Channel parameter determination method and apparatus, storage medium

Abstract

This disclosure relates to a channel parameter determination method, apparatus, and storage medium. The channel parameter determination method provided in this disclosure is applied to a network device and includes: acquiring a first parameter; and determining a second parameter of the channel based on the first parameter and using at least one channel parameter model. This disclosure allows for the prediction of channel parameters to guide terminal blind detection, thereby optimizing transmission processes and reducing power overhead.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and in particular to methods and apparatus for determining channel parameters and storage media. Background Technology

[0002] The PDCCH (Physical Downlink Control Channel) is a channel used to carry DCI (Downlink Control Information). The DCI sent by the base station to the UE (User Equipment) via the PDCCH can be used for UE scheduling. The UE needs to inspect the PDCCH, but it cannot determine the aggregation level the base station will use or the candidate transmission location on the PDCCH. Therefore, the UE can only traverse all possibilities, resulting in significant power consumption. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a channel parameter determination method and apparatus, and a storage medium.

[0004] According to a first aspect of the present disclosure, a channel parameter determination method is provided, applied to a network device, the method comprising: acquiring a first parameter; and determining a second parameter of the channel based on the first parameter and using at least one channel parameter model.

[0005] In one implementation, the channel parameter model is a channel parameter model based on a regression algorithm.

[0006] In another embodiment, the first parameter includes at least one of the following: service type, service occurrence time, terminal movement speed, terminal channel conditions, terminal reception capability, or the cell ID.

[0007] In another embodiment, the terminal channel conditions include at least one of the following: signal-to-interference-plus-noise ratio (SINR) or reference signal received power (RSRP).

[0008] In another embodiment, the second parameter includes at least one of the following: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH.

[0009] In another embodiment, the method further includes: training the at least one channel parameter model using the first parameter.

[0010] In another embodiment, training the at least one channel parameter model includes: outputting a second parameter of the channel at at least one time point; or outputting the second parameter of the channel over at least one time period.

[0011] In another embodiment, obtaining the first parameter includes: obtaining the first parameter of the channel for multiple time periods within a day.

[0012] In another embodiment, the method further includes: updating the at least one channel parameter model based on the first parameter of the channel.

[0013] In another embodiment, the method further includes sending the at least one channel parameter model to a terminal.

[0014] In another embodiment, the method further includes: sending first information, the first information being used to indicate the at least one channel parameter model.

[0015] In another embodiment, the method further includes: sending second information, the second information being used to indicate activation of the at least one channel parameter model, or to indicate deactivation of the at least one channel parameter model.

[0016] According to a second aspect of the present disclosure, a channel parameter determination method is provided, applied to a terminal, the method comprising: acquiring a first parameter; and determining a second parameter of the channel based on the first parameter and utilizing at least one channel parameter model.

[0017] In one implementation, the channel parameter model is a channel parameter model based on a regression algorithm.

[0018] In another embodiment, the first parameter includes at least one of the following: service type, service occurrence time, terminal movement speed, terminal channel conditions, terminal reception capability, or the cell ID.

[0019] In another embodiment, the terminal channel conditions include at least one of the following: signal-to-interference-plus-noise ratio (SINR) or reference signal received power (RSRP).

[0020] In another embodiment, the second parameter includes at least one of the following: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH.

[0021] In another embodiment, the method further includes: training the at least one channel parameter model using the first parameter.

[0022] In another embodiment, training the at least one channel parameter model includes: outputting a second parameter of the channel at at least one time point; or outputting the second parameter of the channel over at least one time period.

[0023] In another embodiment, obtaining the first parameter includes: obtaining the first parameter of the channel for multiple time periods within a day.

[0024] In another embodiment, the method further includes: updating the at least one channel parameter model based on the first parameter of the channel.

[0025] In another embodiment, the method further includes: receiving the at least one channel parameter model sent from a network device.

[0026] In another embodiment, the method further includes: determining one or more channel parameter models among the at least one channel parameter model based on the received signaling.

[0027] In another embodiment, the method further includes switching between multiple channel parameter models based on triggering conditions.

[0028] In another embodiment, the method further includes: receiving first information, the first information being used to indicate the at least one channel parameter model.

[0029] In another embodiment, the method further includes: receiving second information, the second information being used to indicate activation of the at least one channel parameter model, or to indicate deactivation of the at least one channel parameter model.

[0030] According to a third aspect of the present disclosure, a channel parameter determination apparatus is provided, applied to a network device, the apparatus comprising: a parameter acquisition unit configured to acquire a first parameter; and a parameter determination unit configured to determine a second parameter of the channel based on the first parameter and using at least one channel parameter model.

