CN116527454A - Channel estimation method and device based on speech pilot frequency - Google Patents
Channel estimation method and device based on speech pilot frequency Download PDFInfo
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Abstract
The invention provides a channel estimation method and a device based on a speech pilot frequency, comprising the following steps: the method comprises the steps that a sending end sends a voice pilot signal to a receiving end to be subjected to voice function calibration, wherein the voice pilot signal contains original voice function information of the receiving end; the receiving end tests the corresponding language functions according to the language pilot signals, generates test results and feeds the test results back to the sending end; the sending end compares the test result with the original language function information of the receiving end, and calculates the signal deformation condition of the language layer between the sending end and the receiving end by using a preset deviation function; if the signal deformation condition is within a preset acceptable range, the sending end and the receiving end carry out voice communication; otherwise, no verbal communication is performed. The method provided by the invention uses the pilot signal to calibrate the function level of the receiving end in language, thereby realizing the channel estimation of the function level of the language.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and apparatus for channel estimation based on speech pilot.
Background
As is well known, three major application scenarios of 5G are: 1. enhanced mobile broadband (emmbb); 2. large-scale internet of things (MIoT); 3. low latency high reliability (URLLC), such as unmanned, industrial automation, and the like. The application scenes all show great demands on communication speed and communication data volume, but the prior communication technology approaches to the capacity limit of Shannon physical layer, and in the era of everything interconnection, 5G can not bear the great demands on communication speed and throughput gradually, so the communication field starts to explore the source of information, and the pressure in the communication process is hoped to be relieved by reducing the amount of information required to be transmitted. On the other hand, in modern communications, artificial intelligence is tightly coupled with communications, and more object-oriented functions are required for artificial intelligence than a less complex bit stream, which also provides a heuristic for reducing the amount of data from a source.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and apparatus for channel estimation based on an speech pilot, so as to eliminate or improve one or more drawbacks existing in the prior art, and solve the problem that the existing communication technology cannot meet the huge demands of communication rate and throughput and neglect speech functions.
In one aspect, the present invention provides a channel estimation method based on a voice pilot, where the voice pilot is set in each node of a smart network, and according to a channel estimation requirement, the corresponding node in the smart network is used as a transmitting end and a receiving end, and the method is executed at the transmitting end, and includes the following steps:
transmitting a voice pilot signal to a receiving end to be subjected to voice function calibration, wherein the voice pilot signal contains original voice function information of the receiving end;
receiving a test result fed back by the receiving end, wherein the test result is obtained by the receiving end through testing the corresponding language function according to the language pilot signal;
comparing the test result with original language function information of the receiving end, and calculating signal deformation conditions of a language layer between the transmitting end and the receiving end by using a preset deviation function;
if the signal deformation condition is within a preset acceptable range, carrying out voice communication with the receiving end; otherwise, no verbal communication is performed.
In some embodiments of the present invention, before sending the spoken pilot signal to the receiving end to be subjected to the spoken function calibration, the method further comprises:
and based on the RIP protocol updated by the routing table, each node in the intelligent network shares own language function information so as to obtain the original language function information of the receiving end.
In some embodiments of the present invention, after the signal deformation condition is within a preset acceptable range, the method further includes:
re-transmitting the voice pilot signal to the receiving end every preset time period to monitor the channel state;
and stopping communication with the receiving end if the signal deformation condition obtained in the monitoring period exceeds the preset acceptable range.
In some embodiments of the present invention, only one transmitting end is included, and the method further includes:
transmitting a plurality of language pilot signals to the receiving end so as to calibrate the functions of the receiving end in a plurality of languages;
numbering the pilot signals for each language, and marking the priority, wherein the receiving end tests according to the priority of the pilot signals for each language.
In some embodiments of the present invention, a plurality of sending ends are included, and the method further includes:
multiplexing is carried out when a plurality of voice pilot signals are sent to the receiving end so as to reduce interference among the voice pilot signals; the multiplexing process at least comprises one or more of time division multiplexing, frequency division multiplexing and space division multiplexing.
