Tone placement for reference signal optimization

Abstract

Techniques for determining a tone pattern of reference signal transmissions are disclosed. One or more parameters associated with communications with a base station over a time duration can be determined by a user equipment. The user equipment can determine a tone pattern of a reference signal for use in communications with the base station over a future time duration based on the one or more parameters. The user equipment can then transmit the tone pattern to the base station. The user equipment can then receive the reference signal from the base station over the tone pattern.

Description

[0001] open field

[0002] Various aspects of this disclosure generally relate to wireless communications. In some implementations, examples of frequency modulation modes for optimizing the transmission of reference signals between a base station and a user equipment (UE) are described.

[0003] Public background

[0004] Wireless communication systems have undergone several generations of development, including first-generation analog radiotelephone service (1G), second-generation (2G) digital radiotelephone service (including the transitional 2.5G networks), third-generation (3G) high-speed data radio service with Internet capabilities, fourth-generation (4G) services (e.g., LTE, WiMax), and the most recent fifth-generation (5G) services. Currently, there are many different types of wireless communication systems in use, including cellular and Personal Communication Services (PCS) systems. Known examples of cellular systems include cellular analog Advanced Mobile Phone Systems (AMPS), and digital cellular systems based on Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Global System for Mobile Communications (GSM), etc.

[0005] Fifth-generation (5G) mobile standards demand higher data transmission speeds, a greater number of connections, better coverage, and other improvements. The 5G standard (also known as "New Radio" or "NR"), according to the Next Generation Mobile Networks Alliance, is designed to provide tens of megabits per second of data rate to each of tens of thousands of users—for example, gigabit-level connection rates to dozens of users in a shared location such as an office floor. It also needs to support hundreds of thousands of simultaneous connections to support large-scale sensor deployments. Therefore, compared to the current 4G / LTE standards, 5G mobile communication requires significantly improved spectral efficiency. Furthermore, there is a corresponding need for enhanced signaling efficiency and significantly reduced latency compared to current standards.

[0006] Overview

[0007] The following is a simplified overview relating to one or more aspects disclosed herein. Therefore, this overview should not be considered an exhaustive overview relating to all aspects of the conception, nor should it be considered to identify key or decisive elements relating to all aspects of the conception or to depict the scope associated with any particular aspect. Accordingly, the sole purpose of the following overview is to present, in a simplified form, certain concepts relating to one or more aspects of the mechanism disclosed herein before the detailed description given below.

[0008] Systems, apparatus, methods, and computer-readable media for determining and optimizing frequency modulation modes for transmitting reference signals between user equipment and base stations are disclosed.

[0009] According to at least one example, a method includes: determining one or more parameters associated with communication with a base station over a time duration by a user equipment; determining a frequency modulation pattern of a reference signal based on the one or more parameters for use in communication with the base station over a future time duration by the user equipment; and transmitting the frequency modulation pattern to the base station.

[0010] In another example, an apparatus includes one or more memories storing computer-readable instructions, and one or more processors. The one or more processors are configured to execute the computer-readable instructions to: determine one or more parameters associated with communication with a base station over a time duration; determine a frequency modulation pattern of a reference signal based on the one or more parameters for use in future communication with the base station; and transmit the frequency modulation pattern to the base station.

[0011] In another example, a non-transient computer-readable medium is provided including at least one instruction stored thereon, which, when executed by one or more processors, causes the one or more processors to: determine one or more parameters associated with communication with a base station over a time duration; determine a frequency modulation pattern of a reference signal based on the one or more parameters for use in communication with the base station over a future time duration; and transmit the frequency modulation pattern to the base station.

[0012] In another example, an apparatus is provided. The apparatus includes: means for determining one or more parameters associated with communication with a base station over a time duration by a user equipment; means for determining a frequency modulation pattern of a reference signal based on the one or more parameters for use in future communication with the base station over a future time duration; and means for transmitting the frequency modulation pattern to the base station. In another example, an apparatus is provided. The apparatus includes: one or more memories storing computer-readable instructions, and one or more processors. The one or more processors are configured to execute the computer-readable instructions to: determine one or more parameters associated with communication with a user equipment over a time duration; determine a frequency modulation pattern of a reference signal based on the one or more parameters for use in future communication with the user equipment over a future time duration; and transmit the frequency modulation pattern to the user equipment.

[0013] In another example, a method includes: a base station determining one or more parameters associated with communication with a user equipment over a time duration; the base station determining a frequency modulation pattern of a reference signal based on the one or more parameters for use in communication with the user equipment over a future time duration; and the base station transmitting the reference signal to the user equipment using the frequency modulation pattern.

[0014] In another example, a non-transient computer-readable medium is provided including at least one instruction stored thereon, which, when executed by one or more processors, causes the one or more processors to: determine one or more parameters associated with communication with a user equipment over a time duration; determine a frequency modulation pattern of a reference signal based on the one or more parameters for use in communication with the user equipment over a future time duration; and transmit the frequency modulation pattern to the user equipment.

[0015] In another example, an apparatus is provided. The apparatus includes: means for determining one or more parameters associated with communication with a user equipment over a time duration by a base station; means for determining a frequency modulation pattern of a reference signal based on the one or more parameters for use in communication with the user equipment over a future time duration; and means for transmitting the reference signal to the user equipment by the base station using the frequency modulation pattern.

[0016] This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used alone to determine the scope of the claimed subject matter. This subject matter should be understood in conjunction with the appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

[0017] Other objectives and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description. Brief description of the attached diagram

[0019] The accompanying drawings are provided to help describe various aspects of this disclosure, and the drawings are provided for illustrative purposes only and not for limiting the aspects.

[0020] Figure 1 This is a diagram illustrating an example wireless communication system according to some aspects of this disclosure.

[0021] Figure 2A and 2B This is a diagram illustrating an example wireless network architecture based on some aspects of this disclosure.

[0022] Figure 3 The diagram illustrates a design of a base station and user equipment (UE) device according to some aspects of this disclosure, which enables the transmission and processing of signals exchanged between the UE and the base station.

[0023] Figure 4 This is a conceptual diagram illustrating an example of a frame structure based on some aspects of this disclosure.

[0024] Figure 5 This is a conceptual diagram illustrating an example machine learning model that can be configured to facilitate frequency modulation placement optimization according to some aspects of this disclosure.

[0025] Figure 6 This is a flowchart illustrating an example of the process of training a machine learning algorithm for frequency modulation pattern determination according to some aspects of this disclosure.

[0026] Figure 7 This is a flowchart illustrating an example of the process of communicating a customized frequency modulation mode according to some aspects of this disclosure.

[0027] Figures 8A to 8B This is a conceptual diagram illustrating a non-limiting example of a customized irregular frequency modulation pattern arrangement based on some aspects of this disclosure.

[0028] Figure 9 This is a flowchart illustrating an example of the process of communicating a customized frequency modulation mode according to some aspects of this disclosure.

[0029] Figure 10 This is a flowchart illustrating an example of the process of communicating a customized frequency modulation mode according to some aspects of this disclosure.

[0030] Figure 11 This is a flowchart illustrating an example of the process of communicating a customized frequency modulation mode according to some aspects of this disclosure.

[0031] Figure 12 The components of the user equipment according to some aspects of this disclosure are explained.

[0032] Detailed description

[0033] For illustrative purposes, certain aspects and embodiments of this disclosure are provided below. Alternative aspects may be designed without departing from the scope of this disclosure. Furthermore, elements well-known in this disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of this disclosure. Some aspects and embodiments described herein can be applied independently and some can be combined, as will be apparent to those skilled in the art. In the following description, specific details are set forth for illustrative purposes to provide a thorough understanding of embodiments of this application. However, it will be apparent that various embodiments may be practiced without these specific details. The drawings and descriptions are not intended to be limiting.

[0034] This document describes systems, apparatus, processes (also referred to as methods), and computer-readable media (collectively referred to herein as systems and techniques) for optimizing frequency modulation modes for reference signal transmission between base stations (e.g., base stations, new radios, gNodeBs (gB nodes), etc.) and user equipment (UEs).

[0035] As mentioned above, 5G mobile standards demand higher data transmission speeds, a greater number of connections, better coverage, and other improvements. 5G is expected to support hundreds of thousands of simultaneous connections. Therefore, there is room to improve the spectral efficiency of 5G mobile communications by enhancing signaling efficiency and reducing latency. One aspect that can achieve such signaling efficiency and latency reduction is the communication of various uplink and downlink reference signals between user equipment and their respective serving base stations.

[0036] A reference signal is a predefined signal that occupies a specific resource element within the time-frequency grid of a resource block and can be exchanged on one or both of the downlink and uplink physical communication channels. Each reference signal has been defined by the 3rd Generation Partnership Project (3GPP) for specific purposes, such as channel estimation, phase noise compensation, obtaining downlink / uplink channel state information, time and frequency tracking, etc.

[0037] Example reference signals include, but are not limited to, Channel State Information-Reference Signal (CSI-RS), Demodulation Reference Signal (DMRS), and Probe Reference Signal (SRS). Some reference signals (such as CSI-RS) are downlink-specific signals, while others (such as DMRS) are transmitted on both downlink and uplink communication channels. There are also uplink-specific reference signals defined by 3GPP.

[0038] A frequency modulation (FM) pattern can be defined as a specific arrangement of resource elements in a given resource block used to transmit a reference signal. FM patterns are currently predefined in 5G communication standards and are known to both user equipment (UE) and the corresponding base station. Furthermore, the estimated power used to transmit a given resource element is known to both the UE and the base station. Accordingly, both the UE and the base station possess the necessary information to encode / decode the reference signal and perform corresponding measurements.

[0039] Predefined frequency modulation patterns may not be optimized for all environments. In other words, the arrangement or combination of resource elements used to transmit a specific reference signal may not be optimized across all possible conditions under which user equipment and base stations can communicate.

[0040] The systems and techniques described herein dynamically determine (or configure) an optimized frequency modulation (FM) pattern for transmitting a reference signal by one or more base stations and / or one or more UEs. Determining the optimized FM pattern improves signal and spectral efficiency and reduces the overhead associated with transmitting the reference signal for mobile systems (e.g., in 5G mobile systems). In some examples, as described in more detail below, the dynamic determination of the FM pattern can be achieved using machine learning models as well as various other techniques. For example, given conditions for the transmission of the reference signal from the UE to the base station (and / or from the base station to the UE) can be provided as input to the machine learning model. Over time, the machine learning model can be trained to associate various conditions with different resource elements (REs) best suited for the placement of the reference signal's FM pattern to achieve an optimized output (e.g., spectral efficiency). Once trained, the machine learning model can process such inputs and provide an optimized FM pattern for the transmission of the reference signal when they are available as inputs.

[0041] Additional aspects of this disclosure are described in more detail below with reference to the accompanying drawings.

[0042] According to various aspects, Figure 1 Explanation of example wireless communication system 100. Wireless communication system 100 (also referred to as wireless wide area network (WWAN)) may include individual base stations 102 and individual UEs 104.

[0043] As used herein, the terms “User Equipment” (UE) and “Base Station” are not intended to be specific to or otherwise limited to any particular Radio Access Technology (RAT) unless otherwise stated. In general, a UE can be any wireless communication device (e.g., mobile phone, router, tablet computer, laptop computer, and / or tracking device, etc.), wearable device (e.g., smartwatch, smart glasses, wearable ring, and / or extended reality (XR) device (such as virtual reality (VR) headset, augmented reality (AR) headset or glasses, or mixed reality (MR) headset)), vehicle (e.g., car, motorcycle, bicycle, etc.), and / or Internet of Things (IoT) device, etc., for use by a user to communicate over a wireless communication network. A UE can be mobile or can (e.g., at certain times) be stationary and can communicate with a Radio Access Network (RAN). As used herein, the term "UE" may be interchangeably referred to as "access terminal" or "AT," "client device," "wireless device," "subscriber device," "subscriber terminal," "subscriber station," "user terminal," or "UT," "mobile device," "mobile terminal," "mobile station," or variations thereof. Generally, a UE can communicate with the core network via the RAN, and through the core network, the UE can connect to external networks (such as the Internet) and other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for the UE, such as via a wired access network, a wireless local area network (WLAN) (e.g., based on the IEEE 802.11 communication standard), etc.

[0044] A base station may operate according to one of several RATs to communicate with a UE, depending on the network in which it is deployed, and may be alternatively referred to as an Access Point (AP), Network Node, B Node (NB), Evolved B Node (eNB), Next Generation eNB (ng-eNB), New Radio (NR) B Node (also referred to as gNB or gNodeB), etc. A base station may primarily be used to support radio access by the UE, including supporting data, voice, and / or signaling connections with the supported UE. In some systems, the base station may provide edge node signaling functions, while in others, it may provide additional control and / or network management functions. The communication link through which the UE can signal to the base station is called an uplink (UL) channel (e.g., reverse traffic channel, reverse control channel, access channel, etc.). The communication link through which the base station can signal to the UE is called a downlink (DL) or forward link channel (e.g., paging channel, control channel, broadcast channel, forward traffic channel, etc.). As used herein, the term traffic channel (TCH) may refer to an uplink, reverse or downlink, and / or forward traffic channel.

