advancedcomms-march2014

5/27/2018 AdvancedComms-March2014

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1 2014 The MathWorks, Inc.

Communications Systems Design with MATLAB

Houman Zarrinkoub, Development Manager

Peter Sheridan, Senior Account Manager


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Visit The MathWorks Web Site

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MATLAB Releases

Visit http://www.mathworks.com/products/matlab/whatsnew.html


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Learning Resources

Interactive Video

Tutorials Studentslearn the basics outside

of the classroom withself-guided tutorialsprovided by TheMathWorks MATLAB

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Control Systems

Computational Math

Visit www.mathworks.com/academia/student_center/tutorials


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Self-Paced Training

Immediate access (90 days)

Learn at your own pace

Use features and workflowsthat are considered bestpractices

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MATLAB Fundamentals - $200.pp MATLAB Programming Techniques -

$150.pp


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who design and build the products

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software design, web development, control theory,

signal processing, or embedded systems,

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MATLAB

Simulink

10 popular add-on

products Control System Toolbox

Data Acquisition Toolbox

DSP System Toolbox

Image Processing Toolbox

Instrument Control Toolbox

Optimization Toolbox Signal Processing Toolbox

Simulink Control Design

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Student Version


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Further Information

Visit www.mathworks.com/

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Communications Seminar

Houman Zarrinkoub PhD.Product Manager

LTE & Communications Systems

[email protected]


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Agenda

Baseband signal processing Case study: 802.11n/ac/ad as MIMO-OFDM Systems

Accelerating Communications Simulations

Case study: LTE PDCCH processing


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Another resource


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Baseband signal processing


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Understand MIMO-OFDM systems Examine family of WIFI standards (802.11x) as a MIMO-OFDM

system

Effects of OFDM on bandwidth flexibility

Effects of beamforming on higher performance How to evaluate system performance with presence of

interference , large- and small-scale fading, noise

Link adaptation with modulation and coding changing based on

channel conditions

What we learn today


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Why look at MIMO-OFDM systems?


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What does MIMO-OFDM offer?

Broadband Wireless Communications

High data ratesFlexibility in frequency resource

f

H(f)

WiFi

standard

Fc(GHz)

BW

(MHz)

Data rate

(Mbps)

802.11n {5, 2.4} {20, 40} {6.5, 13.5, ,

540}802.11ac 5 {20, 40, 80,

160}{6.5, 13.5, ,

6240}

802.11ad 60 2160 {385, ,6756.75}


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How does a MIMO-OFDM System work?

Inputbits

ModulationChannelcoding

MIMO ....

Transmitter

Channel

Large-scalefading

(path-loss )

Small-scalefading

(Multipath,Dopplereffects)

Interference

NoiseReceiver

Channeldecoding

De-modulation

MIMOReceiver

(Equalizer)

Channelestimation

OFDMreceiver

OFDMreceiver

Output

bits


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OFDM

Multicarrier transmissionscheme Subdivide wideband channel

into multiple narrowbandorthogonal sub-channels

(subcarriers) Enables Flexible transmission

bandwidths (BW) Robust to multipath fading

High spectral efficiency Low-complexity implementation Works with MIMO transmission


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OFDM implementation

Modulated symbols organized in atime-frequency grid

Grid is composed of data, pilots andcontrol signals

Pilots are pre-determined samplesused for channel estimation

In frequency, symbols align withsubcarriers

Subcarriers are multiples offrequency spacing

OFDM modulation is essentially an

Inverse Fourier Transform (IFFT)plus

Cyclic Prefix (CP) insertion: Appendlast N samples to the beginning

CP insertion ensures orthogonality

among frames at the receiver

/=/ < ( ) 1

=subcarriers =

2

OFDM symbol = n

+3pilots

OFDM modulated symbols


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MIMO

Class of Multi-antenna techniques

Advantages of MIMO techniques Boosting overall data rates Increasing reliability of

communication link

Various types of MIMO/SIMO Receive diversity Transmit diversity Beamforming

Spatial multiplexing

WiFi

standard

MIMO Max. Data

rate

(Mbps)

802.11n Up to 4 sub-streams

540

802.11ac Up to 8 sub-streams

6240

802.11ad (Optional)Beamforming

6756.75


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MIMO implementation

Essentially composed of 2 steps

Layer mapping

Precoding

Layer mapping

Splits modulated symbol streaminto multiple substreams

Precoding Transforms (scales) substreams

In beamforming provides beam-

steering In transmit diversity it creates

orthogonal codes

In spatial multiplexing, each sub-stream is steered to a differentdirection

MIMO layer mapping in

Beamforming Transmit diversity

MIMO precoding in

Beamforming Spatial multiplexing


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Channel modeling & propagation scenarios (1)

