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

Abstract

<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>

Max-Min Rate Optimization for Uplink IRS-NOMA With Receive Beamforming

“…In particular, IRS-assisted nonorthogonal multiple access (IRS-NOMA) has been explored extensively to design an efficient platform that enables massive access of explosively increasing Internet-of-things devices [4]- [13]. To accommodate more massive devices, a multiantenna base station (BS) is indispensable for the IRS-NOMA, for which beamforming (BF) at the BS should be optimized jointly with IRS reflection.…”

“…On the other hand, for the uplink IRS-NOMA, IRS reflection optimization was studied mainly with a single-antenna BS [9]- [11] although a few recent studies have considered receive BF for a multiantenna BS [12], [13]. However, these studies aimed to maximize the sum rate which is maximized with the maximum PA of the devices irrelevant of the successive interference cancellation (SIC) order if there are no constraints on the device rates [10]- [13].…”

“…On the other hand, for the uplink IRS-NOMA, IRS reflection optimization was studied mainly with a single-antenna BS [9]- [11] although a few recent studies have considered receive BF for a multiantenna BS [12], [13]. However, these studies aimed to maximize the sum rate which is maximized with the maximum PA of the devices irrelevant of the successive interference cancellation (SIC) order if there are no constraints on the device rates [10]- [13]. A max-min rate optimization problem for the uplnk IRS-NOMA depends on the PA and SIC order unlike the sum rate counterpart but has not been tackled yet although the problem for the downlink was tackled through block coordinate decent (BCD) algorithm 1 optimizing transmit BF at the BS, PA at the BS, and IRS reflection iteratively [7].…”

Max-Min Rate Optimization for Uplink IRS NOMA with Receive Beamforming

“…Particularly IRS-assisted nonorthogonal multiple access (IRS-NOMA) has been extensively investigated to provide an effective platform that allows massive access of explosively increasing Internet-of-things devices [3]- [14]. For the platform, a multiantenna base station (BS) is indispensable so that its beamforming (BF) has been optimized jointly with IRS reflection [3], [4], [6]- [8], [13], [14].…”

“…Particularly IRS-assisted nonorthogonal multiple access (IRS-NOMA) has been extensively investigated to provide an effective platform that allows massive access of explosively increasing Internet-of-things devices [3]- [14]. For the platform, a multiantenna base station (BS) is indispensable so that its beamforming (BF) has been optimized jointly with IRS reflection [3], [4], [6]- [8], [13], [14]. For the downlink IRS-NOMA, to leverage a performance metric such as sum rate [3], energy efficiency [4], reduction of power consumption [6], [8], and minimum rate [7], IRS reflection was jointly optimized with transmit BF for a predetermined successive interference cancellation (SIC) order.…”