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Acknowledgements: Dyonisius Dony Ariananda (TU Delft)
Siavash Shakeri (TU Delft)
Roberto López-Valcarce (UVigo)
EUSIPCO 2014
Lisbon, Portugal
Compressive Covariance Sensing A New Flavor of Compressive Sensing
Geert Leus Delft University of Technology
Zhi Tian Michigan Technological
University
Daniel Romero University of Vigo
2
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
Open Issues
Conclusions
3
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
Open Issues
Conclusions
4
Emerging Challenges
Very large arrays
Sampling rate issue Need for compressive techniques
Impulse radio
Cognitive radio (CR)
(Ultra-)wideband signals
Massive MIMO
Large Arrays
5
Compressed Sensing
Large bandwidths require high sampling rates
Popular alternative is compressive sensing (CS)
o Random linear projections of Nyquist rate samples
o Multiple sparse reconstruction techniques
[Donoho, 2006] [Candès et al, 2006] [Tropp, 2004]
6
Spectrum Estimation
Most compressive spectrum estimation methods estimate the
spectrum (or signal itself) using CS methods
Underdetermined problems sparsity constraint
o High computational complexity
o Difficult performance analysis
Observation: many applications just require second-order
statistics (power spectrum)
Overdetermined problems
o Low computational complexity
o Easy performance analysis
o Sparsity or positivity constraints can also be included
7
Covariance and Spectrum Estimation
Cognitive radio (CR)
frequency spectrum Radar
Doppler + angular spectra
Radio astronomy
spatial spectrum
Medical Imaging
resonance spectrum
Seismic
seismic design response spectrum
8
Acquisition of Wideband Signals
RF circuit choices: multiple NB or single WB ?
Multiple, fixed RF chains
Preset LO filter range
Simple detection per BPF
Single, flexible RF chain
burden on A/D: fs ~ GHz
complex wideband sensing
wideband (WB) circuit
A/D LNA AGC
Fixed LO
Wideband
Sensing
WB filter
SNReff
A/D LNA
LO1
A/D LNA AGC
A/D LNA AGC
LO2
LON
Band 1
Band 2
Band N
multiple narrowband (NB) circuits
NB filter SNReff
AGC
Challenge: reduce the sampling rate without sacrificing bandwidth
9
Angular Spectrum Estimation
Array processing
o Imaging
Optical/radar/ultrasound/acoustic
Radio Astronomy
Seismology
o DoA estimation localization
Source: IAI Inc.
Acquisition and processing hardware
prop. to #antenna elements
Challenge: reduce number of antennas
without sacrificing resolution
10
Roadmap
Introduction
Compressive Covariance Sensing
o Problem definition
o Covariance structures
o Compression schemes
o Modal Analysis
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
Open Issues
Conclusions
11
Compressive Covariance Sensing
Problem definition:
Structure:
Compression operation:
Remarks:
NO SPARSITY NEEDED
compression
uncompressed
signal
compressed
signal
SOS:
Estimate from , multiple ’s, or
12
Second-Order Statistics
Stationary input:
Auto-correlation:
Power Spectral Density (PSD):
compression
uncompressed
signal
compressed
signal
Frequency domain PSD
time frequency
Angular domain PSD
space angle
13
Covariance Structure
All covariance matrices are Hermitian and positive semi-definite
Typical structures
o Toeplitz:
Constant along diagonals
Stationary time-signals
Uniform linear arrays (ULA)
Modal analysis
o Circulant:
Toeplitz + property
diagonal in the freq. domain
o -Banded
Toeplitz + property
Encompassing model: Basis Expansion Model (BEM)
basis matrices
14
Covariance Structure
Toeplitz Circulant
real unknowns real unknowns
d-Banded
real unknowns
BEM representation:
15
Compression Schemes
We consider throughout linear compression schemes
Focus on:
o Frequency PSD Time-domain autocorrelation Time compression
o Angular PSD Space-domain autocorrelation Spatial compression
compression
uncompressed
signal
compressed
signal
16
Periodic Compression
with
Given and estimate
# blocks
Uncompressed
domain
Compressed
domain
Kronecker notation
17
