This paper presents a low-complexity deep neural network (DNN)based multiple-input-multiple-output (MIMO) detector for the BPSK and QPSK constellation cases.

A low-complexity deep neural network (DNN)based multiple-input-multiple-output (MIMO) detector for the BPSK and QPSK constellation cases is presented and it ...

This paper presents a low-complexity deep neural network (DNN)- based multiple-input-multiple-output (MIMO) detector for the. BPSK and QPSK constellation cases.

Jun 15, 2023 · As a result, computationally less expensive detectors can be designed and implemented. And, (iii) by utilizing the “unfolding” method [23], the ...

We review the performance of the interplanetary network over the past year, emphasizing the GRB detection rate, and the speed and accuracy of the localizations.

Jul 24, 2023 · Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven.

Feb 6, 2023 · Next, we develop a deep ADMM unfolding network (DAUN) to learn the ADMM parameter settings from the training data and a position refinement ...

Co-authors ; Deep MIMO detection using ADMM unfolding. MW Un, M Shao, WK Ma, PC Ching. 2019 IEEE Data Science Workshop (DSW), 333-337, 2019. 55, 2019.

Jun 7, 2021 · We address the detection of material defects, which are inside a layered material structure using compressive sensing based multiple-input and multiple-output ...

We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output ...