In this letter, we propose a hyperparameter optimization method for adaptive filtering based on deep unrolling, termed the deep unrolling affine projection (DAP) algorithm. The core idea is to reformulate the iterative structure of the traditional affine projection (AP) algorithm as a multilayer neural network, where each layer corresponds to one iteration and the step size is treated as a trainable parameter. These parameters are optimized through end-to-end supervised learning to enhance convergence speed and steady-state performance. While maintaining the interpretability and computational structure of the original algorithm, DAP leverages modern deep learning techniques to automatically learn hyperparameters from training data. Simulation results for the system identification task demonstrate that DAP outperforms the conventional AP algorithm in both convergence rate and accuracy. Importantly, DAP introduces no additional computational burden since the test phase involves only forward propagation. This makes it an efficient and practical solution for real-time adaptive filtering in engineering applications.
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