Uplink Band Selection Using Adaptive RSS Thresholds
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Solution Overview
Problem
The selection of communication bands in wireless networks, particularly for terminal devices, affects overall system performance due to signal degradation at higher frequencies and limited transmission power, leading to imbalanced load distribution between main and supplementary frequency bands.
Innovation Solution
A method for dynamically adjusting the reception signal strength (RSS) threshold level using measured RSS values and key performance indicators (KPIs) to balance terminal device allocation between higher and lower frequency bands, employing neural networks for predictive threshold adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If terminal devices use higher frequency bands for communication, then throughput is improved, but signal propagation characteristics degrade and transmission power is insufficient
Solution Approach 1:
The communication system is segmented into multiple frequency bands (main communication bands on higher frequencies and supplementary communication bands on lower frequencies), allowing terminal devices to selectively use different bands based on their specific needs and conditions, thus balancing throughput and reliability
Solution Approach 2:
The system dynamically adjusts the reception signal strength threshold levels for different terminal devices based on measured performance metrics and neural network predictions, enabling adaptive selection between main and supplementary frequency bands to optimize both throughput and signal reliability in real-time
2Device complexity
If a fixed RSS threshold is used for frequency band selection, then device complexity is reduced, but system performance is suboptimal due to inability to adapt to changing conditions
Solution Approach 1:
The system implements feedback mechanisms where terminal devices report measured performance metrics (such as RSRP, RSSI, RSRQ) to the network, and the network adjusts RSS threshold levels based on these measurements and neural network predictions, creating a closed-loop system that continuously optimizes performance
Solution Approach 2:
Neural networks are deployed to automatically predict optimal RSS threshold levels based on historical data and current conditions, eliminating the need for manual configuration and enabling the system to self-optimize performance without increasing operational complexity
Data Source
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AI summary
There is provided a solution for determining a threshold for frequency channel selection. According to an aspect, a method comprises: selecting a received signal strength threshold level for determining whether an uplink resource of a terminal device is allocated from a first frequency band or from a second frequency band, wherein the first frequency band is on higher frequencies than the second frequency band, and wherein the first frequency band is for terminal devices having a received signal strength above the received signal strength threshold level and the second frequency band is for terminal devices having the received signal strength below the received signal strength threshold level; outputting the received signal strength threshold level to be broadcasted in a cell; acquiring received signal strength values measured for a plurality of terminal devices in the cell while the received signal strength threshold level has been applied to the cell and further acquiring and at least one performance metric target; and changing the received signal strength threshold level on the basis of the measured received signal strength values and the at least one performance metric target.