Patent Description:
As for example described in 3GPP R1-<NUM> or <CIT>.

Embodiments and aspects that do not fall within the scope of the claims are merely examples used for explanation of the invention. Wording such as "may" and "for example" used in the description in conjunction with features of the independent claims should not be interpreted to mean that those features are merely optional. In some aspects, a method of wireless communication performed by a user equipment (UE) includes identifying multiple resource element (RE) segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled physical resource blocks (PRBs) associated with the scheduled communication; and communicating with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

In some aspects, a UE for wireless communication includes a memory and one or more processors operatively coupled to the memory, the memory and the one or more processors configured to: identify multiple RE segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled PRBs associated with the scheduled communication; and communicate with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

In some aspects, a non-transitory computer-readable medium storing a set of instructions for wireless communication includes one or more instructions that, when executed by one or more processors of a UE, cause the UE to: identify multiple RE segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled PRBs associated with the scheduled communication; and communicate with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

In some aspects, an apparatus for wireless communication includes means for identifying multiple RE segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled PRBs associated with the scheduled communication; and means for communicating with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein the means for communicating with the device includes means for transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

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 scheduling energy autoencoder based noncoherent transmission, 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> 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> 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, the UE <NUM> includes means for identifying multiple resource element (RE) segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled physical resource blocks (PRBs) associated with the scheduled communication, and/or means for communicating with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment. The means for the UE <NUM> to perform operations described herein may include, for example, one or more of antenna <NUM>, demodulator <NUM>, MIMO detector <NUM>, receive processor <NUM>, transmit processor <NUM>, TX MIMO processor <NUM>, modulator <NUM>, controller/processor <NUM>, or memory <NUM>.

In some aspects, the UE <NUM> includes means for receiving signaling that indicates one or more of: a segmentation method associated with the one or more REs in the RE segment, a segmentation method associated with one or more bits in the sub-sequence associated with the RE segment, or the neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

In some aspects, the UE <NUM> includes means for determining that the scheduled communication includes a physical uplink control channel (PUCCH), means for determining a PUCCH format based at least in part on one or more segmentation methods or transmission neural networks associated with modulating the multiple sub-sequences to the one or more REs in the multiple RE segments, and/or means for transmitting the PUCCH based at least in part on the PUCCH format.

In some aspects, the UE <NUM> includes means for determining a PUCCH resource set associated with the PUCCH, and/or means for modulating, for different PUCCH resources within the PUCCH resource set, the multiple sub-sequences to the one or more REs in the multiple RE segments using the one or more segmentation methods or transmission neural networks, wherein the one or more segmentation methods or transmission neural networks are configured by configurations associated with the PUCCH resources.

In some aspects, the UE <NUM> includes means for determining, based at least in part on downlink control information (DCI) that indicates a PUCCH resource for hybrid automatic repeat request acknowledgement (HARQ-ACK) feedback associated with a physical downlink shared channel (PDSCH) scheduled by the DCI, the one or more segmentation methods or transmission neural networks associated with modulating the multiple sub-sequences, and/or means for modulating the multiple sub-sequences to the one or more REs in the multiple RE segments using the one or more segmentation methods or transmission neural networks for the PUCCH resource indicated in the DCI.

In some aspects, the UE <NUM> includes means for determining that the scheduled communication includes a physical downlink control channel (PDCCH); means for determining one or more segmentation methods or transmission neural networks associated with modulating the multiple sub-sequences to the one or more REs in the multiple RE segments based at least in part on one or more resource element group (REG) bundles or control channel elements (CCEs) associated with the PDCCH; and/or means for detecting the source bit sequence in the PDCCH based at least in part on the one or more segmentation methods or transmission neural networks.

In some aspects, the UE <NUM> includes means for determining a priority associated with one or more search spaces or control resource sets (CORESETs) based at least in part on the one or more segmentation methods or transmission neural networks, and/or means for monitoring the PDCCH based at least in part on the priority associated with the one or more search spaces or CORESETs.

In some aspects, the UE <NUM> includes means for determining a priority associated with the one or more search spaces or CORESETs based at least in part on the one or more segmentation methods or transmission neural networks, and/or means for refraining from monitoring the PDCCH based at least in part on the priority associated with the one or more search spaces or CORESETs.

