Channel estimation normally provides information about indoor and outdoor fading channel statistics. The adaptive channel estimation models play an important role to generate the required channel state information (CSI) using the estimated channel coefficient vector. The CSI can be utilized to generate an angle vector that controls the steering mechanism of a beamformer. The beamformer provides better directive gain for linear antenna array and helps to improve the signal to noise ratio of the wireless receiver. The proposed estimation model process the transmitted quadrature amplitude modulation (QAM) data samples in the frequency domain. The adaptive design incorporates norm-based sparsity through block recursive least square (BRLS) algorithm to develop a computationally efficient model. The proposed sparse-FBRLS (Fast BRLS) model has simultaneously addressed the problems of channel estimation and beamforming in case of indoor and outdoor communication. The performance of the model is tested by different performance measures under practical mobility conditions.

中文翻译：

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基于块稀疏自适应滤波的无线信道估计和波束成形

信道估计通常提供有关室内和室外衰落信道统计的信息。自适应信道估计模型在使用估计的信道系数向量生成所需的信道状态信息 (CSI) 方面发挥着重要作用。CSI 可用于生成控制波束成形器转向机制的角度矢量。波束形成器为线性天线阵列提供更好的定向增益，有助于提高无线接收器的信噪比。建议的估计模型在频域中处理传输的正交幅度调制 (QAM) 数据样本。自适应设计通过块递归最小二乘 (BRLS) 算法结合了基于规范的稀疏性，以开发计算效率高的模型。所提出的稀疏 FBRLS（快速 BRLS）模型同时解决了室内和室外通信情况下的信道估计和波束成形问题。在实际移动条件下，通过不同的性能指标来测试模型的性能。