users
• spreading sequences of all users

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Optimum Multiuser Detector

• Optimum ML detector computes the likelihood fn
• 
  and selects
• the sequence that minimizes
  
• The above function can be expressed in the form
• where
• 
  and
• is the correlation matrix with elements
  
• where

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Optimum Multiuser Detector

• results in choices of
  the bits
• of the users
• Thus Optimum Multiuser Detector is highly
  complex
• complexity grows exponentially with number of
  users
• Impractical even for moderate number of users
• Need to know the received signal energies of all
• the users

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Suboptimum Detectors

• Prefer
• Better near-far resistance than Matched Filter
  Detector
• Lesser complexity (linear complexity) than
  Optimum
• Detector
• Linear suboptimum detectors
• Decorrelating detector
• MMSE detector

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Decorrelating Detector
Linear Transformation and Detector
Decision
For the case of 2 users
and
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Decorrelating Detector

• For the case of 2 users
• and
• operation has completely eliminated
  MAI
• components at the output (.e.,
  no NF effect)
• Noise got enhanced (variance increased by a
  factor of )

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Decorrelating Detector

• Alternate representation of Decorrelating
  detector
• By correlating the received signal with the
  modified signature
• waveforms, the MAI is tuned out
  (decorrelated)
• Hence the name decorrelating detector

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MMSE Detector

• Choose the linear transformation that minimizes
• the mean square error between the MF outputs
• and the transmitted data vector

Linear Transformation and Detector
Decision
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MMSE Detector

• Choose the linear transformation
• where is determined so as to minimize the
  
• mean square error (MSE)
• Optimum choice of that minimizes
  is

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MMSE Detector
Linear Transformation and Detector
Decision

• When is small compared to the diagonal
  
• elements of MMSE performance approaches
• Decorrelating detector performance
• When is large becomes (i.e.,
  AWGN
• becomes dominant)

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Adaptive MMSE

• Several adaptation algorithms
• LMS
• RLS
• Blind techniques

Estimate of the data bits
Linear Transversal Filter
Re()
Training bits
Adaptive Algorithm
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Performance Measures

• Bit Error Rate
• Asymptotic efficiency Ratio of SNRs with and
• without
  interference
• represents
  loss due to multiuser
• interference
• Asymptotic efficiency easy to compute than BER

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Performance Measures
Optimum Detector
DC
1.0
MMSE
MF Detector
0.0
-20.0
-10.0
10.0
0.0
20.0
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Subtractive Interference Cancellation

• Multistage interference Cancellation approaches
• Serial (or successive) Interference Canceller
  (SIC)
• sequentially recovers users (recover one user per
  stage)
• data estimate in each stage is used to regenerate
  the interfering signal which is then subtracted
  from the original received signal
• Detects and removes the strongest user first
• Parallel Interference Canceller (PIC)
• Similar to SIC except that cancellations are done
  in parallel

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SIC
MF Detector
MF Detector
Matched Filter
Remodulate Cancel
Remodulate Cancel
Stage-1
Stage-m
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m-th Stage in SIC
MF Detector
MF User m
Select Strongest User
MF User K
Remodulate Cancel
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Performance of SIC

• Good near-far resistance
• Most performance gain in achieved using just 2 to
  3 stages
• High NFR can result in good performance!
• Provided accurate estimates of amptitude and
  timing are available
• Sensitive to amplitude and timing estimation
  errors
• increased loss in performance for amplitude
  estimation errors gt 20
• Some amount of power control may be required to
• compensate for the near-far resistance loss
  due to
• imperfect estimates and low NFR

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PIC
MF User 1
MF User K
Stage 1
Stage j
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Performance of PICPerformance of PIC

• Good near-far resistance
• Similar performance observations as in SIC
• Performance of PIC depends more heavily
• on the relative amplitude levels than on the
• cross-correlations of the user spreading
  codes
• Hybrid SIC/PIC architectures

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DFE Detector
MF User 1
FFF
Centralized Decision Feedback
MF User K
FFF

• Feedback current data decisions of the stronger
  users as well
• DFE multiuser detectors outperform linear
  adaptive receivers
• Complexity, error propagation issues