Wireless signal optimization method and system fusing channel state detection

Abstract

The invention relates to a wireless signal optimization method and system fused with channel state detection, and the method comprises the steps: obtaining detection state parameters of a fused channel, carrying out the scene decision processing, and outputting a classification result; performing transmission matching according to the classification result to obtain a transmission parameter set; performing channel frequency identification on the fusion channel based on the classification result to obtain frequency band resource block features; and carrying out transmission dynamic adaptation and frequency resource allocation based on the transmission parameter set and the frequency band resource block characteristics to obtain wireless control parameters. According to the invention, the complex characteristics of the channel can be reflected more comprehensively, and the accuracy of channel state detection is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless signals, in particular to a wireless signal optimization method and system fusing channel state detection. BACKGROUND

[0002] With the explosive growth of intelligent terminals and the diversification of business scenarios, how to accurately perceive the channel state and dynamically optimize resource allocation has become a key challenge to improve spectrum efficiency and service quality. The existing wireless signal optimization scheme usually has three limitations: first, channel state detection relies on a single dimension parameter, which is difficult to fully reflect the complex channel characteristics such as multipath fading, interference heterogeneity, etc.; second, the transmission strategy and scene adaptation are insufficient, and the fixed parameter configuration cannot match the dynamic changes of business demand; third, the frequency band resource allocation often adopts a static division mode, lacking consideration of the coordination optimization of channel frequency selectivity and business priority. These problems lead to low spectrum utilization and unstable transmission reliability of the existing system in complex wireless environment, which is difficult to meet the communication needs of high-density and differentiated scenarios. SUMMARY

[0003] The main purpose of the present application is to provide a wireless signal optimization method and system fusing channel state detection, which can more comprehensively reflect the complex characteristics of the channel and improve the accuracy of channel state detection.

[0004] To achieve the above purpose, the present application provides a wireless signal optimization method fusing channel state detection, comprising: obtaining detection state parameters of the fusion channel and performing scene decision processing to output a classification result; performing transmission matching according to the classification result to obtain a transmission parameter set; performing channel frequency identification on the fusion channel based on the classification result to obtain frequency band resource block features; performing transmission dynamic adaptation and frequency resource allocation based on the transmission parameter set and the frequency band resource block features to obtain wireless control parameters.

[0005] Further, the obtaining detection state parameters of the fusion channel and performing scene decision processing to output a classification result comprises: performing real-time detection of transmission signals on the fusion channel to extract detection state parameters including instantaneous signal-to-noise ratio, Doppler shift value and time delay spread; performing signal-to-noise ratio scene preliminary screening according to the detection state parameters to obtain a scene category candidate set; performing hierarchical screening processing on the detection state parameters based on the scene category candidate set to obtain a non-high-speed environment marker and a high-speed environment marker; Discriminate the interference intensity of the non-high-speed environment label based on the detection state parameter, to obtain an interference environment subclass; Jointly classify the high-speed environment label and the interference environment subclass to obtain the classification result.

[0006] Further, the transmission parameter set is obtained by performing transmission matching according to the classification result, including: Perform transmission framework mapping construction according to the classification result to obtain a basic transmission framework; Bind modulation parameters to the basic transmission framework and the detection state parameter to obtain a modulation parameter set; Perform pilot control matching and interference coordination adaptation based on the classification result and the detection state parameter to obtain an interference suppression parameter set; Integrate the modulation parameter set and the interference suppression parameter set to obtain the transmission parameter set.

[0007] Further, the interference suppression parameter set is obtained by performing pilot control matching and interference coordination adaptation based on the classification result and the detection state parameter, including: Map the scene pilot density baseline of the classification result to obtain a scene reference pilot density vector; Dynamically scale the scene reference pilot density vector according to the detection state parameter to obtain a pilot density scalar; Construct an interference source based on the classification result and the detection state parameter to obtain interference polarization information; Perform air interface resource conflict detection based on the pilot density scalar and the interference polarization information to obtain the interference suppression parameter set.

[0008] Further, the frequency band resource block feature is obtained by performing channel frequency identification on the fusion channel based on the classification result, including: Extract the channel impulse response and the subcarrier response sequence of the fusion channel by performing channel impulse response extraction and full-band scanning; Divide the scene and calibrate the weight value of the subcarrier response sequence according to the classification result to obtain a resource identification set; Match the correlation of the resource identification set based on the channel impulse response to obtain a time-frequency mapping relationship table; Map the interference of the time-frequency mapping relationship table in combination with the classification result to obtain anti-interference resource information; Compensate and correct the frequency offset of the anti-interference resource information to obtain the frequency band resource block feature.

[0009] Further, the correlation matching of the resource identification set based on the channel impulse response obtains a time-frequency mapping relationship table, comprising: The channel impulse response is decomposed into a plurality of path components to obtain a dominant time delay path feature set; The dominant time delay path feature set is compensated for path fading based on the resource identification set to obtain initial time-frequency correlation information; The initial time-frequency correlation information is filtered based on the classification result to obtain a scene-optimized time-frequency mapping table; The scene-optimized time-frequency mapping table is dynamically distorted and corrected according to the detection state parameter to obtain the time-frequency mapping relationship table.

[0010] Further, the transmission dynamic adaptation and frequency resource allocation are performed based on the transmission parameter set and the frequency band resource block feature to obtain a wireless control parameter, comprising: The transmission parameter set is decoupled into a plurality of strategy combinations; The transmission strategy combination is adapted in multiple dimensions based on the frequency band resource block feature to generate a modulation parameter configuration, pilot density distribution information and an anti-interference configuration set; The modulation parameter configuration, the pilot density distribution information and the anti-interference configuration set are constructed into a full-band scheme to obtain the wireless control parameter.

[0011] Further, the transmission dynamic adaptation and frequency resource allocation are performed based on the transmission parameter set and the frequency band resource block feature to obtain a wireless control parameter, comprising: The transmission strategy combination is split into modulation transmission parameters, pilot transmission parameters and interference suppression transmission parameters; The modulation order of the frequency band resource block feature is adapted based on the modulation transmission parameter to obtain the modulation parameter configuration; The density of the pilot transmission parameters is adaptively adjusted based on the frequency band resource block feature to obtain pilot density distribution information; The interference suppression transmission parameters and the frequency band resource block feature are strategy fusion mapped to obtain an initial anti-interference configuration template; The initial anti-interference configuration template is strategy cooperatively optimized based on the pilot density distribution information to obtain the anti-interference configuration set.

[0012] The application also provides a wireless signal optimization system fusing channel state detection, applied to the wireless signal optimization method fusing channel state detection. The acquisition module is used for acquiring a detection state parameter of a fusion channel, performing scene decision processing, and outputting a classification result. The analysis module is used for performing transmission matching according to the classification result, and obtaining a transmission parameter set. The association module is used for performing channel frequency identification on the fusion channel based on the classification result, and obtaining a frequency band resource block feature. The processing module is used for performing transmission dynamic adaptation and frequency resource allocation based on the transmission parameter set and the frequency band resource block feature, and obtaining a wireless control parameter.

[0013] The wireless signal optimization method and system for fusion channel state detection provided by the application have the following beneficial effects: By acquiring the detection state parameter of the fusion channel and performing scene classification, the limitations of traditional methods relying on single-dimensional parameters are overcome, the complex characteristics of the channel can be more comprehensively reflected, the accuracy of channel state detection is improved, and reliable basis is provided for subsequent optimization. Based on the classification result, transmission matching is performed, so that the transmission parameters can flexibly adapt to the needs of different business scenarios, avoiding the problems of resource waste or insufficient performance caused by fixed configuration, effectively improving the transmission efficiency and reliability. By identifying the frequency band resource block feature of the fusion channel and dynamically adapting the transmission parameters, intelligent allocation of spectrum resources is realized, the frequency band utilization rate is improved, the same frequency interference is reduced, and the overall communication quality is optimized. Through the cooperative optimization of transmission dynamic adaptation and frequency resource allocation, the wireless control parameter is generated, so that the system can adaptively adjust according to different business priorities and channel conditions, maximize the spectrum efficiency, and meet the communication needs in high-density and differentiated scenarios. Considering the channel state, business demand and spectrum characteristics, the transmission strategy is dynamically adjusted, the stability of the system in complex environments such as variable interference and multipath fading is enhanced, and the user experience and network performance are improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is a fusion channel state detection wireless signal optimization method flow chart provided by the application; Figure 2 It is a fusion channel state detection wireless signal optimization system structure diagram provided by the application.

[0015] The implementation of the application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0017] The present application is further described below in combination with the drawings and specific embodiments.

[0018] Referring to Figure 1 The present application provides a wireless signal optimization method based on fusion channel state detection, comprising: Step S1: Obtain the detection state parameters of the fusion channel, and perform scene decision processing to output a classification result; Step S2: Perform transmission matching according to the classification result to obtain a transmission parameter set; Step S3: Perform channel frequency identification on the fusion channel based on the classification result to obtain frequency band resource block features; Step S4: Perform transmission dynamic adaptation and frequency resource allocation based on the transmission parameter set and the frequency band resource block features to obtain wireless control parameters.

