Apparatuses, methods, and systems for automatic detection of interfering cells are disclosed. One method includes monitoring, by each of a plurality of user equipment, one or more characteristic of received wireless signals, wherein the plurality of user equipment is located within a coverage area of a plurality of macro-cells of a wireless network, collecting the monitored one or more characteristics the of the received wireless signal, and adjusting operation of a macro-cell or at least one sector of a sectorized supercell base station based on the collected monitored one or more characteristics of the received wireless signals, wherein at least one sector of the sectorized supercell base station has a wireless signal coverage area that overlaps with the coverage area of plurality of macro-cells of the wireless network.

Apparatuses, methods, and systems for automatic detection of interfering cells are disclosed. One method includes monitoring, by each of a plurality of user equipment, one or more characteristic of received wireless signals, wherein the plurality of user equipment is located within a coverage area of a plurality of macro-cells of a wireless network, collecting the monitored one or more characteristics the of the received wireless signal, and adjusting operation of a macro-cell or at least one sector of a sectorized supercell base station based on the collected monitored one or more characteristics of the received wireless signals, wherein at least one sector of the sectorized supercell base station has a wireless signal coverage area that overlaps with the coverage area of plurality of macro-cells of the wireless network.

This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

One exemplary aspect describes systems and methods for determining normal SLE behavior, determining when a SLE exhibits abnormal deterioration, and determining whether to take an action to mitigate what appears to be an indication of an abnormal SLE.

One exemplary aspect describes systems and methods for determining normal SLE behavior, determining when a SLE exhibits abnormal deterioration, and determining whether to take an action to mitigate what appears to be an indication of an abnormal SLE.

Methods, apparatus, systems, and articles of manufacture are disclosed to perform a self-test of a wireless networking device. Examples disclosed herein include communication controller circuitry to cause the first WNIC to obtain a plurality of data packets from a second WNIC at a frequency. Examples herein further include signal strength determination circuitry to determine a received signal strength indicator (RSSI) value for an antenna of the first WNIC. Examples herein further include performance determination circuitry to increment a counter associated with the antenna when the RSSI value does not satisfy a first threshold and report an error associated with the antenna to a back office facility when the counter associated with the antenna does not satisfy a second threshold.

Methods, apparatus, systems, and articles of manufacture are disclosed to perform a self-test of a wireless networking device. Examples disclosed herein include communication controller circuitry to cause the first WNIC to obtain a plurality of data packets from a second WNIC at a frequency. Examples herein further include signal strength determination circuitry to determine a received signal strength indicator (RSSI) value for an antenna of the first WNIC. Examples herein further include performance determination circuitry to increment a counter associated with the antenna when the RSSI value does not satisfy a first threshold and report an error associated with the antenna to a back office facility when the counter associated with the antenna does not satisfy a second threshold.

A method for providing better connecting signals is disclosed. Coordinates of multiple mobile terminals are obtained. Distances between a mobile Wi-Fi (MIFI) device and the mobile terminals are calculated according to received signal strength indicator (RSSI) values between the mobile terminals and the MIFI device, and a relative coordinate of the MIFI device is then calculated based on the distances. Unit vectors of the mobile terminals relative to the MIFI device are calculated according to the relative coordinate of the MIFI device and the coordinates of the mobile devices. Reference signal receiving power (RSRP) values of the mobile terminals are calculated and are multiplied by the unit vectors to obtain RSRP vectors of the mobile terminals. The RSRP vectors are added up to generate a position where the MIFI device moves to a base station.

A method for providing better connecting signals is disclosed. Coordinates of multiple mobile terminals are obtained. Distances between a mobile Wi-Fi (MIFI) device and the mobile terminals are calculated according to received signal strength indicator (RSSI) values between the mobile terminals and the MIFI device, and a relative coordinate of the MIFI device is then calculated based on the distances. Unit vectors of the mobile terminals relative to the MIFI device are calculated according to the relative coordinate of the MIFI device and the coordinates of the mobile devices. Reference signal receiving power (RSRP) values of the mobile terminals are calculated and are multiplied by the unit vectors to obtain RSRP vectors of the mobile terminals. The RSRP vectors are added up to generate a position where the MIFI device moves to a base station.