Two Time-Scale Learning for Beamforming and Phase Shift Design in RIS-aided Networks
Two Time-Scale Learning for Beamforming and Phase Shift Design in RIS-aided Networks
复制标题
DOI：
10.1109/icc45855.2022.9838777
复制
发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Joohyun Cho;Xiang Huang;Rong-Rong Chen-Rong
中科院分区：
文献类型：
--
作者：
Joohyun Cho;Xiang Huang;Rong-Rong Chen-Rong
在这项工作中，我们开发了一种双时间尺度深度学习方法，用于时变RIS辅助网络中的波束成形和相移（BF-PS）设计。与大多数假设BF-PS设计的完美CSI的现有作品相比，我们考虑了信道估计的成本，并利用长短期记忆（LSTM）网络从估计的信道CSI的有限样本中设计BF-PS。首先设计LSTM信道外推器，以从以慢时间尺度获取的采样信号生成级联BS-RIS用户信道的高分辨率估计。随后，信道外推器的输出被馈送到基于LSTM的两级神经网络中，用于在每个相干时间的快速时间尺度上联合设计BF-PS。为了解决训练开销随RIS元素数量线性增加的关键问题，我们考虑了时间和空间上的各种导频结构和采样模式，以评估所提出的两个时间尺度设计的效率和速率性能。我们的研究结果表明，所提出的两个时间尺度的设计可以实现良好的频谱效率时，考虑到训练所需的导频开销。所提出的设计也优于不采用信道外推器的直接BF-PS设计。这些都证明了在具有合理导频开销的时变信道中应用RIS的可行性。
In this work, we develop a two time-scale deep learning approach for beamforming and phase shift (BF-PS) design in time-varying RIS-aided networks. In contrast to most existing works that assume perfect CSI for BF-PS design, we take into account the cost of channel estimation and utilize Long Short-Term Memory (LSTM) networks to design BF-PS from limited samples of estimated channel CSI. An LSTM channel extrapolator is designed first to generate high resolution estimates of the cascaded BS-RIS-user channel from sampled signals acquired at a slow time scale. Subsequently, the outputs of the channel extrapolator are fed into an LSTM-based two stage neural network for the joint design of BF-PS at a fast time scale of per coherence time. To address the critical issue that training overhead increases linearly with the number of RIS elements, we consider various pilot structures and sampling patterns in time and space to evaluate the efficiency and sum-rate performance of the proposed two time-scale design. Our results show that the proposed two time-scale design can achieve good spectral efficiency when taking into account the pilot overhead required for training. The proposed design also outperforms a direct BF-PS design that does not employ a channel extrapolator. These demonstrate the feasibility of applying RIS in time-varying channels with reasonable pilot overhead.