Maximizing Network Capacity of MPR-Capable Wireless Networkswan/mimo/MPR2015slides.pdf · Outline...
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Maximizing Network Capacity of MPR-Capable WirelessNetworks
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 1 / 25
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Outline
Introduction
Exam Algorithms in Single Interference Domain
Approximation Algorithms in General Networks
PTAS
Summary
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 2 / 25
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Network Model
V : networking nodes
Each v 2 V has MPR capability τ (v)
A: communication links
maximum link length = 1interference radius r > 1Each a 2 A has rate c (a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 3 / 25
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Network Model
V : networking nodes
Each v 2 V has MPR capability τ (v)
A: communication links
maximum link length = 1interference radius r > 1Each a 2 A has rate c (a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 3 / 25
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Network Model
V : networking nodes
Each v 2 V has MPR capability τ (v)
A: communication links
maximum link length = 1interference radius r > 1Each a 2 A has rate c (a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 3 / 25
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Network Model
V : networking nodes
Each v 2 V has MPR capability τ (v)
A: communication links
maximum link length = 1
interference radius r > 1Each a 2 A has rate c (a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 3 / 25
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Network Model
V : networking nodes
Each v 2 V has MPR capability τ (v)
A: communication links
maximum link length = 1interference radius r > 1
Each a 2 A has rate c (a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 3 / 25
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Network Model
V : networking nodes
Each v 2 V has MPR capability τ (v)
A: communication links
maximum link length = 1interference radius r > 1Each a 2 A has rate c (a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 3 / 25
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Independence (i.e. Feasibility) with MPR
Independence (i.e. feasibility) of I A1 MPR Constraint: Each node v is the receiver of at most τ (v) linksin I .
2 Interference-free Constraint: Any two links in I with dierentreceivers are interference-free.
I : collection of all independent subsets of A
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 4 / 25
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Independence (i.e. Feasibility) with MPR
Independence (i.e. feasibility) of I A1 MPR Constraint: Each node v is the receiver of at most τ (v) linksin I .
2 Interference-free Constraint: Any two links in I with dierentreceivers are interference-free.
I : collection of all independent subsets of A
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 4 / 25
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Max-Weighted Independent Set (MWIS)
Given a non-negative weight function w on A, nd anindependent subset I of A with maximum total weight
∑e2Iw (e).
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 5 / 25
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Max-Weighted Independent Set (MWIS)
Given a non-negative weight function w on A, nd anindependent subset I of A with maximum total weight
∑e2Iw (e).
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 5 / 25
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Shortest Link Scheduling (SLS)
A link schedule of d 2 RA+:
S =(Ij , xj ) 2 I R+ : 1 j q
s.t.
d (a) = c (a)q
∑j=1
xj jIj \ fagj ;
length (or latency) of S : kSk := ∑qj=1 xj
SLS: Given a d 2 RA+, nd a shortest link schedule of d .
χ (d): length of a shortest schedules of d
Capacity region of the network:nd 2 RA
+ : χ (d) 1o
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 6 / 25
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Shortest Link Scheduling (SLS)
A link schedule of d 2 RA+:
S =(Ij , xj ) 2 I R+ : 1 j q
s.t.
d (a) = c (a)q
∑j=1
xj jIj \ fagj ;
length (or latency) of S : kSk := ∑qj=1 xj
SLS: Given a d 2 RA+, nd a shortest link schedule of d .
χ (d): length of a shortest schedules of d
Capacity region of the network:nd 2 RA
+ : χ (d) 1o
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 6 / 25
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Shortest Link Scheduling (SLS)
A link schedule of d 2 RA+:
S =(Ij , xj ) 2 I R+ : 1 j q
s.t.
d (a) = c (a)q
∑j=1
xj jIj \ fagj ;
length (or latency) of S : kSk := ∑qj=1 xj
SLS: Given a d 2 RA+, nd a shortest link schedule of d .
χ (d): length of a shortest schedules of d
Capacity region of the network:nd 2 RA
+ : χ (d) 1o
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 6 / 25
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Shortest Link Scheduling (SLS)
A link schedule of d 2 RA+:
S =(Ij , xj ) 2 I R+ : 1 j q
s.t.
d (a) = c (a)q
∑j=1
xj jIj \ fagj ;
length (or latency) of S : kSk := ∑qj=1 xj
SLS: Given a d 2 RA+, nd a shortest link schedule of d .
