Information transmission for managing wireless communications

Abstract

Various embodiments include methods for managing information transmission between a base station and a wireless device. The base station may apply an encoder neural network to auxiliary information that may assist the wireless device in communicating with the base station to generate encoded auxiliary information. The base station may send the encoded auxiliary information to the wireless device via a control channel or a data channel. The wireless device may use the encoded auxiliary information to update one or more behaviors of the wireless device without decoding the encoded auxiliary information. The wireless device may include different neural networks configured to learn how to use the encoded auxiliary information to update one or more behaviors of the wireless device.

Description

[0001] Related Applications

[0002] This application claims the benefit of priority to U.S. Provisional Application No. 62 / 980,804, filed on February 24, 2020, entitled “Managing Information Transmission For Wireless Communication,” the entire contents of which are hereby incorporated by reference into this document for all purposes. Background Art

[0003] In some modem implementations in a wireless device, the wireless device may achieve improvements in wireless communication performance, handle more complex information, and obtain power savings by determining or receiving information about the algorithms used by a base station or the internal state of a base station. For example, with more detailed knowledge of aspects of a scheduler for a base station, the wireless device may determine better channel state feedback, thereby improving the efficiency and accuracy of wireless communications with the base station. However, such information about a base station may be too large to be quickly and efficiently communicated to the wireless device. Furthermore, the information may include proprietary or secret information that the provider of the base station does not wish to reveal in an easily discoverable form. Summary of the invention

[0004] Various aspects include systems and methods for wireless communications performed by a processor of a base station. Various aspects may include: applying an encoder neural network to auxiliary information in the base station to generate encoded auxiliary information; and sending the encoded auxiliary information to a wireless device. In some aspects, the encoder neural network may include an encoder portion of an autoencoder neural network, but no corresponding decoder neural network is provided to the wireless device. In some aspects, sending the encoded auxiliary information to the wireless device may include: sending the encoded auxiliary information to the wireless device via a control channel or a data channel. In some aspects, the encoded auxiliary information may be in a form that the wireless device cannot process to recover the auxiliary information in the base station. In some aspects, the auxiliary information in the base station may include an algorithm or information about an internal state of the base station. Some aspects may also include: using an unsupervised training method to train the encoder neural network and the decoder neural network on a data set of the auxiliary information; using the trained encoder neural network to generate the encoded auxiliary information; and not sharing the decoder neural network with a wireless device.

[0005] Further aspects may include a computing device for use in a base station of a wireless communication system and having a processor configured with processor-executable instructions to perform operations including any of the methods outlined above. Further aspects may include a non-transitory processor-readable storage medium having processor-executable instructions stored thereon, the processor-executable instructions configured to cause a computing device of a base station to perform operations of any of the methods outlined above. Further methods include a base station having means for performing the functionality of any of the methods outlined above.

[0006] Various aspects include a method of wireless communication performed by a processor of a wireless device. Various aspects may include: receiving coded assistance information from a base station; and using the coded assistance information to update one or more behaviors of the wireless device. In some aspects, using the coded assistance information to update one or more behaviors of the wireless device may include: providing the received coded assistance information as an input to a wireless device processing block, and updating one or more behaviors of the wireless device based on the coded assistance information. In some aspects, receiving the coded assistance information may include: receiving the coded assistance information via a control channel or a data channel.

[0007] In some aspects, using the coded auxiliary information may include using the coded auxiliary information as an input to a self-learning neural network. In some aspects, the self-learning neural network may be trained via one of supervised learning or reinforcement learning to optimize a cost or performance metric. In various aspects, the wireless device is not configured to decode the coded auxiliary information.

[0008] Additional aspects may include a wireless device having a processor configured to perform the operations of any of the methods outlined above. Additional aspects may include a non-transitory processor-readable storage medium having processor-executable instructions stored thereon, the processor-executable instructions configured to cause the processor of the wireless device to perform the operations of any of the methods outlined above. Additional methods include a wireless device having a unit for performing the functions of any of the methods outlined above. Additional methods include a system on a chip for use in a wireless device, comprising a processor configured to perform the operations of any of the methods outlined above. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated herein and constitute a part of this specification, illustrate exemplary embodiments of the claims and, together with the general description given above and the detailed description given below, serve to explain features of the claims.

[0010] Figure 1is a system block diagram illustrating an example communication system suitable for implementing any of the various embodiments.

[0011] Figure 2 is a component block diagram illustrating an example computing and wireless modem system suitable for implementing any of the various embodiments.

[0012] Figure 3 is a component block diagram illustrating a software architecture suitable for implementing any of the various embodiments, including radio protocol stacks for user and control planes in wireless communications.

[0013] Figure 4A and 4B is a component block diagram illustrating a system configured for execution by a processor of a base station for managing information transmission for wireless communications in accordance with various embodiments.

[0014] Figure 5 is a process flow diagram illustrating a method for managing information transmission for wireless communications that may be performed by a processor of a base station in accordance with various embodiments.

[0015] Figure 6 is a process flow diagram illustrating a method for managing information transmission for wireless communications that may be performed by a processor of a wireless device in accordance with various embodiments.

[0016] Figure 7 is a process flow diagram illustrating a method for managing information transmission for wireless communications that may be performed by a processor of a wireless device in accordance with various embodiments.

[0017] Figure 8 is a component block diagram of a network computing device suitable for use with the various embodiments.

[0018] Fig. 9 is a component block diagram of a wireless device suitable for use with the various embodiments. DETAILED DESCRIPTION

[0019] Various embodiments will be described in detail with reference to the accompanying drawings. Wherever possible, the same reference numerals will be used throughout the drawings to refer to the same or similar parts. Reference to specific examples and embodiments is for illustrative purposes and is not intended to limit the scope of the claims.

[0020] Various embodiments include systems and methods for managing information transmission for wireless communications between devices such as base stations and wireless devices. Various embodiments may improve the efficiency and accuracy of wireless communications between these devices by providing wireless devices with information that may improve their operation without revealing information that is secret or proprietary to network operators. The base station may form or use state information, algorithms, and other information that may help the wireless device improve its operation or performance. For ease of reference, base station state information, algorithms, and other information that may assist the wireless device are generally and collectively referred to herein as "assistance information." However, such assistance information may be proprietary to the network operator and therefore may not be shared with the wireless device (i.e., not sent to the wireless device). Various embodiments include using a neural network in the base station to convert the assistance information into an encoded form that is sent to the wireless device. For ease of reference, the encoded form of the assistance information is referred to herein as "encoded assistance information." The wireless device is not configured with a trained decoder neural network and therefore cannot recover or discover the proprietary assistance information from the received encoded assistance information. Although the original assistance information cannot be decoded or discovered, the encoded assistance information may be provided to a processing module of the receiving wireless device, and the processing module may use the encoded assistance information to update one or more behaviors of the wireless device. In some embodiments, the wireless device may include a different neural network within the processing block that may be configured to discover or learn how to use the received encoded auxiliary information to update one or more behaviors to improve device functionality or communication with a base station. In such embodiments, the different neural network within the processing block is not a decoder portion of an autoencoder neural network used in the base station to encode the auxiliary information.

[0021] The term "wireless device" is used herein to refer to any or all of the following: wireless router devices, wireless appliances, cellular phones, smart phones, portable computing devices, personal or mobile multimedia players, laptop computers, tablet computers, smartbooks, ultrabooks, handheld computers, wireless email receivers, multimedia Internet-enabled cellular phones, medical devices and apparatus, biometric sensors / devices, wearable devices (including smart watches, smart clothing, smart glasses, smart wristbands, smart jewelry (e.g., smart rings, smart bracelets, etc.)), entertainment devices (e.g., wireless game controllers, music and video players, satellite radio units, 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 business use, wireless communication elements within autonomous and semi-autonomous vehicles), wireless devices attached to or incorporated into various mobile platforms, global positioning system devices, and similar electronic devices that include memory, wireless communication components, and programmable processors.

