Optimizing Transmission Power in Double Self-Sustainable Reconfigurable Intelligent Surfaces Aided Wireless Communications

Core Concepts

The authors propose a double self-sustainable reconfigurable intelligent surfaces (RISs) aided multi-user multiple input multiple output (MIMO) communication system to minimize the transmission power at the base station while guaranteeing the quality of service requirements of the users and meeting the power consumption requirements of the RISs.

Abstract

The authors consider a multi-user MIMO communication system assisted by two self-sustainable RISs. The system comprises a base station (BS) with multiple antennas, RIS1 deployed at the BS side with reflection elements, RIS2 deployed at the users' side with reflection elements, and multiple single-antenna users.
To achieve self-sustainable transmission of the RISs, the RISs are equipped with energy harvesting circuits to harvest energy from the BS. The authors aim to minimize the transmission power at the BS by jointly optimizing the active beamforming at the BS, as well as the phase shifts and amplitude coefficients of the RISs.
The authors employ a block coordinate descent (BCD) algorithm based on the penalty-based method and successive convex approximation (SCA) to alternatively optimize the active beamforming at the BS and the phase shifts, as well as amplitude coefficients of the two RISs. The SCA framework is used to address the quality of service (QoS) and power consumption constraints of the RISs, while the penalty-based method is used to transform the non-convex constraints related to the phase shifts of the RISs.
Simulation results show that the proposed double self-sustainable RISs system can achieve significantly lower power consumption at the BS compared to conventional RIS systems.

Stats

The authors provide the following key figures and metrics:

The transmission power at the BS decreases as the total number of reflection elements increases, but starts to increase when the total number exceeds 140.
The minimum transmission power can be achieved by uniformly distributing the number of reflection elements between the two RISs.

Quotes

None.

Key Insights Distilled From

Double Self-Sustainable Reconfigurable Intelligent Surfaces Aided Wireless Communications

by Ji Wang,Suho... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03101.pdf

Deeper Inquiries

How would the performance of the proposed system be affected by the placement and positioning of the two RISs relative to the BS and users

The performance of the proposed system would be significantly influenced by the placement and positioning of the two RISs relative to the BS and users.

Distance and Coverage: The distance between the RISs and the BS/users would impact the signal strength and coverage area. Placing the RISs closer to the BS can enhance the signal strength for energy harvesting and reflection, improving overall system performance.

Interference Mitigation: Proper positioning can help in mitigating interference. Placing the RISs strategically to reflect signals towards users while minimizing interference can optimize the system's performance.

Multipath Effects: The positioning of RISs can affect multipath propagation. By placing them to exploit constructive interference and minimize destructive interference, the system can achieve better signal quality.

Line of Sight: Ensuring a clear line of sight between the RISs, BS, and users is crucial. Obstructions or improper positioning leading to signal blockages can degrade system performance.

Reflection Angle: Adjusting the angle of reflection from the RISs can optimize signal coverage and strength. Proper alignment can enhance signal propagation efficiency.

What are the potential drawbacks or limitations of the self-sustainable RIS architecture, and how could they be addressed in future research

The self-sustainable RIS architecture, while promising, has potential drawbacks and limitations that need to be addressed in future research:

Energy Harvesting Efficiency: The efficiency of energy harvesting circuits in RISs may vary, impacting the overall system's sustainability. Research focusing on enhancing energy conversion rates is essential.

Complexity and Cost: Implementing energy harvesting components in RISs can increase system complexity and cost. Future studies could explore cost-effective solutions without compromising performance.

Scalability: Scaling up the self-sustainable RIS architecture for larger networks may pose challenges in maintaining energy efficiency and system optimization. Research on scalable solutions is crucial.

Dynamic Environments: Adapting to dynamic environmental conditions, such as changing user locations or channel conditions, can be challenging for self-sustainable RISs. Future research could focus on dynamic optimization algorithms.

Reliability: Ensuring the reliability of energy harvesting and operation of RISs over time is vital. Robustness against failures or degradation in performance needs to be addressed.

Could the proposed optimization framework be extended to consider other system objectives, such as maximizing the energy efficiency or the sum rate, instead of just minimizing the transmission power

The proposed optimization framework could be extended to consider other system objectives, such as maximizing energy efficiency or sum rate, by incorporating additional constraints and objectives into the optimization problem:

Maximizing Energy Efficiency: By introducing constraints related to energy consumption and efficiency, the optimization framework can be modified to maximize energy efficiency while meeting performance requirements.

Sum Rate Maximization: Including objectives related to maximizing the sum rate of the system can involve optimizing beamforming, phase shifts, and amplitude coefficients to enhance overall data transmission rates.

Multi-Objective Optimization: Extending the framework to multi-objective optimization can balance trade-offs between minimizing transmission power, maximizing energy efficiency, and optimizing sum rate, providing a comprehensive approach to system design.

Dynamic Optimization: Incorporating dynamic constraints and objectives that adapt to changing network conditions can further enhance the system's adaptability and performance in real-time scenarios.