Optimizing Application and Network Performance in Mobile Ad-Hoc Networks (ITMANET)

1. Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Thrust 3 Application Metrics and Network Performance Asu Ozdaglar and Devavrat Shah

2. MANET Metrics Constraints Capacity and Fundamental Limits Capacity Layerless Dynamic Networks Delay Models and Dynamics Upper Bound Lower Bound Degrees of Freedom Energy End-to-End Performance and Network Utility Capacity Delay (C*,D*,E*) Utility=U(C,D,E) FLoWS Energy/SNR Fundamental Limits of Wireless Systems Models New MANET Theory Application Metrics Metrics New Paradigms for Upper Bounds Application Metrics and Network Performance

3. Optimizing Application Metrics and Network Performance • Objective: • Developing a framework for optimizing heterogeneous and dynamically varying application metrics and ensuring efficient operationof large-scale decentralized networks with uncertain capabilities and capacities • Providing an interface between application metrics and network capabilities • Focus on a direct involvement of the application in the network, defining services in terms of the function required rather than ratesor other proxies • Application and Network Metrics:utility functions of users-applications, distortion, delay, network stability, energy… • We envision a universal algorithmic architecture: • Capable of balancing (or trading off) application requirements and network resources • Adaptable to variations on the network and user side • Operable in a decentralized manner, scalable • Robust against non-cooperative behavior Algorithmic Architecture for Optimizing Application and Network Performance

4. Thrust Areas Optimization Methods for General Application Metrics Design cross-layer optimization algorithms: Optimize general application metrics (e.g., coupled performance measures, hard-delay constraints) subject to physical layer constraints Jointly optimize over application metrics and coding parameters Completely distributed and scalable Adapt to dynamics in channel characteristics and topology Stochastic Network Algorithms and Performance Analysis Design and seamless integration of queuing-level and flow-level network algorithms that yield desired performance: Flow-level performance optimization and stable network operation Game-Theoretic Models and Multi-Agent Dynamics Network formation and resource allocation models for heterogeneous and non-cooperative users: Dynamics and performance analysis

5. Thrust AchievementsOptimization Methods for General Application Metrics • Cross-Layer Optimization in Wireless Networks under Different Application Delay Metrics (Ng, Medard, Ozdaglar 08) • Joint optimization of different user delay metrics, packet coding parameters, and physical layer parameters • Motivation: In Phase I, random linear network coding shown to achieve significant delay (mean completion time) gains in rateless transmission scenarios, such as file downloads (Eryilmaz, Medard, Ozdaglar 07)

6. Thrust AchievementsOptimization Methods for General Application Metrics • Cross-Layer Optimization in Wireless Networks under Different Application Delay Metrics (Ng, Medard, Ozdaglar 08) • Consider a packet erasure channel with delayed acknowledgment feedback • Introduce a class of different user delay metrics and provide an optimization formulation for a block-by-block coding scheme • Illustrate the tradeoffs between different delay metrics • Extend the framework to a multi-user environment • Joint optimization of packet coding block size and power allocation can be formulated as a convex optimization problem (and therefore solved efficiently) for any convex user delay metric • In existing literature, true for rate-based metrics only in the high SINR regime Delay Region Rate Region

7. Thrust AchievementsOptimization Methods for General Application Metrics • Wireless Network Utility Maximization(NUM) (Boyd, Goldsmith 08) • Existing wireline NUM theory assumes fixed capacity links, steady state operation • A new wireless NUM theory developed: • NUM/Adaptive Modulation: Cross-layer rate and power allocation policies for several practical modulation schemes • Dynamic NUM: Multi-period model and distributed algorithm for dynamic network utility maximization with time-varying utilities and hard-delay requirements • Stochastic NUM: Optimal control policies in random environments; Rate-Delay-Energy tradeoffs; Distributed control policy developed based on model predictive control.

8. Thrust AchievementsOptimization Methods for General Application Metrics • Resource Allocation in Fading Multiple Access Channels (Parandehgheibi, Medard, Ozdaglar 08) • Efficient resource allocation over the information theoretic capacity region of multiple access channel to maximize a general concave utility function of transmission rates • Efficient algorithms that rely on optimization methods and rate-splitting idea • Algorithms use channel state information (does not rely on queue-length information) • Demonstrated superior rate of convergence performance for limited duration communication sessions

9. Thrust AchievementsStochastic Network Algorithms • Performance Optimization for MaxWeight Policies (Meyn 08) • Maxweight scheduling/routing policies have become popular in view of their throughput properties. However, these policies are inflexible with respect to performance (delay) improvement • Extended maxweight using general Lyapunov functions • Demonstrated excellent performance on practical topologies • Distributed implementation for wireless models

