Distributed Strategic Learning for Wireless Engineers

By Hamidou Tembine

© 2012 – CRC Press

496 pages | 45 B/W Illus.

Purchasing Options:
Hardback: 9781439876374
pub: 2012-05-18
US Dollars$139.95

About the Book

Although valued for its ability to allow teams to collaborate and foster coalitional behaviors among the participants, game theory’s application to networking systems is not without challenges. Distributed Strategic Learning for Wireless Engineers illuminates the promise of learning in dynamic games as a tool for analyzing network evolution and underlines the potential pitfalls and difficulties likely to be encountered.

Establishing the link between several theories, this book demonstrates what is needed to learn strategic interaction in wireless networks under uncertainty, randomness, and time delays. It addresses questions such as:

  • How much information is enough for effective distributed decision making?
  • Is having more information always useful in terms of system performance?
  • What are the individual learning performance bounds under outdated and imperfect measurement?
  • What are the possible dynamics and outcomes if the players adopt different learning patterns?
  • If convergence occurs, what is the convergence time of heterogeneous learning?
  • What are the issues of hybrid learning?
  • How can one develop fast and efficient learning schemes in scenarios where some players have more information than the others?
  • What is the impact of risk-sensitivity in strategic learning systems?
  • How can one construct learning schemes in a dynamic environment in which one of the players do not observe a numerical value of its own-payoffs but only a signal of it?
  • How can one learn "unstable" equilibria and global optima in a fully distributed manner?

The book provides an explicit description of how players attempt to learn over time about the game and about the behavior of others. It focuses on finite and infinite systems, where the interplay among the individual adjustments undertaken by the different players generates different learning dynamics, heterogeneous learning, risk-sensitive learning, and hybrid dynamics.

Table of Contents

Introduction to Learning in Games

Basic Elements of Games

Robust Games in Networks

Basic Robust Games

Basics of Robust Cooperative Games

Distributed Strategic Learning

Distributed Strategic Learning in Wireless Networks

Strategy Learning


Strategy Learning under Perfect Action Monitoring

Fully Distributed Strategy-Learning

Stochastic Approximations

Chapter Review

Discussions and Open Issues

Payoff Learning and Dynamics


Learning Equilibrium Payoffs

Payoff Dynamics

Routing Games with Parallel Links

Numerical Values of Payoffs Are Not Observed


Model and Notations


Hybrid and Combined Dynamics

Learning in Games with Continuous Action Spaces

CODIPAS for Stable Games with Continuous Action Spaces 183

CODIPAS-RL via Extremum-Seeking

Designer and Users in an Hierarchical System

From Fictitious Play with Inertia to CODIPAS-RL . . . . . 191

CODIPAS-RL with Random Number of Active Players

CODIPAS for Multi-Armed Bandit Problems

CODIPAS and Evolutionary Game Dynamics

Fastest Learning Algorithms

Learning under Delayed Measurement


Learning under Delayed Imperfect Payoffs

Reacting to the Interference

Learning in Constrained Robust Games


Constrained One-Shot Games

Quality of Experience

Relevance in QoE and QoS satisfaction

Satisfaction Levels as Benchmarks

Satisfactory Solution

Efficient Satisfactory Solution

Learning a Satisfactory Solution

From Nash Equilibrium to Satisfactory Solution

Mixed and Near-Satisfactory Solution

CODIPAS with Dynamic Satisfaction Level

Random Matrix Games

Mean-Variance Response and Demand Satisfaction

Learning under Random Updates


Description of the Random Update Model

Fully Distributed Learning

Dynamic Routing Games with Random Traffic


Mobility-Based Learning in Cognitive Radio Networks

Hybrid Strategic Learning


Chapter Review

Fully Distributed Learning for Global Optima


Resource Selection Games

Frequency Selection Games

User-Centric Network Selection

Markov Chain Adjustment

Pareto Optimal Solutions

Learning in Risk-Sensitive Games


Risk-Sensitive in Dynamic Environment

Risk-sensitive CODIPAS

Risk-Sensitivity in Networking and Communications

Risk-Sensitive Mean Field Learning


Chapter Review


Basics of Dynamical Systems

Basics of Stochastic Approximations

Differential Inclusion

Markovian Noise

Subject Categories

BISAC Subject Codes/Headings:
TECHNOLOGY & ENGINEERING / Mobile & Wireless Communications