[0019] Based on the above steps, the detailed steps are described as follows: Step S1: The channel state detection is performed by a physical layer detection module of the wireless communication system. In an orthogonal frequency division multiplexing symbol period, the radio frequency front end adopts a compressed sensing pilot structure to collect channel matrix data, and extracts three-dimensional state parameters including instantaneous signal-to-noise ratio, Doppler shift value and time delay spread. The instantaneous signal-to-noise ratio is obtained by calculating the difference between the received signal strength indicator and the noise base; the Doppler shift value is derived based on the phase difference of adjacent detection intervals; and the time delay spread is quantified by the root mean square value of the power delay spectrum.

[0020] After receiving the multi-dimensional state parameters, the scene decision processing unit executes a three-level classification mechanism: the first level divides the high / low signal-to-noise ratio scene according to the signal-to-noise ratio threshold; the second level determines the moving state (high / low speed) in combination with the Doppler shift value; and the third level identifies the interference strength (strong / weak interference) based on the time delay spread. The classification result outputs three types of labels of static high signal-to-noise ratio, high speed movement and strong interference environment, each label being accompanied by a confidence weight value. In the decision process, a threshold adaptive mechanism is adopted: the signal-to-noise ratio threshold dynamically floats with the channel coherence time, and the Doppler determination threshold is automatically calibrated according to the carrier frequency. The output classification result is written into a channel state database synchronously for calling by subsequent steps.

[0021] Step S2: The transmission matching is performed by a protocol layer control unit to realize scenario-based policy binding. The input classification result triggers a transmission policy selector to generate a basic transmission framework: a static high signal-to-noise ratio scenario binds a 256QAM modulation and a 100MHz bandwidth allocation; a high-speed mobile scenario activates a pilot density dynamic control module, and a pilot interval is inversely adjusted according to a Doppler shift value; and a strong interference environment scenario starts an interference coordination mode, and reserves an edge protection frequency band and forces a modulation.

[0022] The transmission parameter set is constructed by a multi-layer integration engine: a modulation parameter set includes a modulation order, a coding rate and a hybrid automatic repeat request configuration; a pilot control set defines a pilot pattern density and a time-frequency position mapping rule; and an interference suppression set stores inter-cell interference coordination parameters and power control coefficients. The integration process performs a conflict detection mechanism, and when a high-speed mobile scenario and a strong interference scenario coexist, the interference coordination strategy is preferentially executed and the upper limit of the pilot density is limited. Finally, the output transmission parameter set adopts a versioned storage structure, including a protocol version number, a validity timestamp and a parameter check code, to support policy rollback and incremental update in a dynamic environment.

[0023] Step S3: The channel frequency recognition is performed by a physical layer resource management unit to realize mapping and conversion of channel response characteristics to frequency domain resources. The input classification result drives a channel scanning controller to perform full-band fast scanning on an orthogonal frequency division multiplexing carrier to capture a subcarrier channel response amplitude sequence. The scanning process adopts a sparse Fourier transform algorithm to compress the number of sampling points, and the sampling density is dynamically configured according to the classification result: in a high-speed mobile scenario, the sampling interval is reduced to capture the fast fading characteristics, and in a static scenario, the standard interval is used to reduce the processing overhead.

[0024] The subcarrier response sequence is input into a feature extraction engine to perform scenario-based feature enhancement in combination with the classification result: in a static high signal-to-noise ratio scenario, a subcarrier clustering algorithm is activated to divide continuous subcarriers with optimal channel quality into high-weight resource blocks; and in a strong interference environment scenario, an anti-interference reorganization mode is enabled to aggregate discrete high signal-to-noise ratio subcarriers into virtual resource blocks. The output resource identification set includes the center frequency point, bandwidth range and fading compensation coefficient of each resource block, and is labeled with a scenario adaptation label.

[0025] The channel impulse response is obtained by reference signal cross-correlation calculation, and is input into a time-frequency mapper together with the resource identification set. The mapper performs path fading compensation and time-frequency orthogonality verification to generate a time-frequency mapping relationship table to label the optimal transmission time slot of each resource block. Finally, the mapping table is frequency offset corrected by a Doppler pre-compensation module to output a frequency band resource block feature with time-varying correction marks. The feature data includes a resource block distribution map, a time-frequency mapping relationship and a version check code.

[0026] Step S4: The transmission dynamic adaptation is performed by an air interface resource scheduler to realize accurate binding of transmission strategies and physical resources. The input transmission parameter set is decoupled into three strategy streams of modulation parameter group, pilot control group and interference suppression group by a strategy parser; and the frequency band resource block feature input is parsed into a time-frequency resource map by a resource allocation engine.

[0027] The modulation parameter group is jointly modulated and adapted with the weight value identifier of the resource map: high-order modulation parameters are only bound to high-weight resource blocks, and edge resource blocks are forced to reduce modulation order. The pilot control group performs dynamic density allocation according to the resource block distribution feature: the pilot density is increased to 150% of the standard value in the high-speed mobile marking area, and the reference density is maintained in the low mobility area. The interference suppression group performs map-driven coordination: a protection bandwidth is reserved in the strong interference marking area, and an inter-cell interference coordination protocol is activated.

[0028] The frequency resource allocator receives the adaptation result to perform three-dimensional resource integration: the time domain aggregation factor is proportionally configured according to the time delay extension, and the frequency domain resource block is allocated in descending order of channel quality. The dynamic error calibration module monitors the detection state parameter drift in real time, and triggers the configuration rollback mechanism when the signal-to-noise ratio fluctuation is greater than ±3dB. The final output wireless control parameter includes a frequency domain resource allocation table, a time domain aggregation parameter set and a dynamic calibration threshold, and the scheme is accompanied by a global version label and an effective timestamp, supporting millisecond-level strategy switching on the base station side.

[0029] The wireless signal optimization method provided by the application overcomes the limitations of traditional methods relying on single-dimensional parameters, can more comprehensively reflect the complex characteristics of the channel, thereby improving the accuracy of channel state detection, and providing a reliable basis for subsequent optimization. Based on the classification result, the transmission parameters can be flexibly adapted to the needs of different business scenarios, avoiding the problems of resource waste or insufficient performance caused by fixed configuration, effectively improving the transmission efficiency and reliability. By identifying the frequency band resource block features of the fusion channel and dynamically adapting the transmission parameters, intelligent allocation of spectrum resources is realized, the frequency band utilization is improved, and the overall communication quality is optimized. Through the cooperative optimization of transmission dynamic adaptation and frequency resource allocation, wireless control parameters are generated, so that the system can adaptively adjust according to different business priorities and channel conditions, maximize the spectrum efficiency, and meet the communication needs in high-density and differentiated scenarios. Considering the channel state, business demand and spectrum characteristics, the transmission strategy is dynamically adjusted, and the stability of the system in complex environments such as variable interference and multipath fading is enhanced, and the user experience and network performance are improved.

[0030] In one embodiment, the detection state parameters of the fusion channel are obtained, and scene decision processing is performed to output a classification result, including: In the pilot time slot of OFDM symbol, the RF transceiver sends the probe signal with sparse pilot structure, and the receiving end captures the time-frequency domain mixed signal through the matched filter. The instantaneous SNR is calculated by the difference between the demodulated reference signal received power and the noise floor power, and the noise floor is dynamically updated by the sliding window minimum statistics method. The Doppler shift value is calculated based on the phase difference of the phase reference signal in the continuous two probe periods, and the phase difference is extracted by the Costas loop demodulator, with the frequency shift value accuracy controlled within ±0.5 Hz. The delay spread is calculated by the power delay spectrum of the channel impulse response, and the improved threshold detection method is used to separate the effective multipath components, and the root mean square value of each path delay is calculated as the delay spread value.

[0031] The detection process adopts a double-cycle mechanism to ensure data integrity: the main cycle performs full-parameter detection with a period of 5 ms, and the auxiliary cycle starts 1 ms-level fast re-detection for high-speed mobile scenarios. All parameters are stored in the channel state database after Kalman filtering noise reduction, and the database uses a timestamp index structure to support subsequent steps to call historical parameters according to scene types. The detection module output detection state parameters include three core fields: SNR (floating point, unit dB), Doppler shift value (integer, unit Hz), delay spread (integer, unit ns), each field is attached with confidence identification and time validity mark.

[0032] In the pilot time slot of OFDM symbol, the RF transceiver sends the probe signal with sparse pilot structure, and the receiving end captures the time-frequency domain mixed signal through the matched filter. The instantaneous SNR is calculated by the difference between the demodulated reference signal received power and the noise floor power, and the noise floor is dynamically updated by the sliding window minimum statistics method. The Doppler shift value is calculated based on the phase difference of the phase reference signal in the continuous two probe periods, and the phase difference is extracted by the Costas loop demodulator, with the frequency shift value accuracy controlled within ±0.5 Hz. The delay spread is calculated by the power delay spectrum of the channel impulse response, and the improved threshold detection method is used to separate the effective multipath components, and the root mean square value of each path delay is calculated as the delay spread value.

[0033] The detection process adopts a double-cycle mechanism to ensure data integrity: the main cycle performs full-parameter detection with a period of 5 ms, and the auxiliary cycle starts 1 ms-level fast re-detection for high-speed mobile scenarios (Doppler shift value > 100 Hz). All parameters are stored in the channel state database after Kalman filtering noise reduction, and the database uses a timestamp index structure to support subsequent steps to call historical parameters according to scene types. The detection module output detection state parameters include three core fields: SNR (floating point, unit dB), Doppler shift value (integer, unit Hz), delay spread (integer, unit ns), each field is attached with confidence identification and time validity mark.