<FIG> is a diagram illustrating an example <NUM> of a slot format, in accordance with various aspects of the present disclosure. As shown in <FIG>, time-frequency resources in a radio access network may be partitioned into resource blocks, shown by a single resource block (RB) <NUM>. An RB <NUM> is sometimes referred to as a physical resource block (PRB). An RB <NUM> includes a set of subcarriers (e.g., <NUM> subcarriers) and a set of symbols (e.g., <NUM> symbols) that are schedulable as a unit (e.g., by a base station <NUM>). In some aspects, an RB <NUM> may include a set of subcarriers in a single slot. As shown, a single time-frequency resource included in an RB <NUM> may be referred to as a resource element (RE) <NUM>. An RE <NUM> may include a single subcarrier (e.g., in frequency) and a single symbol (e.g., in time). A symbol may be referred to as an orthogonal frequency division multiplexing (OFDM) symbol. An RE <NUM> may be used to transmit one modulated symbol, which may be a real value or a complex value.

In some telecommunication systems (e.g., NR), RBs <NUM> may span <NUM> subcarriers with a subcarrier spacing of, for example, <NUM> kilohertz (kHz), <NUM>, <NUM>, or <NUM>, among other examples, over a <NUM> millisecond (ms) duration. A radio frame may include <NUM> slots and may have a length of <NUM>. However, a slot length may vary depending on a numerology used to communicate (e.g., a subcarrier spacing, a cyclic prefix format, and/or the like). A slot may be configured with a link direction (e.g., downlink or uplink) for transmission. In some aspects, the link direction for a slot may be dynamically configured.

<FIG> is a diagram illustrating an example <NUM> of coherent wireless communication and an example <NUM> of noncoherent wireless communication, in accordance with various aspects of the present disclosure. The coherent and/or noncoherent wireless communication illustrated in <FIG> may be performed by wireless communication devices, such as a UE <NUM> and a base station <NUM> communicating over an access link, a UE <NUM> and another UE <NUM> communicating over a sidelink, and/or the like.

As shown in <FIG>, and by example <NUM>, coherent wireless communication may involve the use of pilot signals and/or reference signals. A wireless communication device (referred to herein as a "transmitter") may transmit an information bit vector (e.g., a string of bits carrying one or more types of information) by encoding the information bit vector to form one more codewords that each include multiple coded bits. The transmitter may modulate the codewords to form one or more OFDM symbols, generate a pilot signal or reference signal associated with the one or more OFDM symbols (e.g., a demodulation reference signal (DMRS) and/or another suitable reference signal), and transmit the pilot/reference signal and the OFDM symbols over a wireless physical channel (e.g., a PxxCH, which may be a physical downlink control channel (PDCCH), a physical downlink shared channel (PDSCH), a physical uplink control channel (PUCCH), a physical uplink shared channel (PUSCH), a physical sidelink control channel (PSCCH), and/or a physical sidelink shared channel (PSSCH)). The pilot/reference signal and the OFDM symbols may be transmitted over the wireless physical channel to another wireless communication device (referred to as a "receiver").

As further shown in <FIG>, the receiver may receive the pilot/reference signal and the OFDM symbols via the physical channel, and may use the pilot/reference signal to obtain channel state information (CSI) associated with the physical channel. For example, the receiver may demodulate and decode the pilot/reference signal and the OFDM symbols, may perform a channel estimation of the physical channel based at least in part on the demodulation and/or decoding of the pilot/reference signal, and may adjust or modify demodulation and/or decoding parameters for the receiver based at least in part on the channel estimation in order to increase the efficiency and performance of demodulation and/or decoding for the receiver.

In some cases, coherent communication in a wireless system may be suboptimal at a low signal to noise ratio (SNR). For example, the energy used to transmit, decode, and/or measure pilot/reference signals may be wasted because, at low SNR, pilot/reference signals may contain little to no useful information for the receiver. Moreover, attempting to perform a channel estimation at low SNR may result in an inaccurate and/or poor quality channel estimation, which in turn may result in degraded performance in demodulation and/or decoding. Additionally, or alternatively, coherent wireless communication may be suboptimal in other use cases, such as a high Doppler scenario (e.g., when the transmitter and/or the receiver are moving at a fast rate), when transmitted packets have a small payload size (e.g., such that the transmitted packets cannot accommodate the additional payload of a pilot signal or a DMRS), and/or asynchronous communication use cases, among other examples.

Accordingly, as further shown in <FIG>, and by example <NUM>, a transmitter and a receiver may perform noncoherent communication to increase demodulation and/or decoding performance in low SNR scenarios. As described herein, "noncoherent communication" may generally refer to a wireless communication scheme in which the transmitter does not transmit any pilot signals or reference signals for OFDM symbols carrying data/information (e.g., a PxxCH without a DMRS). In this case, the receiver directly demodulates and decodes the received OFDM symbols without performing a channel estimation based on a pilot signal or reference signal.

