AI-Empowered RIS-Assisted Networks: CV-Enabled RIS Selection and DNN-Enabled Transmission
arxiv(2024)
摘要
This paper investigates artificial intelligence (AI) empowered schemes for
reconfigurable intelligent surface (RIS) assisted networks from the perspective
of fast implementation. We formulate a weighted sum-rate maximization problem
for a multi-RIS-assisted network. To avoid huge channel estimation overhead due
to activate all RISs, we propose a computer vision (CV) enabled RIS selection
scheme based on a single shot multi-box detector. To realize real-time resource
allocation, a deep neural network (DNN) enabled transmit design is developed to
learn the optimal mapping from channel information to transmit beamformers and
phase shift matrix. Numerical results illustrate that the CV module is able to
select of RIS with the best propagation condition. The well-trained DNN
achieves similar sum-rate performance to the existing alternative optimization
method but with much smaller inference time.
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