[0034] The scene preliminary screening is performed by a preprocessing unit of the decision engine, realizing coarse-grained classification of channel environment. The signal-to-noise ratio value in the input detection state parameter is input to a dynamic threshold decision maker, which adopts a double-threshold floating mechanism: the basic threshold is set to 15 dB, and is adaptively floated by ±3 dB according to the channel coherence time. When the signal-to-noise ratio is higher than the upper limit of the floating threshold, it is marked as a high signal-to-noise ratio candidate scene; when it is lower than the lower limit, it is marked as a low signal-to-noise ratio candidate scene; and when it is between the threshold interval, a history data backtracking mechanism is started, and the signal-to-noise ratio trend of the previous 5 detection periods is referred to for scene prediction.

[0035] The candidate set generator receives the decision result to construct a three-dimensional scene space: the first dimension distinguishes indoor / outdoor scenes (signal-to-noise ratio > 20 dB and delay spread < 100 ns are judged as indoor); the second dimension divides macro cell / micro cell coverage (signal-to-noise ratio mutation gradient > 5 dB / ms is judged as cell edge); and the third dimension identifies line-of-sight / non-line-of-sight propagation (signal-to-noise ratio fluctuation variance < 2 is judged as line-of-sight). The output scene category candidate set adopts a bitmap encoding structure.

[0036] The candidate set generation process executes a conflict resolution mechanism: when the signal-to-noise ratio is inconsistent with the history data trend, the weighted moving average result is preferentially adopted (the weight of recent data accounts for 70%). The final output candidate set contains 1-3 effective scene identifiers, each identifier is accompanied by a probability weight value, and the total weight is constant at 100%.

[0037] The hierarchical screening process is performed by a mobility analysis unit, realizing accurate classification of terminal motion state. The input scene category candidate set triggers a multi-Doppler shift threshold dynamic configuration mechanism: the indoor scene candidate enables a low sensitivity threshold (50 Hz), the macro cell edge candidate enables a medium sensitivity threshold (100 Hz), and the non-line-of-sight fluctuation candidate enables a high sensitivity threshold (150 Hz). The Doppler shift value is input to a two-stage decision maker to separate the motion state: the first-stage decision maker adopts a fixed threshold of 100 Hz to divide the low-speed / medium-speed moving state; and the second-stage decision maker determines the high-speed moving state based on the scene adaptive threshold, and marks it as a high-speed moving environment when the frequency shift value exceeds the corresponding scene threshold.

[0038] The state marker generator receives the decision result to construct a motion state vector: the low-speed moving state marker is encoded as 00, the medium-speed moving state marker is encoded as 01, and the high-speed environment marker is encoded as 10. The marker generation process executes a time domain smoothing mechanism: the frequency shift value jitter is processed by using a sliding window mean filter, and the window length is configured according to the scene type (the window length is 10 for indoor scenes, and the window length is 5 for macro cell edge).

[0039] The interference intensity discrimination is performed by an interference analysis engine to realize interference feature quantization in a non-high-speed scenario. An interference source feature extraction module is activated by inputting a non-high-speed environment marker to extract a three-dimensional feature vector of a delay spread, a signal-to-noise ratio fluctuation variance, and a number of multipath components. The delay spread is input to a threshold adaptive classifier: when the delay spread < 100 ns, the environment is weak interference; when 100 ns ≤ delay spread < 300 ns, the environment is medium interference; and when the delay spread ≥ 300 ns, the environment is strong interference. The signal-to-noise ratio fluctuation variance performs auxiliary decision: when the variance > 5 dB, the interference level is increased by one level; and when the variance < 2 dB, the interference level is decreased by one level.

[0040] The interference environment subclass generator receives the classification result to construct an interference feature matrix: the weak interference environment is coded as 001, the medium interference environment is coded as 010, and the strong interference environment is coded as 100. The matrix generation process performs a multipath feature compensation mechanism: when the number of multipath components exceeds 6, the interference level is forcibly increased to the strong interference environment. The output interference environment subclass includes an interference type marker, an interference intensity value, and a confidence label, which is calculated by a correlation coefficient of the delay spread and the signal-to-noise ratio fluctuation. The subclass data is written into an interference feature library, which is structured by interference type-intensity two-dimensional index to support subsequent steps to call interference suppression strategies according to the subclass.

[0041] The joint classification is performed by a scene decision center to realize fusion output of the full-scene label. The priority arbitration is performed by inputting a high-speed environment marker and the interference environment subclass: the weak interference marker in the interference environment subclass is shielded when the high-speed mobile marker exists; and the complete interference subclass marker is retained in the non-high-speed mobile environment. The classification engine uses a three-dimensional decision space to construct the final classification: a motion dimension: the high-speed mobile marker is directly mapped to "high-speed mobile scene"; an interference dimension: the strong interference subclass is mapped to "strong interference environment scene"; a signal-to-noise ratio dimension: the high / low signal-to-noise ratio identifier of the scene category candidate set is inherited.

[0042] The classification result generator receives the decision vector to construct a structured output: a 16-bit composite coding structure is used (bits 0-3: motion scene coding, bits 4-7: interference scene coding, bits 8-11: signal-to-noise ratio scene coding, and bits 12-15: confidence level). The confidence level is calculated by a geometric mean of multi-level confidence factors (motion marker confidence × interference subclass confidence × signal-to-noise ratio candidate confidence). The output classification result is attached with a version number and a valid time stamp, which is written into a global scene decision database. The database supports version backtracking query to retain historical classification records for scene migration analysis.

[0043] The embodiment significantly improves the accuracy of channel state classification by jointly analyzing the signal-to-noise ratio, Doppler shift and time delay spread parameters through a three-dimensional scene decision system, and avoids scene misjudgment caused by traditional single parameter decision. A dynamic threshold mechanism is used to adaptively match different propagation environments, ensuring the real-time performance of Doppler shift discrimination in high-speed mobile scenarios, and effectively overcoming the transmission mismatch caused by rapid channel changes. A priority arbitration logic of motion state and interference strength is established to ensure the continuity of high-speed services while intelligently suppressing the influence of strong interference, achieving optimal resource utilization in complex environments. Through the confidence weight and versioned output architecture, reliable decision basis is provided for subsequent transmission strategies, greatly reducing the iteration delay of configuration strategies.

[0044] In one embodiment, transmission matching is performed according to the classification result to obtain a set of transmission parameters, including: The transmission framework mapping is constructed by the protocol control unit, which realizes the initial conversion of the scene label to the transmission strategy. The scene type identification of the input classification result triggers the strategy mapping engine, which has a built-in scene-strategy mapping table: static high signal-to-noise ratio scenes are mapped to high-order modulation frameworks, high-speed mobile scenes are bound to dynamic pilot control frameworks, and strong interference environment scenes are associated with interference coordination frameworks.

[0045] The mapping process uses a priority arbitration mechanism. When the classification result contains multiple scene labels, the dominant framework is selected according to the preset priority (high-speed mobile > strong interference > static high signal-to-noise ratio). The framework constructor generates the basic strategy structure according to the selected framework: the high-order modulation framework includes modulation identification, maximum bandwidth allocation flag and redundancy error disable flag; the dynamic pilot control framework defines the dynamic range of pilot density and Doppler compensation enable bit; the interference coordination framework includes inter-cell interference coordination protocol enable identification and protection band reservation ratio. The output basic transmission framework uses binary encoding format, including framework type identification, parameter configuration domain and version check code, and is stored in the strategy configuration library for subsequent steps.

[0046] The modulation parameter binding is executed by the physical layer parameter configurator, which realizes the precise adaptation of modulation strategy to channel state. The input basic transmission framework is separated by the parser to separate the modulation configuration identification, and the signal-to-noise ratio value of the detected state parameter is used to execute dynamic modulation decision. The modulation decision maker uses a double-threshold floating mechanism: the basic signal-to-noise ratio threshold is set to 20 dB (256QAM) and 10 dB (QPSK), and is dynamically floated ±3 dB according to the time delay spread (when the time delay spread is greater than 200 ns, the threshold is reduced). The modulation order selector receives the decision result and outputs the modulation type identification: when the signal-to-noise ratio is higher than the upper limit of the floating threshold, 256QAM (quadrature amplitude modulation) is selected, when it is between the threshold interval, 64QAM (quadrature amplitude modulation) is selected, and when it is lower than the lower limit, QPSK (quadrature phase shift keying) is forced.

[0047] The modulation parameter set generator integrates modulation type and associated parameters: 256QAM configuration includes modulation order, code rate and constellation rotation angle; 64QAM configuration code rate; QPSK configuration spreading factor. The parameter set is additionally marked with adaptive control: when the Doppler shift value > 100Hz, activate phase noise compensation, when the delay spread > 150ns, enable time domain diversity transmission.

[0048] The output modulation parameter set adopts a hierarchical storage structure: the first layer stores the modulation type and code rate, the second layer records the extension configuration mark, and the third layer reserves the version backtracking pointer. The parameter set is written into the modulation parameter database after cyclic redundancy check, and the database supports index query according to scene type.

