CN116095689A - Terminal bandwidth scheduling method, device and base station in dynamic spectrum sharing network - Google Patents

Terminal bandwidth scheduling method, device and base station in dynamic spectrum sharing network Download PDF

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Publication number
CN116095689A
CN116095689A CN202111298946.7A CN202111298946A CN116095689A CN 116095689 A CN116095689 A CN 116095689A CN 202111298946 A CN202111298946 A CN 202111298946A CN 116095689 A CN116095689 A CN 116095689A
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terminal
bandwidth
mode
csi
machine learning
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周阅天
胡春雷
谢伟良
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China Telecom Corp Ltd
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China Telecom Corp Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10—Dynamic resource partitioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

The disclosure provides a terminal bandwidth scheduling method, device and base station in a dynamic spectrum sharing network, and relates to the technical field of mobile communication. The method for scheduling the terminal bandwidth in the dynamic spectrum sharing network comprises the following steps: under the condition of adopting a single channel state information reference signal (CSI-RS) mode, acquiring Reference Signal Receiving Power (RSRP), channel Quality Indication (CQI) and terminal geographic position information of a new air interface (NR) terminal; determining whether to switch the bandwidth occupation mode of the NR terminal based on a machine learning model according to the RSRP, the CQI and the geographic position information of the terminal; and under the condition that the bandwidth occupation mode of the NR terminal is determined to be switched, sending a bandwidth occupation mode switching instruction to the terminal. By the method, on the basis of realizing bandwidth occupation mode switching of the NR terminal, CSI-RS (channel state information-reference signal) sent by the terminal can be reduced, so that occupation of signaling on frequency band resources is reduced, waste of frequency spectrum resources is reduced, and resource utilization rate is improved.

Description

Terminal bandwidth scheduling method, device and base station in dynamic spectrum sharing network
Technical Field
The disclosure relates to the technical field of mobile communication, in particular to a method, a device and a base station for scheduling terminal bandwidth in a DSS (Dynamic Spectrum Sharing ) network.
Background
In order to realize the purpose of smooth evolution from 4G to 5G, both China telecom and China Union use the strategy of 4G/5G dynamic spectrum sharing (40M DSS) to balance 4G and 5G capacity on 2.1GHz frequency, and use 40MHz large bandwidth jointly.
The 40M DSS technology adopts a strategy that 40M NR exclusive frequency band +20M LTE and NR shared frequency band jointly form 40M large bandwidth. In general, NR terminals schedule as large a bandwidth as possible; when LTE is idle, NR terminals will schedule 40MHZ bandwidth resources.
Disclosure of Invention
An object of the present disclosure is to reduce overhead and improve bandwidth utilization.
According to an aspect of some embodiments of the present disclosure, a terminal bandwidth scheduling method in a DSS network is provided, including: acquiring RSRP (Reference Signal Receiving Power, reference Signal received power), CQI (Channel Quality Indication ) and terminal geographic location information of an NR (New Radio) terminal under the condition of adopting a single CSI-RS (Channel State Information-Reference Signal) mode, wherein the full bandwidth comprises an NR exclusive frequency band and a shared frequency band of NR and LTE (Long Term Evolution ), and the single CSI-RS mode is a mode that the CSI-RS transmitted to the NR terminal only comprises the CSI-RS aiming at the full bandwidth; determining whether to switch a bandwidth occupation mode of an NR terminal based on a machine learning model according to RSRP, CQI and terminal geographical position information, wherein the bandwidth occupation mode comprises a mode that the NR terminal only schedules NR exclusive frequency band resources and allows the NR terminal to schedule full bandwidth resources, the machine learning model is generated according to historical data training in a two-section system CSI-RS mode, and the two-section system CSI-RS mode is a mode that CSI-RSs sent to the NR terminal comprise CSI-RSs aiming at full bandwidth and CSI-RSs aiming at the NR exclusive frequency band; and under the condition that the bandwidth occupation mode of the NR terminal is determined to be switched, sending a bandwidth occupation mode switching instruction to the terminal.
In some embodiments, the terminal bandwidth scheduling method in the DSS network further comprises: determining whether the network is in an idle state; if the network is in an idle state, adopting a two-section CSI-RS mode; and if the network is in a non-idle state, adopting a single CSI-RS mode.
In some embodiments, determining whether the network is in an idle state comprises: determining whether the network is in an idle state according to a preset corresponding relation between the idle state and the time period of the current moment; or determining whether the current network is in an idle state according to the utilization of PRBs (Physical resource blocks ) of the cell.
In some embodiments, the terminal bandwidth scheduling method in the DSS network further comprises: under the condition of adopting a two-section CSI-RS mode, acquiring current RSRP, CQI and terminal geographic position information; determining a mode switching result according to a preset bandwidth occupation mode switching strategy, and establishing the association relation between the current RSRP, CQI, terminal geographic position information and the mode switching result as sample data; and acquiring a machine learning model according to the sample data and a preset machine learning algorithm.