Noncoherent communication schemes rely on a channel coherence principle that channel properties for adjacent coded OFDM symbols (e.g., adjacent in time resources and/or frequency resources) are the same or roughly the same. This permits a transmitter to use differential modulation (e.g., where information is modulated based at least in part on the phase difference between adjacent coded OFDM symbols) and/or sequence-based modulation (e.g., where information is modulated jointly on a sequence of OFDM symbols). However, the longer that channel coherence is used (e.g., the greater the quantity of adjacent coded OFDM symbols that are considered to be coherent), the greater the complexity of encoding at the transmitter and decoding at the receiver. In some channel encoding/decoding techniques, channel coherence may cause an exponential increase in encoding and decoding. Accordingly, noncoherent wireless communication tends to be very challenging to implement in practice.

Some aspects described herein relate to techniques and apparatuses to schedule noncoherent transmission based at least in part on an energy autoencoder. For example, an autoencoder may be an unsupervised neural network that may be trained to efficiently compress and encode data and/or to reconstruct data from a reduced encoded representation that is as close to the original input as possible. Accordingly, as described herein, an energy autoencoder may be an unsupervised neural network that can modulate an input signal (e.g., an input bit sequence) to energies on physical layer resources, and/or demodulate a transmitted signal based at least in part on energies on physical layer resources. For example, in some aspects, a transmitter may use one or more neural networks to modulate a source bit sequence onto RE segments that include one or more REs in one or more PRBs associated with a scheduled communication (e.g., a PxxCH communication), where a total transmit power is normalized over each RE segment (e.g., outputs from the one or more neural networks may include energies that are normalized over the REs in each RE segment). Accordingly, a receiver may use one or more neural networks to demodulate the transmitted bit sequences and thereby detect the source bit sequence. In this way, the energy-based modulation may be robust to fading in a wireless channel, which may be particularly useful for noncoherent transmission where a pilot signal, a DMRS, or another suitable reference signal to enable channel estimation is unavailable. Furthermore, neural networks may offer computational efficiency to configure modulation schemes that are robust to different fading scenarios, and segmenting a source bit sequence with a large number of bits into multiple sub-sequences that each correspond to one RE segment reduces receiver complexity.

<FIG> is a diagram illustrating an example <NUM> of noncoherent wireless communication using transmitter-side and receiver-side neural networks for modulation and detection, in accordance with various aspects of the present disclosure. In some aspects, the noncoherent wireless communication shown in <FIG> may be performed between a transmitter and a receiver in a wireless network. For example, in some aspects, the receiver may be a base station and the transmitter may be a UE that is scheduled to transmit a PUCCH and/or a PUSCH to the base station. Additionally, or alternatively, the transmitter may be a base station and the receiver may be a UE that is scheduled to receive a PDCCH and/or a PDSCH from the base station. Additionally, or alternatively, the transmitter may be a first UE and the receiver may be a second UE, in which case the first UE may be scheduled to transmit and the second UE may be scheduled to receive a PSCCH and/or a PSSCH over a sidelink.

As shown in <FIG>, the transmitter and the receiver may perform noncoherent wireless communication, whereby a transmitted PxxCH does not include a DMRS, a pilot signal, or another reference signal to enable channel estimation at the receiver. In general, as described above, noncoherent wireless communication may increase demodulation and/or decoding performance in low SNR, high Doppler, small packet, and/or asynchronous communication scenarios, among other examples.

For example, when performing noncoherent transmission in which a PxxCH is transmitted without a DMRS, pilot signal, or other signal to enable channel estimation at the receiver, the transmitter may modulate source bits to constellations suitable for noncoherent detection. For example, the transmitter may modulate the source bits to hand-crafted constellations associated with one or more radio resources (e.g., defined across REs associated with a scheduled PxxCH communication from the transmitter to the receiver). At the receiver, the transmitted bits (e.g., the modulated source bits that are transmitted to the receiver over a wireless channel) may be demodulated using maximum likelihood detection, such that detected bits that are decoded or otherwise determined at the receiver approximately reconstruct the source bits. Although noncoherent wireless communication using hand-crafted constellations and maximum likelihood detection may improve demodulation and/or decoding performance in some use cases, hand-crafted constellations and maximum likelihood detection metrics tend to be sensitive to different fading scenarios. For example, hand-crafted constellations and maximum likelihood detection metrics may be robust in the presence of additive white Gaussian noise but non-robust with a large delay spread, among other examples.

