Active Reconfigurable Intelligent Surface for Enhanced Spectrum Sensing in Cognitive Radio Networks

Core Concepts

The proposed active reconfigurable intelligent surface (RIS) can enhance the received signal strength from the primary user and mitigate the underlying interference within the background noise, leading to improved spectrum sensing performance in cognitive radio networks.

Abstract

The content presents an active RIS-assisted spectrum sensing system for cognitive radio networks. The key highlights are:

In opportunistic cognitive radio networks, when the primary signal is very weak compared to the background noise, the secondary user requires long sensing time to achieve reliable spectrum sensing performance, leading to little remaining time for secondary transmission.

To address this issue, the authors propose an active RIS-assisted spectrum sensing system, where the received signal strength from the primary user can be enhanced and the underlying interference within the background noise can be mitigated.

Compared to passive RIS, the active RIS can not only adapt the phase shift of each reflecting element but also amplify the incident signals.

The authors formulate an optimization problem to maximize the detection probability given a maximum tolerable false alarm probability and limited sensing time. This problem is transformed into an equivalent weighted mean square error minimization problem using the WMMSE algorithm.

An iterative optimization approach is proposed to obtain the optimal reflecting coefficient matrix (RCM) for the active RIS.

The authors also study a special case where the direct links are neglected and the RIS-related channels are line-of-sight. The required power budget of the active RIS and passive RIS to achieve a target detection probability are compared.

Simulation results demonstrate the effectiveness of the WMMSE-based RCM optimization approach. The active RIS can outperform the passive RIS when the underlying interference is relatively weak, while the passive RIS performs better in strong interference scenarios.

Stats

The received signal at the secondary user under hypothesis H0 and H1 are respectively given by:
H0: y[t] = PK
k=1 αkhksk[t] + GΦnR[t] + nS[t]
H1: y[t] = h0s0[t] + PK
k=1 αkhksk[t] + GΦnR[t] + nS[t]
where h0 = d0 + GΦf0 and hk = dk + GΦfk.

Quotes

"To tackle this issue, we propose an active reconfigurable intelligent surface (RIS) assisted spectrum sensing system, where the received signal strength from the interested primary user can be enhanced and underlying inter-ference within the background noise can be mitigated as well."
"Notably, we study the reflecting coefficient matrix (RCM) optimization problem to improve the detection probability given a maximum tolerable false alarm probability and limited sensing time."
"Furthermore, the results reveal that the active RIS can outperform the passive RIS when the underlying interference within the background noise is relatively weak, whereas the passive RIS performs better in strong interference scenarios because the same power budget can support a vast number of passive reflecting elements for interference mitigation."

Key Insights Distilled From

Active Reconfigurable Intelligent Surface Enhanced Spectrum Sensing for Cognitive Radio Networks

by Jungang Ge,Y... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2311.16568.pdf

Deeper Inquiries

How can the proposed active RIS-assisted spectrum sensing system be extended to handle more practical scenarios, such as imperfect channel state information or dynamic primary user activities

To extend the proposed active RIS-assisted spectrum sensing system to handle more practical scenarios, such as imperfect channel state information or dynamic primary user activities, several modifications and enhancements can be implemented:

Imperfect Channel State Information (CSI): Introducing robust optimization techniques can help mitigate the impact of imperfect CSI. By incorporating uncertainty models into the optimization framework, the system can adapt to varying channel conditions and improve performance under realistic settings.

Dynamic Primary User Activities: Implementing adaptive algorithms that can dynamically adjust the RIS configuration based on the changing primary user activities can enhance system flexibility. By integrating machine learning algorithms or reinforcement learning techniques, the system can learn and adapt to dynamic environments in real-time.

Reinforcement Learning: Leveraging reinforcement learning algorithms can enable the system to learn optimal RIS configurations through interactions with the environment. By training the system to make decisions based on feedback from the environment, it can adapt to uncertainties and variations in primary user activities.

Multi-Objective Optimization: Considering multiple objectives, such as maximizing detection probability, minimizing interference, and optimizing energy efficiency, can lead to a more comprehensive system design. Multi-objective optimization techniques can balance conflicting goals and provide trade-off solutions for different scenarios.

By incorporating these enhancements, the active RIS-assisted spectrum sensing system can be extended to handle more practical scenarios effectively.

What are the potential challenges and limitations in implementing the active RIS in real-world cognitive radio networks, and how can they be addressed

Implementing active RIS in real-world cognitive radio networks may face several challenges and limitations, including:

Hardware Complexity: The deployment of active RIS requires sophisticated hardware components for signal amplification and phase control. Managing the complexity of the hardware design and ensuring seamless integration with existing network infrastructure can be challenging.

Power Consumption: Active RIS systems may consume more power compared to passive RIS due to signal amplification requirements. Balancing the power consumption with performance gains is crucial to ensure energy efficiency in the network.

Cost Considerations: The cost of deploying and maintaining active RIS infrastructure can be a limiting factor for widespread adoption. Addressing cost concerns through efficient design, deployment strategies, and cost-effective hardware solutions is essential.

Interference Management: Active RIS systems need to effectively mitigate interference from other users or external sources. Developing interference mitigation techniques and algorithms to enhance signal quality and reliability is critical for optimal performance.

To address these challenges, strategies such as efficient hardware design, power optimization techniques, cost-effective solutions, interference management algorithms, and thorough testing and validation in real-world scenarios can help overcome limitations and ensure successful implementation of active RIS in cognitive radio networks.

Given the tradeoffs between active and passive RIS, how can the system design be optimized to achieve the best overall performance in terms of both spectrum sensing and secondary transmission

To optimize the system design for the best overall performance in terms of both spectrum sensing and secondary transmission, the following approaches can be considered:

Dynamic Resource Allocation: Implement dynamic resource allocation algorithms that can adaptively allocate resources between spectrum sensing and secondary transmission based on real-time network conditions. By dynamically adjusting resource allocation, the system can optimize performance based on changing requirements.

Joint Optimization: Consider joint optimization of spectrum sensing and transmission parameters to maximize overall system efficiency. By jointly optimizing RIS configurations, power allocation, and transmission strategies, the system can achieve a balance between spectrum sensing accuracy and secondary transmission quality.

Machine Learning Techniques: Utilize machine learning algorithms to learn and optimize system parameters based on historical data and feedback. By leveraging machine learning for predictive modeling and optimization, the system can continuously improve performance and adapt to changing environments.

Cross-Layer Design: Implement a cross-layer design approach that integrates spectrum sensing, RIS configuration, and transmission protocols at different layers of the network stack. By optimizing interactions between layers, the system can achieve synergistic benefits and enhance overall performance.

By integrating these strategies into the system design, it is possible to achieve the best overall performance in cognitive radio networks, balancing spectrum sensing accuracy with efficient secondary transmission for enhanced network efficiency and reliability.