MU-MIMO Scheduling Using User Type Prediction in Fixed Wireless Access
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Solution Overview
Problem
Current MU-MIMO scheduling methods do not differentiate between mobile and fixed wireless users, leading to suboptimal network resource utilization and capacity.
Innovation Solution
Implement a machine learning model to determine whether a device is mobile or fixed wireless based on signal condition metrics, allowing for differentiated MU-MIMO pairing strategies to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same MU-MIMO parameters are used for both mobile and fixed wireless users, then the scheduling system is simple to implement, but resource utilization and network capacity are suboptimal
Solution Approach 1:
The patent segments users into two distinct categories: mobile users and fixed wireless users. This segmentation allows the base station to apply different MU-MIMO scheduling parameters to each user type, optimizing resource utilization for each category while maintaining manageable system complexity through structured classification.
Solution Approach 2:
The patent applies local quality by tailoring MU-MIMO parameters specifically to each user type's characteristics. Fixed wireless users receive more aggressive scheduling parameters suited for their stationary nature and stable channel conditions, while mobile users receive parameters appropriate for their mobility patterns and varying channel conditions.
2Productivity
If more devices access the router simultaneously, then network capacity increases, but congestion and wait times increase
Solution Approach 1:
The patent implements dynamic scheduling that adapts to real-time channel conditions and user types. By continuously adjusting MU-MIMO parameters based on current network state and user characteristics, the system can accommodate more simultaneous device accesses while dynamically managing wait times through optimized resource allocation.
Solution Approach 2:
The system employs feedback mechanisms where the base station monitors channel conditions, user performance, and network load, then adjusts MU-MIMO scheduling parameters accordingly. This feedback loop enables the network to maintain high capacity while minimizing wait times by responding to changing conditions in real-time.
Data Source
AI summary
Methods and systems for implementing MU-MIMO scheduling in a network are provided. The method begins with determining at least one signal condition metric for a plurality of signals used for communication between a base station and a plurality of devices. A machine learning model is trained to determine whether a device is a mobile device or a fixed wireless device using training data. The training data comprises at least one signal condition metric for the plurality of signals. Users may connect to the network by a mobile connection or through a fixed connection. An output of the machine learning model is then used to predict a type of connection to the network for each device of the plurality of devices. Then, based on the type of connection, MU-MIMO pairings are assigned to at least a portion of the plurality of devices.