Optimizing a cellular network using machine learning
A cellular network, network optimization technology, applied in machine learning, neural learning methods, biological neural network models, etc.
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example 1
[0091] Example 1: A method for a network-optimized controller that includes a network-optimized controller:
[0092]determining performance metrics for optimizing the cellular network;
[0093] determining at least one network configuration parameter affecting the performance metric;
[0094] sending a gradient request message to a plurality of base stations, the gradient request message directing a plurality of wireless transceivers to respectively evaluate a gradient of a performance metric with respect to at least one network configuration parameter;
[0095] receiving gradient report messages generated by a plurality of wireless transceivers from a plurality of base stations, the gradient report messages respectively including gradients;
[0096] using machine learning to analyze gradients to determine at least one optimized network configuration parameter; and
[0097] An optimization message is sent to at least one of the plurality of base stations, the optimization me...
example 2
[0098] Example 2: The method according to Example 1, wherein:
[0099] The plurality of wireless transceivers includes a plurality of base stations.
example 3
[0100] Example 3: The method according to Example 2, wherein:
[0101] The at least one network configuration parameter includes at least one of:
[0102] Downlink transmit power configuration;
[0103] Antenna array configuration;
[0104] phase encoding interval;
[0105] Time division multiplexed pilot pattern;
[0106] Data tone power;
[0107] Data-to-pilot power ratio;
[0108] downlink time slot allocation percentage;
[0109] Subframe configuration;
[0110] switch configuration; or
[0111] Multi-user scheduling configuration.
PUM
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Abstract
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Application Information
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