Secondary cell group selection based on primary frequency band measurement

Abstract

A method for wireless communication by a user equipment (UE) may include: receiving a message requesting channel characteristics of an auxiliary frequency band estimated based on measured characteristics of a channel in a primary frequency band. The method also includes: measuring characteristics of a channel in the primary frequency band. The method estimates the channel characteristics of the auxiliary frequency band based on the primary frequency band characteristic measurement. The estimation may be performed without measuring a signal in the auxiliary frequency band. In addition, the method may report the estimated channel characteristics of the auxiliary frequency band based on the estimated channel characteristics of the auxiliary frequency band and receive an indication of an auxiliary cell group.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. patent application No. 17 / 155,888, filed on January 22, 2021, and entitled “SECONDARY CELL GROUP SELECTION BASED ON PRIMARY FREQUENCY BAND MEASUREMENTS,” which claims the benefit of U.S. Provisional Patent Application No. 62 / 968,965, filed on January 31, 2020, and entitled “SECONDARY CELL GROUP SELECTION BASED ON PRIMARY FREQUENCY BAND MEASUREMENTS,” the disclosures of which are incorporated herein by reference in their entirety. Technical Field

[0003] Aspects of the present disclosure relate generally to wireless communications, and more particularly, to techniques and apparatus for efficient cell measurements to enable, for example, assisted cell selection. Background Art

[0004] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies that can support communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE / LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

[0005] A wireless communication network may include multiple base stations (BSs) that can support communications for multiple user equipments (UEs). User equipments (UEs) may communicate with base stations (BSs) via downlinks and uplinks. A downlink (or forward link) refers to a communication link from a BS to a UE, while an uplink (or reverse link) refers to a communication link from a UE to a BS. As will be described in more detail, a BS may be referred to as a Node B, gNB, access point (AP), radio head, transmit receive point (TRP), new radio (NR) BS, 5G Node B, and the like.

[0006] The above multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different user devices to communicate at the city, country, region, and even global level. New Radio (NR) (which may also be referred to as 5G) is a set of enhancements to the LTE mobile standard promulgated by the Third Generation Partnership Project (3GPP). NR is designed to better integrate with other open standards by improving spectrum efficiency, reducing costs, improving services, utilizing new spectrum, and using orthogonal frequency division multiplexing (OFDM) (CP-OFDM) with cyclic prefix (CP) on the downlink (DL), using CP-OFDM and / or SC-FDM (e.g., also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink (UL), so as to better support mobile broadband Internet access, as well as support beamforming, multiple input multiple output (MIMO) antenna technology and carrier aggregation. However, as the demand for mobile broadband access continues to grow, there is a need for further improvements in NR and LTE technologies. Preferably, these improvements should be applicable to other multiple access technologies and telecommunication standards that adopt these technologies.

[0007] An artificial neural network may include interconnected groups of artificial neurons (e.g., a neuron model). An artificial neural network may be a computing device or represented as a method performed by a computing device. A convolutional neural network (e.g., a deep convolutional neural network) is a feedforward artificial neural network. A convolutional neural network may include a layer of neurons that may be configured in a tiled receptive field. It is desirable to apply neural network processing to wireless communications to achieve greater efficiency. Summary of the invention

[0008] In some aspects of the present disclosure, a method for wireless communication by a user equipment (UE) may include: receiving a message requesting channel characteristics of an auxiliary frequency band estimated based on measured characteristics of a channel in a primary frequency band. The method may also include: measuring the channel characteristics in the primary frequency band, and estimating the channel characteristics of the auxiliary frequency band without measuring a signal in the auxiliary frequency band. In addition, the method may include: reporting the estimated channel characteristics of the auxiliary frequency band. A secondary cell group may be selected based on the estimated channel characteristics.

[0009] In some aspects, a method of wireless communication by a base station includes: configuring a user equipment (UE) to estimate channel characteristics for a secondary frequency band based on actual measurements of a primary frequency band. The method may also include: configuring reporting of the estimated channel characteristics. The method may also include: selecting a secondary cell group (SCG) based on the reported estimated channel characteristics instead of the secondary channel measurements.

[0010] A user equipment (UE) for wireless communication may include a memory and one or more processors operably coupled to the memory. The memory and the one or more processors may receive a message requesting a channel characteristic of an auxiliary frequency band estimated based on the characteristics of a channel measured in a primary frequency band. The UE may also measure the characteristics of a channel in the primary frequency band, and estimate the channel characteristics of the auxiliary frequency band without measuring a signal in the auxiliary frequency band. In addition, the UE may report the estimated channel characteristics of the auxiliary frequency band. A secondary cell group may be selected based on the estimated channel characteristics.

[0011] A base station for wireless communication may include a memory and one or more processors operably coupled to the memory. The memory and the one or more processors may configure a user equipment (UE) to estimate channel characteristics for an auxiliary frequency band based on actual measurements of a primary frequency band. The base station may also be configured to report the estimated channel characteristics. The base station may further select an auxiliary cell group (SCG) based on the reported estimated channel characteristics instead of the auxiliary channel measurements.

[0012] The aspects generally include methods, apparatus, systems, computer program products, non-transitory computer readable media, user equipment, base stations, wireless communication devices, and processing systems as fully described with reference to and as illustrated by the accompanying drawings and description.

[0013] The foregoing has outlined rather broadly the features and technical advantages of examples according to the present disclosure so that the following detailed description may be better understood. Additional features and advantages will be described. The disclosed concepts and specific examples may be easily used as a basis for modifying or designing other structures for achieving the same purpose of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. When considered in conjunction with the accompanying drawings, the characteristics of the disclosed concepts (both their organization and method of operation) and the associated advantages will be better understood from the description below. Each of the figures in the accompanying drawings is provided for the purpose of illustration and description and is not intended to be a definition of limitations to the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to understand the above-mentioned features of the present disclosure in detail, a more specific description of the invention briefly summarized above can be obtained by referring to various aspects (some of which are shown in the accompanying drawings). However, it should be noted that the accompanying drawings only illustrate certain typical aspects of the present disclosure and are therefore not considered to limit the scope of the present disclosure, as the description may allow for other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0015] Figure 1is a block diagram conceptually illustrating an example of a wireless communication network in accordance with various aspects of the present disclosure.

[0016] Figure 2 is a block diagram conceptually illustrating an example of a base station communicating with a user equipment (UE) in a wireless communication network in accordance with various aspects of the present disclosure.

[0017] Figure 3 An exemplary implementation of designing a neural network using a system on a chip (SOC) including a general-purpose processor in accordance with certain aspects of the present disclosure is shown.

[0018] Figure 4A , 4B 4C are diagrams illustrating neural networks according to aspects of the present disclosure.

[0019] Figure 4D is a diagram illustrating an exemplary deep convolutional network (DCN) according to aspects of the present disclosure.

[0020] Figure 5 is a block diagram illustrating an exemplary deep convolutional network (DCN) according to aspects of the present disclosure.

[0021] Figure 6 is a call flow diagram illustrating an exemplary secondary cell group selection process according to aspects of the present disclosure.

[0022] Figure 7 is a flow chart illustrating an exemplary auxiliary band reporting process, for example, performed by a user equipment, according to aspects of the present disclosure.

