Minimal CDMA Recoding Strategies in Power-Controlled Ad-Hoc Wireless Networks

1. Minimal CDMA Recoding Strategies in Power-Controlled Ad-Hoc Wireless Networks Honglei Miao honglei.miao@ee.oulu.fiCentre for Wireless CommunicationsUniversity of Oulu, Finland

2. Outline • Introduction • Problem statement and previous work • New recoding strategies • Simulation Results • Conclusions

3. Introduction • Transmitter Oriented Code Assignment (TOCA) in CDMA based Ad-Hoc wireless network • Each node is assigned one code to be used to transmit it’s message. • Two kinds of collisions can be happened to damage the transmission. • Primary collision where an incoming transmission is damaged by a simultaneous outgoing transmission from the receiving mobile. • Secondary collision where two incoming transmissions garble each other. • Correct and efficient TOCA algorithms should be: • Eliminate all the collisions including primary and secondary collisions. • Minimize the maximum code index assigned to any network node. • Several centralized and distributed heuristics have been proposed for static multihop networks. • Why recoding in Ad-Hoc network? • In a dynamic ad-hoc network, nodes are free to • move about. • connect or disconnect from the network. • Increase or decrease transmission ranges. • These events may introduce new collisions, Recoding is needed to eliminate these new collisions.

4. Introduction (2) • Existed code assignment algorithms are inappropriate for recoding • Centralized code assignment algorithms determine a new code assignment for every node on each event. (costly) • Distributed heuristics assume a static network. (inappropriate) • Minimum recoding algorithms are proposed in this paper. • Distributed, only need communication local to the event. • Minimal recoding, minimize the number of the nodes to be recoded on any network. • Least increase in the maximum code index assigned to the network.

5. Problem statement and previous work • A power controlled ad-hoc network is modelled as a dynamic directed graph G=(V,E). • V = {v1,v2,….,vn} is set of nodes in the network. ri is the transmission range of node vi. ci is the code assigned to node vi. • E = {(vi, vj): i !=j, and dij<=ri} is the set of the directed edges. • TOCA is to assign a code to each node in the network so that the following two constraints are satisfied. • CA1-(Primary) collision avoidance 1: For every edge • CA2-(Secondary) collision avoidance 2: For every pair of edges

6. Problem statement and previous work (2) • Assumption of the events or reconfiguration in the dynamic ad-hoc network • Events occur one after another and not simultaneously. • Nodes move and change their ranges in discrete steps. • Minimal connectivity: A node v can change its configuration iff it has both from-neighbour and to-neighbour. • The goals of an efficient recoding strategy • Minimize the maximum code index used by any node in the network. (hardware consideration) • Minimize the number of nodes that change their codes. • Minimize the overhead of the recoding • Keep the recoding strategy distributed and local.

7. Problem statement and previous work (3) • Previous strategy: CP strategy • The new node and its 1-hop neighbours exchange the information about their old codes and constraints. • Ordering by identities • The new nodes and it’s 1-hop neighbours need to be recoded continuously check if they are the highest (or lowest)-identity node in its vicinity that has not been assigned a code. • Respect for the constraints • If it is the highest (or lowest)-identity node. The lowest available code (not taken by any 1-hop and 2-hop neighbours) is selected.

8. New recoding strategies • Handling Node Join

9. New recoding strategies (2) • From CA1 and CA2, all nodes in 1n, 2n, {n} each need to have codes different from each other. Nodes in 3n need not change their codes since n will be assigned a new code anyway and this will need to be different from any of the codes in 3n. • If a K-sized subset of nodes in 1n U 2n have the same old code, only K-1 nodes need to be changed. • More generally, if they are K nodes in 1n U 2n , and m different codes in 1n U 2n, then only K-m nodes need to be changed to different codes.

10. New recoding strategies (3) • Algorithm for recoding on a node join

11. New recoding strategies (4) • Example of recoding on a node join • 1n = {7}, 2n = {1 2 3 6}, 3n = {}, 4n = {4 5}

12. New recoding strategies (5) • Handling Node Power Increase

13. New recoding strategies (6) • No new constraints are induced among 1n U 2n U 3n U 4n. • All constraints due to CA1 and CA2 added by the new edges involve node n. • Minimum recoding only change the code of n if the old code of n can not satisfy the new constraints. • However, the proposed algorithm may not be the optimal among all minimal recoding strategies. For example, n only have one new constraint with another node m. If n has lots of old constraints and m very few, recoding only m might be more optimal in terms of maximum code index assigned to the network while achieving the minimal recoding bound.

14. New recoding strategies (7) • Handling Node Leaves and Power Decreases • No recoding since no new conflicts are introduced. • Handling Node Movement • Node movement is treated as a pair of consecutive events where the moving node n leaves and joins the network. • Recoding strategy on a node move is similar to that on a node join.

15. Simulation results • The different algorithms are simulated for a long sequence of events. • The proposed algorithms are compared to • BBB algorithm: centralized colouring heuristic, recolor all the nodes at every event. • CP strategy • The performance metrics to be concerned • Maximum code index assigned in the network (the lower the better). • The number of nodes recoded (recoded with a new code different from its old one).

16. Simulation results (2)

17. Simulation results (3)

18. Simulation results (4)

19. Conclusions • A set of recoding strategies Minim for TOCA CDMA recoding in a dynamic ad-hoc network are proposed. • Given an event, the strategy change the codes of the minimum number of mobiles needed to eliminate all collisions in the network. • Simulation results reveal that the Minim approaches trade off a relatively small loss in terms of maximum code index assigned in the network to obtain a significant gain in terms of the total number of instances where a node has to change its code. • The proposed strategies can be very practical in scenarios such as hard real-time systems and high data rate applications running on an ad-hoc network.