Broadcast Radio Tuning Prediction Using Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Broadcast radio receivers experience significant delays when switching between stations, especially in digital standards like DAB and DRM, due to complex processing and limited tuner resources, making it inconvenient for users, especially under weak-signal conditions.
Innovation Solution
A broadcast radio receiving method that utilizes an additional tuner to predict and pre-tune to the next selected service based on user behavior patterns, employing reinforcement learning to adjust predictions and improve prediction accuracy, even in cases where no further tuner is available, by considering previous sequential selections, location, and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multiple tuners are used to prepare for station selection, then tuning time is reduced, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by having the second tuner pre-tune to predicted next stations before the user actually selects them. The system predicts the next station based on current station and user behavior patterns, then the second tuner automatically tunes to this predicted station in advance. When the user selects the predicted station, the audio stream is already ready, eliminating tuning delays without requiring multiple physical tuners for every possible station.
2Reliability
If prediction accuracy is improved by considering more factors, then prediction reliability increases, but computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing prediction on the immediate next station rather than attempting to predict the entire station sequence. Instead of analyzing all possible future stations, the system only predicts the single most likely next station based on local patterns from recent station selections. This localized prediction approach reduces computational complexity while maintaining high reliability for the immediate next station prediction.
3Reliability
If reinforcement learning is used to update predictions, then prediction accuracy improves over time, but processing time increases
Solution Approach 1:
The patent applies partial action by implementing reinforcement learning updates only for the prediction model rather than for all system operations. The RL mechanism selectively updates prediction accuracy based on whether predicted stations match actual user selections, without requiring continuous processing. This partial application of RL maintains improving prediction accuracy while minimizing processing time overhead to only the essential prediction updates.
Data Source
AI summary
The present invention relates to a broadcast radio receiving method addressing the desire for allowing a rapid switching between different services as selected by a user. In order to provide an approach for reducing or even avoiding delays involved with service switching tailored to the context of radio broadcast reception, in particular in view of limited tuner resources, and/or in order to provide an approach for addressing a selection of a new service in case of, for example, signal loss, the prediction focuses on the user's sequential selection behavior.