TY - GEN
T1 - Optimizing IRS-Assisted Uplink NOMA System for Power Constrained IoT Networks
AU - Alaaeldin, Mahmoud
AU - Alsusa, Emad
AU - Seddik, Karim G.
AU - Al-Jarrah, Mohammad
N1 - Funding Information:
VI. CONCLUSIONS This paper proposes an efficient manifold-based optimization technique for the passive beamforming problem at the IRS in the context of uplink NOMA transmission. The proposed algorithm significantly outperforms the SDR-based optimization technique. Simulation results show that the SDR-based technique cannot outperform the optimized OMA scheme in the simulated scenarios. This is because SDR is not well-suited to tune the IRS phases to achieve the full gains of IRS-NOMA scheme. However, the proposed IRS-NOMA manifold-based optimization technique can reap the gains of the IRS-NOMA scheme and show its superiority in the scenarios where IRS-NOMA is expected to do better than the IRS-OMA schemes. ACKNOWLEDGEMENT This work was supported by the European Union’s Horizon 2020 Research and Innovation Program through the Marie Sklodowska-Curie under grant agreement number 812991.
Funding Information:
This work was supported by the European Union's Horizon 2020 Research and Innovation Program through the Marie Sklodowska-Curie under grant agreement number 812991.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper presents a novel approach for power-constrained internet of things (IoT) networks that employ non-orthogonal multiple access (NOMA) and are assisted by an intelligent reflecting surface (IRS) for uplink transmissions. The main objective of this work is to maximize the sum rate of power-constrained IoT networks by jointly designing the IRS phase shifts and the users' transmit power allocation. The proposed solution optimizes the power allocation and phase shifts alternatively. We devise a novel approach to optimize the IRS phase shifts that is based on manifold optimization techniques. Specifically, the IRS phase shifts optimization problem is formulated and solved over the complex circle manifold. Our results show that the proposed method outperforms the widely used semi-definite relaxation (SDR) technique as higher sum rates with less power consumption can be achieved.
AB - This paper presents a novel approach for power-constrained internet of things (IoT) networks that employ non-orthogonal multiple access (NOMA) and are assisted by an intelligent reflecting surface (IRS) for uplink transmissions. The main objective of this work is to maximize the sum rate of power-constrained IoT networks by jointly designing the IRS phase shifts and the users' transmit power allocation. The proposed solution optimizes the power allocation and phase shifts alternatively. We devise a novel approach to optimize the IRS phase shifts that is based on manifold optimization techniques. Specifically, the IRS phase shifts optimization problem is formulated and solved over the complex circle manifold. Our results show that the proposed method outperforms the widely used semi-definite relaxation (SDR) technique as higher sum rates with less power consumption can be achieved.
UR - http://www.scopus.com/inward/record.url?scp=85146975427&partnerID=8YFLogxK
U2 - 10.1109/VTC2022-Fall57202.2022.10012881
DO - 10.1109/VTC2022-Fall57202.2022.10012881
M3 - Conference contribution
AN - SCOPUS:85146975427
T3 - IEEE Vehicular Technology Conference
BT - 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PB - IEEE
T2 - 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Y2 - 26 September 2022 through 29 September 2022
ER -