Satellite communication hierarchical pilot pool method and system based on power service perception

Abstract

This invention discloses a hierarchical pilot pool method, system, and medium for satellite communication based on power service awareness. The method includes: extracting service quality demand characteristics of power services and calculating a comprehensive score, dividing the services into multiple priority queues; constructing a three-layer heterogeneous pilot pool based on the priority queues, namely a dedicated non-contention pilot layer, an anti-frequency offset shared pilot layer, and an unlicensed superimposed pilot layer; the terminal sending a preamble signal according to the priority of the corresponding pilot layer; the satellite side detecting and statistically analyzing the load, collision probability, and access latency of each layer in parallel; dynamically adjusting the resource allocation of each layer using a multi-agent reinforcement learning model and issuing updated configurations; and introducing a disaster triggering mechanism to forcibly increase the resource allocation of the dedicated non-contention pilot layer when the collision rate suddenly increases. This invention achieves differentiated protection for control, acquisition, and video services in power emergency scenarios, possessing low latency, high reliability, large capacity, and adaptive capabilities, filling the gap in hybrid service protection in existing non-terrestrial network standards.

Description

Technical Field

[0001] This invention relates to the field of information and communication engineering technology, and in particular to a method and system for hierarchical pilot pools for satellite communication based on power service awareness. Background Technology

[0002] In non-terrestrial networks (NTNs), the design of the random access preamble directly impacts the terminal's initial synchronization, channel estimation, and access success rate. 3GPP Release 17 / 18 defines enhanced preamble structures for NTN scenarios, including integer multiple frequency offset packet detection for PSS / SSS, joint phase tracking of DM-RS and PT-RS, and an open-loop repetitive transmission mechanism for IoT NTNs. These designs primarily address the Doppler frequency shift and delay spread issues caused by the high-speed motion of low-Earth orbit (LEO) satellites. However, their core assumption is that the terminal possesses GNSS positioning capabilities and operates on a single service type, failing to fully consider the differentiated Quality of Service (QoS) requirements of mixed services and access needs under GNSS denial environments.

[0003] In power industry emergency scenarios, communication terminals (such as distribution automation terminals, differential protection devices, and emergency inspection drones) are often deployed in basements, distribution cabinets, or areas experiencing power outages due to disasters, where GNSS signals are completely unavailable. Meanwhile, power emergency services exhibit distinct hybrid characteristics: control-related services (such as precise load control and differential protection) require end-to-end latency of less than 20 ms and reliability of 99.999%; data collection-related services (such as electricity consumption information collection and distribution network status monitoring) need to support massive connections of thousands of terminals per square kilometer, with latency allowed to be relaxed to the second level; and video-related services (such as on-site inspections and emergency command) require bandwidth of 2-30 Mbps and latency of less than 200 ms. The existing 3GPP NTN pilot framework uses a unified preamble pool and access class prohibition (ACB) mechanism, which cannot simultaneously meet the conflicting requirements of the above three types of services in the same cell: if a large number of dedicated preambles are reserved for control services, the resources of data collection and video services will be wasted; if contention access is adopted completely, the latency and reliability of control services cannot be guaranteed.

[0004] To address the aforementioned issues, the academic community has proposed several enhancement schemes in recent years: Superimposed preamble design based on unlicensed random access (GFRA) can improve pilot pool capacity, but its detection complexity is high; dynamic resource allocation based on multi-agent reinforcement learning significantly improves throughput, but mainly focuses on single service types; frequency offset estimation methods based on beam pointing still have significant errors in GNSS denial environments. However, no research has explicitly mapped the differentiated QoS of mixed power services to the physical layer design of NTN pilot pools, and there is a lack of a complete architecture that can adapt to channel challenges such as large frequency offsets and GNSS denial, and dynamically allocate pilot resources to ensure access for multiple priority services. Summary of the Invention

[0005] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a hierarchical pilot pool method for satellite communication based on power service awareness. Addressing the distinct QoS requirements for latency, reliability, and connection count among control, acquisition, and video services in power emergency scenarios, this invention designs a dynamic management method for a service-aware hierarchical pilot pool. This method quantifies power services into priority queues, constructs a three-layer physical pilot pool (dedicated, contention-free, frequency offset-resistant multiplexing, and license-free overlay), and introduces multi-agent reinforcement learning to dynamically allocate resource proportions and a disaster triggering mechanism. This solves the problem that the existing 3GPP NTN unified pilot pool cannot simultaneously guarantee the differentiated protection of mixed services, achieving the goal of "hard isolation for control services, large capacity for acquisition services, and high efficiency for video services" under various modes such as normal, light load, and disaster.