[0031] According to a fourth aspect of the present disclosure, a channel parameter determination apparatus is provided, applied to a terminal, the apparatus comprising: a parameter acquisition unit configured to acquire a first parameter; and a parameter determination unit configured to determine a second parameter of the channel based on the first parameter and using at least one channel parameter model.

[0032] According to a fifth aspect of the present disclosure, a channel parameter determination apparatus is provided, applied to a network device, the apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: execute the channel parameter determination method provided by any of the technical solutions of the first aspect.

[0033] According to a sixth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor, the processor is able to execute the channel parameter determination method provided by any of the technical solutions of the first aspect.

[0034] According to a seventh aspect of the present disclosure, a channel parameter determination apparatus is provided, applied to a network device, the apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: execute the channel parameter determination method provided by any of the technical solutions of the second aspect above.

[0035] According to an eighth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor, the processor is able to execute the channel parameter determination method provided by any of the technical solutions of the second aspect described above.

[0036] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: channel parameters can be predicted to guide terminal blind detection, thereby optimizing the transmission process and reducing power overhead.

[0037] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0039] Figure 1 This is a schematic diagram illustrating a wireless communication system according to an exemplary embodiment.

[0040] Figure 2 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment.

[0041] Figure 3 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment.

[0042] Figure 4 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment.

[0043] Figure 5 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment.

[0044] Figure 6 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment.

[0045] Figure 7 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment.

[0046] Figure 8 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment.

[0047] Figure 9 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment.

[0048] Figure 10 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment.

[0049] Figure 11 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment.

[0050] Figure 12 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment.

[0051] Figure 13 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment.

[0052] Figure 14 This is a block diagram illustrating an apparatus for determining channel parameters according to an exemplary embodiment.

[0053] Figure 15 This is a block diagram illustrating an apparatus for determining channel parameters according to an exemplary embodiment. Detailed Implementation

[0054] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0055] The channel parameter determination method provided in this disclosure can be applied to... Figure 1 The wireless communication system shown. (Refer to...) Figure 1 As shown, the wireless communication system 100 includes a network device 110 and a terminal 120. The network device 110 and the terminal 120 transmit and receive information through wireless resources.

[0056] Understandable, Figure 1The wireless communication system shown is for illustrative purposes only. The wireless communication system may also include other network devices, such as core network equipment, wireless relay equipment, and wireless backhaul equipment. Figure 1 Not shown in the diagram. This disclosure does not limit the number of network devices and terminals included in the wireless communication system.

[0057] It is further understood that the wireless communication system of this disclosure is a network providing wireless communication functionality. The wireless communication system can employ different communication technologies, such as Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency-Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), and Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA). Based on factors such as capacity, speed, and latency, networks can be categorized as 2G (Generation) networks, 3G networks, 4G networks, or future evolution networks, such as 5G networks. 5G networks can also be referred to as 5G New Radio. For ease of description, this disclosure may sometimes simply refer to the wireless communication network as a network.

[0058] Furthermore, the network device involved in this disclosure can also be referred to as a wireless access network device. This wireless access network device can be: a base station, an evolved NodeB (eNodeB or eNB), a home base station, an access point (AP) in a Wireless Fidelity (WIFI) system, a wireless relay node, a wireless backhaul node, a transmission point (TP), or a transmission and reception point (TRP), etc. It can also be a base station (gNodeB or gNB) in an NR system, or it can be a component or part of a base station. It should be understood that the specific technology and specific device form used in the embodiments of this disclosure are not limited. In this disclosure, the network device can provide communication coverage for a specific geographical area and can communicate with terminals located within that coverage area (cell). Furthermore, when it is a vehicle-to-everything (V2X) communication system, the network device can also be an in-vehicle device.

[0059] Furthermore, the terminal involved in this disclosure may also be referred to as a terminal device, user equipment (UE), mobile station (MS), mobile terminal (MT), etc., and is a device that provides voice and / or data connectivity to a user. For example, a terminal may be a handheld device with wireless connectivity, an in-vehicle device, etc. Currently, some examples of terminals include: smartphones (Mobile Phones), pocket personal computers (PPCs), handheld computers, personal digital assistants (PDAs), laptops, tablets, wearable devices, or in-vehicle devices, etc. In addition, when it is a vehicle-to-everything (V2X) communication system, the terminal device may also be an in-vehicle device. It should be understood that the embodiments of this disclosure do not limit the specific technology or specific device form adopted by the terminal.

