Mobile Station Random Delay for Mass Event Traffic Control
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Solution Overview
Problem
Mass communication events, such as voting, pose significant challenges for mobile wireless communication networks due to high volumes of calls or messages being transmitted in a short time, leading to peak loading issues that are costly and impractical to handle, and existing network-centric solutions often result in user dissatisfaction by delaying or blocking calls.
Innovation Solution
Implementing a random delay mechanism in mobile stations to spread out transmission attempts over a longer time window, reducing peak load on the network without adding overhead traffic, by using algorithms like Gaussian distribution to calculate delays based on preconfigured parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the network is engineered to support peak loading during mass communication events, then the network can handle high volumes of traffic, but the cost and complexity become prohibitive
Solution Approach 1:
The system performs preliminary actions by pre-configuring delay parameters and algorithms in mobile stations before mass communication events occur. Each mobile station is pre-programmed with the ability to calculate and apply random delays, so when the event occurs, the mitigation is already in place without requiring real-time network intervention or infrastructure changes.
Solution Approach 2:
Mobile stations autonomously manage their own transmission timing by calculating random delays using pre-configured parameters and algorithms. Each device independently determines its transmission schedule without requiring network control or coordination, thereby self-mitigating the peak load problem at the source rather than requiring network-side resource allocation.
2Productivity
If network-centric control is used to manage call flows during mass events, then traffic can be controlled, but user experience deteriorates due to apparent delays and call blocking
Solution Approach 1:
Instead of having the network control and delay user transmissions (traditional approach), the invention inverts the control to the mobile station side. Users' devices autonomously delay their own transmissions using random algorithms, achieving traffic control without network-imposed waiting periods or blocking. This reverses who controls the timing: the user's device rather than the network.
Solution Approach 2:
The system applies preliminary delays at the mobile station before transmissions are attempted, rather than imposing delays after network rejection. Users experience no apparent delay because the random delay is applied proactively by their own device, and transmissions occur naturally without network rejection messages or waiting periods.
3Productivity
If transmissions occur simultaneously during mass events, then user participation is maximized, but network resources are overwhelmed
Solution Approach 1:
The invention segments the simultaneous transmission mass into individual staggered transmissions. Each mobile station calculates a unique random delay value, effectively dividing the concentrated traffic into dispersed time slots. This segments the traffic load across time rather than having all users transmit simultaneously, reducing peak resource consumption while maintaining overall participation.
Solution Approach 2:
The system introduces dynamic timing behavior where each mobile station independently adjusts its transmission time based on random delay calculations. Rather than fixed simultaneous transmission times, the system creates dynamic, distributed timing patterns that adapt to individual device characteristics and random variables, naturally spreading traffic without centralized coordination.
Data Source
AI summary
To mitigate the impact of a mass communication event, such as a mass voting event, on the resources of the mobile wireless communication network, each user's mobile station is adapted to appropriately adjust its timing of the transmission during the mass communication event. In a voting example, the mobile station delays sending the user's vote by a random time interval. When implemented in a substantial number of mobile stations, the mobile stations randomly delay their individual transmissions, effectively spreading the traffic over a larger time window, thereby reducing the instantaneous peak load on the wireless network. However, since the solution is implemented in the mobile stations, there is no added overhead traffic related to the event at or around the time of the event. Consequently, handling of the event requires less network capacity to account for this bursty traffic.