In some embodiments, obtaining the machine learning model from the sample data and a predetermined machine learning algorithm comprises: training an initial machine learning algorithm by using sample data to obtain a machine learning model; and updating and training the existing machine learning model by utilizing the sample data newly acquired after the machine learning model is trained and acquired in the previous time.
In some embodiments, the predetermined bandwidth occupancy mode switching policy includes: under the condition that the bandwidth occupation mode is that the NR terminal is allowed to schedule the full bandwidth Resource, if the current RSRP is larger than a preset first threshold value and the CQI corresponding to the current full bandwidth is smaller than a preset second threshold value, determining a first rate of the NR terminal according to the CQI corresponding to the current full bandwidth and the RBs (Resource blocks) distributed in the full bandwidth; determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RB distributed in the NR exclusive frequency band; and under the condition that the second rate is larger than or equal to a first preset proportion of the first rate, determining that the bandwidth occupation mode of the NR terminal is that the NR terminal only schedules the NR exclusive frequency band resource.
In some embodiments, the predetermined bandwidth occupancy mode switching policy includes: under the condition that the bandwidth occupation mode is that the NR terminal only schedules NR exclusive frequency band resources, determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RBs distributed in the NR exclusive frequency band; determining a third rate of the NR terminal according to the CQI corresponding to the current full bandwidth and the RBs distributed in the full bandwidth; and under the condition that the third rate is larger than or equal to a second preset proportion of the second rate, determining that the bandwidth occupation mode of the NR terminal is switched to allow the NR terminal to schedule the full bandwidth resource.
According to an aspect of some embodiments of the present disclosure, there is provided a terminal bandwidth scheduling apparatus, including: an information acquisition unit configured to acquire RSRP, CQI, and terminal geographical location information of an NR terminal in a case where a single CSI-RS mode is adopted, where a full bandwidth includes an NR exclusive band and a shared band of NR and LTE, and the single CSI-RS mode is a mode in which CSI-RS transmitted to the NR terminal includes only CSI-RS for the full bandwidth; a bandwidth occupation mode determining unit configured to determine whether to switch a bandwidth occupation mode of an NR terminal based on a machine learning model according to RSRP, CQI and terminal geographical location information, wherein the bandwidth occupation mode includes an NR terminal scheduling only NR exclusive band resources and allowing the NR terminal to schedule full bandwidth resources, the machine learning model is generated according to historical data training in a two-segment CSI-RS mode, the two-segment CSI-RS mode is a mode in which CSI-RS transmitted to the NR terminal includes CSI-RS for full bandwidth and CSI-RS for NR exclusive band; and a switching unit configured to transmit a bandwidth occupation mode switching instruction to the terminal in case of determining to switch the bandwidth occupation mode of the NR terminal.
In some embodiments, the terminal bandwidth scheduling apparatus further includes a CSI-RS pattern determining unit configured to: determining whether the network is in an idle state; if the network is in an idle state, adopting a two-section CSI-RS mode; and if the network is in a non-idle state, adopting a single CSI-RS mode.
In some embodiments, the terminal bandwidth scheduling apparatus further includes a training unit configured to: under the condition of adopting a two-section CSI-RS mode, acquiring current RSRP, CQI and terminal geographic position information; determining a mode switching result according to a preset bandwidth occupation mode switching strategy, and establishing the association relation between the current RSRP, CQI, terminal geographic position information and the mode switching result as sample data; and acquiring a machine learning model according to the sample data and a preset machine learning algorithm.
In some embodiments, the training unit is configured to train an initial machine learning algorithm using the sample data to obtain a machine learning model; and updating and training the existing machine learning model by utilizing the sample data newly acquired after the machine learning model is trained and acquired in the previous time.
In some embodiments, the predetermined bandwidth occupancy mode switching policy includes: under the condition that the bandwidth occupation mode is that the NR terminal is allowed to schedule the full bandwidth resource, if the current RSRP is larger than a preset first threshold value and the CQI corresponding to the current full bandwidth is smaller than a preset second threshold value, determining a first rate of the NR terminal according to the CQI corresponding to the current full bandwidth and the RBs distributed in the full bandwidth; determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RB distributed in the NR exclusive frequency band; and under the condition that the second rate is larger than or equal to a first preset proportion of the first rate, determining that the bandwidth occupation mode of the NR terminal is that the NR terminal only schedules the NR exclusive frequency band resource.
In some embodiments, the predetermined bandwidth occupancy mode switching policy includes: under the condition that the bandwidth occupation mode is that the NR terminal only schedules NR exclusive frequency band resources, determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RBs distributed in the NR exclusive frequency band; determining a third rate of the NR terminal according to the CQI corresponding to the current full bandwidth and the RBs distributed in the full bandwidth; and under the condition that the third rate is larger than or equal to a second preset proportion of the second rate, determining that the bandwidth occupation mode of the NR terminal is switched to allow the NR terminal to schedule the full bandwidth resource.
