Synaesthesia integrated data transmission method, device and base station

Abstract

The present application provides a synaesthesia integrated data transmission method, device and base station, belonging to the synaesthesia integrated technology field, the synaesthesia integrated data transmission method comprises: the baseband processing unit dynamically configures the compression parameters according to the environmental data collected by the environmental state perception system, and transmits the configured compression parameters to the active antenna unit through the front transmission interface, the compression parameters include compression domain, compression mode and compression mask; the active antenna unit performs the corresponding compression operation according to the received configured compression parameters, and the compressed data is fed back to the baseband processing unit through the front transmission interface, so that the baseband processing unit completes the signal recovery according to the compression parameters. The present application can effectively reduce the transmission requirements and system costs while maintaining high-performance perception capabilities.

Description

Technical Field

[0001] The present application relates to the technical field of synaesthesia integration, and in particular to a synaesthesia integration data transmission method, device and base station. Background Art

[0002] In modern communication systems, especially with the development trend of 5G-A (5G Advanced) and 6G, base stations are designed to have integrated inter-sensory functions, that is, to perform communication and perception (such as radar detection) tasks at the same time. This design is based on the similarity of communication and perception in signal processing, allowing the two to share hardware resources, such as frequency domain channel estimation, PDP (Power Delay Profile) estimation, frequency offset estimation, and beam scanning. Typical base station equipment includes an active antenna unit (AAU) and a baseband processing unit (BBU), which are responsible for RF and baseband signal processing respectively, and exchange data through an enhanced public radio interface (eCPRI).

[0003] Although the design concept of integrated communication and perception is advanced, there are problems in practical applications: in order to realize the perception function, especially the high-precision distance, speed and angle estimation, a large amount of raw perception data needs to be transmitted, which causes the transmission traffic between AAU and BBU to be much higher than the pure communication demand. Existing solutions either sacrifice perception performance to adapt to lower transmission traffic or increase transmission traffic to maintain perception performance, but neither of these two solutions can effectively reduce transmission requirements while maintaining high performance, thereby increasing system cost and complexity. Summary of the invention

[0004] The present application provides a synaesthesia integrated data transmission method, device and base station, aiming to solve the problem of how to effectively reduce transmission requirements and system costs while maintaining high-performance perception capabilities in a communication and perception integrated system.

[0005] In a first aspect, the present application provides a synaesthesia integrated data transmission method, which is applied to a base station, wherein the base station is configured with an environmental state perception system, and the base station includes a baseband processing unit, an active antenna unit, and a fronthaul interface therebetween. The synaesthesia integrated data transmission method includes:

[0006] The baseband processing unit dynamically configures compression parameters according to the environmental data collected by the environmental state perception system, and transmits the configured compression parameters to the active antenna unit through the fronthaul interface, wherein the compression parameters include compression domain, compression mode and compression mask;

[0007] The active antenna unit performs corresponding compression operations according to the received configured compression parameters, and the compressed data is fed back to the baseband processing unit through the fronthaul interface, so that the baseband processing unit completes signal recovery according to the compression parameters.

[0008] In a second aspect, the present application further provides a synaesthesia integrated data transmission device, which is applied to a base station, wherein the base station is configured with an environmental state perception system, and the base station includes a baseband processing unit, an active antenna unit, and a fronthaul interface therebetween. The synaesthesia integrated data transmission device includes:

[0009] A configuration module, which dynamically configures compression parameters according to environmental data collected by the environmental state perception system through a baseband processing unit, and transmits the configured compression parameters to the active antenna unit through a fronthaul interface, wherein the compression parameters include a compression domain, a compression mode, and a compression mask;

[0010] The execution module performs corresponding compression operations according to the received configured compression parameters through the active antenna unit, and the compressed data is fed back to the baseband processing unit through the fronthaul interface, so that the baseband processing unit completes signal recovery according to the compression parameters.

[0011] In a third aspect, the present application further provides a base station, which is used to execute the synaesthesia integrated data transmission method as described in any one of the first aspects.

[0012] The synaesthesia integrated data transmission method, device and base station provided in the present application are intended to solve the problem of effectively reducing transmission requirements and system costs while maintaining high-performance perception capabilities in a communication and perception integrated system. The method uses the environmental data collected by the environmental state perception system through the baseband processing unit to dynamically configure compression parameters. The compression parameters include compression domains (such as distance domains, Doppler domains, angle domains, etc.), compression modes (if the distance domain is selected, the compression mode includes frequency domain compression and distance domain compression) and compression masks (such as the range of controlling data compression). Through dynamic configuration, the system can adjust the compression strategy according to real-time environmental changes, thereby reducing the amount of transmitted data while maintaining perception performance. In addition, the configured compression parameters are transmitted to the active antenna unit through the fronthaul interface. The fronthaul interface is used to transmit compression parameters and compressed data between the baseband processing unit and the active antenna unit. Optimizing the key field design of the fronthaul interface can reduce transmission delays and bandwidth requirements and reduce system costs. Moreover, the active antenna unit performs corresponding compression operations based on the received compression parameters. By performing compression at the active antenna unit end, the amount of data that needs to be transmitted through the fronthaul interface can be reduced, thereby reducing transmission requirements and system costs. At the same time, the compression operation should ensure that the perceived performance is not significantly affected. The compressed data is fed back to the baseband processing unit through the fronthaul interface. The baseband processing unit can analyze the feedback data and further optimize the compression parameters and strategies. Through iterative optimization, data transmission efficiency and perceived performance can be continuously improved while reducing costs.

[0013] Therefore, the present application can effectively reduce transmission requirements and system costs while maintaining high-performance perception capabilities by dynamically configuring compression parameters, efficiently utilizing the fronthaul interface, performing compression operations at the active antenna unit end, feedback and iterative optimization, and system-level optimization and adaptation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 A schematic diagram of the radar system integrated on the base station is shown;

[0016] Figure 2 is a flow chart of the synaesthesia integrated data transmission method provided by the present application;

[0017] Figure 3 It is a schematic diagram of the communication between BBU and AAU provided by the present application;

[0018] Figure 4 It is a schematic diagram of the first method provided in Example 1 of the present application;

[0019] Figure 5 is a schematic diagram of the second clock mode provided in Example 1 of the present application;

[0020] Figure 6 It is a schematic diagram of the first method provided in Example 2 of the present application;

[0021] Figure 7 is a schematic diagram of the second clock mode provided in Example 2 of the present application;

[0022] Figure 8 It is a structural block diagram of the synaesthesia integrated data transmission device provided in this application. DETAILED DESCRIPTION

[0023] In the embodiments of the present application, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] Integrated Sensing and Communication (ISAC), a base station with integrated sensing function, integrates communication and perception functions into the same base station, so that the base station can not only perform data transmission (communication), but also perform environmental perception (such as target detection, positioning, etc.).

