Abstract: An apparatus is disclosed that implements interference and/or clutter cancellation using cross-channel equalization. In example aspects, the apparatus includes a wireless transceiver configured to be connected to multiple feed ports. The wireless transceiver is also configured to transmit an electromagnetic signal using a first feed port of the multiple feed ports. A modulated spur is generated based on the transmission of the electromagnetic signal. The wireless transceiver is additionally configured to receive two versions of a receive signal respectively via two feed ports of the multiple feed ports. The receive signal comprises the modulated spur and a mutual-coupling component associated with the transmission of the electromagnetic signal. The wireless transceiver is further configured to generate a filtered signal by attenuating the mutual-coupling component and the modulated spur within one of the two versions of the receive signal using cross-channel equalization.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a wireless device may transmit a frequency-modulated continuous-waveform (FMCW) dual ramp radar signal over a time duration that sweeps from a first frequency to a second frequency and from the second frequency to the first frequency. The wireless device may receive a reflection of the radar signal with a first reflected part that corresponds to the first part of the radar signal and a second reflected part that corresponds to the second part of the radar signal. The wireless device may compare the first reflected part and a time-inverted version of the second reflected part to estimate a noise pattern. The wireless device may perform an action based at least in part on the noise pattern. Numerous other aspects are described.

Abstract: In some aspects, a radar device may receive a plurality of received signals comprising a plurality of reflected frequency modulated continuous wave radar signals and phase noise. The radar device may obtain a frequency-domain representation of the received signals comprising a plurality of frequency-domain spectrums. The radar device may determine a shaped noise component of the frequency-domain representation corresponding to a negative distance portion of the frequency-domain representation. The radar device may determine a shaped decision boundary for target detection based at least in part on the shaped noise component, wherein the shaped decision boundary corresponds to a positive distance portion of the frequency-domain representation. The radar device may detect a radar target based at least in part on the shaped decision boundary. The radar device may perform an action based at least in part on detecting the radar target. Numerous other aspects are described.

Abstract: In aspects, apparatus and methods of wireless communication, and more specifically of improved mitigation of interference caused by a transmitter are provided. For example, in aspects, a method of interference mitigation of wireless communication is provided including transmitting at least one transmit signal from at least one transmit chain of a user equipment (UE) over a wireless medium, receiving a composite signal that includes a desired receive signal transmitted from a device and a distortion signal, generating a modified composite signal by removing at least a portion of the desired receive signal from the composite signal, generating a distortion signal estimate based on the modified composite signal, and canceling the distortion signal estimate from the composite signal. Numerous other aspects are provided.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multilayer perceptron neural network with Hammerstein structure by dividing an aggressor signal into real and imaginary components, augmenting the components by weight factors, executing a linear combination of the augmented components, and executing a nonlinear sigmoid function for the combined components at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce real and imaginary components of an estimated jammer signal.

Abstract: The various embodiments include methods and apparatuses for cancelling nonlinear interference during concurrent communication of dual-technology wireless communication devices. Nonlinear interference may be estimated using a multilayer perceptron neural network by augmenting aggressor signal(s) by weight factors, executing a linear combination of the augmented aggressor signals, and executing a nonlinear sigmoid function for the combined aggressor signals at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. Multiple hidden layers may repeat the process for the hidden layer output signals. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal. The weight factors may be trained based on a determination of an error of the estimated nonlinear interference.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a block least squares function interference filter by generating aggressor kernel matrices from the aggressor signals, augmenting the aggressor kernel matrices by weight factors and executing a linear combination of the augmented output, at an intermediate layer to produce intermediate layer outputs. At an output layer, a linear filter function may be executed on the intermediate layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multi-layer perceptron neural network with Hammerstein structure by dividing an aggressor signal into real and imaginary components, augmenting the components by weight factors, executing a linear combination of the augmented components, and executing a nonlinear sigmoid function for the combined components at a hidden layer of multi-layer perceptron neural network to produce a hidden layer output signal. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce real and imaginary components of an estimated jammer signal.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multi-model radial basis function neural network with Hammerstein structure by executing a radial basis function on aggressor signals at a hidden layer of the radial basis function neural network with Hammerstein structure to obtain hidden layer outputs, augmenting aggressor signal(s) by weight factors, infusing the hidden layer outputs by infusion factors, and, executing a linear combination of the augmented output, at an intermediate layer to produce a combined hidden layer outputs. At an output layer, a linear filter function may be executed on the hidden layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a support vector regression interference filter by generating one or more aggressor kernels, augmenting the one or more kernels by weight factors, and executing a regression function of the augmented components, to produce an estimated jammer signals. At an output layer, estimated jammer signals may be linearly combined to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

Abstract: The various embodiments include methods and apparatuses for cancelling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a minimum mean squares interference filter by generating aggressor kernels from the aggressor signals, augmenting the aggressor kernels by weight factors and executing a linear combination of the augmented output, at an intermediate layer to produce intermediate layer outputs. At an output layer, a linear filter function may be executed on the intermediate layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a radial basis function neural network with Hammerstein structure by executing a radial basis function on aggressor signals at a hidden layer of the radial basis function neural network with Hammerstein structure to obtain hidden layer outputs, augmenting aggressor signal(s) by weight factors and, executing a linear combination of the augmented output, at an intermediate layer to produce a combined hidden layer outputs. At an output layer, a linear filter function may be executed on the hidden layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a mixed-model block least squares/radial basis function neural network by generating aggressor kernels from the aggressor signals, augmenting the aggressor kernels by weight factors and executing a linear combination of the augmented output, at an intermediate layer to produce intermediate layer outputs. At an output layer, a linear filter function may be executed on the intermediate layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a radial basis function neural network with Hammerstein structure by executing a radial basis function on aggressor signals at a hidden layer of the radial basis function neural network with Hammerstein structure to obtain hidden layer outputs, augmenting aggressor signal(s) by weight factors and, executing a linear combination of the augmented output, at an intermediate layer to produce a combined hidden layer outputs. At an output layer, a linear filter function may be executed on the hidden layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multilayer perceptron neural network with Hammerstein structure by dividing an aggressor signal into real and imaginary components, augmenting the components by weight factors, executing a linear combination of the augmented components, and executing a nonlinear sigmoid function for the combined components at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce real and imaginary components of an estimated jammer signal.

Abstract: Apparatus and methods for wireless communication include receiving, in a time division synchronous code division multiple access (TD-SCDMA) network, a first number of symbols before a midamble, the midamble, and a second number of symbols after the midamble; determining first forward and backward probabilities for a first subset of the first number of symbols and second forward and backward probabilities for a second subset of the second number of symbols; determining first posterior probabilities for the first subset of the first number of symbols and second posterior probabilities for the second subset of the second number of symbols; determining a first target posterior probability and a second target posterior probability; detecting a first target symbol and a second target symbol; and determining a first channel estimate corresponding to the first target symbol and a second channel estimate corresponding to the second target symbol.

Abstract: Apparatus, methods, and computer program product for wireless communication, including receiving a plurality of chips in a time division synchronous code division multiple access (TD-SCDMA) network; performing channel matched filtering, despreading, and descrambling on the plurality of chips to determine a plurality of received symbols for each of a plurality of cells; performing symbol-level inter-cell interference cancellation on the plurality of received symbols to determine a plurality of serving cell symbol estimates; and performing multi-user detection on the plurality of serving cell symbol estimates to determine a plurality of detected serving cell symbols.