According to an aspect of some embodiments of the present disclosure, there is provided a terminal bandwidth scheduling apparatus, including: a memory; and a processor coupled to the memory, the processor configured to perform any of the terminal bandwidth scheduling methods in the DSS network above based on instructions stored in the memory.
According to an aspect of some embodiments of the present disclosure, a computer-readable storage medium is presented, on which computer program instructions are stored, which instructions, when executed by a processor, implement the steps of the terminal bandwidth scheduling method in any one of the above DSS networks.
According to an aspect of some embodiments of the present disclosure, a base station is proposed, comprising any one of the terminal bandwidth scheduling apparatuses above.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the present disclosure, and together with the description serve to explain the present disclosure. In the drawings:
fig. 1 is a flow chart of some embodiments of a terminal bandwidth scheduling method in a DSS network of the present disclosure.
Fig. 2 is a flow chart of further embodiments of a method of terminal bandwidth scheduling in a DSS network of the present disclosure.
Fig. 3 is a flow chart of some embodiments of a model training process of a terminal bandwidth scheduling method in a DSS network of the present disclosure.
Fig. 4 is a flow chart of some embodiments of a two-segment CSI-RS mode of a terminal bandwidth scheduling method in a DSS network of the present disclosure.
Fig. 5 is a schematic diagram of some embodiments of a terminal bandwidth scheduling apparatus of the present disclosure.
Fig. 6 is a schematic diagram of other embodiments of a terminal bandwidth scheduling apparatus of the present disclosure.
Fig. 7 is a schematic diagram of further embodiments of a terminal bandwidth scheduling apparatus of the present disclosure.
Fig. 8 is a schematic diagram of some embodiments of a base station of the present disclosure.
Detailed Description
The technical scheme of the present disclosure is described in further detail below through the accompanying drawings and examples.
The inventor finds that 20M bandwidth of the LTE and NR shared frequency band is interfered by CRS (Channel Reference Signal ) from a neighboring cell, which can cause the defects of poor channel quality, high error rate, reduced MCS (Modulation and Coding Scheme, modulation coding mode) and greatly reduced terminal rate, even the rate of the terminal is possibly reduced to be lower than the rate of NR exclusive bandwidth of the scheduling 20M, and the defects of reduced performance of a 5G network, spectrum resource waste, increased power consumption of the NR terminal and the like are caused.
To solve this problem, when the performance of the NR terminal for scheduling a large bandwidth (greater than 20M) is not good, the base station only schedules the spectrum resource of the 20M NR exclusive area for the NR terminal, and the shared area is reserved for the LTE terminal. Therefore, network performance and spectrum efficiency are improved, and terminal energy consumption can be reduced.
In some embodiments, a mode of configuring two-segment CSI-RS by dynamic DCI (Downlink Control Information ) to measure channel quality and report the channel quality may be adopted, interference situations of two segments of spectrum are monitored in real time, and a terminal rate is calculated at a base station side, so as to determine whether to switch a bandwidth occupation mode.
The disclosure provides a single CSI-RS mode, in which a CSI-RS transmitted to an NR terminal only comprises a full-bandwidth CSI-RS for a DSS, and the CSI-RS for an NR exclusive frequency band is not transmitted to the NR terminal, so that occupation of signaling to frequency band resources is reduced, waste of frequency spectrum resources is reduced, and resource utilization is improved. Meanwhile, the present disclosure proposes a terminal bandwidth scheduling method in a single CSI-RS mode to realize switching of a mode in which an NR terminal occupies a channel in the single CSI-RS mode. In some embodiments, as shown in fig. 1.
In step 121, under the condition of adopting the single CSI-RS mode, the base station acquires RSRP, CQI and terminal geographical location information of the NR terminal, where the full bandwidth includes an NR exclusive band and a shared band of NR and LTE.
In some embodiments, the CQI for the full bandwidth is obtained based on CSI-RS measurements for the full bandwidth of the DSS. In some embodiments, the RSRP of the NR terminal may be obtained by reading the measurement report. In some embodiments, the geographic location information of the terminal may be obtained by using any terminal positioning method, for example, a terminal positioning method based on a base station, reading location information reported by the terminal, and so on. In some embodiments, the full bandwidth has a spectral width of 40M.
In step 122, it is determined whether to switch the bandwidth occupancy mode of the NR terminal based on the machine learning model based on RSRP, CQI and terminal geographical location information. In some embodiments, the bandwidth occupation mode includes that the NR terminal only schedules NR exclusive frequency band resources and allows the NR terminal to schedule full bandwidth resources, and the NR terminal is informed of switching between the two modes through a bandwidth occupation mode switching instruction.
