Patent Description:
Today's communication networks are also more secure, resilient to multipath fading, allow for lower network traffic latencies, provide better communication efficiencies (e.g., in terms of bits per second per unit of bandwidth used, etc.). These and other recent improvements have facilitated the emergence of the Internet of Things (IOT), large scale Machine to Machine (M2M) communication systems, autonomous vehicles, and other technologies that rely on consistent and secure communications.

Additionally, the deployment of neural networks, such as deep neural networks, is gaining momentum in today's communication networks. Neural networks can be used on computing devices for a variety of tasks. Neural networks often use a multilayered architecture in which each layer receives input, performs a computation on the input, and generates an output. The output of a first layer of nodes often becomes an input to a second layer of nodes, the output of a second layer of nodes becomes an input to a third layer of nodes, and so on. As such, computations in a neural network are distributed over a population of processing nodes that make up a computational chain.

<CIT> discloses a concept for mitigating impairments due to transmit noise. A apparatus is provided for mitigating transmitter impairments of a transmit signal, the apparatus comprising an input for an in-phase and a quadrature component of a digital baseband transmit signal, a pre-distortion neural network processor for pre-distorting the input digital baseband transmit signal to obtain a pre-distorted digital baseband signal, wherein the pre-distortion neural network processor is adaptable to a transmitter transfer function of at least one analog transmitter device arranged downstream to the pre-distortion neural network processor, such that a transfer function of the pre-distortion neural network processor approximates an inverse of said transmitter transfer function, and an output for an in-phase and a quadrature component of the pre-distorted digital baseband signal, which may be coupled to an input of the at least one analog transmitter device arranged downstream to the pre-distortion neural network processor.

Advantageous, optional features of the invention are then set out in the appended dependent claims. In the following description, any embodiment referred to and not falling within the scope of the claims is merely an example useful to the understanding of the invention.

Various aspects include systems and methods of wireless communication by transmitting a waveform to a receiving device performed by a processor of a transmitting device. Various aspects may include obtaining transmit waveform distortion information of the transmitting device, compressing the transmit waveform distortion information of the transmitting device into compressed transmit waveform distortion information using an encoder neural network, and sending the compressed transmit waveform distortion information and one or more decoder neural network weights to the receiving device in a configuration that enables the receiving device to use the one or more decoder neural network weights to configure a decoder neural network of the receiving device to recover the transmit waveform distortion information of the transmitting device from the compressed transmit waveform distortion information.

Some aspects may further include determining a model type of the decoder neural network of the transmitting device, and sending the compressed transmit waveform distortion information and the one or more decoder neural network weights to the receiving device may include sending the compressed transmit waveform distortion information, the one or more decoder neural network weights, and the model type to the receiving device. In some aspects, sending the compressed transmit waveform distortion information and the one or more decoder neural network weights to the receiving device may include sending the compressed transmit waveform distortion information and the one or more decoder neural network weights to the receiving device in control information for each slot to be transmitted. In some aspects, sending the compressed transmit waveform distortion information and the one or more decoder neural network weights to the receiving device may include sending the compressed transmit waveform distortion information to the receiving device in control information for each slot to be transmitted and sending the one or more decoder neural network weights to the receiving device in control information at a periodicity greater than every slot to be transmitted. In some aspects, the transmitting device may be a user equipment (UE) computing device and the receiving device may be a base station. In various aspects, the transmitting device may be a base station and the receiving device may be a UE computing device.

Some aspects may further include training the encoder neural network to compress the transmit waveform distortion information of the transmitting device into the compressed transmit waveform distortion information, and training the decoder neural network of the transmitting device to recover the transmit waveform distortion information of the transmitting device from the compressed transmit waveform distortion information, wherein the one or more decoder neural network weights are weights of the trained decoder neural network of the transmitting device. In some aspects, the encoder neural network and the decoder neural network of the of the transmitting device may be trained using unsupervised learning algorithms. In some aspects, training the encoder neural network and training the decoder neural network of the transmitting device may include training the encoder neural network and the decoder neural network of the transmitting device for one transmit antenna of the transmitting device. In some aspects, training the encoder neural network and training the decoder neural network of the transmitting device may include the encoder neural network and the decoder neural network of the transmitting device for each transmit antenna of the transmitting device.

Further aspects may include systems and methods of wireless communication by receiving a waveform from a transmitting device performed by a processor of a receiving device. Various aspects may include receiving compressed transmit waveform distortion information of the transmitting device and one or more weights of a trained decoder neural network of the transmitting device, configuring a decoder neural network of the receiving device using the received one or more weights, and recovering transmit waveform distortion information of the transmitting device from the compressed transmit waveform distortion information of the transmitting device using the configured decoder neural network of the receiving device. Some aspects may further include using the recovered transmit waveform distortion information of the transmitting device to mitigate waveform distortion in a transmit waveform received from the transmitting device.

In some aspects, receiving the compressed transmit waveform distortion information of the transmitting device and the one or more weights of the trained decoder neural network of the transmitting device may include receiving the compressed transmit waveform distortion information of the transmitting device, the one or more weights of the trained decoder neural network of the transmitting device, and a model type of the trained decoder neural network of the transmitting device, and configuring the decoder neural network of the receiving device using the received one or more weights may include configuring the decoder neural network of the receiving device using the received one or more weights and the received model type. In some aspects, the recovered transmit waveform distortion information may be a two-dimensional map of distortion error due to signal clipping of orthogonal frequency division multiplexing (OFDM) symbols within a slot for one or more antennas of the transmitting device. In some aspects, receiving the compressed transmit waveform distortion information of the transmitting device and one or more weights of a trained decoder neural network of the transmitting device may include receiving compressed transmit waveform distortion information of the transmitting device and one or more weights of a trained decoder neural network of the transmitting device in control information for each slot to be transmitted. In some aspects, receiving the compressed transmit waveform distortion information of the transmitting device and one or more weights of a trained decoder neural network of the transmitting device may include receiving compressed transmit waveform distortion information of the transmitting device in control information for each slot to be transmitted and receiving one or more weights of a trained decoder neural network of the transmitting device in control information at a periodicity greater than every slot to be transmitted. In some aspects, the receiving device may be a base station and the transmitting device is a UE computing device. In some aspects, the compressed transmit waveform distortion information and the one or more weights may be received directly from the UE computing device. In some aspects, the compressed transmit waveform distortion information and the one or more weights may be received from a base station other than the transmitting device.

Further aspects may include a base station or a wireless device having a processor configured to perform one or more operations of any of the methods summarized above. Further aspects may include a non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a processor of a base station or a wireless device to perform operations of any of the methods summarized above. Further aspects include a base station or a wireless device having means for performing functions of any of the methods summarized above. Further aspects include a system on chip for use in a base station or a wireless device that includes a processor configured to perform one or more operations of any of the methods summarized above. Further aspects include a system in a package that includes two systems on chip for use in a base station or a wireless device that includes a processor configured to perform one or more operations of any of the methods summarized above.

Various embodiments will be described in detail with reference to the accompanying drawings. References made to particular examples and implementations are for illustrative purposes, and are not intended to limit the scope of the claims.

Various embodiments enable the signaling of transmit (TX) waveform distortion information to a receiving device from a transmitting device. As used herein the term "transmitting device" refers to any device outputting a TX waveform and the term "receiving device" refers to any device attempting to receive that TX waveform. As an example, in uplink (UL) communications in a Third Generation Partnership Project (3GPP) network, the transmitting device may be a User Equipment (UE) computing device and the receiving device may be a base station (e.g., a Next Generation NodeB (gNB)). As another example, in downlink (DL) communications the transmitting device may be a base station (e.g., a gNB) and the receiving device may be a UE computing device.

Various embodiments may employ neural networks executing within transmitting devices to compress TX waveform distortion. In various embodiments, compressed TX waveform distortion information may be conveyed to a receiving device. In various embodiments, the signaling of TX waveform distortion information from a transmitting device to a receiving device may enable a receiving device to mitigate waveform distortion in a transmit waveform received from the transmitting device. In various embodiments, the presence of TX waveform distortion information for the transmitting device decoded by the receiving device may enable the transmitting device to utilize its available transmit power efficiently.

In wireless communications between transmitting devices and receiving devices, distortion of the TX waveform sent from the transmitting device, such as signal clipping, may hinder the successful reception of the TX waveform by the receiving device. Distortion of the TX waveform may be caused by various factors, such as components of the transmitting device itself. For example, power amplifiers of a transmitting device may cause distortion of the TX waveform and the amount of distortion may increase as the power levels of the power amplifiers increase. Conventional transmitting devices mitigate the distortion caused by power amplifiers to the TX waveform by keeping power levels of the power amplifiers below certain levels. This reduction of the power levels of the power amplifiers of the transmitting device can reduce the distortion of the TX waveform, but also can result in less efficient utilization of transmit power (e.g., a less powerful than possible transmit signal).