[0023] Figure 8 is a flow chart illustrating an example secondary cell group selection process, for example, performed by a base station, according to aspects of the present disclosure. DETAILED DESCRIPTION

[0024] The various aspects of the present disclosure are more fully described with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms and should not be construed as being limited to any specific structure or function presented throughout the present disclosure. More specifically, these aspects are provided so that the present disclosure will be thorough and complete, and the scope of the present disclosure will be fully conveyed to those skilled in the art. Based on the teachings, it should be understood by those skilled in the art that the scope of the present disclosure is intended to cover any aspect of the disclosed present disclosure, whether the aspect is implemented independently of any other aspect of the present disclosure or implemented in combination with any other aspect. For example, using any number of aspects set forth, a device can be implemented or a method can be implemented. In addition, the scope of the present disclosure is intended to cover such a device or method implemented using other structures, functions, or structures and functions other than or different from the various aspects of the present disclosure set forth. It should be understood that any aspect of the disclosed present disclosure can be embodied by one or more elements of the claims.

[0025] Several aspects of telecommunication systems will now be presented with reference to various devices and techniques. These devices and techniques will be described in the detailed description below by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements") and illustrated in the accompanying drawings. These elements can be implemented using hardware, software, or a combination thereof. Whether such elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0026] It should be noted that although various aspects may be described using terminology generally associated with 5G and later wireless technologies, various aspects of the present disclosure may be applicable to communication systems based on other generations, including, for example, 3G and / or 4G technologies.

[0027] A user equipment (UE) may have the capability to operate in dual connectivity (DC) mode. Dual connectivity allows the UE to simultaneously send and receive data with multiple cell groups on multiple frequency bands, wherein the multiple cell groups include a primary cell group and one or more secondary cell groups. In some configurations, the primary cell group is based on a first radio access technology (RAT) such as LTE, and the secondary cell group is based on a secondary RAT such as New Radio (NR).

[0028] The base station may select the secondary cell group based on measurements from the UE. The measurement values ​​indicate the best frequency band from the UE's perspective, for example, based on measured reference signal received power (RSRP) of various secondary cell group frequency bands.

[0029] Machine learning techniques have been used to predict channel characteristics in a second frequency band based on measurements in a first frequency band, although RSRP is not typically predicted. The environment for the two frequency bands will be the same (or at least similar) because the propagation channels for each frequency band are similar. It may be the case that measurements on a first frequency band (e.g., frequency range 1 (FR1)) are easier, cheaper, and / or more energy efficient for the UE than measurements on the second frequency band. For example, specific hardware characteristics of frequency range 2 (FR2) may make FR2 measurements less energy efficient for the UE. In addition, omitting measurements on the second frequency band eliminates or at least reduces the measurement overhead for reference signals in the second frequency band. That is, if measurements can be omitted, the UE does not need to detect reference signals in the second frequency band. In other cases, the period of the reference signal can be reduced in the second frequency band.

[0030] According to various aspects of the present disclosure, the UE performs measurements in the primary frequency band, then predicts predicted characteristics of the secondary frequency band based on the first frequency band measurements and reports them to the base station. The base station selects a secondary cell group based on the predicted characteristics of the secondary frequency band.

[0031] Figure 1 1 is a diagram showing a network 100 in which various aspects of the present disclosure may be implemented. The network 100 may be a 5G or NR network or some other wireless network (e.g., an LTE network). The wireless network 100 may include a plurality of BSs 110 (shown as BS 110a, BS 110b, BS 110c, and BS 110d) and other network entities. A BS is an entity that communicates with a user equipment (UE) and may also be referred to as a base station, NR BS, Node B, gNB, 5G Node B (NB), access point, transmit receive point (TRP), etc. Each BS may provide communication coverage for a specific geographic area. In 3GPP, the term "cell" may refer to a coverage area of ​​a BS and / or a BS subsystem serving the coverage area, depending on the context in which the term is used.

[0032] A BS may provide communication coverage for a macro cell, a pico cell, a femto cell, and / or another type of cell. A macro cell may cover a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by UEs with service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEs with service subscription. A femto cell may cover a relatively small geographic area (e.g., a residence) and may allow restricted access by UEs associated with the femto cell (e.g., UEs in a closed subscriber group (CSG)). A BS for a macro cell may be referred to as a macro BS. A BS for a pico cell may be referred to as a pico BS. A BS for a femto cell may be referred to as a femto BS or a home BS. In Figure 1 In the example shown in , BS 110a may be a macro BS for macro cell 102a, BS 110b may be a pico BS for pico cell 102b, and BS 110c may be a femto BS for femto cell 102c. A BS may support one or more (e.g., three) cells. The terms “eNB,” “base station,” “NR BS,” “gNB,” “TRP,” “AP,” “Node B,” “5G NB,” and “cell” may be used interchangeably.

[0033] In some aspects, the cells may not necessarily be stationary, and the geographic area of ​​the cells may move depending on the location of the mobile BS. In some aspects, the BSs may be interconnected with each other and / or with one or more other BSs or network nodes (not shown) in the access network 100 via various types of backhaul interfaces (e.g., direct physical connections, virtual networks, and / or similar interfaces using any appropriate transport networks).

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

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

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

[0037] UE 120 (e.g., 120a, 120b, 120c) can be dispersed throughout the wireless network 100, and each UE can be stationary or mobile. UE can also be referred to as an access terminal, terminal, mobile station, subscriber unit, station, etc. UE can be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet device, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or apparatus, a biometric sensor / device, a wearable device (smart watch, smart clothing, smart glasses, smart wristband, smart jewelry (e.g., smart ring, smart bracelet, etc.)), an entertainment device (e.g., a music or video device, or a satellite radio unit, etc.), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing device, a global positioning system device, or any other suitable device configured to communicate via a wireless or wired medium.

[0038] Some UEs may be considered as machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) UEs. MTC and eMTC UEs include, for example, robots, drones, remote devices, sensors, meters, monitors, location tags, etc., which may communicate with a base station, another device (e.g., a remote device), or some other entity. A wireless node may provide a connection to or to a network (e.g., a wide area network such as the Internet or a cellular network) via, for example, a wired or wireless communication link. Some UEs may be considered as Internet of Things (IoT) devices, and / or may be implemented as NB-IoT (narrowband Internet of Things) devices. Some UEs may be considered as customer premises equipment (CPE). UE 120 may be included inside a housing that houses components of UE 120 (such as a processor component, a memory component, etc.).

[0039] Generally, any number of wireless networks can be deployed in a given geographic area. Each wireless network can support a specific RAT and can operate on one or more frequencies. RAT can also be referred to as radio technology, air interface, etc. Frequency can also be referred to as carrier, channel, etc. Each frequency can support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks can be deployed.

[0040] In some aspects, two or more UEs 120 (e.g., illustrated as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using base station 110 as an intermediary to communicate with each other). For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (e.g., which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, etc.), mesh networks, etc. In this case, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere as being performed by base station 110.

[0041] As noted above, Figure 1 is provided as an example only. Other examples may differ from those described above. Figure 1 Examples described.