10. Thrust AchievementsGame-Theoretic Models and Algorithms • Local Dynamics for Topology Formation (Johari 08) • Efficient topology formation for routing in wireless networks with decentralized cost structures • Existing work on dynamics for topology formation requires global information • Developed decentralized dynamics for topology formation with general cost metrics • Competitive Scheduling in Wireless Collision Channels with Correlated Channel State (Candogan, Menache, Ozdaglar, Parrilo 08) • Competitive scheduling models allow the flexibility to incorporate different user objectives, but focus on users with independent channel models • Developed distributed convergent dynamics and equilibrium characterization for competitive scheduling with correlated channels, which model joint fading effects

11. Inter-Thrust Achievement • Capacity region of a large wireless network (Niesen, Gupta, Shah 08) • Existing scaling results provide one-dimensional characterization of the capacity region in the large network limit • Characterization of dimensional capacity region! • Approach: • Scaling results for networks with random (regular enough) node placement and arbitrary demand • Developed a coding scheme independent of demand requirement, effectively achieving network and physical layer separation • More in Shah’s focus talk

12. Achievements Overview Optimization Theory Distributed efficient algorithms for resource allocation Boyd: Efficient methods for large scale network utility maximization Goldsmith: Layered broadcast source-channel coding Medard, Ozdaglar: Cross-Layer optimization for different application delay metrics and block-by-block coding schemes Medard, Shah: Distributed functional compression Boyd, Goldsmith: Wireless network utility maximization (dynamic user metrics, random environments and adaptive modulation ) Medard, Ozdaglar: Efficient resource allocation in non-fading and fading MAC channels using optimization methods and rate-splitting Ozdaglar: Distributed optimization algorithms for general metrics and with quantized information Goldsmith, Johari: Game-theoretic model for cognitive radio design with incomplete channel information Shah: Capacity region characterization through scaling for arbitrary node placement and arbitrary demand Johari: Local dynamics for topology formation Shah: Low complexity throughput and delay efficient scheduling Ozdaglar: Competitive scheduling in collision channels with correlated channel states Meyn: Generalized Max-Weight policies with performance optim- distributed implementations Game Theory New resource allocation paradigm that focuses on hetereogeneity and competition Stochastic Network Analysis Flow-based models and queuing dynamics

13. Capacity Delay Upper Bound Lower Bound Energy Thrust Synergies: An Example Combinatorial algorithms for upper bounds Shah: Capacity region characterization through scaling for arbitrary node placement and arbitrary demand Thrust 1 Upper Bounds (C*,D*,E*) optimal solution of Boyd, Goldsmith: Wireless network utility maximization (dynamic user metrics, varying environments, adaptive/cooperative coding) Thrust 3 Application Metrics and Network Performance • T3 solves this problem: • Using distributed algorithms • Considering stochastic changes, physical layer constraints and micro-level considerations • Modeling information structures (may lead to changes in the performance region) Medard, Ozdaglar: Cross-Layer optimization for different application delay metrics and block-by-block coding schemes Capacity Delay (C*,D*,E*) Thrust 2 Layerless Dynamic Networks Meyn: Generalized Max-Weight policies with performance optim- distributed implementations Energy Algorithmic constraints and sensitivity analysis may change the dimension of performance region

14. Next Steps Multi-period dynamic NUM for optimally trading-off metrics such as delay, rate, admission costs Layers of bipartite graphs as a model for the network and resource allocation using scheduling and distributed optimization across layers Decentralized implementations for generalized maxweight policies Game-theoretic models for collision channels with partial channel state-correlation Multicast capacity scaling

15. Roadmap for meeting Phase 2 Goals Evolve results in all thrust areas to examine more complex models, robustness/security, more challenging dynamics, and larger networks. Wireless NUM for time-varying user metrics and dynamic network conditions Demonstrate synergies between thrust areas: compare and tighten upper bounds and achievability results for specific models and metrics; apply generalized theory of distortion and utility based on performance regions developed in Thrusts 1-2. Joint optimization of coding, delay metrics, and power allocation Joint optimization of rate and power over information-theoretic capacity region of fading multiple access channels Characterization of capacity region in the large network limit: achievability through coding schemes independent of traffic demand Demonstrate that key synergies between information theory, network theory, and optimization/control lead to at least an order of magnitude performance gain for key metrics. Delay gains of network coding Rate-reliability tradeoffs and performance gains for wireless NUM Performance improvement for generalized Maxweight policies