Accordingly, some aspects described herein relate to a joint transmit-receive (Tx-Rx) design in which artificial intelligence techniques use one or more neural networks in an encoder (e.g., an autoencoder) and a decoder to enable noncoherent wireless communication. For example, as shown by reference number <NUM>, the transmitter may provide source bits (e.g., an input bit sequence) to a transmission neural network that modulates the source bits to a radio resource (e.g., a group of REs in one or more scheduled PRBs associated with a scheduled PxxCH communication from the transmitter to the receiver). As further shown by reference number <NUM>, the receiver may demodulate the transmitted bits (e.g., the modulated source bits that are transmitted to the receiver over a wireless channel) using a reception neural network in order to decode detected bits that approximately reconstruct the source bits.

In some aspects, the transmission neural network used to modulate the source bits and the reception neural network used for demodulating and/or decoding may be jointly trained (e.g., offline using CSI samples). In this way, the joint Tx-Rx design using a transmission neural network at the transmitter and a reception neural network at the receiver may provide data-driven robustness based on channel models, in that autoencoders may not require knowledge of the underlying data distribution of the input or an explicit identification of a structure of the input. For example, a neural network-based encoder/decoder is sometimes applied for CSI feedback in massive multiple input multiple output (MIMO) systems. CSI feedback in MIMO frequency division duplex (FDD) systems is typically associated with significant overhead and relates to sparse channels, which leads to significant compression gains using a neural network-based encoder/decoder. Furthermore, using simple neural networks to enable noncoherent wireless communication may offer comparable or reduced receiver complexity relative to maximum likelihood detection techniques.

<FIG> is a diagram illustrating an example <NUM> associated with scheduling energy autoencoder based noncoherent transmission, in accordance with various aspects of the present disclosure. As shown in <FIG>, example <NUM> includes a UE (e.g., UE <NUM>) that may transmit one or more scheduled communications to, and/or receive one or more scheduled communications from, another device in a wireless network (e.g., wireless network <NUM>). For example, the other device may be a base station (e.g., base station <NUM>) that communicates with the UE via a wireless access link, which may include an uplink and a downlink, and the UE may be scheduled to transmit a PUCCH and/or a PUSCH to the base station and/or to receive a PDCCH and/or a PDSCH from the base station via the wireless access link. Additionally, or alternatively, the other device may be another UE that communicates with the UE via a wireless sidelink, and the UE may be scheduled to transmit a PSCCH and/or a PSSCH to the other UE and/or to receive a PSCCH and/or a PSSCH from the other UE via the wireless sidelink.

In some aspects, as described herein, the UE and the other device may communicate (e.g., transmit and/or receive) scheduled communications using energy-based autoencoders that are suitable for noncoherent transmission (e.g., where a transmitted PxxCH does not include a pilot signal, a DMRS, or another suitable signal to enable channel estimation at the receiver). For example, in some aspects, a transmitter may use a neural network to modulate a source bit sequence onto one or more REs in one or more scheduled PRBs associated with a scheduled communication with only energies (e.g., where a total transmit power is normalized over one or more RE groups), and a neural network can be used at the receiver to demodulate the transmitted bit sequences. In this way, modulating the source bit sequence to energies associated with physical layer resources may be robust to fading, especially in noncoherent wireless communication where a DMRS or other channel estimation signal is unavailable (e.g., because the receiver does not need to know the phase of the received signal, and instead needs to only detect the energies in physical layer resources). Furthermore, using neural networks for transmission and reception may enable computationally efficient modulation schemes that are robust to different fading scenarios (e.g., high Doppler or high delay spread), and segmenting long source bit sequences into multiple sub-sequences can significantly reduce receiver complexity (e.g., because a more complex neural network and/or more complex maximum likelihood detection may be needed to demodulate a long source bit sequence).

