Active Reconfigurable Intelligent Surface-Aided Uplink Massive MIMO System with Imperfect Channel State Information and Phase Noise

Core Concepts

The core message of this article is to investigate the performance of an active reconfigurable intelligent surface (RIS)-aided uplink massive multiple-input multiple-output (MIMO) system in the presence of imperfect channel state information (CSI) and phase noise at the active RIS. The authors derive a closed-form expression for a lower bound of the achievable rate and analyze the power scaling laws of the considered system.

Abstract

The article focuses on an active RIS-aided uplink massive MIMO wireless communication system, considering the phase noise at the active RIS and the direct links between the users and the base station (BS). Key highlights: The authors employ a two-timescale scheme, where the beamforming at the BS is adjusted based on the instantaneous aggregated CSI, and the statistical CSI serves as the basis for designing the phase shifts at the active RIS, to reduce the feedback overhead and computational complexity. The authors derive the analytical closed-form expression of a lower bound of the achievable rate based on the estimated aggregated channel using the linear minimum mean square error (LMMSE) technique. The power scaling laws of the active RIS-aided system are investigated, and the authors find that the thermal noise will cause the lower bound of the achievable rate to approach zero as the number of BS antennas or reflecting elements increases to infinity. An optimization approach based on genetic algorithms is introduced to tackle the phase shift optimization problem, aiming to maximize the minimum user rate and ensure fairness among multiple users. Numerical results reveal that the active RIS can greatly enhance the performance of the considered system under various settings.

Stats

The total power consumption of the uplink active RIS-aided system is given by Ptotal = ∑K k=1 p + Pcir + ξ−1PA, where Pcir = N (PSC + PDC) is the circuit power, and ξ is the power amplifier efficiency. The large-scale fading coefficients for the user-RIS channel, RIS-BS channel, and user-BS channel are modeled as αk = 10−3r−2 UR, β = 10−3d−2.8 RB, and γk = 10−3dUB k −4.2, respectively.

Quotes

"Owing to its capability to amplify the incident signals, active RIS can mitigate the multiplicative fading effect inherent in the passive RIS-aided system." "We find that the thermal noise will cause the lower bound of the achievable rate to approach zero as the number of M or N increases to infinity."

Key Insights Distilled From

Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

by Zhangjie Pen... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03300.pdf

Deeper Inquiries

How can the trade-off between the number of reflecting elements and the power allocation be optimized in active RIS-aided massive MIMO systems

In active reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, the trade-off between the number of reflecting elements and power allocation plays a crucial role in optimizing system performance. Here are some strategies to achieve this optimization: Power-Efficient Reflection: By carefully selecting the number of reflecting elements and their corresponding amplification factors, it is possible to optimize the power allocation in the system. Balancing the amplification capabilities of the active RIS elements with the power constraints of the system can help in achieving an optimal trade-off. Dynamic Power Allocation: Implementing dynamic power allocation schemes based on the channel conditions and user requirements can help in efficiently utilizing the available power resources. Adaptive power allocation algorithms can adjust the power levels of the active RIS elements in real-time to optimize system performance. Energy-Efficient Design: Considering the energy consumption of the active RIS elements, it is essential to design energy-efficient reflecting elements that can amplify the signals effectively while minimizing power consumption. This can involve optimizing the circuit design, biasing power levels, and overall energy efficiency of the active RIS. Performance Metrics: Define specific performance metrics that capture the trade-off between the number of reflecting elements and power allocation. These metrics can include signal-to-noise ratio (SNR), achievable data rates, energy efficiency, and overall system capacity. By evaluating these metrics, it is possible to find the optimal balance between the number of elements and power allocation. By implementing these strategies and considering the trade-off between the number of reflecting elements and power allocation, active RIS-aided massive MIMO systems can be optimized for enhanced performance and efficiency.

What are the potential benefits and challenges of integrating active RIS with other emerging technologies, such as millimeter-wave or terahertz communications

The integration of active RIS with other emerging technologies, such as millimeter-wave or terahertz communications, offers several potential benefits and challenges: Benefits: Enhanced Coverage and Capacity: Active RIS can improve coverage and capacity in millimeter-wave and terahertz communication systems by mitigating path loss and signal blockage issues. Interference Mitigation: Active RIS can be used to mitigate interference in dense communication environments, improving the overall quality of service. Dynamic Beamforming: Integration with millimeter-wave and terahertz technologies allows for dynamic beamforming and beam steering, enabling adaptive communication links for better connectivity. Challenges: Complexity: Integrating active RIS with millimeter-wave and terahertz systems adds complexity to the overall network design and implementation, requiring sophisticated algorithms and coordination mechanisms. Hardware Constraints: The hardware requirements for active RIS in millimeter-wave and terahertz systems may pose challenges in terms of power consumption, size, and cost. Regulatory Considerations: Operating in millimeter-wave and terahertz bands may require adherence to specific regulatory requirements and standards, adding complexity to the deployment of active RIS. By addressing these challenges and leveraging the benefits, the integration of active RIS with millimeter-wave and terahertz communications can lead to significant improvements in network performance and efficiency.

How can the proposed active RIS-aided massive MIMO system be extended to support other communication scenarios, such as downlink transmission or multi-cell cooperation

To extend the proposed active RIS-aided massive MIMO system to support other communication scenarios, such as downlink transmission or multi-cell cooperation, the following approaches can be considered: Downlink Transmission: For downlink transmission, the system can be modified to incorporate base station (BS) beamforming techniques and user-specific precoding. By optimizing the phase shifts at the active RIS for downlink channels, the system can enhance signal transmission to multiple users simultaneously. Multi-Cell Cooperation: In multi-cell scenarios, active RIS can be utilized to improve inter-cell interference coordination and enhance overall network performance. By coordinating the phase shifts of RIS elements across multiple cells, interference can be mitigated, and system capacity can be increased. Resource Allocation: Extending the system to support multi-cell cooperation involves dynamic resource allocation strategies. By optimizing the allocation of power, bandwidth, and beamforming parameters across multiple cells, the system can achieve efficient utilization of resources and improved network performance. Handover and Mobility Management: Implementing seamless handover and mobility management mechanisms is essential for supporting communication scenarios involving multiple cells. Active RIS can play a role in enhancing handover procedures and maintaining connectivity during user mobility. By incorporating these extensions and adaptations, the proposed active RIS-aided massive MIMO system can be tailored to support diverse communication scenarios, including downlink transmission and multi-cell cooperation, while leveraging the benefits of active RIS technology.