[0042] Figure 2 Base station 110 and UE 120 (which may be Figure 1 1. Block diagram of a design 200 of a base station in a base station and a UE in a UE. Base station 110 may be equipped with T antennas 234a through 234t, and UE 120 may be equipped with R antennas 252a through 252r, where, in general, T≥1 and R≥1.

[0043] At the base station 110, the transmit processor 220 may receive data for one or more UEs from the data source 212, select one or more modulation and coding schemes (MCS) for each UE based at least in part on a channel quality indicator (CQI) received from the UE, process (e.g., encode and modulate) the data for the UE based at least in part on the MCS selected for each UE, and provide data symbols for all UEs. The transmit processor 220 may also process system information (e.g., for semi-static resource partitioning information (SRPI), etc.) and control information (e.g., CQI requests, grants, upper layer signaling, etc.), and provide overhead symbols and control symbols. The transmit processor 220 may also generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS)) and synchronization signals (e.g., primary synchronization signals (PSS) and secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, if applicable, and may provide T output symbol streams to T modulators (MOD) 232a to 232t. Each modulator 232 may process a corresponding output symbol stream (e.g., for OFDM, etc.) to obtain an output sample stream. Each modulator 232 may further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. T downlink signals from modulators 232a to 232t may be transmitted via T antennas 234a to 234t, respectively. According to various aspects described in more detail below, a synchronization signal may be generated using position coding to transmit additional information.

[0044] At UE 120, antennas 252a to 252r may receive downlink signals from base station 110 and / or other base stations, and may provide received signals to demodulators (DEMODs) 254a to 254r, respectively. Each demodulator 254 may condition (e.g., filter, amplify, downconvert, and digitize) the received signal to obtain input samples. Each demodulator 254 may further process the input samples (e.g., for OFDM, etc.) to obtain received symbols. MIMO detector 256 may obtain received symbols from all R demodulators 254a to 254r, perform MIMO detection on the received symbols (if applicable), and provide detected symbols. Receive processor 258 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for UE 120 to data sink 260, and provide decoded control information and system information to controller / processor 280. The channel processor may determine reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), channel quality indicator (CQI), etc. In some aspects, one or more components of UE 120 may be included in a housing.

[0045] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information from a controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, CQI, etc.). The transmit processor 264 may also generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be precoded by a TX MIMO processor 266 (if applicable), further processed by modulators 254a through 254r (e.g., for DFT-s-OFDM, CP-OFDM, etc.), and transmitted to the base station 110. At the base station 110, uplink signals from the UE 120 and other UEs may be received by the antenna 234, processed by the demodulator 254, detected by the MIMO detector 236 (if applicable), and further processed by the receive processor 238 to obtain decoded data and control information transmitted by the UE 120. The receive processor 238 may provide decoded data to a data sink 239 and provide decoded control information to a controller / processor 240. The base station 110 may include a communication unit 244 and communicate with the network controller 130 via the communication unit 244. The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292.

[0046] The controller / processor 240 of the base station 110, the controller / processor 280 of the UE 120, and / or Figure 2Any other components in the base station 110 may perform one or more techniques associated with reporting estimated channel characteristics (e.g., for assisting cell group selection), as described in more detail elsewhere. For example, the controller / processor 240 of the base station 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component in may perform or direct e.g. Figure 6 The operations of process 600 and / or other processes as described. Memories 242 and 282 may store data and program codes for base station 110 and UE 120, respectively. Scheduler 246 may schedule UEs for data transmission on the downlink and / or uplink.

[0047] In some aspects, UE 120 may include means for receiving, means for measuring, means for estimating, means for reporting, means for configuring, means for removing, and means for selecting. These means may include combining Figure 2 One or more components of UE 120 or base station 110 are described.

[0048] As mentioned above, it provides Figure 2 This is just an example. Other examples may differ from Figure 2 The content described.

[0049] In some cases, different types of devices supporting different types of applications and / or services can coexist in a cell. Examples of different types of devices include UE phones, customer premises equipment (CPE), vehicles, Internet of Things (IoT) devices, etc. Examples of different types of applications include ultra-reliable low-latency communication (URLLC) applications, massive machine type communication (mMTC) applications, enhanced mobile broadband (eMBB) applications, vehicle-to-everything (V2X) applications, etc. In addition, in some cases, a single device can support different applications or services at the same time.

[0050] Figure 3An exemplary embodiment of a system on chip (SOC) 300 according to certain aspects of the present disclosure is shown, and the SOC 300 may include a central processing unit (CPU) 302 or a multi-core CPU configured to assist in cell group selection. The SOC 300 may be included in the base station 110 or the UE 120. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computing device (e.g., a neural network with weights), delays, frequency band information, and task information may be stored in a memory block associated with a neural processing unit (NPU) 108, in a memory block associated with the CPU 302, in a memory block associated with a graphics processing unit (GPU) 304, in a memory block associated with a digital signal processor (DSP) 306, in a memory block 318, or may be distributed in multiple blocks. Instructions executed at the CPU 302 may be loaded from a program memory associated with the CPU 302, or may be loaded from the memory block 318.

[0051] The SOC 300 may also include additional processing blocks customized for specific functions, such as a GPU 304, a DSP 306, a connection block 310, which may include a fifth generation (5G) connection, a fourth generation long term evolution (4G LTE) connection, a Wi-Fi connection, a USB connection, a Bluetooth connection, etc., and a multimedia processor 312 that may, for example, detect and recognize gestures. In one embodiment, the NPU is implemented in the CPU, DSP, and / or GPU. The SOC 300 may also include a sensor processor 314, an image signal processor (ISP) 316, and / or a navigation module 320, which may include a global positioning system.

[0052] SOC 300 can be based on the ARM instruction set. In one aspect of the present disclosure, the instructions loaded into the general processor 302 may include a code for receiving a message requesting a channel characteristic of an auxiliary frequency band estimated based on measuring the channel characteristic in the primary frequency band. The general processor 302 may also include a code for measuring the channel characteristic in the primary frequency band. The general processor 302 may also include a code for estimating the channel characteristic of the auxiliary frequency band without measuring the signal in the auxiliary frequency band. The general processor 302 may also include a code for reporting the estimated channel characteristic of the auxiliary frequency band. In other aspects of the present disclosure, the general processor 302 may include a code for configuring a user equipment (UE) to estimate the channel characteristic for the auxiliary frequency band based on the measurement of the primary frequency band. The general processor 302 may also include a code for configuring the reporting of the estimated channel characteristic. The general processor 302 may also include a code for selecting a secondary cell group (SCG) based on the estimated channel characteristic instead of the secondary channel measurement.

[0053] Deep learning architectures can perform object recognition tasks by learning to represent the input at successively higher levels of abstraction in each layer, thereby building useful feature representations of the input data. In this way, deep learning addresses a major bottleneck of traditional machine learning. Before the advent of deep learning, machine learning approaches to object recognition problems may have relied heavily on human-engineered features, perhaps combined with shallow classifiers. Shallow classifiers can be two-class linear classifiers, for example, where a weighted sum of the components of a feature vector can be compared to a threshold to predict which class the input belongs to. Human-engineered features can be templates or kernels that are customized by engineers in a specialized field for a specific problem domain. In contrast, deep learning architectures can learn to represent features that are similar to those that a human engineer might design, but through training. In addition, deep networks can learn to represent and recognize new types of features that humans might not have considered.

