Equitting machine learning model based on observed atypical data

Abstract

Techniques and systems for wireless communication are provided. In some examples, a system receives input data for use by a first machine learning (ML) engine to generate an output result. The system may also receive information associated with a first training data set for the first ML engine. The system may also determine a first magnitude of diversity between the received input data and the first training data set based on the received information associated with the first training data set. The system may also determine whether to continue to use the first ML engine to generate the output result based on a comparison of the determined first magnitude of dissimilarity to a first dissimilarity threshold.

Description

Technical Field

[0001] The present disclosure generally relates to machine learning (ML) systems. For example, aspects of the present disclosure relate to systems and techniques for retiring (e.g., stopping use of) ML models based on observed atypical data. Background Art

[0002] Wireless communication systems are deployed to provide a variety of telecommunication and data services including telephone, video, data, messaging, and broadcasting. Broadband wireless communication systems have evolved over several generations, including first generation analog wireless telephone service (1G), second generation (2G) digital wireless telephone service (including transitional 2.5G networks), third generation (3G) high-speed data wireless devices with Internet capabilities, and fourth generation (4G) services (e.g., Long Term Evolution (LTE), WiMax). Examples of wireless communication systems include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, global system for mobile communications (GSM) systems, and the like. Other wireless communication technologies include 802.11 Wi-Fi, Bluetooth, and the like.

[0003] The fifth generation (5G) mobile standard calls for higher data transfer speeds, a greater number of connections, and better coverage, among other improvements. According to the Next Generation Mobile Networks Alliance, the 5G standard (also known as "new radio" or "NR") is designed to provide data rates of tens of megabits per second to each of tens of thousands of users, and 1 gigabit per second to dozens of employees on an office floor. To support large sensor deployments, hundreds of thousands of simultaneous connections should be supported. Artificial intelligence (AI) and ML-based algorithms can be incorporated into 5G and future standards to improve telecommunications and data services. Summary of the invention

[0004] The following presents a simplified summary of the invention related to one or more aspects disclosed herein. Therefore, the following summary of the invention should neither be considered as an exhaustive overview related to all conceived aspects, nor should it be considered to identify key or decisive elements related to all conceived aspects or to delineate the scope associated with any particular aspect. Therefore, the following overview presents certain concepts related to one or more aspects of the mechanisms disclosed herein in a simplified form before the detailed description presented below.

[0005] Described herein are systems and techniques for controlling the use of an AI / ML-based model (which may be referred to simply as an ML model) based on similarity of input data to data used to train the ML model. For example, according to various aspects described herein, the systems and techniques may include determining similarity and / or dissimilarity measures between the input data and the data used to train the ML model. Use of the ML model may be stopped (or exited) based on a comparison of the similarity and / or dissimilarity measures to a threshold value.

[0006] In an illustrative example, an apparatus for wireless communication is provided, the apparatus comprising: at least one memory; and at least one processor (e.g., implemented in a circuit), the at least one processor coupled to the at least one memory and configured to: receive input data for use by a first machine learning (ML) engine to generate an output result. The at least one processor is also configured to: receive information associated with a first training data set for the first ML engine. The at least one processor is also configured to: determine a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set. The at least one processor is also configured to: determine whether to continue using the first ML engine to generate the output result based on a comparison of the determined first amount of dissimilarity with a first dissimilarity threshold.

[0007] In another example, a method for wireless communication is provided. The method includes receiving input data for use by a first machine learning (ML) engine to generate an output result. The method also includes receiving information associated with a first training data set for the first ML engine. The method also includes determining a first dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set. The method also includes determining whether to continue using the first ML engine to generate the output result based on a comparison of the determined first dissimilarity to a first dissimilarity threshold.

[0008] In another example, a non-transitory computer-readable storage medium is provided, the non-transitory computer-readable storage medium including instructions stored thereon, which when executed by at least one processor causes the at least one processor to: receive input data for use by a first machine learning (ML) engine to generate an output result. The instructions also cause the at least one processor to: receive information associated with a first training data set for the first ML engine. The instructions also cause the at least one processor to: determine a first dissimilarity amount between the received input data and the first training data set based on the received information associated with the first training data set. The instructions also cause the at least one processor to: determine whether to continue using the first ML engine to generate the output result based on a comparison of the determined first dissimilarity amount with a first dissimilarity threshold.

[0009] In another example, an apparatus for wireless communication is provided. The apparatus includes: a component for receiving input data for use by a first machine learning (ML) engine to generate an output result; a component for receiving information associated with a first training data set for the first ML engine; a component for determining a first dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set; and a component for determining whether to continue using the first ML engine to generate the output result based on a comparison of the determined first dissimilarity with a first dissimilarity threshold.

[0010] Aspects generally include methods, apparatus, systems, computer program products, non-transitory computer readable media, user equipment, base stations, wireless communication devices and / or processing systems as fully described herein with reference to and as illustrated by the accompanying drawings and description.

[0011] The features and technical advantages of examples according to the present disclosure have been outlined quite extensively above so that the following specific embodiments may be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples may be easily used as a basis for modifying or designing other structures for achieving the same purpose of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. The characteristics of the concepts disclosed herein, both in terms of their organization and method of operation, and the associated advantages will be better understood by considering the following description in conjunction with the accompanying drawings. Each of the figures in the accompanying drawings is provided for the purpose of illustration and description and not as a definition of limitations to the claims.

[0012] Although various aspects are described in the present disclosure by illustrating some examples, it will be understood by those skilled in the art that such aspects can be implemented in many different arrangements and scenarios. The technology described herein can be implemented using different platform types, devices, systems, shapes, sizes and / or packaging arrangements. For example, some aspects can be implemented via integrated chip implementations or other devices based on non-module components (e.g., end-user devices, vehicles, communication equipment, computing equipment, industrial equipment, retail / shopping equipment, medical equipment and / or artificial intelligence devices). Various aspects can be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components and / or system-level components. The equipment incorporating the various aspects and features described may include additional components and features for implementing and practicing the various aspects claimed and described. For example, the transmission and reception of wireless signals may include one or more components for analog and digital purposes (e.g., hardware components, including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders and / or summers). The various aspects described herein are intended to be practiced in various devices, components, systems, distributed arrangements and / or end-user devices of various sizes, shapes and configurations.

[0013] Other objects and advantages associated with the various aspects disclosed herein will be apparent to those skilled in the art based on the drawings and detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Examples of various specific implementations are described in detail below with reference to the following drawings:

[0015] Figure 1 is a block diagram illustrating an example of a wireless communication network according to some examples;

[0016] Figure 2 is a diagram illustrating a design of a base station and a user equipment (UE) device according to some examples, which enables sending and processing of signals exchanged between the UE and the base station;

[0017] Figure 3 is a diagram illustrating an example of a decomposed base station according to some examples;

[0018] Figure 4 is a block diagram illustrating components of user equipment according to some examples;

[0019] Figure 5 illustrates an example architecture of a neural network that may be used in accordance with some aspects of the present disclosure;

[0020] Figure 6 is a block diagram illustrating an ML engine according to aspects of the present disclosure;

[0021] Figure 7 is a block diagram illustrating a technique for exiting a first ML model based on observed atypical data in accordance with aspects of the present disclosure;

[0022] Figure 8 is a flow chart illustrating a process for exiting a first ML model based on observed atypical data in accordance with aspects of the present disclosure; and

[0023] Fig. 9 is a diagram illustrating an example of a system for implementing certain aspects of the present technology. DETAILED DESCRIPTION

[0024] Some aspects and embodiments of the present disclosure are provided below. Some of these aspects and embodiments can be applied independently, and some of them can be applied in combination, which is obvious to those skilled in the art. In the following description, specific details are set forth for explanation purposes in order to provide a thorough understanding of each embodiment of the application. However, it will be apparent that each embodiment can be put into practice without these specific details. Each drawing and description are not intended to be restrictive.

[0025] The following description only provides example embodiments, and is not intended to limit the scope, applicability or configuration of the present disclosure. On the contrary, the following description of the exemplary embodiments will provide an enabling description for realizing the exemplary embodiments to those skilled in the art. It should be understood that various changes can be made to the function and arrangement of elements without departing from the essence and scope of the present application as set forth in the appended claims.

[0026] Various techniques are provided with reference to wireless technologies (e.g., third generation partnership project (3GPP) 5G / new radio (NR) standards) to provide improvements to wireless communications. An example of such a technique is to apply AI / ML-based algorithms to improve various aspects of wireless communications. As an example, wireless communications may use discontinuous reception (DRX). In some cases, DRX may include an enabled state (e.g., on-duration) and a sleep state (e.g., power-off sleep). In the enabled state, a user equipment (UE) or terminal monitors a downlink channel (e.g., a physical downlink control channel (PDCCH)) and receives corresponding data (e.g., on a physical downlink shared channel (PDSCH)). In the sleep state, the UE or terminal turns off (e.g., powers off) a receiving unit and no longer monitors a downlink channel (e.g., PDCCH) to achieve energy saving. In some cases, a wireless network may determine when a UE or terminal may enter a sleep state, and the wireless network may send such a determined indication to the UE or terminal via a wireless device (such as a wireless node). In other cases, a UE or terminal may determine when a UE or terminal may enter a sleep state. In some cases, the AI / ML algorithm may be used to determine when the UE or terminal may enter a sleep state. In some cases, it may be desirable to stop using the AI / ML algorithm and, in some cases, change to a legacy technology for determining when the UE or terminal may enter a sleep state. Although techniques for exiting an ML model based on observed atypical data are described herein in conjunction with wireless technology, it should be understood that such techniques are not intended to be limited to application to wireless technology, but may be applied to other technologies that utilize ML models.

[0027] Described herein are systems, apparatus, electronic devices, methods (also referred to as processes), and computer-readable media (collectively referred to herein as "systems and techniques") for providing a technique for determining when to stop using (or exit) an ML model based on data to be input to the ML model during inference of the ML model (after the ML model has been trained) (referred to as input data). In some cases, based on determining to stop using (or exit) the ML model, the system and technique may revert to, for example, a non-ML-based algorithm or a possibly different ML model. In some aspects, the system and technique may determine similarity and / or dissimilarity measures between the input data and the data used to train the ML model. The system and technique may compare the similarity and / or dissimilarity measures to a threshold value, and based on the comparison, may determine to stop using (or exit) the ML model.

[0028] Additional aspects of the disclosure are described in more detail below.

