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Evolving Strategies for Contentious but Efficient Coexistence in Unlicensed Bands

This study examines the ability of transceivers in unlicensed bands to learn and adapt in order to coexist efficiently. Through the use of genetic algorithms, populations of strategies are evolved to maximize average channel capacity. Winners negotiate and exhibit useful traits such as segregation, self-reliance, callousness, forgiveness, and randomization. The performance of evolved strategies is shown to be effective and stable. Implementation can involve fixing a radio with a strategy or allowing radios to evolve strategies in situ.

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Evolving Strategies for Contentious but Efficient Coexistence in Unlicensed Bands

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  1. Evolving Strategies for Contentious but Efficient Coexistence in Unlicensed Bands N. Clemens C. Rose WINLAB

  2. Our Problem: “survivor” • Scenario: transceivers in an unlicensed band • Transceiver Skills: ‘cognitive’ and agile • Question: can transceivers learn to get along?

  3. System Model 2 Orthogonal ChanNels Equal Cross Gains (g = 0.3) Equal Gaussian Noise Floor (N = 10-3)

  4. The Two-Player Game • Actions: quantized power allocation • All power in channel 1 • All power in channel 2 • Spread equally in both channels • Radio Interaction: mutual interference • Payoff: channel capacity • Strategy: past outcomes govern next action • Goal: maximize average capacity

  5. 1.51.5 7.22 6.96.9 27.2 2.92.9 27.2 6.96.9 7.22 1.51.5 Actions and Payoffs

  6. Player Strategy Structure • History = two previous plays • Next play = S(history) • 9 action pairs per play • 81 two-play histories (9X9) • Strategy  S(k), k=1,…,81 • 4 Possible values for S(.) • Channel 1 • Channel 2 • Spread • Random • Must find good S(.)

  7. Strategy Search • Number of Strategies: 481 • Use Genetic Algorithms • Strategy string  genome • Compose population of strings • Allow “fittest” strategies to “mate” • Discard weaker strategies • Repeat (until tired)

  8. Experiments • Start with random population • Human-engineered evaluator set for comparison • Completely random strategies • Squatters, hoppers, avoiders • Fitness measured against evaluator set • Result: • Populations evolve effective strategies • We distill essential features

  9. Winners’ Characteristics • Winners negotiate among themselves to achieve near-optimality • Winner (red) fares well against random strategies

  10. Examples of Useful Traits • Segregation: • stay on your side of the fence • Self-Reliance: • Push me? I push you back! • Callousness: • exploit passivity • Forgiveness: • to teach and to encourage cooperation • Randomize: • (occasionally) to avoid repeated collision

  11. Schema: identifying common traits • Trait Characterization: • Preference for a certain response • Avoidance of a certain response • Compose a histogram of relative trait frequencies

  12. The Schema Skeleton • Fix the observed traits in a strategy genome • Choose the remaining positions randomly • The performance of this is good! A handful of rules can define a “good” strategy!

  13. Conclusion • Competitive strategies for cognitive radios • effective • stable • Implementation • fix a radio with a strategy • or let radios evolve strategies in situ • Caveat • need to try multi-player games

  14. Thank You!

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