Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network (1905.00152v5)

Abstract: Although the fifth-generation (5G) technologies will significantly improve the spectrum and energy efficiency of today's wireless communication networks, their high complexity and hardware cost as well as increasingly more energy consumption are still crucial issues to be solved. Furthermore, despite that such technologies are generally capable of adapting to the space and time varying wireless environment, the signal propagation over it is essentially random and largely uncontrollable. Recently, intelligent reflecting surface (IRS) has been proposed as a revolutionizing solution to address this open issue, by smartly reconfiguring the wireless propagation environment with the use of massive low-cost, passive, reflective elements integrated on a planar surface. Specifically, different elements of an IRS can independently reflect the incident signal by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained three-dimensional (3D) passive beamforming for signal enhancement or cancellation. In this article, we provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model. We focus on the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only. Furthermore, numerical results are provided to show the potential for significant performance enhancement with the use of IRS in typical wireless network scenarios.

• The paper introduces IRS technology as a passive beamforming method that significantly strengthens signals and reduces interference in wireless networks.
• It details the joint optimization of IRS reflection and base station beamforming to effectively balance coverage extension with interference mitigation.
• Numerical results validate an asymptotic O(N^2) power scaling law, highlighting IRS’s potential for energy-efficient deployment and robust interference suppression.

Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network

The paper "Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network" by Qingqing Wu and Rui Zhang presents a comprehensive paper of Intelligent Reflecting Surface (IRS) technology and its potential applications in enhancing wireless communication networks. This work is set against the backdrop of challenges in achieving the ambitious goals of the forthcoming fifth-generation (5G) and beyond wireless networks, where complexity, cost, and energy consumption are pivotal concerns.

Overview of Intelligent Reflecting Surface (IRS) Technology

The IRS represents a paradigm shift in wireless communications by introducing programmable reflection surfaces composed of numerous low-cost passive elements, each capable of independently adjusting the phase and amplitude of reflected signals. By coordinating these elements, IRS can achieve precise three-dimensional passive beamforming, enhancing desired signals and mitigating interference. This proactive modification of the wireless environment at the physical layer introduces a new degree of freedom (DoF) in wireless communications, contrasting with traditional link adaptation methods.

Key Applications and Advantages

Several typical applications of IRS technology in wireless networks are elucidated in the paper:

• Enhancing Coverage and Signal Quality: By reflecting signals around obstacles, IRS can create virtual line-of-sight (LoS) paths, extending coverage, especially in mmWave communications.
• Improving Physical Layer Security: IRS can be used to negate signals at eavesdroppers, enhancing secure communication.
• Creating Signal Hotspots and Interference-Free Zones: At cell edges, IRS can amplify desired signals and suppress co-channel interference, thus optimizing the link quality and coverage.
• Facilitating Device-to-Device (D2D) Communication: IRS can act as a hub supporting low-power simultaneous D2D transmissions.
• Enhancing Wireless Power Transfer: IRS can optimize the efficiency of simultaneous wireless information and power transfer (SWIPT) in IoT networks.

From an implementation perspective, IRS units are low-profile, conformal, and easily mountable on various surfaces, making them highly compatible and easily integrated into existing wireless networks without extensive hardware modifications.

Comparative Analysis with Existing Technologies

The paper contrasts IRS with related technologies such as active relays, backscatter communication, and active surface-based massive MIMO:

• IRS, unlike active relays, relies on passive elements, avoiding issues of half-duplex operation and self-interference seen in full-duplex relays.
• Differing from traditional backscatter systems like RFID, IRS focuses on enhancing existing communications rather than transmitting its own data, thus, avoiding self-interference concerns.
• In comparison with active surface MIMO systems, IRS scales better with low energy consumption due to its passive nature while providing significant signal amplification.

Design Challenges and Solutions

Passive Beamforming

Designing IRS passive beamforming is inherently complex due to discretized reflection coefficients. The paper suggests practical solutions such as relaxing constraints for continuous values followed by quantization and utilizing heuristic methods to iteratively optimize the reflection parameters. Additionally, joint optimization of IRS reflection and BS transmission beamforming is critical for maximizing network performance, particularly in balancing coverage and interference suppression.

Channel Acquisition

Acquiring accurate channel state information (CSI) is vital for IRS efficiency. When equipped with receive RF chains, IRS can directly estimate channels. Without such hardware, alternative methods like codebook-based beamforming and machine learning can minimize the training overhead and adapt beamforming based on historical data and location correlativity.

Deployment Strategy

Optimal deployment of IRS considers LoS paths for signal enhancement, spatial density for user coverage, and strategic locations for interference management. The paper advocates leveraging machine learning for predicting optimal IRS locations based on performance indicators, thus enabling autonomous deployment in complex environments.

Numerical Validation and Results

The paper provides compelling numerical results demonstrating IRS's effectiveness:

• Signal Power Enhancement: Significant reduction in required BS transmit power with increasing IRS elements, adhering to an asymptotic power scaling law of $\mathcal{O}(N^2)$, highlighting a practical implementation of IRS with discrete phase shifters.
• Interference Suppression: IRS's capability in reducing co-channel interference significantly, achieving near perfect cancellation in scenarios requiring stringent interference management.

Conclusion

This paper highlights the transformative potential of IRS technology in creating smart and reconfigurable wireless environments. Both theoretical foundations and practical implementations are well addressed, paving the way for future research into hybrid wireless networks with intelligent integration of active and passive components. The IRS presents a promising avenue to overcome 5G challenges and beyond, deserving focused efforts in advancing its practical deployment and efficient integration into existing wireless infrastructure.