Multiuser detection in cdma
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Multiuser Detection in CDMA. A. Chockalingam Assistant Professor Indian Institute of Science, Bangalore-12 [email protected] 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

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Multiuser detection in cdma

Multiuser Detection in CDMA

A. Chockalingam

Assistant Professor

Indian Institute of Science, Bangalore-12

[email protected]

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

Matched Filter Detector (MFD)

MF

User 1

MF

User 2

MF

User K

Correlation Matrix

Dept of ECE, IISc, Bangalore


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

Decorrelating Detector

Linear Transformation

and Detector

Decision

For the case of 2 users

and

Dept of ECE, IISc, Bangalore


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

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


Multiuser detection in cdma

SIC

MF

Detector

MF

Detector

Matched

Filter

Remodulate

& Cancel

Remodulate

& Cancel

Stage-1

Stage-m

Dept of ECE, IISc, Bangalore


Multiuser detection in cdma

m-th Stage in SIC

MF Detector

MF

User m

Select

Strongest

User

MF

User K

Remodulate

& Cancel

Dept of ECE, IISc, Bangalore


Multiuser detection in cdma

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


Multiuser detection in cdma

PIC

MF

User 1

MF

User K

Stage 1

Stage j

Dept of ECE, IISc, Bangalore


Multiuser detection in cdma

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


Multiuser detection in cdma

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


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