面向毫米波大规模MIMO波束域信道表征与估计的稀疏化字典学习
Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO
AI总结：
针对毫米波大规模MIMO中傅里叶波束域变换非最优的问题，提出基于学习稀疏化字典的波束域变换与预编码方法，可增强信道稀疏性、减少功率泄漏，提升信道估计质量与波束选择性，最终提高频谱效率。
摘要：
毫米波（mmWave）大规模多输入多输出（mMIMO）被认为是第五代及以后通信系统的关键使能技术。通常使用透镜天线阵列将毫米波大规模MIMO信道转换为信道呈现稀疏性的波束域，这种波束域变换等效于对信道执行傅里叶变换。但由于诸多原因，傅里叶变换未必是最优的变换方式。据此，本文提出使用学习得到的稀疏化字典作为变换算子，生成另一种波束域。由于该字典是通过对实际信道测量数据进行训练得到的，研究表明这种变换可带来两个直接优势：一是增强信道稀疏性，从而实现更高效的导频缩减；二是提升信道表征质量，进而减少潜在的功率泄漏现象。因此，该方法既能改善毫米波大规模MIMO中的信道估计性能，又能简化波束选择过程。此外，出于同样的原因，学习字典也被用作预编码算子。在多种工作场景和环境下开展的大量仿真验证了使用学习字典在提升信道估计质量和波束选择性方面的额外增益，最终可提高频谱效率。
英文摘要：
Millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) is reported as a key enabler in the fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. Still, a Fourier transformation is not necessarily the optimal one, due to many reasons. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace. Since the dictionary is obtained by training over actual channel measurements, this transformation is shown to yield two immediate advantages. First is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second is improving the channel representation quality, and thus reducing the underlying power leakage phenomenon. Consequently, this allows for both improved channel estimation and facilitated beam selection in mmWave mMIMO. Besides, a learned dictionary is also used as the precoding operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and the beam selectivity, thereby improving the spectral efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
