Wave-Controlled Metasurface-Based Reconfigurable Intelligent Surfaces

Abstract: Reconfigurable Intelligent Surfaces (RISs) are programmable metasurfaces that can adaptively steer received electromagnetic energy in desired directions by employing controllable phase shifting cells. Among other uses, an RIS can modify the propagation environment in order to provide wireless access to user locations that are not otherwise reachable by a base station. Alternatively, an RIS can steer the waves away from particular locations in space, to eliminate interference and allow for co-existence of the wireless network with other types of fixed wireless services (e.g., radars, unlicensed radio bands, etc.). The novel approach in this work is a wave-controlled architecture that properly accounts for the maximum possible change in the local reflection phase that can be achieved by adjacent RIS elements. It obviates the need for dense wiring and signal paths that would be required for individual control of every RIS element, and thus offers a substantial reduction in the required hardware. We specify this wave-controlled RIS architecture in detail and discuss signal processing and machine learning methods that exploit it in both point-to-point and multicell MIMO systems. Such implementations can lead to a dramatic improvement in next-generation wireless, radar, and navigation systems where RIS finds wide applications. They have the potential to improve the efficiency of spectrum utilization and coexistence by orders of magnitude.

• The paper presents a novel wave-controlled RIS architecture that reduces hardware complexity by controlling contiguous element groups using standing wave phenomena.
• The study employs full-wave electromagnetic simulations and machine learning techniques to optimize phase profiles for high-directionality beamforming in dynamic environments.
• Performance evaluations demonstrate significant gains in spectral efficiency and cost reductions, making the approach promising for next-generation wireless systems.

Wave-Controlled Metasurface-Based Reconfigurable Intelligent Surfaces

Introduction

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising paradigm for manipulating the wireless propagation environment through programmable, passive metasurfaces capable of adaptively controlling electromagnetic wave reflections. Unlike conventional RF transceivers, RISs do not require downconversion for baseband processing, minimizing thermal noise contributions. They can operate over a wide spectrum range, from low GHz to THz frequencies, allowing for compact, power-efficient implementations.

Wave-Controlled RIS Architecture

The proposed wave-controlled RIS architecture introduces a novel method for reducing hardware complexity and enhancing scalability in RIS deployments. Traditional RIS designs require individual control over a vast number of phase-shifting elements, demanding extensive wiring and power consumption. The wave-controlled approach circumvents these requirements by leveraging standing waves across the metasurface, offering a dramatically reduced hardware footprint.

The architecture is based on contiguous groups of elements controlled as a collective, rather than individually, utilizing full-wave electromagnetic simulations to evaluate surface impedance and optimize reflection characteristics. This method addresses inherent electromagnetic coupling limitations and allows for smoother phase profiles, conducive to high-directionality beamforming.

Signal Processing and Machine Learning in RIS

Implementing signal processing and ML techniques in RIS systems facilitates adaptive control and optimization in dynamic wireless environments. ML algorithms offer potential solutions to complex optimization challenges inherent to RIS configurations where multiple base stations (BSs) and user equipment (UE) interactions demand instant adaptation to channel variations.

Machine learning methods can efficiently manage the computational burden of RIS phase matrix optimization by using training data to predict optimal configurations without explicit CSI evaluation. Moreover, integrating ML capabilities allows for real-time trajectory prediction and dynamic beam allocation, enhancing system resilience in multi-cell networks.

Implementation and Performance Evaluation

The proposed architecture demonstrates significant potential for enhancing next-generation wireless communication systems, particularly at millimeter and terahertz frequencies, where line-of-sight (LOS) and sparse multipath propagation characteristics prevail. This wave-controlled approach simplifies RIS hardware implementations, reducing the number of degrees of freedom required for effective channel manipulation.

The performance evaluations indicate substantial gains in spectral efficiency and interference mitigation capabilities. The reduced hardware complexity results in lower operational costs and power consumption, key factors in advancing economically viable RIS implementations.

Practical Implications and Future Directions

The wave-controlled RIS design holds promise for extensive deployment in various communication scenarios, including urban environments rich with obstacles blocking conventional signal paths. By simplifying the control mechanism and reducing reliance on extensive wiring, the architecture supports cost-effective scaling in dense network deployments.

Future work will focus on developing hybrid systems that integrate the proposed wave-based control technique with emerging materials like graphene to further extend frequency range capabilities. Additionally, expanding machine learning frameworks to accommodate increasingly complex network topologies will be crucial in achieving seamless, robust interactions across diverse RIS-aided communication systems.

Conclusion

In conclusion, the proposed wave-controlled metasurface-based RIS architecture presents a paradigm shift in how intelligent surfaces can be implemented and controlled. By mitigating the hardware complexity traditionally associated with RIS deployments, this approach offers a scalable, efficient solution tailored to the exigencies of modern wireless systems. As such, it lays the groundwork for further explorations into advanced RIS implementations, aiming to enhance spectral efficiency and propagate intelligent surface integration into mainstream communication infrastructures.