Collaborative Cellular Traffic Scheduling for Peak Load Reduction
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Solution Overview
Problem
The rapid proliferation of mobile data traffic exceeds the capacity of cellular networks, leading to peak traffic congestion, with existing solutions either degrading user experience or incurring significant costs through additional infrastructure deployment.
Innovation Solution
Implementing a collaborative scheduling mechanism that allows mobile devices and cellular infrastructure to exchange traffic information, using price data to incentivize delaying or shifting data traffic during peak periods, thereby reducing peak loads without affecting user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional infrastructure is deployed to increase network capacity, then network capacity increases, but deployment costs increase significantly
Solution Approach 1:
The patent changes the temporal parameter of traffic flow by introducing dynamic scheduling that shifts traffic from peak periods to off-peak periods. This temporal redistribution increases effective network capacity without requiring additional physical infrastructure, thereby avoiding high deployment costs while maintaining service quality.
Solution Approach 2:
The patent implements dynamic traffic scheduling that adapts to real-time network conditions. By dynamically adjusting when traffic is transmitted based on network load, the system optimizes capacity utilization without needing to deploy additional static infrastructure, thus improving capacity while controlling costs.
2Productivity
If traffic is delayed to reduce peak loads, then peak traffic is reduced, but user experience may be degraded
Solution Approach 1:
The patent performs preliminary actions by buffering traffic during peak periods and transmitting it during off-peak periods. This advance scheduling allows the system to reduce peak loads while maintaining user experience, as the delayed transmission is planned and executed within acceptable timeframes for various applications.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor network conditions and application requirements to dynamically adjust scheduling decisions. This feedback ensures that traffic delay decisions are made based on real-time information about what can be delayed without impacting user experience, thereby resolving the contradiction between peak reduction and service quality.
Data Source
AI summary
An eNodeB or other cell access point device can receive demand data from mobile devices served by the cell. The demand data can represent an estimate of demand over a future period for network resources (e.g., bandwidth). The cell can aggregate this demand data and determine aggregation data for the time period or for various intervals of the time period, then transmit the aggregation data to the mobile devices. The aggregation data can operate as a collaborative approach to scheduling traffic. For example, data (e.g., delay tolerant data) can be shifted (e.g., delayed for a few seconds) based on an examination of the aggregation data in conjunction a determined priority of the data. Such can be applicable to data traffic not traditionally thought of as delay tolerant such as streaming video or web browsing, and can be accomplished without negatively impacting the quality of service or experience of the client.