CN116865802A - Intelligent beam prediction method and device for deterministic trajectories - Google Patents

Intelligent beam prediction method and device for deterministic trajectories Download PDF

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Publication number
CN116865802A
CN116865802A CN202210286581.4A CN202210286581A CN116865802A CN 116865802 A CN116865802 A CN 116865802A CN 202210286581 A CN202210286581 A CN 202210286581A CN 116865802 A CN116865802 A CN 116865802A
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China
Prior art keywords
mobile terminal
projection position
speed
projection
data
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CN202210286581.4A
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Chinese (zh)
Inventor
孟帆
黄永明
尤肖虎
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Network Communication and Security Zijinshan Laboratory
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Network Communication and Security Zijinshan Laboratory
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Priority to CN202210286581.4A priority Critical patent/CN116865802A/en
Publication of CN116865802A publication Critical patent/CN116865802A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0802—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using antenna selection
    • H04B7/0834—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using antenna selection based on external parameters, e.g. subscriber speed or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0868—Hybrid systems, i.e. switching and combining
    • H04B7/088—Hybrid systems, i.e. switching and combining using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The embodiment of the application provides an intelligent beam prediction method and device for deterministic trajectories, wherein the method comprises the following steps: receiving a received pilot signal and a measurement signal fed back by the mobile terminal; the measurement signal is obtained by performing signal processing on a downlink signal by the mobile terminal; estimating the final observation time based on the projection position and speed of the mobile terminal receiving the pilot signal; estimating the projection position and speed of the mobile terminal based on the measurement signal at the final observation moment; determining the projection position and speed of the mobile terminal at the final observation time based on the projection position and speed of the mobile terminal receiving the pilot signal and the projection position and speed of the mobile terminal measuring the signal; based on the projection position and the speed of the mobile terminal at the final time of observation, the projection position of the mobile terminal at the predicted time in a future period of time is calculated, and then the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time is deduced, so that the beam prediction is realized.

Description

Intelligent beam prediction method and device for deterministic trajectories
Technical Field
The present application relates to the field of communications technologies, and in particular, to an intelligent beam prediction method and apparatus for deterministic trajectories.
Background
Millimeter wave multi-user Multiple Input Multiple Output (MIMO) wireless communication uses a large-scale antenna and a beam forming technology to solve the problem of path loss in a high frequency band, so that space division multiplexing is realized, and the frequency spectrum efficiency is improved. However, mobile scenarios under large-scale antennas, particularly high-speed railway scenarios, frequent beam alignment and tracking cause a significant amount of beam training overhead and significant instruction issuing delay. Therefore, reducing both beam training overhead and instruction issue latency is currently critical in mobile wireless communications. The existing beam management framework still has a large lifting space in the aspect of solving the problems, and the long-term and fine-time granularity beam prediction technology can greatly reduce the beam training overhead and avoid the time delay caused by the instruction issuing.
The method based on model driving in wireless communication has good theoretical guarantee and interpretability, but when facing a complex scene containing an undefined priori, the model driving method cannot effectively solve the technical problems of high beam training overhead and prolonged instruction issuing, and the performance is obviously reduced.
Disclosure of Invention
The embodiment of the application provides an intelligent beam prediction method and device for deterministic trajectories, which are used for solving the technical problems of beam training overhead and instruction issuing delay in beam alignment and tracking in the prior art.
In a first aspect, an embodiment of the present application provides an intelligent beam prediction method for deterministic trajectories, applied to a base station, including:
receiving a received pilot signal and a measurement signal fed back by the mobile terminal; the measurement signal is obtained by performing signal processing on a downlink signal by the mobile terminal;
estimating the final observation time based on the projection position and the projection speed of the mobile terminal receiving the pilot signal; estimating the projection position and speed of the mobile terminal based on the measurement signal at the final observation moment;
determining the projection position and speed of the mobile terminal at the final observation moment based on the projection position and speed of the mobile terminal receiving the pilot signal and the projection position and speed of the mobile terminal based on the measurement signal;
based on the projection position and the speed of the mobile terminal at the final observation time, the projection position of the mobile terminal at the predicted time in a future period is calculated, and then the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time is deduced, so that the beam prediction is realized.
In some embodiments, further comprising:
and receiving a nonlinear mapping model and a data fusion model downloaded by the network equipment.
In some embodiments, the received pilot signal includes all beams in the horizontal direction.
In some embodiments, the measurement signal includes doppler frequency and time of arrival ToA.
In some embodiments, the received pilot signal and the measurement signal are both transmitted at equal time intervals.
In some embodiments, the estimated observation final time is based on a projected position and a velocity of the mobile terminal receiving the pilot signal, comprising:
and outputting a first estimated value set for estimating the final moment of observation based on the projection position and the projection speed of the received pilot signal according to the maximum likelihood criterion.