[0029] Wireless communication networks are deployed to provide various communication services, such as voice, video, packet data, messaging, broadcast, etc. Wireless networks can support both access links for communication between wireless devices. An access link can refer to any communication link between a client device (e.g., user equipment (UE), station (STA), or other client device) and a base station (e.g., 3GPP gNodeB (gNB) for 5G / NR, 3GPP eNodeB (eNB) for LTE, Wi-Fi access point (AP), or other base station) or a decomposed base station (e.g., a central unit, a distributed unit, and / or a radio unit). In one example, the access link between the UE and the 3GPP gNB can be through the Uu interface. In some cases, the access link can support uplink signaling, downlink signaling, connection procedures, etc.

[0030] In some aspects, the wireless communication network may be implemented using one or more modulation schemes. For example, the wireless communication network may be implemented using a quadrature amplitude modulation (QAM) scheme such as 16QAM, 32QAM, 64QAM, etc.

[0031] As used herein, the terms "user equipment" (UE) and "network entity" are not intended to be dedicated to or otherwise limited to any particular radio access technology (RAT), unless otherwise specified. In general, a UE can be any wireless communication device (e.g., a mobile phone, a router, a tablet computer, a laptop computer, and / or a tracking device, etc.), a wearable device (e.g., a smart watch, smart glasses, a wearable ring, and / or an extended reality (XR) device (such as a virtual reality (VR) headset, an augmented reality (AR) headset or glasses, or a mixed reality (MR) headset)), a vehicle (e.g., a car, a motorcycle, a bicycle, etc.), and / or an Internet of Things (IoT) device, etc., for a user to communicate on a wireless communication network. A UE can be mobile or can be stationary (e.g., at certain times) and can communicate with a radio access network (RAN). As used herein, the term "UE" may be interchangeably referred to as an "access terminal" or "AT," "client device," "wireless device," "subscriber equipment," "subscriber terminal," "subscriber station," "user terminal" or "UT," "mobile device," "mobile terminal," "mobile station," or variations thereof. Generally speaking, a UE may communicate with a core network via a RAN, and through the core network, the UE may connect to external networks such as the Internet and to other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for the UE, such as through a wired access network, a wireless local area network (WLAN) network (e.g., based on IEEE 802.11 communication standards, etc.), and the like.

[0032] The network entity may be implemented in a converged or monolithic base station architecture, or alternatively, in a disaggregated base station architecture, and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near-RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. A base station (e.g., having a converged / monolithic base station architecture or a disaggregated base station architecture) may operate in communication with a UE according to one of several RATs depending on the network in which the base station is deployed, and may alternatively be referred to as an access point (AP), a network node, a Node B (NB), an evolved Node B (eNB), a next generation eNB (ng-eNB), a new radio (NR) Node B (also referred to as a gNB or gNodeB), etc. A base station may be primarily used to support wireless access for UEs, including supporting data, voice, and / or signaling connections for the supported UEs. In some systems, a base station may provide edge node signaling functionality, while in other systems, a base station may provide additional control and / or network management functionality. The communication link through which the UE can transmit signals to the base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). The communication link through which the base station can transmit signals to the UE is called a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, or a forward traffic channel, etc.). As used herein, the term traffic channel (TCH) may refer to an uplink, a reverse or downlink, and / or a forward traffic channel.

[0033] The term "network entity" or "base station" (e.g., having an aggregated / monolithic base station architecture or a decomposed base station architecture) may refer to a single physical transmit receive point (TRP) or multiple physical TRPs that may or may not be co-located. For example, where the term "network entity" or "base station" refers to a single physical TRP, the physical TRP may be a base station antenna corresponding to a cell (or several cell sectors) of the base station. Where the term "network entity" or "base station" refers to multiple co-located physical TRPs, these physical TRPs may be antenna arrays of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). Where the term "base station" refers to multiple non-co-located physical TRPs, the physical TRP may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transmission medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, the non-co-located physical TRPs may be a serving base station that receives measurement reports from a UE and a neighbor base station whose reference radio frequency (RF) signal (or "reference signal" for short) the UE is measuring. Because, as used herein, a TRP is the point through which a base station transmits and receives wireless signals, references to transmitting from or receiving at a base station should be understood to refer to the specific TRP of a base station.

[0034] In some specific implementations of supporting UE positioning, a network entity or base station may not support wireless access by the UE (e.g., may not support data, voice, and / or signaling connections with respect to the UE), but may instead send a reference signal to be measured by the UE to the UE, and / or may receive and measure a signal sent by the UE. Such a base station may be referred to as a positioning beacon (e.g., in the case of sending a signal to the UE) and / or as a position measurement unit (e.g., in the case of receiving and measuring a signal from the UE).

[0035] RF signals include electromagnetic waves of a given frequency that transmit information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single "RF signal" or multiple "RF signals" to a receiver. However, due to the propagation characteristics of RF signals through multipath channels, a receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. The same transmitted RF signal on different paths between a transmitter and a receiver may be referred to as a "multipath" RF signal. As used herein, an RF signal may also be referred to as a "wireless signal" or simply a "signal" where it is clear from the context that the term "signal" refers to a wireless signal or an RF signal.

[0036] Various aspects of the systems and techniques described herein are discussed below with respect to the accompanying drawings. According to various aspects, Figure 1 An example of a wireless communication system 100 is illustrated. The wireless communication system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 and various UEs 104. In some aspects, the base station 102 may also be referred to as a "network entity" or a "network node". One or more of the base stations 102 may be implemented in an aggregated or monolithic base station architecture. Additionally or alternatively, one or more of the base stations 102 may be implemented in a decomposed base station architecture and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. The base station 102 may include a macro cell base station (a high-power cellular base station) and / or a small cell base station (a low-power cellular base station). In one aspect, the macro cell base station may include an eNB and / or an ng-eNB (where the wireless communication system 100 corresponds to a long-term evolution (LTE) network), or a gNB (where the wireless communication system 100 corresponds to an NR network), or a combination of both, and the small cell base station may include a femto cell, a pico cell, a micro cell, etc.

[0037] The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) through a backhaul link 122, and interface to one or more location servers 172 (which may be part of the core network 170 or may be external to the core network 170) through the core network 170. Among other functions, the base stations 102 may perform functions related to one or more of the following: transferring user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment tracking, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through the EPC or 5GC) through a backhaul link 134 (which may be wired and / or wireless).

[0038] Base station 102 can communicate wirelessly with UE 104. Each of base stations 102 can provide communication coverage for a corresponding geographic coverage area 110. In one aspect, base station 102 in each coverage area 110 can support one or more cells. A "cell" is a logical communication entity used to communicate with a base station (e.g., on a certain frequency resource, referred to as a carrier frequency, component carrier, carrier, frequency band, etc.), and can be associated with an identifier (e.g., a physical cell identifier (PCI), a virtual cell identifier (VCI), a cell global identifier (CGI)) to distinguish cells operating via the same or different carrier frequencies. In some cases, different cells can be configured according to different protocol types (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB) or other protocol types) that can provide access to different types of UEs. Because a cell is supported by a specific base station, the term "cell" can refer to either or both of the logical communication entity and the base station supporting it, depending on the context. In addition, since a TRP is typically a physical transmission point of a cell, the terms "cell" and "TRP" can be used interchangeably. In some cases, the term "cell" may also refer to a geographic coverage area (eg, a sector) of a base station, as long as a carrier frequency can be detected within some portion of the geographic coverage area 110 and is used for communications within that portion.

[0039] Although the geographic coverage areas 110 of neighboring macrocell base stations 102 may partially overlap (e.g., in a handover area), some of the geographic coverage areas 110 may substantially overlap with the larger geographic coverage area 110. For example, a small cell base station 102' may have a coverage area 110' that substantially overlaps with the coverage areas 110 of one or more macrocell base stations 102. A network that includes both small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB), which may provide services to a restricted group called a closed subscriber group (CSG).

[0040] The communication link 120 between the base station 102 and the UE 104 may include uplink (also known as reverse link) transmissions from the UE 104 to the base station 102 and / or downlink (also known as forward link) transmissions from the base station 102 to the UE 104. The communication link 120 may use MIMO antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may be over one or more carrier frequencies. The allocation of carriers may be asymmetric for the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink than to the uplink).

[0041] The wireless communication system 100 may also include a WLAN AP 150 in communication with a WLAN station (STA) 152 via a communication link 154 in an unlicensed spectrum (e.g., 5 gigahertz (GHz)). When communicating in the unlicensed spectrum, the WLAN STA 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or a listen-before-talk (LBT) procedure before communicating to determine whether a channel is available. In some examples, the wireless communication system 100 may include a device (e.g., UE, etc.) that communicates with one or more UEs 104, base stations 102, APs 150, etc. using an ultra-wideband (UWB) spectrum. The UWB spectrum may range from 3.1 GHz to 10.5 GHz.

[0042] The small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in the unlicensed spectrum, the small cell base station 102' can adopt LTE or NR technology and use the same 5GHz unlicensed spectrum used by the WLAN AP 150. The small cell base station 102' using LTE and / or 5G in the unlicensed spectrum can boost the coverage of the access network and / or increase the capacity of the access network. NR in the unlicensed spectrum can be referred to as NR-U. LTE in the unlicensed spectrum can be referred to as LTE-U, Licensed Assisted Access (LAA) or MulteFire.

[0043] The wireless communication system 100 may also include a millimeter wave (mmW) base station 180, which can operate at mmW frequencies and / or near mmW frequencies to communicate with UE 182. The mmW base station 180 can be implemented in an aggregated or monolithic base station architecture, or alternatively, in a decomposed base station architecture (e.g., including one or more of CU, DU, RU, near RT RIC, or non-RT RIC). Extremely high frequency (EHF) is part of the RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz, with a wavelength between 1 mm and 10 mm. The radio waves in this band may be referred to as millimeter waves. Near mmW can be extended down to a frequency of 3 GHz with a wavelength of 100 mm. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, which is also referred to as centimeter waves. Communications using mmW and / or near mmW radio frequency bands have high path loss and a relatively short range. The mmW base station 180 and the UE 182 may utilize beamforming (transmit and / or receive) on the mmW communication link 184 to compensate for the extremely high path loss and short range. In addition, it should be understood that in alternative configurations, one or more base stations 102 may also transmit using mmW or near-mmW and beamforming. Therefore, it should be understood that the foregoing illustrations are merely examples and should not be construed as limiting the various aspects disclosed herein.

