Multi-Batch Reinforcement Learning for Base Station Traffic Prediction
Solution Overview
Problem
Reinforcement learning algorithms face challenges in real-world applications due to poor data efficiency and inflexibility in offline learning, particularly in scenarios with limited interaction samples, limiting their applicability in tasks like traffic load balancing in communication systems.
Innovation Solution
A method involving multi-batch reinforcement learning using imitation learning and knowledge distillation is employed to generate a generalized AI model for predicting future traffic loads by combining data from multiple base stations, leveraging data augmentation and policy distillation to improve performance across different tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning algorithms are deployed for real-world problems, then control policy performance can be achieved, but data efficiency is poor requiring large number of interactions
Solution Approach 1:
The patent combines multiple data batches from different sources (simulated environment data and real-world data) into a unified training dataset. This merging allows the algorithm to leverage both the abundance of simulated data and the quality of real-world data, reducing the total interaction quantity needed while maintaining policy reliability.
Solution Approach 2:
The patent performs preliminary training in a simulated environment before deploying to real-world applications. This preliminary action in simulation allows the algorithm to learn basic patterns without consuming real-world interactions, so that when deployed, it requires fewer real-world data points to achieve reliable performance.
2Ease of operation
If batch reinforcement learning is performed with limited data, then offline learning is enabled, but learning reliability deteriorates
Solution Approach 1:
The patent merges multiple batches of limited data from different sources into a combined dataset for training. By combining Batch 1 (simulated data) and Batch 2 (real-world data) with complementary information, the algorithm achieves reliable offline learning without requiring large amounts of data from a single source.
Solution Approach 2:
The patent applies different data augmentation strategies to different batches based on their specific characteristics. For example, simulated data may undergo transformations that preserve physical constraints, while real-world data may use augmentations that maintain statistical properties, thereby improving learning reliability for each batch's specific quality characteristics.
3Quantity of substance
If data augmentation is applied to limited datasets, then data quantity increases, but data quality may deteriorate
Solution Approach 1:
The patent applies different augmentation strategies to different batches based on their specific characteristics. For simulated data, augmentations preserve physical constraints and domain-specific properties. For real-world data, augmentations maintain statistical properties and distribution characteristics. This localized approach ensures quantity increase without quality deterioration.
Solution Approach 2:
The patent carefully controls augmentation parameters such as transformation intensity, probability of application, and type of transformation based on the specific batch characteristics. By adjusting these parameters locally for each batch, the system increases data quantity while maintaining quality through controlled, batch-specific augmentation strategies.
Data Source
AI summary
A server may receive a first traffic data and a second traffic data from a first base station and a second base station; obtain a first augmented traffic data for the first base station, based on the first traffic data and a subset data of the second traffic data; obtain a second augmented traffic data for the second base station, based on the second traffic data and a subset data of the first traffic data; obtain a first artificial intelligence (AI) model via imitation learning based on the first augmented traffic data; obtain a second AI model imitation learning based on the second augmented traffic data; obtain a generalized AI model via knowledge distillation from the first AI model and the second AI model; and predict a future traffic load of each of the first base station and the second base station based on the generalized AI model.