In some embodiments, the machine learning model is generated based on historical data training in a two-segment CSI-RS mode, where the two-segment CSI-RS mode is a mode in which CSI-RS transmitted to the NR terminal includes CSI-RS for full bandwidth and CSI-RS for the NR exclusive band. In some embodiments, the history data may include RSRP, CQI and terminal geographical location information of the NR terminal obtained in the two-segment CSI-RS mode, and a result of whether to switch the bandwidth occupation mode of the NR terminal, which is determined in the two-segment CSI-RS mode.
In some embodiments, the machine learning model may be obtained by injecting the fabricated historical data into a machine learning algorithm (e.g., neural network algorithm, etc.), with a sufficient number of exercises.
In step 123, in the case of determining to switch the bandwidth occupation mode of the NR terminal, a bandwidth occupation mode switching instruction is transmitted to the terminal.
By the method, on the basis of realizing bandwidth occupation mode switching of the NR terminal, CSI-RS (channel state information-reference signal) sent by the terminal can be reduced, so that occupation of signaling on frequency band resources is reduced, waste of frequency spectrum resources is reduced, and resource utilization rate is improved.
A flowchart of further embodiments of a terminal bandwidth scheduling method in a DSS network of the present disclosure is shown in fig. 2.
In step 210, it is determined whether the network is in an idle state; if the network is in a non-idle state, then step 220 is performed; if the network is in an idle state, step 230 is performed.
In some embodiments, an idle period may be preset, and step 230 is performed while currently in the idle period; step 220 is performed when not currently in an idle period.
In some embodiments, whether the current network is in an idle state may be determined according to the utilization of PRBs of a cell. When the utilization rate of the PRB is larger than a preset utilization rate threshold value, determining that the PRB is currently in a non-idle period; otherwise, it is determined to be in an idle period.
In step 220, a single CSI-RS mode is adopted, no CSI-RS for the NR exclusive frequency band is transmitted to the NR terminal, and the CSI-RS transmitted to the NR terminal only includes the full bandwidth CSI-RS for the DSS.
In step 221, RSRP, CQI, and terminal geographical location information of the NR terminal are acquired.
In step 222, the RSRP, CQI and terminal geographical location information are input into the trained machine learning model, and the result of whether to switch the bandwidth occupation mode of the NR terminal is obtained.
In step 230, a two-segment CSI-RS mode is employed.
In step 231, the current RSRP, CQI and terminal geographical location information are acquired. In some embodiments, a CQI of an NR exclusive band and a CQI of a full bandwidth are respectively acquired based on a two-segment CSI-RS mode. In some embodiments, the RSRP of the NR terminal may be obtained by reading the measurement report. In some embodiments, the geographic location information of the terminal may be obtained by using any terminal positioning method, for example, a terminal positioning method based on a base station, reading location information reported by the terminal, and so on.
In step 232, a mode switching result is determined according to a predetermined bandwidth occupation mode switching policy, and step 223 is further performed. In some embodiments, RSRP, CQI and terminal geographical location information generated in the current two-segment CSI-RS mode, and mode switch results may be stored for later use as supplemental data to update the training machine learning model.
In step 223, in the case of determining to switch the bandwidth occupation mode of the NR terminal, a bandwidth occupation mode switching instruction is sent to the terminal, and the flow of the current cycle ends. If the bandwidth occupation mode of the NR terminal is determined to be unnecessary to switch, the current state is kept, and monitoring is continuously carried out, so that a bandwidth occupation mode switching instruction is sent at any time when the bandwidth occupation mode is required to be switched.
By the method, a single CSI-RS mode can be adopted under the condition that the network is busy and the frequency band resource is short, so that the bearing capacity of the network is improved; under the conditions that the network is not busy and the frequency band resources are not short, a two-section system CSI-RS mode is adopted, so that on one hand, the monitoring capability is improved, on the other hand, training samples can be accumulated, and the machine learning model is updated and trained, so that the machine learning model can meet the requirement of real-time change of the network environment, and the accuracy of switching the bandwidth occupation mode when the single CSI-RS mode is adopted subsequently is improved.
Because the wireless environment and the comprehensive interference condition of the cell are not unchanged all the time after the network is established, the machine learning model can be continuously updated by training the data generated in the two-section system CSI-RS mode, and the real-time accuracy of the model can be improved. A flowchart of some embodiments of a model training process for a terminal bandwidth scheduling method in a DSS network of the present disclosure is shown in fig. 3.
In step 310, an initial machine learning algorithm is trained based on historical data in a two-segment CSI-RS mode to obtain a machine learning model. In some embodiments, a target machine learning algorithm to be used, such as a deep learning algorithm, may be determined first. And calling historical data in the two-section CSI-RS mode, wherein the historical data can comprise RSRP, CQI and terminal geographic position information in the two-section CSI-RS mode, and marking a switching result under the corresponding condition. In some embodiments, a two-segment CSI-RS pattern may be run for a period of time to accumulate historical data.
