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NOW Foundations and Trends® Books

• F. Baccelli and B. Blaszczyszyn, “Stochastic Geometry and Wireless Networks, Volume 2: Applications,” FnT in Networking, vol. 4, no. 1-2

• F. Baccelli and B. Blaszczyszyn, “Stochastic Geometry and Wireless Networks, Volume 1: Theory,” FnT in Networking, vol. 3, no. 3-4

• M. Haenggi and R. K. Ganti, “Interference in Large Wireless Networks,” FnT in Networking, vol. 3, no. 2

• D. Shah, “Gossip Algorithms,” FnT in Networking, vol. 3, no. 1

• M. Chiang, P. Hande, T. Lan, and C. W. Tan, “Power Control in Wireless Cellular Networks,” FnT in Networking, vol. 2, no. 4

• S. Shakkottai and R. Srikant, “Network Optimization and Control,” FnT in Networking, vol. 2, no. 3

• C. Fragouli and E. Soljanin, “Network Coding Applications,” FnT in Networking, vol. 2, no. 2

• C. Fragouli and E. Soljanin, “Network Coding Fundamentals,” FnT in Networking, vol. 2, no. 1

• G. Kramer, I. Maric, and R. Yates, “Cooperative Communications,” FnT in Networking, vol. 1, no. 3-4

• F. Xue and P. R. Kumar, “Scaling Laws for Ad Hoc Wireless Networks: An Information Theoretic Approach,” FnT in Networking, vol. 1, no. 2

• L. Georgiadis, M. J. Neely, and L. Tassiulas, “Resource Allocation and Cross-Layer Control in Wireless Networks,” FnT in Networking, vol. 1, no. 1

• A. M. Tulino and S. Verdu, “Random Matrix Theory and Wireless Communications,” FnT in Communications and Information Theory, vol. 1, no. 1

• M. J. Wainwright and M. I. Jordan, “Graphical Models, Exponential Families, and Variational Inference,” FnT in Machine Learning, vol. 1, no. 1-2

Information and Communication Theory

• D. J. C. MacKay, “Information Theory, Inference, and Learning Algorithms”

• M. Mezard and A. Montanari, “Information, Physics, and Computation”

• C. E. Shannon, “A Mathematical Theory of Communication”

• D. Tse and P. Viswanath, “Fundamentals of Wireless Communication”

Networking Theory

• M. Bramson, “Stability of Queueing Networks”

• B. Hajek, “Analysis of Computer Networks” (Notes for ECE567)

• V. B. Iversen, “Teletraffic Engineering Handbook”

• F. P. Kelly, “Reversibility and Stochastic Networks”

• S. P. Meyn, “Control Techniques for Complex Networks”

Game Theory

• L. Buttyan and J.-P. Hubaux, “Security and Cooperation in Wireless Networks”

• “Algorithmic Game Theory,” Edited by N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani (Errata)

• Y. Shoham and K. Leyton-Brown, “Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations”

Probability, Random Processes, and Queueing Theory

• I. Adan and J. Resing, “Queueing Theory”

• D. Aldous and J. A. Fill, “Reversible Markov Chains and Random Walks on Graphs”

• L. Devroye, “Non-Uniform Random Variate Generation”

• R. M. Gray, “Probability, Random Processes, and Ergodic Properties”

• R. M. Gray and L. D. Davisson, “Introduction to Statistical Signal Processing”

• C. Grinstead and J. L. Snell, “Introduction to Probability”

• B. Hajek, “An Exploration of Random Processes for Engineers” (Notes for ECE534)

• D. A. Levin, Y. Peres, and E. L. Wilmer, “Markov Chains and Mixing Times”

• R. Lyons and Y. Peres, “Probability on Trees and Networks”

• S. P. Meyn and R. L. Tweedie, “Markov Chains and Stochastic Stability”

• D. Pollard, “Convergence of Stochastic Processes”

• W. Whitt, “Stochastic-Process Limits”

• FREE online books on Queueing Theory, Probability and Stochastic Processes

Optimization

• S. Boyd and L. Vandenberghe, “Convex Optimization”

• A. Schrijver, “A Course in Combinatorial Optimization”

• P. Varaiya, “Lecture Notes on Optimization”

Control Theory

• D. P. Bertsekas and S. E. Shreve, “Stochastic Optimal Control: The Discrete-Time Case”

• L. C. Evans, “An Introduction to Mathematical Optimal Control Theory”

• E. D. Sontag, “Mathematical Control Theory: Deterministic Finite Dimensional Systems”

Graph Theory

• J. A. Bondy and U. S. R. Murty, “Graph Theory with Applications”

• R. Diestel, “Graph Theory”

Machine Learning

• T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”

• D. Michie, D. J. Spiegelhalter, and C. C. Taylor (eds), “Machine Learning, Neural and Statistical Classification”

• N. J. Nilsson, “Introduction to Machine Learning”

• A. Rajaraman and J. D. Ullman, “Mining of Massive Datasets”

Algorithms & Complexity

• S. Arora and B. Barak, “Computational Complexity: A Modern Approach”

• S. Dasgupta, C. H. Papadimitriou, and U.V. Vazirani, “Algorithms”

• J. Erickson, “Algorithms Course Materials”

• O. Goldreich, “Computational Complexity: A Conceptual Perspective”

• O. Goldreich, “P, NP, and NP-Completeness: The Basics of Complexity Theory”

• R. Kannan and S. Vempala, “Spectral Algorithms”

• H. Wilf, “Algorithms and Complexity”

• D. P. Williamson and D. B. Shmoys, “The Design of Approximation Algorithms”

Misc.

• D. P. Bertsekas and J. N. Tsitsiklis, “Parallel and Distributed Computation: Numerical Methods”

• D. J. C. MacKay, “Sustainable Energy - without the hot air”

• A. J. Menezes, P. C. van Oorschot, and S. A. Vanstone, “Handbook of Applied Cryptography”

• Y. Saad, “Iterative Methods for Sparse Linear Systems” (1st edition)

• R. Szeliski, “Computer Vision: Algorithms and Applications”