Mobile context(WiMAX or LTE)

pathloss

pathloss

InterferingBase

station

SignalBasestation

Multipath

fading


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Channel modeling & propagation scenarios (2)

Stationary context(WiFi)

Signalsource

Interference

source

pathloss

pathloss

Multipath

fading


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Performance evaluation with realistic channel models

Large scale simulations

Transceiver system responding to dynamic channel conditions

Evaluating the combined effects of Fading channels

Interfering signals

Non-linearities front-end receivers

Phase noise, Frequency offset, Timing mismatch, IQ imbalance

Channel estimation & Equalization

Antenna arrays & directional propagation

Beamforming & beamsteering

Challenges in design and evaluation of MIMO-

OFDM systems


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Step-by-step MATLAB demo OFDM as the air interface technology of 802.11x

Beamforming as a MIMO technique

Easy-to-follow end-to-end simulation Graphical test bench

Adjustment of channel characteristics onthe fly

Demonstration:

802.11n/ac/ad as a MIMO-OFDMsystem


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Baseband demo workflow


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Version 1: Baseline - Modulation and Coding

Transceiver with modulation, coding, scrambling(No OFDM or MIMO yet)

Channel (Interferer + path loss) (No multipath fading yet)

Isotropic (non-directional) antennas

Antenna configuration (1x1)

Signal

Source (S)

InterferenceSource (I)


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Version 2: Baseline + OFDM

Introduce OFDM transmission of 802.11x Transceiver with modulation, coding, scrambling & OFDM

transmitter & receiver

Channel (Interferer + path loss) (No multipath fading yet)

Signal

Source (S)



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Version 3: Baseline + OFDM +

Receiver-side beamforming

Introduce Receive-side beamforming

Transceiver with modulation, coding, scrambling, OFDM transmitter& receiver & Receive diversity SIMO (receiver beamforming)

Channel with Interferer + path loss (No multipath fading yet)

Receiver has multiple Antennas (1 to 8)

Signal

Source (S)



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Receiver-side beamforming + Multipath fading

Introduce Receive-side beamforming with Multipath fading

Transceiver with modulation, coding, scrambling, OFDM transmitter& receiver & Receive diversity SIMO (receiver beamforming)

Channel with Interferer + path loss + fading

Receiver has multiple Antennas (1 to 8)

Signal

Source (S)



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Transmit-side beamforming + Multipath fading

Introduce Transmit-side beamforming with Multipath fading

Transceiver with modulation, coding, scrambling, MIMO-OFDMtransmitter & receiver


Transmitter has multiple Antennas (1 to 8)

SignalSource (S)



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Version 6: Baseline + OFDM + Multiuser

Transmit-side beamforming + Multipath fading

Introduce Steered transmission(s) in base station (OFDMA)




Signal

Source (S)



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MIMO beamforming + Multipath fading

Introduce MIMO beamforming (both Tx-side and Rx-side)




Signal

Source (S)



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Communications System Toolbox Modulation, Coding, OFDM, MIMO Fading Channels, Visualizations and

Measurements

Phased Array System Toolbox

Beamforming, Beam steering

DSP System Toolbox Mapping User interface parameters into MATLAB parameters

instantaneously, Spectral Analysis

MATLAB

Live interactive MATLAB testbenches

(Optionally) Computer Vision System Toolbox

Providing live data feeds of telemetry data as transmitted bit stream

What MathWorks product features did we

highlight


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Large scale BER, Block Error Rate, Throughput computations Parallel Processing Toolbox

MATLAB Coder (Genrating C code for design bottlenecks)

Analysis of link impairments Synchronization, Phase noise, Frequency offset, Timing mismatch, IQ imbalance

What else can MATLAB do to help with

communications system modeling


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37 2014 The MathWorks, Inc.

Accelerat ing Commun icat ions System

Simulations in MATLAB


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Why simulation acceleration?