[0026] For example, in a smart transportation system, a telesensing integrated base station can simultaneously provide vehicle communication services and perception functions such as traffic flow monitoring and accident warning. This helps improve the efficiency and safety of traffic management and reduce congestion and accidents. In a smart factory environment, a telesensing integrated base station can support communication between devices while monitoring information such as the movement of objects and the location of personnel within the factory. This helps to achieve automated production, optimize logistics, and improve workplace safety. In low-altitude logistics and inspection applications, a telesensing integrated base station can provide a communication link for drones or unmanned aerial vehicles while monitoring and tracking aerial targets. This helps improve logistics efficiency, ensure flight safety, and support remote inspection tasks.

[0027] Please refer to Figure 1 , Figure 1 A schematic diagram of the radar system integrated on the base station is shown. In the three-dimensional space area that the radar can perceive, each dimension (angle dimension, distance dimension, speed dimension) corresponds to a detection parameter of the radar. The angle dimension is related to the beam width of the radar, the distance dimension is the farthest distance that the radar can detect, and the speed dimension is the target speed range that the radar can distinguish. In a base station with integrated interaceptive function, the communication and perception functions have many similarities in signal processing. For example, frequency domain channel estimation is necessary in both communication and perception, PDP estimation (power delay profile estimation) corresponds to distance spectrum estimation in communication, frequency offset estimation corresponds to Doppler spectrum estimation, and beam scanning corresponds to angle spectrum estimation. These similarities mean that the same set of hardware can be reused to implement communication and perception functions, thereby improving efficiency and reducing costs, as shown in the comparison in the following table:

[0028] Table 1

[0029]

[0030] As shown in the table above, the existing NR (New Radio) system, which is part of the 5G communication standard, uses MIMO (Multiple Input Multiple Output) and OFDM (Orthogonal Frequency Division Multiplexing) waveform-based communication signal processing and the RDA (Range-Doppler-Angle) calculation module of the classic radar in principle and implementation. Therefore, in the development of 5G-A (5G Advanced) and 6G (sixth generation mobile communication technology), synaesthesia integration is regarded as an important evolution direction.

[0031] In a typical outdoor base station equipment, the base station equipment includes AAU (Active Antenna Unit) and BBU (Baseband Unit). AAU is mainly responsible for RF signal processing, while BBU is responsible for baseband signal processing. In the communication function, AAU processes MIMO channel-level signals into stream-level signals (symbol estimation) and transmits these signals to BBU through the eCPRI interface to reduce the transmission interface traffic between AAU and BBU. The calculation of this traffic is proportional to the number of streams, bandwidth and IQ bit width, where the IQ bit width of symbol estimation is usually 8 bits.

[0032] However, in the perception function, in order to complete the accurate estimation of distance, speed and angle, multi-time slot joint processing is required. A perception baseband card is set up separately on the BBU to complete it. The perception function requires the AAU to transmit the complete original perception data to the BBU, which results in the perception transmission traffic between the AAU and the BBU being proportional to the channel level, bandwidth and IQ bit width. Since the perception signal needs to process a large dynamic range of strong and weak targets, the IQ bit width of the perception signal is usually 16 bits. In addition, the number of streams in a typical scenario is 1 / 4 of the number of channels, so the transmission traffic demand of the perception signal AAU and BBU is 8 times the transmission traffic demand of the communication signal. The increase in transmission traffic means that the fronthaul traffic of the inter-sensory integrated device (i.e., data transmission between the AAU and the BBU) is significantly expanded, resulting in an increase in the cost of base station equipment with inter-sensory integration capabilities. This is because higher transmission traffic requires more powerful hardware support and higher energy consumption, which will increase the overall cost of the equipment.

[0033] Therefore, the challenges faced in realizing the integrated synaesthesia function are, in particular, how to handle the traffic problem of the fronthaul interface (i.e., the data transmission interface between the AAU and the BBU) while meeting the communication and perception requirements.

[0034] There are two main ways to achieve synaesthesia integration:

[0035] Full specification implementation: This method requires capacity reservation according to the maximum capacity of communication and perception requirements, that is, the full specification configuration consistent with communication is adopted. This can achieve the best inter-sensory performance, but will lead to increased traffic on the fronthaul interface. This means that the bandwidth and processing power of the fronthaul interface need to be increased to meet the increased data transmission requirements, which may increase the cost and complexity of the device.

[0036] Reduced specification implementation: In order to meet the existing fronthaul traffic specifications, the specifications of the sensing function can be reduced, such as reducing the sensing bandwidth, the number of sensing symbols, and the number of channels. This can reduce the traffic demand of the fronthaul interface, but will lead to a significant decrease in sensing performance. For example, reducing the bandwidth will lead to a decrease in distance resolution, reducing the number of sensing symbols will affect Doppler measurement performance, and reducing the number of channels will lead to a decrease in angle resolution.

[0037] However, the current implementation method cannot take into account both performance and reducing interface transmission requirements at the same time. In other words, if you want to maintain the perception performance, you need to increase the traffic of the fronthaul interface; if you want to reduce the traffic of the fronthaul interface, you need to sacrifice the perception performance.

[0038] In traditional synaesthesia integrated systems, all signals received by the AAU are directly forwarded (transparently transmitted) to the BBU, which results in huge fronthaul traffic because a large amount of redundant information that is not actually needed is transmitted. For example, the area detected by the system may far exceed the area that actually needs to be sensed, which means that the system is transmitting and processing a large amount of irrelevant data that is useless for perception applications.

[0039] This application focuses on the actual needs of the perception application and flexibly sets the signal content transmitted between the AAU and the BBU. The purpose of this is to reduce the fronthaul traffic and avoid transmitting invalid information, thereby improving the efficiency and performance of the system. In this way, the system can more accurately collect and process data for the areas that need to be perceived, avoiding unnecessary waste of resources, and may also reduce the cost and complexity of the system.

[0040] Please refer to Figure 2 , Figure 2 A method for transmitting synesthesia integrated data is applied to a base station, wherein the base station is equipped with an environmental state perception system, and the base station includes a baseband processing unit, an active antenna unit, and a fronthaul interface therebetween. The method for transmitting synesthesia integrated data includes:

[0041] S210, the baseband processing unit dynamically configures compression parameters according to the environmental data collected by the environmental status perception system, and transmits the configured compression parameters to the active antenna unit through the fronthaul interface. The compression parameters include compression domain, compression mode and compression mask.

[0042] Exemplarily, the step of dynamically configuring the compression parameters by the baseband processing unit according to the environmental data collected by the environmental state perception system includes:

[0043] S2101, preset initial compression parameters. Specifically, the initial compression parameters are preset based on prior knowledge provided by the environment state perception system. The prior knowledge includes known application scenario features.

[0044] For example, indoor coverage distance, farthest / closest detection distance, etc. Through this prior knowledge, the BBU can preset a preliminary set of compression parameters, laying the foundation for subsequent environmental learning and parameter adjustment.

[0045] S2102, perform an environment learning phase. Specifically, in the environment learning phase, a preset low-cost method is used to preliminarily determine the distance range of the target, and the compression parameters are adjusted and set according to the preliminary determination result of the environment learning.

[0046] Specifically, a preset low-overhead method is used to preliminarily determine the distance range of the target. The goal of this stage is to conduct a preliminary analysis and judgment of the environment with low time and bandwidth overhead. Based on the preliminary judgment results of environmental learning, the BBU will adjust and set the compression parameters to better adapt to the actual environment.

