DEEP LEARNING-BASED DEMAPPER FOR MULTIUSER DETECTION OF NON-ORTHOGONAL MULTIPLE ACCESS SCHEME - Dalam bentuk buku karya ilmiah
ALIFIA SAFRIDA ARINI
Informasi Umum
Kode
24.05.409
Klasifikasi
006.31 - Machine Learning
Jenis
Karya Ilmiah - Thesis (S2) - Reference
Subjek
Communication Engineering-telecommunication Systems
Dilihat
98 kali
Informasi Lainnya
Abstraksi
This thesis proposes a deep learning-based demapper that utilizes feedforward neural networks to learn the complex mapping functions required for multiuser detection in non-orthogonal multiple access (NOMA) system. By utilizing neural networks, the proposed deep learning-based demapper eliminates the need for the system to check each constellation point individually, hence decreasing the computational complexity of the demapping process, while maintaining a good bit-error rate (BER) performances.<br /> <br /> This thesis developed a deep learning-based demapper, trained using a dataset generated with iterative spatial demapping (ISM), to process the received signals from a two-user NOMA scheme. This thesis analyzes two NOMA scenarios: (i) an uncoded scheme that utilizes binary phase-shift keying (BPSK) modulation, and (ii) a coded scheme that employs repetition coding and interleaver to improve transmission reliability. The proposed demapper trained on essential features such as the superposition received sig
Koleksi & Sirkulasi
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Pengarang
| Nama | ALIFIA SAFRIDA ARINI |
| Jenis | Perorangan |
| Penyunting | Khoirul Anwar, Gelar Budiman |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S2 Teknik Elektro |
| Kota | Bandung |
| Tahun | 2024 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