As shown by reference number <NUM>, the UE may be configured as a transmitter or a receiver for one or more scheduled communications. For example, as described above, the UE may be scheduled to transmit a PUCCH or a PUSCH to a base station, to transmit a PSCCH or a PSSCH to another UE, to receive a PDCCH or a PDSCH from a base station, and/or to receive a PSCCH or a PSSCH from another UE. In some aspects, as described herein, the UE may be configured to transmit and/or receive the one or more scheduled communications using RE segments that are based on energy levels associated with the RE segments. For example, as shown, the transmitter may segment a source bit sequence into multiple sub-sequences that are each associated with a respective RE segment, and each RE segment may include one or more REs within one or more scheduled PRBs associated with a scheduled communication. In some aspects, the RE(s) within an RE segment can be contiguous or non-contiguous in a time domain and/or a frequency domain, where contiguous REs may provide reduced encoding and decoding complexity and non-contiguous REs may increase diversity (e.g., using interleaved control channel elements (CCEs) and/or RE group (REG) bundles to improve PDCCH reliability). In some aspects, each sub-sequence and corresponding RE segment may be associated with a neural network that modulates the associated sub-sequence to the REs within the corresponding RE segment. In particular, outputs from each neural network may be energies on a corresponding set of physical layer resources (e.g., the REs within an RE segment), with the energies normalized over the set of physical layer resources (e.g., over the RE segment) based on a transmit power configuration associated with the transmitter.

For example, as shown, a source bit sequence '<NUM>. " may be segmented into four sub-sequences, which include sub-sequences '<NUM>. ," and "<NUM>. " At the transmitter, each sub-sequence is provided to a neural network that modulates the associated sub-sequence to a set of contiguous or non-contiguous REs within an RE segment. In general, the source bit sequence may be associated with a scheduled communication (e.g., a PUCCH, PUSCH, PDCCH, PDSCH, PSSCH, or PSSCH), and the RE segments that are associated with the various sub-sequences (and the REs included within each RE segment) may be included within one or more scheduled PRBs associated with the scheduled communication. For example, <FIG> illustrates an example where the source bit sequence is modulated to the REs in four RE segments (shown by different shadings) that are within one or more scheduled PRBs associated with a scheduled communication. Accordingly, the four sub-sequences associated with the source bit sequence may each be associated with a respective neural network (e.g., NN#<NUM> trough NN#<NUM>), with outputs from the neural networks being energies on the corresponding REs normalized over the RE segment. In this way, the receiver may detect energies on the REs within each RE segment (e.g., using a receiver neural network, as shown, or another suitable technique such as maximum likelihood detection) to demodulate the transmitted sub-sequences and thereby decode a detected bit sequence that approximates the source bit sequence.

Accordingly, as shown by reference number <NUM>, the UE may determine one or more segmentation methods and/or associated neural networks to be used to transmit and/or receive a scheduled communication. For example, the one or more segmentation methods may indicate a technique used to segment one or more scheduled PRBs into the multiple RE segments that correspond to the multiple sub-sequences associated with the source bit sequence. Additionally, or alternatively, the one or more segmentation methods may indicate a technique used to segment one or more RE segments into REs that may be contiguous or non-contiguous in a time domain and/or a frequency domain (e.g., to define how many REs are within an RE segment). Additionally, or alternatively, the one or more segmentation methods may indicate a number of bits to be included in each sub-sequence and/or a technique used to segment the source bit sequence into the multiple sub-sequences.

In some aspects, a base station may signal the segmentation method(s) associated with segmenting the scheduled PRBs into the RE segments, and/or the segmentation method(s) associated with segmenting the source bit sequence into the multiple sub-sequences, to the UE. For example, the base station may be the device that communicates control channels and/or data channels with the UE using noncoherent wireless communication, or may be a device that schedules noncoherent sidelink communication between the UE and the other device. In either case, the base station may transmit, and the UE may receive, signaling that indicates the segmentation method(s) associated with the RE segments and/or the bit sequences, where the signaling may include one or more radio resource control (RRC) and/or downlink control information (DCI) messages. For example, in some aspects, the signaling may include one or more RRC messages that preconfigure a set of N segmentation method options and one or more DCI messages that select one of the N segmentation method options (e.g., when scheduling a PDSCH, PUSCH, and/or PSSCH). Additionally, or alternatively, one or more RRC messages may preconfigure a segmentation method for a particular PUCCH resource, PUCCH resource set, PDCCH search space, and/or PDCCH control resource set (CORESET), among other examples.

In some aspects, as described above, each RE segment may be associated with a neural network that modulates a sub-sequence associated with a source bit sequence to the REs in the corresponding RE segment. For example, as shown in <FIG>, a first sub-sequence (e.g., "<NUM>. ") is associated with a first neural network (e.g., NN#<NUM>) that modulates the first sub-sequence to a first RE segment, a second sub-sequence (e.g., "<NUM>. ") is associated with a second neural network (e.g., NN#<NUM>) that modulates the second sub-sequence to a second RE segment, and so on. Accordingly, in some aspects, a configuration of the neural networks that modulate the various sub-sequences to the different RE segments may depend on the segmentation method(s) that are configured across the one or more scheduled PRBs.

