TY - GEN
T1 - Classification-based Optimal Beamforming for NOMA Wireless Relay Networks
AU - Gau, Rung Hung
AU - Chiu, Hsiao Ting
AU - Lu, Tsung Che
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - In this paper, we study the problem of optimal beamforming for non-orthogonal multiple-access (NOMA) wireless relay networks. For a two-hop wireless relay network that consists of a NOMA broadcasting channel and a Gaussian interference channel, we propose a novel algorithm that efficiently obtains beamforming vectors for maximizing the end-to-end sum rate. We first classifies NOMA wireless relay networks into two classes based on the channel coefficients. For a wireless relay network that belongs to the first class, we use the maximal-ratio transmission (MRT) technique to obtain an optimal set of beamforming vectors. On the other hand, for a wireless relay network that belongs to the second class, we transform the non-convex optimal beamforming problem into a semidefinite programming (SDP) problem and then solve it. Simulation results show that the proposed approach significantly outperforms a number of alternative schemes such as the random-phase beamforming scheme, the maximal-ratio transmission scheme, and the zero-forcing beamforming scheme.
AB - In this paper, we study the problem of optimal beamforming for non-orthogonal multiple-access (NOMA) wireless relay networks. For a two-hop wireless relay network that consists of a NOMA broadcasting channel and a Gaussian interference channel, we propose a novel algorithm that efficiently obtains beamforming vectors for maximizing the end-to-end sum rate. We first classifies NOMA wireless relay networks into two classes based on the channel coefficients. For a wireless relay network that belongs to the first class, we use the maximal-ratio transmission (MRT) technique to obtain an optimal set of beamforming vectors. On the other hand, for a wireless relay network that belongs to the second class, we transform the non-convex optimal beamforming problem into a semidefinite programming (SDP) problem and then solve it. Simulation results show that the proposed approach significantly outperforms a number of alternative schemes such as the random-phase beamforming scheme, the maximal-ratio transmission scheme, and the zero-forcing beamforming scheme.
KW - classification
KW - non-convex optimization
KW - non-orthogonal multiple-access
KW - optimal beamforming vectors
KW - semidefinite programming
KW - wireless relay networks
UR - http://www.scopus.com/inward/record.url?scp=85112479751&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Spring51267.2021.9448831
DO - 10.1109/VTC2021-Spring51267.2021.9448831
M3 - Conference contribution
AN - SCOPUS:85112479751
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
Y2 - 25 April 2021 through 28 April 2021
ER -