Patent Description:
United States Patent Application Publication No. <CIT> relates to machine learning for channel estimation.

In some aspects, a user equipment (UE) for wireless communication may include a memory and one or more processors operatively coupled to the memory. The memory and the one or more processors may be configured to receive, from a base station, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process; and communicate with the base station based at least in part on the resource allocation.

In some aspects, a base station for wireless communication may include a memory and one or more processors operatively coupled to the memory. The memory and the one or more processors may be configured to transmit, to a UE, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process; and communicate with the UE based at least in part on the resource allocation.

In some aspects, a method of wireless communication, performed by a UE, may include receiving, from a base station, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process; and communicating with the base station based at least in part on the resource allocation.

In some aspects, a method of wireless communication, performed by a base station, may include transmitting, to a UE, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process; and communicating with the UE based at least in part on the resource allocation.

Controller/processor <NUM> of base station <NUM>, controller/processor <NUM> of UE <NUM>, and/or any other component(s) of <FIG> may perform one or more techniques associated with communication of a known payload to support a machine learning process, as described in more detail elsewhere herein. For example, controller/processor <NUM> of base station <NUM>, controller/processor <NUM> of UE <NUM>, and/or any other component(s) of <FIG> may perform or direct operations of, for example, process <NUM> of <FIG>, process <NUM> of <FIG>, and/or other processes as described herein. Memories <NUM> and <NUM> may store data and program codes for base station <NUM> and UE <NUM>, respectively. In some aspects, memory <NUM> and/or memory <NUM> may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and/or program code) for wireless communication. For example, the one or more instructions, when executed (e.g., directly, or after compiling, converting, and/or interpreting) by one or more processors of the base station <NUM> and/or the UE <NUM>, may cause the one or more processors, the UE <NUM>, and/or the base station <NUM> to perform or direct operations of, for example, process <NUM> of <FIG>, process <NUM> of <FIG>, and/or other processes as described herein. In some aspects, executing instructions may include running the instructions, converting the instructions, compiling the instructions, and/or interpreting the instructions, among other examples.

In some aspects, UE <NUM> may include means for receiving, from a base station, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process, means for communicating with the base station based at least in part on the resource allocation, and/or the like. In some aspects, such means may include one or more components of UE <NUM> described in connection with <FIG>, such as controller/processor <NUM>, transmit processor <NUM>, TX MIMO processor <NUM>, MOD <NUM>, antenna <NUM>, DEMOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, and/or the like.

In some aspects, base station <NUM> may include means for transmitting, to a UE, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process, means for communicating with the UE based at least in part on the resource allocation, and/or the like. In some aspects, such means may include one or more components of base station <NUM> described in connection with <FIG>, such as antenna <NUM>, DEMOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, controller/processor <NUM>, transmit processor <NUM>, TX MIMO processor <NUM>, MOD <NUM>, antenna <NUM>, and/or the like.

In some wireless networks, one or more network nodes may be configured to run machine learning (ML) processes. The ML processes may be used to establish, enhance, and/or otherwise support functions that the node or nodes are to perform. A machine learning model may be trained using a set of observations. The set of observations may be obtained and/or input from historical data, such as data gathered during one or more processes described herein. For example, the set of observations may include data gathered from transmissions of known payloads, as described elsewhere herein. In some implementations, the machine learning system may receive the set of observations (e.g., as input) from another network device such as a UE or a base station.

A feature set may be derived from the set of observations. The feature set may include a set of variable types. A variable type may be referred to as a feature. A specific observation may include a set of variable values corresponding to the set of variable types. A set of variable values may be specific to an observation. In some cases, different observations may be associated with different sets of variable values, sometimes referred to as feature values. In some implementations, the machine learning system may determine variable values for a specific observation based on input received from a UE and/or base station. For example, the machine learning system may identify a feature set (e.g., one or more features and/or corresponding feature values) from structured data input to the machine learning system, such as by extracting data from a particular column of a table, extracting data from a particular field of a form, extracting data from a particular field of a message, extracting data received in a structured data format, and/or the like. In some implementations, the machine learning system may determine features (e.g., variables types) for a feature set based on input received from a UE and/or a base station, such as by extracting or generating a name for a column, extracting or generating a name for a field of a form and/or a message, extracting or generating a name based on a structured data format, and/or the like. Additionally, or alternatively, the machine learning system may receive input from an operator to determine features and/or feature values. In some implementations, the machine learning system may perform natural language processing and/or another feature identification technique to extract features (e.g., variable types) and/or feature values (e.g., variable values) from text (e.g., unstructured data) input to the machine learning system, such as by identifying keywords and/or values associated with those keywords from the text.

