LSTM-Based Trajectory and Phase-Shift Prediction for RSMA Networks Assisted by AIRS

Sousa Lima, Brena Kelly; Matos-Carvalho, João Pedro; Dinis, Rui; da Costa, Daniel Benevides; Beko, Marko; Oliveira, Rodolfo

http://hdl.handle.net/10362/180160

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Autores

Sousa Lima, Brena Kelly

Matos-Carvalho, João Pedro

Dinis, Rui

da Costa, Daniel Benevides

Beko, Marko

Oliveira, Rodolfo

Resumo(s)

This paper investigates rate-splitting multiple access (RSMA) networks with multiusers assisted by aerial intelligent reflecting surfaces (AIRS). To improve the sum-rate of the system, the UAV’s trajectory and phase-shift vectors are optimized, in which the mobility scenarios with static and dynamic users are explored. In particular, long short-term memory (LSTM)-based frameworks for predicting the UAV’s trajectory and the phase-shift of the reflecting elements of AIRS are proposed. For more insight, a third model is created by combining information from the static and dynamic scenarios. Furthermore, to improve the transmit beamforming at the BS, an algorithm based on alternating optimization (AO) under the assumptions of imperfect successive interference cancelation (SIC) is presented. Training progress and testing results are provided to demonstrate the efficiency of the proposed models. In addition, numerical simulations are presented to verify the performance gains in terms of sum-rate. The simulation results show that the UAV performs better in trajectory prediction and phase-shift when different investigated scenarios are not combined.

Descrição

Funding Information: This work is funded by Fundação para a Ciência e Tecnologia under the project UIDB/50008/2020, UIDB/04111/2020, CEECINST/00147/2018/CP1498/CT0015, as well as Instituto Lusófono de Investigação e Desenvolvimento (ILIND) under Project COFAC/ ILIND/COPELABS/1/2022. This research was partially funded by the European Union’s Horizon Europe Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No. 101086387 and by ROBUST under Grant EXPL/EEI-EEE/0776/2021. Publisher Copyright: © 1972-2012 IEEE.

Palavras-chave

Intelligent reflecting surface (IRS) long short-term memory (LSTM) precoder design rate-splitting multiple access (RSMA) trajectory optimization unmanned aerial vehicle (UAV) Electrical and Electronic Engineering