Systems and methods for Wi-Fi sensing are provided. A method for Wi-Fi sensing carried out by a sensing decision unit in operation on at least one processor configured to execute instructions. Measured channel state information (M-CSI) representing a sensing measurement and receiver front end state information (RFE-SI) are received. According to the RFE-SI, sensing decision input information is determined.

A machine learning based method for channel estimation for a multiple-input multiple-output, MIMO, system, the method including receiving a measured signal y[k] at a receiver of the system; finding subcarriers k of the measured signal y[k]; estimating, with a convolutional neural network, CNN, channel amplitudes ĝ[k] of the measured signal y[k]; reconstructing a channel Ĥ[k], between the receiver and a transmitter of the system, based on the channel amplitudes ĝ[k] and a low resolution whiten measurement matrix Y.sub.w; and adjusting a parameter of the system based on the reconstructed channel Ĥ[k]. The channel amplitudes ĝ[k] are simultaneously estimated by the CNN.

One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.

Systems, devices, and methods for access point discovery in a wireless network are provided. An access point device embeds into a preamble of a transmission packet discovery information including modifications determined by passing in-phase quadrature (IQ) symbols through a finite impulse response (FIR) filter to introduce a phase shift in selected ones of the IQ symbols. The phase shifts are encoded into bits in selected ones of a plurality of subcarriers, bounded by a maximum phase shift and a maximum number of the subcarriers to limit the packet error rate. A convolutional neural network can learn channel state and other information to determine the maximum phase shift and number of subcarriers. A client device can select from among a plurality of modified transmission packets to send a discovery request.

Systems and methods for Wi-Fi sensing are provided. A method for Wi-Fi sensing carried out by a sensing decision unit in operation on at least one processor configured to execute instructions. Measured channel state information (M-CSI) representing a sensing measurement and receiver front end state information (RFE-SI) are received. According to the RFE-SI, sensing decision input information is determined.

A THz UM-MIMO channel estimation method based on the DCNN comprises the steps: the hybrid spherical and planar-wave modeling (HSPM), by taking a sub-array in the antenna array as a unit, employing the PWM within the sub-array, and employing the SWM among the sub-arrays; estimating the channel parameters between the reference sub-arrays at Tx and Rx through a DCNN, including the angles of departure and arrival, the propagation distance and the path gain; deducing the channel parameters between the reference sub-array and other sub-arrays by utilizing the obtained channel parameters and the geometrical relationships among sub-arrays, and recovering the channel matrix; wherein accurate three-dimensional channel modeling is achieved by the HSPM, which possesses high modeling accuracy and low complexity.

Techniques are provide for neural network based positioning of a mobile device. An example method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, determining a channel frequency response or a channel impulse response based on the reference signal information, processing the channel frequency response or the channel impulse response with a neural network, and determining the line of sight delay, the angle of arrival, or the angle of departure value based on an output of the neural network.

Aspects of dynamic and interactive machine learning and feature extraction techniques for performing beam interference management are disclosed. In one aspect, upon entering a coverage area, a UE may transmit a discovery message including one or more machine learning (ML) services for ML service discovery. Based on the ML service discovery, the UE may transmit a session request to establish a data service session between the UE and a network node based on the ML service discovery and other criteria such as extracted features. The network node may receive the ML discovery data and extracted features and aggregate this information with other intelligent network devices to enable the network node to predict a beam blockage during the ML inference data service session. The network node can adapt beam blockage predictions by changing the timing and direction of communications between network entities.

A method of wireless communication performed by a first transmission and reception point (TRP), includes receiving a message indicating a measurement of a reference signal (RS) transmitted in a first band to a wireless device or one or more features of a second band estimated by the wireless device. The first TRP may operate in the second band not overlapping the first band, the one or more features comprise one or more of a channel characteristic, a signal strength, or a beam direction of the second band. The method also includes adjusting one or more parameters of the second band in response to receiving the message. The one or more parameters may include one or both of a channel or a beam direction. The method further includes transmitting signals to a user equipment (UE) in the second band according to the one or more adjusted parameters.