In some embodiments of the present invention, before sending the spoken pilot signal to the receiving end to be subjected to the spoken function calibration, the method further comprises:
transmitting a detection signal to the receiving end to detect software and hardware environment information of the receiving end; the software and hardware environment information at least comprises hardware information, software information for realizing the language function and readable data types.
In some embodiments of the present invention, a plurality of receiving ends are included, and the method further includes:
broadcasting and transmitting the voice pilot signals to a plurality of receiving ends;
and counting network nodes with corresponding language functions in the broadcasting range so as to realize reasonable resource allocation.
In some embodiments of the present invention, when sending the spoken pilot signal to the receiving end to be subjected to the spoken function calibration, the method further includes:
and if the voice pilot signal is transmitted, the voice pilot signal is only forwarded by other nodes in the intelligent network.
In some embodiments of the present invention, only one transmitting end and one receiving end are included, and the method further includes:
the method comprises the steps of sending the same language pilot signals for calibrating the functions of the same language to the receiving end for a plurality of times in a preset short time;
setting a preset protocol according to an application scene, and performing channel estimation processing on the voice pilot signal by the receiving end according to the preset protocol; the preset protocol at least comprises the steps of carrying out channel estimation processing on only one voice pilot signal and carrying out channel estimation processing on each voice pilot signal.
In another aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the steps of the method for speech pilot based channel estimation as described in any of the preceding claims.
The invention has the advantages that:
the invention provides a channel estimation method and a device based on a speech pilot frequency, comprising the following steps: the method comprises the steps that a sending end sends a voice pilot signal to a receiving end to be subjected to voice function calibration, wherein the voice pilot signal contains original voice function information of the receiving end; the receiving end tests the corresponding language functions according to the language pilot signals, generates test results and feeds the test results back to the sending end; the sending end compares the test result with the original language function information of the receiving end, and calculates the signal deformation condition of the language layer between the sending end and the receiving end by using a preset deviation function; if the signal deformation condition is within a preset acceptable range, the sending end and the receiving end carry out voice communication; otherwise, no verbal communication is performed. The method provided by the invention uses the pilot signal to calibrate the function level of the receiving end in language, thereby realizing the channel estimation of the function level of the language.
Further, in the voice communication between the transmitting end and the receiving end, the transmitting end transmits the voice pilot signal to the receiving end again every preset time period, so that the channel state can be monitored, and the channel quality can be found out in time when the channel quality is poor. The preset time period can be set according to the environment complexity and the real-time performance and accuracy requirements, and is suitable for various application scenes.
Further, the method provided by the invention is simultaneously applicable to channel estimation of wired communication and wireless communication. The speech pilot signal can be transmitted end to end, broadcast and transmitted, and can be used for counting the communication nodes with corresponding speech functions in the broadcast range while realizing channel estimation, so that reasonable resource allocation is carried out and the communication efficiency of the intelligent network is improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present invention are not limited to the above-described specific ones, and that the above and other objects that can be achieved with the present invention will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate and together with the description serve to explain the invention. In the drawings:
fig. 1 is a schematic diagram illustrating steps of a method for estimating a channel based on an intra-voice pilot according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating steps of a method for speech pilot based channel estimation according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments and the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. The exemplary embodiments of the present invention and the descriptions thereof are used herein to explain the present invention, but are not intended to limit the invention.
It should be noted here that, in order to avoid obscuring the present invention due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details not greatly related to the present invention are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
It is also noted herein that the term "coupled" may refer to not only a direct connection, but also an indirect connection in which an intermediate is present, unless otherwise specified.
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
It should be emphasized that the references to steps below are not intended to limit the order of the steps, but rather should be understood to mean that the steps may be performed in a different order than in the embodiments, or that several steps may be performed simultaneously.
In order to solve the problem that the existing communication technology cannot meet the huge demands of communication rate and throughput and neglect the speech function, the invention firstly provides a channel estimation method based on speech pilot frequency, wherein the speech pilot frequency is arranged in each node of a intelligent network, and corresponding nodes in the intelligent network are used as a transmitting end and a receiving end according to the channel estimation demands, as shown in fig. 1, the method comprises the following steps S101-S104:
step S101: and sending a voice pilot signal to a receiving end to be subjected to voice function calibration, wherein the voice pilot signal contains original voice function information of the receiving end.