For example, in some aspects, a common segmentation method may be configured across the scheduled PRB(s), in which case each RE segment may have the same number of REs and/or the same number of OFDM symbols. Furthermore, in cases where a common segmentation method is configured across the scheduled PRB(s), each RE segment may be associated with the same number of bits (e.g., the source bit sequence is segmented into multiple sub-sequences that each have the same number of bits). In such cases, where a common segmentation method is indicated or otherwise configured across the scheduled PRB(s), each RE segment may be associated with an identical transmission neural network, which may be signaled to the UE in one or more RRC and/or DCI messages together with the common segmentation method. In some aspects, the transmission neural network used to modulate the sub-sequences onto the RE segments may generally include one or more parameters (e.g., for input neurons, hidden layers, output neurons, and/or weightings, among other examples) that are preconfigured and/or network-configured (or indicated), and neural networks for downlink reception (e.g., PDCCH and/or PDSCH reception) may be configured and/or indicated via one or more RRC messages, a medium access control (MAC) control element (MAC-CE), and/or one or more DCI messages.

Alternatively, in some aspects, a non-common segmentation method may be configured across the scheduled PRB(s). In such cases, the RE segments that are associated with the different sub-sequences may include at least a first RE segment and a second RE segment that have a different number of REs and/or a different number of OFDM symbols. Additionally, or alternatively, in cases where a non-common segmentation method is configured across the scheduled PRB(s), any two RE segments may be associated with a different number of bits (e.g., the source bit sequence may be segmented into multiple sub-sequences that have a non-uniform number of bits). In such cases, where a non-common segmentation method is indicated or otherwise configured across the scheduled PRB(s), different RE segments may be associated with different transmission neural networks, which may be signaled to the UE in one or more RRC and/or DCI messages together with the non-common segmentation method. For example, in some aspects, the different neural networks may include convolutional neural networks with different numbers of kernels and/or different kernel coefficients. In some aspects, the transmission neural networks used to modulate the sub-sequences onto the RE segments may include one or more parameters that are preconfigured and/or network-configured (or indicated), and neural networks for downlink reception may be configured and/or indicated via one or more RRC messages, a MAC-CE, and/or one or more DCI messages, among other examples.

Accordingly, as further shown by reference number <NUM>, the UE and the other device may perform noncoherent wireless communication for a scheduled PxxCH communication using an energy-based autoencoder that is configured in the manner described in further detail above. For example, in some aspects, a transmitter (e.g., the UE or the other device) may use one or more neural networks to modulate the source bit sequence onto corresponding RE segments within one or more scheduled PRBs associated with the scheduled PxxCH communication, with outputs from the neural network(s) being energies on the corresponding physical layer resources (e.g., the RE segments). In some aspects, at the receiver, one or more reception neural networks can be used to demodulate the transmitted bit sequences based at least in part on the energies on the corresponding physical layer resources.

Furthermore, in cases where the scheduled PxxCH is a PUCCH that the UE transmits to a base station using an energy autoencoder, a PUCCH format may be defined based at least in part on the segmentation method(s) associated with segmenting the scheduled PRB(s) into the RE segments, the segmentation method(s) associated with segmenting the source bit sequence into the multiple sub-sequences, and/or the associated neural networks that are used to modulate the sub-sequences onto the RE segments. Additionally, or alternatively, different PUCCH resources within a PUCCH resource set may be associated with the same segmentation method, with different segmentation methods, with the same neural network, and/or with different neural networks. Furthermore, in cases where the PUCCH carries hybrid automatic repeat request acknowledgement (HARQ-ACK) feedback for a PDSCH, one or more DCI messages that schedule the PDSCH may indicate a PUCCH resource for the HARQ-ACK feedback associated with the PDSCH. In such cases, the DCI message(s) scheduling the PDSCH may indicate the segmentation method(s) and/or the associated transmission neural network(s) for the noncoherent transmission of the PUCCH.

