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OutlineIntroduction

PrecodingScheduling (user selection)

Chapter 3: Physical-layer transmission techniques

Section 3.6: Space Division Multiple Access (SDMA)

Instructor: Nguyen Le HungEmail: [email protected]; [email protected]

Department of Electronics & Telecommunications EngineeringDanang University of Technology, University of Danang

Mobile Communications - Chapter 3: Physical-layer transmissions Section 3.6: Space Division Multiple Access (SDMA) 1
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OutlineIntroduction


1 Introduction

SDMA and OFDMMultiuser transmission

2 Precoding

Precoding classificationAn example of linear precodingPower allocation in ZF precodingPossible research problems

3 Scheduling (user selection)Exhaustive selectionGreedy selection

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OutlineIntroduction



SDMA with OFDM

The integration of multi-antenna and OFDM techniques hasprovided remarkable diversity and capacity gains in broadbandwireless communications.In multiuser (MU) transmissions, the use of multiantenna array atthe base station (BS) enables simultaneous transmission of multiple

data streams to multiple users by exploiting spatial separationsamong users.

ABS/eNB

AMS/UE

(a)

IFFT

SU-MIMOprecoder A

BS/eNB

AMS/UE1

(b)

IFFT

MU-MIMOprecoder

AMS/UE2

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OutlineIntroduction



A simple example of multiuser (MU) transmission

1,1h

2,1h

Mh ,1

Base Station

1s

Modulation

Coded bits

of user 1

2s

Modulation

Coded bitsof user 2

1,2h2,2h

Mh ,2

Antenna 1

Antenna M

De-mod

Channel

estimator

User 2

De-mod

Channel

estimator

User 1

1y

2y

1 = 1

=1

1,+2

=1

1,+1, and 2 = 2

=1

2,+1

=1

2,+2


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OutlineIntroduction



Precoding classification

In the so-called space division multiple access (SDMA), multiuserdiversity is the primary factor that increases significantly the systemsum-rate (throughput).

As a result, an appropriate multiuser encoding technique (at the BS)is indispensable to attain the considerable sum-rate gain in SDMA.

It is well-known that dirty paper coding (DPC) is an optimalmultiuser encoding strategy that achieves the capacity limit of MUbroadcast (BC) channels but at the cost of extremely highcomputation burden as the number of users is large.

Recent studies have introduced several suboptimal multiuser

encoding techniques with lower complexity (relative to DPC) thatcan be categorized into:

nonlinear precoding such as: vector perturbation, TomlinsonHarashima techniqueslinear precoding such as: minimum mean squared error (MMSE),zero-forcing.


O tline Precoding classification
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OutlineIntroduction



Multiuser transmission techniques

LTE (4G) system

roa an commun ca ons(high data rate and reliability)

Diversity

Time Freq.SignalSpace

Multi-user

Space

Multipath channelModelin

CSI feedback

Analog Digital

Vectorquantization

Quasi-static Time-variant

BEMs AR

LBG

Grassmannian

Random

Scheduling Precoding

Exhaustivesearch

Greed or iterativesearch

Linearmethods

Non-linearmethods

Codebook-based ones

MMSE BD DPC THP PU2RC

Randomuser selection

VP


Outline Precoding classification
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OutlineIntroduction



An example of linear precoding

1,1h

2,1h

Mh ,1

Base Station

Feedback link of

channel state information (CSI)

1s

X

X

X

1,1w

Modulation

Coded bits

of user 1

2,1w

Mw ,1

2s

X

X

X

1,2w

Modulation

Coded bits

of user 2

2,2w

Mw ,2

1,2h2,2h

Mh ,2

Antenna 1

Antenna M

De-mod

Channel

estimator

User 2

De-mod

Channel

estimator

User 1

1y

2y

1 = 1

=1

1,1,+2

=1

2,1,+1, and2 = 2

=1

2,2,+1

=1

1,2,+2


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OutlineIntroduction



Inter-user interference

The received signals at user- can be determined by

=

=1

,, + =1

,, + , , {1, 2},

(1)

where

=1

,, is called as inter-user interferencethat would significantly degrade the performance of the system.Precoding design is to find the weighting coefficients {,}2=1that satisfy the following condition

=1

,, = 0 with ,

{1, 2} (2)to eliminate the inter-user interference

=1 ,,.

The above technique is called as zero-forcing (ZF) precoding.