Step S102: and receiving a test result fed back by the receiving end, wherein the test result is obtained by the receiving end according to the corresponding language function of the receiving end through testing according to the language pilot signal.
Step S103: comparing the test result with original language function information of the receiving end, and calculating signal deformation conditions of the language layer between the transmitting end and the receiving end by using a preset deviation function.
Step S104: if the signal deformation condition is within a preset acceptable range, carrying out voice communication with the receiving end; otherwise, no verbal communication is performed.
An initial work by Weaver suggests that communication can be divided into three layers. The lowest level is the technical level, defined by shannon's classical information theory, with the emphasis on how to accurately and efficiently transmit symbols (bits) from a transmitter to a receiver. In the middle layer, the semantic layer, semantic information of data is extracted and transmitted through the semantic channel, and in the higher layer, the validity layer, it is responsible for providing the required communication efficiency on the lower two layers. In the invention, three layers of communication from low to high are respectively called a grammar layer, a semantic layer and a speech layer, and the object of the invention is a third layer, namely the speech layer.
Among them, the intelligent profile network is an evolution of the sixth generation (thesixth generation, 6G) mobile communication network, and intelligent profile can be understood as intelligent evolution and native profile.
The voice pilot signal comprises a pseudo-random sequence and corresponding voice function information, different pseudo-random sequences correspond to different voice function information, and can be understood that the pseudo-random sequence is a label of the voice function information, and the voice pilot signal exists in each node of the intelligent network. The function is to calibrate the function level of the language at the receiving end by utilizing the language pilot signal after the receiving end receives the language pilot signal sent by the sending end, thereby carrying out channel estimation of the function level of the language.
Any node in the intelligent network can be used as a transmitting end and a receiving end, and when channel estimation is required, the corresponding node can be calibrated on the function level of language through transmitting the pilot signal for language.
In step S101, the transmitting end transmits a speech pilot signal to the receiving end to be subjected to speech function calibration, where the speech pilot signal includes original speech function information of the receiving end.
The language function refers to functions of each node meeting certain requirements in the intelligent network. The speech function is not only a certain function, but may be, for example, a face recognition speech function, a picture classification speech function, a text translation speech function, or the like. In a smart network, since each communication node is typically combined with artificial intelligence, such nodes are not only capable of receiving bitstreams in conventional communications, but also have a function, which is referred to as a speech function in the present invention.
The original language function information of the receiving end is contained in the language pilot signal, and in the intelligent network, the original language function of each node can be obtained through an algorithm similar to the updating of the routing table. Illustratively, based on the RIP protocol updated by the routing table, each node in the intelligent network shares own language function information, and takes the language function information as original language function information of the node.
Illustratively, the original speech function information of the speech pilot signal for calibrating the face recognition speech function may be a face image; the original language function information of the pilot signal for calibrating the language function of the text translation can be a piece of text.
In some embodiments, since the software and hardware environments of the sending end and the receiving end are different, and may both have the same function of the speech layer, but the process of implementing the function is different, before the sending end sends the speech pilot signal to the receiving end, the sending end sends the detection signal to the receiving end, and detects the software and hardware environment information of the receiving end, so that the following receiving end can correctly identify the speech pilot signal. The software and hardware environment information at least comprises hardware information, software information for realizing the language function and readable data types.
In some embodiments, when the transmitting end transmits the voice pilot information to the designated receiving end, if the voice pilot information is transmitted through other nodes in the intelligent network, the other nodes only forward the voice pilot signal, and do not perform any processing.
In step S102, after receiving the voice pilot signal, the receiving end uses the voice pilot signal to test its own corresponding voice function, generates a test result, and feeds back the test result to the transmitting end.
In some embodiments, when multiple voice pilot signals are transmitted simultaneously on the same channel, they should be distinguished.
