Joint Active and Passive Beamforming Optimization for Intelligent Reflecting Surface Assisted SWIPT under QoS Constraints (1910.06220v2)

Abstract: Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in the paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based method. Moreover, by exploiting the short-range coverage of IRSs, we further propose a low-complexity algorithm by optimizing the phase shifts of all IRSs in parallel.

• The paper introduces a penalty-based iterative algorithm that decouples QoS constraints for joint active and passive beamforming optimization.
• It leverages convex optimization for transmit precoder design and block coordinate descent for efficient IRS phase shift adjustments.
• Numerical results confirm significant AP power savings and extended coverage, highlighting the practical benefits of IRS-assisted SWIPT systems.

Joint Active and Passive Beamforming Optimization for Intelligent Reflecting Surface Assisted SWIPT under QoS Constraints

This paper explores the area of intelligent reflecting surfaces (IRS), a pivotal technological approach aimed at enhancing spectrum and energy efficiency in wireless networks. Employing a strategy of deploying IRSs, the research focuses on enhancing simultaneous wireless information and power transfer (SWIPT) systems. The core proposition involves a dual approach: optimizing both active beamforming at the multi-antenna access point (AP) and passive beamforming via phase shift adjustments at the IRSs.

Problem Formulation

The research targets the minimization of total transmit power at the AP while abiding by quality-of-service (QoS) constraints. These constraints include the signal-to-interference-plus-noise ratio (SINR) for information users (IUs) and energy harvesting requirements for energy users (EUs). The challenge is inherently non-convex due to the coupled nature of the variables involved, which complicates traditional optimization approaches.

Proposed Solution

To address the aforementioned non-convex optimization problem, the authors propose a penalty-based iterative algorithm. This method leverages auxiliary variable transformations for decoupling QoS constraints, thereby facilitating a more tractable optimization landscape. Each iteration comprises three main steps:

1. Transmit Precoders Optimization: Involves a convex quadratic problem, presenting closed-form solutions for optimal precoders.
2. Phase Shifts Optimization: Handles unit-modulus constraints via a block coordinate descent method. This efficiently updates phase shifts across IRS elements.
3. Auxiliary Variables Update: Engages Lagrange duality to solve the problem, capitalizing on the strong duality property under Slater's conditions.

An outer layer further refines this approach by iteratively adjusting the penalty coefficient, converging towards satisfying equality constraints intrinsic to the problem's reformulation.

Numerical Results and Implications

The numerical evaluations underscore considerable performance gains, corroborating the effectiveness of IRSs in reducing AP transmit power. Notably, the paper reveals:

• Distance and Coverage Extension: IRS deployment significantly extends the viable operating range for wireless power transfer, demonstrating a stark contrast against traditional setups.
• Number of Required Energy Beams: Reduced by IRS deployment, occasionally obviating the need for separate energy-dedicated beams due to enhanced channel conditions.
• Joint vs. Independent Beamforming: A combined active and passive beamforming approach is shown to outperform separate strategies, reinforcing the concept of unified IRS-supported design.

From a theoretical standpoint, this research underscores the pivotal alignment of IRSs within the sphere of SWIPT, enhancing both energy efficiency and coverage. Additionally, the reduced complexity of the proposed IRS system suggests a viable route toward practical deployment.

Future Developments

Anticipated progress includes extending the framework to encompass frequency-selective channels and discrete IRS phase shifts, addressing both the practical hardware limits and broader channel diversity. Moreover, expanding the IRS design to multi-user, multi-IRS environments stands as another prospective avenue, offering rich ground for future exploration in enhancing both theoretical insights and practical implementations within the IRS domain.

In conclusion, this paper propels the exploration of IRS in SWIPT systems, showcasing a coherent strategy for optimizing such systems under practical constraints. The numerical insights bridge a gap towards real-world applicability, advocating the practical deployment of IRS technologies in next-generation wireless networks.