Compression in the Time Domain
Nyquist-rate
sampling
MUX
Compressive ADC (conceptual model)
Periodic Acquisition
18
Compression in the Time Domain
Implementations
o Multi-coset sampling [Herley-Wong,1999][Venkataramani-
Bresler,2000]
o Random demodulator [Tropp et al, 2010]
o Modulated wideband converter [Mishali-Eldar,2010]
o Random modulator pre-integrator [Becker, 2011][Yoo et al, 2012]
[Mishali-Eldar,2010]
Modulated wideband converter
19
Compression in the Spatial Domain Uniform linear array (ULA)
Periodic Acquisition
20
Compression in the Spatial Domain
Implementation:
o Sparse some antennas are active subarrays [Hoctor-Kassam, 1990], [Moffet, 1968]
o Dense all antennas are active analog beamforming [Wang-Leus-Pandharipande, 2009],[Wang-Leus, 2010],[Venkateswaran-Van der
Veen, 2010]
[Wang-Leus, 2010]
21
PSD Estimation from
Dense PSD estimation
o Fourier transform
o Application:
Frequency domain PSD estimation of time stationary signals
Angular domain incoherent imaging (continuous source distribution)
Sparse PSD estimation
o Modal analysis
o Application:
Frequency domain frequency estimation of a sum of sinusoids in noise
Angular domain direction of arrival (DoA) estimation (discrete source
distribution)
Estimate from
22
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
o Covariance Estimation
Least Squares
Maximum Likelihood
Modal Analysis
o Covariance Detection
o Sample Statistics Pre-Processing
Sampler Design
Advanced Techniques
Open Issues
Conclusions
23
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
o Covariance Estimation
Least Squares
Maximum Likelihood
Modal Analysis
o Covariance Detection
o Sample Statistics Pre-Processing
Sampler Design
Advanced Techniques
Open Issues
Conclusions
24
Major steps:
1. Identify relation between and
2. Identify relation between and
3. Estimate using (pre-processed) sample estimates
4. Invert the relation with least squares (LS)
5. Reconstruct
Least Squares Estimation [Ariananda-Leus, 2012]
[Leus-Ariananda, 2011]
25
Least Squares Estimation
Sample
Estimate
Least
Squares
Covariance
Reconstruction
Compressed
domain Uncompressed
domain
• Overdetermined system
• Unique reconstruction if
full (column) rank
Design of is critical!!
[Ariananda-Leus, 2012]
[Leus-Ariananda, 2011]
26
LS: Toeplitz/Circulant/Banded Matrices
Toeplitz Circulant
real unknowns real unknowns
d-Banded
real unknowns
27
LS Estimation: PSD
Power Spectrum:
LS estimate:
Improvements:
PSD is non-negative
OR
PSD is sparse (can be relaxed)
OR
Covariance is pos. semidefinite
28
Simulations: Frequency PSD Estimation
Least Squares reconstruction
space of Hermitian Toeplitz d-banded matrices
Complex baseband representation of OFDM signal:
o 16-QAM data symbols
o 8192 tones in band
o 3072 active tones in
o Cyclic prefix length of 1024
o SNR of 10 dB
and
Start with length-42 minimal sparse ruler,
Larger cases by randomly adding extra rows
29
Simulations: Frequency PSD Estimation
MSE of the estimated PSD
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
10 -2
10 -1
Compression rate [M/N]
MS
E
Sparse Ruler (I=549)
Sparse Ruler (I=1646)
Sparse Ruler (I=3291)
Sparse Ruler (I=5485)
Nyquist (I=549)
Nyquist (I=1646)
Nyquist (I=3291)
Nyquist (I=5485)
30
Simulations: Frequency PSD Estimation
Reconstructed PSD
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 -15
-10
-5
0
5
10
Normalized Frequency ( p rad/sample)
Po
wer/
Fre
qu
en
cy (
dB
/rad
/sam
ple
)
Theoretical Noisy PSD
Sparse Ruler (460746 samples, M/N=0.5)
31
Maximum Likelihood
If is well designed:
Maximum Likelihood (ML) estimate:
independent independent It suffices to estimate
32
Maximum Likelihood
[Burg et al, 1982]
Sample estimate
Gaussian Gaussian
Numerical solution Covariance matching
Inverse Iteration Algorithm (IIA)
Trading performance with computation
Pre-processed sample estimates [Romero-Leus,2013a]
Asymptotic approximations: COMET [Ottersten et al, 1998],
SPICE [Stoica et al, 2011], etc.