In some embodiments, after a predetermined amount of training sample processing is completed, or the processing accuracy of the machine learning model reaches a predetermined threshold, the initial training is completed, obtaining the machine learning model. The base station may operate using the method shown in fig. 2.
In step 321, in case of using the two-segment CSI-RS mode (i.e., the network is in an idle state), current RSRP, CQI and terminal geographical location information are acquired.
In step 322, a mode switching result is determined according to a predetermined bandwidth occupation mode switching policy, and an association relationship between the current RSRP, CQI, terminal geographical location information and the mode switching result is established as sample data. In some embodiments, the related data may be stored using memory already present in the base station, or stored in a new memory in the base station.
In step 330, the training of the existing machine learning model is updated with the newly acquired sample data. And when the single CSI-RS mode is adopted subsequently, adopting the updated machine learning model to determine whether to switch the bandwidth occupation mode of the NR terminal. When employed in subsequent use, step 321 is performed.
By the method, training samples can be gradually supplemented and updated in the use process, the updated training samples are adopted to update and train the machine learning model, the environment self-adaptive capacity of the machine learning model is improved, and the accuracy of switching the bandwidth occupation mode of the NR terminal when the single CSI-RS mode is adopted is improved.
A flowchart of some embodiments of a predetermined bandwidth occupancy mode switching strategy in a two-segment CSI-RS mode of a terminal bandwidth scheduling method in a DSS network of the present disclosure is shown in fig. 4. In some embodiments, if the current NR terminal adopts a bandwidth occupation mode that allows the NR terminal to schedule full bandwidth resources, then execution begins at step 401; if the current NR terminal adopts a mode of scheduling only NR exclusive band resources, execution starts at step 406.
In step 401, the bandwidth occupation mode is to allow the NR terminal to schedule the full bandwidth resources.
In step 402, traffic is maintained and channel CQI is measured through configured periodic full bandwidth CSI-RS and RSRP and CQI are periodically reported. It is determined whether RSRP > is a predetermined first threshold a and CQI < a predetermined second threshold b. In some embodiments, if RSRP > a and CQI < b, it means that the channel quality is poor, i.e. there is interference, if the reference signal received power meets the standard, step 403 is performed; otherwise, returning to step 401, the current bandwidth occupation mode is maintained.
In step 403, a first rate X of the NR terminal is determined according to the CQI corresponding to the current full bandwidth and RBs allocated within the full bandwidth.
In step 404, a second rate Y of the NR terminal is determined based on the CQI corresponding to the current NR exclusive band and the RBs allocated in the NR exclusive band.
In step 405, it is determined whether Y is greater than or equal to c X. In some embodiments, the first predetermined proportion c may be a preset fraction, such as 0.7, that is, when the ratio of the rate of the NR terminal in the exclusive band to the total rate is higher than the first predetermined proportion, the bandwidth occupation mode switch may be determined only in the NR exclusive band, and step 406 is performed. Otherwise, the process returns to step 401 to maintain the current bandwidth occupation mode. In some embodiments, the first predetermined ratio may be set and adjusted during use according to the effect.
In step 406, the bandwidth occupation mode of the NR terminal is that the NR terminal schedules only the NR exclusive band resources. In some embodiments, if the previous step is step 405, it is determined to issue a bandwidth occupancy mode switching instruction to the NR terminal.
In step 407, a third rate Z of the NR terminal is determined according to the CQI corresponding to the current full bandwidth and RBs allocated within the full bandwidth.
In step 408, it is determined whether Z.gtoreq.d.Y. In some embodiments, the second predetermined ratio d may be a preset positive number, such as a positive number greater than 1, that is, when the full bandwidth mode is capable of obtaining a higher rate, the method may switch to the full bandwidth mode, and step 401 is performed; otherwise, return to step 406 to maintain the current bandwidth occupancy mode. In some embodiments, the second predetermined ratio may be set and adjusted during use according to the effect.
In some embodiments, to avoid the ping-pong effect, the second predetermined ratio is greater than the inverse of the first predetermined ratio, thereby ensuring that no persistent repeated handoff occurs. Furthermore, training data generated based on the setting is adopted, and a machine learning model obtained through training can avoid the ping-pong effect under a single CSI-RS mode, so that the network stability is improved.
By the method, whether bandwidth occupation mode switching of the NR terminal is performed or not can be determined by using the obtained data in the two-section system CSI-RS mode, on one hand, switching accuracy when the two-section system CSI-RS mode is adopted is ensured, and on the other hand, accurate sample data is provided for machine learning model training, so that switching accuracy when the single CSI-RS mode is adopted is ensured.
A schematic diagram of some embodiments of a terminal bandwidth scheduling apparatus 50 of the present disclosure is shown in fig. 5.