[0044] In some aspects related to 5G, the spectrum in which a wireless network node or entity (e.g., base station 102 / 180, UE 104 / 182) operates is divided into multiple frequency ranges: FR1 (from 450 megahertz (MHz) to 6000 MHz), FR2 (from 24250 MHz to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2). In a multi-carrier system (such as 5G), one of the carrier frequencies is referred to as a "primary carrier" or "anchor carrier" or "primary serving cell" or "PCell", and the remaining carrier frequencies are referred to as "secondary carriers" or "secondary serving cells" or "SCells". In carrier aggregation, the anchor carrier is a carrier operating on the primary frequency (e.g., FR1) used by the UE 104 / 182 and the cell in which the UE 104 / 182 performs an initial radio resource control (RRC) connection establishment procedure or initiates an RRC connection re-establishment procedure. The primary carrier carries all common control channels as well as UE-specific control channels and can be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2), which can be configured and used to provide additional radio resources once an RRC connection is established between the UE 104 and the anchor carrier. In some cases, the secondary carrier may be a carrier in an unlicensed frequency. The secondary carrier may contain only necessary signaling information and signals, for example, since the primary uplink carrier and the primary downlink carrier are typically UE-specific, those signaling information and signals specific to the UE may not be present in the secondary carrier. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. This is also true for the uplink primary carrier. The network is able to change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Because a "serving cell" (whether PCell or SCell) corresponds to a carrier frequency and / or component carrier through which some base stations are communicating, the terms "cell", "serving cell", "component carrier", "carrier frequency", etc. may be used interchangeably.

[0045] For example, still refer to Figure 1, one of the frequencies used by the macrocell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies used by the macrocell base station 102 and / or the mmW base station 180 may be secondary carriers ("SCells"). In carrier aggregation, the base station 102 and / or the UE 104 may use spectrum with a bandwidth of up to Y MHz (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz) per carrier, with up to a total of Yx MHz (x component carriers) in each direction for transmission. The component carriers may be adjacent to each other or may not be adjacent to each other in the spectrum. The allocation of carriers may be asymmetric for the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink than the uplink). The simultaneous transmission and / or reception of multiple carriers enables the UE 104 / 182 to significantly increase its data transmission and / or reception rate. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically result in a doubling of the data rate (i.e., 40 MHz) compared to the data rate obtained with a single 20 MHz carrier.

[0046] In order to operate on multiple carrier frequencies, the base station 102 and / or the UE 104 may be equipped with multiple receivers and / or transmitters. For example, the UE 104 may have two receivers, namely "receiver 1" and "receiver 2", where "receiver 1" is a multi-band receiver that can be tuned to band (i.e., carrier frequency) "X" or band "Y", and "receiver 2" is a single-band receiver that can be tuned to only band "Z". In this example, if the UE 104 is being served in band "X", the band "X" will be referred to as the PCell or active carrier frequency, and "receiver 1" will need to tune from band "X" to band "Y" (SCell) to measure band "Y" (and vice versa). In contrast, regardless of whether the UE 104 is being served in band "X" or band "Y", due to the separate "receiver 2", the UE 104 can measure band "Z" without interrupting the service on band "X" or band "Y".

[0047] The wireless communication system 100 may also include a UE 164 that may communicate with the macrocell base station 102 over the communication link 120 and / or communicate with the mmW base station 180 over the mmW communication link 184. For example, the macrocell base station 102 may support a PCell and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0048] The wireless communication system 100 may also include one or more UEs, such as UE 190, which are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “side links”). Figure 1 In the example of FIG. 1 , UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of base stations 102 (e.g., UE 190 can indirectly obtain cellular connectivity through the D2D P2P link), and has a D2D P2P link 194 with WLAN STA 152 connected to WLAN AP 150 (UE 190 can indirectly obtain WLAN-based Internet connectivity through the D2D P2P link). In an example, D2D P2P links 192 and 194 can use any well-known D2D RAT (such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), etc.) to support.

[0049] Figure 2 A block diagram of a design of a base station 102 and a UE 104 is shown that enables sending and processing of signals exchanged between the UE and the base station according to some aspects of the present disclosure. Design 200 includes components of the base station 102 and the UE 104, which may be Figure 1 1. A base station in base station 102 and a UE in UE 104. Base station 102 may be equipped with T antennas 234a through 234t, and UE 104 may be equipped with R antennas 252a through 252r, where in general T≥1 and R≥1.

[0050] At the base station 102, the transmit processor 220 may receive data for one or more UEs from the data source 212, select one or more modulation and coding schemes (MCS) for each UE based at least in part on a channel quality indicator (CQI) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS selected for the UE, and provide data symbols for all UEs. The transmit processor 220 may also process system information (e.g., for semi-static resource partitioning information (SRPI), etc.) and control information (e.g., CQI requests, grants, upper layer signaling, etc.), and provide overhead symbols and control symbols. The transmit processor 220 may also generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS)) and synchronization signals (e.g., primary synchronization signals (PSS) and secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols where applicable, and may provide T output symbol streams to T modulators (MOD) 232a to 232t. The modulators 232a to 232t are shown as combined modulator-demodulators (MOD-DEMOD). In some cases, the modulator and demodulator may be separate components. Each modulator in the modulators 232a to 232t may process a corresponding output symbol stream (e.g., for an orthogonal frequency division multiplexing (OFDM) scheme, etc.) to obtain an output sample stream. Each modulator in the modulators 232a to 232t may further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. T downlink signals may be sent from the modulators 232a to 232t via T antennas 234a to 234t, respectively. According to certain aspects described in greater detail below, position encoding may be utilized to generate a synchronization signal to convey additional information.

[0051] At UE 104, antennas 252a to 252r may receive downlink signals from base station 102 and / or other base stations and may provide received signals to demodulators (DEMOD) 254a to 254r, respectively. Demodulators 254a to 254r are shown as combined modulator-demodulators (MOD-DEMOD). In some cases, modulators and demodulators may be separate components. Each demodulator in demodulators 254a to 254r may condition (e.g., filter, amplify, down-convert, and digitize) received signals to obtain input samples. Each demodulator in demodulators 254a to 254r may further process input samples (e.g., for OFDM, etc.) to obtain received symbols. MIMO detector 256 may obtain received symbols from all R demodulators 254a to 254r, perform MIMO detection on these received symbols where applicable, and provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for the UE 104 to a data sink 260, and provide decoded control information and system information to a controller / processor 280. The channel processor may determine a reference signal received power (RSRP), a received signal strength indicator (RSSI), a reference signal received quality (RSRQ), a channel quality indicator (CQI), etc.

[0052] On the uplink, at the UE 104, a transmit processor 264 may receive and process data from a data source 262 and control information (e.g., for reports including RSRP, RSSI, RSRQ, CQI, etc.) from a controller / processor 280. The transmit processor 264 may also generate reference symbols for one or more reference signals (e.g., based at least in part on a beta value or set of beta values ​​associated with the one or more reference signals). The symbols from the transmit processor 264 may be precoded by the TX MIMO processor 266, if applicable, further processed by the modulators 254a through 254r (e.g., for DFT-s-OFDM, CP-OFDM, etc.), and sent to the base station 102. At the base station 102, uplink signals from the UE 104 and other UEs may be received by antennas 234a to 234t, processed by demodulators 232a to 232t, detected by a MIMO detector 236 where applicable, and further processed by a receive processor 238 to obtain decoded data and control information transmitted by the UE 104. The receive processor 238 may provide the decoded data to a data sink 239 and the decoded control information to a controller (processor) 240. The base station 102 may include a communication unit 244 and communicate with the network controller 231 via the communication unit 244. The network controller 231 may include a communication unit 294, a controller / processor 290, and a memory 292.

[0053] In some aspects, one or more components of the UE 104 may be included in a housing. The controller 240 of the base station 102, the controller / processor 280 of the UE 104, and / or Figure 2 Any other component of may perform one or more techniques associated with implicit UCI β value determination for NR.

[0054] Memories 242 and 282 may store data and program codes for base station 102 and UE 104, respectively. A scheduler 246 may schedule UEs for data transmission on the downlink, uplink, and / or sidelink.

[0055] In some aspects, the deployment of a communication system such as a 5G New Radio (NR) system can be arranged with various components or components in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a radio access network (RAN) node, a core network node, a network element, or a network equipment (such as a base station (BS), or one or more units (or one or more components) that perform base station functionality) can be implemented in an aggregated or decomposed architecture. For example, a BS (such as a Node B (NB), an evolved NB (eNB), an NR BS, a 5G NB, an access point (AP), a transmit receive point (TRP), or a cell, etc.) can be implemented as an aggregated base station (also referred to as an independent BS or a monolithic BS) or a decomposed base station.

[0056] A converged base station may be configured to utilize a radio protocol stack physically or logically integrated within a single RAN node. A decomposed base station may be configured to utilize a protocol stack physically or logically distributed between two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed in one or more other RAN nodes. A DU may be implemented to communicate with one or more RUs. Each of a CU, a DU, and a RU may also be implemented as a virtual unit, i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0057] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, a disaggregated base station may be used in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (network configurations such as those initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Decomposition may include distributing functionality across two or more units at various physical locations, as well as virtually distributing functionality of at least one unit, which may enable flexibility in network design. Individual units of a disaggregated base station or disaggregated RAN architecture may be configured for wired or wireless communication with at least one other unit.

[0058] Figure 3 A diagram illustrating an example decomposed base station 300 architecture is shown. The decomposed base station 300 architecture may include one or more central units (CUs) 310 that may communicate directly with a core network 320 via a backhaul link, or indirectly with the core network 320 through one or more decomposed base station units, such as a near real-time (near-RT) RAN intelligent controller (RIC) 325 via an E2 link, or a non-real-time (non-RT) RIC 315 associated with a service management and orchestration (SMO) framework 305, or both. The CU 310 may communicate with one or more distributed units (DUs) 330 via respective midhaul links, such as an F1 interface. The DU 330 may communicate with one or more radio units (RUs) 340 via respective fronthaul links. The RU 340 may communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, a UE 104 may be served simultaneously by multiple RUs 340.

[0059] Each of the units (e.g., CU 310, DU 330, RU 340, and near-RT RIC 325, non-RT RIC 315, and SMO framework 305) may include or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each of these units or an associated processor or controller that provides instructions to the communication interface of these units may be configured to communicate with one or more of the other units via a transmission medium. For example, these units may include a wired interface configured to receive a signal or send a signal to one or more of the other units via a wired transmission medium. Additionally, these units may include a wireless interface that may include a receiver, a transmitter, or a transceiver (such as a radio frequency (RF) transceiver) configured to receive a signal or send a signal to one or more of the other units via a wireless transmission medium, or both.