[0060] In NR, the basic building block of a PDCCH is a REG (Resource Element Group). One REG corresponds to the size of a PRB (12 REs) in the frequency domain and the size of an OFDM (Orthogonal Frequency Division Multiplexing) symbol in the time domain. Six REGs form a CCE (Control Channel Element). The CCE is the unit of the search space used by the terminal during blind detection. In current NR systems, a PDCCH can consist of 1, 2, 4, 8, or 16 CCEs. The number of CCEs in a PDCCH is called the aggregation level. When the information bits of a PDCCH are fixed, its aggregation level is mainly determined by the channel conditions. When the terminal's channel conditions are good, a smaller aggregation level can be used. When the channel conditions are poor, a larger aggregation level is chosen. Furthermore, for PDCCHs with different aggregation levels, the protocol supports multiple candidate transmission locations. For example, for a PDCCH with CCE=4, there can be two candidate transmission locations. The base station and the terminal can calculate the CCE numbers of these two candidate locations in the configured control resource set according to preset rules, such as CCE#0 to CCE#3 and CCE#4 to CCE#7. Since the terminal has multiple transmission requirements, it may monitor multiple DCI formats. Therefore, the terminal will also try two DCI sizes during detection. The table below illustrates the possibilities for a terminal to perform blind detection. For example, the terminal will first try aggregation level 1, and at aggregation level 1, it will sequentially detect the corresponding 6 candidate transmission locations, trying two possible DCI sizes at each candidate transmission location: DCI size 1 and DCI size 2. For example, the terminal will then try aggregation level 2, and at aggregation level 2, it will sequentially detect the corresponding 6 candidate transmission locations, trying two possible DCI sizes at each candidate transmission location. Similarly, the terminal will then sequentially try aggregation levels 4 and 8, thus traversing all possibilities.

[0061]

[0062] As mentioned above, the terminal needs to detect the PDCCH, but the terminal cannot determine what level of aggregation the base station will use, or at which candidate PDCCH transmission location the transmission will take place. Therefore, the terminal can only traverse all possibilities, resulting in a large amount of power consumption.

[0063] In recent years, AI (Artificial Intelligence), especially machine learning technology, has developed rapidly. Artificial intelligence excels at uncovering the inherent features and relationships within data, generating corresponding models, and making predictions and adjustments based on these models. Its self-learning, self-optimization, and self-decision-making characteristics have also gained increasing favor across various industries.

[0064] The inventors recognized that current AI regression algorithms, based on data training, can form corresponding models with excellent predictive capabilities. In the field of terminal energy saving, artificial intelligence technology can uncover the potential characteristics and inherent relationships of various business data and terminal data. Therefore, the inventors proposed introducing AI's predictive capabilities into the terminal to predict the aggregation degree and transmission location of PDCCH transmissions, providing guidance for terminal PDCCH detection. Specifically, a PDCCH transmission model is established through machine learning, and this model can predict PDCCH transmission, thereby guiding blind detection on the terminal side. By predicting channel parameters to guide terminal blind detection, the transmission process is optimized and power consumption is reduced.

[0065] This disclosure provides a method for determining channel parameters, which is applied to network devices. Figure 2 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment. (Refer to...) Figure 2 As shown, the channel parameter determination method is applied to network devices and includes the following steps S210 and S220.

[0066] In step S210, the first parameter is obtained.

[0067] In step S220, based on the first parameter, the second parameter of the channel is determined using at least one channel parameter model.

[0068] In some embodiments of this disclosure, the channel parameter model is a channel parameter model based on a regression algorithm. For example, in a channel parameter model based on a regression algorithm, predictions are made based on the correlation between independent and dependent variables. There can be one or more independent variables, and based on the number of independent variables, it can be divided into univariate regression prediction and multivariate regression prediction. Furthermore, based on the correlation between the independent and dependent variables, it can be divided into linear regression prediction methods and nonlinear regression methods.

[0069] It should be noted that the channel parameter model of this disclosure is not limited to a channel parameter model based on a regression algorithm, and other types of models can be used. In some exemplary embodiments, the channel parameter model can employ a trend extrapolation prediction model. In a trend extrapolation prediction model, based on historical and current data of an event, the patterns of how things develop and change over time are sought, thereby predicting the future state of the event. In some exemplary embodiments, the channel parameter model can employ a Kalman filter prediction model. In a Kalman filter prediction model, the minimum mean square error is used as the optimal criterion for estimation, thereby seeking a recursive estimation model. In some exemplary embodiments, the channel parameter model can employ a combined prediction model. In a combined prediction model, multiple prediction methods are used for the same event, and the information provided by these methods is comprehensively utilized to improve the prediction accuracy as much as possible. In some exemplary embodiments, the channel parameter model can employ a BP (Back-Propagation Network) prediction model. In a BP prediction model, through training with sample data, the network weights and thresholds are continuously adjusted so that the error function decreases along the negative gradient direction, approaching the desired output.

[0070] In some embodiments of this disclosure, the first parameter includes at least one of the following: service type, service occurrence time, terminal mobility speed, terminal channel conditions, terminal reception capability, or cell ID. For example, terminal channel conditions include at least one of the following: signal-to-interference-plus-noise ratio (SINR) or reference signal received power (RSRP).