[0045] The term "base station" can refer to a single physical transmit / receive point (TRP) or multiple physical TRPs that may or may not be co-located. For example, when the term "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. When the term "base station" refers to multiple co-located physical TRPs, the physical TRP may be an antenna array of the base station (e.g., in a multiple-input multiple-output (MIMO) system or in the case of beamforming at the base station). When 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 headend (RRH) (a remote base station connected to a serving base station). Alternatively, non-co-located physical TRPs may be the serving base station receiving measurement reports from the UE and a neighboring base station where the UE is measuring its reference RF signal (or simply "reference signal"). Since a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmissions from or receptions at a base station should be understood as references to the specific TRP of that base station.

[0046] Radio frequency (RF) signals, or “RF signals,” encompass electromagnetic waves of a given frequency that transmit information across 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 individual RF signals through multipath channels, a receiver may receive multiple “RF signals” corresponding to each transmitted RF signal. The same RF signal transmitted on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, RF signals may also be referred to as “wireless signals” or simply “signals,” where the context clearly indicates that the term “signal” refers to a wireless signal or an RF signal.

[0047] Reference Figure 1 Base station 102 may include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one aspect, macrocell base stations may include eNB and / or ng-eNB (where wireless communication system 100 corresponds to an LTE network), or gNB (where wireless communication system 100 corresponds to an NR network), or a combination of both, and small cell base stations may include femtocells, picocells, microcells, etc.

[0048] In some implementations that support UE positioning, the base station may not support the UE's radio access (e.g., it may not support data, voice, and / or signaling connections regarding the UE), but may instead transmit reference signals to the UE for measurement, and / or receive and measure signals transmitted by the UE. Such a base station may be referred to as a positioning tower (e.g., in the case of transmitting signals to the UE) and / or as a location measurement unit (e.g., in the case of receiving and measuring signals from the UE).

[0049] Each base station 102 can collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) via a backhaul link 122, and connect to one or more location servers 172 (which may be part of the core network 170 or external to the core network 170) via the core network 170. Among other functions, the base station 102 can also perform functions related to one or more of the following: transmitting user data, radio channel cryptography and decoding, 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, location, and delivery of alarm messages. The base stations 102 can communicate with each other directly or indirectly (e.g., via the EPC or 5GC) via a backhaul link 134 (which may be wired and / or wireless).

[0050] Base station 102 can wirelessly communicate with UE 104. Each base station 102 can provide communication coverage for its respective geographical coverage area 110. In one aspect, one or more cells can be supported by base station 102 in each coverage area 110. A “cell” is a logical communication entity used to communicate with a base station (e.g., on a frequency resource, referred to as a carrier frequency, component carrier, carrier, frequency band, etc.) and can be associated with an identifier (e.g., Physical Cell Identifier (PCI), Virtual Cell Identifier (VCI), 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 that can provide access to different types of UEs (e.g., Machine Type Communication (MTC), Narrowband IoT (NB-IoT), Enhanced Mobile Broadband (eMBB), or others). Since cells are supported by specific base stations, the term “cell” can refer to either or both of the logical communication entity and the base station supporting that logical communication entity, depending on the context. Additionally, since the TRP is typically the physical transmission point of a cell, the terms "cell" and "TRP" are used interchangeably. In some cases, the term "cell" can also refer to the geographical coverage area (e.g., sector) of a base station, in the sense that the carrier frequency can be detected and used for communication within a portion of a geographical coverage area 110.

[0051] While the geographic coverage areas 110 of adjacent macrocell base stations 102 may partially overlap (e.g., in handover areas), some geographic coverage areas 110 may substantially overlap with larger geographic coverage areas 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 and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB) that can provide service to a restricted group known as a Closed Subscriber Group (CSG).

[0052] The communication link 120 between base station 102 and UE 104 may include uplink (also known as reverse link) transmission from UE 104 to base station 102 and / or downlink (also known as forward link) transmission from base station 102 to UE 104. The communication link 120 may use MIMO antenna technologies, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may use one or more carrier frequencies. Carrier allocation may be asymmetric with respect to the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink compared to the uplink).

[0053] The wireless communication system 100 may further include a wireless local area network (WLAN) access point (AP) 150 communicating with a WLAN station (STA) 152 via a communication link 154 in unlicensed spectrum (e.g., 5 GHz). When communicating in unlicensed spectrum, the WLAN STA 152 and / or WLAN AP 150 may perform a clear channel assessment (CCA) or listen-before-speak (LBT) procedure to determine channel availability before communication. In some examples, the wireless communication system 100 may include devices (e.g., UEs, etc.) that communicate with one or more UEs 104, base stations 102, APs 150, etc., using ultra-wideband (UWB) spectrum. The UWB spectrum can range from 3.1 to 10.5 GHz.

[0054] Small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in unlicensed spectrum, small cell base station 102' can employ LTE or NR technology and use the same 5 GHz unlicensed spectrum as used by WLAN AP 150. Small cell base station 102' employing LTE and / or 5G in unlicensed spectrum can enhance access network coverage and / or increase access network capacity. NR in unlicensed spectrum may be referred to as NR-U. LTE in unlicensed spectrum may be referred to as LTE-U, Licensed Assisted Access (LAA), or MulteFire.

[0055] The wireless communication system 100 may further include a millimeter-wave (mmW) base station 180, which can operate in mmW and / or near-mmW frequencies to communicate with the UE 182. Extremely high frequency (EHF) is a portion of the electromagnetic spectrum that contains radio frequency (RF). EHF has a range of 30 GHz to 300 GHz and wavelengths between 1 mm and 10 mm. Radio waves in this band are referred to as millimeter waves. Near-mmW extends down to a frequency of 3 GHz with a wavelength of 100 mm. Ultra-high frequency (SHF) bands extend between 3 GHz and 30 GHz, and are also referred to as centimeter waves. Communication using mmW and / or near-mmW RF bands has high path loss and relatively short range. The mmW base station 180 and the UE 182 can utilize beamforming (transmit and / or receive) on the mmW communication link 184 to compensate for the extremely high path loss and short range. Furthermore, it will be appreciated that in alternative configurations, one or more base stations 102 may also use mmW or near-mmW and beamforming for transmission. Accordingly, it will be understood that the foregoing explanations are merely illustrative and should not be construed as limiting the aspects disclosed herein.

[0056] Transmit beamforming is a technique for focusing RF signals in a specific direction. Conventionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omnidirectionally). Using transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thus providing the receiving device with a faster (in terms of data rate) and stronger RF signal. To change the directivity of the RF signal during transmission, the network node can control the phase and relative amplitude of the RF signal at each of one or more transmitters broadcasting the RF signal. For example, the network node can use an antenna array (referred to as a "phased array" or "antenna array") that generates a beam of RF waves, which can be "guided" to different directions without actually moving the antennas. Specifically, RF currents from the transmitters are fed to the individual antennas with the correct phase relationship so that radio waves from the separate antennas add together in the desired direction to increase radiation, while canceling each other out in the undesired direction to suppress radiation.

[0057] In receive beamforming, a receiver uses a receive beam to amplify an RF signal detected on a given channel. For example, a receiver may increase the gain setting of an antenna array and / or adjust the phase setting of the antenna array in a specific direction to amplify the RF signal received from that direction (e.g., increase its gain level). Thus, when a receiver is referred to as beamforming in a certain direction, it means that the beam gain in that direction is higher than the beam gain along other directions, or that the beam gain in that direction is the highest compared to the beam gains of other receive beams available to the receiver. This results in a stronger received signal strength (e.g., Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal-to-Interference Plus-Noise Ratio (SINR), etc.) of the RF signal received from that direction.

[0058] The receive beam can be spatially dependent. Spatial dependence means that the parameters of the transmit beam used for the second reference signal can be derived from information about the receive beam of the first reference signal. For example, the UE can use a specific receive beam to receive one or more reference downlink reference signals (e.g., Position Reference Signal (PRS), Tracking Reference Signal (TRS), Phase Tracking Reference Signal (PTRS), Cell-Specific Reference Signal (CRS), Channel State Information Reference Signal (CSI-RS), Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS), Synchronization Signal Block (SSB), etc.) from the base station. The UE can then form a transmit beam based on the parameters of the receive beam to transmit one or more uplink reference signals (e.g., Uplink Position Reference Signal (UL-PRS), Detection Reference Signal (SRS), Demodulation Reference Signal (DMRS), PTRS, etc.) to the base station.

[0059] Note that, depending on the entity forming the "downlink" beam, the beam can be either a transmit beam or a receive beam. For example, if a base station is forming a downlink beam to transmit a reference signal to a UE, then the downlink beam is a transmit beam. However, if a UE is forming a downlink beam, then the downlink beam is a receive beam for receiving downlink reference signals. Similarly, depending on the entity forming the "uplink" beam, the beam can be either a transmit beam or a receive beam. For example, if a base station is forming an uplink beam, then the uplink beam is an uplink receive beam, while if a UE is forming an uplink beam, then the uplink beam is an uplink transmit beam.

[0060] In 5G, the spectrum in which radio nodes (e.g., base stations 102 / 180, UE 104 / 182) operate is divided into several frequency ranges: FR1 (from 450 to 6000 MHz), FR2 (from 24250 to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2). In multi-carrier systems (such as 5G), one of the carrier frequencies is referred to as the “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 “SCell.” In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) utilized by UE 104 / 182 and on the cell in which 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 control channels that vary from UE to UE, and can be a carrier in a licensed frequency (however, this is not always the case).

[0061] For example, still refer to Figure 1One of the frequencies utilized by macrocell base station 102 can be an anchor carrier (or "PCell"), and other frequencies utilized by macrocell base station 102 and / or mmW base station 180 can be secondary carriers ("SCell"). In carrier aggregation, base station 102 and / or UE 104 can use a spectrum of up to Y MHz (e.g., 5, 10, 15, 20, 100 MHz) bandwidth per carrier, with up to a total of Yx MHz (x component carriers) for transmission in each direction. Component carriers may be adjacent to each other in the spectrum or may not be adjacent to each other. Carrier allocation may be asymmetrical with respect to downlink and uplink (e.g., more or fewer carriers may be allocated to downlink compared to uplink). Simultaneous transmission and / or reception on multiple carriers allows UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically result in twice the data rate (i.e., 40 MHz) compared to the data rate obtained by a single 20 MHz carrier.

[0062] To operate on multiple carrier frequencies, base station 102 and / or UE 104 are equipped with multiple receivers and / or transmitters. For example, 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', while "Receiver 2" is a single-band receiver that can be tuned to only band 'Z'. In this example, if UE 104 is being served in band 'X', then band 'X' will be referred to as 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 UE 104 is being served in band 'X' or band 'Y', due to the separate "Receiver 2", UE 104 can measure band 'Z' without interrupting service on band 'X' or band 'Y'.

[0063] The wireless communication system 100 may further include a UE 164, which can communicate with the macrocell base station 102 on the communication link 120 and / or with the mmW base station 180 on the mmW communication link 184. For example, the macrocell base station 102 may support 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.

[0064] The wireless communication system 100 may further include one or more UEs (such as UE 190) that 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, UE 190 has a D2D P2P link 192 with a UE 104 connected to a base station 102 (through which UE 190 indirectly obtains cellular connectivity), and a D2D P2P link 194 with a WLANSTA 152 connected to a WLAN AP 150 (through which UE 190 indirectly obtains WLAN-based Internet connectivity). In one example, D2D P2P links 192 and 194 can be supported using any well-known D2D RAT (such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), Bluetooth®, etc.).

[0065] According to various aspects, Figure 2A Example wireless network architecture 200 is explained. For example, 5GC 210 (also referred to as Next Generation Core (NGC)) can be functionally considered as control plane functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and user plane functions 212 (e.g., UE gateway functions, access to data networks, IP routing, etc.), which operate collaboratively to form the core network. User plane interface (NG-U) 213 and control plane interface (NG-C) 215 connect gNB 222 to 5GC 210, specifically to control plane functions 214 and user plane functions 212. In an additional configuration, ng-eNB 224 can also connect to 5GC 210 via NG-C 215 to control plane function 214 and NG-U 213 to user plane function 212. Furthermore, ng-eNB 224 can communicate directly with gNB 222 via backhaul connection 223. In some configurations, the new RAN 220 may have only one or more gNB 222s, while other configurations include both one or more ng-eNB 224s and one or more gNB 222s. The gNB 222 or ng-eNB 224 can be used with UE 204 (e.g., Figure 1 (to communicate with any UE depicted in the text).