[0006] The present invention also proposes a system and medium having the above-mentioned satellite communication hierarchical pilot pool method based on power service awareness.

[0007] A satellite communication preamble signal transmission method based on power service awareness according to a first aspect of the present invention is characterized in that it is executed by a terminal device and includes: Obtain service data from power terminals, extract service quality demand features from them, calculate a comprehensive score based on the service quality demand features, and divide the services into multiple priority queues according to the comprehensive score. Based on the priority queue, the corresponding physical layer pilot layer is determined, and a preamble signal is transmitted on the physical layer pilot layer.

[0008] The satellite communication hierarchical pilot pool method based on power service awareness according to embodiments of the present invention has at least the following beneficial effects: This invention explicitly maps the differentiated QoS of power control, acquisition, and video services to a three-layer physical layer architecture through a service-aware hierarchical pilot pool: a dedicated contention-free layer compresses the access latency of emergency control services to less than 20ms and the collision rate to less than 1%; an anti-frequency offset shared layer uses FDS-LFM pilots to improve the detection probability under a large frequency offset of ±300kHz, expanding the equivalent pilot capacity to 256; and an unlicensed overlay layer utilizes compressed sensing, increasing overload throughput by more than 50% compared to the traditional ACB mechanism. Combined with multi-agent reinforcement learning for dynamic resource allocation and disaster triggering mechanisms, the system balances low latency, high reliability, large capacity, and adaptive capabilities, filling the gap in the existing NTN standard for hybrid service assurance.

[0009] According to some embodiments of the present invention, the step of obtaining service data of power terminals, extracting service quality demand characteristics from them, calculating a comprehensive score based on the service quality demand characteristics, and dividing the services into multiple priority queues according to the comprehensive score specifically includes the following steps for calculating the comprehensive score: The service quality requirements include latency requirements, reliability requirements, and speed requirements; Latency requirements, reliability requirements, and speed requirements are each normalized to a latency sensitivity δ. lat Reliability requirements δ rel and rate requirement δ rate The comprehensive score is calculated using a linear weighted summation, with latency sensitivity having the highest weight.

[0010] According to some embodiments of the present invention, the priority queue includes four levels P0, P1, P2, and P3; wherein: P0 is an emergency control service, mapped to the dedicated contention-free pilot layer; P1 is for power distribution automation services, and P2 is for monitoring and data acquisition services. P1 and P2 are mapped to the frequency offset-resistant shared pilot layer. P3 represents non-urgent or video services and is mapped to the unlicensed overlay pilot layer.

[0011] According to a second aspect of the present invention, a method for hierarchical pilot pool resource allocation in satellite communication based on power service awareness is characterized in that it is executed by the satellite or gateway side and includes: Pre-construct a three-layer heterogeneous pilot pool; It receives and detects preamble signals from the three-layer heterogeneous pilot pool in parallel, and counts the operating status parameters of each physical layer pilot layer in real time. The operating status parameters are used as status inputs, and the resource ratio of each layer in the three-layer heterogeneous pilot pool is dynamically adjusted using a multi-agent reinforcement learning model, and the updated resource configuration parameters are output. The updated resource configuration parameters are broadcast to the power terminal to guide the terminal's subsequent access behavior.