[0071] In some embodiments of this disclosure, the second parameter includes at least one of the following: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH.

[0072] According to embodiments of this disclosure, a second parameter of the channel is determined based on a first parameter and using at least one channel parameter model. This channel parameter model can be a preset channel parameter model or a trained channel parameter model. The second parameter is obtained by inference using the channel parameter model after inputting the first parameter. The first parameter may be, for example, the service type, service occurrence time, terminal mobility speed, terminal channel conditions, terminal reception capability, or the cell ID. The second parameter may be, for example, the aggregation degree of the Physical Downlink Control Channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH. In this embodiment, the first parameter serves as the input for channel parameter model inference, and the second parameter is the output for channel parameter model inference.

[0073] Based on the inference prediction results, the terminal will prioritize detecting the predicted aggregation level, transmission location, and DCI format. When the terminal detects the corresponding DCI, it will stop detecting; otherwise, it will continue detecting.

[0074] Next, the training of the channel parameter model will be briefly described. Figure 3 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment. (Refer to...) Figure 3 As shown, the channel parameter determination method is applied to network devices and includes the following steps S310 and S320.

[0075] In step S310, the first parameter is obtained.

[0076] In step S320, at least one channel parameter model is trained using the first parameter.

[0077] In some embodiments of this disclosure, the channel parameter model can be trained at the terminal. When training the channel parameter model, the characteristics of the input data may include a first parameter, which may be, for example, service type, service occurrence time, terminal movement speed, terminal channel conditions, terminal reception capability, or the cell ID. The labels of the output data may be PDCCH aggregation degree, transmission location, DCI size, etc. When training the channel parameter model, the characteristics of the output data may include a second parameter, which may be, for example, the aggregation degree of the Physical Downlink Control Channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH. In this embodiment, the first parameter serves as the input during channel parameter model training, and the second parameter is the output during channel parameter model training.

[0078] In some embodiments of this disclosure, training at least one channel parameter model includes: outputting a second parameter of the channel at at least one point in time; or outputting a second parameter of the channel over at least one time period. For example, the output of the training data may be the PDCCH transmission status at a certain moment, or it may be the PDCCH transmission status over a period of time.

[0079] In some embodiments of this disclosure, obtaining the first parameter includes obtaining the first parameters of the channel for multiple time periods throughout the day. For example, one channel parameter model can be trained, or multiple channel parameter models can be trained. In exemplary embodiments, for example, according to the terminal's habits, one channel parameter model is trained during the morning peak period (such as 7:30 to 9:30), another channel parameter model is trained during the midday period (such as 11:00 to 13:00), and yet another channel parameter model is trained during the evening peak period (such as 17:00 to 19:00).

[0080] Next, a brief description of the channel parameter model update will be provided. Figure 4 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment. (Refer to...) Figure 4 As shown, the channel parameter determination method is applied to network devices and includes the following steps S410 and S420.

[0081] In step S410, the first parameter is obtained.

[0082] In step S420, at least one channel parameter model is updated based on the first parameter of the channel.

[0083] According to embodiments of this disclosure, network devices can update models based on newly generated data. Once the model on the network device side is updated and the model test is stable, the updated model can be sent to the terminal.

[0084] The use of channel parameter models is then briefly described. In some embodiments of this disclosure, the method further includes sending at least one channel parameter model to the terminal. For example, when the channel parameter models are located on the network device side, the network device can send multiple channel parameter models to the terminal. When multiple channel parameter models exist, the terminal can download multiple models locally, and the network device can instruct the terminal which model to use, or the terminal can switch models based on some triggering conditions.

[0085] In some embodiments of this disclosure, the method further includes: sending first information, the first information being used to indicate at least one channel parameter model. In this embodiment, the channel parameter model is specified by sending information or signaling.

[0086] In some embodiments of this disclosure, the method further includes sending second information, which indicates whether to activate at least one channel parameter model or to deactivate at least one channel parameter model. For example, the network device may send signaling to suggest that the terminal use channel parameter model inference or fall back to a conventional method. For example, when the terminal's regular movement trajectory or regular activity location changes, the channel parameter model may no longer be applicable, in which case the network device may deactivate the use of the channel parameter model.

[0087] According to the exemplary embodiments described above, a transmission model for the PDCCH is established through machine learning. This model can predict PDCCH transmission and guide blind detection at the terminal side. By predicting channel parameters to guide terminal blind detection, the transmission process is optimized and power overhead is reduced. Specifically, on the one hand, based on data-driven machine learning methods, by observing the characteristics of various service data and terminal data and various power-saving parameter settings, optimal parameter configuration models for various services and terminals can be formed, providing more refined and personalized power-saving solutions. On the other hand, adaptive adjustments are made by predicting various service data, terminal data, network conditions, etc. Furthermore, the potential connections or models established in various communication processes based on machine learning can further optimize related processes and reduce power overhead.