The set of observations may be associated with a target variable type. The target variable type may represent a variable having a numeric value (e.g., an integer value, a floating point value, and/or the like), may represent a variable having a numeric value that falls within a range of values or has some discrete possible values, may represent a variable that is selectable from one of multiple options (e.g., one of multiples classes, classifications, labels, and/or the like), may represent a variable having a Boolean value (e.g., <NUM> or <NUM>, True or False, Yes or No), and/or the like. A target variable type may be associated with a target variable value, and a target variable value may be specific to an observation. In some cases, different observations may be associated with different target variable values.

The target variable may represent a value that a machine learning model is being trained to predict, and the feature set may represent the variables that are input to a trained machine learning model to predict a value for the target variable. The set of observations may include target variable values so that the machine learning model can be trained to recognize patterns in the feature set that lead to a target variable value. A machine learning model that is trained to predict a target variable value may be referred to as a supervised learning model, a predictive model, and/or the like. When the target variable type is associated with continuous target variable values (e.g., a range of numbers and/or the like), the machine learning model may employ a regression technique. When the target variable type is associated with categorical target variable values (e.g., classes, labels, and/or the like), the machine learning model may employ a classification technique.

In some implementations, the machine learning model may be trained on a set of observations that do not include a target variable (or that include a target variable, but the machine learning model is not being executed to predict the target variable). This may be referred to as an unsupervised learning model, an automated data analysis model, an automated signal extraction model, and/or the like. In this case, the machine learning model may learn patterns from the set of observations without labeling or supervision, and may provide output that indicates such patterns, such as by using clustering and/or association to identify related groups of items within the set of observations.

In some aspects described herein, an ML process may be utilized to develop a neural network, using supervised learning. A neural network may be a model that includes connected nodes arranged in layers. Weights and biases may be assigned to the connections. A weight influences how much a given node activates a node in the next layer. A bias is a threshold that can help weed out activations that may give false positives.

In some aspects, the neural network may be used for processing received MIMO signals. ML processes may include the building of algorithms and/or models and may run on UEs, BSs, and/or jointly across UEs and BSs (e.g., in the case of distributed algorithms, and/or the like). While neural networks may be trained offline, the neural networks may additionally, or alternatively, be configured to be trained using known wireless network transmissions as training data to fine-tune the models with regard to network channels, noise, and/or other environmental characteristics.

A generalized neural network for use as a MIMO demapper may be represented, for example, as y = Hx + n, where y is the received vector, x is the transmitted symbol vector, n is the noise vector, and finally H is the channel matrix. The neural network may be trained offline to determine x̂ (estimated symbols) and may benefit from being fine-tuned using online training. The inputs of the neural network may be received observations (y), and estimated channel matrix (H), and the outputs may be detected transmitted symbols, x̂. To perform online training, the neural network may be provided with the ground truth labels (x̂), and training data, in the form of transmissions having known payloads (e.g., data that the device is aware of or that can be regenerated by the device) may be sent to the device (e.g., UE and/or BS) on which the ML model is implemented. The received observations, y, as well as the estimated channel matrix (H) may be the inputs to the neural network, and the known payload (x̂) may be the output (ground truth labels). This way the device (UE or gNB) can perform further online training, without having to decode y to use the decoded x̂ as the ground truth labels for the neural network. Because the payloads are known, the neural network can use the training data to learn how to interpret the symbols in the presence of channel characteristics, noise characteristics, and/or the like.

