Chapter 9: Detection and Estimation Theory

Learning Objectives

• Formulate binary and M-ary detection as hypothesis testing and derive MAP and ML decision rules
• Compute exact and approximate BER/SER for PAM, QAM, and PSK in AWGN using the Q-function and its representations
• State and apply the Cramer-Rao lower bound (CRLB) to assess estimator quality and derive ML and MMSE estimators
• Derive average error probability for coherent detection over Rayleigh and Ricean fading channels using the MGF approach
• Explain the role of diversity order in determining high-SNR BER slope and contrast exponential vs algebraic BER decay
• Design pilot-based channel estimation using LS and MMSE estimators and analyse their MSE performance
• Compare coherent, non-coherent, and differentially coherent detection in terms of performance and CSI requirements