A method, system, device and medium for dynamic spectrum allocation in satellite-ground cooperation

Abstract

This disclosure provides a dynamic spectrum allocation method, system, device, and medium for satellite-ground collaboration. The method includes: activating an SDR module in an idle time slot, scanning a designated frequency band broadcast by the satellite to generate a local spectrum fingerprint, and uploading it to the satellite after differential privacy encryption; receiving a federated learning global training timing instruction from the satellite, completing local iterative training of a lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instruction, and encrypting and uploading the generated model gradient parameters to the satellite; receiving a spectrum allocation instruction from the satellite and determining the transmit and receive frequencies according to the spectrum allocation instruction; fusing GNSS and IMU data to calculate the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite in real time. Embodiments of this disclosure can achieve two-way Doppler compensation between satellite and ground, improving frequency shift correction accuracy and uplink transmission reliability.

Description

Technical Field

[0001] This disclosure relates to the field of satellite communication technology, and in particular to a dynamic spectrum allocation method, system, device and medium for satellite-ground coordination. Background Technology

[0002] Currently, the main methods for allocating satellite spectrum for direct mobile phone connections include licensed exclusive use of spectrum, unlicensed spectrum sharing, dynamic sharing of terrestrial cellular spectrum, and satellite-to-ground spectrum sharing assisted by IRS (Intelligent Reflecting Surface). While each of these methods has its applicability in different scenarios, none has solved the core challenge of dynamic satellite-to-ground spectrum allocation.

[0003] Satellite spectrum resources for direct mobile phone connections are extremely scarce. Large-scale development, especially for the deep development of 6G networks, necessitates the reuse of terrestrial mobile network frequency bands (such as the 1.9-2.2GHz S-band). However, co-channel interference between satellite and ground networks leads to a 30%-50% capacity reduction. Existing technologies mainly suffer from the following drawbacks:

[0004] (1) Static spectrum allocation has poor adaptability: Static spectrum allocation (such as seven-color multiplexing) cannot adapt to the differences in traffic volume between beams. The beam blocking rate in urban areas is as high as 50%, while the beam utilization rate in suburban areas is less than 40%, resulting in an imbalance in spectrum resource utilization.

[0005] (2) Excessive overhead of dynamic spectrum coordination signaling: Existing dynamic spectrum sharing (such as cognitive radio) requires real-time satellite-to-ground signaling interaction, with a single satellite having a daily signaling volume of over 100GB, which is difficult to support the large-scale communication needs of tens of thousands of constellations.

[0006] (3) Spectrum prediction poses a risk of data privacy leakage: Building wireless environment maps and other projects requires centralized uploading of terminal spectrum data, which violates relevant privacy protection regulations and makes it difficult for the technical solution to be commercially implemented;

[0007] (4) The Doppler compensation has a one-way problem: the existing scheme only completes the frequency shift compensation through the satellite end, ignoring the dynamic influence of the mobile terminal's motion state on the uplink communication signal, and the frequency shift correction accuracy is insufficient. Summary of the Invention

[0008] This disclosure provides a dynamic spectrum allocation method, system, device, and medium for satellite-ground coordination, which addresses the problems of existing mobile phone direct satellite spectrum allocation methods, such as poor adaptability of static spectrum allocation, excessive signaling overhead for dynamic spectrum coordination, data privacy leakage risks in spectrum prediction, and one-wayness of Doppler compensation.

[0009] Firstly, this disclosure provides a dynamic spectrum allocation method for satellite-ground coordination, applied to mobile terminals, the method comprising:

[0010] Activate the software-defined radio (SDR) module during idle time slots, scan the specified frequency band broadcast by the satellite to generate a local spectrum fingerprint, and upload it to the satellite after differential privacy encryption. This enables the satellite to integrate the local spectrum fingerprints uploaded by all mobile terminals and form a satellite-ground collaborative sensing system.

[0011] The system receives a global training timing instruction for federated learning from the satellite terminal. Based on the local historical spectrum data, it completes local iterative training of the lightweight spectrum prediction model according to the global training timing instruction. The generated model gradient parameters are encrypted and uploaded to the satellite terminal, so that the satellite terminal can aggregate the model gradient parameters received from multiple mobile terminals to generate a globally optimized spectrum prediction model.

[0012] The system receives a spectrum allocation instruction from the satellite and determines the transmission and reception frequency based on the spectrum allocation instruction. The spectrum allocation instruction is generated by the satellite after performing global dynamic spectrum scheduling and allocation based on the spectrum prediction data of the satellite-ground collaborative sensing system and the global optimized spectrum prediction model, and the location and motion state context data of the mobile terminal.

[0013] By integrating GNSS and IMU data from the Global Navigation Satellite System, the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite are calculated in real time.

[0014] The pre-compensated frequency offset is calculated using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command. The transmission frequency is then pre-compensated based on the pre-compensated frequency offset. Subsequently, the pre-compensated uplink transmission is initiated so that after the satellite receives the uplink transmission, it performs a second precise calibration of the Doppler frequency shift by combining the real-time transmission status of the satellite-to-ground link and the motion trajectory of the mobile terminal.

[0015] Furthermore, the generation of a local spectral fingerprint from the designated frequency band broadcast by the scanning satellite specifically includes:

[0016] The system scans the designated frequency band broadcast by the satellite, collects the interference intensity and spectrum occupancy data of the designated frequency band, and generates the local spectrum fingerprint by combining the location hash value.

[0017] Furthermore, the step of completing the local iterative training of the lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instructions specifically includes:

[0018] Synchronize the local training clock according to the global training timing instructions, and complete at least one round of iteration of the lightweight spectrum prediction model based on the local training clock and local historical spectrum data, calculating only the model gradient parameters to complete the local iterative training.

[0019] Furthermore, the spectrum allocation command received from the satellite terminal specifically includes:

[0020] The spectrum allocation command issued by the satellite is received through a quantum-encrypted control channel.

[0021] Furthermore, the calculation of the pre-compensation frequency offset using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command is specifically performed according to the following formula:

[0022]

[0023] In the formula, To pre-compensate for frequency offset, For the velocity vector, The satellite elevation angle is [value]. At the speed of light, The uplink carrier frequency in the spectrum allocation instruction.

[0024] Furthermore, the method also includes:

[0025] When a sudden interference is detected in the designated frequency band, the interference information is uploaded to the satellite so that the satellite can generate a backup frequency band allocation strategy based on the global optimized spectrum prediction model.

[0026] Receive the spare frequency band allocation instruction issued by the satellite based on the spare frequency band allocation strategy.

[0027] Secondly, this disclosure provides a dynamic spectrum allocation method for satellite-ground coordination, applied to the satellite end, the method comprising:

[0028] Receive local spectrum fingerprints uploaded by multiple mobile terminals after differential privacy encryption. The local spectrum fingerprint is generated by the mobile terminal activating the software-defined radio (SDR) module in an idle time slot, scanning the specified frequency band broadcast by the satellite, and then uploading it after differential privacy encryption.