[0049] The pilot control matching is performed by the air interface resource management unit, which realizes the accurate adaptation of pilot resource configuration and channel dynamic characteristics. The scene type identifier of the input classification result triggers the pilot density baseline mapping module, which has built-in scene-pilot density mapping relationship: static high signal-to-noise ratio scene maps the reference density 0.1 symbol / subcarrier, high-speed mobile scene maps 0.3 symbol / subcarrier, and strong interference environment scene maps 0.2 symbol / subcarrier. The Doppler shift value in the state parameter is input into the dynamic scaling controller, which calculates the scaling factor according to the frequency shift value and outputs the pilot density scalar.

[0050] The interference coordination adaptation is performed by the interference suppression engine, which realizes the conversion of interference source characteristics to suppression strategy. The interference strength mark of the classification result and the delay spread in the state parameter are input to perform interference source feature extraction: when the delay spread < 100ns, generate a weak interference feature vector, when 100ns ≤ delay spread < 300ns, construct a medium interference feature matrix, and when delay spread ≥ 300ns, generate a strong interference polarization tensor. The feature data is input into the strategy fusioner to perform three-dimensional mapping: weak interference features are bound to power backoff strategy (backoff 3dB), medium interference features are associated with frequency domain interference coordination protocol, and strong interference features are activated spatial beamforming suppression.

[0051] The interference suppression parameter set generator integrates pilot density scalar and interference suppression strategy: pilot density scalar is converted into pilot pattern distribution instruction, and interference suppression strategy is encoded into protocol control word. The configuration set adopts a hierarchical packaging structure: the physical layer stores the pilot density instruction and beamforming coefficient, the protocol layer records the ICIC parameter and power backoff value, and the control layer reserves the scene adaptation mark and version number. The output configuration set is written into the interference suppression database after hash check, and the database supports fast retrieval according to the Doppler shift value index.

[0052] The multi-layer parameter integration is performed by a transmission protocol stack control center to realize the collaborative integration of physical layer and protocol layer parameters. An input modulation parameter set is separated by a parser into a modulation order, a code rate and an adaptive flag; an interference suppression parameter set is decoupled into pilot distribution instructions, an interference suppression protocol and a power control coefficient. A parameter binding engine performs three-dimensional correlation mapping: the modulation order is bound to the pilot density instruction to generate a time-frequency resource allocation map, the code rate is correlated to the interference suppression protocol to construct an error correction coordination strategy, and the adaptive flag and the power control coefficient are combined to generate an environment adaptation factor.

[0053] The integration process performs conflict detection and resolution mechanisms: when the pilot density requirement of a high-speed mobile scenario conflicts with the power backoff of a strong interference environment, priority arbitration is started (the pilot density priority is higher than the power backoff); when the spectral efficiency requirement of 256-order quadrature amplitude modulation conflicts with the bandwidth reservation of interference coordination, partial bandwidth reuse mode is forcibly activated. The output transmission parameter set adopts a tree storage structure: the root node stores a global version number and a valid time stamp, the first-level child node records modulation-pilot joint parameters, the second-level child node stores interference suppression-power control linkage parameters, and the leaf node reserves environment adaptation factors and conflict resolution records. The parameter set is written into a transmission strategy library after cyclic redundancy check, and the library structure adopts a scene type-channel quality double indexing mechanism to support millisecond-level strategy loading on the base station side.

[0054] This embodiment ensures the real-time performance of Doppler shift discrimination in a high-speed mobile scenario by adaptively matching different propagation environments through a dynamic threshold mechanism, effectively overcoming transmission mismatch caused by rapid channel changes. The priority arbitration logic of motion state and interference strength is established to intelligently suppress the impact of strong interference while ensuring the continuity of high-speed services, achieving optimal resource utilization in complex environments. Through the confidence weight and versioned output architecture, reliable decision basis is provided for subsequent transmission strategies, greatly reducing the iteration delay of configuration strategies.

[0055] In one embodiment, pilot control matching and interference coordination adaptation are performed based on the classification result and the detection state parameter to obtain an interference suppression parameter set, including: The scene pilot density baseline mapping process realizes a conversion mechanism from a scene classification label to a pilot density baseline value. The scene type identifier of the input classification result triggers the baseline mapping rule library call, and the rule library presets the corresponding relationship between the scene type and the pilot density baseline value: a static high signal-to-noise ratio scenario is associated with a low density baseline value, a high-speed mobile scenario is bound to a medium density baseline value, and a strong interference environment scenario is mapped to a high density baseline value. The mapping executor calculates the weighted baseline value in the composite scene according to the priority weight of the scene label, and the priority weight is allocated according to the dynamic influence degree of the scene.

[0056] The reference value generation module constructs a multi-dimensional density vector structure, and the vector dimensions strictly correspond to the number of subcarrier group divisions of the communication system. Each dimension stores the reference density value of the corresponding subcarrier group, and the generation process performs cross-dimension consistency verification: when the difference between the scene labels of adjacent subcarrier groups exceeds the preset level threshold, the density smoothing interpolation algorithm is activated to eliminate the step change. The output scene reference pilot density vector adopts a floating-point array storage format, accompanied by a scene label index and a confidence identifier. The confidence identifier is generated by weighted calculation of the scene confidence of the classification result, and the reference value review process is triggered when the confidence is lower than the system threshold.

[0057] The dynamic scaling process realizes the adaptive adjustment of pilot density to the dynamic characteristics of the channel. The input scene reference pilot density vector is separated into subcarrier group reference values by the dimension resolver, and the Doppler shift component in the detection state parameter is input into the scaling factor calculation engine. The engine adopts a nonlinear scaling strategy: when the frequency shift value is below the first frequency shift threshold, the reference value remains unchanged; when the frequency shift value is between the first threshold and the second threshold, the scaling factor is inversely proportional to the frequency shift value; when the frequency shift value is higher than the second frequency shift threshold, the scaling factor saturates to the system upper limit value. The threshold configuration is dynamically adjusted according to the scene type: the high-speed moving scene enables the high-frequency shift threshold group, and the static scene adopts the low-frequency shift threshold group.

[0058] The scaling execution unit implements dimension-by-dimension scaling operation on the reference density vector: each subcarrier group density value is multiplied by the corresponding scaling factor, and the scaling result is constrained within the system-defined density feasible region. The boundary of the feasible region is set according to the pilot overhead limit of the communication protocol: the lower limit ensures the basic channel estimation performance, and the upper limit avoids excessive resource occupation. The pilot density scalar generator aggregates the processing results, and the output scalar value is the weighted average of the scaled density of each dimension. The scalar is attached with a frequency offset compensation state identifier: when the maximum scaling amplitude exceeds the system alarm threshold, the frequency offset alarm flag is activated. The output pilot density scalar adopts a structured data format, including the scalar value, the frequency offset alarm state, and the number of valid dimensions, and is written into the frequency offset compensation parameter repository.

[0059] The interference source construction is performed by the interference feature extraction engine, realizing multi-dimensional feature modeling of the interference environment. The interference intensity label of the input classification result triggers the interference source identification module, which calls the time delay spread in the detection state parameter to perform interference level division: when the time delay spread is below the low interference threshold, a weak interference feature vector is generated; when the time delay spread is between the low interference threshold and the high interference threshold, a medium interference feature tensor is constructed; and when the time delay spread exceeds the high interference threshold, a strong interference polarization tensor is generated. The interference threshold value is dynamically configured according to the scene type: the static scene adopts a loose threshold group, and the high-speed moving scene enables a strict threshold group.

[0060] The feature matrix generator receives the feature data to perform spatial polarization modeling: weak interference feature vectors are mapped to single-dimensional power spectrum density distribution, medium interference feature tensors are decomposed into time-frequency interference correlation graphs, and strong interference polarization tensors are converted into spatial domain beam interference patterns. The modeling process performs feature dimension reduction processing: core interference components are extracted through principal component analysis algorithm, and pseudo-features caused by channel noise are removed. The output interference polarization information adopts a complex matrix structure, the real part stores the interference intensity distribution, and the imaginary part records the polarization angle information. The matrix is accompanied by interference source confidence labels and time effectiveness identifiers, and is written into the interference feature database for conflict detection calling. The confidence label is generated by calculating the historical fluctuation variance of the time delay expansion quantity, and when the variance exceeds the threshold, the feature recheck process is triggered.

[0061] The air interface resource conflict detection is performed by the resource arbitrator to realize the collaborative optimization of pilot resource configuration and interference suppression strategy. The input pilot density scalar generates a pilot resource occupation graph through the density-resource converter, and the interference polarization information is input into the interference suppression strategy mapper to generate a set of interference coordination parameters. The resource occupation graph and the interference coordination parameter set are input into the conflict analysis engine, which performs three-dimensional conflict scanning: the time domain dimension detects the overlap conflict of pilot symbols and interference suppression time slots, the frequency domain dimension identifies the position collision of pilot subcarriers and interference protection frequency bands, and the spatial domain dimension checks the polarization conflict of beamforming direction and pilot transmission angle.