[0047] S2103, updating and optimizing the compression parameters by means of alternating iterations. Specifically, the compression parameters are continuously updated and optimized by means of alternating iterations so that the compression parameters can be adaptively adapted to environmental changes, thereby determining the compression parameters.

[0048] That is, the compression parameters are continuously updated and optimized so that they can adaptively match environmental changes. This process is carried out in an alternating iterative manner, that is, the two steps of environmental learning and parameter adjustment are continuously alternating until the compression parameters can effectively adapt to environmental changes.

[0049] By executing steps S2101 to S2103, including presetting initial parameters, preliminary judgment and parameter adjustment in the environmental learning phase, and continuously optimizing parameters through alternating iterations, the system is ensured to be able to adaptively match environmental changes. This approach is intended to improve the performance and efficiency of the system, especially when environmental conditions are constantly changing.

[0050] Exemplarily, the system also needs to add multiple key fields to the fronthaul interface to ensure that the communication between the BBU and the AAU is more efficient, accurate and flexible, thereby optimizing the system's fronthaul traffic and improving the system's performance and adaptability. The key fields are used to transmit various parameters required for compression operations, such as compression domain (frequency domain / distance domain), compression mode (frequency comb / effective range selection), compression position (frequency domain position / distance spectrum position), etc. These parameters are the basis for the AAU to perform compression operations. Through the key fields, the BBU can clearly instruct the AAU how to perform compression operations. This helps reduce misunderstandings and errors and ensures that the AAU can accurately execute the BBU's instructions. The key fields enable the BBU to dynamically adjust the compression parameters according to environmental changes and pass these adjustments to the AAU in real time. This enables the system to quickly adapt to different environments and application scenarios. By precisely controlling each dimension of the compression operation, the system can optimize the fronthaul traffic and reduce unnecessary bandwidth consumption while maintaining the necessary perceptual performance. This helps to improve the overall performance and efficiency of the system. The key fields enable the system to adaptively match environmental changes and ensure that the system can operate stably under various conditions by continuously updating and optimizing the compression parameters.

[0051] Exemplarily, the multiple key fields cover various dimensions of the compression operation, including but not limited to the following:

[0052] Compression domain flag (RDA-Flag): used to define the domain for compression in the range domain, Doppler domain and angle domain;

[0053] Distance domain compression mode (R-Mode): used to define the compression mode in the distance domain, including frequency domain compression and distance domain compression;

[0054] Distance domain compression mask (R-Pattern): a mask used to define compression, including data at compressible locations and data at incompressible locations;

[0055] Doppler domain compression mode (D-Mode): used to define the compression mode in the Doppler domain, including time domain compression and velocity domain compression;

[0056] Doppler domain compression mask (D-Pattern): a mask used to define compression, including data at compressible locations and data at incompressible locations;

[0057] Angle domain compression mode (A-Mode): used to define the compression mode in the angle domain, including antenna domain compression and angle domain compression;

[0058] Angle domain compression mask (A-Pattern): A mask used to define compression, including data in compressible locations and data in incompressible locations.

[0059] The BBU dynamically configures compression parameters based on environmental data collected by the environmental status perception system. The environmental status perception system may include sensors, radars, or other monitoring equipment to collect information about the surrounding environment, such as interference levels, target distribution, signal strength, etc. The BBU decides how to compress the data to be transmitted to the AAU through the fronthaul interface based on these environmental data. The compression parameters include compression domain, compression mode, and compression mask. The compression domain is the domain selected for compression. For example, the compression domain can be the distance domain, Doppler domain, and angle domain. The compression mode is to select the domain to be compressed in the selected compression domain. For example, if the distance domain is selected, you can choose whether to perform frequency domain compression or distance domain compression.

[0060] In this way, the BBU can optimize data transmission according to real-time environmental conditions, reduce the traffic demand of the fronthaul interface, and improve the efficiency and performance of the system. This dynamic configuration and data compression mechanism is essential to achieve efficient communication and perception functions, especially under limited bandwidth and processing power.

[0061] S220, the active antenna unit performs corresponding compression operations according to the received configured compression parameters, and the compressed data is fed back to the baseband processing unit through the fronthaul interface, so that the baseband processing unit completes signal recovery according to the compression parameters.

[0062] Specifically, the BBU has dynamically configured compression parameters based on the environmental data collected by the environmental status perception system, and transmitted these compression parameters to the AAU through the fronthaul interface. After receiving these compression parameters, the AAU will perform corresponding data compression operations based on these parameters. This means that the AAU will compress the signal data it receives to reduce the amount of data, thereby reducing the traffic demand of the fronthaul interface. After the compression operation is completed, the AAU will feed the compressed data back to the BBU through the fronthaul interface. In this way, the BBU can receive and process the compressed data without causing processing delays or system performance degradation due to excessive data volume. The purpose of this process is to optimize data transmission, reduce the traffic of the fronthaul interface, and improve the efficiency and performance of the system. By dynamically configuring compression parameters and performing corresponding compression operations, the system can optimize data transmission according to real-time environmental conditions.

[0063] This application sets the signal content transmitted between the AAU and the BBU according to the specific needs of the application scenario or through learning of the perceived environment, and introduces a new fronthaul interface format, allowing the independent or joint compression domain and compression mode (i.e., compression Pattern) of the RDA domain (Range-Doppler-Angle) to be selected on demand. The content transmitted is the range domain signal, Doppler domain signal, and angle domain signal required by the application, rather than all received signals. With the support of the new fronthaul interface, the transmission of redundant perception signals is reduced, thereby reducing the demand for fronthaul bandwidth. Bandwidth resources can be used more efficiently and unnecessary transmissions can be reduced. Under the same fronthaul bandwidth, this application can provide better perception performance compared to those solutions that use reduced specifications.

[0064] Please refer to Figure 3 , Figure 3 Schematic diagram of BBU and AAU communication provided by this application. The synaesthesia integrated data transmission process shown in the figure includes the initial compression parameter configuration process and the update compression parameter configuration step. The details are as follows:

[0065] (1) The initial compression parameter configuration steps include:

[0066] S1, scene parameter input, that is, the application end receives and inputs corresponding scene parameters according to specific scenarios (such as indoor low-speed short-distance coverage, outdoor low-speed long-distance coverage, outdoor high-speed long-distance coverage, etc.).

[0067] S2, initial compression parameter configuration, that is, the baseband processing unit receives these scene parameters and completes the configuration of the initial compression parameters accordingly.

[0068] S3, compression parameter transmission, that is, the baseband processing unit transmits the configured compression parameters in the form of data packets to the active antenna unit through the fronthaul interface.

[0069] S4, compression parameter parsing, that is, the active antenna unit parses the received data packet to obtain the compression parameter.

[0070] (2) Update the compression parameter configuration steps, including:

[0071] S5, detection signal sending, that is, the baseband processing unit sends a detection signal to the active antenna unit, and the detection signal is transmitted through the air interface.

