A New Approach to Channel Access Scheduling in Ad Hoc Networks

1. A New Approach to Channel Access Scheduling in Ad Hoc Networks Lichun Bao School of ICS University of California, Irvine J.J. Garcia-Luna-Aceves School of Engineering University of California, Santa Cruz

2. Administrator: UxDMA needs the whole network topology, while distributed solution needs local topology and schedule resolution. Existing Solutions for Channel Access: • Random Access Scheme: • ALOHA, CSMA/CA (FAMA, MACA, MACAW, IEEE 802.11) : with/without RTS/CTS handshakes. • Difficulties providing fairness, QoS. • Scheduled Access Scheme: • Node/Link Activation. • FDMA/TDMA/CDMA in multihop networks: graph coloring problem — UxDMA. University of California

3. Our Solution: Scheduled Access • Problem description: • Given a set of contenders Mi of an entity i in contention context t, how does i determine whether itself is the winner during t ? • Topology dependence: • Exactly two-hop neighbor information required to resolve contentions. • Two-hop neighbors are acquired by each node broadcasting its one-hop neighbor set. University of California

4. Example Settings: • Omni-directional Antenna; • Time slotted channel access; • Equal transmission range; • 4 nodes; • Each node knows its one- and two-hop neighbors — Mi. University of California

5. Goals to Achieve: • Collision-free — avoid hidden terminal problem, no waste on transmissions; • Fair — the probability of accessing the channel is proportional to contention; • Live — capable of yielding at least one transmission each time slot. University of California

6. i j l k 0 1 2 3 time t Illustrations by Example: i wins j & l win k wins University of California

7. Neighbor-aware Contention Resolution (NCR): • In each contention context (time slot t): • Compute priorities • i is the winner for channel access if:  University of California

8. Attributes of NCR: • Collision freedom; • Fairness; • Liveliness; • 2-coloring: • An entity colors itself if it red has the highest priority among its contenders. • Otherwise, it has transparent color. University of California

9. NCR-MI (Multiple Identities): • Dynamic Resource Allocation. • A node i may have Ii pseudo identities. • k-th identity is denoted as • Ii is dynamically chosen by i according to traffic requirement. • Each identity of i gives i a chance to win a contention. The more identities, the better chance of channel access. University of California

10. NCR-MI Specification: • Compute the priority on each pseudo identity of every contender: • For l-th identity of contender k, we have: • i is the winner for channel access one of its priority is the greatest among its contenders.  University of California

11. Channel Access Probability: • Dependent on the number of pseudo identities and the density of the neighborhood. • Channel access probability: • Bandwidth allocation University of California

12. i j l k Bandwidth Allocation Example: • Channel access probability for individual nodes: • Spatial channel reuse ratio: University of California

13. Delay & Throughput Analysis: • Data packet service at entity i modeled as M/G/1 queuing system with server vacation. • Delay (Pollaczek-Kinchin formula): • Throughput: University of California

14. Delay Curves: University of California

15. Channel Access Scheduling Protocols: • Node Activation Multiple Access (NAMA): • Entity type: node • Time division: • Block • Section • Part • Time-slot University of California

16. Block 0 1 ....... 50 51 Membership Section: Neighbor Maintenance Section 0 1 Part 0 1 2 Time Slot NAMA Time Division Illustrated: University of California

17. Part 0 Part 1 1,5,6,8,10 2,3,4,7,9 Section 0 8 1,10 1,5,6 5,8 10 6 4,9 9 2,3,7 3 2,3,4,7,9 No occupied by anyone Everyone tries to use 2,4,7 Section 1 Contenders resolve contention using NCR NAMA Illustrated: Fully connected network with 10 nodes. ID: 1~10. University of California

18. Neighbor Protocol: • One-hop neighbor information broadcasting. • New node starting up. • Link addition and deletion. • Old neighbor going down can be treated as multiple link deletions. • Membership section: send signals. University of California

19. Channel Access Scheduling Protocols (continued): • Link Activation Multiple Access (LAMA): • Direct Sequence Spread Spectrum, available pseudo-noise code set: Cpn • Received-Oriented Code Assignment (ROCA) • Contenders of node i : • Once Mi is decided, LAMA follows NCR. University of California

20. 1 5 14 19 11 c 23 21 8 c 20 At time t, the priority of each node is computed. 6 LAMA Illustrated: d Node i tries to activate its adjacent links on code c Both j and k are assigned code c a j e b i c k f g i can activate either link (i,j) or (i,k). University of California

21. Channel Access Scheduling Protocols (continued): • Pair-wise Link Activation Multiple Access (PAMA): • Contending entities are directed edges; • Priorities are computed for each link; • Dynamic code assignment: • Contenders of a link are its adjacent links. University of California

22. a 13 5 14 23 k f c b i c 21 51 7 11 c g PAMA Illustrated: 1. Directional links 2. Only one direction shown for simplicity 3. Hidden terminal avoidance: link (i,k) and (f,g) assigned the same code — compare node priorities of i and f. University of California

23. Summary — Unified Algorithm: • Determine the entity type (node/link); • Find out the contender set; • Run NCR to determine if the entity is active in the current time slot; • Resolve hidden terminal problem. University of California

