Reconfigurable Intelligent Surfaces (RIS) Assisted Wireless Networks: Collaborative Regulation, Deployment Modes, and Field Testing

Conceitos essenciais

This article analyzes and discusses the two deployment modes of RIS-Assisted wireless networks, namely Network Controlled Mode and Standalone Mode. It presents three typical collaboration scenarios of RIS networks, including multi-RIS collaboration, multi-user access, and multi-cell coordination, and proposes collaborative regulation mechanisms for RIS. The article also establishes simulation models of these scenarios and provides numerical simulation results, as well as builds an actual field test environment to conduct preliminary field tests and verification.

Resumo

The article begins by introducing the progress of RIS in engineering application research and industrialization, as well as in academic research. It then categorizes the deployment mode of RIS into two distinct types: Network-Controlled Mode (NCM) and Standalone Mode (SAM), and conducts a comprehensive analysis and comparison of these modes.
The article presents three typical collaboration scenarios of RIS networks: multi-RIS collaboration, multi-user access, and multi-cell coordination. For multi-RIS collaboration, it discusses the challenges in achieving coherent Coordinated Multi-point Joint Transmission (CoMP-JT) due to phase differences between RISs, and proposes solutions for phase alignment.
For multi-user access, the article explores two approaches for RIS collaborative regulation in NCM mode: Pattern Addition (PA)-based multi-beam mechanism and RIS blocking mechanism. It also discusses two solutions for RIS non-collaborative regulation in SAM mode, involving the use of a simple network entity to perform multi-UE CSI measurements.
For multi-cell coordination, the article presents three schemes to address the interference problem caused by RIS deployment at the cell edge: predefined signal strength profile, narrow beam shaping, and beam width adjustment.
The article then establishes simulation models for the three scenarios and provides rich numerical simulation results. It also builds an actual field test environment and conducts preliminary field tests and verification using a specially designed and processed RIS prototype.
Finally, the article outlines the anticipated trends and challenges for the future.

Estatísticas

The received signal-to-noise ratio (SINR) of user i is given by:
γ_i = p_i / (Σ_j≠i ||H_i Φ_j||^2 + σ_i^2)
where p_i is the power allocated to user i, σ_i is the noise at the receiver of user i, H_i is the downlink channel between RIS and user i, and Φ_j is the regulation matrix of RIS j.

Citações

"Without loss of generality, taking Downlink (DL) as an example and referring to RIS channel model formula (2), the RIS-based CoMP-JT channel model H_DL,CoMP^JT can be expressed as equation (5)."
"Assuming two RISs, ris_1 and ris_2, the independently calculated regulation matrices are Φ_ris,1^0 c_ris,1 and Φ_ris,2^0 c_ris,2. Network base station (NB) coordinates and updates the regulation matrices of ris_1 and ris_2: keeping the regulation matrix Φ_ris,1 of ris_1 unchanged, and updating the ris_2 regulation matrix Φ_ris,2 to align with the phase of ris_1."

Principais Insights Extraídos De

RIS Assisted Wireless Networks: Collaborative Regulation, Deployment Mode and Field Testing

by Yajun Zhao às arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08644.pdf

Perguntas Mais Profundas

How can the collaborative regulation mechanisms for RIS be extended to support more complex network scenarios, such as multi-network coexistence or dynamic network topologies

To extend collaborative regulation mechanisms for Reconfigurable Intelligent Surfaces (RIS) to more complex network scenarios like multi-network coexistence or dynamic network topologies, several strategies can be implemented.

Multi-Network Coexistence: In scenarios where multiple networks coexist, RIS can play a crucial role in managing interference and optimizing signal propagation. By incorporating intelligent algorithms that consider the presence of multiple networks, RIS can dynamically adjust its regulation matrix to minimize interference and enhance overall network performance. This may involve advanced coordination mechanisms between RIS units in different networks to ensure efficient spectrum utilization and seamless connectivity.

Dynamic Network Topologies: In dynamic network environments where topologies change frequently, RIS can adapt by employing real-time optimization algorithms. By continuously monitoring network conditions and adjusting the regulation matrix based on dynamic topology changes, RIS can enhance coverage, improve signal quality, and mitigate interference. This adaptive approach requires robust communication protocols and coordination mechanisms to facilitate seamless transitions between different network configurations.

