Abstract:
Various devices and methods are provided that use signaling to support advanced wireless receivers. For example, a method includes receiving an input signal at a user equipment. The input signal includes a desired signal and an interfering signal, where the desired signal defines symbols using constellations. The method also includes obtaining information identifying a wireless channel used by the interfering signal and a modulation type used to modulate data in the interfering signal. The method further includes recovering the symbols from the desired signal using the information.
Abstract:
Methods and systems for providing joint power control (PC) and scheduling in a wireless network are provided. In one example, a method includes generating a near-optimal power pattern for PC and scheduling in accordance with long term channel statistics. The near-optimal PC solution may be generated by first generating a set of possible power patterns in accordance with likely scheduling scenarios, then statistically narrowing the set of possible power patterns to identify the most commonly used power patterns, and finally selecting one of the most commonly used power patterns as the near-optimal power pattern. In another example, a table of optimal PC solutions are provided for performing distributed PC and scheduling in an adaptive and/or dynamic manner.
Abstract:
Methods and systems for facilitating uplink power control (PC) and scheduling in a wireless network are provided. In one example, common interference patterns are obtained from long term channel statistics, and used to perform local PC and scheduling by distributed base stations (eNBs). In some implementations, the common interference patterns are obtained through statistical narrowing techniques that identify common ones out of a plurality of potential interference patterns. The common interference patterns may specify maximum interference thresholds and/or individual eNB-to-eNB interference thresholds which may govern the local PC and scheduling decisions of the distributed eNBs.
Abstract:
Increased resource utilization efficiency can be improved by modeling path costs during admission and path-selection. Specifically, path costs for candidate paths are modeled based on load characteristics (e.g., current load, load variation, etc.) of links in the candidate paths. Path costs can represent any quantifiable cost or liability associated with transporting a service flow over the corresponding path. For example, path costs can correspond to a probability that at least one link in the path will experience an outage when transporting the service flow, a price charged by a network operator (NTO) for transporting the traffic flow over the candidate path, or a total network cost for transporting the flow over a candidate path. The candidate path having the lowest path cost is selected to transport a service flow.
Abstract:
Embodiments are provided for scheduling resources considering data rate-efficiency and fairness trade-off. A value of Jain's fairness index (JFI) is determined for transmitting a service to a plurality of users, and accordingly a sum of throughputs is maximized for transmitting the service to the users. Alternatively, a sum of throughputs is determined first and accordingly the JFI is maximized. Maximizing the sum of throughputs or JFI includes selecting a suitable value for a tuning parameter in an efficiency and fairness trade-off relation model. In accordance with the values of sum of throughputs and JFI, a plurality of resources are allocated for transmitting the service to the users. For static or quasi-static channels, the relation model is a convex function with a monotonic trade-off property. For ergodic time varying channels, the tuning parameter is selected by solving the relation model using a gradient-based approach.
Abstract:
Methods and systems for providing joint power control (PC) and scheduling in a wireless network are provided. In one example, a method includes generating a near-optimal power pattern for PC and scheduling in accordance with long term channel statistics. The near-optimal PC solution may be generated by first generating a set of possible power patterns in accordance with likely scheduling scenarios, then statistically narrowing the set of possible power patterns to identify the most commonly used power patterns, and finally selecting one of the most commonly used power patterns as the near-optimal power pattern. In another example, a table of optimal PC solutions are provided for performing distributed PC and scheduling in an adaptive and/or dynamic manner.
Abstract:
Embodiments are provided for scheduling resources considering data rate-efficiency and fairness trade-off. A value of Jain's fairness index (JFI) is determined for transmitting a service to a plurality of users, and accordingly a sum of throughputs is maximized for transmitting the service to the users. Alternatively, a sum of throughputs is determined first and accordingly the JFI is maximized. Maximizing the sum of throughputs or JFI includes selecting a suitable value for a tuning parameter in an efficiency and fairness trade-off relation model. In accordance with the values of sum of throughputs and JFI, a plurality of resources are allocated for transmitting the service to the users. For static or quasi-static channels, the relation model is a convex function with a monotonic trade-off property. For ergodic time varying channels, the tuning parameter is selected by solving the relation model using a gradient-based approach.
Abstract:
Methods and systems for facilitating uplink power control (PC) and scheduling in a wireless network are provided. In one example, common interference patterns are obtained from long term channel statistics, and used to perform local PC and scheduling by distributed base stations (eNBs). In some implementations, the common interference patterns are obtained through statistical narrowing techniques that identify common ones out of a plurality of potential interference patterns. The common interference patterns may specify maximum interference thresholds and/or individual eNB-to-eNB interference thresholds which may govern the local PC and scheduling decisions of the distributed eNBs.
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.