аналитика - Wireless Communications - # Active RIS-Aided Massive MIMO Uplink with Low-Resolution ADCs

Active Reconﬁgurable Intelligent Surface-Aided Massive MIMO Uplink Systems with Low-Resolution Analog-to-Digital Converters

Q: How can the active RIS be further optimized to achieve even higher performance gains over the passive RIS

To achieve even higher performance gains over passive RIS, the active RIS can be further optimized in several ways: Dynamic Phase Control: Implementing dynamic phase control algorithms that adaptively adjust the phase shifts of the RIS elements in real-time based on channel conditions can optimize the system performance. Machine Learning Techniques: Utilizing machine learning algorithms to predict optimal phase configurations for the RIS based on historical data and current channel conditions can enhance the efficiency of the active RIS. Hybrid Beamforming: Incorporating hybrid beamforming techniques that combine analog and digital beamforming can improve the beamforming capabilities of the active RIS, leading to better signal focusing and interference mitigation. Energy-Efficient Designs: Developing energy-efficient hardware components for the active RIS, such as low-power amplifiers and intelligent control circuits, can reduce power consumption while maintaining high performance levels.

Q: What are the potential challenges and trade-offs in implementing active RIS-aided massive MIMO systems in real-world scenarios

Implementing active RIS-aided massive MIMO systems in real-world scenarios may face several challenges and trade-offs: Hardware Complexity: The deployment of active RIS requires additional hardware components such as amplifiers and control circuits, increasing system complexity and cost. Power Consumption: Active RIS systems consume more power compared to passive RIS due to the active components, leading to higher energy consumption and potential thermal management issues. Channel Estimation: Accurate channel estimation is crucial for optimizing the performance of active RIS, but it can be challenging in dynamic environments with fast-changing channel conditions. Interference Management: Coordinating multiple active RIS elements to mitigate interference and optimize signal reflections requires sophisticated algorithms and coordination mechanisms. Regulatory Compliance: Compliance with regulatory requirements for radio frequency emissions and power levels is essential when deploying active RIS technology in real-world scenarios.

Q: What other communication scenarios or applications could benefit from the integration of active RIS technology

Integration of active RIS technology can benefit various communication scenarios and applications, including: Satellite Communications: Active RIS can enhance satellite communication systems by improving link quality, increasing coverage, and mitigating interference from terrestrial sources. Internet of Things (IoT): Active RIS can optimize wireless connectivity in IoT networks by enhancing signal strength, extending coverage, and reducing energy consumption for IoT devices. Smart Cities: Implementing active RIS in smart city infrastructure can improve wireless communication for smart grids, traffic management systems, and public safety applications. Industry 4.0: Active RIS technology can enhance wireless connectivity in industrial environments, enabling efficient communication for automation, robotics, and remote monitoring applications. Vehicular Networks: Active RIS can improve communication reliability and coverage in vehicular networks, supporting connected vehicles, intelligent transportation systems, and autonomous driving technologies.

Основные понятия

An active reconﬁgurable intelligent surface (RIS) can enhance the performance of massive MIMO uplink systems with low-resolution analog-to-digital converters (ADCs) by adjusting the phase shifts and amplifying the received signals.

Аннотация

The paper considers an active RIS-aided multi-user uplink massive MIMO system with low-resolution ADCs. The key highlights are:

A closed-form approximate expression for the sum achievable rate (AR) is derived, where the maximum ratio combination (MRC) processing and low-resolution ADCs are applied at the base station.
The system performance is analyzed, and a genetic algorithm (GA)-based method is proposed to optimize the RIS's phase shifts for enhancing the system performance.
Numerical results verify the accuracy of the derivations and demonstrate that the active RIS has a notable performance gain over the passive RIS.
Applying low-resolution ADCs in this system can balance the system performance with the deployment costs.

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Статистика

The sum achievable rate increases as the number of base station antennas M and the number of RIS reflecting elements N go larger.
When the total power PT is lower than 20 dBm, the passive RIS outperforms the active RIS, but the active RIS can achieve a higher rate under sufficient energy conditions.
With 4-bit quantization, the system can achieve similar performance to the case with high-resolution ADCs.

Цитаты

"Active RIS is capable of approaching the upper limit of system performance while requiring fewer components such as M = 64 and N = 16."
"Applying low-resolution ADCs in this system can balance the system performance with the deployment costs."

Ключевые выводы из

Active RIS-Aided Massive MIMO Uplink Systems with Low-Resolution ADCs

by Zhangjie Pen... в arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13875.pdf

Active RIS-Aided Massive MIMO Uplink Systems with Low-Resolution ADCs

Дополнительные вопросы

How can the active RIS be further optimized to achieve even higher performance gains over the passive RIS

To achieve even higher performance gains over passive RIS, the active RIS can be further optimized in several ways:

Dynamic Phase Control: Implementing dynamic phase control algorithms that adaptively adjust the phase shifts of the RIS elements in real-time based on channel conditions can optimize the system performance.
Machine Learning Techniques: Utilizing machine learning algorithms to predict optimal phase configurations for the RIS based on historical data and current channel conditions can enhance the efficiency of the active RIS.
Hybrid Beamforming: Incorporating hybrid beamforming techniques that combine analog and digital beamforming can improve the beamforming capabilities of the active RIS, leading to better signal focusing and interference mitigation.
Energy-Efficient Designs: Developing energy-efficient hardware components for the active RIS, such as low-power amplifiers and intelligent control circuits, can reduce power consumption while maintaining high performance levels.

What are the potential challenges and trade-offs in implementing active RIS-aided massive MIMO systems in real-world scenarios

Implementing active RIS-aided massive MIMO systems in real-world scenarios may face several challenges and trade-offs:

Hardware Complexity: The deployment of active RIS requires additional hardware components such as amplifiers and control circuits, increasing system complexity and cost.
Power Consumption: Active RIS systems consume more power compared to passive RIS due to the active components, leading to higher energy consumption and potential thermal management issues.
Channel Estimation: Accurate channel estimation is crucial for optimizing the performance of active RIS, but it can be challenging in dynamic environments with fast-changing channel conditions.
Interference Management: Coordinating multiple active RIS elements to mitigate interference and optimize signal reflections requires sophisticated algorithms and coordination mechanisms.
Regulatory Compliance: Compliance with regulatory requirements for radio frequency emissions and power levels is essential when deploying active RIS technology in real-world scenarios.

What other communication scenarios or applications could benefit from the integration of active RIS technology

Integration of active RIS technology can benefit various communication scenarios and applications, including:

Satellite Communications: Active RIS can enhance satellite communication systems by improving link quality, increasing coverage, and mitigating interference from terrestrial sources.
Internet of Things (IoT): Active RIS can optimize wireless connectivity in IoT networks by enhancing signal strength, extending coverage, and reducing energy consumption for IoT devices.
Smart Cities: Implementing active RIS in smart city infrastructure can improve wireless communication for smart grids, traffic management systems, and public safety applications.
Industry 4.0: Active RIS technology can enhance wireless connectivity in industrial environments, enabling efficient communication for automation, robotics, and remote monitoring applications.
Vehicular Networks: Active RIS can improve communication reliability and coverage in vehicular networks, supporting connected vehicles, intelligent transportation systems, and autonomous driving technologies.