Methods, devices, and systems for spectrum resource utilization

Abstract

This disclosure describes methods, systems, and apparatus for spectrum resource utilization. One method includes: determining, by a base station, at least one granular level of spectrum resource utilization and a cooperation level, wherein the granularity level of the spectrum resource represents a level at the frequency resource granularity, and the cooperation level represents a combination level with a cooperation model. Another method includes: determining, by a user equipment (UE), at least one granular level of spectrum resource utilization and a cooperation level, wherein the granularity level of the spectrum resource represents a level at the frequency resource granularity, and the cooperation level represents a combination level with a cooperation model.

Description

Technical Field

[0001] This disclosure generally relates to wireless communications. Specifically, this disclosure relates to methods, apparatus, and systems for spectrum resource utilization. Background Technology

[0002] Wireless communication technology is propelling the world towards an increasingly interconnected and networked society. High-speed, low-latency wireless communication relies on efficient network resource management and allocation between user equipment and wireless access network nodes (including but not limited to base stations). Next-generation networks promise to provide high-speed, low-latency, and ultra-reliable communication capabilities, meeting the needs of various industries and users.

[0003] In wireless communication systems, spectrum resources are utilized by each carrier within a frequency band. In some implementations, a bandwidth part (BWP) granularity is introduced in the new radio (NR), which is typically a portion of a carrier, since the receive and transmit bandwidth of user equipment (UE) does not need to be as large as the cell's bandwidth. Spectrum resources are utilized at multiple granularities, such as BWP, carrier, frequency band, and combinations of frequency bands. In some implementations, when resources on a single cell are almost fully utilized, higher capacity can be supported through carrier aggregation (CA); simultaneously, uplink (UL) coverage can be improved by configuring a supplementary uplink (SUL) carrier alongside a non-supplementary uplink carrier within the same cell. However, several issues / problems exist associated with some implementations. For example, the utilization of spectrum resources at each granularity may be fixed and specific; and / or cells may be defined based on carrier granularity; and / or how to achieve intelligent or efficient utilization of spectrum resources.

[0004] This disclosure describes various embodiments for spectrum resource utilization that address at least one of the issues / problems discussed in this disclosure, thereby improving the efficiency of spectrum resource utilization and enhancing the telecommunications sector. As a non-limiting example, this disclosure describes embodiments for more intelligent and / or more efficient utilization of one or more granular spectrum resources when using artificial intelligence / machine learning (AI / ML) or other technologies. Summary of the Invention

[0005] This document relates to methods, systems, and apparatuses for wireless communication, and more specifically, to the utilization of spectrum resources. Various embodiments in this disclosure may include novel methods for spectrum resource utilization operations, which facilitate more efficient use of spectrum resources, improve telecommunications resource utilization efficiency, and / or enhance the performance of wireless communication.

[0006] In one embodiment, this disclosure describes a method for wireless communication. The method includes: determining, by a base station, at least one granular level of spectrum resource utilization and a cooperation level, wherein the granular level of the spectrum resource represents a level at the frequency resource granularity, and the cooperation level represents a combination level with a cooperation model.

[0007] In another embodiment, this disclosure describes another method for wireless communication. The method includes: determining, by a user equipment (UE), at least one granular level of spectrum resource utilization and a cooperation level, wherein the granular level of the spectrum resource represents a level at the granularity of frequency resources, and the cooperation level represents a combination level with a cooperation model.

[0008] In some other embodiments, an apparatus for wireless communication may include a memory storing instructions and processing circuitry communicating with the memory. When the processing circuitry executes the instructions, it is configured to implement the methods described above.

[0009] In some other embodiments, a device for wireless communication may include a memory storing instructions and processing circuitry communicating with the memory. When the processing circuitry executes the instructions, it is configured to implement the methods described above.

[0010] In some other embodiments, a computer-readable medium includes instructions that, when executed by a computer, cause the computer to perform the methods described above. The computer-readable medium may be a non-transient computer-readable medium.

[0011] The above and other aspects and their embodiments are described in more detail in the accompanying drawings, description and claims. Attached Figure Description

[0012] Figure 1A An example of a wireless communication system including a wireless network node and one or more user devices is shown.

[0013] Figure 1B An example at the granular level of spectrum resources is shown.

[0014] Figure 1C An example of changing the position and / or width of the bandwidth portion (BWP) is shown.

[0015] Figure 2 An example of a network node is shown.

[0016] Figure 3 An example of a user device is shown.

[0017] Figure 4A A flowchart of a method for wireless communication is shown.

[0018] Figure 4B A flowchart of another method for wireless communication is shown.

[0019] Figure 5 Exemplary embodiments of this disclosure are shown.

[0020] Figure 6 Another exemplary embodiment of this disclosure is shown.

[0021] Figure 7A Another exemplary embodiment of this disclosure is shown.

[0022] Figure 7B Another exemplary embodiment of this disclosure is shown.

[0023] Figure 8A Another exemplary embodiment of this disclosure is shown.

[0024] Figure 8B Another exemplary embodiment of this disclosure is shown. Detailed Implementation

[0025] This disclosure will now be described in detail with reference to the accompanying drawings, which form a part of this disclosure and illustrate specific examples of embodiments by way of illustration. However, it should be noted that this disclosure may be implemented in a variety of different forms, and therefore, the subject matter covered or claimed is intended to be construed as not being limited to any of the embodiments set forth below.

[0026] Throughout the specification and claims, terms may have suggestive or implied meanings in the context, in addition to their expressly stated meanings. Similarly, the phrases “in one embodiment” or “in some embodiments” as used herein do not necessarily refer to the same embodiment, and the phrases “in another embodiment” or “in other embodiments” as used herein do not necessarily refer to different embodiments. For example, the claimed subject matter is intended to include, in whole or in part, exemplary embodiments or combinations of embodiments.