[0012] According to some embodiments of the present invention, the step of pre-constructing a three-layer heterogeneous pilot cell specifically includes: The three-layer heterogeneous pilot pool includes: a dedicated competition-free pilot layer for the highest priority services, a frequency offset-resistant shared pilot layer for the middle priority services, and an unlicensed superimposed pilot layer for the lowest priority services. The dedicated contention-free pilot layer uses dedicated resource blocks under orthogonal time-frequency space-time OTFS modulation, and each terminal is allocated a unique time-delay-Doppler domain resource block; The frequency offset-resistant shared pilot layer uses frequency domain superimposed linear frequency modulation (FDS-LFM) preambles and superimposed multiple linear frequency modulation waveforms with different initial phases in the frequency domain. The unlicensed superimposed pilot layer uses superimposed preamble transmission. Each user randomly selects multiple orthogonal preamble sequences for superposition and transmission. The receiving end uses a compressed sensing detection algorithm to detect users. According to some embodiments of the present invention, in the step of taking the running state parameters as state input, using a multi-agent reinforcement learning model to dynamically adjust the resource proportion of each layer in the three-layer heterogeneous pilot pool, and outputting the updated resource configuration parameters, the initial baseline of the resource proportion is set as follows: 20% for dedicated non-contention pilot layer, 50% for anti-frequency offset shared pilot layer, and 30% for unlicensed superimposed pilot layer.

[0013] According to some embodiments of the present invention, in the step of using the operating state parameters as state input, dynamically adjusting the resource proportion of each layer in the three-layer heterogeneous pilot pool using a multi-agent reinforcement learning model, and outputting updated resource configuration parameters, the state vector of the multi-agent reinforcement learning model includes: normalized load of each layer, preamble collision probability, average access latency, and service arrival rate of each layer; its action vector is the adjustment amount of the resource proportion of each layer; its reward function includes: service quality SLA satisfaction reward, overload penalty, and action smoothing penalty.

[0014] According to some embodiments of the present invention, the calculation method for the service quality SLA satisfaction reward is as follows: a latency threshold and a collision rate threshold are set for each layer. When the average access latency and collision rate of a certain layer both meet the threshold, a positive reward is given, and the reward weight of the dedicated contention-free pilot layer is the highest. The overload penalty is calculated based on the degree to which the actual load of each layer exceeds the preset load threshold. The action smoothing penalty is used to suppress the violent oscillation of resource ratio.

[0015] According to some embodiments of the present invention, the step of using the operating state parameters as state input, dynamically adjusting the resource proportion of each layer in the three-layer heterogeneous pilot pool using a multi-agent reinforcement learning model, and outputting updated resource configuration parameters also introduces a disaster triggering mechanism: when the short-term collision rate increment exceeds a preset threshold, the resource proportion of the dedicated non-contention pilot layer is forcibly increased to its preset upper limit, and resources are borrowed from the anti-frequency offset shared pilot layer.

[0016] According to a third aspect of the present invention, a satellite communication hierarchical pilot pool system based on power service awareness is characterized in that it comprises: Terminal equipment is used to acquire service data of power terminals, extract service quality demand features from them, calculate a comprehensive score based on the service quality demand features, divide the service into multiple priority queues according to the comprehensive score, and send preamble signals on the corresponding physical layer pilot layer according to the priority queues. A satellite or gateway device is used to pre-build a three-layer heterogeneous pilot pool; receive and detect preamble signals from the three-layer heterogeneous pilot pool in parallel, and statistically analyze the operating status parameters of each physical layer pilot layer in real time; use the operating status parameters as status input, dynamically adjust the resource ratio of each layer in the three-layer heterogeneous pilot pool using a multi-agent reinforcement learning model, and output the updated resource configuration parameters; and broadcast the updated resource configuration parameters to the power terminal to guide the terminal's subsequent access behavior.

[0017] According to a third aspect of the present invention, a computer-readable storage medium stores computer-executable instructions for performing the above-described power service-aware satellite communication hierarchical pilot pool method.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic diagram illustrating the steps of the hierarchical pilot pool method for satellite communication based on power service awareness, according to an embodiment of the present invention. Figure 2 A flowchart illustrating the service classification and grading process of the satellite communication hierarchical pilot pool method based on power service awareness provided in this embodiment of the invention; Figure 3 A flowchart of dynamic resource allocation for power satellites using a power service-aware hierarchical pilot pool method for satellite communication, provided in an embodiment of the present invention; Figure 4 This is a structural block diagram of a satellite communication hierarchical pilot pool system based on power service awareness, provided for an embodiment of the present invention. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0021] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0022] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a hierarchical pilot pool method for satellite communication based on power service awareness, comprising the following steps: Step S1: Obtain service data from the power terminal, extract its service quality requirement characteristics, calculate a comprehensive score based on the service quality requirement characteristics, and divide the service into multiple priority queues according to the comprehensive score. For example... Figure 2 As shown, it specifically includes: Step S1.1 Power Business Input and 3D Feature Extraction Obtain service data from power terminals and extract Quality of Service (QoS) requirement features, including latency requirements. Reliability requirements and rate requirements Let the first... The QoS requirements of similar services are determined by the triplet. Characterization; The aforementioned requirements are normalized to latency sensitivity. δ lat Reliability requirements δ rel and rate requirements δ rate : Delay sensitivity δ lat : (1) in Take the base delay for power control operations. The smaller, The closer it is to 1, the higher the sensitivity.

