Abstract: Systems and methods are disclosed for graph-based distributed parameter coordination in a communications network. In general, discrete local parameters to be coordinated among communication nodes in the communications network and their respective performance metrics, or costs, are modeled using a factor graph. Based on the factor graph, a variant of a sum-product algorithm, namely the min-sum algorithm, is applied in order for the communication nodes, through iterative message passing of reduced size messages with their neighboring communication nodes, to decide upon optimal values for the local parameters for the communication nodes that collectively optimize a global performance metric across the communications network. In one embodiment, the communications network is a wireless communications network. In one specific embodiment, the wireless communications network is a cellular communications network.

Abstract: Systems and methods are disclosed for graph-based distributed parameter coordination in a communications network. In general, discrete local parameters to be coordinated among communication nodes in the communications network and their respective performance metrics, or costs, are modeled using a factor graph. Based on the factor graph, a variant of a sum-product algorithm, namely the min-sum algorithm, is applied in order for the communication nodes, through iterative message passing of reduced size messages with their neighboring communication nodes, to decide upon optimal values for the local parameters for the communication nodes that collectively optimize a global performance metric across the communications network. In one embodiment, the communications network is a wireless communications network. In one specific embodiment, the wireless communications network is a cellular communications network.

Abstract: Systems and methods are disclosed for graph-based distributed parameter coordination in a communication network. In general, discrete local parameters to be coordinated among communication nodes in the communication network and their respective performance metrics, or costs, are modeled using a factor graph. Based on the factor graph, a variant of the sum-product algorithm, namely the min-sum algorithm, is applied in order for the communication nodes, through iterative message passing with their neighboring communication nodes, to decide upon optimal values for the local parameters for the communication nodes that collectively optimize a global performance metric across the communication network. In one embodiment, the communication network is a wireless communication network. In one specific embodiment, the wireless communication network is a cellular communication network.

Abstract: A network node jointly precodes multi-user (MU) multiple-input multiple-output (MIMO) transmissions simultaneously sent from geographically distributed base stations to a plurality of mobile terminals over associated downlink MU-MIMO channels. The node receives feedback that describes statistics of the downlink MU-MIMO channels, including channel mean and covariance. The node then computes, based on the channel means and covariances, uplink input covariances for the mobile terminals that would collectively maximize a first or second-order approximation of the ergodic capacity of dual uplink MU-MIMO channels, subject to a global transmit power constraint that comprises the sum of individual transmit power constraints for the base stations.

Abstract: A network node jointly precodes multi-user (MU) multiple-input multiple-output (MIMO) transmissions simultaneously sent from geographically distributed base stations to a plurality of mobile terminals over associated downlink MU-MIMO channels. The node receives feedback that describes statistics of the downlink MU-MIMO channels, including channel mean and covariance. The node then computes, based on the channel means and covariances, uplink input covariances for the mobile terminals that would collectively maximize a first or second-order approximation of the ergodic capacity of dual uplink MU-MIMO channels, subject to a global transmit power constraint that comprises the sum of individual transmit power constraints for the base stations.

Abstract: According to the teachings presented herein, each base station in a group of base stations is linked to an associated terminal as a receiver-transmitter pair. These receiver-transmitter pairs reuse channelization resources, such that each terminal represents a source of other-cell interference (also referred to as multi-user interference or MUI) for other terminals in neighboring cells that are reusing all or some of the same channelization resources. Accordingly, the base stations implement a gaming-based algorithm to mitigate MUI for the multiple-input-multiple-output (MIMO) uplink signals received from their associated terminals. More particularly, each base station functions as a player in a game, in which the allowed gaming action is the selection of the precoding matrix to be used for MIMO uplink transmissions to the base station from an associated terminal.