[0062] The conflict resolution arbitrator receives the conflict analysis result to perform dynamic strategy adjustment: the time domain conflict enables symbol offset mechanism, the frequency domain conflict activates subcarrier remapping, and the spatial domain conflict adopts beam deflection compensation. The adjustment strategy is optimized by the cost evaluation module to generate a set of interference suppression parameters: including time slot offset, subcarrier remapping table and beam deflection angle coefficient. The configuration set adopts a hierarchical packaging structure: the physical layer stores symbol-level adjustment parameters, the protocol layer records resource block reallocation instructions, and the control layer retains conflict resolution logs. The output configuration set is accompanied by version number and effective timestamp, and is written into the interference suppression strategy library after cyclic redundancy check, supporting the base station side to load the strategy according to the scene type.

[0063] This embodiment realizes accurate adaptation of pilot resource configuration to channel dynamic characteristics through scene pilot density baseline mapping and dynamic scaling mechanism, significantly improving the channel estimation accuracy in high-speed mobile scenarios. The interference source construction technology is used to establish a multi-dimensional interference feature model, effectively identifying the interference polarization characteristics in complex environments, and enhancing the pertinence of the interference suppression strategy. Based on the arbitration mechanism of air interface resource conflict detection, the pilot resource allocation and interference suppression strategy are collaboratively optimized to eliminate time-frequency-space three-dimensional resource conflicts and ensure business transmission continuity. The confidence feedback and version control architecture form a closed-loop optimization system, continuously improving the reliability of resource configuration decision-making and significantly reducing the strategy iteration delay.

[0064] In one embodiment, the channel frequency identification is performed on the fusion channel based on the classification result, and a frequency band resource block feature is obtained, including: The channel impulse response extraction is performed by a physical layer detection module to realize accurate capture of channel time domain characteristics. The receiving end performs cross-correlation operation on the locally stored reference signal copy and the received signal to calculate a discrete time domain impulse response sequence. The impulse response sequence contains time delay and amplitude information of multipath components, and a threshold detection method is used to separate effective multipath components and eliminate pseudo paths caused by noise. The full-band scanning process activates the fast Fourier transform engine of the orthogonal frequency division multiplexing system to perform amplitude response measurement on all subcarriers in the system bandwidth. The scanning adopts a sparse sampling strategy to reduce processing overhead, and the sampling density is dynamically configured according to the scene type of the classification result: the sampling point density is increased in the high-speed mobile scene, and the number of sampling points is reduced in the static scene.

[0065] The channel impulse response output is a complex sequence structure, the real part records the path amplitude attenuation value, and the imaginary part stores the phase offset. The subcarrier response sequence generates an amplitude-frequency distribution map, and the map marks the center frequency and normalized amplitude of each subcarrier. The output data is attached with a time stamp and a channel coherence time identifier, and is stored in the channel feature database after cyclic redundancy check. The detection process performs double-loop quality control: the main loop completes full parameter acquisition, and the auxiliary loop triggers fast re-detection for channel sudden change scenes (Doppler shift exceeding threshold).

[0066] The scene division is performed by a frequency domain resource management unit to realize subcarrier grouping and scene label binding. The scene type identifier of the input classification result activates the subcarrier clustering engine, which divides the carrier groups according to the scene requirements: the continuous subcarrier grouping strategy is used in the static high signal-to-noise ratio scene, the discrete subcarrier aggregation mode is enabled in the high-speed mobile scene, and the interference avoidance type grouping is performed in the strong interference environment scene. The grouping process performs channel quality consistency check: when the signal-to-noise ratio difference of subcarriers in a group exceeds the threshold, the intra-group reallocation is started to ensure that the subcarriers in the same group have similar fading characteristics.

[0067] The weight marker receives the grouping result to perform quality quantization: the subcarrier group weight in the static scene is linearly marked based on the average signal-to-noise ratio, the weight in the high-speed mobile scene introduces a Doppler shift compensation coefficient, and the weight in the strong interference scene is calculated inversely combined with the interference noise ratio. The marking process adopts normalization processing, and the highest weight corresponds to the optimal channel quality subcarrier group. The output resource identifier set is constructed as a two-dimensional matrix structure: the row vector indexes the subcarrier group number, and the column vector stores the intra-group subcarrier list, group weight and scene adaptation label. The identifier set is attached with a confidence factor and a timeliness identifier, and the confidence factor is calculated inversely proportional to the channel quality variance in the group. The matrix data is written into the resource identifier database after hash check.

[0068] The correlation matching is performed by a time-frequency resource mapping engine, realizing joint optimization of subcarrier group characteristics and time-domain channel response. The input channel impulse response sequence is extracted by a multipath separator to extract the dominant propagation path characteristics, including path time delay, amplitude attenuation and phase shift three-dimensional parameters. The resource identifier set is input into a subcarrier group parser to separate the center frequency, bandwidth range and weight label of each subcarrier group. The matching process performs path-subcarrier joint analysis: high-weight subcarrier groups are preferentially bound to propagation paths with the smallest time delay extension, and low-weight subcarrier groups are associated with time-domain responses with rich multipath.

[0069] The mapping relationship constructor receives the analysis results to generate a time-frequency correlation matrix: the row vector of the matrix indexes the time slot number, the column vector identifies the subcarrier group number, and the matrix element stores the path-subcarrier binding strength value. The binding strength is calculated by the product of the path attenuation coefficient and the subcarrier signal-to-noise ratio, and when the strength value is below the threshold, the remapping process is started. The output time-frequency mapping relationship table uses a sparse matrix storage format, including valid binding entries, time slot-subcarrier mapping pairs and path fading compensation coefficients. The table is attached with time-frequency orthogonality check marks and version numbers, and is written into the time-frequency resource library after consistency verification.

[0070] The interference mapping is performed by an anti-interference optimizer, realizing intelligent avoidance of resource allocation to interference sources. The interference strength label of the input classification result triggers the interference source positioning module, which calls the time slot-subcarrier mapping pairs in the time-frequency mapping relationship table to perform conflict detection: in a strong interference scenario, adjacent cell interference hot time slots are scanned, and in a medium interference environment, same-frequency band interference subcarrier clusters are detected. The remapping engine receives the detection results to perform dynamic adjustment: the subcarrier groups in the interference hot time slots start the time slot offset strategy, and the interference subcarrier cluster coverage area enables the frequency domain resource block migration.

[0071] The resource map generator integrates the adjustment results to construct a three-dimensional anti-interference map: the time domain dimension marks the conflict-free transmission time slot window, the frequency domain dimension marks the clean subcarrier block, and the spatial domain dimension records the beam null direction. The map generation process performs resource continuity guarantee: after time slot offset, the time slot aggregation compensation mechanism is activated, and after subcarrier migration, the virtual resource block reorganization is performed. The output anti-interference resource information uses a rasterized storage structure, including a time-frequency resource distribution matrix, an interference avoidance label set and a migration cost coefficient, and the map is written into the anti-interference resource library after topology verification.

[0072] The embodiment realizes accurate matching of subcarrier resources and propagation paths through a time-frequency mapping relationship table construction mechanism, significantly improving frequency resource utilization efficiency. By using interference mapping technology, time-frequency resource conflicts in interference hotspot areas are effectively identified and avoided, ensuring the continuity of business transmission. The frequency offset compensation correction mechanism dynamically suppresses the influence of Doppler shift, ensuring the orthogonality of subcarriers in high-speed mobile scenarios. The versioned resource management architecture forms a closed-loop optimization system, continuously improving the reliability of resource configuration decisions and significantly reducing the strategy iteration delay in dynamic environments.

[0073] In one embodiment, the resource identification set is matched based on the channel impulse response to obtain a time-frequency mapping relationship table, including: The multi-path component decomposition is performed by a channel feature extraction engine to realize effective path separation of the channel impulse response. The input channel impulse response sequence is separated into effective multi-path components by a threshold detection module. The detection module uses a dynamic threshold strategy: the main path amplitude is used as the reference threshold, and the secondary path amplitude is lower than the reference threshold by a set proportion and is considered as a noise path and is removed. The effective path is input into a path feature extractor, and the extractor outputs path features including three-dimensional parameters of time delay offset, path attenuation coefficient and phase rotation angle.

[0074] The dominant path selector receives the path features to perform path screening: the paths are sorted in descending order of path attenuation coefficient, and the first several paths with attenuation lower than the system threshold are selected as the dominant paths. The screening process performs path correlation verification: when the time delay difference is less than the channel coherence time, the paths are combined into equivalent path features. The output dominant time delay path feature set is constructed as a structured array: the array elements store the path index number, time delay value, attenuation value and phase value, and the array is attached with path validity identifier and confidence factor. The confidence factor is generated by calculating the historical stability of the path amplitude, and when the confidence is lower than the threshold, the path review process is triggered. The feature set is written into the multi-path feature database after cyclic redundancy check.

[0075] The path fading compensation is performed by a time-frequency resource mapper to realize joint optimization of path characteristics and subcarrier resources. The input dominant time delay path feature set is separated into path delay and attenuation parameters by a path parser, and the resource identification set is input into a subcarrier group parser to obtain subcarrier group center frequency and weight label. The compensation engine performs path-subcarrier joint compensation calculation: high-weight subcarrier groups are bound to dominant paths with minimum time delay extension, and low-weight subcarrier groups are associated with path clusters with rich multi-paths.