[0027] Generally, terms can be understood at least in part from their use in context. For example, terms used herein, such as “and,” “or,” or “and / or,” can include a variety of meanings, which can depend at least in part on the context in which these terms are used. Typically, “or,” when used in an associative list, such as A, B, or C, means A, B, and C in an inclusive sense, and A, B, or C in an exclusive sense. Furthermore, the terms “one or more” or “at least one,” as used herein, can be used, at least in part on context, to describe any feature, structure, or characteristic in a singular sense, or to describe a combination of features, structures, or characteristics in a plural sense. Similarly, terms such as “a,” “an,” or “the” can also be understood to convey either a singular or a plural usage, at least in part on context. Furthermore, the terms “based on” or “determined by” can be understood as not necessarily intended to convey an exclusive set of factors, but rather to allow for the existence of additional factors that are not necessarily explicitly described, which, too, depends at least in part on the context.

[0028] This disclosure describes methods and apparatus for utilizing spectrum resources.

[0029] Next-generation (NG) mobile communication systems are propelling the world towards an increasingly interconnected and networked society. High-speed, low-latency wireless communication relies on efficient network resource management and allocation between user equipment and radio access network nodes (including but not limited to radio base stations). NG networks promise to provide high-speed, low-latency, and ultra-reliable communication capabilities, meeting the needs of various industries and users.

[0030] The fourth-generation mobile communication technology (4G), Long-Term Evolution (LTE) or LTE-Advance (LTE-A), the fifth-generation mobile communication technology (5G), and the future sixth-generation mobile communication technology (6G) are facing increasing demands. Based on current development trends, 4G and 5G systems are being developed to support features such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC).

[0031] In wireless communication systems, spectrum resources are utilized by each carrier within a frequency band. In some implementations, a bandwidth portion (BWP) granularity is introduced in the New Radio (NR), which is typically a portion of a carrier, since the receive and transmit bandwidth of user equipment (UE) does not need to be as large as the cell's bandwidth. Spectrum resources are utilized at multiple granularities, such as BWP, carrier, frequency band, and combinations of frequency bands. In some implementations, higher capacity can be supported through carrier aggregation (CA) when resources on a single cell are almost fully utilized; simultaneously, uplink (UL) coverage can be improved by configuring a supplementary uplink (SUL) carrier together with a non-supplementary uplink carrier in the same cell. However, some issues / problems exist associated with some implementations. For example, the utilization of spectrum resources at each granularity may be fixed and specific; and / or the cell may be defined based on carrier granularity; and / or how to achieve intelligent or efficient utilization of spectrum resources.

[0032] This disclosure describes various embodiments for spectrum resource utilization that address at least one of the issues / problems discussed herein, thereby improving the efficiency of spectrum resource utilization and enhancing the telecommunications sector. In some embodiments, artificial intelligence / machine learning (AI / ML) can be used in 5G, 6G, or more advanced wireless communication systems to improve the efficiency of the communication systems. As a non-limiting example, this disclosure describes embodiments for more intelligent and / or more efficient utilization of one or more granular spectrum resources when using artificial intelligence / machine learning (AI / ML) or other technologies.

[0033] Figure 1A A wireless communication system 100 is illustrated, comprising a wireless network node (or wireless communication node, e.g., a network base station) 118 and one or more user equipments (UEs) (or wireless communication devices) 110. The wireless network node may include a network base station, which may be a nodeB (NB, e.g., gNB) in a mobile telecommunications context. Each UE may wirelessly communicate with the wireless network node via one or more wireless channels 115 for uplink / downlink communication. For example, a first UE 110 may wirelessly communicate with the wireless network node 118 via a channel including multiple wireless channels during a specific time period. The network base station 118 may send higher-layer signaling to the UE 110. The higher-layer signaling may include configuration information for communication between the UE and the base station. In one embodiment, the higher-layer signaling may include a Radio Resource Control (RRC) message.

[0034] Figure 2 An example of an electronic device 200 implementing a network base station is shown. The example electronic device 200 may include wireless transmit / receive (Tx / Rx) circuitry 208 for transmitting / receiving communications with a UE and / or other base stations. The electronic device 200 may also include network interface circuitry 209 (e.g., optical or wired interconnect, Ethernet, and / or other data transmission media / protocols) for communicating between the base station and other base stations and / or the core network. The electronic device 200 may optionally include an input / output (I / O) interface 206 for communicating with operators, etc.

[0035] Electronic device 200 may also include system circuitry 204. System circuitry 204 may include one or more processors 221 and / or memory 222. Memory 222 may include operating system 224, instructions 226, and parameters 228. Instructions 226 may be configured for use by one or more processors 124 to perform functions of the network node. Parameters 228 may include parameters that support the execution of instructions 226. For example, parameters may include network protocol settings, bandwidth parameters, radio frequency mapping allocation, and / or other parameters.

[0036] Figure 3 An example of an electronic device (e.g., a user equipment (UE)) implementing terminal device 300 is shown. UE 300 may be a mobile device, such as a smartphone or a mobile communication module installed in a vehicle. UE 300 may include a communication interface 302, system circuitry 304, input / output interfaces (I / O) 306, display circuitry 308, and storage device 309. The display circuitry may include a user interface 310. System circuitry 304 may include any combination of hardware, software, firmware, or other logic / circuit. System circuitry 304 may be implemented, for example, using one or more systems on a chip (SoC), application-specific integrated circuits (ASICs), discrete analog and digital circuits, and other circuits. System circuitry 304 may be part of an implementation of any desired functionality in UE 300. In this regard, system circuitry 304 may include logic that facilitates, for example, decoding and playing music and video (e.g., MP3, MP4, MPEG, AVI, FLAC, AC3, or WAV decoding and playback); running applications; accepting user input; saving and retrieving application data; establishing, maintaining, and terminating cellular phone calls or data connections (as an example, for an internet connection); establishing, maintaining, and terminating wireless network connections, Bluetooth connections, or other connections; and displaying relevant information on user interface 310. User interface 310 and input / output (I / O) interface 306 may include a graphical user interface, a touch-sensitive display, haptic feedback or other haptic outputs, voice or facial recognition inputs, buttons, switches, speakers, and other user interface elements. Additional examples of I / O interface 306 may include a microphone, video and still image cameras, temperature sensors, vibration sensors, rotation and orientation sensors, headphone and microphone input / output jacks, a Universal Serial Bus (USB) connector, a memory card slot, a radiation sensor (e.g., an IR sensor), and other types of inputs.