[0072] S6, receiving and compressing the air interface echo signal, that is, after the active antenna unit receives the air interface echo signal, it compresses it and sends the compressed data back to the baseband processing unit in the form of a data packet.

[0073] S7, signal decompression and perception recovery, that is, after receiving the data packet, the baseband processing unit performs a decompression operation, recovers the perception signal, and completes the estimation of the distance, angle and speed of the target based on the recovered perception signal.

[0074] S8, environment learning and parameter updating, that is, the baseband processing unit performs environment learning according to the perception signal, obtains the distance distribution, angle distribution and speed distribution information of the target, and dynamically updates the compression parameters according to this information and scene parameter settings.

[0075] S9, updating parameter transmission, that is, the baseband processing unit transmits the updated compression parameters to the active antenna unit through the fronthaul interface.

[0076] S10, updating parameter parsing, that is, the active antenna unit parses the updated data packet to obtain new compression parameters.

[0077] The above steps S5 to S10 are performed in a periodic manner to ensure that the system is continuously optimized and adapts to the dynamic environment.

[0078] The synaesthesia integrated data transmission method of the present application is described below through embodiments.

[0079] Embodiment 1: Process of compressing and restoring Range domain (distance domain) information.

[0080] The Range domain refers to the signal characteristics related to the target distance. In a radar system, the distance of the target is determined by measuring the time delay between the transmitted signal and the received signal. This time delay is proportional to the distance of the target. In signal processing, this time delay information can be represented in the frequency domain to form the Range domain. Forward compression refers to compressing the data before it is transmitted from the AAU at the front end to the BBU at the back end. Doing so can reduce the amount of data, thereby reducing the traffic demand of the fronthaul interface and improving the efficiency of data transmission. Recovery refers to decompressing the compressed data after the data reaches the BBU to restore the original data format and content. In this way, the BBU can process the data for further signal processing and analysis.

[0081] In the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, there are two key performance parameters related to the Range domain: maximum distance and distance resolution.

[0082] Maximum distance ( ):

[0083] ;

[0084] This formula represents the maximum distance that a radar or communication system can detect or communicate in a MIMO-OFDM system. Where c is the speed of light (about 3×10^8 meters per second), is the duration of the OFDM symbol, that is, the transmission time of each OFDM symbol. From this formula, we can see that the maximum distance It is proportional to the duration of the OFDM symbol. Because the longer the duration of the OFDM symbol, the greater the time delay that the system can detect, and thus the greater the distance that can be detected.

[0085] Distance resolution ( ):

[0086] ;

[0087] This formula represents the minimum distance difference between two targets at different distances that a radar or communication system can distinguish in a MIMO-OFDM system, i.e., the distance resolution. Among them, B is the transmission bandwidth of the signal. From this formula, we can see that the distance resolution is inversely proportional to the transmission bandwidth of the signal. The larger the bandwidth, the smaller the distance that the system can distinguish, i.e., the higher the distance resolution.

[0088] Under the NR standard, the MIMO-OFDM system can sense distances exceeding 5 km and 1 km respectively in the FR1 (Frequency Range 1, i.e., the frequency band below 6 GHz) and FR2 (Frequency Range 2, i.e., the millimeter wave frequency band). These distances far exceed the sensing distances required for many synaesthesia integration scenarios (such as smart factories, indoor health monitoring, low-altitude drone take-off and landing facilities, etc.), which usually only require a detection distance of less than 100 meters, or even no more than 300 meters. Since the AAU can sense farther distances than actually required, it is not necessary to pass the entire distance dimension signal before passing the signal to the BBU. Doing so can reduce the fronthaul traffic, that is, reduce the amount of data transmitted from the AAU to the BBU, thereby improving the efficiency of the system.

[0089] Exemplarily, there are two ways to implement distance dimension signal compression: a first way is a frequency comb compression method; a second way is an effective range selection compression method of the R spectrum.

[0090] For the first method, please refer to Figure 4 , Figure 4 This is a schematic diagram of the first method provided in Example 1 of the present application. Figure 4It shows that the transmitted signal is sparsely placed within the full bandwidth. That is, the signal is not transmitted continuously throughout the frequency band from the lowest frequency to the highest frequency, but is placed in a certain interval or sparse manner. That is, the signal is not densely distributed within the frequency band, but is regularly spaced apart, forming a sparse distribution pattern.

[0091] In some embodiments, in the above step S220, performing the corresponding compression operation includes performing a distance domain compression operation, including the following steps:

[0092] S301, determine the combing fraction. Specifically, first determine the maximum detection distance in the case of continuous resource element transmission, and select the combing fraction according to the actual perception requirements and the fronthaul traffic limit. The combing fraction defines how many resource elements are sampled in the frequency domain to achieve sparse representation of the signal.

[0093] Specifically, in order to reduce the number of resource elements (REs) within the bandwidth, frequency comb can be used. Frequency comb is a technique that reduces the number of consecutive REs by selectively allocating REs in the frequency domain. For example, if the comb number is S, every S resource elements are sampled in the frequency domain.

[0094] S302, calculating the unambiguous detection distance after combing. Specifically, the maximum detection distance is divided by the selected combing number to obtain the unambiguous detection distance after combing.

[0095] Specifically, assuming that the detectable unambiguous distance of continuous RE transmission is A, then through S combing (i.e. selecting one of the S REs), the detectable unambiguous distance becomes A / S. This means that through frequency combing, the effective detection distance of the system is reduced, but at the same time the fronthaul traffic is also reduced.

[0096] S303, generating compressed data. Specifically, based on the unambiguous detection distance after the combing, generating compressed data.

[0097] Among them, the data generated after the compression operation are sparsely represented because they are obtained by selectively sampling resource elements in the frequency domain.

[0098] The first method is that it can effectively reduce the fronthaul traffic without losing the range resolution. By selecting the appropriate comb fraction, the system performance can be optimized while meeting the perception requirements.

[0099] For the second method, please refer to Figure 5 , Figure 5 It is a schematic diagram of the second clock mode provided in Example 1 of the present application. Figure 5 Demonstrates on-demand selection of R-region transfer. That is, data transfer is not fixed or preset, but is determined based on actual needs. For example, when and which data to transfer can be dynamically determined based on user requests, the real-time status of the system, the importance of the data, or other factors. On-demand selection of R-region transfer means that the system dynamically determines and transfers a specific data region based on current needs or conditions.

[0100] In some embodiments, in the above step S220, performing the corresponding compression operation includes performing a distance domain compression operation, including the following steps:

[0101] S401, determining a sensing distance range. Specifically, determining a distance range that the system needs to sense based on actual application scenarios.

[0102] Specifically, the design of the system needs to take into account specific application scenarios, such as smart factories, indoor health monitoring, low-altitude drone take-off and landing facilities, etc. These scenarios usually have specific distance detection requirements.

[0103] S402, analyzing the distance spectrum. Specifically, analyzing the distance spectrum of the system to identify key areas corresponding to the determined perception distance range.

[0104] Specifically, the system's range spectrum (R spectrum) is analyzed to identify key areas that correspond to the determined perception distance range. The range spectrum is a frequency spectrum graph that reflects the signal strength at different distances. By analyzing this graph, it is possible to determine which areas the system needs to focus on.