From algorithm exploration to system design Size and complexity of models increases

Time needed for a single simulation increases

Number of test cases increases

Test cases become larger

Need to reduce simulation time during design

simulation time for large scale testing during prototyping


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MATLAB is quite fast

Optimized and widely-used libraries BLAS Basic Linear Algebra Subroutines (multithreaded)

LAPACK Linear Algebra Package

JIT (Just In Time) Acceleration On-the-fly multithreaded code generation for increased speed

Built-in support for vector and matrix operations


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Application

LTE Physical Downlink Control Channel (PDCCH)


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Workflow

Start with a baseline algorithm

Profile it to introduce a performance yardstick

Introduce the following optimizations: Better MATLAB serial programming techniques

Using System objects

MATLAB to C code generation (MEX)

Parallel Computing

GPU-optimized System objects Rapid Accelerator mode of simulation in Simulink


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Simulation acceleration options in MATLAB

MATLAB to C

Users Code

GPU

processing

ParallelComputing

Better MATLAB

code

System objects


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Profiling MATLAB algorithms

Profiler summarizesMATLAB code execution total time spent within each

function which lines of code use the

most processing time

Helps identify algorithmbottlenecks


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Effective MATLAB programming techniques

Pre-allocation Initialize an array using its final size

Helps avoid dynamically resizing arrays in a loop

Vectorization Convert code from using scalar loops to using matrix/vectoroperations

Helps MATLAB leverage processor-optimized libraries forvector processing

Example of pre-allocation

y=[];

forn=1:LEN/Tx

G=[u(idx1(n)) u(idx2(n));...

-conj(u(idx2(n))) conj(u(idx1(n)))];

y=[y;G];

end

y=complex(zeros(LEN,Tx));

y(idx1,1)=u(idx1);

y(idx1,2)=u(idx2);

y(idx2,1)=-conj(u(idx2));

y(idx2,2)=conj(u(idx1));


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Using System objects of

DSP & Communications System Toolboxes

System objects facilitate stream processing

Can accelerate simulation because Decouple declaration from the execution of the algorithms

Reduce overhead of parameter handling in the loop

Most of them implemented as MATLAB executables (MEX)

Example of System objects

functions = Alamouti_DecoderS(u,H)

%#codegen

% STBC Combiner

persistenthTDDec

ifisempty(hTDDec)

hTDDec= comm.OSTBCCombiner(...

'NumTransmitAntennas',2,'NumReceiveAntennas',2);

end

s = step(hTDDec, u, H);


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MATLAB to C code generation

MATLAB Coder

Automatically generatea MEX function

Call the generated MEX

file within testbench Verify same numerical

results

Assess the baseline

function and thegenerated MEX functionfor speed


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Task 1 Task 2 Task 3 Task 4Task 1 Task 2 Task 3 Task 4

Parallel Simulation Runs

Time Time

TOOLBOXES

BLOCKSETS

Worker

Worker

Worker

Worker

>> Demo


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Summary

matlabpool available workers No modification of algorithm

Useparforloop instead of forloop

Parallel computation or simulation

leads to further acceleration More cores = more speed


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Simulation acceleration options in MATLAB

MATLAB to C

Users Code

GPU

processing

Parallel

Computing

Better MATLAB

code

System objects


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What is a Graphics Processing Unit (GPU)

Originally for graphics acceleration, now also used forscientific calculations

Massively parallel array of integer and

floating point processors Typically hundreds of processors per card

GPU cores complement CPU cores

Dedicated high-speed memory


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Why would you want to use a GPU?

Speed up execution of computationally intensivesimulations

For example: Performance: A\b with Double Precision


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Options for Targeting GPUs

1) Use GPU with MATLAB built-in functions

2) Execute MATLAB functions elementwiseon the GPU

3) Create kernels from existing CUDA codeand PTX files

Ease

ofUse

G

reaterContro

l


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Data Transfer between MATLAB and GPU

% Push data from CPU to GPU memory

Agpu = gpuArray(A)

% Bring results from GPU memory back to CPU

B = gather(Bgpu)


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GPU Processing with

Communications System Toolbox

Alternative implementationfor many System objectstake advantage of GPUprocessing

Use Parallel ComputingToolbox to execute manycommunications algorithmsdirectly on the GPU

Easy-to-use syntax

Dramatically accelerate

simulations

GPU System objects

comm.gpu.TurboDecoder

comm.gpu.ViterbiDecoder

comm.gpu.LDPCDecoder

comm.gpu.PSKDemodulator

comm.gpu.AWGNChannel


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Impressive coding gain

High computational complexity

Bit-error rate performance as a function of number ofiterations

Example: Turbo Coding

= comm.TurboDecoder(NumIterations, numIter,


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Acceleration with GPU System objects

