Active Reconfigurable Intelligent Surface-aided Deep Learning Communication Systems
Reconfigurable Intelligent Surfaces(RIS)represent one of the most promising physical layer technologies for future wireless communication systems,creating a novel communications paradigm that evolves from adapting to environmental conditions to re-constructing electromagnetic propagation environment.However,due to the"multiplicative fading"effect,RIS can only achieve negligible capacity gains in typical communication scenarios,a fact widely overlooked in many existing studies.To address this,active RIS can effectively counteract the significant path loss of"multiplicative fading"by actively amplifying the reflected signals.In this pa-per,we introduce a communication system aided by an active RIS that employs an End-to-End(E2E)learning strategy.By using a deep learning network,we can jointly optimize the precoding and power allocation ratio at the Base Station(BS)and RIS,as well as the com-biner matrix design at the User Equipment(UE),thus avoiding the high complexity resulting from the alternating optimization inherent in traditional schemes.Specifically,we utilize three Deep Neural Networks(DNN)to implement the precoding matrix and power alloca-tion at BS,and the combiner matrix design on UE,and use a learnable parameter vector to characterize the phase shifts in RIS.Simula-tion results demonstrate that the proposed deep learning-based active RIS transmission scheme outperforms conventional passive RIS and no-RIS schemes in terms of Bit Error Rate(BER).
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