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.

A method for determining a reliability of a wireless device to estimate its location under a simulated environmental condition includes simulating an environmental condition inside a test chamber by controlling a physical parameter. The test chamber contains the wireless user device configured to estimate a location of the wireless user device based on reference signals received from a signal source. The method includes communicating a pattern of reference signals having varying signal propagation characteristics in the test chamber. The method includes receiving an indication of an estimated location calculated by the wireless user device based on the pattern of reference signals. The reliability of the wireless device to estimate its location under the simulated environmental condition is determined based on a comparison of the estimated location with the simulated location.

A method for determining a reliability of a wireless device to estimate its location under a simulated environmental condition includes simulating an environmental condition inside a test chamber by controlling a physical parameter. The test chamber contains the wireless user device configured to estimate a location of the wireless user device based on reference signals received from a signal source. The method includes communicating a pattern of reference signals having varying signal propagation characteristics in the test chamber. The method includes receiving an indication of an estimated location calculated by the wireless user device based on the pattern of reference signals. The reliability of the wireless device to estimate its location under the simulated environmental condition is determined based on a comparison of the estimated location with the simulated location.

Disclosed are a method, network device, and system for transmit power control during D2D communication. The method comprises: a network device determines the maximum allowed transmit power of a first terminal on the basis of the maximum level of interference that a cellular network can handle and/or the maximum level of interference that the first terminal is allowed to cause to a receiving terminal, and sends said maximum allowed transmit power of the first terminal to the first terminal, said first terminal being the sending device of D2D communication; and the first terminal determines transmit power on the basis of said maximum allowed transmit power. By means of the present technical solutions, when determining transmit power, a D2D sending device takes into account the maximum interference a cellular network can handle and/or the maximum level of interference that may be caused to a receiving terminal, thereby reducing the interference caused to D2D communicating terminals or to terminals of other cells using the resources of a cellular network.

Disclosed are a method, network device, and system for transmit power control during D2D communication. The method comprises: a network device determines the maximum allowed transmit power of a first terminal on the basis of the maximum level of interference that a cellular network can handle and/or the maximum level of interference that the first terminal is allowed to cause to a receiving terminal, and sends said maximum allowed transmit power of the first terminal to the first terminal, said first terminal being the sending device of D2D communication; and the first terminal determines transmit power on the basis of said maximum allowed transmit power. By means of the present technical solutions, when determining transmit power, a D2D sending device takes into account the maximum interference a cellular network can handle and/or the maximum level of interference that may be caused to a receiving terminal, thereby reducing the interference caused to D2D communicating terminals or to terminals of other cells using the resources of a cellular network.

Artificial Intelligence (AI) means are disclosed for enabling network operators to optimize 5G and 6G messaging performance, in real-time. AI models, or fieldable algorithms derived therefrom, can select an appropriate modulation scheme according to network conditions. Modulation variables can then be adjusted to optimize performance, such as throughput or failure rates, for low or high traffic densities. Three development phases are described: network data acquisition including faults experienced under various network conditions, AI structure tuning for accurate prediction of performance, and implementation of a fieldable algorithm based on the AI structure. Network operators can use the fieldable algorithm to compare predicted performance metrics in real-time, according to various operating conditions (such as available modulation schemes), and thereby adjust particular modulation parameters (such as amplitude or phase levels).

Artificial Intelligence (AI) means are disclosed for enabling network operators to optimize 5G and 6G messaging performance, in real-time. AI models, or fieldable algorithms derived therefrom, can select an appropriate modulation scheme according to network conditions. Modulation variables can then be adjusted to optimize performance, such as throughput or failure rates, for low or high traffic densities. Three development phases are described: network data acquisition including faults experienced under various network conditions, AI structure tuning for accurate prediction of performance, and implementation of a fieldable algorithm based on the AI structure. Network operators can use the fieldable algorithm to compare predicted performance metrics in real-time, according to various operating conditions (such as available modulation schemes), and thereby adjust particular modulation parameters (such as amplitude or phase levels).

Systems and methods are provided for using automated mobile computing device testing to ensure that a given targeted message is actually appearing in an intended manner for various types of intended recipients. The intended content or format of a target message to be presented by each of a plurality of mobile computing devices may be based on characteristics of each device. The targeted message may be sent to each device, and an indication of the actual content and/or format of the targeted message as presented by each device may be received and compared to the intended content or format of the targeted message for each device. Errors associated with the targeted message may be detected based on any discrepancies between the intended content and/or format of the targeted message for each device and the actual content or format of the targeted message as presented by each device.

Systems and methods are provided for using automated mobile computing device testing to ensure that a given targeted message is actually appearing in an intended manner for various types of intended recipients. The intended content or format of a target message to be presented by each of a plurality of mobile computing devices may be based on characteristics of each device. The targeted message may be sent to each device, and an indication of the actual content and/or format of the targeted message as presented by each device may be received and compared to the intended content or format of the targeted message for each device. Errors associated with the targeted message may be detected based on any discrepancies between the intended content and/or format of the targeted message for each device and the actual content or format of the targeted message as presented by each device.

Channel availability check optimization may be provided. A plurality of Pulse Repetition Intervals (PRIs) may be determined for a respective plurality of bursts on a respective plurality of frequencies. A list of at least a portion of the plurality of frequencies may be generated. The list may include a plurality of bias factors respectively indicating a probability that each of the respective plurality of bursts was a radar burst based on the respective plurality of PRIs. An Access Point (AP) may perform a plurality of preemptive Channel Availability Checks (CACs) on each of the respective plurality of frequencies on the list in order of highest probability to lowest probability based on the plurality of bias factors.