[0022] The term "system on chip" (SOC) is used herein to refer to a single integrated circuit (IC) chip that includes multiple resources and / or processors integrated on a single substrate. A single SOC may include circuits for digital, analog, mixed signal, and radio frequency functions. A single SOC may also include any number of general and / or special processors (digital signal processors, modem processors, video processors, etc.), memory blocks (e.g., ROM, RAM, flash memory, etc.), and resources (e.g., timers, voltage regulators, oscillators, etc.). The SOC may also include software for controlling the integrated resources and processors and for controlling peripheral devices.

[0023] The term "system-in-package" (SIP) may be used herein to refer to a single module or package that contains multiple resources, computing 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, a SIP may include one or more multi-chip modules (MCMs) on which multiple ICs or semiconductor dies are packaged into a unified substrate. A SIP may also include multiple independent SOCs that are coupled together via high-speed communication circuits and are tightly packaged, for example, on a single motherboard or in a single wireless device. The proximity of the SOCs facilitates high-speed communication and the sharing of memory and resources.

[0024] In some wireless devices (e.g., in some wireless device modem implementations), increased knowledge of algorithms used by a base station or information about the internal states of a base station may enable the wireless device to improve wireless communication performance, efficiency, and accuracy. For example, increased knowledge of algorithms used by a base station scheduler or its internal states may enable the wireless device to derive improved channel state feedback. Examples of such information include: whether the base station will utilize a sounding reference signal (SRS) in its scheduling and how; whether the base station will pair wireless devices via multi-user (MU) multiple-input multiple-output (MIMO) at the scheduled time; any scheduling delays at the base station; whether the base station employs an outer loop for scheduling, and if so, what type of algorithm it may use; the beam management algorithm of the base station; and other appropriate information or algorithms. However, these algorithms and this information may be too large to be quickly and efficiently communicated to the wireless device. In addition, this information may be proprietary or secret information that the owner of the base station (e.g., a wireless network service provider) does not wish to reveal in a form that can be easily discovered by competitors.

[0025] Various embodiments include methods that utilize a trained encoder neural network in a base station to convert auxiliary information (e.g., state information, algorithms, instructions, and other appropriate auxiliary information) into coded auxiliary information with significantly reduced information density that cannot be used to recover the auxiliary information without a decoder neural network that is not shared with a wireless device. Since the wireless device is not configured with a corresponding trained decoder neural network, the wireless device does not decode the coded auxiliary information. Instead, the wireless device may receive the coded auxiliary information and apply the information to internal processing blocks or functions to use the information to update one or more behaviors of the wireless device without being able to reproduce the base station auxiliary information.

[0026] In various embodiments, the base station may use an unsupervised learning approach to train an encoder neural network that uses an autoencoder process to learn how to compress auxiliary information (i.e., base station algorithms, state conditions, and other information, etc.) into encoded auxiliary information. For example, the base station processor may use unsupervised learning to train an autoencoder neural network to compress an input of auxiliary information into encoded auxiliary information using a decoder neural network that recovers the information for comparison with the input information. A loss function may be calculated based on the delta between the actual input and the reconstructed input, and an optimizer function may train both the encoder and the decoder to reduce the loss function (i.e., the reconstruction loss). Once trained, the encoder neural network may be used by the base station processor to generate the encoded auxiliary information that is sent for use by the wireless device, while the decoder neural network remains known only to the base station and is not provided to the wireless device or configured in the wireless device. Therefore, the wireless device receives the encoded auxiliary information in a form that cannot be used by the device to recover or understand the auxiliary information. However, the encoded auxiliary information will have characteristics determined by or dependent on the auxiliary information, and therefore includes information that the receiving wireless device can learn over time to manage or update one or more behaviors to better communicate with the base station.

[0027] In some embodiments, the base station may send the coded assistance information to the wireless device via a control channel. The wireless device may be configured with circuits, algorithms, or neural networks (different from the autoencoder neural network used in the base station) to utilize the coded assistance information, for example, to update one or more behaviors of the wireless device. In some embodiments, the wireless device may not initially be configured with an algorithm or trained neural network configured to transform the received coded assistance information, but instead may include a self-learning neural network in the processing block that learns over time how to update one or more behaviors in response to specific characteristics or changes observed in the received coded assistance information.

[0028] Figure 1 1 is a system block diagram illustrating an example communication system 100 suitable for implementing any of the various embodiments. The communication system 100 may be a 5G New Radio (NR) network or any other suitable network such as a Long Term Evolution (LTE) network.

[0029] The communication system 100 may include a heterogeneous network architecture including a core network 140 and various mobile devices (shown as Figure 1 120a-120e in the communication system 100). The communication system 100 may also include multiple base stations 110 (shown as BS 110a, BS 110b, BS 110c and BS 110d) and other network entities. A base station is an entity that communicates with wireless devices (mobile devices) and may also be referred to as a NodeB, NodeB, LTE evolved NodeB (eNB), access point (AP), radio head, transmit receive point (TRP), new radio base station (NR BS), 5G NodeB (NB), next generation NodeB (gNB), etc. Each base station may provide communication coverage for a specific geographic area. In 3GPP, the term "cell" may refer to the coverage area of ​​a base station, a base station subsystem serving the coverage area, or a combination thereof, depending on the context in which the term is used.

[0030] Base stations 110a-110d 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 (e.g., 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 (e.g., a residence) and may allow restricted access by mobile devices associated with the femto cell (e.g., 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 Figure 1 In the example shown in , base station 110a can be a macro BS for macro cell 102a, base station 110b can be a pico BS for pico cell 102b, and base station 110c can be a femto BS for femto cell 102c. Base stations 110a-110d can support one or more (e.g., three) cells. The terms "eNB", "base station", "NR BS", "gNB", "TRP", "AP", "Node B", "5G NB" and "cell" may be used interchangeably herein.

[0031] In some examples, the cell may not be stationary, and the geographic area of ​​the cell may move depending on the location of the mobile base station. In some examples, the base stations 110a-110d may be interconnected with each other and with one or more other base stations or network nodes (not shown) in the communication system 100 using any suitable transport network, through various types of backhaul interfaces (such as direct physical connections, virtual networks, or combinations thereof).

[0032] The base stations 110a-110d may communicate with the core network 140 over a wired or wireless communication link 126. The wireless devices 120a-120e may communicate with the base stations 110a-110d over a wireless communication link 122.

[0033] The wired communication link 126 may use various wired networks (e.g., Ethernet, television cable, telephone, fiber optic, and other forms of physical network connections) that may use one or more wired communication protocols (such as Ethernet, Point-to-Point Protocol, High-Level Data Link Control (HDLC), Advanced Data Communications Control Protocol (ADCCP), and Transmission Control Protocol / Internet Protocol (TCP / IP)).

[0034] The communication system 100 may also include a relay station (e.g., relay BS 110d). A relay station is an entity that can receive data transmissions from an upstream station (e.g., a base station or a mobile device) and send the data to a downstream station (e.g., a wireless device or a base station). A relay station may also be a mobile device that is capable of relaying transmissions for other wireless devices. Figure 1 In the example shown in FIG. 1 , a relay station 110d can communicate with a macro base station 110a and a wireless device 120d to facilitate communication between the base station 110a and the mobile device 120d. A relay station may also be referred to as a relay base station, a relay base station, a relay, or the like.