Specifically, when only one transmitting end is included, that is, when a plurality of voice pilot signals in a channel are transmitted by one transmitting end, the transmitting end shall number each voice pilot signal and mark priority, so that the receiving end can correctly distinguish the voice pilot signals for calibrating different voice functions and test each voice function according to the priority.
When multiple transmitting ends are included, that is, when multiple pilot signals for languages in a channel are transmitted by different transmitting ends, transmission of pilot signals for each language should be multiplexed according to a transmission rule of conventional communication, so as to reduce interference between pilot signals for each language. Wherein the multiplexing process at least comprises one or more of time division multiplexing, frequency division multiplexing and space division multiplexing.
In some embodiments, when only one transmitting end and one receiving end are included, when the transmitting end transmits the same voice pilot signal for calibrating the same voice function to the receiving end multiple times within a preset short time, the receiving end performs channel estimation processing on the voice pilot signal according to a preset protocol. Specifically, when the default condition is not set in advance, the preset protocol may be that only one of the voice pilot signals is subjected to channel estimation processing, that is, the receiving end only processes one of the received multiple same voice pilot signals; in some special application scenarios, such as complex and changeable environments and high requirements for real-time performance and accuracy, the preset protocol may be to perform channel estimation processing on each voice pilot signal, that is, the receiving end needs to process and timely feed back each received voice pilot signal.
In step S103, the test result is compared with the original speech function information of the receiving end, and the signal deformation condition of the speech layer between the transmitting end and the receiving end is calculated by using a preset deviation function.
The bias function needs to be selected according to specific language functions, and the classification task and the NLP (natural language processing) task are different in measurement criteria, so that different language functions have different bias functions.
Illustratively, the face recognition function of the receiving end is calibrated, and the transmitting end transmits a voice pilot signal to the receiving end, wherein the voice pilot signal comprises a face image with a label in a face database; after receiving the voice pilot signal, the receiving end obtains a corresponding face image; the receiving end carries out face recognition on the face image to obtain a group of recognition data, wherein the recognition data are as follows: the probability of recognizing the person a is 0.9, the probability of recognizing the person B is 0.07, and the probability of recognizing the person C is 0.03; and feeding the group of identification data back to the sending end, and comparing the fed-back identification data with original language function information by the sending end to judge whether the identification of the receiving end is correct or not so as to complete channel estimation of the language layer.
In step S104, based on the channel estimation of the speech function layer in steps S101 to S103, obtaining the signal deformation condition of the speech layer between the transmitting end and the receiving end, if the signal deformation condition is within the preset acceptable range, it is indicated that the channel between the transmitting end and the receiving end can perform speech communication at the moment, and the two begin to perform speech communication; if the signal deformation condition is not within the preset acceptable range, the signal deformation condition indicates that the channel quality between the sending end and the receiving end is poor at the moment, and the voice communication can not be carried out.
In some embodiments, if the signal deformation condition is within the preset acceptable range, when the sending end and the receiving end have started to perform the voice communication, the sending end re-sends the voice pilot signal to the receiving end every preset time period so as to monitor the channel state and prevent the channel from being degraded; if the receiving end does not meet the requirement of carrying out the voice communication during the monitoring, namely the signal deformation condition exceeds the preset acceptable range, the transmitting end stops the communication with the receiving end.
Based on the above steps S101 to S104, channel estimation based on the speech pilot is achieved, and whether the transmitting end and the receiving end communicate is determined according to the channel estimation result.
In some embodiments, the method provided by the invention further comprises: different speech functions may show different deviations under the same channel condition, that is, under the condition of single-shot single-reception, one speech pilot frequency of the transmitting end can calibrate the speech functions of a plurality of receiving ends at the same time, so as to obtain different channel estimates of different speech function layers under the same channel condition.
In some embodiments, the method provided by the invention further comprises: the speech pilot signal may be transmitted end-to-end or broadcast. When the method is used for broadcasting, the transmitting end broadcasts the voice pilot signals to a plurality of receiving ends, so that channel estimation can be realized on channels among the plurality of receiving ends, and statistics can be carried out on the receiving ends (namely network nodes) with corresponding voice functions in a broadcasting range, thereby reasonably distributing resources and improving the communication efficiency of the intelligent network.
