Mobility Increases The Capacity of Ad-hoc Wireless Networks By Grossglauser and Tse

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/wreasons: • 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 Pi(t) - transmission power, ij(t) - channel gain,  - signal to interference ratio, No - 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: • 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 • 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.

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. ResultsMobile 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 : 1st phase • Scheduling of packet from relays to final destination: 2nd 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 !!

36. Thank you !!!