CN116761223B - Method for realizing 4G radio frequency communication by using 5G baseband chip and vehicle-mounted radio frequency system - Google Patents
Method for realizing 4G radio frequency communication by using 5G baseband chip and vehicle-mounted radio frequency system Download PDFInfo
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Abstract
A method for realizing 4G radio frequency communication by using a 5G baseband chip and a vehicle-mounted radio frequency system are disclosed. The method comprises the steps of integrating a 4G radio frequency module in a 5G baseband chip, enabling the 5G baseband chip to simultaneously support signal processing of 5G and 4G, then establishing a bidirectional data transmission channel between the 5G baseband chip and the 4G radio frequency module, realizing data exchange between the 5G baseband chip and the 4G radio frequency module, and then dynamically switching working modes of the 5G baseband chip and the 4G radio frequency module according to network environment and user requirements, so as to realize seamless switching between the 5G and the 4G. Thus, the reliability of implementing 4G radio frequency communication by using the 5G baseband chip can be improved.
Description
Technical Field
The application relates to the field of communication, in particular to a method for realizing 4G radio frequency communication by utilizing a 5G baseband chip and a vehicle-mounted radio frequency system.
Background
With the rapid development of 5G communication technology, there is an increasing demand for high-speed, low-delay communication. However, since the construction and coverage of 5G networks is still underway, there are still a large number of 4G network users. For example, although the existing car system adopts a 5G module, the existing car system does not actually use the radio frequency function of 5G, but only uses the high performance of 5G. Thus, the waste of resources such as chips and the like is caused, and the cost is increased. Therefore, in order to meet the requirements of users, it is necessary to simultaneously consider the communications of the 5G and 4G networks, and implementing the 4G radio frequency communication requirement by using the 5G baseband chip becomes a feasible solution.
However, in the existing scheme, since the 5G baseband chip itself is designed for the radio frequency scheme of the 5G frequency band, and the 4G communication adopts a different radio frequency scheme, the radio frequency performance of the 5G communication may not be as good as that of the radio frequency scheme specifically designed for the 4G communication. This causes signal attenuation, so that the transmission distance is limited, and the signal strength is lowered, thereby degrading the communication quality. In addition, when the 4G radio frequency communication is realized, the 5G baseband chip may generate interference with other devices or signal sources, so that the signal quality is reduced, the error rate is increased, and the reliability of the communication is affected.
Therefore, an optimized scheme for implementing 4G radio frequency communication using a 5G baseband chip is desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a method for realizing 4G radio frequency communication by using a 5G baseband chip and a vehicle-mounted radio frequency system. The bidirectional data transmission channel is established between the 5G baseband chip and the 4G radio frequency module, and signal transmission optimization is performed by utilizing signal coding-decoding in the data exchange process of the 5G baseband chip and the 4G radio frequency module so as to ensure the communication quality, thereby improving the reliability of realizing 4G radio frequency communication by utilizing the 5G baseband chip.
According to one aspect of the present application, there is provided a method for implementing 4G radio frequency communication using a 5G baseband chip, including:
integrating a 4G radio frequency module in the 5G baseband chip, so that the 5G baseband chip can support 4G signal processing;
a bidirectional data transmission channel is established between the 5G baseband chip and the 4G radio frequency module, so that data exchange between the 5G baseband chip and the 4G radio frequency module is realized; the self-switching of the 4G network is realized, and the working modes of the 5G baseband chip and the 4G radio frequency module are dynamically switched according to the network environment and the user requirements.
According to another aspect of the present application, there is provided a vehicle-mounted radio frequency system, comprising:
the transmission signal acquisition module is used for acquiring a transmission signal transmitted to the 5G baseband chip by the 4G radio frequency module;
the signal analysis module is used for carrying out signal analysis on the transmission signal to obtain the waveform characteristics of the transmission signal; and
and the optimized transmission signal generation module is used for generating an optimized transmission signal based on the waveform characteristics of the transmission signal.
