Single-Carrier RAN Link Tuning for Power-Sensitive UE Coverage
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Solution Overview
Problem
Existing radio access networks (RANs) fail to provide improved coverage, robustness, and capacity for power-sensitive or low data rate user equipment (UEs) due to the inability to implement multiple carrier aggregation and dual connectivity, leading to inefficient resource consumption.
Innovation Solution
A network device employs a machine learning model to determine the quantity of repetitions, modulation coding scheme (MCS) reductions, and block error rate (BLER) adjustments for a robust single carrier RAN link, utilizing a trained neural network model to enhance uplink coverage and robustness for power-sensitive or low data rate UEs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple carrier aggregation and dual connectivity are implemented, then RAN coverage and link robustness are improved, but device complexity and power consumption increase for power-sensitive UEs
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting repetition quantities, MCS values, and BLER thresholds based on channel conditions and UE capabilities. The network device processes multiple carrier information through a machine learning model to determine optimal parameter configurations for single carrier operation, enabling robust communication without requiring complex multi-carrier aggregation hardware.
2Productivity
If multiple carrier aggregation is implemented, then RAN capacity and data rate are improved, but resource consumption increases for power-sensitive UEs
Solution Approach 1:
The patent extracts and processes only the necessary multiple carrier information through a machine learning model to determine optimal single carrier configurations. Instead of fully implementing multi-carrier aggregation, the system extracts key parameters from multiple carrier scenarios and applies them to single carrier operation, achieving capacity improvements while avoiding the full resource overhead of multi-carrier implementation.
3Use of energy by moving object
If single carrier bandwidth is used for power-sensitive UEs, then power consumption is reduced, but RAN coverage and link robustness deteriorate
Solution Approach 1:
The patent applies preliminary action by having the network device determine the quantity of repetitions, MCS reductions, and BLER adjustments before actual data transmission. The machine learning model processes multiple carrier information in advance to pre-calculate optimal single carrier parameters, ensuring that coverage and robustness are maintained from the outset of communication without requiring complex real-time adjustments during transmission.
4Device complexity
If single carrier operation is used, then device complexity is reduced, but resource efficiency and network performance deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the network device monitors channel conditions, UE performance, and transmission outcomes to continuously refine the single carrier parameters. The machine learning model processes feedback information to adjust repetition quantities, MCS values, and BLER thresholds dynamically, optimizing resource efficiency while maintaining simple single carrier operation.
Data Source
AI summary
A device described herein may detect entry into a coverage area of a first frequency band provided by a network device and transmit by the device, capability information to the network device, the capability information indicating that the device is a power sensitive device or a narrow band device. The device then receiving an indication of a quantity of repetitions to extend uplink coverage for the device, based on the capability information; and enabling by the device, the quantity of repetitions for uplink transmissions to extend uplink coverage.