Network Cooperation for
Client-AP Association Optimization
Akash Baid, Ivan Seskar, Dipankar
Raychaudhuri
WINLAB, Rutgers University
Introduction
→Motivation
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System
Model
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Problem
Formulation
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Optimal
Solution
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Simulation
Results
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Conclusion
• Exponential rise in no. of planned WiFi deployments telecom, cable, service companies
• Large WiFi networks leverage years of research on
enterprise WLAN management
• However, less focus on how one managed network
interacts/interferes/coordinates with another
APs of two
networks in
Brooklyn area of
New York City
This work: Study the effect of inter-network interference
on the intra-network performance optimization
Operational Cooperation Model
→Motivation
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System
Model
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Problem
Formulation
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Optimal
Solution
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Simulation
Results
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Conclusion
• Each network periodically shares the info about the
location and operating channels of its APs with all other
networks operating in the same area
• Clients belonging to one network cannot join other
networks
• Advantages of operational coop. over full access coop:
– Authentication functionality within each network
– Extra capacity provisioning not required
– A network can retain the control of sessions, policy, and billing
We show how each network can optimize client-AP
associations to minimize the effects of inter-network
interference.
Motivating Example
→Motivation
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System
Model
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Problem
Formulation
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Optimal
Solution
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Simulation
Results
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Conclusion
27 Mbps
Mbps
54
48 Mbps
24
36 Mbps
Default Selection:
Connect to closest (AP1)
Intra-network
optimization:
Take AP load into
account (AP2)
Inter-network
optimization:
Take effect of foreign
APs into account (AP3)
Intra-network optimization of client-AP associations can
lead to inefficient results in presence of foreign networks
System Model
○
Motivation
→System
Model
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○
• 𝑁 independently operated WiFi networks: indexed by 𝑖
• 𝐴𝑖 Access Points; 𝑈𝑖 Clients in the 𝑖 th network
Problem
Formulation
𝑥𝑖𝑗 (k)
Optimal
Solution
connection state between the 𝑗th client and 𝑘th
AP of the 𝑖 th network
𝑝𝑖𝑗 (k)
fraction of time provided by the AP to the client
𝑟𝑖𝑗 (k)
effective bit rate
○
Simulation
Results
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Conclusion
Effective rate of 𝑗th client of 𝑖 th network:
𝐵𝑖𝑘
𝐶𝑖𝑘
set of co-channel foreign APs within carrier sense range
set of co-channel foreign APs outside carrier sense but
within interference range (potential hidden nodes)
System Model
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Motivation
→System
Model
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Problem
Formulation
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Optimal
Solution
○
Simulation
Results
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Conclusion
• Assumptions:
– No priority order between clients
– Each AP enforces proportional fairness between
connected clients ⇒ equal time share [Liew’05]
– Only downlink traffic (from APs to clients)
– Full buffer (clients always have pending data requests
at the AP)
• Average channel time for a client:
Parameter in the range (0,1) which
captures the average effect of hidden
node interference per interferer
Intra-Network Optimization
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Motivation
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System
Model
→Problem
Formulation
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Optimal
Solution
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Simulation
Results
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Conclusion
• Each network 𝑖 aims to maximize the sum utility of all its
clients by controlling the association 𝑥𝑖𝑗 (k)
• However, in this case, a network does not know what
foreign networks are doing ⇒ no 𝐵𝑖𝑘 , 𝐶𝑖𝑘 terms
• Non-linear integer program:
Cooperative Optimization
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Motivation
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System
Model
→Problem
• The optimization is now done cooperatively by all
networks
– For each AP of each network, the number of interferers is known
Formulation
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Optimal
Solution
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Simulation
Results
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Conclusion
• Combined optimization problem:
Solving the integer program
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Motivation
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System
Model
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Solution
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– Essentially the same approach as [Yang’08]
Problem
Formulation
→Optimal
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• For intra-network problem:
Simulation
Results
Non-linear
integer program
Relaxed
discretized
linear program
Shmoys &
Tardos’
rounding
process
– 2 + ∈ approximate solution in polynomial time
Conclusion
• For cooperative problem:
– Once the inter-network parameters are known,
problem decomposes into 𝑁 different problem
– Each network can individually solve the problem
using the same approach as above
Simulation
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Motivation
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System
Model
○
Problem
Formulation
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Optimal
Solution
→Simulation
• Comparison between:
– Least Distance: Each client connects to the closest AP of the
same network (benchmark case)
– Intra-Network Optimization: Each network optimizes the
association pattern of its clients.