[0076] The compensation calculation adopts a decay weighting mechanism: a path decay coefficient is multiplied by a subcarrier group weight to generate a compensation weight, and the weight value is normalized to serve as a path-subcarrier binding strength reference. The matrix generator receives the compensation result to construct initial time-frequency association information: the matrix row index corresponds to the time slot number, the column index identifies the subcarrier group number, and the matrix element stores the path-subcarrier binding strength value. The matrix generation process performs an orthogonality constraint check: when the binding path delay difference of adjacent time slots is less than the guard interval, the path isolation flag is forcibly enabled. The output initial time-frequency association information adopts a sparse storage format, including valid binding entries, path fading compensation coefficients, and time-frequency orthogonality identifiers, and the matrix is written into the time-frequency mapping library with a version backtracking pointer.

[0077] The scene adaptation filter is executed by the time-frequency optimization engine to realize dynamic matching of resource mapping relationship and channel scene. The initial time-frequency association information is separated into binding strength values and orthogonality identifiers by the matrix parser, and the scene type identifier of the classification result triggers the filter parameter configuration: the frequency domain smoothing filter is enabled in the static high signal-to-noise ratio scene, the time domain anti-jitter filter is activated in the high-speed mobile scene, and the spatial domain interference suppression filter is associated in the strong interference environment scene. The filtering process performs multi-dimensional optimization: the frequency domain filter eliminates the binding strength jump between subcarriers, the time domain filter suppresses the path switching jitter between time slots, and the spatial domain filter avoids resource allocation in the direction of the interference source.

[0078] The optimized mapping table generator receives the filtering result to reconstruct the time-frequency relationship: the continuous binding of high-weight subcarrier groups is strengthened in the static scene, the time slot aggregation factor is increased to improve transmission robustness in the high-speed scene, and the protection time slot is inserted to block interference conduction in the strong interference scene. The output scene-optimized time-frequency mapping table adopts a rasterized storage structure: the row vector identifies the time slot aggregation block number, the column vector records the subcarrier group cluster index, and the raster element stores the optimized binding strength value and the scene adaptation label. The table is written into the optimized mapping resource library after the orthogonality recheck, with the filtering confidence identifier and the version iteration number attached.

[0079] The dynamic distortion correction is executed by the frequency offset compensation unit to eliminate the time-frequency resource distortion caused by the Doppler shift. The scene-optimized time-frequency mapping table is separated into time slot aggregation block and subcarrier group cluster information by the time-frequency parser, and the Doppler shift component in the detection state parameter is input into the distortion calculator. The calculator generates a frequency offset compensation vector: the vector dimension is consistent with the number of time slot aggregation blocks, and each dimension stores the time delay compensation amount of the corresponding block. The compensation amount is dynamically calculated by the product of the frequency shift value and the symbol period, and the compensation direction is determined according to the terminal motion history trajectory prediction.

[0080] The correction executor implements three-dimensional correction on the mapping table: the time domain dimension applies time delay compensation to the time slot aggregation block, the frequency domain dimension adjusts the center frequency offset of the subcarrier group cluster, and the space domain dimension rotates the beamforming angle to match the terminal position. After correction, the distortion residual detection is performed: when the residual value exceeds the threshold, the iterative compensation process is started. The time-frequency mapping relationship table generator integrates the output: the table contains the corrected time-frequency grid data, the frequency offset suppression coefficient and the motion trajectory prediction identifier. The output table is stored in a versioned tree structure: the root node records the global correction parameters, the leaf node stores the subcarrier-level mapping relationship, and the Doppler stability verification is written into the time-frequency mapping database.

[0081] The embodiment constructs a mechanism for leading time delay path features, accurately separates the effective signal path in multipath propagation, and significantly improves the reliability of time-frequency resource allocation. The path fading compensation technology realizes the dynamic adaptation of subcarrier resources and channel path characteristics, effectively suppresses the signal distortion caused by multipath fading. The scene adaptation filtering technology optimizes the time-frequency mapping relationship according to the channel environment characteristics, and guarantees the optimality of resource allocation in static and high-speed moving scenes. The dynamic distortion correction mechanism real-time suppresses the time-frequency grid distortion caused by Doppler shift, and ensures the orthogonality of signal transmission in high-speed dynamic environment. The closed-loop feedback architecture and version control strategy continuously optimize the system stability, greatly reducing the iteration delay of resource allocation strategy.

[0082] In one embodiment, based on the transmission parameter set and the frequency band resource block characteristics, the transmission dynamic adaptation and frequency resource allocation are performed to obtain wireless control parameters, including: The multi-strategy group decoupling is performed by a protocol stack analysis engine to realize the strategy dimension separation of composite transmission parameters. The input transmission parameter set is identified by a structured parser to identify parameter type tags: modulation parameter tags bind modulation order identifiers and coding rate configurations, pilot control tags associate pilot density ranges and time-frequency position mapping rules, and interference suppression tags store inter-cell coordination protocol identifiers and protection bandwidth ratios. The decoupling engine performs parameter clustering according to the strategy type: modulation class parameters are aggregated into modulation transmission parameters, pilot control class parameters are constructed into pilot transmission parameters, and interference suppression class parameters are generated into interference suppression transmission parameters.

[0083] The decoupling process performs strategy conflict detection: when the high-order modulation identifier of the modulation transmission parameter coexists with the forced downshift marker of the interference suppression group, the priority arbitration mechanism is started (the priority of the interference suppression is higher than that of the modulation strategy). The output transmission strategy combination adopts a tree storage structure: the root node records the global strategy version number, the first-level child node stores the modulation transmission parameter set, the second-level child node mounts the pilot transmission parameter configuration set, and the third-level child node reserves the interference suppression transmission parameter protocol set. The combination data is attached with a strategy effective timestamp and a conflict resolution log, which is written into the strategy decoupling database after cyclic redundancy check.

[0084] Multi-dimensional strategy adaptation is performed by the air interface resource coordinator to realize dynamic binding of physical resource characteristics and transmission strategies. The input frequency band resource block characteristics are separated by the characteristic parser into subcarrier group weight distribution maps and anti-interference resource identifiers, and the transmission strategy combination is input into the strategy adaptation engine.

[0085] The engine performs three-dimensional strategy mapping: Modulation strategy adaptation: modulation transmission parameters are input into the modulation binder, which executes hierarchical modulation decision in combination with the subcarrier group weight map. High-weight subcarrier groups are bound to high-order modulation parameters (256-order quadrature amplitude modulation), medium-weight groups are associated with medium-order modulation (64-order quadrature amplitude modulation), and low-weight groups are forced to robust modulation (quadrature phase shift keying). The decision process introduces a time delay expansion compensation factor, and the time delay expansion exceeds the threshold to reduce the modulation level.

[0086] Pilot control adaptation: pilot transmission parameters are input into the density distributor, which generates pilot density distribution information in combination with anti-interference resource identifiers. The matrix row index corresponds to the time slot number, the column index identifies the subcarrier group number, and the matrix element stores the scenario-based pilot density value. The pilot density in the strong interference area is increased to a set proportion of the reference value, and the pilot symbol proportion in the high-speed mobile area is increased.

[0087] Interference suppression adaptation: interference suppression transmission parameters are input into the coordination optimizer, which performs joint anti-interference configuration generation based on anti-interference resource identifiers. The configuration set includes the spatial beamforming angle set, the frequency domain protection bandwidth allocation table, and the time domain interference coordination protocol stack. The configuration process performs interference hotspot avoidance mechanism, and the avoidance marker is strictly bound with the interference avoidance identifier in the resource block characteristics.

[0088] The output data is written into the strategy adaptation library after version consistency verification, and the result is attached with resource adaptation confidence labels and timeliness identifiers.

[0089] The full-band scheme is constructed by the resource integration engine to perform multi-dimensional transmission strategy spectrum-level collaborative operation. The modulation parameter configuration is input into the modulation mapper to convert into a subcarrier-level modulation order distribution map, which marks the modulation type and coding rate parameters of each subcarrier group. The pilot density distribution information is input into the pilot scheduler to generate a time-frequency two-dimensional pilot resource occupation map, which defines the distribution density of pilot symbols in the time slot and subcarrier dimensions. The anti-interference configuration set is decomposed by the protocol parser into the spatial beamforming coefficient set, the frequency domain protection bandwidth allocation table, and the time domain interference coordination instruction stack. The construction process performs three-dimensional resource superposition operation: the modulation order distribution map and the pilot resource occupation map perform time-frequency grid alignment, the high modulation order area synchronously increases the pilot symbol density, and the low modulation order area reduces the pilot resource proportion. The alignment process introduces orthogonal constraint verification to eliminate the risk of inter-symbol interference.

[0090] Anti-interference configuration and spectrum resource perform collaborative integration: spatial domain beamforming coefficient is bound to high interference sensitive subcarrier group, frequency domain guard band allocation avoids interference hot spot frequency band and enables transition subcarrier to smooth spectrum mutation, time domain interference coordination instruction is inserted into special interference suppression time slot. The global integration layer receives the superposition result to generate a full-band resource allocation template, which marks the modulation type identification, pilot density value, beam pointing angle and interference suppression mark of each resource block. The template is checked by the topology conflict detection engine and the initial optimization configuration scheme is output. The scheme uses a hierarchical packaging structure for storage: the physical layer stores the modulation pilot joint parameter set, the protocol layer records the interference coordination protocol instruction stack, and the control layer reserves the resource conflict resolution log and version label. The scheme is accompanied by a global effective time window identifier and a configuration confidence factor, which is written into the initial configuration database after cyclic redundancy check.