In the present invention, the speech pilot signal is suitable for channel estimation in both wired communication and wireless communication. The speech communication is based on the traditional grammar communication, so that the speech communication is also applicable to various schemes and guidelines of grammar communication in general, such as network slicing, NOMA, MIMO and other grammar communication fields, and is also applicable to the transmission of speech pilot signals.
The invention is further described below with reference to a specific embodiment that implements calibration of the face recognition function of the receiving end, as shown in fig. 2, where the transmitting end is denoted as a and the receiving end is denoted as B, and includes the following steps a to E:
step A: the method comprises the steps that a sending end A sends a voice pilot signal related to a face recognition function to a receiving end B which needs to conduct face recognition function calibration, wherein the voice pilot signal comprises original voice function information of the receiving end B, and the original voice function information is a face image with a label.
And (B) step (B): and after receiving the voice pilot signal, the receiving end B extracts information carried in the voice pilot signal, namely a face image, and performs functional test according to the face image. Specifically, the receiving end performs face recognition on the face image to obtain a group of recognition data, wherein the recognition data is as follows: the probability of identifying the person as the third person is 0.9, the probability of identifying the person as the fourth person is 0.07, and the probability of identifying the person as the fifth person is 0.03; and feeding the set of identification data back to the sending end A as a test result.
Step C: after receiving the test result fed back by the receiving end B, the sending end A compares the test result with an original face recognition function, specifically extracts a label of a face image in original language function information, judges whether the test result in the receiving end is correct, and judges the signal deformation condition of the language function layer between the sending end and the receiving end by using a deviation function of the face recognition function.
Step D: if the sending end A finds that the face recognition function of the receiving end B is within an acceptable range after judging, judging that the face recognition function of the receiving end B can perform the voice communication at the moment, and starting the voice communication of the face recognition function; if the sending end A judges that the face recognition function of the receiving end B can not meet the expected requirement, the sending end A judges that the channel quality between the sending end A and the receiving end B is poor at the moment, and the voice communication can not be carried out.
Step E: if the sender a and the receiver B have started such communication in step D, the sender a needs to send the voice pilot signal with the face recognition function again every preset time, i.e. repeat step a to monitor the channel state, prevent the channel from deteriorating, and if the requirement of such communication between the sender a and the receiver B cannot be met, the sender a can find and stop the communication in time.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method for speech pilot based channel estimation.
Accordingly, the present invention also provides an apparatus comprising a computer apparatus including a processor and a memory, the memory having stored therein computer instructions for executing the computer instructions stored in the memory, the apparatus implementing the steps of the method as described above when the computer instructions are executed by the processor.
The embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the edge computing server deployment method described above. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disk, a removable memory disk, a CD-ROM, or any other form of storage medium known in the art.
In summary, the present invention provides a method and apparatus for channel estimation based on an intra-speech pilot, including: the method comprises the steps that a sending end sends a voice pilot signal to a receiving end to be subjected to voice function calibration, wherein the voice pilot signal contains original voice function information of the receiving end; the receiving end tests the corresponding language functions according to the language pilot signals, generates test results and feeds the test results back to the sending end; the sending end compares the test result with the original language function information of the receiving end, and calculates the signal deformation condition of the language layer between the sending end and the receiving end by using a preset deviation function; if the signal deformation condition is within a preset acceptable range, the sending end and the receiving end carry out voice communication; otherwise, no verbal communication is performed. The method provided by the invention uses the pilot signal to calibrate the function level of the receiving end in language, thereby realizing the channel estimation of the function level of the language.
Further, in the voice communication between the transmitting end and the receiving end, the transmitting end transmits the voice pilot signal to the receiving end again every preset time period, so that the channel state can be monitored, and the channel quality can be found out in time when the channel quality is poor. The preset time period can be set according to the environment complexity and the real-time performance and accuracy requirements, and is suitable for various application scenes.
