Prediction-Based Radio Parameter Balancing for Wireless Systems
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Solution Overview
Problem
Current wireless communication systems fail to maintain a consistent minimum performance level for mobile applications across an area, as local radio conditions can vary significantly due to terrain, interference, and shared network effects, leading to service degradation without adequate countermeasures.
Innovation Solution
A prediction-based method and system where User Equipment (UE) sends location information and required radio parameters to the Radio Access Network (RAN), which associates this data with signal coverage predictions to proactively adjust transmission settings, such as communication technology and spectrum layers, to ensure minimum performance requirements are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the network provides general area coverage marking as 4G or 5G covered, then overall connectivity is available, but local performance varies significantly due to terrain, shadowing effects, and interference
Solution Approach 1:
The system performs preliminary actions by predicting future radio conditions based on current location and historical data before actual degradation occurs. The network proactively adjusts transmission parameters in advance to prevent performance drops, rather than reacting after problems manifest.
Solution Approach 2:
The system dynamically adapts transmission parameters based on real-time location updates and predicted radio conditions. The network continuously monitors UE movement and adjusts modulation schemes, coding rates, and resource allocation to maintain consistent performance despite changing environmental factors.
2Reliability
If the UE operates in a default communication mode, then device complexity is reduced, but the network cannot guarantee minimum required performance when moving through areas with varying radio conditions
Solution Approach 1:
The network performs self-service by autonomously determining and adjusting transmission parameters based on UE location and predicted conditions. The system automatically selects appropriate communication modes and configurations without requiring complex UE-side decision-making, centralizing intelligence in the network infrastructure.
Solution Approach 2:
The system implements feedback mechanisms where the network continuously monitors UE performance and location, compares actual conditions against predicted conditions, and adjusts transmission parameters accordingly. This closed-loop control ensures minimum performance requirements are maintained while adapting to changing radio conditions.
3Ease of operation
If countermeasures are taken only after service termination or degradation occurs, then system complexity is minimized, but user experience suffers with disappointing results
Solution Approach 1:
The system takes preliminary action by predicting radio condition degradation before it affects service quality. The network proactively prepares and applies compensatory transmission adjustments in advance, preventing performance drops rather than reacting after problems occur.
Solution Approach 2:
The system applies preliminary anti-action by implementing countermeasures that oppose potential degradation before it manifests. The network predicts adverse conditions and pre-applies corrective transmission parameter adjustments to neutralize their impact before they can degrade service quality.
4Reliability
If the application performs performance balancing via buffering and codec switching, then local performance control is improved, but the process starts only after connectivity degradation has already occurred
Solution Approach 1:
The network performs preliminary action by predicting future radio conditions and proactively adjusting transmission parameters before degradation occurs. This prevents the need for reactive application-side buffering and codec switching, eliminating the time delay associated with post-degradation responses.
Solution Approach 2:
The network acts as an intermediary between the UE and the radio environment, absorbing the complexity of performance balancing. Instead of the application directly responding to degradation, the network mediates by predicting conditions and adjusting transmission parameters to maintain performance, eliminating the need for application-side reactive measures.
Data Source
Figure 1A~1C
Figure 2
Figure 3(a)~3(c)
AI summary
A method for prediction-based radio parameters balancing in a wireless communication in a system, the system comprising a User Equipment, UE, and a radio access network, RAN, the UE comprising an application used or to be used by a user of the UE, the method comprising the steps of: sending, by the UE to the RAN, location information of the UE; sending, by the UE to the RAN, a request for a set of radio parameters required by the application; associating, by the RAN, the location information and the requested set of radio parameters with signal coverage prediction information of the area where the UE is located; providing, by the RAN, based on the above combined information, a transmission between the UE and the RAN that satisfies the required set of radio parameters by the application over the area where the UE is located; monitoring and updating, by the RAN, the location of the UE; determining, by the RAN, based on the above combined information including the updated location of the UE, whether a change of the transmission is necessary for satisfying the set of radio parameters required by the application; and when the RAN determines that a change of the transmission is necessary: changing the transmission to satisfy the required set of radio parameters by the application over the area where the UE is located.