[0029] The local spectrum fingerprints uploaded by all mobile terminals, after being encrypted with differential privacy, are integrated to form a space-ground collaborative sensing system;

[0030] A global training timing instruction for federated learning is sent to the mobile terminal so that the mobile terminal can complete the local iterative training of the lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instruction, and encrypt and upload the generated model gradient parameters to the satellite.

[0031] The model gradient parameters received from multiple mobile terminals are aggregated to generate a globally optimized spectrum prediction model.

[0032] After performing global dynamic spectrum scheduling and allocation based on the spectrum prediction data of the aforementioned satellite-ground collaborative sensing system and the global optimized spectrum prediction model, and the location and motion state context data of the mobile terminal, a spectrum allocation instruction corresponding to each mobile terminal is generated, and the spectrum allocation instruction is sent to the corresponding mobile terminal.

[0033] The system receives the uplink transmission initiated by the mobile terminal after pre-compensation, and performs secondary precise calibration of the Doppler frequency shift by combining the real-time transmission status of the satellite-to-ground link and the motion trajectory of the mobile terminal. The uplink transmission is initiated by the mobile terminal by fusing GNSS and IMU data to calculate the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite in real time, calculating the pre-compensated frequency offset using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command, and then pre-compensating the transmission frequency according to the pre-compensated frequency offset.

[0034] Furthermore, the integration of all received local spectrum fingerprints uploaded by mobile terminals after differential privacy encryption to form a satellite-ground collaborative sensing system specifically includes:

[0035] A unique radio frequency key corresponding to each mobile terminal is generated by a spaceborne quantum random number generator (QRNG).

[0036] The mobile phone identity and data integrity of each mobile terminal are verified using the exclusive radio frequency key, and invalid data in the local spectrum fingerprint after differential privacy encryption is removed.

[0037] By integrating the local spectrum fingerprint after removing invalid data, a real-time spectrum occupancy and interference distribution map is generated, forming the aforementioned satellite-ground collaborative sensing system.

[0038] Furthermore, the aggregation of model gradient parameters from multiple received mobile terminals to generate a globally optimized spectrum prediction model specifically includes:

[0039] Within a preset window period, model gradient parameters uploaded by multiple mobile terminals are collected, the model gradient parameters are weighted and fused, and quantum noise is injected during the fusion process to generate the globally optimized spectrum prediction model.

[0040] Furthermore, after generating the globally optimized spectrum prediction model, the method further includes:

[0041] The updated global model parameters are sent to the mobile terminal to trigger the next round of local training, while retaining the global model from multiple previous rounds.

[0042] Furthermore, the step of sending the spectrum allocation instruction to the corresponding mobile terminal specifically includes:

[0043] The spectrum allocation command is sent to the corresponding mobile terminal through a quantum-encrypted control channel.

[0044] Furthermore, the method also includes:

[0045] Receive interference information uploaded by the mobile terminal when it detects sudden interference in the specified frequency band;

[0046] A backup frequency band allocation strategy is generated based on the interference information and the global optimized spectrum prediction model.

[0047] Based on the aforementioned backup frequency band allocation strategy, a backup frequency band allocation instruction is sent to the corresponding mobile terminal.

[0048] Thirdly, this disclosure provides a dynamic spectrum allocation system for satellite-ground collaboration, including a mobile terminal and a satellite terminal;

[0049] The mobile terminal is used to execute the dynamic spectrum allocation method for satellite-ground coordination described in the first aspect;

[0050] The satellite is used to execute the dynamic spectrum allocation method for satellite-ground cooperation described in the second aspect.

[0051] Fourthly, this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, the one or more computer programs being executed by the at least one processor to enable the at least one processor to perform the satellite-ground coordinated dynamic spectrum allocation method described in the first or second aspect above.

[0052] Fifthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the dynamic spectrum allocation method for satellite-ground coordination described in the first or second aspect above.

[0053] The dynamic spectrum allocation method, system, equipment, and medium for satellite-ground collaboration provided in this disclosure enable mobile terminals to activate the SDR module during idle time slots to scan designated frequency bands broadcast by the satellite, generate local spectrum fingerprints, and encrypt and upload them. This allows the satellite to integrate and form a satellite-ground collaborative sensing system, achieving dynamic collaborative sensing of satellite and ground spectrum resources and improving spectrum resource utilization. By receiving global training timing instructions for federated learning from the satellite, a lightweight spectrum prediction model is iteratively trained locally based on local historical spectrum data, and the model gradient parameters are encrypted and uploaded. This allows the satellite to aggregate and generate a globally optimized spectrum prediction model, achieving accurate prediction of spectrum status while ensuring data privacy and reducing satellite-ground signaling interaction overhead. By fusing GNSS and IMU data to calculate the velocity vector and satellite elevation angle relative to the satellite in real time, and using the velocity vector, satellite elevation angle, and uplink carrier frequency to calculate pre-compensated frequency offset and pre-compensate the transmission frequency before initiating uplink transmission, the satellite can perform secondary accurate calibration of Doppler frequency shift by combining the real-time transmission status of the satellite-ground link and the mobile terminal's motion trajectory. This achieves two-way Doppler compensation between satellite and ground, improving frequency shift correction accuracy and uplink transmission reliability. This solves the problems of existing methods for direct satellite spectrum allocation for mobile phones, such as poor adaptability of static spectrum allocation, excessive signaling overhead for dynamic spectrum coordination, data privacy leakage risks in spectrum prediction, and one-way Doppler compensation. Attached Figure Description

[0054] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the embodiments of the present disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings:

[0055] Figure 1 A flowchart illustrating a dynamic spectrum allocation method for satellite-ground coordination provided in this embodiment of the disclosure;

[0056] Figure 2 A schematic diagram illustrating a scenario of a satellite-ground coordinated dynamic spectrum allocation method provided in an embodiment of this disclosure;

[0057] Figure 3 A flowchart illustrating yet another dynamic spectrum allocation method for satellite-ground coordination provided in this disclosure embodiment;

[0058] Figure 4 A block diagram of a satellite-ground coordinated dynamic spectrum allocation system provided in this disclosure embodiment;

[0059] Figure 5 This is a block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0060] To enable those skilled in the art to better understand the technical solutions of this disclosure, exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments of this disclosure to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0061] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0062] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0063] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Words such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0064] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0065] Figure 1 A flowchart illustrating a dynamic spectrum allocation method for satellite-ground coordination provided in this embodiment of the disclosure. (Refer to...) Figure 1 The method includes:

[0066] Step S101: Activate the SDR (Software Defined Radio) module in an idle time slot, scan the specified frequency band broadcast by the satellite to generate a local spectrum fingerprint, and upload it to the satellite after differential privacy encryption, so that the satellite can integrate the local spectrum fingerprints uploaded by all mobile terminals after differential privacy encryption to form a satellite-ground cooperative sensing system.

