Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable
Intelligent Surface-Aided Tera-Hertz Massive MIMO
- URL: http://arxiv.org/abs/2209.08456v1
- Date: Sun, 18 Sep 2022 03:07:37 GMT
- Title: Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable
Intelligent Surface-Aided Tera-Hertz Massive MIMO
- Authors: Minghui Wu, Zhen Gao, Yang Huang, Zhenyu Xiao, Derrick Wing Kwan Ng,
and Zhaoyang Zhang
- Abstract summary: Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems.
However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging.
This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
- Score: 56.022764337221325
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reconfigurable intelligent surface (RIS) can significantly enhance the
service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO)
communication systems. However, obtaining accurate high-dimensional channel
state information (CSI) with limited pilot and feedback signaling overhead is
challenging, severely degrading the performance of conventional spatial
division multiple access. To improve the robustness against CSI imperfection,
this paper proposes a deep learning (DL)-based rate-splitting multiple access
(RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically,
we first propose a hybrid data-model driven DL-based RSMA precoding scheme,
including the passive precoding at the RIS as well as the analog active
precoding and the RSMA digital active precoding at the base station (BS). To
realize the passive precoding at the RIS, we propose a Transformer-based
data-driven RIS reflecting network (RRN). As for the analog active precoding at
the BS, we propose a match-filter based analog precoding scheme considering
that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the
RSMA digital active precoding at the BS, we propose a low-complexity
approximate weighted minimum mean square error (AWMMSE) digital precoding
scheme. Furthermore, for better precoding performance as well as lower
computational complexity, a model-driven deep unfolding active precoding
network (DFAPN) is also designed by combining the proposed AWMMSE scheme with
DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding
scheme to achieve higher spectral efficiency, we propose a CSI acquisition
network (CAN) with low pilot and feedback signaling overhead, where the
downlink pilot transmission, CSI feedback at the user equipments (UEs), and CSI
reconstruction at the BS are modeled as an end-to-end neural network based on
Transformer.
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