Provided is a setting device and the like with which correct estimation of a communication band is possible. The setting device 101 has a transmission unit 102 that, on the basis of a first timing at which a first information processing device 401 transmits to a second information processing device 402 a first signal for measuring a communication band which pertains to a communication network 403, transmits to the second information processing device 402 a setting signal for setting a communication unit 407 of the second information processing device 402 to a communication-enabled state.

Systems and methods that use adaptive control of communication protocol parameters in a network of moving things. Each network device may share information identifying communication protocol parameters used by the network device through periodic broadcasts, which may be received by neighboring network devices that are within communication range. The neighboring network devices may then adjust their own communication protocol parameters accordingly when attempting to communicate with the first network device and their own neighboring network devices. Network devices that have not received the shared information may use information about their geographic location and the geographic location of neighboring network devices to estimate values of some communication protocol parameters that will provide more effective communication.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

One or more computing devices, systems, and/or methods are provided. In an example, a method includes instantiating a first user equipment (UE) module on a first computing device and instantiating a second UE module on a second computing device connected to the first computing device by a wired network. A first base station (BS) module associated with the first UE module is instantiated on a third computing device coupled to the wired network and a second BS module associated with the second UE module is instantiated on a fourth computing device coupled to the wired network. A first resource allocation map is sent from the first BS module to the second BS module. An interference metric is generated based on the first resource allocation map. A data transmission between the second UE module and the second BS module is modulated based on the interference metric.

There is provided mechanisms for handling a change in a physical environment in which a network provides service. A method includes obtaining an indication of change in the physical environment. The method includes performing an action when the indication fulfils a condition. The action pertains to at least one of obtaining radio measurements from network equipment operating in the network, providing radio measurement configuration to the network equipment, providing network configuration to the network equipment.

There is provided mechanisms for handling a change in a physical environment in which a network provides service. A method includes obtaining an indication of change in the physical environment. The method includes performing an action when the indication fulfils a condition. The action pertains to at least one of obtaining radio measurements from network equipment operating in the network, providing radio measurement configuration to the network equipment, providing network configuration to the network equipment.

Systems, apparatuses and methods may provide for technology that adjusts, via a short-term subsystem, a communications parameter for one or more of wireless communication devices based on data from one or more of a plurality of sensors. The technology may also determine, via a neural network, a prediction of future performance of the wireless network based on a state of the network environment, wherein the state of the network environment includes information from the short-term subsystem and location information about the wireless communication devices and other objects in the environment, and determine a change in network configuration to improve a quality of communications in the wireless network based on the prediction of future performance of the wireless network. The technology may further generate generic path loss models based on time-stamped RSSI maps and record a sequence of events that cause a significant drop in RSSI to determine a change in network configuration.

Systems, apparatuses and methods may provide for technology that adjusts, via a short-term subsystem, a communications parameter for one or more of wireless communication devices based on data from one or more of a plurality of sensors. The technology may also determine, via a neural network, a prediction of future performance of the wireless network based on a state of the network environment, wherein the state of the network environment includes information from the short-term subsystem and location information about the wireless communication devices and other objects in the environment, and determine a change in network configuration to improve a quality of communications in the wireless network based on the prediction of future performance of the wireless network. The technology may further generate generic path loss models based on time-stamped RSSI maps and record a sequence of events that cause a significant drop in RSSI to determine a change in network configuration.

A method and device for testing a base station comprising one or more radio units and a baseband unit connectable to the one or more radio units, wherein the device includes a configuration module configured to generate a first test case configuration associated with the one or more radio units and the baseband unit, where the first test case configuration includes a first protocol stack including a first protocol associated with a first layer and a second protocol associated with a second layer, and where a first set of protocol parameters associated with the first protocol is in a first namespace and a second set of protocol parameters associated with the second protocol is in a second namespace that is distinct from the first name space.