Variational Bayesian inference-based joint estimation method for underwater acoustic OFDM under impulsive interference

To address the severe performance degradation of underwater acoustic orthogonal frequency division multiplexing (OFDM) communication in the presence of impulsive interference, a channel estimation method based on variational Bayesian inference is proposed. This method exploits the sparse characteristics of the underwater acoustic channel and impulsive interference. By utilizing mean-field variational Bayesian inference, this approach decomposes the posterior probability distributions of the channel vector and impulsive interference vector into simple probability distributions for fitting respectively. Iterative estimation is performed based on pilot subcarriers until convergence is achieved, resulting in the maximum a posteriori estimation of the channel and impulsive interference. The proposed method alleviates the problem that one cannot separate the sparsity of channel vector and interference vector in the joint estimation method. Meanwhile, it significantly reduces the computational complexity. Based on this, a joint estimation method of interference, channel, and symbols based on variational Bayesian inference is further proposed, where the unknown symbols are integrated into the variational Bayesian inference framework for iterative estimation with interference and channel, leading to more accurate symbol estimates. Simulation and experimental results demonstrate the effectiveness of the proposed algorithms. Compared to the existing methods, the proposed approach achieves lower error rates and complexity.