[0105] S403, selecting a valid part. Specifically, in the distance spectrum, select and mark the valid parts corresponding to the perception range, wherein the valid parts contain key distance detection information.

[0106] Specifically, in the distance spectrum, the valid parts corresponding to the sensing range are selected and marked, which contain key distance detection information. This means that the system only transmits the distance detection results within the sensing range, while the results outside the sensing range are not transmitted. In this way, the fronthaul traffic can be reduced while maintaining the distance resolution.

[0107] The role of the second method is to reduce the fronthaul traffic without affecting the system perception performance by analyzing and selecting the effective part of the distance spectrum, thereby improving the efficiency and performance of the system.

[0108] Exemplarily, in a MIMO-OFDM system, the specific process and related parameters for presetting and implementing range domain compression are as follows:

[0109] Preset Range domain compression mode:

[0110] (a) Mode preset can be preset based on prior information of the synaesthesia application scenario. For example, the compression mode can be preset based on information such as indoor coverage distance, farthest / shortest detection distance, etc.

[0111] (b) Another way is to complete the mode preset through the two-step method of "environment learning + application". In the environmental learning stage, the distance range of the target is initially determined with low overhead (such as time and bandwidth), and then the compression mode is set. These two steps are iterated alternately, so that the system can adaptively match the environment.

[0112] Parameter contents of the compression mode of the Range domain:

[0113] Compression domain: Determines whether the compression operation is performed in the frequency domain or the distance domain, that is, frequency domain / distance domain.

[0114] Compression Pattern: Specifies the specific location in the frequency domain or distance spectrum (R spectrum) where compression is performed, that is, the frequency domain position / R spectrum position.

[0115] The BBU passes the above parameters to the AAU through the fronthaul interface, and the AAU completes the Range domain compression according to the instructions. This means that the baseband processing unit (BBU) will pass the relevant parameters of the compression mode to the active antenna unit (AAU), and the AAU will perform the range domain compression operation based on these parameters.

[0116] Implementing Range domain compression involves additional fronthaul interface information. This means that when performing range domain compression, some additional key fields need to be added to the fronthaul interface so that the AAU can perform the compression operation correctly. For example, the added key fields are as follows:

[0117] Table 2

[0118]

[0119] After performing the distance domain compression operation, the AAU sends the compressed information to the BBU through the fronthaul interface. This information is compressed to reduce the fronthaul traffic. After receiving this compressed information, the BBU will perform a decompression operation according to the previously configured fronthaul parameters. These fronthaul parameters include detailed information about the compression operation, such as compression domain, compression mode, compression position, etc. Through the decompression operation, the BBU can restore the original distance domain information content. This means that the BBU can extract the original distance detection results from the compressed data for subsequent processing and analysis.

[0120] Embodiment 2: A process of compressing and restoring Doppler domain information.

[0121] The Doppler domain involves the frequency shift caused by the motion of the target relative to the receiver, which can be used to detect and analyze the speed and direction of movement of the target. During the Doppler domain forward compression process, the system compresses the information related to the Doppler effect. This involves selectively sampling key parts of the Doppler spectrum to reduce the amount of data that needs to be transmitted. The compression operation can be based on preset parameters or dynamically adjusted parameters to adapt to different application scenarios and environmental conditions. At the receiving end, after the BBU receives the compressed Doppler domain information, it will perform a decompression operation to restore the original Doppler spectrum information. This process involves using the parameters set during compression to reconstruct the complete Doppler spectrum, so that the speed and direction of movement of the target can be accurately analyzed.

[0122] In MIMO-OFDM systems, there are two key performance parameters related to the Doppler domain: maximum speed ( ) and velocity resolution (Δv).

[0123] Maximum speed ( ):

[0124] ;

[0125] This formula calculates the maximum speed that the system can detect. Where λ is the wavelength of the signal (usually related to the carrier frequency, λ = c / f, c is the speed of light, f is the carrier frequency), is the duration of an OFDM symbol. The maximum speed is calculated based on the maximum value of the Doppler frequency shift, that is, when the target moves at the maximum speed, the frequency deviation caused will not exceed half of the OFDM subcarrier spacing.

[0126] Velocity resolution (Δv):

[0127] ;

[0128] This formula calculates the minimum speed difference that the system can resolve. Where c is the speed of light, N is the number of OFDM symbols, is the duration of an OFDM symbol. The speed resolution reflects the system's ability to distinguish targets of different speeds, which depends on the total observation time of the OFDM system and the number of subcarriers.

[0129] These two parameters are critical to the performance of MIMO-OFDM systems, especially in application scenarios that require accurate perception and tracking of fast-moving targets. The maximum speed determines the fastest movement speed that the system can detect, while the speed resolution determines how small the speed difference can be distinguished by the system. By optimizing these parameters, the system's perception performance can be improved, ensuring that targets can still be accurately detected and tracked in complex and dynamic environments.

[0130] The NR MIMO-OFDM system is designed in the FR1 (Frequency Range 1) and FR2 (Frequency Range 2) frequency bands to sense very high speeds, exceeding 500 m / s and 200 m / s, respectively. However, in many synaesthesia integration scenarios, such as smart factories, indoor health monitoring, low-altitude drone take-off and landing facilities, etc., the actual required detection speed is usually much lower than these values, such as less than 10 m / s or 30 m / s. Since the speed requirements in actual application scenarios are much lower than the capabilities of the system design, the active antenna unit (AAU) does not need to pass the entire Doppler signal to the baseband processing unit (BBU). This reduces the amount of data that needs to be transmitted, thereby reducing the fronthaul traffic.

[0131] Exemplarily, there are two ways to implement Doppler signal compression: a first way is a time domain comb compression method; a second way is a V spectrum effective range selection compression method.

[0132] For the first method, please refer to Figure 6 , Figure 6 This is a schematic diagram of the first method provided in Example 2 of the present application. Figure 6 It shows that the transmitted signals are sparsely placed throughout the time. That is, from the time the signal starts to be emitted to the time it ends, the signal is not emitted continuously, but is placed in a certain interval or sparse manner. That is, the signal is not densely distributed on the time axis, but is regularly spaced apart, forming a sparse distribution pattern.

[0133] In some embodiments, in the above step S220, performing the corresponding compression operation includes performing a Doppler domain compression operation, specifically including:

[0134] S501, determining a speed range that the system needs to sense. Specifically, the speed range that the system needs to sense is determined according to an actual application scenario.

[0135] For example, in scenarios such as smart factories or indoor health monitoring, only lower speeds may need to be detected, while higher speeds may need to be detected in other scenarios. This step is the basis for subsequent compression operations because it determines which speed ranges of data need to be retained.

[0136] S502, calculating the detection speed after combing. Specifically, the detection speed after combing is obtained by dividing the detectable speed of continuous OFDM symbol transmission by the selected combing number.

[0137] Specifically, the system divides the detectable speed (assumed to be A) of continuous OFDM symbols by the selected combing number (assumed to be S), that is, A / S, to obtain the detection speed after combing. Combing means sending a symbol every certain number of OFDM symbols, thereby reducing the amount of data transmitted. In this way, the system can reduce the amount of data that needs to be transmitted while maintaining speed resolution.

