Abstract: 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.

Abstract: Procedures are disclosed to enable a wireless device to determine its alignment direction toward a base station or another device in 5G or 6G, using a “phased beam-alignment pulse”, which is a transmitted pulse having phase modulation that varies with angle. For example, the pulse may be transmitted spanning 360 degrees of angle, and may be phase modulated varying from 0 to 360 degrees of phase in the same angular range. A user device can receive the phased beam-alignment pulse and immediately determine, from the phase, the alignment angle toward the transmitter. In another embodiment, the transmitter transmits a uniform, non-directional pulse, and the receiver receives it using an antenna configured to impose an angle-dependent phase shift, thereby indicating the alignment direction. With either method, wireless entities can align their beams rapidly and efficiently, using just one or two resource elements, without complex encoding or time-consuming handshaking.

Abstract: Enhanced phase-noise mitigation is possible at low-to-no cost. Communication at the high frequencies envisioned for late 5G and 6G will require much better phase-noise control than current frequency bands, because the tight margins will result in excessive phase faults and greatly reduced throughput. The disclosed examples show how to use two modulation schemes to provide the best phase margins at the final step. For example, the message can be initially modulated in classical amplitude-phase modulation as transmitted, but is received and processed using convenient QAM orthogonal components. Then the receiver can convert the results back to the amplitude-phase modulation scheme analytically, and can finally demodulate using calibrated amplitude and phase levels derived from a proximate demodulation reference.

Abstract: To provide improved phase noise tolerance and improved identification of certain fault types, a modulation/demodulation procedure is disclosed for 5G and 6G. The transmitter can modulate a message according to the amplitude and phase of the overall waveform to be emitted, modulated according to predetermined amplitude and phase levels of the modulation scheme. The receiver can then separate the received waveform into orthogonal I and Q branches and measure their branch amplitudes, as usual. The receiver can then convert the branch amplitude measurements back into the original amplitude-phase modulation parameters using formulas provided. The receiver can then demodulate the message by comparing the overall amplitude and phase of each message element to the predetermined amplitude and phase levels of the modulation scheme, which thereby provides substantially increased phase noise tolerance at high frequencies.

Abstract: Artificial Intelligence (AI) is well-suited to mitigate message faults by combining analog and digital information in 5G and 6G communications. The analog information includes everything measureable about the waveform signal as-received, and the digital information includes the error-detection code accompanying the message. For example, the AI model can localize the most likely faulted message elements according to amplitude fluctuations or phase deviations or other signaling irregularity, and can then use the error-detection code to calculate the corrected values of the faulted message elements. The AI model can also check the error-detection code itself for faults and consistency, as well as a demodulation reference that was used to demodulate the message, thereby avoiding a defective mitigation if either of those is faulted. The AI model can provide output including the most likely corrected version of the message, as well as a comparison with other possible versions, if any.

Abstract: For efficient communication in 5G and 6G, transmission beams are to be aligned with each user device as soon as possible during the initial access procedure. Prior-art procedures for downlink beam alignment consume large amounts of power and resources. Therefore, low-complexity formats and procedures are disclosed for a new user device to indicate its angular position relative to the base station upon entering the network. In one embodiment, the SSB message is broadcast isotropically, along with test signals which are transmitted in different directions. The user device indicates which test signal is best received, thereby indicating its angular direction. In a second embodiment, the test signals and feedback messages are appended to various entry messages after initial contact. Using either method, the base station and user device can then aim their beams toward the other, for enhanced signal quality thereafter.

Abstract: Wireless messages in 5G and 6G include error-detection codes appended or embedded in the message, such as Polar, LDPC, Turbo, and CRC codes. Analog fault tests based on the waveform signal of each message element can enhance the fault localization and mitigation, in many cases. Unexpected amplitude or phase variations, ambiguous modulation values, and polarization irregularities in or between message elements can localize the likely faulted message element(s). Turbo, Polar, and certain LDPC versions employ soft-decision decoding natively, and therefore can include the waveform results as extrinsic information. Other codes, such as basic parity, CRC, and basic LDPC, use hard-decision processes, but when combined with waveform diagnostics, can localize and mitigate faults. In life-threatening situations, rapid mitigation of a faulted message may save lives.

Abstract: In a message modulated according to orthogonal amplitude-modulated component signals in 5G or 6G, the receiver can attempt to recover a corrupted message by evaluating the modulation quality of each component signal in each message element. The modulation quality of each component signal may be determined according to a distance between the amplitude of the component signal and the closest amplitude level of the modulation scheme, as determined by a prior demodulation reference. The modulation quality may also be determined by the SNR and amplitude stability of the component signal. Upon detecting a corrupted message, the receiver can identify the faulted message elements according to modulation quality, and if the faulted message elements are clustered in a portion of the message (as is common), the receiver can request that just the faulted portion be retransmitted, saving time and bandwidth.

Abstract: 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.

Abstract: In a traffic emergency, there is no time for a human to integrate multiple sensor data streams and devise a plan for avoiding a collision. Only the electronic reflexes of a trained automatic system can provide evasive action in time. Disclosed is an artificial intelligence (AI) model trained to recognize an imminent collision based on sensor data, rapidly devise and test a large number of possible sequences of actions, some drawn from a library of previously-successful strategies and others invented by the AI model. If any sequence can avoid the collision, the AI model implements that sequence immediately. If none of the sequences can avoid the collision, the AI model calculates the harm caused by each sequence and picks the one that causes the least harm (fatalities, injuries, etc.) for implementation. AI is needed to find a possible solution in time to implement it and thereby mitigate the imminent collision.

