Autoencoder-based Communications with Reconfigurable Intelligent Surfaces

Abstract

This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-to-end communication performance at the receiver. The RIS is a software-defined array of unit cells that can be controlled in terms of the scattering and reflection profiles to focus the incoming signals from the transmitter to the receiver. The benefit of the RIS is to improve the coverage and spectral efficiency for wireless communications by overcoming physical obstructions of the line-of-sight (LoS) links. The selection process of the RIS beam codeword (out of a pre-defined codebook) is formulated as a DNN, while the operations of the transmitter-receiver pair are modeled as two DNNs, one for the encoder (at the transmitter) and the other one for the decoder (at the receiver) of an autoencoder, by accounting for channel effects including those induced by the RIS in between. The underlying DNNs are jointly trained to minimize the symbol error rate at the receiver. Numerical results show that the proposed design achieves major gains in error performance with respect to various baseline schemes, where no RIS is used or the selection of the RIS beam is separated from the design of the transmitter-receiver pair.

RIS-Assisted MIMO Communication Systems: Model-based versus Autoencoder Approaches

“…where B is the mini-batch size and x ∈ C Nt is the output of the last fully connected layer. In (11), the predicted signal x is the transmitted signal with a transmit power level.…”

“…Similar to the normalization layer of the transmitter, these channel layers are custom layers with untrainable parameters to perform the complex multiplication between signals and channels. Different from [11], in the training process, the channels are changing along with every transmitted symbol. Therefore, the trained neural networks can encode the data bit streams with the aware of channel condition instead of only a function of the bit information as in the conventional modulation schemes.…”

“…Although different machine learning approaches have been successfully applied in RIS systems, there is very limit work that focuses on improving signal detection performance and reliability of the system. In [11], the authors considered a jointly transmitter, receiver and RIS phaseshift design. In this framework, an autoencoder approach is proposed to enhance the BER performance of RIS-assited SISO system.…”

“…Furthermore, the authors in [12] proposed a autoencoder approach-based design for RIS-assited MIMO systems to improve data detection performance. However, the works in [11] and [12] are limited since the autoencoders are trained based on one deterministic channel realization. This not only makes the autoencoder can work only for one specific channel but also does not really reflex the practical aspects of physical radio channels.…”

“…The previous works[11],[12] assumed sufficiently large coherent time, so their autoencoder architectures may not enable them to cope with the rapid changes of propagation channels in practice.…”