Network Cooperation for Client-AP Association Optimization Akash Baid, Ivan Seskar, Dipankar Raychaudhuri WINLAB, Rutgers University

Introduction 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 → Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution ○ Simulation Results ○ Conclusion

Operational Cooperation Model 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. → Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution ○ Simulation Results ○ Conclusion

Motivating Example Chosen AP 54 Mbps 48 Mbps 36 Mbps 27 Mbps 24 Mbps Chosen AP 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 → Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution ○ Simulation Results ○ Conclusion

System Model fraction of time provided by the AP to the client effective bit rate 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) ○ Motivation → System Model ○ Problem Formulation ○ Optimal Solution ○ Simulation Results ○ Conclusion

System Model 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) Parameter in the range (0,1) which captures the average effect of hidden node interference per interferer Average channel time for a client: ○ Motivation → System Model ○ Problem Formulation ○ Optimal Solution ○ Simulation Results ○ Conclusion

Intra-Network Optimization ○ Motivation ○ System Model → Problem Formulation ○ Optimal Solution ○ Simulation Results ○ Conclusion

Cooperative Optimization The optimization is now done cooperatively by all networks – For each AP of each network, the number of interferers is known Combined optimization problem: ○ Motivation ○ System Model → Problem Formulation ○ Optimal Solution ○ Simulation Results ○ Conclusion

Solving the integer program Non-linear integer program Relaxed discretized linear program Shmoys & Tardos’ rounding process ○ Motivation ○ System Model ○ Problem Formulation → Optimal Solution ○ Simulation Results ○ Conclusion

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 2-6 overlapping networks, APs/network, clients/network Two types of deployments: – Uniform-random – Clustered ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution → Simulation Results ○ Conclusion

Random Deployment 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 ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution → Simulation Results ○ Conclusion

Simulation Results 2 Networks, 25 APs, 150 clients per network 2x gains in low rate clients, slight gain in median ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution → Simulation Results ○ Conclusion

Simulation Results Gains consistent as no. of overlapping networks increase, loss in mean rate reduces ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution → Simulation Results ○ Conclusion

Clustered Deployment 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 1 st network clustered in 3 rectangular regions of size 200x200 meters each – APs of 2 nd network still uniformly random across the area – Client placement, other parameters still same as before ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution → Simulation Results ○ Conclusion

Clustered Deployment Net 1 APs are clustered Effect of Net 2 APs is less Info from Net 2 not very useful ⇓ ⇓ Net 1 APs are clustered Effect of the cluster of Net 1 APs on Net 2 is high Info from Net 1 helps a lot ⇓ ⇓ ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution → Simulation Results ○ Conclusion

Moving Forward ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution ○ Simulation Results → Conclusion New initiative towards network collaboration for spectrum allocation Starting under the new NSF EARS program

Moving Forward ○ Motivation ○ System Model ○ Problem Formulation ○ Optimal Solution ○ Simulation Results → Conclusion Software Defined Network (SDN) approach to implementing network collaboration

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Motivating Example Default Selection: Closest APIntra-Network Optimization Inter-Network Optimization Chosen AP Chosen AP Chosen AP