Sum Rate Optimization of IRS-Aided Uplink Muliantenna NOMA with Practical Reflection

In this letter, we consider intelligent reflecting surface (IRS) aided nonorthogonal multiple access (NOMA) for the uplink employing multiple receive antennas in order to achieve high spectral efficiency and massive connectivity. In particular, the phase shifts of the IRS are optimized under a generalized reflection model to maximize the sum rate. For the unit modulus reflection, a determinant-maximization problem is formulated and solved through extended semidefinite relaxation (max-det). For the practical reflection, we apply the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm by deriving the gradient of the complicated objective function. Based on the results, we observe that the max-det solution provides a near-optimal performance but at high complexity for a large number N of IRS elements, while the L-BFGS relieves the complexity issue for a large N and provides a performance comparable to or better than a conventional sequential optimization at a reduced computational time.