[0088] It should be noted that those skilled in the art will understand that the various implementation methods / embodiments described above in this disclosure can be used in conjunction with the foregoing embodiments, or they can be used independently. Whether used alone or in conjunction with the foregoing embodiments, the implementation principle is similar. In this disclosure, some embodiments are described as implementations used together; of course, those skilled in the art will understand that such illustrative examples are not intended to limit the embodiments of this disclosure.

[0089] This disclosure provides a method for determining channel parameters, applied to a terminal. Figure 5 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment. (Refer to...) Figure 5 As shown, the channel parameter determination method is applied to the terminal and includes the following steps S510 and S520.

[0090] In step S510, the first parameter is obtained.

[0091] In step S520, based on the first parameter, the second parameter of the channel is determined using at least one channel parameter model.

[0092] In some embodiments of this disclosure, the channel parameter model is a channel parameter model based on a regression algorithm.

[0093] In some embodiments of this disclosure, the first parameter includes: service type, service occurrence time, terminal movement speed, terminal channel conditions, terminal receiving capability, or the cell ID. For example, terminal channel conditions include: signal-to-interference-plus-noise ratio (SINR) or reference received signal power (RSRP).

[0094] In some embodiments of this disclosure, the second parameter includes: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH.

[0095] Figure 6 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment. (Refer to...) Figure 6 As shown, the channel parameter determination method is applied to the terminal and includes the following steps S610 and S620.

[0096] In step S610, the first parameter is obtained.

[0097] In step S620, at least one channel parameter model is trained using the first parameter.

[0098] In some embodiments of this disclosure, training at least one channel parameter model includes: outputting a second parameter of the channel at at least one point in time; or outputting a second parameter of the channel over at least one time period.

[0099] In some embodiments of this disclosure, obtaining the first parameter includes obtaining the first parameter of the channel for multiple time periods within a day.

[0100] Figure 7 This is a flowchart illustrating a channel parameter determination method according to an exemplary embodiment. (Refer to...) Figure 7 As shown, the channel parameter determination method is applied to the terminal and includes the following steps S710 and S720.

[0101] In step S710, the first parameter is obtained.

[0102] In step S720, at least one channel parameter model is updated based on the first parameter of the channel.

[0103] According to embodiments of this disclosure, the terminal can update the model based on newly generated data.

[0104] In some embodiments of this disclosure, the method further includes: receiving at least one channel parameter model sent from a network device. For example, the method further includes: determining one or more channel parameter models from at least one channel parameter model based on received signaling. For example, the method further includes: switching between multiple channel parameter models based on triggering conditions. According to the embodiments described above, when the channel parameter models are located on the network device side, the network device can send multiple channel parameter models to the terminal. When multiple channel parameter models exist, the terminal can download multiple models locally, and the network device can instruct the terminal which model to use, or the terminal can switch models based on some triggering conditions.

[0105] In some embodiments of this disclosure, the method further includes: receiving first information, the first information being used to indicate at least one channel parameter model. In this embodiment, the channel parameter model is specified by receiving information or signaling sent by a network device.

[0106] In some embodiments of this disclosure, the method further includes receiving second information, which indicates whether to activate at least one channel parameter model or deactivate at least one channel parameter model. For example, by receiving signaling sent by a network device, the terminal uses the channel parameter model for inference or falls back to a conventional method. For example, when the terminal's regular movement trajectory or regular activity location changes, the channel parameter model is no longer applicable, and in this case, the terminal can fall back to a conventional method.

[0107] It should be noted that those skilled in the art will understand that the various implementation methods / embodiments described above in this disclosure can be used in conjunction with the foregoing embodiments, or they can be used independently. Whether used alone or in conjunction with the foregoing embodiments, the implementation principle is similar. In this disclosure, some embodiments are described as implementations used together; of course, those skilled in the art will understand that such illustrative examples are not intended to limit the embodiments of this disclosure.

[0108] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0109] Based on the same concept, embodiments of this disclosure provide a channel parameter determination apparatus applied to network devices.

[0110] Figure 8 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment. (Refer to...) Figure 8 As shown, the channel parameter determination device 800 is applied to a network device and includes a parameter acquisition unit 810 and a parameter determination unit 820.

[0111] The parameter acquisition unit 810 is configured to acquire the first parameter.

[0112] The parameter determination unit 820 is configured to determine the second parameter of the channel based on the first parameter and using at least one channel parameter model.

[0113] In some embodiments of this disclosure, the channel parameter model is a channel parameter model based on a regression algorithm.

[0114] In some embodiments of this disclosure, the first parameter includes: service type, service occurrence time, terminal movement speed, terminal channel conditions, terminal receiving capability, or the cell ID. For example, terminal channel conditions include: signal-to-interference-plus-noise ratio (SINR) or reference received signal power (RSRP).