33
Simulations: Wideband Spectrum Sensing
primary transmitters
-th transmitter normalized PSD
received power:
Estimators:
o LS estimators: Weighted LS and constrained & weighted LS
o Approx. ML estimation: SIIA
bandpass Gaussian signals with disjoint support
white Gaussian noise
C-ADC:
[Romero-Leus, 2013a]
34
Simulations: Wideband Spectrum Sensing
Strong compression ratios may result in a small performance loss
35
Modal Analysis: Observation Model
# sources/sinusoids
Space domain Time domain
36
What kind of sources do we have?
o Uncorrelated sources: is diagonal is Toeplitz
o Correlated sources? [Ariananda-Leus, 2012, 2013]
Modal Analysis
[Pillai et al,1985] [Abramovich et al,1998,1999]
[Pal-Vaidyanathan, 2010, 2011] [Shakeri-Ariananda-Leus, 2012]
[Yen-Tsai-Wang, 2013], [Krieger-Kochman-Wornell, 2013]
Uncompressed domain Compressed domain
37
Modal Analysis: Standard Methods
Correlation matrix of :
More measurements than sources
MUSIC:
MVDR:
[Bresler, 2008], [Mishali-Eldar, 2009]
[Wang-Pandharipande-Leus, 2010]
38
Modal Analysis: Gridding-Based Methods Grid of frequencies/angles and virtual
sources/sinusoids on this grid source vector
is overcomplete basis for but is sparse
Sparse reconstruction [Malioutov et al, 2005] [Mishali-Eldar, 2009] [Tropp et al, 2010]
More measurements than sources!!
39
Modal Analysis: Virtual Sampler/Array
Uncorrelated sources:
represents a virtual sampler/array of virtual
samples/antennas receiving virtual sources/sinusoids
equations
unknowns
40
Modal Analysis: Virtual Sampler/Array
Problem: virtual sources are constant or fully coherent
o Gridding
: LS : sparsity/positivity
Sparse sampling (antenna selection):
o MUSIC or MVDR with spatial smoothing
Virtual sampler/array should be uniform!
sparse ruler
o Translate problem into circulant covariance matrix
Uniform gridding required with grid points
circular sparse ruler
[Shakeri-Ariananda-Leus, 2012]
41
Simulations: DoA Estimation
Least Squares and MUSIC reconstruction
space of Hermitian Toeplitz matrices
ULA of and available antenna positions (aperture)
o Minimal sparse ruler array Virtual ULA of 2x36-1 antennas
o Two-level nested array
Inner array of 5 and outer array of 6 antennas
Virtual ULA of 2x36-1 antennas
o Co-prime array
9 antennas spacing 2 and 3 antennas spacing 9
Virtual ULA of only 2x20-1 antennas
1600 time samples
SNR of 0 dB
42
Simulations: DoA Estimation
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-40
-35
-30
-25
-20
-15
-10
-5
0
Direction of Arrival [sin()]
No
rma
lize
d S
pe
ctr
um
[d
B]
Least Squares Method vs. MUSIC Method
LS method
MUSIC method
Reconstructed spectrum using LS and MUSIC for the minimal sparse ruler array
(S=17 sources with 10 degrees of separation; for LS S=71)
43
Simulations: DoA Estimation
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Direction of Arrival [sin()]
No
rma
lize
d S
pe
ctr
um
[d
B]
Least Squares Method vs. MUSIC Method
LS method
MUSIC method
Reconstructed spectrum using LS and MUSIC for the minimal sparse ruler array
(continuous source from 30 to 40 degrees; for LS S=71)
44
Simulations: DoA Estimation
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-60
-50
-40
-30
-20
-10
0
Direction of Arrival [sin()]
No
rma
liz
ed
Sp
ec
tru
m [
dB
]
Minimal Sparse Ruler Array
Two-Level Nested Array
Coprime Array
Reconstructed spectrum using MUSIC (S=21 sources with 7 degrees separation)