[0023] Reliability requirements δ rel : (2) For example correspond , correspond .

[0024] Rate requirements δ rate : (3) For EC business ( ), It is a negative value; for EV video services ( ), Relatively large.

[0025] Step S1.2: Obtain a four-level priority queue by mapping business hierarchy. The overall score is calculated using a linear weighted summation method. Among them, latency sensitivity has the highest weight, and it is recommended to select [value]. , , .

[0026] Threshold division: (4) Based on fitting of measured data, a typical threshold is taken. , , .

[0027] Based on the comprehensive score, the services are divided into four priority queues from P0 to P3: if S≥4.0, it is P0 (emergency control service); if 2.5≤S<4.0, it is P1 (distribution automation service); if 1.5≤S<2.5, it is P2 (monitoring and data acquisition service); and if S<1.5, it is P3 (non-emergency / video service). As a special case, the power differential protection service is directly hard-coded as P0.

[0028] Furthermore, step S1 also includes: Step S1.3 Power Scenario Specialization Power differential protection services: (5) Calculated , , , In actual judgment, due to the extreme importance of differential protection, it is directly hardcoded as P0 in engineering, and the model weights can be adjusted to... Highlight latency.

[0029] Step S2: Pre-construct a three-layer heterogeneous pilot pool, with each layer corresponding to the priority queue. Specifically, this includes: Step S2.1 Resource Proportion Constraints Based on the priority queue, a physical layer pilot layer is allocated to each priority level, forming a three-layer heterogeneous pilot pool: Layer-1 (Dedicated Contention-Free Pilot Layer): Corresponds to P0 services. It employs dedicated resource blocks under Orthogonal Time-Frequency-Space (OTFS) modulation, with each terminal allocated a unique delay-Doppler domain resource block. The transmitted signal is converted into a time-domain signal through inverse symptotic Fourier transform, and the receiver recovers it through two-dimensional matched filtering, achieving contention-free and ACB-free access.

[0030] Layer-2 (Frequency Offset Resistant Shared Pilot Layer): Corresponds to P1 and P2 services. It employs a frequency-domain superimposed linear frequency modulation (FDS-LFM) preamble, superimposing M LFM waveforms with different initial phases in the frequency domain. (6) in For frequency modulation slope, This is the initial phase. After IFFT, the time-domain signal exhibits ideal autocorrelation characteristics.

[0031] Layer-3 (Unlicensed Pilot Layer): Corresponds to P3 service. It uses superimposed preamble transmission; each user randomly selects L orthogonal preamble sequences (usually L=3) for superposition and transmission. The received signal model is as follows: (7) in For active users, For transmission power, For channel coefficients, For users Selected pilot index.

[0032] When user activity is sparse ( This model can be transformed into a compressed sensing problem. The pilot pool capacity (maximum number of users it can support) is extended to that of traditional schemes. The approximate value can be achieved by multiples, and in practice, it can be obtained through OMP / AMP detection. (Empirical formula).

[0033] The initial baseline for the three-tier resource allocation is set as follows: Layer-1 accounts for 20%, Layer-2 accounts for 50%, and Layer-3 accounts for 30%, and the following conditions are met: (8) Step S3: Based on the priority queue division result, control the power terminal to send a preamble signal on the physical layer pilot layer corresponding to its priority queue.

[0034] According to the priority assigned in step S1, the control power terminals transmit preamble signals on their corresponding pilot layers: P0 terminals use dedicated OTFS resource blocks in Layer-1; P1 / P2 terminals use FDS-LFM preambles in Layer-2; and P3 terminals use superimposed preambles in Layer-3. Each layer transmits according to preset power, repetition count, and PRACH time slot parameters.

