Abstract:
A method for configuring a first base station within a cluster in a communications system having a plurality of cluster includes optimizing an operating parameter of the first base station in accordance with first utility function results from a first utility function associated with the first base station and second utility function results from a second utility function associated with a second base station within the cluster, the first utility function results and the second utility function results according to multiple settings for the operating parameter of the first base station, a first initialized setting of the operating parameter for the second base station, and a second initialized setting of the operating parameter for an external base station outside the cluster. The method also includes sharing the optimized operating parameter with the external base station.
Abstract:
Embodiments are provided for network resource allocation considering user experience, satisfaction, and operator interest. An embodiment method by a network component for allocating network resources includes evaluating, for a user, a QoE for each flow of a plurality of flows in network traffic in according with a QoE model, and further evaluating, for an operator, a revenue associated with the flows in accordance with a revenue model. A plurality of priorities that correspond to the flows are calculated in accordance with the QoE for the user and the revenue for the operator. The method further includes identifying a flow of the flows with a highest value of the priorities, and allocating a network resource for the flow. In an embodiment, the QoE model is a satisfaction model that provides a measure of user satisfaction for each flow in accordance with a subscription or behavior class of the user.
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:
In one embodiment, method for controlling multiple wireless access nodes includes receiving, by a central controller from a base station (BS), a message indicating a channel state information (CSI) and determining a state transition function in accordance with the message. The method also includes determining a belief state in accordance with the state transition function and determining cooperation for a plurality of BSs including the BS in accordance with the belief state to produce a cooperation decision. Additionally, the method includes transmitting, by the central controller to the BS, the cooperation decision.
Abstract:
Embodiments are provided for assessing radio resource requirements using virtual bin virtualization. An embodiment method includes receiving a service request from a user equipment (UE) in a geographical bin. Resource requirements are then obtained, from a lookup table (LUT), for a serving radio node and neighbor radio nodes associated with the geographic bin of the UE. The LUT comprises a plurality of entries that map combinations of path losses of wireless links for the serving radio node and neighbor radio nodes to corresponding combinations of resource requirements. The entries of the path losses further include one or more service specific and network node parameters for the serving radio nodes and neighbor radio nodes, which are also mapped to the resource requirements. The obtained resource requirements are then assessed, including deciding whether to serve the UE according to the resource requirements and to resource availability.
Abstract:
A system and method for agile wireless access network is provided. A method embodiment for agile radio access network management includes determining, by a network controller, capabilities and neighborhood relations of radio nodes in the radio access network. The network controller then configures a backhaul network infrastructure for the radio access network in accordance with the capabilities and the neighborhood relations of the radio nodes.
Abstract:
In one embodiment, method for controlling multiple wireless access nodes includes receiving, by a central controller from a base station (BS), a message indicating a channel state information (CSI) and determining a state transition function in accordance with the message. The method also includes determining a belief state in accordance with the state transition function and determining cooperation for a plurality of BSs including the BS in accordance with the belief state to produce a cooperation decision. Additionally, the method includes transmitting, by the central controller to the BS, the cooperation decision.
Abstract:
A method embodiment for transmission scheduling includes implementing, by a first base station (BS), a soft-persistent scheduling scheme. The soft-persistent scheduling scheme includes allocating a first resource block to a first UE and other resource blocks to other UEs for a first transmission time interval (TTI), calculating a first priority of the first UE for the first resource block for a second TTI, wherein calculating the first priority involves including a first bonus in the first priority, and wherein the second TTI is later than the first TTI, calculating other priorities for the other UEs and the other resource blocks for the second TTI, and allocating the first and the other resource blocks to the first and other UEs for the second TTI in accordance with the first priority of the first UE as modified by the first bonus and the other priorities of the other UEs.
Abstract:
Embodiments are provided for uplink measurement based mechanism and control using user equipment (UE) centric sounding signals. The mechanism provides an alternative to DL-measurement dominated system control. Based on UL-measurements at TPs, the network obtains knowledge of users' channel and timing information, traffic, and interference, and is thus able to perform better control, including TP and UE clustering and optimization, and power control and link adaptation. In an embodiment method, a TP receives one-to-one mapping information indicating a plurality of UE IDs and a plurality of sounding channels assigned to the corresponding UE IDs. When the TP detects a sounding reference signal (SRS) from a UE, the TP is able to identify the UE using the detected SRS and the one-to-one mapping information. The TP then obtains measurement information for the identified UE, enabling better control and communications for uplink and downlink transmissions between multiple TPs and the UE.