Intelligent Reflecting Surface Phase Control for Terahertz Coverage
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Solution Overview
Problem
Existing wireless communication systems face challenges in securing signal transmission distance and improving network capacity, particularly in the terahertz band, due to severe path loss and atmospheric absorption, which are crucial for achieving high data rates and low latency in 6G communication systems.
Innovation Solution
The implementation of an intelligent reflecting surface (IRS) with low-power reflecting elements to enhance signal quality by adding a new path of high path gain, combined with deep learning-based phase shift control (D-PSC) for optimizing data rates, and methods for initial access and angle/path gain estimation between network entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning-based phase shift control is implemented to maximize data rate, then data rate increases significantly, but system complexity increases due to AI model integration
Solution Approach 1:
The patent introduces an intelligent reflecting surface (IRS) as an intermediary component between the base station and user equipment. The IRS includes a deep learning processor that independently performs phase shift optimization using trained neural network models, acting as a mediator that handles the complex AI computations without requiring direct integration into the main communication system architecture. This allows the system to achieve high data rates while isolating the complexity to a dedicated intermediate component.
Solution Approach 2:
The system is segmented into distinct functional modules: the base station, user equipment, and intelligent reflecting surface with its own deep learning processor. The AI-based phase shift control functionality is separated into a dedicated IRS unit rather than being integrated into the base station or UE. This segmentation allows the complex deep learning operations to be performed in a specialized subsystem, improving overall system productivity while containing complexity in a modular fashion.
2Quantity of substance
If IRS phase shifts are optimized using deep learning to improve spectral efficiency, then spectral efficiency increases, but computational resources and energy consumption increase
Solution Approach 1:
The deep learning neural network models are trained offline in advance to learn the optimal phase shift mappings for various channel conditions. During actual communication operation, the pre-trained models are deployed at the IRS to perform rapid phase shift adjustments based on current channel state information. This preliminary training action separates the computationally intensive learning phase from the operational phase, allowing high spectral efficiency during communication while minimizing real-time energy consumption at the IRS.
3Ease of manufacture
If conventional communication methods are used in terahertz bands, then implementation is simpler, but signal transmission distance and coverage are severely limited due to path loss and atmospheric absorption
Solution Approach 1:
The intelligent reflecting surface acts as an intermediary to create alternative signal propagation paths between the base station and user equipment. The IRS receives terahertz signals from the base station, applies deep learning-optimized phase shifts to compensate for path loss and atmospheric absorption effects, and reflects the enhanced signals to reach distant or previously unreachable locations. This intermediary approach extends transmission distance without requiring changes to the fundamental terahertz communication implementation.
Solution Approach 2:
The system dynamically changes the phase shift parameters of the IRS reflecting elements based on deep learning model predictions and current channel conditions. By adjusting these phase parameters in real-time, the system compensates for the severe path loss and atmospheric absorption inherent in terahertz bands, effectively extending the signal transmission distance while maintaining implementation simplicity at the physical layer.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The IRS enhances signal quality in dead zones and improves network capacity by mitigating interference and increasing received signal strength, while enabling efficient data communication and access procedures.
Implementation Method 1
deep learning-based phase shift control (D-PSC) architecture for intelligent reflecting surfaces (IRS) to maximize data rate by optimizing IRS phase shifts
Implementation Method 2
optimizing IRS phase shifts and beamforming, leveraging the IRS's ability to modify radio channels
Data Source
AI summary
The disclosure relates to a 5G or a 6th generation (6G) communication system for supporting a higher data transmission rate after a 4th generation (4G) communication system such as LTE. An operating method of a BS in a wireless communication system includes broadcasting a power signal of an IRS, broadcasting an SSB, and requesting receive beam reporting from a control unit of the IRS.