The information obtaining unit 510 can obtain, by using a single CSI-RS mode, RSRP, CQI, and terminal geographical location information of an NR terminal, where the full bandwidth includes an NR exclusive band and a shared band of NR and LTE.
The bandwidth occupation mode determining unit 520 can determine whether to switch the bandwidth occupation mode of the NR terminal based on the machine learning model according to RSRP, CQI, and terminal geographical location information. In some embodiments, the bandwidth occupation mode includes that the NR terminal only schedules NR exclusive frequency band resources and allows the NR terminal to schedule full bandwidth resources, and the NR terminal is informed of switching between the two modes through a bandwidth occupation mode switching instruction. In some embodiments, the machine learning model is generated based on historical data training in a two-segment CSI-RS mode, where the two-segment CSI-RS mode is a mode in which CSI-RS transmitted to the NR terminal includes CSI-RS for full bandwidth and CSI-RS for the NR exclusive band. In some embodiments, the history data may include RSRP, CQI and terminal geographical location information of the NR terminal obtained in the two-segment CSI-RS mode, and a result of whether to switch the bandwidth occupation mode of the NR terminal, which is determined in the two-segment CSI-RS mode.
The switching unit 530 can transmit a bandwidth occupation mode switching instruction to the terminal in case of determining to switch the bandwidth occupation mode of the NR terminal.
The device can reduce CSI-RS transmitted to the terminal on the basis of realizing bandwidth occupation mode switching of the NR terminal, thereby reducing occupation of signaling on frequency band resources, reducing waste of frequency spectrum resources and improving resource utilization rate.
In some embodiments, as shown in fig. 5, the terminal bandwidth scheduling apparatus may further include a CSI-RS pattern determining unit 540 capable of determining whether the network is in an idle state. If the network is in an idle state, adopting a two-section CSI-RS mode; and if the network is in a non-idle state, adopting a single CSI-RS mode. In some embodiments, the CSI-RS pattern determining unit 540 may determine whether the network is in an idle state according to a predetermined correspondence between idle states and time periods, and the time period in which the current time is located, or determine whether the current network is in an idle state according to the utilization of PRBs of the cell.
The device can adopt a single CSI-RS mode under the condition that the network is busy and the frequency band resource is short, so that the bearing capacity of the network is improved; under the conditions that the network is not busy and the frequency band resources are not short, a two-section system CSI-RS mode is adopted, so that on one hand, the monitoring capability is improved, on the other hand, training samples can be accumulated, and the accuracy of switching the bandwidth occupation mode when the single CSI-RS mode is adopted subsequently is improved.
In some embodiments, as shown in fig. 5, the terminal bandwidth scheduling apparatus may further include a training unit 550, capable of acquiring current RSRP, CQI and terminal geographical location information in the case of using the two-segment CSI-RS mode, and determining a mode switching result according to a predetermined bandwidth occupation mode switching policy. In some embodiments, the predetermined bandwidth occupancy mode switching policy may be as shown in the fig. 4 embodiment. The training unit 550 establishes an association relationship between the current RSRP, CQI, terminal geographical location information, and a mode switching result as sample data, and then acquires a machine learning model according to the sample data and a predetermined machine learning algorithm. In some embodiments, the training unit may train an initial machine learning algorithm using the sample data to obtain the machine learning model at the beginning of the generation of the machine learning model; in some embodiments, the training unit may further update training the existing machine learning model using the sample data newly acquired after the previous training and obtaining the machine learning model.
The device can gradually supplement and update the training sample in the use process, update and train the machine learning model by adopting the updated training sample, improve the environment self-adaption capability of the machine learning model and improve the accuracy of switching the bandwidth occupation mode of the NR terminal when the single CSI-RS mode is adopted.
A schematic structural diagram of an embodiment of a terminal bandwidth scheduling apparatus of the present disclosure is shown in fig. 6. The terminal bandwidth scheduling apparatus includes a memory 601 and a processor 602. Wherein: the memory 601 may be a magnetic disk, flash memory or any other non-volatile storage medium. The memory is used for storing instructions in the corresponding embodiment of the terminal bandwidth scheduling method in the DSS network above. The processor 602 is coupled to the memory 601 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 602 is configured to execute instructions stored in the memory, so that occupation of signaling on frequency band resources can be reduced, waste of spectrum resources can be reduced, and resource utilization rate can be improved.
In one embodiment, as also shown in fig. 7, the terminal bandwidth scheduling apparatus 700 includes a memory 701 and a processor 702. The processor 702 is coupled to the memory 701 through a BUS 703. The terminal bandwidth scheduler 700 may also be connected to an external storage device 705 via a storage interface 704 for invoking external data, and to a network or another computer system (not shown) via a network interface 706. And will not be described in detail herein.
