Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design (1809.01423v2)

Abstract: Intelligent reflecting surface (IRS) is envisioned to have abundant applications in future wireless networks by smartly reconfiguring the signal propagation for performance enhancement. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and suppress interference at one or more designated receivers. In this paper, we study an IRS-enhanced point-to-point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. As a result, the user simultaneously receives the signal sent directly from the AP as well as that reflected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the AP and (passive) reflect beamforming by the phase shifters at the IRS. We first propose a centralized algorithm based on the technique of semidefinite relaxation (SDR) by assuming the global channel state information (CSI) available at the IRS. Since the centralized implementation requires excessive channel estimation and signal exchange overheads, we further propose a low-complexity distributed algorithm where the AP and IRS independently adjust the transmit beamforming and the phase shifts in an alternating manner until the convergence is reached. Simulation results show that significant performance gains can be achieved by the proposed algorithms as compared to benchmark schemes. Moreover, it is verified that the IRS is able to drastically enhance the link quality and/or coverage over the conventional setup without the IRS.

• The paper introduces centralized and distributed beamforming algorithms that jointly optimize active transmit and passive reflect beamforming in MISO systems.
• It employs semidefinite relaxation to solve non-convex problems, achieving near-optimal signal-to-noise ratios with gains that scale by the square of IRS elements.
• The findings demonstrate IRS's potential to enhance network coverage and energy efficiency, offering promising solutions for next-generation wireless networks.

Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design

This paper presents a rigorous paper on the enhancement of wireless networks through the deployment of Intelligent Reflecting Surfaces (IRS). It focuses on a multiple-input single-output (MISO) wireless system where an IRS assists communication between a multi-antenna access point (AP) and a single-antenna user. The objective is to maximize the received signal power by optimizing active transmit beamforming at the AP and passive reflect beamforming at the IRS.

Key Contributions

The paper proposes both centralized and distributed algorithms to tackle the joint beamforming optimization problem. It leverages techniques like semidefinite relaxation (SDR) to handle the non-convexity inherent in this setup:

• Centralized Algorithm: Assumes global channel state information at the IRS, exploiting SDR to provide both an upper bound and a high-quality approximate solution. While optimal, this method incurs significant overhead in channel estimation and signal exchange.
• Distributed Algorithm: Reduces complexity by allowing the AP and IRS to independently adjust transmit beamforming and phase shifts in an alternating manner until convergence. This approach avoids excessive signal exchange while effectively enhancing performance.

Numerical Results and Insights

Simulation results demonstrate considerable performance gains over benchmark schemes, validating the efficacy of the proposed algorithms. Key observations include:

• The IRS significantly enhances link quality and coverage compared to conventional setups.
• The user SNR benefit scales with the number of IRS elements, exhibiting a $N^2$ gain, where $N$ is the number of reflecting elements.
• The distributed algorithm achieves near-optimal performance with minimal iterations, indicating low implementation complexity.

Implications and Future Developments

The research highlights the potential of IRS in achieving both spectral and energy efficiency in next-generation wireless networks. By leveraging only passive elements, IRS can offer substantial performance improvements without additional power consumption. The findings suggest promising applications in indoor environments where coverage and interference management are critical concerns.

The paper opens avenues for further exploration in:

1. Dynamic IRS Deployment: Investigating IRS deployment strategies in real-world, dynamic environments.
2. Multi-user Scenarios: Extending the framework to multi-user systems to understand its scalability and efficiency.
3. Integration with Advanced Technologies: Examining synergy with technologies like mmWave or massive MIMO to capitalize on combined gains.

This paper provides a foundational approach to IRS-enhanced networks, demonstrating both theoretical rigor and practical viability. As the research on IRS progresses, its role in shaping future wireless networks continues to expand, warranting deeper exploration and experimentation.