Compared with the prior art, the method for realizing 4G radio frequency communication by using the 5G baseband chip and the vehicle-mounted radio frequency system provided by the application have the advantages that the 4G radio frequency module is integrated in the 5G baseband chip firstly, so that the 5G baseband chip can support 4G signal processing, then a bidirectional data transmission channel is established between the 5G baseband chip and the 4G radio frequency module, the data exchange between the 5G baseband chip and the 4G radio frequency module is realized, then the working modes of the 5G baseband chip and the 4G radio frequency module are dynamically switched according to the network environment and the user requirements, and the seamless switching of the 4G network is realized. Thus, the reliability of implementing 4G radio frequency communication by using the 5G baseband chip can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below, the following drawings not being drawn to scale with respect to actual dimensions, emphasis instead being placed upon illustrating the gist of the present application.
Fig. 1 is a flowchart of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application.
Fig. 2 is a flowchart of substep S120 of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the application.
Fig. 3 is a schematic diagram of the architecture of substep S120 of the method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application.
Fig. 4 is a flowchart of substep S122 of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the application.
Fig. 5 is a flowchart of a training step of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application.
Fig. 6 is a block diagram of an in-vehicle radio frequency system according to an embodiment of the present application.
Fig. 7 is an application scenario diagram of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the application. 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 also within the scope of the application.
As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Although the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server. The modules are merely illustrative, and different aspects of the systems and methods may use different modules.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Aiming at the technical problems, the technical conception of the application is to establish a bidirectional data transmission channel between the 5G baseband chip and the 4G radio frequency module, and to realize signal transmission optimization by utilizing signal coding-decoding in the data exchange process of the 5G baseband chip and the 4G radio frequency module so as to ensure the communication quality, thereby improving the reliability of realizing 4G radio frequency communication by utilizing the 5G baseband chip.
Fig. 1 is a flowchart of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application. As shown in fig. 1, a method for implementing 4G radio frequency communication by using a 5G baseband chip according to an embodiment of the present application includes the steps of: s110, integrating a 4G radio frequency module in a 5G baseband chip, so that the 5G baseband chip can support 4G signal processing; s120, a bidirectional data transmission channel is established between the 5G baseband chip and the 4G radio frequency module, so that data exchange between the 5G baseband chip and the 4G radio frequency module is realized; and S130, dynamically switching the working modes of the 5G baseband chip and the 4G radio frequency module according to the network environment and the user requirements, so as to realize seamless switching of the 4G network.
Fig. 2 is a flowchart of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application. Fig. 3 is a schematic architecture diagram of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application. As shown in fig. 2 and 3, according to an embodiment of the present application, a method for implementing 4G radio frequency communication by using a 5G baseband chip establishes a bidirectional data transmission channel between the 5G baseband chip and a 4G radio frequency module, and implements data exchange between the 5G baseband chip and the 4G radio frequency module, including the steps of: s121, acquiring a transmission signal transmitted to a 5G baseband chip by a 4G radio frequency module; s122, carrying out signal analysis on the transmission signal to obtain the waveform characteristics of the transmission signal; and S123, generating an optimized transmission signal based on the transmission signal waveform characteristics.
Specifically, in the technical scheme of the application, firstly, a transmission signal transmitted to a 5G baseband chip by a 4G radio frequency module is obtained. Next, it is considered that there are different data information in different application scenarios due to the transmission signal, and a large amount of information is generally contained in the transmission signal. Therefore, to better understand and analyze the transmitted signal, it is necessary to divide it into a plurality of sampling windows for processing to extract and analyze the segments of the respective local signal features of the transmitted signal. That is, the method of sectioning the transmission signal and adopting the sliding window can better adapt to the data analysis requirements of the transmission signal in different scenes, can effectively avoid interference and noise in the signal, and improves the accuracy and reliability of data processing. Specifically, in the technical scheme of the application, sliding window sampling based on a sampling window is performed on the transmission signal so as to obtain a plurality of transmission signal local sampling window signals. It will be appreciated that sliding window sampling is a signal processing method for discretizing a continuous signal by sliding a window of fixed size over the continuous signal and sampling at each window location to obtain samples of the local signal. In signal analysis of a transmission signal, sliding window sampling is used to obtain a plurality of transmission signal local sampling window signals, and by dividing a continuous signal into a plurality of windows and then sampling at each window position, local signal samples in each window can be obtained, and the local signal samples can be used for subsequent feature extraction and analysis. The window size of the sliding window samples may be selected according to specific requirements, smaller windows may provide higher temporal resolution, but may result in information loss. Larger windows may provide more comprehensive frequency information, but may result in reduced time resolution, and selecting an appropriate window size may require a trade-off depending on the particular application scenario. Sliding window sampling is a method for taking local samples of a continuous signal by sliding a window over the continuous signal and sampling for subsequent signal analysis and feature extraction.
