Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32611, USA.
J Acoust Soc Am. 2011 Jul;130(1):249-62. doi: 10.1121/1.3578458.
This paper addresses multi-input multi-output (MIMO) communications over sparse acoustic channels suffering from frequency modulations. An extension of the recently introduced SLIM algorithm, which stands for sparse learning via iterative minimization, is presented to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). The sparseness is exploited through a hierarchical Bayesian model, and because GoSLIM is user parameter free, it is easy to use in practical applications. Moreover this paper considers channel equalization and symbol detection for various MIMO transmission schemes, including both space-time block coding and spatial multiplexing, under the challenging channel conditions. The effectiveness of the proposed approaches is demonstrated using in-water experimental measurements recently acquired during WHOI09 and ACOMM10 experiments.
本文研究了在遭受频率调制的稀疏水声信道中进行多输入多输出（MIMO）通信。本文提出了一种对稀疏和频率调制水声信道进行估计的扩展 SLIM 算法，SLIM 算法的全称是通过迭代最小化进行稀疏学习。该扩展算法被称为广义 SLIM（GoSLIM）。通过分层贝叶斯模型来利用稀疏性，并且由于 GoSLIM 没有用户参数，因此在实际应用中易于使用。此外，本文还考虑了各种 MIMO 传输方案（包括空时分组编码和空间复用）的信道均衡和符号检测，在具有挑战性的信道条件下。最近在 WHOI09 和 ACOMM10 实验中获取的水下实验测量结果证明了所提出方法的有效性。
