[Paper Review] MISO Wireless Communication Systems via Intelligent Reflecting Surfaces

Xianghao Yu, Dongfang Xu+1 more|arXiv (Cornell University)|Apr 27, 2019

Advanced Wireless Communication Technologies16 references205 citations

TL;DR

The paper jointly designs AP beamforming and IRS phase shifts in a MISO system using fixed-point iteration and manifold optimization, achieving higher spectral efficiency with lower complexity than SDR. It also shows large-scale IRSs outperform enlarging AP antenna arrays for spectral and energy efficiency.

ABSTRACT

Intelligent reflecting surfaces (IRSs) have received considerable attention from the wireless communications research community recently. In particular, as low-cost passive devices, IRSs enable the control of the wireless propagation environment, which is not possible in conventional wireless networks. To take full advantage of such IRS-assisted communication systems, both the beamformer at the access point (AP) and the phase shifts at the IRS need to be optimally designed. However, thus far, the optimal design is not well understood. In this paper, a point-to-point IRS-assisted multiple-input single-output (MISO) communication system is investigated. The beamformer at the AP and the IRS phase shifts are jointly optimized to maximize the spectral efficiency. Two efficient algorithms exploiting fixed point iteration and manifold optimization techniques, respectively, are developed for solving the resulting non-convex optimization problem. The proposed algorithms not only achieve a higher spectral efficiency but also lead to a lower computational complexity than the state-of-the-art approach. Simulation results reveal that deploying large-scale IRSs in wireless systems is more efficient than increasing the antenna array size at the AP for enhancing both the spectral and the energy efficiency.

Motivation & Objective

• Motivate energy-efficient wireless design by reconfiguring the propagation environment with intelligent reflecting surfaces (IRSs).
• Jointly optimize the AP beamformer and IRS phase shifts to maximize spectral efficiency under a power constraint.
• Develop computationally efficient algorithms with provable local optimality for non-convex unit-modulus constraints.
• Evaluate the performance and complexity relative to existing SDR approaches and analyze the impact of IRS size on efficiency.

Proposed method

• Formulate joint optimization of beamforming vector f and IRS phase shifts via a non-convex problem with unit-modulus constraints.
• Recast as a QCQP and solve via two novel approaches: a fixed-point iteration (Algorithm 1) guaranteeing a locally optimal v and MRT for f,
• and a manifold optimization (Algorithm 2) on the complex circle manifold with Riemannian gradients, vector transport, and retraction.
• Provide initialization via an eigenvector-based relaxation and phase extraction to start the algorithms.
• Compare against the SDR-based baseline in terms of spectral efficiency and computational complexity.

Experimental results

Research questions

• RQ1 Can joint optimization of AP beamforming and IRS phase shifts be efficiently solved to achieve high spectral efficiency in MISO IRS-assisted systems?
• RQ2 Do fixed-point iteration and manifold optimization yield locally optimal solutions that outperform SDR in both performance and complexity?
• RQ3 How does IRS size (M) affect spectral efficiency and energy efficiency relative to increasing AP antenna count?
• RQ4 Is MRT at the AP sufficient when the IRS and direct channels are strong, and how does joint design benefit other operating regimes?

Key findings

• The proposed fixed-point and manifold optimization methods achieve similar spectral efficiency and outperform SDR in simulations.
• Both proposed algorithms converge to locally optimal solutions under unit-modulus constraints.
• SDR-based solutions are computationally heavier, especially for large M, while the proposed methods scale more favorably.
• Simulations show deploying large-scale IRSs yields higher spectral and energy efficiency than enlarging the AP antenna array.
• IRS-assisted systems outperform MRT without IRSs, and increasing M is more efficient than increasing Nt for spectral gain, with greater gains as M grows.