Then, considering that the representation form of the transmission signal in the time domain is a waveform diagram, in order to effectively capture the characteristics of each local area in the transmission signal, in the technical scheme of the application, the local sampling window signals of the plurality of transmission signals are respectively subjected to characteristic mining through a local sampling window signal characteristic extractor based on a convolutional neural network model so as to extract local implicit characteristic distribution information related to the transmission signal in the local sampling window signals of each transmission signal, thereby obtaining waveform characteristic vectors of the local sampling window signals of the plurality of transmission signals.
Further, since the hidden features of the local sampling window signals of the transmission signals have an association relation based on the whole transmission signals, in order to effectively perform feature analysis of the transmission signals, the transmission signals are optimized to increase the reliability of implementing 4G radio frequency communication by using a 5G baseband chip.
Accordingly, as shown in fig. 4, the signal analysis of the transmission signal to obtain the waveform characteristics of the transmission signal includes: s1221, carrying out sliding window sampling based on a sampling window on the transmission signal to obtain a plurality of transmission signal local sampling window signals; s1222, the local sampling window signals of the transmission signals are respectively passed through a local sampling window signal characteristic extractor based on a convolutional neural network model to obtain waveform characteristic vectors of the local sampling window signals of the transmission signals; and S1223, performing association coding on the signal waveform characteristic vectors of the plurality of transmission signal local sampling windows to obtain a global transmission signal waveform characteristic vector as the transmission signal waveform characteristic. Convolutional neural networks (Convolutional Neural Network, CNN for short) are a deep learning model, particularly suitable for processing data with a grid structure, such as images and speech. The core idea of convolutional neural networks is to extract and learn the features of the image through the convolutional layer, the pooling layer and the fully connected layer. Wherein the convolution layer convolves the input image with a set of learnable filters (also called convolution kernels) to extract local features of the image. The convolution operation may capture low-level features such as edges, textures, etc. in the image. The pooling layer is used for downsampling the output of the convolution layer, reducing the size of the feature map and retaining important features. Common pooling operations have maximum pooling and average pooling. The pooling operation can enhance the translational invariance of the model and reduce the number of parameters, thereby improving the efficiency and generalization capability of the model. The fully connected layer connects the outputs of the convolutional and pooling layers, and the combination and transformation of features is performed by a series of fully connected and activation functions. Finally, classifying by a softmax function to obtain probability distribution of the input image belonging to each category. The convolutional neural network can gradually extract and learn advanced features of the image through the multi-layer convolution, pooling and stacking of full-connection layers, so that accurate classification and identification of the image are realized.
It is worth mentioning that the associative coding (Encoding) is a process of converting data into another representation. It reduces the dimensionality of the data and retains important information by extracting and representing key features of the data. In associative coding, data is mapped to a low-dimensional feature space, typically a coded vector or coded representation, which captures important features of the data and can be used for subsequent data analysis, model training, or other tasks. Associative coding is commonly used in the fields of data compression, feature extraction, data representation, and the like. It can reduce the cost of storage and computation by reducing the dimensionality of the data while preserving important information of the data. By converting data into a more compact representation, associative coding may help better understand and process complex data. Common associated Coding methods include Principal Component Analysis (PCA), self-encoder (autocoder), sparse Coding (spark Coding), and the like. These methods perform the data's dimension reduction and feature extraction through different mathematical and statistical techniques to better represent the data's structure and pattern. Associative coding is a process of converting data into another form of representation that reduces the dimensionality of the data and retains important information by extracting and representing key features of the data.
