Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Thrust 2 Layerless Dynamic Networks Lizhong Zheng, Todd Coleman

Capacity Delay Power Upper Bound Lower Bound Capacity and Fundamental Limits Capacity Delay Power Utility=U(C,D,E) Application and Network Optimization (C*,D*,E*) Constraints Degrees of Freedom Models and Dynamics Application Metrics and Network Performance Layerless Dynamic Networks New Paradigms for Upper Bounds MANET Metrics Fundamental Limits of Wireless Systems Application Metrics Models New MANET Theory Metrics

Layerless Dynamic Networks Dynamic : Separation of functionalities by different time scales no longer optimal. –Time varying channel/network environments, lack of information, high overhead costs; –Data/side information available in a variety of forms, with a wide range of quality/precision/reliability; –Broadcasting and interference, beyond point-to-point communications; Layerless: New signaling schemes, new metrics –Network information theory: cooperative/cognitive transmissions, relay and soft information processing; –Dynamic information exchange, feedbacks and multi-way communication; –Heterogeneous data processing, prioritizing data by different levels of reliability; networking based on new interface to the physical layer; –The principle of network coding, transmit-collect-combine pieces of information, generalized form and coordination in dynamic networks; –Operating with imperfect side information, robustness and universal designs.

Thrust Areas Network information theory –Multi-terminal communication schemes with cooperative and cognitive signaling, interference mitigation, broadcast/relay; Generalizing network coding Structured code designs –Efficient transmission of heterogeneous data and applications; Universal and robust algorithms Feedback and dynamic communication problems

Recent Thrust Achievements: Relaying, forwarding, and combining soft information Likelihood forwarding – Koetter General relaying for multicast – Goldsmith Interference forwarding -- Goldsmith Broadcasting Channel with Cognitive Relay – Goldsmith Multicasting with relay – Goldsmith –Structured codes based on the structure of the network; –Finite field channel / linear codes vs. Gaussian channel / nested lattice codes Sum capacity for deterministic interference channels -- El Gamal Broadcasting Channel with 3+ receivers – El Gamal (focus talk) Analytical study for zig-zag decoding -- Medard

Recent Thrust Achievements: Structured codes and Applications Broadcasting with layered source codes – Goldsmith Generalized capacity/distortion for joint source channel codes – Effros & Goldsmith UEP: performance limits and applications – Zheng Joint source channel coding with limited feedback -- Goldsmith –Successive refinement with progressive transmissions –Dynamic of channel blocks between layers Distortion-outage tradeoff -- Medard & Zheng –Comparison between multiple description and multi-resolution codes –New interface to the physical layer Low complexity channel coding with Polar codes – Effros

Recent Thrust Achievements: Generalized Network Coding Contents feedback with Network Coding – Effros Linear representation in Network Coding – Effos When linear coding suffices for all interior nodes? Low SNR Relay Networks – Medard Peaky signaling with network coding Distributed network coding with coded side information – Effros Multiple uni-cast with Network Coding -- Medard

Generalization to finite state broadcast channels (FSBC) - Goldsmith Control principles for feedback channels – Coleman Dynamic coding problem interpreted with stochastic control Reverse iterated function system (RIFS) decoding algorithms (w/ linear complexity) achieve capacity Lyapunov exponents of dynamical systems provide clean way to demonstrate how posterior matching achieves capacity Tilted posterior matching for feedback channels -- Zheng (focus talk) Joint source channel code with outage, with limited feedback -- Goldsmith Exploiting mobility in relay networks -- Moulin Recent Thrust Achievements: Feedback, channel memory, and dynamics

Provide building blocks for large networks, translate design constraints into network modeling assumptions Thrust Synergies Thrust 2 Dynamic Network Information theory: improving performance in presence of interference, cooperation, and dynamic environment Thrust 3 Application Metrics and Network Performance Guide problem formulation by identifying application constraints and relevant performance metrics Provide achievable performance region, based on which distributed algorithms and resource allocation over large networks are designed Thrust 1 New Paradigm of outer bounds Performance benchmark and design justification General Relaying, interference forwarding -- Goldsmith Posterior matching for feedback channels – Coleman, Zheng Joint source channel coding with outage – Goldsmith Network scalability, robust and distributed algorithms

Dynamic Network Information Theory CSI, feedback, and robustness Structured coding Thrust 2 Achievements Overview Goldsmith: general relaying, soft combining Zheng: Mismatched receivers Goldsmith: Broadcasting with layered code Effros: two stage polar codes Moulin: Universal Decoding in MANETs Goldsmith: Feedback and Directed Information Coleman: Control principle for feedback channels Zheng: tilted matching for feedback channels Medard, Zheng: Diversity-distortion tradeoff Goldsmith: Joint source channel coding / outage Effros: linear representation of network coding Goldsmith: Interference forwarding Cover: coordination capacity El Gamal: BC with 3+ receivers Effros: distributed network coding with coded side information Goldsmith: Multicast with relay; BC with cognitive relay Moulin: exploiting mobility of relay networks

Thrust Achievement Summary 1.Revolutionize upper bounding techniques (thrust 1) 2.Determine the optimal channel/network “coding” that achieves these capacity upper bounds when possible, and characterize for which classes of networks gaps still exist between achievability and upper bounds, and why. –Coordination capacity – Cover (focus talk); 3.Develop new achievability results for key performance metrics based on networks designed as a single probabilistic mapping with dynamics over multiple timescales –General framework for systematic development and comparison of signaling schemes –New metrics and new interfaces Error-erasure, distortion-outage, joint source channel codes; –New signaling techniques Generalized network coding, soft information combining, interference management; Message embedding, layered codes; Robust and universal algorithms; Dynamic communication problems, feedback, mobility; 4.Develop a generalized theory of rate distortion and network utilization (thrust 3) 5.Demonstrate the consummated union between information theory, networks, and control; and why all three are necessary ingredients in this union. –Large network results; –Control view of dynamic information exchange

Thrust Challenges Formulations beyond capacity –Philosophical alignment of achievability and converse methodologies –Bounds vs. approximations –New metrics focusing on delays and robustness –Hidden dimensions of limits Coordination and Networks –Not all communication has to be reliable: how to measure/utilize soft information for end-to-end reliability? –Robustness and imperfect side information –Coordination overhead Coded side info. / unintended helper / confidential messages Feedback and dynamic communication problems: are rate and error exponent the right metrics? Impacts and insights to practical problems