In some embodiments, the estimating the final moment of observation is based on the projected position and velocity of the mobile terminal of the measurement signal, comprising:
and outputting a second estimated value set for estimating the projection position and the speed of the mobile terminal at the final moment of observation based on the measurement signals according to the maximum likelihood criterion.
In some embodiments, the determining the projection position and velocity of the mobile terminal at the final time of observation based on the projection position and velocity of the mobile terminal based on the received pilot signal and the projection position and velocity of the mobile terminal based on the measurement signal includes:
Based on the first set of estimate values and the second set of estimate values, a final set of estimate values of projection position and velocity is determined.
In some embodiments, the calculating the projection position of the mobile terminal at the predicted time in a future period based on the projection position and the speed of the mobile terminal at the final observation time, and further deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time includes:
calculating the projection position of the mobile terminal at the predicted moment in a future period of time based on the projection position and the speed of the mobile terminal at the final observation moment, and further calculating the projection distance;
calculating a horizontal arrival angle and a departure angle based on the projection position and the projection distance of the mobile terminal at the predicted moment;
and deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted moment based on the horizontal arrival angle and the departure angle.
In some embodiments, further comprising:
and sending the analog precoding of the receiving end of the mobile terminal to the mobile terminal.
In a second aspect, an embodiment of the present application provides an intelligent beam prediction method for deterministic trajectories, applied to a network device, including:
Acquiring training data;
training a nonlinear mapping model and a data fusion model based on the training data;
downloading the trained nonlinear mapping model and the trained data fusion model to a base station; the nonlinear mapping model is used for estimating the projection position and the projection speed of the mobile terminal based on the received pilot signals and the measurement signals; the data fusion model is used for fusing the projection position and the projection speed of the mobile terminal based on the received pilot signal and the measurement signal, determining the projection position and the projection speed of the mobile terminal at the final observation moment, and further realizing beam prediction.
In some embodiments, the acquiring training data includes:
acquiring data fusion data sent by a base station, and storing the data fusion data in a data warehouse module;
and obtaining nonlinear module data through geometric measurement, and storing the nonlinear module data in a data warehouse module.
In some embodiments, training the nonlinear mapping model and the data fusion model based on the training data comprises:
training a data fusion model based on the data fusion data;
and training a nonlinear mapping model based on the nonlinear module data.
In a third aspect, an embodiment of the present application provides a base station, including a memory, a transceiver, and a processor;
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the intelligent beam prediction method for deterministic trajectories as described in the first aspect above.
In a fourth aspect, an embodiment of the present application provides a network device, including a memory, a transceiver, and a processor;
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the intelligent beam prediction method for deterministic trajectories as described in the second aspect above.
In a fifth aspect, an embodiment of the present application provides an intelligent beam prediction apparatus for deterministic trajectories, including:
the receiving and transmitting module is used for receiving a received pilot signal and a measurement signal fed back by the mobile terminal; the measurement signal is obtained by performing signal processing on a downlink signal by the mobile terminal;
the nonlinear mapping module is used for estimating the projection position and the speed of the mobile terminal which receives the pilot signal at the final observation moment; estimating the projection position and speed of the mobile terminal based on the measurement signal at the final observation moment;
The data fusion module is used for determining the projection position and the projection speed of the mobile terminal at the final observation moment based on the projection position and the projection speed of the mobile terminal based on the pilot signal and the projection position and the projection speed of the mobile terminal based on the measurement signal;
and the analog precoding module is used for calculating the projection position of the mobile terminal at the predicted moment in a period of time in the future based on the projection position and the speed of the mobile terminal at the final observation moment, and further deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted moment to realize beam prediction.
In a sixth aspect, an embodiment of the present application provides an intelligent beam prediction apparatus for deterministic trajectories, including:
the acquisition module is used for acquiring training data;
the training module is used for training the nonlinear mapping model and the data fusion model based on the training data;
the sending module is used for downloading the trained nonlinear mapping model and the trained data fusion model to the base station; the nonlinear mapping model is used for estimating the projection position and the projection speed of the mobile terminal based on the received pilot signals and the measurement signals; the data fusion model is used for fusing the projection position and the projection speed of the mobile terminal based on the received pilot signal and the measurement signal, determining the projection position and the projection speed of the mobile terminal at the final observation moment, and further realizing beam prediction.
In a seventh aspect, embodiments of the present application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the intelligent beam prediction method for deterministic trajectories as described in the first or second aspect above.
In an eighth aspect, embodiments of the present application further provide a computer-readable storage medium storing a computer program for causing a computer to perform the steps of the intelligent beam prediction method for deterministic trajectories according to the first or second aspect as described above.
In a ninth aspect, embodiments of the present application further provide a communication device readable storage medium storing a computer program for causing a communication device to perform the steps of the intelligent beam prediction method for deterministic trajectories as described in the first or second aspect above.