More specifically, in step S1223, performing association encoding on the plurality of transmission signal local sampling window signal waveform feature vectors to obtain a global transmission signal waveform feature vector as the transmission signal waveform feature, including: the plurality of transmission signal local sampling window signal waveform feature vectors are passed through a context encoder based on a converter module to obtain the global transmission signal waveform feature vector. It should be appreciated that the context-based encoder (Context Encoder based on Transformer Module) of the converter module is a method of encoding using the converter module for converting signal waveform feature vectors of a plurality of transmission signal local sampling windows into global transmission signal waveform feature vectors. The converter module is a neural network model based on self-attention mechanisms, originally used for natural language processing tasks such as machine translation. The method has strong modeling capability and parallel computing capability, and is suitable for processing sequence data. In this step, a context-based encoder of the converter module takes as input signal waveform feature vectors of a plurality of transmission signal local sampling windows, which vectors are encoded by a self-attention mechanism. The self-attention mechanism may learn the relevance between different locations, helping to capture global context information. The signal waveform feature vectors of the plurality of local sampling windows can be converted into one global transmission signal waveform feature vector by a context encoder based on the converter module. This global feature vector captures important features of the entire transmitted signal, which can be used for subsequent signal analysis, model training, or other tasks. A context encoder based on a converter module is a method of encoding using the converter module for converting signal waveform feature vectors of a plurality of transmission signal local sampling windows into global transmission signal waveform feature vectors, which can capture global context information and extract important features of the transmission signal.
And then, the global transmission signal waveform characteristic vector is passed through a decoder to generate an optimized transmission signal. That is, decoding is performed by using the global implicit associated feature distribution information of the transmission signal, so that signal transmission optimization is performed, and communication quality is ensured. In particular, here, the decoder may perform signal reconstruction from information of the global transmission signal waveform feature vector, thereby reducing distortion and noise of the transmission signal. In addition, the decoder can adjust and optimize the transmission signal according to a specific optimization target so as to improve the transmission efficiency and reliability. Thus, the optimized transmission signal generated by the decoder may have better quality and performance.
Accordingly, generating an optimized transmission signal based on the transmission signal waveform characteristics, comprising: the global transmission signal waveform feature vector is passed through a decoder to generate the optimized transmission signal. It should be appreciated that the decoder is a component in the convolutional neural network model for converting the input eigenvectors into output signals. In the transmission signal optimization scenario, the decoder performs signal reconstruction according to the information of the global transmission signal waveform feature vector, so as to reduce distortion and noise of the transmission signal. The specific structure and parameters of the decoder are determined by a specific model design and typically include a plurality of convolution layers, pooling layers and full-join layers for extracting and learning features and converting them into optimized transmission signals. The goal of the decoder is to adjust and optimize the transmission signal according to specific optimization objectives to increase transmission efficiency and reliability, thereby generating an optimized transmission signal with better quality and performance.
Further, the method for implementing 4G radio frequency communication by using the 5G baseband chip according to the present application further includes a training step for training the local sampling window signal feature extractor based on the convolutional neural network model, the context encoder based on the converter module, and the decoder. It should be understood that the training step refers to the process of training the local sampling window signal feature extractor, the context encoder and the decoder. In a method of implementing 4G radio frequency communication using a 5G baseband chip, these components need to learn and optimize their parameters through training so that they can efficiently extract signal features, encode and decode transmission signals. The purpose of the training step is to enable these components to learn parameters suitable for signal feature extraction, encoding and decoding, through a large number of data samples and specific optimization objectives. During training, supervised learning is typically performed using known input signals and corresponding target output signals. By comparing the differences between the model output and the target output, the parameters of the model are updated using a back-propagation algorithm so that the model can gradually optimize and adapt to the characteristics of the input signal. Through the training step, the local sampling window signal feature extractor can learn the ability to extract local signal features, the context encoder can learn the ability to convert local features to global features, and the decoder can learn the ability to reconstruct and optimize signals based on global features. In this way, in practical applications, these components can perform feature extraction, encoding and decoding on the real-time signal according to the knowledge learned by training, so as to realize an optimized transmission signal.
