Interference Considerations for QoS in MANETs Rajarshi Gupta, John Musacchio, Jean Walrand {guptar, musacchj, University of California, Berkeley

Why Interference is critical In wired networks, all links may be used simultaneously In MANET, neighboring links interfere Interference Range (Ix) > Transmission Range (Tx) For simulations Transmission range = 500m Interference range = 1 km

Overview Previously assumed approximate models No interference across clusters Only one hop interference New Contribution Model MANET with accurate interference considerations b MAC protocol Interference based on distance Heuristic QoS algorithms incorporating interference effects Simulation study to validate theoretical models "Adaptive Quality of Service for a Mobile Ad Hoc Network”, A. Dimakis, L. He, J. Musacchio, H-S W. So, T. Tung, and J. Walrand, MWCN "A Wireless Overlay Network with QoS Capabilities“, E. Magana, D. Morato, H.W. So, B. Hodge, J. Walrand, and P. Varaiya, Technical Report.

Conflict Graph Interference between links in graph G may be modeled as Conflict Graph CG Link from node i to node j in G => vertex L ij in CG Edge in CG between L ij and L pq iff L ij and L pq interfere with each other Incorporates protocol versions With/out RTS-CTS (simulations only without) Consideration for MAC-layer acknowledgements Two links (i.e. vertices in CG) can not be active simultaneously if there is a edge connecting them "Impact of Interference on Multi-hop Wireless Network Performance”, K. Jain, J. Padhye, V. N. Padmanabhan, and L. Qiu, ACM Mobicom 2003.

Ideal Solution Goal Maximize concurrent transmissions Schedule ‘many’ non-interfering links Solution Identify maximal sets of non-neighboring links, i.e Independent Sets in the C.G Schedule the Independent Sets s.t. the QoS requirements are met for flows Very hard problem (even if centralized) Finding all independent sets itself is NP-hard Then need to appropriately schedule “A New Model for Packet Scheduling in Multihop Wireless Networks”, H. Luo, S. Lu, and V. Bhargavan, ACM Mobicom 2000.

Alternative Solution: Cliques Clique = Complete Subgraph Maximal Clique = Clique not a subset of any other Only one vertex in a clique may be active at once Capacity in ad-hoc networks closely related to cliques in CG Maximal Cliques: ABC, BCEF, CDF

Proposed Clique-based Mechanism Objectives Fully distributed processing Functions only with localized information Dynamic Computationally efficient i.e. quick Can work (less accurately) even with incomplete information Heuristic mechanism

State Information Exchange All nodes have GPS to know their position Nodes need to know about all their interference neighbors Their locations Allocated flows at each neighbor Need message exchanges between interference neighbors Usually available in local neighborhood Works with incomplete information, but may yield sub- optimal decisions Each node has the logical information to compute its CG subgraph, but explicit computation not required

Computing Cliques General algorithms take exponential time Propose faster heuristic algorithm Key observations for an interference CG All links sharing cliques with this link must lie within a radius of Ix (interference range) Links that together form a clique must all lie within a diameter Ix

Heuristic Clique Algorithm Use a disk of radius Ix/2 to scan a disk of radius Ix around link Each position of scanning disk generates a clique Shrink set of cliques by remembering previous clique and checking containment Can further shrink to set of maximal cliques Time taken to generate cliques that the link belongs to ~1 sec to get heuristically shrunk set of cliques <15 sec to shrink to set of maximal cliques

Theoretical Result Unfortunately, capacity constraints based on cliques are not sufficient Only work for Perfect Graphs Need a scaling factor of for sufficiency Flows that satisfy scaled clique constraints have a realizable schedule Clique constraints suggest a rate of 0.5 per link But only 0.4 per link is achievable “Graph Imperfection I”, S. Gerke and C. McDiarmid, Journal of Combinatorial Theory, Series B, vol. 83 (2001), pp

Complete Distributed Mechanism Local link state exchange: position, flow Distributedly compute all maximal cliques Recompute upon topology change Requested flow (rate + path) checked by all nodes in neighborhood of path Check allocated and requested flows against clique constraints scaled by 0.46 Admit flows if satisfied

Visualization of Algorithm Plot ad-hoc nodes and links Color of a link denotes allocated resources on link, considering interference over cliques, expressed as % of theoretical capacity Allocated flows paths shown in gray bandwidths shown in list

OPNET Simulation Model

Comparing Model with Simulation X-axis = minimum spare capacity amongst all cliques Y-axis = percentage of traffic received Blue = Average over all flows Red = Worst amongst all flows Each point indicates a simulation run (some runs are non-uniform) Vertical bars indicate spare capacities of -2%, 5% and 10%

Received vs Sent Rates -- 3 Flows -- 4 Flows -- 5 Flows Clique Predicted Limit – 3 Flows Clique Predicted Limit – 4 Flows Clique Predicted Limit – 5 Flows All flows have the same sending rate X-axis: average rate of sent traffic Y-axis: average rate of received traffic Vertical lines show theoretical capacity limits predicted by clique constraints

Next Phase of Work Make further use of interference knowledge Distributed QoS routing algorithm for a general MANET To be used also for distributed intra-cluster routing in a clustered MANET Incorporate mobility in simulations Handle multiple classes of service