Metropolitan-Scale Radio Resource Management€¦ · Metropolitan-Scale Radio Resource Management...
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Background Spectrum management Optimized allocation Performance conclusion Metropolitan-Scale Radio Resource Management Dongning Guo in collaboration with Zhiyi Zhou, Binnan Zhuang, Ermin Wei, Michael L. Honig Department of Electrical Engineering and Computer Science Northwestern University LIDS Smart Urban Infrastructures Workshop May 11, 2017 Dongning Guo p. 1 Metropolitan-Scale Radio Resource Management
Transcript of Metropolitan-Scale Radio Resource Management€¦ · Metropolitan-Scale Radio Resource Management...
Background Spectrum management Optimized allocation Performance conclusion
Metropolitan-Scale Radio Resource Management
Dongning Guo
in collaboration with Zhiyi Zhou, Binnan Zhuang, Ermin Wei, Michael L. Honig
Department of Electrical Engineering and Computer ScienceNorthwestern University
LIDS Smart Urban Infrastructures WorkshopMay 11, 2017
Dongning Guo p. 1
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
New spectrum and technologies for 5G and IoT
I Heterogeneous frequency resources, including lower frequencies andmillimeter wave bands (e.g., 700 MHz, 28 GHz, 37 GHz, 39 GHz, 64-71GHz, ...);
I Many small cells;
I Many antennas (massive MIMO);
I Full duplex;
I Massive/grant-free access for IoT;
I Non-orthogonal multiple access for IoT;
I Traffic and interference will vary significantly from cell to cell.
Dongning Guo p. 2
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
New spectrum and technologies for 5G and IoT
I Heterogeneous frequency resources, including lower frequencies andmillimeter wave bands (e.g., 700 MHz, 28 GHz, 37 GHz, 39 GHz, 64-71GHz, ...);
I Many small cells;
I Many antennas (massive MIMO);
I Full duplex;
I Massive/grant-free access for IoT;
I Non-orthogonal multiple access for IoT;
I Traffic and interference will vary significantly from cell to cell.
Dongning Guo p. 2
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
New spectrum and technologies for 5G and IoT
I Heterogeneous frequency resources, including lower frequencies andmillimeter wave bands (e.g., 700 MHz, 28 GHz, 37 GHz, 39 GHz, 64-71GHz, ...);
I Many small cells;
I Many antennas (massive MIMO);
I Full duplex;
I Massive/grant-free access for IoT;
I Non-orthogonal multiple access for IoT;
I Traffic and interference will vary significantly from cell to cell.
Dongning Guo p. 2
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
New spectrum and technologies for 5G and IoT
I Heterogeneous frequency resources, including lower frequencies andmillimeter wave bands (e.g., 700 MHz, 28 GHz, 37 GHz, 39 GHz, 64-71GHz, ...);
I Many small cells;
I Many antennas (massive MIMO);
I Full duplex;
I Massive/grant-free access for IoT;
I Non-orthogonal multiple access for IoT;
I Traffic and interference will vary significantly from cell to cell.
Dongning Guo p. 2
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
New spectrum and technologies for 5G and IoT
I Heterogeneous frequency resources, including lower frequencies andmillimeter wave bands (e.g., 700 MHz, 28 GHz, 37 GHz, 39 GHz, 64-71GHz, ...);
I Many small cells;
I Many antennas (massive MIMO);
I Full duplex;
I Massive/grant-free access for IoT;
I Non-orthogonal multiple access for IoT;
I Traffic and interference will vary significantly from cell to cell.
Dongning Guo p. 2
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
New spectrum and technologies for 5G and IoT
I Heterogeneous frequency resources, including lower frequencies andmillimeter wave bands (e.g., 700 MHz, 28 GHz, 37 GHz, 39 GHz, 64-71GHz, ...);
I Many small cells;
I Many antennas (massive MIMO);
I Full duplex;
I Massive/grant-free access for IoT;
I Non-orthogonal multiple access for IoT;
I Traffic and interference will vary significantly from cell to cell.
Dongning Guo p. 2
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
New spectrum and technologies for 5G and IoT
I Heterogeneous frequency resources, including lower frequencies andmillimeter wave bands (e.g., 700 MHz, 28 GHz, 37 GHz, 39 GHz, 64-71GHz, ...);
I Many small cells;
I Many antennas (massive MIMO);
I Full duplex;
I Massive/grant-free access for IoT;
I Non-orthogonal multiple access for IoT;
I Traffic and interference will vary significantly from cell to cell.
Dongning Guo p. 2
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Metropolitan-scale deployment
0 500 1000 1500 2000 2500 3000 3500 40000
500
1000
1500
2000
2500
3000
3500
4000
Dongning Guo p. 3
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Radio resource management (RRM)At any time, which access points (APs) should serve a given user equipment(UE)?What frequencies and powers should each AP-UE link use?