Accordingly, as shown in fig. 5, the training step includes: s210, acquiring training data, wherein the training data comprises training transmission signals and real signals of the optimized transmission signals; s220, sliding window sampling based on a sampling window is carried out on the training transmission signals so as to obtain a plurality of training transmission signal local sampling window signals; s230, the local sampling window signals of the training transmission signals are respectively passed through the local sampling window signal characteristic extractor based on the convolutional neural network model to obtain waveform characteristic vectors of the local sampling window signals of the training transmission signals; s240, passing the plurality of training transmission signal local sampling window signal waveform feature vectors through the context encoder based on the converter module to obtain training global transmission signal waveform feature vectors; s250, passing the training global transmission signal waveform feature vector through the decoder to obtain a decoding loss function value; and S260, training the local sampling window signal feature extractor based on the convolutional neural network model, the context encoder based on the converter module and the decoder based on the decoding loss function value and through gradient descent direction propagation, wherein the cross-domain attention transfer optimization of feature distribution is performed on a weight matrix of the decoder in each iteration of the training.
Particularly, in the technical scheme of the application, the plurality of training transmission signal local sampling window signals are considered to be obtained by sampling the training transmission signals by sliding windows based on sampling windows, so that the signal waveform source image semantic distribution of the plurality of training transmission signal local sampling window signals has time sequence discontinuity, and the plurality of training transmission signal local sampling window signals respectively pass through a local sampling window signal characteristic extractor based on a convolutional neural network model to obtain signal waveform image characteristic semantic expression of waveform characteristic vectors of the plurality of training transmission signal local sampling window signals to have a certain degree of time sequence distribution difference. Thus, although the plurality of training transmission signal local sampling window signal waveform feature vectors extract context timing related features through the context encoder based on the translator module, the training global transmission signal waveform feature vectors still have a differentiated local distribution.
In this way, when the training global transmission signal waveform feature vector is decoded by a decoder to obtain an optimized transmission signal, the differential local feature distribution representation may have better distribution transferability than other feature representations when the weight matrix of the decoder is adapted with respect to some feature representations, for example, in consideration of the distribution transferability difference of the differential local feature distribution representation in the domain transfer process of decoding generation, and vice versa. Therefore, the weight matrix of the decoder needs to adaptively optimize the waveform feature vector of the training global transmission signal, so as to improve the training effect of generating training by decoding the waveform feature vector of the training global transmission signal through the decoder, namely, improve the decoding generation speed and the accuracy of the obtained optimized transmission signal. The applicant of the present application therefore, in each iteration of the weighting matrix of the decoder, for said weighting matrixCross-domain attention to feature distributionAnd (5) transferring and optimizing.
Accordingly, in a specific example, in each iteration of the training, the cross-domain attention-transfer optimization of feature distribution is performed on the weight matrix of the decoder with the following optimization formula; wherein, the optimization formula is:
wherein,is a weight matrix of the decoder, +.>Is of the scale +.>，/>To->Is the weight matrixIs->Individual row vectors>Representing the two norms of the feature vector, +.>Is to the weight matrix +.>The sum value of each row vector of (a) is arranged to obtain a row vector, and +.>And->All represent a single layer convolution operation, ">Representing matrix multiplication +.>Representing a transpose operation->Representing the weight matrix of the decoder after iteration.
Here, the feature distribution-based cross-domain attention transfer optimization is based on a weight matrix of the decoder for different representations of feature distributions of feature vectors of the training global transmission signal waveforms existing in a feature space domain and a target domainCross-domain diversity feature representation of waveform feature vectors relative to the training global transmission signal by +.>Is focused by convolution operations to enhance the transferability of cross-domain gaps of good transferred feature distributions in a diversified feature distribution while suppressing negative transfer (negative transfer) of bad transferred feature distributions to be based on the weight matrix ∈ ->Realizing a weight matrix by self relative to the distribution structure of the waveform characteristic vector of the training global transmission signal>The unsupervised domain transfer self-adaptive optimization, so that the training effect of generating training by decoding the training global transmission signal waveform characteristic vector through a decoder is improved, namely, the decoding generation speed and the accuracy of the obtained optimized transmission signal are improved. In this way, signal encoding-decoding can be utilizedThe transmission of the row signals is optimized to ensure the communication quality, thereby improving the performance and user experience of the communication system and enhancing the reliability of realizing 4G radio frequency communication by using the 5G baseband chip.
