Connecting Theory and Practice – Part A

Spring 2013Final Part A Presentation

TechnionIsrael Institute of Technology

Professor: Yonina EldarSupervisor:

Debby Cohen

Consultants:

Eli Shoshan, Rolf Hilgendorf

Students: Etgar Israeli, Shahar Tsiper

Project Definition and Goals Work Plan Review Project Main Activities:

◦ Matlab Algorithm + Integration◦ AWR Activities

Simulations Gantt

Contents

Understanding, fixing and improving the Matlab code

Learning AWR tool and Modeling MWC Deeper understanding of the main issues the

system suffers from Integrating Co-Projects into one Matlab code

Main Challenges

◦ Matlab Reconstruction◦ AWR Activities – Part A◦ AWR Activities – Part B◦ A-Matrix Calibration◦ MWC Development Support Systems

Project Main Stages

Matlab reconstruction algorithm◦ The current algorithm

What is there, what is missing Tuning detection SBR4 (SBR2 ?) SBR Special How do we want to display results of reconstruction

Modulate back up to show the original signal Combine slices, find signal and demodulate to

baseband Enter first draft of MWC schematic

Work Plan Review – Matlab

Completed

Starting AWR activities◦ Understand current schematics of analog part of

new MWC◦ Get understanding of AWR tool◦ Define method for in- and output files

Matlab , CSV etc.◦ Enter first draft of MWC schematic

Work Plan Review – AWR activities

Completed

Matlab◦ Calculating recovery success % with correlation

technique◦ Complete rewrite of Sample and Expander

algorithms◦ Implementing support recovery with thresholds◦ Complete rewrite and debug of MWC System code◦ Full consistency check with article

Additional Tasks Completed

Integration from co-projects◦ Integrating & comparing different Mixing Series◦ Integrating Filter Banks Expander

Finally - Achieving great recovery results without noticeable redundant harmonics

Additional Tasks Completed

AWR◦ Precise modelling of most linear components

using S Parameters – Waiting for new card schematics to complete

◦ Mixing Series full integration into AWR◦ Almost complete modelling of old MWC card

More details in last meeting’s summary

More tasks completed

Main issues solved:◦ Redundant Harmonics have been (almost) eliminated◦ Reconstruction (-1) factor has been removed. Entire

Reconstruction method has been rebuilt◦ Parameters names are now consistent throughout the code -

mostly L, L0, m, M, etc. ◦ Fixing “minor” things – for example:

exp(jωt) vs. exp(-jωt) A.’ (transpose) vs. A’ (conj-transpose) A=SFD vs. A=conj(SFD)

Matlab Code Debug

Features Added◦ Constructing matlab libraries by subjects◦ Consistency Check◦ Error check – Original vs. Recovery signal◦ Improving signal generation (qpsk, sinc and it’s

powers)◦ Integrating different mixing series

Matlab Code Improvements

Full article-code consistency check has been implemented

Conditions must be met or user must authorize manual override

Consistency Check

Error check – Original vs. Recovery◦Comparison Method: Correlation between Original & Recovery

Signal:

◦ Where :◦ c = xcorr(x,y) returns the Cross-Correlation sequence in a length 2N-1 vector, where x and y

are length N vectors (if x and y are not the same length, the shorter vector is padded to the

length of the longer vector).

By default, xcorr computes raw correlation with no normalization.

◦ In the denominator, 2-norm of x and y – for normalization

◦ Matlab code (function handle):

CorrXY = @(x,y)max(abs(xcorr(x,y)))/(norm(x,2)*norm(y,2));

max | ( , ) | , 0 1|| || || ||xcorr x yx y

Error check – Original vs. Recovery

Parameter ValueSignal Type QPSK

Mix Series Type GoldN 4

Hardware Channels

4

q 5B 20MhzFp 24Mhz

Fnyq 6.144GhzM 263

Expander Type FIR

Frequency Domain Zoom-In

Time Domain Zoom-In

Console OutputParameter ValueSignal Type QPSK

Mix Series Type GoldN 4

Hardware Channels 4q 5B 20MhzFp 24Mhz

Fnyq 6.144GhzM 264

Expander Type FIR

MWC Analog Schematics

AWR – Top System ViewSig

Generator (AWR

or Matlab)

Pre Processi

ngSplitter + Mixer

Post Processi

ngSampling

+ A/D

Output to

Matlab Server

AWR – Hierarchy

Output to Matlab Server

Sampling + A/D

Post Processing

Splitter + Mixer

Pre Processing

Sig Generator (AWR or Matlab)

