CN113630299A - Deep learning communication processing system and communication link applying same - Google Patents
Deep learning communication processing system and communication link applying same Download PDFInfo
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Abstract
The invention relates to a deep learning communication processing system and a communication link applying the same, comprising a deep learning module and a communication processing module; the deep learning module is in communication connection with the communication processing module through a first internal interface so as to carry out interaction of parameter information; the deep learning module is in communication connection with the communication processing module through a second internal interface so as to carry out interaction of images, sounds and text information; the deep learning module is provided with a first external interface and a second external interface; connecting a deep learning module to a cloud end to update kernel parameters and algorithms in the deep learning module; the communication processing module is provided with a third external interface and a fourth external interface; the communication processing module is connected with external equipment to carry out data interaction. The deep learning communication processing idea can be integrated into a communication link, the deep learning communication processing can be expanded and improved, and a brand new design idea is provided for development of an intelligent communication technology.
Description
Technical Field
The invention relates to the technical field of intelligent high-speed communication, in particular to a deep learning communication processing system and a communication link applying the same.
Background
With the advance of technology, the demand for high-speed communication is increasing. In the existing high-speed communication technology, a mobile phone or a group client usually accesses to a radio access network through a base station, processes the signal through a radio access network or an IP radio access network or a packet transport network scheme, transmits the signal to a base station controller or a radio network controller, and transmits the signal to a core network, and an internal network element of the core network carries the signal through an IP bearer network. The communication scheme is complex in design, difficult to expand and update and poor in communication intelligence.
Disclosure of Invention
The invention aims to solve the technical problem of providing a deep learning communication processing system and a communication link applying the same, which can integrate the deep learning communication processing idea into the communication link, and the deep learning communication processing can be expanded and improved, so that a brand new design idea and a good thinking space are provided for the design and development of an intelligent communication technology.
In order to solve the technical problem, the invention provides a deep learning communication processing system and a communication link applying the same, which comprises a deep learning module and a communication processing module; the deep learning module is in communication connection with the communication processing module through a first internal interface so as to carry out interaction of parameter information; the deep learning module is in communication connection with the communication processing module through a second internal interface so as to carry out interaction of images, sounds and text information; the deep learning module is provided with a first external interface and a second external interface; the deep learning module is connected to a cloud end through the first external interface and the second external interface, and the cloud end transmits external information into the deep learning module to update kernel parameters and algorithms in the deep learning module; the communication processing module is provided with a third external interface and a fourth external interface; and the communication processing module is connected with external equipment through the third external interface and the fourth external interface so as to carry out data interaction.
Preferably, the deep learning module comprises a convolution operation group unit and an operation array unit; the communication processing module comprises a layer one processing unit, a layer two processing unit and a radio frequency front end unit; the layer one processing unit is in communication connection with the operation array unit through the first internal interface; the operation array unit is connected with the radio frequency front end unit through a first internal interface so as to transmit synchronous information.
Preferably, the first internal interface comprises an AXI interface and the second internal interface comprises a DMA bus.
Preferably, the layer two processing unit is in communication connection with the convolution operation group unit through the second internal interface to perform data interaction.
Preferably, a Yolo algorithm, a Cluster algorithm and a Kalman algorithm run in the deep learning module.
Preferably, the Yolo algorithm runs in the convolution operation group unit, and the Cluster algorithm and the Kalman algorithm are performed in the operation array unit.
Preferably, when the layer two processing unit in the communication processing module performs information interaction processing with the deep learning module, the method specifically includes the following steps: the layer two processing unit acquires image information and analyzes the image information; transmitting the analyzed image information to the deep learning unit, operating a Yolo algorithm in the convolution operation group unit, finishing processing on the analyzed image information, and obtaining a first processing result; the communication processing unit acquires the first processing result.
Preferably, when the layer one processing unit in the communication processing module performs information interaction processing with the deep learning module, the method specifically includes the following steps: the layer one processing unit acquires modulation and demodulation data and sends the modulation and demodulation data to the deep learning unit; the Cluster algorithm is operated in the operation array unit, and the modulation and demodulation data are operated to obtain a second processing result; the Cluster algorithm is used for operating the modulation and demodulation data to obtain a second processing result; and the communication processing module acquires the second processing result.
Preferably, when the radio frequency front end unit in the communication processing module performs information interaction processing with the deep learning module, the method specifically includes the following steps: the radio frequency front end unit acquires frame header data block information and transmits the frame header data block information to a deep learning unit; the operating array unit operates the Kalman algorithm and calculates the frame header data block information to obtain a third processing result; and the communication processing module acquires the third processing result.
A communication link using a deep learning communication processing system, preferably comprising: a deep learning communication processing system and a communication link; the deep learning communication processing system is inserted into the communication link to realize continuous data transmission.
Compared with the prior art, the technical scheme of the invention has the following advantages:
1. the deep learning communication processing system comprises a deep learning module and a communication processing module; the deep learning communication processing system has the advantages that data interaction can be carried out between the deep learning module and the communication processing module, and the deep learning communication processing system is ingenious in design and easy to realize.