In summary, the method for implementing 4G radio frequency communication by using the 5G baseband chip according to the embodiments of the present application is illustrated, which can establish a bidirectional data transmission channel between the 5G baseband chip and the 4G radio frequency module, and implement signal transmission optimization by using signal encoding-decoding to ensure communication quality in the data exchange process of the 5G baseband chip and the 4G radio frequency module, so as to improve reliability of implementing 4G radio frequency communication by using the 5G baseband chip.
Fig. 6 is a block diagram of an in-vehicle radio frequency system 100 according to an embodiment of the present application. As shown in fig. 6, the vehicle-mounted radio frequency system 100 according to the embodiment of the present application includes: a transmission signal acquisition module 110, configured to acquire a transmission signal transmitted to the 5G baseband chip by the 4G radio frequency module; a signal analysis module 120, configured to perform signal analysis on the transmission signal to obtain a transmission signal waveform characteristic; and an optimized transmission signal generation module 130, configured to generate an optimized transmission signal based on the transmission signal waveform characteristics.
In one example, in the above-mentioned on-vehicle radio frequency system 100, the signal analysis module 120 includes: the sliding window sampling unit is used for carrying out sliding window sampling based on the sampling window on the transmission signals so as to obtain a plurality of transmission signal local sampling window signals; the characteristic extraction unit is used for enabling the partial sampling window signals of the plurality of transmission signals to respectively pass through a partial sampling window signal characteristic extractor based on a convolutional neural network model so as to obtain waveform characteristic vectors of the partial sampling window signals of the plurality of transmission signals; and the association coding unit is used for carrying out association coding on the signal waveform characteristic vectors of the plurality of transmission signal local sampling windows so as to obtain a global transmission signal waveform characteristic vector as the transmission signal waveform characteristic.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective modules in the above-described in-vehicle radio frequency system 100 have been described in detail in the above description of the method of implementing 4G radio frequency communication using the 5G baseband chip with reference to fig. 1 to 5, and thus, repetitive descriptions thereof will be omitted.
As described above, the in-vehicle radio frequency system 100 according to the embodiment of the present application may be implemented in various wireless terminals, such as a server or the like having an algorithm for implementing 4G radio frequency communication using a 5G baseband chip. In one example, the in-vehicle radio frequency system 100 according to embodiments of the present application may be integrated into a wireless terminal as a software module and/or hardware module. For example, the in-vehicle radio frequency system 100 may be a software module in the operating system of the wireless terminal, or may be an application developed for the wireless terminal; of course, the in-vehicle radio frequency system 100 may also be one of a plurality of hardware modules of the wireless terminal.
Alternatively, in another example, the in-vehicle radio frequency system 100 and the wireless terminal may be separate devices, and the in-vehicle radio frequency system 100 may be connected to the wireless terminal through a wired and/or wireless network and transmit the interactive information in a agreed data format.
Fig. 7 is an application scenario diagram of a method for implementing 4G radio frequency communication using a 5G baseband chip according to an embodiment of the present application. As shown in fig. 7, in this application scenario, first, a transmission signal (e.g., D illustrated in fig. 7) transmitted to a 5G baseband chip by a 4G radio frequency module is acquired, and then, the transmission signal is input to a server (e.g., S illustrated in fig. 7) in which an algorithm for implementing 4G radio frequency communication using the 5G baseband chip is deployed, wherein the server can process the transmission signal using the algorithm for implementing 4G radio frequency communication using the 5G baseband chip to generate an optimized transmission signal.
It should be appreciated that existing vehicle systems use high performance of 5G, but do not actually use 5G modules, which results in wasted chip and increased cost. In order to solve the problem, a 4G module based on a 5G SOC is adopted, and all domestic radio frequency devices are used for reconstruction, so that the vehicle-mounted application meeting the requirements of customers is realized, and the cost is reduced.