45
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
o Covariance Estimation
Least Squares
Maximum Likelihood
Modal Analysis
o Covariance Detection
o Sample Statistics Pre-Processing
Sampler Design
Advanced Techniques
Open Issues
Conclusions
46
Covariance Detection
Binary hypothesis test
Problem statement:
Depending on prior information: Neyman-Pearson, generalized
likelihood ratio test (GLRT), Bayesian, etc
Noise covariance
Signal covariance
Given decide or
GLRT:
[Kay, 1998]
47
Covariance Detection
GLRT: alternative formulation
Exact computation of ML estimates expensive
Alleviate computational cost
o Replace ML estimates by approximations
o Smoothing/cropping sample statistics
BEM formulation:
[Romero-Leus,2013a]
[VázquezVilar-LópezValcarce, 2011]
given
48
Covariance Detection: Spectrum Sensing
Wideband spectrum sensing formulation:
GLRT:
Efficiency approximate ML estimators
#signals
Power -th signal
Is the -th primary
user transmitting
ML estimator under
ML estimator under
[Romero-Leus,2013a]
[VázquezVilar-LópezValcarce, 2011]
49
Simulation: Wideband Spectrum Sensing
Test on the user with
primary transmitters
-th transmitter normalized PSD
received power:
Estimators:
o LS estimators: WLS, CWLS
o ML estimator: LIKES
o Approx. ML estimation: SSPICE, SIIA, SLIKES
bandpass Gaussian signals with disjoint support
white Gaussian noise
C-ADC:
[Romero-Leus,2013a]
50
Simulation: Wideband Spectrum Sensing
Approximate ML estimators achieve a similar performance at a much reduced cost
51
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
o Covariance Estimation
Least Squares
Maximum Likelihood
Modal Analysis
o Covariance Detection
o Sample Statistics Pre-processing
Sampler Design
Advanced Techniques
Open Issues
Conclusions
52
Sample Statistics Pre-Processing
LS and ML work on
Sometimes several observations of are available
• Spatial auto-correlation average along time
• Temporal auto-correlation average along space Dual domain averaging
53
Sample Statistics Pre-Processing
Dual domain averaging
54
Sample Statistics Pre-Processing
MUX
MUX
MUX
Dual domain averaging
55
Sample Statistics Pre-Processing
If a single observation of is given
o Raw sample estimate may result in poor performance
o Smoothing exploiting periodic structure:
Controlling the bias/variance trade-off windowing/cropping
o Given blocks make be with
o May also help to control complexity
stationary
Rows of
stationary
Smoothed estimate
56
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
o Design criteria
o Sparse samplers
o Dense samplers
Advanced Techniques
Open Issues
Conclusions
57
Sampler Design: Compression Model
General structure for
o Temporal compression
o Spatial compression
o …
Model can represent
o Periodic sampling
o Non-periodic sampling
o Sparse sampling
o Dense sampling
58
Design Problem
# blocks
Uncompressed
domain
Compressed
domain Compression Ratio
Find conditions for to allow estimation of from
Maximize the compression ratio among the admissible samplers
Goals
59
Covariance Structure
Modal analysis
Multi-band signal
Toeplitz subspace
Banded subspace
Circulant subspace
Uncompressed
domain
Compressed
domain
Dimension:
60
Design Criteria
The identifiability of is preserved
linearly independent linearly independent
A matrix defines an -covariance sampler
if the associated function is invertible.