[0066] Another optional aspect may include location server 230, which may communicate with 5GC 210 to provide location assistance to UE 204. Location server 230 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules extending across multiple physical servers, etc.), or alternatively, each may correspond to a single server. Location server 230 may be configured to support one or more location services for UE 204, which UE 204 can connect to via the core network, 5GC 210, and / or via the Internet (not explained). Furthermore, location server 230 may be integrated into a component of the core network, or alternatively, may be external to the core network. In some examples, location server 230 may be operated by the operator or provider of 5GC 210, a third party, an original equipment manufacturer (OEM), or other parties. In some cases, multiple location servers may be provided, such as location servers for carriers, location servers for specific device OEMs, and / or other location servers. In such cases, location-aided data can be received from the operator's location server, and other auxiliary data can be received from the OEM's location server.

[0067] According to various aspects, Figure 2B Another example wireless network architecture 250 is described. For example, 5GC 260 can be functionally considered as both a control plane function (provided by Access and Mobility Management Function (AMF) 264) and a user plane function (provided by User Plane Function (UPF) 262), which operate collaboratively to form the core network (i.e., 5GC 260). User plane interface 263 and control plane interface 265 connect ng-eNB 224 to 5GC 260, specifically to UPF 262 and AMF 264, respectively. In an additional configuration, gNB 222 can also connect to 5GC 260 via control plane interface 265 to AMF 264 and user plane interface 263 to UPF 262. Furthermore, ng-eNB 224 can communicate directly with gNB 222 via backhaul connection 223, with or without gNB direct connectivity to 5GC 260. In some configurations, the new RAN 220 may have only one or more gNB222s, while other configurations include both one or more ng-eNB 224s and one or more gNB 222s. The gNB 222 or ng-eNB224 can be used with UE 204 (e.g., Figure 1 The base station of the new RAN 220 communicates with the AMF 264 via the N2 interface and with the UPF 262 via the N3 interface.

[0068] The functions of AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transmission of Session Management (SM) messages between UE 204 and Session Management Function (SMF) 266, transparent proxy service for routing SM messages, access authentication and access authorization, transmission of Short Message Service (SMS) messages between UE 204 and Short Message Service Function (SMSF) (not shown), and Security Anchor Functionality (SEAF). AMF 264 also interacts with Authentication Server Function (AUSF) (not shown) and UE 204, and receives an intermediate key established as a result of the UE 204 authentication process. In the case of authentication based on the UMTS (Universal Mobile Telecommunications System) Subscriber Identity Module (USIM), AMF 264 retrieves security material from the AUSSF. The functions of AMF 264 also include Security Context Management (SCM). The SCM receives a key from the SEAF, which is used by the SCM to derive a key that varies depending on the access network. The functionality of AMF 264 also includes: location service management for regulatory services, transmission of location service messages between UE 204 and Location Management Function (LMF) 270 (which acts as location server 230), transmission of location service messages between the new RAN 220 and LMF 270, allocation of EPS bearer identifiers for interoperability with Evolved Packet Systems (EPS), and UE 204 mobility event notification. Furthermore, AMF 264 also supports functionality for non-3GPP access networks.

[0069] The functions of UPF 262 include: acting as an anchor point for intra / inter-RAT mobility (where applicable), acting as an external Protocol Data Unit (PDU) session point interconnecting to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, quality of service (QoS) handling for user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in the downlink), uplink traffic verification (Service Data Flow (SDF) to QoS Flow mapping), transport-level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding one or more "end markers" to the source RAN node. UPF 262 may also support the transmission of location service messages on the user plane between UE 204 and a location server (such as Secure User Plane Positioning (SUPL) Location Platform (SLP) 272).

[0070] The functions of SMF 266 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, traffic bootstrapping configuration at UPF 262 to route traffic to the correct destination, partial control of policy enforcement and QoS, and downlink data notification. The interface used by SMF 266 to communicate with AMF 264 is called the N11 interface.

[0071] Another optional aspect may include LMF 270, which can communicate with 5GC 260 to provide location assistance to UE 204. LMF 270 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules extending across multiple physical servers, etc.), or alternatively, each may correspond to a single server. LMF 270 may be configured to support one or more location services for UE 204, which can connect to LMF 270 via the core network, 5GC 260, and / or via the Internet (not explained). SLP 272 supports similar functionality to LMF 270, but while LMF 270 can communicate with AMF 264, the new RAN 220, and UE 204 on the control plane (e.g., using interfaces and protocols designed to convey signaling messages rather than voice or data), SLP 272 can communicate with UE 204 and external clients on the user plane (e.g., using protocols designed to carry voice and / or data, such as Transmission Control Protocol (TCP) and / or IP). Figure 2B (Not shown in the image) communicates.

[0072] On one hand, the LMF 270 and / or SLP 272 can be integrated with base stations such as gNB 222 and / or ng-eNB 224. When integrated into gNB 222 and / or ng-eNB 224, the LMF 270 and / or SLP 272 may be referred to as a “Location Management Component” or “LMC”. However, as used herein, references to LMF 270 and SLP 272 include both cases where LMF 270 and SLP 272 are components of the core network (e.g., 5GC 260) and cases where LMF 270 and SLP 272 are components of the base station.

[0073] Figure 3 A block diagram of a base station 102 and a UE 104 designed according to some aspects of this disclosure is shown, which implements the transmission and processing of signals exchanged between the UE and the base station. Design 300 includes components of base station 102 and UE 104, which may be... Figure 1One of the base stations 102 and one of the UEs 104. The base station 102 may be equipped with T antennas 334a to 334t, while the UE 104 may be equipped with R antennas 352a to 352r, where generally T ≥ 1 and R ≥ 1.

[0074] At base station 102, transmit processor 320 can receive data destined for one or more UEs from data source 312, select one or more modulation and coding schemes (MCS) for each UE based at least in part on channel quality indicators (CQI) received from each UE, process (e.g., encode and modulate) the data destined for each UE based at least in part on the MCS selected for each UE, and provide data symbols for all UEs. Transmit processor 320 can also process system information (e.g., semi-static resource allocation information (SRPI) and control information (e.g., CQI requests, grants, upper-layer signaling, etc.) and provide overhead symbols and control symbols. Transmit processor 320 can also generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS)) and synchronization signals (e.g., primary synchronization signal (PSS) and secondary synchronization signal (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 330 can perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols where applicable, and can provide T output symbol streams to T modulators (MODs) 332a to 332t. The modulators 332a to 332t are shown as combined modulator-demodulators (MOD-DEMODs). In some cases, the modulators and demodulators can be separate components. Each modulator in the modulators 332a to 332t can 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 332a to 332t can further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. The T downlink signals can be transmitted from the modulators 332a to 332t via T antennas 334a to 334t, respectively. Based on some aspects described in more detail below, position coding can be used to generate synchronization signals to convey additional information.

[0075] At UE 104, antennas 352a to 352r can receive downlink signals from base station 102 and / or other base stations and can provide the received signals to demodulators (DEMODs) 354a to 354r respectively. Demodulators 354a to 354r are shown as combined modulator-demodulators (MOD-DEMODs). In some cases, the modulator and demodulator can be separate components. Each demodulator in demodulators 354a to 354r can condition (e.g., filter, amplify, downconvert, and digitize) the received signal to obtain an input sample. Each demodulator in demodulators 354a to 354r can further process the input sample (e.g., for OFDM, etc.) to obtain received symbols. MIMO detector 356 can obtain the received symbols from all R demodulators 354a to 354r, perform MIMO detection on these received symbols where applicable, and provide detected symbols. The receiver processor 358 can process (e.g., demodulate and decode) these detected symbols, provide the decoded data for UE 104 to the data sink 360, and provide the decoded control information and system information to the controller / processor 380. The channel processor can determine the Reference Received Power (RSRP), Received Signal Strength Indicator (RSSI), Reference Received Quality (RSRQ), Channel Quality Indicator (CQI), etc.

[0076] On the uplink, at UE 104, the transmit processor 364 can receive and process data from data source 362 and control information from controller / processor 380 (e.g., reports including RSRP, RSSI, RSRQ, CQI, etc.). The transmit processor 364 can also generate reference symbols for one or more reference signals (e.g., based at least in part on a β value or set of β values ​​associated with the one or more reference signals). Symbols from the transmit processor 364 can be pre-encoded by the TX MIMO processor 366, further processed by modulators 354a to 354r (e.g., for DFT-s-OFDM, CP-OFDM, etc.), and transmitted to base station 102. At base station 102, uplink signals from UE 104 and other UEs can be received by antennas 336a to 334t, processed by demodulators 332a to 332t, detected by MIMO detector 336 where applicable, and further processed by receiver processor 338 to obtain decoded data and control information transmitted by UE 104. Receiver processor 338 can provide the decoded data to data sink 339 and the decoded control information to controller (processor) 340. Base station 102 may include communication unit 344 and communicates with network controller 331 via communication unit 344. Network controller 331 may include communication unit 394, controller / processor 390, and memory 392.

[0077] In some respects, one or more components of UE 104 may be included in the housing. These include the controller 340 of base station 102, the controller / processor 380 of UE 104, and / or Figure 3 Any other component(s) may perform one or more techniques associated with the determination of implicit UCI β values ​​for NR.

[0078] Memory 342 and 382 may store data and program code for base station 102 and UE 104, respectively. Scheduler 346 may schedule UE for data transmission on downlink and / or uplink.

[0079] In some implementations, UE 104 may include: means for determining one or more parameters associated with a communication channel between UE 104 and base station 102; means for determining a frequency modulation pattern for a reference signal based on the one or more parameters; and means for transmitting the frequency modulation pattern to the base station. In some cases, the frequency modulation pattern may be used by the base station to transmit the reference signal to UE 104.

[0080] In some implementations, base station 102 may include: means for determining one or more parameters associated with a communication channel between UE 104 and base station 102; means for determining a frequency modulation mode for a reference signal based on the one or more parameters; and means for transmitting the frequency modulation mode to UE 104. In some cases, the frequency modulation mode may be used by UE 104 to transmit a reference signal to base station 102.

[0081] As mentioned above, a frequency modulation pattern can be defined as a specific arrangement of resource elements in a given resource block for transmitting reference signals between a UE (such as one of UEs 104) and a base station (such as base station 102). Currently, frequency modulation patterns are predefined in 5G communication standards. Predefined frequency modulation patterns may not be optimized for all environments. In other words, the arrangement or combination of resource elements used to transmit a specific reference signal may not be optimized across all possible conditions of user equipment and base station operation. Therefore, signal efficiency and latency reduction in 5G mobile systems can be improved by dynamically determining the optimal frequency modulation pattern configuration.

[0082] Resource blocks can be transmitted over UL or DL ​​between UE 104 and base station 102 using radio frames. Various radio frame structures can be used to support downlink and uplink transmissions between network nodes (e.g., base station and UE). Figure 4 Figure 400 is an example illustrating a downlink frame structure according to some aspects of this disclosure. Other wireless communication technologies may have different frame structures and / or different channels.

[0083] NR (and LTE) utilize OFDM on the downlink and single-carrier frequency division multiplexing (SC-FDM) on the uplink. However, unlike LTE, NR also has the option to use OFDM on the uplink. OFDM and SC-FDM divide the system bandwidth into multiple (K) orthogonal subcarriers, which are often referred to as frequency modulation, frequency slots, etc. Each subcarrier can be modulated with data. Generally, modulation symbols are transmitted in the frequency domain for OFDM and in the time domain for SC-FDM. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers (K) can depend on the system bandwidth. For example, the subcarrier spacing can be 15 kHz, and the minimum resource allocation (resource block) can be 12 subcarriers (or 180 kHz). Therefore, for system bandwidths of 1.25, 2.5, 5, 10, or 20 MHz, the nominal Fast Fourier Transform (FFT) size can be equal to 128, 256, 512, 1024, or 2048, respectively. The system bandwidth can also be divided into subbands. For example, a subband can cover 1.08 MHz (i.e., 6 resource blocks), and for system bandwidths of 1.25, 2.5, 5, 10, or 20 MHz, there can be 1, 2, 4, 8, or 16 subbands, respectively.

[0084] LTE supports single-parameter design (subcarrier spacing, symbol length, etc.). In contrast, NR supports multiple-parameter design (µ). For example, subcarrier spacings (SCS) of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz or greater can be available. Table 1 below lists some of the various parameters used for different NR parameter designs.