[0067] The idle time slot refers to the communication gap when the mobile terminal is not engaged in cellular communication or service data transmission, such as background standby time or idle time during the DRX (Discontinuous Reception) cycle, to avoid interfering with the user's normal communication experience. The software-defined radio (SDR) module is configured to initiate radio frequency sampling and spectrum analysis within this time slot, scanning only the specified frequency band broadcast by the satellite (e.g., 1.9-2.2 GHz S-band), without additionally occupying full-band resources. The local spectrum fingerprint is used to characterize the spectrum environment characteristics of the mobile terminal's location. The differential privacy encryption processing is performed after the local spectrum fingerprint is generated, by adding noise perturbations to desensitize the data, ensuring that the data uploaded to the satellite cannot be used to deduce the original spectrum information of a single mobile terminal, thus avoiding the leakage of original spectrum data. The satellite-ground collaborative sensing system is generated by the satellite integrating the local spectrum fingerprints uploaded by multiple mobile terminals, and is used to describe the spectrum occupancy status and interference distribution across the entire area.

[0068] Specifically, after receiving local spectrum fingerprints uploaded by multiple mobile terminals and encrypted with differential privacy, the satellite first generates a unique radio frequency key for each mobile terminal using an onboard QRNG (Quantum Random Number Generator). Then, it verifies the mobile phone identity and data integrity of each mobile terminal based on the unique radio frequency key, removes invalid data from the local spectrum fingerprints encrypted with differential privacy, and integrates the local spectrum fingerprints after removing invalid data to generate a real-time spectrum occupancy and interference distribution map, thus forming the satellite-ground collaborative sensing system.

[0069] In some embodiments, generating a local spectrum fingerprint by scanning a specified frequency band broadcast by the satellite specifically includes:

[0070] The system scans the designated frequency band broadcast by the satellite, collects the interference intensity and spectrum occupancy data of the designated frequency band, and generates the local spectrum fingerprint by combining the location hash value.

[0071] Specifically, the designated frequency band for scanning satellite broadcasts refers to the mobile terminal activating the SDR module during idle time slots and performing spectrum energy detection within the designated frequency band. This involves collecting the RSSI (Received Signal Strength Indication) values ​​of each sub-band within that frequency band as interference strength, and simultaneously recording the occupancy status of each sub-band as spectrum occupancy data. The location hash value is a fixed-length digest value generated by hashing the mobile terminal's current location information. It is used to associate spectrum data with geographical location while avoiding directly exposing precise location information. The local spectrum fingerprint is generated by fusing the interference strength, spectrum occupancy data, and location hash value. For example, the three are encapsulated into a spectrum sensing record according to a preset data format.

[0072] Step S102: Receive the global training timing instruction for federated learning from the satellite, complete the local iterative training of the lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instruction, and encrypt and upload the generated model gradient parameters to the satellite so that the satellite can aggregate the model gradient parameters received from multiple mobile terminals to generate a globally optimized spectrum prediction model.

[0073] Specifically, the global training timing instructions for federated learning are periodically issued by the satellite to synchronize the training start time and iteration cycle of each mobile terminal, avoiding aggregation failure caused by inconsistent training progress of multiple terminals; the local historical spectrum data is the local spectrum fingerprint collected and stored by the mobile terminal in past idle time slots, which does not need to be uploaded to the satellite and only completes model training locally; the lightweight spectrum prediction model is, for example, a lightweight LSTM (Long Short-Term Memory) network with less than 100KB of model parameters, which is adapted to the computing power and storage resources of the mobile terminal and avoids excessive load on the terminal performance.

[0074] Specifically, each mobile terminal encrypts and uploads the model gradient parameters generated during training to the satellite. The satellite collects the model gradient parameters uploaded by multiple mobile terminals within a preset window period, performs weighted fusion of the model gradient parameters, and injects quantum noise during the fusion process to generate the globally optimized spectrum prediction model. At the same time, the satellite sends the updated global model parameters to the mobile terminals, triggering the next round of local training, and retains the global model from multiple previous rounds.

[0075] In some embodiments, the step of completing the local iterative training of the lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instructions specifically includes:

[0076] Synchronize the local training clock according to the global training timing instructions, and complete at least one round of iteration of the lightweight spectrum prediction model based on the local training clock and local historical spectrum data, calculating only the model gradient parameters to complete the local iterative training.

[0077] Specifically, the synchronized local training clock refers to aligning the training timer of the mobile terminal with the global timing of the satellite according to the global training timing instructions, ensuring that multiple terminals complete training iterations within the same time window; the calculation of only model gradient parameters means that during the local iteration process, only the gradient change values ​​of the model parameters are output, without transmitting the complete model weights or original spectrum data, further reducing the risk of data leakage; the at least one iteration can be adjusted according to the terminal's computing power and training needs, for example, 2-3 iterations can be performed when computing power is sufficient to improve model accuracy.

[0078] Step S103: Receive the spectrum allocation instruction sent by the satellite and determine the transmission and reception frequency according to the spectrum allocation instruction. The spectrum allocation instruction is generated by the satellite after performing global dynamic spectrum scheduling and allocation based on the spectrum prediction data of the satellite-ground cooperative sensing system, the global optimized spectrum prediction model, and the location and motion state context data of the mobile terminal.

[0079] Specifically, the spectrum allocation command is sent from the satellite to the mobile terminal via the downlink channel, and its content includes at least the uplink and downlink carrier frequency information allocated to the mobile terminal. The satellite-ground cooperative sensing system is used to provide the current real-time global spectrum occupancy and interference distribution status, and the global optimized spectrum prediction model is used to provide spectrum hole prediction results for future time periods (e.g., the next 5 minutes). The mobile terminal's position and motion context data includes the mobile terminal's GNSS (Global Navigation Satellite System) coordinates, velocity vector, and satellite elevation angle. The satellite integrates the above three types of data to perform global dynamic spectrum scheduling and allocation, generating spectrum allocation commands for each mobile terminal to achieve efficient use of spectrum resources and interference avoidance.

[0080] In some embodiments, receiving the spectrum allocation instruction sent by the satellite terminal specifically includes:

[0081] The spectrum allocation command issued by the satellite is received through a quantum-encrypted control channel.

[0082] Specifically, the quantum-encrypted control channel refers to a dedicated control channel established using high-order quantum noise encryption technology, through which the satellite transmits spectrum allocation commands. During channel establishment, the satellite uses an onboard quantum random number generator (QRNG) to generate an encryption key to encrypt the spectrum allocation commands; the mobile terminal receives the commands and restores the command content through a corresponding decryption mechanism. The quantum-encrypted control channel is used to prevent spectrum allocation commands from being eavesdropped on, tampered with, or forged during transmission, ensuring the security of spectrum scheduling commands.

[0083] Step S104: Integrate GNSS and IMU (Inertial Measurement Unit) data to calculate the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite in real time.

[0084] Specifically, the mobile terminal integrates real-time GNSS and IMU data to calculate the velocity vector and satellite elevation angle relative to the satellite, providing accurate motion parameter support for subsequent Doppler frequency offset pre-compensation.

