Artificial intelligence procedures are disclosed for localizing faults in corrupted messages in 5G and 6G, and for correcting those faults based on measured parameters such as backgrounds and message signals according to pulse-amplitude modulation. An AI model with multiple adjustable variables may be “trained” using a large number of message events, including faulted messages, to determine which message elements are likely faulted, based on input parameters such as modulation quality, SNR, and other signal properties. The receiving entity can then attempt a grid search to correct the faulted message elements, or request a retransmission. For field use by base stations and user devices, an algorithm may be developed based on the AI model, and configured to predict which message elements are likely faulted. By detecting and correcting message faults, networks may increase reliability and reduce latency while avoiding most retransmission costs and delays, according to some embodiments.

Disclosed are methods for avoiding, detecting, and mitigating message faults. Due to the expected large increase in electromagnetic background energy in in dense 5G and 6G networks, message faults are likely to dramatically increase, along with their costs. To avoid intermittent interference, a user device can monitor the noise level and request that the base station store incoming messages while the noise level is too high. Likewise, if a user device receives a faulted message while the noise level is high, the user device can delay the retransmission until the noise subsides. If the user device has received two faulted messages (a likely scenario in crowded urban/industrial/sporting environments), the user device can merge the two versions while selecting the message elements with the best quality (based on modulation, SNR, stability, and other criteria) and may thereby obtain a corrected message version, without resorting to a third transmission of the message.

Techniques for improved wireless reliability are provided. It is determined that a client device is at least one of an augmented reality (AR) or a virtual reality (VR) device. A default set of retry parameters and a second set of retry parameters are determined, where the second set of retry parameters are more robust than the default set of retry parameters. Data is transmitted to the client device using the second set of retry parameters.

A data transmitting method, includes: generating, by a first device, data messages based on a to-be-sent data stream; transmitting, by the first device, the data messages to a second device based on a packet transmission rate; recording, by the first device, the number of transmitted data messages; when the number of the transmitted data messages reaches a preset number for one batch, transmitting, by the first device, a verification message to the second device; receiving, by the first device, a verification acknowledgement returned by the second device, and determining, by the second device, a packet loss rate of the batch of data messages; updating, by the first device, the packet transmission rate based on packet loss rates of multiple batches of data messages.

Described herein are apparatus, system, and method for optimizing performance of one or more communication units by a remote server. The method comprises: collecting data from the one or more communication units; generating a policy, for each of the one or more communication units, based on the collected data; and sending the policy to each of the one or more communication units, wherein the policy comprises conditions for operation of the one or more communication units, wherein the one or more communication units implement the policy according to time-varying data. The system comprises: one or more communication units; and a server operable to communicate with the one or more communication units, wherein the server comprises: a memory; and a processor, coupled to the memory, and operable to perform the method discussed above.

A system for determining a channel margin of a data transmission channel (DTC) using error correction under real-world channel conditions is described. The system includes a monitoring unit, an operating state determining unit and a data processing unit. The monitoring unit monitors data transmission along the DTC and estimates a statistical distribution of errors (H) in the transmission of data. The operating state determining unit determines a current value of an operating state parameter for the DTC. The data processing unit determines a reference channel margin associated with said current value of the operating state parameter for a reference channel and the error correction scheme employed, provides a statistical distribution of errors (HR) associated with said reference channel for said current value of said operating state parameter, compute a deviation of H and HR, and computes a reduction of the reference channel margin.

A central challenge in next-generation 5G/6G networks is achieving high message reliability despite very dense usage and unavoidable signal fading at high frequencies. To provide enhanced fault detection, localization, and mitigation, the disclosed procedures can enable an AI model (or an algorithm derived from it) to discriminate between faulted and unfaulted message elements according to signal quality, modulation parameters, and other inputs. The AI model can estimate the likelihood that each message element is faulted, and predict the most probable corrected value, among other outputs. The AI model can also consider the quality of a demodulation reference used to demodulate the message, and the quality of the associated error-detection code. The AI model can also consider previously received messages to the same receiver, or messages of a similar type. Fault mitigation by the receiver can save substantial time and resources by avoiding a retransmission. Many other aspects are disclosed.

Methods and devices for transmitting data via a transmission medium. One example method includes ascertaining a probability of at least one transmission error during a future data transmission, and determining, based on the probability, whether the future data transmission should be at least temporarily suspended.

The present invention relates to a data transmission control method, an information sending end and receiving end, and an aerial vehicle image transmission system. The data transmission control method includes: receiving data frames sent by a sending end, the data frames being sequentially sent by the sending end in an order of a data frame sequence; and returning an acknowledgement signal corresponding to a currently-received data frame N to the sending end, to enable the information sending end to determine a current network status according to the acknowledgement signal, and adjusting data encoding quality of the sent data frame based on the current network status. In the method, delays for image quality and transmission speed to recover when a network status recovers can be effectively reduced by rapidly determining a current network status based on the feedback of an acknowledgement signal.

Message reliability is a key requirement of 5G/6G communications. In many challenging network environments, two successive retransmissions of a message can both be corrupted, greatly reducing reliability. Therefore, methods are disclosed for identifying faulted message elements according to a metric that includes the waveform or SNR of the message element, its modulation quality, and a consistency check between the received versions. The receiver can then assemble a merged message version by selecting the higher quality version of each message element from the two (or more) corrupted versions, and thereby avoid requesting yet another retransmission. In addition, the receiver can monitor the background level and, if it is above a predetermined limit, can request that the receiver store the message for a predetermined time, or until the background level subsides below the limit.