Optimization of Energy-Constrained IRS-NOMA Using a Complex Circle Manifold Approach

Abstract

This work investigates the performance of intelligent reflective surfaces (IRSs) assisted uplink nonorthogonal multiple access (NOMA) in energy-constrained networks. Specifically, we formulate and solve two optimization problems; the first aims at minimizing the sum of users’ transmit power, while the second targets maximizing the system-level energy efficiency (EE). The two problems are solved by jointly optimizing the users’ transmit powers and the beamforming coefficients at the IRS, subject to the users’ individual uplink rate and transmit power constraints. A novel and low-complexity algorithm is developed to optimize the IRS beamforming coefficients by optimizing the objective function over the complex circle manifold (CCM). To efficiently optimize the IRS phase shifts over the manifold, the optimization problem is reformulated into a feasibility expansion problem which is reduced to a max-min signal-to-interference-plus-noise ratio (SINR). Then, with the aid of a smoothing technique, the exact penalty method is applied to transform the problem from constrained to unconstrained. The proposed solution is compared against three semi-definite programming (SDP)-based benchmarks which are semi-definite relaxation (SDR), SDP-difference of convex (SDP-DC) and sequential rank-one constraint relaxation (SROCR). The results show that the manifold algorithm provides better performance than the SDP-based benchmarks, and at a much lower computational complexity for both the transmit power minimization and EE maximization problems. The results also reveal that IRS-NOMA is only superior to orthogonal multiple access (OMA) when the users’ target achievable rate requirements are relatively high.