[0054] A deep learning architecture can learn a hierarchy of features. For example, if presented with visual data, the first layer can learn to recognize relatively simple features in the input stream, such as edges. In another example, if presented with auditory data, the first layer can learn to recognize spectral power at specific frequencies. The second layer, taking the output of the first layer as input, can learn to recognize combinations of features, such as simple shapes for visual data or combinations of sounds for auditory data. For example, higher layers can learn to represent complex shapes in visual data or words in auditory data. Even higher layers can learn to recognize common visual objects or spoken phrases.

[0055] Deep learning architectures can perform particularly well when applied to problems that have a natural hierarchical structure. For example, the classification of motor vehicles can benefit from first learning to recognize wheels, windshields, and other features. These features can be combined in different ways at higher levels to recognize cars, trucks, and airplanes.

[0056] Neural networks can be designed with a variety of connection patterns. In a feedforward network, information is passed from lower layers to higher layers, where each neuron in a given layer transmits to neurons in higher layers. As described above, hierarchical representations can be established in consecutive layers of a feedforward network. Neural networks can also have recurrent or feedback (also known as top-down) connections. In a recurrent connection, the output from a neuron in a given layer can be transmitted to another neuron in the same layer. Recurrent architectures may help identify patterns that span more than one block of input data, which is passed to the neural network in sequence. The connections from neurons in a given layer to neurons in lower layers are called feedback (or top-down) connections. Networks with many feedback connections may be helpful when the recognition of high-level concepts may help distinguish specific low-level features of the input.

[0057] The connections between layers of a neural network can be fully connected or partially connected. Figure 4A An example of a fully connected neural network 402 is shown. In the fully connected neural network 402, a neuron in a first layer may transmit its output to every neuron in a second layer, such that every neuron in the second layer will receive input from every neuron in the first layer. Figure 4B An example of a locally connected neural network 404 is shown. In the locally connected neural network 404, a neuron in a first layer may be connected to a limited number of neurons in a second layer. More generally, the locally connected layers of the locally connected neural network 404 may be configured such that each neuron in the layer will have the same or similar connection pattern, but the connection strengths may have different values ​​(e.g., 410, 412, 414, and 416). The locally connected connection patterns may produce spatially different receptive fields in higher layers because higher layer neurons in a given region may receive inputs that have properties tuned through training to a restricted portion of the total input to the network.

[0058] An example of a locally connected neural network is a convolutional neural network. Figure 4C An example of a convolutional neural network 406 is shown. The convolutional neural network 406 can be configured such that the connection strengths associated with the inputs of each neuron in the second layer are shared (e.g., 408). Convolutional neural networks can be well suited for problems where the spatial location of the inputs is meaningful.

[0059] One type of convolutional neural network is a deep convolutional network (DCN). Figure 4D A detailed example of a DCN 400 is shown, which is designed to recognize visual features in an image 426 input from an image capture device 430 (e.g., a vehicle-mounted camera). The DCN 400 of the current example can be trained to recognize traffic signs and numbers provided on traffic signs. Of course, the DCN 400 can be trained for other tasks, such as recognizing lane markings or recognizing traffic lights.

[0060] The DCN 400 may be trained using supervised learning. During training, an image, such as an image 426 of a speed limit sign, may be presented to the DCN 400, and a forward pass may be calculated to produce an output 422. The DCN 400 may include a feature extraction portion and a classification portion. Upon receiving the image 426, a convolutional layer 432 may apply a convolutional kernel (not shown) to the image 426 to generate a first set of feature maps 418. As an example, the convolutional kernel for the convolutional layer 432 may be a 5x5 kernel that generates a 28x28 feature map. In this example, because four different feature maps are generated in the first set of feature maps 418, four different convolutional kernels are applied to the image 426 at the convolutional layer 432. A convolutional kernel may also be referred to as a filter or a convolutional filter.

[0061] The first set of feature maps 418 may be subsampled by a max pooling layer (not shown) to generate a second set of feature maps 420. The max pooling layer reduces the size of the first set of feature maps 418. That is, the size of the second set of feature maps 420, e.g., 14x14, is smaller than the size of the first set of feature maps 418, e.g., 28x28. The reduced size provides similar information to subsequent layers while reducing memory consumption. The second set of feature maps 420 may be further convolved via one or more subsequent convolutional layers (not shown) to generate one or more sets of subsequent feature maps (not shown).

[0062] exist Figure 4D In the example of , the second set of feature maps 420 are convolved to generate a first feature vector 424. In addition, the first feature vector 424 is further convolved to generate a second feature vector 428. Each feature of the second feature vector 428 can include a number corresponding to a possible feature of the image 426, such as "symbol", "60", and "100". A softmax function (not shown) can convert the numbers in the second feature vector 428 into probabilities. In this way, the output 422 of the DCN 400 is the probability that the image 426 includes one or more features.

[0063] In this example, the probabilities for "sign" and "60" in output 422 are higher than the probabilities for the others (e.g., "30," "40," "50," "70," "80," "90," and "100") of output 422. Before training, output 422 produced by DCN 400 may be incorrect. Therefore, the error between output 422 and the target output may be calculated. The target output is the ground truth of image 426 (e.g., "sign" and "60"). The weights of DCN 400 may then be adjusted so that output 422 of DCN 400 is more closely aligned with the target output.

[0064] To adjust the weights, the learning algorithm may calculate a gradient vector with respect to the weights. The gradient may indicate the amount by which the error will increase or decrease if the weights are adjusted. At the top layer, the gradient may correspond directly to the value of the weights connecting the neurons activated in the penultimate layer and the neurons in the output layer. In lower layers, the gradient may depend on the value of the weights and the calculated error gradients of higher layers. The weights may then be adjusted to reduce the error. This way of adjusting weights may be referred to as "backward propagation" because it involves a "backward pass" through the neural network.

[0065] In practice, the error gradient of the weights may be computed over a small number of examples so that the computed gradient is close to the true error gradient. This approximation method may be referred to as stochastic gradient descent. Stochastic gradient descent may be repeated until the achievable error rate for the entire system has stopped decreasing or until the error rate has reached a target level. After learning, a new image (e.g., a speed limit sign of image 426) may be presented to the DCN, and a forward pass through the network may produce an output 422 that may be considered an inference or prediction of the DCN.

[0066] A deep belief network (DBN) is a probabilistic model comprising multiple layers of hidden nodes. A DBN can be used to extract a hierarchical representation of a training data set. A DBN can be obtained by stacking layers of a finite Boltzmann machine (RBM). RBM is an artificial neural network that can learn a probability distribution over a set of inputs. Since RBM can learn a probability distribution without information about the category to which each input should be classified, RBM is often used for unsupervised learning. Using a mixed unsupervised and supervised paradigm, the bottom RBM of a DBN can be trained in an unsupervised manner and used as a feature extractor, while the top RBM can be trained in a supervised manner (on the joint distribution of inputs from the previous layer and the target category) and can be used as a classifier.