Training data may include, for example, known reference signals; known payloads of physical downlink control channel (PDCCH) transmissions, physical uplink control channel (PUCCH) transmissions, physical downlink shared channel (PDSCH) transmissions, physical uplink shared channel (PUSCH) transmissions; periodically repeating system information blocks; usual unicast transmissions; and/or the like. Regular data transmissions such as those indicated above may be treated as known once they have been decoded. However, this approach may require excessive memory and computation overhead. For example, received modulation symbols may need to be stored until decoding completes.

In some aspects, techniques and apparatuses are provided for communication of a known payload to support an ML process (e.g., a process for training a neural network). In some aspects, a UE may receive, from a BS, an indication of a resource allocation for a data communication having a known payload and communicate with the BS based at least in part on the resource allocation. In this way, the UE and BS may both coordinate information regarding which transmissions will include the known payload, so that the receiving node (UE and/or BS) can receive the transmissions and process the data contained therein (e.g., use the payload data for an ML process) without decoding the transmissions.

In some aspects, the known payload may be generated based on a scrambling seed configured using radio resource control (RRC) messages. The scrambling seed may generate bits for an encoding process so that the known payload transmissions can be treated more like reference signals than data packets. In this way, the network nodes may be able to process the transmitted data without decoding signals. In some aspects, the known payload is transmitted using a dedicated logical channel that includes assigned priority rules and multiplexing restrictions, so that receiving network nodes may be aware that the transmissions include known payload data. In some aspects, for example, transmissions of the dedicated logical channel may be prohibited from being multiplexed with any other logical channel, so that the known aspect of the data is not corrupted.

In some aspects, known payload transmissions may be assigned a lowest priority as compared to other types of transmissions, so that the transmission of known payloads to support ML processes does not interrupt the regular flow of network traffic. In some aspects, multiple dedicated logical channels for transmitting known payloads for ML processes may be defined and may be assigned priority relative to one another (e.g., based on logical channel identifiers (IDs), and/or the like). In some aspects, specifications may prohibit transmission of uplink control information (UCI) on PUSCH transmissions that include known payloads, so that known payload transmissions are not obscured by UCI. In some aspects, exceptions to known payload transmissions having a lowest priority may be made in cases where an ML model is out of date or exhibiting performance that fails to satisfy a performance threshold. In this way, to the extent that known payload transmissions may facilitate maintenance of the regular flow of network traffic, the transmissions may be prioritized.

<FIG> is a diagram illustrating an example <NUM> of communication of a known payload to support a machine learning process, in accordance with various aspects of the present disclosure. As shown, a BS <NUM> and a UE <NUM> may communicate with one another.

As shown by reference number <NUM>, the BS <NUM> may transmit, and the UE <NUM> may receive, an indication of a resource allocation for a data communication having a known payload. The known payload may include data associated with a machine learning process, as described above. In some aspects, for example, the neural network associated with the BS <NUM> and/or UE <NUM> may go through an offline training phase in order to find the weights and biases of the neural network. The neural network may be deployed in another environment (e.g., a wireless network) which is different from the offline environment. The transmission of known data may be used to help the BS <NUM> and/or UE <NUM> perform online training and refine the neural network parameters (weights and biases) for tailoring the neural network parameters to the specific environment in which the BS <NUM> and UE <NUM> have been deployed.

In some aspects, the indication of the resource allocation may be carried in at least one of a radio resource control (RRC) message, a downlink control information (DCI) communication, medium access control (MAC) control element (CE), or a combination thereof. As shown by reference number <NUM>, the UE <NUM> may communicate with the BS <NUM> based at least in part on the resource allocation. Communicating with the BS <NUM> may include transmitting the known payload to the base station and/or receiving the known payload from the BS <NUM>.

In some aspects, the resource allocation and corresponding communication is configured, in accordance with various rules, specifications, and/or the like, so that both the BS <NUM> and the UE <NUM> can know and/or agree on the payload in advance of the communication so that the receiving entity can process the payload data (e.g., use the payload data in an ML process) without decoding the transmissions.