[0035] Step S4: On the satellite or gateway side, receive and detect the preamble signal from the three-layer heterogeneous pilot pool in parallel, and statistically analyze the load, collision probability and average access delay of each physical layer pilot layer in real time.

[0036] On the satellite or gateway side, multi-user superimposed signals are received and distributed to three parallel detectors according to time-frequency resources or code patterns: Layer-1 detector: Two-dimensional matched filtering recovers dedicated resource blocks; Layer-2 detector: Based on FDS-LFM correlation peak detection; Layer-3 detector: compressed sensing combined with user and data detection.

[0037] After the test is completed, the normalized load of each layer is calculated. Collision probability and average access latency The collision probability is estimated using a sliding window: (9) (10) The average access latency is calculated using a weighted average. (11) Step S5: Using the statistically obtained load, collision probability, and average access delay as state inputs, dynamically adjust the resource ratio of each layer in the three-layer heterogeneous pilot pool using a multi-agent reinforcement learning model, and output the updated resource configuration parameters.

[0038] Using the statistics obtained in step S4 as state input, the resource allocation of the three layers is dynamically adjusted using a multi-agent reinforcement learning model, such as... Figure 3 As shown. Define the state vector: (12) in, The arrival rate of the i-th layer of services is estimated using an exponentially weighted moving average: (13) Adjustment amount of output resource allocation for each agent i Action vector : (14) The resource percentage update rule is as follows: (15) and normalization ,make sure .

[0039] The boundaries of each layer are set according to the power service guarantee requirements as follows: - Layer-1 (P0): , - Layer-2 (P1 / P2): , - Layer-3 (P3): , The reward function consists of three parts: (16) Among them, the reward , The indicator function indicates the average delay of the i-th layer. Threshold and collision rate The value is 1 when the threshold is reached, and 0 otherwise.

[0040] Thresholds: Layer-1 (20ms, 1%); Layer-2 (1s, 5%); Layer-3 (5s, 10%).

[0041] Overload penalty : (17) Among them, the penalty coefficient threshold , , .

[0042] Smooth motion penalty : (18) To prevent drastic fluctuations in resource allocation.

[0043] The MADDPG framework, employing centralized training and distributed execution, is used. Each agent has an Actor network and a Critic network. Each agent i possesses: Actor Network Based on local state (Include (etc.) output .

[0044] Critic Network : Input global state The Q-value is output based on the actions of all agents.

[0045] Critic loss function (TD error minimization): Experience Replay Pool Discount factor (set to 0.95) Target Critic Network Target Actor Network Actor policy gradient: Target network soft update (per C steps): (Soft update coefficient).

[0046] Disaster Triggering Mechanism: Define short-term collision rate increment : (19) window size This corresponds to 5 PRACH cycles; This represents the average collision rate under normal circumstances, such as 2%.

[0047] when Emergency mode is triggered at any time. .

[0048] Emergency actions: (20) At the same time, temporarily increased To 0.3, and reduce Up to 0.5 (borrowing resources from Layer-2). The duration of the emergency is determined by ultra-short-term forecasts: when Exit after 3 cycles.

[0049] Step S6: The updated resource configuration parameters are broadcast to the power terminal to guide the terminal's subsequent access behavior.

[0050] The updated resource allocation (such as the number of preambles at each layer, ACB factor, etc.) in step S5 is sent to the power terminal via broadcast information (SIB). The terminal uses the new resource configuration the next time it accesses the network, thus forming a closed loop.

[0051] Example 2 In step S1 of Embodiment 1, as a preferred option, the weights can be adjusted to w1=0.8, w2=0.1, and w3=0.1 to further highlight the priority of latency-sensitive services (such as differential protection).

[0052] In step S2, the frequency modulation slope of the FDS-LFM preamble of Layer-2 μ m It can be assigned according to a pseudo-random sequence to reduce cross-correlation interference.

[0053] In step S5, multi-agent reinforcement learning can employ algorithms such as MADDPG, QMIX, or MAPPO. The network structure consists of three fully connected layers (with 256, 128, and 64 hidden nodes respectively), a discount factor γ = 0.95, and an experience replay pool size of 10. 5 .

