Wireless Channel Control Using Predicted Quality in Industrial RAN
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Solution Overview
Problem
Industrial wireless communication systems face challenges in maintaining Ultra Reliable Low Latency Communication (URLLC) due to varying radio coverage in flexible production areas, leading to packet losses that can trigger safety modes in receivers, and existing RICs do not adequately address these issues.
Innovation Solution
A method utilizing a trained data-driven model, such as LSTM-based recurrent neural networks, to predict future channel quality data, enabling proactive control of communication channels through link adaptation, redundancy, and antenna reconfiguration based on channel quality and location data, ensuring survival time compliance and application availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wireless communication is used in flexible production areas, then adaptability and mobility are improved, but communication reliability deteriorates due to varying radio coverage
Solution Approach 1:
The system performs preliminary actions by predicting future channel quality data before actual communication failures occur. The RIC uses machine learning models to forecast channel conditions and proactively adjusts communication parameters, reconfigures antennas, or activates redundancy mechanisms in advance, preventing packet losses that would otherwise trigger safety modes.
2Reliability
If packet loss mitigation measures are implemented, then communication reliability is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the RIC continuously monitors channel quality data, packet loss patterns, and communication performance. This feedback loop enables dynamic adjustment of communication parameters, intelligent activation of redundancy only when predicted necessary, and adaptive antenna reconfiguration, achieving high reliability without permanently maintaining complex mitigation infrastructure.
3Reliability
If proactive channel control is implemented using prediction models, then communication reliability is improved, but computational requirements and processing time increase
Solution Approach 1:
The system employs periodic action by updating channel quality predictions at regular intervals and using recurrent neural network models that efficiently process sequential data. The RIC periodically re-evaluates channel conditions and adjusts communication parameters in rhythmic cycles, balancing computational load with the need for timely responses to changing radio conditions.
Data Source
Figure 1~3
AI summary
The invention relates to a method for computer-implemented optimized controlling of a communication channel (2) of a wireless communication system (1) in an industrial environment, the wireless communication system (1) comprising a sending communication device (10), a receiving communication device (20) and a wireless communication network (30) having at least one antenna (311, 312), a radio access network (320) configured to be controlled by a RAN intelligent controller, RIC, and a core network (330). The communication channel (2) is established between the sending communication device (10) and the receiving communication device (20) via the wireless communication network (30). The following steps are performed: a) obtaining channel quality data (CQD) of the communication channel (2), the channel quality data (CQD) being current data obtained from the radio access network (320); b) determining future channel quality data (FCQD) of the communication channel (2) by processing the obtained channel quality data (CQD) by a trained data driven model (MO), where the obtained channel quality data (CQD) are fed as digital input to the trained data driven model (MO) and the trained data driven model (MO) provides the future channel quality data (FCQD) as a digital output; c) processing the future channel quality data (FCQD) in order to determine radio resource allocation parameters (RSAP); and d) reconfigure the at least one antenna (311, 312) using the determined radio resource allocation parameters (RSAP).