[0085]

[0086] Table 1

[0087] In one example, a parameter design of 15 kHz is used. Therefore, in the time domain, a 10-millisecond (ms) frame is divided into 10 equal-sized subframes, each 1 ms long, and each subframe includes one time slot. Figure 4 In this context, time is represented horizontally (e.g., on the X-axis), where time increases from left to right, while frequency is represented vertically (e.g., on the Y-axis), where frequency increases (or decreases) from bottom to top.

[0088] Resource grids can be used to represent time slots, each of which includes one or more time-concurrent resource blocks (RBs) in the frequency domain (also known as physical RBs (PRBs)). Figure 4 An example of resource block (RB) 402 has been explained. The resource grid is further divided into multiple resource elements (REs). See reference. Figure 4RB 402 includes multiple REs, including resource elements (REs) 404. RE 404 may correspond to a symbol length in the time domain and a subcarrier in the frequency domain. Figure 4 In the parameter design, for a normal cyclic prefix, RB 402 can contain 12 consecutive subcarriers in the frequency domain and 7 consecutive symbols in the time domain, for a total of 84 REs (such as RE 404). For an extended cyclic prefix, RB can contain 12 consecutive subcarriers in the frequency domain and 6 consecutive symbols in the time domain, for a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

[0089] Some REs carry downlink reference (pilot) signals (DL-RS). DL-RS may include, but is not limited to, PRS, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, etc. Figure 4 Example locations of REs carrying DL-RS are explained (where each RE is labeled "R"). Figure 4 The following examples will describe CSI-RS and TRS as illustrative examples of DL-RS. Throughout this disclosure, although references may be made to CSI-RS and / or TRS, it should be understood that TRS is a specific type of CSI-RS. However, this disclosure is not limited thereto, and the dynamic-based techniques described herein for determining frequency modulation patterns can be equally applied to any other DL-RS and / or UL reference (pilot) signal (UL-RS).

[0090] The set of resource elements (REs) used to transmit CSI-RS and / or TRS is called a frequency modulation pattern. This frequency modulation pattern can span multiple REs on a single RB, multiple REs on multiple RBs in the frequency domain, and span 'N' (e.g., one or more) consecutive symbols within a time slot in the time domain, or it can span a single RE in a given RB.

[0091] Figure 4 The position of the RE (marked "R") in RB 402 indicates an example frequency modulation pattern for CSI-RS signals and / or an example frequency modulation pattern for TRS signals. This frequency modulation pattern can be defined using REs in RB 402 with (time (subcarrier), frequency (OFDM symbol)) coordinates. For example, in Figure 4 In the example, the frequency modulation mode can be defined by REs in RB 402 with coordinates (2,4), (4,4), (6,4), and (8,4). These coordinates define... Figure 4 The location of RE marked "R" in the code. 3GPP defines various other frequency modulation modes for the CSI-RS and TRS of the 5G standard. Figure 4The frequency modulation patterns shown, as well as other frequency modulation patterns specified by 3GPP, are predefined. In other words, when CSI-RS or TRS is to be transmitted from base station 102 to UE 104 on the DL channel (which can be in each RB, every other RB, etc.), base station 102 uses one of these predefined frequency modulation patterns to transmit CSI-RS and / or TRS to UE 104.

[0092] Predefined frequency modulation patterns that rely on decoupling from the conditions (channel conditions) for communication with the UE and the base station can lead to redundant and / or otherwise inefficient use of network resources to transmit CSI-RS and / or TRS from the base station to the UE. An example of such inefficient use of network resources includes the overhead associated with the transmission of reference signals, which can be reduced by implementing the techniques described below for determining the frequency modulation patterns used for reference signal transmission.

[0093] As mentioned above, this document describes systems and techniques for determining a frequency modulation pattern (also referred to as a customized and / or optimized frequency modulation pattern) for reference signal transmission between a UE and a base station. In some cases, these systems and techniques may be implemented by a base station (such as base station 102). In some examples, these systems and techniques may be implemented by a UE (such as UE 104). These systems and techniques can determine and communicate the customized frequency modulation pattern based on the current and / or previous conditions of the communication channel between the UE and the base station. The systems and techniques described herein can also utilize trained machine learning models to determine the customized and / or optimized frequency modulation pattern for reference signal transmission between the UE and the base station.

[0094] Configuring the UE and / or base station to determine an optimized (and / or customized) frequency modulation mode (e.g., whenever CSI-RS and / or TRS will be transmitted to the UE on the DL channel) can improve network resource utilization efficiency at the physical layer, reduce overhead associated with the transmission of reference signals (e.g., for some RS frequency modulations, based on Energy Per Resource Element (EPRE) = 0, as described herein), etc.

[0095] For example, under certain conditions, such as UE mobility conditions, environmental conditions, and / or the transmission and reception capabilities of the UE and the base station (e.g., the UE has an advanced receiver that can estimate the channel using lower RS ​​frequency modulation, etc.), using Figure 4The example frequency modulation pattern described herein may be redundant or inefficient. In this case, and in others, transmitting CSI-RS or TRS on four different REs may be redundant. Using the systems and techniques described herein, the UE and / or base station can customize the frequency modulation pattern used for the transmission of the reference signal under such or other conditions. As a non-limiting example, using one or two REs instead of four frequency modulations to transmit CSI-RS or TRS may be sufficient.

[0096] In another example, environmental conditions may result in suboptimal signal reception (below a threshold) at UE 104. In this instance, UE 104 may transmit CSI-RS or TRS on more than four REs. In yet another example, and depending on the channel and device configuration, some REs in a given RB may be reserved for communication of other pilot signals, which would interfere with the REs used to transmit CSI-RS and / or TRS signals.

[0097] While the foregoing has described several examples where using predefined frequency modulation patterns might be suboptimal, various other situations may exist where using custom or alternative frequency modulation patterns may be advantageous compared to predefined patterns defined in the standard. Custom frequency modulation patterns can be determined by generating new patterns based on channel conditions. In some cases, the new frequency modulation pattern is not defined in the 3GPP standard. For example, a new frequency modulation pattern may include custom (e.g., irregular) frequency modulation placement within a resource block. Illustrative examples of such new frequency modulation patterns are discussed below. Figure 8A and Figure 8B This has been described. In either case, whether a new frequency modulation mode is determined or an existing mode is used, identifying the optimal frequency modulation mode for transmitting CSI-RS or TRS signals can improve spectral efficiency and reduce latency.

[0098] In some examples, the frequency modulation mode optimization systems and techniques described herein can take channel conditions or other factors into account when determining the optimized frequency modulation mode. For example, a UE (e.g., referenced above) Figure 1 The UE 104 described herein receives data from (e.g., referenced above) on the DL channel from (various) base stations (e.g., as described above). Figure 1 The UE is connected to one or more base stations 102 as described above, which receive CSI-RS and / or TRS. The UE may use CSI-RS to perform one or more measurements, including but not limited to channel throughput, mobility, and reference signal received power (RSRP) during beam management, frequency and / or time tracking, precoding based on demodulation and UL reciprocity, etc. Similarly, the UE may use TRS to perform one or more measurements, including but not limited to time and frequency tracking, path delay spread, and Doppler spread, etc.

[0099] Such measurements performed by the UE may vary depending on channel conditions (e.g., environmental conditions, mobility state, etc.) and the specific frequency modulation patterns transmitted to the UE by its respective base station via CSI-RS and / or TRS. Accordingly, the UE can determine over time which frequency modulation patterns lead to better measurements for a given set of channel conditions.

[0100] In some examples, machine learning models can be used to determine optimized frequency modulation patterns and / or EPRE values. For example, as described in more detail below, a training dataset can be used to train the machine learning model. In some aspects, the training dataset may include channel conditions, different frequency modulation patterns on which the UE receives CSI-RS and TRS signals, measurements obtained from the results performed by the receiving UE under such channel conditions and frequency modulation patterns, measured throughput, etc. Once trained, the machine learning model can take channel conditions as input (e.g., one or more parameters associated with the communication channel between UE 104 and the base station 102 to which it is connected, such as the mobility state of UE 104, the environmental conditions in which UE 104 is operating, etc.) and provide a recommended frequency modulation pattern as output for base station 102 to use in transmitting CSI-RS and / or TRS to UE 104 on the DL channel.

[0101] Now refer to Figures 5 to 10 An example to describe the frequency modulation mode optimization process.

[0102] Figure 5 An example neural architecture of a neural network 500, which can be trained for frequency modulation placement optimization according to some aspects of this disclosure, is described. The example neural 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 base station 102 and / or UE 104. The neural network 500 can be a feedforward neural network or any other known or under-development neural network or machine learning model.

[0103] Neural network description 502 may include the complete specification of neural network 500, including Figure 5 The neural architecture shown is illustrated. For example, neural network description 502 may include a description or specification of the architecture of neural network 500 (e.g., layers, layer interconnections, number of nodes in each layer, etc.); input and output descriptions indicating how the inputs and outputs are formed or processed; indications of activation functions, operations or filters in the neural network, etc.; neural network parameters such as weights, biases, etc.; and so on.

[0104] The neural network 500 may reflect the neural architecture defined in the neural network description 502. In this non-limiting example, the neural network 500 includes an input layer 503 that can receive one or more sets of input data. The input data can be any type of data, such as one or more parameters associated with a communication channel (e.g., the DL communication channel between UE 104 and base station 102, such as environmental conditions, UE mobility state, etc.), various measurements performed by UE 104 using previously transmitted CSI-RS or TRS (e.g., the frequency modulation pattern used to transmit CSI-RS or TRS in a previous RB, etc.).

[0105] The neural network 500 may include hidden layers 504A to 504N (collectively referred to as "504"). Hidden layers 504 may include n hidden layers, where n is an integer greater than or equal to 1. The number of hidden layers may include the number of layers required to achieve the desired processing result and / or to present the intended outcome. In an illustrative example, any layer of hidden layers 504 may include: data representing one or more data provided at input layer 503, such as one or more parameters associated with a communication channel (e.g., a DL communication channel between UE 104, such as environmental conditions, UE mobility state, etc.), previously used frequency modulation patterns used by base station 102 to transmit CSI-RS and / or TRS signals to UE 104, one or more measurements performed by UE 104 using previously used frequency modulation patterns and associated throughput, increments (differences) between measurements performed by UE 104 using different frequency modulation patterns, and so on.

[0106] The neural network 500 further includes an output layer 506 that provides the output produced by the processing performed by the hidden layer 504. In an illustrative example, the output layer 506 may provide output data based on the input data. In an example context relating to determining the frequency modulation pattern for transmitting CSI-RS and / or TRS, the output data may include a recommended frequency modulation pattern to be used by the base station 102 to transmit CSI-RS and / or TRS to the UE 104 on the DL channel.

[0107] In this example, neural network 500 is a multi-layer neural network with 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 information while processing it. In some cases, neural network 500 may include a feedforward neural network. In other cases, neural network 500 may include a recurrent neural network, which may have loops that allow information to be carried across nodes as input is read in.

[0108] Information can be exchanged between nodes via node-to-node interconnects between layers. Nodes in input layer 503 can activate a set of nodes in the first hidden layer 504A. For example, as shown, each input node of input layer 503 is connected to each node in the first hidden layer 504A. Nodes in hidden layer 504A can transform information by applying an activation function to the information of each input node. The information derived from the transformation can then be passed to and can activate nodes in the next hidden layer (e.g., 504B), which can execute their own specified functions. Example functions include convolution, upsampling, data transformation, pooling, and / or any other suitable function. The output of a hidden layer (e.g., 504B) can then activate nodes in the next hidden layer (e.g., 504N), and so on. The output of the last hidden layer can activate one or more nodes in output layer 506, providing the output at that point. In some cases, although nodes in neural network 500 (e.g., nodes 508A, 508B, 508C) are shown as having multiple output lines, a node has a single output and all lines shown as output from a single node represent the same output value.

[0109] In some cases, each node or the interconnections between nodes can have weights derived from a set of parameters trained on the neural network 500. For example, interconnections between nodes can represent learned pieces of information related to the interconnected nodes. Interconnections can have numerical weights that can be tuned (e.g., based on the training dataset), allowing the neural network 500 to adapt to the input and learn as it processes more data.

[0110] The neural network 500 can be pre-trained to process features from the data in the input layer 503 using different hidden layers 504 in order to provide output through the output layer 506.

[0111] In some cases, the neural network 500 can use a training process called backpropagation to adjust the weights of its nodes. Backpropagation can include forward pass, loss function, back pass, and weight update. Forward pass, loss function, back pass, and parameter update can be performed on a single training iteration. This process can be repeated for each training dataset up to a certain number of iterations until the weights of these layers are accurately tuned (e.g., to meet configurable thresholds determined based on experimental and / or empirical research).

