Performance Analysis of a Reconfigurable Intelligent Surface-Assisted Spectrum Sharing System

Kernkonzepte

This work presents a comprehensive performance analysis of a reconfigurable intelligent surface (RIS)-assisted underlay spectrum sharing system, where a secondary network shares the spectrum licensed to a primary network.

Zusammenfassung

The authors propose a RIS-assisted underlay spectrum sharing system, where a secondary network shares the spectrum licensed to a primary network. The secondary network consists of a secondary source (SS), an RIS, and a secondary destination (SD), operating in a Rician fading environment.
The key highlights and insights are:

The RIS-assisted secondary network shares the spectrum of the primary network without deteriorating the performance of the primary link by adopting transmit power adaptation at the SS. The SS can adjust its transmit power to limit the interference power at the primary receiver (PR) to a specified threshold, while also considering a peak power constraint.

The deployment of the RIS in the secondary network is of significant practical importance as it can enable communication between the SS and SD even when a direct link is not available. Unlike relaying and backscatter communication techniques, the RIS can effectively control the reflected signal towards the SD while restricting the interference towards the PR.

Novel analytical expressions are derived for the cumulative distribution function (CDF) and probability density function (PDF) of the signal-to-noise ratio (SNR) at the SD in terms of the incomplete H-function, considering both the Rician fading model and the transmit power adaptation at the SS.

Building upon the SNR statistics, the authors analyze the outage probability, ergodic capacity, and average bit error rate (BER) of the secondary network, and provide novel exact expressions in terms of the H-function and the incomplete H-function.

Asymptotic expressions are also obtained for the performance measures when the peak power of the SS is high, which are shown to closely match the exact results.

The derived analytical results are validated through extensive Monte-Carlo simulations, confirming the correctness of the theoretical analysis.

Statistiken

The transmit power at the secondary source is given by Ps = min{Q/|h0|^2, P}, where P is the peak power of the secondary source and Q is the interference power limit at the primary receiver.
The instantaneous SNR at the secondary destination is given by ρ = min{QR^2/|h0|^2, PR^2}, where R^2 = Σ_{n=1}^N (α_n β_n)^2 and N is the number of RIS elements.

Zitate

"The deployment of the RIS in the secondary network is of significant practical importance due to the following reasons: (i) In a situation where no direct link is available between the SS and the SD, the RIS is deployed in an effort to make the communication between the SS and the SD not only possible but also reliable. (ii) Unlike relaying and backscatter communication techniques, the reflected wave from the RIS can be effectively controlled and programmed in real-time due to the adjustable phase shifts of the RIS."
"Expressing the cumulative distribution function (CDF) and probability density function (PDF) of the SNR in the RIS-assisted secondary network, while considering both the Rician model and the transmit power adaption at the SS, in closed-form is very challenging. Consequently, the performance analysis is mathematically intricate, requiring the solution of difficult integrals to quantify the ergodic capacity and the average BER."

Wichtige Erkenntnisse aus

Performance Analysis of RIS-Assisted Spectrum Sharing Systems

by Yazan H. Al-... um arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13494.pdf

Tiefere Fragen

How can the performance of the RIS-assisted secondary network be further improved by optimizing the phase shifts of the RIS elements?

Optimizing the phase shifts of the RIS elements plays a crucial role in enhancing the performance of the RIS-assisted secondary network. By strategically adjusting the phase shifts, the RIS can manipulate the propagation of signals in a way that minimizes interference, maximizes signal strength, and improves overall communication reliability. Here are some ways in which optimizing the phase shifts can further improve the network performance:

Beamforming: By adjusting the phase shifts of the RIS elements, beamforming can be effectively implemented. Beamforming allows the RIS to focus the transmitted signal towards the intended receiver, increasing the signal strength and reducing interference from other directions.

Channel Equalization: Optimizing the phase shifts can help in equalizing the channel response across different paths, compensating for signal attenuation and multipath effects. This can lead to improved signal quality and reliability.

Interference Mitigation: By intelligently adjusting the phase shifts, the RIS can nullify or minimize interference from other sources, including neighboring networks or unwanted signals, leading to a cleaner communication environment.