Machine Learning and AI: Leveraging machine learning and artificial intelligence algorithms can enhance the collaborative regulation capabilities of RIS in complex network scenarios. By analyzing vast amounts of data, predicting network behavior, and optimizing regulation parameters, RIS can intelligently adapt to diverse network environments. Machine learning models can learn from network dynamics and user behavior to optimize RIS performance in multi-network and dynamic topology scenarios.

Inter-Network Coordination: Establishing communication protocols for inter-network coordination can enable RIS units in different networks to exchange information and collaborate effectively. By sharing channel state information, coordinating beamforming strategies, and synchronizing regulation actions, RIS units can collectively optimize network performance across multiple networks. This coordination is essential for ensuring seamless connectivity, reducing interference, and maximizing the benefits of RIS technology in complex network environments.

What are the potential trade-offs and design considerations in implementing the RIS blocking mechanism for multi-user access, particularly in terms of the number of RIS sub-blocks and the blocking ratio factor

Implementing the RIS blocking mechanism for multi-user access involves several trade-offs and design considerations, particularly regarding the number of RIS sub-blocks and the blocking ratio factor.

Number of RIS Sub-Blocks: The number of RIS sub-blocks determines the granularity of regulation and the level of interference control. Increasing the number of sub-blocks allows for more precise regulation of individual user signals but may lead to increased complexity in managing interference and optimizing beamforming. On the other hand, reducing the number of sub-blocks simplifies the system but may limit the flexibility and performance of multi-user access.

Blocking Ratio Factor: The blocking ratio factor determines the proportion of energy allocated to each user's signal within a sub-block. A higher blocking ratio concentrates more energy on the target user, enhancing signal quality but potentially reducing fairness among users. Conversely, a lower blocking ratio spreads energy more evenly among users, promoting fairness but potentially compromising signal strength and coverage. Balancing the blocking ratio factor is crucial to optimizing system performance while ensuring equitable access for all users.

Interference Management: The RIS blocking mechanism must effectively manage interference between users sharing the same sub-block or adjacent sub-blocks. By dynamically adjusting the blocking ratio and beamforming strategies based on channel conditions and user requirements, the system can mitigate interference and optimize signal quality. Intelligent interference mitigation algorithms and adaptive beamforming techniques are essential for maximizing spectral efficiency and user satisfaction in multi-user access scenarios.

Regulation Complexity: The complexity of calculating and optimizing the regulation matrix for each RIS sub-block impacts system performance and resource utilization. Design considerations should focus on balancing regulation complexity with computational efficiency to ensure real-time responsiveness and scalability in multi-user access environments. Simplifying regulation algorithms while maintaining performance is key to achieving seamless connectivity and optimal user experience.

Given the limitations of RIS non-collaborative regulation in the Standalone Mode, how can the performance and flexibility of multi-user access be further improved without relying on network coordination

In the Standalone Mode, where RIS operates without network coordination, enhancing the performance and flexibility of multi-user access requires innovative approaches to overcome the limitations of non-collaborative regulation.

Intelligent Beamforming: Implementing intelligent beamforming techniques based on user feedback and environmental conditions can optimize signal transmission and reception without network coordination. By dynamically adjusting beamforming parameters to align with user requirements and channel characteristics, RIS can enhance signal quality and coverage for multiple users simultaneously.

Dynamic Channel Estimation: Utilizing advanced channel estimation algorithms that adapt to changing channel conditions can improve the accuracy of CSI measurement and regulation matrix calculation in non-collaborative scenarios. By continuously updating channel information and optimizing beamforming strategies based on real-time feedback, RIS can enhance multi-user access performance and adapt to dynamic network environments.

Self-Optimization Algorithms: Developing self-optimization algorithms that enable RIS to autonomously adjust regulation parameters based on local observations and user interactions can enhance system flexibility and performance. By incorporating machine learning and AI capabilities, RIS can learn from user behavior, predict network dynamics, and optimize regulation strategies without external coordination, thereby improving multi-user access efficiency and user satisfaction.

Adaptive Resource Allocation: Implementing adaptive resource allocation mechanisms that dynamically allocate transmission power, beamforming resources, and bandwidth based on user demand and network conditions can optimize system performance in non-collaborative environments. By intelligently managing resource allocation and prioritizing user requirements, RIS can maximize spectral efficiency, minimize interference, and ensure fair access for all users without relying on network coordination.

By integrating these advanced technologies and strategies, RIS in Standalone Mode can enhance multi-user access performance, flexibility, and efficiency, providing seamless connectivity and optimal user experience in diverse network scenarios.