Various embodiments enable neural networks at receiving devices to recover TX waveform distortion information of a transmitting device and use the recovered TX waveform distortion information of the transmitting device to mitigate waveform distortion in a TX waveform received from the transmitting device. The presence of TX waveform distortion information for the transmitting device at the receiving device may enable the transmitting device to utilize its available transmit power efficiently (e.g., by using power amplifiers at full power) because the distortion caused by power amplifiers may be mitigated at the receiving device side.

In various embodiments, a transmitting device may obtain TX waveform distortion information of the transmitting device. In some embodiments, the TX waveform distortion information may be a two-dimensional map of distortion error due to signal clipping of orthogonal frequency division multiplexing (OFDM) symbols within a slot for one or more antennas of the transmitting device. The TX waveform distortion information may be configured such that a receiving device may use the TX waveform distortion information of the transmitting device to mitigate waveform distortion in a TX waveform received from the transmitting device. For example, the TX waveform distortion information may be used by a receiving device to mitigate the TX waveform distortion of multiple OFDM symbols. The TX waveform distortion information may depend on the data that may be sent on each slot because the data to be sent may impact the OFDM waveform. Thus, the provisioning of TX waveform distortion information for each slot may enable a receiving device to mitigate the TX waveform distortion on a per slot basis.

In various embodiments, a transmitting device may include an encoder neural network and decoder neural network pair. In various embodiments, the encoder neural network and the decoder neural network may be deep neural networks. In various embodiments, there may be an encoder and decoder pair for each transmit antenna of the transmitting device. The encoder neural network may be configured to compress information. For example, the encoder neural network may be configured to compress TX waveform distortion information into compressed TX waveform distortion information, and the decoder neural network may be a configured to decompress information. For example, the decoder neural network may be configured to recover the TX waveform distortion information from compressed TX waveform distortion information. In various embodiments, a receiving device may also include a decoder neural network.

In various embodiments, a transmitting device may train an encoder neural network of the transmitting device to compress the TX waveform distortion information of the transmitting device into compressed TX waveform distortion information of the transmitting device. In various embodiments, a transmitting device may train a decoder neural network of the transmitting device to recover the TX waveform distortion information of the transmitting device from the compressed TX waveform distortion information of the transmitting device. In various embodiments, the encoder neural network of the transmitting device and the decoder neural network of the of the transmitting device may be trained using unsupervised learning algorithms. In various embodiments, training the encoder neural network of the transmitting device and training the decoder neural network of the transmitting device may include training the encoder neural network and the decoder neural network for one transmit antenna of the transmitting device. In various embodiments, training the encoder neural network of the transmitting device and training the decoder neural network of the transmitting device may be performed periodically. For example, training may occur upon initial start-up of the transmitting device, daily, upon registration with a new network, etc. In various embodiments, the encoder neural network of the transmitting device and the decoder neural network of the of the transmitting device may be trained using unsupervised learning algorithms.

In various embodiments, a transmitting device may determine a model type of the trained decoder neural network of the transmitting device and weights of the trained decoder neural network of the transmitting device.

A model type may be a structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. Model types may be actual representations of the neural network elements themselves and/or descriptors (e.g., model names, model numbers, model tags) that indicate the structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. In various embodiments, a model type of a decoder neural network of the transmitting device may provide information to a receiving device to reconstitute a specific structure of the decoder neural network as was trained at the transmitting device.

Weights of a neural network may be the values associated with the interconnections between the nodes of the neural network after training of the neural network. The weights of the trained decoder neural network of the transmitting device may be the values associated with the interconnections between the nodes of the decoder neural network after training at the transmitting device. In various embodiments, two decoder neural networks having the same model type may have the same structure such that applying the same weights to the two decoder neural networks will result in the same decompressed output of the two decoder neural networks based on the same compressed input being provided to each decoder neural network.

In various embodiments, a receiving device having a model type of a trained decoder neural network of the transmitting device and weights of the trained decoder neural network of the transmitting device may configure a decoder neural network of the receiving device to recover the same decompressed output as would be recovered by the trained decoder neural network of the transmitting device from a common compressed input without having to spend time to actually train the decoder neural network of the receiving device.

In various embodiments, a transmitting device may send the compressed TX waveform distortion information of the transmitting device, a model type of the trained decoder neural network of the transmitting device, and weights of the trained decoder neural network of the transmitting device to the receiving device. The compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent to the receiving device in a configuration that enables the receiving device to use the model type and the weights to configure a decoder neural network of the receiving device to recover the TX waveform distortion information of the transmitting device from the compressed TX waveform distortion information of the transmitting device. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent together. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent separately. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent in overhead information exchanged between the transmitting computing device and the receiving computing device. In some embodiments, the compressed TX waveform distortion information of the transmitting device may be sent in control information for each slot to be transmitted. The compressed TX waveform distortion information may depend on the data that may be sent on each slot because the data to be sent may impact the OFDM waveform. Providing the compressed TX waveform distortion information on a per slot basis may enable a receiving device to mitigate the TX waveform distortion on a per slot basis. In some embodiments, compressed TX waveform distortion information and the model type and the weights may be signaled at different periodicities. In some embodiments, the model type and the weights may be signaled less frequently than the compressed TX waveform distortion information of the transmitting device. For example, compressed TX waveform distortion information may be sent in control information for each slot and the weights may be sent at a periodicity greater than every slot (e.g., less frequently than the compressed TX waveform distortion information). Weights and/or model types may be sent at much larger time scales than compressed TX waveform distortion information as the encoder neural network and decoder neural network of the transmitting device may be updated infrequently. In some embodiments, a model type may not need to be sent as the model type may already be known at a receiving device.

In various embodiments, a receiving device may receive compressed TX waveform distortion information of a transmitting device, a model type of a trained decoder neural network of the transmitting device, and weights of the trained decoder neural network of the transmitting device. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent in overhead information. In some embodiments, the compressed TX waveform distortion information of the transmitting device may be received in control information for each slot to be transmitted. In some embodiments, a model type may not need to be received as the model type may already be known at the receiving device. As one example, a single default model type may be used for both the decoder neural network of the transmitting device and the decoder neural network of the receiving device. As another example, the model type may be known based on device type, network, or other settings at the receiving device.

In some embodiments, the compressed TX waveform distortion information, the model type, and the weights may be received directly from the transmitting device. For example, the transmitting device may be a UE computing device and the UE computing device may send the compressed TX waveform distortion information, the model type, and the weights to a base station as part of initial registration procedures between the UE computing device and the base station to receive services in the cell served by the base station. The base station may be the receiving device and may utilize the compressed TX waveform distortion information, the model type, and the weights to mitigate distortion in the TX waveform sent by the UE computing device.

In some embodiments, the compressed TX waveform distortion information, the model type, and the weights may be received from a base station other than the transmitting device. For example, the compressed TX waveform distortion information, the model type, and the weights may be values stored and shared among devices in a communication network.

In some embodiments, the compressed TX waveform distortion information, the model type, and/or the weights of base stations may be shared among devices, such that a device may receive the compressed TX waveform distortion information, the model type, and the weights for another device indirectly. As one example, base stations of neighbor cells may share their model types and weights with one another and with UE computing devices in their respective cells to support UE computing device mobility and handoff. As another example, UE computing model type and weights may be centrally stored such that a base station may retrieve the model type and the weights for a UE computing device upon discovery of the UE computing device without the UE computing device needing to directly transmit the model type and the weights to the base station, etc. As another example, base station compressed TX waveform distortion information, model type, and/or weights may be centrally stored such that a UE computing device may retrieve the compressed TX waveform distortion information, the model type, and/or the weights for a next base station before entering the coverage area of that base station and without the UE computing device needing to directly receive the compressed TX waveform distortion information, the model type, and/or the weights from that next base station, etc..

In various embodiments, a receiving device may configure a decoder neural network of the receiving device using the received model type and weights. A model type may be a structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. Model types may be actual representations of the neural network elements themselves and/or descriptors (e.g., model names, model numbers, model tags) that indicate the structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. Weights of a neural network may be the values associated with the interconnections between the nodes of the neural network after training of the neural network. In various embodiments, a receiving device having a model type of a trained decoder neural network of the transmitting device and weights of the trained decoder neural network of the transmitting device may configure a decoder neural network of the receiving device to recover the same decompressed output as would be recovered by the trained decoder neural network of the transmitting device from the compressed TX waveform distortion information of the transmitting device received from the transmitting device. In this manner, the receiving device may configure its decoder neural network as a trained neural network without having to spend time to train its decoder neural network.

