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Multiuser Detection in CDMA. A. Chockalingam Assistant Professor Indian Institute of Science, Bangalore-12 achockal@ece.iisc.ernet.in http://ece.iisc.ernet.in/~achockal. Outline. Near-Far Effect in CDMA CDMA System Model Conventional MF Detector Optimum Multiuser Detector

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multiuser detection in cdma

Multiuser Detection in CDMA

A. Chockalingam

Assistant Professor

Indian Institute of Science, Bangalore-12

achockal@ece.iisc.ernet.in

http://ece.iisc.ernet.in/~achockal

slide2

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

Dept of ECE, IISc, Bangalore

slide3

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

Dept of ECE, IISc, Bangalore

slide4

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 =NTc

Tc

N : Processing Gain

PN Signal

t

Dept of ECE, IISc, Bangalore

slide5

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)

Dept of ECE, IISc, Bangalore

slide6

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.

Dept of ECE, IISc, Bangalore

slide7

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

Dept of ECE, IISc, Bangalore

slide8

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

Dept of ECE, IISc, Bangalore

slide9

Matched Filter Detector (MFD)

MF

User 1

MF

User 2

MF

User K

Correlation Matrix

Dept of ECE, IISc, Bangalore

slide10

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

Dept of ECE, IISc, Bangalore

slide11

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

Dept of ECE, IISc, Bangalore

slide12

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

Dept of ECE, IISc, Bangalore

slide13

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

Dept of ECE, IISc, Bangalore

slide14

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

Dept of ECE, IISc, Bangalore

slide15

Decorrelating Detector

Linear Transformation

and Detector

Decision

For the case of 2 users

and

Dept of ECE, IISc, Bangalore

slide16

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 )

Dept of ECE, IISc, Bangalore

slide17

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

Dept of ECE, IISc, Bangalore

slide18

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

Dept of ECE, IISc, Bangalore

slide19

MMSE Detector

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

Dept of ECE, IISc, Bangalore

slide20

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)

Dept of ECE, IISc, Bangalore

slide21

Adaptive MMSE

  • Several adaptation algorithms
    • LMS
    • RLS
  • Blind techniques

Estimate of the

data bits

Linear

Transversal

Filter

Re()

Training bits

Adaptive

Algorithm

Dept of ECE, IISc, Bangalore

slide22

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

Dept of ECE, IISc, Bangalore

slide23

Performance Measures

Optimum Detector

DC

1.0

MMSE

MF Detector

0.0

-20.0

-10.0

0.0

10.0

20.0

Dept of ECE, IISc, Bangalore

slide24

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

Dept of ECE, IISc, Bangalore

slide25

SIC

MF

Detector

MF

Detector

Matched

Filter

Remodulate

& Cancel

Remodulate

& Cancel

Stage-1

Stage-m

Dept of ECE, IISc, Bangalore

slide26

m-th Stage in SIC

MF Detector

MF

User m

Select

Strongest

User

MF

User K

Remodulate

& Cancel

Dept of ECE, IISc, Bangalore

slide27

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 > 20 %
  • Some amount of power control may be required to

compensate for the near-far resistance loss due to

imperfect estimates and low NFR

Dept of ECE, IISc, Bangalore

slide28

PIC

MF

User 1

MF

User K

Stage 1

Stage j

Dept of ECE, IISc, Bangalore

slide29

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

Dept of ECE, IISc, Bangalore

slide30

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

Dept of ECE, IISc, Bangalore