Signal Generator Analog generated input from AWR Digital input from Matlab is partially implemented

In this block all of the components are accurately modeled with S-Parameters

Pre Processing

AWR unable to model mixer in the way we intended. Fallback option – Mathematical Multiplier Mixing series are generated in Matlab

Mixer

LPF-105 was found inadequate for signal properties Missing properties for buffers and output driver

Post Processing

Quantization doesn’t work perfectly yet - WIP

Sampling + A/D

4 digital channels are multiplexed into 1 channel That channel is demuxed in Matlab environment Timing AWR and Matlab with triggers – WIP Full DSP of AWR output and A matrix calibration – Main open Task

Output to Matlab Server

Understanding AWR Signal Types Signals in AWR can be modeled with 4 different

types:◦ Real Signal – Signals are modeled using real numbers

as a very dense function of time (5*Fnyquist)◦ Complex – Signals are modeled using complex

numbers as a dense function of time◦ Complex Envelope – Signal data contains carrier wave

and modulated information separately◦ Digital Signal – I/O to/from Matlab

Complex Envelope Type AWR utilizes the CE representation of signals whenever possible

to gain the tremendous advantage in simulation:

This representation, that is utilized in all mixer components to shorten simulations by order-of-magnitude, annihilates the wide spectrum of the signals that are mixed with the series

S-Parameters - Background

S-Parameter files contain the behavior of linear components for different frequencies

They represent the following LTI system:

Usually our components will have b1 and a2 connected to GND Information for adding components using S-Parameters will be

documented in the project book

S-Parameters - Example LFCN-105_Plus25degC.S2P

Mix Series Modeling Frequency, M and tRise were all taken into

account:

Sine Signal as MWC Input Sine wave was used as MWC input Fp = Fnyq/M = 6.144GHz/261 ≈ 23.5Mhz Sine wave frequency - 400Mhz ≈ 17 Fp At the MWC output, we expect to see evenly spaced

deltas, by 23.5Mhz between them Different hardware channels should differ only by

amplitude Results comply with theory

Reminder: the mixer is implemented as a Mathematical Multiplier!

Sine Signal MWC Output – Zoom Out

dB

Sine Signal MWC OutputdB

Simulations – Results & Conclusions

◦Different kind of simulations had been made, with Constant/variable SNR. For example:

Demonstrate fp ≥ B condition Comparing Different mixing series Collapsed vs. non collapsed channels –

m = const { m ≡ q*HardwareChannels } Demonstrate m ≥ 2N Condition

Demonstrate M ≥ L condition

Comparison Method (Reminder)

◦ Comparison Method: Correlation between Original & Recovery Signal:

◦ Where :◦ c = xcorr(x,y) returns the Cross-Correlation sequence in a length 2N-1 vector, where x and

y are length N vectors (if x and y are not the same length, the shorter vector is padded to

the length of the longer vector).

By default, xcorr computes raw correlation with no normalization.

◦ In the denominator, 2-norm of x and y – for normalization

◦ Matlab code (function handle):

CorrXY = @(x,y)max(abs(xcorr(x,y)))/(norm(x,2)*norm(y,2));

max | ( , ) | , 0 1|| || || ||xcorr x yx y

Different fp comparison

For constant B, sampling with different fp rate

fp = [0.8,1,1.2,1.4]*B were taken

fp ≥ B is necessary for blind recovery

Different fp comparison

Simulation Parameters:Signal – 'sinc‘Mix Series Type - 'GoldSeries'N = 6Hardware Channels = 4q = 5B = 20MhzFp = 16,20,24,28 Mhz M = 499Fnyq = 6.144GhzNumber of simulations = 10

Comparison Between Different Mixing Series - WIP

Series Types:◦ Random Sequences (Using Matlab Function ‘rndsrc’)◦ One Random Sequence and it’s shift◦ Gold Sequence◦ One Gold Sequence and it’s shift◦ Lu M Sequences◦ Lu Lagendre Sequences

SNR = 30

Comparison Between Different Mixing Series – Ch=4, q=5

Simulation Parameters:Signal – 'sinc'N = 6HardwareChannels = 4q = 5B = 20MhzFp = 24MhzM = 263Fnyq = 6.144GhzSNR = 30dBNumber of simulations = 300

Comparison Between Different Mixing Series – Ch=20, q=1

Simulation Parameters:Signal – 'sinc'N = 6HardwareChannels = 20q = 1B = 20MhzFp = 24MhzM = 263Fnyq = 6.144GhzSNR = 30dBNumber of simulations = 120

Comparison Between Different Mixing Series

Interim conclusions: For HardwareChannels=4, q=5:

◦ ‘rndsrc’ – Most of the times gives good results ◦ Lu M and shifted Sequences - Mediocre results ◦ Gold Sequence – best results.◦ Lu Lagendre – bad results! (most of the time

Support recovery doesn’t succeed)

Comparison Between Different Mixing Series

Interim conclusions: For HardwareChannels=20, q=1:

◦ ‘rndsrc’ – Best results.◦ Gold Sequence – Mediocre results.◦ Lu Lagendre Sequence – Mediocre results

Different M comparison

For constant parameters, sampling with different series length

M = [155, 191, 263, 299] were taken fp = 24Mhz, q=5, fnyq =6.144*109

L0 = = 130 L = 2L0+1 = 261 M = = 257

M ≥ L and M ≥ Mmin is necessary for blind recovery

Different M comparison

Simulation Parameters:Signal – 'sinc‘Mix Series Type - 'GoldSeries'N = 6Hardware Channels = 4q = 5B = 20MhzFp = 24 Mhz M = [155, 191, 263, 299]Fnyq = 6.144GhzNumber of Simulations = 30

Collapsed vs. non collapsed – Sim1

For constant parameters, and constant SNR sampling with different number of q & hardware channels

HardwareChannels = [1,2,3,4,6,10,20] were taken q = [1,3,5,9,15,21] were taken m ≡ q*HardwareChannels

m≥2N is necessary for blind recovery

Collapsed vs. non collapsed – Sim1

Simulation Parameters:Signal – 'sinc‘Mix Series Type - 'GoldSeries'N = 6Hardware Channels = [1:4,6,10,20] q = [1,3,5,9,15,21] B = 20MhzFp = 16,20,24,28 Mhz M = 263Fnyq = 6.144GhzSNR = 30dBNumber of simulations = 30

Collapsed vs. non collapsed – Sim2

Let m ≡ q*HardwareChannels Setting m = constant, Comparing results for variable

SNR m = 105 was taken

◦ HardwareChannels = [105,35,21,15,1] and respectively q = [1,3,5,7,105] were taken

◦ N = 42 was taken

Collapsed vs. non collapsed – Sim2

Simulation Parameters:Signal – 'sinc‘Mix Series Type - 'Gold'N = 42Hardware Channels = [105,35,21,15,1] q = [1,3,5,7,105] B = 12MhzFp = 24 Mhz M = 399Fnyq = 6.144GhzNum of simulations = 30

Deeper theory understanding, improving and integrating new technologies into the Matlab code

Solving AWR issues Combining AWR and Matlab into one seamless

system Implementing the solutions on the actual system Writing comprehensive literature, covering main

methodologies used in AWR

Future Challenges

AWR◦ AWR Analog output synchronization with Matlab

Server – WIP◦ Track&Hold + A/D – WIP◦ Mixer and buffers aren’t modeled correctly – More

research necessary.

Tasks in progress - AWR

AWR◦ Inserting digital signal to AWR from Matlab◦ Experimenting with AWR Noise Figures until

reaching adequate levels◦ Solving Mixer’s modelling issues

More details in last meeting’s summary

Open Tasks

Matlab:◦ Used for full modeling of the MWC system –

Already given – need to be fixed

◦ Calibration Methods AWR:

◦ Implementing an analog model of the entire MWC system.

◦ Linking the analog AWR frontend and the digital Matlab backend

Labview:◦ Implementing calibration procedure

Systems Used In Project

Project Gantt – 2nd Stage Summer

2013week 1 20/10

week 2 27/10

week 3 3/11

week 4 10/11

week 5 17/11

week 6 24/11

week 7 1/12

Mid Presentation

week 8 8/12

week 9 15/12

week 10 22/12

week 11 29/12

week 12 05/01

week 13 12/01

week 14 19/01

Writing AWR literature                              

Refine MWC design                             

creation the input mixing series (AWR or matlab)    

                         

Integrating AWR output to Matlab                             

Implemetation of T&H + A/D - AWR                             

Get final models for all components                             

Integrating matlab signal to AWR                             

Ensure synchronization between patterns                             

Enter final schematic after solving AWR issues

              

           

Basic Verification of output data using matlab

                        

   

AWR and Matlab real-time loopback           

              

Anti-aliasing filter response               

            

Creat logic design for LabView               

             

Implementing the solutions on the actual system                              

Thank You!

Spring 2013Final Part A Presentation

Supervisors: Rolf Hilgendorf, Debby CohenStudents: Etgar Israeli, Shahar Tsiper

TechnionIsrael Institute of

Technology