[0112] Neural Network 500 can include any suitable neural or deep learning network. One example includes a Convolutional Neural Network (CNN), which consists of an input layer and an output layer, with multiple hidden layers between the input and output layers. The hidden layers of a CNN include a series of convolutional layers, non-linear layers, pooling layers (for downsampling), and fully connected layers. In other examples, Neural Network 500 can represent any other neural network or deep learning network, such as an autoencoder, a deep belief network (DBN), a recurrent neural network (RNN), etc.

[0113] Once trained, the neural network 500 can receive one or more parameters as input associated with the communication channel between base station 102 and UE 104. Such parameters may include, but are not limited to, the environmental conditions under which base station 102 and UE 104 are communicating (e.g., weather conditions, indoor / outdoor channel conditions and / or cellular or wireless connectivity, transmission capabilities and power of base station 102 and / or UE 104, etc.). In some aspects, environmental conditions may further include the mobility state of UE 104 (e.g., how fast UE 104 moves toward or away from base station 102, etc.), the multipath characteristics of the channel, and / or various measurements performed by UE 104 using previously transmitted CSI-RS or TRS (e.g., the frequency modulation pattern used in the previous RB for transmitting CSI-RS or TRS, etc.). Based on these environmental conditions, an optimal frequency modulation pattern configuration can be determined and provided, for example, as an output recommendation of the frequency modulation pattern to be used for transmitting the next CSI-RS or TRS from base station 102 to UE 104.

[0114] In some examples, the receiver (e.g., base station 102 and / or UE 104) can monitor the received REs in real time (e.g., when REs are received) or near real time over time and / or frequency, and can determine over a period of time which REs are more suitable for the transmission of the reference signal (RS). In some cases, even the frequency modulation pattern used for data transmission can be interpreted as a pilot after it is decoded. The receiver can also determine the optimized power distribution of the frequency modulation pattern over which the reference signal is received. Once the optimized power distribution is determined, the receiver can feed the optimized pattern back to the transmitter for the transmission of the reference signal. Depending on the desired implementation, the transmitter may be associated with the same equipment as the receiver or may be associated with different equipment, such as another base station 102 and / or UE 104.

[0115] The trained neural network 500 can record parameters, channel conditions, and / or other information (e.g., UE location, etc.) and associate them with various frequency modulation (FM) modes used for reference signal transmission. As mentioned herein, channel conditions may include indoor and / or outdoor channel conditions, UE mobility state, multipath channel characteristics, and / or other channel conditions, or any combination thereof. For example, the trained neural network 500 can remember which FM modes were used with the recorded information so that they can be used in the future. Based on subsequent measurement and channel estimation processes, the trained neural network 500 can identify which FM modes are best suited for a given set of parameters and channel conditions. In the future, once such channel conditions occur or are detected, the trained neural network 500 can identify and use the most suitable FM mode to transmit the reference signal under the detected channel conditions. In some examples, the trained neural network 500 can be continuously updated or retrained (e.g., using online learning methods). For example, the trained neural network can be configured to be optimized whenever a reference signal is transmitted between base station 102 and UE 104 under various conditions.

[0116] As will be described below, the trained neural network 500 can be deployed at UE 104 or alternatively at base station 102. In one example, when the machine learning model is deployed at base station 102, base station 102 can receive one or more input parameters from UE 104 and determine the optimal frequency modulation mode of CSI-RS or TRS as the output of the trained neural network.

[0117] As mentioned above, the output of the trained neural network 500, whether implemented at base station 102 or UE 104, can be a customized and novel frequency modulation pattern not previously used for the transmission of CSI-RS or TRS signals. Alternatively, the output of the trained neural network 500 can be a previously used frequency modulation pattern, i.e., the optimal frequency modulation pattern determined by the trained neural network 500 for a given set of one or more parameters provided as input.

[0118] Figure 6 This is a flowchart of a process 600 for training a machine learning algorithm (such as a neural network 500) for frequency modulation mode determination according to some aspects of this disclosure. (The flowchart will be combined with...) Figure 5 To describe Figure 6 The neural network 500 can be implemented at base station 102 or UE 104.

[0119] In operation 610, neural controller 501 receives a description of the structure of neural network 500 (e.g., from base station 102), including but not limited to the architecture and layer definitions of neural network 500, layer interconnections, input and output descriptions, activation functions, operations, filters, and parameters (such as weights, coefficients, biases, etc.). In some examples, this description may be received from the device based on user input received by the device (e.g., input via an input device, such as a keyboard, mouse, touchscreen interface, and / or other types of input devices). In some examples, operation 610 is optional and may not be performed. For example, neural network 500 may vary depending on the UE (e.g., performed by the UE), and therefore the description and specific configuration of neural network 500 may be provided by UE 104. In operation 620, neural network 500 is generated based on the description received at operation 610. Using the description, neural controller 501 generates appropriate input layers, intermediate layers, and output layers, which have defined interconnections between layers and / or any weights or coefficients assigned to them. These weights and / or other coefficients can be set to initial values, which will be modified during training, as described below. In some examples, operation 620 is optional and may not be performed (e.g., when neural network 500 is different from the UE).

[0120] In operation 630, once the neural network 500 is defined, a training dataset is provided to the input layer 503 of the neural network 500. As described above, the training dataset may include, but is not limited to, various frequency modulation patterns used to transmit reference signals between base station 102 and UE 104, one or more parameters associated with the communication channel (e.g., a DL communication channel between UE 104 and base station 102), such as weather conditions, cellular or wireless connectivity, transmission capacity and power of base station 102 and / or UE 104, environmental conditions, UE mobility state, indoor / outdoor conditions, multipath characteristics of the communication channel, etc. Furthermore, previously used frequency modulation patterns for communication from base station 102 to UE 104, one or more measurements performed by UE 104 using previously used frequency modulation patterns and associated throughput, differences between measurements performed by UE 104 using different frequency modulations, etc., may be used when training the neural network 500. In some examples, there may not be an explicit dedicated training dataset for training the neural network 500, or the training dataset may not necessarily be a predetermined set of conditions and associated frequency modulation patterns. For example, in some cases, neural network 500 may alternatively be trained using information associated with the conditions under which base station 102 and UE 104 are using frequency modulation modes to transmit and transmit reference signals. In such examples, real-time data may be used for real-time training of neural network 500, for example, using online learning methods.

[0121] In operation 640, the training dataset is used to train neural network 500. In one example, training neural network 500 is an iterative process repeated multiple times, each time validated against a test dataset. The test dataset may include a set containing one or more parameters that are analogous to parameters used as part of the training dataset and the associated output frequency modulation pattern. During each iteration, the output at output layer 506 is compared to the test dataset, and the increment (Δ) between the output at output layer 506 at that iteration and the optimized output defined in the test dataset is determined. The weights and other coefficients of the individual layers can be adjusted based on this increment. This iterative process can continue until the increment for any given set of input parameters is less than a threshold. The threshold can be a configurable parameter determined based on experimental and / or empirical research.

[0122] In operation 650, and once the neural network 500 is trained, the trained neural network 500 is deployed at base station 102 and / or UE 104. As described below, given a set of input parameters associated with the communication channel between base station 102 and UE 104, the trained neural network can then be used to determine a frequency modulation pattern (e.g., a frequency modulation pattern for CSI-RS or TRS). The periodicity of the determined frequency modulation pattern can depend on any number of factors, including but not limited to the periodicity configured for the transmission of the reference signal, such as every subframe or RB, every other subframe or RB, every frame, etc. As channel conditions or other parameters change, the receiving device (e.g., base station 102 or UE 104 on which the trained neural network 500 is deployed) can retrain the neural network 500 to determine an optimized frequency modulation pattern and / or channel conditions for the new conditions.

[0123] In operation 660, a trigger condition for retraining neural network 500 is detected. This command can be received after the trained neural network 500 is deployed and after each instance of determining a frequency modulation mode for CSI-RS or TRS. In other words, whenever the trained neural network 500 determines a frequency modulation mode for CSI-RS and TRS transmission, the corresponding parameters used as input and the frequency modulation mode are used to retrain and optimize the trained neural network 500. The corresponding parameters used as input and the frequency modulation mode are provided as part of the received command for retraining neural network 500. In another example, the command can be received upon detection of a trigger condition, which will be referred to below. Figure 7 and Figure 9To further describe. Examples of such triggering conditions may include, but are not limited to, a threshold degradation of the performance of the frequency modulation mode recommended by the neural network 500 for transmitting the reference signal (where the threshold may be determined based on experimental and / or empirical studies), channel estimation error when the reference signal is used for channel estimation (e.g., when the channel estimation error reaches and / or exceeds a configurable threshold more than a certain number of times within a certain period of time, where the number of times and the time period are configurable parameters determined based on experimental and / or empirical studies), etc.

[0124] In operation 670, neural network 500 is retrained using the corresponding parameters and frequency modulation pattern received as input as part of the command at operation 660. Retraining neural network 500 may include adjusting the weights, coefficients, and / or biases at different nodes in different layers of neural network 500 using the input and the frequency modulation pattern. Operations 660 and 670 (retraining neural network 500) can be repeated consecutively, resulting in increased accuracy of neural network 500 over time.

[0125] Figure 7 This is a flowchart of process 700 for customizing a frequency modulation mode based on some aspects of this disclosure. Figure 7 The process 700 will be described from the perspective of UE 104. It should be understood that UE 104 may have one or more processors configured to execute one or more computer-readable instructions stored in one or more associated memories of UE 104 to implement… Figure 7 The steps. In the description Figure 7 During operation, UE 104 can be a receiving device and base station 102 can be a transmitting device.

[0126] In operation 710, UE 104 determines one or more parameters associated with communication between base station 102 and UE 104 (and / or parameters associated with UE 104 and other devices such as other base stations and / or other UEs). Communication between base station 102 and UE 104 may be conducted over a communication channel used over a time period (e.g., the current communication channel between base station 102 and UE 104). As described above, the one or more parameters may include, but are not limited to: the location of UE 104 (e.g., relative to base station 102), the environmental conditions associated with the location of UE 104, the location of base station 102, the environmental conditions associated with the location of base station 102, indoor / outdoor channel conditions, multipath characteristics of the channel, the mobility state of UE 104 (e.g., how fast UE 104 moves toward or away from base station 102), various measurements performed by UE 104 using previously transmitted CSI-RS or TRS (e.g., the frequency modulation pattern used to transmit CSI-RS or TRS in a previous RB), and a recommendation provided as output for the frequency modulation pattern to be used by base station 102 to transmit the next CSI-RS (e.g., TRS) to UE 104, any combination thereof, and / or other parameters. The one or more parameters may be measured by UE 104 over a time period (e.g., 1ms, 5ms, 10s, one minute, etc.).

[0127] In operation 720, UE 104 determines a frequency modulation pattern for a reference signal (e.g., CSI-RS, TRS, etc.) to be used in future communication between base station 102 and UE 104. In one example, UE 104 determines the frequency modulation pattern based on one or more parameters determined at operation 710. This frequency modulation pattern may identify one or more symbols and one or more locations within a resource block for placing those symbols. In one example, UE 104 may use... Figure 5 A trained machine learning model (e.g., a trained neural network 500) is used to determine the frequency modulation pattern. In another example, one or more signal processing techniques may be applied to determine the frequency modulation pattern. For example, UE 104 may have advanced receiver capabilities built in, which allow UE 104 to use less frequency modulation for a reference signal (which can be used for channel estimation).

[0128] As described above, a trained machine learning model can determine a frequency modulation (FM) mode based on one or more parameters determined at operation 710. In some cases, UE 104 may have several machine learning models, each tuned / trained to determine an optimized FM mode under given conditions corresponding to one or more parameters (e.g., the location of UE 104, environmental conditions associated with the location of UE 104, the location of base station 102, environmental conditions associated with the location of base station 102, indoor / outdoor channel conditions, multipath characteristics of the channel, mobility state of UE 104, etc.). In such cases, UE 104 can determine which tuned / trained machine learning model to use based on the one or more parameters. In some cases, the one or more parameters can be provided as input to a machine learning model that has been tuned / trained over time to identify an optimized FM mode for any given set of parameters (e.g., the one or more parameters). In such cases, the machine learning model can run inference on the one or more parameters to determine an optimized FM mode based on the one or more parameters.