In various embodiments, a receiving device may recover the TX waveform distortion information of the transmitting device from the compressed TX waveform distortion information of the transmitting device using the configured decoder neural network of the receiving device. In some embodiments, the recovered TX waveform distortion information may include a two-dimensional map of distortion error due to signal clipping of OFDM symbols within a slot for one or more antennas of the transmitting device.

In various embodiments, a receiving device may use the recovered TX waveform distortion information of the transmitting device to mitigate waveform distortion in a TX waveform received from the transmitting device. Mitigating the waveform distortion may include using the recovered TX waveform distortion information to reconstitute the original TX waveform signal at the receiving device. By mitigating the waveform distortion, the receiving device may compensate for any distortion caused by the transmitting device itself, such as distortion caused by power amplifiers of the transmitting device.

The terms "wireless device" and "UE computing device" are used interchangeably herein to refer to any one or all of wireless router devices, wireless appliances, cellular telephones, smartphones, portable computing devices, personal or mobile multi-media players, laptop computers, tablet computers, smartbooks, ultrabooks, palmtop computers, wireless electronic mail receivers, multimedia Internet-enabled cellular telephones, medical devices and equipment, biometric sensors/devices, wearable devices including smart watches, smart clothing, smart glasses, smart wrist bands, smart jewelry (e.g., smart rings, smart bracelets, etc.), entertainment devices (e.g., wireless gaming controllers, music and video players, satellite radios, etc.), wireless-network enabled Internet of Things (IoT) devices including smart meters/sensors, industrial manufacturing equipment, large and small machinery and appliances for home or enterprise use, wireless communication elements within autonomous and semiautonomous vehicles, wireless devices affixed to or incorporated into various mobile platforms, global positioning system devices, and similar electronic devices that include a memory, wireless communication components and a programmable processor.

The term "system in a package" (SIP) may be used herein to refer to a single module or package that contains multiple resources, computational units, cores and/or processors on two or more IC chips, substrates, or SOCs. For example, a SIP may include a single substrate on which multiple IC chips or semiconductor dies are stacked in a vertical configuration. Similarly, the SIP may include one or more multichip modules (MCMs) on which multiple ICs or semiconductor dies are packaged into a unifying substrate. A SIP may also include multiple independent SOCs coupled together via high speed communication circuitry and packaged in close proximity, such as on a single motherboard or in a single wireless device. The proximity of the SOCs facilitates high speed communications and the sharing of memory and resources.

<FIG> is a system block diagram illustrating an example communication system <NUM> suitable for implementing any of the various embodiments. The communications system <NUM> may be a <NUM> New Radio (NR) network, or any other suitable network such as a Long Term Evolution (LTE) network.

The communications system <NUM> may include a heterogeneous network architecture that includes a core network <NUM> and a variety of mobile devices (illustrated as wireless device 120a-120e in <FIG>). The communications system <NUM> may also include a number of base stations (illustrated as the BS 110a, the BS 110b, the BS 110c, and the BS 110d) and other network entities. A base station is an entity that communicates with wireless devices (mobile devices or UE computing devices), and also may be referred to as an NodeB, a Node B, an LTE evolved nodeB (eNB), an access point (AP), a radio head, a transmit receive point (TRP), a New Radio base station (NR BS), a <NUM> NodeB (NB), a Next Generation NodeB (gNB), or the like. Each base station may provide communication coverage for a particular geographic area. In 3GPP, the term "cell" can refer to a coverage area of a base station, a base station subsystem serving this coverage area, or a combination thereof, depending on the context in which the term is used.

A base station 110a- <NUM>10d may provide communication coverage for a macro cell, a pico cell, a femto cell, another type of cell, or a combination thereof. A macro cell may cover a relatively large geographic area (for example, several kilometers in radius) and may allow unrestricted access by mobile devices with service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by mobile devices with service subscription. A femto cell may cover a relatively small geographic area (for example, a home) and may allow restricted access by mobile devices having association with the femto cell (for example, mobile devices in a closed subscriber group (CSG)). A base station for a macro cell may be referred to as a macro BS. A base station for a pico cell may be referred to as a pico BS. A base station for a femto cell may be referred to as a femto BS or a home BS. In the example illustrated in <FIG>, a base station 110a may be a macro BS for a macro cell 102a, a base station 110b may be a pico BS for a pico cell 102b, and a base station 110c may be a femto BS for a femto cell 102c. A base station 110a-110d may support one or multiple (for example, three) cells.

In some examples, a cell may not be stationary, and the geographic area of the cell may move according to the location of a mobile base station. In some examples, the base stations 110a-110d may be interconnected to one another as well as to one or more other base stations or network nodes (not illustrated) in the communications system <NUM> through various types of backhaul interfaces, such as a direct physical connection, a virtual network, or a combination thereof using any suitable transport network.

The base station 110a-110d may communicate with the core network <NUM> over a wired or wireless communication link <NUM>. The wireless device 120a-120e (UE computing device) may communicate with the base station 110a-110d over a wireless communication link <NUM>.

The communications system <NUM> also may include relay stations (e.g., relay BS 110d). A relay station is an entity that can receive a transmission of data from an upstream station (for example, a base station or a mobile device) and transmit the data to a downstream station (for example, a wireless device or a base station). A relay station also may be a mobile device that can relay transmissions for other wireless devices. In the example illustrated in <FIG>, a relay station 110d may communicate with macro the base station 110a and the wireless device 120d in order to facilitate communication between the base station 110a and the wireless device 120d. A relay station also may be referred to as a relay base station, a relay base station, a relay, etc..

The wireless devices (UE computing devices) 120a, 120b, 120c may be dispersed throughout communications system <NUM>, and each wireless device may be stationary or mobile. A wireless device also may be referred to as an access terminal, a terminal, a mobile station, a subscriber unit, a station, etc..

A macro base station 110a may communicate with the communication network <NUM> over a wired or wireless communication link <NUM>. The wireless devices 120a, 120b, 120c may communicate with a base station 110a-110d over a wireless communication link <NUM>.

The wireless communication links <NUM>, <NUM> may include a plurality of carrier signals, frequencies, or frequency bands, each of which may include a plurality of logical channels. The wireless communication links <NUM> and <NUM> may utilize one or more radio access technologies (RATs). Examples of RATs that may be used in a wireless communication link include 3GPP LTE, <NUM>, <NUM>, <NUM> (e.g., NR), GSM, Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX), Time Division Multiple Access (TDMA), and other mobile telephony communication technologies cellular RATs. Further examples of RATs that may be used in one or more of the various wireless communication links <NUM>, <NUM> within the communication system <NUM> include medium range protocols such as Wi-Fi, LTE-U, LTE-Direct, LAA, MuLTEfire, and relatively short range RATs such as ZigBee, Bluetooth, and Bluetooth Low Energy (LE).

For example, the spacing of the subcarriers may be <NUM> and the minimum resource allocation (called a "resource block") may be <NUM> subcarriers (or <NUM>). Consequently, the nominal Fast File Transfer (FFT) size may be equal to <NUM>, <NUM>, <NUM>, <NUM> or <NUM> for system bandwidth of <NUM>, <NUM>, <NUM>, <NUM> or <NUM> megahertz (MHz), respectively. For example, a subband may cover <NUM> (i.e., <NUM> resource blocks), and there may be <NUM>, <NUM>, <NUM>, <NUM> or <NUM> subbands for system bandwidth of <NUM>, <NUM>, <NUM>, <NUM> or <NUM>, respectively.

While descriptions of some embodiments may use terminology and examples associated with LTE technologies, various embodiments may be applicable to other wireless communications systems, such as a new radio (NR) or <NUM> network. NR may utilize OFDM with a cyclic prefix (CP) on the uplink (UL) and downlink (DL) and include support for half-duplex operation using time division duplex (TDD). A single component carrier bandwidth of <NUM> may be supported. NR resource blocks may span <NUM> sub-carriers with a sub-carrier bandwidth of <NUM> over a <NUM> millisecond (ms) duration. Each radio frame may consist of <NUM> subframes with a length of <NUM>. Consequently, each subframe may have a length of <NUM>. Each subframe may indicate a link direction (i.e., DL or UL) for data transmission and the link direction for each subframe may be dynamically switched. Each subframe may include DL/UL data as well as DL/UL control data. Multiple Input Multiple Output (MIMO) transmissions with precoding may also be supported. MIMO configurations in the DL may support up to eight transmit antennas with multi-layer DL transmissions up to eight streams and up to two streams per wireless device. Multi-layer transmissions with up to <NUM> streams per wireless device may be supported. Aggregation of multiple cells may be supported with up to eight serving cells. Alternatively, NR may support a different air interface, other than an OFDM-based air interface.