[0085] Step S105: Calculate the pre-compensated frequency offset using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command, and pre-compensate the transmission frequency according to the pre-compensated frequency offset. Then, initiate the pre-compensated uplink transmission so that after the satellite receives the uplink transmission, it can perform a second precise calibration of the Doppler frequency shift by combining the real-time transmission status of the satellite-to-ground link and the motion trajectory of the mobile terminal.

[0086] Specifically, the calculation of the pre-compensation frequency offset using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command is performed according to the following formula:

[0087]

[0088] In the formula, To pre-compensate for frequency offset, For the velocity vector, The satellite elevation angle is [value]. At the speed of light, The uplink carrier frequency in the spectrum allocation instruction.

[0089] Specifically, the RF front-end of the mobile terminal integrates a Doppler pre-distortion module. This module adjusts the RF transmission frequency accordingly before the mobile terminal transmits the signal based on the calculated pre-compensation frequency offset, thus achieving pre-compensation for the uplink signal. The pre-compensated uplink transmission significantly reduces the initial frequency offset deviation of the satellite-to-ground link, facilitating secondary calibration by the satellite end using real-time transmission status of the satellite-to-ground link and information such as the mobile terminal's motion trajectory. This enables bidirectional collaborative compensation between satellite and ground, further improving frequency shift correction accuracy and uplink signal demodulation performance.

[0090] In some embodiments, the method further includes:

[0091] When a sudden interference is detected in the designated frequency band, the interference information is uploaded to the satellite so that the satellite can generate a backup frequency band allocation strategy based on the global optimized spectrum prediction model.

[0092] Receive the spare frequency band allocation instruction issued by the satellite based on the spare frequency band allocation strategy.

[0093] Specifically, during spectrum sensing or uplink transmission, the mobile terminal monitors the signal quality and interference intensity of a specified frequency band in real time. When a sudden interference situation is detected, such as the interference intensity exceeding a preset threshold, the mobile terminal encrypts and uploads the interference information, including the location information when the interference occurred, the interference intensity, and the interference information of the affected frequency band, to the satellite. The satellite analyzes the interference situation in real time based on the interference information and the global optimized spectrum prediction model, generates a backup frequency band allocation strategy adapted to the current communication state, and sends a backup frequency band allocation instruction to the corresponding mobile terminal. The mobile terminal switches to the backup frequency band to continue communication according to the instruction, so as to avoid the impact of sudden interference on satellite-to-ground communication.

[0094] It should be noted that, during the research and practice of existing technologies, the inventors discovered five core defects in existing mobile phone direct-to-satellite spectrum allocation methods: First, they adopt a centralized allocation logic dominated by ground base stations, neglecting the global spectrum scheduling capabilities of the satellite end, resulting in a lack of satellite-ground collaboration and low resource utilization; Second, interference suppression relies solely on centralized satellite-borne hardware such as RIS reflectors and beamforming, failing to utilize the distributed sensing capabilities of mobile terminals, leading to narrow interference monitoring coverage and poor real-time performance; Third, spectrum prediction uses centralized model training, requiring the uploading of raw terminal data, resulting in high privacy leakage risks, delayed model updates, and wasted local terminal computing power; Fourth, Doppler frequency offset is only corrected unidirectionally at the satellite end, failing to utilize the local pre-compensation capabilities of the mobile phone chip, leading to delayed correction, slow synchronization, and high uplink bit error rate; Fifth, they do not integrate contextual information such as mobile phone location and motion status, only statically allocating spectrum, which cannot adapt to the dynamic transmission characteristics of satellite-ground links. To solve the above problems, this application provides a satellite-ground collaborative dynamic spectrum allocation method, relying on three core improvements to achieve full-domain sensing, privacy security, accurate allocation, and efficient transmission. The main improvement ideas are as follows:

[0095] 1. Dual-mode spectrum sensing architecture for mobile terminals and satellite terminals

[0096] To address the shortcomings of "lack of space-ground coordination and no global scheduling" and "relying solely on centralized spaceborne hardware interference suppression without utilizing the distributed sensing capabilities of mobile phones," a distributed space-ground spectrum sensing scheme suitable for civilian mobile phones is proposed. This scheme transforms ordinary mobile phones into distributed spectrum sensors and constructs a dual-mode architecture of "local sensing at the mobile terminal + quantum verification at the satellite end." It fully meets privacy compliance requirements and makes up for the shortcomings of distributed sensing and space-ground coordinated sensing.

[0097] 2. Federated Learning-Driven Distributed Spectral Hole Prediction

[0098] To address the third drawback—"centralized spectrum prediction requires uploading raw data, which poses high privacy risks, leads to delayed model updates, and wastes terminal computing power"—a lightweight federated learning framework is built using the local computing power of mobile terminals. This framework allows for local training on the terminal without uploading raw data, and quantum noise is injected during gradient aggregation on the satellite side to prevent attacks. It balances data privacy and prediction accuracy, improving the prediction accuracy by 2 percentage points compared to traditional centralized models. This enables distributed collaborative training and makes better use of the local computing power of the terminal.

[0099] 3. Motion Adaptive Chip-Level Doppler Precompensation

[0100] To address the fourth defect ("only satellite-side unidirectional Doppler compensation, resulting in correction lag, slow synchronization, and high bit error rate") and the fifth defect ("lack of integration of mobile phone motion and location information, and static spectrum allocation"), this solution integrates mobile phone GNSS+IMU motion and location data to achieve terminal chip-level pre-compensation. Combined with secondary calibration at the satellite end, a bidirectional compensation mechanism is constructed, significantly shortening synchronization time and reducing uplink bit error rate. This overcomes the shortcomings of existing technologies that lack terminal-side pre-compensation mechanisms and simultaneously achieves dynamic adaptation based on motion context. Supporting solutions include: using quantum noise high-order encryption to build a dedicated spectrum control channel to prevent spectrum command tampering and data forgery, ensuring security throughout the entire transmission process.

[0101] Based on the above improvements, the steps executed by this satellite-ground coordinated dynamic spectrum allocation method on the mobile terminal include:

[0102] Step 1: Lightweight Spectrum Sensing and Encrypted Reporting

[0103] The mobile terminal monitors idle communication slots, automatically activates the built-in lightweight SDR module, scans the 1.9-2.2GHz satellite-ground shared frequency band, collects data such as interference intensity and spectrum occupancy, combines it with location information to generate a location hash value, and fuses it to form a local spectrum fingerprint. After differential privacy encryption processing, it is uploaded to the satellite terminal to prevent the leakage of original sensitive information. The entire process does not affect normal communication and solves the shortcomings of existing technologies that lack terminal-based distributed interference perception.