– Cooperative Optimization: All networks share information for
optimizing the client association
Results
○
Conclusion
• 2-6 overlapping networks, 15-35 APs/network, 50-250
clients/network
• Two types of deployments:
– Uniform-random
– Clustered
Random Deployment
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Motivation
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System
Model
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Problem
Formulation
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Optimal
Solution
→Simulation
• APs and clients uniformly placed in a 500 x 500m area
• Minimum separation of 50m between 2 APs of same
network; no minimum across networks
• Frequency selection: each AP chooses one of the three
orthogonal channels in the 2.4 GHz range that minimizes
the number of co-channel APs in its range
Results
○
Conclusion
- Carrier Sense radius: 215m
- Interference radius: 250m
- Physical data rates 𝑟𝑖𝑗 𝑘
selected based on distance
between the client 𝑗 and AP 𝑘
Simulation Results
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Motivation
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System
Model
○
Problem
Formulation
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Optimal
Solution
→Simulation
Results
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Conclusion
• 2 Networks, 25 APs, 150 clients per network
• 2x gains in low rate clients, slight gain in median
Simulation Results
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Motivation
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System
Model
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Problem
Formulation
10 percentile client throughput
Optimal
Solution
0.6
Results
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Conclusion
Throughput (Mbps)
→Simulation
Throughput (Mbps)
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• Gains consistent as no. of overlapping networks increase,
loss in mean rate reduces
0.4
0.2
0
N=2 N=3 N=4
Least Distance
2
Mean client throughput
1.5
1
0.5
0
N=2 N=3 N=4
Intra-network Optim.
Cooperative Optim.
Clustered Deployment
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Motivation
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System
Model
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Problem
Formulation
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Optimal
Solution
→Simulation
Results
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Conclusion
• Aim is to study topology-specific interference patterns
• Reflects realistic scenarios where some networks have
dense deployments in a popular spot
• Two network example:
– APs of 1st network clustered in 3 rectangular regions of size
200x200 meters each
– APs of 2nd network still uniformly random across the area
– Client placement, other parameters still same as before
Clustered Deployment
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Motivation
System
Model
Problem
Formulation
0.8
Optimal
Solution
Least Distance
Intra-Network
Cooperative
Net 1 APs are clustered
⇓
0.6
Effect of Net 2 APs is less
0.4
⇓
Info from Net 2 not very
useful
0.2
0
-2
10
→Simulation
Results
-1
0
10
10
1
10
2
10
Client Rates (Mbps)
Conclusion
Network 2
1
0.8
CDF
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Network 1
1
CDF
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Least Distance
Intra-Network
Cooperative
Net 1 APs are clustered
⇓
0.6
0.4
7x Gain in
10 %ile
throughput
0.2
0
-2
10
Effect of the cluster of
Net 1 APs on Net 2 is high
⇓
Info from Net 1 helps a lot
-1
10
0
10
Client Rates (Mbps)
1
10
2
10
Moving Forward
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Motivation
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System
Model
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Problem
Formulation
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Optimal
Solution
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Simulation
Results
→ Conclusion
• New initiative towards network collaboration for
spectrum allocation
• Starting under the new NSF EARS program
Moving Forward
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Motivation
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System
Model
○
Problem
Formulation
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Optimal
Solution
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Simulation
Results
→ Conclusion
• Software Defined Network (SDN) approach to
implementing network collaboration
Thanks !
Questions?
Extras
Motivating Example
Default Selection: Closest AP
Intra-Network Optimization
Inter-Network Optimization