Demodulation of 5G and 6G messages involves complex demodulation reference signals that occupy valuable resource grid area. Disclosed are numerous configurations of short-form demodulation reference types that provide sufficient modulation information to enable a receiver to determine all of the predetermined modulation levels of the modulation scheme, while consuming minimal resources. Selection of the appropriate modulation scheme and demodulation reference type generally depends on many competing factors. Therefore, an AI model may be required. The AI model may be trained on network data to recommend when a different modulation scheme would be beneficial, as the network default or to serve a particular user device. The AI model may be configured to select between the disclosed short-form demodulation references and prior-art demodulation reference signals, thereby optimizing the subsequent network performance as well as individual user satisfaction, while minimizing costs, bandwidth, power, and especially avoiding interference with neighboring cells.

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.

Methods, systems, and devices for wireless communication are described. A communication device, such as a user equipment (UE) may receive a set of demodulation reference signal (DMRS) samples including a first subset of DMRS samples associated with a first power level and a second subset of DMRS samples associated with a second power level. The UE may perform a digital post distortion operation based on the first subset of DMRS samples associated with the first power level and the second subset of DMRS samples associated with the second power level. The UE may receive the wireless communication based on performing the digital post distortion operation.

Methods, systems, and devices for wireless communication are described. A communication device, such as a user equipment (UE) may receive a set of demodulation reference signal (DMRS) samples including a first subset of DMRS samples associated with a first power level and a second subset of DMRS samples associated with a second power level. The UE may perform a digital post distortion operation based on the first subset of DMRS samples associated with the first power level and the second subset of DMRS samples associated with the second power level. The UE may receive the wireless communication based on performing the digital post distortion operation.

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.

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.

A dither amplitude circuit has a current circuit for receiving DC voltage signals and generating a direct current on a basis thereof, and generating a dither signal, and a dither current on the basis thereof, applying the dither current to the direct current in order to obtain a drive current, artificially modulating the amplitude of the dither signal or a directly modulated drive current with an amplitude modulation deviation on a periodic basis such that a quantization threshold is reliably exceeded. The dither amplitude circuit also has a return current detection circuit that generates numerous quantized digital values in a time period on a basis of the drive current flowing through a load, such that the dither amplitude can be determined from the numerous digital values.

A signal receiving method include: demodulating a signal received from a transmitting apparatus to generate values based on 1024-quadrature amplitude modulation (QAM); splitting the values into a plurality of groups; deinterleaving the plurality of groups based on a preset interleaving order; and decoding values of the deinterleaved plurality of groups based on a low density parity check (LDPC) code, a code rate of the LDPC code being 6/15 and a code length of the LDPC code being 64800, wherein the plurality of groups are deinterleaved based on a predetermined equation.

A tracking and rejection filter for use in a receiver of a radio includes a selectable filter configured to provide an output digital in-phase signal and an output digital quadrature signal based on a center frequency, a digital in-phase signal corresponding to an in-phase component of a received radio frequency signal, and a digital quadrature signal corresponding to a quadrature component of the received radio frequency signal. The tracking and rejection filter includes a select circuit configured to select the center frequency of the selectable filter according to whether an interfering signal is detected in a target frequency band of the received radio frequency signal. The center frequency is selected from a predetermined frequency and an estimated center frequency determined using an instantaneous frequency signal. The instantaneous frequency signal is based on the digital in-phase signal and the digital quadrature signal.

A modulation format estimation device 100 includes: a frequency shift correction unit 112 configured to estimate the amount of a frequency shift using a baseband signal acquired from a received signal and correct the baseband signal based on an estimation result; a frequency error generation unit 122 configured to generate a plurality of frequency errors from a range set based on an error occurring in the estimation of the frequency shift amount; a frequency error introduction unit 123 configured to acquire learning baseband signals in which each of a plurality of source signals modulated by different modulation formats is frequency-shifted by each frequency error; and a modulation format estimation unit 113 configured to input a corrected baseband signal to a first machine learning model created by machine learning using learning data including the plurality of learning baseband signals and a label, and estimate a modulation format of the received signal.