1 Multi-radio Channel Allocation in Competitive Wireless Networks Mark Felegyhazi, Mario Čagalj, Jean-Pierre Hubaux EPFL, Switzerland IBC’06, Lisbon, Portugal July 4, 2006
2 System model and game
3 Problem formulation channel allocation in cellular networks emerging wireless networks (mesh NWs, cognitive radio NWs) – self-organizing – devices can use multiple radios graph coloring might not be appropriate
4 System model (1/2) N communicating pairs of devices sender and receiver are synchronized C orthogonal channels single collision domain if they use the same channel devices have multiple radios k radios at each device k ≤ C
5 System model (2/2) N communicating pairs of devices C orthogonal channels k radios at each device number of radios by each of the devices in pair i on channel c → example: more radios on one channel ? Intuition:
6 Channel model channels with the same properties R(k c ) – total rate on any channel c
7 Multi-radio channel allocation game selfish users (communicating pairs) non-cooperative game G – communicating pairs → players – channel allocation → strategy – total rate of the player → utility strategy: strategy matrix: utility:
8 Concepts Nash equilibrium: The strategy matrix S * defines a Nash Equilibrium (NE), if none of the players can unilaterally change its strategy to increase its utility. Pareto-optimality: S * is a Pareto-optimal channel allocation, if one cannot improve the utility of any player i without decreasing the utility of at least one other player j. where is the set of strategies for all players except i There is no strategy matrix S’ such that: with strict inequality for at least one player i.
9 Results
10 Non-conflicting case Fact 1: If, then any channel allocation with is a Nash equilibrium.
11 Lemma 1: Each player should use all of his radios. Full usage of radios p4p4 p4p4
12 Flat channel allocation Proposition 1: If S * is a Nash equilibrium, then for any channel b and c. Consider two arbitrary channels b and c, where: k b ≥ k c NE candidate: p4p4 p4p4
13 Nash Equilibria (1/2) Theorem 1: If for any two channels b and c the conditions, hold, then S * is a Nash equilibrium. Consider two arbitrary channels b and c, where: k b ≥ k c Nash Equilibrium: Use one radio per channel. p4p4 p2p2
14 Nash Equilibria (2/2) Theorem 1: If the following conditions hold: ; ;, then S * is a Nash equilibrium. Consider two arbitrary channels b and c, where: k b ≥ k c Nash Equilibrium: Use more radios on certain channels. channels with the minimum number of radios →
15 Pareto optimality Theorem 2: If the rate function R(·) is constant, then any Nash equilibrium channel allocation is Pareto-optimal.
16 Centralized algorithm Assign radios to the channels sequentially. p1p1 p1p1 p1p1 p1p1 p2p2 p2p2 p2p2 p2p2 p3p3 p3p3 p3p3 p3p3 p4p4 p4p4 p4p4 p4p4
17 Conclusion Wireless networks with multi-radio devices Users of the devices are selfish players Model using game theory Results for a Nash equilibrium: – players should use all their radios – flat allocation over channels – Nash equilibria: one radio per channel for each player or unfair NE NE are Pareto-optimal for the constant rate function Simple centralized algorithm
18 Future work General topology networks Specific network scenarios: mesh networks The effect of the MAC protocol Distributed convergence algorithms (to the NE)