An information processing method, includes obtaining, by a communication device, first information related to configuration information of a target AI model, where the first information includes measurement-related information and/or at least one candidate data processing policy; and determining, by the communication device, input data of the target AI model or a target data processing policy based on the measurement-related information and/or each candidate data processing policy; where the target data processing policy is used to indicate a preprocessing policy for the measurement-related information or a preprocessing policy for the input data of the target AI model.

Some embodiments of the present disclosure provide for use of a linear chirp signal as a basis for a sensing signal. Modification of the linear chirp signal by a signature function can allow a receiver of the sensing signal to determine an identity for a source of the sensing signal. Accordingly, upon processing the received sensing signal to obtain path parameter estimates, the receiver can direct a transmission of an indication of the path parameter estimates to the source of the sensing signal. Aspects of the present application relate to performing multi-node, multi-path channel estimation on the basis of processing the received sensing signal. Conveniently, the processing is performed with low complexity.

Disclosed are techniques for communication. In an aspect, a radio access network (RAN) node transmits, to a network entity, a set of machine learning positioning capabilities supported by the RAN node, wherein the set of machine learning positioning capabilities includes a list of identifiers of a set of machine learning positioning functionalities supported by the RAN node, wherein the set of machine learning positioning functionalities is associated with a set of machine learning models that support the set of machine learning positioning functionalities, and wherein each machine learning positioning functionality of the set of machine learning positioning functionalities is associated with one or more machine learning models of the set of machine learning models, and transmits, to the network entity, one or more measurements of one or more sounding reference signal (SRS) resources transmitted by a user equipment (UE).

A ceiling fan or similar air-moving device can include a motor for rotating one or more blades to drive a volume of air about a space. A connector can be used to connect the blade to a blade iron to mount the blade to the motor. The connector can include a set of receptacles configured to insert into openings on the blade, with the set of receptacles connected by a set of arms. A set of mount posts on the blade iron can seat within the receptacles to mount the blade to the blade iron.

In a method for sending signals in network communications, an apparatus obtains a plurality of extended sequences, with each extended sequence comprising an original sequence and an additional sequence. In each extended sequence, the additional sequence is located before the original sequence, and the additional sequence includes last m codes of the original sequence. Alternatively, the additional sequence is located after the original sequence, and the additional sequence includes first n codes of the original sequence. The apparatus generates a plurality of target sequences based on the plurality of extended sequences and a security code corresponding to each extended sequence. The apparatus then generates a first impulse signal based on the plurality of target sequences, and sending the first impulse signal.

In a digital communication system there is provided a method for OFDM channel estimation that jointly considers the effects of coarse timing error and multipath propagation. The method uses an iterative channel estimation technique, which considers the practical scenario of fractional timing error and non-sample space echo delays. The method does not require channel state information such as second-order statistic of the channel impulse responses or the noise power. Moreover, timing error can be conveniently obtained with the proposed technique. Simulation shows that, when comparing OFDM channel estimation techniques under DOCSIS 3.1 realistic channel conditions, the proposed algorithm significantly outperforms conventional methods.

A data processing device includes a control channel model simple estimation apparatus performs control channel simple estimation, which does not include filter processing, a linear prediction apparatus performs linear prediction on a result obtained by the simple estimation, a pilot reconstruction apparatus reconstructs a first estimation result obtained by the linear prediction, a control channel data regeneration apparatus regenerates a pilot part in antenna data of a control channel of the user, and a control channel interference canceller removes the regenerated pilot part from antenna data of multiple users in order to obtain a control parameter part of the antenna data of the control channels of the multiple users.

The present application describes a self-interference channel estimation system. The system includes a signal generator configured to generate a swept tone having a frequency ranging from an upper edge of a reception band to a lower edge of the reception band, and configured to generate a message signal. The system includes an infinite impulse response (IIR) filter configured to determine an infinite impulse response of a self-interference channel based upon the swept tone. The system includes a processor configured to characterize the self-interference channel based upon the infinite impulse response.

A method for detecting (DET) a signal in a communication system including a plurality of communication channels, from a plurality of noisy values respectively representative of the transmission through the communication channels, is provided. The propagation in the communication channels is characterized by at least one variable. The set of values that can be taken by the variable is divided into a plurality of ranges. The method includes receiving (E4) the signals respectively transmitted in the plurality of communication channels, and detecting (E20) a signal corresponding to a value of the variable lying within one of the plurality of ranges by comparing, with a predetermined threshold, a value taken by a correlator linked to the range in question and calculated as a function of the noisy values. A channel estimation method, a detection device, a channel estimation device and a computer program are also described.

A method to be performed at a station configured to connect to a Long Term Evolution radio access network (LTE-RAN) to utilize enhanced Multimedia Broadcast Multicast Services using a Multicast-Broadcast Single-Frequency Network (MBSFN). The method including receiving a MBSFN subframe having a MBSFN subframe structure including a plurality of Orthogonal Frequency-Division Multiplexing (OFDM) symbols, a first one of the OFDM symbols having a first reference symbol inserted therein, a second one of the OFDM symbols having a second reference symbol inserted therein, determining a rate of change of channel conditions being experienced by the station and performing a non-destaggered channel estimation when the rate of change of channel conditions is greater than a predetermined threshold, the non-destaggered channel estimation using a first Channel Impulse Response (CIR) at the first OFDM symbol and a second CIR at the second OFDM symbol.