MOTIVATION

Communication Anywhere-Anytime-Anytype

Increasing Demand for Wireless Services

Unique Problems compared to Wireline communications

2

SMART ANTENNA SYSTEMS

Wireless systems broadcast signals across wide areas to individual users

Most of energy is wasted while little reaches

the intended user

Spatial processing characterizes the RF environment

through antenna arrays and innovative digital signal processing sotware

Additional dimension of functionality

3

ADDED DIMENSION IN WIRELESS COMMUNICATION

Space division compelements existing air interfaces

Frequency Frequency Frequency

Tim

e

Tim

e Tim

e

Tim

e

FDMA TDMA CDMA

NEW DIMENSION--SPACE

Tim

e

Tim

e

Tim

e

Spac

e

Spac

e

Frequency Frequency Frequency

Spac

e

SPACE DIVISION MULTIPLE ACCESS (SDMA)

AMPS GSM IS-95

CochannelInterference



toward intended users – away from unintended users

4

WIDEBAND CHANNEL MODEL

The symbol period, , is comparable to multipath spread,

max

Channel Impulse Responseh(t)

time

ampl

itude

T (Symbol Period)

Single user case with paths to receiver

Vector channel model with an antenna array

5

UPLINK PROCESSING FOR WIRELESS BASESTATIONS

Objective: Combine the intended user signals while suppressing the

co-channel signals.

How? Estimate the wireless channel parameters ( ) based on the

data received

s (n)

s (n)

s (n)1

2

h (n)

h (n)

h (n)

P

2

1

P

x(n)

Channels BaseStationSignals

User

6

CODE DIVISION MULTIPLE ACCESS (CDMA)

Uses available bandwidth efficiently

Distinguishes users by codewords

Modulate &Code

Spreading Code Noise & OtherCDMA Signals

Decode&

Demod

DespreadingCode

Channel&

Delay

Synchronous CDMA All signals must be synchronized

Narrowband CDMA

RAKE receiver coherently resolves multipath fading

Wideband CDMA (W-CDMA)

Emerging third-generation standards

Estimate (vector) FIR channel parameters for symbol estimation

7

CDMA SYSTEMS WITH PERIODIC SPREADING

Single receiver with Users

Blindly estimate

Assume knowledge of spreading codes

Assume knowledge of propagation delays .

8

DATA FORMULATION

Baseband signal, single receiver, P users:

denotes the user index

is the number of users

are the information symbols

is the symbol duration

is a signature waveform

is the spreading code of user

is the code length

are the channels

9

MATRIX FORM

Represent the data vector of samples with a symbol period in a matrix form

.

.

....

.

.

.

Stack successive samples of the signal in vector form

.

.

.

.

.

.

is defined as the smoothing factor.

10

INPUT/OUTPUT STRUCTURE

shows the algebraic relation between the input and output

.

.

....

. . .. . .

.

.

....

.

.

....

Signature waveform matrix has rich structure

.

.

....

. . .. . .

.

.

.

.

.

....

.

.

....

. . ....

11

MULTI-USER CHANNEL ESTIMATION

Estimate signature vectors determine the channel vectors

Data model when including additive white noise

Perform subspace decomposition on by a singular value decomposition

(SVD)

Vectors in span the signal subspace, .

Vectors in span the noise subspace.

12

MULTI-USER CHANNEL ESTIMATION

Estimate from without knowledge of

Orthogonality between noise and signal subspace

Using yields

is a Toeplitz matrix of spreading codes

is in the null space of

13

PROPOSED ALGORITHM FOR A SINGLE RECEIVER

1. Construct data matrix

2. Apply SVD to , or eigenvalue decomposition (EVD) of , to obtain

orthogonal subspace .

3. For each user, estimate the channel vector

4. Reconstruct signature vectors and

Exploits fact that each signature vector is a linear function of a unique

spreading code [Torlak & Xu, IEEE-TSP Jan. 1997]

14

CAPACITY INCREASE USING ANTENNA ARRAY

Problem: Existing systems are not designed to accommodate more than

(spreading gain) users, i.e. P

Goal: Adjust the algorithm to manage an overloaded system (P )

Proposed algorithm in an overloaded system breaks down since the

dimension of the orthogonal subspace reduces

Additional orthogonal vectors are required to guarantee more equations

than unknowns.

