A method for controlling multi-band switching of an RRU

Abstract

The present invention relates to the technical field of wireless base station equipment, and particularly relates to a method for controlling multi-channel frequency band switching of an RRU. The method includes the following steps: extracting frequency band specification parameters through the hardware specification of a 2.1G RRU device and the target frequency band standard specification data to obtain a 2.1G radio frequency parameter set and a target frequency band parameter set; performing frequency band difference analysis based on the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data, and performing spectrum mapping relationship analysis and spectrum mapping relationship modeling based on the frequency band difference data to obtain an initial spectrum mapping relationship model. The present invention realizes the switching control of the target frequency band by adding a frequency band distributor control unit to the intermediate frequency circuit of the RRU; uses a four-to-one data selector to realize the directional selection and output of signals in different frequency bands according to the address code provided by the control subsystem; and realizes the software switching of the mixed frequency signal through the control subsystem, avoiding the complexity and cost brought by hardware switching.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless base station equipment, and in particular to a method for controlling multi-channel frequency band switching of an RRU. Background Art

[0002] With the development of the 5G 3.5GHz + 2.1GHz hybrid network, after the construction of 5G 2.1G devices, due to frequency band conflicts, 4G 2.1G devices will be taken out of service. These devices will become idle materials in the warehouse and cannot be reused in the existing network.

[0003] At the same time, by analyzing the development and operation requirements of the 4G network, there are still a large number of demand points for the coverage and expansion of the 4G network in many scenarios. In response to this demand, through research on the performance and principle of 4G 2.1G devices, the FPGA part of the 2.1G RRU can support multi-band control functions, but the RFIC part cannot support the switching of multiple frequency bands and can only support a single 2.1G frequency band.

[0004] After technological innovation and transformation, it is planned to transform the retired 4G devices in the 2.1G frequency band into 1.8G or 800M devices and put them back into the existing network for use, truly revitalizing the 2.1G device resources. The transformed devices can be used for blind spot filling or expansion of the 4G wireless network, accelerating the elimination of 4G coverage blind spots, and can provide good capacity and network speed, improving the overall coverage quality of the 4G network. Summary of the Invention

[0005] Based on this, it is necessary to provide a method for controlling multi-channel frequency band switching of an RRU to solve at least one of the above technical problems.

[0006] To achieve the above object, a method for controlling multi-channel frequency band switching of an RRU includes the following steps:

[0007] Step S1: Extract frequency band specification parameters through the 2.1G RRU device hardware specification and target frequency band standard specification data to obtain a 2.1G radio frequency parameter set and a target frequency band parameter set; perform frequency band difference analysis based on the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data, perform spectrum mapping relationship analysis based on the frequency band difference data and perform spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model; generate a spectrum mapping table based on the initial spectrum mapping relationship model to obtain a spectrum mapping table;

[0008] Step S2: Perform digital signal sampling on the 2.1G RRU device to obtain a 2.1G digital signal sample set; perform spectrum mapping index conversion on the 2.1G digital signal sample set according to the spectrum mapping table to obtain a target frequency band digital signal sample index set; extract broadband information from the target frequency band parameter set to obtain target frequency band broadband information data; perform intermediate frequency signal reconstruction according to the target frequency band digital signal sample index set and the target frequency band broadband information data to obtain a reconstructed intermediate frequency signal stream;

[0009] Step S3: Extract filter parameters from the reconstructed intermediate frequency signal stream to obtain a filter parameter set; perform coefficient calculation processing according to the filter parameter set to obtain a filter coefficient set; construct a multi-band filter according to the filter coefficient set to obtain multi-band filter parameters;

[0010] Step S4: Monitor the network status through the FPGA network interface module to obtain network status data; perform model initialization processing according to the network status data to obtain an initial model; perform model frequency band training on the initial model according to the multi-band filter parameters to obtain a frequency band allocation model; obtain real-time network status data; generate a frequency band allocation control strategy according to the frequency band allocation model and the real-time network status data to obtain frequency band allocation control strategy data;

[0011] Step S5: Obtain real-time frequency band information data; extract an instruction switching method according to the current frequency band information data and the frequency band allocation control strategy data to obtain instruction switching method data; set switching parameters according to the instruction switching method data to obtain instruction switching parameters; generate a switching instruction according to the instruction switching method data and the instruction switching parameters to obtain frequency band switching control instruction data;

[0012] Step S6: Transmit the frequency band switching control instruction data to the RRU control system, perform frequency band switching execution processing on the reconstructed intermediate frequency signal stream according to the frequency band switching control instruction data and the multi-band filter parameters to obtain a filtered intermediate frequency signal stream; perform radio frequency enhancement processing on the filtered intermediate frequency signal stream to obtain a switched radio frequency signal, so as to implement the frequency band switching operation of the network.

[0013] The present invention analyzes the specification parameters of the 2.1G RRU and the target frequency band, finds the differences between the two, and thereby establishes a preliminary spectrum mapping relationship model, and finally generates a spectrum mapping table. This mapping table provides a guiding basis for the accurate conversion of 2.1G signals to target frequency band signals. According to the spectrum mapping table established in step S1, the 2.1G digital signal is resampled and rearranged to make it preliminarily consistent with the target frequency band in both the time domain and the frequency domain. Then, digital interpolation is performed according to the bandwidth information of the target frequency band to further improve the signal quality, and finally the reconstructed intermediate frequency signal is obtained. Analyze the reconstructed intermediate frequency signal, extract key parameters, and optimize according to the FPGA resource limitations to design a multi-band filter that can effectively filter out-of-band interference and noise. This filter can provide high-quality target frequency band signals for subsequent signal processing links. By real-time monitoring the network status and using machine learning technology, according to information such as network load and the number of users, the optimal frequency band allocation scheme is intelligently predicted. This data-driven frequency band allocation strategy can effectively improve the utilization rate of network resources and enhance the overall performance of the network. According to the optimal frequency band predicted in step S4 and the current state of the RRU, select an appropriate switching method and set corresponding switching parameters, and finally generate a specific frequency band switching control instruction. This instruction contains all the information required for the RRU to perform frequency band switching. The RRU control system configures the hardware according to the received switching instruction and performs corresponding signal processing, such as filtering, frequency conversion, power amplification, etc., and finally realizes the smooth switching of the RRU from the current frequency band to the target frequency band, completing the frequency band switching operation of the network. Therefore, the present invention uses SDR technology to perform digital signal processing, multi-band filter design, frequency band allocation control strategy generation, and frequency band switching control instruction generation through FPGA, realizes flexible frequency band switching control, does not require hardware modification, realizes efficient frequency band switching and signal processing, dynamically generates frequency band allocation control strategies, and improves network performance. Realize the frequency band switching of retired 2.1G RRU devices to make them support 1.8G or 800M networks.

[0014] Preferably, step S1 includes the following steps:

[0015] Step S11: Extract key radio frequency parameters from the hardware specification of the 2.1G RRU device to obtain a 2.1G radio frequency parameter set;

[0016] Step S12: Obtain the standard specification data of the target frequency band; define the target frequency band parameters according to the standard specification data of the target frequency band to obtain a target frequency band parameter set;

[0017] Step S13: Perform frequency band difference analysis according to the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data, perform spectrum mapping relationship analysis according to the frequency band difference data and perform spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model;

[0018] Step S14: Obtain the digital signal sampling rate data of the 2.1G RRU device; perform sampling rate matching on the initial model of the spectrum mapping relationship according to the digital signal sampling rate data of the 2.1G RRU device to obtain the spectrum mapping relationship model;

[0019] Step S15: Generate index mapping rules according to the spectrum mapping relationship model to obtain index mapping rule data; construct a spectrum mapping table according to the index mapping rule data to obtain the spectrum mapping table.

[0020] In the present invention, by extracting the key radio frequency parameters in the hardware specification of the 2.1G RRU device, a 2.1G radio frequency parameter set is formed, providing basic data support for subsequent frequency band difference analysis and spectrum mapping relationship establishment. Obtain the standard specification data of the target frequency band, and define the parameter set of the target frequency band according to these data, providing a target basis for subsequent frequency band difference analysis and spectrum mapping relationship establishment. By comparing and analyzing the differences between the 2.1G radio frequency parameter set and the target frequency band parameter set, such as differences in center frequency, bandwidth, transmit power, etc., a mapping relationship between the two frequency bands is established, and a preliminary spectrum mapping relationship model is constructed based on this, providing a conversion basis for subsequent signal processing. Obtain the digital signal sampling rate data of the 2.1G RRU device, and perform sampling rate matching on the preliminary spectrum mapping relationship model according to this data to ensure that the model can adapt to the actual signal processing process and improve the accuracy of spectrum mapping. According to the final spectrum mapping relationship model, generate clear index mapping rule data, and construct a complete spectrum mapping table based on this. This spectrum mapping table will serve as the key basis for spectrum mapping index conversion in the subsequent signal processing process, guiding the accurate conversion of signals between different frequency bands.

[0021] Preferably, step S13 includes the following steps:

[0022] Step S131: Perform frequency band difference analysis according to the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data, where the frequency band difference data includes center frequency difference data, bandwidth difference data, transmit power difference data, and receive sensitivity difference data;

[0023] Step S132: Calculate the frequency offset value according to the center frequency difference data to obtain the frequency offset value; construct a bandwidth adjustment model according to the bandwidth difference data to obtain the bandwidth adjustment model;

[0024] Step S133: Establish a power matching relationship according to the transmit power difference data to obtain power matching relationship data; establish a sensitivity matching relationship according to the receive sensitivity difference data to obtain sensitivity matching relationship data;

[0025] Step S134: Based on the frequency offset value, bandwidth adjustment model, power matching relationship data, and sensitivity matching relationship data, perform spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model.