Focus on
Toeplitz subspaces
banded subspaces
Circulant subspaces
Universal cov. samplers
61
Sparse Samplers
Architectures:
o Space domain sampling Sub-array
o Time domain sampling C-ADC
Set representation:
62
(Linear) Sparse Rulers
Difference set:
Sparse ruler:
Minimal sparse ruler
Suboptimal designs: nested, co-prime, …
[Rédei-Rényi, 1949] [Leech, 1956] [Wichmann, 1963] [Moffet, 1968] [Miller, 1971] [Wild,
1987] [Pearson et al, 1990] [Linebarger et al, 1993] [Ariananda-Leus, 2012]
[Wichmann, 1963] [Pearson et al, 1990] [Linebarger et al, 1993] [Pumphrey, 1993]
[Pal-Vaidyanathan, 2010] [Pal-Vaidyanathan, 2011]
is a length- sparse ruler
w/o repetition
63
Circular Sparse Rulers
Modular difference set:
Circular sparse ruler:
Minimal circular sparse ruler
is a length- circular sparse ruler
w/o repetition
[Singer, 1938] [Miller, 1971] [Ariananda-Leus, 2012] [Romero-Leus, 2013b]
[Krieger-Kochman-Wornell, 2013] [Romero-LópezValcarce-Leus, 2014]
64
Sparse Samplers: Toeplitz Subspace
Sparse sampler Toeplitz subspace
covariance sampler
linear sparse ruler
Optimum Sampler
minimal linear sparse ruler
[Rédei-Rényi, 1949] [Leech, 1956] [Pearson et al, 1990][Romero-LópezValcarce-Leus, 2014]
65
Sparse Samplers: Circulant Subspace
Sparse sampler Circulant subspace
covariance sampler
circular sparse ruler
Optimum Sampler
minimal circular sparse ruler
[Romero-LópezValcarce-Leus, 2014]
66
Sparse Samplers: Banded Subspace
Sparse sampler -banded subspace
covariance sampler
linear sparse ruler
circular sparse ruler
incomp. sparse ruler
[Ariananda-Leus, 2012][Romero-LópezValcarce-Leus, 2014]
67
Dense Samplers
Architectures:
o Space domain sampling
o Time domain sampling C-ADC
Design:
o Similar to CS random designs
o Existing random designs
Continuous distributions
Cov. samplers with probability one
Attain compression limits
analog
beamforming
[Romero-LópezValcarce-Leus, 2014]
68
-covariance sampler a.s. iff
Random Sampling: Compression Limits
Toeplitz subspace:
Banded subspace:
Circulant subspace:
drawn from a cont. distrib.
[Romero-LópezValcarce-Leus, 2014]
69
Optimal Samplers: Summary
(transpose the table)
70
Sampler Design: Summary
71
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
o Cyclostationarity
o Multiband spectrum estimation
o Distributed Compressive Covariance Sensing
o Dynamic Sampling
Open Issues
Conclusions
72
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
o Cyclostationarity
Estimation
Sampler Design
o Multiband spectrum estimation
o Distributed Compressive Covariance Sensing
o Dynamic Sampling
Open Issues
Conclusions
73
Cyclostationary modulated signals
Cyclic features reveal critical signal parameters:
o carrier frequency
o symbol rate
o modulation type
o timing, phase etc.
Non-cyclic signals (e.g. noise) do not possess cycle frequencies
Periodic autocorrelation Cyclic spectrum
2x Fourier Transform
74
Noise Suppression in the Cyclic Domain
Power spectrum density (PSD)
(a = 0)
Spectral correlation density (SCD)
Hig
h S
NR
L
ow
SN
R
Multi-harmonics
peaks at
Energy detection vs. cyclic feature detection e.g., [Sahai-Cabric, 2005]
✔ no noise components when
75
BPSK signal alone
Source Separation in Cyclic Domain
Cyclostationarity-based approach to detection
o resilient against Gaussian noise
o robust to multipath
o can differentiate modulation types and separate interference
o insensitive to unknown signal parameters
Overlapping in PSD, separable in SCD
Spectral correlation density (SCD) e.g., [Gardner, 1988]
White noise plus
five AM interferences BPSK in noise plus
five AM interferers
76
Second-order Cyclic Statistics
Suppose is periodic in t with period T
Periodic autocorrelation and cyclic spectrum
Cyclic frequency: Frequency: f
Stack samples over a block of multiple ( ) cyclic periods
o # samples per block:
compression
uncompressed
signal
compressed
signal
T=1: x[t] stationary T>1: x[t] cyclostationary
77
Covariance Structure
Cyclostationary signals with period T
Covariance structure
o Within one cyclic period: dense, with DoF = T2
o Over a block of N cyclic periods
block Toeplitz
DoF
e.g.
