ML-Based CSI Measurement Relaxation for 5G UE Power Optimization
Solution Overview
Problem
In wireless communication systems, particularly in 5G networks, the current methods for configuring measurement gaps lead to unnecessary power consumption and inefficient use of radio resources, as devices perform excessive measurements, including those that are not necessary due to immobility or incorrect targeting, resulting in performance degradation and suboptimal machine learning outputs.
Innovation Solution
Implementing a machine learning model that allows user equipment (UE) to predict when to perform measurements and when not to, enabling the network to configure measurement gaps dynamically based on UE feedback, thereby reducing unnecessary measurements and optimizing data throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurement gaps are configured to ensure accurate CSI measurements, then measurement precision is improved, but power consumption increases and productivity decreases
Solution Approach 1:
The measurement gap configuration is made dynamic by allowing the network device to adjust the measurement gap pattern based on UE mobility state and channel conditions. The network device receives mobility information from the UE and configures appropriate measurement gaps only when necessary, rather than using fixed periodic measurement gaps. This dynamic adaptation resolves the contradiction by maintaining measurement precision when needed while reducing power consumption when the UE is stationary or mobility is low.
Solution Approach 2:
The system changes the measurement gap parameters (periodicity, duration, offset) based on UE feedback and network conditions. The network device can modify the measurement gap configuration parameters dynamically, adjusting the measurement density and timing to match actual channel variation characteristics. This parameter adaptation allows the system to maintain adequate measurement precision while minimizing unnecessary measurements that consume power.
2Reliability
If measurement gaps are configured frequently to track channel changes, then reliability is improved, but productivity decreases due to excessive switching between carriers
Solution Approach 1:
The measurement gap configuration is dynamically adjusted based on actual channel conditions and UE mobility state. The network device receives mobility information and channel state feedback from the UE, then configures measurement gaps adaptively - using more frequent gaps when channel conditions are rapidly changing and reducing gap frequency when conditions are stable. This dynamic approach maintains channel tracking reliability while minimizing interruptions to data transmission, thus preserving productivity.
Solution Approach 2:
Instead of configuring measurement gaps at maximum frequency to ensure reliability, the system uses partial action by configuring only the necessary number of measurement gaps based on actual needs. The network device determines the appropriate measurement gap density according to UE mobility state and channel conditions, avoiding excessive measurements that would reduce productivity. This partial action approach achieves adequate reliability without the penalty of excessive carrier switching.
3Loss of energy
If the network configures measurement relaxation to reduce power consumption, then energy efficiency is improved, but measurement precision may deteriorate
Solution Approach 1:
The measurement relaxation mechanism incorporates feedback loops where the UE reports mobility information, channel state, and measurement quality to the network device. The network device uses this feedback to determine whether measurement relaxation is appropriate and adjusts the relaxation level accordingly. This feedback mechanism ensures that measurement precision is maintained at acceptable levels while achieving power consumption reduction through selective relaxation of measurement requirements.
Solution Approach 2:
The system changes measurement parameters (such as measurement frequency, resources allocated, and reporting intervals) when relaxation is applied. The network device can modify these parameters dynamically based on UE state and network conditions, ensuring that measurement precision remains adequate even when relaxation is active. This parameter adaptation allows the system to achieve energy efficiency improvements without sacrificing essential measurement accuracy.
4Productivity
If machine learning models are used to predict measurement needs, then productivity is improved by reducing unnecessary measurements, but device complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary between the UE mobility state/channel conditions and the measurement gap configuration decisions. Rather than directly complex logic in the network device determining measurement needs, the ML model processes input features (mobility information, channel state, historical data) and outputs recommended measurement configurations. This intermediary approach improves productivity by enabling sophisticated prediction while managing device complexity by encapsulating the complexity within the ML model framework.
Solution Approach 2:
The UE provides self-service by collecting and reporting mobility information and channel state measurements to the network device. The network device uses this self-reported data as input to the ML model, reducing the need for complex sensing and monitoring infrastructure. The UE's self-service capability simplifies the overall system complexity while enabling the ML model to make accurate predictions about measurement needs, thereby improving productivity.
Data Source
AI summary
Various techniques are provided for a method including communicating, by a user equipment (UE) to a network device, a message including a measurement relaxation request, receiving, by the UE from the network device, a message including one of a measurement relaxation approval or a measurement relaxation denial, in response to receiving the measurement relaxation approval predicting, by the UE, a measurement relaxation configuration using a machine learning model, communicating, by the UE to the network device, a message including the measurement relaxation configuration, receiving, by the UE from the network device, a message including a measurement relaxation acknowledgement, and reporting, by the UE to the network device, measurements based on the measurement relaxation configuration.