[0067] A Deep Convolutional Network (DCN) is a network of convolutional networks configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning, where the input and output targets are known for many examples and used to modify the weights of the network by using a gradient descent method.

[0068] The DCN can be a feed-forward network. In addition, as described above, the connections from neurons in the first layer of the DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of the DCN can be used for fast processing. For example, the computational burden of the DCN can be much smaller than that of a similarly sized neural network that includes loops or feedback connections.

[0069] The processing of each layer of the convolutional network can be thought of as a spatially invariant template or basis projection. If the input is first decomposed into multiple channels, such as the red, green, and blue channels of a color image, then the convolutional network trained on the input can be thought of as three-dimensional, with two spatial dimensions along the axes of the image and a third dimension that captures color information. The output of the convolutional connection can be thought of as forming a feature map in a subsequent layer, where each element of the feature map (e.g., 220) receives input from a series of neurons in the previous layer (e.g., feature map 218) and input from each of the multiple channels. The values ​​in the feature map can be further processed with nonlinearities, such as correction, max(0,x). The values ​​from neighboring neurons can be further pooled, which corresponds to downsampling and can provide additional local invariance and dimensionality reduction. Normalization, which corresponds to whitening, can also be applied by lateral inhibition between neurons in the feature map.

[0070] The performance of deep learning architectures can increase as more labeled data points become available or as computing power increases. Modern deep neural networks are often trained using thousands of times more computing resources than were available to a typical researcher fifteen years ago. New architectures and training paradigms can further improve the performance of deep learning. Rectified linear units can reduce the training problem known as vanishing gradients. New training techniques can reduce overfitting, allowing larger models to achieve better generalization. Encapsulation techniques can abstract the data in a given receptive field and further improve overall performance.

[0071] Figure 5 is a block diagram illustrating a deep convolutional network 550. The deep convolutional network 550 may include multiple different types of layers based on connections and weight sharing. Figure 5 As shown in , the deep convolutional network 550 includes convolution blocks 554A and 554B. Each convolution block 554A and 554B can be configured with a convolution layer (CONV) 356, a normalization layer (LNorm) 558, and a maximum pooling layer (MAXPOOL) 560.

[0072] The convolution layer 556 may include one or more convolution filters that can be applied to the input data to generate a feature map. Although only two convolution blocks 554A, 554B are shown, the present disclosure is not limited thereto, and any number of convolution blocks 554A, 554B may be included in the deep convolution network 550 according to design preferences. The normalization layer 558 may normalize the output of the convolution filter. For example, the normalization layer 558 may provide whitening or lateral suppression. The maximum pooling layer 560 may provide downsampling aggregation in space for local invariance and dimensionality reduction.

[0073] For example, the parallel filter bank of the deep convolutional network can be loaded on the CPU 302 or GPU 304 of the SOC 300 to achieve high performance and low power consumption. In an alternative embodiment, the parallel filter bank can be loaded on the DSP 306 or ISP 316 of the SOC 300. In addition, the deep convolutional network 550 can access other processing blocks that may exist on the SOC 300, such as the sensor processor 314 and the navigation module 320 dedicated to sensors and navigation, respectively.

[0074] The deep convolutional network 550 may also include one or more fully connected layers 562 (FC1 and FC2). The deep convolutional network 550 may also include a logistic regression (LR) layer 564. Between each layer 556, 558, 560, 562, 564 of the deep convolutional network 550 are weights (not shown) to be updated. The output of each layer (e.g., 556, 558, 560, 562, 564) can be used as an input to subsequent layers (e.g., 556, 558, 560, 562, 564) in the deep convolutional network 550 to learn hierarchical feature representations from the input data 552 (e.g., images, audio, video, sensor data, and / or other input data) provided at the first convolutional block 554A. The output of the deep convolutional network 550 is a classification score 566 for the input data 552. The classification score 566 can be a set of probabilities, where each probability is a probability of the input data (including a feature from a feature set).

[0075] As mentioned above, it provides Figure 3-5 As an example. Other examples may differ from Figure 3-5 The content described.

[0076] As described above, a user equipment (UE) may have the capability to operate in dual connectivity (DC) mode. Dual connectivity allows a UE to simultaneously send and receive data with multiple cell groups on multiple frequency bands, wherein the multiple cell groups include a primary cell group and one or more secondary cell groups. In some configurations, the primary cell group is based on a first radio access technology (RAT) such as LTE, and the secondary cell group is based on a secondary RAT such as new radio (NR). The following description mentions these two radio access technologies, although other radio access technologies are possible for either cell group.

[0077] The base station may select the secondary cell group based on measurements from the UE. The measurements indicate the best band from the UE's perspective (e.g., based on measured reference signal received power (RSRP) for various NR bands).

[0078] Machine learning techniques have been used to predict channel characteristics in a second frequency band based on measurements in a first frequency band, although RSRP is not typically predicted. The environment for both frequency bands will be the same (or at least similar) because the propagation channels for each frequency band are similar. It may be the case that measurements on a first frequency band (e.g., frequency range 1 (FR1)) are easier, cheaper, and / or more energy efficient for a UE than measurements on a second frequency band. For example, specific hardware characteristics of frequency range 2 (FR2) may make FR2 measurements less energy efficient for a UE. In addition, omitting measurements on a second frequency band eliminates or at least reduces the measurement overhead for reference signals in the second frequency band. That is, if measurements can be omitted, the UE does not need to detect reference signals in the second frequency band. In other cases, the period of the reference signal may be reduced in the second frequency band. According to aspects of the present disclosure, a UE performs measurements in a primary frequency band, then predicts characteristics of an auxiliary frequency band based on the first frequency band measurements and reports them to a base station, enabling the base station to select an auxiliary cell group based on the predicted characteristics of the auxiliary frequency band.

[0079] Figure 6 is a call flow diagram illustrating an exemplary secondary cell group selection process according to various aspects of the present disclosure. Figure 7 7 is a flow chart illustrating an exemplary secondary band reporting process, for example, performed by a user device, in accordance with various aspects of the present disclosure. Exemplary process 700 is an example of reporting estimated channel characteristics (e.g., for secondary cell group selection based on measurements in a primary band). In some aspects, the primary band is lower than the secondary band. For example, the primary band may be lower than 6 GHz, while the secondary band may be higher than 20 GHz.

[0080] Process 700 may operate in a dual connectivity scenario where New Radio (NR) and LTE are deployed, where a primary cell 110 operates in LTE and a secondary cell 111 operates in NR. This scenario may be referred to as dual connectivity. UE 120 initially connects to primary cell 110 in an LTE network and subsequently connects to secondary cell 111 in an NR network via an RRC connection reconfiguration procedure. Figure 6 and Figure 7 In the dual connectivity example, the LTE base station 110 corresponds to the master cell group (MCG), and the NR base station 111 corresponds to the secondary cell group (SCG).