[0129] In some examples, frequency modulation patterns can be used for DL ​​reference signals (e.g., CSI-RS or other DL reference signals) transmitted from base station 102 to UE 104. For example, the frequency modulation pattern can be a novel frequency modulation pattern, such as an irregular arrangement of customized REs with different (time, frequency) coordinates. Figures 8A to 8B Non-limiting examples of customized irregular frequency modulation pattern arrangements based on some aspects of this disclosure are explained. Figure 8A As mentioned above (refer to the reference). Figure 4 The aforementioned Figure 4 Example of configuration 800 of RB 402 with resource element (RE) 404. Figure 8A Configuration 800 in the text refers to a customized irregular arrangement of REs for frequency modulation mode, also known as an irregular combination of REs, including REs 802, REs 804, and REs 806. Conversely, Figure 4 The frequency modulation arrangement is predefined and conventional, as defined by 3GPP standards. Figure 8A The REs 802, 804, and 806 shown are placed across RB 402 without any set pattern (repetition or periodicity) for their placement within RB 402. In example configuration 800, REs 802, 804, and 806 have (time, frequency) coordinates (1,12), (2,4), and (4,9), and define a non-limiting example of a customized irregular frequency modulation pattern.

[0130] Figure 8BThis is another example of a customized irregular frequency modulation pattern arrangement as shown in configuration 850. In this example, the frequency modulation pattern of configuration 850 is defined by two RE clusters, including cluster 852 and cluster 860. Cluster 852 includes REs 854, 856, and 858, while cluster 860 includes REs 862, 864, 866, and 868. Each cluster can be defined by REs within threshold positions of each other in RB 402 in time and / or frequency. For example, REs 854, 856, and 858 in cluster 852 are separated by at most one subcarrier (time) and / or one OFDM symbol (frequency). Accordingly, REs 854, 856, and 858 are within the thresholds of two OFDM symbols and two subcarriers of each other. In another example, REs 862, 864, 866, and 868 in cluster 860 are separated by at most one subcarrier (time) and / or two OFDM symbols (frequency). Accordingly, REs 862, 864, 866, and 868 are within the thresholds of three OFDM symbols and two subcarriers to each other. Accordingly, the thresholds for time and frequency can be different and can be as referenced. Figure 8B The example clusters 852 and 860 are different. RE clusters (such as clusters 852 and 860) can also be called RE clusters. Although Figure 8B Two example clusters are interpreted as forming an example frequency modulation pattern (e.g., for CSI-RS, such as TRS), but this disclosure is not limited thereto and the frequency modulation pattern can be formed by a single cluster or more than two clusters.

[0131] In some cases, the frequency modulation pattern (FM pattern) can be a regular FM pattern (e.g., with a defined and repeatable RE arrangement) that has not yet been defined by the 3GPP standard. In some examples, the FM pattern determined at Operation 720 can be one of several existing FM patterns defined by the 3GPP standard. Examples of such existing FM patterns are... Figure 4 The RE of RB 402 is shown in the figure (i.e., the one specified by “R”). Figure 4 The existing frequency modulation mode is used for Figure 4 The example shown is an example of the predefined rule arrangement of REs for the frequency modulation mode.

[0132] In operation 730, UE 104 may transmit (send) the frequency modulation pattern to base station 102. Base station 102 may determine whether to use the frequency modulation pattern to transmit CSI-RS (e.g., TRS) to UE 104 on the downlink channel. In operation 740, UE 104 may receive CSI-RS (e.g., TRS) from base station 102 on the DL channel via one or more transceivers of UE 104. In operation 750, UE 104 may perform one or more measurements based on the CSI-RS or TRS, including but not limited to measuring channel throughput, measuring reference signal received power (RSRP) during mobility and beam management, frequency and / or time tracking, precoding based on demodulation and UL reciprocity, etc. In some cases, UE 104 may use CSI-RS (e.g., TRS) to determine one or more measurements, including but not limited to time and frequency tracking, path delay spread, and Doppler spread, etc.

[0133] In operation 760, UE 104 may determine whether a triggering condition for retraining neural network 500 has occurred. In one example, the triggering condition may be a change in the mobility state of UE 104. In another example, the triggering condition may be the determination of a frequency modulation mode at operation 720. In some cases, the triggering condition may be the execution of operation 720. Examples of such triggering conditions may further include, but are not limited to, a threshold degradation of the performance of the frequency modulation mode recommended by neural network 500 for transmitting the reference signal (where the threshold may be determined based on experimental and / or empirical studies), the channel estimation error when the reference signal is used for channel estimation (e.g., when the channel estimation error reaches and / or exceeds a configurable threshold more than a certain number of times over a period of time, where the number of times and the time period are configurable parameters determined based on experimental and / or empirical studies), any combination thereof, and / or other triggering conditions.

[0134] If, during operation 760, UE 104 determines that the triggering condition has not yet occurred, then process 700 reverts to operation 710, and operations 710 to 760 can be repeated periodically (e.g., depending on the frequency at which base station 102 transmits CSI-RS (e.g., TRS) to UE 104 (e.g., every subframe (1 ms), every other subframe, etc.)) to determine the frequency modulation pattern for CSI-RS (e.g., for TRS transmission). However, if, during operation 760, UE 104 determines that the triggering condition has occurred, then during operation 770, UE 104 can proceed as described above. Figure 6Operations 660 and 670 are used to retrain neural network 500. For example, one or more parameters determined at operation 710 and / or the frequency modulation pattern determined at operation 720 can be provided as input to input layer 503 of neural network 500 to retrain neural network 500. In one example, this retraining may involve adjusting the coefficients, biases, and / or weights of different nodes (e.g., nodes 508A, 508B, 508C) at different network layers of neural network 500. Thereafter, process 700 can revert to operation 710 and UE 104 can perform this periodically depending on the frequency at which base station 102 transmits CSI-RS or TRS to UE 104 (e.g., every subframe (1ms), every other subframe, etc.). Figure 7 The process.

[0135] As mentioned above, using Figure 5 The process of determining the frequency modulation mode by training a neural network 500 can be performed at base station 102. Figure 9 This is a flowchart of an example process 900 for conveying a customized frequency modulation mode according to some aspects of this disclosure. Figure 9 The process 900 will be described from the perspective of base station 102. It should be understood that base station 102 may have one or more processors configured to execute one or more computer-readable instructions stored in one or more associated memories of base station 102 to implement… Figure 9 The steps. In the description Figure 9 During operation, base station 102 can be a receiving device and UE 104 can be a transmitting device.

[0136] In operation 910, base station 102 determines one or more parameters associated with communication between base station 102 and UE 104. Such communication between base station 102 and UE 104 may occur over a time-duration communication channel (e.g., the current communication channel between base station 102 and UE 104). In one example, base station 102 determines these parameters by receiving them from UE 104. Base station 102 may receive these parameters from UE 104 over one or more of the Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control Layer. These parameters may be measured by UE 104 over a time period (e.g., 1 ms, 5 ms, 10 s, one minute, etc.).

[0137] As described above, the one or more parameters include, but are not limited to: the location of UE 104 (e.g., relative to base station 102), the environmental conditions associated with the location of UE 104, the location of base station 102, the environmental conditions associated with the location of base station 102, indoor / outdoor channel conditions, the multipath characteristics of the channel, the mobility state of UE 104 (e.g., how fast UE 104 moves toward or away from base station 102), various measurements performed by UE 104 using previously transmitted CSI-RS or TRS (e.g., the frequency modulation pattern used to transmit CSI-RS or TRS in a previous RB), and recommendations provided as output for the frequency modulation pattern to be used by base station 102 to transmit the next CSI-RS or TRS to UE 104, any combination thereof, and / or other parameters.

[0138] In operation 920, base station 102 determines a frequency modulation pattern for a reference signal (e.g., CSI-RS, TRS, etc.) to be used in future communication between base station 102 and UE 104. In one example, base station 102 determines the frequency modulation pattern based on one or more parameters determined at operation 910. This frequency modulation pattern may identify one or more symbols and one or more locations within a resource block for placing those one or more symbols. In one example, base station 102 may use... Figure 5 A trained machine learning model (e.g., a trained neural network 500) determines the frequency modulation pattern. As described above, the trained machine learning model can receive one or more parameters determined at operation 910 as input and provide the frequency modulation pattern as output at operation 920. In another example, one or more signal processing techniques can be applied to determine the frequency modulation pattern. For example, UE 104 may have advanced receiver capabilities built in, allowing UE 104 to use less frequency modulation for a reference signal (e.g., the reference signal can be used for channel estimation). In another example, base station 102 can determine the frequency modulation pattern by receiving it from UE 104.

[0139] In some examples, the frequency modulation pattern can be used for DL ​​reference signals (e.g., CSI-RS or other DL reference signals) transmitted from base station 102 to UE 104. In some examples, the frequency modulation pattern can be a new predefined rule frequency modulation pattern not yet defined by the 3GPP standard. In another example, the frequency modulation pattern can be a new, customized, irregular arrangement of REs with different (time, frequency) coordinates. (See above for reference.) Figure 8A and 8B Two non-limiting examples of customized irregular frequency modulation pattern arrangements are described. In some examples, the frequency modulation pattern determined at Operation 910 can be one of several existing frequency modulation patterns defined by 3GPP standards. Examples of such existing frequency modulation patterns are shown in... Figure 4 The RE of RB 402 is shown in the figure (i.e., the one specified by “R”). Figure 4 The existing frequency modulation mode is used for Figure 4 The example shown is an example of the predefined rule arrangement of REs for the frequency modulation mode.

[0140] In operation 930, base station 102 transmits (or sends) CSI-RS or TRS to UE 104 on the DL channel using the frequency modulation pattern determined at operation 920. As described above, UE 104 can detect and decode CSI-RS or TRS, and perform one or more measurements based on the CSI-RS or TRS, including but not limited to measuring channel throughput, measuring mobility and reference signal received power (RSRP) during beam management, frequency and / or time tracking, precoding based on demodulation and UL reciprocity, etc. Similarly, the receiving UE can use TRS to perform one or more measurements, including but not limited to time and frequency tracking, path delay spread, and Doppler spread, etc.

[0141] In operation 940, UE 104 may determine whether a triggering condition for retraining neural network 500 has occurred. In one example, the triggering condition may be a change in the mobility state of UE 104. Such a triggering condition may be communicated by UE 104 to base station 102 on the UL channel. In another example, the triggering condition may be the determination of a frequency modulation mode at operation 920. In some cases, the triggering condition may be the execution of operation 920. Examples of such triggering conditions may further include, but are not limited to, a threshold degradation of the performance of the frequency modulation mode recommended by neural network 500 for transmitting reference signals (where the threshold may be determined based on experimental and / or empirical studies), the channel estimation error when the reference signal is used for channel estimation (e.g., when the channel estimation error reaches and / or exceeds a configurable threshold more than a certain number of times within a certain period of time, where the number of times and the time period are configurable parameters determined based on experimental and / or empirical studies), any combination thereof, and / or other triggering conditions.

[0142] If, during operation 940, base station 102 determines that the triggering condition has not yet occurred, then process 900 reverts to operation 910, and operations 910 to 940 can be repeated periodically (depending on the frequency at which base station 102 transmits CSI-RS or TRS to UE 104 (e.g., every subframe (1 ms), every other subframe, etc.)) to determine the frequency modulation pattern used for CSI-RS or TRS transmission. However, if, during operation 940, base station 102 determines that the triggering condition has occurred, then during operation 950, UE 104 can proceed as described above. Figure 6Operations 660 and 670 are used to retrain neural network 500. For example, one or more parameters determined at operation 710 and / or the frequency modulation pattern determined at operation 720 can be provided as input to input layer 503 of neural network 500 to retrain neural network 500. In one example, this retraining may involve adjusting the coefficients, biases, and / or weights of different nodes (e.g., nodes 508A, 508B, 508C) at different network layers of neural network 500. Thereafter, process 900 can return to operation 910 and base station 102 can perform this operation periodically depending on the frequency at which base station 102 transmits CSI-RS or TRS to UE 104 (e.g., every subframe (1ms), every other subframe, etc.). Figure 9 The process.

[0143] Figure 10 This is a flowchart of an example process 1000 for conveying a customized frequency modulation mode according to some aspects of this disclosure. Figure 10 Process 1000 is described from the perspective of the user equipment, and in some examples it may be the same as UE 104. In operation 1010, process 1000 includes the user equipment determining one or more parameters associated with communication with the base station over a time duration. In some cases, the one or more parameters include at least the location of the user equipment, the environmental conditions associated with that location, the mobility state of the user equipment, any combination thereof, and / or other parameters.

[0144] In operation 1020, process 1000 includes the user equipment determining a frequency modulation pattern of a reference signal based on one or more parameters for use in future time-duration communications with the base station. In some aspects, the frequency modulation pattern is a customized irregular arrangement of resource elements in a resource block used to transmit the reference signal. In some cases, the frequency modulation pattern is determined based on multiple existing frequency modulation patterns used to transmit the reference signal. In some cases, the frequency modulation pattern is determined using a machine learning model (such as...). Figure 5 The frequency modulation pattern is determined by a neural network (500). In some aspects, the machine learning model receives one or more parameters as input and provides the frequency modulation pattern as output. In some examples, the user equipment can determine the machine learning model to use based on one or more parameters (e.g., from multiple previously trained / tuned models), as described above. In some cases, the machine learning model can be retrained upon detection of a triggering condition. In some cases, the triggering condition is at least one of a change in the mobility state of the user equipment and a channel estimation error, as described above. In some cases, the reference signal can be any of a channel state information-reference signal (CSI-RS), a tracking reference signal (TRS), or any other downlink reference signal.