In some aspects, as indicated above, the BS <NUM> may transmit the indication of the resource allocation in an RRC message. In some aspects, the RRC message may include an indication of a scrambling seed. In some aspects, the RRC message may include a configuration of a scrambling seed generation process by which the UE may generate the scrambling seed. In some aspects, the UE <NUM> may generate the known payload based at least in part on the scrambling seed. In some aspects, generating the known payload based at least in part on the scrambling seed may include using the scrambling seed to generate bits to be encoded.

In some aspects, the data communication may include an uplink data communication, and the UE <NUM> may generate one or more padding bits, associated with the known payload, using the scrambling seed. For example, in some aspects, padding bits may be used when the transport block size allocation is larger than the number of bits available. In uplink communications, if the pad is large enough to be able to insert a buffer status report (BSR), a BSR may be inserted (which may be referred to as "padding-BSR"). In some aspects, a wireless communication standard, configuration, or dynamic indication may indicate that the known payloads not have pads (or not have pads large enough for a BSR) or that padding BSR is not to be used.

For example, the UE <NUM> may either generate the padding bits using the same scrambling seed proposed for known payloads or set the padding bits to zero. Additionally, or alternatively, as indicated above, some aspects include generating one or more padding bits associated with the known payload without including a buffer status report in the one or more padding bits.

In some aspects, as indicated above, the BS <NUM> may transmit the indication of the resource allocation in DCI. In some aspects, the DCI may include a radio network temporary identifier (RNTI) associated with the known payload. In some aspects, the DCI may include a DCI format associated with the known payload.

In some aspects, the communication includes transmitting or receiving the known payload using a dedicated logical channel. The dedicated logical channel are subjected to certain priority and/or multiplexing rules. In some aspects, for example, the dedicated logical channel is not to be multiplexed with another logical channel. In some aspects, the dedicated logical channel is not to be transmitted on a transport block that includes a medium access control (MAC) control element.

In some aspects, the BS <NUM> and/or UE <NUM> may transmit the known payload according to a priority associated with the dedicated logical channel. The priority associated with the dedicated logical channel may be configured relative to a priority associated with another dedicated logical channel or channels. In some aspects, the priority associated with the dedicated logical channel may be lower relative to a priority associated with a user data communication. The user data may be carried on an uplink and/or downlink channel.

In some aspects, the transmitting entity (the UE <NUM> and/or the BS <NUM>) may determine that a machine learning model associated with the machine learning process is out of date or demonstrating performance that fails to satisfy a performance threshold. The transmitting entity may transmit the known payload according to a priority associated with the dedicated logical channel, where the priority associated with the dedicated logical channel is higher relative to a priority associated with at least one other communication based at least in part on determining that the machine learning model associated with the machine learning process is out of date or demonstrating performance that fails to satisfy a performance threshold.

In some aspects, the transmitting and/or receiving entity may detect a collision between the communication associated with the dedicated logical channel and another communication. The transmitting and/or receiving entity may abandon the communication associated with the dedicated logical channel for a time period associated with the other communication based at least in part on detecting the collision. In some aspects, the indication of the resource allocation may indicate a dedicated semi-persistent scheduling grant onto which only the dedicated logical channel is to be mapped. In some aspects, the indication of the resource allocation may indicate a configured grant onto which only the dedicated logical channel is to be mapped.

<FIG> is a diagram illustrating an example <NUM> associated with communication of a known payload to support a machine learning process, in accordance with the present disclosure. As shown in <FIG>, a BS <NUM> and a UE <NUM> may communicate with one another.

As show by reference number <NUM>, the BS <NUM> may transmit, and the UE <NUM> may receive, a scrambling seed configuration. The scrambling seed configuration may be transmitted using an RRC message. In some aspects, the RRC message may include an indication of a scrambling seed. In some aspects, the RRC message may include a configuration of a scrambling seed generation process by which the UE may generate the scrambling seed. In some aspects, the UE <NUM> may generate the known payload based at least in part on the scrambling seed. In some aspects, generating the known payload based at least in part on the scrambling seed may include using the scrambling seed to generate bits to be encoded.