Alternatively, in cases where the scheduled PxxCH is a PDCCH that a base station transmits to the UE using an energy autoencoder, the segmentation method(s) associated with segmenting the scheduled PRB(s) into the RE segments and/or segmenting the source bit sequence into the multiple sub-sequences may be based on one or more REG bundles and/or one or more CCEs. Furthermore, the PDCCH may be associated with an aggregation level, a search space, and/or a CORESET based at least in part on the configured segmentation method(s) and/or the associated neural network(s) used to modulate the sub-sequences onto the RE segments associated with the PDCCH (e.g., different aggregation levels, search spaces, and/or CORESETS can be configured or defined for different segmentation methods and/or neural networks). In addition, in cases where the PDCCH is overbooked (e.g., where a blind detection or channel estimation limit is lower than a determined number of PDCCH decodings) and/or the UE is configured to operate in a power saving mode (e.g., a discontinuous reception (DRX) mode and/or a sleep mode, among other examples), the UE may determine a priority associated with a particular PDCCH search space and/or CORESET based at least in part on the segmentation method(s) and/or associated neural network(s). For example, search spaces and/or CORESETs associated with different segmentation methods and/or neural networks may have different priorities, whereby the UE may determine whether to monitor or refrain from monitoring a search space and/or a CORESET associated with a PDCCH based on the priority associated with the segmentation method and/or the associated neural network corresponding to the search space and/or CORESET. For example, a search space or a CORESET that is associated with a neural network having a large number neurons may have a relatively low priority when the UE is operating in a DRX mode in cases where blind detection or channel estimation limits are lower than the determined number of PDCCH decodings.

<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>) performs operations associated with scheduling energy autoencoder based noncoherent transmission.

As shown in <FIG>, in some aspects, process <NUM> includes identifying, as claimed, multiple RE segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled PRBs associated with the scheduled communication (block <NUM>). For example, the UE (e.g., using identification component <NUM>, depicted in <FIG>) may identify multiple RE segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled PRBs associated with the scheduled communication, as described above.

As further shown in <FIG>, in some aspects, process <NUM> includes communicating, as claimed, with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment (block <NUM>). For example, the UE (e.g., using communication component <NUM>, depicted in <FIG>) may communicate with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment, as described above.

In a first aspect, the multiple RE segments include at least one RE segment in which the one or more REs are contiguous.

In a second aspect, alone or in combination with the first aspect, the multiple RE segments include at least one RE segment in which the one or more REs are non-contiguous.

In a third aspect, alone or in combination with one or more of the first and second aspects, an output from the neural network includes the respective energies on the one or more REs normalized over the RE segment.

In a fourth aspect, alone or in combination with one or more of the first through third aspects, process <NUM> includes receiving signaling that indicates one or more of a segmentation method associated with the one or more REs in the RE segment, a segmentation method associated with one or more bits in the sub-sequence associated with the RE segment, or the neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the signaling includes one or more RRC or DCI messages.

In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, each RE segment has the same number of REs, symbols, and bits as each other RE segment.

In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, the multiple RE segments are each associated with an identical neural network that modulates the sub-sequence associated with a respective RE segment to the one or more REs in the respective RE segment.

In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, the multiple RE segments include a first RE segment and a second RE segment that include one or more of a different number of REs, a different number of symbols, or a different number of bits.

In a ninth aspect, alone or in combination with one or more of the first through eighth aspects, different RE segments are associated with different neural networks that are used to modulate the sub-sequence associated with a respective RE segment to the one or more REs in the respective RE segment.

In a tenth aspect, alone or in combination with one or more of the first through ninth aspects, the different neural networks include different convolutional neural networks with different numbers or different coefficients of kernels.

In an eleventh aspect, alone or in combination with one or more of the first through tenth aspects, the scheduled communication includes a non-coherent control channel transmission or a non-coherent data channel transmission.

In a twelfth aspect, alone or in combination with one or more of the first through eleventh aspects, communicating with the device includes determining that the scheduled communication includes a PUCCH, determining a PUCCH format based at least in part on one or more segmentation methods or transmission neural networks associated with modulating the multiple sub-sequences to the one or more REs in the multiple RE segments, and transmitting the PUCCH based at least in part on the PUCCH format.

In a thirteenth aspect, alone or in combination with one or more of the first through twelfth aspects, transmitting the PUCCH includes determining a PUCCH resource set associated with the PUCCH, and modulating, for different PUCCH resources within the PUCCH resource set, the multiple sub-sequences to the one or more REs in the multiple RE segments using the one or more segmentation methods or transmission neural networks, wherein the one or more segmentation methods or transmission neural networks are configured by configurations associated with the PUCCH resources.

In a fourteenth aspect, alone or in combination with one or more of the first through thirteenth aspects, transmitting the PUCCH includes determining, based at least in part on DCI that indicates a PUCCH resource for HARQ-ACK feedback associated with a PDSCH scheduled by the DCI, the one or more segmentation methods or transmission neural networks associated with modulating the multiple sub-sequences, and modulating the multiple sub-sequences to the one or more REs in the multiple RE segments using the one or more segmentation methods or transmission neural networks for the PUCCH resource indicated in the DCI.