[0054] Example 3 Another embodiment of the present invention provides a different satellite communication hierarchical pilot pool system based on power service awareness, such as... Figure 4 As shown, the system 40 includes: Service classification mapping module 401: Configured on the power terminal side, it is used to perform the functions of step 1 above, namely, to obtain service data, extract QoS features, calculate comprehensive scores and divide priority queues.

[0055] Layered pilot pool construction module 402: Configured on the satellite or gateway side, it is used to perform the function of step 2 above, that is, to construct a three-layer heterogeneous pilot pool including Layer-1 (dedicated and contention-free), Layer-2 (anti-frequency offset sharing), and Layer-3 (license-free superposition), and set the initial resource ratio.

[0056] Terminal preamble transmission module 403: configured on the power terminal side, used to perform the function of step 3 above, that is, to select the corresponding pilot layer according to priority and send the preamble signal.

[0057] Receiver detection and statistics module 404: Configured on the satellite or gateway side, it is used to perform the function of step 4 above, namely, to detect the three-layer signals in parallel and to count the load, collision probability and average access delay of each layer in real time.

[0058] Dynamic resource allocation module 405: Configured on the satellite or gateway side, it is used to perform the function of step 5 above, that is, to dynamically adjust the resource ratio using a multi-agent reinforcement learning model, and includes a disaster triggering unit to detect short-term collision rate increments and trigger emergency mode when the threshold is exceeded.

[0059] Configuration distribution module 406: Configured on the satellite or gateway side, it is used to perform the function of step 6 above, that is, to distribute the updated resource configuration parameters to the terminal through broadcast information.

[0060] The modules work together through data interfaces and broadcast channels to achieve a complete process of "service awareness - hierarchical mapping - dynamic allocation - closed-loop update".

[0061] The power service-aware satellite communication hierarchical pilot pool system of this embodiment can execute the power service-aware satellite communication hierarchical pilot pool method provided in this embodiment of the invention. The implementation principle is similar and will not be described again here.

[0062] Example 4 Another embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for performing the above-described... Figure 1 The illustrated method is a hierarchical pilot pool for satellite communication based on power service awareness.

[0063] This invention explicitly maps the differentiated QoS of power control, acquisition, and video services to a three-layer physical layer architecture through a service-aware hierarchical pilot pool: a dedicated contention-free layer compresses the access latency of emergency control services to less than 20ms and the collision rate to less than 1%; an anti-frequency offset shared layer uses FDS-LFM pilots to improve the detection probability under a large frequency offset of ±300kHz, expanding the equivalent pilot capacity to 256; and an unlicensed overlay layer utilizes compressed sensing, increasing overload throughput by more than 50% compared to the traditional ACB mechanism. Combined with multi-agent reinforcement learning for dynamic resource allocation and disaster triggering mechanisms, the system balances low latency, high reliability, large capacity, and adaptive capabilities, filling the gap in the existing NTN standard for hybrid service assurance.

[0064] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can 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. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, 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, as is known to those skilled in the art, communication media typically contain computer-readable 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.

[0066] The above is a detailed description of the preferred embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for transmitting satellite communication preamble signals based on power service awareness, characterized in that, Executed by the terminal device, including: Obtain service data from power terminals, extract service quality demand features from them, calculate a comprehensive score based on the service quality demand features, and divide the services into multiple priority queues according to the comprehensive score. Based on the priority queue, the corresponding physical layer pilot layer is determined, and a preamble signal is transmitted on the physical layer pilot layer.

2. The method according to claim 1, characterized in that, The step of obtaining service data from power terminals, extracting service quality demand characteristics from them, calculating a comprehensive score based on the service quality demand characteristics, and dividing the services into multiple priority queues according to the comprehensive score specifically includes the following steps for calculating the comprehensive score: The service quality requirements include latency requirements, reliability requirements, and speed requirements; Latency requirements, reliability requirements, and speed requirements are each normalized to a latency sensitivity δ. lat Reliability requirements δ rel and rate requirement δ rate The comprehensive score is calculated using a linear weighted summation, with latency sensitivity having the highest weight.

3. The method according to claim 2, characterized in that, The priority queue includes four levels: P0, P1, P2, and P3; wherein: P0 is an emergency control service, mapped to the dedicated contention-free pilot layer; P1 is for power distribution automation services, and P2 is for monitoring and data acquisition services. P1 and P2 are mapped to the frequency offset-resistant shared pilot layer. P3 represents non-urgent or video services and is mapped to the unlicensed overlay pilot layer.