[0026] In the present invention, by comparing and analyzing the 2.1G RF parameter set and the target frequency band parameter set, key data such as the center frequency difference, bandwidth difference, transmit power difference, and receive sensitivity difference are extracted. These difference data comprehensively reflect the differences between the two frequency bands, providing a basis for establishing an accurate spectrum mapping relationship subsequently. According to the center frequency difference data, the frequency offset value is calculated, which is used to guide the subsequent translation operation of the signal in the frequency domain to achieve the alignment of different center frequencies. At the same time, according to the bandwidth difference data, a bandwidth adjustment model is constructed to reasonably compress or expand the signal to ensure that the signal can adapt to the bandwidth requirements of the target frequency band. According to the transmit power difference data, a power matching relationship is established to guide the adjustment of the signal's transmit power to ensure that the signal can be transmitted at an appropriate power in the target frequency band, avoiding the signal being too strong or too weak. At the same time, according to the receive sensitivity difference data, a sensitivity matching relationship is established to guide the receiver to receive the signal in the target frequency band to ensure that the signal can be correctly received and demodulated. By comprehensively using the frequency offset value, bandwidth adjustment model, power matching relationship data, and sensitivity matching relationship data obtained in steps S132 and S133, a preliminary spectrum mapping relationship model is established. This model can initially realize the mapping of 2.1G frequency band signals to target frequency band signals, laying a foundation for subsequent more refined spectrum mapping adjustment.

[0027] Preferably, step S2 includes the following steps:

[0028] Step S21: Use the FPGA to perform digital signal sampling on the 2.1G RRU device to obtain a 2.1G digital signal sample set; use a preset frequency band allocator control unit to generate an address code to obtain address code data;

[0029] Step S22: Use a preset four-way data selector to perform sample index mapping on the 2.1G digital signal sample set according to the spectrum mapping table and the address code data to obtain sample index data;

[0030] Step S23: Use a preset four-way data selector to rearrange the 2.1G digital signal sample set according to the sample index data and the address code data to obtain a target frequency band digital signal sample index set;

[0031] Step S24: Resample the 2.1G digital signal sample set according to the target frequency band digital signal sample index set to obtain target frequency band digital signal samples;

[0032] Step S25: Extract the broadband information from the target frequency band parameter set to obtain the target frequency band broadband information data; perform digital signal interpolation on the target frequency band digital signal samples according to the target frequency band broadband information data to obtain the reconstructed intermediate frequency signal stream.

[0033] In the present invention, by using the FPGA to perform high-speed digital signal sampling on the 2.1G RRU device, the original 2.1G digital signal sample set is obtained, providing a data basis for subsequent spectrum mapping and signal reconstruction. Meanwhile, the preset frequency band allocator control unit is used to generate address code data for indexing identification in the subsequent data selection and rearrangement processes. Using the preset four-way data selector, according to the spectrum mapping table and address code data generated in step S1, the 2.1G digital signal sample set is subjected to sample index mapping, mapping the original signal samples to the corresponding positions in the target frequency band, preparing for subsequent data rearrangement. Again using the preset four-way data selector, according to the sample index data and address code data obtained in step S22, the 2.1G digital signal sample set is subjected to data rearrangement, making it re-ordered according to the spectrum structure of the target frequency band, facilitating subsequent resampling and interpolation operations. According to the target frequency band digital signal sample index set obtained in step S23, the 2.1G digital signal sample set is resampled to obtain signal samples at the sampling rate of the target frequency band, initially completing the conversion of the signal from the 2.1G frequency band to the target frequency band. Extract the bandwidth information from the target frequency band parameter set, and perform digital signal interpolation on the target frequency band digital signal samples obtained in step S24 according to this information to make up for the signal distortion that occurs during the resampling process, and finally obtain the reconstructed intermediate frequency signal stream, preparing for subsequent filtering and radio frequency processing.

[0034] Preferably, step S3 includes the following steps:

[0035] Step S31: Use the FPGA to perform spectrum analysis on the reconstructed intermediate frequency signal stream and identify the target frequency band signal range to obtain the signal spectrogram;

[0036] Step S32: Extract the filter parameters from the signal spectrogram to obtain the filter parameter set; perform coefficient calculation processing according to the filter parameter set to obtain the filter coefficient set;

[0037] Step S33: Obtain the FPGA resource limit data; perform filter structure optimization according to the filter coefficient set and the FPGA resource limit data to obtain the filter structure optimization data;

[0038] Step S34: Generate multi-band filter parameters according to the filter structure optimization data and the filter coefficient set.

[0039] The present invention performs spectrum analysis on the reconstructed intermediate frequency signal stream by using an FPGA to identify the specific range of the target frequency band signal on the spectrogram, providing accurate spectrum information for subsequent filter parameter extraction. According to the signal spectrogram obtained in step S31, key parameters required for constructing the filter are extracted, such as the center frequency, bandwidth, stopband attenuation, etc., to form a filter parameter set. And based on this parameter set, combined with specific filter design methods, the coefficient set of the filter is calculated, providing a parameter basis for subsequent filter implementation. Obtain the resource limitation data of the FPGA, such as logic resources, storage resources, etc., and according to these limitation data and the filter coefficient set obtained in step S32, optimize the structure of the filter, such as adopting a cascade structure, a parallel structure or a multi-rate structure, etc., to save the FPGA resources to the greatest extent and improve the system efficiency. According to the filter structure optimization data and the filter coefficient set obtained in step S33, generate the final multi-band filter parameters for configuring the filter module inside the FPGA to achieve precise filtering of the target frequency band signal and provide high-quality signals for subsequent radio frequency processing.

[0040] Preferably, step S32 includes the following steps:

[0041] Step S321: Perform spectrum peak detection on the signal spectrogram to obtain the center frequency data of the target frequency band;

[0042] Step S322: Estimate the bandwidth according to the broadband information data of the target frequency band to obtain the bandwidth range data of the target frequency band;

[0043] Step S323: Calculate the filter parameters according to the center frequency data of the target frequency band and the bandwidth range data of the target frequency band to obtain a filter parameter set;

[0044] Step S324: Select the filter type according to the filter parameter set to obtain the filter type data;

[0045] Step S325: Calculate the filter coefficients according to the filter type data and the filter parameter set; optimize the coefficients of the filter coefficients to obtain a filter coefficient set.

[0046] The present invention analyzes the signal spectrogram, detects the spectral peak, and thus accurately obtains the center frequency data of the target frequency band, providing a basis for setting the center frequency of the subsequent filter. Based on the bandwidth information data of the target frequency band and combining the spectral analysis results, the actual bandwidth of the target signal is estimated to obtain the bandwidth range data of the target frequency band, providing a reference for the bandwidth design of the subsequent filter. According to the center frequency data and bandwidth range data of the target frequency band obtained in steps S321 and S322, and combining the performance index requirements of the filter (such as stopband attenuation, transition band roll-off, etc.), a set of filter parameters that meet the requirements is calculated, such as passband cut-off frequency, stopband cut-off frequency, filter order, etc. According to the set of filter parameters calculated in step S323, a suitable filter type is selected. Different types of filters have different amplitude-frequency characteristics and phase-frequency characteristics, and need to be selected according to actual requirements. According to the filter type selected in step S324 and the set of filter parameters calculated in step S323, the filter coefficients are calculated using a filter design algorithm. To further improve the performance of the filter, such as reducing the passband ripple and increasing the stopband attenuation, the filter coefficients can be optimized.

[0047] Preferably, step S4 includes the following steps:

[0048] Step S41: Monitor the network status through the FPGA network interface module to obtain network status data;

[0049] Step S42: Extract features from the multi-band filter parameters and network status data to obtain a set of feature vectors;

[0050] Step S43: Perform model initialization processing according to the network status data to obtain an initial model; perform model frequency band training on the initial model according to the set of feature vectors to obtain a frequency band allocation model;

[0051] Step S44: Obtain real-time network status data; perform frequency band allocation prediction on the real-time network status data according to the frequency band allocation model to obtain frequency band allocation strategy data; generate a frequency band allocation control strategy according to the frequency band allocation strategy data and a preset frequency band allocator control unit to obtain frequency band allocation control strategy data.

[0052] The present invention monitors the network status in real time by using the FPGA network interface module, such as network load, number of users, interference situation, etc., and collects this information to form network status data, providing a basis for subsequent frequency band allocation decisions. Extract key features from the multi-band filter parameters obtained in step S3 and the network status data obtained in step S41, such as signal strength, signal-to-noise ratio, user distribution, interference strength, etc., and combine these features into a feature vector set to prepare for subsequent model training. According to the historical network status data, select a suitable model and initialize it, such as a neural network, decision tree, etc., to obtain an initial model. Then, use the feature vector set extracted in step S42 to train the initial model so that the model can learn the relationship between the network status and the optimal frequency band allocation, and finally obtain a frequency band allocation model that can predict the optimal frequency band allocation according to the network status. Obtain the latest network status data in real time and input it into the frequency band allocation model trained in step S43 to perform frequency band allocation prediction and obtain preliminary frequency band allocation strategy data. Finally, combine the preset frequency band allocation rules and the frequency band allocator control unit to adjust and optimize the preliminary frequency band allocation strategy data to generate the final frequency band allocation control strategy data for guiding the frequency band switching operation of the RRU.

[0053] Preferably, step S43 includes the following steps:

[0054] Step S431: Perform model selection according to the network status data to obtain model selection data; set the learning rate according to the model selection data to obtain model learning rate data; analyze the number of hidden layers according to the model selection data to obtain model hidden layer number data;

[0055] Step S432: Generate an initial model according to the model learning rate data and the model hidden layer number data to obtain an initial model;

[0056] Step S433: Obtain historical frequency band data; train the initial model according to the feature vector set and the historical frequency band data to obtain a trained model;

[0057] Step S434: Evaluate the trained model according to the feature vector set and the historical frequency band data to obtain model evaluation data; adjust the trained model according to the model evaluation data to obtain a frequency band allocation model.

[0058] The present invention obtains model selection data by analyzing the characteristics and scale of network state data. According to the selected model type and data scale, an appropriate model learning rate is set to obtain model learning rate data. At the same time, according to the model complexity and data feature dimension, the number of hidden layers of the model is determined to obtain model hidden layer number data. The selection of these parameters aims to construct a model that can effectively learn the relationship between network state and frequency band allocation. According to the parameters such as the model type, learning rate, and number of hidden layers determined in step S431, the model parameters are initialized, and an initial frequency band allocation model is built to prepare for subsequent model training. The best frequency band allocation data corresponding to historical network state data is collected, and using these historical data and the feature vector set extracted in step S42, the initial model generated in step S432 is trained, and the model parameters are continuously adjusted to enable it to accurately predict the best frequency band allocation scheme according to the input feature vectors. Another part of the historical data and the feature vector set are used to evaluate the model trained in step S433 to test the generalization ability and prediction accuracy of the model. According to the evaluation results, the model is further adjusted, such as modifying the model structure, adjusting hyperparameters, etc., and finally a frequency band allocation model with excellent performance is obtained.

