Selective Mmwave(选择性毫米波)研究综述
Selective Mmwave 选择性毫米波 - Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. [1] Further, SBL based Kalman filter (SBL-KF) for time and frequency selective mmWave multiple-input multiple-output (MIMO) hybrid architecture is presented. [2] This paper considers frequency-selective mmWave channels and proposes a channel estimation algorithm based on compressive sensing (CS) theory. [3] In this paper, we propose a new strategy to track the frequency selective mmWave channel under blockage. [4] doubly-selective mmWave MIMO channels, via the MMV sparse Kalman filtering-based SIP (MK-SIP) technique for tracking the CSI, and subsequently also to the joint Kalman filtering-based SIP (JK-SIP) for data-aided CSI acquisition. [5] In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. [6] In this paper, Adaptive Cross-Entropy (ACE) based Kalman Hybrid Precoder for the frequency selective mmWave MIMO channel in time domain approach is proposed. [7] The unique aspects of the proposed scheme are that it exploits the group-sparsity inherent in the equivalent beamspace channel vector of the frequency-selective mmWave MIMO channel and also considers the effect of correlated noise in the equivalent system model due to RF-combining. [8] Towards this end, the doubly selective mmWave MIMO channel is represented in terms of the beamspace channel vector employing suitable sparsifying dictionary matrices comprising of the transmit and receive array response vectors. [9] To this end, in this paper we jointly optimize user selection and beam allocation under a wideband frequency selective mmWave channel. [10] In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. [11] Further, an online recursive hierarchical Bayesian Kalman Filter is developed for the estimation of a time-selective mmWave MIMO channel. [12]随后，设计了一种在线 G-SBL (O-SBL) 变体，用于估计双选择性 mmWave MIMO OFDM 信道，该信道具有低处理延迟并利用时间相关性。 [1] 此外，还提出了用于时间和频率选择性毫米波多输入多输出 (MIMO) 混合架构的基于 SBL 的卡尔曼滤波器 (SBL-KF)。 [2] 本文考虑频率选择性毫米波信道，提出了一种基于压缩感知（CS）理论的信道估计算法。 [3] 在本文中，我们提出了一种新的策略来跟踪阻塞下的频率选择性毫米波信道。 [4] 双选择性毫米波 MIMO 通道，通过基于 MMV 稀疏卡尔曼滤波的 SIP (MK-SIP) 技术来跟踪 CSI，随后还通过基于联合卡尔曼滤波的 SIP (JK-SIP) 进行数据辅助 CSI 采集。 [5] 在本文中，我们专注于频率选择性毫米波信道的信道跟踪问题，并提出了两种新颖的信道跟踪算法。 [6] 在本文中，提出了一种基于自适应交叉熵（ACE）的卡尔曼混合预编码器，用于时域频率选择性毫米波 MIMO 信道方法。 [7] 所提出方案的独特之处在于它利用了频率选择性毫米波 MIMO 信道的等效波束空间信道向量中固有的组稀疏性，并且还考虑了由于射频组合导致的等效系统模型中相关噪声的影响。 [8] 为此，双选择性毫米波 MIMO 信道根据波束空间信道向量表示，该信道向量采用合适的稀疏字典矩阵，包括发射和接收阵列响应向量。 [9] 为此，在本文中，我们共同优化了宽带频率选择性毫米波信道下的用户选择和波束分配。 [10] 在这方面，我们利用频率选择性毫米波信道的稀疏结构，并将信道估计问题表述为频域中的稀疏信号重构。 [11] 此外，开发了一种在线递归分层贝叶斯卡尔曼滤波器，用于估计时间选择性毫米波 MIMO 信道。 [12]
Frequency Selective Mmwave 频率选择性毫米波