Solution: spatially oversample by means of multiple receivers

15

CAPACITY INCREASE USING ANTENNA ARRAY

Assume receivers at the base-station

......

and subspace space relation between and still hold

Orthogonal vectors in increases from to

Signal space remains fixed while noise space increases

Extend our proposed algorithm to handle

16

A-CDMA SYSTEM SIMULATION

Average bit error rate (BER) for different receivers with estimated and

actual channels

Spreading gain=16, M=1, SNR=10 dB, and 9 users

0 1 2 3 4 5 6 7 8 9 1010

10

10

10

10

SNR [dB]

BE

R

__ :actual channels

M=1, P=9

0 1 2 3 4

0Channel response, real part

Time [T]0 1 2 3 4

0

0.05Channel response, imaginary part

Time [T]

0 1

0

0.5

1

0 0.1

0

0.1

RAKE Receiver

17

A-CDMA SYSTEM SIMULATION

Average BER for overloaded A-CDMA systems

Spreading gain=16, M=2, SNR=10 dB, and 18 users

0 1 2 3 4 5 6 7 8 9 10

10

10

10

10

10

SNR [dB]

BE

R

___:actual channels

M=2, P=18

0 1

0

0.5

0 1

0

0.5

0 0.1

0

0.1

user #11:RAKE Recceiver

0 0.1

0

0.05

0.1

user #18:RAKE Receiver

18

CDMA WITH APERIODIC SPREADING SEQUENCES

Aperiodic spreading sequences

distribute signal spectrum uniformly

have inherent interference averaging capabilities

are beneficial to the soft capacity in IS-95 CDMA systems

Subspace-based methods require periodic spreading sequences

Current methods in CDMA sytems with aperiodic spreading sequences

1-D RAKE receivers

2-D RAKE receivers (with an antenna array)

Principal Component (PC) Algorithm

Proposed a new iterative blind channel estimation method

Channel estimation at chip level

Joint block equalization at symbol level

19

DATA MODEL

CDMA system with users

is the chip period

is the noise vector

is the chip delay index assumed to be known.

Wideband channel model

Source symbols are drawn from a finite alphabet

Pseudo-noise (PN) spreading codes

20

STEP 1: ESTIMATION OF CHANNELS GIVEN TRANSMITTED SYMBOLS

Construct the data matrix of the signal

...

is constructed from the estimated input symbols and the code sequence for

th user

Solution for the above equation

Most of is known due to the known PN spreading sequence.

21

STEP 2: ESTIMATION OF SYMBOLS GIVEN CHANNELS

Stack the spatial data samples so that the data matrix

...

are to be estimated

Estimated wideband channel parameters and spreading codes form

. . .

blocks

...

22

DEFINITION OF AND SOLUTION FOR

is the shifted blocks of the codes

...

...

...

...

.

.

.

...

...

.

.

.

...

.

.

.

columns

Each block covers one symbol period and has Toeplitz structure

Use to solve for , then

23

ALGORITHM SUMMARY

and allow us to use both frameworks to exploit the

discrete-alphabet property of CDMA signals and knowledge of spreading

codes.

Adopt Iterative Least Squares with Projection (ILSP) algorithm originally

developed by Talwar, Viberg, and Paulraj for TDMA systems.

Update iteratively, which updates , and under the constraint that

the information symbols are from finite alphabets.

Continue to iterate until or converge.

24

ALGORITHM OUTLINE

1. Randomly choose and set

2.

(a) where

...

(b) Construct with the channel estimates and PN sequences.

(c) is estimated through

(d) Project to closest discrete values.

3. Continue until .

25

SIMULATION OF CDMA SYSTEM WITH APERIODIC SPREADING

Signal constellation for 1-D RAKE, 2-D RAKE, and the proposed method

for M=1 and M=2 cases

Spreading gain=16, SNR=15 dB, and

4 5 6 7 8 9 1010

10

100

Number of Users

MS

E

2D RAKE

M=1

M=2

M=1

M=2

Proposed Method

1D RAKE

-2 0 2

-2

0

2

1D-RAKE with PC-MMSE-1 0 1

-0.5

0

0.5

Proposed Method

P=8,M=1

-1 0 1

-0.5

0

0.5

1

Proposed Method

P=13,M=2

0 5-4

-2

0

2

2D-RAKE with PC-MMSE

26

CONTRIBUTIONS AND OTHER RESEARCH

Uplink processing for W-CDMA with smart antennas

Blind multi-user channel estimation

CDMA with aperiodic spreading codes

Downlink processing for multi-transmitter diversity

Designing weight vectors for maximum capacity

Smart Antenna Testbed development

Statistical vector channel modeling

Real-time signal processing

TDMA with smart antennas

Fast source separation algorithms for Digital Wireless Applications

27

FUTURE DIRECTIONS

1G and 2G are optimized for Voice Communications (FDMA, TDMA,

CDMA) at 8-13 kbps

3G technology must deliver data at 144 kbps-2 Mbps

– Coordinated system concept

– Wideband-CDMA : The Universal Platform

Fourth-generation systems

– Multimedia/Telemedicine/Internet access

– Accommodating mixed traffic

Wideband testbed development with antenna arrays

– Channel propagation studies

– Experimental validations of algorithms

28

estimation and capacity of channels in smart antenna

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