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:
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 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:
Iterative sequential selection techniques can be used to efficiently compute RB assignment sequences in relay-assisted networks. Embodiment techniques construct a graphical representation of a cyclic group based on a selected pattern in a set of patterns and a selected cyclic-shift in a plurality of cyclic shifts. Remaining patterns are placed in a unitary group, and an iterative sequential selection technique is used to evaluate the remaining patterns in the unitary group for each of the cyclic shifts over a sequence of iterations, thereby complete the list of RB assignment sequences. At the end of each iteration, a new RB assignment sequence is added based on the pattern, cyclic shift tuple producing the fewest collisions with occupied resource blocks of the graphical representation.
Abstract:
Iterative sequential selection techniques can be used to efficiently compute RB assignment sequences in relay-assisted networks. Embodiment techniques construct a graphical representation of a cyclic group based on a selected pattern in a set of patterns and a selected cyclic-shift in a plurality of cyclic shifts. Remaining patterns are placed in a unitary group, and an iterative sequential selection technique is used to evaluate the remaining patterns in the unitary group for each of the cyclic shifts over a sequence of iterations, thereby complete the list of RB assignment sequences. At the end of each iteration, a new RB assignment sequence is added based on the pattern, cyclic shift tuple producing the fewest collisions with occupied resource blocks of the graphical representation.
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:
Embodiments are provided for enabling dynamic pricing of services to users. The amount of usage all or different services are quantized into quanta of minimum usage units, such as to an amount of effective bits (eBits) in communications services. The amount of usage of the different services is weighted differently, such as according to service cost or demand, to obtain corresponding amount of quanta of usage or eBits for each service. The amount of quanta of usage or eBits for the different services is summed up to provide a total amount of quanta of usage or eBits. The total amount of quanta of usage or eBits is converted into a total charge, as a product of the total amount of quanta of usage or eBits, or a monotonic function of the total amount, and a fixed charge rate per quanta of usage or eBit.
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.