78
Compressive samples
Compressive measurements: M samples per block
Corss-correlation of compressive samples
It can be shown that
v.s.
79
Vector-form Relationships: Covariances
Assuming limited support we obtain
is a block-circulant matrix, which allows for block-diagonalization
80
Vector-form Relationships: Cyclic Spectrum
Since , delays in
Hence, uniquely determined by samples in f
Since is related to by DFT and DTFT operations:
is a invertible matrix, so
81
Cyclic Spectrum Reconstruction
If then we can use simple least squares (LS)
When this is only possible if (no compression)
When (e.g., when ) this is possible even for
Advantages
Computational simplicity
Performance guarantees (e.g., probability of false alarm)
When the period of the sampler is larger than the period of the
cyclostationarity, we can reconstruct the cyclic spectrum by LS
82
C-ADC
(sub-Nyquist)
Sparse cyclic
spectrum
reconstruction
Cyclic feature
detection
Cyclic feature
classification
Wideband aspect:
multiple signal sources
frequency pow
er
Cyclic Spectrum Estimation for CR
83
Example: Robustness to Rate Reduction
Probability of detection vs. compression ratio (PFA= 0.1, #blocks=32, 200)
Monitored band |fmax| < 300 MHz
2 sources (noise-free): PU1 - BPSK at 150MHz;
PU2 - QPSK at 225MHz; Ts=0.02667μs
Cisco 802.11 DSSS
Spread spectrum
50%
compression
50%
compression
84
Example: Robustness to Noise Uncertainty
Outperforms energy detection and insensitive to noise uncertainty
Receiver Operating Characteristic (ROC): PD vs PFA (SNR=5dB, 50% compression)
Energy detection
(noise uncertainty = 0, 1, 2, 3dB)
cyclic
85
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
o Cyclostationarity
Estimation
Sampler Design
o Multiband spectrum estimation
o Distributed Compressive Covariance Sensing
o Dynamic Sampling
Open Issues
Conclusions
86
Sampler Design for Cyclostationary Signals
Sampler design for reconstructing cyclic spectrum
o Multiple samplers used periodically across N blocks
Proposition: it is possible to reconstruct non-
sparse 2D cyclic spectrum in closed form when
Original signal x[n] Structure of Rx Compressed signal y[n]
Stationary Toeplitz Cyclostationary
Cyclostationary
(period T)
Block Toeplitz
(block size NT )
Cyclostationary
(period M)
Circular
sparse ruler
[Leus-Tian, 2011]
87
Cyclostationary Signals: Intuition
Stationary signal
Input: stationary with
cross-correlations
Output: cyclostationary
with period
cross-correlations
Increase of degrees of freedom!
88
Cyclostationary Signals: Intuition
Cyclostationary signal
Input: cyclostationary
with period
cross-correlations
Output: cyclostationary
with period Q
cross-correlations
No increase of degrees of freedom!
89
Cyclostationary Signals: Intuition
Cyclostationary signal
Input: cyclostationary
with period
cross-correlations
Output: cyclostationary
with period
cross-correlations
To increase degrees of freedom the period of the sampler needs to be
larger than the period of the cyclostationarity!
[Leus-Tian, 2011]
90
Our goals:
reconstruct from using least squares (LS)
System of Equations
must have full column rank.
selection matrix
collection of
correlations of
collection of
correlations of
at blocks-lag at blocks-lag
91
Minimal Sparse Ruler Sampling
One possible sampling pattern that leads to a full
column rank minimal sparse ruler based design
Example:
Other are set to
identity matrix empty matrix
Minimal sparse ruler based design obtain the minimum
compression for the case when each is equal to either [] or
92
Example
Consider based on length-5 minimal sparse ruler, we
have: and
All
have full
column rank
93
Exploiting Correlations Between -blocks
94
System of equations:
correlations of
When is not constrained to either [] or
greedy algorithm [Ariananda-Leus, 2014] to design
sub-optimal compression rate
at blocks-lag
Exploiting Correlations Between -blocks
95
full
column
rank!
full
column
rank!