[0037] Reference Figure 3The communication interface 302 may include radio frequency (RF) transmitting (Tx) and receiving (Rx) circuitry 316, which processes the transmission and reception of signals via one or more antennas 314. The communication interface 302 may include one or more transceivers. These transceivers may be wireless transceivers, which include modulation / demodulation circuitry, digital-to-analog converters (DACs), shapers, analog-to-digital converters (ADCs), filters, waveform shapers, preamplifiers, power amplifiers, and / or other logic for transmission and reception via one or more antennas or (for some devices) via a physical (e.g., wired) medium. The transmitted and received signals may follow any of a variety of formats, protocols, modulations (e.g., QPSK (Quadrature Phase Shift Keying), 16-QAM (Quadrature Amplitude Modulation), 64-QAM, or 256-QAM), channels, bit rates, and encodings. As a specific example, the communication interface 302 may include a transceiver that supports transmission and reception under the following standards: 2G (the 2nd generation mobile communication technology), 3G (the 3rd generation mobile communication technology), BT (Bluetooth), WiFi (wireless fidelity), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA)+, 4G / Long Term Evolution (LTE), 5G standard and / or 6G standard.However, the technologies described below, whether derived from the 3rd Generation Partnership Project (3GPP), the GSM (Global System for Mobile Communications) Association, 3GPP2, the IEEE (Institute of Electrical and Electronics Engineers), or other partners or standards bodies, are applicable to other wireless communication technologies.

[0038] Reference Figure 3 System circuitry 304 may include one or more processors 321 and memory 322. Memory 322 stores, for example, an operating system 324, instructions 326, and parameters 328. Processor 321 is configured to execute instructions 326 to implement the desired functions of UE 300. Parameters 328 can provide and specify configuration and operational options for instructions 326. Memory 322 may also store any BT, WiFi, 3G, 4G, 5G, 6G, or other data that UE 300 will send or has received via communication interface 302. In various embodiments, system power for UE 300 may be provided by power storage devices such as batteries or transformers.

[0039] This disclosure describes various embodiments for mode switching operations, which may be partly or entirely derived from the above. Figure 2 and Figure 3 The network base stations and / or user equipment described herein are implemented. Various embodiments in this disclosure can achieve efficient utilization of spectrum resources in telecommunications systems, which can improve overall communication efficiency and / or enhance the latency performance of communication services.

[0040] In some implementations of New Radio (NR), spectrum resources are utilized by each carrier within a frequency band defined in the specification, as illustrated in some examples in Table 1, which may be similar to Long Term Evolution (LTE). Since the receive and transmit bandwidth of User Equipment (UE) does not need to be as large as the cell bandwidth, a bandwidth portion (BWP) granularity is introduced in NR, which is typically a portion of a carrier. Figure 1B As shown, spectrum resources can be utilized at various granularities (band combination (BC) (152), frequency band (154), carrier (156), and BWP (158)), and cells can be defined based on carrier granularity. A BC may include one or more frequency bands, a frequency band may include one or more carriers, and / or a carrier may include one or more BWPs.

[0041] In some implementations, for example Figure 1C Each BWP shown can be commanded to change its width (e.g., shrink during periods of low activity to save power); and / or the position of the BWP can be moved in the frequency domain (e.g., to increase scheduling flexibility); and / or the subcarrier spacing can be commanded to change (e.g., to allow different services).

[0042] Table 1: Examples of some NR operating frequency bands

[0043] In some implementations, artificial intelligence / machine learning (AI / ML) or other air interface-specific technologies may be used for certain functions, such as enhanced channel state information (CSI) feedback to reduce overhead or improve accuracy, enhanced beam management to achieve beam prediction, thereby reducing overhead / latency or improving beam selection accuracy, and enhanced positioning accuracy for different scenarios. In some implementations, the AI / ML model may include an algorithm pre-trained / initialized based on a training dataset, which can then be used to generate a set of outputs based on a set of inputs. The AI / ML model may be deployed on the UE side, the gNB side, or both sides. Note that the AI / ML model can refer to a general term or be considered a collaborative model used to describe the UE's ability to perform a processing method, a function, or a feature. This function, processing method, or feature may be implemented by one or more AI / ML models.

[0044] In some implementations, for carrier aggregation (CA) operation, multiple cells can be used for simultaneous transmission, with each cell using one carrier. The maximum bandwidth of the NR carrier can be 100MHz in the first frequency range (FR1) and 400MHz in the second frequency range (FR2). To achieve even wider bandwidth, carrier aggregation (CA) of up to 16 NR carriers is further supported. Both intra-band CA and inter-band CA are supported. For inter-band CA, CA with different parameter sets is also supported, for example, CA between NR carriers in FR1 and NR carriers in FR2.

[0045] In some implementations, for Supplemental Uplink (SUL) operation, two uplink (UL) carriers can be configured for the UE as a normal uplink and a supplemental uplink for a single downlink (DL) carrier within the same cell. Uplink transmissions on these two UL carriers are network-controlled to avoid temporal overlap of the Physical Uplink Shared Channel / Physical Uplink Control Channel (PUSCH / PUCCH) transmissions. Overlapping transmissions on the PUSCH are avoided through scheduling, while overlapping transmissions on the PUCCH are avoided through configuration (the PUCCH can only be configured for one of the two UL carriers of the cell). Furthermore, each uplink supports initial access; that is, random access can be performed on either the normal uplink or the SUL.

[0046] In some embodiments of this disclosure, different levels of AI / ML applications may be used to achieve intelligent or efficient utilization of spectrum resources at one or more granularities (e.g., BWP, carrier, frequency band, combination of frequency bands). In some embodiments, to use a unified framework to support requirements from both CA and SUL, different levels of unified cell definition / management may be employed to match / align different granularities of spectrum resources.

[0047] Reference Figure 4A This disclosure describes various embodiments of a method 400 for wireless communication. The method 400 can be performed by a wireless communication node (e.g., a base station or a radio access network (RAN)). The method 400 may include step 410: determining by the base station at least one granularity level of spectrum resource utilization and a cooperation level, wherein the granularity level of the spectrum resource represents a level at the granularity of the frequency resource, and / or the cooperation level represents a combined level with a cooperation model.