[0059] Preferably, step S5 includes the following steps:

[0060] Step S51: Use the FPGA data parsing module to parse the frequency band allocation control strategy data to obtain target frequency band information data and switching instruction information data;

[0061] Step S52: Obtain real-time frequency band information data through the FPGA communication interface module; analyze the instruction switching method based on the real-time frequency band information data and the target frequency band information data to obtain instruction switching method data;

[0062] Step S53: Set switching parameters according to the instruction switching method data to obtain instruction switching parameters;

[0063] Step S54: Generate a switching instruction according to the switching instruction information data, the instruction switching method data, the instruction switching parameters, and the target frequency band information data to obtain frequency band switching control instruction data.

[0064] The present invention analyzes the frequency band allocation control strategy data generated in step S4 by using an FPGA data analysis module to extract target frequency band information data, such as the center frequency and bandwidth of the target frequency band, as well as handover instruction information data, such as handover time and handover method, etc., to prepare for the subsequent generation of handover instructions. The current frequency band information of the RRU is obtained through the FPGA communication interface module, such as the center frequency and bandwidth of the current frequency band, etc. Then, based on the real-time frequency band information data and the target frequency band information data analyzed in step S51, the best instruction handover method for realizing frequency band handover is analyzed, such as hard handover, soft handover or seamless handover, etc., to obtain instruction handover method data. According to the instruction handover method determined in step S52, corresponding handover parameters are set, such as the handover time for hard handover, the power slope for soft handover, the phase compensation for seamless handover, etc., to obtain instruction handover parameters, which are used to ensure the smoothness and stability of the frequency band handover process. By comprehensively using the handover instruction information data analyzed in step S51, the instruction handover method data analyzed in step S52, the instruction handover parameters set in step S53, and the target frequency band information data, the final frequency band handover control instruction data is generated, which is used to control the RRU to perform specific frequency band handover operations.

[0065] Preferably, step S6 includes the following steps:

[0066] Step S61: Use the FPGA communication interface module to transmit the frequency band handover control instruction data to the RRU control system, extract handover parameters according to the frequency band handover control instruction data, and obtain frequency band handover parameters;

[0067] Step S62: Integrate the frequency band handover parameters and the multi-band filter parameters to obtain a set of handover parameters; configure the filter according to the set of handover parameters to obtain the configured filter data;

[0068] Step S63: Perform digital filtering processing on the reconstructed intermediate frequency signal stream according to the configured filter data to obtain digital filtering data; perform signal reconstruction on the digital filtering data according to the set of handover parameters to obtain the filtered intermediate frequency signal stream;

[0069] Step S64: Perform digital-to-analog conversion on the filtered intermediate frequency signal stream to obtain analog intermediate frequency signal data; perform upper sideband processing on the analog intermediate frequency signal data to obtain an analog radio frequency signal;

[0070] Step S65: Perform power amplification processing on the analog radio frequency signal to obtain the handover radio frequency signal, so as to realize the frequency band handover operation of the network.

[0071] In the present invention, the frequency band switching control instruction data generated in step S5 is transmitted to the RRU control system by using the FPGA communication interface module. The RRU control system extracts specific frequency band switching parameters according to the received instruction data, so as to prepare for the subsequent signal processing link. The frequency band switching parameters extracted in step S61 are integrated with the multi-band filter parameters generated in step S3 to form a complete switching parameter set. Then, the multi-band filter is configured according to this parameter set, for example, the center frequency, bandwidth, etc. of the filter are updated to obtain the configured filter data, so as to prepare for the subsequent signal filtering. The configured filter in step S62 is used to perform digital filtering on the intermediate frequency signal stream reconstructed in step S2 to filter out out-of-band interference and noise, and obtain clean digital filtering data. Then, the digital filtering data is signal-reconstructed according to the switching parameter set, for example, frequency shifting, amplitude adjustment, etc. are performed to obtain the filtered intermediate frequency signal stream that meets the requirements of the target frequency band. The filtered intermediate frequency signal stream obtained in step S63 is subjected to digital-to-analog conversion to obtain an analog intermediate frequency signal. Then, the analog intermediate frequency signal is up-converted to shift it to the radio frequency frequency range of the target frequency band to obtain an analog radio frequency signal. The analog radio frequency signal obtained in step S64 is subjected to power amplification processing to make it reach the target transmission power, and finally the switched radio frequency signal is obtained and transmitted through the antenna to complete the frequency band switching operation of the network.

[0072] The present invention analyzes the specification parameters of the 2.1G RRU and the target frequency band, finds the differences between the two, and establishes a preliminary spectrum mapping relationship model based on this, and finally generates a spectrum mapping table. This mapping table provides a guiding basis for the accurate conversion of 2.1G signals to target frequency band signals. According to the spectrum mapping table established in step S1, the 2.1G digital signal is resampled and rearranged to make it preliminarily consistent with the target frequency band in both the time domain and the frequency domain. Then, digital interpolation is performed according to the bandwidth information of the target frequency band to further improve the signal quality, and finally the reconstructed intermediate frequency signal is obtained. The reconstructed intermediate frequency signal is analyzed, key parameters are extracted, and optimization is carried out according to the FPGA resource constraints, and a multi-band filter capable of effectively filtering out-of-band interference and noise is designed. This filter can provide high-quality target frequency band signals for subsequent signal processing links. By real-time monitoring the network status and using machine learning technology, according to information such as network load and the number of users, the best frequency band allocation scheme is intelligently predicted. This data-driven frequency band allocation strategy can effectively improve the utilization rate of network resources and enhance the overall performance of the network. According to the best frequency band predicted in step S4 and the current state of the RRU, a suitable switching method is selected, and corresponding switching parameters are set, and finally a specific frequency band switching control instruction is generated. This instruction contains all the information required for the RRU to perform frequency band switching. The RRU control system configures the hardware according to the received switching instruction and performs corresponding signal processing, such as filtering, frequency conversion, power amplification, etc., and finally realizes the smooth switching of the RRU from the current frequency band to the target frequency band and completes the frequency band switching operation of the network. Therefore, the present invention uses SDR technology to perform digital signal processing, multi-band filter design, frequency band allocation control strategy generation, and frequency band switching control instruction generation through FPGA, realizes flexible frequency band switching control, does not require hardware modification, realizes efficient frequency band switching and signal processing, dynamically generates frequency band allocation control strategies, and improves network performance. Realize the frequency band switching of retired 2.1G RRU devices to support 1.8G or 800M networks. Description of the Drawings

[0073] Figure 1 It is a schematic diagram of the step flow of the method for multi-channel frequency band switching control of RRU;

[0074] Figure 2 For Figure 1 It is a schematic diagram of the detailed implementation step flow of step S2 in

[0075] Figure 3 For Figure 1 It is a schematic diagram of the detailed implementation step flow of step S4 in

[0076] Figure 4 It is a schematic diagram of the principle of the frequency band allocator control unit of a method for multi-channel frequency band switching control of RRU;

[0077] Figure 5 It is a schematic diagram of a small system of a frequency allocation control unit for a method of RRU multi-band switching control.

[0078] The realization of the purpose, functional features and advantages of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0079] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be called the second unit, and similarly the second unit may be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0082] To achieve the above object, please refer to Figures 1 to 5 , a method for RRU multi-band switching control, comprising the following steps:

[0083] Step S1: Extract band specification parameters through the 2.1G RRU device hardware specification and the target band standard specification data to obtain a 2.1G radio frequency parameter set and a target band parameter set; perform band difference analysis based on the 2.1G radio frequency parameter set and the target band parameter set to obtain band difference data, perform spectrum mapping relationship analysis based on the band difference data and perform spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model; generate a spectrum mapping table based on the initial spectrum mapping relationship model to obtain a spectrum mapping table;

[0084] Step S2: Perform digital signal sampling on the 2.1G RRU device to obtain a 2.1G digital signal sample set; perform spectrum mapping index conversion on the 2.1G digital signal sample set according to the spectrum mapping table to obtain a target frequency band digital signal sample index set; perform broadband information extraction on the target frequency band parameter set to obtain target frequency band broadband information data; perform intermediate frequency signal reconstruction according to the target frequency band digital signal sample index set and the target frequency band broadband information data to obtain a reconstructed intermediate frequency signal stream;

[0085] Step S3: Extract filter parameters according to the reconstructed intermediate frequency signal stream to obtain a filter parameter set; perform coefficient calculation processing according to the filter parameter set to obtain a filter coefficient set; construct a multi-band filter according to the filter coefficient set to obtain multi-band filter parameters;

[0086] Step S4: Monitor the network status through the FPGA network interface module to obtain network status data; perform model initialization processing according to the network status data to obtain an initial model; perform model frequency band training on the initial model according to the multi-band filter parameters to obtain a frequency band allocation model; obtain real-time network status data; generate a frequency band allocation control strategy according to the frequency band allocation model and the real-time network status data to obtain frequency band allocation control strategy data;

[0087] Step S5: Obtain real-time frequency band information data; extract an instruction switching method according to the current frequency band information data and the frequency band allocation control strategy data to obtain instruction switching method data; set switching parameters according to the instruction switching method data to obtain instruction switching parameters; generate a switching instruction according to the instruction switching method data and the instruction switching parameters to obtain frequency band switching control instruction data;

[0088] Step S6: Transmit the frequency band switching control instruction data to the RRU control system, perform frequency band switching execution processing on the reconstructed intermediate frequency signal stream according to the frequency band switching control instruction data and the multi-band filter parameters to obtain a filtered intermediate frequency signal stream; perform radio frequency enhancement processing on the filtered intermediate frequency signal stream to obtain a switched radio frequency signal, so as to realize the frequency band switching operation of the network.

