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SUPER: Sparse signals with Unknown Phases Efficiently Recovered
Sheng Cai, Mayank Bakshi, Sidharth Jaggi and Minghua Chen
The Chinese University of Hong Kong
b
Compressive Sensing
2
?
? n
m
k
I. Introduction
b
Compressive Phase Retrieval
2
?
?
k
I. Introduction
-2eiπ/3
Complex number
m
n
2 x → -x
x →eiθx
b
Compressive Phase Retrieval?
?
k
I. Introduction
Applications: X-ray crystallography, Optics, Astronomical imaging…
m
n
2
Complex number
Compressive Phase Retrieval
2
?
?
I. Introduction
Our contribution: 1. O(k) number of measurements (best known O(k)[1]) 2. O(klogk) decoding complexity (best known O(knlogn) [2])
[1] H. Ohlsson and Y. C. Eldar, “On conditions for uniqueness in sparse phase retrieval,” e-prints, arXiv:1308.5447[2] K. Jaganathan, S. Oymak, and B. Hassibi, “Sparse phase retrieval: Convex algorithms and limitations,” in 2013 IEEE International Symposium on Information Theory Proceedings (ISIT), 2013, pp. 1022–1026.
bm
n
x2
x6
x5
x4
x3
b1
b2
b3
b4
x1
Bipartite graph
n signal nodes O(k) measurement nodes
k non-zerocomponents
II. Overview/High-Level Intuition
3
x2
x6
x5
x4
x3
b1
b2
b3
b4
x1
Bipartite Graph → Measurement Matrix
II. Overview/High-Level Intuition & IV. Measurement Design
x1 x2 x3 x4 x5 x6
b1
b2
b3
b4
Adjacent Matrix
4
x2
x6
x4
b1
b2
b3
b4
Useful Measurement Nodes
n signal nodes O(k) measurement nodes
II. Overview/High-Level Intuition
5
b1
b2
b3
b4
x2
x6
x4
Doubleton
Multiton
Singleton
Useful Measurement Nodes
II. Overview/High-Level Intuition
5
b1
b2
b3
b4
x2
x6
x4
Doubleton
Multiton
Singleton
Useful Measurement Nodes
Magnitude recovery
Phase recovery
Resolvable
Resolvable
Δ
|x2+x4|
|x2|
|x4|Solving a quadratic equation
“Cancelling out” process:
II. Overview/High-Level Intuition & V. Reconstruction Algorithm
5
Three Phases
…
n signal nodes
… …
Seeding Phase: Singletons and Resolvable Doubletons
… Geometric-decay Phase:Resolvable Multitons
…
Cleaning-up Phase:Resolvable Multitons
O(k) measurement nodes
II. Overview/High-Level Intuition
6
Seeding Phase
II. Overview/High-Level Intuition
…
n signal nodes
…
GI
H
7
Seeding Phase
II. Overview/High-Level Intuition
…
n signal nodes
…
GI
“Sigma” Structure
7
x1 x2Hx2
x1
Seeding Phase
II. Overview/High-Level Intuition
…
n signal nodes
…
GI
H’
7
H
1/2
Geometric-decay phase
II. Overview/High-Level Intuition
…
n signal nodes
…
GII,l
1/4
H’
8
H
Geometric-decay phase
II. Overview/High-Level Intuition
…
n signal nodes
…
GII,l
1/8 O(k/logk)
O(loglogk) stages
H’
8
H
Cleaning-up Phase
II. Overview/High-Level Intuition
…
n signal nodes
…
GIII
|V(H’)|=k
H’
9
H
Seeding Phase
II. Overview/High-Level Intuition & III. Graph Properties
…
n signal nodes
…
ck measurement nodes
…
GI
with prob. 1/k
H
H ’Many SingletonsMany Doubletons
10
Geometric-decay phase
II. Overview/High-Level Intuition & III. Graph Properties
…
n signal nodes
…
ck/2 measurement nodes
…
GII,lH
H ’
with prob. 2/k
O(loglogk)
Many Multitons
11
Geometric-decay phase
II. Overview/High-Level Intuition & III. Graph Properties
…
n signal nodes
…
ck/4 measurement nodes
…
GII,lH
H ’
with prob. 4/k
O(k/logk)
O(loglogk)
11
Many Multitons
Cleaning-up Phase
II. Overview/High-Level Intuition & III. Graph Properties
…
n signal nodes
…
c(k/logk)log(k/logk) = O(k) measurement nodes
…
GIIIH
H ’
|V(H’)|=k
with prob. logk/k
Many Multitons
12
x2
x6
x5
x4
x3
b1
b2
b3
b4
x1
Bipartite Graph → Measurement Matrix
II. Overview/High-Level Intuition & IV. Measurement Design
Adjacent Matrix
13
x1 x2 x3 x4 x5 x6
b1
b2
b3
b4
x2
x5
b1
x1
Bipartite Graph → Measurement Matrix
II. Overview/High-Level Intuition & IV. Measurement Design
x1 x2 x3 x4 x5 x6
b1
b2
b3
b4
13
x2
x5
b1
x1
Bipartite Graph → Measurement Matrix
II. Overview/High-Level Intuition & IV. Measurement Design
x1 x2 x3 x4 x5 x6
b1
b2
b3
b4
α = (π/2)/nunit phase
b1,1
b1,2
b1,3
b1,4
b1,5
13
x2
x5
b1
x1
Bipartite Graph → Measurement Matrix
II. Overview/High-Level Intuition & V. Reconstruction Algorithm
α = (π/2)/nunit phase
b1,1
b1,2
b1,3
b1,4
b1,5
arctan(b1,2/ib1,1)/ α = 2Guess:
x2 ≠ 0 and |x2| = b1,1/cos2α
Verify: |x2| = b1,5 ?
13
Seeding Phase: Giant Connected Component
VI. Parameters Design
O(k) different edges in graph H’(By Coupon Collection)
O(k) right nodesEach edge appears with prob. 1/k
O(k) right nodes are singletons O(k) right nodes are doubletons
Size of H’ is (1-fI)k(By percolation results)
H
H’
EXPECTATION!
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Geometric-decay Phase
VI. Parameters Design
O(fII,l-1k) different nodes appendedin graph H’ (By Coupon Collection)
O(fII,l-1k) right nodesEach edge appears with prob. 1/fII,l-1k
O(fII,l-1k) right nodes are resolvablemultitons
H
H’
EXPECTATION!
15
VII. Performance of Algorithm
Number of Measurements
…
n signal nodes
… ……
…
Seeding Phase
Geometric-decayPhase
Cleaning-upPhase
cfII,l-1k measurement nodes
O(k)
ck measurement nodes
O(k)
c(k/logk)log(k/logk) measurement nodes
O(k)
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Decoding Complexity and Correctness
• BFS: O(|V|+|E|) for a graph G(V,E). O(k) in the seeding phase.• “Cancelling out”: O(logk) for a right node. Overall decoding
complexity is O(klogk).
• (1-εII,l-1)fII,l-1<gII,l-1<(1+εII,l-1)flI,l-1 hold for all l.
– Generalized/traditional coupon collection– Chernoff bound– Percolation results– Union bound
VII. Performance of Algorithm
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THANK YOU謝謝