[0048] Reference Figure 4B This disclosure describes various embodiments of a method 450 for wireless communication. The method 450 can be performed by a wireless communication device (e.g., a user equipment). The method 450 may include: step 460, whereby the user equipment (UE) determines at least one granular level of utilization of spectrum resources and a cooperation level, wherein the granular level of spectrum resources represents a level at the granularity of frequency resources, and / or the cooperation level represents a combined level with a cooperation model.

[0049] In some implementations, the granularity level of the spectrum resource includes at least one of the following: bandwidth portion (BWP) level, carrier level, frequency band level, or frequency band combination (BC) level.

[0050] In some implementations, the collaboration level includes at least one of the following: a first collaboration level, a second collaboration level, or a third collaboration level; and / or the collaboration model includes an artificial intelligence and / or machine learning (ML) model.

[0051] In some implementations, the granularity level of the spectrum resource is the BWP level; and / or the cooperation model is combined with each BWP of the BWP set or a portion of the BWP set, and / or the BWPs combined with the cooperation model for a group of UEs include common BWPs or common frequency resources (CFRs).

[0052] In some implementations, the granularity of the spectrum resource is at the carrier level; and / or the UE determines one or more candidates for carrier aggregation (CA) combinations, or the UE determines that the carrier is in one of the following states: active, deactivated, or dormant: determines that the carrier is in a state based on an indication from the base station, recommends that state, and / or performs an operation to put the carrier in a state and delivers the result of the operation to the base station.

[0053] In some implementations, the granularity of the spectrum resource is at the frequency band level; and / or in response to one frequency band in a frequency band pair being combined with a cooperation model, the other frequency band in the frequency band pair being combined with the cooperation model, or in response to one frequency band in a frequency band group being combined with a cooperation model, all other frequency bands in the frequency band group being combined with the cooperation model.

[0054] In some implementations, in response to the cooperation level being a first cooperation level, all parameters related to the granularity level of the spectrum resource are determined by the cooperation model.

[0055] In some implementations, in response to the cooperation level being a second cooperation level, some parameters related to the granularity level of the spectrum resource are determined by the cooperation model.

[0056] In some implementations, in response to the third cooperation level, after the parameters of the granularity level of the spectrum resource are initially determined without a cooperation model, the cooperation level for the granularity level of the spectrum resource is switched to another cooperation level.

[0057] In some implementations, the granular utilization mode for the spectrum resource switches between a cooperative mode and a non-cooperative mode.

[0058] In some implementations, the method may further include switching between cooperative and non-cooperative modes of utilization, including switching between different frequency resources at the granularity level of the spectrum resource.

[0059] In some implementations, the method may further include switching between cooperative and non-cooperative modes of utilization, including switching within the same frequency resources at the granularity level of the spectrum resource.

[0060] In some implementations, utilization of at least one granular level of spectrum resources includes multi-level cells, wherein each level of the multi-level cells is a cell associated with a granular level of spectrum resources.

[0061] In some implementations, the cell is determined based on at least one of the following: the highest level of the multi-level cell, the lowest level of the multi-level cell, or each level of the multi-level cell.

[0062] In some implementations, a multi-level cell includes at least one of the following: a multi-level physical cell identifier (PCID), at least one cell with the highest-level PCID being used independently, at least one parameter of a higher-level cell in the multi-level cell including the same parameter used for lower-level cells in the multi-level cell, and / or the number of cells in the multi-level cell is determined by the number of highest-level PCIDs.

[0063] In some implementations, at least one cell group is determined by cells of the same level, or at least one cell group is determined by cells of different levels.

[0064] In some implementations, at least one cell for initial access is determined by one of the following: cells at each level, cells at only the highest or lowest level, or cells at a subset of levels.

[0065] This disclosure describes various non-limiting embodiments, which are for illustrative purposes only and are not intended to impose limitations. In some embodiments, AI / ML is used as a non-limiting example, and other methods / models may also be used in combination with spectrum utilization.

[0066] Example Set I In various embodiments that combine spectrum utilization with AI / ML, one of the following granularities of spectrum resources can be used to combine with AI / ML.

[0067] In some implementations, BWP-level utilization can be combined with AI / ML. Baseline combination can be a configuration where each BWP can be combined with AI / ML; or a subset of BWPs can be combined with AI / ML. Optionally, this configuration can reference some auxiliary information, such as UE preferences, i.e., which BWP or subset of BWPs the UE expects to combine with AI / ML, and this auxiliary information is delivered or reported by the UE to the base station. Optionally, for a group of UEs, common BWPs or common frequency resources (CFRs) can be used for AI / ML-based BWPs.

[0068] In some implementations, carrier-level utilization can be combined with AI / ML. Baseline combination can be a configuration where each carrier can be combined with AI / ML; or a subset of carriers can be combined with AI / ML. Optionally, this configuration can reference auxiliary information, such as UE preferences—that is, which carrier or subset of carriers the UE expects to combine with AI / ML—and this auxiliary information is delivered or reported by the UE to the base station. Optionally, for a group of UEs, a common carrier can be used for AI / ML-based carriers. Optionally, in the case of carrier aggregation (CA), the carriers or cells used for CA can be configured based on reports of auxiliary information. One or more candidates for UE-preferred CA combinations can be delivered or reported by the UE to the base station. Furthermore, carriers or cells that are active, deactivated, and / or dormant can be combined with AI / ML to make them more intelligent. Optionally, carriers or cells that are active, deactivated, and / or dormant can be determined by the UE. In one alternative (Alt.1), carrier or cell activation, deactivation, and / or sleep can still be based on instructions from the network (e.g., base station), and the UE can deliver or recommend preferred carriers or cells for activation, deactivation, and / or sleep. In another alternative (Alt.2), the UE can perform carrier or cell activation, deactivation, and / or sleep operations and pass the results to the network (NW). In Alt.2, the NW may or may not perform instructions for carrier / cell (de)activation / sleep, and / or the UE may ignore or discard such instructions upon receipt.

