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<p>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.</p><div></div>

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Federated learning (FL) has emerged as a promising framework to exploit massive data generated by edge devices in developing a common learning model while preserving the privacy of local data. In implementing FL over wireless networks, the participation of more devices is encouraged to alleviate the training inefficiency due to irregular local data but it tends to increase communication latency. To solve this problem, we address non-orthogonal multiple access (NOMA) assisted by intelligent reflecting surfaces (IRSs) to accommodate more devices and tailor their channels favorably to the FL performance. For the FL with IRS-NOMA, we minimize the total latency by reducing the latency per training round dominated by local computation and uplink communication through optimization of IRS-NOMA strategies and improving the training efficiency under irregular local data through active device selection. We then propose an auction-based IRS allocation that utilizes the optimized total latency for the valuation of the IRSs when multiple base stations of different operators share their neighboring IRSs. Winner determination (WD) and payment methods are devised with multiple bids on IRS subsets in a way of maximizing social welfare. The results show that the proposed latency minimizing algorithm outperforms the benchmarks by improving both communication and training efficiency through device selection combined with IRS-NOMA optimization. In addition, the auction mechanism with the proposed WD outperforms the benchmarks, where the social welfare is improved by constructing each bid with the valuation on multiple IRSs and increasing the number of bids submitted.

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.