1. Illinois Center for Wireless Systems Wireless Networks: Algorithms and Optimization R. Srikant ECE/CSL

2. 2 Researchers & Selected Topics Topics Tamer Basar Chris Hadjicostis Bruce Hajek Jennifer Hou P. R. Kumar Sean Meyn R. S. Sreenivas R. Srikant Mathematical Tools Cross-layer designOptimization Power ControlDistributed Algorithms Distributed MACInformation Theory Multi-channel, multi- antenna protocols Game Theory Performance AnalysisSystems & Control SecurityStochastic Processes

3. 3 Technical Approach Establish fundamental limits of performance of single-hop and multi-hop wireless networks Translate algorithms into practical protocols for wireless networks accounting for overhead, complexity Design distributed algorithms to achieve or approximate the above performance limits

4. 4 Example Project: Clean-Slate Design  Sponsor: NSF  Organizations: UIUC, Princeton, Texas-Austin, Purdue, Ohio State  Goal: Clean-state design of wireless networks  Is there an optimal network architecture? Should it be layer separated (PHY, MAC, network, transport, etc.) or cross-layered?  Are there near-optimal architectures that tradeoff between efficiency, robustness, signaling overhead, complexity, etc.?  Develop methodologies to evaluate alternatives  Key difficulty: No notion of a reliable bit-pipe between a pair of nodes

5. 5 Example Project: From Theory to Practice  Sponsor: DARPA  Companies: Lockheed-Martin, Alcatel-Lucent  Universities: UIUC, Stanford, Princeton, UCSB  Pose the problem of fair, efficient resource allocation as a convex optimization problem  Obtain a solution using dual decomposition theory  Find approximate solutions to optimal routing, power control and MAC algorithms  Devise low-overhead signalling protocol to enable implementation of approximately optimal algorithms  Implement in a 30-50 node MANET

6. 6 Example Project: Security & Trustworthiness  Sponsor: Motorola  Organizations: UIUC  Design algorithms for identifying and isolating misbehaving users in multi-hop wireless networks and devise incentive mechanisms to encourage cooperative behavior  Algorithms have to be distributed  No single user should have an incentive to deviate from socially responsible behavior  Robust to coalitions  Low overhead

7. Recent Results: Optimal Architecture 7  What does it mean to “optimally” allocate resources?

8. Unlike TCP, source does not react to end-to-end congestion; instead hop-by-hop congestion control Congestion Control for Flow f : Decrease queue length if ingress queue length is small Ingress Queue length 20608020 Weights= Backpressures -40-20 60  Cross-layer solution is optimal: power, time slots, routing are all allocated at the same time scale  On the other hand, layering is beneficial for proliferation: can minimal coupling of functionality among layers reap most of the performance benefits? Optimal Solution

9. 9 Recent Results: Scheduling & QoS  Problem: Given QoS constraints on the packet delay at the router, what is the optimal scheduling policy?  Scheduling policy can use queue length, delay of HoL packet, channel conditions in making a decision

10. Key Result  Using queue-length or head-of-line packet delay information can dramatically improve performance 10 QLB: Queue- length based scheduling policy Greedy: Schedule the user with the best channel Number of users Network Throughput

11. Recent Results: Enforcing Cooperation  Problem: Detect misbehaving nodes and provide incentives for all nodes to act in a socially responsible manner  Detecting misbehavior is difficult in wireless networks  Example:  C asks D to send a packet to E  When D transmits to E, B transmits to A  D  E transmission is successful, but C does not know 11 ABCDE

12. Game-Theoretic Solution  DARWIN: Distributed, Adaptive Reputation mechanism for WIreless Networks  Collect each node’s reputation based on its forwarding behavior; errors will occur occasionally  Punishment for misbehavior: Tit-for- tat strategy  Accounting for errors: Be contrite (accept punishments assuming that they are due to error)  Above behavior is optimal from a game-theoretic point-of-view: deviation from cooperation is not fruitful 12  Can be incorporated into 802.11 or other protocols  Overhead is fixed, independent of network load

13. Summary  Design of optimal architectures and algorithms for wireless networks  Develop new theory and translate it into practice  Theory-driven protocol stack design can lead dramatic gains in network performance 13