In a tenth aspect, embodiments of the present application also provide a chip-product-readable storage medium storing a computer program for causing a chip product to perform the steps of the intelligent beam prediction method for deterministic trajectories as described in the first or second aspect above.
The intelligent beam prediction method and the intelligent beam prediction device for the deterministic track provided by the embodiment of the application are driven by the model and the data cooperatively, are beneficial to reducing the beam training overhead and the instruction issuing time delay in beam alignment and tracking, and improve the frequency spectrum efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is one of the flow diagrams of the intelligent beam prediction method for deterministic trajectories provided by an embodiment of the present application;
FIG. 2 is a flow diagram of an intelligent beam prediction system for deterministic trajectories provided by an embodiment of the present application;
FIG. 3 is a second flow chart of an intelligent beam prediction method for deterministic trajectories according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a base station according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a network device according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an intelligent beam prediction apparatus for deterministic trajectories according to an embodiment of the present application;
fig. 7 is a schematic diagram of a second embodiment of an intelligent beam prediction apparatus for deterministic trajectories according to the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 1 is one of flow diagrams of an intelligent beam prediction method for deterministic trajectories according to an embodiment of the present application, as shown in fig. 1, an embodiment of the present application provides an intelligent beam prediction method for deterministic trajectories, an execution subject of which may be a base station, and the method includes:
step 101, receiving a received pilot signal and a measurement signal fed back by a mobile terminal; the measurement signal is obtained by the mobile terminal performing signal processing on the downlink signal.
In some embodiments, further comprising:
and receiving a nonlinear mapping model and a data fusion model downloaded by the network equipment.
In some embodiments, the received pilot signal includes all beams in the horizontal direction.
In some embodiments, the measurement signal includes doppler frequency and time of arrival ToA.
In some embodiments, the received pilot signal and the measurement signal are both transmitted at equal time intervals.
102, estimating the projection position and speed of the mobile terminal at the final observation moment based on the received pilot signal; and estimates a projection position and a speed of the mobile terminal at the final observation time based on the measurement signal.
In some embodiments, estimating the final time of observation based on the projected position and velocity of the mobile terminal receiving the pilot signal includes:
according to a maximum likelihood criterion, a first set of estimated values is output that estimate the final moment of observation based on the projected position and velocity of the received pilot signal.
In some embodiments, estimating the projected position and velocity of the mobile terminal at the final point in time of observation based on the measurement signal includes:
and outputting a second estimated value set for estimating the projection position and the speed of the mobile terminal at the final moment of observation based on the measurement signals according to the maximum likelihood criterion.
In some embodiments, determining the projected position and velocity of the mobile terminal at the final time of observation based on the projected position and velocity of the mobile terminal based on the received pilot signal and based on the projected position and velocity of the mobile terminal of the measurement signal comprises:
based on the first set of estimates and the second set of estimates, a final set of estimates of projection position and velocity is determined.
Step 103, determining the projection position and speed of the mobile terminal at the final time of observation based on the projection position and speed of the mobile terminal receiving the pilot signal and the projection position and speed of the mobile terminal measuring the signal.
In some embodiments, after determining the projected position and velocity of the mobile terminal based on the received pilot signal and the projected position and velocity of the mobile terminal based on the measured signal, a data fusion module in the base station estimates the final projected position and velocity of the mobile terminal based on the pilot signal and the measured signal.
And 104, calculating the projection position of the mobile terminal at the predicted time in a future period based on the projection position and the speed of the mobile terminal at the final time, and further deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time to realize beam prediction.
In some embodiments, based on the projection position and the speed of the mobile terminal at the final time of observation, the projection position of the mobile terminal at the predicted time is calculated in a future period of time, and then the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time is deduced, including:
calculating the projection position of the mobile terminal at the predicted moment in a future period of time based on the projection position and the speed of the mobile terminal at the final observation moment, and further calculating the projection distance;
calculating a horizontal arrival angle and a departure angle based on the projection position and the projection distance of the mobile terminal at the predicted moment;
and deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted moment based on the horizontal arrival angle and the departure angle.
In some embodiments, further comprising:
and sending the analog precoding of the receiving end of the mobile terminal to the mobile terminal.
Specifically, fig. 2 is a schematic flow chart of an intelligent beam prediction system for deterministic trajectories according to an embodiment of the present application, and as shown in fig. 2, the intelligent beam prediction system for deterministic trajectories according to an embodiment of the present application includes a base station, a plurality of mobile terminals, and a network device (may also be referred to as a "wireless network intelligent control platform").