χ (d): length of a shortest schedules of d
Capacity region of the network:nd 2 RA
+ : χ (d) 1o
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 6 / 25
![Page 17: Maximizing Network Capacity of MPR-Capable Wireless Networkswan/mimo/MPR2015slides.pdf · Outline Introduction Exam Algorithms in Single Interference Domain Approximation Algorithms](https://reader035.fdocuments.us/reader035/viewer/2022081522/5fb9045b264edc5999420738/html5/thumbnails/17.jpg)
Shortest Link Scheduling (SLS)
A link schedule of d 2 RA+:
S =(Ij , xj ) 2 I R+ : 1 j q
s.t.
d (a) = c (a)q
∑j=1
xj jIj \ fagj ;
length (or latency) of S : kSk := ∑qj=1 xj
SLS: Given a d 2 RA+, nd a shortest link schedule of d .
χ (d): length of a shortest schedules of d
Capacity region of the network:nd 2 RA
+ : χ (d) 1o
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 6 / 25
![Page 18: Maximizing Network Capacity of MPR-Capable Wireless Networkswan/mimo/MPR2015slides.pdf · Outline Introduction Exam Algorithms in Single Interference Domain Approximation Algorithms](https://reader035.fdocuments.us/reader035/viewer/2022081522/5fb9045b264edc5999420738/html5/thumbnails/18.jpg)
Multi ow
k unicast requests.
Fj : the set of ows in (V ,E ) of the j-th requestA k- ow is a sequence f = hf1, f2, , fki with fj 2 Fj 8j 2 [k ]
valfj: value of fj
∑kj=1fj : cumulative ow of f
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 7 / 25
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Multi ow
k unicast requests.
Fj : the set of ows in (V ,E ) of the j-th request
A k- ow is a sequence f = hf1, f2, , fki with fj 2 Fj 8j 2 [k ]
valfj: value of fj
∑kj=1fj : cumulative ow of f
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 7 / 25
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Multi ow
k unicast requests.
Fj : the set of ows in (V ,E ) of the j-th requestA k- ow is a sequence f = hf1, f2, , fki with fj 2 Fj 8j 2 [k ]
valfj: value of fj
∑kj=1fj : cumulative ow of f
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 7 / 25
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Multi ow
k unicast requests.
Fj : the set of ows in (V ,E ) of the j-th requestA k- ow is a sequence f = hf1, f2, , fki with fj 2 Fj 8j 2 [k ]
valfj: value of fj
∑kj=1fj : cumulative ow of f
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 7 / 25
![Page 22: Maximizing Network Capacity of MPR-Capable Wireless Networkswan/mimo/MPR2015slides.pdf · Outline Introduction Exam Algorithms in Single Interference Domain Approximation Algorithms](https://reader035.fdocuments.us/reader035/viewer/2022081522/5fb9045b264edc5999420738/html5/thumbnails/22.jpg)
Multi ow
k unicast requests.
Fj : the set of ows in (V ,E ) of the j-th requestA k- ow is a sequence f = hf1, f2, , fki with fj 2 Fj 8j 2 [k ]
valfj: value of fj
∑kj=1fj : cumulative ow of f
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 7 / 25
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Maximum Weighted Multi ow (MWMF)
Given that each request j has a weight wj > 0 per unit of its ow, nd a multi ow f = hf1, , fki and a link schedule S of∑kj=1fj such that the length of kSk 1 and the total weight of
f given by
∑kj=1val (fj )wj .
is maximized.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 8 / 25
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Maximum Concurrent Multi ow (MCMF)
Given that each request j has a demand dj > 0, nd amulti ow f = hf1, , fki and a link schedule S of ∑k
j=1fj suchthat kSk 1 and the concurrency of f given by
min1jk
val (fj ) /dj .
is maximized.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 9 / 25
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Algorithmic Issues
At least as hard as those without MPR capability (i.e., τ (v) = 18v 2 V )
Non-applicability of traditional graph-theoretic techniques due to thenon-binary nature of the link independence
Still the same approximality?
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 10 / 25
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Algorithmic Issues
At least as hard as those without MPR capability (i.e., τ (v) = 18v 2 V )Non-applicability of traditional graph-theoretic techniques due to thenon-binary nature of the link independence
Still the same approximality?
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 10 / 25
![Page 27: Maximizing Network Capacity of MPR-Capable Wireless Networkswan/mimo/MPR2015slides.pdf · Outline Introduction Exam Algorithms in Single Interference Domain Approximation Algorithms](https://reader035.fdocuments.us/reader035/viewer/2022081522/5fb9045b264edc5999420738/html5/thumbnails/27.jpg)
Algorithmic Issues
At least as hard as those without MPR capability (i.e., τ (v) = 18v 2 V )Non-applicability of traditional graph-theoretic techniques due to thenon-binary nature of the link independence
Still the same approximality?
