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

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

3. 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: Our solution to the dynamic problems
Network information theory: cooperative/cognitive transmissions, relay and soft information processing, feedbacks;
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.

4. Thrust Areas Network information theory Generalizing network coding
Multi-terminal communication schemes with cooperative and cognitive signaling, interference mitigation, broadcast/relay;
Generalizing network coding
Dynamic environment and feedbacks;
Structured code designs
Efficient transmission of heterogeneous data;
Universal and robust algorithms
Reducing the requirement of coordination overhead;
Feedback
Moving towards larger networks

5. Recent Thrust Achievements: Relaying, forwarding, and combining soft information
Likelihood forwarding – Koetter
General relaying for multicast – Goldsmith
DMT for multi-hop networks -- Goldsmith
Cognitive interference / Z channels -- Goldsmith
Based on generalized Gel’fand-Pinsker channel;
Optimal structure for interference.
Interference forwarding -- Goldsmith
Taking advantage of the structure of codebooks;
Forwarding interference so it can be decoded and cancelled.
MIMO cognitive networks -- Goldsmith
Spatial degrees of freedom for cognition and cooperation;
Cooperation in broadcasting, multiple prime users.
relay
dest1
dest2
source 1
source 2

6. Recent Thrust Achievements: Structured code designs
Broadcasting with layered source codes – Goldsmith
Generalized capacity/distortion for joint source channel codes – Effros & Goldsmith
UEP: performance limits and applications -- Zheng
Theoretical limits for heterogeneous data transmissions;
Unifying resource allocation between control and data messages, a new interface to the physical layer.
Layered joint source channel codes -- Medard & Zheng
Distortion-diversity tradeoff: a performance metric for dynamic source-channel problems;
Multiple descriptions carried by layered channel codes

7. Generalized Network Coding
Recent Thrust Achievements:
Generalized Network Coding
Using feedbacks with linear network coding – Medard
ACK used to estimate transmission time and channel condition;
Improve delay/energy efficiency for TDD systems;
Contents feedback with Network Coding – Effros
Avoid unnecessary retransmissions;
Strictly increase capacity region for the butterfly network and multi-terminal source coding.
Distributed source coding with network coding – Effros
Classical example of source networks
extended with network coding

8. Finite-State Broadcast Channel
Recent Thrust Achievements:
Feedback, channel memory, and dynamics
Network coding with feedback
Indecomposable finite state channel with Feedback -- Goldsmith
Appropriate model of dynamics;
Tx-Rx synchronization to achieve the maximum over all channel states;
Generalization to finite state broadcast channels (FSBC) - Goldsmith
Superposition codetree at the encoder;
User cooperation included;
DMDT for multi-hop MIMO Relay Network
- Goldsmith
Diversity-Multiplexing-Delay tradeoff;
Optimal ARQ protocol: fractional variable ARQ;
Control principles for feedback channels -- Coleman
Feedback channel as a control problem;
Low complexity iterative encoder to achieve capacity
Finite-State Broadcast Channel
Rx1 Xi
Rx2
Yi-1 Zi
p(yi,zi,si|xi,si-1)
Si-1 Si D
Zi-1 Yi

9. Recent Thrust Achievements: Towards Larger Networks
Interference Mitigating Mobility Strategies – Moulin
Using mobility to actively avoid interference to others;
Optimal mobility strategy to dynamically enlarge capacity region.
Scaling law for heterogeneous large networks – Shah
Tree networks for hierarchical cooperative relay;
Arbitrary traffic/ node placement.
Networks with Side Information – Effros
Distributed source coding joint with network coding;
Successive refinement for source / side information for multiple sinks.
Graphical scheduling -- Medard & Koetter
Hyper graph to describe conflicts;
Distributed algorithms for hyperarc scheduling.

10. Thrust Synergies Thrust 1 Thrust 2 Thrust 3
New Paradigm of outer bounds
Provide building blocks for large networks, translate design constraints into network modeling assumptions
Performance benchmark and design justification
UEP: bit-wise. vs
message-wise -- Zheng
Network equivalence
–Effros, Medard &Koetter
Thrust 2
Dynamic Network Information theory: improving performance in presence of interference, cooperation, and dynamic environment
Provide achievable performance region, based on which distributed algorithms and resource allocation over large networks are designed
Guide problem formulation by identifying application constraints and relevant performance metrics
Network scalability, robust and distributed algorithms
Cooperative models for wireless NUM
– Boyd & Goldsmith
Thrust 3
Application Metrics and Network Performance

11. Thrust 2 Achievements: Previous
Dynamic Network Information Theory
Goldsmith: general relaying, soft combining
Goldsmith: Cognitive users and interference
Zheng: Embedded Coding and UEP
Moulin: Information flow via timing
Goldsmith: Broadcasting with layered code
Coleman: Joint Source/Channel Coding in Networks
Effros, Goldsmith: Generalized capacity, distortion, and joint source/channel coding.
Zheng: Euclidean Information Theory
Goldsmith: Degraded FS Broadcast Channels
Moulin: Universal Decoding in MANETs
Goldsmith: Feedback and Directed Information
Coleman: Rate Distortion of Poisson Processes
Moulin: Error/erasure tradeoff for compound channel
Goldsmith: DMT for multi-hop networks
Coleman: “E-type” broadcasting channels
Zheng: Message embedding in feedback channels
Medard, Zheng: Diversity-distortion tradeoff
Goldsmith: Interference forwarding
CSI, feedback, and robustness
Structured coding

12. Thrust 2 Achievements: Recent
Dynamic Network Information Theory
Systematic study of cooperative and cognitive communications, and interference management, with well defined metrics, and a set of novel signaling concepts
Model and characterize the effect of dynamics, in the context of feedbacks and network coding, with limited reliability and precision
Efficient coding with heterogeneous data, new interface to the physical layer, unified view of overhead costs
Large Networks
Goldsmith: cognitive interference/Z channel
Goldsmith: Interference Forwarding
Goldsmith: MIMO cognitive network
Goldsmith: Finite State (BC) channels with Feedback
Medard, Zheng: Diversity-distortion tradeoff
Zheng: UEP and applications
Effros: Network coding and distributed Source coding
Medard: Network coding with FB in TDD
Effros: Contents Feedback for Network Coding
Zheng: Message-wise UEP and Feedbacks
Coleman: Control Principle for feedback designs
Moulin: Mobility to mitigate interference
Shah: Scaling of heterogeneous networks
Medard Koetter: Hypergraph scheduling
CSI, feedback, and robustness
Structured coding

13. Thrust Alignment with Phase 2 Goals
Evolve results in all thrust areas to examine more complex models, robustness/security, more challenging dynamics, and larger networks.
Network coding for classical distributed source coding problem;
Impact of dynamics for feedback (BC) channels;
Multiple cooperative/cognitive scenarios;
Network coding with feedback;
Demonstrate synergies between thrust areas
Research clustering within the thrust:
Dynamic channel models with novel multi-user signaling;
Network coding used for distributed source coding;
Synergies between thrusts:
Distributed algorithms for scheduling in networks
Models for wireless NUM with interference and dynamics (focus talk. Boyd & Goldsmith)
Network coding capacity with selfish users (focus talk, Effros)
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.
Gains by modeling dynamics, allowing cooperative/cognitive transmissions, and utilizing feedbacks；
Gains by novel signaling (interference forwarding and message embedding)