The base station comprises a base station receiving and transmitting end, a parameter estimation module, a data fusion module, a nonlinear mapping module and an analog precoding module. The mobile terminal comprises a mobile terminal receiving and transmitting end and a signal processing module. The wireless network intelligent control platform comprises a data warehouse module and an Artificial Intelligence (AI) model training module. And the plurality of mobile terminals do nonlinear deterministic track motion in the service range of the base station.
A data warehouse module in the wireless network intelligent control platform accumulates training data uploaded by the base station and is used for training a data fusion model; the training data of the geometric measurements are accumulated for training the nonlinear mapping model.
The AI model training module in the wireless network intelligent control platform comprises a data fusion model and a nonlinear mapping model, and the model training is realized by using the data of the data warehouse module in an offline training stage; and the wireless network intelligent control platform downloads the trained data fusion model and the nonlinear mapping model to the base station.
A signal processing module in the mobile terminal estimates Doppler (Doppler) frequency and time to reach ToA and uploads the base station through a transceiver end of the mobile terminal.
The transceiver in the mobile terminal transmits the estimated Doppler frequency, time arrival ToA and received pilot signal in the uplink and receives the mobile terminal analog precoding transmitted by the base station in the downlink.
The base station is used for transmitting a group of pilot signals to each mobile terminal in the observation stage, the mobile terminal is used for receiving the group of pilot signals transmitted by the base station, the mobile terminal obtains measurement signals through signal processing, and the pilot signals and the measurement signals are fed back to the base station.
The transceiver end in the base station is used for receiving the feedback pilot signal and the measurement signal.
A parameter estimation module in the base station, which uses a nonlinear mapping module to estimate the projection position and speed of the mobile terminal based on the pilot signal and the measurement signal; the base station uploads the projection position and speed of the mobile terminal estimated based on the pilot signal and the measurement signal to a data warehouse module in the wireless network intelligent control platform, and tag data.
The data fusion module in the base station estimates the final projection position and speed of the mobile terminal according to the projection position and speed of the mobile terminal based on the pilot signal and the measurement signal.
And the analog precoding module in the base station predicts the optimal beam code word in a future period of time according to the final projection position and speed of the mobile terminal and the nonlinear mapping module, namely, the analog precoding of the transmitting end of the base station and the analog precoding of the receiving end of the mobile terminal, so as to realize beam prediction.
The base station transmits signals using base station transmitting end analog precoding, and the mobile terminal receives signals using mobile terminal receiving end analog precoding.
The intelligent beam prediction method for deterministic trajectories provided by the embodiment of the application is driven by the model and the data cooperatively, is beneficial to reducing the beam training overhead and the instruction issuing time delay in beam alignment and tracking, and improves the frequency spectrum efficiency.
Fig. 3 is a second flowchart of an intelligent beam prediction method for deterministic trajectories according to an embodiment of the present application, as shown in fig. 3, where an execution body of the intelligent beam prediction method for deterministic trajectories may be a network device, for example, a wireless network intelligent control platform, and includes:
step 301, acquiring training data.
In some embodiments, obtaining training data includes:
acquiring data fusion data sent by a base station and storing the data fusion data in a data warehouse module;
nonlinear module data is obtained by geometric measurements and stored in a data warehouse module.
Step 302, training the nonlinear mapping model and the data fusion model based on training data.
In some embodiments, training the nonlinear mapping model and the data fusion model based on the training data includes:
training a data fusion model based on the data fusion data;
a nonlinear mapping model is trained based on the nonlinear module data.
And 303, downloading the trained nonlinear mapping model and the trained data fusion model to the base station.
In some embodiments, a nonlinear mapping model is used to estimate the projected position and velocity of a mobile terminal based on received pilot signals and based on measurement signals; the data fusion model is used for fusing the projection position and the projection speed of the mobile terminal based on the received pilot signal and the measurement signal, determining the projection position and the projection speed of the mobile terminal at the final observation moment, and further realizing beam prediction.
Specifically, as shown in fig. 2, the wireless network intelligent control platform obtains data fusion data from the base station through the process 1a, and stores the data fusion data in the data warehouse module.
The wireless network intelligent control platform obtains nonlinear module data (track data and the like) from the geometric measurement module through the process 1b, and stores the nonlinear module data in the data warehouse module.
The wireless network intelligent control platform trains the data fusion model through the data fusion data collected in the process 1c, and the trained data fusion model is downloaded to the data fusion module of the base station through the process 1 d.
The wireless network intelligent control platform trains the nonlinear mapping model through nonlinear module data collected in the process 1e, and the trained nonlinear mapping model is downloaded to the nonlinear module of the base station through the process 1 f.
The intelligent beam prediction method for deterministic trajectories provided by the embodiment of the application is driven by the model and the data cooperatively, is beneficial to reducing the beam training overhead and the instruction issuing time delay in beam alignment and tracking, and improves the frequency spectrum efficiency.