3 1 5
4 4 6 1
1 3 1 5 2
4 4 6 1
1 4 4
AP 3 UE 1AP 3AP 3 UE 1AP 3 UE 5AP 3
AP 2AP 2 UE 4AP 2 UE 3AP 2 UE 6AP 2 UE 5
AP 1AP 1 UE 1AP 1AP 1 UE 4AP 1 UE 4
frequency
time
Dongning Guo p. 4
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Radio resource management (RRM)At any time, which access points (APs) should serve a given user equipment(UE)?What frequencies and powers should each AP-UE link use?
3 1 5
4 4 6 1
1 3 1 5 2
4 4 6 1
1 4 4
AP 3 UE 1AP 3AP 3 UE 1AP 3 UE 5AP 3
AP 2AP 2 UE 4AP 2 UE 3AP 2 UE 6AP 2 UE 5
AP 1AP 1 UE 1AP 1AP 1 UE 4AP 1 UE 4
frequency
time
Dongning Guo p. 4
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Timescales
1 millisecond
opportunisticscheduling
1 second
ICICeICIC
1 minute
self-organizingnetwork (SON)
1 day
AP placement& configuration
I Scheduling is likely to be distributed; cooperation is local;
I The aggregate traffic demand and large-scale fading vary slowly;
I Coarse resource allocation can be carried out over a metropolitan area.
Dongning Guo p. 5
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Timescales
1 millisecond
opportunisticscheduling
1 second
ICICeICIC
1 minute
self-organizingnetwork (SON)
1 day
AP placement& configuration
I Scheduling is likely to be distributed; cooperation is local;
I The aggregate traffic demand and large-scale fading vary slowly;
I Coarse resource allocation can be carried out over a metropolitan area.
Dongning Guo p. 5
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Timescales
1 millisecond
opportunisticscheduling
1 second
ICICeICIC
1 minute
self-organizingnetwork (SON)
1 day
AP placement& configuration
I Scheduling is likely to be distributed; cooperation is local;
I The aggregate traffic demand and large-scale fading vary slowly;
I Coarse resource allocation can be carried out over a metropolitan area.
Dongning Guo p. 5
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Timescales
1 millisecond
opportunisticscheduling
1 second
ICICeICIC
1 minute
self-organizingnetwork (SON)
1 day
AP placement& configuration
I Scheduling is likely to be distributed; cooperation is local;
I The aggregate traffic demand and large-scale fading vary slowly;
I Coarse resource allocation can be carried out over a metropolitan area.
Dongning Guo p. 5
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Cloud radio access network (C-RAN)
(NEC labs)
Dongning Guo p. 6
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Our vision: C-RAN + Metropolitan-area RRM
Metropolitan-arearadio resourcemanagement(Ma-RRM)
C-RAN scheduler
Dongning Guo p. 7
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Our vision: C-RAN + Metropolitan-area RRM
Metropolitan-arearadio resourcemanagement(Ma-RRM)
C-RAN scheduler
(average) traffic conditions
Dongning Guo p. 7
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Our vision: C-RAN + Metropolitan-area RRM
Metropolitan-arearadio resourcemanagement(Ma-RRM)
C-RAN scheduler
(average) traffic conditions
(average) channel state information
Dongning Guo p. 7
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Our vision: C-RAN + Metropolitan-area RRM
Metropolitan-arearadio resourcemanagement(Ma-RRM)
C-RAN scheduler
(average) traffic conditions
(average) channel state information
queue states
Dongning Guo p. 7
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Our vision: C-RAN + Metropolitan-area RRM
Metropolitan-arearadio resourcemanagement(Ma-RRM)
C-RAN scheduler
(average) traffic conditions
(average) channel state information
queue states
UE-AP associationscarrier assignmentssubcarrier assignments
Dongning Guo p. 7
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Our vision: C-RAN + Metropolitan-area RRM
Metropolitan-arearadio resourcemanagement(Ma-RRM)
C-RAN scheduler
(average) traffic conditions
(average) channel state information
queue states
UE-AP associationscarrier assignmentssubcarrier assignments
routing
Dongning Guo p. 7
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Radio network model
AP 1 AP 2 AP 3
UE 1 UE 2
λ1 λ2
Dongning Guo p. 8
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Orthogonal spectrum reuse
1 2 3
UE 1 UE 2
Dongning Guo p. 9
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Orthogonal spectrum reuse
AP 3
AP 2
AP 1
spectrum (Hz)
1 2 3
UE 1 UE 2
