Optimizing Elements Allocation for Joint Active and Passive Intelligent Reflecting Surface Aided Wireless Communication

Core Concepts

The core message of this article is to determine the optimal allocation of elements between an active intelligent reflecting surface (AIRS) and a passive intelligent reflecting surface (PIRS) in a joint AIRS-PIRS aided wireless communication system to maximize the achievable rate at the receiver.

Abstract

The article considers a wireless communication system where a single-antenna transmitter communicates with a single-antenna receiver, aided by a pair of AIRS and PIRS. The authors aim to determine the optimal number of elements for the AIRS and PIRS, as well as the reflection amplitude factors, to maximize the achievable rate at the receiver. The authors analyze two transmission schemes: Tx → AIRS → PIRS → Rx (TAPR) and Tx → PIRS → AIRS → Rx (TPAR). For the TAPR scheme, the authors formulate an optimization problem to jointly optimize the number of AIRS and PIRS elements and the reflection amplitude factor of the AIRS. For the TPAR scheme, a similar optimization problem is formulated. The authors provide suboptimal solutions in closed-form for both schemes, which reveal that the PIRS should be allocated more elements than the AIRS in both schemes to achieve the optimized rate. Additionally, the authors show that the TAPR scheme outperforms the TPAR scheme in terms of achievable rate when the distance between the second IRS and the receiver is sufficiently small or the AIRS amplification power is adequately large. The authors also analyze the signal-to-noise ratio (SNR) scaling orders for both schemes, demonstrating that the considered double-IRS aided systems can achieve linear SNR scaling orders, which is higher than the single-active-IRS case. Simulation results are provided to evaluate the proposed algorithms and compare the rate performance of the AIRS and PIRS jointly aided wireless system with various benchmark systems, validating the analysis.

Stats

Pt = 20dBm Pv = 10dBm σ^2_0 = σ^2_v = -80dBm Wact = 1.2 Wpas = 1

Quotes

None

Key Insights Distilled From

Joint Active And Passive IRS Aided Wireless Communication

by Chaoying Hua... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06880.pdf

Deeper Inquiries

How can the proposed elements allocation strategies be extended to scenarios with more than two cascaded IRSs

The proposed elements allocation strategies for joint AIRS-PIRS systems can be extended to scenarios with more than two cascaded IRSs by following a similar optimization framework. In cases where there are multiple cascaded IRSs, the elements allocation problem becomes more complex due to the increased number of reflecting surfaces and paths. However, the fundamental principle of optimizing the number of elements for each IRS based on the total deployment budget and amplification noise considerations remains the same. To extend the strategies to scenarios with more than two cascaded IRSs, the optimization problem would involve determining the optimal allocation of elements for each IRS in the cascade, considering the signal amplification and noise factors at each stage. By iteratively optimizing the elements allocation for each IRS in the cascade, it is possible to maximize the achievable rate while ensuring efficient use of resources and minimizing interference.

What are the potential practical challenges and limitations in implementing the joint AIRS-PIRS system in real-world deployments

Implementing a joint AIRS-PIRS system in real-world deployments may face several practical challenges and limitations. Some of these challenges include: Hardware Complexity: Integrating both active and passive IRS elements in a joint system can increase hardware complexity, requiring sophisticated design and implementation. Power Consumption: Active IRS elements consume power for signal amplification, which can lead to increased power consumption in the system. Managing power consumption while maintaining performance is crucial. Channel Estimation: Accurate channel estimation is essential for optimizing the elements allocation in the joint system. Practical challenges in channel estimation, such as channel coherence time and pilot contamination, can impact system performance. Deployment and Calibration: Deploying and calibrating a large number of IRS elements in real-world environments can be challenging. Ensuring proper alignment, synchronization, and calibration of the elements is crucial for system effectiveness. Interference and Coexistence: Coexistence with other wireless systems and potential interference issues need to be addressed in joint AIRS-PIRS deployments to ensure seamless operation and performance. Addressing these challenges requires a comprehensive understanding of the system requirements, careful system design, efficient resource management, and robust implementation strategies.

How can the joint AIRS-PIRS system be integrated with other emerging wireless technologies, such as massive MIMO or millimeter-wave communications, to further enhance the system performance

Integrating the joint AIRS-PIRS system with other emerging wireless technologies, such as massive MIMO or millimeter-wave communications, can further enhance system performance by leveraging the unique capabilities of each technology. Here are some ways to integrate the joint AIRS-PIRS system with other technologies: Massive MIMO: Combining the joint AIRS-PIRS system with massive MIMO technology can enhance spatial multiplexing gains and improve spectral efficiency. By coordinating beamforming and precoding techniques between the systems, it is possible to achieve higher data rates and better coverage. Millimeter-Wave Communications: Integrating the joint AIRS-PIRS system with millimeter-wave communications can leverage the high bandwidth and directional characteristics of millimeter-wave frequencies. By optimizing beamforming and reflection patterns at both the IRS and the millimeter-wave transceivers, it is possible to achieve high data rates and low latency communication. Network Slicing: Implementing network slicing techniques can enable the joint AIRS-PIRS system to cater to diverse use cases and applications with varying requirements. By dynamically allocating resources and optimizing the system configuration based on specific slice requirements, the system can provide customized services efficiently. AI and Machine Learning: Leveraging AI and machine learning algorithms can enhance the optimization of the joint AIRS-PIRS system by adapting to changing environmental conditions, user dynamics, and traffic patterns. AI can be used for intelligent resource allocation, interference management, and predictive maintenance, improving overall system performance. By integrating the joint AIRS-PIRS system with these emerging technologies, it is possible to create a robust and efficient wireless communication system that meets the demands of future networks.