Some mobile devices may be considered machine-type communication (MTC) or evolved or enhanced machine-type communication (eMTC) mobile devices. MTC and eMTC mobile devices include, for example, robots, drones, remote devices, sensors, meters, monitors, location tags, etc., that may communicate with a base station, another device (for example, remote device), or some other entity. A wireless node may provide, for example, connectivity for or to a network (for example, a wide area network such as Internet or a cellular network) via a wired or wireless communication link. Some mobile devices may be considered Internet-ofThings (IoT) devices or may be implemented as NB-IoT (narrowband internet of things) devices. A wireless device 120a-120e may be included inside a housing that houses components of the wireless device, such as processor components, memory components, similar components, or a combination thereof.

In general, any number of communication systems and any number of wireless networks may be deployed in a given geographic area. Each communications system and wireless network may support a particular radio access technology (RAT) and may operate on one or more frequencies. A RAT also may be referred to as a radio technology, an air interface, etc. A frequency also may be referred to as a carrier, a frequency channel, etc. Each frequency may support a single RAT in a given geographic area in order to avoid interference between communications systems of different RATs.

In some embodiments, two or more mobile devices 120a-e (for example, illustrated as the wireless device 120a and the wireless device 120e) may communicate directly using one or more sidelink channels <NUM> (for example, without using a base station <NUM> as an intermediary to communicate with one another). For example, the wireless devices 120a-120e may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, or similar protocol), a mesh network, or similar networks, or combinations thereof. In this case, the wireless device 120a-120e may perform scheduling operations, resource selection operations, as well as other operations described elsewhere herein as being performed by the base station 110a.

<FIG> is a component block diagram illustrating an example computing and wireless modem system <NUM> suitable for implementing any of the various embodiments. Various embodiments may be implemented on a number of single processor and multiprocessor computer systems, including a system-on-chip (SOC) or system in a package (SIP).

With reference to <FIG> and <FIG>, the illustrated example computing system <NUM> (which may be a SIP in some embodiments) that includes a two SOCs <NUM>, <NUM> coupled to a clock <NUM>, a voltage regulator <NUM>, and one or more wireless transceivers <NUM> configured to send and receive wireless communications via one or more antennas <NUM> to/from wireless devices, such as a base station 110a. In some embodiments, the first SOC <NUM> operate as central processing unit (CPU) of the wireless device that carries out the instructions of software application programs by performing the arithmetic, logical, control and input/output (I/O) operations specified by the instructions. In some embodiments, the second SOC <NUM> may operate as a specialized processing unit. For example, the second SOC <NUM> may operate as a specialized <NUM> processing unit responsible for managing high volume, high speed (e.g., <NUM> Gbps, etc.), and/or very high frequency short wave length (e.g., <NUM> mmWave spectrum, etc.) communications.

The first SOC <NUM> may include a digital signal processor (DSP) <NUM>, a modem processor <NUM>, a graphics processor <NUM>, an application processor <NUM>, one or more coprocessors <NUM> (e.g., vector co-processor) connected to one or more of the processors, memory <NUM>, custom circuity <NUM>, system components and resources <NUM>, an interconnection/bus module <NUM>, one or more temperature sensors <NUM>, a thermal management unit <NUM>, and a thermal power envelope (TPE) component <NUM>. The second SOC <NUM> may include a <NUM> modem processor <NUM>, a power management unit <NUM>, an interconnection/bus module <NUM>, a plurality of mmWave transceivers <NUM>, memory <NUM>, and various additional processors <NUM>, such as an applications processor, packet processor, etc. The plurality of mmWave transceivers <NUM> may be connected to one or more antennas <NUM> and may be configured to send and receive wireless communications via the one or more antennas <NUM> to/from wireless devices, such as a base station 110a.

The first and second SOC <NUM>, <NUM> may include various system components, resources and custom circuitry for managing sensor data, analog-to-digital conversions, wireless data transmissions, and for performing other specialized operations, such as decoding data packets and processing encoded audio and video signals for rendering in a web browser. For example, the system components and resources <NUM> of the first SOC <NUM> may include power amplifiers, voltage regulators, oscillators, phase-locked loops, peripheral bridges, data controllers, memory controllers, system controllers, access ports, timers, and other similar components used to support the processors and software clients running on a wireless device. The system components and resources <NUM> and/or custom circuitry <NUM> may also include circuitry to interface with peripheral devices, such as cameras, electronic displays, wireless communication devices, external memory chips, etc..

The first and second SOC <NUM>, <NUM> may communicate via interconnection/bus module <NUM>. The various processors <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, may be interconnected to one or more memory elements <NUM>, system components and resources <NUM>, and custom circuitry <NUM>, and a thermal management unit <NUM> via an interconnection/bus module <NUM>. Similarly, the processor <NUM> may be interconnected to the power management unit <NUM>, the mmWave transceivers <NUM>, memory <NUM>, and various additional processors <NUM> via the interconnection/bus module <NUM>. The interconnection/bus module <NUM>, <NUM>, <NUM> may include an array of reconfigurable logic gates and/or implement a bus architecture (e.g., CoreConnect, AMBA, etc.). Communications may be provided by advanced interconnects, such as high-performance networks-on chip (NoCs).

The first and/or second SOCs <NUM>, <NUM> may further include an input/output module (not illustrated) for communicating with resources external to the SOC, such as a clock <NUM> and a voltage regulator <NUM>. Resources external to the SOC (e.g., clock <NUM>, voltage regulator <NUM>) may be shared by two or more of the internal SOC processors/cores.

In addition to the example SIP <NUM> discussed above, various embodiments may be implemented in a wide variety of computing systems, which may include a single processor, multiple processors, multicore processors, or any combination thereof.

<FIG> is a software architecture diagram illustrating a software architecture <NUM> including a radio protocol stack for the user and control planes in wireless communications suitable for implementing any of the various embodiments. With reference to <FIG>, the wireless device (UE computing device) <NUM> may implement the software architecture <NUM> to facilitate communication between a wireless device <NUM> (e.g., the wireless device 120a-120e, <NUM>) and the base station <NUM> (e.g., the base station 110a) of a communication system (e.g., <NUM>). In various embodiments, layers in software architecture <NUM> may form logical connections with corresponding layers in software of the base station <NUM>. The software architecture <NUM> may be distributed among one or more processors (e.g., the processors <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>). While illustrated with respect to one radio protocol stack, in a multi-SIM (subscriber identity module) wireless device, the software architecture <NUM> may include multiple protocol stacks, each of which may be associated with a different SIM (e.g., two protocol stacks associated with two SIMs, respectively, in a dual-SIM wireless communication device). While described below with reference to LTE communication layers, the software architecture <NUM> may support any of variety of standards and protocols for wireless communications, and/or may include additional protocol stacks that support any of variety of standards and protocols wireless communications.

The software architecture <NUM> may include a Non-Access Stratum (NAS) <NUM> and an Access Stratum (AS) <NUM>. The NAS <NUM> may include functions and protocols to support packet filtering, security management, mobility control, session management, and traffic and signaling between a SIM(s) of the wireless device (e.g., SIM(s) <NUM>) and its core network <NUM>. The AS <NUM> may include functions and protocols that support communication between a SIM(s) (e.g., SIM(s) <NUM>) and entities of supported access networks (e.g., a base station). In particular, the AS <NUM> may include at least three layers (Layer <NUM>, Layer <NUM>, and Layer <NUM>), each of which may contain various sub-layers.

In the user and control planes, Layer <NUM> (L1) of the AS <NUM> may be a physical layer (PHY) <NUM>, which may oversee functions that enable transmission and/or reception over the air interface. Examples of such physical layer <NUM> functions may include cyclic redundancy check (CRC) attachment, coding blocks, scrambling and descrambling, modulation and demodulation, signal measurements, MIMO, etc. The physical layer may include various logical channels, including the Physical Downlink Control Channel (PDCCH) and the Physical Downlink Shared Channel (PDSCH).

In the user and control planes, Layer <NUM> (L2) of the AS <NUM> may be responsible for the link between the wireless device <NUM> and the base station <NUM> over the physical layer <NUM>. In the various embodiments, Layer <NUM> may include a media access control (MAC) sublayer <NUM>, a radio link control (RLC) sublayer <NUM>, and a packet data convergence protocol (PDCP) <NUM> sublayer, each of which form logical connections terminating at the base station <NUM>.