The technical scheme of the 4G module based on the 5G SOC can be designed and implemented according to the following steps, a proper 5G SOC chip is selected in terms of a hardware design part, a proper 5G SOC chip is selected according to requirements, and the chip needs to support a 4G network and has enough performance and stability; the domestic radio frequency device is selected for integration, so that the compatibility of the device and a 5G SOC chip is ensured, and the requirement of a vehicle-mounted environment is met; according to the specification requirements of the 5G SOC and the radio frequency device, circuit design and layout are carried out, and stability and reliability of signal transmission are ensured; and carrying out electromagnetic compatibility test on designed hardware to ensure that no interference or external interference is generated in a vehicle-mounted environment. Regarding the software development part, a driver of a 4G module is developed for the 5G SOC chip, and the development of the driver is performed according to the technical document and specification requirements of the 5G SOC chip so as to realize the connection and communication functions of the 4G network; the module identification and the matching degree optimization are carried out, and the module can be ensured to accurately identify and match the vehicle-mounted equipment through a software algorithm and the optimization, so that the stability and the matching degree of the system are improved; stability testing and optimization are performed, and stability and reliability of the module under long-time operation and various environmental conditions are ensured through a large number of tests and optimizations. Regarding the integration and test part, the designed hardware and software are integrated, the hardware and the software are integrated, the normal cooperation of the hardware and the software is ensured, and the corresponding debugging and optimization are performed; functional testing and performance evaluation were performed: performing functional test and performance evaluation on the integrated 4G module to ensure that the integrated 4G module can meet the requirements of clients and has good performance and stability; and (3) carrying out integrated test on the 4G module and the vehicle-mounted system, and ensuring the compatibility and stability of the 4G module and other systems. Through the design and implementation steps, the 4G module based on the 5G SOC can be constructed, and the same performance but lower cost effect can be achieved.
Further, the working process of the 4G module based on the 5G SOC can be divided into the following steps: firstly, hardware design is required, including circuit board design and layout, and the process involves selecting a proper 5G SOC chip and a radio frequency device and designing corresponding circuit connection and layout; after the hardware design is completed, selected radio frequency devices are required to be integrated on a circuit board, wherein the radio frequency devices comprise an antenna, a filter, a power amplifier and the like and are used for processing the transmission and the reception of wireless signals; the next is the MODEM software development phase, which includes writing MODEM drivers and frequency band control logic to ensure that the 5G SOC can communicate correctly with the 4G module, and also requires module identification and matching optimization to ensure compatibility and stability of the module and the vehicle-to-machine system; after software development is completed, the 4G module needs to be integrated into the vehicle-mounted system and tested, and the tests comprise a functional test, an RF index performance test and a compatibility test so as to ensure the normal operation of the 4G module and good compatibility with the vehicle-mounted system. Through the steps, the 4G module based on the 5G SOC can realize the same functions and performances as the traditional 4G module, but the cost is lower.
Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present application and is not to be construed as limiting thereof. Although a few exemplary embodiments of this application have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this application. Accordingly, all such modifications are intended to be included within the scope of this application as defined in the following claims. It is to be understood that the foregoing is illustrative of the present application and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The application is defined by the claims and their equivalents.
Claims (7)
1. A method for implementing 4G radio frequency communication using a 5G baseband chip, comprising:
integrating the 5G baseband chip and the 4G radio frequency module through a circuit board, so that the 5G baseband chip can support 5G and 4G signal processing at the same time;
a bidirectional data transmission channel is established between the 5G baseband chip and the 4G radio frequency module, so that data exchange between the 5G baseband chip and the 4G radio frequency module is realized; according to the network environment and the user requirements, dynamically switching the working modes of the 5G baseband chip and the 4G radio frequency module to realize seamless switching between the 5G and the 4G;
a bidirectional data transmission channel is established between a 5G baseband chip and a 4G radio frequency module, so as to realize data exchange between the 5G baseband chip and the 4G radio frequency module, comprising:
acquiring a transmission signal transmitted to a 5G baseband chip by a 4G radio frequency module;
carrying out signal analysis on the transmission signal to obtain the waveform characteristics of the transmission signal; and
generating an optimized transmission signal based on the transmission signal waveform characteristics;
the signal analysis of the transmission signal to obtain the waveform characteristics of the transmission signal comprises the following steps:
carrying out sliding window sampling based on a sampling window on the transmission signal to obtain a plurality of transmission signal local sampling window signals;
the local sampling window signals of the transmission signals are respectively passed through a local sampling window signal characteristic extractor based on a convolutional neural network model to obtain waveform characteristic vectors of the local sampling window signals of the transmission signals; and
and performing associated coding on the signal waveform characteristic vectors of the plurality of transmission signal local sampling windows to obtain global transmission signal waveform characteristic vectors serving as the transmission signal waveform characteristics.