[0104] Step 2: Federated Learning Local Training and Gradient Upload

[0105] A lightweight LSTM spectrum prediction model with less than 100KB of parameters is pre-deployed locally on the mobile terminal. Each federated training cycle is fixed at 5 minutes (matching the spectrum prediction duration) and strictly follows the following timing sequence: ① Time slot alignment: Receives global training timing instructions from the satellite and synchronizes the local training clock; ② Local iteration: Completes 2-3 rounds of lightweight model iteration based on local historical spectrum data, calculating only model gradient parameters without uploading original spectrum or location data; ③ Gradient reporting: Immediately after training, encrypted gradients are uploaded. Data from the current round is automatically removed if it fails to report within the timeout period to avoid affecting global aggregation; ④ Waiting for updates: Receives global model parameters from the satellite and waits for the next training round to trigger. The entire process is computed locally, without consuming core communication computing power, thus solving the problems of privacy leaks and wasted computing power in centralized training.

[0106] Step 3: Motion sensing and chip-level Doppler pre-compensation

[0107] The mobile terminal integrates real-time GNSS and IMU data to calculate context parameters such as velocity vector, elevation angle, and position relative to the satellite. This triggers the RF front-end Doppler predistortion module to calculate the pre-compensation frequency offset according to the formula, thus shifting the uplink transmission frequency in advance and completing the pre-compensation on the terminal side.

[0108]

[0109] in, To pre-compensate for frequency offset, This refers to the velocity vector (i.e., relative velocity) of the mobile terminal relative to the satellite. The satellite elevation angle. At the speed of light, The uplink carrier frequency (i.e., the frequency transmitted from the terminal to the satellite, which is allocated by the satellite base station) in the spectrum allocation command is dynamically pre-compensated based on motion state, replacing the traditional one-way hysteresis compensation. The mobile terminal receives the spectrum allocation command from the satellite through a quantum-encrypted control channel, adjusts the radio frequency parameters, adapts to the allocated frequency band and the pre-compensation frequency, completes the satellite-to-ground link access, and synchronously and continuously updates sensing and motion data to support the next round of dynamic allocation.

[0110] It should be noted that, , , It was obtained based on real-time GNSS data combined with satellite ephemeris data.

[0111] Accordingly, based on the above improvements, the steps performed at the satellite end of this satellite-ground coordinated dynamic spectrum allocation method include:

[0112] Step 1: Quantum Encryption Data Verification and Global Perception

[0113] The satellite receives the encrypted spectrum fingerprint from the mobile terminal and generates a unique radio frequency key through the onboard quantum random number generator (QRNG) to complete the verification of terminal identity and data integrity, and eliminate forged and invalid data. It integrates valid data from the entire domain to generate a real-time spectrum occupancy and interference distribution map, and combines the advantages of satellite wide-area coverage to carry out global spectrum situation analysis, make up for the blind spots of satellite-based independent sensing, and build a satellite-ground collaborative sensing system to solve the problem that existing technologies lack a global spectrum scheduling foundation.

[0114] Step 2: Federated Gradient Aggregation and Global Model Update

[0115] The satellite-based training follows a fixed 5-minute single-round training cycle, strictly synchronized with the training sequence of mobile terminals, and executes a global aggregation process: ① Gradient collection: Collects all valid gradient data from mobile terminals within the window period, and stops receiving data after the window period closes; ② Quantum-encrypted aggregation: Weighted fusion of gradients, with quantum noise injected in real time during the aggregation process to prevent model inversion and gradient stealing attacks; ③ Model update: Generates a globally optimized LSTM model with a prediction accuracy of 97%, and simultaneously completes lightweight model compression; ④ Parameter distribution: Immediately distributes the updated global model parameters to all mobile terminals on the network, triggering the next round of local training; ⑤ Model caching: Retains the global model from the past 3 rounds to cope with packet loss during transmission, enabling real-time iterative updates of the model and solving the problem of delayed updates in centralized training.

[0116] Step 3: Doppler compensation calibration and performance optimization

[0117] The satellite receives the pre-compensation parameters from the mobile terminal and, combined with the real-time transmission status of the satellite-to-ground link and the mobile terminal's motion trajectory, completes a second precise calibration to achieve bidirectional collaborative compensation. Compared with traditional one-way compensation, the satellite-to-ground synchronization time is reduced to 1 / 2-1 / 3, and the uplink bit error rate is reduced by 10 times. At the same time, the compensation parameters are dynamically adjusted in combination with the terminal's motion status to adapt to the dynamic transmission characteristics of the satellite-to-ground link, thus solving the shortcomings of existing technologies such as compensation lag and lack of dynamic adaptation.

[0118] Step 4: Global Dynamic Allocation and Encryption Command Issuance

[0119] The satellite combines global perception results, federated learning spectrum prediction data, and mobile terminal motion and location context to carry out global dynamic spectrum scheduling and allocation, breaking the allocation logic dominated by the ground base station and realizing the overall resource coordination of the entire domain. The final command is issued through a quantum-encrypted control channel to prevent command eavesdropping and tampering. At the same time, the spectrum status is monitored in real time, and the entire process of perception, training, compensation and allocation is triggered in a 5-minute cycle to achieve dynamic closed-loop control and fully adapt to the needs of satellite-ground integrated communication.

[0120] Figure 2 A scenario diagram illustrating a satellite-ground collaborative dynamic spectrum allocation method is provided, showcasing the collaborative architecture of mobile terminals, satellites, gateway stations, and ground base stations. In this architecture: the mobile terminal acts as a distributed spectrum sensing node and local computing unit, performing spectrum sensing, local iterative training of a lightweight spectrum prediction model, and Doppler pre-compensation; the satellite, as the core of satellite-ground collaborative scheduling, integrates multi-terminal spectrum fingerprints to form a satellite-ground collaborative sensing system, aggregates gradient parameters to generate a globally optimized spectrum prediction model, and issues dynamic spectrum allocation commands; the gateway station, as a ground access node, ensures stable transmission of the satellite-ground link; and the ground base station, as a spectrum environment signal source within a specified frequency band, provides the data foundation for distributed spectrum sensing for the mobile terminal. Figure 2 The scenario diagram shown corresponds to the following dynamic spectrum allocation methods for satellite-ground coordination:

[0121] (1) Access phase:

[0122] The mobile terminal receives the spectrum map broadcast by the satellite, combines its own perceived motion state with the spectrum of the specified frequency band to generate a local spectrum fingerprint (including the location hash value), and the data is processed by the lightweight model on the terminal side, then differentially encrypted for privacy, and uploaded through the chain-established channel.

[0123] After receiving and decrypting the information from the mobile terminal, the satellite processes the terminal data and combines it with the onboard large model to generate a spectrum allocation command, which is then sent to the mobile terminal to achieve two-way collaboration.

[0124] (2) Communication phase:

[0125] The mobile terminal determines the transmission and reception frequency according to the instructions from the satellite, performs Doppler frequency shift pre-compensation, initiates uplink transmission, realizes bidirectional Doppler compensation, and compresses the synchronization time to about 1 / 2 to 1 / 3 of the traditional solution.

[0126] (3) Interference response:

[0127] When sudden interference is detected, the federated learning model generates a backup frequency band allocation strategy in real time.