[0089] The present invention analyzes the specification parameters of the 2.1G RRU and the target frequency band, finds the differences between the two, and establishes a preliminary spectrum mapping relationship model based on this, and finally generates a spectrum mapping table. This mapping table provides a guiding basis for the accurate conversion of 2.1G signals to target frequency band signals. According to the spectrum mapping table established in step S1, the 2.1G digital signal is resampled and rearranged to make it preliminarily consistent with the target frequency band in both the time domain and the frequency domain. Then, digital interpolation is performed according to the bandwidth information of the target frequency band to further improve the signal quality, and finally the reconstructed intermediate frequency signal is obtained. Analyze the reconstructed intermediate frequency signal, extract key parameters, and optimize according to the FPGA resource constraints to design a multi-band filter that can effectively filter out-of-band interference and noise. This filter can provide high-quality target frequency band signals for subsequent signal processing. By real-time monitoring the network status and using machine learning technology, according to information such as network load and number of users, the optimal frequency band allocation scheme is intelligently predicted. This data-driven frequency band allocation strategy can effectively improve the utilization rate of network resources and enhance the overall performance of the network. According to the optimal frequency band predicted in step S4 and the current status of the RRU, select an appropriate switching method and set corresponding switching parameters, and finally generate a specific frequency band switching control instruction. This instruction contains all the information required for the RRU to perform frequency band switching. The RRU control system configures the hardware according to the received switching instruction and performs corresponding signal processing, such as filtering, frequency conversion, power amplification, etc., and finally realizes the smooth switching of the RRU from the current frequency band to the target frequency band, completing the frequency band switching operation of the network. Therefore, the present invention uses SDR technology to perform digital signal processing, multi-band filter design, frequency band allocation control strategy generation, and frequency band switching control instruction generation through FPGA, realizes flexible frequency band switching control, does not require hardware modification, realizes efficient frequency band switching and signal processing, dynamically generates frequency band allocation control strategies, and improves network performance. Realize the frequency band switching of retired 2.1G RRU devices to support 1.8G or 800M networks.

[0090] In an embodiment of the present invention, refer to Figure 1 As described, it is a schematic diagram of the step flow of the method for multi-channel frequency band switching control of the RRU of the present invention. In this example, the method for multi-channel frequency band switching control of the RRU includes the following steps:

[0091] Step S1: Extract frequency band specification parameters through the 2.1G RRU device hardware specification and the target frequency band standard specification data to obtain a 2.1G radio frequency parameter set and a target frequency band parameter set; perform frequency band difference analysis according to the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data, perform spectrum mapping relationship analysis based on the frequency band difference data and perform spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model; generate a spectrum mapping table according to the initial spectrum mapping relationship model to obtain a spectrum mapping table;

[0092] In the embodiment of the present invention, the 2.1G RRU hardware specification and the target frequency band standard specification are analyzed, key radio frequency parameters are extracted to form a parameter set. The two parameter sets are compared and analyzed, the differences such as the center frequency and bandwidth are calculated, and a preliminary spectrum mapping model is established. According to the 2.1G RRU sampling rate adjustment model, a spectrum mapping table of the corresponding relationship between the 2.1G signal and the target frequency band signal is finally generated.

[0093] Step S2: Perform digital signal sampling on the 2.1G RRU device to obtain a 2.1G digital signal sample set; perform spectrum mapping index conversion on the 2.1G digital signal sample set according to the spectrum mapping table to obtain a target frequency band digital signal sample index set; extract broadband information from the target frequency band parameter set to obtain target frequency band broadband information data; perform intermediate frequency signal reconstruction according to the target frequency band digital signal sample index set and the target frequency band broadband information data to obtain a reconstructed intermediate frequency signal stream;

[0094] In the embodiment of the present invention, the FPGA is used to sample the 2.1G RRU signal to obtain a digital signal sample set. According to the spectrum mapping table, the sample index is mapped to the corresponding position in the target frequency band, and data rearrangement is performed. The signal is resampled at the target frequency band sampling rate, and digital interpolation is performed according to the target frequency band bandwidth information, and finally a reconstructed intermediate frequency signal is obtained.

[0095] Step S3: Extract filter parameters according to the reconstructed intermediate frequency signal stream to obtain a filter parameter set; perform coefficient calculation processing according to the filter parameter set to obtain a filter coefficient set; construct a multi-band filter according to the filter coefficient set to obtain multi-band filter parameters;

[0096] In the embodiment of the present invention, spectrum analysis is performed on the reconstructed intermediate frequency signal to identify the target frequency band signal range and extract filter parameters. Filter coefficients are calculated according to the parameters, and the filter structure is optimized according to the FPGA resource limitations, and finally multi-band filter parameters are generated for configuring the filter module of the FPGA.

[0097] Step S4: Monitor the network status through the FPGA network interface module to obtain network status data; perform model initialization processing according to the network status data to obtain an initial model; perform model frequency band training on the initial model according to the multi-band filter parameters to obtain a frequency band allocation model; obtain real-time network status data; generate a frequency band allocation control strategy according to the frequency band allocation model and the real-time network status data to obtain frequency band allocation control strategy data;

[0098] In an embodiment of the present invention, an FPGA network interface module is used to obtain network status data such as network load and the number of users. A suitable machine learning model is selected and trained using historical network status and frequency band allocation data to obtain a frequency band allocation model. Network status data is obtained in real time, and the model is used to predict the optimal frequency band allocation scheme, and the final frequency band allocation control strategy data is generated according to preset rules.

[0099] Step S5: Obtain real-time frequency band information data; extract the instruction switching method according to the current frequency band information data and the frequency band allocation control strategy data to obtain instruction switching method data; set switching parameters according to the instruction switching method data to obtain instruction switching parameters; perform switching instruction generation processing according to the instruction switching method data and the instruction switching parameters to obtain frequency band switching control instruction data;

[0100] In an embodiment of the present invention, the FPGA reads the current operating frequency band information of the RRU and compares it with the target frequency band information generated in step S4 to determine the instruction switching method. Set switching parameters according to the switching method, such as switching time, power slope, etc. Finally, integrate the switching instruction information, switching method, switching parameters, and target frequency band information to generate complete frequency band switching control instruction data.

[0101] Step S6: Transmit the frequency band switching control instruction data to the RRU control system, perform frequency band switching execution processing on the reconstructed intermediate frequency signal stream according to the frequency band switching control instruction data and the multi-band filter parameters to obtain a filtered intermediate frequency signal stream; perform radio frequency enhancement processing on the filtered intermediate frequency signal stream to obtain a switched radio frequency signal to implement the frequency band switching operation of the network;

[0102] Step S5: The FPGA transmits the frequency band switching control instruction data to the RRU control system. The RRU control system analyzes the instruction data, extracts the switching parameters, and configures the multi-band filter and radio frequency module accordingly. Finally, perform filtering, frequency conversion, power amplification, etc. on the reconstructed intermediate frequency signal to generate a radio frequency signal that meets the target frequency band requirements and transmit it through the antenna to complete the frequency band switching operation.

[0103] Preferably, step S1 includes the following steps:

[0104] Step S11: Extract key radio frequency parameters from the hardware specification of the 2.1G RRU device to obtain a 2.1G radio frequency parameter set;

[0105] Step S12: Obtain target frequency band standard specification data; define target frequency band parameters according to the target frequency band standard specification data to obtain a target frequency band parameter set;

[0106] Step S13: Perform frequency band difference analysis based on the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data. Perform spectrum mapping relationship analysis based on the frequency band difference data and conduct spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model;

[0107] Step S14: Obtain the digital signal sampling rate data of the 2.1G RRU device; perform sampling rate matching on the initial spectrum mapping relationship model according to the digital signal sampling rate data of the 2.1G RRU device to obtain a spectrum mapping relationship model;

[0108] Step S15: Generate index mapping rule data according to the spectrum mapping relationship model; construct a spectrum mapping table according to the index mapping rule data.

[0109] In the present invention, key radio frequency parameters in the hardware specification of the 2.1G RRU device are extracted to form a 2.1G radio frequency parameter set, providing basic data support for subsequent frequency band difference analysis and spectrum mapping relationship establishment. Obtain the standard specification data of the target frequency band, and define the parameter set of the target frequency band according to these data, providing a target basis for subsequent frequency band difference analysis and spectrum mapping relationship establishment. By comparing and analyzing the differences between the 2.1G radio frequency parameter set and the target frequency band parameter set, such as differences in center frequency, bandwidth, transmit power, etc., establish a mapping relationship between the two frequency bands, and construct a preliminary spectrum mapping relationship model based on this, providing a conversion basis for subsequent signal processing. Obtain the digital signal sampling rate data of the 2.1G RRU device, and perform sampling rate matching on the preliminary spectrum mapping relationship model according to this data to ensure that the model can adapt to the actual signal processing process and improve the accuracy of spectrum mapping. According to the final spectrum mapping relationship model, generate clear index mapping rule data, and construct a complete spectrum mapping table based on this. This spectrum mapping table will serve as the key basis for spectrum mapping index conversion in the subsequent signal processing process, guiding the accurate conversion of signals between different frequency bands.

[0110] In the embodiments of the present invention, analyze the hardware specification of the 2.1G RRU device, extract key radio frequency parameters related to the frequency band from it, such as operating frequency band, bandwidth, transmit power, receive sensitivity, etc., and record these parameters to form a 2.1G radio frequency parameter set. Analyze the standard specification documents of the target frequency band, such as the 3GPP protocol, obtain relevant information of the target frequency band from it, such as frequency band range, channel bandwidth, modulation method, etc., and define the parameters of the target frequency band according to this information to form a target frequency band parameter set. Compare the 2.1G radio frequency parameter set with the target frequency band parameter set, analyze the differences between the two in terms of center frequency, bandwidth, transmit power, receive sensitivity, etc., and establish a spectrum mapping relationship between the 2.1G frequency band and the target frequency band based on these difference data to construct a preliminary spectrum mapping relationship model. Obtain the sampling rate data of its digital signal from the hardware specification of the 2.1G RRU device or the relevant configuration file. According to this sampling rate data, adjust the initial spectrum mapping relationship model established in step S13 to ensure that the model can adapt to the actual signal sampling rate and obtain the final spectrum mapping relationship model. According to the spectrum mapping relationship model obtained in step S14, formulate detailed index mapping rules, clearly stipulate the corresponding relationship between the 2.1G frequency band signal sample index and the target frequency band signal sample index. Finally, construct a complete spectrum mapping table according to these index mapping rules to guide the subsequent signal processing process.