[0081] UE 120 performs initial registration with MCG 110 at time t0. MCG 110 may add one or more SCGs 111, although Figure 6 Only one SCG 111 is shown in FIG. To add an SCG 111, Figure 6 and Figure 7As shown, process 700 may include: receiving a message requesting an estimate of the channel characteristics of the auxiliary frequency band based on the channel characteristics in the primary frequency band at time t1 (block 705). For example, the modified RRC connection reconfiguration message from the primary base station 110 may configure the UE 120 to send the channel characteristic estimate of the auxiliary frequency band. The modified message may request the estimated channel characteristics for the SCG 111. Additional reports based on existing CSI-RS signaling or new CSI-RS signals may be configured to report the estimated channel characteristics. The UE 120 (e.g., using the antenna 252, the DEMOD 254, the MIMO detector 256, the receive processor 258, the controller / processor 280, etc.) may receive the message.

[0082] like Figure 6 and Figure 7 As shown, in some aspects, process 700 may include: measuring characteristics of a channel in a primary frequency band at time t2 (block 710). For example, UE 120 (e.g., using antenna 252, DEMOD 254, MIMO detector 256, receive processor 258, controller / processor 280, etc.) may measure characteristics of a channel (e.g., LTE sub 6GHz band). Some examples of measurements that UE 120 may perform are reference signal received power (RSRP) measurements in a first frequency band, positioning information of UE 120 (e.g., which may be used to learn beam directions), raw channel measurements, channel impulse response (CIR) measurements, angle of departure (AoD) measurements (e.g., AoD of the strongest multipath component (MPC)), delay profile of the strongest MPC, and / or other measurements in the first frequency band. These measurements may be inputs to a machine learning algorithm.

[0083] like Figure 6 and Figure 7 As shown, in some aspects, process 700 may include: estimating channel characteristics of the auxiliary frequency band at time t3. The estimation may be a neural network prediction based on the primary frequency band performance measurement. The estimation is performed without measuring the signal in the auxiliary frequency band (block 720). For example, UE 120 (e.g., using controller / processor 280, etc.) may ignore any configuration for auxiliary frequency band (e.g., new radio (NR)) measurement and instead perform the estimation of the channel characteristics using artificial intelligence (AI).

[0084] like Figure 6 and Figure 7As shown, in some aspects, process 700 may include: reporting the estimated channel characteristics at time t4 (block 730). For example, UE 120 (e.g., using antenna 252, MOD 254, TX MIMO detector 266, transmit processor 264, controller / processor 280, etc.) may report the estimated channel characteristics instead of the actual values ​​measured for NR. The report may be based on a type of CSI-RS signaling, as described above. The report may also include: a list of frequency bands to which the estimated channel characteristics are applicable.

[0085] Alternatively, if Figure 6 and Figure 7 As shown, process 700 may include: receiving an indication of a secondary cell group determined based on estimated channel characteristics at time t5 (block 740). For example, the primary base station 110 determines the secondary cell group 111 based on the estimation report received from the UE 120 and the status report from the secondary base station. The determination of the primary base station 110 is based on the estimated channel characteristics, not the actual channel measurement. The UE 120 receives an indication from the primary base station of the secondary cell group (SCG). The UE 120 receives the indication using, for example, the antenna 252, the DEMOD 254, the MIMO detector 256, the receive processor 258, the controller / processor 280, etc. At time t6, the UE 120 is connected to the secondary cell group 111.

[0086] Figure 8 8 is a flow chart illustrating an exemplary secondary cell group selection process 800, for example, performed by a base station, according to aspects of the present disclosure. The exemplary process 800 is an example of configuring reporting of estimated channel characteristics (e.g., for secondary cell group selection based on measurements in a primary frequency band). Figure 8 As shown in , in some aspects, process 800 may include configuring a user equipment (UE) (e.g., using neural network processing of actual measurements of a primary frequency band) to estimate channel characteristics for a secondary frequency band (block 810). The base station may configure the UE using antenna 234, MOD 232, TX MIMO processor 230, transmit processor 220, controller / processor 240, scheduler 246, etc.

[0087] like Figure 8As shown in, in some aspects, process 800 may include: configuring a report of estimated channel characteristics (box 820). In some aspects, the primary base station may configure a new report for estimated channel characteristics based on an existing CSI-RS signal. Alternatively, the primary base station may configure a new CSI-RS signal and a new corresponding report for the UE. The configuration may be through an RRC message, such as an RRCConnectionReconfiguration message. The base station may configure the UE using antenna 234, MOD 232, TX MIMO processor 230, transmit processor 220, controller / processor 240, and scheduler 246, etc.

[0088] like Figure 8 As shown in , in some aspects, process 800 may include selecting a secondary cell group (SCG) based on estimated channel characteristics rather than actual secondary channel measurements (block 830). The base station selects the SCG using, for example, controller / processor 240 or the like.

[0089] although Figure 7 and Figure 8 Example blocks of processes 700, 800 are shown, but in some aspects, processes 700, 800 may include Figure 7 and Figure 8 , or more blocks, or fewer blocks, or different blocks, or blocks arranged differently than those depicted in . Additionally or alternatively, two or more blocks of processes 700, 800 may be performed in parallel. Similarly, Figure 6 The process shown in can include Figure 6 Additional processes, fewer processes, different processes, or a different arrangement of processes than those depicted in the drawings. Two or more processes may be performed in parallel.

[0090] Implementation examples are described in the following numbered clauses.

[0091] 1. A method for wireless communication via a user equipment (UE), comprising:

[0092] receiving a message requesting channel characteristics of the auxiliary frequency band estimated based on measuring characteristics of the channel in the primary frequency band;

[0093] measuring characteristics of the channel in the primary frequency band;

[0094] estimating channel characteristics of the auxiliary frequency band based on measured characteristics of the channel in the primary frequency band, the estimating occurring without measuring signals in the auxiliary frequency band; and

[0095] Estimated channel characteristics of the auxiliary frequency band are reported.

[0096] 2. The method of clause 1, further comprising: receiving an indication of a secondary cell group, the secondary cell group being determined based on the estimated channel characteristics of the secondary frequency band.

[0097] 3. A method according to any preceding clause, wherein the measured characteristic of the channel in the primary frequency band is a measurement of a signal transmitted by a non-serving cell.

[0098] 4. A method according to any preceding clause, wherein the non-serving cell comprises a first base station of a first radio access technology (RAT) and a second base station of a second RAT co-located.

[0099] 5. A method according to any of the preceding clauses, further comprising: based on determining that the estimated channel characteristics of the potential secondary cell group are below a threshold, excluding the potential secondary cell group from consideration.

[0100] 6. A method according to any of the preceding clauses, wherein measuring the characteristics of the channel in the main frequency band comprises at least one of the following: measuring a reference signal received power (RSRP) of the channel in the main frequency band, performing channel estimation of the channel in the main frequency band, measuring a channel impulse response (CIR) of the channel in the main frequency band, measuring a departure angle of the channel in the main frequency band, measuring an arrival angle of the channel in the main frequency band, measuring a channel path delay of the channel in the main frequency band, determining a user equipment (UE) location, and analyzing a power delay profile of the channel in the main frequency band.

[0101] 7. A method according to any of the preceding clauses, further comprising:

[0102] reporting the estimated channel characteristics of the secondary frequency band based on an existing channel state information (CSI) signal and a reporting configuration received from a primary base station in a radio resource control (RRC) message; and

[0103] A measurement of the characteristic of the channel in the primary frequency band is reported based on the existing CSI signal.

