IRS Reflector Partitioning for Interference-Aware Link Configuration
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Solution Overview
Problem
IRS deployment can lead to additional interference at receivers, message decoding failures, and multiple hybrid automatic repeat request (HARQ) based retransmissions when utilized by different nodes simultaneously.
Innovation Solution
Adaptive IRS partitioning and configuration based on message decoding status and channel quality, with joint adaptation of receiver design, IRS partitioning, beamforming, and transmission parameters to reduce interference and improve network fairness and end-to-end data transmission delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional signal processing techniques are used at the access point, then implementation is straightforward, but the radio environment cannot be actively controlled and signal reflections cannot be turned on or off
Solution Approach 1:
An intelligent reflecting surface (IRS) is introduced as an intermediary device between the access point and user equipment. The IRS consists of multiple reflecting elements that can independently adjust their phase and amplitude to control signal reflections. This mediator enables active control of the radio environment without requiring complex signal processing at the access point, thus resolving the contradiction between adaptability and device complexity.
Solution Approach 2:
The reflecting elements of the IRS are designed with dynamic phase shifters and amplitude controllers that can adjust their parameters in real-time based on channel conditions. This dynamic capability allows the system to adaptively control signal reflections, transform static passive reflection into active controllable reflection, and resolve the contradiction between adaptability and device complexity by distributing the control functionality to the IRS rather than the access point.
2Measurement precision
If deep learning models with many parameters are trained offline, then model accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The deep learning model is segmented into two parts: a large-scale model trained offline on historical data to capture general channel characteristics, and a smaller online model that performs rapid adaptation to current channel conditions. This segmentation allows the system to benefit from the accuracy of large models while reducing the computational burden and training time required for real-time operation, thus resolving the contradiction between measurement precision and loss of time.
Solution Approach 2:
Channel statistics and characteristics are pre-computed and stored during an offline training phase using historical data. These pre-computed parameters are then used to initialize and guide the online channel estimation process. By performing preliminary actions offline, the system reduces the computational requirements and time needed for real-time channel estimation, resolving the contradiction between measurement precision and loss of time.
3Productivity
If channel statistics are pre-computed offline, then online estimation is accelerated, but historical data requirements increase
Solution Approach 1:
The system dynamically adjusts the parameters of the channel statistics model based on the available historical data and current channel conditions. By changing parameters such as coherence time, Doppler spread, and path loss exponents adaptively, the system can achieve accurate online estimation with less historical data, thus resolving the contradiction between productivity and quantity of substance.
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
Enhances network fairness and reduces average end-to-end data transmission delay by dynamically adapting IRS partitioning and configurations, ensuring improved performance for UEs with weak channel quality without compromising those with strong channel quality.
Implementation Method 1
an intelligent reflecting surface (IRS) that comprises a number of reflecting elements, each element controllable to reflect an incident wave with a controllable phase and/or amplitude
Data Source
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AI summary
A method (200) for configuring a set of reflectors (120), wherein the set of configurable reflectors are provided by a set of one or more intelligent reflecting surfaces, IRSs (105). The method includes determining (s202) a first set of one or more IRS configuration parameters, wherein the first set of IRS configuration parameters comprises at least one of: (a) a first link quality metric for a first link between a first CD and an access network node, wherein the first link comprises a first channel between the first CD and the set of one or more IRSs and a second channel between the set of one or more IRSs and the access network node; (b) a first quality-of-service, QoS, requirement for the first CD; or (c) a transmission status for the first CD. The method also includes, based on the first set of IRS configuration parameters, partitioning (s204) the set of configurable reflectors, wherein the partitioning comprises defining at least a first subset of the set of configurable reflectors and a second subset of the set of configurable reflectors. The method also includes configuring (s206) the first subset of the configurable reflectors and configuring the second subset of the configurable reflectors.