Abstract:
Predicting mobile station migration between geographical locations of a wireless network can be achieved using a migration probability database. The database can be generated based on statistical information relating to the wireless network, such as historical migration patterns and associated mobility information (e.g., velocities, bin location, etc.). The migration probability database consolidates the statistical information into mobility prediction functions for estimating migration probabilities/trajectories based on dynamically reported mobility parameters. By example, mobility prediction functions can compute a likelihood that a mobile station will migrate between geographic regions based on a velocity of the mobile station. Accurate mobility prediction may improve resource provisioning efficiency during admission control and path selection, and can also be used to dynamically adjust handover margins.
Abstract:
In one embodiment, a method for a method of estimating an effective bandwidth of a traffic source includes obtaining a first traffic pattern from a first traffic source. Also, the method includes setting a first effective bandwidth between a mean data rate of the first traffic source and a peak data rate of the first traffic source. Additionally, the method includes determining a first outage rate of the first traffic source in accordance with the first traffic pattern and the first effective bandwidth.
Abstract:
System and method embodiments for mobility prediction in a wireless network enable the wireless network to determine the location of a wireless device with minimal transmissions from the wireless device. In an embodiment, the method includes negotiating with a mobile device to determine a mobility prediction algorithm and a condition upon which the mobile wireless device will report the actual location of the mobile device, training the mobility prediction algorithm using prior mobile wireless device location and timestamp information, determining a predicted location of the mobile device using the mobility prediction algorithm, and setting an predicted location for the mobile device at a time as the actual location for the mobile device at the time when failing to receive a location report from the mobile wireless device, wherein the mobile device transmits actual location information after the training period only if the condition is met.
Abstract:
Predicting mobile station migration between geographical locations of a wireless network can be achieved using a migration probability database. The database can be generated based on statistical information relating to the wireless network, such as historical migration patterns and associated mobility information (e.g., velocities, bin location, etc.). The migration probability database consolidates the statistical information into mobility prediction functions for estimating migration probabilities/trajectories based on dynamically reported mobility parameters. By example, mobility prediction functions can compute a likelihood that a mobile station will migrate between geographic regions based on a velocity of the mobile station. Accurate mobility prediction may improve resource provisioning efficiency during admission control and path selection, and can also be used to dynamically adjust handover margins.
Abstract:
Interference costs on virtual radio interfaces can be modeled as a function of loading in a wireless network to estimate changes in spectral efficiency and/or resource availability that would result from a provisioning decision. In one example, this modeling is achieved through cost functions that are developed from historical and/or simulated resource cost data corresponding to the wireless network. The cost data may include interference data, spectral efficiency data, and/or loading data for various links over a common period of time (e.g., a month, a year, etc.), and may be analyzed and/or consolidated to obtain correlations between interference costs and loading on the various links in the network. As an example, a cost function may specify an interference cost on one virtual link as a function of loading on one or more neighboring virtual links.
Abstract:
Systems and methods reduce redundancy in a data representation. The data is divided into a plurality of data portions. The data portions are used to encode a plurality of compressed data portions, wherein the compressed data portions correspond to a subset of the data portions and comprise less redundant data than the subset of the data portions. The compressed data portions are also encoded in accordance with data in the remaining data portions. The compressed data portions are transmitted instead of the subset of the data portions with the remaining data portions according to a sequence of data portions. Each of the compressed data portions is transmitted upon receiving an acknowledgment message that indicates successful transmission of a previous data portion or compressed data portion in the sequence of data portions.
Abstract:
A method for optimizing uplink power control settings in a wireless network, the method comprising generating a first gene pool comprising a set of parent genes, wherein each parent gene comprises a set of first generation power control solutions for a set of base stations in the wireless network. The method may further include performing natural selection on the first gene pool to generate a second gene pool comprising selected ones of the set of parent genes, wherein the selected parent genes are chosen by probabilistically selecting some of the parent genes based on fitness values assigned to the parent genes. The method may further include evolving the second gene pool into a descendent gene, wherein the descendent gene comprises a set of local power control solutions for the set of base station in the wireless network.
Abstract:
Interference costs on virtual radio interfaces can be modeled as a function of loading in a wireless network to estimate changes in spectral efficiency and/or resource availability that would result from a provisioning decision. In one example, this modeling is achieved through cost functions that are developed from historical and/or simulated resource cost data corresponding to the wireless network. The cost data may include interference data, spectral efficiency data, and/or loading data for various links over a common period of time (e.g., a month, a year, etc.), and may be analyzed and/or consolidated to obtain correlations between interference costs and loading on the various links in the network. As an example, a cost function may specify an interference cost on one virtual link as a function of loading on one or more neighboring virtual links.
Abstract:
Predicting mobile station migration between geographical locations of a wireless network can be achieved using a migration probability database. The database can be generated based on statistical information relating to the wireless network, such as historical migration patterns and associated mobility information (e.g., velocities, bin location, etc.). The migration probability database consolidates the statistical information into mobility prediction functions for estimating migration probabilities/trajectories based on dynamically reported mobility parameters. By example, mobility prediction functions can compute a likelihood that a mobile station will migrate between geographic regions based on a velocity of the mobile station. Accurate mobility prediction may improve resource provisioning efficiency during admission control and path selection, and can also be used to dynamically adjust handover margins.
Abstract:
A method for optimizing uplink power control settings in a wireless network, the method comprising generating a first gene pool comprising a set of parent genes, wherein each parent gene comprises a set of first generation power control solutions for a set of base stations in the wireless network. The method may further include performing natural selection on the first gene pool to generate a second gene pool comprising selected ones of the set of parent genes, wherein the selected parent genes are chosen by probabilistically selecting some of the parent genes based on fitness values assigned to the parent genes. The method may further include evolving the second gene pool into a descendent gene, wherein the descendent gene comprises a set of local power control solutions for the set of base station in the wireless network.