[0035] The communication system 100 may be a heterogeneous network including different types of base stations (e.g., macro base stations, pico base stations, femto base stations, relay base stations, etc.). These different types of base stations may have different transmit power levels, different coverage areas, and different impacts on interference in the communication system 100. For example, a macro base station may have a high transmit power level (e.g., 5 to 40 watts), while a pico base station, a femto base station, and a relay base station may have a lower transmit power level (e.g., 0.1 to 2 watts).

[0036] A network controller 130 may be coupled to a set of base stations and may provide coordination and control for these base stations. The network controller 130 may communicate with the base stations via a backhaul. The base stations may also communicate with each other directly or indirectly, for example, via a wireless or wired backhaul.

[0037] Wireless devices 120a, 120b, 120c may be dispersed throughout communication system 100, and each wireless device may be stationary or mobile. A wireless device may also be referred to as an access terminal, terminal, mobile station, subscriber unit, station, or the like.

[0038] The macro base station 110a may communicate with the communication network 140 over a wired or wireless communication link 126. The wireless devices 120a, 120b, 120c may communicate over a wireless communication link 122 with the base stations 110a-110d.

[0039] The wireless communication links 122, 124 may include multiple carrier signals, frequencies or frequency bands, each of which may include multiple logical channels. The wireless communication links 122 and 124 may utilize one or more radio access technologies (RATs). Examples of RATs that may be used in the wireless communication links include 3GPP LTE, 3G, 4G, 5G (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 phone communication technology cellular RATs. Additional examples of RATs that may be used in one or more of the various wireless communication links 122, 124 within the communication system 100 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)).

[0040] Some wireless networks (e.g., LTE) utilize orthogonal frequency division multiplexing (OFDM) on the downlink and single carrier frequency division multiplexing (SC-FDM) on the uplink. OFDM and SC-FDM divide the system bandwidth into multiple (K) orthogonal subcarriers, which are also commonly referred to as tones, frequency bins, etc. Each subcarrier can be modulated with data. Typically, modulation symbols are sent in the frequency domain using OFDM and in the time domain using SC-FDM. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers (K) can depend on the system bandwidth. For example, the spacing of the subcarriers can be 15kHz and the minimum resource allocation (referred to as a "resource block") can be 12 subcarriers (or 180kHz). Therefore, for system bandwidths of 1.25, 2.5, 5, 10, or 20 megahertz (MHz), the nominal fast file transfer (FFT) size can be equal to 128, 256, 512, 1024, or 2048, respectively. The system bandwidth may also be partitioned into subbands. For example, a subband may cover 1.08 MHz (ie, 6 resource blocks), and there may be 1, 2, 4, 8, or 16 subbands for system bandwidths of 1.25, 2.5, 5, 10, or 20 MHz, respectively.

[0041] Although the description of some embodiments may use terms and examples associated with LTE technology, the various embodiments may be applicable to other wireless communication systems, such as new radio (NR) or 5G networks. NR can utilize OFDM with a cyclic prefix (CP) on the uplink (UL) and downlink (DL), and can include support for half-duplex operation using time division duplex (TDD). A single component carrier bandwidth of 100MHz can be supported. NR resource blocks can span 12 subcarriers with a subcarrier bandwidth of 75kHz in a duration of 0.1 milliseconds (ms). Each radio frame can consist of 50 subframes and have a length of 10ms. Therefore, each subframe can have a length of 0.2ms. Each subframe can indicate the link direction (ie, DL or UL) for data transmission, and the link direction for each subframe can be dynamically switched. Each subframe can include DL / UL data and DL / UL control data. Beamforming can be supported and the beam direction can be dynamically configured. Multiple-input multiple-output (MIMO) transmission with precoding can also be supported. MIMO configurations in the DL may support up to eight transmit antennas with multi-layer DL transmissions of up to eight streams and up to two streams per wireless device. Multi-layer transmissions with up to 2 streams per wireless device may be supported. Aggregation of multiple cells with up to eight serving cells may be supported. Alternatively, NR may support different air interfaces other than OFDM-based air interfaces.

[0042] Some mobile devices may be considered to be 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., which may communicate with a base station, another device (e.g., a remote device), or some other entity. A wireless node may provide a connection to a network (e.g., a wide area network such as the Internet or a cellular network) or a connection to a network, for example, via a wired or wireless communication link. Some mobile devices may be considered to be Internet of Things (IoT) devices or may be implemented as NB-IoT (narrowband Internet of Things) devices. Wireless devices 120a-120e may be included inside a housing that houses components of the wireless device (such as a processor component, a memory component, a similar component, or a combination thereof).

[0043] Generally, any number of communication systems and any number of wireless networks can be deployed in a given geographic area. Each communication system and wireless network can support a specific radio access technology (RAT) and can operate on one or more frequencies. RAT can also be referred to as radio technology, air interface, etc. Frequency can also be referred to as carrier, frequency channel, etc. Each frequency can support a single RAT in a given geographic area to avoid interference between communication systems of different RATs. In some cases, NR or 5G RAT networks can be deployed.

[0044] In some embodiments, two or more mobile devices 120a-120e (e.g., shown as wireless device 120a and wireless device 120e) may communicate directly using one or more sidelink channels 124 (e.g., without using base stations 110a-110d as an intermediary to communicate with each other). For example, the wireless devices 120a-120e may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, or similar protocols), mesh networks, or similar networks, or combinations thereof. In this case, the wireless devices 120a-120e may perform scheduling operations, resource selection operations, and other operations described elsewhere herein as being performed by the base station 110a.

[0045] Figure 2 is a component block diagram illustrating an example computing and wireless modem system 200 suitable for implementing any of the various embodiments. The various embodiments may be implemented on a number of single-processor and multi-processor computer systems including a system on a chip (SOC) or a system in a package (SIP).

[0046] Reference Figure 1 and Figure 2 , the example computing system shown (which may be a SIP in some embodiments) includes: two SOCs 202, 204, which are coupled to a clock 206; a voltage regulator 208; and a wireless transceiver 266, which is configured to send wireless communications to a wireless device (such as a base station 110a) via an antenna (not shown) and receive wireless communications from the wireless device via the antenna. In some embodiments, the first SOC 202 operates as a central processing unit (CPU) of the wireless device, which executes the instructions by performing arithmetic, logic, control, and input / output (I / O) operations specified by the instructions of the software application. In some embodiments, the second SOC 204 can operate as a dedicated processing unit. For example, the second SOC 204 can operate as a dedicated 5G processing unit, which is responsible for managing communications of large capacity, high speed (e.g., 5Gbps, etc.) and / or extremely high frequency short wavelengths (e.g., 28GHz millimeter wave spectrum, etc.).

[0047] The first SOC 202 may include a digital signal processor (DSP) 210, a modem processor 212, a graphics processor 214, an application processor 216, one or more coprocessors 218 (e.g., vector coprocessors) connected to one or more of these processors, memory 220, custom circuits 222, system components and resources 224, an interconnect / bus module 226, one or more temperature sensors 230, a thermal management unit 232, and a thermal power envelope (TPE) component 234. The second SOC 204 may include a 5G modem processor 252, a power management unit 254, an interconnect / bus module 264, a plurality of millimeter wave transceivers 256, a memory 258, and various additional processors 260 (such as an application processor, a packet processor, etc.).

