Abstract: A system described herein may provide a technique for the intelligent, automated, and dynamic selection of anchor evolved Node Bs (“eNBs”) for Next Generation Node Bs (“gNBs”) in a Non-Standalone (“NSA”) wireless network environment. An eNB may be selected as an anchor eNB based on distance to a particular gNB, and one or more sectors of the anchor eNB may be selected as anchors based on coverage areas associated with the sectors. Sectors of additional eNBs may be identified based on frequent handovers between the identified sectors of the anchor eNB and sectors of the additional eNBs. Selected eNBs and/or sectors may be notified of the designation as an anchor, and may thus provide gNB carrier information to connected User Equipment (“UE”), which may facilitate the connection of such UEs to the gNB.

Abstract: A system described herein may provide a technique for the intelligent, automated, and dynamic selection of anchor evolved Node Bs (“eNBs”) for Next Generation Node Bs (“gNBs”) in a Non-Standalone (“NSA”) wireless network environment. An eNB may be selected as an anchor eNB based on distance to a particular gNB, and one or more sectors of the anchor eNB may be selected as anchors based on coverage areas associated with the sectors. Sectors of additional eNBs may be identified based on frequent handovers between the identified sectors of the anchor eNB and sectors of the additional eNBs. Selected eNBs and/or sectors may be notified of the designation as an anchor, and may thus provide gNB carrier information to connected User Equipment (“UE”), which may facilitate the connection of such UEs to the gNB.

Abstract: A system described herein may provide a technique for the intelligent, automated, and dynamic selection of anchor evolved Node Bs (“eNBs”) for Next Generation Node Bs (“gNBs”) in a Non-Standalone (“NSA”) wireless network environment. An eNB may be selected as an anchor eNB based on distance to a particular gNB, and one or more sectors of the anchor eNB may be selected as anchors based on coverage areas associated with the sectors. Sectors of additional eNBs may be identified based on frequent handovers between the identified sectors of the anchor eNB and sectors of the additional eNBs. Selected eNBs and/or sectors may be notified of the designation as an anchor, and may thus provide gNB carrier information to connected User Equipment (“UE”), which may facilitate the connection of such UEs to the gNB.

Abstract: A system described herein may provide a technique for the intelligent, automated, and dynamic selection of anchor evolved Node Bs (“eNBs”) for Next Generation Node Bs (“gNBs”) in a Non-Standalone (“NSA”) wireless network environment. An eNB may be selected as an anchor eNB based on distance to a particular gNB, and one or more sectors of the anchor eNB may be selected as anchors based on coverage areas associated with the sectors. Sectors of additional eNBs may be identified based on frequent handovers between the identified sectors of the anchor eNB and sectors of the additional eNBs. Selected eNBs and/or sectors may be notified of the designation as an anchor, and may thus provide gNB carrier information to connected User Equipment (“UE”), which may facilitate the connection of such UEs to the gNB.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.