In the control plane, Layer <NUM> (L3) of the AS <NUM> may include a radio resource control (RRC) sublayer <NUM>. While not shown, the software architecture <NUM> may include additional Layer <NUM> sublayers, as well as various upper layers above Layer <NUM>. In various embodiments, the RRC sublayer <NUM> may provide functions including broadcasting system information, paging, and establishing and releasing an RRC signaling connection between the wireless device <NUM> and the base station <NUM>.

In various embodiments, the PDCP sublayer <NUM> may provide uplink functions including multiplexing between different radio bearers and logical channels, sequence number addition, handover data handling, integrity protection, ciphering, and header compression. In the downlink, the PDCP sublayer <NUM> may provide functions that include in-sequence delivery of data packets, duplicate data packet detection, integrity validation, deciphering, and header decompression.

In the uplink, MAC sublayer <NUM> may provide functions including multiplexing between logical and transport channels, random access procedure, logical channel priority, and hybrid-ARQ (HARQ) operations. In the downlink, the MAC layer functions may include channel mapping within a cell, de-multiplexing, discontinuous reception (DRX), and HARQ operations.

While the software architecture <NUM> may provide functions to transmit data through physical media, the software architecture <NUM> may further include at least one host layer <NUM> to provide data transfer services to various applications in the wireless device <NUM>. In some embodiments, application-specific functions provided by the at least one host layer <NUM> may provide an interface between the software architecture and the general purpose processor <NUM>.

In other embodiments, the software architecture <NUM> may include one or more higher logical layer (e.g., transport, session, presentation, application, etc.) that provide host layer functions. For example, in some embodiments, the software architecture <NUM> may include a network layer (e.g., Internet Protocol (IP) layer) in which a logical connection terminates at a packet data network (PDN) gateway (PGW). In some embodiments, the software architecture <NUM> may include an application layer in which a logical connection terminates at another device (e.g., end user device, server, etc.). In some embodiments, the software architecture <NUM> may further include in the AS <NUM> a hardware interface <NUM> between the physical layer <NUM> and the communication hardware (e.g., one or more radio frequency (RF) transceivers).

<FIG> illustrates an example neural network <NUM> that may be implemented in a computing device for implementing any of the various embodiments. With reference to <FIG>, any device in a communication system (e.g., <NUM>), such as a wireless device (UE computing device) (e.g., wireless device 120a-120e, <NUM>, <NUM>) and/or a base station (e.g., the base station 110a, <NUM>, etc.), may implement the neural network <NUM>. The neural network <NUM> may be a neural network dedicated to any purpose, such as an encoder neural network, a decoder neural network, etc. As one example, the neural network <NUM> may be a feed-forward deep neural network. The neural network <NUM> may be distributed among one or more processors (e.g., the processors <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>).

The neural network <NUM> may include an input layer <NUM>, intermediate layer(s) <NUM>, and an output layer <NUM>. Each of the layers <NUM>, <NUM>, <NUM> may include one or more processing nodes that receive input values, perform computations based the input values, and propagate the result (activation) to the next layer. The structure of the neural network may be the description of the types, numbers, and/or interconnections (e.g., layers <NUM>, <NUM>, <NUM> layout) of the nodes in the neural network <NUM>. Model types may be actual representations of the neural network elements themselves (e.g., the layers <NUM>, <NUM>, <NUM> and the nodes therein) and/or descriptors (e.g., model names, model numbers, model tags) that indicate the structure of the neural network <NUM>, such as a description of the types, numbers, and/or interconnections (e.g., layer <NUM>, <NUM>, <NUM> layout) of the nodes in the neural network <NUM>. As an example, the model of neural network <NUM> as illustrated in <FIG> may be an input layer <NUM> with a single input node X, an intermediate layer <NUM> with four nodes Y1, Y2, Y3, and Y4, and an output layer <NUM> with a single output node Z. While illustrated with respect to specific layers <NUM>, <NUM>, <NUM> and nodes X, Y1, Y2, Y3, Y4, and Z, the neural network <NUM> may include additional layers, nodes, and/or interconnections therebetween and the neural network <NUM> may be any type of neural network <NUM>.

In feed-forward neural networks, such as the neural network <NUM>, all of the computations are performed as a sequence of operations on the outputs of a previous layer. The final set of operations generate the output of the neural network, such as a compressed information in an encoder neural network, decompressed information in a decoder neural network, etc. Weights of a neural network <NUM> may be the values associated with the interconnections between the nodes of the neural network <NUM> after training of the neural network <NUM>. For example, the weights W11 illustrated in <FIG> are values associated with the interconnections between the X node of the neural network <NUM> and the Y nodes, Y1, Y2, Y3, Y4, after training of the neural network <NUM>. The final output of the neural network <NUM> may correspond to a task that the neural network <NUM> may be performing, such as operating as an encoder to compress TX waveform distortion information, operating as a decoder to recover TX waveform distortion information from compressed TX waveform information, etc..

In the neural network <NUM>, learning may be accomplished during a training process in which the values of the weights of each layer <NUM>, <NUM>, <NUM> are determined. After the training process is complete, the neural network <NUM> may successfully perform its intended purpose. For example, a trained encoder network may successfully compress information, such as successfully compress TX waveform distortion information into compressed TX distortion waveform information. As another example, a trained decoder neural network may successfully decompress information, such as successfully decompress compressed TX distortion waveform information into TX waveform distortion information (also referred to as recovered TX waveform distortion information).

Training the neural network <NUM> may include causing the neural network <NUM> to process a task for which an expected/desired output is known, and comparing the output generated by the neural network <NUM> to the expected/desired output. Training may be supervised or unsupervised training. During training, the weights of the neural network <NUM> may be updated until the output of the neural network <NUM> matches the expected/desired output. The weights of the trained decoder neural network of the transmitting device may be the values associated with the interconnections between each node of the decoder neural network after training at the transmitting device. For example, the weights of the trained neural network <NUM> as illustrated in <FIG> may include the weights W11 that are values associated with the interconnections between the X node of the neural network <NUM> and the Y nodes, Y1, Y2, Y3, Y4, after training of the neural network <NUM>. For example, in the example illustrated in <FIG>, Y may be related to X by the equation Y = W11 * X where Y=[Y1, Y2, Y3, Y4] and W11 is a 1x4 matrix such that W11 are the weights, and similarly Z may be related to Y by other weights.

Providing the model of the neural network <NUM> and the weights of the trained neural network <NUM> may enable another instance of the same neural network <NUM> to be created. In various embodiments, two decoder neural networks having the same model type may have the same structure such that applying the same weights to the two decoder neural networks will result in the same output (e.g., same decompressed output when the neural network <NUM> is a decoder neural network) of the two neural networks based on the same input (e.g., the same compressed input, such as compressed TX waveform distortion information) being provided to each decoder neural network. In this manner, a device having a model type of a trained neural network <NUM> and weights of the trained neural network <NUM> may configure a second neural network to correspond to neural network <NUM> (e.g., configure that second neural network to be a copy of the trained neural network <NUM>) to generate the same output as would be generated by the trained neural network <NUM> for a same input without having to spend time to actually train the second neural network.

<FIG> is a process flow diagram of an example <NUM> for wireless communication performed by a processor of a transmitting device transmitting a waveform to a receiving device according to various embodiments. With reference to <FIG>, the method <NUM> may be implemented by a processor (such as <NUM>, <NUM>, <NUM> or <NUM>) of a transmitting device, such as a wireless device (UE computing device) (e.g., wireless device 120a-120e, <NUM>, <NUM>) and/or a base station (e.g., the base station 110a, <NUM>, etc.) implementing one or more neural networks (e.g., neural network <NUM>). As one example, in UL communications in a 3GPP network, the transmitting device may be a UE computing device and the receiving device may be a base station (e.g., a gNB). As another example, in DL communications in a 3GPP network, the transmitting device may be a base station (e.g., a gNB) and the receiving device may be a UE computing device.

In block <NUM>, the processor of the transmitting device may perform operations including obtaining TX waveform distortion information of the transmitting device. In some embodiments, the TX waveform distortion information may be a two-dimensional map of distortion error due to signal clipping of orthogonal frequency division multiplexing (OFDM) symbols within a slot for one or more antennas of the transmitting device. The TX waveform distortion information may be configured such that a receiving device may use the TX waveform distortion information of the transmitting device to mitigate waveform distortion in a TX waveform received from the transmitting device. For example, the TX waveform distortion information may be used by a receiving device to mitigate the TX waveform distortion of multiple OFDM symbols.

In block <NUM>, the processor of the transmitting device may perform operations including training an encoder neural network to compress the TX waveform distortion information of the transmitting device into compressed TX waveform distortion information of the transmitting device.

