This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation.

中文翻译：

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多输入多输出水声通信的贝叶斯迭代信道估计和涡轮均衡

本文研究了一种适用于单载波、多输入多输出 (MIMO) 水声 (UWA) 通信的稳健接收器方案，该方案使用稀疏贝叶斯学习算法进行嵌入 Turbo 均衡 (TEQ) 的迭代信道估计。我们推导出了一种对 MIMO UWA 信道空间相关性进行建模的逐块稀疏贝叶斯学习框架，其中提出了一种更稳健的期望最大化算法，用于更新信道脉冲响应、残余噪声和信道协方差矩阵的联合估计。通过利用 MIMO UWA 信道的空间相关稀疏性和训练序列中的二阶先验信道统计数据，所提出的贝叶斯信道估计器不仅具有相对较低的复杂性，而且对决定信道稀疏性和恢复精度的超参数具有更稳定的控制。此外，本文提出了一种具有连续软干扰消除的低复杂度空时软判决反馈均衡器 (ST-SDFE)。通过海底 2008 表面过程和声学通信实验评估，改进的稀疏贝叶斯学习信道估计算法在鲁棒性和复杂度方面均优于传统贝叶斯算法，同时具有比正交匹配追踪更好的估计精度和改进的比例归一化最小均值平方算法。