[0060] In some aspects, CU 310 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function may be implemented with an interface configured to communicate signals with other control functions hosted by CU 310. CU 310 may be configured to handle user plane functionality (i.e., central unit-user plane (CU-UP)), control plane functionality (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some specific implementations, CU 310 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface such as an E1 interface. As needed, CU 310 may be implemented to communicate with DU 330 for network control and signaling.

[0061] DU 330 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RU 340. In some aspects, DU 330 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) at least in part according to a functional split such as that defined by the Third Generation Partnership Project (3GPP). In some aspects, DU 330 may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by DU 330 or with control functions hosted by CU 310.

[0062] The lower layer functionality may be implemented by one or more RUs 340. In some deployments, a RU 340 controlled by a DU 330 may correspond to a logical node that hosts RF processing functions or low PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, based at least in part on a functional split (such as a lower layer functional split). In such an architecture, the RU 340 may be implemented to handle over-the-air (OTA) communications with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable the DU 330 and CU 310 to be implemented in a cloud-based RAN architecture such as a vRAN architecture.

[0063] The SMO framework 305 may be configured to support RAN deployment and configuration of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 305 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 390) to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, and near-RT RIC 325. In some specific implementations, the SMO framework 305 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 311) via the O1 interface. Additionally, in some specific implementations, the SMO framework 305 may communicate directly with one or more RUs 340 via the O1 interface. The SMO framework 305 may also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305 .

[0064] The non-RT RIC 315 may be configured to include logic functions that implement non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 325. The non-RT RIC 315 may be coupled to or communicate with the near-RT RIC 325 (such as via an A1 interface). The near-RT RIC 325 may be configured to include logic functions that implement near-real-time control and optimization of RAN elements and resources via data collection and actions through an interface (such as via an E2 interface) that connects one or more CUs 310, one or more DUs 330, or both, and the O-eNB with the near-RT RIC 325.

[0065] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 325 and may be received from a non-network data source or from a network function at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or the near-RT RIC 325 may be configured to adjust RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 305 (such as via reconfiguration of O1) or via the creation of RAN management policies (such as A1 policies).

[0066] Figure 4 An example of a computing system 470 of a wireless device 407 is illustrated. The wireless device 407 may include a client device such as a UE (e.g., UE 104, UE 152, UE 190) or other type of device that can be used by an end user (e.g., a station (STA) configured to communicate using a Wi-Fi interface). For example, the wireless device 407 may include a mobile phone, a router, a tablet computer, a laptop computer, a tracking device, a wearable device (e.g., a smart watch, glasses, an extended reality (XR) device (such as a virtual reality (VR), augmented reality (AR) or mixed reality (MR) device), etc.), an Internet of Things (IoT) device, an access point, and / or another device configured to communicate via a wireless communication network. The computing system 470 includes software and hardware components that can be electrically coupled or communicatively coupled via a bus 489 (or can communicate in other ways, as the case may be). For example, the computing system 470 includes one or more processors 484. One or more processors 484 may include one or more CPUs, ASICs, FPGAs, APs, GPUs, VPUs, NSPs, microcontrollers, dedicated hardware, any combination thereof, and / or other processing devices or systems. One or more processors 484 may use a bus 489 to communicate between cores and / or with one or more memory devices 486.

[0067] The computing system 470 may also include one or more memory devices 486, one or more digital signal processors (DSPs) 482, one or more subscriber identity modules (SIMs) 474, one or more modems 476, one or more wireless transceivers 478, one or more antennas 487, one or more input devices 472 (e.g., a camera, a mouse, a keyboard, a touch-sensitive screen, a touchpad, a keypad, a microphone, and / or the like), and one or more output devices 480 (e.g., a display, a speaker, a printer, and / or the like).

[0068] In some aspects, computing system 470 may include one or more RF interfaces configured to send and / or receive radio frequency (RF) signals. In some examples, the RF interface may include components such as modem 476, wireless transceiver 478, and / or antenna 487. One or more wireless transceivers 478 may send and receive wireless signals (e.g., signal 488) from one or more other devices via antenna 487, such as other wireless devices, network devices (e.g., base stations such as eNBs and / or gNBs, Wi-Fi access points (APs) such as routers, range extenders, etc.), cloud networks, etc. In some examples, computing system 470 may include multiple antennas or antenna arrays that can facilitate simultaneous transmission and reception functionality. Antenna 487 may be an omnidirectional antenna so that radio frequency (RF) signals can be received from all directions and transmitted in all directions. Wireless signal 488 may be sent via a wireless network. The wireless network may be any wireless network, such as a cellular or telecommunications network (e.g., 3G, 4G, 5G, etc.), a wireless local area network (e.g., a WiFi network), a Bluetooth TM network and / or other networks.

[0069] In some examples, wireless signals 488 may be transmitted directly to other wireless devices using sidelink communications (e.g., using a PC5 interface, using a DSRC interface, etc.). Wireless transceiver 478 may be configured to transmit RF signals via antenna 487 for performing sidelink communications according to one or more transmit power parameters that may be associated with one or more regulatory modes. Wireless transceiver 478 may also be configured to receive sidelink communication signals having different signal parameters from other wireless devices.

[0070] In some examples, one or more wireless transceivers 478 may include an RF front end that includes one or more components such as an amplifier, a mixer for down-converting a signal (also known as a signal multiplier), a frequency synthesizer (also known as an oscillator) that provides a signal to the mixer, a baseband filter, an analog-to-digital converter (ADC), one or more power amplifiers, and other components. The RF front end may generally handle the selection of wireless signals 488 and the conversion of the wireless signals to baseband frequencies or intermediate frequencies, and may convert the RF signals to the digital domain.

[0071] In some cases, computing system 470 may include a coding-decoding device (or CODEC) configured to encode and / or decode data transmitted and / or received using one or more wireless transceivers 478. In some cases, computing system 470 may include an encryption-decryption device or component configured to encrypt and / or decrypt data transmitted and / or received by one or more wireless transceivers 478 (e.g., according to the AES and / or DES standards).

[0072] One or more SIMs 474 may each securely store an International Mobile Subscriber Identity (IMSI) number and associated keys assigned to a user of the wireless device 407. The IMSI and keys may be used to identify and authenticate a subscriber when accessing a network provided by a network service provider or operator associated with the one or more SIMs 474. One or more modems 476 may modulate one or more signals to encode information for transmission using one or more wireless transceivers 478. One or more modems 476 may also demodulate signals received by one or more wireless transceivers 478 in order to decode the transmitted information. In some examples, one or more modems 476 may include a Wi-Fi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and / or other types of modems. One or more modems 476 and one or more wireless transceivers 478 may be used to communicate data of one or more SIMs 474.

[0073] The computing system 470 may also include (and / or be in communication with) one or more non-transitory machine-readable storage media or storage devices (e.g., one or more memory devices 486), which may include, but are not limited to, local and / or network accessible storage, disk drives, drive arrays, optical storage devices, solid-state storage devices such as RAM and / or ROM, which may be programmable, flash-updatable, etc. Such storage devices may be configured to implement any suitable data storage, including, but not limited to, various file systems, database structures, etc.

[0074] In various embodiments, the functionality may be stored as one or more computer program products (e.g., instructions or code) in the memory device 486 and executed by the one or more processors 484 and / or the one or more DSPs 482. The computing system 470 may also include software elements (e.g., located within the one or more memory devices 486) including, for example, an operating system, device drivers, executable libraries, and / or other code, such as one or more applications, which may include computer programs that implement the functionality provided by the various embodiments and / or may be designed to implement methods and / or configure systems, as described herein.

[0075] Figure 5An example architecture of a neural network 500 that can be used according to some aspects of the present disclosure is illustrated. The example architecture of the neural network 500 can be defined by an example neural network description 502 in a neural controller 501. The neural network 500 is an example of a machine learning model that can be deployed and implemented at a base station 102, a central unit (CU) 310, a distributed unit (DU) 330, a radio unit (RU) 340, and / or a UE 104. The neural network 500 can be a feed-forward neural network or any other known or to be developed neural network or machine learning model.

[0076] Neural network description 502 may include a complete specification of neural network 500, including Figure 5 . For example, neural network description 502 may include: a description or specification of the architecture of neural network 500 (e.g., layers, layer interconnects, number of nodes in each layer, etc.); input and output descriptions indicating how inputs and outputs are formed or processed; indications of activation functions in the neural network, operations or filters in the neural network, etc.; neural network parameters such as weights, biases, etc.; and the like.

[0077] Neural network 500 may reflect the neural architecture defined in neural network description 502. Neural network 500 may include any suitable neural or deep learning type of network. In some cases, neural network 500 may include a feedforward neural network. In other cases, neural network 500 may include a recursive neural network, which may have loops that allow information to be carried across nodes when reading inputs. Neural network 500 may include any other suitable neural network or machine learning model. An example includes a convolutional neural network (CNN), which includes an input layer and an output layer, with multiple hidden layers between the input layer and the output layer. The hidden layers of the CNN include a series of hidden layers as described below, such as convolutional layers, nonlinear layers, pooling layers (for downsampling), and fully connected layers. In other examples, neural network 500 may represent any other neural network or deep learning network, such as an autoencoder, a deep belief network (DBN), a recursive neural network (RNN), etc.

[0078] exist Figure 5In a non-limiting example, neural network 500 includes an input layer 503 that can receive one or more input data sets. The input data can be any type of data (e.g., image data, video data, network parameter data, user data, etc.). Neural network 500 can include hidden layers 504A to 504N (hereinafter collectively referred to as "504"). Hidden layer 504 can include n number of hidden layers, where n is an integer greater than or equal to 1. The number of hidden layers can include the number of layers required for the desired processing results and / or presentation intent. In an illustrative example, any of hidden layers 504 can include data representing one or more of the data provided at input layer 503. Neural network 500 also includes an output layer 506 that provides output resulting from the processing performed by hidden layer 504. Output layer 506 can provide output data based on the input data.

[0079] exist Figure 5 In the example of , the neural network 500 is a multi-layer neural network of interconnected nodes. Each node can represent a piece of information. The information associated with these nodes is shared between different layers, and each layer retains the information when processing the information. Information can be exchanged between the nodes through the node-to-node interconnects between the layers. The nodes of the input layer 503 can activate a set of nodes in the first hidden layer 504A. For example, as shown in the figure, each input node of the input layer 503 is connected to each node of the first hidden layer 504A. The nodes of the hidden layer 504A can transform the information by applying an activation function to the information of each input node. Then, the information derived from the transformation can be passed to the nodes of the next hidden layer (e.g., 504B) and can activate the nodes that can be passed to the next hidden layer (e.g., 504B), which can perform their own specified functions. Example functions include convolution, upsampling, data transformation, pooling and / or any other suitable function. The output of the hidden layer (e.g., 504B) can then activate the nodes of the next hidden layer (e.g., 504N), and so on. The output of the last hidden layer may activate one or more nodes of the output layer 506, providing an output at that point. In some cases, although nodes in the neural network 500 (e.g., nodes 508A, 508B, 508C) are shown as having multiple output lines, the node may have a single output and all lines shown as outputs from the node may represent the same output value.