Dongning Guo p. 9
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Orthogonal spectrum reuse
AP 3
AP 2
AP 1
spectrum (Hz)
1 2 3
UE 1 UE 2
Dongning Guo p. 9
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Orthogonal spectrum reuse
AP 3
AP 2
AP 1
spectrum (Hz)
1 2 3
UE 1 UE 2
Dongning Guo p. 9
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Full spectrum reuse
1 2 3
UE 1 UE 2
Dongning Guo p. 10
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Full spectrum reuse
AP 3
AP 2
AP 1
spectrum (Hz)
1 2 3
UE 1 UE 2
Dongning Guo p. 10
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Full spectrum reuse
AP 3
AP 2
AP 1
spectrum (Hz)
1 2 3
UE 1 UE 2
Dongning Guo p. 10
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1}
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2}
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2} y{3}
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2} y{3} y{1,2}
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2} y{3} y{1,2} y{2,3}
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2} y{3} y{1,2} y{2,3} y{1,3}
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2} y{3} y{1,2} y{2,3} y{1,3} y{1,2,3}
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2} y{3} y{1,2} y{2,3} y{1,3} y{1,2,3}
1 2
1 2
1 2
1 2
1 2 1 2
1 2
1 2
1 2
1 2
1 2
1 2
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Fully flexible spectrum allocation
AP 3
AP 2
AP 1
spectrum (Hz)y{1} y{2} y{3} y{1,2} y{2,3} y{1,3} y{1,2,3}
1 2
1 2
1 2
1 2
1 2 1 2
1 2
1 2
1 2
1 2
1 2
1 2
x{1,3}1→1 x
{1,3}1→2
x{1,3}3→1 x
{1,3}3→2
1 2 3
UE 1 UE 2
Dongning Guo p. 11
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
The basic formulation
maximizer,w,y
u(r1, . . . , rk)
subject to rj =∑
A⊂{1,...,n}
∑i∈A
sAi→jwAi→j , j = 1, . . . , k
k∑j=1
wAi→j ≤ yA, A ⊂ {1, . . . , n}, i ∈ A
∑A⊂{1,...,n}
yA = 1
wAi→j ≥ 0, j = 1, . . . , k, A ⊂ {1, . . . , n}, i ∈ A.
I It is a convex optimization problem.I It has an optimal solution that activates at most k patterns.I It has kn2n−1 + 2n + k variables.
Dongning Guo p. 12
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
The basic formulation
maximizer,w,y
u(r1, . . . , rk)
subject to rj =∑
A⊂{1,...,n}
∑i∈A
sAi→jwAi→j , j = 1, . . . , k
k∑j=1
wAi→j ≤ yA, A ⊂ {1, . . . , n}, i ∈ A
∑A⊂{1,...,n}
yA = 1
wAi→j ≥ 0, j = 1, . . . , k, A ⊂ {1, . . . , n}, i ∈ A.
I It is a convex optimization problem.I It has an optimal solution that activates at most k patterns.I It has kn2n−1 + 2n + k variables.
Dongning Guo p. 12
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
The basic formulation
maximizer,w,y
u(r1, . . . , rk)
subject to rj =∑
A⊂{1,...,n}
∑i∈A
sAi→jwAi→j , j = 1, . . . , k
k∑j=1
wAi→j ≤ yA, A ⊂ {1, . . . , n}, i ∈ A
∑A⊂{1,...,n}
yA = 1
wAi→j ≥ 0, j = 1, . . . , k, A ⊂ {1, . . . , n}, i ∈ A.
I It is a convex optimization problem.I It has an optimal solution that activates at most k patterns.I It has kn2n−1 + 2n + k variables.
Dongning Guo p. 12
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
A user-centric scalable model
N1 N21 2 3
UE 1 UE 2
λ1 λ2
I Each UE can be served only by a cluster of APs in its neighborhood;
I Out-of-cluster APs treated as stationary noise sources;
I Equivalent reformulation as iterative binary linear programming withO(k) variables with guaranteed optimality gap;
I Solved using a highly efficient iterative pattern-pursuit algorithm.
Dongning Guo p. 13
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
A user-centric scalable model
N1 N21 2 3
UE 1 UE 2
λ1 λ2
I Each UE can be served only by a cluster of APs in its neighborhood;
I Out-of-cluster APs treated as stationary noise sources;
I Equivalent reformulation as iterative binary linear programming withO(k) variables with guaranteed optimality gap;
I Solved using a highly efficient iterative pattern-pursuit algorithm.
Dongning Guo p. 13
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
A user-centric scalable model
N1 N21 2 3
UE 1 UE 2
λ1 λ2
I Each UE can be served only by a cluster of APs in its neighborhood;
I Out-of-cluster APs treated as stationary noise sources;
I Equivalent reformulation as iterative binary linear programming withO(k) variables with guaranteed optimality gap;
I Solved using a highly efficient iterative pattern-pursuit algorithm.