The problem of finding the weighting coefficients {,}2=1 can beeasily solved by expressing received signals in a vector form.


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OutlineIntroduction



Zero forcing (ZF) precoding formulation

In the presence of two users, the previous equations become12

=

1,1 . . . 1,2,1 . . . 2,

1,1 2,1...

...1, 2,

12

+

12

.

In the presence of users, the received signal can be expressed by:

y = HWs+ z, (3)

where y =

1..

.

, H =

1,1 . . . 1,..

.

..

.

..

.,1 . . . ,

, s =

1..

.

W =

1,1 . . . ,1... . . .

...1, . . . ,

= [w1, . . . ,w] with

w = [,1

, . . . , ,]

, and z = [1

, . . . , ]

.Mobile Communications - Chapter 3: Physical-layer transmissions Section 3.6: Space Division Multiple Access (SDMA) 9

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IntroductionPrecoding

Scheduling (user selection)

gAn example of linear precodingPower allocation in ZF precodingPossible research problems

Zero-forcing precoding formulation (cont.)

To eliminate inter-user interference, precoding matrix W can bedetermined by

W = H(HH

)1H (4)

so thaty = HWs+ z = s+ z. (5)

With precoding, the received signal can be written by

y = Hx+ z, (6)

where x = [1, . . . , ]

= Ws are the transmitted signals in a

vector form at antennas in the base station.Under the power constraint of max at the BS, one has

=12

=

[ x 2

] max, (7)


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gAn example of linear precodingPower allocation in ZF precodingPossible research problems

Power allocation in ZF precoding

The power constraint (7) is equivalent to=1

max. (8)

where =(HH

)1, and =

After ZF precoding, the received signals at users are given by

y =

1...

=

11

...

+

1...

(9)Hence, the resultant sum-rate of the multiuser system is

= max:

=1 max

=1log2 (1 + ) (bps/Hz) (10)


OutlineI d i

Precoding classificationA l f li di
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An example of linear precodingPower allocation in ZF precodingPossible research problems

Power allocation in ZF precoding (cont.)

The optimal power allocation [, {1,...,}] in (10) can beeasily determined by the following waterfilling process

= (/ 1)+ (11)

where + denotes max(, 0), and the water level is chosen to

satisfy=1

( )+ = max. (12)

Given a set of selected users = {1,...,}, the above precodingprocess attempts to eliminate the inter-user interference andmaximize the system sum-rate.

The problem of how to perform user selection (finding the set = {1,...,}) with a reasonable complexity for maximizing thesystem sum-rate will be addressed in the next section.


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Precoding classificationA l f li di
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An example of linear precodingPower allocation in ZF precodingPossible research problems

Precoding in LTE downlink transmissions

Data bits

ofuser 1

Channel

encoderInterleaver Layer

mapper

MQAM

mapper

MQAMmapper

Precoding

OFDMA

modulator

OFDMA

modulator

Precoding matrix

generator

Recovered data bits Channel decoder

Channel

Estimator

OFDMA

Demodulator

BER evaluator

ofuser 1

OFDMA

Demodulator

Channel State

Information (CSI)

MIMO

demapper

Limited feedback

link

User 1

Base Station (BS)

Data bits

ofuser N

Channel

encoderInterleaver Layer

mapper

MQAM

mapper

MQAM

mapper

WX

Y= W*X

BER evaluator

ofuser N

Multipath fading

channel

User N


OutlineIntroduction Exhaustive selection
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Exhaustive selectionGreedy selection

Exhaustive selection

Given a precoding technique, scheduling (user selection) is to find aset of users among all active users to maximize the system sum-rate.

Obviously, the simple optimal method for user selection is exhaustivesearch but its complexity is impractically high as the number of usersis large.


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Exhaustive selectionGreedy selection

Greedy selection

Greedy user selection algorithm1 Initialization: 0 = {1, 2,...,} is the set of all available users

indices0 = {} is the set of selected users initially assigned to a null set. = 0 stands for the number of selected users, initially set to zero.

0 = 0 is the system sum-rate of selected users, initially set to zero.2 Repetition: Assuming that selecting user in the set

maximizes the resulting sum-rate of the system called max.

= + 1If max < 1 or > or > go to Step 3 otherwise do:

= max = 1

{} (select one more user)

= 1{} (ignore user- in later consideration)

Go to Step 2.

3 Stop the user selection process and compute the ZF weightingvectors based on the composite channel matrix of selected users.

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section 3 6 sdma v1

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