2. The method for implementing 4G radio frequency communication using a 5G baseband chip according to claim 1, wherein performing associated coding on the plurality of transmission signal local sampling window signal waveform feature vectors to obtain a global transmission signal waveform feature vector as the transmission signal waveform feature, comprises:
the plurality of transmission signal local sampling window signal waveform feature vectors are passed through a context encoder based on a converter module to obtain the global transmission signal waveform feature vector.
3. The method for implementing 4G radio frequency communication using a 5G baseband chip of claim 2, wherein generating an optimized transmission signal based on the transmission signal waveform characteristics comprises:
the global transmission signal waveform feature vector is passed through a decoder to generate the optimized transmission signal.
4. The method for implementing 4G radio frequency communication using a 5G baseband chip of claim 3, further comprising a training step for training the convolutional neural network model-based local sampling window signal feature extractor, the translator module-based context encoder, and the decoder.
5. The method for implementing 4G radio frequency communication using a 5G baseband chip of claim 4, wherein the training step comprises:
acquiring training data, wherein the training data comprises training transmission signals and real signals of the optimized transmission signals;
sampling the training transmission signals by a sliding window based on a sampling window to obtain a plurality of training transmission signal local sampling window signals;
the local sampling window signals of the training transmission signals are respectively passed through the local sampling window signal characteristic extractor based on the convolutional neural network model to obtain waveform characteristic vectors of the local sampling window signals of the training transmission signals;
passing the plurality of training transmission signal local sampling window signal waveform feature vectors through the context encoder based on the converter module to obtain training global transmission signal waveform feature vectors;
passing the training global transmission signal waveform feature vector through the decoder to obtain a decoding loss function value; and training the convolutional neural network model-based local sampling window signal feature extractor, the converter module-based context encoder, and the decoder based on the decoding loss function values and by gradient descent direction propagation, wherein a cross-domain attention-transfer optimization of feature distribution is performed on a weight matrix of the decoder in each iteration of the training.
6. The method for implementing 4G radio frequency communication using a 5G baseband chip of claim 5, wherein in each iteration of the training, a cross-domain attention-transfer optimization of feature distribution is performed on the weight matrix of the decoder with the following optimization formula;
wherein, the optimization formula is:
wherein M is the weight matrix of the decoder, and M is m×m, V 1 To V m Is the M row vectors of the weight matrix M, I.I 2 Representing the two norms of the feature vector (Σ) j m i,j Is a row vector obtained by arranging the summation value of each row vector of the weight matrix M, and cov 1 (. Cndot.) and cov 2 Table of the sameA single-layer convolution operation is shown,representing matrix multiplication (.) T Representing a transpose operation, M' represents a weight matrix of the decoder after iteration.
7. A vehicle-mounted radio frequency system, comprising:
the transmission signal acquisition module is used for acquiring a transmission signal transmitted to the 5G baseband chip by the 4G radio frequency module;
the signal analysis module is used for carrying out signal analysis on the transmission signal to obtain the waveform characteristics of the transmission signal; and
the optimized transmission signal generation module is used for generating an optimized transmission signal based on the waveform characteristics of the transmission signal;
the signal analysis module comprises:
the sliding window sampling unit is used for carrying out sliding window sampling based on the sampling window on the transmission signals so as to obtain a plurality of transmission signal local sampling window signals;
the characteristic extraction unit is used for enabling the partial sampling window signals of the plurality of transmission signals to respectively pass through a partial sampling window signal characteristic extractor based on a convolutional neural network model so as to obtain waveform characteristic vectors of the partial sampling window signals of the plurality of transmission signals; and
and the association coding unit is used for carrying out association coding on the signal waveform characteristic vectors of the plurality of transmission signal local sampling windows so as to obtain a global transmission signal waveform characteristic vector as the transmission signal waveform characteristic.
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