Fig. 4 is a schematic structural diagram of a network device according to an embodiment of the present application, as shown in fig. 4, where the network device includes a memory 420, a transceiver 400, and a processor 410, where:
a memory 420 for storing a computer program; a transceiver 400 for transceiving data under the control of the processor 410; a processor 410 for reading the computer program in the memory 420 and performing the following operations:
receiving a received pilot signal and a measurement signal fed back by the mobile terminal; the measurement signal is obtained by performing signal processing on a downlink signal by the mobile terminal;
estimating the final observation time based on the projection position and the projection speed of the mobile terminal receiving the pilot signal; estimating the projection position and speed of the mobile terminal based on the measurement signal at the final observation moment;
Determining the projection position and speed of the mobile terminal at the final observation moment based on the projection position and speed of the mobile terminal receiving the pilot signal and the projection position and speed of the mobile terminal based on the measurement signal;
based on the projection position and the speed of the mobile terminal at the final observation time, the projection position of the mobile terminal at the predicted time in a future period is calculated, and then the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time is deduced, so that the beam prediction is realized.
Specifically, the transceiver 400 is configured to receive and transmit data under the control of the processor 410.
Wherein in fig. 4, a bus architecture may comprise any number of interconnected buses and bridges, and in particular one or more processors represented by processor 410 and various circuits of memory represented by memory 420, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. Transceiver 400 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc. The processor 410 is responsible for managing the bus architecture and general processing, and the memory 420 may store data used by the processor 410 in performing operations.
The processor 410 may be a Central Processing Unit (CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or a complex programmable logic device (Complex Programmable Logic Device, CPLD), or it may employ a multi-core architecture.
In some embodiments, further comprising:
and receiving a nonlinear mapping model and a data fusion model downloaded by the network equipment.
In some embodiments, the received pilot signal includes all beams in the horizontal direction.
In some embodiments, the measurement signal includes doppler frequency and time of arrival ToA.
In some embodiments, the received pilot signal and the measurement signal are both transmitted at equal time intervals.
In some embodiments, the estimated observation final time is based on a projected position and a velocity of the mobile terminal receiving the pilot signal, comprising:
and outputting a first estimated value set for estimating the final moment of observation based on the projection position and the projection speed of the received pilot signal according to the maximum likelihood criterion.
In some embodiments, the estimating the final moment of observation is based on the projected position and velocity of the mobile terminal of the measurement signal, comprising:
And outputting a second estimated value set for estimating the projection position and the speed of the mobile terminal at the final moment of observation based on the measurement signals according to the maximum likelihood criterion.
In some embodiments, the determining the projection position and velocity of the mobile terminal at the final time of observation based on the projection position and velocity of the mobile terminal based on the received pilot signal and the projection position and velocity of the mobile terminal based on the measurement signal includes:
based on the first set of estimate values and the second set of estimate values, a final set of estimate values of projection position and velocity is determined.
In some embodiments, the calculating the projection position of the mobile terminal at the predicted time in a future period based on the projection position and the speed of the mobile terminal at the final observation time, and further deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time includes:
calculating the projection position of the mobile terminal at the predicted moment in a future period of time based on the projection position and the speed of the mobile terminal at the final observation moment, and further calculating the projection distance;
calculating a horizontal arrival angle and a departure angle based on the projection position and the projection distance of the mobile terminal at the predicted moment;
And deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted moment based on the horizontal arrival angle and the departure angle.
In some embodiments, further comprising:
and sending the analog precoding of the receiving end of the mobile terminal to the mobile terminal.
Specifically, the base station provided by the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is a base station, and can achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.
Fig. 5 is a schematic structural diagram of a network device according to an embodiment of the present application, as shown in fig. 5, where the network device includes a memory 520, a transceiver 500, and a processor 510, where:
a memory 520 for storing a computer program; a transceiver 500 for transceiving data under the control of the processor 510; a processor 510 for reading the computer program in the memory 520 and performing the following operations:
acquiring training data;
training a nonlinear mapping model and a data fusion model based on the training data;
downloading the trained nonlinear mapping model and the trained data fusion model to a base station; the nonlinear mapping model is used for estimating the projection position and the projection speed of the mobile terminal based on the received pilot signals and the measurement signals; the data fusion model is used for fusing the projection position and the projection speed of the mobile terminal based on the received pilot signal and the measurement signal, determining the projection position and the projection speed of the mobile terminal at the final observation moment, and further realizing beam prediction.
Specifically, the transceiver 500 is used to receive and transmit data under the control of the processor 510.
Where in FIG. 5, a bus architecture may comprise any number of interconnected buses and bridges, with various circuits of the one or more processors, as represented by processor 510, and the memory, as represented by memory 520, being linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. Transceiver 500 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc. The processor 510 is responsible for managing the bus architecture and general processing, and the memory 520 may store data used by the processor 510 in performing operations.