In the embodiment, the data instruction is stored by the memory, and then the instruction is processed by the processor, so that the occupation of signaling to frequency band resources can be reduced, the waste of frequency spectrum resources is reduced, and the resource utilization rate is improved.
In another embodiment, a computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method in the corresponding embodiment of the terminal bandwidth scheduling method in a DSS network. It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
A schematic diagram of some embodiments of a base station 80 of the present disclosure is shown in fig. 8. The base station 80 includes a terminal bandwidth scheduling device 810 in addition to a base station structure in the related art, such as an antenna, etc. The terminal bandwidth scheduler 810 may be any of those mentioned above. In some embodiments, a register and an artificial intelligence platform can be added at the base station side, and the register and the artificial intelligence platform form the terminal bandwidth scheduling device 810.
The base station can reduce CSI-RS transmitted to the terminal on the basis of realizing bandwidth occupation mode switching of the NR terminal, thereby reducing occupation of signaling on frequency band resources, reducing waste of frequency spectrum resources and improving resource utilization rate.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Thus far, the present disclosure has been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
Finally, it should be noted that: the above embodiments are merely for illustrating the technical solution of the present disclosure and are not limiting thereof; although the present disclosure has been described in detail with reference to preferred embodiments, those of ordinary skill in the art will appreciate that: modifications may be made to the specific embodiments of the disclosure or equivalents may be substituted for part of the technical features; without departing from the spirit of the technical solutions of the present disclosure, it should be covered in the scope of the technical solutions claimed in the present disclosure.

Claims (16)

1. A method for scheduling terminal bandwidth in dynamic spectrum sharing DSS network includes:
in case of employing the single channel state information reference signal CSI-RS pattern,
acquiring reference signal receiving power RSRP, channel quality indication CQI and terminal geographical position information of a new air interface NR terminal, wherein the full bandwidth comprises an NR exclusive frequency band and a shared frequency band of NR and long term evolution LTE, and the single CSI-RS mode is a mode that CSI-RS sent to the NR terminal only comprises CSI-RS aiming at the full bandwidth;
determining whether to switch a bandwidth occupation mode of the NR terminal based on a machine learning model according to the RSRP, the CQI and the geographic position information of the terminal, wherein the bandwidth occupation mode comprises a mode that the NR terminal only schedules NR exclusive frequency band resources and allows the NR terminal to schedule full bandwidth resources, the machine learning model is generated according to historical data training in a two-section system CSI-RS mode, and the two-section system CSI-RS mode is a mode that the CSI-RS sent to the NR terminal comprises the CSI-RS aiming at the full bandwidth and the CSI-RS aiming at the NR exclusive frequency band;
and under the condition that the bandwidth occupation mode of the NR terminal is determined to be switched, sending a bandwidth occupation mode switching instruction to the terminal.
2. The method of claim 1, further comprising:
determining whether the network is in an idle state;
if the network is in an idle state, adopting the two-section CSI-RS mode;
and if the network is in a non-idle state, adopting the single CSI-RS mode.
3. The method of claim 2, wherein the determining whether the network is in an idle state comprises:
determining whether the network is in an idle state according to a preset corresponding relation between the idle state and the time period of the current moment; or (b)
And determining whether the current network is in an idle state according to the utilization rate of Physical Resource Blocks (PRBs) of the cell.
4. The method of claim 2, further comprising:
in the case of the two-segment CSI-RS mode,
acquiring current RSRP, CQI and terminal geographic position information;
determining a mode switching result according to a preset bandwidth occupation mode switching strategy, and establishing the association relation between the current RSRP, CQI, terminal geographic position information and the mode switching result as sample data;
and acquiring the machine learning model according to the sample data and a preset machine learning algorithm.
5. The method of claim 4, wherein the obtaining the machine learning model from the sample data and a predetermined machine learning algorithm comprises:
training an initial machine learning algorithm by using the sample data to obtain the machine learning model; and
and updating the machine learning model which is trained by utilizing the sample data which is newly acquired after the machine learning model is trained in the previous time.
6. The method of claim 4, wherein the predetermined bandwidth occupancy mode switching policy comprises:
in case the bandwidth occupation mode is to allow the NR terminal to schedule the full bandwidth resources,
if the current RSRP is greater than the preset first threshold value and the CQI corresponding to the current full bandwidth is less than the preset second threshold value, then
Determining a first rate of an NR terminal according to the CQI corresponding to the current full bandwidth and the Resource Blocks (RBs) allocated in the full bandwidth;
determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RB distributed in the NR exclusive frequency band;
and under the condition that the second rate is larger than or equal to a first preset proportion of the first rate, determining that the bandwidth occupation mode of the NR terminal is switched to be that the NR terminal only schedules NR exclusive frequency band resources.
