Spatially Correlated Rician Fading Channels in IRS-Enhanced MIMO Networks

In recent years, Intelligent Reflecting Surfaces (IRSs) have emerged as a promising technology for enhancing the performance of wireless communication systems. IRSs are essentially metamaterials that can be programmed to manipulate the propagation of electromagnetic waves. By strategically deploying IRSs in the environment, it is possible to improve the coverage, capacity, and energy efficiency of wireless networks.

In scenarios with spatially correlated Rician fading channels, IRSs can be used to exploit the multipath propagation to improve the signal-to-noise ratio (SNR) and spectral efficiency. Spatial correlation arises when the received signals at different antennas are not independent due to their proximity. This dependence can be beneficial in IRS-enhanced MIMO networks, as it allows the IRS to effectively steer the reflected signals to specific users.

Benefits of using IRSs in spatially correlated Rician fading channels:

• Improved SNR: By reflecting the signals from multiple paths, IRSs can increase the SNR at the receiver, leading to improved data rates and reduced error rates.
• Enhanced spectral efficiency: IRSs can be used to create constructive interference at the receiver, which can effectively increase the bandwidth available for communication.
• Reduced power consumption: By reflecting the signals efficiently, IRSs can help to reduce the transmit power required at the base station, leading to lower energy consumption.
• Reduced multipath interference: IRSs can be used to redirect or cancel out unwanted multipath components, which can improve the overall performance of the communication system.

Challenges of using IRSs in spatially correlated Rician fading channels:

• Channel estimation: Accurately estimating the channel state information (CSI) is crucial for designing effective IRSs-based beamforming strategies. However, due to the complex propagation environment, estimating the CSI in spatially correlated Rician fading channels can be challenging.
• Hardware complexity: Implementing large-scale IRSs with a high number of reflecting elements requires complex hardware and careful calibration.
• Limited controllability: The effectiveness of IRSs depends on their ability to control the reflected signals. However, this controllability is limited by factors such as the size and shape of the IRS, as well as the operating frequency.

Research directions:

• Developing efficient algorithms for channel estimation in spatially correlated Rician fading channels.
• Designing IRSs-based beamforming strategies that exploit the spatial correlation for improved performance.
• Investigating new materials and designs for IRSs to improve their controllability and efficiency.
• Analyzing the performance of IRSs in different network scenarios and fading conditions.

Overall, IRSs have the potential to significantly improve the performance of wireless networks with spatially correlated Rician fading channels. However, several challenges need to be addressed before IRSs can be widely adopted in real-world deployments.

Hi!

1. I wouldn't recommend using the exponential correlation model because it is not a physically motivated model, so it is unable to model the spatial correlation at a UPA. You can instead consider the model that we describe in the following paper: https://arxiv.org/pdf/2108.04633.pdf

It describes the spatial correlation between the antennas when there are scattering clusters in specific angular directions.

2. All physically motivated models are based on the steering vectors (called array response vectors in my paper). The starting point is Eq. (2) which is the integral over steering vectors multiplied by factors that determine how much signal energy comes from different directions.

A Rician fading channel is given by "array steering vector * a random phase shift + correlated Rayleigh fading channel". You can use the model described above to create the spatial correlated Rayleigh fading part. One option is to place a scattering cluster around the LOS angle.