arXiv subjects

Alexander James Fernandes

Publications and source records attributed to Alexander James Fernandes.

3 recordsLinked to original sources

arXiv2026

Model-based Deep Learning for Joint RIS Phase Shift Compression and WMMSE Beamforming

A model-based deep learning (DL) architecture is proposed for reconfigurable intelligent surface (RIS)-assisted multi-user communications to reduce the number of bits required for transmitting phase shift information from the access point (AP) to the RIS controller. The AP computes the phase shifts and compresses them into a binary control message that is sent to the RIS controller for element configuration. To help reduce beamformer mismatches caused by phase shift compression errors, the beamformer is updated with the actual (decompressed) RIS phase shifts. By unrolling the iterative weighted minimum mean square error (WMMSE) algorithm within the wireless communication-informed DL architecture, joint phase shift compression and WMMSE beamforming can be trained end-to-end. Simulation results demonstrate that incorporating compression-aware beamforming significantly improves sum-rate performance, even when the number of control bits is lower than the number of RIS elements.

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arXiv2023

Channel Estimation for Reconfigurable Intelligent Surface MIMO with Tensor Signal Modelling

We consider a narrowband MIMO reconfigurable intelligent surface (RIS)-assisted wireless communication system and use tensor signal modelling techniques to individually estimate all communication channels including the non-RIS channels (direct path) and decoupled RIS channels. We model the received signal as a third-order tensor composed of two CANDECOMP/PARAFAC decomposition terms for the non-RIS and the RIS-assisted links, respectively, and we propose two channel estimation methods based on an iterative alternating least squares (ALS) algorithm: The two-stage RIS OFF-ON method estimates each of the non-RIS and RIS-assisted terms in two pilot training stages, whereas the enhanced alternating least squares (E-ALS) method improves upon the ALS algorithm to jointly estimate all channels over the full training duration. A key benefit of both methods compared to the traditional least squares (LS) solution is that they exploit the structure of the tensor model to obtain decoupled estimates of all communication channels. We provide the computational complexities to obtain each of the channel estimates for our two proposed methods. Numerical simulations are used to evaluate the accuracy and verify the computational complexities of the proposed two-stage RIS OFF-ON, and E-ALS, and compare them to the traditional LS methods. Results show that E-ALS will obtain the most accurate estimate while only having a slightly higher run-time than the two-stage method.

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arXiv2023

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO with Hardware Impairments

We consider the problem of channel estimation in a multiple-input-multiple-output (MIMO) full-duplex (FD) wireless communication system assisted by a reconfigurable intelligent surface (RIS) with hardware impairments (HI) occurring at the transceivers and RIS elements. We propose an unbiased channel estimator that requires knowledge of only the first and second order statistics of the HI, for which we derive closed form expressions. The proposed estimator reduces to the maximum likelihood estimator (MLE) in the case of ideal hardware. We also describe FD and HD orthogonal pilot schemes that minimize the mean square error of the MLE in the case of ideal hardware. We verify the performance of the estimator under varying conditions of transceiver and RIS HI via numerical simulations.

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