Efficient Anadromic Gradient Descent-Based Off-Grid Underwater Acoustic Channel Estimation for Long-Term IoUT Systems

Underwater acoustic communication (UAC) is one of the core technologies for the Internet of Underwater Things (IoUT). Channel estimation (CE) is crucial for achieving reliable UAC performance. However, existing underwater acoustic CE methods involve a trade-off between computational overhead and estimation accuracy. To relieve this issue, this paper proposes an efficient and high-precision CE algorithm. Based on compressed sensing theory, the algorithm first obtains an on-grid delay estimate via orthogonal matching pursuit (OMP). Subsequently, we optimize the delay estimation result beyond the grid constraint using an anadromic gradient descent (AGD) algorithm combined with the Armijo backtracking line search approach. The simulation results demonstrate that the proposed AGD-Armijo algorithm achieves comparable CE performance to existing same type of off-grid CE algorithms, while improving the iterative convergence rate by approximately 52.7%. Furthermore, to address the pseudo-path estimation problem caused by off-grid delay errors during the on-grid estimation stage, we propose a two-stage optimization off-grid channel estimation (TSO-OGCE) method. The TSO-OGCE can effectively suppress pseudo-paths and achieve reliable estimation performance by introducing a single-path off-grid optimization step. We have demonstrated the feasibility and effectiveness of the above methods through field experiments at Songhua Lake.