Throughput Improvement in Ad hoc networks using the Channel MAC

1. Throughput Improvement in Ad hoc networks using the Channel MAC Manzur Ashraf ITR, University of South Australia

2. Contents • Motivation: opportunistic communication • The Channel MAC protocol • Analytical model • Discrete event simulation • Challenges: Practical implementation

3. Motivation: Opportunistic communication Channel variations can be exploited by transmitting information opportunistically when and where the channel is strong. • Goldsmith et al. (97) Point-to-point communication • David Tse, et al. (98)  Multi-user communication impacts Multi-user diversity resource allocated to strong user at a time problem Contrary to Space-time code concepts Beamforming using dumb antennas (same signal xmitted with time-varying phase and power to produce scattering) Limited gain in slow fading/poor channel fluctuations solution

4. Channel MAC: Idea Improved throughput, fairness, etc Channel Randomness Scheduling

5. Contd. (Fix a threshold & transmit) threshold

6. Contd. threshold

7. Contd. threshold

8. Contd. threshold

9. Contd. threshold

10. threshold Threshold Negligible Propagation delay minimize collision Probability that more than 1 channel becomes good at an instance is zero assumptions

11. Throughput calculation threshold T Throughput of one channel Using Monte Carlo Process, we can approximate the Throughput

12. Rayleigh-faded (Jakes model) Channel Results Node speed=10 km/h Carrier Freq=2000 Mhz

13. Channel-model OFF, Idle time assumptions ON > 0 l3 l1 l2 Any general Inter-arrival distribution function? Arrival-rate= r =Level crossing rate Arrival points time Mixture of Weibull distribution:

14. Specific: 2-state channel model In 1-user Channel MAC, the inter-arrival distribution is a shifted exponential dist.

15. 1-user Channel MAC Proposition 1: In 1-user Channel MAC, the arrival point process is approximated by a Renewal process.

16. Proposition 2: The shifted exponential distribution function (by Prop. 1) results in a non-Poisson renewal arrival process. Problem How to get the inter-arrival distribution for the Superposition of a number of non-Poisson renewal processes?

17. Solution: Approximation approach Grigelionis theorem, 1963: • If points of each individual processes are (a) suitably sparse and (b) no one process dominates the rest, then the distribution of the point process is close to Poisson. Fairness of true random arrival processes Ref: B. Grigelionis, “On the convergence of sums of random step processes to a poisson process”, Theory Prob. Appl., No. 8, pp 177-182, 1963 A Poisson process is often a good approximation for a superposition process if many processes are being superposed. Ref: P. Keuhn, “Approximate analysis of general queuing networks by decomposition”, IEEE transactions of communications, Vol. com-27, No. 1, 1979, pp 113-126.

18. N-user Channel MAC Proposition 3: The arrival points of the Superpositioned n-user Channel MAC converges asymptotically to a Poisson point process as per our assumptions. Proposition 4: In a Poisson Point process, if n number of arrival points occur in an interval of T, the expected delay of the first arrival point in T is T/(n+1). Since, points of a homogeneous Poisson process in an interval are independently and uniformly distributed.

19. Exp. Idle time time PACKET Prop. 4

20. Throughput Exp. Packet transmission time Throughput = Exp. Packet transmission time + Exp Idle time

21. Discrete event simulation using NS-2 • NS version 2.27 • Nodes communicate using half-duplex radio based on the the Channel MAC mechanism at 1 Mbps. • The transmission range of a node is set to 250 m and the career sense threshold is set to 550 m. • For simplicity, the ARP (Address Resolution Protocol) is assumed to have the hardware address for the destination (i.e. ARP broadcasting is absent).

22. Static routing technique is used incorporating the NOAH (No Ad hoc routing) extension of NS2. • CMU-extension for Ricean fading (time-correlated) PIFS DATA ACK DATA ACK SIFS Packets and overheads

23. Single-hop network P. Pham, S. Perreau, A. Jayasuriya, “New cross layer design approach to ad hoc networks under rayleigh fading”, IEEE journal on selected areas in communications: special issue on wireless ad hoc networks, 2005.

24. A chain multi-hop network When p=0.85

25. Random topologies All single-hop flows; Randomly distributed over 1500 X 1500 sq.meter Monte Carlo approximation

26. Challenges for the practical Implementation [Selecting threshold] How can we select “p” (probability of a good channel) at the transmitter? Issues include: how to calculate the received mean power? how many symbols are required to be transmitted to calculate the received mean power?

27. [Scalability]  How long the channel can be predicted (with reasonable accuracy) for transmitting data considering a Rayleigh (or any other suitable) channel-fading model? Is the channel prediction scheme is scalable with any number of nodes?

28.  Do we need another control channel for periodic broadcast of the channel information?  With the large number of users, is it feasible to use multiple channel in a coordinated way?  With small number of users, is it feasible to use multiple channels with the Channel MAC mechanism applied to each sub-channel?

29. [Routing]  How the broadcasting technique can be improved considering the ‘channel fading’ (deriving the CSI)? At the moment we only focus on point to point type communications with channel MAC, but to implement routing we need a proper broadcast mechanism.

30. [Rate adaptive MAC: Again Channel Prediction!] Design of a rate-adaptive Channel MAC: ARF, RBAR, OAR,MOAR etc are rate-adaptive protocols in the IEEE 802.11 domain.  [ Receiver calculates rate based on SNR/ Received signal strength ] Is it feasible to implement similar rate adaptation technique for the Channel MAC? How the channel prediction inaccuracy will affect the rate-adaptation performance?

31. Q/A Manzur.Ashraf@postgrads.unisa.edu.au