[0145] In operation 1030, process 1000 includes transmitting the frequency modulation pattern to the base station. In some cases, transmitting the frequency modulation pattern may include transmitting one or more identified symbols and one or more locations within a resource block for placing the one or more identified symbols. In some cases, the frequency modulation pattern may be transmitted to the base station on one or more of the Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control Layer. In some cases, and while transmitting the frequency modulation pattern to the base station, user equipment may use a transceiver and receive a reference signal with the frequency modulation pattern from the base station (e.g., on a resource element corresponding to the frequency modulation pattern).

[0146] Figure 11 This is a flowchart of an example process 1100 for conveying a customized frequency modulation mode according to some aspects of this disclosure. Figure 11 Process 1100 is described from the perspective of the base station, and in some examples it may be the same as base station 102. In operation 1110, process 1100 includes the base station determining one or more parameters associated with communication with user equipment over a time period. In some cases, the one or more parameters include at least the location of the user equipment, the environmental conditions associated with that location, the mobility status of the user equipment, any combination thereof, and / or other parameters.

[0147] In operation 1120, process 1100 includes determining a frequency modulation pattern of a reference signal by the base station and based on one or more parameters for use in future time-duration communications with user equipment. In some aspects, the frequency modulation pattern is a customized irregular arrangement of resource elements in a resource block used to transmit the reference signal. In some cases, the frequency modulation pattern is determined from multiple existing frequency modulation patterns used to transmit the reference signal. In some cases, the frequency modulation pattern is determined using a machine learning model (such as...) Figure 5 The frequency modulation pattern is determined by a neural network (500). In some aspects, the machine learning model receives one or more parameters as input and provides the frequency modulation pattern as output. In some examples, the base station can determine the machine learning model to use based on one or more parameters (e.g., from multiple previously trained / tuned models), as described above. In some cases, the machine learning model can be retrained upon detection of a triggering condition. In some cases, the triggering condition is at least one of a change in the mobility state of the user equipment and a channel estimation error, as described above. In some cases, the reference signal can be any of the channel state information-reference signal (CSI-RS), tracking reference signal (TRS), or any other downlink reference signal.

[0148] In operation 1130, process 1100 includes the base station transmitting the reference signal to the user equipment using the frequency modulation pattern. In some cases, the frequency modulation pattern may be transmitted to the user equipment on one or more of the Physical Downlink Control Channel (PDCCH), MAC Control Element (MAC-CE), or Radio Resource Control Layer. In some cases, the user equipment may use a transceiver and receive the reference signal with the frequency modulation pattern from the base station (e.g., on a resource element corresponding to the frequency modulation pattern).

[0149] Based on the above reference Figures 4-11 The various examples of frequency modulation mode optimization described will now be described, explaining the components of UE104. Figure 12 .

[0150] Figure 12 An example of the computing system 1270 of User Equipment (UE) 1207 has been explained. UE 1207 can be compared with the above reference. Figures 1 to 10 The UE 104 described is identical. In some examples, UE 1207 may include a mobile phone, router, tablet computer, laptop computer, tracking device, wearable device (e.g., smartwatch, glasses, XR device, etc.), Internet of Things (IoT) device, and / or other devices used by the user to communicate over a wireless communication network. Computing system 1270 includes software and hardware components that can be electrically coupled (or may appropriately otherwise be in communication) via bus 1289. For example, computing system 1270 includes one or more processors 1284. One or more processors 1284 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. Bus 1289 may be used by one or more processors 1284 to communicate between cores and / or with one or more memory devices 1286.

[0151] The computing system 1270 may also include one or more memory devices 1286, one or more digital signal processors (DSPs) 1282, one or more subscriber identity modules (SIMs) 1274, one or more modems 1276, one or more wireless transceivers 1278, an antenna 1287, one or more input devices 1272 (e.g., a camera, mouse, keyboard, touchscreen, touchpad, keypad, microphone, etc.) and one or more output devices 1280 (e.g., a display, speaker, printer, etc.).

[0152] One or more wireless transceivers 1278 can transmit and receive wireless signals (e.g., signal 1288) to and from one or more other devices via antenna 1287. These other devices may be one or more other UEs, network devices (e.g., base stations such as eNBs and / or gNBs, WiFi routers, etc.), cloud networks, etc. As described herein, one or more wireless transceivers 1278 may include combined transmitters / receivers, discrete transmitters, discrete receivers, or any combination thereof. In some examples, computing system 1270 may include multiple antennas. Wireless signal 1288 can be transmitted via a wireless network. The wireless network can 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), Bluetooth, etc. TM Networks and / or other networks. In some examples, one or more wireless transceivers 1278 may include a radio frequency (RF) front end, which includes one or more components such as amplifiers, a mixer (also known as a signal multiplier) for down-converting the signal, a frequency synthesizer (also known as an oscillator) that supplies the 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 generally handles the selection of the wireless signal 1288 and its conversion to baseband or intermediate frequency, and can convert the RF signal to the digital domain.

[0153] In some cases, computing system 1270 may include a decoder-decoder (or CODEC) configured to encode and / or decode data transmitted and / or received using one or more wireless transceivers 1278. In some cases, computing system 1270 may include an encryption-decryption device or component configured to encrypt and / or decrypt (e.g., according to AES and / or DES standards) data transmitted and / or received by one or more wireless transceivers 1278.

[0154] One or more SIMs 1274 may each securely store an International Mobile Subscriber Identity (IMSI) number and associated key assigned to a user of UE 1207. The IMSI and key can be used to identify and authenticate the subscriber when accessing a network provided by a network service provider or operator associated with one or more SIMs 1274. One or more modems 1276 may modulate one or more signals to encode information for transmission using one or more radio transceivers 1278. One or more modems 1276 may also demodulate signals received by one or more radio transceivers 1278 to decode the transmitted information. In some examples, one or more modems 1276 may include a 4G (or LTE) modem, a 5G (or NR) modem, a Bluetooth™ modem, a modem configured for vehicle-to-everything (V2X) communications, and / or other types of modems. In some examples, one or more modems 1276 and one or more radio transceivers 1278 may be used to transmit data for one or more SIMs 1274.

[0155] The computing system 1270 may also include (and / or communicate with) one or more non-transitory machine-readable storage media or storage devices (e.g., one or more memory devices 1286), 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.

[0156] In various embodiments, functionality may be stored as one or more computer program products (e.g., instructions or code) in memory devices 1286 and executed by one or more processors 1284 and / or one or more DSPs 1282. The computing system 1270 may also include software elements (e.g., residing within one or more memory devices 1286) including, for example, operating systems, device drivers, executable libraries, and / or other code, such as one or more application programs, which may include computer programs implementing the functionality provided by the various embodiments, and / or may be designed to implement methods and / or configure systems as described herein.

[0157] Specific details have been provided in the foregoing description to offer a thorough understanding of the various embodiments and examples presented herein, but those skilled in the art will recognize that this application is not limited thereto. Therefore, although illustrative embodiments of this application have been described in detail herein, it is to be understood that the various inventive concepts may be implemented and employed in a variety of other ways, and the appended claims are not intended to be construed as including these variations unless limited by the prior art. Furthermore, the embodiments may be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of this specification. Accordingly, this specification and the accompanying drawings should be considered illustrative rather than limiting. For illustrative purposes, the methods are described in a particular order. It should be understood that in alternative embodiments, the methods may be performed in a different order than described.

[0158] For clarity, in some instances, the technology of the present invention may be presented as including various functional blocks, which include devices, device components, steps or routines in methods implemented in software or a combination of hardware and software. Additional components may be used in addition to those shown in the drawings and / or described herein. For example, circuits, systems, networks, processes and other components may be shown as components in block diagram form to avoid obscuring these embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures and techniques may be shown without the need for unnecessary detail to avoid obscuring the embodiments.

[0159] Furthermore, those skilled in the art will appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in connection with the aspects disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

[0160] The various embodiments described above may be processes or methods, depicted as flowcharts, diagrams, data flow graphs, structural diagrams, or block diagrams. Although a flowchart may describe operations as a sequential process, many operations may be performed in parallel or concurrently. Furthermore, the order of operations may be rearranged. A process terminates when its operations are completed, but a process may have additional steps not included in the figures. A process may correspond to a method, function, procedure, subroutine, subroutine, etc. When a process corresponds to a function, its termination corresponds to the function returning to the calling function or the main function.

[0161] The processes and methods described in the examples above can be implemented using stored computer-executable instructions or computer-executable instructions otherwise available from a computer-readable medium. These instructions may include, for example, instructions and data that cause or otherwise configure a general-purpose computer, special-purpose computer, or processing device to perform a function or group of functions. Parts of the computer resources used are accessible via a network. The computer-executable instructions may be, for example, binary files, intermediate format instructions (such as assembly language), firmware, and source code. Examples of computer-readable media that can be used to store instructions, information used during the methods according to the described examples, and / or information created include hard disks or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, etc.

[0162] In some embodiments, computer-readable storage devices, media, and memories may include cables or wireless signals containing bit streams, etc. However, when referred to, non-transient computer-readable storage media explicitly exclude media such as energy, carrier signals, electromagnetic waves, and the signals themselves.

[0163] Those skilled in the art will appreciate that information and signals can be represented using any of a variety of different techniques and skills. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to throughout the above description may, in some cases, be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or light particles, or any combination thereof, depending in part on the specific application, in part on the desired design, in part on the corresponding technology, etc.

[0164] The various descriptive logic blocks, modules, and circuits described in conjunction with the aspects disclosed herein can be implemented or executed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any form factor of various form factors. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., computer program products) for performing necessary tasks can be stored in a computer-readable or machine-readable medium. A processor can perform the necessary tasks. Examples of form factors include: laptop devices, smartphones, mobile phones, tablet devices, or other small form factor personal computers, personal digital assistants, rack-mount devices, self-standing devices, etc. The functionality described herein can also be implemented using peripheral devices or plug-in cards. As a further example, such functionality can also be implemented on a circuit board within different chips or different processes executed on a single device.

[0165] Instructions, media for conveying these instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functionality described in this disclosure.

[0166] The techniques described herein can also be implemented using electronic hardware, computer software, firmware, or any combination thereof. These techniques can be implemented using any of a variety of devices, such as general-purpose computers, wireless communication handsets, or multi-purpose integrated circuit devices, including applications in wireless communication handsets and other devices. Any feature described as a module or component can be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, these techniques can be implemented at least in part by a computer-readable data storage medium comprising program code, including instructions that, when executed, perform one or more of the methods, algorithms, and / or operations described above. The computer-readable data storage medium can form part of a computer program product and may include packaging material. The computer-readable medium may include memory or data storage media, such as random access memory (RAM) (such as synchronous dynamic random access memory (SDRAM)), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage media, and so on. These technologies may additionally or alternatively be implemented, at least in part, by computer-readable communication media carrying or conveying program code in the form of instructions or data structures that can be accessed, read, and / or executed by a computer, such as propagated signals or waves.

[0167] The program code can 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 arrays (FPGAs), or other equivalent integrated or discrete logic circuit systems. Such processors can be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor can 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 cooperating with a DSP core, or any other such configuration. Accordingly, the term "processor" as used herein may refer to any of the foregoing structures, any combination of the foregoing structures, or any other structure or apparatus suitable for implementing the techniques described herein.

[0168] The foregoing description provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of this disclosure. Rather, the following description of the exemplary embodiments will provide those skilled in the art with enabling descriptions for implementing the exemplary embodiments. It should be understood that various changes may be made to the function and arrangement of the elements without departing from the spirit and scope of this application as set forth in the appended claims.

[0169] The terms “exemplary” and / or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” and / or “example” is not necessarily to be construed as superior to or better than other aspects. Similarly, the term “aspects of this disclosure” does not require that all aspects of this disclosure include the features, advantages, or modes of operation discussed.

[0170] Those skilled in the art will appreciate that the less than (“<”) and greater than (“>”) symbols or terms used herein may be replaced by the less than or equal to (“≤”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this specification.

[0171] When the components are described as being “configured” to perform certain operations, such configurations can be achieved, for example, by designing electronic circuits or other hardware to perform the operations, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits), or any combination thereof.

[0172] The phrase “coupled to” means that any component is physically connected directly or indirectly to another component, and / or that any component is in communication with another component directly or indirectly (e.g., connected to that other component via a wired or wireless connection and / or other suitable communication interface).

