Disclosed is an electronic device, including a housing, a first communication circuit disposed in the housing and configured to support omnidirectional wireless communication, a second communication circuit disposed in the housing and configured to support directional wireless communication using beamforming, a processor disposed in the housing and operatively coupled to the first communication circuit and the second communication circuit, and a memory disposed in the housing and operatively coupled to the processor. The processor may be configured to receive at least one first radio signal through a communication channel from an external device capable of supporting the omnidirectional wireless communication and the directional wireless communication using the first communication circuit, determine a state of the communication channel based on at least part of the at least one first radio signal, and activate the second communication circuit based on at least part of the determined state of the communication channel wherein the second communication circuit is configured to receive a second radio signal from the external device.

A transmission apparatus including the number of antennas different from a reception apparatus and performing transmission by SC-MIMO to and from the reception apparatus includes a training signal generation unit that generates a known signal predetermined, a CP addition unit that adds a CP to each symbol of a transmission signal including the known signal, a weight generation unit that generates a transmission weight based on a transposed adjugate matrix that is a product of a channel matrix estimated based on the known signal by the reception apparatus and a complex conjugate transpose of the channel matrix, and a transmission beam formation unit that uses the transmission weight to form a transmission beam for the transmission signal where the CP is added.

A battery pack comprises an enclosure; a plurality of network nodes that communicate with each other inside the enclosure and that generate a unique radio frequency (RF) signature; and a special-purpose computer processor that compares an incoming channel impulse response (CIR) of the unique radio frequency (RF) signature corresponding to an incoming packet to a plurality of stored valid RF CIR signatures and executes a resemblance metric to accept or reject the incoming packet.

In accordance with a first aspect of the present disclosure, a channel equalizer is provided for use in a near field communication (NFC) device, the channel equalizer comprising: a filter configured to receive an input signal and to generate a filtered output signal; an estimator configured to determine filter coefficients to be used by said filter; a synchronizer configured to determine when to enable the channel equalizer and to provide one or more corresponding control signals to the estimator. In accordance with a second aspect of the present disclosure, a corresponding method of operating a channel equalizer for use in a near field communication (NFC) device is conceived.

A method and devices for configuring a data transmission network are disclosed. The method is for configuring a data transmission network, executed by a configuration device, wherein the data transmission network comprises at least one transmitter, at least one receiver with a communication channel between the transmitter and the receiver, the method comprising: training a machine learning model of the data transmission network, wherein the machine learning model comprises at least a transmitter model including a transmitter neural network, a channel model, and a receiver model including a receiver neural network by providing a message within a sequence of messages; generating a group of transmission symbols for each message in the sequence of messages using the transmitter neural network; concatenating the groups of transmission symbols together as a sequence of transmission symbols; simulating transmission of the sequence of transmission symbols over the communication channel using the channel model to the receiver; analysing a sequence of received symbols using the reception neural network to generate a decoded message; and updating the machine learning model based on an output of said reception neural network. In this way, the machine learning model can be trained using representative sequences of message, which improves performance when deployed in a real network.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a wireless communication device may determine, based at least in part on two or more measurements on a channel taken at different points in time, that the channel is classified as static. The wireless communication device may perform at least one optimization based at least in part on determining that the channel is classified as static. For example, the at least one optimization may include modifying a channel state feedback procedure, reducing a periodicity associated with a measurement gap, modifying a filtering associated with measurements of the channel, reducing a threshold associated with beam switching, and/or refraining from performing at least one filtering at a radio frequency receiver of the wireless communication device. Numerous other aspects are described.

A communication device includes: a plurality of wireless communication sections, each of which is configured to wirelessly receive a signal from another communication device; and a control section configured to select a wireless communication section from which a specific reception time corresponding to an optimum parameter that is a reliability parameter indicating that the specific reception time is most appropriate for a processing target is detected, as a transmission communication section serving as a wireless communication section that transmits a signal from among the plurality of wireless communication sections, and configured to measure a distance between the transmission communication section and the other communication device on the basis of the specific reception time detected from a correlation computation result obtained from signals transmitted/received between the other communication device and the selected transmission communication section.

A radio receiver includes a channel estimator processing circuit including: a feature extractor configured to extract one or more features from a received signal, the features including a channel correlation estimated based on a reference signal in a current slot, the estimated channel correlation indicating a rate of change of a wireless channel over time; and a Doppler spread estimator configured to estimate a Doppler spread of the wireless channel by supplying the features to one or more Doppler shift predictors trained on training data across a training signal-to-noise ratio (SNR) range and across a training Doppler shift range, each Doppler shift predictor being trained on a portion of the training data corresponding to a different portion of the training data.

Some examples described herein provide for an integrated circuit including a continuous time linear equalizer (CTLE) circuit and a method of operating the integrated circuit. In an example, an integrated circuit includes a transconductance amplifier stage and a transimpedance amplifier stage. The transconductance amplifier stage has a first input node and a first output node. The transconductance amplifier stage includes a first complementary device inverter. The transimpedance amplifier stage has a second input node and a second output node. The first output node is electrically connected to the second input node. The transimpedance amplifier stage includes a second complementary device inverter.

A channel information processing method and apparatus. The method includes: receiving first information and second information from a terminal, where the first information includes a first parameter without uplink/downlink channel reciprocity, the first parameter is determined based on downlink channel estimation, the second information is used to indicate a deviation between a second parameter and a third parameter that have uplink/downlink channel reciprocity, the second parameter is determined based on uplink channel estimation, and the third parameter is determined based on downlink channel estimation; and determining channel information of a downlink channel based on the first information, the second information, and the second parameter.