[0048] Each processor 210, 212, 214, 216, 218, 252, 260 may include one or more cores, and each processor / core may perform operations independently of other processors / cores. For example, the first SOC 202 may include a processor that executes a first type of operating system (e.g., FreeBSD, LINUX, OS X, etc.) and a processor that executes a second type of operating system (e.g., MICROSOFT WINDOWS 10). In addition, any one or all of the processors 210, 212, 214, 216, 218, 252, 260 may be included as part of a processor cluster architecture (e.g., a synchronous processor cluster architecture, an asynchronous or heterogeneous processor cluster architecture, etc.).

[0049] The first SOC 202 and the second SOC 204 may include various system components, resources and custom circuits for managing sensor data, analog-to-digital conversion, wireless data transmission and for performing other special operations, such as decoding data packets and processing encoded audio and video signals to present in a web browser. For example, the system components and resources 224 of the first SOC 202 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 for supporting processors and software clients running on wireless devices. System components and resources 224 and / or custom circuits 222 may also include circuits that interface with peripheral devices (such as cameras, electronic displays, wireless communication devices, external memory chips, etc.).

[0050] The first SOC 202 and the second SOC 204 can communicate via an interconnect / bus module 250. The various processors 210, 212, 214, 216, 218 can be interconnected to one or more memory elements 220, system components and resources 224, and custom circuits 222, and thermal management units 232 via interconnect / bus modules 226. Similarly, the processor 252 can be interconnected to a power management unit 254, a millimeter wave transceiver 256, a memory 258, and various additional processors 260 via an interconnect / bus module 264. The interconnect / bus modules 226, 250, 264 can include an array of reconfigurable logic gates and / or implement a bus architecture (e.g., CoreConnect, AMBA, etc.). Communication can be provided through advanced interconnects such as high-performance networks on chip (NoCs).

[0051] The first SOC 202 and / or the second SOC 204 may also include an input / output module (not shown) for communicating with resources external to the SOC, such as a clock 206 and a voltage regulator 208. Resources external to the SOC (e.g., clock 206, voltage regulator 208) may be shared by two or more of the internal SOC processors / cores.

[0052] In addition to the example SIP 200 discussed above, various embodiments may be implemented in a wide variety of computing systems, which may include a single processor, multiple processors, multi-core processors, or any combination thereof.

[0053] Figure 3 is a component block diagram illustrating a software architecture 300 suitable for implementing any of the various embodiments, the software architecture 300 including a radio protocol stack for user and control planes in wireless communications. Figure 1-3, the wireless device 320 may implement the software architecture 300 to facilitate communication between the wireless device 320 (e.g., the wireless devices 120a-120e, 200) and a base station 350 (e.g., base station 110) of a communication system (e.g., 100). In various embodiments, the layers in the software architecture 300 may form a logical connection with the corresponding layers in the software of the base station 350. The software architecture 300 may be distributed between one or more processors (e.g., processors 212, 214, 216, 218, 252, 260). Although described with respect to one radio protocol stack, in a multi-SIM (subscriber identity module) wireless device, the software architecture 300 may include multiple protocol stacks, each of which may be associated with a different SIM (e.g., two protocol stacks associated with two SIMs in a dual SIM wireless communication device, respectively). Although described below with reference to the LTE communication layer, the software architecture 300 may support any of a variety of standards and protocols for wireless communication, and / or may include additional protocol stacks that support any of a variety of standards and protocols for wireless communication.

[0054] The software architecture 300 may include a non-access stratum (NAS) 302 and an access stratum (AS) 304. The NAS 302 may include functions and protocols for supporting packet filtering, security management, mobility control, session management, and services and signaling between a SIM (e.g., SIM 204) of a wireless device and its core network 140. The AS 304 may include functions and protocols for supporting communication between a SIM (e.g., SIM 204) and an entity (e.g., a base station) of a supported access network. Specifically, the AS 304 may include at least three layers (layer 1, layer 2, and layer 3), each of which may include various sublayers.

[0055] In the user and control planes, layer 1 (L1) of AS 304 may be a physical layer (PHY) 306, which may oversee functions for transmitting and / or receiving over an air interface via a wireless transceiver (e.g., 256). Examples of such physical layer 306 functions may include cyclic redundancy check (CRC) appending, coding blocks, scrambling and descrambling, modulation and demodulation, signal measurement, MIMO, etc. The physical layer may include various logical channels, including a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH).

[0056] In the user and control planes, Layer 2 (L2) of AS 304 may be responsible for the link above physical layer 306 between wireless device 320 and base station 350. In various embodiments, Layer 2 may include a medium access control (MAC) sublayer 308, a radio link control (RLC) sublayer 310, and a packet data convergence protocol (PDCP) 312 sublayer, each of which forms a logical connection that terminates at base station 350.

[0057] In the control plane, layer 3 (L3) of AS 304 may include a radio resource control (RRC) sublayer 3. Although not shown, the software architecture 300 may include additional layer 3 sublayers and various upper layers above layer 3. In various embodiments, the RRC sublayer 313 may provide functions including broadcasting system information, paging, and establishing and releasing RRC signaling connections between the wireless device 320 and the base station 350.

[0058] In various embodiments, the PDCP sublayer 312 may provide uplink functions including multiplexing between different radio bearers and logical channels, sequence number addition, handover data processing, integrity protection, encryption, and header compression. In the downlink, the PDCP sublayer 312 may provide functions including in-sequence delivery of data packets, duplicate data packet detection, integrity verification, decryption, and header decompression.

[0059] In the uplink, the RLC sublayer 310 may provide segmentation and concatenation of upper layer data packets, retransmission of lost data packets, and automatic repeat request (ARQ). In the downlink, the RLC sublayer 310 functions may include reordering of data packets to compensate for out-of-order reception, reassembly of upper layer data packets, and ARQ.

[0060] In the uplink, the MAC sublayer 308 can provide functions including: multiplexing between logical channels and transport channels, random access procedures, logical channel priorities, and hybrid ARQ (HARQ) operations. In the downlink, MAC layer functions may include channel mapping within a cell, demultiplexing, discontinuous reception (DRX), and HARQ operations.

[0061] While the software architecture 300 may provide functionality for sending data over a physical medium, the software architecture 300 may also include at least one host layer 314 to provide data transmission services to various applications in the wireless device 320. In some embodiments, the application-specific functionality provided by the at least one host layer 314 may provide an interface between the software architecture and the general processor 206.

[0062] In other embodiments, the software architecture 300 may include one or more higher logical layers (e.g., transport, session, presentation, application, etc.) that provide host layer functionality. For example, in some embodiments, the software architecture 300 may include a network layer (e.g., an Internet Protocol (IP) layer) in which a logical connection terminates at an external packet data network (PDN) gateway (PGW) in a mobile operator's network. In some embodiments, the software architecture 300 may include an application layer in which a logical connection terminates at another device (e.g., an end-user device, a server, etc.). In some embodiments, the software architecture 300 may also include a hardware interface 316 between the physical layer 306 and communication hardware (e.g., one or more radio frequency (RF) transceivers) in the AS 304.

[0063] Figure 4A and 4B is a block diagram illustrating components of a system 400 configured to manage information transmission for wireless communications performed by a processor of a base station according to various embodiments. Figure 1-4B , the system 400 may include a base station 402 (e.g., 110a-110d, 200, 350) and a wireless device 404 (e.g., 120a-120e, 200, 320). The base station 402 and the wireless device 404 may communicate over a wireless communication link 122, which may provide the wireless device 404 with access to a wireless communication network 424 (in Figure 1 Access to the device (various aspects of which are shown in FIG. 1 ).