[0104] 8. A method according to any of the preceding clauses, further comprising:

[0105] reporting a measurement of the characteristic of the channel in the primary frequency band based on a first type of channel state information (CSI) signal; and

[0106] The estimated channel characteristics of the secondary frequency band are reported based on a second type of CSI signal and a reporting configuration received from a primary base station in a radio resource control (RRC) message and corresponding to the second type of CSI signal.

[0107] 9. A method according to any preceding clause, wherein reporting the estimated channel characteristics further comprises reporting a list of frequency bands to which the estimated channel characteristics apply.

[0108] 10. A method according to any preceding clause, wherein the primary frequency band is of a lower frequency than the secondary frequency band.

[0109] 11. A method according to any of the preceding clauses, wherein the estimated channel characteristics of the auxiliary frequency band are performed using neural network processing.

[0110] 12. A method for wireless communication by a base station, comprising:

[0111] configuring a user equipment (UE) to estimate channel characteristics of the secondary frequency band based on measurements of the primary frequency band;

[0112] configuring reporting of estimated channel characteristics of the auxiliary frequency band; and

[0113] A secondary cell group (SCG) is selected based on the estimated channel characteristics of the secondary frequency band rather than measurements of the secondary frequency band.

[0114] 13. A method as described in clause 12, wherein configuring a report comprises configuring a new report of the estimated channel characteristics for the auxiliary frequency band based on an existing channel state information (CSI) signal.

[0115] 14. A method as described in clause 12, wherein configuring reporting comprises configuring a new channel state information (CSI) signal and a new corresponding report of the estimated channel characteristics for the auxiliary frequency band.

[0116] 15. A user equipment (UE) for wireless communication, comprising:

[0117] Memory, and

[0118] at least one processor operatively coupled to the memory,

[0119] The memory and the at least one processor are configured to:

[0120] receiving a message requesting channel characteristics of the auxiliary frequency band estimated based on measuring characteristics of the channel in the primary frequency band;

[0121] measuring characteristics of the channel in the primary frequency band;

[0122] estimating channel characteristics of the auxiliary frequency band based on measured characteristics of the channel in the primary frequency band, the estimating occurring without measuring signals in the auxiliary frequency band; and

[0123] Estimated channel characteristics of the auxiliary frequency band are reported.

[0124] 16. A UE as claimed in clause 15, wherein the at least one processor is further configured to receive an indication of a secondary cell group, the secondary cell group being determined based on the estimated channel characteristic.

[0125] 17. A UE as described in any preceding clause, wherein the measured characteristic of the channel in the primary frequency band is a measurement of a signal transmitted by a non-serving cell.

[0126] 18. A UE as described in any of the preceding clauses, wherein the non-serving cell comprises a first base station of a first radio access technology (RAT) and a second base station of a second RAT co-located.

[0127] 19. A UE according to any of the preceding clauses, wherein the at least one processor is further configured to: based on determining that the estimated channel characteristics of the potential secondary cell group are below a threshold, not consider the potential secondary cell group.

[0128] 20. A UE according to any of the preceding clauses, wherein the at least one processor is further configured to measure characteristics by at least one of the following: measuring a reference signal received power (RSRP) of the channel in the main frequency band, performing channel estimation of the channel in the main frequency band, measuring a channel impulse response (CIR) of the channel in the main frequency band, measuring a departure angle of the channel in the main frequency band, measuring an arrival angle of the channel in the main frequency band, measuring a channel path delay of the channel in the main frequency band, determining a user equipment (UE) position, and analyzing a power delay profile of the channel in the main frequency band.

[0129] 21. A UE as described in any of the preceding clauses, wherein the at least one processor is further configured to:

[0130] reporting the estimated channel characteristics of the secondary frequency band based on an existing channel state information (CSI) signal and a reporting configuration received from a primary base station in a radio resource configuration (RRC) message; and

[0131] A measurement of the characteristic of the channel in the primary frequency band is reported based on the existing CSI signal.

[0132] 22. A UE as described in any preceding clause, wherein the at least one processor is further configured to:

[0133] reporting a measurement of the characteristic of the channel in the primary frequency band based on a first type of channel state information (CSI) signal; and

[0134] The estimated channel characteristics of the secondary frequency band are reported based on a second type of CSI signal and a reporting configuration received from a primary base station in a radio resource control (RRC) message and corresponding to the second type of CSI signal.

[0135] 23. A UE according to any preceding clause, wherein the at least one processor is further configured to report the estimated channel characteristics by reporting a list of frequency bands to which the estimated channel characteristics apply.

[0136] 24. A UE as described in any preceding clause, wherein the primary frequency band is at a lower frequency than the secondary frequency band.

[0137] 25. A UE according to any of the preceding clauses, wherein the at least one processor is further configured to: use neural network processing to perform estimation.

[0138] 26. A base station for wireless communication, comprising:

[0139] Memory, and

[0140] at least one processor operably coupled to the memory, the memory and the at least one processor configured to:

[0141] configuring a user equipment (UE) to estimate channel characteristics of the secondary frequency band based on measurements of the primary frequency band;

[0142] configuring reporting of estimated channel characteristics of the auxiliary frequency band; and

[0143] A secondary cell group (SCG) is selected based on the estimated channel characteristics of the secondary frequency band rather than measurements of the secondary frequency band.

[0144] 27. A base station as described in clause 26, wherein the at least one processor is further configured to configure a new report of the estimated channel characteristics for the secondary frequency band based on an existing channel state information (CSI) signal.

[0145] 28. A base station as described in clause 26, wherein the at least one processor is further configured to configure a new channel state information (CSI) signal and a new corresponding report of the estimated channel characteristics for the auxiliary frequency band.

[0146] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise forms disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the various aspects.

[0147] As used, the term "component" is intended to be broadly interpreted as hardware, firmware, and / or a combination of hardware and software. As used, a processor is implemented with hardware, firmware, and / or a combination of hardware and software.

[0148] Some aspects are described in conjunction with a threshold value. As used, satisfying a threshold can represent a value greater than a threshold, greater than or equal to a threshold, less than a threshold, less than or equal to a threshold, equal to a threshold, not equal to a threshold, etc., depending on the context.

[0149] It will be clear that the systems and / or methods described herein may be implemented with various forms of hardware, firmware, and / or combinations of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting in any respect. Therefore, the operation and behavior of the systems and / or methods are described without reference to specific software code, it being understood that software and hardware may be designed to implement the systems and / or methods based at least in part on the specification.

[0150] Even if a specific combination of features is recorded in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. In fact, many of these features can be combined in a manner that is not specifically recorded in the claims and / or specifically disclosed in the specification. Although each dependent claim listed below can only directly depend on one claim, the disclosure of various aspects includes the combination of each dependent claim with each other claim in the claim set. The phrase "at least one of" referring to the list of items refers to any combination of those items, including a single member. For example, "at least one of a, b or c" is intended to cover a, b, c, ab, ac, bc and abc, and any combination with multiples of the same element (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc and ccc or any other ordering of a, b and c).

