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Multiuser Detection in CDMA

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Multiuser Detection in CDMA

A. Chockalingam

Assistant Professor

Indian Institute of Science, Bangalore-12

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

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

- Linear Detectors

Dept of ECE, IISc, Bangalore

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

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

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

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

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

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

Matched Filter Detector (MFD)

MF

User 1

MF

User 2

MF

User K

Correlation Matrix

Dept of ECE, IISc, Bangalore

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

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

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

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

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

Decorrelating Detector

Linear Transformation

and Detector

Decision

For the case of 2 users

and

Dept of ECE, IISc, Bangalore

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 )

- operation has completely eliminated MAI

Dept of ECE, IISc, Bangalore

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

- By correlating the received signal with the modified signature

Dept of ECE, IISc, Bangalore

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

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

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

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

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

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

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

- Serial (or successive) Interference Canceller (SIC)

Dept of ECE, IISc, Bangalore

SIC

MF

Detector

MF

Detector

Matched

Filter

Remodulate

& Cancel

Remodulate

& Cancel

Stage-1

Stage-m

Dept of ECE, IISc, Bangalore

m-th Stage in SIC

MF Detector

MF

User m

Select

Strongest

User

MF

User K

Remodulate

& Cancel

Dept of ECE, IISc, Bangalore

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

PIC

MF

User 1

MF

User K

Stage 1

Stage j

Dept of ECE, IISc, Bangalore

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

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