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When is a random network (almost) connected? How much information can it carry? How can you find a particular destination within the network? And how do you approach these questions - and others - when the network is random? The analysis of communication networks requires a fascinating synthesis of random graph theory, stochastic geometry and percolation theory to provide models for both structure and information flow. This book is the first comprehensive introduction for graduate students and scientists to techniques and problems in the field of spatial random networks. The selection of material is driven by applications arising in engineering, and the treatment is both readable and mathematically rigorous. Though mainly concerned with information-flow-related questions motivated by wireless data networks, the models developed are also of interest in a broader context, ranging from engineering to social networks, biology, and physics.

Understand the relationship between information theory and the physics of wave propagation with this expert guide. Balancing fundamental theory with engineering applications, it describes the mechanism and limits for the representation and communication of information using electromagnetic waves. Information-theoretic laws relating functional approximation and quantum uncertainty principles to entropy, capacity, mutual information, rate distortion, and degrees of freedom of band-limited radiation are derived and explained. Both stochastic and deterministic approaches are explored, and applications for sensing and signal reconstruction, wireless communication, and networks of multiple transmitters and receivers are reviewed. With end-of-chapter exercises and suggestions for further reading enabling in-depth understanding of key concepts, it is the ideal resource for researchers and graduate students in electrical engineering, physics and applied mathematics looking for a fresh perspecti