In block <NUM>, the processor of the transmitting device may perform operations including training a decoder neural network to recover the TX waveform distortion information of the transmitting device from the compressed TX waveform distortion information of the transmitting device.

In various embodiments, a transmitting device may include an encoder neural network and decoder neural network pair. In some embodiments, the encoder neural network and the decoder neural network may be deep neural networks. In some embodiments, there may be an encoder and decoder pair for each transmit antenna of the transmitting device. The encoder neural network may be configured to compress information. For example, the encoder neural network may be configured to compress TX waveform distortion information into compressed TX waveform distortion information. The decoder neural network may be a configured to decompress information. For example, the decoder neural network may be configured to recover the TX waveform distortion information from compressed TX waveform distortion information.

In some embodiments, the encoder neural network of the transmitting device and the decoder neural network of the of the transmitting device may be trained using unsupervised learning algorithms. In some embodiments, training the encoder neural network of the transmitting device and training the decoder neural network of the transmitting device may include training the encoder neural network and the decoder neural network for one transmit antenna of the transmitting device. In some embodiments, training the encoder neural network of the transmitting device and training the decoder neural network of the transmitting device may be performed periodically. For example, training may occur upon initial start-up of the transmitting device, daily, upon registration with a new network, etc. In some embodiments, the encoder neural network of the transmitting device and the decoder neural network of the of the transmitting device may be trained using unsupervised learning algorithms.

In block <NUM>, the processor of the transmitting device may perform operations including determining a model type of the trained decoder neural network of the transmitting device and one or more weights of the trained decoder neural network of the transmitting device. A model type may be a structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. Model types may be actual representations of the neural network elements themselves and/or descriptors (e.g., model names, model numbers, model tags) that indicate the structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. In some embodiments, a model type of a decoder neural network of the transmitting device may provide information to a receiving device to reconstitute a specific structure of the decoder neural network as was trained at the transmitting device. Weights of a neural network may be the values associated with the interconnections between the nodes of the neural network after training of the neural network. The weights of the trained decoder neural network of the transmitting device may be the values associated with the interconnections between the nodes of the decoder neural network after training at the transmitting device. In some embodiments, two decoder neural networks having the same model type may have the same structure such that applying the same weights to the two decoder neural networks will result in the same decompressed output of the two decoder neural networks based on the same compressed input being provided to each decoder neural network. In this manner, a receiving device having a model type of a trained decoder neural network of the transmitting device and weights of the trained decoder neural network of the transmitting device may configure a decoder neural network of the receiving device to recover the same decompressed output as would be recovered by the trained decoder neural network of the transmitting device from a common compressed input without having to spend time to actually train the decoder neural network of the receiving device.

In block <NUM>, the processor of the transmitting device may perform operations including compressing the transmit waveform distortion information for the transmitting device into compressed transmit waveform distortion information using the encoder neural network. In some embodiments, the compression of the transmit waveform distortion information for the transmitting device into compressed transmit waveform distortion information may be performed after the encoder neural network is trained to achieve a selected level of compression for the transmit waveform distortion information. For example, the trained encoder neural network may compress the obtained TX waveform distortion information to the selected level of compression for sending to the receiving device.

In block <NUM>, the processor of the transmitting device may perform operations including sending the compressed TX waveform distortion information of the transmitting device, the one or more weights, and/or the model type to the receiving device in a configuration that enables the receiving device to use the one or more weights and/or the model type to configure a decoder neural network of the receiving device to recover the TX waveform distortion information of the transmitting device from the compressed TX waveform distortion information. The compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent to the receiving device in a configuration that enables the receiving device to use the model type and the weights to configure a decoder neural network of the receiving device to recover the TX waveform distortion information of the transmitting device from the compressed TX waveform distortion information of the transmitting device. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent together. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent separately. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be sent in overhead information exchanged between the transmitting computing device and the receiving computing device. In some embodiments, the compressed TX waveform distortion information of the transmitting device may be sent in control information for each slot to be transmitted. The compressed TX waveform distortion information may depend on the data that may be sent on each slot because the data to be sent may impact the OFDM waveform. Providing the compressed TX waveform distortion information on a per slot basis may enable a receiving device to mitigate the TX waveform distortion on a per slot basis. In some embodiments, compressed TX waveform distortion information and the model type and the weights may be signaled at different periodicities. In some embodiments, the model type and the weights may be signaled less frequently than the compressed TX waveform distortion information of the transmitting device. For example, compressed TX waveform distortion information may be sent in control information for each slot and the weights may be sent at a periodicity greater than every slot (e.g., less frequently than the compressed TX waveform distortion information). Weights and/or model types may be sent at much larger time scales than compressed TX waveform distortion information as the encoder neural network and decoder neural network of the transmitting device may be updated infrequently.

<FIG> is a process flow diagram of an example method <NUM> for wireless communication performed by a processor of a receiving device receiving a waveform from a transmitting device according to various embodiments. With reference to <FIG>, the method <NUM> may be implemented by a processor (such as <NUM>, <NUM>, <NUM> or <NUM>) of a receiving device, such as a wireless device (UE computing device) (e.g., wireless device 120a-120e, <NUM>, <NUM>) and/or a base station (e.g., the base station 110a, <NUM>, etc.) implementing one or more neural networks (e.g., neural network <NUM>). The operations of method <NUM> may be performed in conjunction with the operations of method <NUM> (<FIG>). As one example, in UL communications in a 3GPP network, the receiving device may be a base station (e.g., a gNB) and the transmitting device may be a UE computing device. As another example, in DL communications in a 3GPP network, the receiving device may be a UE computing device and the transmitting device may be a base station (e.g., a gNB).

In block <NUM>, the processor of the receiving device may perform operations including receiving a compressed TX waveform distortion information of the transmitting device, one or more weights of a trained decoder neural network of the transmitting device, and/or a model type of the trained decoder neural network of the transmitting device. For example, the compressed TX waveform distortion information, the model type, and the weights may be the compressed TX waveform distortion information, the model type, and the weights of a transmitting device performing operations of method <NUM> of <FIG>. In some embodiments, the compressed TX waveform distortion information of the transmitting device, the model type, and the weights may be received in overhead information. In some embodiments, the compressed TX waveform distortion information of the transmitting device may be received in control information for each slot to be transmitted.

In some embodiments, the model type and the weights may be received directly from the transmitting device. For example, the transmitting device may be a UE computing device and the UE computing device may send the model type and the weights to a base station as part of initial registration procedures between the UE computing device and the base station to receive services in the cell served by the base station. The base station may be the receiving device and may utilize the model type and the weights to mitigate distortion in the TX waveform sent by the UE computing device. In some embodiments, the model type and the weights may be received from a base station other than the transmitting device. For example, the model type and the weights may be values stored and shared among devices in a communication network. The model type and the weights of base stations may be shared among devices, such that a device may receive the model type and the weights for another device indirectly. As one example, base stations of neighbor cells may share their model types and weights with one another and with UE computing devices in their respective cells to support UE computing device mobility and handoff. As another example, UE computing model type and weights may be centrally stored such that a base station may retrieve the model type and the weights for a UE computing device upon discovery of the UE computing device without the UE computing device needing to directly transmit the model type and the weights to the base station, etc..

In some embodiments, base station compressed TX waveform distortion information may be centrally stored in addition to the model type, and/or weights so that a UE computing device may retrieve the compressed TX waveform distortion information, the model type, and/or the weights for a next base station before entering the coverage area of that base station and without the UE computing device needing to directly receive the compressed TX waveform distortion information, the model type, and/or the weights from that next base station, etc. Storing of compressed TX waveform distortion information may be useful in circumstances in which network side transmitters send the same data multiple times (e.g., a service description that is sent periodically) and a UE computing device uses the compressed TX waveform distortion information from a next cell to be set up ahead of time to receive the same data the next cell.

In block <NUM>, the processor of the receiving device may perform operations including configuring a decoder neural network of the receiving device using the received one or more weights and/or the received model type. A model type may be a structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. Model types may be actual representations of the neural network elements themselves and/or descriptors (e.g., model names, model numbers, model tags) that indicate the structure of the neural network, such as a description of the types, numbers, and/or interconnections (e.g., layer layout) of the nodes in the neural network. Weights of a neural network may be the values associated with the interconnections between the nodes of the neural network after training of the neural network. In some embodiments, a receiving device having a model type of a trained decoder neural network of the transmitting device and weights of the trained decoder neural network of the transmitting device may configure a decoder neural network of the receiving device to recover the same decompressed output as would be recovered by the trained decoder neural network of the transmitting device from the compressed TX waveform distortion information of the transmitting device received from the transmitting device. In this manner, the receiving device may configure its decoder neural network as a trained neural network without having to spend time to actually train its decoder neural network.

