insight - Wireless Communications - # Enhancing User Fairness in Wireless Powered Communication Networks with STAR-RIS

Maximizing User Fairness in Wireless Powered Communication Networks with Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces

Q: How can the proposed STAR-RIS assisted WPCN be extended to scenarios with more than two users?

To extend the proposed STAR-RIS assisted Wireless Powered Communication Network (WPCN) to scenarios with more than two users, several modifications and enhancements can be considered. First, the system model must be adapted to accommodate multiple users, which involves redefining the channel models to include additional user links and their respective channel coefficients. This can be achieved by employing a multi-user channel estimation technique to ensure accurate channel state information (CSI) for all users. Next, the optimization problems for both the Energy Splitting Non-Orthogonal Multiple Access (ES-NOMA) and Time Switching Time Division Multiple Access (TS-TDMA) strategies need to be reformulated to maximize the minimum throughput across all users. This involves extending the objective function to account for the throughput of each user, thereby ensuring fairness among them. The optimization variables, such as time allocation, transmit power, and beamforming vectors, will also need to be expanded to include all users. Moreover, advanced resource allocation techniques, such as user grouping and dynamic power allocation, can be implemented to enhance performance. For instance, in the ES-NOMA strategy, users can be grouped based on their channel conditions, allowing for more efficient power distribution and improved throughput. In the TS-TDMA strategy, time slots can be dynamically allocated based on user demand and channel conditions, further optimizing the overall system performance. Finally, the implementation of advanced algorithms, such as machine learning-based approaches, can be explored to adaptively manage the resource allocation and beamforming strategies in real-time, ensuring optimal performance in a multi-user environment.

Q: What are the potential challenges and trade-offs in implementing the ES-NOMA and TS-TDMA strategies in practical STAR-RIS assisted WPCNs?

Implementing the ES-NOMA and TS-TDMA strategies in practical STAR-RIS assisted WPCNs presents several challenges and trade-offs. Complexity of Beamforming Design: The STAR-RIS's capability to simultaneously transmit and reflect signals introduces additional complexity in beamforming design. For both ES-NOMA and TS-TDMA, the optimization of active and passive beamforming must consider the interactions between multiple users, which can lead to highly coupled optimization problems that are difficult to solve. User Fairness vs. Throughput Maximization: While both strategies aim to enhance user fairness, there is often a trade-off between maximizing the minimum throughput and achieving the highest overall system throughput. In ES-NOMA, prioritizing certain users may lead to unfairness for others, while in TS-TDMA, the allocation of time slots may not fully utilize the available resources if user demands are not balanced. Channel Estimation and Feedback: Accurate channel state information is crucial for both strategies. However, in practical scenarios, the overhead associated with channel estimation and feedback can be significant, especially in dynamic environments with rapidly changing channel conditions. This can lead to outdated CSI, negatively impacting the performance of both strategies. Hardware Limitations: The implementation of STAR-RIS technology requires advanced hardware capable of supporting the necessary phase shifts and amplitude adjustments. The cost and complexity of deploying such hardware can be a barrier to practical implementation, particularly in large-scale networks. Interference Management: In ES-NOMA, managing interference between users is critical, especially when users are transmitting simultaneously. Effective interference cancellation techniques, such as successive interference cancellation (SIC), must be implemented, which can add to the system's complexity.

Core Concepts

The core message of this paper is to enhance user fairness in wireless powered communication networks (WPCNs) by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Two STAR-RIS operating protocol-driven transmission strategies, namely energy splitting non-orthogonal multiple access (ES-NOMA) and time switching time division multiple access (TS-TDMA), are proposed to effectively eliminate the doubly-near-far effect in WPCNs.

Abstract

The paper proposes a STAR-RIS assisted WPCN, where a STAR-RIS is deployed to support both downlink wireless power transfer (WPT) and uplink wireless information transfer (WIT) between a multi-antenna hybrid access point (HAP) and two single-antenna users.

To enhance user fairness, the authors investigate the minimum throughput maximization (MTM) problem based on two STAR-RIS operating protocol-driven transmission strategies: ES-NOMA and TS-TDMA.

For the ES-NOMA strategy:

The HAP first transmits energy to the users in the form of broadcasting, and then the users utilize the harvested energy to simultaneously transmit information to the HAP.
A two-layer iterative algorithm is proposed to solve the resulting intractable problem. In the inner layer, the authors use the block coordinate descent (BCD) framework to iteratively optimize the highly-coupled variables. In the outer layer, the optimal time allocation is determined via one-dimensional search.

For the TS-TDMA strategy:

The HAP utilizes orthogonal time slots to minimize interference, whether it is transmitting energy to different users or receiving information from different users.
The authors first employ the maximum-ratio transmission (MRT) beamformer to determine the optimal active beamforming and passive beamforming. Then, the optimal time allocation is obtained by solving a standard convex problem.

The numerical results show that: 1) the STAR-RIS can achieve considerable performance improvements for both strategies compared to the conventional RIS; 2) TS-TDMA is preferred for single-antenna scenarios, whereas ES-NOMA is better suited for multi-antenna scenarios; and 3) the superiority of ES-NOMA over TS-TDMA is enhanced as the number of STAR-RIS elements increases.

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Stats

The number of connected Internet-of-Things (IoT) devices will surge at an average annual rate of 12% and reach 125 billion in 2030 from 27 billion in 2017.

Quotes

"The STAR-RIS can achieve considerable performance improvements for both strategies compared to the conventional RIS."
"TS-TDMA is preferred for single-antenna scenarios, whereas ES-NOMA is better suited for multi-antenna scenarios."
"The superiority of ES-NOMA over TS-TDMA is enhanced as the number of STAR-RIS elements increases."