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 10 / 25
![Page 28: Maximizing Network Capacity of MPR-Capable Wireless Networkswan/mimo/MPR2015slides.pdf · Outline Introduction Exam Algorithms in Single Interference Domain Approximation Algorithms](https://reader035.fdocuments.us/reader035/viewer/2022081522/5fb9045b264edc5999420738/html5/thumbnails/28.jpg)
Roadmap
Introduction
Exam Algorithms in Single Interference Domain
Approximation Algorithms in General Networks
PTAS
Summary
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 11 / 25
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Exact Algorithm for MWIS
A set of minδin (v) , τ (v) heaviest links in δin (v) for some
v 2 V .
Enumeration
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 12 / 25
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Exact Algorithm for MWIS
A set of minδin (v) , τ (v) heaviest links in δin (v) for some
v 2 V .Enumeration
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 12 / 25
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Exact Algorithm for SLS
B: links with positive demands
Concatenation of SLSs of the demands by δinB (v) for all v 2 VSLS of the demands by δinB (v)
Length
max
8<: maxa2δinB (v )
d (a)
c (a),
∑a2δinB (v )d(a)c(a)
τ (v)
9=; .A wrap-around scheme
(b)(a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 13 / 25
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Exact Algorithm for SLS
B: links with positive demands
Concatenation of SLSs of the demands by δinB (v) for all v 2 V
SLS of the demands by δinB (v)
Length
max
8<: maxa2δinB (v )
d (a)
c (a),
∑a2δinB (v )d(a)c(a)
τ (v)
9=; .A wrap-around scheme
(b)(a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 13 / 25
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Exact Algorithm for SLS
B: links with positive demands
Concatenation of SLSs of the demands by δinB (v) for all v 2 VSLS of the demands by δinB (v)
Length
max
8<: maxa2δinB (v )
d (a)
c (a),
∑a2δinB (v )d(a)c(a)
τ (v)
9=; .A wrap-around scheme
(b)(a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 13 / 25
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Exact Algorithm for SLS
B: links with positive demands
Concatenation of SLSs of the demands by δinB (v) for all v 2 VSLS of the demands by δinB (v)
Length
max
8<: maxa2δinB (v )
d (a)
c (a),
∑a2δinB (v )d(a)c(a)
τ (v)
9=; .
A wrap-around scheme
(b)(a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 13 / 25
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Exact Algorithm for SLS
B: links with positive demands
Concatenation of SLSs of the demands by δinB (v) for all v 2 VSLS of the demands by δinB (v)
Length
max
8<: maxa2δinB (v )
d (a)
c (a),
∑a2δinB (v )d(a)c(a)
τ (v)
9=; .A wrap-around scheme
(b)(a)
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 13 / 25
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Capacity Region
Ω =
8<:d 2 RA+ : ∑
v2Vmax
8<: maxa2δin(v)
d (a)
c (a),
∑a2δin(v)d(a)c(a)
τ (v)
9=; 19=; .
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 14 / 25
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Exact Algorithms for MWMF, MCMF
MWMF:
max ∑kj=1 wj val (fj )
s.t. fj 2 Fj , 81 j k;∑kj=1 fj 2 Ω.
MCMF:
max φs.t. fj 2 Fj , 81 j k;
val (fj ) φdj , 81 i k;∑kj=1 fj 2 Ω.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 15 / 25
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Exact Algorithms for MWMF, MCMF
MWMF:
max ∑kj=1 wj val (fj )
s.t. fj 2 Fj , 81 j k;∑kj=1 fj 2 Ω.