Further, SBL based Kalman filter (SBL-KF) for time and frequency selective mmWave multiple-input multiple-output (MIMO) hybrid architecture is presented. [1] In this paper, we propose a new strategy to track the frequency selective mmWave channel under blockage. [2] In this paper, Adaptive Cross-Entropy (ACE) based Kalman Hybrid Precoder for the frequency selective mmWave MIMO channel in time domain approach is proposed. [3] To this end, in this paper we jointly optimize user selection and beam allocation under a wideband frequency selective mmWave channel. [4]此外，还提出了用于时间和频率选择性毫米波多输入多输出 (MIMO) 混合架构的基于 SBL 的卡尔曼滤波器 (SBL-KF)。 [1] 在本文中，我们提出了一种新的策略来跟踪阻塞下的频率选择性毫米波信道。 [2] 在本文中，提出了一种基于自适应交叉熵（ACE）的卡尔曼混合预编码器，用于时域频率选择性毫米波 MIMO 信道方法。 [3] 为此，在本文中，我们共同优化了宽带频率选择性毫米波信道下的用户选择和波束分配。 [4]
selective mmwave mimo 选择性毫米波 Mimo
Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. [1] doubly-selective mmWave MIMO channels, via the MMV sparse Kalman filtering-based SIP (MK-SIP) technique for tracking the CSI, and subsequently also to the joint Kalman filtering-based SIP (JK-SIP) for data-aided CSI acquisition. [2] In this paper, Adaptive Cross-Entropy (ACE) based Kalman Hybrid Precoder for the frequency selective mmWave MIMO channel in time domain approach is proposed. [3] The unique aspects of the proposed scheme are that it exploits the group-sparsity inherent in the equivalent beamspace channel vector of the frequency-selective mmWave MIMO channel and also considers the effect of correlated noise in the equivalent system model due to RF-combining. [4] Towards this end, the doubly selective mmWave MIMO channel is represented in terms of the beamspace channel vector employing suitable sparsifying dictionary matrices comprising of the transmit and receive array response vectors. [5] Further, an online recursive hierarchical Bayesian Kalman Filter is developed for the estimation of a time-selective mmWave MIMO channel. [6]随后，设计了一种在线 G-SBL (O-SBL) 变体，用于估计双选择性 mmWave MIMO OFDM 信道，该信道具有低处理延迟并利用时间相关性。 [1] 双选择性毫米波 MIMO 通道，通过基于 MMV 稀疏卡尔曼滤波的 SIP (MK-SIP) 技术来跟踪 CSI，随后还通过基于联合卡尔曼滤波的 SIP (JK-SIP) 进行数据辅助 CSI 采集。 [2] 在本文中，提出了一种基于自适应交叉熵（ACE）的卡尔曼混合预编码器，用于时域频率选择性毫米波 MIMO 信道方法。 [3] 所提出方案的独特之处在于它利用了频率选择性毫米波 MIMO 信道的等效波束空间信道向量中固有的组稀疏性，并且还考虑了由于射频组合导致的等效系统模型中相关噪声的影响。 [4] 为此，双选择性毫米波 MIMO 信道根据波束空间信道向量表示，该信道向量采用合适的稀疏字典矩阵，包括发射和接收阵列响应向量。 [5] 此外，开发了一种在线递归分层贝叶斯卡尔曼滤波器，用于估计时间选择性毫米波 MIMO 信道。 [6]
selective mmwave channel 选择性毫米波频道
This paper considers frequency-selective mmWave channels and proposes a channel estimation algorithm based on compressive sensing (CS) theory. [1] In this paper, we propose a new strategy to track the frequency selective mmWave channel under blockage. [2] In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. [3] To this end, in this paper we jointly optimize user selection and beam allocation under a wideband frequency selective mmWave channel. [4] In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. [5]本文考虑频率选择性毫米波信道，提出了一种基于压缩感知（CS）理论的信道估计算法。 [1] 在本文中，我们提出了一种新的策略来跟踪阻塞下的频率选择性毫米波信道。 [2] 在本文中，我们专注于频率选择性毫米波信道的信道跟踪问题，并提出了两种新颖的信道跟踪算法。 [3] 为此，在本文中，我们共同优化了宽带频率选择性毫米波信道下的用户选择和波束分配。 [4] 在这方面，我们利用频率选择性毫米波信道的稀疏结构，并将信道估计问题表述为频域中的稀疏信号重构。 [5]