mwc-system expander implementation using filter banks final presentation
DESCRIPTION
MWC-System Expander Implementation using Filter Banks Final Presentation. Supervisors: Professor Yonina Eldar Deborah Cohen Raz Lifshitz , 052856721 Assaf Bismut , 300316684. Goals. Learn the principles of the Sub Nyquist theory and MWC system - PowerPoint PPT PresentationTRANSCRIPT
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MWC-System
Expander Implementation using Filter Banks
Final Presentation
Supervisors: Professor Yonina Eldar Deborah Cohen
Raz Lifshitz, 052856721Assaf Bismut, 300316684
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Goals
- Learn the principles of the Sub Nyquist theory and MWC system
- Comprehend the purpose of the expander
- Comprehend the theory behind the implementation
- Implement the expander using filter banks
- Compare the performance between the old implementation
and the new implementation
MWC Expander Theory ComparisonConclusionsFilter Banks
Goals
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Learn the principles of the Sub Nyquist theory and MWC system
Goals MWC Expander Theory ComparisonConclusionsFilter Banks
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Goals MWC Expander Theory ComparisonConclusions
MWC – Part I: Analog to Digital ComponentInput: The sparse signal (original signal)Output: M digital channels
Filter Banks
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Input: M digital channelsOutput: The final recovered signal
Goals MWC Expander Theory ComparisonConclusions
MWC – Part II: DSP Component
Filter Banks
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Goals MWC Expander Theory ComparisonConclusions
Comprehend the Purpose of the Expander
Filter Banks
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The main issue: In order to reconstruct the original signal, M equations are necessary, where every equation is represented by a physical channel. M must fulfill: M>2N, where N is the number of bands of the original signal. Thus, the burden on hardware becomes significant.
Trading channels for sampling rateThe solution: In order to reduce hardware overload, we will combine every “Q” channels into one. This will be done by increasing the bandwidth of each channel by Q. As a result the sampling rate is expedited. For separating back the channels, we will use the Expander.
Preventing burden on hardware
Goals MWC Expander Theory ComparisonConclusionsFilter Banks
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Goals MWC Expander Theory ComparisonConclusions
ExpanderYi[n]
M channelsYi,Q[n]
Q*M channels
For example, Q=3, in the Frequency domain:
Expander
Yi[n]Xi,1[n]
Xi,2[n]Xi,3[n]
Expander – General Architecture
Filter Banks
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Goals MWC ExpanderTheory ComparisonConclusions
Comprehend the theory behind the implementation
Filter Banks
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Q
Q
Q
Q
Goals MWC ExpanderTheory ComparisonConclusions
Primary implementation Hybrid implementation
LPF Q LPF Q
Expander Implementations
Filter Banks
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Goals MWC ExpanderTheory ComparisonConclusions
Expander ImplementationFilter Banks
Filter Banks
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Goals MWC ExpanderTheory ComparisonConclusions
Polyphase - TheoryGiven FIR filter order N
The filter can be written as
Filter Banks
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Goals MWC ExpanderTheory ComparisonConclusions
Or
The result is M decimated filters
Filter Banks
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Goals MWC ExpanderTheory ComparisonConclusionsFilter Banks
Q
Q
Q
Q
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Goals MWC ExpanderTheory ComparisonConclusions
We will observe the case:
Filter Banks - Theory
Filter Banks
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Goals MWC ExpanderTheory ComparisonConclusions
We will observe the case:
Filter Banks - Theory
H0 can be written as a sum of its polyphase parts:
Filter Banks
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Goals MWC ExpanderTheory Filter Banks ComparisonConclusions
Set it to the Hk (z) phrase (the wanted filter):
DFT of (P0,P1….Pq-1 )
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Implement the Expander using filter banks
Goals MWC Expander Theory FilterBanksComparisonConclusions
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Goals MWC Expander Theory FilterBanksComparisonConclusions
Expander ImplementationFinal Filter Banks Architecture
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Goals MWC Expander Theory FilterBanksComparisonConclusions
Input = IDFT Output
CircularSignalShift
DC
DCFast Frequencies
Fast Frequencies
Fast Frequencies
Output
Low Frequencies
Low Frequencies
Low Frequencies
Low Frequencies
Circular Signal Shift
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InitialPhaseFixer
Goals MWC Expander Theory FilterBanksComparisonConclusions
Unlike we assumed in theory, as a result of the analog filter, the first sample of the signal doesn't arrive in t=0.
In the simulator we implement the analog filter by using an digital filter. The order of the digital filter is 5,000.