[0111] Preferably, step S13 includes the following steps:

[0112] Step S131: Perform frequency band difference analysis based on the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data, where the frequency band difference data includes center frequency difference data, bandwidth difference data, transmit power difference data, and receive sensitivity difference data;

[0113] Step S132: Calculate the frequency offset value according to the center frequency difference data to obtain the frequency offset value; construct a bandwidth adjustment model according to the bandwidth difference data to obtain the bandwidth adjustment model;

[0114] Step S133: Establish a power matching relationship according to the transmit power difference data to obtain power matching relationship data; establish a sensitivity matching relationship according to the receive sensitivity difference data to obtain sensitivity matching relationship data;

[0115] Step S134: Perform spectrum mapping relationship modeling based on the frequency offset value, bandwidth adjustment model, power matching relationship data, and sensitivity matching relationship data to obtain an initial spectrum mapping relationship model.

[0116] The present invention extracts key data such as center frequency difference, bandwidth difference, transmit power difference, and receive sensitivity difference by comparing and analyzing the 2.1G RF parameter set and the target frequency band parameter set. These difference data comprehensively reflect the differences between the two frequency bands and provide a basis for establishing an accurate spectrum mapping relationship subsequently. The frequency offset value is calculated based on the center frequency difference data to guide the subsequent translation operation of the signal in the frequency domain to achieve the alignment of different center frequencies. At the same time, a bandwidth adjustment model is constructed according to the bandwidth difference data to reasonably compress or expand the signal to ensure that the signal can adapt to the bandwidth requirements of the target frequency band. A power matching relationship is established based on the transmit power difference data to guide the adjustment of the signal in transmit power to ensure that the signal can be transmitted at an appropriate power in the target frequency band and avoid the signal being too strong or too weak. At the same time, a sensitivity matching relationship is established according to the receive sensitivity difference data to guide the receiver to receive the signal in the target frequency band to ensure that the signal can be correctly received and demodulated. By comprehensively using the frequency offset value, bandwidth adjustment model, power matching relationship data, and sensitivity matching relationship data obtained in steps S132 and S133, a preliminary spectrum mapping relationship model is established. This model can initially realize the mapping of 2.1G frequency band signals to target frequency band signals and lay a foundation for subsequent more refined spectrum mapping adjustment.

[0117] In the embodiment of the present invention, the 2.1G RF parameter set and the target frequency band parameter set are compared, and the center frequency difference, bandwidth difference, transmit power difference, and receive sensitivity difference are calculated respectively, and these difference data are recorded to form frequency band difference data. The frequency offset value is directly calculated according to the center frequency difference data obtained by step S131. At the same time, according to the bandwidth difference data, a suitable bandwidth adjustment algorithm, such as linear interpolation method, polynomial fitting method, etc., is selected to construct a bandwidth adjustment model. According to the transmit power difference data, a corresponding relationship between the transmit power in the 2.1G frequency band and the transmit power in the target frequency band is established to form power matching relationship data. Similarly, according to the receive sensitivity difference data, a corresponding relationship between the receive sensitivity in the 2.1G frequency band and the receive sensitivity in the target frequency band is established to form sensitivity matching relationship data. The frequency offset value and bandwidth adjustment model calculated in step S132, and the power matching relationship data and sensitivity matching relationship data established in step S133 are integrated into a model to construct a preliminary spectrum mapping relationship model.

[0118] Preferably, step S2 includes the following steps:

[0119] Step S21: Use FPGA to sample the digital signals of the 2.1G RRU device to obtain a 2.1G digital signal sample set; use a preset frequency band allocator control unit to generate an address code to obtain address code data;

[0120] Step S22: Use a preset four - to - one data selector to perform sample index mapping on the 2.1G digital signal sample set according to the spectrum mapping table and the address code data, obtaining sample index data;

[0121] Step S23: Use a preset four - to - one data selector to perform data rearrangement on the 2.1G digital signal sample set according to the sample index data and the address code data, obtaining the digital signal sample index set of the target frequency band;

[0122] Step S24: Resample the 2.1G digital signal sample set according to the digital signal sample index set of the target frequency band, obtaining the digital signal samples of the target frequency band;

[0123] Step S25: Extract broadband information from the target frequency band parameter set, obtaining the broadband information data of the target frequency band; Interpolate the digital signal samples of the target frequency band according to the broadband information data of the target frequency band, obtaining the reconstructed intermediate - frequency signal stream.

[0124] The present invention obtains the original 2.1G digital signal sample set by using an FPGA to perform high - speed digital signal sampling on a 2.1G RRU device, providing a data basis for subsequent spectrum mapping and signal reconstruction. Meanwhile, a preset frequency band allocator control unit is used to generate address code data for index identification in subsequent data selection and rearrangement processes. Using a preset four - to - one data selector, according to the spectrum mapping table and the address code data generated in step S1, perform sample index mapping on the 2.1G digital signal sample set, mapping the original signal samples to the corresponding positions in the target frequency band, preparing for subsequent data rearrangement. Once again, use a preset four - to - one data selector, according to the sample index data obtained in step S22 and the address code data, perform data rearrangement on the 2.1G digital signal sample set, re - sorting it according to the spectrum structure of the target frequency band, facilitating subsequent resampling and interpolation operations. According to the digital signal sample index set of the target frequency band obtained in step S23, resample the 2.1G digital signal sample set, obtaining signal samples at the sampling rate of the target frequency band, initially completing the conversion of the signal from the 2.1G frequency band to the target frequency band. Extract the bandwidth information from the target frequency band parameter set, and interpolate the digital signal samples of the target frequency band obtained in step S24 according to this information, compensating for signal distortion during the resampling process, and finally obtaining the reconstructed intermediate - frequency signal stream, preparing for subsequent filtering and radio - frequency processing.

[0125] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0126] Step S21: Use the FPGA to sample the digital signals of the 2.1G RRU device to obtain a 2.1G digital signal sample set; use a preset frequency band allocator control unit to generate address code data;

[0127] In the embodiment of the present invention, the ADC module inside the FPGA is used to sample the digital intermediate frequency signals of the 2.1G RRU device, and the sampled digital signals are stored in the cache of the FPGA to form a 2.1G digital signal sample set. At the same time, a preset counter or state machine module is used as the frequency band allocator control unit to generate address code data for data selection and rearrangement.

[0128] Step S22: Use a preset four-to-one data selector to perform sample index mapping on the 2.1G digital signal sample set according to the spectrum mapping table and the address code data to obtain sample index data;

[0129] In the embodiment of the present invention, the corresponding entries in the spectrum mapping table are selected according to the address code data, and the index values of each sample in the 2.1G digital signal sample set are mapped to obtain the sample index data corresponding to the target frequency band.

[0130] Step S23: Use a preset four-to-one data selector to perform data rearrangement on the 2.1G digital signal sample set according to the sample index data and the address code data to obtain a target frequency band digital signal sample index set;

[0131] In the embodiment of the present invention, according to the address code data and the sample index data obtained in step S22, a four-to-one data selector is used to select the corresponding samples from the 2.1G digital signal sample set and rearrange them in the order of the target frequency band to form a target frequency band digital signal sample index set.

[0132] Step S24: Resample the 2.1G digital signal sample set according to the target frequency band digital signal sample index set to obtain target frequency band digital signal samples;

[0133] In the embodiment of the present invention, according to the target frequency band digital signal sample index set, the corresponding samples are extracted from the 2.1G digital signal sample set and rearranged at the sampling rate of the target frequency band to obtain target frequency band digital signal samples.

[0134] Step S25: Extract broadband information from the target frequency band parameter set to obtain target frequency band broadband information data; perform digital signal interpolation on the target frequency band digital signal samples according to the target frequency band broadband information data to obtain a reconstructed intermediate frequency signal stream;

[0135] In the embodiment of the present invention, the bandwidth information of the target frequency band is read from the target frequency band parameter set, and according to this information, a suitable digital interpolation algorithm, such as linear interpolation, cubic spline interpolation, etc., is selected to perform interpolation processing on the target frequency band digital signal samples obtained in step S24, and finally the reconstructed intermediate frequency signal stream is obtained.

[0136] Preferably, step S3 includes the following steps:

[0137] Step S31: Use the FPGA to perform spectrum analysis on the reconstructed intermediate frequency signal stream and identify the signal range of the target frequency band to obtain a signal spectrogram;

[0138] Step S32: Extract filter parameters from the signal spectrogram to obtain a filter parameter set; perform coefficient calculation processing according to the filter parameter set to obtain a filter coefficient set;

[0139] Step S33: Obtain FPGA resource limit data; optimize the filter structure according to the filter coefficient set and the FPGA resource limit data to obtain optimized filter structure data;

[0140] Step S34: Generate multi-band filter parameters according to the optimized filter structure data and the filter coefficient set to obtain multi-band filter parameters.

[0141] The present invention performs spectrum analysis on the reconstructed intermediate frequency signal stream by using the FPGA to identify the specific range of the target frequency band signal on the spectrogram, providing accurate spectrum information for subsequent filter parameter extraction. According to the signal spectrogram obtained in step S31, the key parameters required for constructing the filter, such as center frequency, bandwidth, stopband attenuation, etc., are extracted to form a filter parameter set. And according to this parameter set, combined with specific filter design methods, the coefficient set of the filter is calculated, providing a parameter basis for subsequent filter implementation. Obtain the resource limit data of the FPGA, such as logic resources, storage resources, etc., and optimize the structure of the filter according to these limit data and the filter coefficient set obtained in step S32, such as using a cascade structure, a parallel structure or a multi-rate structure, etc., to save the FPGA resources to the greatest extent and improve the system efficiency. According to the optimized filter structure data and the filter coefficient set obtained in step S33, the final multi-band filter parameters are generated for configuring the filter module inside the FPGA to achieve precise filtering of the target frequency band signal and provide high-quality signals for subsequent radio frequency processing.

[0142] In an embodiment of the present invention, the FFT module inside the FPGA is used to perform spectrum analysis on the intermediate frequency signal stream after reconstruction in step S2, and a spectrogram of the signal is obtained. According to the center frequency and bandwidth information of the target frequency band, the range of the target frequency band signal is identified on the spectrogram. According to the range of the target frequency band signal identified in step S31 and the preset filter performance indicators, such as passband bandwidth, stopband attenuation, transition band roll-off, etc., parameters such as the center frequency, bandwidth, and order of the filter are determined to form a filter parameter set. Then, according to the selected filter type and the parameter set, a filter design algorithm is used to calculate the coefficients of the filter to form a filter coefficient set. The data manual of the FPGA chip is queried or relevant tools are used to obtain the resource limit data of the FPGA, such as the number of logic units, memory size, maximum operating frequency, etc. According to the scale of the filter coefficient set and the resource limit data of the FPGA, a suitable filter structure is selected, such as a direct form structure, a cascade form structure, a parallel form structure, etc., and the filter structure is optimized, such as reducing the computational complexity and reducing resource occupancy, to obtain optimized filter structure data. According to the optimized filter structure data determined in step S33 and the filter coefficient set calculated in step S32, the final multi-band filter parameters are generated for configuring the filter module inside the FPGA.