[0128] The dynamic spectrum allocation method for satellite-ground collaboration provided in this disclosure involves a mobile terminal activating the SDR module during idle time slots to scan designated frequency bands broadcast by the satellite, generating a local spectrum fingerprint, and encrypting and uploading it. This enables the satellite to integrate and form a satellite-ground collaborative sensing system, achieving dynamic collaborative sensing of satellite and ground spectrum resources and improving spectrum resource utilization. By receiving global training timing instructions for federated learning from the satellite, a lightweight spectrum prediction model is iteratively trained locally based on local historical spectrum data, and the model gradient parameters are encrypted and uploaded. This allows the satellite to aggregate and generate a globally optimized spectrum prediction model, achieving accurate prediction of spectrum status while ensuring data privacy and reducing satellite-ground signaling interaction overhead. By fusing GNSS and IMU data to calculate the velocity vector and satellite elevation angle relative to the satellite in real time, and using the velocity vector, satellite elevation angle, and uplink carrier frequency to calculate pre-compensated frequency offset and pre-compensate the transmission frequency before initiating uplink transmission, the satellite combines the real-time transmission status of the satellite-ground link and the mobile terminal's motion trajectory to perform secondary accurate calibration of the Doppler frequency shift, achieving two-way Doppler compensation between satellite and ground, improving frequency shift correction accuracy and uplink transmission reliability. This solves the problems of existing methods for direct satellite spectrum allocation for mobile phones, such as poor adaptability of static spectrum allocation, excessive signaling overhead for dynamic spectrum coordination, data privacy leakage risks in spectrum prediction, and one-way Doppler compensation.

[0129] Figure 3 A flowchart of another dynamic spectrum allocation method for satellite-ground coordination provided in this disclosure is shown below. Figure 3 This satellite-ground coordinated dynamic spectrum allocation method is applied to the satellite end, including:

[0130] Step S201: Receive local spectrum fingerprints uploaded by multiple mobile terminals after differential privacy encryption. The local spectrum fingerprint is generated by the mobile terminal activating the software-defined radio (SDR) module in an idle time slot, scanning the specified frequency band broadcast by the satellite, and then uploading it after differential privacy encryption.

[0131] The idle time slot refers to the communication interval when the mobile terminal is not engaged in cellular communication or service data transmission, such as background standby time or idle time during non-continuous DRX reception cycles, to avoid interfering with the user's normal communication experience. The software-defined radio (SDR) module is configured to initiate radio frequency sampling and spectrum analysis within this time slot, scanning only the designated frequency band broadcast by the satellite (e.g., 1.9-2.2 GHz S-band), without additionally occupying full-band resources. The local spectrum fingerprint is used to characterize the spectrum environment characteristics of the mobile terminal's location. The differential privacy encryption process is performed after the local spectrum fingerprint is generated, by adding noise perturbations and other methods to desensitize the data, ensuring that the data uploaded to the satellite cannot be used to deduce the original spectrum information of a single mobile terminal, thereby avoiding the leakage of original spectrum data.

[0132] Step S202: Integrate the local spectrum fingerprints uploaded by all mobile terminals after differential privacy encryption to form a satellite-ground collaborative sensing system.

[0133] Specifically, the satellite-ground collaborative sensing system is generated by integrating local spectrum fingerprints uploaded by multiple mobile terminals from the satellite end, and is used to describe the spectrum occupancy status and interference distribution across the entire region.

[0134] In some embodiments, the integration of the local spectrum fingerprints uploaded by all received mobile terminals after differential privacy encryption to form a satellite-ground collaborative sensing system specifically includes:

[0135] A unique radio frequency key corresponding to each mobile terminal is generated by a spaceborne quantum random number generator (QRNG).

[0136] The mobile phone identity and data integrity of each mobile terminal are verified using the exclusive radio frequency key, and invalid data in the local spectrum fingerprint after differential privacy encryption is removed.

[0137] By integrating the local spectrum fingerprint after removing invalid data, a real-time spectrum occupancy and interference distribution map is generated, forming the aforementioned satellite-ground collaborative sensing system.

[0138] Specifically, after receiving the encrypted spectrum fingerprint uploaded by the mobile terminal, the satellite generates a unique radio frequency key corresponding to the mobile terminal's identity using an onboard quantum random number generator (QRNG). This key, based on the true randomness of quantum random numbers, is unpredictable and unique. The satellite uses this key to decrypt and verify the integrity of the encrypted data. If the verification passes, the mobile terminal's identity is confirmed as legitimate and the data has not been tampered with during transmission; if the verification fails, the data is deemed invalid and discarded. The satellite then fuses the valid local spectrum fingerprints after removing invalid data according to geographical location to generate a real-time spectrum occupancy and interference distribution map covering the specified area. This distribution map is the core data foundation of the satellite-ground collaborative sensing system, used to describe the real-time status of the entire spectrum environment.

[0139] Step S203: Send a global training timing instruction for federated learning to the mobile terminal so that the mobile terminal can complete the local iterative training of the lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instruction, and encrypt and upload the generated model gradient parameters to the satellite.

[0140] Specifically, the satellite sends global training timing instructions to each mobile terminal at a preset cycle to standardize the training start time and iteration cycle for each terminal. Upon receiving the instruction, the mobile terminal initiates local training, iteratively training the lightweight spectrum prediction model using locally stored historical spectrum data, and encrypts and uploads the calculated model gradient parameters to the satellite. Through this mechanism, the satellite can obtain local model update information from each terminal without collecting the original spectrum data from the mobile terminals.

[0141] Step S204: Aggregate the model gradient parameters of the received multiple mobile terminals to generate a globally optimized spectrum prediction model.

[0142] Specifically, the satellite collects model gradient parameters uploaded by multiple mobile terminals within a preset window period, performs weighted fusion of the model gradient parameters, and injects quantum noise during the fusion process to generate the globally optimized spectrum prediction model. The preset window period is a gradient collection time window set by the satellite and can be adaptively adjusted according to the constellation size and the number of terminals.

[0143] In some embodiments, after generating the globally optimized spectrum prediction model, the method further includes:

[0144] The updated global model parameters are sent to the mobile terminal to trigger the next round of local training, while retaining the global model from multiple previous rounds.

[0145] Specifically, after the satellite completes the global model update, it sends the updated global model parameters to the mobile terminals participating in this round of training via the downlink channel, triggering the next round of local training and forming a federated learning closed loop of "local training - gradient upload - global aggregation - model distribution". Simultaneously, the satellite retains global model parameters from previous rounds (e.g., the most recent three rounds) in its local cache. If some mobile terminals fail to receive the latest model parameters in time due to channel quality or network congestion, they can obtain the historical model through a retransmission mechanism before the next round of training. This avoids terminals being unable to participate in federated learning due to packet loss, thereby improving the system's robustness.