[0138] S503, generating data after compression operation based on the combed detection speed.

[0139] That is to say, the system only transmits speed data within the perception range and ignores speed information that exceeds actual requirements. This method can effectively reduce the fronthaul traffic while ensuring that the system's perception performance is not affected.

[0140] The first method is to compress the Doppler signal by time-domain combing to reduce the forward traffic and maintain the speed resolution. This method is suitable for application scenarios with specific requirements for speed detection.

[0141] For the second method, please refer to Figure 7 , Figure 7 It is a schematic diagram of the second clock mode provided in Example 2 of the present application. Figure 7 Demonstrates on-demand selection of Doppler zone transmission. That is, data transmission is not fixed or preset, but determined based on actual needs. For example, it is possible to dynamically decide when and what data to transmit based on user requests, the real-time status of the system, the importance of the data, or other factors. On-demand selection of Doppler zone transmission means that the system dynamically determines and transmits data areas associated with a specific Doppler effect based on current needs or conditions.

[0142] In some embodiments, in the above step S220, performing the corresponding compression operation includes performing a Doppler domain compression operation, specifically including:

[0143] S601, determining a speed range that the system needs to sense. Specifically, determining a speed range that the system needs to sense according to an actual application scenario.

[0144] For example, in some scenarios, only lower speeds may need to be detected, while in other scenarios, higher speeds may need to be detected. This step is the basis for subsequent compression operations because it determines which speed ranges of data need to be retained.

[0145] S602, analyzing the speed spectrum of the system. Specifically, by analyzing the speed spectrum of the system, the speed corresponding to the perception range is determined.

[0146] Specifically, the velocity spectrum (V spectrum) is a frequency spectrum representation of the system's response to targets of different speeds. By analyzing the velocity spectrum, the system can identify which parts of the data are related to the speed within the perception range.

[0147] S603, selecting and marking a valid part. Specifically, in the speed spectrum, a valid part corresponding to the perception range is selected and marked, wherein the valid part contains key speed detection information.

[0148] Specifically, in the speed spectrum, the system selects and marks the valid parts corresponding to the perception range. These valid parts contain key speed detection information, while other parts are considered non-perceptible or irrelevant. In this way, the system can focus on processing and transmitting data that is meaningful to the current application scenario.

[0149] S604, generating compressed data. Specifically, generating compressed data based on the speed detection information of the selected effective part.

[0150] That is to say, the system only transmits speed data within the perception range and ignores speed information that exceeds actual requirements. This method can effectively reduce the fronthaul traffic while ensuring that the system's perception performance is not affected.

[0151] The second method is to compress the Doppler signal by selecting the effective range of the V spectrum to reduce the forward traffic and maintain the speed resolution. This method is suitable for application scenarios with specific requirements for speed detection.

[0152] Exemplarily, in a MIMO-OFDM system, the specific process and parameter contents of presetting and implementing the Doppler domain compression mode are as follows:

[0153] Preset compression mode for Doppler domain:

[0154] (a) Mode preset can be preset based on prior information of the synaesthesia application scenario. For example, the compression mode can be set based on known application requirements (such as minimum / maximum detection speed).

[0155] (b) Another way is to complete the mode preset through the two-step method of "environment learning + application". First, the system learns the environment with low overhead (time, bandwidth, etc.) and makes a preliminary judgment on the speed range of the target. Then, the compression mode is set based on these preliminary judgments. This method uses two steps to alternately iterate, allowing the system to adaptively match the environment.

[0156] The parameters of the compression mode in the Doppler domain are as follows:

[0157] Compression Domain: Specifies that the compression operation will be performed in a certain signal domain, which can be the time domain or the velocity domain.

[0158] Compression Pattern: Specifies the specific pattern of the compression operation, such as at a specific position in the time domain or at a specific position in the velocity spectrum (V spectrum).

[0159] The BBU transmits the above compression mode parameters to the AAU via the fronthaul interface. The AAU completes the Doppler domain compression operation according to the received instructions.

[0160] Implementing Doppler domain compression involves additional fronthaul interface information. This means that when performing Doppler domain compression, some additional key fields need to be added to the fronthaul interface so that the AAU can perform the compression operation correctly. For example, the added key fields are as follows:

[0161] Table 3

[0162]

[0163] The BBU receives the compressed information sent by the AAU. This information is the result of compression by the AAU in the Doppler domain (i.e., speed domain). The compression operation is intended to reduce the fronthaul traffic while maintaining the necessary perception performance. The BBU decompresses the compressed information according to the pre-configured fronthaul parameters. These fronthaul parameters include information such as the compression domain and compression pattern, which are determined during the compression mode preset stage. Through the decompression operation, the BBU is able to restore the original Doppler domain information content. This means that the BBU can restore the same speed detection information as in the uncompressed state, so as to continue subsequent processing and analysis.

[0164] In some embodiments, in the above step S220, performing the corresponding compression operation includes performing a Doppler domain compression operation, specifically including:

[0165] Determine the angle range that the system needs to perceive based on the actual application scenario;

[0166] analyzing the angular spectrum of the system to determine the angles corresponding to the perceptual range;

[0167] In the angle spectrum, a valid part of the angle spectrum corresponding to the perception range is selected and marked, and the valid part contains key angle detection information.

[0168] In some embodiments, in the above step S220, performing the corresponding compression operation includes performing a Doppler domain compression operation, specifically including:

[0169] Determine the angle range that the system needs to perceive based on the actual application scenario;

[0170] The antenna spacing is determined based on the unambiguous angle range requirements, and some antenna domain signals are selected to complete the compression operation.

[0171] In summary, the purpose of this application is to optimize the transmission efficiency of perception signals in MIMO-OFDM systems, especially when processing distance domain and Doppler dimension signals, using frequency comb and time domain comb compression strategies, as well as effective range selection methods, to reduce the amount of data in the distance domain and Doppler dimension. The specific process is as follows: First, preset or dynamically adjust the compression mode to ensure that it matches the needs of the application scenario. Then, through the fronthaul interface, the compression parameters are passed to the AAU to instruct it to perform the compression operation. The AAU compresses the signal according to the received parameters to reduce the amount of data. Subsequently, the BBU receives the compressed information sent by the AAU. The BBU decompresses this information based on the preset fronthaul parameters to restore the original distance domain and Doppler domain information content. This process not only reduces unnecessary data transmission and reduces fronthaul traffic, but also improves the overall efficiency and flexibility of the system.

[0172] Through the above steps, the perception performance is not affected while the amount of data is reduced, thereby achieving the best balance between system performance and resource utilization.

[0173] The synaesthesia integrated data transmission device provided by the present application is described below. The synaesthesia integrated data transmission device described below and the synaesthesia integrated data transmission method described above can be referred to each other.

