Abstract: Networks operating at high frequencies in 5G and 6G may reduce the incidence of phase faulting by declaring that, above a specified frequency, messages are to be modulated according to multiplexed amplitude-phase modulation, instead of the QAM modulation generally used at lower frequencies. Multiplexed amplitude-phase modulation can provide larger phase margins than QAM of the same order, by arranging the modulation phase levels to be equally spaced-apart which they are not in QAM. For example, with 4 amplitude and 4 phase levels (16 states), the various modulation states can be separated by 90 degrees of phase, whereas in 16QAM the minimum phase separation is only 36.9 degrees, a serious problem at higher frequencies where phase noise predominates. In addition, QAM cannot accommodate non-square modulation tables, which are readily provided by amplitude-phase modulation, further enhancing fault-mitigation options.

Abstract: Vehicles can be operated according to an artificial intelligence model contained in an on-board processor. The AI model can analyze sensor data, such as visible or infrared images of traffic, and determine when a collision is possible, whether it has become imminent, and whether the collision is avoidable or unavoidable using sequences of accelerations, braking, and steering. The AI model can also select the most appropriate sequence of actions from a large plurality of calculated sequences to avoid the collision if avoidable, and to minimize the harm if unavoidable. The AI model can also cause a processor to actuate linkages connected to the throttle (or electric power control), brakes (or regenerative braking), and steering to implement the selected sequence of actions. Thus the collision can be avoided or mitigated by an ADAS system or a fully autonomous vehicle.

Abstract: Wireless message faults are an increasing problem in 5G and especially 6G due to network crowding and pathloss attenuation at higher frequencies, among other emergent problems. Therefore, disclosed are methods to diagnose and repair faulted messages by the receiver. For example, an AI program can determine the signal quality of each message element according to the modulation quality, SNR, and other waveform measurements, and thereby identify just a portion of the message that contains all of the likely faults. That portion can then be retransmitted instead of the entire message, saving substantial time. In addition, the AI program can select which message elements of the two versions has the best signal quality, and prepare a merged message from those best-received signals.

Abstract: A wireless receiver in 5G and 6G can localize message faults by defining an “acceptance region” surrounding each of the predetermined allowed modulation states of a modulation scheme. Then, upon receiving a message, the receiver can measure (or calculate from the I and Q branches) the received amplitude and phase of the waveform signal in each message element. If the received amplitude and phase are within one of the acceptance regions, the message element according to the allowed state in that acceptance region. If the received amplitude and phase are not within the acceptance region of any of the allowed states, that message element is faulted. In addition, the receiver can increment an amplitude fault tally and a phase fault tally according to which parameter—amplitude or phase—is outside the acceptance regions, thereby greatly simplifying recovery of the message without a retransmission.

Abstract: A wireless receiver in 5G and 6G can localize message faults by defining an “acceptance region” surrounding each of the predetermined allowed modulation states of a modulation scheme. Then, upon receiving a message, the receiver can measure (or calculate from the I and Q branches) the received amplitude and phase of the waveform signal in each message element. If the received amplitude and phase are within one of the acceptance regions, the message element according to the allowed state in that acceptance region. If the received amplitude and phase are not within the acceptance region of any of the allowed states, that message element is faulted. In addition, the receiver can increment an amplitude fault tally and a phase fault tally according to which parameter—amplitude or phase—is outside the acceptance regions, thereby greatly simplifying recovery of the message without a retransmission.

Abstract: Cybersecurity is a critical requirement for 5G-Advanced and 6G, in part due to the large attack surface represented by the exponentially increasing number of reduced-capability devices needing wireless service but intensely cost-constrained by their applications. Therefore, disclosed is a network topology that enables an unlimited number of basic sensor/actuator “end devices” to communicate, securely, with a user device of a managed 3GPP-compliant network. The user device or “hub” device serves as both manager and gateway, and more importantly, as a firewall preventing attackers from penetrating the managed network through the end devices. In addition, the end devices can obtain iron-clad protection by booting and operating from ROM only, and transferring only pre-configured replies up to their hub device. The topology enables the hub and end devices to perform the edge work largely autonomously, while insulating the managed network from cyberattacks.