[0064] Base station 402 may include one or more processors 428 coupled to electronic storage 426 and to a wireless transceiver (e.g., 266). Wireless transceiver 266 may be configured to: receive messages to be sent in downlink transmissions from processor 428; and to transmit such messages to wireless device 404 via an antenna (not shown). In some embodiments, base station 402 may receive messages from wireless communication network 424 for relay to wireless device 404. Similarly, wireless transceiver 266 may be configured to: receive messages from wireless device 404 in uplink transmissions; and to pass such messages (e.g., via a modem (e.g., 252) that demodulates the messages) to one or more processors 428 for ultimate relay to wireless communication network 424.

[0065] Processor 428 may be configured by machine readable instructions 406. Machine readable instructions 406 may include one or more instruction modules. Instruction modules may include computer program modules. Instruction modules may include one or more of the following: communication information and instruction determination module 408, neural network training module 410, encoder neural network application module, wireless communication module 414, or other instruction modules.

[0066] The communication information and instruction determination module 408 may be configured to determine base station algorithms, information used by the processor to generate communication information and instructions for the wireless device to enable communication with the base station.

[0067] The neural network training module 410 may be configured to train an encoder neural network to compress base station algorithms, information, and corresponding data useful for enabling communications with a base station into coded auxiliary information that is processed by a decoder neural network to recover base station algorithms, information, and corresponding data useful for enabling communications with a base station, wherein the difference is used to update the encoder neural network using an unsupervised learning approach. For training (i.e., in a base station, or in another network device or element), the encoder neural network may be part of an autoencoder consisting of an encoder neural network and a decoder neural network. The encoder and decoder pairs in the autoencoder are jointly trained so that a cost metric or loss function (e.g., mean squared error) between the input to the encoder and the output from the decoder is minimized. The encoder neural network trained in this manner will convert the base station algorithm or information into a coded output that a wireless device that does not have access to the decoder neural network cannot use to reproduce the base station algorithm or information. However, the coded output may be useful for various operations of the wireless device in terms of optimizing communication performance, reducing complexity, and / or reducing power consumption.

[0068] The neural network application module 412 may be configured to apply the encoder neural network to a base station algorithm or information used by the processor to generate encoded auxiliary information for transmission to the wireless device 404 .

[0069] The wireless communication module 414 may be configured to send the encoded assistance information to the wireless device 404. In some embodiments, the encoded assistance information is sent by the base station on a data channel. In some embodiments, the encoded assistance information is sent by the base station on a control channel.

[0070] The wireless device 404 may include one or more processors 432 coupled to the electronic storage 430 and a wireless transceiver (e.g., 266). The wireless transceiver 266 may be configured to receive messages to be sent in uplink transmissions from the processor 432 and to transmit such messages to the base station 402 via an antenna (not shown). Similarly, the wireless transceiver 266 may be configured to receive messages from the base station 402 in downlink transmissions and to communicate such messages to the one or more processors 432 (e.g., via a modem (e.g., 252) which demodulates the messages).

[0071] The processor 432 may be configured by machine readable instructions 434. The machine readable instructions 406 may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of the following: wireless communication module 436, encoded auxiliary information processing module 438, modem communication module 440, or other instruction modules.

[0072] The wireless communication module 436 may be configured to receive the encoded auxiliary information of the base station encoder neural network from the base station 402. In some embodiments, the wireless communication module 436 may be configured to receive the encoded auxiliary information of the base station encoder neural network via a control channel.

[0073] The coded assistance information processing module 438 may be configured to use the received coded assistance information to update one or more wireless device behaviors. The coded assistance information processing module 438 is not capable of decoding (i.e., is not configured to decode) the original assistance information that is input to the base station coding neural network. In some embodiments, the coded assistance information processing module 438 may include a self-learning neural network (different from the base station neural network) that may learn over time how to use the coded assistance information received from the base station for various purposes, such as updating one or more behaviors in a manner that improves wireless device performance.

[0074] The modem communication module 440 may be configured to enable communications with the base station 402 .

[0075] In some embodiments, base station 402 and wireless device 404 may be operatively connected via one or more electronic communication links, such as wireless communication link 122. It will be appreciated that this is not intended to be limiting, and the scope of the present disclosure includes embodiments in which base station 402 and wireless device 404 may be operatively connected via some other communication medium.

[0076] Electronic storage 426, 430 may include non-transitory storage media that electronically stores information. The electronic storage media of electronic storage 426, 430 may include one or both of the following: system storage provided integrally (i.e., substantially non-removably) with base station 402 or wireless device 404; and / or removable storage that is removably connected to base station 402 or wireless device 404 via, for example, a port (e.g., a universal serial bus (USB) port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage 426, 430 may include one or more of the following: optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy disk drive, etc.), charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drives, etc.), and / or other electronically readable storage media. Electronic storage 426, 430 may include one or more virtual storage resources (e.g., cloud storage, virtual private networks, and / or other virtual storage resources). Electronic storage 426, 430 may store software algorithms, information determined by processors 428, 432, information received from base station 402 or wireless device 404, or other information that enables base station 402 or wireless device 404 to function as described herein.

[0077] Processors 428, 432 may be configured to provide information processing capabilities in base station 402. Therefore, processors 428, 432 may include one or more of the following: a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. Although processors 428, 432 are shown as a single entity, this is for illustrative purposes only. In some embodiments, processors 428, 432 may include multiple processing units and / or processor cores. The processing units may be physically located within the same device, or processors 428, 432 may represent processing functions of multiple devices operating in coordination. Processors 428, 432 may be configured to execute modules 408-414 and modules 436-440 and / or other modules by software; hardware; firmware; some combination of software, hardware and / or firmware, and / or other mechanisms for configuring processing capabilities on processors 428, 432. As used herein, the term "module" may refer to any component or component set that performs the functions attributed to the module. This may include one or more physical processors, processor readable instructions, circuitry, hardware, storage media, or any other component during execution of processor readable instructions.

[0078] The description of the functionality provided by the different modules 408-414 and modules 436-440 described below is for illustrative purposes and is not intended to be limiting, as any of the modules 408-414 and modules 436-440 may provide more or less functionality than described. For example, one or more of the modules 408-414 and modules 436-440 may be eliminated, and some or all of their functionality may be provided by other modules 408-414 and modules 436-440. As another example, the processors 428, 432 may be configured to execute one or more additional modules that may perform some or all of the functionality attributed to one of the modules 408-414 and modules 436-440 below.

[0079] Figure 5 is a process flow diagram illustrating a method 500 for managing information transmission for wireless communication that may be performed by a processor of a base station according to various embodiments. Figure 1-5 , the method 500 may be implemented by a processor (eg, 210 , 212 , 214 , 216 , 218 , 252 , 260 , 428 ) of a base station (eg, base stations 110 a - 110 d , 350 , 402 ).

[0080] In block 502, a processor may apply an encoder neural network to auxiliary information in a base station to generate encoded auxiliary information. In some embodiments, the encoder neural network may be trained to convert the auxiliary information into encoded auxiliary information, wherein the receiving wireless device may provide the encoded auxiliary information to a processing block of the wireless device to update one or more behaviors of the wireless device without reproducing a base station algorithm or information. In some embodiments, the base station algorithm or information includes information about an internal state of the base station. The means for performing the functions of the operations in block 502 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428).

[0081] In some embodiments, the base station algorithm or information may include, for example, whether the base station will utilize SRS in its scheduling, and in what manner or way to utilize SRS. The base station algorithm or information may include, for example, whether the base station will pair the wireless device via MU-MIMO at the scheduled time. As another example, the base station algorithm or information may include an indication of any scheduling delay at the base station. As another example, the base station algorithm or information may include whether the base station uses an outer loop for scheduling, and if so, what type of algorithm it can use. The base station algorithm or information may, for example, include a beam management algorithm for the base station. The base station algorithm or information may include any other suitable information or algorithm, including any combination of the foregoing.