In block <NUM>, the processor of the receiving device may perform operations including recovering TX waveform distortion information of the transmitting device from the compressed TX waveform distortion information using the configured decoder neural network of the receiving device. In some embodiments, the TX waveform distortion information may include a two-dimensional map of distortion error due to signal clipping of OFDM symbols within a slot for one or more antennas of the transmitting device. The receiving device may recover the same two-dimensional map of distortion that was originally created at the transmitting device.

In block <NUM>, the processor of the receiving device may perform operations including using the TX waveform distortion information of the transmitting device to mitigate waveform distortion in a TX waveform received from the transmitting device. Mitigating the waveform distortion may include using the TX waveform distortion information to reconstitute the original TX waveform signal at the receiving device. In this manner, by mitigating the waveform distortion, the receiving device may compensate for any distortion caused by the transmitting device itself, such as distortion caused by power amplifiers of the transmitting device.

<FIG> illustrates example interactions of a receiving device and transmitting device performing operations of methods <NUM> and <NUM> to leverage neural networks to provide distortion information from the transmitting device to the receiving device. With reference to <FIG>, interactions illustrated and discussed may be implemented by a processor (such as <NUM>, <NUM>, <NUM> or <NUM>) of a transmitting device <NUM>, such as a wireless device (UE computing device) (e.g., wireless device 120a-120e, <NUM>, <NUM>) and/or a base station (e.g., the base station 110a, <NUM>, etc.) implementing one or more neural networks (e.g., neural network <NUM>) transmitting a TX waveform to a receiving device <NUM>, such as a wireless device (UE computing device) (e.g., wireless device 120a-120e, <NUM>, <NUM>) and/or a base station (e.g., the base station 110a, <NUM>, etc.) implementing one or more neural networks (e.g., neural network <NUM>).

The transmitting device <NUM> may obtain a two-dimensional distortion map <NUM> of the distortion error due to signal clipping for each transmit antenna of the transmitting device <NUM>. The two-dimensional distortion map <NUM> is shown as an error map of a single transmit antenna for ease of illustration. Each cell in the grid represents a time domain sample within an OFDM symbol with the shaded blocks <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> representing different levels of transmit waveform distortion, typically represented as a complex number.

The transmitting device <NUM> may input the two-dimensional distortion map <NUM> to an encoder neural network <NUM> (e.g., neural network <NUM>) of the transmitting device <NUM>. The output of the encoder neural network <NUM> may be compressed TX waveform distortion information <NUM>. The compressed TX waveform distortion information <NUM> may be output by the transmitting device <NUM> to the decoder neural network <NUM> (e.g., neural network <NUM>) of the transmitting device <NUM> as the input to the decoder neural network <NUM>.

The transmitting device <NUM> may train its encoder neural network <NUM> and decoder neural network <NUM> until a selected level of compression is achieved by the encoder neural network <NUM> and the two-dimensional distortion map <NUM> is correctly output by the decoder neural network <NUM>. For example, the decoder neural network <NUM> of the transmitting device <NUM> may be considered trained when the output compressed TX waveform distortion information <NUM> that is the compressed TX waveform distortion map <NUM> provided as input to the decoder neural network <NUM> of the transmitting device <NUM> results in the decoder neural network <NUM> outputting a correct copy of the original distortion map <NUM>.

In response to the decoder neural network <NUM> of the transmitting device <NUM> being trained, the transmitting device <NUM> may determine the model type of the trained decoder neural network <NUM> of the transmitting device <NUM> and the weights of the trained decoder neural network <NUM> of the transmitting device <NUM>. Once the encoder neural network <NUM> and the decoder neural network <NUM> are trained, the transmitting device <NUM> may send the compressed TX waveform distortion information <NUM> and the model type and weights of the trained decoder neural network <NUM> to the receiving device <NUM>.

The receiving device <NUM> may configure its decoder neural network <NUM> using the received model type and weights. In this manner, the decoder neural network <NUM> may effectively be configured to be a copy of the trained decoder neural network <NUM> without having to actually undergo training of the decoder neural network <NUM>.

The receiving device <NUM> may input the compressed TX waveform distortion information <NUM> into its decoder neural network <NUM> and the output may be the original distortion map <NUM>. The receiving device <NUM> may use the distortion map <NUM> to mitigate TX waveform distortion in a transmit waveform received from the transmitting device <NUM>.

<FIG> illustrates additional example interactions of the receiving device <NUM> and transmitting device <NUM> of <FIG> performing operations of methods <NUM> and <NUM> to leverage neural networks to provide distortion information to the receiving device <NUM> from the transmitting device <NUM>. With reference to <FIG>, interactions illustrated and discussed may be implemented by a processor (such as <NUM>, <NUM>, <NUM> or <NUM>) of a transmitting device <NUM>, such as a wireless device (UE computing device) (e.g., wireless device 120a-120e, <NUM>, <NUM>) and/or a base station (e.g., the base station 110a, <NUM>, etc.) implementing one or more neural networks (e.g., neural network <NUM>) transmitting a TX waveform to a receiving device <NUM>, such as a wireless device (UE computing device) (e.g., wireless device 120a-120e, <NUM>, <NUM>) and/or a base station (e.g., the base station 110a, <NUM>, etc.) implementing one or more neural networks (e.g., neural network <NUM>).

<FIG> illustrates that OFDM symbols <NUM> may be output as a digital representation of the transmit waveform (x) passed to a digital-to-analog converter (D2A) <NUM> and power amplifier <NUM> for transmission via an antenna <NUM> of the transmitting device <NUM> in a TX waveform <NUM>. The TX waveform <NUM> may be an analog signal sent over the air to the receiving device <NUM>. The operations to transmit the TX waveform <NUM>, such as the power amplification, etc., may cause signal distortion which may be measured by tapping the signal output to the antenna <NUM> and passing that output to an analog-to-digital converter (A2D) <NUM>. The output of the A2D <NUM> may be a digital representation of the distorted TX waveform (y). The distortion present in the distorted TX waveform (y) may be a function of the distortion caused and/or experienced in the transmit chain of the transmitting device <NUM>. The digital representation of the transmit waveform (x) and the digital representation of the distorted TX waveform (y) may be passed to a comparator <NUM>. The comparator <NUM> may determine the difference between the two waveforms, such as y-x, and that difference may be the TX waveform distortion information of the transmitting device <NUM>. That TX waveform distortion information of the transmitting device <NUM> (e.g., y-x) may be provided to the encoder neural network <NUM> of the transmitting device <NUM>. The encoder neural network <NUM> may compress the TX waveform distortion information of the transmitting device <NUM> into compressed TX waveform distortion information <NUM>. Additionally, the TX waveform distortion information of the transmitting device <NUM> (e.g., y-x) may be provided to a training module <NUM> configured to control training of the encoder neural network <NUM> and the decoder neural network <NUM>. For example, the training module <NUM> may apply one or more loss functions used for training the encoder neural network <NUM> and/or the decoder neural network <NUM>, such as a mean squared error (MSE) loss function.

In a training mode of operation, the compressed TX waveform distortion information <NUM> may be passed to the decoder neural network <NUM> of the transmitting device <NUM> and the decoder neural network <NUM> may recover TX waveform distortion information (e.g., y-x) from the compressed TX waveform distortion information <NUM>. The output of the decoder neural network <NUM> may be output to the training module <NUM> and compared to the TX waveform distortion information input to the encoder neural network <NUM>. The training module <NUM> of the transmitting device <NUM> may train its encoder neural network <NUM> and decoder neural network <NUM> until a selected level of compression is achieved by the encoder neural network <NUM> and the TX waveform distortion information of the transmitting device <NUM> (e.g., y-x) is correctly output by the decoder neural network <NUM>. The training may include applying a loss function, such as MSE loss, between the input to the encoder neural network <NUM> and the output of the decoder neural network <NUM>. For example, the decoder neural network <NUM> of the transmitting device <NUM> may be considered trained when the output compressed TX waveform distortion information <NUM> that is the compressed TX waveform distortion information <NUM> provided as input to the decoder neural network <NUM> of the transmitting device <NUM> results in the decoder neural network <NUM> outputting a correct copy of the original TX waveform distortion information of the transmitting device <NUM> (e.g., a correct copy of y-x).