[0143] Preferably, step S32 includes the following steps:

[0144] Step S321: Perform spectrum peak detection on the signal spectrogram to obtain the center frequency data of the target frequency band;

[0145] Step S322: Perform bandwidth estimation according to the broadband information data of the target frequency band to obtain the bandwidth range data of the target frequency band;

[0146] Step S323: Calculate filter parameters according to the center frequency data of the target frequency band and the bandwidth range data of the target frequency band to obtain a filter parameter set;

[0147] Step S324: Select a filter type according to the filter parameter set to obtain filter type data;

[0148] Step S325: Calculate filter coefficients according to the filter type data and the filter parameter set to obtain filter coefficients; optimize the coefficients of the filter coefficients to obtain a filter coefficient set.

[0149] By analyzing the signal spectrogram, the present invention detects the spectral peak, thereby accurately obtaining the central frequency data of the target frequency band, providing a basis for setting the central frequency of the subsequent filter. Based on the bandwidth information data of the target frequency band and combining the spectral analysis results, the actual bandwidth of the target signal is estimated to obtain the bandwidth range data of the target frequency band, providing a reference for the bandwidth design of the subsequent filter. According to the central frequency data and bandwidth range data of the target frequency band obtained in steps S321 and S322, and combining the performance index requirements of the filter (such as stopband attenuation, transition band roll-off, etc.), a set of filter parameters that meet the requirements is calculated, such as passband cut-off frequency, stopband cut-off frequency, filter order, etc. According to the set of filter parameters calculated in step S323, a suitable filter type is selected. Different types of filters have different amplitude-frequency characteristics and phase-frequency characteristics, and need to be selected according to actual needs. According to the filter type selected in step S324 and the set of filter parameters calculated in step S323, the coefficients of the filter are calculated using the filter design algorithm. In order to further improve the performance of the filter, such as reducing the passband ripple, increasing the stopband attenuation, etc., the filter coefficients can be optimized.

[0150] In the embodiment of the present invention, a peak search algorithm, such as the maximum search method, threshold detection method, etc., is used to analyze the signal spectrogram obtained in step S31, find the spectral peak representing the target frequency band signal, and record the frequency value corresponding to the peak as the central frequency data of the target frequency band. According to the standard bandwidth information of the target frequency band and combining the signal spectrogram obtained in step S31, the spectral width of the target frequency band signal is analyzed, and according to certain rules, such as the 3dB bandwidth method, equivalent rectangular bandwidth method, etc., the bandwidth range data of the target frequency band is estimated. According to the central frequency data of the target frequency band obtained in step S321 and the bandwidth range data of the target frequency band obtained in step S322, and combining the preset filter performance indexes, such as passband bandwidth, stopband attenuation, transition band roll-off, etc., the filter design formula is used to calculate the parameters such as the passband cut-off frequency, stopband cut-off frequency, filter order of the filter, forming a set of filter parameters. According to the set of filter parameters calculated in step S323 and the performance characteristics of different types of filters, such as the passband flatness of the Butterworth filter, the stopband attenuation speed of the Chebyshev filter, the transition band steepness of the elliptic filter, etc., the most suitable filter type is selected as the final filter type to be used. According to the filter type selected in step S324 and the set of filter parameters calculated in step S323, the corresponding filter design formula is used to calculate the coefficients of the filter. Then, the filter coefficients are optimized according to actual needs to obtain the final set of filter coefficients.

[0151] Preferably, step S4 includes the following steps:

[0152] Step S41: Monitor the network status through the FPGA network interface module to obtain network status data;

[0153] Step S42: Extract features from the multi - band filter parameters and the network status data to obtain a set of feature vectors;

[0154] Step S43: Perform model initialization processing based on the network status data to obtain an initial model; perform model frequency band training on the initial model according to the set of feature vectors to obtain a frequency band allocation model;

[0155] Step S44: Obtain real - time network status data; perform frequency band allocation prediction on the real - time network status data according to the frequency band allocation model to obtain frequency band allocation strategy data; generate a frequency band allocation control strategy by means of the frequency band allocation strategy data and a preset frequency band allocator control unit to obtain frequency band allocation control strategy data.

[0156] The present invention monitors the network status in real time by using the FPGA network interface module, such as network load, number of users, interference situation, etc., and collects this information to form network status data, providing a basis for subsequent frequency band allocation decisions. Extract key features from the multi - band filter parameters obtained in step S3 and the network status data obtained in step S41, such as signal strength, signal - to - noise ratio, user distribution, interference intensity, etc., and combine these features into a set of feature vectors to prepare for subsequent model training. According to historical network status data, select a suitable model and initialize it, such as a neural network, decision tree, etc., to obtain an initial model. Then, use the set of feature vectors extracted in step S42 to train the initial model so that the model can learn the relationship between the network status and the optimal frequency band allocation, and finally obtain a frequency band allocation model that can predict the optimal frequency band allocation according to the network status. Obtain the latest network status data in real time and input it into the frequency band allocation model trained in step S43 for frequency band allocation prediction to obtain preliminary frequency band allocation strategy data. Finally, combine the preset frequency band allocation rules and the frequency band allocator control unit to adjust and optimize the preliminary frequency band allocation strategy data to generate the final frequency band allocation control strategy data for guiding the frequency band switching operation of the RRU.

[0157] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes:

[0158] Step S41: Monitor the network status through the FPGA network interface module to obtain network status data;

[0159] In the embodiment of the present invention, an FPGA network interface module, such as an Ethernet controller, is used to read network status information sent by a network management system or other network devices, such as network load, number of users, interference intensity, etc., and store this information in the cache of the FPGA to form network status data.

[0160] Step S42: Extract features from the multi-band filter parameters and the network status data to obtain a set of feature vectors;

[0161] In the embodiment of the present invention, key features are extracted from the multi-band filter parameters obtained in step S3, such as the center frequency, bandwidth, stopband attenuation, etc. of the filter. At the same time, key features are extracted from the network status data obtained in step S41, such as network load, number of users, interference intensity, etc. These features are combined together to form a set of feature vectors.

[0162] Step S43: Perform model initialization processing according to the network status data to obtain an initial model; perform model frequency band training on the initial model according to the set of feature vectors to obtain a frequency band allocation model;

[0163] In the embodiment of the present invention, according to historical network status data and frequency band allocation data, a suitable machine learning model is selected, such as a neural network, decision tree, support vector machine, etc., and the model is initialized with random parameters to obtain an initial model. Then, the set of feature vectors extracted in step S42 and the corresponding historical best frequency band allocation data are used to train the initial model, and the model parameters are continuously adjusted until the prediction accuracy of the model meets the requirements to obtain the final frequency band allocation model.

[0164] Step S44: Obtain real-time network status data; perform frequency band allocation prediction on the real-time network status data according to the frequency band allocation model to obtain frequency band allocation strategy data; generate a frequency band allocation control strategy according to the frequency band allocation strategy data and a preset frequency band allocator control unit to obtain frequency band allocation control strategy data;

[0165] In the embodiment of the present invention, an FPGA network interface module is used to obtain the latest network status data in real time, convert it into feature vectors, and input it into the frequency band allocation model trained in step S43 to obtain the best frequency band allocation scheme predicted by the model, that is, frequency band allocation strategy data. Then, according to preset frequency band allocation rules, such as priority rules, load balancing rules, etc., the frequency band allocation strategy data predicted by the model is adjusted and optimized to finally generate frequency band allocation control strategy data.

[0166] Preferably, step S43 includes the following steps:

[0167] Step S431: Perform model selection based on the network status data to obtain model selection data; perform learning rate setting based on the model selection data to obtain model learning rate data; perform hidden layer quantity analysis based on the model selection data to obtain model hidden layer quantity data;

[0168] Step S432: Generate an initial model based on the model learning rate data and the model hidden layer quantity data to obtain an initial model;

[0169] Step S433: Obtain historical frequency band data; perform model training on the initial model according to the feature vector set and the historical frequency band data to obtain a trained model;

[0170] Step S434: Perform model evaluation on the trained model according to the feature vector set and the historical frequency band data to obtain model evaluation data; perform model adjustment on the trained model according to the model evaluation data to obtain a frequency band allocation model.

[0171] The present invention analyzes the characteristics and scale of the network status data to obtain model selection data. According to the selected model type and data scale, a suitable model learning rate is set to obtain model learning rate data. At the same time, according to the model complexity and data feature dimension, the number of hidden layers of the model is determined to obtain model hidden layer quantity data. The selection of these parameters aims to construct a model that can effectively learn the relationship between the network status and frequency band allocation. According to the parameters such as the model type, learning rate, and number of hidden layers determined in Step S431, the model parameters are initialized, and an initial frequency band allocation model is built to prepare for subsequent model training. The best frequency band allocation data corresponding to the historical network status data is collected, and using these historical data and the feature vector set extracted in Step S42, the initial model generated in Step S432 is trained, and the model parameters are continuously adjusted to enable it to accurately predict the best frequency band allocation scheme according to the input feature vectors. Another part of the historical data and the feature vector set are used to evaluate the model trained in Step S433 to test the generalization ability and prediction accuracy of the model. According to the evaluation results, the model is further adjusted, such as modifying the model structure, adjusting hyperparameters, etc., and finally a frequency band allocation model with excellent performance is obtained.