[0082] In some embodiments, the set of base station algorithms or information may be denoted as X, and the encoder algorithm may be trained to encode a compact message m (referred to elsewhere herein as coded auxiliary information), which may be denoted as where m is the encoder function f applied to the base station algorithm or information set X enc,θ , and θ represents the weights of the encoder neural network. In some embodiments, the compact message m comprises one dimension or fewer dimensions than the set X. In some embodiments, the dimensionality reduction when converting the set X of base station algorithms or information into the compact message m is due to lossy compression performed by the encoder algorithm.

[0083] In various embodiments, the encoder algorithm can be trained using an unsupervised learning method by using a decoder neural network to recover X from m, and adjusting the weights of the encoder and decoder neural networks based on the difference or error until a highly compressed compact message m is produced that can be decoded with an acceptable difference or error. In unsupervised learning of a codec pair of neural networks, the encoder neural network and the decoder neural network can be trained together. The processor can train the decoder neural network to output data, which can be represented as and can be defined as in is the decoder function f applied to the compact message m dec,φ is defined, and φ represents the weight of the decoder neural network. In some embodiments, can be expressed as The processor may determine a loss function for training the encoder and decoder neural networks. For example, the loss function may be expressed as It means X and The processor may apply a loss function during the training of the encoder neural network and the decoder neural network to reduce the distance or difference between X and The distance or difference between them is thus increased, thereby increasing the amount of information about X included in the compact message m.

[0084] In block 504, the processor may send the encoded assistance information to the wireless device. In some embodiments, the base station may send the encoded assistance information (e.g., m) to the wireless device via a control channel. In some embodiments, the base station may send the encoded assistance information (e.g., m) to the wireless device via a data channel. The means for performing the functions of the operations in block 504 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) coupled to a wireless transceiver (e.g., 266).

[0085] The method 500 may be repeated continuously or periodically as the processor may again perform the operations of block 502 .

[0086] Figure 6 is a process flow diagram illustrating a method 600 for managing information transmission for wireless communication that may be performed by a processor of a wireless device according to various embodiments. Figure 1-6 , the method 600 may be implemented by a processor (eg, 210, 212, 214, 216, 218, 252, 260, 432) of a wireless device (eg, 120a-120e, 200, 320, 404).

[0087] In block 602, a processor may receive encoded auxiliary information of a base station encoder neural network from a base station. For example, the processor may receive a compact message m as described from a base station. In some embodiments, the processor may receive the encoded auxiliary information via a control channel. In some embodiments, the processor may receive the encoded auxiliary information via a data channel. The means for performing the functions of the operations in block 602 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) coupled to a wireless transceiver (e.g., 266).

[0088] In block 604, the processor may provide the received coded auxiliary information as an input to a processing block of the wireless device, and may update one or more behaviors of the wireless device based on the coded auxiliary information. In some embodiments, the processor may provide the coded auxiliary information to a modem block of the wireless device. In some embodiments, the modem block may update one or more behaviors based on the coded auxiliary information. In some embodiments, the processing block of the wireless device may be configured to use the coded auxiliary information without decoding the coded auxiliary information, thereby avoiding reproducing the base station algorithm or information input to the base station encoder neural network. In some embodiments, one or more behaviors of the wireless device may change as a function of the input coded auxiliary information. In some embodiments, the processing block may include a self-learning neural network that may learn how to use the received coded auxiliary information to improve the performance of the wireless device. In some embodiments, the neural network of the processing block may be trained with the coded auxiliary information as one of its inputs. In some embodiments, the neural network of the processing block may learn (or may be trained) over time how to use the coded auxiliary information to update one or more wireless device behaviors to improve the performance of the wireless device (e.g., improve one or more aspects of communication with a base station). In some embodiments, updating one or more behaviors of the wireless device may include using the coded auxiliary information as an input to an internal algorithm so that its output becomes a function of the coding assistance. The means for performing the function of the operation in block 604 may include a processor (eg, 210, 212, 214, 216, 218, 252, 260, 432) and a modem block or modem processor (eg, 212, 252).

[0089] The method 600 may be repeated continuously or periodically as the processor may again perform the operations of block 602 .

[0090] Figure 7 is a process flow diagram illustrating a method 700 according to some embodiments, which may be performed by a processor of a wireless device as part of method 600 to train a self-learning neural network to learn how to use received coded auxiliary information to improve the performance of the wireless device. Figure 1-7 , the method 700 may be implemented by a processor (eg, 210, 212, 214, 216, 218, 252, 260, 432) of a wireless device (eg, 120a-120e, 200, 320, 404).

[0091] In block 604 ( Figure 6), in block 702, the processor may provide the received coded auxiliary information as an input to a processing block of the wireless device, and the processing block may update one or more behaviors based on the coded auxiliary information. In some embodiments, the processing block may include a neural network that receives the coded auxiliary information as one of its inputs. In such embodiments, the weights of the neural network may be trained via back-propagating gradients of a loss function relative to the weights of the neural network (which may be referred to as supervised learning). In some embodiments, the processing block may include a neural network that does not include a differentiable loss function. In these embodiments, gradient back-propagation may be used to train the neural network via reinforcement learning techniques.

[0092] In the case where the internal algorithm and / or behavior is not a neural network, the algorithm / behavior can learn how to use the received encoded auxiliary information in a trial and error manner as follows: In block 604, the processor can compare the performance of the wireless device before and after updating one or more behaviors of the wireless device. For example, the processor can evaluate the performance of one or more operations of the wireless device before and after updating one or more behaviors of the wireless device to determine whether the performance of the wireless device has been improved or worsened by the adjustment. As another example, the processor can evaluate the quality of the communication link with the base station before and after updating one or more behaviors of the wireless device to determine whether the link quality has been improved or worsened by the update. The means for performing the functions of the operations in block 702 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) and a modem block or modem processor (e.g., 212, 252).

[0093] In block 704, the processor may update a neural network in a processing block of the wireless device based on the comparison made in block 702 and the received coded auxiliary information. For example, for a given coded auxiliary information, the processor may update (e.g., adjust, change, etc.) one or more weights of the neural network in a direction that makes the performance of the wireless device better. In the case of supervised learning, this is typically accomplished by calculating and back-propagating the gradient of a loss function with respect to the weights of the neural network. By updating the weights of the neural network based on such improvements, a self-learning neural network in the wireless device may learn over time how to use the received coded auxiliary information to improve device performance. In some embodiments, the updating of the neural network may be performed by unsupervised learning. In some embodiments, the updating of the neural network may be performed by unsupervised learning or training. The means for performing the functions of the operations in block 704 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) and a modem block or modem processor (e.g., 212, 252).

[0094] The processor may then continue to execute the described block 602 ( Figure 6 ) operation.

[0095] Various embodiments (including method 500) may be performed in various network computing devices (eg, in a base station). Figure 8 An example of a network computing device is shown in Figure 8 is a component block diagram of a network computing device 800 suitable for use with various embodiments. Such a network computing device may include at least Figure 8 Components shown in . Figure 1-8 , the network computing device 800 may include a processor 801 coupled to a volatile memory 802 (e.g., 426) and a large capacity non-volatile memory (such as a disk drive 803). The network computing device 800 may also include a peripheral memory access device, such as a floppy disk drive, a compact disc (CD), or a digital video disc (DVD) drive 806 coupled to the processor 801. The network computing device 800 may also include a network access port 804 (or interface) coupled to the processor 801, which is used to establish a data connection with a network (such as the Internet and / or a local area network coupled to other system computers and servers). The network computing device 800 may be connected to one or more antennas for sending and receiving electromagnetic radiation that can be connected to a wireless communication link. The network computing device 800 may include additional access ports for coupling to peripherals, external memory, or other devices, such as USB, Firewire, Thunderbolt, etc.