[0151] None of the elements, actions or instructions used should be interpreted as critical or essential unless explicitly described as such. In addition, as used, the articles "a" and "an" are intended to include one or more entries and can be used interchangeably with "one or more". In addition, as used, the terms "set" and "group" are intended to include one or more entries (e.g., related entries, irrelevant entries, combinations of related entries and irrelevant entries, etc.), and can be used interchangeably with "one or more". In the case of only one entry being expected, the phrase "only one" or similar language is used. In addition, as used, the terms "has", "have", "having" and / or similar terms are intended to be open terms. In addition, unless otherwise expressly stated, the phrase "based on" is intended to mean "based at least in part on".

Claims

1. A method for wireless communication via a user equipment (UE), comprising: estimating channel characteristics of a secondary frequency band based on measured characteristics of a channel in a primary frequency band, the estimating occurring without measuring signals in the secondary frequency band, the primary frequency band detected in signals received from a non-serving cell of a first radio access technology (RAT), the non-serving cell comprising a first base station of the first RAT collocated with a second base station of a second RAT; reporting the estimated channel characteristics of the auxiliary frequency band to a base station of a serving cell of the first RAT; as well as An indication of a secondary cell group is received, the secondary cell group being determined based on the estimated channel characteristics of the secondary frequency band.

2. The method according to claim 1, further comprising: When it is determined that the estimated channel characteristic of a potential secondary cell group is below a threshold, the potential secondary cell group is not considered.

3. The method according to claim 1, wherein: The measurement of the characteristics of the channel in the main frequency band also includes at least one of the following: measuring a reference signal received power (RSRP) of the channel in the main frequency band, performing channel estimation of the channel in the main frequency band, measuring a channel impulse response (CIR) of the channel in the main frequency band, measuring a departure angle of the channel in the main frequency band, measuring an arrival angle of the channel in the main frequency band, measuring a channel path delay of the channel in the main frequency band, determining a user equipment (UE) location, and analyzing a power delay profile of the channel in the main frequency band.

4. The method according to claim 1, further comprising: reporting the estimated channel characteristics of the secondary frequency band based on measuring an existing channel state information (CSI) signal and a reporting configuration received from a primary base station in a radio resource control (RRC) message; as well as A measurement of the characteristic of the channel in the primary frequency band is reported based on the existing CSI signal.

5. The method according to claim 1, further comprising: reporting a measurement of the characteristic of the channel in the primary frequency band based on measuring a first type of channel state information (CSI) signal; as well as The estimated channel characteristics of the secondary frequency band are reported based on measuring a second type of CSI signal and a reporting configuration received from a primary base station in a radio resource control (RRC) message and corresponding to the second type of CSI signal.

6. The method according to claim 1, wherein: Reporting the estimated channel characteristics further includes reporting a list of frequency bands to which the estimated channel characteristics apply.

7. The method according to claim 1, wherein: The main frequency band has a lower frequency than the auxiliary frequency band.

8. The method according to claim 1, wherein: Estimating the channel characteristics of the auxiliary frequency band is performed using a neural network process.

9. A method for wireless communication by a base station, comprising: configuring a user equipment (UE) to estimate channel characteristics of a secondary frequency band based on measurements of signals in a primary frequency band received from a non-serving cell of a first radio access technology (RAT), the non-serving cell comprising a first base station of the first RAT collocated with a second base station of a second RAT; configuring to report the estimated channel characteristics of the auxiliary frequency band; as well as A secondary cell group (SCG) is selected based on the estimated channel characteristics of the secondary frequency band without using measurements of the secondary frequency band.

10. The method according to claim 9, wherein: Configuring the report further includes configuring a new report of estimated channel characteristics for the auxiliary frequency band based on an existing channel state information (CSI) signal.

11. The method according to claim 9, wherein: Configuring the reporting further includes configuring a new channel state information (CSI) signal and a new corresponding report of the estimated channel characteristics for the auxiliary frequency band.

12. A user equipment (UE) for wireless communication, comprising: Memory, and at least one processor operably coupled to the memory, the memory and the at least one processor configured to: estimating channel characteristics of a secondary frequency band based on measured characteristics of a channel in a primary frequency band, the estimating occurring without measuring signals in the secondary frequency band, the primary frequency band detected in signals received from a non-serving cell of a first radio access technology (RAT), the non-serving cell comprising a first base station of the first RAT collocated with a second base station of a second RAT; reporting the estimated channel characteristics of the auxiliary frequency band to a base station of a serving cell of the first RAT; as well as An indication of a secondary cell group is received, the secondary cell group being determined based on the estimated channel characteristics of the secondary frequency band.

13. The UE according to claim 12, wherein: The at least one processor is further configured to, upon determining that the estimated channel characteristic of a potential secondary cell group is below a threshold, disregard the potential secondary cell group.

14. The UE according to claim 12, wherein: The measurement of the characteristics in the main frequency band also includes at least one of the following: measuring a reference signal received power (RSRP) of the channel in the main frequency band, performing channel estimation of the channel in the main frequency band, measuring a channel impulse response (CIR) of the channel in the main frequency band, measuring a departure angle of the channel in the main frequency band, measuring an arrival angle of the channel in the main frequency band, measuring a channel path delay of the channel in the main frequency band, determining a user equipment (UE) location, and analyzing a power delay profile of the channel in the main frequency band.

15. The UE according to claim 12, wherein: The at least one processor is further configured to: reporting the estimated channel characteristics of the secondary frequency band based on measuring an existing channel state information (CSI) signal and a reporting configuration received from a primary base station in a radio resource configuration (RRC) message; as well as Based on measuring the existing CSI signal, a measurement of the characteristic of the channel in the primary frequency band is reported.

16. The UE according to claim 12, wherein: The at least one processor is further configured to: reporting a measurement of the characteristic of the channel in the primary frequency band based on measuring a first type of channel state information (CSI) signal; as well as The estimated channel characteristics of the secondary frequency band are reported based on measuring a second type of CSI signal and a reporting configuration received from a primary base station in a radio resource control (RRC) message and corresponding to the second type of CSI signal.

17. The UE according to claim 12, wherein: The at least one processor is further configured to report the estimated channel characteristics by reporting a list of frequency bands to which the estimated channel characteristics apply.

18. The UE according to claim 12, wherein: The main frequency band has a lower frequency than the auxiliary frequency band.

19. The UE according to claim 12, wherein: The at least one processor is further configured to estimate the channel characteristics of the auxiliary frequency band using neural network processing.

20. A base station for wireless communication, comprising: Memory, and at least one processor operably coupled to the memory, the memory and the at least one processor configured to: configuring a user equipment (UE) to estimate channel characteristics of a secondary frequency band based on measurements of signals in a primary frequency band received from a non-serving cell of a first radio access technology (RAT), the non-serving cell comprising a first base station of the first RAT collocated with a second base station of a second RAT; configuring to report the estimated channel characteristics of the auxiliary frequency band; as well as A secondary cell group (SCG) is selected based on the estimated channel characteristics of the secondary frequency band without using measurements of the secondary frequency band.

21. The base station according to claim 20, wherein: The at least one processor is further configured to configure a new report of estimated channel characteristics for the secondary frequency band based on an existing channel state information (CSI) signal.

22. The base station according to claim 20, wherein: The at least one processor is further configured to configure a new channel state information (CSI) signal and a new corresponding report of the estimated channel characteristics for the secondary frequency band.

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