ML-Predicted Wireless Broadcast for Bandwidth Reduction
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Solution Overview
Problem
Existing wireless broadcast technologies waste bandwidth when broadcasting content to a single user or users who request content at slightly different times, due to synchronization issues and signaling overhead, which limits the efficiency of wireless bandwidth usage.
Innovation Solution
A system that uses machine learning to predict user behavior and proactively downloads content to multiple users connected to the same cell site, enabling simultaneous broadcast of content to those likely to request it, thereby reducing wireless bandwidth requirements and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If wireless broadcast is used to deliver content to multiple users, then wireless bandwidth consumption is reduced, but bandwidth is wasted when broadcasting to a single user or users with different request times
Solution Approach 1:
The system performs preliminary actions by using machine learning to predict which users are likely to request content before they actually do. Content is proactively delivered to predicted users in advance, transforming reactive unicast into proactive targeted broadcast. This resolves the contradiction by ensuring broadcast only occurs when multiple users are predicted to need the content, eliminating wasted bandwidth on single-user broadcasts.
Solution Approach 2:
The system implements feedback loops where actual user requests are continuously monitored and fed back into the machine learning model. This feedback refines predictions over time, improving the accuracy of identifying users who would benefit from broadcast delivery. The feedback mechanism ensures broadcast efficiency improves as the system learns user behavior patterns, resolving the reliability concern.
2Productivity
If content is broadcast to users who request at slightly different times, then more users can receive content simultaneously, but synchronization issues prevent successful broadcast
Solution Approach 1:
The machine learning model performs preliminary analysis of user request patterns to identify groups of users who are likely to request content within a predictable time window. By predicting requests before they occur, the system can synchronize content delivery to these users without dealing with the complexity of real-time synchronization of actually occurring requests at different times.
Solution Approach 2:
The system dynamically adjusts broadcast timing and target user groups based on predicted request patterns rather than fixed synchronization protocols. This dynamic approach allows the system to adapt to varying user behavior while maintaining synchronization, avoiding the complexity of rigid time-synchronization requirements.
3Ease of operation
If unicast is used to deliver content to each user individually, then content delivery is simple and direct, but wireless bandwidth consumption increases significantly
Solution Approach 1:
The system merges multiple unicast delivery operations into a single broadcast operation by identifying users with similar content requests using machine learning. Instead of delivering content separately to each user (multiple unicast), the system combines deliveries into targeted broadcast groups, maintaining the simplicity of automated delivery while dramatically reducing bandwidth consumption.
Solution Approach 2:
The system creates virtual copies of user request patterns through machine learning models, allowing it to simulate and predict future requests. These predicted request copies enable the system to prepare and deliver content via broadcast before actual requests occur, avoiding the need for multiple separate unicast deliveries while maintaining delivery simplicity through automated prediction-based grouping.
Data Source
AI summary
Systems and methods for improved efficiency in content streaming and downloading. The system enables a first list including a plurality of user equipment (UE) in communication with a wireless base station (WBS) to be identified based on the likelihood that each UE will download a piece of content. The process starts in response to a UE requesting the content from the WBS or some other triggering event. The system can use the content and subscriber history from the plurality of UEs along with machine learning, or some other algorithm, to identify likely candidates. The system can calculate a similarity score, or similar, comparing the requested content to the content history for each of the plurality of UEs. The system can add each UE with a sufficiently high similarity score to a second list. The content can be broadcast to all UEs on the second list in a single broadcast transmission.