[0069] In some implementations, band-level utilization can be combined with AI / ML. Baseline combination can be a configuration where each band can be combined with AI / ML; or a subset of bands can be combined with AI / ML. Optionally, this configuration can reference auxiliary information, such as UE preferences, i.e., which band or subset of bands the UE expects to combine with AI / ML, and this auxiliary information is delivered or reported by the UE to the base station. Optionally, for a group of UEs, a common band can be used for AI / ML-based carriers. Optionally, when band pairs or band groups are configured by the network or reported by the UE, limitations may include: if one band in a band pair or band group is configured / combined with AI / ML, other bands(s)(s) can also be configured / combined with AI / ML. Some implementations may be beneficial for Tx handover and can be performed on AI / ML-based band pairs.

[0070] In some implementations, band combination-level utilization can be combined with AI / ML. Baseline combination can be a configuration where each band combination can be combined with AI / ML; or a subset of band combinations can be combined with AI / ML. Optionally, this configuration can reference auxiliary information, such as UE preferences—that is, which band combination or subset of band combinations the UE expects to combine with AI / ML—and this auxiliary information can be delivered or reported by the UE to the base station. Optionally, for a group of UEs, a common band combination can be used for AI / ML-based carriers.

[0071] Various embodiments can provide the following benefits: these methods enable intelligent or efficient utilization of one or more granularities of spectrum resources (e.g., BWP, carrier, frequency band, combination of frequency bands), which can be achieved by combining different levels of spectrum resources with AI / ML; and / or any granularity of spectrum resources can be combined with AI / ML.

[0072] Example Set II In various embodiments, when spectrum utilization can be combined with AI / ML, in other embodiments, at least one granularity of spectrum resources can be used to combine with AI / ML.

[0073] In some implementations, at least one AI / ML or collaboration level can be combined with a granularity of spectrum resource utilization.

[0074] At the first level (e.g., n1 level), all parameters of the granularity of the spectrum resource can be determined by AI / ML; and / or all parameters related to the granularity of the spectrum resource can be determined by AI / ML.

[0075] At the second level (e.g., n2 level), some parameters of the granularity of spectrum resources (and / or some parameters related to the granularity of spectrum resources) can be determined by AI / ML. Alternatively, other parameters can be determined by conventional methods, such as network configuration or UE reports.

[0076] At the third level (e.g., level n3), the application of granular spectrum resources can be directed to an AI / ML mode with a higher level (e.g., level n2 or level n1). That is, the default application of granular spectrum resources is based on a conventional approach, which is not integrated with AI / ML. After the granularity of spectrum resources is determined by the conventional approach, an AI / ML mode with a higher level (e.g., level n2 or level n1) can be switched via AI / ML functionality.

[0077] BWP utilization can be considered as a non-limiting example. When BWP-level utilization can be combined with AI / ML, the baseline combination can be a configuration of each BWP or part of the BWP combined with AI / ML. Optionally, this configuration can reference some auxiliary information, such as UE preferences, i.e., which BWP or part of the BWP the UE expects to combine with AI / ML, and this auxiliary information is delivered or reported by the UE. Optionally, for a group of UEs, a common BWP or common frequency resource (CFR) can be used for AI / ML-based BWPs. Optionally, at least one AI / ML level can be combined with BWP utilization.

[0078] For an example using the n1 level, all parameters of the BWP can be determined by AI / ML. That is, both the parameter set (numerology) and location parameters are determined by the AI / ML model. The parameter set parameters include subcarrier spacing (SCS), cyclic prefix, etc.; and / or location parameters include location and bandwidth, etc. In some implementations, parameters related to the BWP can also be determined by AI / ML, such as the parameters of the signal or channel within the BWP.

[0079]

[0080] For an example using level n2, some parameters of the BWP can be determined by AI / ML. Optionally, other parameters are determined by conventional methods (e.g., network configuration or UE reporting). That is, some parameters can be determined by AI / ML, while others are determined by conventional methods. For example, parameters of the parameter set are determined by AI / ML, while location parameters are determined by conventional methods. In another example, location parameters are determined by AI / ML, while parameters of the parameter set are determined by conventional methods. Note that some parameters related to the BWP (e.g., parameters of signals or channels within the BWP) can also be determined by AI / ML.

[0081] For examples using the n3 level, BWP applications can be directed to an AI / ML mode with a higher level. In other words, BWP's default application is based on a traditional approach that does not integrate with AI / ML; once BWP is determined using the traditional approach, it can be switched to an AI / ML mode with a higher level via AI / ML functionality.

[0082] Various embodiments can provide the following benefits: these methods enable intelligent or efficient utilization of one or more granularities of spectrum resources (e.g., BWP, carrier, frequency band, combination of frequency bands), which can be achieved by combining spectrum resources with AI / ML; and / or any granularity of spectrum resources can be combined with AI / ML.

[0083] Example Set III In various embodiments, when spectrum utilization can be combined with AI / ML, at least one granularity of the spectrum resource can be used for combination with AI / ML. Optionally, at least one AI / ML level can be combined with a granularity of spectrum resource utilization.

[0084] In some implementations, switching between AI / ML mode and fallback mode can be supported for a certain granularity of spectrum resource utilization, wherein the fallback mode can be the traditional mode.

[0085] For the first option (Option 1), the switching between AI / ML mode and fallback mode can occur between different resources at the granularity of spectrum resource utilization, such as BWP / carrier / band switching. At least one resource is configured / applied with AI / ML, and at least one resource is not configured / applied with AI / ML. AI / ML mode or fallback mode can be implemented by switching to or from that at least one resource. Optionally, different levels of AI / ML or cooperation can be configured / applied for different resources at the granularity of spectrum resource utilization. That is, for non-fallback mode, there may be switching between AI / ML or cooperation levels.

[0086] For the second option (Option 2), the switching between AI / ML mode and fallback mode may occur within the same resource at the granularity of spectrum resource utilization, for example, within a BWP / carrier / band. A resource at this granularity of spectrum resource utilization may be configured / applied with AI / ML, and AI / ML mode or fallback mode can be implemented based on each resource and through switching. Optionally, different levels of AI / ML or cooperation levels can be configured / applied for each resource. That is, for non-fallback mode, there may be switching between AI / ML or cooperation levels.