[0172] In the embodiments of the present invention, according to the characteristic dimensions, data volume, and requirements for model prediction accuracy and computational complexity of network state data, a suitable machine learning model is selected. For example, for scenarios with lower characteristic dimensions and smaller data volumes, a decision tree or support vector machine model can be selected; for scenarios with higher characteristic dimensions and larger data volumes, a neural network model can be selected. After determining the model type, a suitable learning rate is selected based on experience or using the cross-validation method, and the number of hidden layers (if using a neural network) is determined according to the data complexity and model expressiveness. According to parameters such as the model type, learning rate, and number of hidden layers selected in step S431, the model parameters are initialized using random values or pre-trained values to construct an initial frequency band allocation model. Historical network state data and its corresponding optimal frequency band allocation data are collected. The initial model generated in step S432 is trained using the feature vector set extracted in step S42 and the corresponding historical optimal frequency band allocation data. For example, for a neural network model, the model parameters can be updated using the backpropagation algorithm until the prediction accuracy of the model meets the requirements, and a trained model is obtained. Another part of the historical network state data and frequency band allocation data, as well as the feature vector set extracted in step S42, are used to evaluate the model trained in step S433, and metrics such as the prediction accuracy and recall rate of the model are calculated to evaluate the generalization ability of the model. According to the evaluation results, the model is adjusted. For example, for a neural network model, the network structure can be adjusted, the activation function can be modified, and regularization methods can be used, etc., until the performance of the model reaches the expected goal, and a final frequency band allocation model is obtained.

[0173] Preferably, step S5 includes the following steps:

[0174] Step S51: Use the FPGA data parsing module to parse the frequency band allocation control strategy data to obtain the target frequency band information data and the switching instruction information data;

[0175] Step S52: Obtain the real-time frequency band information data through the FPGA communication interface module; analyze the instruction switching method based on the real-time frequency band information data and the target frequency band information data to obtain the instruction switching method data;

[0176] Step S53: Set the switching parameters according to the instruction switching method data to obtain the instruction switching parameters;

[0177] Step S54: Generate a switching instruction according to the switching instruction information data, the instruction switching method data, the instruction switching parameters, and the target frequency band information data to obtain the frequency band switching control instruction data.

[0178] In the present invention, the FPGA data parsing module is utilized to parse the frequency band allocation control strategy data generated in step S4, extract the target frequency band information data, such as the center frequency and bandwidth of the target frequency band, and the handover instruction information data, such as the handover time and handover mode, etc., to prepare for the subsequent generation of handover instructions. The FPGA communication interface module is used to obtain the frequency band information where the RRU is currently located, such as the center frequency and bandwidth of the current frequency band. Then, based on the real-time frequency band information data and the target frequency band information data parsed in step S51, the best instruction handover mode for implementing frequency band handover is analyzed, such as hard handover, soft handover or seamless handover, etc., to obtain the instruction handover mode data. According to the instruction handover mode determined in step S52, the corresponding handover parameters are set, such as the handover time for hard handover, the power slope for soft handover, the phase compensation for seamless handover, etc., to obtain the instruction handover parameters, which are used to ensure the smoothness and stability of the frequency band handover process. By comprehensively utilizing the handover instruction information data parsed in step S51, the instruction handover mode data analyzed in step S52, the instruction handover parameters set in step S53, and the target frequency band information data, the final frequency band handover control instruction data is generated, which is used to control the RRU to execute specific frequency band handover operations.

[0179] In an embodiment of the present invention, the built-in logic resources or embedded processors of the FPGA are used to parse the frequency band allocation control strategy data generated in step S4, extract information such as the center frequency and bandwidth of the target frequency band, and information such as the handover instruction type and handover time, and store them in the corresponding registers or memory addresses respectively. The FPGA communication interface module, such as the SPI or I2C interface, is used to read information such as the center frequency and bandwidth of the current working frequency band of the RRU. Then, based on the read real-time frequency band information and the target frequency band information parsed in step S51, a suitable instruction handover mode is selected, such as hard handover, soft handover or seamless handover, and the selected handover mode is stored in the specified register or memory address. According to the instruction handover mode selected in step S52, the corresponding handover parameters are set. For example, for hard handover, the handover time is set; for soft handover, the power drop slope and time are set; for seamless handover, the phase compensation parameters are set, etc. The set handover parameters are stored in the specified register or memory address. According to the handover instruction information parsed in step S51, the instruction handover mode selected in step S52, the handover parameters set in step S53, and the target frequency band information, they are assembled into complete frequency band handover control instruction data and sent to the RRU control system.

[0180] Preferably, step S6 includes the following steps:

[0181] Step S61: Use the FPGA communication interface module to transmit the frequency band switching control instruction data to the RRU control system, extract switching parameters according to the frequency band switching control instruction data, and obtain the frequency band switching parameters;

[0182] Step S62: Integrate the frequency band switching parameters and the multi-band filter parameters to obtain a set of switching parameters; configure the filter according to the set of switching parameters to obtain the configured filter data;

[0183] Step S63: Perform digital filtering on the reconstructed intermediate frequency signal stream according to the configured filter data to obtain digital filtering data; perform signal reconstruction on the digital filtering data according to the set of switching parameters to obtain the filtered intermediate frequency signal stream;

[0184] Step S64: Perform digital-to-analog conversion on the filtered intermediate frequency signal stream to obtain analog intermediate frequency signal data; perform upper sideband processing on the analog intermediate frequency signal data to obtain an analog radio frequency signal;

[0185] Step S65: Perform power amplification on the analog radio frequency signal to obtain the switched radio frequency signal, so as to implement the frequency band switching operation of the network.

[0186] In the present invention, the frequency band switching control instruction data generated in step S5 is transmitted to the RRU control system by using the FPGA communication interface module. The RRU control system extracts specific frequency band switching parameters according to the received instruction data, preparing for the subsequent signal processing. Integrate the frequency band switching parameters extracted in step S61 with the multi-band filter parameters generated in step S3 to form a complete set of switching parameters. Then, configure the multi-band filter according to this set of parameters, such as updating the center frequency, bandwidth, etc. of the filter, to obtain the configured filter data, preparing for the subsequent signal filtering. Use the filter configured in step S62 to perform digital filtering on the intermediate frequency signal stream reconstructed in step S2, filtering out out-of-band interference and noise to obtain clean digital filtering data. Then, perform signal reconstruction on the digital filtering data according to the set of switching parameters, such as frequency shifting, amplitude adjustment, etc., to obtain the filtered intermediate frequency signal stream that meets the requirements of the target frequency band. Perform digital-to-analog conversion on the filtered intermediate frequency signal stream obtained in step S63 to obtain an analog intermediate frequency signal. Then, perform up-conversion processing on the analog intermediate frequency signal to shift it to the radio frequency frequency range of the target frequency band to obtain an analog radio frequency signal. Perform power amplification on the analog radio frequency signal obtained in step S64 to make it reach the target transmission power, finally obtain the switched radio frequency signal, and transmit it through the antenna to complete the frequency band switching operation of the network.

[0187] In an embodiment of the present invention, an FPGA communication interface module, such as an SPI or I2C interface, is used to send the frequency band switching control instruction data generated in step S5 to an RRU control system, such as a baseband processing unit. After receiving the instruction data, the RRU control system parses parameters such as the center frequency, bandwidth, and transmit power of the target frequency band from it. Integrate the frequency band switching parameters parsed in step S61 with the multi-band filter parameters generated in step S3 to form a switching parameter set including filter configuration parameters and radio frequency parameters. According to the switching parameter set, the RRU control system configures parameters such as the center frequency and bandwidth of the multi-band filter, and updates parameters such as the operating frequency and transmit power of the radio frequency module. The RRU control system filters the intermediate frequency signal stream reconstructed in step S2 using the configured multi-band filter to remove out-of-band interference and noise. Then, according to the target frequency band center frequency set in the switching parameter set, frequency shift the filtered digital signal and adjust the signal amplitude to the target transmit power to obtain a filtered intermediate frequency signal stream meeting the requirements of the target frequency band. The RRU control system uses a DAC module to convert the filtered intermediate frequency signal stream obtained in step S63 into an analog signal. Then, use a mixer to mix the analog intermediate frequency signal with the local oscillator signal to shift the signal frequency to the radio frequency of the target frequency band to obtain an analog radio frequency signal. The RRU control system uses a power amplifier to amplify the analog radio frequency signal obtained in step S64 to make it reach the target transmit power, and transmits the amplified radio frequency signal through an antenna to complete the frequency band switching operation.

[0188] The present invention analyzes the specification parameters of the 2.1G RRU and the target frequency band, finds the differences between the two, and establishes a preliminary spectral mapping relationship model based on this, and finally generates a spectral mapping table. This mapping table provides a guiding basis for the accurate conversion of 2.1G signals to target frequency band signals. According to the spectral mapping table established in step S1, the 2.1G digital signal is resampled and rearranged to make it initially consistent with the target frequency band in both the time domain and the frequency domain. Then, digital interpolation is performed according to the bandwidth information of the target frequency band to further improve the signal quality, and finally the reconstructed intermediate frequency signal is obtained. The reconstructed intermediate frequency signal is analyzed, key parameters are extracted, and optimized according to the FPGA resource limitations, and a multi-band filter capable of effectively filtering out-of-band interference and noise is designed. This filter can provide high-quality target frequency band signals for subsequent signal processing links. By real-time monitoring the network status and using machine learning techniques, according to information such as network load and the number of users, the optimal frequency band allocation scheme is intelligently predicted. This data-driven frequency band allocation strategy can effectively improve the network resource utilization rate and enhance the overall network performance. According to the optimal frequency band predicted in step S4 and the current state of the RRU, a suitable switching method is selected and corresponding switching parameters are set, and finally a specific frequency band switching control instruction is generated. This instruction contains all the information required for the RRU to perform frequency band switching. The RRU control system configures the hardware according to the received switching instruction and performs corresponding signal processing, such as filtering, frequency conversion, power amplification, etc., and finally realizes the smooth switching of the RRU from the current frequency band to the target frequency band, completing the network frequency band switching operation. Therefore, the present invention utilizes SDR technology to perform digital signal processing, multi-band filter design, frequency band allocation control strategy generation, and frequency band switching control instruction generation through FPGA, realizes flexible frequency band switching control, does not require hardware modification, realizes efficient frequency band switching and signal processing, dynamically generates frequency band allocation control strategies, and improves network performance. Realize the frequency band switching of retired 2.1G RRU devices to make them support 1.8G or 800M networks.

