1. Mobility Increases The Capacity of Ad-hoc Wireless Networks By Grossglauser and Tse Gautam Pohare Heli Mehta Computer Science University of Southern California

2. Outline Characteristics & Technical challenges of mobile adhoc networks. Diversity and its analysis Gupta and Kumar Model The authors new approach Session and Transmission Model

3. Outline (Cont’d) Results Two-phase Scheduling Sender/Receiver centric approach Distributed Implementation Related Work Conclusion and Critiques

6. Technical Challenges Hardware –Small, lightweight,low power Communication Systems –Harsh-time varying propagation environment –Scarce radio spectrum –Data rates

7. Mobile Wireless Network Characteristic: –time variation of the channel strength of the underlying communication links. Reasons: –Multi path fading –Path loss via distance attenuation –Shadowing by obstacles –Interference from other users.

8. Use of Diversity for Time Variation Point-to-point Diversity can be obtained by: –Interleaving of coded bits –Combining of multipaths –multiple antennas or multiple basestations Multiuser diversity:  Basic Idea: performance ↑ thro many independent signal paths between tx-rc.  Multiple users are communicating to the base station via time-varying fading channel.  Motivation from Knopp and Humblet’s paper.

9. Diversity Analysis: Obtained by: - Allocating channel to the user who can best exploit it. - Overall throughput maximized. But Problems in this ……  It incurs additional delay.  Additional delay [buffer pkts until channel becomes strong]  More diversity can be obtained on asynchronous networks  Examples: Email, Event notification

10. More diversity can be obtained on asyn n/w reasons: N/w topology changes due to user mobility Theorems we will see which proves this. Focus is on applications that can tolerate delays of several mins/hrs like email,….

11. Gupta and Kumar Model: Fixed Ad-hoc network with Relaying –Nodes are immobile –Each node can be source, destination or relay Result: –As the number of nodes per unit area n increases, the throughput per S-D pair decreases approximately 1/  n.

12. Problems with fixed Adhoc N/w : Excessive interference in long range direct communication. If relaying used - # of hops increase (root of n) Nodes carry lot of relay traffic. Hence, more delay, less throughput. Scalability problems !!! - Traffic rate per S-D actually goes down to Zero as n increases.

13. New Approach by the authors is : Avg long-term throughput per S-D pair can be kept constant even if n increases ! Totally contrasting to fixed model by G & K. How is this achieved ??? Source node sends packets to every other nodes within its radius which act as relays. Relays, when come near to the destination node, deliver packet. Many relay nodes => more probability its close to dest node. Imp: Max 1 Relay => Max 2 Hops S-D. So η ↑. Fault Tolerance !!!

14. Models Session Model: –Each node is source node for one session and destination node for another session. –Source-Destination association does not change with time. –Each node has an infinite buffer capacity. –Nodes themselves move.

15. Transmission Model (Time slots present) At time t, node I transmits data at rate R packet/second to node j if P i (t) - transmission power,  ij (t) - channel gain,  - signal to interference ratio, N o - background noise power, L - processing gain

16. Transmission Model (Cont’d) The channel gain is given by (α > 2, Xi and Xj are node locations)

17. 2 Models used for experiments: One with packets directly transmitted from S-D Another nodes can be relays. Scheduler, depending on scheduling policy, chooses senders and receivers based on power levels Long term throughput improvement is feasible.

18. Results: Thus within a factor of  log n, the throughput per S-D pair goes to zero like R/  n in the case when the nodes are fixed. Fixed Node with Relaying ( G & K Model): – The following results yield upper and lower bounds on the throughput. Theorem : There exits c and c’ such that

19. Results (Cont’d): Fixed Node with Relaying ( G & K Model): Reason for throughput becoming zero for fixed nodes: As Model scales, relay nodes ↑ by √n In mobile nodes with Relaying, max # of hops is 2. Without relaying, no way of high throughput as n increases.

20. Results Mobile nodes without relaying:: Theorem: Direct transmission between the source and destination nodes, and no relaying !

21. Results Mobile nodes without relaying:: Theorem (Contd) : If c is any constant satisfying This result says that without relaying, the throughput per S-D pair goes to zero at least as fast as

22. Problems with mobile nodes w/o relaying Too much long range communication Resulting interference limits # concurrent transmissions. Hence throughput low.

23. Results Mobile Nodes with Relaying Why you need the relaying? the source and destination become nearest neighbors for small fraction of time(1/n) Concurrent O(n) transmission are possible How??? Theorem: For the scheduling policy , the expected number E[Nt] of feasible sender-receiver pairs is  (n).

24. Mobile Nodes with Relaying gives : Transmission to nearest nodes. O(n) Concurrent successful transmissions per time slot. Receiver power at the nearest neighbor is of same order as total interference from O(n) interferers. At max 2 hops for packet to reach from source to destination

25. Two phase scheduling Scheduling of packet transmission from source to destination : 1 st phase Scheduling of packet from relays to final destination: 2 nd phase Interleaved phase Scheduling policy depends only on location of the node

26. Two-phase scheduling policy

27. Throughput analysis: Throughput = probability of two nodes to be selected as sender receiver pair Throughput per each S-D pair turns out to be O(1) : theorem 3.5

28. Sender-Centric Vs. Receiver Centric Approach Sender-Centric means it is senders that selects the closest receiver to send to. Receiver-Centric means it is receivers that selects the closest sender from which receive.

29. Analysis Problem with receiver centric approach: -Two receivers may select the same sender Adv of receiver centric approach: In receiver centric approach, signal from selected sender is always the strongest. SIR Ratio – the interference in receiver centric approach is stochastically smaller

30. Throughput for sender centric approach

31. Numerical Results For given α there is an optimal sender density Θ that maximizes the throughput. If density is too low the channel reuse has not been exploited. If density is too large the interference power becomes too dominant Select Θ carefully

32. Distributed Implementation –Node decide whether it wants to be sender or receiver –Even phase: Sender forward packets from source to relays. –Odd phase: Sender forward packets from relays to destinations. –Uncoordinated access –Future work: Study of local scheduling strategies and their impact on end to end delay.

33. Related Work Infostation focused on maximizing the capacity of a point to point channel in fix power budget Our focus: spatial reuse of channel to achieve higher throughput. Related interference model studies the probability of capturing a single receiver Gupta & Kumar model

34. Conclusion Results show that direct communication between sources and destinations alone cannot achieve higher throughput because they are too far apart most of the time Delay tolerant applications can take advantage of node mobility to significantly increase the throughput capacity of networks.

35. Critiques: The scheduling policy given is totally abstract. There will be duplicate packets problem when multiple relays send the same pkt to the destination. No explanation for this. No way for real-time data – audio,video etc. No communication protocol specified which is crucial for efficiency in wireless networks All Theoretical concepts, no practical implementation model !!