[0146] Step S205: After performing global dynamic spectrum scheduling and allocation based on the spectrum prediction data of the satellite-ground collaborative sensing system and the global optimized spectrum prediction model and the location and motion state context data of the mobile terminal, a spectrum allocation instruction corresponding to each mobile terminal is generated, and the spectrum allocation instruction is sent to the corresponding mobile terminal.

[0147] Specifically, the satellite-ground collaborative sensing system provides the current real-time global spectrum occupancy and interference distribution status; the global optimized spectrum prediction model provides spectrum hole prediction results for future time periods (e.g., the next 5 minutes); and the mobile terminal's location and motion context data includes the mobile terminal's GNSS coordinates, velocity vector, and satellite elevation angle. The satellite integrates these three types of data to perform global dynamic spectrum scheduling and allocation, generating spectrum allocation instructions for each mobile terminal to achieve efficient spectrum resource utilization and interference avoidance.

[0148] In some embodiments, sending the spectrum allocation instruction to the corresponding mobile terminal specifically includes:

[0149] The spectrum allocation command is sent to the corresponding mobile terminal through a quantum-encrypted control channel.

[0150] Specifically, the quantum encryption control channel refers to a dedicated control channel established using quantum noise high-order encryption technology, through which the satellite sends spectrum allocation commands.

[0151] Step S206: Receive the uplink transmission initiated by the mobile terminal after pre-compensation, and perform secondary precise calibration of the Doppler frequency shift by combining the real-time transmission status of the satellite-to-ground link and the motion trajectory of the mobile terminal. The uplink transmission is initiated by the mobile terminal by fusing GNSS and IMU data, calculating the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite in real time, calculating the pre-compensation frequency offset using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command, and then pre-compensating the transmission frequency according to the pre-compensation frequency offset.

[0152] Specifically, the RF front-end of the mobile terminal integrates a Doppler pre-distortion module. Based on the calculated pre-compensation frequency offset, this module adjusts the RF transmission frequency accordingly before the mobile terminal transmits the signal, thereby achieving pre-compensation of the uplink signal. The uplink transmission after pre-compensation can significantly reduce the initial frequency offset deviation of the satellite-to-ground link. When the satellite receives the uplink transmission initiated by the mobile terminal after pre-compensation, it performs secondary calibration by combining the real-time transmission status of the satellite-to-ground link and the motion trajectory of the mobile terminal, thereby achieving two-way collaborative compensation between satellite and ground, further improving the frequency shift correction accuracy and uplink signal demodulation performance.

[0153] In some embodiments, the method further includes:

[0154] Receive interference information uploaded by the mobile terminal when it detects sudden interference in the specified frequency band;

[0155] A backup frequency band allocation strategy is generated based on the interference information and the global optimized spectrum prediction model.

[0156] Based on the aforementioned backup frequency band allocation strategy, a backup frequency band allocation instruction is sent to the corresponding mobile terminal.

[0157] Specifically, during spectrum sensing or uplink transmission, the mobile terminal monitors the signal quality and interference intensity of a specified frequency band in real time. When a sudden interference situation is detected, such as the interference intensity exceeding a preset threshold, the mobile terminal encrypts and uploads the interference information, including the location information when the interference occurred, the interference intensity, and the interference information of the affected frequency band, to the satellite. The satellite analyzes the interference situation in real time based on the interference information and the global optimized spectrum prediction model, generates a backup frequency band allocation strategy adapted to the current communication state, and sends a backup frequency band allocation instruction to the corresponding mobile terminal. The mobile terminal switches to the backup frequency band to continue communication according to the instruction, so as to avoid the impact of sudden interference on satellite-to-ground communication.

[0158] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further. Those skilled in the art will understand that in the above methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0159] Figure 4 This is a block diagram of a satellite-ground coordinated dynamic spectrum allocation system provided in an embodiment of this disclosure.

[0160] Reference Figure 4 This disclosure provides a dynamic spectrum allocation system for satellite-ground collaboration, including a mobile terminal 11 and a satellite terminal 12;

[0161] The mobile terminal 11 is used to execute the above-mentioned dynamic spectrum allocation method for satellite-ground coordination of the mobile terminal;

[0162] The satellite terminal 12 is used to execute the aforementioned satellite-to-ground collaborative dynamic spectrum allocation method.

[0163] Figure 5 This is a block diagram of an electronic device provided in an embodiment of the present disclosure.

[0164] Reference Figure 5 This disclosure provides an electronic device comprising: at least one processor 701; at least one memory 702; and one or more I / O interfaces 703 connected between the processor 701 and the memory 702; wherein the memory 702 stores one or more computer programs executable by the at least one processor 701, the one or more computer programs being executed by the at least one processor 701 to enable the at least one processor 701 to execute the aforementioned satellite-ground coordinated dynamic spectrum allocation method.

[0165] This disclosure also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the aforementioned dynamic spectrum allocation method for satellite-ground coordination. The computer-readable storage medium may be volatile or non-volatile.

[0166] In summary, the satellite-ground collaborative dynamic spectrum allocation method, system, device, and medium provided in this disclosure activate the SDR module in idle time slots to scan the designated frequency band broadcast by the satellite, generate a local spectrum fingerprint, and encrypt and upload it. This enables the satellite to integrate and form a satellite-ground collaborative sensing system, achieving dynamic collaborative sensing of satellite and ground spectrum resources and improving spectrum resource utilization. By receiving the global training timing instructions for federated learning issued by the satellite, the system completes local iterative training of a lightweight spectrum prediction model based on local historical spectrum data and encrypts and uploads the model gradient parameters. This allows the satellite to aggregate and generate a globally optimized spectrum prediction model, achieving accurate prediction of spectrum situation while ensuring data privacy and reducing satellite-ground signaling interaction overhead. By fusing GNSS and IMU data to calculate the velocity vector and satellite elevation angle relative to the satellite in real time, and using the velocity vector, satellite elevation angle, and uplink carrier frequency to calculate pre-compensated frequency offset and pre-compensate the transmission frequency before initiating uplink transmission, the satellite combines the real-time transmission status of the satellite-ground link and the mobile terminal's motion trajectory to perform secondary accurate calibration of the Doppler frequency shift, achieving two-way Doppler compensation between satellite and ground, improving frequency shift correction accuracy and uplink transmission reliability. This solves the problems of existing methods for direct satellite spectrum allocation for mobile phones, such as poor adaptability of static spectrum allocation, excessive signaling overhead for dynamic spectrum coordination, data privacy leakage risks in spectrum prediction, and one-way Doppler compensation.