Version Elapsed time Acceleration

CPU 8 hours 1.0

1 GPU 40 minutes 12.0

Cluster of 4GPUs

11 minutes 43.0

Same numerical results

= comm.TurboDecoder(NumIterations, N,

= comm.gpu.TurboDecoder(NumIterations, N,

= comm.AWGNChannel(= comm.gpu.AWGNChannel(


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Key Operations in Turbo Coding Function

CPU GPU Version 1

% Turbo EncoderhTEnc = comm.TurboEncoder('TrellisStructure',poly2trellis(4, [13 15], 13),..

'InterleaverIndices', intrlvrIndices)

% AWG Noise

hAWGN = comm.AWGNChannel('NoiseMethod', 'Variance');

% BER measurement

hBER = comm.ErrorRate;

% Turbo Decoder

hTDec = comm.TurboDecoder(

'TrellisStructure',poly2trellis(4, [13 15], 13),...

'InterleaverIndices', intrlvrIndices,'NumIterations', numIter);

ber = zeros(3,1); %initialize BER output

%% Processing loop

while( ber(1) < MaxNumErrs && ber(2) < MaxNumBits)

data = randn(blkLength, 1)>0.5;

% Encode random data bits

yEnc = step(hTEnc, data);

%Modulate, Add noise to real bipolar data

modout = 1-2*yEnc;

rData = step(hAWGN, modout);

% Convert to log-likelihood ratios for decoding

llrData = (-2/noiseVar).*rData;

% Turbo Decode

decData = step(hTDec, llrData);

% Calculate errors

ber = step(hBER, data, decData);

end



% AWG Noise


% BER measurement


% Turbo Decoder

hTDec = comm.gpu.TurboDecoder(

'TrellisStructure',poly2trellis(4, [13 15], 13),...


ber = zeros(3,1); %initialize BER output

%% Processing loop






modout = 1-2*yEnc;




% Turbo Decode


% Calculate errors


end


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Profile results in Turbo Coding Function

CPU GPU Version 1

% Turbo Encoder


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Key Operations in Turbo Coding Function

CPU GPU Version 2



% AWG Noise


% BER measurement


% Turbo Decoder

hTDec = comm.TurboDecoder('TrellisStructure',poly2trellis(4, [13 15], 13),...


%% Processing loop






modout = 1-2*yEnc;




% Turbo Decode


% Calculate errors


end



% AWG Noise

hAWGN = comm.gpu.AWGNChannel ('NoiseMethod', 'Variance');

% BER measurement


% Turbo Decoder - setup for Multi-frame or Multi-user processing

numFrames = 30;

hTDec = comm.gpu.TurboDecoder('TrellisStructure',poly2trellis(4, [13 15], 13),...

'InterleaverIndices', intrlvrIndices,'NumIterations',numIter,

NumFrames,numFrames);

%% Processing loop


data = randn(numFrames*blkLength, 1)>0.5;


yEnc = gpuArray(multiframeStep(hTEnc, data, numFrames));


modout = 1-2*yEnc;




% Turbo Decode


% Calculate errors

ber=step(hBER, data, gather(decData));

end


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Profile results in Turbo Coding Function

CPU GPU Version 2

% Turbo Encoder


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Things to note when targeting GPU

Minimize data transfer between CPU and GPU.

Using GPU only makes sense if data size is large.

Some functions in MATLAB are optimized and can befaster than the GPU equivalent (eg. FFT).

Use arrayfun to explicitly specify elementwiseoperations.


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Summary

Acceleration methodologies in MATLAB & Simulink Technology /

Product1. Best Practices in Programming

Vectorization & pre-allocation

Environment tools. (i.e. Profiler, Code Analyzer)

MATLAB, Toolboxes,

System Toolboxes

2. Better Algorithms

Ideal environment for algorithm exploration

Rich set of functionality (e.g. System objects)

MATLAB, Toolboxes,

System Toolboxes

3. More Processors or Cores

High level parallel constructs (e.g. parfor, matlabpool)

Utilize cluster, clouds, and grids

Parallel Computing

Toolbox,

MATLAB Distributed

Computing Server

4. Refactoring the Implementation

Compiled code (MEX)

GPUs, FPGA-in-the-Loop

MATLAB, MATLAB Coder,

Parallel Computing

Toolbox


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Q & A

Thank You

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