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.

A key requirement for 5G and 6G networking is reliability. Message faults are inevitable, and therefore procedures are needed to identify each fault location in a message and, if possible, to rectify it. Disclosed herein are artificial intelligence AI models and procedures for mitigating faults in wireless messages by (a) evaluating the signal quality of each message element according to waveform features and modulation deviations, (b) evaluating the fault probability of each message element by seeking correlations, which may be subtle, among the various waveform measurements including polarization and frequency offset, and (c) correcting the faults according to the message type, apparent format, intent or meaning, typical previous messages of a similar type, correlations of bit patterns and symbol sequences, error-detection codes if present, and other content-based indicators uncovered during model development. Automatic, real-time fault localization and correction may save substantial time and resources while substantially enhancing messaging reliability.

A classification apparatus includes a memory and a processor. The memory is configured to store rules corresponding to a corpus of rules in respective rule entries, each rule includes a respective set of unmasked bits having corresponding bit values, and at least some of the rules include masked bits. The rules in the corpus conform to respective Rule Patterns (RPs), each RP defining a respective sequence of masked and unmasked bits. The processor is configured to cluster the RPs, using a clustering criterion, into extended Rule Patterns (eRPs) associated with respective hash tables including buckets for storing rule entries. The clustering criterion aims to minimize an overall number of the eRPs while meeting a collision condition that depends on a specified maximal number of rule entries per bucket.

A receiver device is configured to receive a packet. The receiver device includes a physical layer circuit. The physical layer circuit is configured to demodulate the packet to acquire at least one indicator associated with the packet, and determine whether the packet is an abnormal packet or not according to the at least one indicator. If the packet is the abnormal packet, the physical layer circuit drops the packet.

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.

Message faults are an increasing problem for 5G and expected 6G networks, due to growth, crowding, and signal fading problems. Disclosed are procedures for determining which particular message element of a corrupted message is faulted, and optionally the most likely correction. A receiver can identify the faulted message element by measuring the fluctuations, in phase and amplitude, of the waveform of each message element, as well as the modulation quality, frequency offset, and other signal measurements. Faulted message elements are likely to have higher fluctuations, higher modulation deviations, and higher signal irregularities, than the unfaulted ones. Mitigation can then be applied to the faulted message elements, thereby recovering the correct message and avoiding a costly retransmission delay. AI models may enhance the fault detection sensitivity by exploiting correlations between the various waveform measurement parameters, and then may predict the corrected value of the faulted message elements.

A method for delivering payloads through an interface includes receiving an out-of-sequence data transfer unit, DTU; segmenting the out-of-sequence DTU into at least one data unit; determining at least one priority level of the at least one data unit; and for any particular data unit among the at least one data unit, (1) selectively forwarding a payload of the particular data unit to the interface in response to priority level thereof satisfying a priority condition; or (2) holding the particular data unit in response to the priority level thereof not satisfying the priority condition.

Examples disclosed herein relate to interpreting a device communication based on process state. In one implementation, first electronic device transmits a request for data to a second electronic device. For example, the second electronic device may not include a capability to independently send a message to the first electronic device. The first electronic device may determine based on stored process state information whether a response received from the second electronic device is associated with at least one of an acknowledgment of previously data sent, an amount of data available to transmit to the first electronic device, and the available data.

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.

A non-transitory computer readable medium storing at least one program, wherein a wireless communication method is performed while the program is executed. The wireless communication method comprises: (a) receiving a plurality of data groups, wherein the data groups do not pass an error checking procedure; (b) selecting a portion of at least one of the data groups; and (c) reconstructing a reconstruction data group based on the portions selected in the step (b).