[0091] Dynamic error calibration is realized by a closed-loop control unit to improve configuration accuracy under real-time channel fluctuations. The initial optimization configuration scheme is input into the scheme parser to separate the physical layer modulation pilot parameters and the protocol layer interference coordination instructions. The signal-to-noise ratio fluctuation component, Doppler shift drift amount and time delay spread change amount in the detection state parameter are input into the error calculator. The calculator generates a three-dimensional error compensation vector: the signal-to-noise ratio fluctuation component drives the modulation order threshold floating compensation vector, the Doppler shift drift amount generates the pilot density recalibration vector, and the time delay spread change amount derives the time slot aggregation factor update vector.

[0092] The calibration executor implements parameter correction on the initial scheme: the modulation order threshold dynamically floats according to the compensation vector and executes the down-order protection trigger mechanism, the pilot density distribution is remapped to the anti-frequency offset density scalar according to the recalibration vector, and the time slot aggregation factor adjusts the transmission robustness according to the time delay spread change amount. After correction, the residual detection process is performed, and when the signal-to-noise ratio fluctuation exceeds the system tolerance, the modulation order forced down-order mechanism is activated. The frequency band edge optimizer receives the calibration result to perform frequency spectrum boundary enhancement: using the edge subcarrier weight distribution characteristics of the frequency band resource block characteristics, an anti-leakage window function is applied to the spectrum boundary area to suppress out-of-band radiation interference, and a spread spectrum transmission mode is enabled for low signal-to-noise ratio edge subcarrier cluster to improve signal coverage capability. The optimization process performs energy redistribution strategy: reduce the proportion of redundant power in the central area, increase the transmission power of edge subcarriers and inject power compensation factor. The output wireless control parameters are constructed into a tree-shaped storage structure: the root node stores the global calibration parameter set and optimization strategy identifier, the first branch node hangs the physical layer calibration parameter set, the second branch node associates the protocol layer optimization instruction set, and the leaf node records the frequency band edge enhancement coefficient and power compensation factor.

[0093] The embodiment realizes three-dimensional coordination of modulation strategy, pilot distribution and interference suppression through a full-band resource allocation template construction mechanism, significantly improving the efficiency of spectrum resource utilization. The dynamic error calibration technology compensates for the configuration deviation caused by channel parameter fluctuation in real time, ensuring transmission reliability in high-speed mobile scenarios. The band edge optimization mechanism enhances the signal coverage capability of the spectrum boundary area, effectively suppressing out-of-band interference radiation. The tree-shaped configuration structure supports millisecond-level strategy loading and version backtracking, significantly reducing the configuration switching delay in dynamic environments. The closed-loop control architecture continuously optimizes the accuracy of resource configuration, forming a self-enhancing system for transmission performance.

[0094] In one embodiment, multi-dimensional strategy adaptation is performed on the transmission strategy combination based on the frequency band resource block characteristics to generate modulation parameter configuration, pilot density distribution information and anti-interference configuration set, including: Strategy splitting is performed by a protocol stack parser engine to realize dimensional separation of composite transmission strategies. The input transmission strategy combination is identified by a structured parser to identify strategy type identification: modulation strategy identification binds high-order modulation identification, coding rate configuration and constellation rotation parameters; pilot control strategy identification associates pilot density dynamic range, time-frequency location mapping rule and Doppler compensation enable bit; interference suppression strategy identification stores inter-cell interference coordination protocol identification, protection bandwidth reservation proportion and beamforming suppression coefficient. The splitting engine performs clustering separation according to strategy function: modulation class parameters are aggregated into modulation transmission parameter data structure, pilot control class parameters construct pilot transmission parameter matrix, and interference suppression class parameters generate interference suppression transmission parameter protocol stack.

[0095] The splitting process performs a strategy conflict arbitration mechanism: when the high-order modulation identification of the modulation transmission parameter coexists with the forced downgrading mark of the interference suppression transmission parameter, the priority arbitration logic is activated (interference suppression priority is higher than modulation strategy). The output strategy group adopts a tree-shaped storage architecture: the root node records the global strategy version label, the first branch node mounts the modulation transmission parameter parameter set (including modulation type, code rate configuration and adaptive mark), the second branch node associates the pilot transmission parameter configuration matrix (defines density range, location mapping and compensation parameters), and the third branch node reserves the interference suppression transmission parameter protocol set (stores coordination protocol, protection bandwidth and beam suppression coefficient). The output data is accompanied by a strategy effective timestamp and an arbitration log, which is written into the strategy group database after cyclic redundancy check.

[0096] Modulation order adaptation is performed by a physical layer modulation controller to achieve accurate matching of modulation strategy and spectral resources. Input modulation transmission parameters are separated by a parameter parser into modulation order constraint identification and adaptive control markers, and frequency band resource block characteristics are input into a characteristic parser to extract subcarrier group weight distribution graphs. An adaptation engine performs hierarchical modulation decision: high weight subcarrier groups are bound to high order modulation identification (256 order quadrature amplitude modulation) of modulation transmission parameters, medium weight groups are associated with medium order modulation identification (64 order quadrature amplitude modulation), and low weight groups are forced to enable robust modulation identification (quadrature phase shift keying) of modulation transmission parameters.

[0097] A decision process introduces a delay spread compensation mechanism: when the delay spread identification in the frequency band resource block characteristics exceeds the system threshold, a modulation order reduction protection strategy is activated (high order modulation is reduced to medium order, and medium order is reduced to robust modulation). The output modulation parameter configuration is built as a composite data structure: the basic layer stores the modulation type identification and code rate configuration, the enhanced layer records the adaptive control markers (phase noise compensation is activated when the delay spread exceeds the limit, and spread spectrum transmission is enabled when the Doppler shift exceeds the threshold), and the control layer retains the modulation version number and effective condition identification. After the configuration data is verified by modulation performance simulation, it is written into the modulation parameter library, and the library structure supports fast retrieval by subcarrier group weight index.

[0098] Density adaptive adjustment processing is performed by a pilot resource optimizer to achieve accurate response of pilot configuration to dynamic characteristics of the channel. Input pilot transmission parameters are separated by a strategy parser into pilot density reference range, time-frequency location mapping rule and Doppler compensation enable identification, and frequency band resource block characteristics are input into a characteristic extractor to obtain subcarrier group weight distribution graphs and anti-interference resource identification. An adjustment engine performs three-dimensional adaptive processing: a weight-driven density allocation mechanism associates high weight subcarrier groups with the upper limit density value of pilot transmission parameters, medium weight groups are bound to the median value of the reference density, and low weight groups maintain the lower limit density. The allocation process introduces a delay spread compensation factor to trigger the density proportion increase of the high weight group; an interference avoidance adaptation mechanism performs pilot location remapping through anti-interference resource identification to avoid hot time slot pilot insertion in strong interference identification areas; a dynamic compensation mechanism activates dynamic scaling of frequency shift components according to the compensation enable identification, and the scaling ratio is inversely related to the frequency shift value.

[0099] Output pilot density distribution information is built as a time-frequency grid structure, with row index corresponding to time slot number, column index identifying subcarrier group number, and matrix element storing scenario-based pilot density value. The matrix is additionally labeled with density confidence and time effectiveness, and the confidence label is generated by inversely proportional calculation of the subcarrier group weight variance, and the matrix is written into the pilot configuration library after orthogonal verification.

[0100] The policy fusion mapping is performed by the interference coordination engine to realize deep binding of the interference suppression policy and the physical resource. The input interference suppression transmission parameter is separated by a protocol parser to obtain a cell coordination protocol identifier, a protection bandwidth ratio and a beamforming suppression coefficient. The frequency band resource block feature is input into a feature decoupler to extract anti-interference resource information and a time-frequency mapping relationship table.

[0101] The fusion process performs multi-level policy injection. The frequency domain fusion layer binds the protection bandwidth ratio parameter to the interference hotspot frequency band of the anti-interference resource information to generate a frequency domain protection band allocation table. The allocation table performs bandwidth continuity constraint injection to smooth the spectrum mutation of the transition subcarrier. The spatial domain fusion layer maps the beamforming suppression coefficient to the interference source direction identifier to generate a spatial domain beam null angle set. The beam null angle set is accompanied by a beam width adaptive identifier to realize dynamic adjustment driven by interference strength. The time domain fusion layer cooperates with the cell coordination protocol identifier and the time-frequency mapping relationship table to generate an interference coordination time slot insertion instruction stack. The instruction stack defines the time slot position, length and power backoff value. The output initial anti-interference configuration template adopts a hierarchical storage architecture. The physical layer stores the frequency domain protection allocation table. The protocol layer records the spatial domain beam null parameter set. The control layer retains the time domain coordination instruction stack and the fusion confidence label. The template is written into the anti-interference configuration library after being checked by the conflict resolution engine.

[0102] The policy coordination optimization is performed by the anti-interference coordinator to realize global coordination of the pilot resource and the interference suppression policy. The input pilot density distribution information is separated by a matrix parser to obtain time-frequency density distribution data. The initial anti-interference configuration template is input into a template decoupler to separate the frequency domain protection table, the spatial domain beam set and the time domain instruction stack.