The processor 510 may be a Central Processing Unit (CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or a complex programmable logic device (Complex Programmable Logic Device, CPLD), and may also employ a multi-core architecture.
In some embodiments, the acquiring training data includes:
acquiring data fusion data sent by a base station, and storing the data fusion data in a data warehouse module;
and obtaining nonlinear module data through geometric measurement, and storing the nonlinear module data in a data warehouse module.
In some embodiments, training the nonlinear mapping model and the data fusion model based on the training data comprises:
training a data fusion model based on the data fusion data;
and training a nonlinear mapping model based on the nonlinear module data.
Specifically, the network device provided by the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is a network device, and can achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.
Fig. 6 is one of schematic structural diagrams of an intelligent beam prediction apparatus for deterministic trajectories according to an embodiment of the present application, and as shown in fig. 6, an intelligent beam prediction apparatus for deterministic trajectories according to an embodiment of the present application includes a transceiver module 601, a nonlinear mapping module 602, a data fusion module 603, and an analog precoding module 604, where:
The transceiver module 601 is configured to receive a received pilot signal and a measurement signal fed back by the mobile terminal; the measurement signal is obtained by performing signal processing on a downlink signal by the mobile terminal; the nonlinear mapping module 602 is configured to estimate a projection position and a projection speed of the mobile terminal based on the received pilot signal at a final observation time; estimating the projection position and speed of the mobile terminal based on the measurement signal at the final observation moment; the data fusion module 603 is configured to determine a projection position and a projection speed of the mobile terminal at a final time of observation based on the projection position and the projection speed of the mobile terminal based on the received pilot signal and the projection position and the projection speed of the mobile terminal based on the measurement signal; the analog pre-coding module 604 is configured to calculate, based on the projection position and the speed of the mobile terminal at the final observation time, the projection position of the mobile terminal at the predicted time within a period of time in the future, and further infer analog pre-coding of the base station transmitting end and the mobile terminal receiving end at the predicted time, so as to implement beam prediction.
Specifically, the intelligent beam prediction device for deterministic trajectories provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is a base station, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in the embodiment are omitted.
Fig. 7 is a second schematic structural diagram of an intelligent beam prediction apparatus for deterministic trajectories according to an embodiment of the present application, as shown in fig. 7, the embodiment of the present application provides an intelligent beam prediction apparatus for deterministic trajectories, which includes an acquisition module 701, a training module 702, and a transmission module 703, wherein:
the acquisition module 701 is configured to acquire training data; training module 702 is configured to train the nonlinear mapping model and the data fusion model based on the training data; the sending module 703 is configured to download the trained nonlinear mapping model and the data fusion model to the base station; the nonlinear mapping model is used for estimating the projection position and the projection speed of the mobile terminal based on the received pilot signals and the measurement signals; the data fusion model is used for fusing the projection position and the projection speed of the mobile terminal based on the received pilot signal and the measurement signal, determining the projection position and the projection speed of the mobile terminal at the final observation moment, and further realizing beam prediction.
Specifically, the intelligent beam prediction apparatus for deterministic trajectories provided in the embodiments of the present application can implement all the method steps implemented by the method embodiments in which the execution body is a network device, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiments in the embodiments are omitted herein.
It should be noted that the division of the units/modules in the above embodiments of the present application is merely a logic function division, and other division manners may be implemented in practice. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In some embodiments, there is also provided a computer readable storage medium storing a computer program for causing a computer to perform the steps of the intelligent beam prediction method for deterministic trajectories provided by the above-described method embodiments.
Specifically, the computer readable storage medium provided by the embodiment of the present application can implement all the method steps implemented by the above method embodiments and achieve the same technical effects, and the parts and beneficial effects that are the same as those of the method embodiments in this embodiment are not described in detail herein.
It should be noted that: the computer readable storage medium may be any available medium or data storage device that can be accessed by a processor including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (e.g., ROM, EPROM, EEPROM, nonvolatile memory (NAND FLASH), solid State Disk (SSD)), etc.
In addition, it should be noted that: the terms "first," "second," and the like in embodiments of the present application are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the "first" and "second" distinguishing between objects generally are not limited in number to the extent that the first object may, for example, be one or more.
In the embodiment of the application, the term "and/or" describes the association relation of the association objects, which means that three relations can exist, for example, a and/or B can be expressed as follows: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The term "plurality" in embodiments of the present application means two or more, and other adjectives are similar.
The technical scheme provided by the embodiment of the application can be suitable for various systems, in particular to a 5G system. For example, suitable systems may be global system for mobile communications (global system of mobile communication, GSM), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) universal packet Radio service (general packet Radio service, GPRS), long term evolution (long term evolution, LTE), LTE frequency division duplex (frequency division duplex, FDD), LTE time division duplex (time division duplex, TDD), long term evolution-advanced (long term evolution advanced, LTE-a), universal mobile system (universal mobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX), 5G New air interface (New Radio, NR), and the like. Terminal devices and network devices are included in these various systems. Core network parts such as evolved packet system (Evloved Packet System, EPS), 5G system (5 GS) etc. may also be included in the system.