24. Performance (Delay — Fully Connected): University of California

25. Performance (Delay — Multi-hop Network): University of California

26. Performance (Throughput — Fully Connected) University of California

27. Performance (Throughput — Multi-hop) University of California

28. Comparison with Static Scheduling Algorithm (UxDMA): University of California

29. Coloring Efficiency Comparison with UxDMA: University of California

30. 4 10 8 e b a 6 5 d c 7 3 1 g h f Problems with NAMA • Inefficient activation in certain scenarios. • For example, only one node, a, can be activated according NAMA, although several other opportunities exist. —— We want to activate g and d as well. University of California

31. Node + Link (Hybrid) Activation • Additional assumption • Radio tranceiver is capable of code division channelization (DSSS —— direct sequence spread spectrum) • Code set is C . • Code assignment for each node is per time slot: i .code = i .prio mod |C | University of California

32. Hybrid Activation Multiple Access (HAMA) • Node state classification per time slot according to their priorities. • Receiver (Rx): intermediate prio among one-hop neighbors. • Drain (DRx): lowest prio amongst one-hop. • BTx: highest prio among two-hop. • UTx: highest prio among one-hop. • DTx: highest prio among the one-hop of a drain. University of California

33. HAMA (cont.) • Transmission schedules: • BTx —> all one-hop neighbors. • UTx —> selected one-hops, which are in Rx state, and the UTx has the highest prio among the one-hop neighbors of the receiver. • DTx —> Drains (DRx), and the DTx has the highest prio among the one-hops of the DRx. University of California

34. 4-DRx 8-Rx 10-BTx e b a 5-DTx 6-Rx d c 7-UTx 3-DRx 1-DRx g h f HAMA Operations • Suppose no conflict in code assignment. • Nodal states are denoted beside each node: • Node D converted from Rx to DTx. • Benefit: one-activation in NAMA to four possible activations in HAMA. University of California

35. Neighbor Protocol (Need) • Purpose: propagate neighbor updates. • Cannot be based on NCR — requires a priori neighbor information. • Only way: • Random access. • Broadcast. • No acknowledgement: why? Efficiency, broadcast. • Use retransmission to improve reliability. • Why not TSMA: Topology-dependent. University of California

36. Neighbor Protocol (Method) • Insert random access section after ROMA. • Send short signals carrying neighbor updates (256 bytes). • Problem formulation: • How to regulate interval t and number n of retransmissions to have low latency to deliver messages with given (high) probability p . University of California

37. Neighbor Protocol (Results) • Reliability: deliver-probability p =99%. • Retransmission interval: t =1.44N— only depends on N (the number of two hop neighbors). • Number of retransmission: n =6.7≈7 — only depends on p . • Suppose 2Mbps bandwidth, 2 second delay, 20 two-hop neighbors — random access sections cost 9.6% of the channel resource. University of California

38. Performance Analysis • Modeling • Infinite plane with node density ρ (100 nodes per 1000mX1000m area). • Transmission range r (0m~500m). • Derive average per-node throughput according to node-distribution and node geometric relations. • Analyze both NAMA and HAMA. University of California

39. Comparison between NAMA and HAMA • HAMA has higher throughput than NAMA: • Similar at low transmission range r . • 3-4 times higher throughput at higher r . University of California

40. Comparison withCSMA and CSMA/CA (1) • Throughput of CSMA (CA) taken from the work of Yu et al. [ICNP’02]. • Load conversion: • CSMA (CA) always fully loaded. Differ at channel access probability p’ and size ldata. • HAMA load depends on packet arrival rate λ λ=p’· ldata /(1+p’· ldata ) • Compare the throughput S in the one-hop neighborhood N= ρπr² (ρ: node density; r Tx range). University of California

41. Two scenarios: long data packet (100 time slots) and short data packet (10 time slot) Different contention levels in each scenario. Comparison with CSMA and CSMA/CA (2) University of California

42. Comparison withCSMA and CSMA/CA (3) • HAMA gives the constant S at high load, whereas CSMA and CSMA/CA degrade. • HAMA differs by the shift reaching the highest S. • When the data packet is shorter, the collision vulnerable period becomes longer relatively in CSMA and CSMA/CA, thus lower throughput. University of California

43. Comparison with NAMA and UxDMA through Simulations • UxDMA schedules broadcast only, like NAMA does. • Network generated by placing 100 nodes in 1000mX1000m area. No movement. • Transmission range: 100m, 200m, 300m, 400m. • Code set size |C |=30. • Simulation duration: 100,000 time slots. University of California

44. Throughput (1) University of California

45. Throughput (2) • HAMA collected throughput of broadcast and unicast traffics separately. • Overall throughput of HAMA and NAMA is compared with the theoretical analyses — matches well. • NAMA is worse than UxDMA sometimes, HAMA is always better than UxDMA. University of California

46. Delay University of California

47. Delay Explained • UxDMA always has lower delay. • HAMA has separate delay attributes for unicast and broadcast, because they are transmitted using separate transmission opportunities. • NAMA and HAMA have the same broadcast delay. University of California

48. Conclusions: • Collision-free scheduling algorithm; • Minimum topology information needed; • Better throughput than static scheduling algorithms. • More activation opportunities can be explored in NAMA —— HAMA. • HAMA needs code division channelization. • Theoretical analyses reveal higher throughput in HAMA than in NAMA. • Scheduled approach gives higher throughput than random access approach (CSMA, CSMA/CA). University of California