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To reap the benefits of reconfigurable intelligent surfaces (RIS), channel state information (CSI) is generally required. However, CSI acquisition in RIS systems is challenging and often results in very large pilot overhead, especially in unstructured channel environments. Consequently, the RIS channel estimation problem has attracted a lot of interest and also been a subject of intense study in recent years. In this paper, we propose a decision-directed RIS channel estimation framework for general unstructured channel models. The employed RIS contains some hybrid elements that can simultaneously reflect and sense the incoming signal. We show that with the help of the hybrid RIS elements, it is possible to accurately recover the CSI with a pilot overhead proportional to the number of users. Therefore, the proposed framework substantially improves the system spectral efficiency compared to systems with passive RIS arrays since the pilot overhead in passive RIS systems is proportional to the number of RIS elements times the number of users. We also perform a detailed spectral efficiency analysis for both the pilot-directed and decision-directed frameworks. Our analysis takes into account both the channel estimation and data detection errors at both the RIS and the BS. Finally, we present numerous simulation results to verify the accuracy of the analysis as well as to show the benefits of the proposed decision-directed framework.
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Two Time-Scale Learning for Beamforming and Phase Shift Design in RIS-aided Networks
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.
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- Award ID(s):
- 1824558
- PAR ID:
- 10388696
- Date Published:
- Journal Name:
- ICC 2022 - IEEE International Conference on Communications
- Page Range / eLocation ID:
- 2627 to 2632
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC45855.2022.9838777
