Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module for minimizing the mean square error (MSE) of parameter estimation at the fusion center (FC). The linear transceiver design framework is initially proposed for the general scenario with arbitrary SNR sensor observations, followed by a special case with high-SNR sensor observations scenario. Our analysis also incorporates the channel correlation. The MMSE channel estimates are determined for the sensors (SNs), followed by a robust transceiver design procedure that is resilient to the channel state information (CSI) uncertainty arising due to the channel estimation error, an aberration that is unavoidable in practical implementations. Our simulation results demonstrate the improved performance of the proposed BL framework and optimal MMSE transceiver design in sparse parameter estimation relying on realistic imperfect channel estimates over the benchmarks.

中文翻译：

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基于不完善CSI的MIMO无线传感器网络中基于贝叶斯学习的线性分散稀疏参数估计

针对多输入多输出 (MIMO) 无线传感器网络 (WSN) 中基于贝叶斯学习 (BL) 的稀疏参数向量估计，提出了最优线性最小均方误差 (MMSE) 收发器设计技术。我们提出的收发器设计依赖于从 BL 模块获得的专业化理论和超参数估计，以最小化融合中心 (FC) 参数估计的均方误差 (MSE)。线性收发器设计框架最初是针对具有任意 SNR 传感器观测的一般场景提出的，然后是具有高 SNR 传感器观测场景的特殊情况。我们的分析还包含了信道相关性。MMSE 信道估计是针对传感器 (SN) 确定的，随后是稳健的收发器设计程序，该程序对由于信道估计错误引起的信道状态信息 (CSI) 不确定性具有弹性，这是在实际实现中不可避免的畸变。我们的仿真结果证明了所提出的 BL 框架和最佳 MMSE 收发器设计在稀疏参数估计中的改进性能，依赖于基准上的现实不完美信道估计。