Abstract: Mobile user devices, seeing a base station, can usually detect multiple base stations and access points capable of supporting communications in 5G or 6G. However, some of these may be crowded or distant, while others may be close enough to provide excellent reception. To assist the mobile user device in selecting an optimal base station for signal quality, the user device can include a network database that lists the locations and frequencies (and other data) about available base stations and access points. The user device can then use an artificial intelligence (AI) model, or an algorithm derived from AI, to select the base station or access point likely to provide the best signal quality while in transit. This would be much faster and simpler than the current alternative, of blindly searching through all the detectable base station sequentially.

Abstract: An unsolved problem in 5G and 6G is how a mobile user device can select the most appropriate base station or access point for communication. Currently, the user device is forced to do a tedious and time-consuming blind search, and even then is not guaranteed to have found the best one. Therefore, to assist user devices (especially reduced-capability IoT devices) in network discovery, disclosed herein is a “hailing” message that the user device can broadcast, and determine from the base station reply messages which one is closest, or has the best signal, or other criterion. The base stations, upon detecting the hailing message, can use an AI model to determine whether they can accommodate another user device (optionally with coordination among multiple access points for load-leveling), and if so, can transmit a reply message after a delay related to the received power in the hailing message.

Abstract: When two different messages are transmitted at the same time in 5G or 6G, the message is “collided”. The resulting interference causes a message fault, necessitating a costly retransmission. Disclosed is a modulation scheme in which the modulation states are configured so that a message element can be collided by an intruder message, yet the receiver can still recover the original message element. In other cases, depending on the colliding states, the receiver can narrow the possible values of each faulted message element to just two or three possibilities, thereby greatly reducing the amount of time required for testing each combination against an error-detection code. Especially under high-noise conditions, the collision-proof modulation scheme may enable enhanced throughput by identifying and mitigating the faulted message element, and may thereby avoid unnecessary retransmissions.

Abstract: Message faulting is an increasing problem in 5G and future 6G due to network crowding, receiver motion, signal fading at higher frequencies, and greater phase-noise sensitivity. Disclosed herein are methods for analyzing waveform features of the received signal using artificial intelligence, and identifying the likely faulted message elements according to correlations of those waveform features. For example, after demodulating, the receiver can identify a subset of message elements that are all demodulated according to the same modulation level, and can measure a signal parameter for each message element in the subset. The processor can then average the deviations in the subset, and compare those message elements to the average for the subset. If one of the message elements shows an anomalously large deviation from the average, that message element is likely faulted.

Abstract: An autonomous or semi-autonomous vehicle can detect an imminent collision according to sensor data and responsively select an action or a sequence of actions to avoid the collision if avoidable, and to reduce or minimize the harm of the collision if unavoidable. An artificial intelligence model may be used to process the sensor data, detect the imminent collision, and select the collision avoidance action or actions, or the harm-reduction or harm-minimization action or actions. For example, when the collision is considered unavoidable, the vehicle may apply the brakes to reduce the vehicle's speed and therefore, the severity of the impact. When the collision is considered avoidable, the vehicle may automatically apply steering to avoid the potential collision, such as when the vehicle is departing a lane and may collide with a vehicle traveling in the same or opposite direction.

Abstract: Autonomous vehicles, and user-driven vehicles with an emergency intervention capability, can communicate to avoid collisions using 5G/6G technology, but this level of cooperation is possible only if the threatened vehicles have already determined the relative location and wireless address of the other vehicle. Disclosed is a method for wireless vehicles in traffic to exchange distance and angular information of the other vehicles in view, from which a position map can be prepared indicating the relative locations of each participating and non-participating vehicle. In addition, the traffic map can be annotated with the wireless addresses of the participating vehicles, thereby enabling them to communicate instantly in an emergency. The traffic map may be prepared or updated by one of the vehicles in traffic, or by a roadside access point. Satellite data is not necessary for the relative localization, but may be included if available.

Abstract: Disclosed is a method to demodulate messages according to two different modulation schemes in 5G and 6G, and thereby identifying which message elements are likely faulted. The two modulation schemes are QAM in which the signal is a sum of two orthogonal amplitude-modulated “branch” signals, and classical amplitude-phase modulation in which each message element's raw signal is both amplitude and phase modulated. The two schemes have similar information density but different noise sensitivities. Therefore, a receiver can compare the demodulated message using one modulation scheme to the same message demodulated according to the other modulation scheme, and flag any message elements that demodulate differently. In addition, one modulation scheme may be more effective than the other depending on conditions.