As shown by reference number <NUM>, the UE <NUM> may transmit, and the BS <NUM> may receive, a request for data. For example, the UE <NUM> may determine that a neural network hosted by the UE <NUM> is outdated and should be updated through training. The UE <NUM> may transmit the request for data based at least in part on that determination.

As shown by reference number <NUM>, the BS <NUM> transmits, and the UE <NUM> receives, an indication of a resource allocation for a data communication having a known payload, as described above in connection with <FIG>. As shown by reference <NUM>, the BS <NUM> transmits, and the UE <NUM> receives, a data communication that includes the known payload. The UE <NUM> may use the data communication to update the neural network through a training process.

In some aspects, instead of a downlink data transfer, the data transfer may be an uplink transfer. For example, the BS <NUM> may determine that a neural network maintained at the BS <NUM> is outdated. The BS <NUM> may, based at least in part on that determination, transmit a resource allocation to the UE <NUM> without first sending a data request.

<FIG> is a diagram illustrating an example process <NUM> performed, for example, by a UE, in accordance with various aspects of the present disclosure. Example process <NUM> is an example where the UE (e.g., UE <NUM> and/or the like) performs operations associated with communication of a known payload to support an ML process.

As shown in <FIG>, in some aspects, process <NUM> may include receiving, from a base station, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process (block <NUM>). For example, the UE (e.g., using receive processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) may receive, from a base station, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process, as described above.

As further shown in <FIG>, in some aspects, process <NUM> may include communicating with the base station based at least in part on the resource allocation (block <NUM>). For example, the UE (e.g., using receive processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) may communicate with the base station based at least in part on the resource allocation, as described above.

In a first aspect, receiving the indication of the resource allocation comprises receiving at least one of a radio resource control message, a medium access control (MAC) control element, or a downlink control information communication.

In a second aspect, alone or in combination with the first aspect, process <NUM> includes receiving, from the base station, an indication of a scrambling seed; and generating the known payload based at least in part on the scrambling seed, wherein communicating with the base station comprises transmitting the known payload to the base station or receiving the known payload from the base station.

In a third aspect, alone or in combination with the second aspect, the scrambling seed is carried in a radio resource control message.

In a fourth aspect, alone or in combination with one or more of the second through third aspects, generating the known payload based at least in part on the scrambling seed comprises using the scrambling seed to generate bits to be encoded.

In a fifth aspect, alone or in combination with one or more of the second through fourth aspects, the data communication comprises an uplink data communication, the method further comprising generating one or more padding bits, associated with the known payload, using the scrambling seed.

In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, the data communication comprises an uplink data communication, the method further comprising generating one or more padding bits associated with the known payload, the one or more padding bits each has a value of zero.

In a seventh aspect, alone or in combination with one or more of the first through fifth aspects, the data communication comprises an uplink data communication, process <NUM> further including generating one or more padding bits associated with the known payload without including a buffer status report in the one or more padding bits.

In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, communicating with the base station comprises transmitting or receiving the known payload using a dedicated logical channel.

In a ninth aspect, alone or in combination with the eighth aspect, the dedicated logical channel is not to be multiplexed with another logical channel.

In a tenth aspect, alone or in combination with one or more of the eighth through ninth aspects, uplink control information (UCI) is not to be carried by a physical uplink shared channel (PUSCH) communication that includes the known payload.

In an eleventh aspect, alone or in combination with one or more of the eighth through tenth aspects, the dedicated logical channel is not to be transmitted in a transport block that includes a medium access control (MAC) control element.

In a twelfth aspect, alone or in combination with one or more of the eighth through eleventh aspects, process <NUM> includes transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority associated with the dedicated logical channel is configured relative to a priority associated with another dedicated logical channel.

In a thirteenth aspect, alone or in combination with one or more of the eighth through twelfth aspects, process <NUM> includes transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority associated with the dedicated logical channel is lower relative to a priority associated with a user data communication.

