TY - GEN
T1 - Deep learning-based multi-user multi-dimensional constellation design in code domain non-orthogonal multiple access
AU - Han, Minsig
AU - Seo, Hanchang
AU - Abebe, Ameha T.
AU - Kang, Chung G.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in part by Samsung Research, Samsung Electronics and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2020R1A2C1009984).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered as a multi-user multi-dimensional modulation (MU-MDM) design. However, the sheer complexity of assigning multiple bits from multiple users to signal points in multi-dimension signal space, while minimizing bit-error rate (BER), had limited its practicality. Inspired by its ability to approximate complex optimization methods, this paper proposes an autoencoder (AE)-based MU-MDM design. In this regard, a novel loss function is proposed which simultaneously considers Euclidean distance between signal points and Hamming distance between bits assigned to neighboring signal points. Extensive simulation results show that the proposed AE-based design has 1.5dB gain over the state-of-the-art MU-MDM designs in both sparse and dense codebook setting. Furthermore, it is shown that the low complexity of deep learning (DL)-based receiver allows for employing dense CD-NOMA as compared to the conventional receivers which require codebooks to be sparse to reduce its implementation complexity.
AB - Codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered as a multi-user multi-dimensional modulation (MU-MDM) design. However, the sheer complexity of assigning multiple bits from multiple users to signal points in multi-dimension signal space, while minimizing bit-error rate (BER), had limited its practicality. Inspired by its ability to approximate complex optimization methods, this paper proposes an autoencoder (AE)-based MU-MDM design. In this regard, a novel loss function is proposed which simultaneously considers Euclidean distance between signal points and Hamming distance between bits assigned to neighboring signal points. Extensive simulation results show that the proposed AE-based design has 1.5dB gain over the state-of-the-art MU-MDM designs in both sparse and dense codebook setting. Furthermore, it is shown that the low complexity of deep learning (DL)-based receiver allows for employing dense CD-NOMA as compared to the conventional receivers which require codebooks to be sparse to reduce its implementation complexity.
KW - Autoencoder
KW - Codebook design
KW - Deep learning
KW - Multi-dimension constellation
KW - Non-orthogonal multiple access (NOMA)
KW - Sparse code multiple access (SCMA)
UR - http://www.scopus.com/inward/record.url?scp=85090283943&partnerID=8YFLogxK
U2 - 10.1109/ICCWorkshops49005.2020.9145347
DO - 10.1109/ICCWorkshops49005.2020.9145347
M3 - Conference contribution
AN - SCOPUS:85090283943
T3 - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
BT - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
Y2 - 7 June 2020 through 11 June 2020
ER -