[0115] In some embodiments of this disclosure, the second parameter includes: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH.

[0116] Figure 9 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment. (Refer to...) Figure 9 As shown, the channel parameter determination device 900 is applied to network equipment and includes a parameter acquisition unit 910 and a model training unit 920.

[0117] The parameter acquisition unit 910 is configured to acquire the first parameter.

[0118] The model training unit 920 is configured to train at least one channel parameter model using the first parameter.

[0119] In some embodiments of this disclosure, the model training unit 920 is configured to output a second parameter of the channel at at least one point in time; or to output a second parameter of the channel over at least one time period.

[0120] In some embodiments of this disclosure, the parameter acquisition unit 910 is configured to acquire first parameters of the channel for multiple time periods within a day.

[0121] Figure 10 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment. (Refer to...) Figure 10 As shown, the channel parameter determination device 1000 is applied to a network device and includes a parameter acquisition unit 1010 and a model update unit 1020.

[0122] The parameter acquisition unit 1010 is configured to acquire the first parameter.

[0123] The model update unit 1020 is configured to update at least one channel parameter model based on the first parameter of the channel.

[0124] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0125] Based on the same concept, embodiments of this disclosure provide a channel parameter determination device applied to a terminal.

[0126] Figure 11 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment. (Refer to...) Figure 11 As shown, the channel parameter determination device 1100 is applied to a terminal and includes a parameter acquisition unit 1110 and a parameter determination unit 1120.

[0127] The parameter acquisition unit 1110 is configured to acquire the first parameter.

[0128] The parameter determination unit 1120 is configured to determine the second parameter of the channel based on the first parameter and using at least one channel parameter model.

[0129] In some embodiments of this disclosure, the channel parameter model is a channel parameter model based on a regression algorithm.

[0130] In some embodiments of this disclosure, the first parameter includes: service type, service occurrence time, terminal movement speed, terminal channel conditions, terminal receiving capability, or the cell ID. For example, terminal channel conditions include: signal-to-interference-plus-noise ratio (SINR) or reference received signal power (RSRP).

[0131] In some embodiments of this disclosure, the second parameter includes: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH.

[0132] Figure 12 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment. (Refer to...) Figure 12 As shown, the channel parameter determination device 1200 is applied to a terminal and includes a parameter acquisition unit 1210 and a model training unit 1220.

[0133] The parameter acquisition unit 1210 is configured to acquire the first parameter.

[0134] The model training unit 1220 is configured to train at least one channel parameter model using the first parameter.

[0135] In some embodiments of this disclosure, the model training unit 1220 is configured to output a second parameter of the channel at at least one point in time; or to output a second parameter of the channel over at least one time period.

[0136] In some embodiments of this disclosure, the parameter acquisition unit 1210 is configured to acquire first parameters of the channel for multiple time periods within a day.

[0137] Figure 13 This is a block diagram illustrating a channel parameter determination apparatus according to an exemplary embodiment. (Refer to...) Figure 13 As shown, the channel parameter determination device 1300 is applied to a terminal and includes a parameter acquisition unit 1310 and a model update unit 1320.

[0138] The parameter acquisition unit 1310 is configured to acquire the first parameter.

[0139] The model update unit 1320 is configured to update at least one channel parameter model based on the first parameter of the channel.

[0140] It is understood that the channel parameter determination apparatus provided in this disclosure includes hardware structures and / or software modules corresponding to each function in order to achieve the above-mentioned functions. In conjunction with the units and algorithm steps of the various examples disclosed in this disclosure, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of this disclosure.

[0141] Figure 14 This is a block diagram illustrating an apparatus 1400 for determining channel parameters according to an exemplary embodiment. For example, apparatus 1400 may be a terminal. The terminal may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0142] Reference Figure 14 The device 1400 may include one or more of the following components: processing component 1402, memory 1404, power component 1406, multimedia component 1408, audio component 1410, input / output (I / O) interface 1412, sensor component 1414, and communication component 1416.

[0143] Processing component 1402 typically controls the overall operation of device 1400, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 1402 may include one or more processors 1420 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 1402 may include one or more modules to facilitate interaction between processing component 1402 and other components. For example, processing component 1402 may include a multimedia module to facilitate interaction between multimedia component 1408 and processing component 1402.

[0144] Memory 1404 is configured to store various types of data to support the operation of device 1400. Examples of this data include instructions for any application or method operating on device 1400, contact data, phonebook data, messages, pictures, videos, etc. Memory 1404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0145] The power component 1406 provides power to the various components of the device 1400. The power component 1406 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 1400.