Further, the method provided by the invention is simultaneously applicable to channel estimation of wired communication and wireless communication. The speech pilot signal can be transmitted end to end, broadcast and transmitted, and can be used for counting the communication nodes with corresponding speech functions in the broadcast range while realizing channel estimation, so that reasonable resource allocation is carried out and the communication efficiency of the intelligent network is improved.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave.
It should be understood that the invention is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present invention.
In this disclosure, features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The channel estimation method based on the language pilot frequency is characterized in that the language pilot frequency is arranged in each node of an intelligent network, and corresponding nodes in the intelligent network are used as a transmitting end and a receiving end according to the channel estimation requirement, and the method is executed at the transmitting end and comprises the following steps:
transmitting a voice pilot signal to a receiving end to be subjected to voice function calibration, wherein the voice pilot signal contains original voice function information of the receiving end;
receiving a test result fed back by the receiving end, wherein the test result is obtained by the receiving end through testing the corresponding language function according to the language pilot signal;
comparing the test result with original language function information of the receiving end, and calculating signal deformation conditions of a language layer between the transmitting end and the receiving end by using a preset deviation function;
if the signal deformation condition is within a preset acceptable range, carrying out voice communication with the receiving end; otherwise, no verbal communication is performed.
2. The method of claim 1, wherein before transmitting the voice pilot signal to the receiving end to be subjected to voice function calibration, the method further comprises:
and based on the RIP protocol updated by the routing table, each node in the intelligent network shares own language function information so as to obtain the original language function information of the receiving end.
3. The method of claim 1, wherein if the signal distortion condition is within a preset acceptable range, after performing the voice communication with the receiving end, the method further comprises:
re-transmitting the voice pilot signal to the receiving end every preset time period to monitor the channel state;
and stopping communication with the receiving end if the signal deformation condition obtained in the monitoring period exceeds the preset acceptable range.
4. The method of claim 1, comprising only one transmitting end, the method further comprising:
transmitting a plurality of language pilot signals to the receiving end so as to calibrate the functions of the receiving end in a plurality of languages;
numbering the pilot signals for each language, and marking the priority, wherein the receiving end tests according to the priority of the pilot signals for each language.
5. The method for speech pilot based channel estimation of claim 1, comprising a plurality of transmitting ends, the method further comprising:
multiplexing is carried out when a plurality of voice pilot signals are sent to the receiving end so as to reduce interference among the voice pilot signals; the multiplexing process at least comprises one or more of time division multiplexing, frequency division multiplexing and space division multiplexing.
6. The method of claim 1, wherein before transmitting the voice pilot signal to the receiving end to be subjected to voice function calibration, the method further comprises:
transmitting a detection signal to the receiving end to detect software and hardware environment information of the receiving end; the software and hardware environment information at least comprises hardware information, software information for realizing the language function and readable data types.
7. The method of speech pilot based channel estimation according to claim 1, comprising a plurality of receiving ends, the method further comprising:
broadcasting and transmitting the voice pilot signals to a plurality of receiving ends;
and counting network nodes with corresponding language functions in the broadcasting range so as to realize reasonable resource allocation.
8. The method for channel estimation based on a speech pilot according to claim 1, wherein when sending a speech pilot signal to a receiving end to be subjected to speech function calibration, the method further comprises:
and if the voice pilot signal is transmitted, the voice pilot signal is only forwarded by other nodes in the intelligent network.
9. The method of claim 1, comprising only one transmitting end and one receiving end, the method further comprising:
the method comprises the steps of sending the same language pilot signals for calibrating the functions of the same language to the receiving end for a plurality of times in a preset short time;
setting a preset protocol according to an application scene, and performing channel estimation processing on the voice pilot signal by the receiving end according to the preset protocol; the preset protocol at least comprises the steps of carrying out channel estimation processing on only one voice pilot signal and carrying out channel estimation processing on each voice pilot signal.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 9.
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| CN116701410B (en) * | 2023-08-07 | 2023-11-14 | 北京大学 | Method and system for storing memory state data for data language of digital networking |
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