DYNAMIC POWER ALLOCATION AND ROUTING FOR TIME-VARYING WIRELESS NETWORKS

1. DYNAMIC POWER ALLOCATION AND ROUTING FOR TIME-VARYING WIRELESS NETWORKS Michael J. Neely, Eytan Modiano and Charles E.Rohrs Presented by Ruogu Li Department of Electrical and Computer Engineering The Ohio State University

2. CONTENTS • Overview • System Model and Assumptions • Network Capacity Region • Centralized DRPC Policy • Proof of stability • Enhanced DRPC Policy • Decentralized DRPC Policy • Conclusion • Future work

3. OVERVIEW • We consider dynamic routing and power allocation for a wireless network with time-varying channels: • The network consists of power constrained nodes; • Transmission rates over the links are determined by allocated power; • Packets randomly enter the system at each node and wait in output queues to be transmitted to their destinations; • We developed a joint routing and power allocating policy (DRPC) that stabilizes the system and provides bounded average delay.

4. SYSTEM MODEL AND ASSUMPTIONS • A wireless network with nodes; • Time is slotted, channel state stays the same in one slot; • Multiple data stream randomly enter the system with source and destination ; • Each node can transmit data over multiple links simultaneously; • Power is assigned to links at each node.

5. SYSTEM MODEL AND ASSUMPTIONS • Power constraint at each node: • Transmission rate on each link is determined by a rate-power curve , where is the power matrix, and is the channel state matrix; • Channel state represents, for example, attenuation and/or noise levels; it is known to the controller at the beginning of each time slot;

6. SYSTEM MODEL AND ASSUMPTIONS • The power curve is assumed to be upper semi-continuous in the power matrix for all states ; • The power matrix , where is the set of acceptable power allocations.

7. SYSTEM MODEL AND ASSUMPTIONS • Each node queues data according to their destinations; • We classify all data flowing through the network as belonging to a particular commodity , representing the destination node for the data; • Define as the rate offered to commodity traffic along link ;

8. SYSTEM MODEL AND ASSUMPTIONS • The input process of the network are stationary and ergodic with rates . • represents the incoming rate at node of commodity . The matrix is the corresponding matrix with diagonal entries equal to zero. • Further assume that the second moment of is bounded every time slot by some finite maximum value regardless of past history.

9. SYSTEM MODEL AND ASSUMPTIONS • The control decision variables are: • Power allocation, choose such that ; • Routing/Scheduling, choose such that • The backlog of bits in node destined for node c is represent by (the queue length).

10. NETWORK CAPACITY REGION • A queueing system is said to be stable if the queue length does not ‘blow up’ when time goes to infinity; • The network capacity region is the closure f the set of all rates matrices that can be stably supported over the network, considering all possible algorithms.

11. NETWORK CAPACITY REGION • Example: • The capacity region will be

12. CENTRALIZED DRPC POLICY • Dynamic Routing and Power Control (DRPC) Policy: • For all links , find commodity such that and define • Power allocation: choose a matrix such that • Routing: define transmission rate as follows: , if and , otherwise

13. CENTRALIZED DRPC POLICY • It is inspired by the maximum differential backlog algorithms developed by Tassiulas and Ephremids; • An extension of the maximum differential backlog algorithm which maximize the throughput of a constrained network; • Thus DRPC Policy maximizes the throughput of the network. Ref: L. Tassiulas and A. Ephremids, “Stability properites of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks,” IEEE trans. Autom. Control, vol. 37, no.12, Dec 1992

14. CENTRALIZED DRPC POLICY • Stability of DRPC Policy • Theorem: Suppose an N-node wireless network has capacity region and rate matrix such that for some . Then, the above DPRC policy stabilize the system and guarantees bounded average congestion. • Proof of Stability of DRPC Policy • Basic idea: prove the stability of the system using a Lyapunov function. • A function is a Lyapunov candidate function if it is locally positive definite, i.e. • The choice of the Lyapunov function is based on the problem.

15. CENTRALIZED DRPC POLICY • The proof in the paper is very complicated; • We consider a similar simpler case using the same approach; • Single base station sends out data to N users; • Data arrive at base station with rate , same assumption for the arriving process; User 1 User 2 User 3 User N B.S.

16. CENTRALIZED DRPC POLICY • Constraint for the base station , ‘power constraint’ with linear rate power curve, denote the set of feasible by ; • Control variable: choose ; • The arrive rates satisfy , where is the capacity region; • Policy: choose User 1 User 2 User 3 User N B.S.

17. CENTRALIZED DRPC POLICY • Queue evolution: • Choose the Lyapunov function • Thus the Lyapunov drift is given by

18. CENTRALIZED DRPC POLICY • Notice that ; • Thus • From the assumption of , we know that second moment,

19. CENTRALIZED DRPC POLICY • Thus we get which is an simplified version of (21) in the paper; • Sum over 1 through T-1 and take expectation on both sides, we get • From the non negativity of the Lyapunov function,

20. CENTRALIZED DRPC POLICY • Taking the limit of the above inequality • Thus we proved the stability of the system under our policy; • Using Little’s Law we can get the bound on delay; • The proof in the paper is an extension of this simple case.

21. ENHANCED DRPC POLICY • Potential problem of DRPC Policy: • When the network is lightly loaded, very little information is contained in the backlog values; • Packets may wander in the network, resulting long delays; • Solution: • Adding a restricted set of desirable routes; • But restricting the routes may be harmful in time varying channels; • Enhanced DRPC Algorithm is introduced to solve this problem.

22. ENHANCED DRPC POLICY • Basic idea: implementing a ‘bias’ in the DRPC Policy so that in low loading situations, nodes are inclined to route packets in the direction of their destinations; • Define and define as the maximizer of ; • Power allocation and routing is done as before;

23. ENHANCED DRPC POLICY • The parameters can be chosen as scaled hop count estimates between nodes and , so that, in the absence of backlog information, data is routed to reduce the remaining distance to the destination; • The values are any weights for prioritizing commodity service in node ; • It can be shown that this enhanced DRPC Policy can stabilize the system for any and .

24. DECENTRALIZED DRPC POLICY • The DRPC Policy is a centralized control; • Hard to implement in reality; • The authors provided a simple decentralized approximation without proof; • Nodes have current neighbors; • The current neighbors of a node is defined as the set of the nodes to which node can currently transmit and receive.

25. DECENTRALIZED DRPC POLICY • The Decentralized DRPC Policy • At the beginning of each time slot, nodes randomly decide to transmit with probability . All transmitting nodes send a control signal of power where is globally known; • Define as the set of all transmitting nodes. Each node measures its total resulting interference and send this quantity over a control channel to all neighbors; • Each transmitting user decides to transmit using full power to the single neighbor who maximizes

26. CONCLUSION • We have formulated a general power allocation problem for a multinode wireless network with time-varying channels and adaptive transmission rates; • The network capacity region was established; • A DRPC algorithm is developed and shown to stabilize the network whenever the arrival rate matrix is within the capacity region.

27. FUTURE WORK • The DRPC policy is based on maximum backlog differential algorithm which tries to maximize the throughput of the network, but other network control metrics such as minimizing the delay are not considered. • In the policy, we need to find which is not a trivial problem. A straightforward exhaustive search may not work for large networks. Many works have been done on this, for example, using greedy algorithm.

28. Thank you