[0146] Multimedia component 1408 includes a screen that provides an output interface between the device 1400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 1408 includes a front-facing camera and / or a rear-facing camera. When the device 1400 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0147] Audio component 1410 is configured to output and / or input audio signals. For example, audio component 1410 includes a microphone (MIC) configured to receive external audio signals when device 1400 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1404 or transmitted via communication component 1416. In some embodiments, audio component 1410 also includes a speaker for outputting audio signals.

[0148] I / O interface 1412 provides an interface between processing component 1402 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0149] Sensor assembly 1414 includes one or more sensors for providing state assessments of various aspects of device 1400. For example, sensor assembly 1414 may detect the on / off state of device 1400, the relative positioning of components such as the display and keypad of device 1400, changes in position of device 1400 or a component of device 1400, the presence or absence of user contact with device 1400, orientation or acceleration / deceleration of device 1400, and temperature changes of device 1400. Sensor assembly 1414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 1414 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0150] Communication component 1416 is configured to facilitate wired or wireless communication between device 1400 and other devices. Device 1400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 1416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1416 further includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0151] In an exemplary embodiment, the apparatus 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0152] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1404 including instructions, which can be executed by a processor 1420 of the device 1400 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0153] Figure 15This is a block diagram illustrating an apparatus 1500 for determining channel parameters according to an exemplary embodiment. Apparatus 1500 may be a network device. (Refer to...) Figure 15 The apparatus 1500 includes a processing component 1522, which further includes one or more processors, and memory resources represented by memory 1532 for storing instructions, such as application programs, that can be executed by the processing component 1522. The application programs stored in memory 1532 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1522 is configured to execute instructions to perform the methods described above.

[0154] Device 1500 may further include: a power supply component 1526 configured to perform power management of device 1500; a wired or wireless network interface 1550 configured to connect device 1500 to a network; and an input / output (I / O) interface 1558. Device 1500 can operate on an operating system, such as Windows Server, stored in memory 1532. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0155] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1532 including instructions, which can be executed by a processing component 1522 of the apparatus 1500 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0156] It is understood that in this disclosure, "multiple" refers to two or more, and other quantifiers are similar. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The singular forms "a," "the," and "the" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0157] It is further understood that the terms "first," "second," etc., are used to describe various types of information, but this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another, and do not indicate a specific order or degree of importance. In fact, the expressions "first," "second," etc., are completely interchangeable. For example, without departing from the scope of this disclosure, first information can also be referred to as second information, and similarly, second information can also be referred to as first information.

[0158] It is further understood that although operations are described in a specific order in the accompanying drawings in this disclosure, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the shown operations to obtain the desired result. In certain scenarios, multitasking and parallel processing may be advantageous.

[0159] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0160] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining channel parameters, characterized in that, Applied to network devices, the method includes: Obtain the first parameters of the channel for multiple time periods within a day; Using the first parameter, multiple channel parameter models are trained, wherein each channel parameter model corresponds to a time period. Based on the first parameter, the second parameter of the channel is determined using at least one channel parameter model; The training of the at least one channel parameter model includes: Output the second parameter of the channel at at least one point in time; or output the second parameter of the channel over at least one time period; The channel parameter model is a channel parameter model based on a regression algorithm. The regression algorithm is a linear regression algorithm or a nonlinear regression algorithm, which is determined based on the correlation between the independent and dependent variables of the channel parameter model. The first parameter includes at least one of the following: service type and the time when the service occurs. The second parameter includes at least one of the following: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH; Among them, the aggregation degree, the transmission location and the DCI format are the ones that the terminal will detect first, and the terminal will stop detecting when it detects the corresponding DCI.

2. The method according to claim 1, characterized in that, The first parameter also includes at least one of the following: Terminal moving speed, terminal channel conditions, terminal receiving capability, or the cell ID.

3. The method according to claim 2, characterized in that, The terminal channel conditions include at least one of the following: Signal-to-interference-plus-noise ratio (SINR) or reference signal received power (RSRP).

4. The method according to claim 1, characterized in that, The method further includes: The at least one channel parameter model is updated based on the first parameter of the channel.

5. The method according to claim 1, characterized in that, The method further includes: Send the at least one channel parameter model to the terminal.

6. The method according to claim 1, characterized in that, The method further includes: Send a first message, which is used to indicate the at least one channel parameter model.

7. The method according to claim 1, characterized in that, The method further includes: Send a second message, which is used to indicate activation of the at least one channel parameter model, or to indicate deactivation of the at least one channel parameter model; When the terminal's regular movement trajectory or regular activity location changes, the second information is used to indicate the deactivation of the at least one channel parameter model.