Abstract: A receiver may use a trained AI model to recover a faulted 5G/6G message by interpreting the meaning or intent of the message by correlating the message content with one of the “expected” message types. For example, the AI model may consider changes to the message, for consistency with an associated error-detection code, thereby producing a series of candidate messages. The AI model can then determine a likelihood that each of the candidate messages is correct, in the context of the receiver (such as an action or condition of the receiver, or a planned activity of the receiver) or is commonly received in that context. For example, the AI model can be trained to recognize the expected messages or message types, and thereby indicate which candidate message has the highest likelihood of being correct. The AI model may also consider waveform parameters to identify likely faults.

Abstract: An enhanced modulation scheme for 5G and 6G is disclosed in which amplitude modulation includes a zero-power level, which is readily recognized by the receiver as a special demarcation. This can be used, for example, to unambiguously indicate the start and end of a message, such as a downlink control message, which receivers are generally stressed to detect. The zero-power level can be just one branch of a QAM symbol, or the entire symbol, in various embodiments. Advantageously, the enhanced modulation scheme increases the throughput due to the larger number of encoded states, yet requires no increase in transmission power. In some embodiments, the throughput is increased yet the transmission power is actually reduced, with no loss in SNR. Other aspects are disclosed.

Abstract: Feedback messages are essential for controlling transmission beams between network devices in 5G and 6G. Disclosed are brief signals and procedures for a user device to provide feedback to a base station on beam direction, transmission power, beam width, and other transmission parameters. The resource cost is so low, the base station can provide alignment test signals with each downlink message, and the user device can provide feedback selecting a best-received test signal as part of each uplink message, such as an acknowledgement. With such near-real-time beam fine-tuning, base stations and user devices can maintain the tight directional communications required for next-generation communications, without burdening the network with cumbersome feedback messages of prior art. Numerous other aspects are disclosed.

Abstract: In 5G-Advanced and 6G, due to the higher frequencies involved, phase noise is expected to be a major source of faults. Disclosed herein is a small (single resource element) phase-tracking reference signal that also provides an updated amplitude calibration. The compact phase-tracking reference signal, in QAM, includes a first branch at a maximum amplitude and an orthogonal second branch at either the maximum amplitude or zero amplitude, as transmitted. The receiver can readily determine a phase rotation angle according to a ratio of the two branch amplitudes, and also an amplitude calibration according to the vector magnitude of the received branches, thereby negating both amplitude noise and phase noise.

Abstract: A receiver may use a trained AI model to recover a faulted 5G/6G message by interpreting the meaning or intent of the message by correlating the message content with one of the “expected” message types. For example, the AI model may consider changes to the message, for consistency with an associated error-detection code, thereby producing a series of candidate messages. The AI model can then determine a likelihood that each of the candidate messages is correct, in the context of the receiver (such as an action or condition of the receiver, or a planned activity of the receiver) or is commonly received in that context. For example, the AI model can be trained to recognize the expected messages or message types, and thereby indicate which candidate message has the highest likelihood of being correct. The AI model may also consider waveform parameters to identify likely faults.

Abstract: In 5G-Advanced and 6G, due to the higher frequencies involved, phase noise is expected to be a major source of faults. Disclosed herein is a small (single resource element) phase-tracking reference signal that also provides an updated amplitude calibration. The compact phase-tracking reference signal, in QAM, includes a first branch at a maximum amplitude and an orthogonal second branch at either the maximum amplitude or zero amplitude, as transmitted. The receiver can readily determine a phase rotation angle according to a ratio of the two branch amplitudes, and also an amplitude calibration according to the vector magnitude of the received branches, thereby negating both amplitude noise and phase noise.

Abstract: In busy 5G and 6G networks, precise timing and synchronization are key to maintaining throughput with low fault rates. Disclosed are systems and methods for adjusting each user device's clock for proper reception, including downlink propagation delays, uplink propagation delays, round-trip propagation delays, and Doppler shifts, individually for each user device, and including any uplink/downlink asymmetries. The clock adjustment and timing advance of each user device is based on a predetermined transmission schedule for timing signals, broadcast by the base station. The Doppler shift is measured by the base station, according to uplink timing signals, and communicated to the user device in a single final timing signal. The single final timing signal is either frequency-shifted by the measured Doppler shift, or delayed proportional to the Doppler shift, either of which indicates, to the user device, how to apply the correct timing to future uplink messages.