Abstract: A major goal of 5G and especially 6G is reliable, low-latency communication. Unfortunately, higher density networks result in increasing interference, and higher frequency bands inevitably have signal fading problems, leading to frequency message faults. To restore high-speed, high-reliability messaging, methods are disclosed for evaluating the signal quality of each message element of a received message so that any faulting can be localized to the message elements with the lowest signal quality. Numerous contributions to signal quality are disclosed, including modulation, amplitude and phase stability, polarization and inter-symbol irregularities, expected message format and meaning, and common or unexpected bit sequences. Many further aspects are included.

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.

Abstract: Messages are transmitted in closely-spaced subcarriers in 5G and 6G, configured so that each subcarrier signal is orthogonal to the adjacent subcarrier signals. However, many effects can penetrate that orthogonality—distortion, interference, frequency variations, amplitude variations, crosstalk, etc.—collectively termed energy spill-over. To combat this problem, a receiver can determine the total energy spill-over into adjacent subcarriers by measuring a residual signal in a subcarrier with no transmission, adjacent to another subcarrier with a known transmission. The receiver can measure the amplitude, phase, temporal or spectral properties, and so forth of the residual signal. The receiver can then correct the message during signal processing, by calculating a function of the residual signal and subtracting it from each digitized subcarrier signal of a message.

Abstract: The present disclosure shows how artificial intelligence (AI) can analyze the large number of physical, mental, and other intangible metrics - as well as analyzing all available background information - to determine which athlete in a list of potential candidates is most likely to succeed in the quarterback position in American-style football, according to the draft-candidate procedures of the National Football League. Data on each quarterback candidate’s strength, agility, speed, mental focus, and other features can be provided as input to the AI model, which then provides a prediction of the quarterback candidate’s probability of success at the next highest level, and especially on the fiercely-grueling and merciless playing field of NFL football, where failure and defeat is not an option.

Abstract: Artificial Intelligence (AI) can rapidly evaluate a faulted message in 5G or 6G, calculate a likelihood that each message element is faulted, and optionally suggest a most probable corrected version for each of the likely faulted message elements. To do so, the AI takes in numerous factors besides the message itself, such as the modulation quality of each message element, the proximity and quality of a nearest demodulation reference, a signal-to-noise ratio of the message element, a measure of current electromagnetic noise during the message element, an expected format or expected codewords based on prior messages or convention, and other factors. The AI model can then provide guidance as to mitigation, such as choosing whether to request a retransmission or attempting to vary the likely faulted message elements. The AI model can be adapted to fixed-site computers or to the more limited computers of a mobile user device.

Abstract: 5G and 6G wireless networks can use artificial intelligence models to optimize performance by measuring the rate of message faults, and in particular the rate of various types of faults. For example, the amplitude faults include a modulation amplitude distorted by a small or large amplitude change, and whether the faults cluster in the high or low amplitude modulation portions of a constellation chart, among many other inputs related to network operations. The artificial intelligence model can be configured to predict the subsequent network performance according to each modulation scheme available to the network, thereby enabling the network to select a more effective modulation scheme. Alternatively, the artificial intelligence model can select the preferred modulation scheme and recommend the change to the network operators, or it can implement the change automatically if enabled to do so.

Abstract: Reliable communication in 5G and planned 6G networks depends on the fast, realtime decision-making regarding signal quality, beam parameters, and modulation. Rapid-fire messaging among hordes of diverse users in a busy urban setting will require efficient procedures for managing beam parameters in realtime, under complex changing conditions. The required time scales have already shrunk below the reflex time of humans. Therefore, artificial intelligence procedures are required for certain key aspects of feedback beam management. Disclosed are AI models for (a) instant evaluation of signal quality based on received signal properties and backgrounds, (b) incremental adjustment of beam parameters on demand, and (c) adapting the modulation scheme and size, based on fault patterns and QoS requirements. Each of these AI models can be tuned using network data, and then adapted for field use by a base station or a user device, for realtime beam parameter optimization.

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: Vehicles in traffic cannot coordinate their actions properly in 5G and 6G without knowing the location and the wireless address of the other vehicle. GNSS signals are generally too slow and too imprecise to discern vehicles in, for example, adjacent lanes. Directional wireless beams are subject to reflections from conducting surfaces, producing chaotic signals and false locations if more than one vehicle is within the transmission beam. To provide precise localization in traffic, methods are disclosed for multiple vehicles (or other mobile devices) to acquire satellite signals simultaneously, and then analyze the data differentially, thereby canceling major uncertainties (such as propagation variations, ephemeris motion, and clock jitter), and thereby determining the relative positions precisely. Unlike prior-art “precision” positioning methods, the disclosed methods do not require averaging multiple acquisitions.

Abstract: Message faults are inevitable in the high-throughput environment of 5G and planned 6G. Retransmissions are costly in time and resources, while generating extra backgrounds and interference. Therefore, methods are disclosed for recovering a faulted message by identifying and correcting each mis-demodulated message element. The faulted message elements generally have substantially lower modulation quality than the correctly demodulated elements, and can be identified by determining the modulation quality of each received message element. If the number of faulted message elements is small, the receiver may correct them using a grid search tested by an associated error-detection code. If the number of faults exceeds a predetermined threshold, the receiver can request a retransmission, and then assemble a merged copy of the message by selecting the message element with the best modulation quality from each version.

Abstract: Amplitude noise, phase noise, and interference can be mitigated in 5G and 6G by exploiting advantages of two different modulation schemes. A message may be modulated according to a first modulation scheme, such as multiplexed amplitude and phase modulation, and then received (including noise and interference) according to a second modulation scheme, such as QAM (quadrature amplitude modulation). In addition, a compact demodulation reference can be transmitted wherein a first resource element exhibits a particular phase along with a maximum and a minimum branch amplitude, and a second resource element is blank. The receiver calibrates the amplitude levels according to the demodulation reference, calculates the phase noise according to a ratio of the two branch amplitudes, and measures the interference according to the unpowered (blank) second resource element. The receiver can then demodulate the message according to the second modulation scheme, while correcting for phase noise, fading, and interference.

