Power Control Algorihms for VANET

1. Power Control Algorihmsfor VANET Presented by Yuhao Zheng

2. Motivation • Transmission range in Vehicular Ad Hoc Networks (VANET) varies a lot: longer range How to decide? shorter range

3. Papers to be Presented Today • [25] A Power Control Algorithm with High Channel Availability for Vehicular Ad Hoc Networks • G. Caizzone, P. Giacomazzi, L. Musumeci, G. Verticale • [26] Assignment of Dynamic Transmission Range Based on Estimation of Vehicle Density • M. M. Artimy, W. Robertson, W. J. Phillips

4. Paper #1 of 2 – [26] • Outline • Traffic Theory • Density-MTR Relationship • Density Estimation • The Algorithm • Performance • Conclusion

5. Traffic Theory • Three basic quantities: • The flow q, measures the number of vehicles that pass an observer per unit time • The density k, represents the number of vehicles per unit distance • The speed u, is the distance a vehicle travels per unit time • Fundamental Traffic Flow relationship: • q = u ⋅ k

6. Speed-Density Relationship • λ – the sensitivity of the vehicle interaction • kJ – the maximum vehicle density at traffic jam • Proposed by Pipes

7. Flow-Density Relationship

8. MTR-Density Relationship • MTR = minimum tranmission range that guarantees that a network is connected • Simulation Model:

9. MTR-Density Relationship • Racetrack, single lane approximated maximum MTR absolute maximum MTR

10. MTR-Density Relationship • Racetrack, three lanes

11. MTR-Density Relationship • Intersection

12. Density Estimation • Ts – the time a test vehicle is stopped • T – windows size (T=10s) • n – parameter that indicates the QoS (n=0) • λ – normalized λ (1/λ=2s)

13. Density Estimation given k measure Ts

14. Density Estimation

15. The Algorithm

16. Performance • Metrics: • Number of network partitions • Average transmission range

17. Performance • Racetrack, single lane

18. Performance • Racetrack, three lanes

19. Performance • Intersection

20. Conclusion of Paper [26] • Pros • Non-centralized algorithm • Apapts to vehicles mobility • Cons • Need to determine some parameter, such as n, λ, T

21. Paper #2 of 2 – [25] • Outline • Model Definitions • The Algorithm • Performance • Conclusion

22. Model Definitions • Neighborhood relation • The notation A←B indicates that node B is a neighbor of node A. • Node B is a neighbor of node A iff A can receive B’s message. • The neighborhood relation is not commutative, as if B is a neighbor of A, A may not be a neighbor of B. This may happen if A and B transmit at different powers.

23. Model Definitions • Neighborhood of a node • The neighborhood of node A is defined as the set of nodes N(A) such that ∀n∈ N(A) : A←n. • Reach of a node • The reach of node A is defined as the set R(A) of nodes such that ∀n∈R(A) : n←A.

24. Model Definitions • Reciprocal neighborhood relation • Nodes A and B are reciprocal neighbors if A←B and B←A. The reciprocal neighborhood relation between nodes A and B is indicated as A↔B. • Reciprocal neighborhood of a node • The reciprocal neighborhood of node A is defined as the set of nodes NR(A) such that ∀n∈ NR(A) : A↔n . From previous definitions, NR(A)=N(A)∩R(A).

25. The Algorithm • Baisc idea: • Maintain an appropriate number of neighbors • Pin = -35dBm, Pmax = 24dBm, Δ = 1dB • required min/max number of reciprocal neighbors: min=3 max=8

26. Performance • Compare with: • Fixed transmission range = 500m / 800m • Metrics: • Carried load • Percentage of isolated users • Average number of reciprocal neighbors • Percentage of time passed in disconnection • Average distance of reached nodes

27. Performance

28. Conclusion of Paper [25] • Pros • Non-centralized algorithm • High scalability over a wide range and user densities • Insensitive to user speed • High user channel availability • Cons • Isolated users during transient periods

29. Questions? Presented by Yuhao Zheng Thank you!