11results about "Modulation type identification" patented technology

Disclosed are systems and techniques for wireless communications. For instance, a network entity may obtain a first plurality of bits from a first buffer, wherein a start position for obtaining the first plurality of bits from the first buffer is based on a redundancy version order. In some cases, the network entity may determine, based on a modulation order and a probability distribution associated with the first plurality of bits, at least one scaling factor for constellation mapping the first plurality of bits. In some examples, the network entity may map, based on the at least one scaling factor, the first plurality of bits to one or more symbols.

Methods, systems, and devices for wireless communications are described. In some examples, a wireless communication device (such as a network entity) may output a downlink control channel message using at least a first resource element (RE) of a first control resource set (CORESET) and using at least a second RE of a second CORESET. The first CORESET and the second CORESET may be linked and may be associated with different transmission configuration indicator (TCI) states. Additionally, each of the first RE and the second RE may be associated with a same payload of the downlink control channel message, and modulation of a first set of symbols mapped to the first RE may be linked to modulation of a second set of symbols mapped to the second RE based on the first CORESET and the second CORESET being linked.

A system includes one or more data processors. The system further includes a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations. The operations include receiving a stream of in-phase components and quadrature phase components as IQ data. The IQ data indicates which subcarriers each of the IQ data is to be sent. The operations further include determining a maximum absolute value associated with the IQ data, determining a constellation measure of a largest bounding box covering all subcarriers of the IQ data, and compressing the IQ data to obtain compressed IQ data based on the maximum absolute value and the constellation measure.

Methods, systems, and devices for wireless communications are described. The described techniques provide for a user equipment (UE) to communicate usingπ2binary phase shift keying (BPSK) modulation according to extended coding rates. In some cases, the UE may receive configuration information that supports modulation orders forπ2 ⁢BPSKmodulation extended to include modulation orders otherwise only configured for quadrature phase shift keying (QPSK) modulation. Further, the UE may support dynamic switching betweenπ2 ⁢BPSKand QPSK modulation. For example, the UE may transmit or receive control signaling indicating a modulation order for an uplink transmission, where the UE or a network entity may determine which modulation scheme and order to use according to communication parameters associated with the uplink transmission, the UE, or both.

The invention relates to the field of Radio Frequency (RF) signal classification and recognition, specifically a method for training of Convolutional Neural Networks (CNN) and applying the trained CNN for the classification or identification of digitally modulated RF signals. The method (100) comprises providing a Deep Learning CNN, providing input digital signals (102), generating an input data set from those signals (103), processing the input data set to provide a training set (104), training the CNN (105). The training (105) comprises the steps of providing time-based signals comprising at least a first symbol timing and generating IQ representations of those signals. The method is suitable for classifying digital signals, and is particularly suited to digitally modulated communications signals.

This invention discloses a modulation signal identification method based on a GRU, with the following specific steps: Step 1, the transmitting end generates several modulation signals of different modulation types, processes them through an AMR Input Switcher module, and outputs a modulation signal with an inserted category index, which is then radiated to the air interface channel; Step 2, the interfering end generates an interference signal and radiates it to the target air interface channel to obtain a damaged I / Q signal; Step 3, the receiving end receives the damaged I / Q signal and preprocesses the received signal to identify the preprocessed signal. This invention's method exhibits high identification accuracy under complex environmental conditions such as high-intensity noise interference and channel impairment.

The present application relates to the technical field of unmanned aerial vehicle remote identification signal detection equipment, and discloses an unmanned aerial vehicle RID signal space-ground terminal complementary anti-signal collision decoding method, comprising: an unmanned aerial vehicle broadcasts an RID signal at a certain time in a certain area on the ground; a ground receiver in the area receives and demodulates the broadcast RID signal, and then transmits data back to a ground data center; an aerial receiver receives the broadcast RID signal of the unmanned aerial vehicle, and transmits a mixed signal back to the ground data center; the received data is reconstructed to obtain a reconstructed RID signal; the mixed signal is subtracted from the reconstructed RID signal to obtain a residual signal; and the residual signal is demodulated. A system for implementing the above unmanned aerial vehicle RID signal space-ground terminal complementary anti-signal collision decoding method is also disclosed. The present application ensures stable and reliable operation of the receiving and decoding system.

The application provides a communication signal automatic modulation recognition method based on symplectic geometry modal decomposition, belongs to the field of signal automatic modulation recognition, and carries out phase space reconstruction on a preprocessed modulation signal, obtains a trajectory matrix and constructs a covariance matrix, decomposes the covariance matrix, obtains an eigenvector matrix and an eigenvalue, constructs and converts a reconstruction matrix, and obtains a dynamic modal component; the dynamic modal component with an eigenvalue greater than a set value is taken as a main modal, the dynamic modal component with an eigenvalue less than the set value is superimposed to form a residual signal, a multi-channel time domain modal feature matrix is constructed, and a spectrum diagram is generated; the multi-channel time domain modal feature matrix and the spectrum diagram are input into a trained SGFN model, the multi-channel time domain modal feature matrix enters a time domain branch, the spectrum diagram enters a frequency domain branch, a time domain eigenvector and a frequency domain eigenvector are weightedly fused, classification is carried out, and a modulation type is obtained; and a system is also provided; and the modulation recognition accuracy under low signal-to-noise ratio and small sample conditions is improved.

The invention relates to the field of Radio Frequency (RF) signal classification and recognition, specifically a method of training a Convolutional Neural Network (CNN) and applying the trained CNN fo

Methods, systems, and devices for wireless communications are described. In some examples, a wireless communication device (such as a network entity) may output a downlink control channel message using at least a first resource element (RE) of a first control resource set (CORESET) and using at least a second RE of a second CORESET. The first CORESET and the second CORESET may be linked and may be associated with different transmission configuration indicator (TCI) states. Additionally, each of the first RE and the second RE may be associated with a same payload of the downlink control channel message, and modulation of a first set of symbols mapped to the first RE may be linked to modulation of a second set of symbols mapped to the second RE based on the first CORESET and the second CORESET being linked.

The present application belongs to the technical field of electronic reconnaissance, and relates to a M-APSK signal format identification method based on a mixed Gaussian model. The present application provides a method for simultaneously identifying the modulation type and constellation distribution of an APSK signal. In combination with the characteristics that different LDPC identifiers correspond to different constellation distributions and the radius of inner and outer constellation points, the method can effectively identify the modulation type, LDPC identifier and constellation distribution of an APSK signal under the premise that a reconnaissance user does not know the LDPC identifier, and provide signal demodulation parameters for a subsequent demodulator. The present application has the beneficial effect that according to the characteristics that different LDPC identifiers correspond to different constellation distributions in the DVB-S2 communication standard, the present application designs an inter-class modulation identification method for M-APSK signals. The method can effectively identify the LDPC identifier and modulation order, and the constellation distribution of an APSK signal. The algorithm of the present application has small computation amount, simple structure, high identification accuracy, and can provide effective assistance for a communication reconnaissance device to demodulate an APSK signal.