Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization

Abstract: Intelligent reflecting surface (IRS) that enables the control of wireless propagation environment has recently emerged as a promising cost-effective technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper the practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Based on the proposed model and considering an IRS-aided multiuser system with an IRS deployed to assist in the downlink communications from a multi-antenna access point (AP) to multiple single-antenna users, we formulate an optimization problem to minimize the total transmit power at the AP by jointly designing the AP transmit beamforming and the IRS reflect beamforming, subject to the users' individual signal-to-interference-plus-noise ratio (SINR) constraints. Iterative algorithms are proposed to find suboptimal solutions to this problem efficiently by utilizing the alternating optimization (AO) or penalty-based optimization technique. Moreover, we analyze the asymptotic performance loss of the IRS-aided system that employs practical phase shifters but assumes the ideal phase shift model for beamforming optimization, as the number of IRS elements goes to infinity. Simulation results unveil substantial performance gains achieved by the proposed beamforming optimization based on the practical phase shift model as compared to the conventional ideal model.

• The paper introduces a practical phase shift model that accounts for phase-dependent amplitude variations in IRS hardware.
• It formulates and solves a non-convex joint transmit and IRS reflect beamforming optimization problem using iterative algorithms.
• Numerical results demonstrate that the proposed model significantly improves energy efficiency and reliability in IRS-aided multiuser systems.

Overview of "Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization"

The paper, "Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization," by Abeywickrama et al., investigates the performance of intelligent reflecting surfaces (IRS) in wireless communication systems by introducing a practical phase shift model. This study deviates from prior literature, which often assumes an ideal phase shift model with perfect signal reflection independent of phase shifts, a hypothesis challenged by real-world hardware constraints. The work aims to enhance the efficiency of IRS by considering the phase-dependent amplitude variation in signal reflection.

Theoretical Developments

The authors introduce a novel phase shift model that accounts for practical constraints, particularly the phase-dependent reflection amplitude observed in semiconductor devices. The model's parameters are relevant for a range of devices, including varactor diodes and PIN diodes, offering a more comprehensive framework for IRS systems deployed in various communication environments. This model provides insights into the optimal balance between reflection amplitude and phase alignment, overcoming the significant performance degradation seen in conventional designs.

Methodology and Optimization

Focusing on an IRS-aided multiuser system, the paper develops an optimization problem targeting the minimization of total transmit power from a multi-antenna access point (AP) to multiple single-antenna users. The problem is complex, given the non-convex nature of joint AP transmit and IRS reflect beamforming design, and involves constraints on the user's signal-to-interference-plus-noise ratio (SINR). To address this, the authors propose iterative algorithms utilizing alternating optimization (AO) and penalty-based methods for finding suboptimal solutions efficiently. These solutions consider IRS hardware imperfections, contrasting the idealized models traditionally used.

Numerical Results and Performance Analysis

Simulation results illustrate the efficacy of the proposed solutions. The optimized beamforming using the practical phase shift model exhibits significant performance gains and reliability over the conventional ideal model, demonstrating the critical importance of the realistic consideration of IRS hardware. Notably, the practical model's superiority becomes more pronounced as the number of IRS elements increases, aligning with theoretical predictions. The numerical analysis further extends to scenarios involving discrete phase shifts, shedding light on practical implementation challenges and solutions.

Implications and Future Directions

This research underscores the necessity of realistic IRS models in wireless communication to enhance energy efficiency and performance. By challenging the idealized assumptions prevalent in IRS modeling, this work opens pathways for more accurate and feasible designs in practical scenarios. The implications are vast, particularly for systems utilizing OFDM, NOMA, and SWIPT, where IRS can be strategically deployed to maximize gains.

Future research directions could include deploying the proposed phase shift model across various IRS-enabled applications, analyzing its impact on large-scale networks, and incorporating the model into different frequency bands and evolving wireless protocols. Further exploration might also consider advancements in semiconductor technologies that could refine the model's parameters, ultimately advancing IRS technology towards a mainstream solution in wireless communications.

In conclusion, while the study advances the understanding of IRS within practical constraints, it calls for continued investigation into optimizing these surfaces, suggesting that the integration of hardware-aware models is crucial for the realistic and efficient deployment of IRS in next-generation wireless networks.