To provide a mobile terminal test device capable of efficiently performing a test by facilitating switching of parameter settings when switching a simulated base station. The mobile terminal test device includes a control unit 6 that, when a test is performed by simulating a plurality of base stations, retains a parameter common to the plurality of simulated base stations and a parameter individual for each of the plurality of simulated base stations, as the parameters for simulating the base stations, and sets the parameter common to the simulated base stations as it is, and switches and sets the parameter individual for each of the simulated base stations.

To provide a mobile terminal test device capable of efficiently performing a test by facilitating switching of parameter settings when switching a simulated base station. The mobile terminal test device includes a control unit 6 that, when a test is performed by simulating a plurality of base stations, retains a parameter common to the plurality of simulated base stations and a parameter individual for each of the plurality of simulated base stations, as the parameters for simulating the base stations, and sets the parameter common to the simulated base stations as it is, and switches and sets the parameter individual for each of the simulated base stations.

The embodiments of the present invention provide a method, apparatus for cross-protocol opportunistic routing, an electronic device, and a storage medium, the method includes: when there is a first data packet in a low-power wireless network, simulating the first data packet to generate a second data packet including to-be-transmitted data in the first data packet; obtaining identification information of a destination node in the first data packet, and selecting a low-power node with the lowest delay to the destination node in the low-power wireless network, except the first low-power node, as a forwarding low-power node based on the identification information of the destination node; sending the generated second data packet to the forwarding low-power node, so that the forwarding low-power node forwards the to-be-transmitted data to the destination node. By using high-power nodes, when there is a data packet in the low-power node, the data packet can be sent in time without being transmitted in a reserved idle channel, thereby reducing the transmission delay of the data packet from the source node to the destination node in the low-power wireless network.

A radio frequency (RF) receive circuit is described herein. In accordance with one embodiment, the RF receive circuit includes a mixer configured to receive an RF input signal to down-convert the RF input signal into a base-band or intermediate frequency (IF) band, an analog-to-digital converter (ADC), and a signal processing chain coupled between the mixer and the ADC. The signal processing chain includes at least two circuit nodes. The RF receive circuit further includes an oscillator circuit that is configured to generate a test signal. The oscillator circuit is coupled to the signal processing chain and is configured to selectively feed the oscillator signal into one of the at least two circuit nodes.

A radio frequency (RF) receive circuit is described herein. In accordance with one embodiment, the RF receive circuit includes a mixer configured to receive an RF input signal to down-convert the RF input signal into a base-band or intermediate frequency (IF) band, an analog-to-digital converter (ADC), and a signal processing chain coupled between the mixer and the ADC. The signal processing chain includes at least two circuit nodes. The RF receive circuit further includes an oscillator circuit that is configured to generate a test signal. The oscillator circuit is coupled to the signal processing chain and is configured to selectively feed the oscillator signal into one of the at least two circuit nodes.

A client device and method for analysis of a predetermined set of parameters associated with a radio coupling to a WLAN is provided. The client device includes a memory and a radio coupled to at least one processor. The at least one processor executes in the memory a first client Wireless Local Area Network (WLAN) stack having a plurality of layers configured to couple the radio to a WLAN. The at least one processor also executes in the memory a second client WLAN stack emulating the plurality of layers of the first client WLAN stack. The at least one processor is configured to receive, at the second client WLAN stack, data from the plurality of layers of the first client WLAN stack and analyze a predetermined set of WLAN parameters of the client device based on the data received from the plurality of layers of the first client WLAN stack.

A modular test instrument for performing tests and measurements in a network is disclosed. The modular test instrument may include a modular processing unit comprising a processor and memory, the modular processing unit connectable to at least one modular test unit or modular test subunit. The modular test instrument may also include a modular display unit connectable to the modular processing unit or the modular test unit. Display modularity may enable quick and cost-efficient display replacement when damage, malfunction, or failure is incurred. Furthermore, the modular test instrument may include an additional modular test subunit connectable to at least one of the modular processing unit or the modular test unit. When the modular processing unit is fitted with the modular display unit, the modular least, or modular test subunit, for example, the modular test instrument may form an integrated test instrument for performing any number of tests and measurements associated with installation, troubleshooting, or maintenance of a long-term evolution (LTE) or 5G network.

The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained machine learning model, the input data representative of a network environment of the wireless network, wherein the trained machine learning model is configured to provide, based on the input data, output data representative of an expected performance of a plurality of configurations of network components with respect to power consumption and performance of the wireless network; select a configuration of a network component from the plurality of configurations based on the output data of the trained machine learning model; and instruct an operation of the network component according to the selected configuration; and a memory coupled with the processor, the memory storing the input data provided to the trained machine learning model and/or the output data from the trained machine learning model.

The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained machine learning model, the input data representative of a network environment of the wireless network, wherein the trained machine learning model is configured to provide, based on the input data, output data representative of an expected performance of a plurality of configurations of network components with respect to power consumption and performance of the wireless network; select a configuration of a network component from the plurality of configurations based on the output data of the trained machine learning model; and instruct an operation of the network component according to the selected configuration; and a memory coupled with the processor, the memory storing the input data provided to the trained machine learning model and/or the output data from the trained machine learning model.

Systems and methods for network diagnostics are provided. Various embodiments allow for a diagnostic application to be automatically pushed to one or more mobile devices on a network. A diagnostic platform can select one or more mobile devices to perform a set of tests (e.g., Wi-Fi connections, cellular connections, download speeds, initiate phone calls, etc.) to evaluate the network performance. The diagnostic application can then be accessed (or installed) by the diagnostic platform. Messages that include the captured data about the network performance can be sent from each of the mobile devices to the diagnostic platform. Various analytics can then be generated about the network and performance of specific device configurations.