7. The method of claim 4 or 6, wherein the predetermined bandwidth occupancy mode switching policy comprises:
in case the bandwidth occupation mode is that the NR terminal schedules only NR exclusive band resources,
determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RB distributed in the NR exclusive frequency band;
determining a third rate of the NR terminal according to the CQI corresponding to the current full bandwidth and the RBs distributed in the full bandwidth;
and under the condition that the third rate is larger than or equal to a second preset proportion of the second rate, determining to switch the bandwidth occupation mode of the NR terminal to allow the NR terminal to schedule full bandwidth resources.
8. A terminal bandwidth scheduling apparatus, comprising:
an information acquisition unit configured to acquire reference signal received power RSRP, channel quality indicator CQI, and terminal geographical location information of a new air interface NR terminal in a case where a single channel state information reference signal CSI-RS mode is adopted, where the full bandwidth includes an NR exclusive frequency band and a shared frequency band of NR and long term evolution LTE, and the single CSI-RS mode is a mode in which CSI-RS transmitted to the NR terminal includes only CSI-RS for the full bandwidth;
a bandwidth occupation mode determining unit configured to determine whether to switch a bandwidth occupation mode of the NR terminal based on a machine learning model according to the RSRP, the CQI, and terminal geographical location information, wherein the bandwidth occupation mode includes an NR terminal scheduling only NR exclusive band resources and allowing the NR terminal to schedule full bandwidth resources, the machine learning model is generated according to historical data training in a two-segment CSI-RS mode, the two-segment CSI-RS mode is a mode in which CSI-RS transmitted to the NR terminal includes CSI-RS for the full bandwidth and CSI-RS for the NR exclusive band;
and a switching unit configured to send a bandwidth occupation mode switching instruction to the terminal in the case of determining to switch the bandwidth occupation mode of the NR terminal.
9. The apparatus of claim 8, further comprising a CSI-RS pattern determining unit configured to:
determining whether the network is in an idle state;
if the network is in an idle state, adopting the two-section CSI-RS mode;
and if the network is in a non-idle state, adopting the single CSI-RS mode.
10. The apparatus of claim 9, further comprising a training unit configured to:
in the case of the two-segment CSI-RS mode,
acquiring current RSRP, CQI and terminal geographic position information;
determining a mode switching result according to a preset bandwidth occupation mode switching strategy, and establishing the association relation between the current RSRP, CQI, terminal geographic position information and the mode switching result as sample data;
and acquiring the machine learning model according to the sample data and a preset machine learning algorithm.
11. The apparatus of claim 10, wherein the training unit is configured to train an initial machine learning algorithm using the sample data to obtain the machine learning model; and updating the machine learning model which is trained by using the sample data which is newly acquired after the machine learning model is trained last time.
12. The apparatus of claim 10, wherein the predetermined bandwidth occupancy mode switching policy comprises:
in case the bandwidth occupation mode is to allow the NR terminal to schedule the full bandwidth resources,
if the current RSRP is greater than the preset first threshold value and the CQI corresponding to the current full bandwidth is less than the preset second threshold value, then
Determining a first rate of an NR terminal according to the CQI corresponding to the current full bandwidth and the Resource Blocks (RBs) allocated in the full bandwidth;
determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RB distributed in the NR exclusive frequency band;
and under the condition that the second rate is larger than or equal to a first preset proportion of the first rate, determining that the bandwidth occupation mode of the NR terminal is switched to be that the NR terminal only schedules NR exclusive frequency band resources.
13. The apparatus of claim 10 or 12, wherein the predetermined bandwidth occupancy mode switching policy comprises:
in case the bandwidth occupation mode is that the NR terminal schedules only NR exclusive band resources,
determining a second rate of the NR terminal according to CQI corresponding to the current NR exclusive frequency band and RB distributed in the NR exclusive frequency band;
determining a third rate of the NR terminal according to the CQI corresponding to the current full bandwidth and the RBs distributed in the full bandwidth;
and under the condition that the third rate is larger than or equal to a second preset proportion of the second rate, determining to switch the bandwidth occupation mode of the NR terminal to allow the NR terminal to schedule full bandwidth resources.
14. A terminal bandwidth scheduling apparatus, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of any of claims 1-7 based on instructions stored in the memory.
15. A computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any of claims 1 to 7.
16. A base station comprising the terminal bandwidth scheduling apparatus according to any one of claims 8 to 14.
CN202111298946.7A 2021-11-04 2021-11-04 Terminal bandwidth scheduling method, device and base station in dynamic spectrum sharing network Pending CN116095689A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117098082A (en) * 2023-10-18 2023-11-21 深圳国人无线通信有限公司 Method and system for 5G voice fallback 4G of DSS scene

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117098082A (en) * 2023-10-18 2023-11-21 深圳国人无线通信有限公司 Method and system for 5G voice fallback 4G of DSS scene
CN117098082B (en) * 2023-10-18 2024-02-09 深圳国人无线通信有限公司 Method and system for 5G voice fallback 4G of DSS scene

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