Abstract: Embodiments are provided for using association rule mining to analyze performance counters of a mobile network, including converting a plurality of performance counters of a mobile network into a plurality of key performance indicators (KPIs), quantizing each KPI into one value of a set of values associated with that KPI, creating a set of items having multiple subsets corresponding to respective KPIs, where each item of a subset corresponds to a respective value of a particular set of values associated with a particular KPI, and generating association rules based, at least in part, on the set of items. In further embodiments, the quantizing the plurality of KPIs includes quantizing a first KPI into a first value of a set of three or more values associated with the first KPI.

Abstract: Embodiments are provided for using association rule mining to analyze performance counters of a mobile network, including converting a plurality of performance counters of a mobile network into a plurality of key performance indicators (KPIs), quantizing each KPI into one value of a set of values associated with that KPI, creating a set of items having multiple subsets corresponding to respective KPIs, where each item of a subset corresponds to a respective value of a particular set of values associated with a particular KPI, and generating association rules based, at least in part, on the set of items. In further embodiments, the quantizing the plurality of KPIs includes quantizing a first KPI into a first value of a set of three or more values associated with the first KPI.

Abstract: An LAA-LTE wireless device obtains data to be transmitted in subframes over a shared spectrum. The wireless device transmits a reservation message over the shared spectrum. The reservation message indicates that an initial synchronization message will be retransmitted a specified number of times at specified intervals, such as with a predetermined number of subframes between each retransmitted synchronization message. The wireless device transmits the initial synchronization message over the shared spectrum.

Abstract: A first wireless device determines the duration of a clear channel assessment interval based on feedback signals before transmitting data to a second wireless device. The first wireless device receives feedback signals, such as acknowledgement (ACK) and negative acknowledgement (NAK) signals, from one or more second wireless devices. The first wireless device determines a ratio of NAK/ACK signals in the feedback signals for a predetermined amount of time. The first wireless device determines whether the wireless medium is free by monitoring for wireless signals during a clear channel assessment interval. The duration of the clear channel assessment interval is based on the ratio of NAK/ACK signals. If the wireless medium is free for the duration of the clear channel assessment, the first wireless device wirelessly transmits data to at least one of the second wireless devices.

Abstract: A method is provided in one example embodiment and includes gathering at a small cell base station information to populate a neighbor cell list of the small cell base station; gathering measurement reports from connected mode user equipment (“UE”) at the small cell base station; and determining whether uplink interference has exceeded a predetermined threshold and generating an alarm to a central node when the uplink interference has exceeded the predetermined threshold. Upon receipt of notification, the central node triggers an action on an aggressor node, wherein the aggressor node is a node servicing at least one aggressor user equipment (“UE”) that significantly contributes to the uplink interference as determined from the gathered information and reports.

Abstract: A method is provided in one example embodiment and may include training a statistical model representing radio access point loads or load changes for radio access points, wherein the statistical model is trained using, at least in part, historical measurement data associated with previous user equipment (UE) handovers among the radio access points and wherein the historical measurement data used to train the statistical model is gathered before and after the previous UE handovers; collecting current measurement data associated with a source radio access point and a target radio access point; and calculating a predicted load or load change for the target radio access point for one or more potential UE handovers from the source radio access point to the target radio access point for one or more UE based, at least in part, on application of the current measurement data to the trained statistical model.

Abstract: A wireless device accesses a shared spectrum equitably in a self-organizing manner by determining success indices and adjusting courtesy parameters. The wireless device transmits wireless transmissions in a frame subframe structure over a shared spectrum, each of the subframes bounded by subframe boundaries at predetermined time intervals. The success index represents a measure of success in transmitting wireless transmissions in the shared spectrum. The wireless device performs a clear channel assessment to determine whether the shared spectrum is available at the end of the clear channel assessment time interval. The wireless device determines a gap interval between the end of the clear channel assessment time interval and the next subframe boundary and transmits the success index during the gap interval.