[0096] Various embodiments (including methods 600 and 700) may be performed in various wireless devices (e.g., wireless devices 120a-120e, 200, 320, 402), in Fig. 9 An example of a wireless device is shown in Fig. 9 is a component block diagram of a wireless device 900 suitable for use with various embodiments. Figure 1-9 , the wireless device 900 may include a first SOC 202 (e.g., a SOC-CPU) coupled to a second SOC 204 (e.g., a 5G-capable SOC). The first SOC 202 and the second SOC 204 may be coupled to internal memory 430, 916, a display 912, and a speaker 914. In addition, the wireless device 900 may include an antenna 904 for sending and receiving electromagnetic radiation, which may be connected to a wireless data link and / or a cellular telephone transceiver 266 coupled to one or more processors in the first SOC 202 and / or the second SOC 204. The wireless device 900 may also include a menu selection button or rocker switch 920 for receiving user input.

[0097] The wireless device 900 may also include a sound coding / decoding (CODEC) circuit 910 that digitizes the sound received from the microphone into a data packet suitable for wireless transmission, and decodes the received sound packet to generate an analog signal, which is provided to the speaker to generate sound. In addition, one or more processors in the processors in the first SOC 202 and the second SOC 204, the wireless transceiver 266, and the CODEC 910 may include a digital signal processor (DSP) circuit (not shown separately).

[0098] The processors of the network computing device 900 and the wireless device 900 may be any programmable microprocessor, microcomputer, or one or more multi-processor chips that may be configured by software instructions (applications) to perform various functions, including the functions of the various embodiments described below. In some mobile devices, multiple processors may be provided, such as one processor within the SOC 204 dedicated to wireless communication functions, and one processor within the SOC 202 dedicated to running other applications. Before software applications are accessed and loaded into the processor, they may be stored in the memory 426, 430, 916. The processor may include internal memory sufficient to store application software instructions.

[0099] As used in this application, the terms "component", "module", "system", etc. are intended to include computer-related entities, such as but not limited to hardware, firmware, a combination of hardware and software, software, or software in execution, which is configured to perform specific 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 file, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a wireless device and a wireless device may be referred to as a component. One or more components may be located in a process and / or a thread of execution, and a component may be located on a processor or core and / or distributed between two or more processors or cores. In addition, these components may be executed from various non-temporary 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 communication methods associated with networks, computers, processors, and / or processes.

[0100] A variety of different cellular and mobile communication services and standards may be available or expected in the future, all of which may be implemented and benefit from various embodiments. Such services and standards include, for example, the Third Generation Partnership Project (3GPP), Long Term Evolution (LTE) system, third generation wireless mobile communication technology (3G), fourth generation wireless mobile communication technology (4G), fifth generation wireless mobile communication technology (5G), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), 3GSM, General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA) system (e.g., cdmaOne, CDMA1020TM), Enhanced Data Rates for GSM Evolution (EDGE), Advanced Mobile Phone System (AMPS), Digital AMPS (IS-136 / 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 and 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, unless specifically stated in the language of the claims, any reference to terms and / or technical details related to individual telecommunication standards or technologies is for illustrative purposes only and is not intended to limit the scope of the claims to a particular communication system or technology.

[0101] The various embodiments shown and described are provided as examples only to illustrate various features of the claims. However, the 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 shown and described. In addition, the claims are not intended to be limited by any one of the example embodiments. For example, one or more of the operations of methods 500, 600, and 700 may replace or be combined with one or more operations of methods 500, 600, and 700.

[0102] The aforementioned method description and process flow chart are provided only as illustrative examples, and are not intended to require or imply that the operations of the various embodiments must be performed in the order given. As will be appreciated by those skilled in the art, the order of operations in the aforementioned embodiments can be performed in any order. Words such as "thereafter", "subsequently", "next" are not intended to limit the order of operations; these words are used to guide the reader through the description of the method. In addition, any reference to a claim element in a singular form (e.g., using the article "a", "an" or "the") should not be interpreted as limiting the element to the singular.

[0103] Various illustrative logic blocks, modules, components, circuits and algorithmic operations described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, various illustrative components, frames, modules, circuits and operations have been generally described above around their functions. Whether this function is implemented as hardware or software depends on specific applications and the design constraints imposed on the overall system. Technicians can implement the described functions in a changing manner for each specific application, but such an embodiment decision should not be interpreted as causing a departure from the scope of the claims.

[0104] The hardware for implementing the various illustrative logics, logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed using a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), 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. The general purpose processor may be a microprocessor, but in an alternative manner, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of receiver intelligent objects, for example, 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 circuits specific to a given function.

[0105] In one or more embodiments, the functions 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 codes on a non-transitory computer-readable storage medium or a non-transitory processor-readable storage medium. The operation of the method or algorithm disclosed herein may be embodied in a processor-executable software module or a processor-executable instruction, which may reside on a non-transitory computer-readable or processor-readable storage medium. A non-transitory computer-readable or processor-readable storage medium may be any storage medium that can be accessed by a computer or processor. By way of example and not limitation, such a non-transitory computer-readable or processor-readable storage medium may include RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, disk storage or other magnetic storage smart objects, or any other medium that can be used to store desired program codes in the form of instructions or data structures and can be accessed by a computer. As used herein, disks and optical disks include compact disks (CDs), laser optical disks, optical disks, digital versatile disks (DVDs), floppy disks, and Blu-ray disks, wherein disks typically reproduce data magnetically, and optical disks reproduce data optically with lasers. Combinations of the above are also included within the scope of non-transitory computer-readable and processor-readable media. In addition, the operations of a method or algorithm may reside as one or any combination of codes and / or instructions, or codes and / or instruction sets, on a non-transitory processor-readable storage medium and / or a computer-readable storage medium, which may be incorporated into a computer program product.

[0106] The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the claims. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the claims. Therefore, the present disclosure is not intended to be limited to the embodiments shown herein, but is to be given the widest scope consistent with the appended claims and the principles and novel features disclosed herein.

Claims

1. A method for managing information transmission for wireless communication performed by a processor of an access point, comprising: applying an encoder neural network to the auxiliary information in the access point to generate encoded auxiliary information; as well as The encoded auxiliary information is sent to a wireless device for the wireless device to use the encoded auxiliary information to update one or more behaviors of the wireless device, wherein the wireless device is not configured to decode the encoded auxiliary information, and wherein using the encoded auxiliary information includes: using the encoded auxiliary information as an input to a self-learning neural network.

2. The method according to claim 1, wherein: Sending the coded assistance information to the wireless device comprises: sending the coded assistance information to the wireless device via a control channel or a data channel; and The encoded assistance information is in a form that the wireless device cannot process to recover the assistance information in the access point.

3. The method according to claim 1, wherein: The assistance information in the access point includes an algorithm or information about an internal state of the access point.

4. The method according to claim 1, further comprising: Using an unsupervised training method to train the encoder neural network and the decoder neural network on the dataset of the auxiliary information; generating the encoded auxiliary information using a trained encoder neural network; as well as The decoder neural network is not shared with wireless devices.