[0080] In some cases, each node or interconnections between nodes may have a weight that is a set of parameters derived from training neural network 500. For example, an interconnection between nodes may represent a piece of information learned about the interconnected nodes. The interconnections may have numerical weights that may be tuned (e.g., based on a training data set), thereby allowing neural network 500 to adapt to inputs and learn as more data is processed.

[0081] The neural network 500 may be pre-trained to process features of the data from the input layer 503 using different hidden layers 504 in order to provide an output through the output layer 506. For example, in some cases, the neural network 500 may use a training process known as back propagation to adjust the weights of the nodes. Back propagation may include a forward pass, a loss function, a backward pass, and a weight update. The forward pass, loss function, backward pass, and parameter update may be performed for one training iteration. The process may be repeated for each training data set for a certain number of iterations until the weights of the layers are accurately tuned (e.g., to meet a configurable threshold determined based on experiments and / or empirical studies).

[0082] More and more ML (e.g., AI) algorithms (e.g., models) are being incorporated into a variety of technologies, including wireless telecommunications standards. Since ML technology is a powerful but still developing field, ML performance can be monitored to ensure the desired behavior. In some cases, ML performance can be monitored based on the results generated from one or more outputs of the ML model. In some cases, the performance of the ML model can refer to how well one or more outputs of a particular ML model perform relative to a given task using performance metrics such as throughput, accuracy of predictions, combinations thereof, and / or other performance metrics. In some cases, the performance of the ML model can be measured relative to a similar performance metric of another algorithm, such as a legacy algorithm or another ML algorithm. In some cases, the performance metric can be related to the parameters of the communication link. As an example, the performance of the ML model can be evaluated based on the throughput of the ML model relative to the expected throughput of the legacy algorithm. As another example, the performance of the ML model can refer to how accurately the predictions made by the ML match the actual results. As a more detailed example, in the case where an ML model is used to predict a DRX cycle, the prediction of the ML model may be monitored based on whether there are transmissions transmitted to the UE while the UE is in a DRX off state, and / or how often the UE exits the DRX off state to monitor transmissions when there are no transmissions to the UE. In the event that the ML model performance drops below one or more threshold levels, the use of the ML model for predicting and scheduling the DRX cycle may be discontinued.

[0083] In some cases, monitoring ML performance based on results adds latency because the ML model first generates a prediction, the prediction is used to implement an action, the result of the action is monitored, feedback on the result is provided, and the feedback is used to evaluate ML performance. In some scenarios, such as for low-latency communications or extended reality applications (e.g., virtual reality, augmented reality, etc.), the latency caused by results-based ML model performance monitoring may be undesirable. In some cases, monitoring the data input to the ML model for determining whether to stop using the ML model can provide lower latency in results-based ML model performance monitoring and solve the latency problem.

[0084] Figure 6 600 . As an example, one or more devices in a wireless system may include the ML engine 600. In some cases, the ML engine 600 may be similar to the neural network 500. In this example, the ML engine 600 includes three parts: an input 602 to the ML engine 600, the ML engine, and an output 604 from the ML engine 600. The input 602 to the ML engine 600 may be data that the ML engine 600 may use to make predictions or otherwise operate on it. As an example, the ML engine 600 configured to select an RF beam may take as input 602 data about current RF conditions, location information, network load, etc. As another example, data related to packets transmitted to a UE may be input 602 to the ML engine 600 configured to predict a DRX schedule for the UE along with historical packet data. In some cases, the output 604 may be a prediction or other information generated by the ML engine 600, and the output 604 may be used to configure the wireless device, adjust settings, parameters, operating modes, etc. Continuing with the previous example, the ML engine 600 configured to select an RF beam may output 604 an RF beam or a set of RF beams that may be used. Similarly, the ML engine 600 configured to predict a DRX schedule for a UE may output a DRX schedule for the UE.

[0085] Figure 7 is a block diagram illustrating a technique 700 for exiting a first ML model based on observed atypical data in accordance with aspects of the present disclosure. In this example, an input monitoring engine 702 can be coupled to an ML engine 704. The ML engine 704 can execute the first ML model. In some cases, the first ML model can make predictions about configuring, monitoring, adjusting settings, parameters, operating modes, etc. of a wireless device and / or a wireless connection based on the input data 706. As shown in Figure 6, the ML engine 704 may receive input data 706 (via the input monitoring engine 702) to make predictions or otherwise generate output 712A. The output 712A (e.g., of a first ML model executed on the ML engine 704) may be used to configure the wireless device, adjust settings, parameters, operating modes, etc.

[0086] exist Figure 7 , the input data 706 of the ML engine 704 may be input to the input monitoring engine 702 before being passed to the ML engine 704. The input monitoring engine 702 may also receive information about the training data 708 for training the first ML model of the ML engine 704 (e.g., information about the data used to train the ML model). In some cases, the information about the training data 708 may include data or a subset thereof used to train the first ML model. In some cases, the data used to train the first ML model may be encrypted, hashed, or otherwise encoded. In some examples, the information about the training data 708 may include information about the data used to train the first ML model, such as statistical information, keywords, distribution information, data ranges, relative relationships between vector values, and the like. For example, in the case where the first ML model of the ML engine 704 is configured to predict DRX scheduling, certain applications (such as gaming applications) may be associated with specific network traffic characteristics (such as packet size, time domain traffic pattern, etc.), and the information about the training data 708 may include information describing such statistical information about packet size, information about traffic patterns, keywords in headers, etc., which may indicate the presence of gaming applications. In some cases, a time window for the input data 706 may also be provided. For example, the gNB may configure and / or indicate to the UE one or more time windows corresponding to the input data 706. In some cases, information about the training data 708 may be provided to the wireless device (such as the UE) by another wireless device (such as the gNB). In some cases, the information about the training data 708 may be provided to the wireless device via an online code repository or database.

[0087] The input monitoring engine 702 may measure the amount of dissimilarity (e.g., difference) between the input data 706 and the information about the training data 708 relative to a threshold 710. In some cases, the threshold 710 may be provided along with the information about the training data 708. If the input monitoring engine 702 determines that the amount of dissimilarity between the input data 706 and the information about the training data 708 exceeds the threshold 710, the input monitoring engine 702 may stop the use of the first ML model of the ML engine 704 and switch to another engine (such as a legacy engine 714) to generate output 712B. In some cases, the output 712B may be the same type of output as the output 712A, but the output 712B may be generated without using the first ML model of the ML engine 704. As an example, if the ML engine 704 is configured to predict a DRX schedule, the other engine may implement another algorithm for generating a DRX schedule. In some cases, the other algorithm may be a legacy algorithm, such as an algorithm used to generate a DRX schedule in a previous version. In some cases, the other algorithm may be another ML model (e.g., a second ML model). In some cases, an indication that the wireless device has stopped using the first ML model of the ML engine 704 may be transmitted to another device. For example, in the event that the UE stops using an ML model (such as a model that predicts a DRX schedule), the UE may send an indication to another wireless device (such as to a gNB) indicating that the UE is reverting to a legacy DRX schedule. In some cases, the indication that the wireless device has stopped using the first ML model may be implicit. For example, if the gNB stops using the first ML model to predict the DRX schedule, the gNB may send a new DRX schedule generated using a legacy algorithm to the UE.

[0088] In some cases, the amount of dissimilarity between the input data 706 and the information about the training data 708 can be based on the difference between a statistical parameter or set of statistical parameters describing the input data 706 and the information about the training data 708. Examples of statistical parameters may include a mean, a variance, a higher-order statistical moment, or a relationship between the input data 706 and the information about the training data 708. In some cases, the amount of dissimilarity can be based on statistics of observed temporal variations of the input, a Wasserstein distance (e.g., an earth-mover's distance or a Kantorovich-Rubinstein metric), a Kullback-Leibler divergence, a Jensen-Shannon divergence, a Kolmogorov-Smirnov test, or any other distance measure. In some cases, the dissimilarity measure can be the minimum of the differences between the observed input distribution and a scaled (e.g., multiplied by a factor) version of the training data distribution (e.g., the minimum between scaled versions with different scaling factors). In some cases, the dissimilarity measure can be the minimum of the differences between the observed input distribution and a transformed version of the training data distribution (e.g., the minimum between transformations with different transformation parameters). One or more transformations may be applied. In some examples, the transformation may be a linear transformation (e.g., a shift, translation, scaling, etc.) or a nonlinear transformation. The particular transformation may be implementation specific and may depend, for example, on what is expected of the input. For example, where the input comprises a Rayleigh fading channel, the channel coefficients may have a circularly symmetric complex Gaussian distribution, which may suggest that rotations with a fixed phase should be allowed and the applied transformation may take such distribution into account. In some cases, the type of transformation applied may depend on the structure of the first ML model. For example, some convolutional neural networks are shift (e.g., translation) invariant, which may suggest that a shifted or translated (e.g., where a vector value is added to a value, another vector, etc.) version of the training input should still be considered typical.

[0089] In some cases, the measure of similarity may be based on output from another ML model (e.g., a similarity measurement ML model). In some cases, the other ML model may be a relatively simple ML model (compared to the ML model of the ML engine 704) to help avoid additional computational overhead / complexity. In some cases, a relatively simpler other ML model may be used because the accuracy of the other ML model may be relatively low compared to the ML model used for the ML engine 704. The accuracy of the other ML model may be low because the consequences when the other ML model outputs an inaccurate result may be relatively small compared to the ML model used for the ML engine 704. For example, if the other ML model generates an inaccurate result, such as a true or false positive, the consequence may be that the use of the ML model in the ML engine 704 is stopped, and a fallback to the legacy engine 714 may occur, for example, and the fallback may be preferred compared to the inaccurate result from the ML model used for the ML engine 704. In some cases, the other ML model may be a generative adversarial network (GAN). In general, a GAN can be used to generate data similar to the target data. The GAN can be used to find a minimum (or local minimum) of the observed dissimilarity of the inputs, and a class of transformations on the training inputs (e.g., before comparing the minimum to a threshold 710). In some cases, the class of transformations can be predefined.

