Resource Allocation in Intelligent Reflecting Surface Assisted NOMA Systems

Zuo, Jiakuo; Liu, Yuanwei; Qin, Zhijin; Al-Dhahir, Naofal

doi:10.1109/tcomm.2020.3016742

Cited by 224 publications

(112 citation statements)

References 27 publications

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“…existing research work on IRS has covered a broad range of topics such as, but not limited to, power and energy optimization [1], [8], [13], [33], physical layer security [7], resource allocation with non orthogonal multiple access (NOMA) [9], full duplex cognitive radio [5], and symbol-level precoding [11]. The integration of IRS and simultaneous wireless information and power transfer (SWIPT) is considered in [4].…”

Section: A Related Workmentioning

confidence: 99%

IRS-Assisted UAV Communications with Imperfect Phase Compensation

Al-Dweik¹,

Al‐Jarrah²,

Alsusa³

et al. 2020

Preprint

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<div>This work presents a performance analysis on unmanned aerial vehicles (UAVs) assisted wireless</div><div>communications systems, where one of the UAVs supports intelligent reflecting surfaces (IRS). As the</div><div>estimation and compensation of the end-to-end phase for each propagation path is prone to errors, imperfect</div><div>phase compensation at the IRS is taken into consideration. The performance is derived in terms of symbol</div><div>error rate (SER) and outage probability, where the phase error is modeled using the von Mises distribution.</div><div>The air-to-air (A2A) channel for</div><div>each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are</div><div>compared to the Rician channel model. The obtained results reveal that the considered A2A model can be</div><div>used to accurately represent the A2A channel with Rician fading.</div>

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Section: A Related Workmentioning

confidence: 99%

IRS-Assisted UAV Communications with Imperfect Phase Compensation

Al-Dweik¹,

Al‐Jarrah²,

Alsusa³

et al. 2020

Preprint

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“…to work with other emerging techniques such as deep learning [17], cognitive radio (CR) network [18], wireless powered communication network (WPCN) [19], full-duplex (FD) communication [20], non-orthogonal multiple access (NOMA) [21], and directional modulation (DM) [22], respectively. Summarizing the literature, the manifold optimization [10], the majorizationminimization (MM) method [13], and the semi-definite programming (SDP) with Gaussian randomization (GR) [18] are commonly used to optimize the RC.…”

Section: Introductionmentioning

confidence: 99%

Weighted Sum Secrecy Rate Maximization Using Intelligent Reflecting Surface

Niu

Chu

Zhou

et al. 2021

IEEE Trans. Commun.

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“…Second, how to optimize the IRS reflection coefficients (including both reflection amplitudes and phase shifts in general) to unveil the full benefit offered by IRS is another challenging problem, due to their involved difficult-to-handle uni-modular constraints and intricate coupling in passive beamforming optimization. In the literature, various algorithms have been proposed to optimize the IRS reflection phase shifts (continuous or discrete), based on the instantaneous CSI (I-CSI) [6]- [12], [26]- [29]. In [30], the authors further investigated the effect of IRS amplitude control on the passive beamforming performance, and showed that under imperfect CSI, additional performance gains can be achieved by jointly controlling the reflection amplitudes as compared to the case with full reflection and phase-shift control only.…”

Section: Introductionmentioning

confidence: 99%

Two-timescale Beamforming Optimization for Intelligent Reflecting Surface Enhanced Wireless Network

Zhao

Zhang

2020

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral/energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming (i.e., IRS phase shifts) is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming (i.e., transmit precoding vectors at the access point (AP)) is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the AP, subject to the users' individual quality of service (QoS) constraints on the achievable long-term average rate. The formulated stochastic optimization problem is nonconvex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term passive beamforming problem and a family of shortterm active beamforming problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. We show that both the long-term and short-term problems can be efficiently solved and the proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes.

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Resource Allocation in Intelligent Reflecting Surface Assisted NOMA Systems

Cited by 224 publications

References 27 publications

IRS-Assisted UAV Communications with Imperfect Phase Compensation

IRS-Assisted UAV Communications with Imperfect Phase Compensation

Weighted Sum Secrecy Rate Maximization Using Intelligent Reflecting Surface

Two-timescale Beamforming Optimization for Intelligent Reflecting Surface Enhanced Wireless Network

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