Abstract: Inter-vehicle signaling is essential for cooperative collision mitigation. Disclosed are systems and methods for autonomous or semi-autonomous vehicles to identify each other, localize each other, and then cooperate in avoiding a collision if avoidable, and minimizing the harm of the collision if not avoidable. Examples include simultaneous wireless and infrared signals that enable other vehicles to specifically identify each cooperating vehicle, so that an evasion strategy can be developed. Additionally, the signaling can include, in the wireless messages or the infrared signals, or both, the wireless address of the transmitting vehicle, thereby enabling unicast communication and greatly improved coordination thereafter. This system will save lives.

Abstract: Amplitude noise, phase noise, and interference can be mitigated in 5G and 6G by exploiting advantages of two different modulation schemes. A message may be modulated according to a first modulation scheme, such as multiplexed amplitude and phase modulation, and then received (including noise and interference) according to a second modulation scheme, such as QAM (quadrature amplitude modulation). In addition, a compact demodulation reference can be transmitted wherein a first resource element exhibits a particular phase along with a maximum and a minimum branch amplitude, and a second resource element is blank. The receiver calibrates the amplitude levels according to the demodulation reference, calculates the phase noise according to a ratio of the two branch amplitudes, and measures the interference according to the unpowered (blank) second resource element. The receiver can then demodulate the message according to the second modulation scheme, while correcting for phase noise, fading, and interference.

Abstract: Uplink messages in 5G and 6G are expected to arrive at the base station in alignment with the base station's resource grid, at the proper time and frequency. Disclosed are lean procedures and compact timing signals that can enable user devices to maintain synchronization with a base station's resource grid. Shaped timing signals are disclosed that, when measured by a receiver, can indicate whether the receiver's clock is synchronized with the transmitter's clock, or is in disagreement, and in which direction, and by how much. The receiver thereby determines the clock error by amplitude measurements only, since the timing signal is configured to convert the timing error into a readily determined amplitude value, which the receiver can quantify using normal amplitude-demodulation procedures. The receiver's amplitude resolution corresponds to the time resolution achievable. No special time-measurement signal processing is required. No synchronization messages or other legacy overhead are required.

Abstract: Feedback based on signal reception is essential for managing wireless communications in 5G and planned 6G networks. Disclosed procedures include brief test signals and brief responsive feedback signaling, including modulation schemes that encode multiple parameter feedback with minimal consumption of resources and power. For example, user device can indicate, in a very terse code, an improved beam direction, a beam power adjustment, and various scheduling/acknowledgement options. In addition, the custom modulation schemes can provide high phase noise tolerance for high-frequency operations, yet can be demodulated without a prior amplitude calibration for low-complexity user devices, at negligible cost in energy and resources. Many other aspects are disclosed.

Abstract: Synchronization between a base station and user devices is crucial for high-frequency 5G and especially 6G communications. Disclosed is a fast, compact timing signal comprising an abrupt change in a downlink signal modulation, centrally positioned in a predetermined symbol-time. Each user device can receive the downlink timing signal, digitize the received signal, and determine a specific time at which the modulation switches. The user device can then determine whether the specific time is at a midpoint of the predetermined symbol-time, and if not, can determine a timing offset according to the difference. Since the user device's symbol-time boundaries are determined by the user device's clock, whereas the timing signal is determined by the base station, the timing offset generally indicates a setting error of the user device's clock, in which case the user device can reset its clock to agree with the base station clock.

Abstract: An ultra-lean, low-complexity 5G/6G procedure for adjusting transmission beam parameters for optimal reception by a particular user device. The transmitter can append multiple brief test signals to each message, using a different transmission parameter for each test signal. The receiver can reply (optionally in an acknowledgement) indicating which test signal was best received. The transmitter can then use the selected value of the transmission parameter for the next message, thereby successively approaching the optimal value. Each adjustment step can be a predetermined increment value, which is added or subtracted from the previous value depending on which test signal was selected by the receiver. As conditions change (or as the user device moves), the beam parameter can be updated again upon each downlink transmission, thereby tracking the user device's preferred transmission value in realtime.

Abstract: A 5G/6G network can include a modulation scheme for uplink and downlink messaging using a special zero-power modulation level, in addition to the regular amplitude modulation levels of, for example, QAM. The receiver can demodulate each message element by comparing the amplitude to the various amplitude levels, including the zero-power level, and thereby increase the bits per message element, and hence the communication throughput, at no increase in transmitted power. In addition, the zero-power states can reveal intrusive noise and interference in the proximate message, enabling correction before the demodulation. The zero-power amplitude may be added to conventional modulation schemes, such as an additional zero-power amplitude level in QAM, PSK, and amplitude-phase modulation schemes, thereby providing greater versatility at little or no cost and no additional transmission power.

Abstract: Currently in 5G and 6G, user devices are required to search a large number of possible combinations of frequencies, start times, message lengths, and error-codes to find their downlink control messages. Disclosed is a faster and much simpler method for user devices to determine which downlink messages are intended for which user device, and also to determine the beginning and ending of each message within a stream of arriving data. The base station can affix a predetermined demarcation bit-sequence to the beginning of each message, and a different demarcation to the ending of the message. As an option, the demarcations may also serve as a demodulation reference, including predetermined amplitudes of the modulation scheme, immediately proximate to the message. Downlink demarcations may also include a characteristic feature, such as a gap of no transmission, for easy recognition and for evaluating noise.

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.