MCMF:
max φs.t. fj 2 Fj , 81 j k;
val (fj ) φdj , 81 i k;∑kj=1 fj 2 Ω.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 15 / 25
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Roadmap
Introduction
Exam Algorithms in Single Interference Domain
Approximation Algorithms in General Networks
PTAS
Summary
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 16 / 25
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Spatial Division
0
0
0
0 0
0
(a) (b)
1 2 34 5 6 7
8
11
0
9 1011
1 2 34 5 6 7
8 9 10
111 2 3
4 5 6 78 9 10
11
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 10
hexagon cells of diameter r 1
associate a link with a cell by receiver
cell labelling: all co-label cells are apart > r + 1
# of labels 7 λ 12 for r 3
80 i < λ, Si the set of non-empty cells with label i
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 17 / 25
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Spatial Division
0
0
0
0 0
0
(a) (b)
1 2 34 5 6 7
8
11
0
9 1011
1 2 34 5 6 7
8 9 10
111 2 3
4 5 6 78 9 10
11
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 10
hexagon cells of diameter r 1associate a link with a cell by receiver
cell labelling: all co-label cells are apart > r + 1
# of labels 7 λ 12 for r 3
80 i < λ, Si the set of non-empty cells with label i
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 17 / 25
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Spatial Division
0
0
0
0 0
0
(a) (b)
1 2 34 5 6 7
8
11
0
9 1011
1 2 34 5 6 7
8 9 10
111 2 3
4 5 6 78 9 10
11
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 10
hexagon cells of diameter r 1associate a link with a cell by receiver
cell labelling: all co-label cells are apart > r + 1
# of labels 7 λ 12 for r 3
80 i < λ, Si the set of non-empty cells with label i
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 17 / 25
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Spatial Division
0
0
0
0 0
0
(a) (b)
1 2 34 5 6 7
8
11
0
9 1011
1 2 34 5 6 7
8 9 10
111 2 3
4 5 6 78 9 10
11
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 10
hexagon cells of diameter r 1associate a link with a cell by receiver
cell labelling: all co-label cells are apart > r + 1
# of labels 7 λ 12 for r 3
80 i < λ, Si the set of non-empty cells with label i
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 17 / 25
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Spatial Division
0
0
0
0 0
0
(a) (b)
1 2 34 5 6 7
8
11
0
9 1011
1 2 34 5 6 7
8 9 10
111 2 3
4 5 6 78 9 10
11
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 1011
1 2 34 5 6 7
8 9 10
hexagon cells of diameter r 1associate a link with a cell by receiver
cell labelling: all co-label cells are apart > r + 1
# of labels 7 λ 12 for r 3
80 i < λ, Si the set of non-empty cells with label i
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 17 / 25
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Divide-And-Conquer Algorithm for MWIS
Conquer: For each non-empty cell S , IS a MWIS of all linksassociated with S .
Combination: Output the heaviest one amongSS2Si IS : 0 i < λ
λ-approixmate
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 18 / 25
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Divide-And-Conquer Algorithm for MWIS
Conquer: For each non-empty cell S , IS a MWIS of all linksassociated with S .
Combination: Output the heaviest one amongSS2Si IS : 0 i < λ
λ-approixmate
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 18 / 25
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Divide-And-Conquer Algorithm for MWIS
Conquer: For each non-empty cell S , IS a MWIS of all linksassociated with S .
Combination: Output the heaviest one amongSS2Si IS : 0 i < λ
λ-approixmate
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 18 / 25
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Divide-And-Conquer Algorithm for SLS
Conquer: 8non-empty cell S , ΓS a SLS of the demands by linksassociated with S .
Combination:
Merge: 80 i < λ, Πi merged schedule from ΓS for all S 2 SiConcatenation: Output Π concatenation of Πi for 0 i < λ
kΠk =λ1∑i=0
maxS2Si
∑v2VS
max
8<: maxa2δin(v )
d (a)
c (a),
∑a2δin(v )d(a)c(a)
τ (v)
9=;
λ-approximate solution.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 19 / 25
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Divide-And-Conquer Algorithm for SLS
Conquer: 8non-empty cell S , ΓS a SLS of the demands by linksassociated with S .
Combination:
Merge: 80 i < λ, Πi merged schedule from ΓS for all S 2 SiConcatenation: Output Π concatenation of Πi for 0 i < λ
kΠk =λ1∑i=0
maxS2Si
∑v2VS
max
8<: maxa2δin(v )
d (a)
c (a),
∑a2δin(v )d(a)c(a)
τ (v)
9=;λ-approximate solution.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 19 / 25
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Divide-And-Conquer Algorithm for SLS
Conquer: 8non-empty cell S , ΓS a SLS of the demands by linksassociated with S .
Combination:
Merge: 80 i < λ, Πi merged schedule from ΓS for all S 2 Si
Concatenation: Output Π concatenation of Πi for 0 i < λ
kΠk =λ1∑i=0
maxS2Si
∑v2VS
max
8<: maxa2δin(v )
d (a)
c (a),
∑a2δin(v )d(a)c(a)
τ (v)
9=;λ-approximate solution.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 19 / 25
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Divide-And-Conquer Algorithm for SLS
Conquer: 8non-empty cell S , ΓS a SLS of the demands by linksassociated with S .
Combination:
Merge: 80 i < λ, Πi merged schedule from ΓS for all S 2 SiConcatenation: Output Π concatenation of Πi for 0 i < λ
kΠk =λ1∑i=0
maxS2Si
∑v2VS
max
8<: maxa2δin(v )
d (a)
c (a),
∑a2δin(v )d(a)c(a)
τ (v)
9=;
λ-approximate solution.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 19 / 25
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Divide-And-Conquer Algorithm for SLS
Conquer: 8non-empty cell S , ΓS a SLS of the demands by linksassociated with S .