[0087] In some implementations, not all resources at the granularity of spectrum resource utilization are configured / applied with AI / ML, or at least one resource at the granularity of spectrum resource utilization may be configured / applied without AI / ML.

[0088] BWP utilization can be considered as a non-limiting example. When BWP-level utilization can be combined with AI / ML, the baseline combination can be a configuration of each BWP or part of the BWP combined with AI / ML. Optionally, this configuration can reference some auxiliary information, such as UE preferences, i.e., which BWP or part of the BWP the UE expects to combine with AI / ML, and this auxiliary information is delivered or reported by the UE. Optionally, for a group of UEs, a common BWP or common frequency resource (CFR) can be used for AI / ML-based BWPs. Optionally, at least one AI / ML or cooperative level can be combined with BWP utilization. Optionally, when the granularity of spectrum resource utilization is BWP, switching between AI / ML mode and fallback mode can be supported by one of the following options.

[0089] For example, for option 1, switching may occur between different BWPs. At least one BWP is configured / applied with AI / ML, and at least one BWP is not configured / applied with AI / ML. AI / ML mode or fallback mode can be achieved by switching to or from that at least one BWP. Figure 5 As shown, BWP#1 (510) is in fallback mode, therefore BWP#1 is not configured / applied with AI / ML or collaborative mode, while BWP#2 (520) is configured / applied with AI / ML or collaborative mode. Switching between AI / ML mode and fallback mode can be supported by switching BWPs between BWP#1 and BWP#2. In some implementations, different levels of AI / ML or collaborative modes can be configured / applied for different resources at the granularity of spectrum resource utilization. For example... Figure 5As shown, BWP#1 is not configured / applied with AI / ML or collaboration mode, BWP#2 is configured / applied with AI / ML or collaboration mode at level n1, BWP#3 (530) is configured / applied with AI / ML or collaboration mode at levels n1 / n2, and / or BWP#4 (540) is configured / applied with AI / ML or collaboration mode at levels n1 / n2 / n3. For example, switching between different levels of AI / ML mode and fallback mode can be supported by switching BWPs between BWP#1, BWP#2, and / or BWP#3.

[0090] For the example of option 2, switching may occur within the same BWP. BWPs can be configured / applied with AI / ML; AI / ML mode or fallback mode can be implemented by switching on each BWP. For example, BWP#1 is configured / applied with no AI / ML or collaboration mode; furthermore, BWP#1 can switch to AI / ML or collaboration mode when needed. Optionally, different levels of AI / ML or collaboration can be configured / applied to each BWP. For example, BWP#1 is configured / applied with no AI / ML or collaboration mode; furthermore, BWP#1 can switch to a certain level of AI / ML or collaboration mode when needed.

[0091] Various embodiments can provide the following benefits: these methods enable intelligent or efficient utilization of one or more granularities of spectrum resources (e.g., BWP, carrier, frequency band, combination of frequency bands), which can be achieved by combining different levels of spectrum resources with AI / ML, and by switching between AI / ML modes and fallback modes; and / or any granularity of spectrum resources can be combined with AI / ML.

[0092] Example Set IV In various embodiments, when spectrum utilization can be combined with AI / ML, in other embodiments, at least one granularity of spectrum resources can be used for combination with AI / ML. Multi-level (also known as unified cell) approaches can be introduced for different levels of management to match or align different granularities of spectrum resources. Multi-level cells can be combined with AI / ML. Optionally, multi-level cells can support requirements from both CA and SUL.

[0093] In some implementations, such as Figure 6 As shown, each level of a multi-level cell (e.g., cell (600)) can be associated with a spectrum resource BC / band / carrier / BWP. Multi-level cells can operate in several potential types (e.g., two types).

[0094] For the first type (Type 1), each level of a multi-level cell can be utilized equally. As an example of a non-restrictive approach, such as... Figure 7A As shown, cells #1 / 2 / 3 / 4 are utilized equally. For example, as... Figure 7B As shown, cells #1 / 2 / 3 / 4 can be optionally combined with different levels of spectrum resources without overlap.

[0095] For the second type (Type 2), each level of the multi-level cell can be utilized as a nested structure. In a non-restricted example, such as Figure 8A As shown, cells #1 / 2 / 3 / 4 are used as a nested structure, for example, as Figure 8B As shown, cells #1 / 2 / 3 / 4 can be optionally combined with different levels of spectrum resources that overlap.

[0096] In some implementations, cell determination can be based on a level and performed through one of the following options. For the first option (Option 1), the cell is determined or defined based on each level in a multi-level cell system corresponding to a granularity of spectrum resources. In some implementations, Option 1 can be combined with Type 1 multi-level cells. Furthermore, each level at the granularity of the spectrum resource can be determined as a cell; for example, a BWP can be deployed as a cell. Therefore, more cells can be derived.

[0097] For the second option (Option 2), the cell can be determined or defined based on the highest level, lowest level, or level corresponding to the granularity of a spectrum resource within the multi-level cell hierarchy. In some implementations, Option 2 can be combined with Type 2 multi-level cells. Furthermore, details of Option 2 may include at least one of the following. For the first alternative (Alt. 1), a multi-level physical cell identifier (PCID) can be applied to the multi-level cells. For example, a first synchronization signal (SS0) with N0 indices corresponds to the highest-level PCID, a second synchronization signal (SS1) with N1 indices corresponds to the second-level PCID, a third synchronization signal (SS2) with N2 indices corresponds to the third-level PCID, and so on. In some implementations, each BWP may contain a synchronization signal block (SSB); or, in some implementations, each BWP may not contain a synchronization signal block (SSB). For the second alternative (Alt. 2), only the cell with the highest-level PCID can operate effectively / independently, or cells with each level of PCID can operate independently. Optionally, there are some restrictions / associations between cells with lower-level PCIDs and cells with higher-level PCIDs. For the third alternative (Alt.3), there are certain restrictions / associations between lower-level and higher-level cells. These restrictions / associations may include: for one or more configuration functions / parameters of a higher-level cell, the same / subset of configurations can be applied to the same function / parameter of a lower-level cell. For the fourth alternative (Alt.4), the number of cells can be determined solely by the number of highest-level PCIDs. Alternatively, the number of cells can be determined by the number of both the highest-level PCIDs and the additional-level PCIDs.