Abstract: The future of manufacturing, agriculture, distribution, healthcare, and virtually every other labor-intensive endeavor is robotic—a multitude of autonomous, mobile, robotic systems. One of the many problems this will bring is the coordination of independently-navigating robots in limited spaces. Communication is the key to coordination. Fixed-position and mobile robots can identify and localize each other in real-time using pulses of visible or infrared light, synchronized with wireless messages in 5G or 6G. A fixed-position robotic assembly device can identify a mobile robot bringing raw components, by exchanging synchronized pulses and messages. Busy robots in a distribution center can avoid collisions and improve throughput by coordinating with other proximate robots, using the communication tools provided herein. Fixed-position robots can enforce boundary conditions and provide oversight, keeping innumerable mobile devices in-lane and on-task. Many other aspects and applications are provided.

Abstract: In 5G and 6G, efficient communication relies on narrow communication beams directed between the transmitter and the intended recipient. However, the optimal beam direction changes whenever the user moves, as in traffic. The user device can inform the base station of the user's initial location, speed, and direction of travel, so the base station can calculate the appropriate beam direction versus time. However, if the mobile user device changes in speed or direction, this may result in an erroneous calculation by the base station. The directional error may be insignificant if the directional error is much less than the beam width. The mobile user device can calculate the directional error based on the change in speed or direction, and when the directional error becomes comparable to the beam width of the base station, the user can transmit a message to the base station indicating the new location, speed, and direction.

Abstract: 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.

Abstract: Wireless messages in 5G and 6G include error-detection codes appended or embedded in the message, such as Polar, LDPC, Turbo, and CRC codes. Analog fault tests based on the waveform signal of each message element can enhance the fault localization and mitigation, in many cases. Unexpected amplitude or phase variations, ambiguous modulation values, and polarization irregularities in or between message elements can localize the likely faulted message element(s). Turbo, Polar, and certain LDPC versions employ soft-decision decoding natively, and therefore can include the waveform results as extrinsic information. Other codes, such as basic parity, CRC, and basic LDPC, use hard-decision processes, but when combined with waveform diagnostics, can localize and mitigate faults. In life-threatening situations, rapid mitigation of a faulted message may save lives.

Abstract: High-frequency communications in 5G and especially 6G will require precise synchronization of user devices with the base station, including periodically setting the user device clock time and clock rate to mitigate oscillator drift. The base station can assist user devices by periodically providing a timing signal containing a mid-symbol timestamp point, which is a signal that includes an abrupt change in phase or amplitude centered in the symbol-time. A receiver can analyze the timing signal and determine precisely the time of arrival of the timestamp point, and correct the receiver's clock to ensure that uplink messages will then arrive at the base station synchronized with the base station's resource grid. In addition, the base station can provide two timing signals in which the mid-symbol timestamp points are separated by a predetermined separation, thereby assisting the user devices in adjusting their clock rates.

Abstract: 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.

Abstract: Phase noise is a limiting factor in high-frequency 5G and 6G communications. Disclosed is a multiplexed amplitude-phase modulation scheme that can provide extremely wide phase noise margins at high frequencies. The transmitter can transmit a wave modulated in amplitude and phase, configured to provide a wide separation of phase states. The receiver, on the other hand, demodulates the message using quadrature amplitude modulation QAM, since that is generally more economical and technically preferred for signal processing. The demodulated message, however, still retains the large phase margins. As a further benefit, the examples illustrate non-square and asymmetric modulation schemes, which can extend the noise margins even further. By modulating with amplitude and phase, but demodulating with orthogonal branch signals, wireless networks can expand into high-frequency bandwidths while retaining high reliability and high throughput, as required for wireless applications of tomorrow.

Abstract: In addition to the normal modulation states of 5G and 6G (BPSK, QPSK, 16QAM, etc.), the modulation scheme may include one or more zero-power states in which an amplitude is transmitted with very low or zero power. The receiver can detect the zero-power state and treat that state as an additional modulation state of the modulation scheme, thereby increasing the information content of each message element due to the additional number of modulation states available for encoding. Alternatively, the zero-power state or states may be used for special options, such as indicating a beginning or an ending of the message. Zero-power states may also be used to separate the message from an associated demodulation reference or to separate sequential messages. Substantial power may be saved since the zero-power states require very little (or no) transmitter power.

Abstract: A searchable, portable network database of base station information can greatly simplify discovery, selection, and registration of user devices on a preferred cell in 5G or 6G. In addition, based on location, the user device can select the base station that is likely to provide the best signal. The network database can provide all of the static system information of the SSB message, which enables prospective user devices to receive downlink messages without searching for the SSB. In addition, the network database can provide all of the static system information contained in the SIB 1 message, which enables prospective user devices to transmit uplink messages without searching for the SIB1. Initial synchronization can be obtained by receiving any broadcast from the base station, or by receiving timing signals from a navigation satellite, among other ways. The user device can thereby avoid most or all of the arduous prior-art cell discovery steps.

Abstract: 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.