[0103] The optimization process performs three-dimensional coordination correction. Pilot-frequency domain coordination performs frequency band alignment verification. The high-density pilot area avoids the protection band edge to generate a joint frequency domain resource allocation map. Pilot-spatial domain coordination optimizes beam pointing. The high-density pilot time slot enhances the beam main lobe gain to output a spatial domain pilot enhancement coefficient set. Pilot-time domain coordination maps the pilot time slot position to avoid strong interference suppression time slots to generate a time domain pilot-interference coordination joint time slot table. The output anti-interference configuration set integrates the three-dimensional coordination results and adopts a versioned tree structure for storage. The root node records the global optimization identifier. The branch node hangs the joint frequency domain resource allocation map and the spatial domain pilot enhancement coefficient set. The leaf node stores the time domain joint time slot table and the conflict resolution log. The configuration set is written into the joint anti-interference strategy library after being verified for multi-dimensional orthogonality.

[0104] The embodiment realizes accurate response of pilot resource configuration to channel quality and interference characteristics through a pilot density adaptive adjustment mechanism, significantly improving channel estimation accuracy in a high-speed mobile scenario. The deep fusion mapping of interference suppression strategy and physical resources effectively identifies the interference polarization characteristics and generates a directional suppression scheme, significantly reducing signal distortion in a strong interference environment. Based on the three-dimensional cooperative optimization of pilot distribution and anti-interference strategy, the resource allocation conflicts in time, frequency and space domains are eliminated, and the continuity of service transmission is ensured. The versioned tree storage structure supports millisecond-level strategy backtracking and loading, forming a resource configuration closed-loop optimization system, continuously improving the dynamic adaptation capability of the system. The confidence label and conflict resolution mechanism ensure the reliability of the strategy decision, providing robust protection for complex communication environments.

[0105] Referring to Figure 2 The application also provides a wireless signal optimization system fusing channel state detection, applied to the wireless signal optimization method fusing channel state detection of any one of the above, comprising: The acquisition module is used to obtain the detection state parameters of the fusion channel and perform scene decision processing, and output a classification result; The analysis module is used to perform transmission matching according to the classification result, and obtain a transmission parameter set; The association module is used to perform channel frequency identification on the fusion channel based on the classification result, and obtain frequency band resource block features; The processing module is used to perform transmission dynamic adaptation and frequency resource allocation based on the transmission parameter set and the frequency band resource block features, and obtain wireless control parameters.

[0106] The wireless signal optimization system fusing channel state detection provided by the application overcomes the limitations of traditional methods relying on single-dimensional parameters by obtaining the detection state parameters of the fusion channel and performing scene classification, can more comprehensively reflect the complex characteristics of the channel, thereby improving the accuracy of channel state detection and providing a reliable basis for subsequent optimization. Transmission matching based on the classification result enables transmission parameters to flexibly adapt to the needs of different business scenarios, avoiding resource waste or performance deficiency problems caused by fixed configuration, effectively improving transmission efficiency and reliability. By identifying the frequency band resource block features of the fusion channel and dynamically adapting to the transmission parameters, intelligent allocation of spectrum resources is realized, improving frequency band utilization, reducing co-frequency interference, and optimizing overall communication quality. Through the cooperative optimization of transmission dynamic adaptation and frequency resource allocation, wireless control parameters are generated, enabling the system to adaptively adjust according to different business priorities and channel conditions, maximizing spectrum efficiency and meeting the communication needs in high-density and differentiated scenarios.

[0107] It should be noted that, for the convenience and brevity of description, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0108] The above only describes the preferred embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields based on the content of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A wireless signal optimization method integrating channel state detection, characterized in that: include: Obtain the detection status parameters of the fusion channel, perform scene decision processing, and output the classification results; Perform transmission matching according to the classification results to obtain a transmission parameter set; Performing channel frequency identification on the fused channel based on the classification result to obtain frequency band resource block characteristics; Based on the transmission parameter set and the frequency band resource block characteristics, dynamic transmission adaptation and frequency resource allocation are performed to obtain wireless control parameters.

2. The wireless signal optimization method integrating channel state detection according to claim 1, characterized in that: The acquisition of the detection state parameters of the fusion channel, performing scene decision processing, and outputting the classification results include: Performing real-time detection of the transmission signal of the fusion channel to extract detection state parameters including instantaneous signal-to-noise ratio, Doppler frequency shift value and delay spread; Performing a preliminary signal-to-noise ratio scene screening based on the detection state parameters to obtain a scene category candidate set; Performing hierarchical screening processing on the detection state parameters based on the scene category candidate set to obtain a non-high-speed environment label and a high-speed environment label; Performing interference intensity discrimination on the non-high-speed environment marker based on the detection state parameter to obtain an interference environment subclass; The high-speed environment label and the interference environment subclass are jointly classified to obtain the classification result.

3. The wireless signal optimization method integrating channel state detection according to claim 1, characterized in that: The performing transmission matching according to the classification result to obtain a transmission parameter set includes: Performing transmission framework mapping and construction according to the classification results to obtain a basic transmission framework; Binding the basic transmission framework and the detection state parameter to obtain a modulation parameter set; Perform pilot control matching and interference coordination adaptation based on the classification result and the detection state parameter to obtain an interference suppression parameter set; Multi-layer parameter integration is performed on the modulation parameter set and the interference suppression parameter set to obtain the transmission parameter set.

4. The wireless signal optimization method integrating channel state detection according to claim 3, characterized in that: The performing pilot control matching and interference coordination adaptation based on the classification result and the detection state parameter to obtain an interference suppression parameter set includes: Performing scene pilot density baseline mapping on the classification result to obtain a scene reference pilot density vector; Dynamically scaling the scene reference pilot density vector according to the detection state parameter to obtain a pilot density scalar; Performing interference source construction on the classification result and the detection state parameter to obtain interference polarization information; Air interface resource conflict detection is performed based on the pilot density scalar and the interference polarization information to obtain the interference suppression parameter set.

5. The wireless signal optimization method integrating channel state detection according to claim 1, characterized in that: The performing channel frequency identification on the fused channel based on the classification result to obtain frequency band resource block characteristics includes: performing channel impulse response extraction and full-band scanning on the fused channel to obtain a channel impulse response and a subcarrier response sequence; Performing scenario division and weight calibration on the subcarrier response sequence according to the classification result to obtain a resource identification set; Performing correlation matching on the resource identifier set based on the channel impulse response to obtain a time-frequency mapping relationship table; performing interference mapping on the time-frequency mapping relationship table in combination with the classification result to obtain anti-interference resource information; Frequency offset compensation correction is performed on the anti-interference resource information to obtain the frequency band resource block characteristics.

6. The wireless signal optimization method integrating channel state detection according to claim 5, characterized in that: The performing correlation matching on the resource identifier set based on the channel impulse response to obtain a time-frequency mapping relationship table includes: Decomposing the channel impulse response into multipath components to obtain a dominant delay path feature set; performing path fading compensation on the dominant delay path feature set according to the resource identification set to obtain initial time-frequency correlation information; Performing scene adaptation filtering on the initial time-frequency association information based on the classification result to obtain a scene optimized time-frequency mapping table; Dynamic distortion correction is performed on the scene-optimized time-frequency mapping table according to the detection state parameters to obtain the time-frequency mapping relationship table.

7. The wireless signal optimization method integrating channel state detection according to claim 1, characterized in that: The performing dynamic transmission adaptation and frequency resource allocation based on the transmission parameter set and the frequency band resource block characteristics to obtain wireless control parameters includes: Decoupling the transmission parameter set into multiple strategy groups to obtain a transmission strategy combination; Performing multi-dimensional strategy adaptation on the transmission strategy combination based on the characteristics of the frequency band resource block to generate modulation parameter configuration, pilot density distribution information and anti-interference configuration set; A full-band solution is constructed for the modulation parameter configuration, the pilot density distribution information, and the anti-interference configuration set to obtain the wireless control parameters.

8. The wireless signal optimization method integrating channel state detection according to claim 7, characterized in that: The performing multi-dimensional strategy adaptation on the transmission strategy combination based on the frequency band resource block characteristics to generate modulation parameter configuration, pilot density distribution information and anti-interference configuration set includes: Performing strategy decomposition on the transmission strategy combination to obtain modulation transmission parameters, pilot transmission parameters, and interference suppression transmission parameters; Adapting the modulation order of the frequency band resource block characteristics according to the modulation transmission parameters to obtain the modulation parameter configuration; Performing density adaptive adjustment processing on the pilot transmission parameter based on the frequency band resource block characteristics to obtain pilot density distribution information; Performing strategy fusion mapping on the interference suppression transmission parameters and the frequency band resource block characteristics to obtain an initial anti-interference configuration template; The initial anti-interference configuration template is subjected to strategy collaborative optimization based on the pilot density distribution information to obtain the anti-interference configuration set.

9. A wireless signal optimization system integrating channel state detection, characterized in that: The wireless signal optimization method for integrating channel state detection according to any one of claims 1 to 8 comprises: An acquisition module, which is used to obtain detection state parameters of the fusion channel, perform scene decision processing, and output classification results; An analysis module, configured to perform transmission matching based on the classification result to obtain a transmission parameter set; an association module, configured to perform channel frequency identification on the fused channel based on the classification result to obtain frequency band resource block characteristics; A processing module is used to perform dynamic transmission adaptation and frequency resource allocation based on the transmission parameter set and the frequency band resource block characteristics to obtain wireless control parameters.

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