5. A computing device for use in an access point of a wireless communication system, comprising: A processor configured with processor-executable instructions to perform operations including: applying an encoder neural network to the auxiliary information in the access point to generate encoded auxiliary information; as well as The encoded auxiliary information is sent to a wireless device for the wireless device to use the encoded auxiliary information to update one or more behaviors of the wireless device, wherein the wireless device is not configured to decode the encoded auxiliary information, and wherein using the encoded auxiliary information includes: using the encoded auxiliary information as an input to a self-learning neural network.

6. The computing device of claim 5, wherein: The processor is also configured with processor-executable instructions to perform operations such that: Sending the coded assistance information to the wireless device comprises: sending the coded assistance information to the wireless device via a control channel or a data channel; and The encoded assistance information is in a form that the wireless device cannot process to recover the assistance information in the access point.

7. The computing device of claim 5, wherein: The processor is also configured with processor-executable instructions to perform operations such that the assistance information in the access point includes an algorithm or information regarding an internal state of the access point.

8. The computing device of claim 5, wherein: The processor is further configured with processor-executable instructions to perform operations further comprising: Using an unsupervised training method to train the encoder neural network and the decoder neural network on the dataset of the auxiliary information; generating the encoded auxiliary information using a trained encoder neural network; as well as The decoder neural network is not shared with wireless devices.

9. A non-transitory processor-readable medium having processor-executable instructions stored thereon, the processor-executable instructions configured to cause a computing device in an access point of a wireless communication system to perform operations comprising: applying an encoder neural network to the auxiliary information in the access point to generate encoded auxiliary information; and The encoded assistance information is sent to a wireless device for the wireless device to use the encoded assistance information to update one or more behaviors of the wireless device, wherein: The wireless device is not configured to decode the coded assistance information, and wherein using the coded assistance information comprises using the coded assistance information as input to a self-learning neural network.

10. The non-transitory processor-readable medium of claim 9, wherein: The stored processor-executable instructions are further configured to cause a computing device in an access point of a wireless communication system to perform operations such that: Sending the coded assistance information to the wireless device comprises: sending the coded assistance information to the wireless device via a control channel or a data channel; and The encoded assistance information is in a form that the wireless device cannot process to recover the assistance information in the access point.

11. The non-transitory processor-readable medium of claim 9, wherein: The stored processor-executable instructions are further configured to cause a computing device in an access point of a wireless communication system to perform operations such that the assistance information in the access point includes an algorithm or information about an internal state of the access point.

12. The non-transitory processor-readable medium of claim 9, wherein: The stored processor-executable instructions are further configured to cause a computing device in an access point of a wireless communication system to perform operations further including: Using an unsupervised training method to train the encoder neural network and the decoder neural network on the dataset of the auxiliary information; generating the encoded auxiliary information using a trained encoder neural network; as well as The decoder neural network is not shared with wireless devices.

13. An access point for use in a wireless communication system, comprising: means for applying an encoder neural network to the auxiliary information in the access point to generate encoded auxiliary information; as well as A unit for sending the encoded auxiliary information to a wireless device for the wireless device to use the encoded auxiliary information to update one or more behaviors of the wireless device, wherein the wireless device is not configured to decode the encoded auxiliary information, and wherein using the encoded auxiliary information includes: using the encoded auxiliary information as an input to a self-learning neural network.

14. The access point of claim 13, wherein: The means for sending the coded assistance information to the wireless device comprises: means for sending the coded assistance information to the wireless device via a control channel or a data channel; and The encoded assistance information is in a form that the wireless device cannot process to recover the assistance information in the access point.

15. The access point according to claim 13, wherein: The assistance information in the access point includes an algorithm or information about an internal state of the access point.

16. The access point of claim 13, further comprising: means for training the encoder neural network and the decoder neural network on the dataset of the auxiliary information using an unsupervised training method; as well as Means for generating the encoded auxiliary information using a trained encoder neural network.

17. A method performed by a processor of a wireless device, comprising: receiving coded assistance information from an access point; as well as Using the encoded assistance information to update one or more behaviors of the wireless device, wherein the wireless device is not configured to decode the encoded assistance information, and wherein using the encoded assistance information comprises: using the encoded assistance information as an input to a self-learning neural network.

18. The method according to claim 17, wherein: Using the coded assistance information to update one or more behaviors of the wireless device includes providing the received coded assistance information as input to a wireless device processing block and updating one or more behaviors of the wireless device based on the coded assistance information.

19. The method of claim 17, wherein: Receiving the coded assistance information includes receiving the coded assistance information via a control channel or a data channel.

20. The method according to claim 17, wherein: The self-learning neural network is trained via one of supervised learning or reinforcement learning to optimize a cost or performance metric.

21. A wireless device comprising: A processor configured with processor-executable instructions to perform operations including: receiving coded assistance information from an access point; as well as Using the encoded assistance information to update one or more behaviors of the wireless device, wherein the wireless device is not configured to decode the encoded assistance information, and wherein using the encoded assistance information comprises: using the encoded assistance information as an input to a self-learning neural network.

22. The wireless device of claim 21, wherein: The processor is also configured with processor-executable instructions to perform operations such that: using the encoded auxiliary information to update one or more behaviors of the wireless device includes: providing the received encoded auxiliary information as input to a wireless device processing block, and updating one or more behaviors of the wireless device based on the encoded auxiliary information.

23. The wireless device of claim 21, wherein: The processor is also configured with processor-executable instructions to perform operations such that: Receiving the coded assistance information includes receiving the coded assistance information via a control channel or a data channel.

24. The wireless device of claim 21, wherein: The processor is also configured with processor-executable instructions to perform operations such that the self-learning neural network is trained via one of supervised learning or reinforcement learning to optimize a cost or performance metric.

25. A non-transitory processor-readable medium having processor-executable instructions stored thereon, the processor-executable instructions configured to cause a processor of a wireless device to perform operations comprising: receiving coded assistance information from an access point; and using the encoded assistance information to update one or more behaviors of the wireless device, wherein: The wireless device is not configured to decode the coded assistance information, and wherein using the coded assistance information comprises using the coded assistance information as input to a self-learning neural network.

26. The non-transitory processor-readable medium of claim 25, wherein: The stored processor-executable instructions are also configured to cause the processor of the wireless device to perform operations such that using the encoded auxiliary information to update one or more behaviors of the wireless device includes: providing the received encoded auxiliary information as input to the wireless device processing block, and updating one or more behaviors of the wireless device based on the encoded auxiliary information.

27. The non-transitory processor-readable medium of claim 25, wherein: The stored processor-executable instructions are further configured to cause the processor of the wireless device to perform operations such that: Receiving the coded assistance information includes receiving the coded assistance information via a control channel or a data channel.

28. The non-transitory processor-readable medium of claim 25, wherein: The stored processor-executable instructions are further configured to cause the processor of the wireless device to perform operations such that the self-learning neural network is trained via one of supervised learning or reinforcement learning to optimize a cost or performance metric.

29. A wireless device comprising: means for receiving coded assistance information from an access point; as well as Means for using the encoded assistance information to update one or more behaviors of the wireless device, wherein the wireless device is not configured to decode the encoded assistance information, and wherein using the encoded assistance information comprises using the encoded assistance information as an input to a self-learning neural network.

30. The wireless device of claim 29, wherein: The means for using the coded assistance information to update one or more behaviors of the wireless device includes means for providing the received coded assistance information as input to a wireless device processing block and updating one or more behaviors of the wireless device based on the coded assistance information.

31. The wireless device of claim 29, wherein: Means for receiving the coded assistance information includes means for receiving the coded assistance information via a control channel or a data channel.

32. The wireless device of claim 29, wherein: The self-learning neural network is trained via one of supervised learning or reinforcement learning to optimize a cost or performance metric.

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