Abstract: An LAA-LTE wireless device obtains data to be transmitted in subframes over a shared spectrum. The wireless device transmits a reservation message over the shared spectrum. The reservation message indicates that an initial synchronization message will be retransmitted a specified number of times at specified intervals, such as with a predetermined number of subframes between each retransmitted synchronization message. The wireless device transmits the initial synchronization message over the shared spectrum.

Abstract: A wireless device accesses a shared spectrum equitably in a self-organizing manner by determining success indices and adjusting courtesy parameters. The wireless device transmits wireless transmissions in a frame subframe structure over a shared spectrum, each of the subframes bounded by subframe boundaries at predetermined time intervals. The success index represents a measure of success in transmitting wireless transmissions in the shared spectrum. The wireless device performs a clear channel assessment to determine whether the shared spectrum is available at the end of the clear channel assessment time interval. The wireless device determines a gap interval between the end of the clear channel assessment time interval and the next subframe boundary and transmits the success index during the gap interval.

Abstract: A first wireless device determines the duration of a clear channel assessment interval based on feedback signals before transmitting data to a second wireless device. The first wireless device receives feedback signals, such as acknowledgement (ACK) and negative acknowledgement (NAK) signals, from one or more second wireless devices. The first wireless device determines a ratio of NAK/ACK signals in the feedback signals for a predetermined amount of time. The first wireless device determines whether the wireless medium is free by monitoring for wireless signals during a clear channel assessment interval. The duration of the clear channel assessment interval is based on the ratio of NAK/ACK signals. If the wireless medium is free for the duration of the clear channel assessment, the first wireless device wirelessly transmits data to at least one of the second wireless devices.

Abstract: A method is provided in one example embodiment and may include training a statistical model representing radio access point loads or load changes for radio access points, wherein the statistical model is trained using, at least in part, historical measurement data associated with previous user equipment (UE) handovers among the radio access points and wherein the historical measurement data used to train the statistical model is gathered before and after the previous UE handovers; collecting current measurement data associated with a source radio access point and a target radio access point; and calculating a predicted load or load change for the target radio access point for one or more potential UE handovers from the source radio access point to the target radio access point for one or more UE based, at least in part, on application of the current measurement data to the trained statistical model.

Abstract: Embodiments are provided for using association rule mining to analyze performance counters of a mobile network, including converting a plurality of performance counters of a mobile network into a plurality of key performance indicators (KPIs), quantizing each KPI into one value of a set of values associated with that KPI, creating a set of items having multiple subsets corresponding to respective KPIs, where each item of a subset corresponds to a respective value of a particular set of values associated with a particular KPI, and generating association rules based, at least in part, on the set of items. In further embodiments, the quantizing the plurality of KPIs includes quantizing a first KPI into a first value of a set of three or more values associated with the first KPI.

Abstract: A method is provided in one example embodiment and may include training a statistical model representing radio access point loads or load changes for radio access points, wherein the statistical model is trained using, at least in part, historical measurement data associated with previous user equipment (UE) handovers among the radio access points and wherein the historical measurement data used to train the statistical model is gathered before and after the previous UE handovers; collecting current measurement data associated with a source radio access point and a target radio access point; and calculating a predicted load or load change for the target radio access point for one or more potential UE handovers from the source radio access point to the target radio access point for one or more UE based, at least in part, on application of the current measurement data to the trained statistical model.

Abstract: A method is provided in one example embodiment and may include training a statistical model representing radio access point loads or load changes for radio access points, wherein the statistical model is trained using, at least in part, historical measurement data associated with previous user equipment (UE) handovers among the radio access points and wherein the historical measurement data used to train the statistical model is gathered before and after the previous UE handovers; collecting current measurement data associated with a source radio access point and a target radio access point; and calculating a predicted load or load change for the target radio access point for one or more potential UE handovers from the source radio access point to the target radio access point for one or more UE based, at least in part, on application of the current measurement data to the trained statistical model.