The terminal device according to the embodiment of the present application may be a device that provides voice and/or data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem, etc. The names of the terminal devices may also be different in different systems, for example in a 5G system, the terminal devices may be referred to as User Equipment (UE). The wireless terminal device may communicate with one or more Core Networks (CNs) via a radio access Network (Radio Access Network, RAN), which may be mobile terminal devices such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, e.g., portable, pocket, hand-held, computer-built-in or vehicle-mounted mobile devices that exchange voice and/or data with the radio access Network. Such as personal communication services (Personal Communication Service, PCS) phones, cordless phones, session initiation protocol (Session Initiated Protocol, SIP) phones, wireless local loop (Wireless Local Loop, WLL) stations, personal digital assistants (Personal Digital Assistant, PDAs), and the like. The wireless terminal device may also be referred to as a system, subscriber unit (subscriber unit), subscriber station (subscriber station), mobile station (mobile), remote station (remote station), access point (access point), remote terminal device (remote terminal), access terminal device (access terminal), user terminal device (user terminal), user agent (user agent), user equipment (user device), and embodiments of the present application are not limited in this respect.
The network device according to the embodiment of the present application may be a base station, where the base station may include a plurality of cells for providing services for the terminal. A base station may also be called an access point or may be a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or other names, depending on the particular application. The network device may be operable to exchange received air frames with internet protocol (Internet Protocol, IP) packets as a router between the wireless terminal device and the rest of the access network, which may include an Internet Protocol (IP) communication network. The network device may also coordinate attribute management for the air interface. For example, the network device according to the embodiment of the present application may be a network device (Base Transceiver Station, BTS) in a global system for mobile communications (Global System for Mobile communications, GSM) or code division multiple access (Code Division Multiple Access, CDMA), a network device (NodeB) in a wideband code division multiple access (Wide-band Code Division Multiple Access, WCDMA), an evolved network device (evolutional Node B, eNB or e-NodeB) in a long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), a home evolved base station (Home evolved Node B, heNB), a relay node (relay node), a home base station (femto), a pico base station (pico), etc., which are not limited in the embodiment of the present application. In some network structures, the network device may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, which may also be geographically separated.
Multiple-input Multiple-output (Multi Input Multi Output, MIMO) transmissions may each be made between a network device and a terminal device using one or more antennas, and the MIMO transmissions may be Single User MIMO (SU-MIMO) or Multiple User MIMO (MU-MIMO). The MIMO transmission may be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, or may be diversity transmission, precoding transmission, beamforming transmission, or the like, depending on the form and number of the root antenna combinations.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. 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-executable instructions. These computer-executable 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 processor-executable instructions may also be stored in a processor-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 processor-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 processor-executable 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.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (18)

1. An intelligent beam prediction method for deterministic trajectories, applied to a base station, comprising:
receiving a received pilot signal and a measurement signal fed back by the mobile terminal; the measurement signal is obtained by performing signal processing on a downlink signal by the mobile terminal;
estimating the final observation time based on the projection position and the projection speed of the mobile terminal receiving the pilot signal; estimating the projection position and speed of the mobile terminal based on the measurement signal at the final observation moment;
determining the projection position and speed of the mobile terminal at the final observation moment based on the projection position and speed of the mobile terminal receiving the pilot signal and the projection position and speed of the mobile terminal based on the measurement signal;
based on the projection position and the speed of the mobile terminal at the final observation time, the projection position of the mobile terminal at the predicted time in a future period is calculated, and then the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time is deduced, so that the beam prediction is realized.
2. The intelligent beam prediction method for deterministic trajectories according to claim 1, further comprising:
And receiving a nonlinear mapping model and a data fusion model downloaded by the network equipment.
3. The intelligent beam prediction method for deterministic trajectories according to claim 1, wherein the received pilot signal comprises all beams in the horizontal direction.
4. The intelligent beam prediction method for deterministic trajectories according to claim 1, wherein the measurement signal comprises doppler frequency and time of arrival ToA.
5. The intelligent beam prediction method for deterministic trajectories according to claim 1, wherein the received pilot signal and the measurement signal are both transmitted at equal time intervals.
6. The intelligent beam prediction method for deterministic trajectories according to claim 1, wherein the estimated observation final instants are based on projected positions and velocities of the mobile terminals receiving pilot signals, comprising:
and outputting a first estimated value set for estimating the final moment of observation based on the projection position and the projection speed of the received pilot signal according to the maximum likelihood criterion.