Example: , and
as well as
full column
rank!
full column
rank!
Exploiting Correlations Between -blocks
96
constrained to or for each
Maximum compression minimal circular sparse ruler
Example:
Other are set to
Exploiting Correlations Between -blocks
[Ariananda-Leus, 2014]
97
8 10 12 14 16 18 20 220.2
0.25
0.3
0.35
0.4
0.45
The value of N
Ac
hie
ve
d C
om
pre
ss
ion
Ra
te
Minimal Circular SparseRuler (all T)
Greedy Algorithm T=18
Greedy Algorithm T=22
Greedy Algorithm T=26
Greedy Algorithm T=30
Compression Rate
98
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
o Cyclostationarity
o Multiband spectrum estimation
o Distributed Compressive Covariance Sensing
o Dynamic Sampling
Open Issues
Conclusions
99
Temporal Compression
Nyquist-
rate
samples
MUX
...
...
analog-to-information converter (AIC)
100
Spatial Compression
uniform linear
array (ULA)
...
...
...
...
[Krieger et al, 2013]
101
With compression vector:
Data Model
Compute the DTFT:
and
Nyquist-rate case vector
We then have:
102
Multi-bin Approach
Write and collect in
We have an IDFT matrix
Consider the multiband model and divide into bins
...
[Mishali-Eldar, 2009]
103
Diagonal/Circulant Correlation Matrix
If the bin size is equal to the largest band
is diagonal [Yen-Tsai-Wang, 2013]
The correlation matrix of :
is circulant [Ariananda-Romero-Leus, 2013, 2014]
104
full column rank
universal and M/N > 1/2
full column rank
circular sparse ruler
Diagonal/Circulant Correlation Matrix Given
Multicoset sampling/array
...
...
If has full column rank, we can perform LS ( )
ML methods can also be employed [Romero-Leus, 2013]
[Yen-Tsai-Wang, 2013]
[Ariananda-Romero-Leus, 2013, 2014]
105
Sample Estimates
Q: Over which domain do we average?
A: The dual domain!
...
...
...
...
Spatial-domain
Compression
106
Sample Estimates Time-domain
Compression
...
MUX ... MUX ...
...
MUX ...
...
107
Simulations
Number of samples: block size:
Number of blocks:
Sensing based on length- minimal circular sparse ruler
=> active cosets:
Averaged over 200 independent realizations
Six bands (user signals) => details can be found in the paper.
White Gaussian noise for each realization; variance:
108
Simulations
-0.4 -0.2 0 0.2 0.4
0
50
100
150
200
2p ( 2p) (radian)
No
rma
liz
ed
Ma
gn
itu
de
/Fre
qu
en
cy
(mW
/ra
dia
n/s
am
ple
)
M/N=0.278
Nyquist rate
109
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
o Cyclostationarity
o Multiband spectrum estimation
o Distributed Compressive Covariance Sensing
o Dynamic Sampling
Open Issues
Conclusions
110
Distributed Sensing: Partial Observation
Each sensor cluster
may estimate a subset
of the total lags
Sampling rate reduction
[Ariananda-Romero-Leus, 2014]
LS estimation
111
Distributed Sensing: Sampler Design
Necessary condition
Number
of sensor
clusters
Modular dif. set
Non-overlapping circular
Golumb rulers
[Ariananda-Romero-Leus, 2014]
112
Simulation
113
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
o Cyclostationarity
o Multiband spectrum estimation
o Distributed Compressive Covariance Sensing
o Dynamic Sampling
Open Issues
Conclusions
114
Dynamic Sampling
Changing sub-array configurations allows detecting more correlated
sources than antennas
Uncorrelated sources:
represents a virtual sampler/array of virtual
samples/antennas receiving virtual sources/sinusoids
equations
unknowns
115
Dynamic Sampling
Assume the sources are correlated
Problem: degrees of freedom are not increased
Solution: periodic compression with slots per period
o Periodic (in time) antenna selection
o Periodic (in space) sample selection
[Ariananda-Leus, 2012]
116
Correlated Sources
Combining the correlations from slots
represents a virtual sampler/array of virtual samples/antennas
receiving virtual sources
Spatial smoothing not directly possible, so gridding left:
Over-determined: LS
Under-determined: sparsity or positivity constraints
Universality: every pair of samples appears in a period
117
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
Open Issues
Conclusions
118
Open Issues
Super resolution
Big data
Combining spatial and
temporal domains
Extensions to Doppler
spectrum, imaging, …
Applications to radar,
MRI, seismic, radio
astronomy, …
119
Open Issues
Large-scale networks
Sample size issue sparsity-enforcing regularization
Internet backbone network (Abilene) Disease gene network
120
Sparse event detection using large-scale wireless sensor nets
o Local phenomena induce spatially sparse signals
o Desiderata: energy efficiency, scalability, robustness
Centralized
FUSION
CENTER
Decentralized
FUSION
CENTER Scalability Robustness
Infrastructureless
Structural Health Monitoring
Floor 1
Floor 2
Floor 3
Floor 4
Floor 5
Floor 6
Floor 7
Floor 8
Floor 9
Floor 10
Floor 11
Floor 12
Bay 1 Bay 2 Bay 3 Bay 4 Bay 5 Bay 6 Bay 7 Bay 8 Bay 9 Bay 10
(2,1) (2,2) (2,3) (2,4) (2,5) (2,6) (2,7) (2,8) (2,9) (2,10)
(3,1) (3,2) (3,3) (3,4) (3,5) (3,6) (3,7) (3,8) (3,9) (3,10)
(4,1) (4,2) (4,3) (4,4) (4,5) (4,6) (4,7) (4,8) (4,9) (4,10)
(5,1) (5,2) (5,3) (5,4) (5,5) (5,6) (5,7) (5,8) (5,9) (5,10)
(6,1) (6,2) (6,3) (6,4) (6,5) (6,6) (6,7) (6,8) (6,9) (6,10)
(7,1) (7,2) (7,3) (7,4) (7,5) (7,6) (7,7) (7,8) (7,9) (7,10)
(8,1) (8,2) (8,3) (8,4) (8,5) (8,6) (8,7) (8,8) (8,9) (8,10)
(9,1) (9,2) (9,3) (9,4) (9,5) (9,6) (9,7) (9,8) (9,9) (9,10)
(10,1) (10,2) (10,3) (10,4) (10,5) (10,6) (10,7) (10,8) (10,9) (10,10)
(11,1) (11,2) (11,3) (11,4) (11,5) (11,6) (11,7) (11,8) (11,9) (11,10)
(12,1) (12,2) (12,3) (12,4) (12,5) (12,6) (12,7) (12,8) (12,9) (12,10)
(13,1) (13,2) (13,3) (13,4) (13,5) (13,6) (13,7) (13,8) (13,9) (13,10)
[Ling-Tian et al, 2009]
Distributed optimization
[Ling-Tian, 2010, 2011]
Open Issues
121
Roadmap
Introduction
Compressive Covariance Sensing
Covariance Estimation and Detection
Sampler Design
Advanced Techniques
Open Issues
Conclusions
122
Conclusions: CS vs. CCS
Compressed Covariance Sensing Compressed Sensing
Aims at recovering statistics Aims at recovering the signal
Lossy Lossless
Use sparse sampling
Use random sampling
No sparsity is required Sparsity is required
Linear/non-linear reconstruction Non-linear reconstruction
Overdetermined Underdetermined
123
Conclusions
Aiming at reconstructing the second-order statistics
o Compressive samples
o Linear compression
o Linear/non-linear covariance structures
Estimation/detection:
o LS
o ML
o GLRT
Sampler design
o Sparse samplers
o Dense samplers
Advanced techniques
124
Thank you!
125
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