In response to the decoder neural network <NUM> of the transmitting device <NUM> being trained, the transmitting device <NUM> may determine the model type of the trained decoder neural network <NUM> of the transmitting device <NUM> and/or the one or more weights of the trained decoder neural network <NUM> of the transmitting device <NUM>. Once the encoder neural network <NUM> and the decoder neural network <NUM> are trained, the transmitting device <NUM> may send the compressed TX waveform distortion information <NUM>, the model type of the trained decoder neural network <NUM>, and/or the one or more weights of the trained decoder neural network <NUM> to the receiving device <NUM>. The compressed TX waveform distortion information <NUM>, the model type of the trained decoder neural network <NUM>, and/or the one or more weights of the trained decoder neural network <NUM> may be sent in various manners, such as in overhead signaling, out-of-band signaling, etc..

The receiving device <NUM> may configure its decoder neural network <NUM> using the received one or more weights and/received model type. In this manner, the decoder neural network <NUM> may effectively be configured to be a copy of the trained decoder neural network <NUM> without having to actually undergo training of the decoder neural network <NUM>.

The receiving device <NUM> may input the compressed TX waveform distortion information <NUM> into its decoder neural network <NUM> and the output may be the original TX waveform distortion information of the transmitting device <NUM> (e.g., a recovered copy of y-x). The receiving device <NUM> may pass this TX waveform distortion information of the transmitting device <NUM> (e.g., a recovered copy of y-x) to the receiver <NUM> to mitigate TX waveform distortion in a transmit waveform <NUM> received via an antenna <NUM> of the receiving device <NUM> from the transmitting device <NUM>. The receiver <NUM> of the receiving device <NUM> may use the recovered TX waveform distortion information of the transmitting device <NUM> (e.g., a recovered copy of y-x) to mitigate waveform distortion and receive the OFDM symbols <NUM> transmitted in the transmit waveform <NUM>.

Various embodiments may be implemented on a variety of wireless network devices, an example of which is illustrated in <FIG> in the form of a wireless network computing device <NUM> functioning as a network element of a communication network, such as a base station. Such network computing devices may include at least the components illustrated in <FIG>. With reference to <FIG>, the network computing device <NUM> may typically include a processor <NUM> coupled to volatile memory <NUM> and a large capacity nonvolatile memory, such as a disk drive <NUM>. The network computing device <NUM> may also include a peripheral memory access device such as a floppy disc drive, compact disc (CD) or digital video disc (DVD) drive <NUM> coupled to the processor <NUM>. The network computing device <NUM> may also include network access ports <NUM> (or interfaces) coupled to the processor <NUM> for establishing data connections with a network, such as the Internet and/or a local area network coupled to other system computers and servers. The network computing device <NUM> may be coupled to one or more antennas for sending and receiving electromagnetic radiation for establishing a wireless communication link. The network computing device <NUM> may include additional access ports, such as USB, Firewire, Thunderbolt, and the like for coupling to peripherals, external memory, or other devices.

Various embodiments may be implemented on a variety of wireless devices (e.g., the wireless device 120a-120e, <NUM>, <NUM>), an example of which is illustrated in <FIG> in the form of a smartphone <NUM>. The smartphone <NUM> may include a first SOC <NUM> (e.g., a SOC-CPU) coupled to a second SOC <NUM> (e.g., a <NUM> capable SOC). The first and second SOCs <NUM>, <NUM> may be coupled to internal memory <NUM>, <NUM>, a display <NUM>, and to a speaker <NUM>. Additionally, the smartphone <NUM> may include an antenna <NUM> for sending and receiving electromagnetic radiation that may be connected to a wireless data link and/or cellular telephone transceiver <NUM> coupled to one or more processors in the first and/or second SOCs <NUM>, <NUM>. Smartphones <NUM> typically also include menu selection buttons or rocker switches <NUM> for receiving user inputs.

A typical smartphone <NUM> also includes a sound encoding/decoding (CODEC) circuit <NUM>, which digitizes sound received from a microphone into data packets suitable for wireless transmission and decodes received sound data packets to generate analog signals that are provided to the speaker to generate sound. Also, one or more of the processors in the first and second SOCs <NUM>, <NUM>, wireless transceiver <NUM> and CODEC <NUM> may include a digital signal processor (DSP) circuit (not shown separately).

The processors of the wireless network computing device <NUM> and the smart phone <NUM> may be any programmable microprocessor, microcomputer or multiple processor chip or chips that can be configured by software instructions (applications) to perform a variety of functions, including the functions of the various embodiments described below. In some mobile devices, multiple processors may be provided, such as one processor within an SOC <NUM> dedicated to wireless communication functions and one processor within an SOC <NUM> dedicated to running other applications. Typically, software applications may be stored in the memory <NUM>, <NUM> before they are accessed and loaded into the processor. The processors may include internal memory sufficient to store the application software instructions.

As used in this application, the terms "component," "module," "system," and the like are intended to include a computer-related entity, such as, but not limited to, hardware, firmware, a combination of hardware and software, software, or software in execution, which are configured to perform particular operations or functions. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a wireless device and the wireless device may be referred to as a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one processor or core and/or distributed between two or more processors or cores. In addition, these components may execute from various non-transitory computer readable media having various instructions and/or data structures stored thereon. Components may communicate by way of local and/or remote processes, function or procedure calls, electronic signals, data packets, memory read/writes, and other known network, computer, processor, and/or process related communication methodologies.

A number of different cellular and mobile communication services and standards are available or contemplated in the future, all of which may implement and benefit from the various embodiments. Such services and standards include, e.g., third generation partnership project (3GPP), long term evolution (LTE) systems, third generation wireless mobile communication technology (<NUM>), fourth generation wireless mobile communication technology (<NUM>), fifth generation wireless mobile communication technology (<NUM>), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), 3GSM, general packet radio service (GPRS), code division multiple access (CDMA) systems (e.g., cdmaOne, CDMA1020TM), enhanced data rates for GSM evolution (EDGE), advanced mobile phone system (AMPS), digital AMPS (IS-<NUM>/TDMA), evolution-data optimized (EV-DO), digital enhanced cordless telecommunications (DECT), Worldwide Interoperability for Microwave Access (WiMAX), wireless local area network (WLAN), Wi-Fi Protected Access I & II (WPA, WPA2), and integrated digital enhanced network (iDEN). Each of these technologies involves, for example, the transmission and reception of voice, data, signaling, and/or content messages. It should be understood that any references to terminology and/or technical details related to an individual telecommunication standard or technology are for illustrative purposes only, and are not intended to limit the scope of the claims to a particular communication system or technology unless specifically recited in the claim language.

Various embodiments illustrated and described are provided merely as examples to illustrate various features of the claims. However, features shown and described with respect to any given embodiment are not necessarily limited to the associated embodiment and may be used or combined with other embodiments that are shown and described. Further, the claims are not intended to be limited by any one example embodiment. For example, one or more of the operations of the methods <NUM> and/or <NUM> may be substituted for or combined with one or more operations of the methods <NUM> and/or <NUM>.

The hardware used to implement various illustrative logics, logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor may also be implemented as a combination of receiver smart objects, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some operations or methods may be performed by circuitry that is specific to a given function.

In one or more embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable storage medium or non-transitory processor-readable storage medium. The operations of a method or algorithm disclosed herein may be embodied in a processor-executable software module or processor-executable instructions, which may reside on a non-transitory computer-readable or processor-readable storage medium. Non-transitory computer-readable or processor-readable storage media may be any storage media that may be accessed by a computer or a processor. By way of example but not limitation, such non-transitory computer-readable or processor-readable storage media may include RAM, ROM, EEPROM, FLASH memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage smart objects, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Combinations of the above are also included within the scope of non-transitory computer-readable and processor-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non-transitory processor-readable storage medium and/or computer-readable storage medium, which may be incorporated into a computer program product.

Claim 1:
A method (<NUM>) of wireless communication performed by a processor of a transmitting device transmitting a waveform to a receiving device, comprising:
obtaining (<NUM>) transmit waveform distortion information of the transmitting device;
compressing (<NUM>) the transmit waveform distortion information of the transmitting device into compressed transmit waveform distortion information using an encoder neural network, wherein the encoder neural network is trained to compress the transmit waveform distortion information of the transmitting device into the compressed transmit waveform distortion information; and
sending (<NUM>) the compressed transmit waveform distortion information and one or more decoder neural network weights to the receiving device in a configuration that enables the receiving device to use the one or more decoder neural network weights to configure a decoder neural network of the receiving device to recover the transmit waveform distortion information of the transmitting device from the compressed transmit waveform distortion information, wherein the decoder neural network is trained to recover the transmit waveform distortion information of the transmitting device from the compressed transmit waveform distortion information; and
wherein the one or more decoder neural network weights are weights of the trained decoder neural network of the transmitting device.