In a fifteenth aspect, alone or in combination with one or more of the first through fourteenth aspects, communicating with the device includes determining that the scheduled communication includes a PDCCH, determining one or more segmentation methods or transmission neural networks associated with modulating the multiple sub-sequences to the one or more REs in the multiple RE segments based at least in part on one or more REG bundles or CCEs associated with the PDCCH, and detecting the source bit sequence in the PDCCH based at least in part on the one or more segmentation methods or transmission neural networks.

In a sixteenth aspect, alone or in combination with one or more of the first through fifteenth aspects, the source bit sequence is detected based at least in part on one or more aggregation levels associated with the one or more segmentation methods or transmission neural networks.

In a seventeenth aspect, alone or in combination with one or more of the first through sixteenth aspects, the source bit sequence is detected based at least in part on one or more search spaces or CORESETs associated with the one or more segmentation methods or transmission neural networks.

In an eighteenth aspect, alone or in combination with one or more of the first through seventeenth aspects, process <NUM> includes determining a priority associated with the one or more search spaces or CORESETs based at least in part on the one or more segmentation methods or transmission neural networks, and monitoring the PDCCH based at least in part on the priority associated with the one or more search spaces or CORESETs.

In a nineteenth aspect, alone or in combination with one or more of the first through eighteenth aspects, process <NUM> includes determining a priority associated with the one or more search spaces or CORESETs based at least in part on the one or more segmentation methods or transmission neural networks, and refraining from monitoring the PDCCH based at least in part on the priority associated with the one or more search spaces or CORESETs.

<FIG> is a block diagram of an example apparatus <NUM> for wireless communication. The apparatus <NUM> may be a UE, or a UE may include the apparatus <NUM>. In some aspects, the apparatus <NUM> includes a reception component <NUM> and a transmission component <NUM>, which may be in communication with one another (for example, via one or more buses and/or one or more other components). As shown, the apparatus <NUM> may communicate with another apparatus <NUM> (such as a UE, a base station, or another wireless communication device) using the reception component <NUM> and the transmission component <NUM>. As further shown, the apparatus <NUM> includes one or more of an identification component <NUM>, a communication component <NUM>, or a monitoring component <NUM>, among other examples.

In some aspects, the apparatus <NUM> may be configured to perform one or more operations described herein in connection with <FIG>. Additionally, or alternatively, the apparatus <NUM> may be configured to perform one or more processes described herein, such as process <NUM> of <FIG>. In some aspects, the apparatus <NUM> and/or one or more components shown in <FIG> may include one or more components of the UE described above in connection with <FIG>. Additionally, or alternatively, one or more components shown in <FIG> may be implemented within one or more components described above in connection with <FIG>. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and executable by a controller or a processor to perform the functions or operations of the component.

The identification component <NUM> may identify multiple RE segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more REs in one or more scheduled PRBs associated with the scheduled communication. The communication component <NUM> may communicate, or may cause reception component <NUM> and/or transmission component <NUM> to communicate, with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with a source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

The reception component <NUM> may receive signaling that indicates one or more of a segmentation method associated with the one or more REs in the RE segment, a segmentation method associated with one or more bits in the sub-sequence associated with the RE segment, or the neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.

The monitoring component <NUM> may determine a priority associated with the one or more search spaces or CORESETs based at least in part on the one or more segmentation methods or transmission neural networks, and the monitoring component <NUM> may monitor the PDCCH based at least in part on the priority associated with the one or more search spaces or CORESETs.

The monitoring component <NUM> may determine a priority associated with the one or more search spaces or CORESETs based at least in part on the one or more segmentation methods or transmission neural networks, and the monitoring component <NUM> may refrain from monitoring the PDCCH based at least in part on the priority associated with the one or more search spaces or CORESETs.

Claim 1:
A method of wireless communication performed by a user equipment, UE, comprising:
identifying (<NUM>) multiple resource element, RE, segments associated with a scheduled communication that includes a source bit sequence, wherein the multiple RE segments each include one or more Res in one or more scheduled physical resource blocks, PRBs, associated with the scheduled communication; and
communicating (<NUM>) with a device based at least in part on respective energies on the one or more REs included in the multiple RE segments associated with the scheduled communication, wherein the multiple RE segments are each associated with a sub-sequence associated with the source bit sequence segmented into multiple sub-sequences, and wherein communicating with the device includes transmitting or detecting the sub-sequence associated with an RE segment, among the multiple RE segments, using a neural network that modulates the sub-sequence associated with the RE segment to the one or more REs in the RE segment.