Hybrid Evolutionary-Based Sparse Channel Estimation for IRS-Assisted mmWave MIMO Systems
Hybrid Evolutionary-Based Sparse Channel Estimation for IRS-Assisted mmWave MIMO Systems
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DOI：
10.1109/twc.2021.3105405
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发表时间：
2022-03-01
影响因子：
10.4
通讯作者：
Wong, Kai-Kit
中科院分区：
文献类型：
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作者：
Chen, Zhen;Tang, Jie;Wong, Kai-Kit
智能反射面（IRS）辅助毫米波（mmWave）通信系统已经成为用于覆盖扩展和容量增强的有前景的技术。IRS的现有工作大多假设完美的信道状态信息（CSI），这有助于导出上限性能，但由于IRS的无源元件没有信号处理能力而难以在实践中实现。本文提出了一种基于IRS的毫米波多输入多输出（MIMO）系统的压缩信道估计方法。为了减少训练开销，利用毫米波信道的固有稀疏性。利用Kronecker积的性质，将IRS辅助的毫米波信道转化为稀疏信号恢复问题，该问题涉及两个相互竞争的代价函数项（测量误差和稀疏项）。现有的稀疏恢复算法使用正则化参数来解决组合矛盾目标函数，这导致次优解。为了解决这个问题，一个混合多目标进化范式来解决稀疏恢复问题，它可以克服困难的正则化参数值的选择。仿真结果表明，在较宽的仿真设置范围内，该方法与现有的信道估计方法相比，具有较好的误码性能。
The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.