[0090] In some cases, after the input, the monitoring engine 702 may stop the use of the first ML model of the ML engine 704 and switch to another ML model, such as a second ML model that performs a similar function to the first ML model. For example, where the first ML model is configured to predict the DRX schedule, the second ML model may also be configured to predict the DRX schedule. However, different ML models may have different tolerances for different types of data. For example, the first ML model may be trained to make highly accurate predictions given a particular environment (e.g., the type of input data 706), and may produce relatively inaccurate predictions given a change in the environment (e.g., when the input data 706 changes). The second ML model may be trained to be more robust to changes in the environment, but may make relatively inaccurate predictions for a particular environment (such as an environment in which the first ML model is configured to make accurate predictions). In such cases, the input monitoring engine 702 may receive information about the training data 708 corresponding to the first ML model and information about the training data 708 corresponding to the second ML model. The input monitoring engine 702 can compare similarity measures of the input data 706 (e.g., for past time intervals) with information about training data 708 associated with the first ML model and information about training data 708 associated with the second ML model, and select the ML model with the best similarity (e.g., compared to corresponding thresholds on similarity / dissimilarity). In some cases, different ML models can have different thresholds of input dissimilarity for triggering an exit. For example, where a first ML model can be trained to make highly accurate predictions given a particular environment, the first ML model can be associated with (e.g., configured with) a relatively lower dissimilarity / similarity threshold 710 than a second ML model that can be more robust to changing environments.

[0091] In some cases, stopping the use of the first ML model based on a similarity / dissimilarity threshold 710 (e.g., atypical data) as applied to the input data 706 can be applied in conjunction with ML model performance monitoring. As indicated above, ML performance can be monitored based on results generated from the output of the ML model. In some cases, ML performance monitoring can be integrated with input data monitoring by modifying the threshold 710 using ML performance monitoring. That is, the threshold 710 value can be adjusted based on the performance of the first ML model. For example, a lower threshold 710 of dissimilarity can be applied based on relatively poor past performance. As an example, in the case where the first ML model predicts which RF beams to use, different thresholds 710 can be applied based on prediction errors in past time intervals. In the absence of any prediction errors in the past time intervals, a relatively large threshold 710 (e.g., a large dissimilarity between the input data 706 and the information about the training data 708) can be applied. If the percentage of prediction errors is lower than, for example, 10%, a smaller threshold 710 can be applied. If the percentage of prediction errors is higher than, for example, 10%, an even smaller threshold 710 can be applied. In some cases, if the percentage error is above another percentage, for example, then use of the first ML model is stopped based on performance monitoring.

[0092] As indicated above, techniques for stopping the use of a first ML based on atypical input data may be performed on a variety of wireless devices, including user-side wireless devices (such as UEs) and network-side wireless devices (such as eNBs, gNBs, etc.). In some cases where a client-side wireless device may stop the use of a first ML based on atypical input data, the client-side wireless device may feed back information to the network. In some cases, the feedback information may indicate, for example, that the UE is using the first ML model, that the UE has stopped using the first ML model, and in some cases, why the UE has stopped using the first ML model.

[0093] In some cases, the client-side wireless device may also provide feedback on the measured similarity / dissimilarity of the input data 706 to the information about the training data 708. In some cases, the feedback on the measured similarity / dissimilarity may be part of periodic and / or semi-periodic feedback on the performance of the ML model. For example, the UE may generally be configured to provide periodic and / or semi-periodic feedback on the performance of the ML model, and the feedback on the measured similarity / dissimilarity may be included in such feedback. Resources for feedback may be configured for the UE by the gNB. For example, the gNB may send a downlink control information message, resource information for providing feedback (e.g., measured similarity / dissimilarity information and / or feedback generally on the performance of the ML model). The UE may send feedback to the gNB via, for example, an uplink (UL) medium access control (MAC) control element (MAC CE), as UL data on a physical uplink shared channel (PUSCH), as uplink control information (UCI) on the PUSCH, or on a physical uplink control channel (PUCCH).

[0094] In some cases, after use of the first ML model is stopped based on the dissimilarity between the input data 706 and the information about the training data 708, use of the first ML model may be resumed. In some cases, use of the first ML model may be resumed, for example, if a dissimilarity measure between the input data 706 (in the past time interval) and the information about the training data 708 (or an updated version thereof received from the network) drops below a certain threshold (e.g., a resume threshold). In some cases, the resume threshold may be different from the threshold used to stop use of the first ML model. For example, the resume threshold may be lower than the threshold used to stop use of the first ML model (e.g., greater similarity between the input data 706 and the information about the training data 708) to help induce hysteresis-type behavior to avoid excessive switching between using the first ML model and stopping use of the first ML model.

[0095] Figure 8 is a flow chart illustrating a process 800 for exiting a first ML model based on observed atypical data according to aspects of the present disclosure. In some cases, the process 800 for exiting a first ML model can be performed, for example, by the input monitoring engine 702. At operation 802, the process 800 can include: receiving input data for use by a first machine learning (ML) engine to generate output results. At operation 804, the process 800 can include: receiving information associated with a first training data set for the first ML engine. In some cases, the information associated with the first training data set includes at least a portion of the first training data set. In some cases, the information associated with the first training data set includes one or more statistical parameters.

[0096] At operation 806, process 800 may include determining a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set. In some cases, the first amount of dissimilarity is based on one or more statistical parameters.

[0097] At operation 808, process 800 may include determining whether to continue using the first ML engine to generate output results based on a comparison of the determined first dissimilarity amount to a first dissimilarity threshold. In some cases, process 800 may also include ceasing to use the first ML engine to generate output results based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold. In some cases, the output results of the first ML engine are used to determine a parameter associated with the device.

[0098] In some cases, process 800 may also include: determining parameters using a legacy algorithm based on determining that the first dissimilarity amount exceeds a first dissimilarity threshold. In some cases, process 800 may also include: switching to a second ML engine to generate output results based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold. In some cases, process 800 may also include: receiving information associated with a second training data set for a second ML engine; determining a second dissimilarity amount between the received input data and the second training data set based on the received information associated with the second training data set; and switching to the second ML engine based on a comparison of the second dissimilarity amount with a second dissimilarity threshold, the second dissimilarity threshold being associated with the second ML engine.

[0099] In some cases, the process 800 may also include: based on determining that the first dissimilarity amount is within the first dissimilarity threshold, using the first ML engine to generate an output result based on the input data. In some cases, the process 800 may also include: applying a transformation to one of the input data or information associated with the first training data set. In some cases, the process 800 may also include: using the ML model to determine the first dissimilarity amount. In some cases, the process 800 may also include: determining a configuration parameter based on the output result; and sending an indication of the configuration parameter to the user device. In some cases, the process 800 may also include: determining a configuration parameter based on the output result; and sending an indication of the configuration parameter to the network device.

[0100] In some examples, the processes described herein (e.g., process 800 and / or other processes described herein) may be performed by a computing device or apparatus (e.g., a UE or a base station). In another example, process 800 may be performed by Figure 1 In another example, process 800 may be performed by UE 104. Figure 1 The base station 102 executes.

[0101] Fig. 9 is a diagram illustrating an example of a system for implementing certain aspects of the present technology. Specifically, Fig. 9 An example of a computing system 900 is illustrated, which may be any computing device, for example, constituting an internal computing system, a remote computing system, a camera, or any components thereof, wherein the components of the system communicate with each other using a connection 905. The connection 905 may be a physical connection using a bus, or a direct connection into the processor 910, such as in a chipset architecture. The connection 905 may also be a virtual connection, a networked connection, or a logical connection.

[0102] In some embodiments, computing system 900 is a distributed system, where the functionality described in the present disclosure may be distributed within a data center, multiple data centers, a peer-to-peer network, etc. In some embodiments, one or more of the described system components represent a number of such components that each perform some or all of the functionality for which the component is described. In some embodiments, a component may be a physical device or a virtual device.

[0103] The example system 900 includes at least one processing unit (CPU or processor) 910 and connections 905 that communicatively couple various system components including system memory 915, such as read only memory (ROM) 920 and random access memory (RAM) 925, to the processor 910. The computing system 900 may include a cache 912 of high-speed memory directly connected to the processor 910, in close proximity to the processor, or integrated as part of the processor.

[0104] Processor 910 may include any general purpose processor and hardware or software services such as services 932, 934, and 936 stored in storage device 930 configured to control processor 910, as well as special purpose processors where software instructions are incorporated into the actual processor design. Processor 910 may essentially be a completely independent computing system containing multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0105] To enable user interaction, the computing system 900 includes an input device 945 that can represent any number of input mechanisms, such as a microphone for voice, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, voice, etc. The computing system 900 may also include an output device 935, which may be one or more of a plurality of output mechanisms. In some cases, a multimodal system may enable a user to provide multiple types of input / output to communicate with the computing system 900.

[0106] The computing system 900 may include a communication interface 940, which may generally govern and manage user input and system output. The communication interface may perform or facilitate receiving and / or sending wired or wireless communications using wired and / or wireless transceivers, including using an audio jack / plug, a microphone jack / plug, a Universal Serial Bus (USB) port / plug, an Apple TM Lightning TM Ports / plugs, Ethernet ports / plugs, Fiber optic ports / plugs, Dedicated wired ports / plugs, 3G, 4G, 5G and / or other cellular data network wireless signal transfer, Bluetooth TM Wireless signal transfer, Bluetooth TM Low energy (BLE) wireless signal transfer, IBEACON TM Wireless signal transfer, radio frequency identification (RFID) wireless signal transfer, near field communication (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, visible light communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), infrared (IR) communication wireless signal transfer, public switched telephone network (PSTN) signal transfer, integrated services digital network (ISDN) signal transfer, ad hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. The communication interface 940 may also include one or more global navigation satellite system (GNSS) receivers or transceivers for determining the location of the computing system 900 based on receiving one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the United States' Global Positioning System (GPS), Russia's Global Navigation Satellite System (GLONASS), China's BeiDou Navigation Satellite System (BDS), and Europe's Galileo GNSS. There is no restriction to operating on any particular hardware arrangement, and thus the base features herein may be easily substituted for improved hardware or firmware arrangements as they are developed.

