Paper Info
Title
Sum Rate Optimization of IRS-Aided Uplink Muliantenna NOMA with Practical Reflection
Abstract
Recently, intelligent reflecting surfaces (IRSs) have drawn huge attention as a promising solution for 6G networks to enhance diverse performance metrics in a cost-effective way. For massive connectivity toward a higher spectral efficiency, we address an intelligent reflecting surface (IRS) to an uplink nonorthogonal multiple access (NOMA) network supported by a multiantenna receiver. We maximize the sum rate of the IRS-aided NOMA network by optimizing the IRS reflection pattern under unit modulus and practical reflection. For a moderate-sized IRS, we obtain an upper bound on the optimal sum rate by solving a determinant maximization (max-det) problem after rank relaxation, which also leads to a feasible solution through Gaussian randomization. For a large number of IRS elements, we apply the iterative algorithms relying on the gradient, such as Broyden-Fletcher-Goldfarb-Shanno (BFGS) and limited-memory BFGS algorithms for which the gradient of the sum rate is derived in a computationally efficient form. The results show that the max-det approach provides a near-optimal performance under unit modulus reflection, while the gradient-based iterative algorithms exhibit merits in performance and complexity for a large-sized IRS with practical reflection.
Year
DOI
Venue
2022
10.3390/s22124449
SENSORS
Keywords
DocType
Volume
intelligent reflecting surface, nonorthogonal multiple access, practical reflection, multiple receive antennas
Journal
22
Issue
ISSN
Citations 
12
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jinho Choi11642206.06
Jinho Choi21642206.06
Luiggi Cantos300.34
Y.-H. Kim415821.90