The apparatus operates communicates with a second wireless device during an active period and performs a radio impairment calibration during a periodic silent period. The calibration may include a receiver calibration, e.g., including gain state training. The calibration may comprise a transmitter calibration.

A faulted message element in 5G or 6G can often be identified according to its modulation parameters, including a large deviation of the branch amplitudes from the predetermined amplitude levels of the modulation scheme, and/or the SNR of the branch amplitudes, and/or an amplitude variation of the raw signal or the branches during the message element, and/or an inconsistency between the modulation state as determined by the amplitude and phase of the raw waveform versus the amplitudes of the orthogonal branch signals, among other measures of modulation quality. An AI model may be necessary to correlate the various quality measures, and optionally to determine the correct demodulation of faulted message elements. Costly, time-consuming retransmissions may be avoided by determining the correct demodulation of each message element at the receiver, thereby improving throughput and reliability with fewer delays.

A wideband transceiver for a gateway is presented. The wideband transceiver may interface with the gateway’s processors over an interface that carries data encapsulated in baseband frames. The wideband transceiver may comprise a modulator and a high power amplifier and may improve a transmitted signal quality and may utilize a wideband for wireless communications, for example, for a satellite communication system.

An orthogonal frequency-division multiplexing (OFDM) based radar signal comprising Q sub-carriers adapted to push an IQ-imbalance component out of a subset of L contiguous range bins of range profiles derived out of the received radar signal and wherein L is at most Q/2, is disclosed.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit, to a base station, a capability message indicating a capability supporting network-side phase noise compensation. The UE may transmit, to the base station, phase tracking reference signals based on transmitting the capability message. In an example, the phase tracking reference signals may include a UE phase noise component which may be associated with a local oscillator of the UE. The UE may receive, from the base station, a compensated downlink transmission that is compensated based on the UE phase noise component. In generating the compensated downlink transmission, the base station may apply a multiplication factor associated with the estimated UE phase noise component to the compensated downlink transmission.

A method of pre-compensating for transmitter in-phase (I) and quadrature (Q) mismatch (IQMM) may include sending a signal through an up-converter of a transmit path to provide an up-converted signal, determining the up-converted signal, determining one or more IQMM parameters for the transmit path based on the determined up-converted signal, and determining one or more pre-compensation parameters for the transmit path based on the one or more IQMM parameters for the transmit path. In some embodiments, the up-converted signal may be determined through a receive feedback path. In some embodiments, the up-converted signal may be determined through an envelope detector.

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.

A wireless communication apparatus includes: a plurality of receivers provided so as to correspond to a plurality of respective antennas; a calibration transmitter; and a control unit configured to release, when a UL calibration is executed, connection between the antennas and the respective receivers, and determine a calibration weight to be applied to each of the receivers based on a UL calibration signal transmitted from the calibration transmitter and a UL calibration signal received by each of the receivers.

Techniques are provided for calibrating signal currents in a radio frequency signal generator system, such as an arbitrary waveform generator system. A device comprises a current measurement circuit and a current imbalance correction circuit. The current measurement circuit is configured, during a calibration process, to measure a first current in a first signal path of a radio frequency signal generator, and to measure a second current in a second signal path of the radio frequency signal generator. The current imbalance correction circuit is configured to adjust a current level in at least one of the first signal path and the second signal path of the radio frequency signal generator to correct for an imbalance between the measured first current and the measured second current.

Artificial Intelligence (AI) means are disclosed for enabling network operators to optimize 5G and 6G messaging performance, in real-time. AI models, or fieldable algorithms derived therefrom, can select an appropriate modulation scheme according to network conditions. Modulation variables can then be adjusted to optimize performance, such as throughput or failure rates, for low or high traffic densities. Three development phases are described: network data acquisition including faults experienced under various network conditions, AI structure tuning for accurate prediction of performance, and implementation of a fieldable algorithm based on the AI structure. Network operators can use the fieldable algorithm to compare predicted performance metrics in real-time, according to various operating conditions (such as available modulation schemes), and thereby adjust particular modulation parameters (such as amplitude or phase levels).