7. The intelligent beam prediction method for deterministic trajectories according to claim 6, wherein the estimated observation final instants are based on projected positions and velocities of mobile terminals of the measurement signals, comprising:
And outputting a second estimated value set for estimating the projection position and the speed of the mobile terminal at the final moment of observation based on the measurement signals according to the maximum likelihood criterion.
8. The intelligent beam prediction method for deterministic trajectories according to claim 7, wherein said determining the projected position and velocity of the mobile terminal at the final point of observation based on the projected position and velocity of the mobile terminal based on the received pilot signal and the projected position and velocity of the mobile terminal based on the measurement signal comprises:
based on the first set of estimate values and the second set of estimate values, a final set of estimate values of projection position and velocity is determined.
9. The intelligent beam prediction method for deterministic trajectories according to claim 1, wherein said calculating the projected position of the mobile terminal at the predicted time in a future period based on the projected position and velocity of the mobile terminal at the final time of observation, further deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted time, comprises:
calculating the projection position of the mobile terminal at the predicted moment in a future period of time based on the projection position and the speed of the mobile terminal at the final observation moment, and further calculating the projection distance;
Calculating a horizontal arrival angle and a departure angle based on the projection position and the projection distance of the mobile terminal at the predicted moment;
and deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted moment based on the horizontal arrival angle and the departure angle.
10. The intelligent beam prediction method for deterministic trajectories according to claim 1, further comprising:
and sending the analog precoding of the receiving end of the mobile terminal to the mobile terminal.
11. An intelligent beam prediction method for deterministic trajectories, applied to a network device, comprising:
acquiring training data;
training a nonlinear mapping model and a data fusion model based on the training data;
downloading the trained nonlinear mapping model and the trained data fusion model to a base station; the nonlinear mapping model is used for estimating the projection position and the projection speed of the mobile terminal based on the received pilot signals and the measurement signals; the data fusion model is used for fusing the projection position and the projection speed of the mobile terminal based on the received pilot signal and the measurement signal, determining the projection position and the projection speed of the mobile terminal at the final observation moment, and further realizing beam prediction.
12. The intelligent beam prediction method for deterministic trajectories according to claim 11, wherein the acquiring training data comprises:
acquiring data fusion data sent by a base station, and storing the data fusion data in a data warehouse module;
and obtaining nonlinear module data through geometric measurement, and storing the nonlinear module data in a data warehouse module.
13. The intelligent beam prediction method for deterministic trajectories according to claim 12, wherein training a nonlinear mapping model and a data fusion model based on the training data comprises:
training a data fusion model based on the data fusion data;
and training a nonlinear mapping model based on the nonlinear module data.
14. A base station comprising a memory, a transceiver, and a processor;
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the intelligent beam prediction method for deterministic trajectories according to any of claims 1-10.
15. A network device comprising a memory, a transceiver, and a processor;
A memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the intelligent beam prediction method for deterministic trajectories according to any of claims 11-13.
16. An intelligent beam prediction apparatus for deterministic trajectories, comprising:
the receiving and transmitting module is used for receiving a received pilot signal and a measurement signal fed back by the mobile terminal; the measurement signal is obtained by performing signal processing on a downlink signal by the mobile terminal;
the nonlinear mapping module is used for estimating the projection position and the speed of the mobile terminal which receives the pilot signal at the final observation moment; estimating the projection position and speed of the mobile terminal based on the measurement signal at the final observation moment;
the data fusion module is used for determining the projection position and the projection speed of the mobile terminal at the final observation moment based on the projection position and the projection speed of the mobile terminal based on the pilot signal and the projection position and the projection speed of the mobile terminal based on the measurement signal;
and the analog precoding module is used for calculating the projection position of the mobile terminal at the predicted moment in a period of time in the future based on the projection position and the speed of the mobile terminal at the final observation moment, and further deducing the analog precoding of the base station transmitting end and the mobile terminal receiving end at the predicted moment to realize beam prediction.
17. An intelligent beam prediction apparatus for deterministic trajectories, comprising:
the acquisition module is used for acquiring training data;
the training module is used for training the nonlinear mapping model and the data fusion model based on the training data;
the sending module is used for downloading the trained nonlinear mapping model and the trained data fusion model to the base station; the nonlinear mapping model is used for estimating the projection position and the projection speed of the mobile terminal based on the received pilot signals and the measurement signals; the data fusion model is used for fusing the projection position and the projection speed of the mobile terminal based on the received pilot signal and the measurement signal, determining the projection position and the projection speed of the mobile terminal at the final observation moment, and further realizing beam prediction.
18. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for causing a computer to execute the intelligent beam prediction method for deterministic trajectories according to any one of claims 1 to 13.
CN202210286581.4A 2022-03-22 2022-03-22 Intelligent beam prediction method and device for deterministic trajectories Pending CN116865802A (en)

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