8. A method for determining channel parameters, characterized in that, Applied to a terminal, the method includes: Obtain the first parameters of the channel for multiple time periods within a day; Using the first parameter, multiple channel parameter models are trained, wherein each channel parameter model corresponds to a time period. Based on the first parameter, the second parameter of the channel is determined using at least one channel parameter model; The training of the at least one channel parameter model includes: Output the second parameter of the channel at at least one point in time; or output the second parameter of the channel over at least one time period; The channel parameter model is a channel parameter model based on a regression algorithm. The regression algorithm is a linear regression algorithm or a nonlinear regression algorithm, which is determined based on the correlation between the independent and dependent variables of the channel parameter model. The first parameter includes at least one of the following: service type and the time when the service occurs. The second parameter includes at least one of the following: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH; The terminal will prioritize detecting and predicting the aggregation level, the transmission location, and the DCI format, and will stop detecting when the terminal detects the corresponding DCI.

9. The method according to claim 8, characterized in that, The first parameter also includes at least one of the following: Terminal moving speed, terminal channel conditions, terminal receiving capability, or the cell ID.

10. The method according to claim 9, characterized in that, The terminal channel conditions include at least one of the following: Signal-to-interference-plus-noise ratio (SINR) or reference signal received power (RSRP).

11. The method according to claim 8, characterized in that, The method further includes: The at least one channel parameter model is updated based on the first parameter of the channel.

12. The method according to claim 8, characterized in that, The method further includes: Receive the at least one channel parameter model sent from the network device.

13. The method according to claim 12, characterized in that, The method further includes: Based on the received signaling, determine one or more channel parameter models among the at least one channel parameter model.

14. The method according to claim 12, characterized in that, The method further includes: Depending on the triggering conditions, it switches between multiple channel parameter models.

15. The method according to claim 8, characterized in that, The method further includes: Receive first information, which is used to indicate the at least one channel parameter model.

16. The method according to claim 8, characterized in that, The method further includes: Send a second message, which is used to indicate activation of the at least one channel parameter model, or to indicate deactivation of the at least one channel parameter model; When the terminal’s regular movement trajectory or regular activity location changes, the second information is used to indicate the deactivation of the at least one channel parameter model.

17. A channel parameter determination device, characterized in that, Applied to network devices, the device includes: The parameter acquisition unit is configured to acquire first parameters of the channel for multiple time periods within a day; The model training unit is configured to train multiple channel parameter models using the first parameter, wherein each channel parameter model corresponds to a time period. The parameter determination unit is configured to determine the second parameter of the channel based on the first parameter and using at least one channel parameter model; The training of the at least one channel parameter model includes: Output the second parameter of the channel at at least one point in time; or output the second parameter of the channel over at least one time period; The channel parameter model is a channel parameter model based on a regression algorithm. The regression algorithm is either a linear regression algorithm or a nonlinear regression algorithm, and the linear regression algorithm or the nonlinear regression algorithm is determined based on the correlation between the independent variables and the dependent variables of the channel parameter model; The first parameter includes at least one of the following: service type and the time when the service occurs. The second parameter includes at least one of the following: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH; Among them, the aggregation degree, the transmission location and the DCI format are the ones that the terminal will detect first, and the terminal will stop detecting when it detects the corresponding DCI.

18. A channel parameter determination device, characterized in that, Applied to a terminal, the device includes: The parameter acquisition unit is configured to acquire first parameters of the channel for multiple time periods within a day; The model training unit is configured to train multiple channel parameter models using the first parameter, wherein each channel parameter model corresponds to a time period. The parameter determination unit is configured to determine the second parameter of the channel based on the first parameter and using at least one channel parameter model; The training of the at least one channel parameter model includes: Output the second parameter of the channel at at least one point in time; or output the second parameter of the channel over at least one time period; The channel parameter model is a channel parameter model based on a regression algorithm. The regression algorithm is either a linear regression algorithm or a nonlinear regression algorithm, and the linear regression algorithm or the nonlinear regression algorithm is determined based on the correlation between the independent variables and the dependent variables of the channel parameter model; The first parameter includes at least one of the following: service type and service occurrence time. The second parameter includes at least one of the following: the aggregation degree of the physical downlink control channel (PDCCH), the transmission location of the PDCCH, or the DCI size of the PDCCH; The aggregation level, transmission location, and DCI format are prioritized for detection by the terminal, and the terminal stops detection when it detects the corresponding DCI.

19. A channel parameter determination device, characterized in that, Applied to network devices, the device includes: processor; Memory used to store processor-executable instructions; The processor is configured to execute the channel parameter determination method according to any one of claims 1-7.

20. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor, enable the processor to perform the channel parameter determination method according to any one of claims 1-7.

21. A channel parameter determination device, characterized in that, Applied to network devices, the device includes: processor; Memory used to store processor-executable instructions; The processor is configured to execute the channel parameter determination method according to any one of claims 8-16.

22. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor, enable the processor to perform the channel parameter determination method according to any one of claims 8-16.

Images