In a fourteenth aspect, alone or in combination with one or more of the eighth through thirteenth aspects, process <NUM> includes transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority associated with the dedicated logical channel is lower relative to a priority associated with a reference signal.

In a fifteenth aspect, alone or in combination with one or more of the eighth through fourteenth aspects, process <NUM> includes determining that a machine learning model associated with the machine learning process is out of date or demonstrating performance that fails to satisfy a performance threshold; and transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority is associating with the dedicated logical channel is higher relative to a priority associated with at least one other communication based at least in part on determining that the machine learning model associated with the machine learning process is out of date or demonstrating performance that fails to satisfy a performance threshold.

In a sixteenth aspect, alone or in combination with one or more of the eighth through fifteenth aspects, process <NUM> includes detecting a collision between the communication associated with the dedicated logical channel and another communication; and abandoning the communication associated with the dedicated logical channel for a time period associated with the other communication based at least in part on detecting the collision.

In a seventeenth aspect, alone or in combination with one or more of the eighth through sixteenth aspects, the indication of the resource allocation indicates a dedicated semi-persistent scheduling grant onto which only the dedicated logical channel is to be mapped.

In an eighteenth aspect, alone or in combination with one or more of the eighth through seventeenth aspects, the indication of the resource allocation indicates a configured grant onto which only the dedicated logical channel is to be mapped.

In a nineteenth aspect, alone or in combination with one or more of the first through eighteenth aspects, the indication of the resource allocation indicates at least one of a periodic functionality of the dedicated logic channel, a semipersistent functionality of the dedicated logic channel, aperiodic functionality of the dedicated logic channel, or a combination thereof.

In a twentieth aspect, alone or in combination with one or more of the first through nineteenth aspects, receiving the indication of the resource allocation comprises receiving downlink control information (DCI), the DCI comprising a radio network temporary identifier associated with the known payload.

In a twenty-first aspect, alone or in combination with one or more of the first through twentieth aspects, receiving the indication of the resource allocation comprises receiving DCI, the DCI comprising a new DCI format associated with the known payload.

In a twenty-second aspect, alone or in combination with one or more of the first through twenty-first aspects, the data communication comprises a downlink data communication, the process <NUM> further comprising: receiving the downlink data communication, and processing the downlink data communication to extract the data associated with the machine learning process without decoding the downlink data communication.

<FIG> is a diagram illustrating an example process <NUM> performed, for example, by a base station, in accordance with various aspects of the present disclosure. Example process <NUM> is an example where the base station (e.g., base station <NUM> and/or the like) performs operations associated with communication of a known payload to support a machine learning process.

As shown in <FIG>, in some aspects, process <NUM> may include transmitting, to a UE, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process (block <NUM>). For example, the base station (e.g., using transmit processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) may transmit, to a UE, an indication of a resource allocation for a data communication having a known payload, the known payload comprising data associated with a machine learning process, as described above.

As further shown in <FIG>, in some aspects, process <NUM> may include communicating with the UE based at least in part on the resource allocation (block <NUM>). For example, the base station (e.g., using transmit processor <NUM>, receive processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) may communicate with the UE based at least in part on the resource allocation, as described above.

In a first aspect, transmitting the indication of the resource allocation comprises transmitting at least one of a radio resource control message, a MAC-CE, or a downlink control information communication.

In a second aspect, alone or in combination with the first aspect, process <NUM> includes transmitting, to the UE, an indication of a scrambling seed; and receiving, from the UE, the known payload, wherein the known payload is based at least in part on the scrambling seed.

In a fourth aspect, alone or in combination with one or more of the first through third aspects, the data communication comprises a downlink data communication, the process <NUM> further comprising generating one or more padding bits, associated with the known payload, using the scrambling seed.

In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the data communication comprises a downlink data communication, the process <NUM> further comprising generating one or more padding bits associated with the known payload, the one or more padding bits each has a value of zero.

In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, communicating with the UE comprises transmitting or receiving the known payload using a dedicated logical channel.