Combination:
Merge: 80 i < λ, Πi merged schedule from ΓS for all S 2 SiConcatenation: Output Π concatenation of Πi for 0 i < λ
kΠk =λ1∑i=0
maxS2Si
∑v2VS
max
8<: maxa2δin(v )
d (a)
c (a),
∑a2δin(v )d(a)c(a)
τ (v)
9=;λ-approximate solution.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 19 / 25
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Approximate Capacity Region
Φ =
8<:d 2 RA+ :
λ1∑i=0
maxS2Si
∑v2VS
max
8<: maxa2δin(v)
d (a)
c (a),
∑a2δin(v)d(a)c(a)
τ (v)
9=; 19=; .
λ-approximate:Φ Ω λΦ.
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 20 / 25
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Exact Restricted Muti ows
Φ-restricted MWMF:
max ∑kj=1 wj val (fj )
s.t. fj 2 Fj , 81 j k;∑kj=1 fj 2 Φ.
Φ-restricted MCMF:
max φs.t. fj 2 Fj , 81 j k;
val (fj ) φdj , 81 i k;∑kj=1 fj 2 Φ.
λ-approximations
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 21 / 25
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Exact Restricted Muti ows
Φ-restricted MWMF:
max ∑kj=1 wj val (fj )
s.t. fj 2 Fj , 81 j k;∑kj=1 fj 2 Φ.
Φ-restricted MCMF:
max φs.t. fj 2 Fj , 81 j k;
val (fj ) φdj , 81 i k;∑kj=1 fj 2 Φ.
λ-approximations
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 21 / 25
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Exact Restricted Muti ows
Φ-restricted MWMF:
max ∑kj=1 wj val (fj )
s.t. fj 2 Fj , 81 j k;∑kj=1 fj 2 Φ.
Φ-restricted MCMF:
max φs.t. fj 2 Fj , 81 j k;
val (fj ) φdj , 81 i k;∑kj=1 fj 2 Φ.
λ-approximations
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 21 / 25
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Roadmap
Introduction
Exam Algorithms in Single Interference Domain
Approximation Algorithms in General Networks
PTAS
Summary
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 22 / 25
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PTAS
Constant-bounded the maximum MPR capability τ := maxv2V τ (v)
# of independent links whose interference ranges contained in squareof side L is at most Then,
jI j 4
π/ arcsin r12r 1
τ
πL2.
PTAS for MWIS: shifting + dynamic programming
PTAS for SLS, MWMF, and MCMF: approximation-preservingreductions from MWIS
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 23 / 25
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PTAS
Constant-bounded the maximum MPR capability τ := maxv2V τ (v)
# of independent links whose interference ranges contained in squareof side L is at most Then,
jI j 4
π/ arcsin r12r 1
τ
πL2.
PTAS for MWIS: shifting + dynamic programming
PTAS for SLS, MWMF, and MCMF: approximation-preservingreductions from MWIS
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 23 / 25
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PTAS
Constant-bounded the maximum MPR capability τ := maxv2V τ (v)
# of independent links whose interference ranges contained in squareof side L is at most Then,
jI j 4
π/ arcsin r12r 1
τ
πL2.
PTAS for MWIS: shifting + dynamic programming
PTAS for SLS, MWMF, and MCMF: approximation-preservingreductions from MWIS
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 23 / 25
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PTAS
Constant-bounded the maximum MPR capability τ := maxv2V τ (v)
# of independent links whose interference ranges contained in squareof side L is at most Then,
jI j 4
π/ arcsin r12r 1
τ
πL2.
PTAS for MWIS: shifting + dynamic programming
PTAS for SLS, MWMF, and MCMF: approximation-preservingreductions from MWIS
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 23 / 25
![Page 62: Maximizing Network Capacity of MPR-Capable Wireless Networkswan/mimo/MPR2015slides.pdf · Outline Introduction Exam Algorithms in Single Interference Domain Approximation Algorithms](https://reader035.fdocuments.us/reader035/viewer/2022081522/5fb9045b264edc5999420738/html5/thumbnails/62.jpg)
Roadmap
Introduction
Exam Algorithms in Single Interference Domain
Approximation Algorithms in General Networks
PTAS
Summary
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 24 / 25
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Summary
Exam Algorithms in Single Interference Domain
Approximation Algorithms in General Networks
PTAS
Future work: arbitrary interference radii
P.-J. Wan, F. Al-dhelaan, X. Jia, B. Wang, and G. Xing ([email protected])Maximizing Network Capacity of MPR-Capable Wireless Networks 25 / 25