ULTRALOGIC 6G, LLC

A base station can maintain a transmission beam toward a user device, even in changing conditions, by realtime incremental feedback in 5G and 6G. The incremental feedback is a compact one-of-three request, from the user device, indicating a higher, keep-same, or lower adjustment of a transmission parameter such as the power or beam angle. The base station then adds or subtracts a predetermined increment to that transmission parameter, thereby improving reception with each downlink message. Such incremental adjustment continues with each message to the user device, until the optimum value is reached, at which time the user device selects the keep-same choice. For efficient optimization, the base station can vary the increment size, for example escalating to larger sizes upon repeated same-sign requests, and then de-escalating to smaller increments for fine-tuning and beam optimization. Many other aspects are disclosed.

Billions of low-cost autonomous sensors and actuators are expected to join 5G and 6G networks. Most of them will use DRX (discontinuous reception) to save power. However, if an incoming message arrives while the device is in sleep-mode, a complex process is required for the device to obtain its message. Instead, a fast, lean polling method is provided, in which the base station can inform the user devices periodically of their messages on hold, using an encoded matrix of bits, one bit per user device in the network. When a user device wakes up and receives the matrix, it can determine, according to its bit position, whether it has messages waiting. If so, the user device can then transmit a very brief signal in an allocated reply region, at a specific location corresponding to its bit position in the matrix, thereby requesting the message or messages on hold.

In 5G and 6G, a message received with even a single-bit fault generally discarded and a retransmission is requested. However, the faulted message contains a wealth of information that the receiver can use to avoid, or at least mitigate, such faults thereafter. Disclosed is a method for comparing a faulted message with an unfaulted copy, thereby determining which part of the message is faulted, and specifically how it was faulted. For example, the fault may have been an amplitude fault in which a demodulated amplitude differs by one level from the initially modulated amplitude, or it may be a phase fault in which the received phase differs by one phase level, or there may be a displacement by multiple amplitude or phase levels (a non-adjacent fault). Different mitigation strategies are disclosed for each situation, including AI models configured to select a suitable modulation scheme to combat specific faults.

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.

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.

The guard-space reference disclosed herein is a signal transmitted in the guard spaces separating message data intervals, and configured to reveal amplitude noise or phase noise or both, using 5G or 6G technology. For example, the transmitter can transmit an I-branch with a predetermined amplitude level, and an orthogonal Q branch with zero amplitude, in the guard space. The receiver can measure the received amplitude and phase of the guard-space reference, subtract the initial amplitude and phase, and thereby measure both phase noise and amplitude noise. The receiver can then subtract the measured amplitude and phase effects from the message data, thereby negating both phase noise and amplitude noise. Guard-space references disclosed herein can preserve the inter-subcarrier orthogonality, inter-symbol separation, and signal circularity advantages of prior art, while additionally providing both amplitude noise and phase noise mitigation. Examples are suitable for wireless standards.

Disclosed are procedures and examples of feedback messages that enable a user device to request transmission adjustments in 5G and 6G. The feedback message may indicate a selected beam direction, a power adjustment request, a grant request, and an acknowledgement, among other things. For example, a downlink message may include several beam test signals, each transmitted in a different direction, and the user device can indicate, in the feedback message, which test signal was best received. Thus the feedback enables the base station to adjust its beam toward the user device upon each message. In addition, the user device can determine the amplitude or signal quality received in the favored test signal, and can then include an incremental power adjustment request in the feedback message. In addition, the user device can request a retransmission and/or an uplink grant, and can optionally provide a BSR as well, in 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.

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.

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. Since the amplitude-phase modulation scheme provides substantially larger phase margins than QAM with the same information content, substantially higher frequencies can be accessed while retaining high reliability.