[0189] Reference Figure 4 , as the frequency band allocator control unit for RRU multi-channel frequency band switching control, the downlink transmission link adopts the up-conversion signal acquisition technology. The input end of the link signal is the intermediate frequency signal output by the digital-to-analog (DAC) (i.e., Spectrum 1, Spectrum 2, Spectrum 3, and Spectrum 4 in the figure), and the output end is the radio frequency analog signal required by the protocol. The Pc end transmits the reset operation data to the frequency band allocation control unit after preliminary control processing by the small system control unit. The intermediate frequency signal is up-converted by the frequency band allocation control unit to obtain Spectrum 1-4, and then further amplified and gain-controlled by the power amplifier, and finally filtered by the duplex filter.

[0190] Reference Figure 5, as a small system of the frequency allocation control unit for RRU multi-band switching control, in which the STM32 single-chip microcomputer is linked with the power supply circuit, the software interface circuit and the clock circuit, and the operation data transmission control is performed by the reset system. The data after the transmission control processing is exchanged with the band allocation control unit.

[0191] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0192] The above are only the specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for controlling multi-band switching of an RRU, characterized in that, It includes the following steps: Step S1: Extract band specification parameters through the 2.1G RRU device hardware specification and target band standard specification data to obtain a 2.1G radio frequency parameter set and a target band parameter set; perform band difference analysis based on the 2.1G radio frequency parameter set and the target band parameter set to obtain band difference data, perform spectrum mapping relationship analysis based on the band difference data and conduct spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model; generate a spectrum mapping table based on the initial spectrum mapping relationship model to obtain a spectrum mapping table; Step S2: Sample the digital signals of the 2.1G RRU device to obtain a 2.1G digital signal sample set; perform spectrum mapping index conversion on the 2.1G digital signal sample set according to the spectrum mapping table to obtain a target band digital signal sample index set; extract broadband information from the target band parameter set to obtain target band broadband information data; reconstruct the intermediate frequency signal based on the target band digital signal sample index set and the target band broadband information data to obtain a reconstructed intermediate frequency signal stream; Step S3: Extract filter parameters based on the reconstructed intermediate frequency signal stream to obtain a filter parameter set; perform coefficient calculation processing based on the filter parameter set to obtain a filter coefficient set; construct a multi-band filter based on the filter coefficient set to obtain multi-band filter parameters; Step S4: Monitor the network status through the FPGA network interface module to obtain network status data; perform model initialization processing based on the network status data to obtain an initial model; train the model band of the initial model according to the multi-band filter parameters to obtain a band allocation model; obtain real-time network status data; generate a band allocation control strategy based on the band allocation model and the real-time network status data to obtain band allocation control strategy data; Step S5: Obtain real-time band information data; extract the instruction switching method based on the current band information data and the band allocation control strategy data to obtain instruction switching method data; set switching parameters according to the instruction switching method data to obtain instruction switching parameters; generate a switching instruction based on the instruction switching method data and the instruction switching parameters to obtain band switching control instruction data; Step S6: Transmit the band switching control instruction data to the RRU control system, and perform band switching execution processing on the reconstructed intermediate frequency signal stream according to the band switching control instruction data and the multi-band filter parameters to obtain a filtered intermediate frequency signal stream; perform radio frequency enhancement processing on the filtered intermediate frequency signal stream to obtain a switched radio frequency signal, so as to realize the band switching operation of the network.

2. The method for RRU multi-band frequency switching control according to claim 1, wherein Step S1 includes the following steps: Step S11: Extract key radio frequency parameters from the 2.1G RRU device hardware specification to obtain a 2.1G radio frequency parameter set; Step S12: Obtain the target band standard specification data; define the target band parameters according to the target band standard specification data to obtain a target band parameter set; Step S13: Perform frequency band difference analysis based on the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data. Perform spectrum mapping relationship analysis based on the frequency band difference data and conduct spectrum mapping relationship modeling to obtain an initial spectrum mapping relationship model; Step S14: Obtain the digital signal sampling rate data of the 2.1G RRU device; perform sampling rate matching on the initial spectrum mapping relationship model according to the digital signal sampling rate data of the 2.1G RRU device to obtain a spectrum mapping relationship model; Step S15: Generate index mapping rules according to the spectrum mapping relationship model to obtain index mapping rule data; construct a spectrum mapping table according to the index mapping rule data to obtain a spectrum mapping table.

3. The method for RRU multi-channel frequency band switching control according to claim 2, wherein Step S13 includes the following steps: Step S131: Perform frequency band difference analysis based on the 2.1G radio frequency parameter set and the target frequency band parameter set to obtain frequency band difference data, where the frequency band difference data includes center frequency difference data, bandwidth difference data, transmit power difference data, and receive sensitivity difference data; Step S132: Calculate the frequency offset value according to the center frequency difference data to obtain the frequency offset value; construct a bandwidth adjustment model according to the bandwidth difference data to obtain a bandwidth adjustment model; Step S133: Establish a power matching relationship according to the transmit power difference data to obtain power matching relationship data; establish a sensitivity matching relationship according to the receive sensitivity difference data to obtain sensitivity matching relationship data; Step S134: Perform spectrum mapping relationship modeling based on the frequency offset value, bandwidth adjustment model, power matching relationship data, and sensitivity matching relationship data to obtain an initial spectrum mapping relationship model.

4. The method for RRU multi-band switching control according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Use the FPGA to sample the digital signal of the 2.1G RRU device to obtain a 2.1G digital signal sample set; use a preset frequency band allocator control unit to generate an address code to obtain address code data; Step S22: Use a preset four-way data selector to perform sample index mapping on the 2.1G digital signal sample set according to the spectrum mapping table and the address code data to obtain sample index data; Step S23: Use a preset four-way data selector to rearrange the data of the 2.1G digital signal sample set according to the sample index data and the address code data to obtain a target frequency band digital signal sample index set; Step S24: Resample the 2.1G digital signal sample set according to the target frequency band digital signal sample index set to obtain target frequency band digital signal samples; Step S25: Extract broadband information from the target frequency band parameter set to obtain target frequency band broadband information data; perform digital signal interpolation on the target frequency band digital signal samples according to the target frequency band broadband information data to obtain a reconstructed intermediate frequency signal stream.

5. The method for RRU multi-channel frequency band switching control according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Use the FPGA to perform spectrum analysis on the reconstructed intermediate frequency signal stream and identify the target frequency band signal range to obtain a signal spectrogram; Step S32: Extract filter parameters from the signal spectrogram to obtain a filter parameter set; perform coefficient calculation processing according to the filter parameter set to obtain a filter coefficient set; Step S33: Obtain FPGA resource limit data; optimize the filter structure according to the filter coefficient set and the FPGA resource limit data to obtain filter structure optimization data; Step S34: Generate multi-band filter parameters according to the filter structure optimization data and the filter coefficient set to obtain multi-band filter parameters.

6. The method for RRU multi-band switching control according to claim 5, characterized in that, Step S32 includes the following steps: Step S321: Detect the spectral peak of the signal spectrogram to obtain the center frequency data of the target frequency band; Step S322: Estimate the bandwidth according to the broadband information data of the target frequency band to obtain the bandwidth range data of the target frequency band; Step S323: Calculate filter parameters according to the center frequency data of the target frequency band and the bandwidth range data of the target frequency band to obtain a filter parameter set; Step S324: Select a filter type according to the filter parameter set to obtain filter type data; Step S325: Calculate filter coefficients according to the filter type data and the filter parameter set to obtain filter coefficients; optimize the coefficients of the filter coefficients to obtain a filter coefficient set.

7. The method for RRU multi-band switching control according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Monitor the network status through the FPGA network interface module to obtain network status data; Step S42: Extract features from the multi-band filter parameters and the network status data to obtain a feature vector set; Step S43: Perform model initialization processing according to the network status data to obtain an initial model; perform model frequency band training on the initial model according to the feature vector set to obtain a frequency band allocation model; Step S44: Obtain real-time network status data; perform frequency band allocation prediction on the real-time network status data according to the frequency band allocation model to obtain frequency band allocation strategy data; generate a frequency band allocation control strategy according to the frequency band allocation strategy data and a preset frequency band allocator control unit to obtain frequency band allocation control strategy data.

8. The method for RRU multi-channel frequency band switching control according to claim 7, wherein Step S43 includes the following steps: Step S431: Select a model according to the network status data to obtain model selection data; set the learning rate according to the model selection data to obtain model learning rate data; analyze the number of hidden layers according to the model selection data to obtain model hidden layer number data; Step S432: Generate an initial model according to the model learning rate data and the model hidden layer number data to obtain an initial model; Step S433: Obtain historical frequency band data; perform model training on the initial model according to the feature vector set and the historical frequency band data to obtain a trained model; Step S434: Evaluate the trained model according to the feature vector set and the historical frequency band data to obtain model evaluation data; adjust the trained model according to the model evaluation data to obtain a frequency band allocation model.

9. The method for RRU multi-channel frequency band switching control according to claim 1, wherein Step S5 includes the following steps: Step S51: Use the FPGA data parsing module to parse the frequency band allocation control strategy data to obtain target frequency band information data and switching instruction information data; Step S52: Obtain real-time frequency band information data through the FPGA communication interface module; analyze the instruction switching method based on the real-time frequency band information data and the target frequency band information data to obtain instruction switching method data; Step S53: Set switching parameters according to the instruction switching method data to obtain instruction switching parameters; Step S54: Generate a switching instruction based on the switching instruction information data, the instruction switching method data, the instruction switching parameters, and the target frequency band information data to obtain frequency band switching control instruction data.

10. The method for RRU multi-channel frequency band switching control according to claim 1, characterized in that, Step S6 includes the following steps: Step S61: Use the FPGA communication interface module to transmit the frequency band switching control instruction data to the RRU control system, extract switching parameters according to the frequency band switching control instruction data to obtain frequency band switching parameters; Step S62: Integrate the frequency band switching parameters and the multi-band filter parameters to obtain a set of switching parameters; configure the filter according to the set of switching parameters to obtain the configured filter data; Step S63: Perform digital filtering on the reconstructed intermediate frequency signal stream according to the configured filter data to obtain digital filtering data; reconstruct the signal on the digital filtering data according to the set of switching parameters to obtain the filtered intermediate frequency signal stream; Step S64: Perform digital-to-analog conversion on the filtered intermediate frequency signal stream to obtain analog intermediate frequency signal data; perform upper sideband processing on the analog intermediate frequency signal data to obtain an analog radio frequency signal; Step S65: Perform power amplification on the analog radio frequency signal to obtain the switched radio frequency signal, so as to realize the frequency band switching operation of the network.

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