2. The deep learning communication processing system is not only realizable, but also improvable and higher in intelligence.
3. The invention also discloses a communication link applying the deep learning communication processing system, and the deep learning communication processing system is inserted into the communication link and can effectively integrate the deep learning idea into the high-speed communication system.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a diagram of an architecture of a deep learning communication processing system according to the present invention;
FIG. 2 is a schematic diagram of data interaction between a deep learning module and a communication processing module according to the present invention;
fig. 3 is a diagram of a communication architecture in which a deep learning communication processing system is plugged into an optical fiber link according to an embodiment of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Referring to fig. 1 to 3, the present invention discloses a deep learning communication processing system and a communication link using the deep learning communication processing system.
The deep learning communication processing system comprises a deep learning module and a communication processing module.
The deep learning module is in communication connection with the communication processing module through a first internal interface, and the first internal interface is a user-defined interface or a general interface and is used for transmitting parameter information between the deep learning module and the communication processing module.
The deep learning module is in communication connection with the communication processing module through a second internal interface, the second internal interface is a user-defined interface or a general interface and can be used for transmitting big data information between the deep learning module and the communication processing module, and the big data information comprises images, sounds, character information and the like.
The deep learning module is provided with a first external interface and a second external interface. The first external interface and the second external interface are both external interfaces of the deep learning communication processing system, and the first external interface and the second external interface are both universal interfaces. Through the first external interface, the deep learning communication processing system can be connected to the cloud end, and the cloud end transmits external information into the deep learning module for updating kernel parameters in the deep learning module. Through the second external interface, the deep learning communication processing system can be connected to the cloud end and used for updating the basic algorithm in the deep learning module.
The communication processing module is provided with a third external interface and a fourth external interface. The third external interface is a general interface, and the deep learning communication processing system can be connected with the existing equipment or unit to transmit information through the third external interface. The fourth external interface is a custom interface, and information can be mutually transmitted among a plurality of deep learning communication processing systems in a wireless mode through the fourth external interface.
Furthermore, the core unit in the deep learning module is a convolution operation unit and an operation array unit. The communication processing module comprises a layer-one processing unit, a layer-two processing unit and a radio frequency front end unit. The first layer one processing unit and the operation array unit are connected through a first internal interface in a communication mode, and the first internal interface can be preferably an AXI interface. The arithmetic array unit and the radio frequency front end unit realize the mutual transmission of the synchronous information through an AXI interface.
And the layer two processing units in the communication processing module are connected with the convolution operation group unit through a second internal interface and perform data interaction. The second internal interface may preferably be a DMA bus.
When the deep learning module processes data, the algorithms used by the deep learning module include, but are not limited to, a Yolo algorithm, a Cluster algorithm and a Kalman algorithm. The above Yolo algorithm mainly operates in the convolution operation group unit, the operation array module plays a role of auxiliary calculation, and the above Cluster algorithm and Kalman algorithm are mainly performed in the operation array unit.
The deep learning communication processing system comprises the following working processes: when the layer two processing unit in the communication processing module and the deep learning module perform information interaction processing, the method specifically comprises the following steps:
and the layer two processing unit acquires and analyzes the image information, transmits the analyzed image information to a Buff Area1 position in the deep learning unit through the XDMA interface, and simultaneously indicates that the interruption one is effective and informs the deep learning unit to process the event.
And after receiving the interrupt signal, the deep learning unit completes the Yolo operation by the convolution operation group unit, completes the processing of the analyzed image information and obtains a first processing result. Then, the first processing result is put back to the position of the Buff Area1 (buffer Area 1) in the deep learning unit, and is valid through Indicator1 (Indicator 1), indicating that the processing of the communication processing module is completed, after receiving Indicator1, the communication processing module takes the processing result of the Yolo operation from the position of the Buff Area1 (buffer Area 1) in an XDMA interface access manner, and then packages the processing result, and continues the communication of the communication processing module.
When the layer one processing unit in the communication processing module performs information interaction processing with the deep learning module, the method specifically comprises the following steps:
the layer one processing unit passes the transmitted and received modem data to the Buff Area2 (buffer 2) location in the deep learning module over the XDMA interface while indicating that Interrupt2 (Interrupt 2) is valid and telling the deep learning module to handle this event.
After receiving Interrupt2 (Interrupt 2) information, the deep learning module completes Cluster operation by the operation array unit, the Cluster algorithm performs operation on the modulation and demodulation data to obtain a second processing result, and the second processing result is put back to the Buff Area2 (buffer 2) position in the deep learning module while Indicator2 (indication 2) is an effective indication and indicates the communication processing module that the processing is completed. After receiving Indicator2 (indication 2), the communication processing module extracts the processing result of the Cluster operation from the position of Buff Area2 (buffer 2) in an AXI bus transmission mode to determine the modulation and demodulation mode and the demodulation decision boundary adopted by the communication processing module for subsequent communication.