[0098] In some implementations, cell groups or cell aggregations can be determined based on levels and through one of the following options. For the first option (Option 1), cell groups or cell aggregations can be determined by cells at the same level. That is, multi-level cell groups can be derived. For the second option (Option 2), cell groups or cell aggregations can be determined by cells at different levels. That is, multi-level cell groups can also be derived, and non-independent lower-level cells can be generated before grouping. In some implementations, constraints / associations may change due to grouping; for example, after grouping, lower-level cell groups may have some constraints / associations with higher-level cell groups, regardless of the level of each cell within the cell group.

[0099] In some implementations, the cell used for initial access can be determined by one of the following options. For the first option (Option 1), cells of every level can be used for initial access. Optionally, if a PCID is detected and the cell is not a valid / independent cell, the remaining procedures for initial access can be based on the associated higher / highest level cell (primary cell), or still based on that cell. For the second option (Option 2), initial access can be performed using only one level of cells or cells from a subset of levels. Optionally, the highest or lowest level cell can be used for initial access. After initial access, cells of different levels can be used (e.g., via cell handover).

[0100] In some implementations, multi-level cells can be combined with AI / ML. When the AI / ML model is located on the network (NW) side, the (re)generation / determination of multi-level cells can be determined by the model, for example, determining whether to determine single-level or multi-level cells; determining whether the functions of each level of cells are the same or different; the relationships or configurations between cells can be determined by the model, for example, restrictions / associations between cells; the resources of one or more cells can be determined by the model, for example, what granularity can be applied, and restrictions / associations between cells. Optionally, information for inference (e.g., preferred cells for each level or multi-level, preferred granularity of cells for one or more levels) can be reported to enhance the model. That is, this information can be reported from the UE to assist the NW-side AI / ML model in inference / prediction.

[0101] In some implementations, multi-level cells can be combined with AI / ML. When the AI / ML model is located on the UE side, there may be labels for supervised or semi-supervised learning, and further classification / prediction can be performed within these labels. Optionally, the label type can be at least one of the following: a set of parameters with finite combinations, such as one or more granularities of spectrum resources, or one or more levels of cells. Optionally, the label and / or other training data can be requested, reported, or sent by the UE to the network (NW). Optionally, the signaling used for the above label derivation or transmission can be indicated by the network to the UE, wherein the signaling can be L1 signaling (e.g., downlink control information (DCI)), or L2 signaling (e.g., medium access control (MAC) control element (CE)), or L3 signaling (e.g., radio resource control (RRC)). Optionally, in the case of unsupervised learning or reinforcement learning without labels, the classification type of the inference can be derived, such as mapping schemes, interleaving methods, etc. Optionally, the UE may report prediction information to the NW, wherein the prediction information may be based on the output inferred from an AI / ML model, such as multi-level cell determination and the resources corresponding to each level of cell. Optionally, the UE may report prediction information for N future time instances to the NW, wherein the prediction information may be based on the output inferred from an AI / ML model. Optionally, the UE may report the confidence / probability information of the prediction information to the NW.

[0102] Various embodiments can provide the following benefits: these methods enable intelligent or efficient utilization of one or more granularities of spectrum resources (e.g., BWP, carrier, frequency band, frequency band combination), which can be achieved by combining different levels of spectrum resources with different levels of AI / ML; a single-level cell can use a single granularity of spectrum resources, multi-level cells can be used to associate different granularities of spectrum resources; and / or any granularity of spectrum resources can be combined with AI / ML.

[0103] This disclosure describes various embodiments for enhancing spectrum utilization. When spectrum utilization is combined with AI / ML, at least one of the following spectrum resources can be applied: BWP, carrier, frequency band, or combination of frequency bands, and the utilization of these spectrum resource levels can be combined with AI / ML. Optionally, for a group of UEs, a common BWP or common frequency resource (CFR) can be used for an AI / ML-based BWP. Optionally, the UE can deliver or report one or more candidates of CA combinations preferred by the UE. Optionally, carrier / cell (de)activation / sleep can be determined by the UE. As an alternative, carrier / cell (de)activation / sleep can still be based on indications from the network, and the UE can deliver or recommend preferred carriers / cells for (de)activation / sleep. As another alternative, the UE can perform carrier / cell (de)activation / sleep operations and deliver the results to the NW. Optionally, if one frequency band in a band pair or band group is configured / combined with AI / ML, other frequency bands can also be configured / combined with AI / ML.

[0104] In various embodiments, at least one AI / ML level can be combined with a granularity of spectrum resource utilization. n1 level: All parameters of the spectrum resource granularity can be determined by AI / ML; n2 level: Some parameters of the spectrum resource granularity can be determined by AI / ML; n3 level: The application of the spectrum resource granularity can be directed to an AI / ML mode with a higher level.

[0105] In various embodiments, the switching between AI / ML mode and fallback mode for spectrum resource utilization granularity can be supported by one of the following options: Option 1: Switching the granularity of spectrum resource utilization between different resources. Option 2: Switching the granularity of spectrum resource utilization within the same resource.

[0106] In various embodiments, each level in a multi-level cell can be associated with spectrum resources of BC / band / carrier / BWP. Cells can be determined or defined based on the highest level, lowest level, or each level of the multi-level cell, corresponding to a granularity of spectrum resources. As an alternative, multi-level PCIDs can be applied to multi-level cells. Alternatively, only cells with the highest-level PCID may be able to operate independently / effectively, or cells with PCIDs at each level may be able to operate independently; the number of cells may be determined solely by the number of highest-level PCIDs. Optionally, cell groups or cell aggregations can be determined by cells at the same level. Optionally, cells used for initial access can be determined by one of the following options: Option 1: Cells at each level can be used for initial access. Option 2: Only cells at one level or cells at a subset of levels can be used for initial access.

[0107] This disclosure describes methods, apparatus, and computer-readable media for wireless communication. This disclosure addresses the issue of spectrum resource utilization. The methods, apparatus, and computer-readable media described in this disclosure can improve the performance of wireless communication, thereby increasing efficiency and overall performance. The methods, apparatus, and computer-readable media described in this disclosure can improve the overall efficiency of wireless communication systems.