[0174] Please refer to Figure 8 , Figure 8 The module block diagram of the synaesthesia integrated data transmission device provided by the present application. A synaesthesia integrated data transmission device 700 is applied to a base station, the base station is configured with an environmental state perception system, the base station includes a baseband processing unit, an active antenna unit and a fronthaul interface therebetween, and the synaesthesia integrated data transmission device 700 includes a configuration module 710 and an execution module 720.

[0175] Exemplarily, the configuration module 710 dynamically configures compression parameters according to the environmental data collected by the environmental state perception system through the baseband processing unit, and transmits the configured compression parameters to the active antenna unit through the fronthaul interface, wherein the compression parameters include a compression domain and a compression mode, wherein the compression domain indicates that compression is performed in a certain signal domain, and the compression mode indicates that a portion of the signal position in the selected compression domain needs to be compressed;

[0176] Exemplarily, the execution module 720 performs corresponding compression operations according to the received configured compression parameters through the active antenna unit, and the compressed data is fed back to the baseband processing unit through the fronthaul interface.

[0177] Exemplarily, multiple key fields are added to the fronthaul interface, and the multiple key fields cover various dimensions of the compression operation, specifically including:

[0178] Compression domain flag: used to define which domains in the range domain, Doppler domain, and angle domain are to be compressed;

[0179] Distance domain compression mode: used to define the compression mode in the distance domain, including frequency domain compression and distance domain compression;

[0180] Distance domain compression mask: a mask used to define compression, including which data locations can be compressed and which data locations cannot be compressed;

[0181] Doppler domain compression mode: used to define the compression mode in the Doppler domain, including time domain compression and velocity domain compression;

[0182] Doppler domain compression mask: a mask used to define compression, including which data locations can be compressed and which data locations cannot be compressed;

[0183] Angle domain compression mode: used to define the compression mode in the angle domain, including antenna domain compression and angle domain compression;

[0184] Angle domain compression mask: A mask used to define compression, including which data locations can be compressed and which data locations cannot be compressed.

[0185] Exemplarily, the execution module 720 is further configured to:

[0186] Determine the maximum detection range in the case of continuous resource element delivery, and select the comb fraction based on the actual perception needs and fronthaul traffic constraints. The comb fraction defines how many resource elements are sampled in the frequency domain to achieve sparse representation of the signal.

[0187] Divide the maximum detection distance by the selected combing number to obtain the unambiguous detection distance after combing;

[0188] Based on the unambiguous detection distance after combing, data subjected to compression operation is generated.

[0189] Exemplarily, the execution module 720 is further configured to:

[0190] Determine the distance range that the system needs to perceive based on the actual application scenario;

[0191] Analyzing the system's distance spectrum to identify those critical regions corresponding to the determined perceptual distance ranges;

[0192] In the distance spectrum, those effective parts corresponding to the perception range are selected and marked, wherein the effective parts contain key distance detection information;

[0193] Based on the distance detection information of the selected effective part, data subjected to the compression operation is generated.

[0194] Exemplarily, the execution module 720 is further configured to:

[0195] Determine the speed range that the system needs to sense based on the actual application scenario;

[0196] Divide the detectable rate of continuous OFDM symbol transmission by the selected combing number to obtain the detection rate after combing;

[0197] Based on the combed detection speed, data after compression operation is generated.

[0198] Exemplarily, the execution module 720 is further configured to:

[0199] Determine the speed range that the system needs to sense based on the actual application scenario;

[0200] Analyze the system's speed spectrum to determine which parts correspond to speeds within the perceptual range;

[0201] In the speed spectrum, a valid part of the speed spectrum corresponding to the perception range is selected and marked, wherein the valid part contains key speed detection information;

[0202] Based on the speed detection information of the selected effective portion, data subjected to a compression operation is generated.

[0203] Exemplarily, the configuration module 710 is further used to:

[0204] Based on the prior knowledge provided by the environmental state perception system, the initial compression parameters are preset. The prior knowledge includes known application scenario features;

[0205] During the environmental learning phase, a preset low-overhead method is used to preliminarily determine the distance range of the target, and the compression parameters are adjusted and set according to the preliminary judgment results of the environmental learning;

[0206] The compression parameters are determined by continuously updating and optimizing the compression parameters by adopting an alternating iterative method so as to enable the compression parameters to adaptively adapt to environmental changes.

[0207] Exemplarily, the compression parameters are applied to range domain signals, Doppler domain signals and angle domain signals; the compression domain of the range domain signal includes the frequency domain and the distance domain, and the compression mode of the range domain signal includes the frequency domain position and the range spectrum position; the compression domain of the Doppler signal includes the time domain and the velocity domain, and the compression mode of the Doppler signal includes the time domain position and the velocity spectrum position; the compression domain of the angle domain signal includes the angle domain and the antenna domain, and the compression mode of the angle domain signal includes the angle domain position and the angle spectrum position.

[0208] It should be noted here that the above-mentioned synaesthesia integrated data transmission device provided in the embodiment of the present application can implement the method steps implemented in the above-mentioned synaesthesia integrated data transmission method embodiment, and can achieve the same technical effects. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0209] In some embodiments, the present application further provides a base station, which is used to execute the synaesthesia integrated data transmission method as described above.

[0210] Specifically, the base station is designed to perform the above-described synaesthesia integrated data transmission method. That is, the base station has the functions and capabilities required to implement the method and can operate according to the steps and processes specified in the method to optimize the transmission of perceptual signals in the MIMO-OFDM system.

[0211] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the synaesthesia integrated data transmission method provided by the above methods.

[0212] On the other hand, the present application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the above-mentioned synaesthesia integrated data transmission method.

[0213] The embodiments of the present application provide an electronic device, a computer program product, and a processor-readable storage medium, on which the computer program stored enables the processor to implement all the method steps implemented in the above-mentioned method embodiments and to achieve the same technical effects. The parts and beneficial effects of this embodiment that are the same as those in the method embodiments will not be described in detail herein.

[0214] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0215] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0216] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A synaesthesia integrated data transmission method, characterized in that: Applied to a base station, the base station is equipped with an environmental state perception system, the base station includes a baseband processing unit, an active antenna unit and a fronthaul interface therebetween, the interaural integrated data transmission method includes: The baseband processing unit dynamically configures compression parameters according to the environmental data collected by the environmental state perception system, and transmits the configured compression parameters to the active antenna unit through the fronthaul interface, wherein the compression parameters include compression domain, compression mode and compression mask; The active antenna unit performs corresponding compression operations according to the received configured compression parameters, and the compressed data is fed back to the baseband processing unit through the fronthaul interface, so that the baseband processing unit completes signal recovery according to the compression parameters; The step of dynamically configuring compression parameters includes: Presetting initial compression parameters based on prior knowledge provided by the environmental state perception system, wherein the prior knowledge includes known application scenario characteristics; During the environmental learning phase, a preset low-overhead method is used to preliminarily determine the distance range of the target, and the compression parameters are adjusted and set according to the preliminary judgment results of the environmental learning; Adopting an alternating iterative method to continuously update and optimize the compression parameters so that the compression parameters can be adaptively adapted to environmental changes, thereby determining the compression parameters; Among them, the step of updating the compression parameter configuration includes: the baseband processing unit sends a detection signal to the active antenna unit, and the signal is transmitted through the air interface; after the active antenna unit receives the air interface echo signal, it compresses it and sends the compressed data back to the baseband processing unit in the form of a data packet; after the baseband processing unit receives the data packet, it performs a decompression operation to restore the perception signal; the baseband processing unit completes the estimation of the distance, angle and speed of the target according to the restored perception signal; the baseband processing unit performs environmental learning according to the perception signal, obtains the distance distribution, angle distribution and speed distribution information of the target, and dynamically updates the compression parameters according to the information and scene parameter settings, and transmits the updated compression parameters to the active antenna unit through the fronthaul interface, and the active antenna unit parses the updated data packet to obtain new compression parameters.

2. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The synaesthesia integrated data transmission method specifically further includes a primary compression parameter configuration step, and the primary compression parameter configuration step includes: The application end receives and inputs corresponding scene parameters according to the specific scenario; the baseband processing unit receives the scene parameters, and completes the configuration of the initial compression parameters accordingly, and transmits the configured compression parameters in the form of data packets to the active antenna unit through the fronthaul interface. The active antenna unit parses the received data packets to obtain the compression parameters.

3. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The synaesthesia integrated data transmission method further includes: A plurality of key fields are added to the fronthaul interface, and the plurality of key fields cover various dimensions of the compression operation, specifically including: Compression domain flag: used to define the domain to be compressed in the range domain, Doppler domain and angle domain; Distance domain compression mode: used to define the compression mode in the distance domain, including frequency domain compression and distance domain compression; Distance domain compression mask: a mask used to define compression, including data in compressible locations and data in incompressible locations; Doppler domain compression mode: used to define the compression mode in the Doppler domain, including time domain compression and velocity domain compression; Doppler domain compression mask: a mask used to define compression, including data at compressible locations and data at incompressible locations; Angle domain compression mode: used to define the compression mode in the angle domain, including antenna domain compression and angle domain compression; Angle Domain Compression Mask: A mask used to define compression, including data in compressible locations and data in incompressible locations.

4. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The performing of the corresponding compression operation includes performing a distance domain compression operation, specifically including: Determine the maximum detection range in the case of continuous resource element delivery, and select the comb fraction based on the actual perception needs and fronthaul traffic constraints. The comb fraction defines how many resource elements are sampled in the frequency domain to achieve sparse representation of the signal. Divide the maximum detection distance by the selected combing number to obtain the unambiguous detection distance after combing; Based on the unambiguous detection distance after combing, data subjected to compression operation is generated.

5. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The performing of the corresponding compression operation includes performing a distance domain compression operation, specifically including: Determine the distance range that the system needs to perceive based on the actual application scenario; analyzing the system's distance spectrum to identify critical regions corresponding to the determined perception distance ranges; In the distance spectrum, a valid part corresponding to the perception range is selected and marked, wherein the valid part contains key distance detection information; Based on the distance detection information of the selected effective portion, data subjected to a compression operation is generated.

6. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The performing of the corresponding compression operation includes performing a Doppler domain compression operation, specifically including: Determine the speed range that the system needs to sense based on the actual application scenario; Divide the detectable speed of continuous OFDM symbol transmission by the selected combing number to obtain the unambiguous detection speed after combing; Based on the combed detection speed, data after compression operation is generated.

7. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The performing of the corresponding compression operation includes performing a Doppler domain compression operation, specifically including: Determine the speed range that the system needs to sense based on the actual application scenario; Analyze the system's velocity spectrum to determine the velocities that correspond to the perceptual range; In the speed spectrum, a valid part of the speed spectrum corresponding to the perception range is selected and marked, wherein the valid part contains key speed detection information; Based on the speed detection information of the selected effective portion, data subjected to a compression operation is generated.

8. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The performing of the corresponding compression operation includes performing an angle domain compression operation, specifically including: Determine the angle range that the system needs to perceive based on the actual application scenario; analyzing the angular spectrum of the system to determine the angles corresponding to the perceptual range; In the angle spectrum, a valid part corresponding to the perception range in the angle spectrum is selected and marked, and the valid part contains key angle detection information.

9. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The performing of the corresponding compression operation includes performing an angle domain compression operation, specifically including: Determine the angle range that the system needs to perceive based on the actual application scenario; The antenna spacing is determined based on the unambiguous angle range requirements, and some antenna domain signals are selected to complete the compression operation.

10. The synaesthesia integrated data transmission method according to claim 1, characterized in that: The compression parameters are applied to range domain signals, Doppler domain signals and angle domain signals; the compression domain of the range domain signal includes the frequency domain and the range domain, and the compression mode of the range domain signal includes the frequency domain position and the range spectrum position; the compression domain of the Doppler domain signal includes the time domain and the velocity domain, and the compression mode of the Doppler domain signal includes the time domain position and the velocity spectrum position; the compression domain of the angle domain signal includes the angle domain and the antenna domain, and the compression mode of the angle domain signal includes the antenna position and the angle spectrum position.

11. A synaesthesia integrated data transmission device, characterized in that: Applied to a base station, the base station is equipped with an environmental state perception system, the base station includes a baseband processing unit, an active antenna unit and a fronthaul interface therebetween, and the interaural integrated data transmission device includes: The configuration module dynamically configures compression parameters according to the environmental data collected by the environmental state perception system through the baseband processing unit, and transmits the configured compression parameters to the active antenna unit through the fronthaul interface, wherein the compression parameters include a compression domain, a compression mode, and a compression mask; wherein the step of dynamically configuring the compression parameters includes: Presetting initial compression parameters based on prior knowledge provided by the environmental state perception system, wherein the prior knowledge includes known application scenario characteristics; During the environmental learning phase, a preset low-overhead method is used to preliminarily determine the distance range of the target, and the compression parameters are adjusted and set according to the preliminary judgment results of the environmental learning; Adopting an alternating iterative method to continuously update and optimize the compression parameters so that the compression parameters can be adaptively adapted to environmental changes, thereby determining the compression parameters; Among them, the step of updating the compression parameter configuration includes: the baseband processing unit sends a detection signal to the active antenna unit, and the signal is transmitted through the air interface; after the active antenna unit receives the air interface echo signal, it compresses it and sends the compressed data back to the baseband processing unit in the form of a data packet; after the baseband processing unit receives the data packet, it performs a decompression operation to restore the perception signal; the baseband processing unit completes the estimation of the distance, angle and speed of the target according to the restored perception signal; the baseband processing unit performs environmental learning according to the perception signal, obtains the distance distribution, angle distribution and speed distribution information of the target, and dynamically updates the compression parameters according to the information and scene parameter settings, and transmits the updated compression parameters to the active antenna unit through the fronthaul interface, and the active antenna unit parses the updated data packet to obtain the new compression parameters; The execution module performs corresponding compression operations according to the received configured compression parameters through the active antenna unit, and the compressed data is fed back to the baseband processing unit through the fronthaul interface, so that the baseband processing unit completes signal recovery according to the compression parameters.

12. A base station, characterized in that: The base station is used to execute the synaesthesia integrated data transmission method according to any one of claims 1 to 10.

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