[0167] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0168] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0169] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0170] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0171] This disclosure has disclosed exemplary embodiments, and although specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A dynamic spectrum allocation method for satellite-ground coordination, characterized in that, Applied to mobile terminals, the method includes: Activate the software-defined radio (SDR) module during idle time slots, scan the specified frequency band broadcast by the satellite to generate a local spectrum fingerprint, and upload it to the satellite after differential privacy encryption. This enables the satellite to integrate the local spectrum fingerprints uploaded by all mobile terminals and form a satellite-ground collaborative sensing system. The system receives a global training timing instruction for federated learning from the satellite terminal. Based on the local historical spectrum data, it completes local iterative training of the lightweight spectrum prediction model according to the global training timing instruction. The generated model gradient parameters are encrypted and uploaded to the satellite terminal, so that the satellite terminal can aggregate the model gradient parameters received from multiple mobile terminals to generate a globally optimized spectrum prediction model. The system receives a spectrum allocation instruction from the satellite and determines the transmission and reception frequency based on the spectrum allocation instruction. The spectrum allocation instruction is generated by the satellite after performing global dynamic spectrum scheduling and allocation based on the spectrum prediction data of the satellite-ground collaborative sensing system and the global optimized spectrum prediction model, and the location and motion state context data of the mobile terminal. By integrating GNSS and IMU data from the Global Navigation Satellite System, the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite are calculated in real time. The pre-compensated frequency offset is calculated using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command. The transmission frequency is then pre-compensated based on the pre-compensated frequency offset. Subsequently, the pre-compensated uplink transmission is initiated so that after the satellite receives the uplink transmission, it performs a second precise calibration of the Doppler frequency shift by combining the real-time transmission status of the satellite-to-ground link and the motion trajectory of the mobile terminal.

2. The method according to claim 1, characterized in that, The process of generating a local spectral fingerprint by scanning the designated frequency band broadcast by the satellite specifically includes: The system scans the designated frequency band broadcast by the satellite, collects the interference intensity and spectrum occupancy data of the designated frequency band, and generates the local spectrum fingerprint by combining the location hash value.

3. The method according to claim 1, characterized in that, The step of completing the local iterative training of the lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instructions specifically includes: Synchronize the local training clock according to the global training timing instructions, and complete at least one round of iteration of the lightweight spectrum prediction model based on the local training clock and local historical spectrum data, calculating only the model gradient parameters to complete the local iterative training.

4. The method according to claim 1, characterized in that, The spectrum allocation instructions received from the satellite terminal specifically include: The spectrum allocation command issued by the satellite is received through a quantum-encrypted control channel.

5. The method according to claim 1, characterized in that, The pre-compensation frequency offset is calculated using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command, specifically according to the following formula: In the formula, To pre-compensate for frequency offset, For the velocity vector, The satellite elevation angle is [value]. At the speed of light, The uplink carrier frequency in the spectrum allocation instruction.

6. The method according to claim 1, characterized in that, The method further includes: When a sudden interference is detected in the designated frequency band, the interference information is uploaded to the satellite so that the satellite can generate a backup frequency band allocation strategy based on the global optimized spectrum prediction model. Receive the spare frequency band allocation instruction issued by the satellite based on the spare frequency band allocation strategy.

7. A dynamic spectrum allocation method for satellite-ground coordination, characterized in that, Applied to satellite terminals, the method includes: Receive local spectrum fingerprints uploaded by multiple mobile terminals after differential privacy encryption. The local spectrum fingerprint is generated by the mobile terminal activating the software-defined radio (SDR) module in an idle time slot, scanning the specified frequency band broadcast by the satellite, and then uploading it after differential privacy encryption. The local spectrum fingerprints uploaded by all mobile terminals, after being encrypted with differential privacy, are integrated to form a space-ground collaborative sensing system; A global training timing instruction for federated learning is sent to the mobile terminal so that the mobile terminal can complete the local iterative training of the lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instruction, and encrypt and upload the generated model gradient parameters to the satellite. The model gradient parameters received from multiple mobile terminals are aggregated to generate a globally optimized spectrum prediction model. After performing global dynamic spectrum scheduling and allocation based on the spectrum prediction data of the aforementioned satellite-ground collaborative sensing system and the global optimized spectrum prediction model, and the location and motion state context data of the mobile terminal, a spectrum allocation instruction corresponding to each mobile terminal is generated, and the spectrum allocation instruction is sent to the corresponding mobile terminal. The system receives the uplink transmission initiated by the mobile terminal after pre-compensation, and performs secondary precise calibration of the Doppler frequency shift by combining the real-time transmission status of the satellite-to-ground link and the motion trajectory of the mobile terminal. The uplink transmission is initiated by the mobile terminal by fusing GNSS and IMU data to calculate the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite in real time, calculating the pre-compensated frequency offset using the velocity vector, satellite elevation angle, and uplink carrier frequency in the spectrum allocation command, and then pre-compensating the transmission frequency according to the pre-compensated frequency offset.

8. The method according to claim 7, characterized in that, The process of integrating the local spectrum fingerprints uploaded by all received mobile terminals after differential privacy encryption to form a satellite-ground collaborative sensing system specifically includes: A unique radio frequency key corresponding to each mobile terminal is generated by a spaceborne quantum random number generator (QRNG). The mobile phone identity and data integrity of each mobile terminal are verified using the exclusive radio frequency key, and invalid data in the local spectrum fingerprint after differential privacy encryption is removed. By integrating the local spectrum fingerprint after removing invalid data, a real-time spectrum occupancy and interference distribution map is generated, forming the aforementioned satellite-ground collaborative sensing system.

9. The method according to claim 7, characterized in that, The aggregation of model gradient parameters from multiple received mobile terminals to generate a globally optimized spectrum prediction model specifically includes: Within a preset window period, model gradient parameters uploaded by multiple mobile terminals are collected, the model gradient parameters are weighted and fused, and quantum noise is injected during the fusion process to generate the globally optimized spectrum prediction model.

10. The method according to claim 7, characterized in that, After generating the globally optimized spectrum prediction model, the method further includes: The updated global model parameters are sent to the mobile terminal to trigger the next round of local training, while retaining the global model from multiple previous rounds.

11. The method according to claim 7, characterized in that, The step of sending the spectrum allocation instruction to the corresponding mobile terminal specifically includes: The spectrum allocation command is sent to the corresponding mobile terminal through a quantum-encrypted control channel.

12. The method according to claim 7, characterized in that, The method further includes: Receive interference information uploaded by the mobile terminal when it detects sudden interference in the specified frequency band; A backup frequency band allocation strategy is generated based on the interference information and the global optimized spectrum prediction model. Based on the aforementioned backup frequency band allocation strategy, a backup frequency band allocation instruction is sent to the corresponding mobile terminal.

13. A satellite-ground coordinated dynamic spectrum allocation system, characterized in that, Including mobile terminals and satellite terminals; The mobile terminal is used to execute the dynamic spectrum allocation method for satellite-ground coordination as described in any one of claims 1-6; The satellite terminal is used to execute the dynamic spectrum allocation method for satellite-ground coordination as described in any one of claims 7-12.

14. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to perform the satellite-ground coordinated dynamic spectrum allocation method as described in any one of claims 1-6, or the satellite-ground coordinated dynamic spectrum allocation method as described in any one of claims 7-12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic spectrum allocation method for satellite-ground coordination as described in any one of claims 1-6, or the dynamic spectrum allocation method for satellite-ground coordination as described in any one of claims 7-12.

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