[0107] The storage device 930 may be a non-volatile and / or non-transitory and / or computer-readable memory device and may be a hard disk or other type of computer-readable medium that can store data accessible by a computer, such as a magnetic cassette, a flash memory card, a solid-state memory device, a digital versatile disk, a cassette, a floppy disk, a floppy disk, a hard disk, a magnetic tape, a magnetic stripe / magnetic stripe, any other magnetic storage medium, flash memory, a memristor memory, any other solid-state memory, a compact disk-read only memory (CD-ROM) optical disk, a rewritable compact disk (CD) optical disk, a digital video disk (DVD) optical disk, a Blu-ray disc (BDD) optical disk, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Card, or a memory card. card, a smart card chip, an EMV chip, a subscriber identity module (SIM) card, a mini / micro / nano / pico SIM card, another integrated circuit (IC) chip / card, a random access memory (RAM), a static RAM (SRAM), a dynamic RAM (DRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash EPROM (FLASH EPROM), a cache memory (e.g., a layer 1 (L1) cache, a layer 2 (L2) cache, a layer 3 (L3) cache, a layer 4 (L4) cache, a layer 5 (L5) cache, other (L#) cache), a resistive random access memory (RRAM / ReRAM), a phase change memory (PCM), a spin transfer torque RAM (STT-RAM), another memory chip or box, and / or a combination thereof.

[0108] The storage device 930 may include software services, servers, services, etc., which, when the code defining such software is executed by the processor 910, causes the system to perform functions. In some embodiments, hardware services that perform specific functions may include software components for performing functions stored in a computer-readable medium connected to necessary hardware components such as the processor 910, connection 905, output device 935, etc. The term "computer-readable medium" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, containing or carrying instructions and / or data. Computer-readable media may include non-transient media in which data may be stored and does not include carrier waves and / or transient electronic signals propagated wirelessly or through wired connections. Examples of non-transient media may include, but are not limited to, disks or tapes, optical storage media (such as compact discs (CDs) or digital versatile discs (DVDs)), flash memory, memory, or memory devices. Computer readable media may store thereon code and / or machine executable instructions, which may represent a procedure, function, subprogram, program, routine, subroutine, module, software package, category, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, independent variables, parameters, or memory contents. Information, independent variables, parameters, data, etc. may be passed, forwarded, or sent via any suitable means, including memory sharing, message passing, token passing, network sending, etc.

[0109] Specific details are provided in the above description to provide a thorough understanding of each embodiment and each example provided herein, but those skilled in the art will recognize that the present application is not limited thereto. Thus, although the exemplary embodiments of the present application have been described in detail herein, it is to be understood that the inventive concept can be embodied and adopted in various other ways, and the appended claims are intended to be interpreted as including such variations, unless limited by the prior art. Various features and aspects of the above-mentioned applications can be used individually or in combination. In addition, without departing from the broader scope of this specification, the embodiments can be used in any number of environments and applications beyond the environment and application described herein. Therefore, the description and the accompanying drawings should be considered as illustrative rather than restrictive. For the purpose of illustration, each method is described in a specific order. It should be understood that in an alternative embodiment, each method can be performed in a different order than described.

[0110] For clarity of explanation, in some instances, the present technology may be presented as including separate functional blocks, which include devices, device components, steps or routines in the method embodied in software or a combination of hardware and software. Additional components other than those components shown in the drawings and / or described herein may be used. For example, circuits, systems, networks, processes and other components may be shown as components in block diagram form to avoid burying these embodiments in unnecessary details. In other cases, known circuits, processes, algorithms, structures and techniques may be shown without necessary details to avoid confusing various embodiments.

[0111] In addition, it will be appreciated by those skilled in the art that the various exemplary logic blocks, modules, circuits and algorithmic steps described in conjunction with the various aspects 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 exemplary components, blocks, modules, circuits and steps have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints proposed to the entire system. Those skilled in the art can implement the described functions in different ways for each specific application, but such specific implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0112] Individual embodiments may be described above as processes or methods depicted as flow charts, flow diagrams, data flow diagrams, structure diagrams, or block diagrams. Although a flow chart may describe an operation as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. The process is terminated when the operations of the process are completed, but the process may have additional steps not included in the accompanying drawings. A process may correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, the termination of the process may correspond to the function returning to the calling function or main function.

[0113] The processes and methods according to the above examples can be implemented using stored computer executable instructions or otherwise available computer executable instructions from a computer readable medium. Such instructions may include, for example, instructions and data that configure a general-purpose computer, a special-purpose computer, or a processing device to perform a certain function or group of functions. Portions of the computer resources used may be accessed via a network. Computer executable instructions may be, for example, binary, intermediate format instructions such as assembly language, firmware, source code. Examples of computer readable media that can be used to store instructions, information used, and / or information created during the methods according to the described examples include disks or optical disks, flash memories, USB devices with non-volatile memory, networked storage devices, etc.

[0114] In some embodiments, computer readable storage devices, media, and memories may include wired or wireless signals containing bit streams, etc. However, when referred to, non-transitory computer readable storage media explicitly excludes media such as power consumption, carrier signals, electromagnetic waves, and signals themselves.

[0115] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof in some cases, depending in part on the specific application, in part on the desired design, in part on the corresponding technology, etc.

[0116] The various illustrative logic blocks, modules, and circuits described in conjunction with the various aspects disclosed herein may be implemented or executed using hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof, and may be implemented in any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., computer program products) for performing the necessary tasks may be stored in a computer-readable or machine-readable medium. The processor may perform the necessary tasks. Examples of form factors include: laptops, smartphones, mobile phones, tablet devices, or other small form factor personal computers, personal digital assistants, rack-mounted devices, stand-alone devices, etc. The functions described herein may also be embodied in peripheral devices or add-in cards. By way of further example, such functionality may also be implemented on circuit boards in different chips or different processes executed on a single device.

[0117] The instructions, the media for conveying such instructions, the computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functionality described in this disclosure.

[0118] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices, such as general-purpose computers, wireless communication devices, or integrated circuit devices with multiple uses, including applications in wireless communication devices, mobile phones, and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be implemented at least in part by a computer-readable data storage medium including a program code, which includes instructions for executing one or more of the above-mentioned methods, algorithms, and / or operations when executed. A computer-readable data storage medium may form part of a computer program product, which may include packaging materials. A computer-readable medium may include a memory or data storage medium, such as a random access memory (RAM) (such as a synchronous dynamic random access memory (SDRAM)), a read-only memory (ROM), a non-volatile random access memory (NVRAM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic or optical data storage medium, and the like. Additionally or alternatively, the techniques may be implemented at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as a propagated signal or wave.

[0119] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Such processors may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; however, in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Therefore, the term "processor" as used herein may refer to any of the aforementioned structures, any combination of the aforementioned structures, or any other structure or device suitable for implementing the techniques described herein.

[0120] It should be understood by those of ordinary skill in the art that the less than ("<") and greater than (">") symbols or terms used herein may be replaced by less than or equal to ("≤") and greater than or equal to ("≥") symbols, respectively, without departing from the scope of the present specification.

[0121] Where a component is described as being “configured to” perform certain operations, such configuration may be achieved, for example, by designing electronic circuits or other hardware to perform the operations, by programming programmable electronic circuits (e.g., a microprocessor or other suitable electronic circuits) to perform the operations, or any combination thereof.

[0122] The phrases “coupled to” or “communicatively coupled to” refer to any component being physically connected directly or indirectly to another component, and / or any component being in communication with another component directly or indirectly (e.g., connected to the other component via a wired or wireless connection and / or other suitable communication interface).

[0123] Claim language or other language stating "at least one of" a set and / or "one or more of" a set indicates that one member of the set or multiple members of the set (in any combination) satisfies the claim. For example, claim language stating "at least one of A and B" or "at least one of A or B" means A, B, or A and B. In another example, claim language stating "at least one of A, B, and C" or "at least one of A, B, or C" means A, B, C, or A and B, or A and C, or B and C, A and B and C, or any repetition is information or data (e.g., A and A, B and B, C and C, A and A and B, etc.), or any other ordering, repetition, or combination of A, B, and C. The language "at least one of" a set and / or "one or more of" a set does not limit the set to the items listed in the set. For example, claim language stating "at least one of A and B" or "at least one of A or B" may mean A, B, or A and B, and may additionally include items not listed in the set of A and B.

[0124] Illustrative aspects of the present disclosure include:

[0125] Aspect 1. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor, the at least one processor being coupled to the memory and configured to: receive input data for use by a first machine learning (ML) engine to generate an output result; receive information associated with a first training data set for the first ML engine; determine a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set; and determine whether to continue using the first ML engine to generate the output result based on a comparison of the determined first amount of dissimilarity with a first dissimilarity threshold.

[0126] Aspect 2. The apparatus of claim 1, wherein the at least one processor is further configured to: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, stop using the first ML engine to generate the output result.

[0127] Aspect 3. The apparatus of claim 2, wherein the output result of the first ML engine is used to determine a parameter associated with the apparatus, and wherein the at least one processor is further configured to: determine the parameter using a legacy algorithm based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold.

[0128] Aspect 4. The apparatus of claim 2, wherein the at least one processor is further configured to: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, switch to a second ML engine to generate the output result.

[0129] Aspect 5. The apparatus of claim 4, wherein the at least one processor is further configured to: receive information associated with a second training data set for the second ML engine; determine a second amount of dissimilarity between the received input data and the second training data set based on the received information associated with the second training data set; and switch to the second ML engine based on a comparison of the second amount of dissimilarity with a second dissimilarity threshold associated with the second ML engine.

[0130] Aspect 6. The apparatus of claim 1, wherein the at least one processor is further configured to: based on determining that the first dissimilarity amount is within the first dissimilarity threshold, use the first ML engine to generate the output result based on the input data.

[0131] Aspect 7. The apparatus of claim 1, wherein the information associated with the first training data set comprises at least a portion of the first training data set.

[0132] Aspect 8. The apparatus of claim 1, wherein the information associated with the first training data set comprises one or more statistical parameters.

[0133] Aspect 9. The apparatus of claim 1, wherein the first dissimilarity measure is based on one or more statistical parameters.

[0134] Aspect 10. The apparatus of claim 1, wherein the at least one processor is further configured to apply a transformation to one of the input data or the information associated with the first training data set.

[0135] Aspect 11. The apparatus of claim 1, wherein the at least one processor is further configured to: determine the first dissimilarity measure using an ML model.

[0136] Aspect 12. The apparatus of claim 1, wherein the apparatus comprises a network device, and wherein the at least one processor is further configured to: determine a configuration parameter based on the output result; and send an indication of the configuration parameter to a user equipment.

[0137] Aspect 13. The apparatus of claim 1, wherein the apparatus comprises a user equipment, and wherein the at least one processor is further configured to: determine a configuration parameter based on the output result; and send an indication of the configuration parameter to a network device.

[0138] Aspect 14. A method for wireless communication, the method comprising: receiving input data for use by a first machine learning (ML) engine to generate an output result; receiving information associated with a first training data set for the first ML engine; determining a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set; and determining whether to continue using the first ML engine to generate the output result based on a comparison of the determined first amount of dissimilarity with a first dissimilarity threshold.

