BER Performance Analysis of Linear, DFE, and MLSE Equalizers in Static Channels with Deep Nulls

This demonstration illustrates the Bit Error Rate (BER) performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel featuring deep spectral nulls. The evaluation initiates with an MLSE equalizer configured with perfect channel knowledge, then compares performance using a simplified yet imperfect channel estimation algorithm for MLSE equalization. BER results are determined through Monte Carlo simulations, which involve generating random symbol sequences, applying channel distortion models with deep nulls, and processing signals through each equalizer type. The demo highlights how these equalizers can operate seamlessly across multiple data blocks while preserving internal state continuity between blocks—critical for maintaining equalizer performance in practical implementations. Code implementation typically involves using sliding window techniques for MLSE's Viterbi algorithm, tap weight updates for DFE adaptation, and matrix inversion methods for linear equalizer coefficient calculation.