When the radio frequency front end unit in the communication processing module and the deep learning module perform information interaction processing, the method specifically comprises the following steps:
the radio frequency front end unit in the communication processing module transmits the received frame header data block to a Buff Area3 (buffer 3) position in the deep learning module through the XDMA interface, and indicates that Interrupt3 (Interrupt 3) is valid, and informs the deep learning module to process the event.
After the deep learning module receives Interrupt3 (Interrupt 3), the arithmetic array unit completes Kalman operation to obtain a third processing result, and the third processing result after operation is put back to a Buff Area3 position in the deep learning module, and an Indicator3 (indication 3) is valid to indicate that the communication processing module completes the processing. After receiving Indicator3 (indication 3), the communication processing module takes out the processing result of Kalman operation from Buff Area3 in the form of AXI bus, at this time, the data of the radio frequency front end unit part is completed synchronously, and the subsequent communication of the communication processing module is synchronized by the information.
The invention also discloses a communication link applying the deep learning communication processing system, which comprises the communication link and the deep learning communication processing system.
Specifically, the deep learning communication processing system is inserted into a communication link. Based on this, the invention also discloses the first embodiment:
as shown in fig. 3, a simplified version of the mobile communication architecture is shown. A radio Access network, that is, ran (radio Access network), is selected, and simply, mobile terminals are all accessed to a base station in a communication network, and then from the base station to a telecommunication room. At present, data transmission from a telecommunication room to a bearer network of a backbone network and between a base station and a base station is generally carried out in an optical fiber mode.
However, the optical fiber is often damaged artificially or aged naturally, in order to prevent the phenomenon, two deep learning communication processing systems are accessed through the third external interface, and data can be transmitted continuously through the fourth external interface, so that smooth continuation of Backhaul is ensured.
As will be appreciated by one skilled in the art, 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, disk storage, CD-ROM, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A deep learning communication processing system, comprising:
the deep learning module and the communication processing module;
the deep learning module is in communication connection with the communication processing module through a first internal interface so as to carry out interaction of parameter information;
the deep learning module is in communication connection with the communication processing module through a second internal interface so as to carry out interaction of images, sounds and text information;
the deep learning module is provided with a first external interface and a second external interface; the deep learning module is connected to a cloud end through the first external interface and the second external interface, and the cloud end transmits external information into the deep learning module to update kernel parameters and algorithms in the deep learning module;
the communication processing module is provided with a third external interface and a fourth external interface; and the communication processing module is connected with external equipment through the third external interface and the fourth external interface so as to carry out data interaction.
2. The deep learning communication processing system of claim 1, wherein the deep learning module comprises a convolution operation group unit and an operation array unit; the communication processing module comprises a layer one processing unit, a layer two processing unit and a radio frequency front end unit;
the layer one processing unit is in communication connection with the operation array unit through the first internal interface; the operation array unit is connected with the radio frequency front end unit through a first internal interface so as to transmit synchronous information.
3. The deep learning communication processing system of claim 1, wherein the first internal interface comprises an AXI interface and the second internal interface comprises a DMA bus.
4. The system of claim 2, wherein the layer two processing unit is communicatively connected to the convolution operation group unit through the second internal interface for data interaction.
5. The system of claim 2, wherein a Yolo algorithm, a Cluster algorithm and a Kalman algorithm are run in the deep learning module.
6. The system of claim 5, wherein the Yolo algorithm is operated in the convolution operation group unit, and the Cluster algorithm and the Kalman algorithm are performed in the operation array unit.
7. The deep learning communication processing system according to claim 5, wherein when the layer two processing unit in the communication processing module performs information interaction processing with the deep learning module, the deep learning communication processing system specifically includes the following steps:
the layer two processing unit acquires image information and analyzes the image information;
transmitting the analyzed image information to the deep learning unit, operating a Yolo algorithm in the convolution operation group unit, finishing processing on the analyzed image information, and obtaining a first processing result;
the communication processing unit acquires the first processing result.
8. The deep learning communication processing system of claim 5, wherein when the layer one processing unit in the communication processing module performs information interaction processing with the deep learning module, the deep learning communication processing system specifically includes the following steps:
the layer one processing unit acquires modulation and demodulation data and sends the modulation and demodulation data to the deep learning unit;
the Cluster algorithm is operated in the operation array unit, and the modulation and demodulation data are operated to obtain a second processing result;
and the communication processing module acquires the second processing result.
9. The deep learning communication processing system of claim 5, wherein when the rf front-end unit in the communication processing module performs information interaction processing with the deep learning module, the deep learning communication processing system specifically includes the following steps:
the radio frequency front end unit acquires frame header data block information and transmits the frame header data block information to a deep learning unit;
the operating array unit operates the Kalman algorithm and calculates the frame header data block information to obtain a third processing result;
and the communication processing module acquires the third processing result.
10. A communication link employing a deep learning communication processing system, comprising: a communication link and a deep learning communication processing system as claimed in any one of claims 1 to 9;
the deep learning communication processing system is inserted into the communication link to realize continuous data transmission.
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