Dongning Guo p. 13
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
A user-centric scalable model
N1 N21 2 3
UE 1 UE 2
λ1 λ2
I Each UE can be served only by a cluster of APs in its neighborhood;
I Out-of-cluster APs treated as stationary noise sources;
I Equivalent reformulation as iterative binary linear programming withO(k) variables with guaranteed optimality gap;
I Solved using a highly efficient iterative pattern-pursuit algorithm.
Dongning Guo p. 13
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
1000 APs and 2500 UEs: topology and association
Dongning Guo p. 14
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Local topology and allocation
900 950 1000 1050 1100 1150 1200 1250 1300
x (meters)
150
200
250
300
350
400
450
500
550
y (m
eter
s)
1/10
2/83/18
4/5
5/3
6/13
7/5 8/39/6
10/6
11/0
12/513/13
14/20
15/516/12
17/19
18/10
19/620/12
21/11
22/323/11
24/3
25/5
26/11
27/16
28/14
29/10
30/18
31/191
23 4 5
6
78
9
10
11 12
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Spectrum allocation
123456789
101112
Acc
ess
poin
ts
4
101316
2326252830
4248
12
19
24252830
4
101415
2326252830
3
12101216
232725
29
4
101416
2226252831
4246
12
2024
252830
48
12
19
24252830
4248
1119
242529
3
5111415111824
252831
3
512
161824
2529
4
914171824
252831
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12
202127252831
4148
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2327252830
4147
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4246
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2024
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35
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252830
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3
8
121519
2425
29
4
101315
232725
29
1
512
161822262529
4146
12
202226252830
3
511141711182227252830
4147
12
19
24252830
4
101415
2126252830
3
511
1824
2529
4
12101216
212625
29
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8
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29
4246
1119
262529
3
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2327252830
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121619
2425
29
Dongning Guo p. 15
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Baseline schemes
I Full-spectrum reuse with strongest AP association;
I Full-spectrum reuse with optimal AP-UE association;
I A theoretical lower bound.
Dongning Guo p. 16
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Baseline schemes
I Full-spectrum reuse with strongest AP association;
I Full-spectrum reuse with optimal AP-UE association;
I A theoretical lower bound.
Dongning Guo p. 16
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Baseline schemes
I Full-spectrum reuse with strongest AP association;
I Full-spectrum reuse with optimal AP-UE association;
I A theoretical lower bound.
Dongning Guo p. 16
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
1000 APs and 2500 UEs: delays
0 2 4 6 8 10 12 14 16 18 20
Packet arrival rate (packets/second)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Ave
rage
pac
ket d
elay
(se
cond
s)
Delay, full-spectrum-reuse + MaxRSRP
Delay, full-spectrum-reuse + user association
Delay, proposed scheme
Delay lower bound
Dongning Guo p. 17
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
1000 APs and 2500 UEs: packet-level simulation
0 5 10 15 20 25 30
Traffic load(packets/second/UE)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2A
vera
ge p
acke
t del
ay (
seco
nds)
Actual delay, full-spectrum-reuse + MaxRSRPActual delay, full-spectrum-reuse + user associationActual delay, proposed scheme
Dongning Guo p. 18
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Conclusion
I A vision for metropolitan-area multiple-timescale resource management;
I A user-centric scalable model;
I A highly efficient iterative pattern-pursuit algorithm;
I Guaranteed optimality gap;
I The framework is potentially applicable to some other metropolitan-scaleresource management problems.
Dongning Guo p. 19
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Conclusion
I A vision for metropolitan-area multiple-timescale resource management;
I A user-centric scalable model;
I A highly efficient iterative pattern-pursuit algorithm;
I Guaranteed optimality gap;
I The framework is potentially applicable to some other metropolitan-scaleresource management problems.
Dongning Guo p. 19
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Conclusion
I A vision for metropolitan-area multiple-timescale resource management;
I A user-centric scalable model;
I A highly efficient iterative pattern-pursuit algorithm;
I Guaranteed optimality gap;
I The framework is potentially applicable to some other metropolitan-scaleresource management problems.
Dongning Guo p. 19
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Conclusion
I A vision for metropolitan-area multiple-timescale resource management;
I A user-centric scalable model;
I A highly efficient iterative pattern-pursuit algorithm;
I Guaranteed optimality gap;
I The framework is potentially applicable to some other metropolitan-scaleresource management problems.
Dongning Guo p. 19
Metropolitan-Scale Radio Resource Management
Background Spectrum management Optimized allocation Performance conclusion
Conclusion
I A vision for metropolitan-area multiple-timescale resource management;
I A user-centric scalable model;
I A highly efficient iterative pattern-pursuit algorithm;
I Guaranteed optimality gap;
I The framework is potentially applicable to some other metropolitan-scaleresource management problems.
Dongning Guo p. 19
Metropolitan-Scale Radio Resource Management