Key Insights Distilled From

Enhancing User Fairness in Wireless Powered Communication Networks with STAR-RIS

by Guangyu Zhu,... at arxiv.org 10-03-2024

https://arxiv.org/pdf/2410.01330.pdf

Enhancing User Fairness in Wireless Powered Communication Networks with STAR-RIS

Deeper Inquiries

How can the proposed STAR-RIS assisted WPCN be extended to scenarios with more than two users?

To extend the proposed STAR-RIS assisted Wireless Powered Communication Network (WPCN) to scenarios with more than two users, several modifications and enhancements can be considered. First, the system model must be adapted to accommodate multiple users, which involves redefining the channel models to include additional user links and their respective channel coefficients. This can be achieved by employing a multi-user channel estimation technique to ensure accurate channel state information (CSI) for all users.
Next, the optimization problems for both the Energy Splitting Non-Orthogonal Multiple Access (ES-NOMA) and Time Switching Time Division Multiple Access (TS-TDMA) strategies need to be reformulated to maximize the minimum throughput across all users. This involves extending the objective function to account for the throughput of each user, thereby ensuring fairness among them. The optimization variables, such as time allocation, transmit power, and beamforming vectors, will also need to be expanded to include all users.
Moreover, advanced resource allocation techniques, such as user grouping and dynamic power allocation, can be implemented to enhance performance. For instance, in the ES-NOMA strategy, users can be grouped based on their channel conditions, allowing for more efficient power distribution and improved throughput. In the TS-TDMA strategy, time slots can be dynamically allocated based on user demand and channel conditions, further optimizing the overall system performance.
Finally, the implementation of advanced algorithms, such as machine learning-based approaches, can be explored to adaptively manage the resource allocation and beamforming strategies in real-time, ensuring optimal performance in a multi-user environment.

What are the potential challenges and trade-offs in implementing the ES-NOMA and TS-TDMA strategies in practical STAR-RIS assisted WPCNs?

Implementing the ES-NOMA and TS-TDMA strategies in practical STAR-RIS assisted WPCNs presents several challenges and trade-offs.

Complexity of Beamforming Design: The STAR-RIS's capability to simultaneously transmit and reflect signals introduces additional complexity in beamforming design. For both ES-NOMA and TS-TDMA, the optimization of active and passive beamforming must consider the interactions between multiple users, which can lead to highly coupled optimization problems that are difficult to solve.

User Fairness vs. Throughput Maximization: While both strategies aim to enhance user fairness, there is often a trade-off between maximizing the minimum throughput and achieving the highest overall system throughput. In ES-NOMA, prioritizing certain users may lead to unfairness for others, while in TS-TDMA, the allocation of time slots may not fully utilize the available resources if user demands are not balanced.

Channel Estimation and Feedback: Accurate channel state information is crucial for both strategies. However, in practical scenarios, the overhead associated with channel estimation and feedback can be significant, especially in dynamic environments with rapidly changing channel conditions. This can lead to outdated CSI, negatively impacting the performance of both strategies.

Hardware Limitations: The implementation of STAR-RIS technology requires advanced hardware capable of supporting the necessary phase shifts and amplitude adjustments. The cost and complexity of deploying such hardware can be a barrier to practical implementation, particularly in large-scale networks.

Interference Management: In ES-NOMA, managing interference between users is critical, especially when users are transmitting simultaneously. Effective interference cancellation techniques, such as successive interference cancellation (SIC), must be implemented, which can add to the system's complexity.

How can the performance of the STAR-RIS assisted WPCN be further improved by considering other advanced communication techniques, such as full-duplex, massive MIMO, or millimeter-wave communications?

The performance of the STAR-RIS assisted WPCN can be significantly enhanced by integrating advanced communication techniques such as full-duplex, massive MIMO, and millimeter-wave communications.

Full-Duplex Communication: Implementing full-duplex capabilities allows simultaneous transmission and reception of signals, which can effectively double the throughput of the WPCN. By utilizing full-duplex communication, the hybrid access point (HAP) can transmit energy to users while simultaneously receiving their information, thereby improving the overall efficiency of the network. This can be particularly beneficial in scenarios with high user density, where maximizing throughput is essential.

Massive MIMO: The integration of massive MIMO technology can enhance the spatial diversity and multiplexing capabilities of the WPCN. By equipping the HAP with a large number of antennas, the system can serve multiple users simultaneously with improved signal quality and reduced interference. Massive MIMO can also facilitate more effective beamforming, allowing for better targeting of users and improved energy transfer efficiency.

Millimeter-Wave Communications: Utilizing millimeter-wave frequencies can provide higher bandwidth and increased data rates, which are crucial for supporting the growing demands of IoT devices in WPCNs. The high-frequency bands can enable more efficient energy transfer and information transmission, particularly in scenarios where users are in close proximity to the HAP. However, the implementation of millimeter-wave communications requires careful consideration of propagation characteristics and potential obstacles, as these frequencies are more susceptible to attenuation and blockage.

Cooperative Communication: Incorporating cooperative communication techniques, where users can relay information to each other or to the HAP, can further enhance the performance of the WPCN. This can improve reliability and throughput, especially in scenarios where some users may have poor channel conditions.

Advanced Resource Allocation Algorithms: The use of advanced algorithms, such as reinforcement learning or genetic algorithms, can optimize resource allocation dynamically based on real-time network conditions. These algorithms can adaptively manage power allocation, time slots, and beamforming strategies, ensuring optimal performance in varying environments.

By leveraging these advanced communication techniques, the STAR-RIS assisted WPCN can achieve significant improvements in throughput, user fairness, and overall system efficiency, making it a more robust solution for future wireless communication networks.