Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems
Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems
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DOI：
10.1109/tsp.2018.2873506
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发表时间：
2018-05
影响因子：
5.4
通讯作者：
Cheng Qian;Xiao Fu;N. Sidiropoulos;Ye Yang
中科院分区：
文献类型：
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作者：
Cheng Qian;Xiao Fu;N. Sidiropoulos;Ye Yang
3GPP建议将联合收割机双极化（DP）天线阵列与双方向（DD）信道模型相结合用于下行链路信道估计。这种组合在高容量通信和简约的信道建模之间取得了良好的平衡，并且还带来了用于可达范围内的下行链路信道状态信息的有限反馈方案，因为这种信道可以由几个关键参数完全表征。然而，大多数现有的DD模型下的信道估计工作尚未考虑DP阵列，这可能是因为复杂的阵列流形和算法设计的困难。在本文中，我们首先揭示了在发送器和接收器处具有DP阵列的DD信道可以自然地建模为低秩张量，从而可以通过张量分解算法有效地估计信道的关键参数。在理论方面，我们证明了DD-DP参数是可识别的温和条件下，利用低秩张量的可识别性。此外，压缩张量分解算法的开发，以减轻下行链路的训练开销。我们表明，通过使用明智地设计的导频结构，信道参数仍然保证通过压缩张量分解公式识别，即使当导频序列的大小比传统的信道识别方法，如线性最小二乘和匹配滤波所需的要小得多。大量的仿真实验证明了该方法的有效性。
The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for downlink channel state information within reach—since such channel can be fully characterized by several key parameters. However, most existing channel estimation work under the DD model has not yet considered DP arrays, perhaps because of the complex array manifold and the resulting difficulty in algorithm design. In this paper, we first reveal that the DD channel with DP arrays at the transmitter and receiver can be naturally modeled as a low-rank tensor, and thus the key parameters of the channel can be effectively estimated via tensor decomposition algorithms. On the theory side, we show that the DD–DP parameters are identifiable under mild conditions, by leveraging identifiability of low-rank tensors. Furthermore, a compressed tensor decomposition algorithm is developed for alleviating the downlink training overhead. We show that, by using judiciously designed pilot structure, the channel parameters are still guaranteed to be identified via the compressed tensor decomposition formulation even when the size of the pilot sequence is much smaller than what is needed for conventional channel identification methods, such as linear least squares and matched filtering. Extensive simulations are employed to showcase the effectiveness of the proposed method.