Dynamic Adaptation: Real-time optimization of phase shifts based on changing channel conditions and network requirements can ensure adaptive and efficient signal transmission, maximizing network performance under varying circumstances.

Energy Efficiency: Optimizing the phase shifts can also contribute to energy efficiency by directing the transmitted energy towards the desired direction, reducing wastage and improving overall network sustainability.

In essence, by fine-tuning the phase shifts of the RIS elements, the network can achieve better signal coverage, reduced interference, improved reliability, and enhanced overall performance.

What are the potential challenges and practical considerations in implementing the proposed RIS-assisted spectrum sharing system in real-world scenarios?

Implementing the proposed RIS-assisted spectrum sharing system in real-world scenarios comes with several challenges and practical considerations that need to be addressed for successful deployment. Some of the key challenges and considerations include:

Hardware Complexity: The deployment of RIS elements adds complexity to the network infrastructure, requiring careful planning for installation, maintenance, and synchronization of the elements.

Channel Estimation: Accurate channel estimation is crucial for optimizing the phase shifts of the RIS elements. Dealing with dynamic channel conditions and achieving precise channel state information adds complexity to the system.

Regulatory Compliance: Spectrum sharing involves adherence to regulatory requirements and coordination with existing spectrum holders. Ensuring compliance with spectrum regulations and licensing constraints is essential.

Interference Management: Coordinating spectrum sharing while mitigating interference with primary users and other secondary networks is a critical challenge. Efficient interference management strategies need to be implemented.

Security and Privacy: Protecting the network from security threats and ensuring user privacy in a shared spectrum environment are paramount. Robust security measures and encryption protocols must be in place.

Cost and Scalability: The cost of deploying RIS elements and the scalability of the system to accommodate a growing number of users and devices are important considerations. Balancing cost-effectiveness with performance is crucial.

Integration with Existing Networks: Seamless integration of the RIS-assisted system with existing network infrastructures and protocols without causing disruptions is essential for successful deployment.

Addressing these challenges and considerations through thorough planning, robust system design, efficient resource management, and continuous optimization is key to the successful implementation of the RIS-assisted spectrum sharing system in real-world scenarios.

How can the proposed framework be extended to consider more complex primary and secondary network topologies, such as multiple primary transmitters and receivers, or multiple secondary sources and destinations?

Extending the proposed framework to accommodate more complex primary and secondary network topologies involves adapting the system design and analytical models to account for the increased network complexity. Here are some ways in which the framework can be extended:

Multiple Primary Transmitters and Receivers: To incorporate multiple primary transmitters and receivers, the system model needs to be expanded to include the interactions and interference scenarios between these entities. Analyzing the impact of multiple primary transmitters on the secondary network's performance and optimizing resource allocation becomes crucial.

Multiple Secondary Sources and Destinations: Introducing multiple secondary sources and destinations requires considering the interactions and communication links among these entities. The system model should account for the dynamic routing of signals, interference management, and resource allocation among multiple secondary nodes.

Network Scheduling and Coordination: With multiple transmitters and receivers in both primary and secondary networks, efficient network scheduling and coordination mechanisms need to be developed. This includes optimizing transmission schedules, managing interference, and ensuring fair resource allocation.

Dynamic Spectrum Access: Extending the framework to support dynamic spectrum access for multiple nodes involves developing intelligent algorithms for spectrum sharing, interference avoidance, and adaptive modulation and coding schemes based on real-time network conditions.

Advanced Signal Processing Techniques: Incorporating advanced signal processing techniques such as multi-user MIMO, massive MIMO, and cooperative communication can enhance the performance of the network with multiple transmitters and receivers.

Resource Management and Optimization: Implementing sophisticated resource management algorithms to allocate spectrum, power, and bandwidth efficiently among multiple nodes while ensuring QoS requirements for all users.

By addressing these aspects and tailoring the framework to accommodate more complex network topologies, the system can be extended to support diverse communication scenarios and provide enhanced performance in multi-user, multi-node environments.