In an seventh aspect, alone or in combination with the sixth aspect, the dedicated logical channel is not to be multiplexed with another logical channel.

In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, the dedicated logical channel is not to be transmitted in a transport block that includes a medium access control (MAC) control element.

In a ninth aspect, alone or in combination with the eighth aspect, the process <NUM> includes transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority associated with the dedicated logical channel is configured relative to a priority associated with another dedicated logical channel.

In a tenth aspect, alone or in combination with one or more of the eighth through ninth aspects, the process <NUM> includes transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority associated with the dedicated logical channel is lower relative to a priority associated with a user data communication.

In an eleventh aspect, alone or in combination with one or more of the eighth through tenth aspects, the process <NUM> includes transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority associated with the dedicated logical channel is lower relative to a priority associated with a reference signal.

In a twelfth aspect, alone or in combination with one or more of the eighth through eleventh aspects, process <NUM> includes determining that a machine learning model associated with the machine learning process is out of date or demonstrating performance that fails to satisfy a performance threshold; and transmitting the known payload according to a priority associated with the dedicated logical channel, wherein the priority is associating with the dedicated logical channel is higher relative to a priority associated with at least one other communication based at least in part on determining that the machine learning model associated with the machine learning process is out of date or demonstrating performance that fails to satisfy a performance threshold.

In a thirteenth aspect, alone or in combination with one or more of the eighth through twelfth aspects, process <NUM> includes detecting a collision between the communication associated with the dedicated logical channel and another communication; and abandoning the communication associated with the dedicated logical channel for a time period associated with the other communication based at least in part on detecting the collision.

In a fourteenth aspect, alone or in combination with one or more of the eighth through thirteenth aspects, the indication of the resource allocation indicates a dedicated semi-persistent scheduling grant onto which only the dedicated logical channel is to be mapped.

In a fifteenth aspect, alone or in combination with one or more of the eighth through fourteenth aspects, the indication of the resource allocation indicates a configured grant onto which only the dedicated logical channel is to be mapped.

In a sixteenth aspect, alone or in combination with one or more of the eighth through fifteenth aspects, the indication of the resource allocation indicates at least one of a periodic functionality of the dedicated logic channel, a semipersistent functionality of the dedicated logic channel, a periodic functionality of the dedicated logic channel, or a combination thereof.

In an seventeenth aspect, alone or in combination with one or more of the eighth through sixteenth aspects, transmitting the indication of the resource allocation comprises transmitting DCI, the DCI comprising a radio network temporary identifier associated with the known payload.

In an eighteenth aspect, alone or in combination with one or more of the eighth through seventeenth aspects, transmitting the indication of the resource allocation comprises transmitting DCI, the DCI comprising a new DCI format associated with the known payload.

In a nineteenth aspect, alone or in combination with one or more of the first through eighteenth aspects, the data communication comprises an uplink data communication, the method further comprising: receiving the uplink data communication, and processing the uplink data communication to extract the data associated with the machine learning process without decoding the uplink data communication.

Further disclosure is included in the appendix. The appendix is provided as an example only, and is to be considered part of the specification. A definition, illustration, or other description in the appendix does not supersede or override similar information included in the detailed description or figures. Furthermore, a definition, illustration, or other description in the detailed description or figures does not supersede or override similar information included in the appendix. Furthermore, the appendix is not intended to limit the disclosure of possible aspects.

Claim 1:
A user equipment, UE, (<NUM>) for wireless communication, comprising:
a memory; and
one or more processors operatively coupled to the memory, the memory and the one or more processors configured to:
receive, from a base station, an indication of a resource allocation for a data communication, the data communication having a known payload, the known payload comprising data associated with a machine learning process, the known payload being known to both the UE and the base station; and
communicate with the base station based at least in part on the resource allocation, wherein, the memory and the one or more processors, when communicating with the base station, are configured to transmit or receive the known payload using a dedicated logical channel, wherein the dedicated logical channel is subject to an assigned priority rule and/or multiplexing restriction.