Initial Phase Fixer
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Goals MWC Expander Theory FilterBanksComparisonConclusions
* Looking at one equation
Expander output (Before phase fix) Expected Expander output
InitialPhaseFixer
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Goals MWC Expander Theory FilterBanksComparisonConclusions
Initial Phase Fixer
Output Input
InitialPhaseFixer
. . .
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Goals MWC Expander Theory FilterBanksComparisonConclusions
Initial Phase problem – solution #2
Another solution to fix the initial phase problem is by setting the analog filter length in a way that : t0 MOD qT =0When t0 is the filter delay
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Goals MWC Expander Theory FilterBanksComparisonConclusions
DFT VS FFTProblem: A unit that uses the FFT algorithm to calculate the IDFT can’t provide Wq
kn twiddles because q is an odd number (Therefore q isn’t a power of 2).
Though it is faster to calculate the IDFT of a signal using the FFT algorithm than the direct way. It is negligible because we calculate the IDFT of a short signal (q=3,5…15).
Hardware that calculates the IDFT directly is less common then the ones that uses the FFT algorithm and therefore more expensive, less accurate, and less efficient.
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Goals MWC Expander Theory FilterBanksComparisonConclusions
DFT VS FFTA way to use the FFT unit:Though Q is an odd number. The expander can be designed for every number including those in the power of two.
Example: Q=4
Expander (Q=4)(Using FFT)
* Looking at a single channel.
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Expander (Q=4)(Using FFT)
L=4 LPF M=3
We can use Interpolation/ Decimation before the expander that uses the FFT unit to get the same output
EXP for q=3
Goals MWC Expander Theory FilterBanksComparisonConclusions
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Goals MWC Expander Theory FilterBanksComparisonConclusions
NFAA (No Filter at All)
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Goals MWC Expander Theory FilterBanksComparisonConclusions
NFAA (No Filter at All)
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Goals MWC Expander Theory FilterBanksComparisonConclusions
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Compare the performance between the old implementation and the new implementation
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Quantity of CM (Complexity Multiplies) -Theory 1. Primary: Calculate CM using the fast rate frequency for both LPF’s and the
exponents.2. Hybrid: Calculate CM using the fast rate frequency only for the
exponents. While it use a lower rate to calculate the CM of the LPF’s3. Ours: Calculate CM using the low rate frequency for the LPF, IDFT, and the
exponents.
Table: Number of CM per second Filter Banks Polyphase Primary
M[(L+q2 + q-1)/q] fs(q) ≈ M(L/q)fs(q) M[(q-1)+L]fs(q) ≈ MLfs(q) M[Lq+(q-1)]fs (q)
(*) CM with one was not included, therefore there are only q-1 CM with exponents
M: Hardware Channels ; L: Digital Filter length q: Parameter q of the MWC system ; CM: Complex Multiplies
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Quantity of CM (Complexity Multiplies) -Theory
2 4 6 8 10 12 14 160
1
2
3
4
5
6x 10
12 Multiply/Sec
q
Multip
ly/Se
c
HybridOursNFAAPrimary
2 4 6 8 10 12 14 160
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
11 Multiply/Sec
q
Multip
ly/Se
c
HybridOursNFAA
Ours Hybrid PrimaryM[(L+q2 + q-1)/q] fs(q) M[(q-1)+L]fs(q) M[Lq+(q-1)]fs (q)
(*) While fs(q) =fs(base) *q ; fs,(base) =2.4e+07
PolyphaseFilter BanksNo Filter
Primary
PolyphaseFilter BanksNo Filter
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Quantity of CM (Complexity Multiplies) - Simulation
N = 4
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Quantity of CM (Complexity Multiplies) - Simulation
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Correlations - Simulation
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
SNR Simulation
In the case that Mq >2N, there is a 100 % success.For the special case that Mq =2N :
Average of:(q, N, M) = (3,6,4), (5,10,4), (7,14,4)
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
SNR Simulation
(q, N, M) = (3,6,4) (q, N, M) = (5,10,4)
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Primary VS Filter BanksPrimary VS Filter Banks implementation ParameterFilter Banks Imp. requires less Complex Multiplies per second than the Primary imp.
Complex Multiplies per second
Filter Banks imp. has lower execution time than the Primary Imp.
Execution time
Both of the imp. have the same sensitivity to the SNR SNR
Both of the imp. have the same delay Delay
When the signal passes the recovery support stage the correlation of the Filter Banks imp. is slightly better than the primary imp.
Correlation
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Goals MWC Expander Theory FilterBanks ComparisonConclusions
Appendix
Plot of the polyphase filter:
q = 5
q = 3
q = 7