[0108] In some other embodiments, a computer-readable medium includes instructions that, when executed by a computer, cause the computer to perform the methods described above. The computer-readable medium may be referred to as a non-transitory computer-readable media (CRM), which can store data for a longer period of time, such as a flash drive or optical disc (CD), or store data for a short period of time when powered, such as a storage device or random access memory (RAM). In some embodiments, computer-readable instructions may be included in software embodied in one or more tangible, non-transitory computer-readable media. Such a non-transitory computer-readable medium may be a medium associated with a user-accessible mass storage device, or a medium associated with certain short-term storage devices (e.g., internal mass storage or ROM) having non-transitory characteristics. Software implementing various embodiments of this disclosure may be stored in such a device and executed by a processor (or processing circuitry). Depending on specific needs, the computer-readable medium may include one or more storage devices or chips. The software may cause a processor (including a CPU, GPU, FPGA, etc.) to perform a specific process or a specific portion of a specific process described herein, including defining data structures stored in RAM and modifying such data structures according to a software-defined process.

[0109] References to features, advantages, or similar language throughout this specification do not imply that all features and advantages achievable using this solution should be included or are all included in any single implementation thereof. Rather, the language referring to features and advantages is to be understood as meaning that a particular feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of this solution. Therefore, the discussion of features and advantages, and similar language throughout this specification, may, but do not necessarily, refer to the same embodiments.

[0110] Furthermore, the features, advantages, or characteristics described in this solution can be combined in one or more embodiments in any suitable manner. Those skilled in the art will recognize from the description herein that this solution can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages that may not be present in all embodiments of this solution may be recognized in certain embodiments.

Claims

1. A method for wireless communication, comprising: The base station determines at least one granular level of spectrum resource utilization and cooperation level, wherein, The granularity level of spectrum resources refers to the level of granularity of frequency resources, and The collaboration level represents the level of combination with the collaboration model.

2. A method for wireless communication, comprising: The user equipment (UE) determines at least one granular level of spectrum resource utilization and cooperation level, wherein, The granularity level of spectrum resources refers to the level of granularity of frequency resources, and The collaboration level represents the level of combination with the collaboration model.

3. The method according to any one of claims 1 and 2, wherein, The granularity level of the spectrum resources includes at least one of the following: bandwidth portion (BWP) level, carrier level, frequency band level, or frequency band combination (BC) level.

4. The method according to any one of claims 1 and 3, wherein, The collaboration level includes at least one of the following: a first collaboration level, a second collaboration level, or a third collaboration level; and The collaborative model includes artificial intelligence (AI) and / or machine learning (ML) models.

5. The method according to any one of claims 1 to 4, wherein, The granularity of the spectrum resources is at the BWP level; and The collaboration model is combined with each BWP in the BWP set or a portion of the BWP set, or A BWP for a group of UEs in conjunction with the aforementioned collaboration model includes a common BWP or a common frequency resource (CFR).

6. The method according to any one of claims 1 to 4, wherein, The granularity of the spectrum resources is at the carrier level; and The UE determines one or more candidates for carrier aggregation (CA) combinations, or The UE determines that the carrier is in one of the following states: active, deactivated, or dormant, through the following methods: Based on indications from the base station, the carrier is determined to be in a certain state, and that state is recommended, or An operation is performed to bring the carrier to a certain state, and the result of the operation is delivered to the base station.

7. The method according to any one of claims 1 to 4, wherein, The granularity of the spectrum resources is at the frequency band level; and In response to one frequency band in the frequency band pair being combined with the cooperation model, the other frequency band in the frequency band pair being combined with the cooperation model, or In response to one frequency band in the frequency band group being combined with the cooperation model, all other frequency bands in the frequency band group are combined with the cooperation model.

8. The method according to any one of claims 1 to 4, wherein, In response to the cooperation level being the first cooperation level, all parameters related to the granularity level of the spectrum resource are determined by the cooperation model.

9. The method according to any one of claims 1 to 4, wherein, In response to the cooperation level being the second cooperation level, some parameters related to the granularity level of the spectrum resources are determined by the cooperation model.

10. The method according to any one of claims 1 to 4, wherein, In response to the third cooperation level, after the parameters of the granularity level of the spectrum resource are initially determined without a cooperation model, the cooperation level for the granularity level of the spectrum resource is switched to another cooperation level.

11. The method according to any one of claims 1 to 4, wherein, The granular utilization mode of the spectrum resources can switch between cooperative and non-cooperative modes.

12. The method according to claim 11, wherein, Switching between the cooperative mode and the non-cooperative mode includes switching between different frequency resources at the granularity level of the spectrum resources.

13. The method according to claim 11, wherein, Switching the utilization mode between the cooperative mode and the non-cooperative mode includes switching within the same frequency resources at the granularity level of the spectrum resources.

14. The method according to any one of claims 1 to 4, wherein, The utilization of the spectrum resources at at least one granular level includes multi-level cells, wherein each level of the multi-level cells is a cell associated with a granular level of the spectrum resources.

15. The method according to claim 14, wherein, The cell is determined based on at least one of the following: the highest level of the multi-level cell, the lowest level of the multi-level cell, or each level of the multi-level cell.

16. The method according to claim 15, wherein, The multi-level cell includes at least one of the following: Multilevel Physical Cell Identifier (PCID) At least one cell with the highest-level PCID is used independently. At least one parameter of a higher-level cell in the multi-level cell system includes the same parameters used for lower-level cells in the multi-level cell system, or The number of cells in the multi-level cell system is determined by the number of the highest-level PCIDs.

17. The method of claim 15, wherein, At least one cell group is determined by cells of the same level, or At least one cell group is determined by cells at different levels.

18. The method according to claim 15, wherein, At least one cell for initial access is determined by one of the following: cells at each level, cells at only the highest or lowest level, or cells at a subset of levels.

19. A wireless communication device, comprising a processor and a memory, wherein, The processor is configured to read code from the memory and implement the method according to any one of claims 1 to 18.

20. A computer program product comprising computer-readable program medium code stored thereon, the computer-readable program medium code, when executed by a processor, causing the processor to implement the method according to any one of claims 1 to 18.

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