基于K学习的Sub-6GHz辅助毫米波信道信息获取

Acquisition of Channel State Information Based on K-Learning Sub-6GHz Assist mmWave

毫米波通信是下一代蜂窝通信之类的大数据通信的可行方案。然而,因为毫米波频率具有极大的路径损耗,所以为了减轻毫米波路径损耗,混合模拟/数字波束成形可以作为一种技术来降低路径损耗。对于获得高波束源增益至关重要的是在发射机处获得准确的毫米波信道信息。该文着重讨论具有大规模MIMO阵列的毫米波通信系统中的信道状态信息获取问题。由于信道信息获取是一种开销很大的方法,因此该文考虑了一种开销低的精确信道估计方案。通过在子-6GHz处提取的支持信息来辅助毫米波信道信息的获取,将毫米波信道信息获取公式化为压缩感测问题,并使用广义近似消息传递(GAMP)算法获得信道信息。使用子-6GHz信道的支持分布信息扩展了GAMP算法。此外,基于K最近邻的思想,根据子-6GHz的支持分布信息重新设计GAMP算法。仿真结果表明,与现有的信道估计算法相比,该算法不仅可以提高估计精度还能降低导频开销。

Millimeter-wave(mmWave)communication is a practicable scheme for big data communication,such as next-generation cellular communication.However,mmWave frequencies have an extremely large path loss,for this,hybrid analog/digital beamforming could serve as an awesome technique to reduce such loss.This paper concentrates on the channel state information acquirement problem in mmWave communication systems with massive multiple-input multiple-output(MIMO)arrays.Because the channel state information acquirement is a method of significant overhead,we consider an accurate channel estimation scheme with low overhead.This paper proposes using support information extracted at Sub-6GHz to aid the mmWave channel state information acquirement.We formulate mmWave channel state information acquirement as a compressive sensing problem and use generalized approximate message passing(GAMP)algorithm.We also extend the GAMP algorithm with support distribution information from Sub-6GHz channel.Furthermore,based on the K nearest neighbor idea,we redesign the GAMP algorithm depending on Sub-6GHz support distribution information.Simulation results show that the out-of-band information aided mmWave channel estimation is capable of reducing the pilot overhead greatly and channel estimation accuracy can be improved as well.