[0173] The language of the claims or other languages ​​that use "at least one" and / or "one or more" in a set of statements indicate that one or more members of that set (in any combination) satisfy the claim. For example, the claim language that states "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, the claim language that states "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, or A, B, and C. The language that uses "at least one" and / or "one or more" in a set does not limit the set to the items listed in that set. For example, the claim language that states "at least one of A and B" or "at least one of A or B" can mean A, B, or A and B, and may additionally include items not listed in the set of A and B.

[0174] The explanatory aspects of this disclosure include:

[0175] Aspect 1: A method comprising: determining one or more parameters associated with communication with a base station over a time duration by a user equipment; determining a frequency modulation pattern of a reference signal based on the one or more parameters for use in communication with the base station over a future time duration by the user equipment; and transmitting the frequency modulation pattern to the base station.

[0176] Aspect 2: The method of aspect 1, wherein the frequency modulation pattern is determined from a plurality of existing frequency modulation patterns used to transmit a reference signal.

[0177] Aspect 3: The method of either Aspect 1 or 2, wherein the frequency modulation mode is formed by an irregular arrangement of resource elements in a resource block used to transmit a reference signal.

[0178] Aspect 4: The method of any of Aspects 1 to 3 further includes: receiving a reference signal having the frequency modulation mode from a base station.

[0179] Aspect 5: The method of any of Aspects 1 to 4, wherein the frequency modulation mode is determined using a machine learning model.

[0180] Aspect 6: The method of any of Aspects 1 to 5, wherein the machine learning model outputs the frequency modulation pattern based on one or more parameters.

[0181] Aspect 7: The method of any of Aspects 1 to 6 further includes: retraining the machine learning model upon detection of the triggering condition.

[0182] Aspect 8: The method of any of Aspects 1 to 7, wherein the triggering condition is at least one of a change in the mobility state of the user equipment and a channel estimation error.

[0183] Aspect 9: The method of any of Aspects 1 to 8, wherein the reference signal is a Channel State Information-Reference Signal (CSI-RS).

[0184] Aspect 10: The method of any of Aspects 1 to 9, wherein the reference signal is a tracking reference signal (TRS).

[0185] Aspect 11: The method of any of Aspects 1 to 10, wherein the reference signal is a downlink reference signal.

[0186] Aspect 12: The method of any of Aspects 1 to 11, wherein transmitting the frequency modulation mode includes transmitting one or more identified symbols and one or more locations within a resource block for placing the one or more identified symbols.

[0187] Aspect 13: The method of any of Aspects 1 to 12, wherein the frequency modulation mode is transmitted to the base station on one or more of the Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control Layer.

[0188] Aspect 14: The method of any of Aspects 1 to 13, wherein the one or more parameters include at least the location of the user equipment, the environmental conditions associated with the location, and the mobility status of the user equipment.

[0189] Aspect 15: An apparatus comprising one or more memories storing computer-readable instructions, and one or more processors. The one or more processors are configured to execute the computer-readable instructions to: determine one or more parameters associated with communication with a base station over a time duration; determine a frequency modulation pattern of a reference signal based on the one or more parameters for use in communication with the base station over a future time duration; and transmit the frequency modulation pattern to the base station.

[0190] Aspect 16: The apparatus of aspect 15, wherein the frequency modulation pattern is determined from a plurality of existing frequency modulation patterns used to transmit a reference signal.

[0191] Aspect 17: An apparatus as described in either Aspect 15 or 16, wherein the frequency modulation mode is formed by an irregular arrangement of resource elements in a resource block used to transmit a reference signal.

[0192] Aspect 18: An apparatus of any of Aspects 15 to 17, wherein the one or more processors are further configured to execute computer-readable instructions to receive a reference signal having the frequency modulation mode from a base station.

[0193] Aspect 19: An apparatus of any of Aspects 15 to 18, wherein the one or more processors are further configured to execute computer-readable instructions to determine the frequency modulation mode using a machine learning model.

[0194] Aspect 20: An apparatus as described in any of Aspects 15 to 19, wherein the machine learning model is configured to output the frequency modulation mode based on one or more of the parameters.

[0195] Aspect 21: An apparatus of any of Aspects 15 to 20, wherein the one or more processors are further configured to execute computer-readable instructions to retrain the machine learning model upon detection of a triggering condition.

[0196] Aspect 22: A device of any of Aspects 15 to 21, wherein the triggering condition is at least one of a change in the mobility state of the device and a channel estimation error.

[0197] Aspect 23: An apparatus of any of Aspects 15 to 22, wherein the reference signal is a Channel State Information-Reference Signal (CSI-RS).

[0198] Aspect 24: The apparatus of any of Aspects 15 to 23, wherein the reference signal is a tracking reference signal (TRS).

[0199] Aspect 25: An apparatus of any of Aspects 15 to 24, wherein the reference signal is a downlink reference signal.

[0200] Aspect 26: An apparatus of any of Aspects 15 to 25, wherein the one or more processors are configured to execute computer-readable instructions to transmit the frequency modulation mode by transmitting one or more identified symbols and one or more locations within a resource block for placing the one or more identified symbols.

[0201] Aspect 27: An apparatus of any of Aspects 15 to 26, wherein the frequency modulation mode is transmitted to a base station on one or more of the Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control Layer.

[0202] Aspect 28: A device as described in any of Aspects 15 to 27, wherein the one or more parameters include at least the location of the device, the environmental conditions associated with the location, and the mobility status of the device.

[0203] Aspect 29: One or more non-transient computer-readable media comprising computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform any of the operations according to aspects 1 to 14.

[0204] Aspect 30: An apparatus comprising means for performing operations according to any one of aspects 1 to 14.

[0205] Aspect 31: A method comprising: determining one or more parameters associated with communication with a user equipment over a time duration by a base station; determining, based on the one or more parameters, a frequency modulation pattern of a reference signal for use in communication with the user equipment over a future time duration by the base station; and transmitting the reference signal to the user equipment by the base station using the frequency modulation pattern.

[0206] Aspect 32: The method of any of Aspect 31, wherein the frequency modulation pattern is determined from a plurality of existing frequency modulation patterns used to convey a reference signal.

[0207] Aspect 33: The method of either aspect 31 or 32, wherein the frequency modulation mode is formed by an irregular arrangement of resource elements in a resource block for conveying a reference signal.

[0208] Aspect 34: The method of any of aspects 31 to 33 further includes receiving the frequency modulation mode from the user equipment.

[0209] Aspect 35: The method of any of Aspects 31 to 34, wherein receiving the frequency modulation mode includes receiving one or more identified symbols and one or more locations within a resource block for placing the one or more identified symbols.

[0210] Aspect 36: The method of any of Aspects 31 to 35, wherein the frequency modulation mode is received on one or more of the Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control Layer.

[0211] Aspect 37: The method of any of Aspects 31 to 36, wherein the frequency modulation mode is determined using a machine learning model.

[0212] Aspect 38: The method of any of Aspects 31 to 37, wherein the machine learning model outputs a frequency modulation pattern based on one or more parameters.

[0213] Aspect 39: The method of any of Aspects 31 to 38, wherein the reference signal is a channel state information-reference signal (CSI-RS).

[0214] Aspect 40: The method of any of Aspects 31 to 39, wherein the reference signal is a tracking reference signal (TRS).

[0215] Aspect 41: The method of any of Aspects 31 to 40, wherein the reference signal is a downlink reference signal.

[0216] Aspect 42: The method of any of Aspects 31 to 41, wherein the one or more parameters include at least the location of the user equipment, the environmental conditions associated with the location, and the mobility status of the user equipment.

[0217] Aspect 43: An apparatus comprising one or more memories storing computer-readable instructions therein, and one or more processors. The one or more processors are configured to perform operations according to any of aspects 31 to 42.

[0218] Aspect 44: One or more non-transient computer-readable media comprising computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform any of the operations pursuant to aspects 31 to 42.

[0219] Aspect 45: An apparatus comprising means for performing operations according to any one of aspects 31 to 42.

Claims

1. A wireless communication method performed by a user equipment (UE), the method comprising: Determine one or more parameters associated with communication with network devices over a time period; Using a machine learning model, a first frequency modulation mode of the first reference signal is determined based on one or more parameters for use in communication with the network device over a future time period; Transmit information corresponding to the first frequency modulation mode to the network device; The machine learning model is retrained upon detection of the triggering condition. Using the retrained machine learning model, a second frequency modulation mode of the second reference signal is determined based on changes in one or more parameters, wherein the second frequency modulation mode is different from the first frequency modulation mode, and wherein the triggering condition includes at least one of a change in the mobility state of the UE or a channel estimation error. as well as The network device is transmitted information corresponding to the second frequency modulation mode.

2. The method of claim 1, wherein the first frequency modulation mode and the second frequency modulation mode are determined from a plurality of existing frequency modulation modes for conveying the first reference signal and the second reference signal.

3. The method of claim 1, wherein the first frequency modulation mode is formed by a first irregular arrangement of resource elements in a first resource block for conveying the first reference signal, and wherein the second frequency modulation mode is formed by a second irregular arrangement of resource elements in a second resource block for conveying the second reference signal.

4. The method of claim 1, further comprising: Receive the first reference signal having the first frequency modulation mode from the network device.

5. The method of claim 1, wherein the machine learning model outputs the first frequency modulation mode based on the one or more parameters, and outputs the second frequency modulation mode based on the change of the one or more parameters.

6. The method of claim 1, wherein the first reference signal is a channel state information-reference signal (CSI-RS).

7. The method of claim 1, wherein the first reference signal is a tracking reference signal TRS.

8. The method of claim 1, wherein the first reference signal is a downlink reference signal.

9. The method of claim 1, wherein transmitting the information corresponding to the first frequency modulation mode includes transmitting one or more identified symbols and one or more locations within a resource block for placing the one or more identified symbols.

10. The method of claim 1, wherein the information corresponding to the first frequency modulation mode is transmitted to the network device via one or more of the Physical Uplink Control Channel (PUCCH), the MAC Control Element (MAC-CE), or the Radio Resource Control (RRC) layer.

11. The method of claim 1, wherein the one or more parameters include at least the location of the user equipment, environmental conditions associated with the location, and the mobility status of the user equipment.

12. An apparatus for wireless communication, comprising: It contains one or more memory locations that store computer-readable instructions; as well as One or more processors, the one or more processors being configured to execute the computer-readable instructions to: Determine one or more parameters associated with communication with network devices over a time period; Using a machine learning model, a first frequency modulation pattern of a first reference signal is determined based on one or more parameters for use in future time-duration communication with the network device; information corresponding to the first frequency modulation pattern is transmitted to the network device. The machine learning model is retrained upon detection of the triggering condition. Using the retrained machine learning model, a second frequency modulation mode of the second reference signal is determined based on changes in one or more parameters, wherein the second frequency modulation mode is different from the first frequency modulation mode, and wherein the triggering condition includes at least one of a change in the mobility state of the device or a channel estimation error. as well as The network device is transmitted information corresponding to the second frequency modulation mode.

13. The apparatus of claim 12, wherein the first frequency modulation mode and the second frequency modulation mode are determined from a plurality of existing frequency modulation modes for conveying the first reference signal and the second reference signal.

14. The apparatus of claim 12, wherein the first frequency modulation pattern is formed by a first irregular arrangement of resource elements in a first resource block for conveying the first reference signal, and wherein the second frequency modulation pattern is formed by a second irregular arrangement of resource elements in a second resource block for conveying the second reference signal.

15. The apparatus of claim 12, wherein the one or more processors are further configured to execute the computer-readable instructions to: Receive the first reference signal having the first frequency modulation mode from the network device.

16. The apparatus of claim 12, wherein the machine learning model is configured to output the first frequency modulation mode based on the one or more parameters, and to output the second frequency modulation mode based on the change of the one or more parameters.

17. The apparatus of claim 12, wherein the first reference signal is a channel state information-reference signal (CSI-RS).

18. The apparatus of claim 12, wherein the first reference signal is a tracking reference signal TRS.

19. The apparatus of claim 12, wherein the first reference signal is a downlink reference signal.

20. The apparatus of claim 12, wherein the one or more processors are configured to execute the computer-readable instructions to transmit the information corresponding to the first frequency modulation mode by transmitting one or more identified symbols and one or more locations within a resource block for placing the one or more identified symbols.

21. The apparatus of claim 12, wherein the one or more processors are configured to execute the computer-readable instructions to transmit the information corresponding to the first frequency modulation mode to the network device on one or more of the Physical Uplink Control Channel (PUCCH), the MAC Control Element (MAC-CE), or the Radio Resource Control (RRC) layer.

22. The apparatus of claim 12, wherein the one or more parameters include at least the location of the apparatus, environmental conditions associated with the location, and the mobility state of the apparatus.

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