[0139] Aspect 15. The method of claim 14, further comprising: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, ceasing to use the first ML engine to generate the output result.

[0140] Aspect 16. The method of claim 15, wherein the output of the first ML engine is used to determine a parameter associated with a device, and the method further comprises: based on determining that the first amount of dissimilarity exceeds the first dissimilarity threshold, determining the parameter using a legacy algorithm.

[0141] Aspect 17. The method of claim 15, further comprising: switching to a second ML engine to generate the output result based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold.

[0142] Aspect 18. The method of claim 17, further comprising: receiving information associated with a second training data set for the second ML engine; determining a second amount of dissimilarity between the received input data and the second training data set based on the received information associated with the second training data set; and switching to the second ML engine based on a comparison of the second amount of dissimilarity to a second dissimilarity threshold associated with the second ML engine.

[0143] Aspect 19. The method of claim 14, further comprising: based on determining that the first dissimilarity amount is within the first dissimilarity threshold, using the first ML engine to generate the output result based on the input data.

[0144] Aspect 20. The method of claim 14, wherein the information associated with the first training data set includes at least a portion of the first training data set.

[0145] Aspect 21. The method of claim 14, wherein the information associated with the first training data set includes one or more statistical parameters.

[0146] Aspect 22. The method of claim 14, wherein the first dissimilarity measure is based on one or more statistical parameters.

[0147] Aspect 23. The method of claim 14, further comprising applying a transformation to one of the input data or the information associated with the first training data set.

[0148] Aspect 24. The method of claim 14, further comprising: determining the first dissimilarity measure using an ML model.

[0149] Aspect 25. The method of claim 14, further comprising: determining a configuration parameter based on the output result; and sending an indication of the configuration parameter to a user equipment.

[0150] Aspect 26. The method of claim 14, further comprising: determining a configuration parameter based on the output result; and sending an indication of the configuration parameter to a network device.

[0151] Aspect 27. A non-transitory computer-readable storage medium, comprising instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to: receive input data for use by a first machine learning (ML) engine to generate an output result; receive information associated with a first training data set for the first ML engine; determine a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set; and determine whether to continue using the first ML engine to generate the output result based on a comparison of the determined first amount of dissimilarity with a first dissimilarity threshold.

[0152] Aspect 28. The non-transitory computer-readable storage medium of claim 27, wherein the instructions further cause the at least one processor to: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, cease using the first ML engine to generate the output result.

[0153] Aspect 29. The non-transitory computer-readable storage medium of claim 28, wherein the output of the first ML engine is used to determine a parameter associated with the device, and wherein the instructions further cause the at least one processor to: determine the parameter using a legacy algorithm based on determining that the first amount of dissimilarity exceeds the first dissimilarity threshold.

[0154] Aspect 30. The non-transitory computer-readable storage medium of claim 28, wherein the instructions further cause the at least one processor to: based on determining that the first amount of dissimilarity exceeds the first dissimilarity threshold, switch to a second ML engine to generate the output result.

[0155] Aspect 31. A non-transitory computer-readable storage medium according to claim 30, wherein the instructions further cause the at least one processor to: receive information associated with a second training data set for the second ML engine; determine a second amount of dissimilarity between the received input data and the second training data set based on the received information associated with the second training data set; and switch to the second ML engine based on a comparison of the second amount of dissimilarity to a second dissimilarity threshold, the second dissimilarity threshold being associated with the second ML engine.

[0156] Aspect 32. The non-transitory computer-readable storage medium of claim 27, wherein the instructions further cause the at least one processor to: based on determining that the first dissimilarity amount is within the first dissimilarity threshold, use the first ML engine to generate the output result based on the input data.

[0157] Aspect 33. The non-transitory computer-readable storage medium of claim 27, wherein the information associated with the first training data set comprises at least a portion of the first training data set.

[0158] Aspect 34. The non-transitory computer-readable storage medium of claim 27, wherein the information associated with the first training data set comprises one or more statistical parameters.

[0159] Aspect 35. The non-transitory computer-readable storage medium of claim 27, wherein the first dissimilarity measure is based on one or more statistical parameters.

[0160] Aspect 36. The non-transitory computer-readable storage medium of claim 27, wherein the instructions further cause the at least one processor to: apply a transformation to one of the input data or the information associated with the first training data set.

[0161] Aspect 37. The non-transitory computer-readable storage medium of claim 27, wherein the instructions further cause the at least one processor to: determine the first dissimilarity measure using an ML model.

[0162] Aspect 38. A non-transitory computer-readable storage medium according to claim 27, wherein the apparatus comprises a network device, and wherein the instructions further cause the at least one processor to: determine a configuration parameter based on the output result; and send an indication of the configuration parameter to a user device.

[0163] Aspect 39. A non-transitory computer-readable storage medium according to claim 27, wherein the apparatus comprises a user device, and wherein the instructions further cause the at least one processor to: determine a configuration parameter based on the output result; and send an indication of the configuration parameter to a network device.

[0164] Aspect 40. An apparatus for wireless communications, the apparatus comprising one or more means for performing the operations of any one of claims 14 to 26.

Claims

1. A device for wireless communication, the device comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: receiving input data for use by a first machine learning (ML) engine to generate output results; receiving information associated with a first training data set for the first ML engine; determining a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set; as well as Based on a comparison of the determined first dissimilarity amount with a first dissimilarity threshold, it is determined whether to continue using the first ML engine to generate the output result.

2. The apparatus of claim 1, wherein the at least one processor is further configured to: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, cease using the first ML engine to generate the output result.

3. The apparatus of claim 2, wherein the output of the first ML engine is used to determine a parameter associated with the apparatus, and wherein the at least one processor is further configured to: determine the parameter using a legacy algorithm based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold.

4. The apparatus of claim 2, wherein the at least one processor is further configured to: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, switch to a second ML engine to generate the output result.

5. The apparatus of claim 4, wherein the at least one processor is further configured to: receiving information associated with a second training data set for the second ML engine; determining a second amount of dissimilarity between the received input data and the second training data set based on the received information associated with the second training data set; and Switching to a second ML engine is performed based on a comparison of the second dissimilarity measure to a second dissimilarity threshold value, the second dissimilarity threshold value being associated with the second ML engine.

6. The apparatus of claim 1, wherein the at least one processor is further configured to: based on determining that the first dissimilarity amount is within the first dissimilarity threshold, use the first ML engine to generate the output result based on the input data.

7. The apparatus of claim 1, wherein the information associated with the first training data set comprises at least a portion of the first training data set.

8. The apparatus of claim 1, wherein the information associated with the first training data set comprises one or more statistical parameters.

9. The apparatus of claim 1, wherein the first dissimilarity measure is based on one or more statistical parameters.

10. The apparatus of claim 1, wherein the at least one processor is further configured to apply a transformation to one of the input data or the information associated with the first training data set.

11. The apparatus of claim 1, wherein the at least one processor is further configured to: determine the first dissimilarity measure using an ML model.

12. The apparatus of claim 1, wherein the apparatus comprises a network device, and wherein the at least one processor is further configured to: determining configuration parameters based on the output results; and An indication of the configuration parameter is sent to a user equipment.

13. The apparatus of claim 1, wherein the apparatus comprises a user equipment, and wherein the at least one processor is further configured to: determining configuration parameters based on the output results; and An indication of the configuration parameter is sent to a network device.

14. A method for wireless communication, the method comprising: receiving input data for use by a first machine learning (ML) engine to generate output results; receiving information associated with a first training data set for the first ML engine; determining a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set; as well as Based on a comparison of the determined first dissimilarity amount with a first dissimilarity threshold, it is determined whether to continue using the first ML engine to generate the output result.

15. The method according to claim 14, further comprising: Based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, ceasing to use the first ML engine to generate the output result.

16. The method of claim 15, wherein the output of the first ML engine is used to determine parameters associated with a device, and the method further comprises: Based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, the parameter is determined using a legacy algorithm.

17. The method according to claim 15, further comprising: Based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, switching to a second ML engine to generate the output result.

18. The method according to claim 17, further comprising: receiving information associated with a second training data set for the second ML engine; determining a second amount of dissimilarity between the received input data and the second training data set based on the received information associated with the second training data set; as well as Switching to a second ML engine is performed based on a comparison of the second dissimilarity measure to a second dissimilarity threshold value, the second dissimilarity threshold value being associated with the second ML engine.

19. The method of claim 14, wherein the information associated with the first training data set includes at least a portion of the first training data set.

20. The method of claim 14, wherein the information associated with the first training data set includes one or more statistical parameters.

21. The method of claim 14, wherein the first dissimilarity measure is based on one or more statistical parameters.

22. The method according to claim 14, further comprising: A transformation is applied to one of the input data or the information associated with the first training data set.

23. The method according to claim 14, further comprising: determining configuration parameters based on the output results; as well as An indication of the configuration parameter is sent to a user equipment.

24. The method according to claim 15, further comprising: determining configuration parameters based on the output results; as well as An indication of the configuration parameter is sent to a network device.

25. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor, cause the at least one processor to: receiving input data for use by a first machine learning (ML) engine to generate output results; receiving information associated with a first training data set for the first ML engine; determining a first amount of dissimilarity between the received input data and the first training data set based on the received information associated with the first training data set; as well as Based on a comparison of the determined first dissimilarity amount with a first dissimilarity threshold, it is determined whether to continue using the first ML engine to generate the output result.

26. The non-transitory computer-readable storage medium of claim 25, wherein the instructions further cause the at least one processor to: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, cease using the first ML engine to generate the output result.

27. The non-transitory computer-readable storage medium of claim 26, wherein the output of the first ML engine is used to determine a parameter associated with a device, and wherein the instructions further cause the at least one processor to: determine the parameter using a legacy algorithm based on determining that the first amount of dissimilarity exceeds the first dissimilarity threshold.

28. The non-transitory computer-readable storage medium of claim 26, wherein the instructions further cause the at least one processor to: based on determining that the first dissimilarity amount exceeds the first dissimilarity threshold, switch to a second ML engine to generate the output result.

29. The non-transitory computer readable storage medium of claim 25, wherein the instructions further cause the at least one processor to: determining configuration parameters based on the output results; and An indication of the configuration parameter is sent to a user equipment.

30. The non-transitory computer-readable storage medium of claim 25, wherein the instructions further cause the at least one processor to: determining configuration parameters based on the output results; and An indication of the configuration parameter is sent to a network device.

31. An apparatus for wireless communications, the apparatus comprising one or more means for performing the operations of any of claims 14 to 24.

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