An Introduction to Statistical Communication Theory: An IEEE Press Classic Reissue
This IEEE Classic Reissue provides at an advanced level, a uniquely fundamental exposition of the applications of Statistical Communication Theory to a vast spectrum of important physical problems. Included are general analysis of signal detection, estimation, measurement, and related topics involving information transfer.
Using the statistical Bayesian viewpoint, renowned author David Middleton employs statistical decision theory specifically tailored for the general tasks of signal processing. Dr. Middleton also provides a special focus on physical modeling of the canonical channel with real-world examples relating to radar, sonar, and general telecommunications. This book offers a detailed treatment and an array of problems and results spanning an exceptionally broad range of technical subjects in the communications field.
Complete with special functions, integrals, solutions of integral equations, and an extensive, updated bibliography by chapter, An Introduction to Statistical Communication Theory is a seminal reference, particularly for anyone working in the field of communications, as well as in other areas of statistical physics. (Originally published in 1960.)
Why Read This Book
You will gain a rigorous, physically grounded foundation in statistical communication theory that connects Bayesian decision methods to real-world radar, sonar, and telecommunications problems. The book’s emphasis on statistical modeling of channels and interference gives you the tools to move beyond formulas and reason about performance and design in noisy, nonideal environments.
Who Will Benefit
Researchers and senior engineers in signal processing, communications, or radar/sonar who need a mathematically rigorous treatment of detection, estimation, and statistical channel modeling to inform system design and analysis.
Level: Advanced — Prerequisites: Solid background in probability and random processes, linear algebra, signals and systems, and basic familiarity with estimation/detection concepts and calculus.
Key Takeaways
- Apply Bayesian decision theory to formulate and solve detection and estimation problems under realistic channel and noise models.
- Derive and analyze optimal detectors and estimators (MAP, ML, likelihood-ratio tests) and compute performance metrics for communications and radar scenarios.
- Model physical channels and non-Gaussian interference statistically to predict system behavior and robustness.
- Perform spectral analysis and use transforms (e.g., Fourier/FFT-based techniques) within a statistical framework for measurement and signal characterization.
- Design and analyze adaptive filtering and statistically based measurement strategies for improved parameter estimation in dynamic environments.
Topics Covered
- Preface and Overview of Statistical Communication Theory
- Mathematical Preliminaries: Probability, Random Variables, and Stochastic Processes
- Bayesian Decision Theory and Statistical Inference
- Hypothesis Testing and Detection: Likelihood Ratios and Error Rates
- Estimation Theory: Point Estimation, Cramer–Rao Bounds, and Bayesian Estimators
- Measurement Theory and Performance Assessment
- Random Processes, Spectral Analysis, and the Role of the FFT
- Statistical Models of Noise, Clutter, and Interference (including non-Gaussian models)
- Channel Modeling for Communications: Fading, Multipath, and Statistical Channels
- Radar and Sonar Applications: Detection, Tracking, and Parameter Estimation
- Adaptive Filtering and Sequential Estimation
- Information Transfer, Capacity Considerations, and Applications
- Worked Problems, Examples, and Advanced Topics
Languages, Platforms & Tools
How It Compares
Covers similar foundational territory to Van Trees' Detection, Estimation, and Modulation Theory and Kay's Fundamentals of Statistical Signal Processing, but Middleton emphasizes Bayesian decision perspectives and detailed physical channel modeling with extensive radar/sonar examples.