4. A method for hierarchical pilot pool resource allocation in satellite communication based on power service awareness, characterized in that, Performed by the satellite or gateway side, including: Pre-construct a three-layer heterogeneous pilot pool; It receives and detects preamble signals from the three-layer heterogeneous pilot pool in parallel, and counts the operating status parameters of each physical layer pilot layer in real time. The operating status parameters are used as status inputs, and the resource ratio of each layer in the three-layer heterogeneous pilot pool is dynamically adjusted using a multi-agent reinforcement learning model, and the updated resource configuration parameters are output. The updated resource configuration parameters are broadcast to the power terminal to guide the terminal's subsequent access behavior.

5. The method according to claim 4, characterized in that, The step of pre-constructing a three-layer heterogeneous pilot pool specifically includes: The three-layer heterogeneous pilot pool includes: a dedicated competition-free pilot layer for the highest priority services, a frequency offset-resistant shared pilot layer for the middle priority services, and an unlicensed superimposed pilot layer for the lowest priority services. The dedicated contention-free pilot layer uses dedicated resource blocks under orthogonal time-frequency space-time OTFS modulation, and each terminal is allocated a unique time-delay-Doppler domain resource block; The frequency offset-resistant shared pilot layer uses frequency domain superimposed linear frequency modulation (FDS-LFM) preambles and superimposed multiple linear frequency modulation waveforms with different initial phases in the frequency domain. The unlicensed superimposed pilot layer uses superimposed preamble transmission. Each user randomly selects multiple orthogonal preamble sequences for superposition and transmission. The receiving end uses a compressed sensing detection algorithm to detect users.

6. The method according to claim 5, characterized in that, In the step of using the operating status parameters as state input, dynamically adjusting the resource proportion of each layer in the three-layer heterogeneous pilot pool using a multi-agent reinforcement learning model, and outputting the updated resource configuration parameters, the initial baseline of the resource proportion is set as follows: 20% for dedicated non-contention pilot layer, 50% for anti-frequency offset shared pilot layer, and 30% for unlicensed superimposed pilot layer.

7. The method according to claim 5, characterized in that, In the step of taking the operating state parameters as state input, dynamically adjusting the resource proportion of each layer in the three-layer heterogeneous pilot pool using a multi-agent reinforcement learning model, and outputting the updated resource configuration parameters, the state vector of the multi-agent reinforcement learning model includes: normalized load of each layer, preamble collision probability, average access latency, and service arrival rate of each layer; its action vector is the adjustment amount of the resource proportion of each layer; its reward function includes: service quality SLA satisfaction reward, overload penalty, and action smoothing penalty.

8. The method according to claim 7, characterized in that, The calculation method for the service quality SLA satisfaction reward is as follows: a latency threshold and a collision rate threshold are set for each layer. When the average access latency and collision rate of a certain layer both meet the threshold, a positive reward is given, and the reward weight of the dedicated contention-free pilot layer is the highest. The overload penalty is calculated based on the degree to which the actual load of each layer exceeds the preset load threshold. The action smoothing penalty is used to suppress the violent fluctuation of resource ratio.

9. The method according to claim 1, characterized in that, The step of using the operating state parameters as state input, dynamically adjusting the resource ratio of each layer in the three-layer heterogeneous pilot pool using a multi-agent reinforcement learning model, and outputting the updated resource configuration parameters also introduces a disaster triggering mechanism: when the short-term collision rate increment exceeds a preset threshold, the resource ratio of the dedicated non-contention pilot layer is forcibly increased to its preset upper limit, and resources are borrowed from the anti-frequency offset shared pilot layer.

10. A satellite communication hierarchical pilot pool system based on power service awareness, characterized in that, include: Terminal equipment is used to acquire service data of power terminals, extract service quality demand features from them, calculate a comprehensive score based on the service quality demand features, divide the service into multiple priority queues according to the comprehensive score, and send preamble signals on the corresponding physical layer pilot layer according to the priority queues. Satellite or gateway equipment for pre-constructing a three-layer heterogeneous pilot pool; It receives and detects preamble signals from the three-layer heterogeneous pilot pool in parallel, and counts the operating status parameters of each physical layer pilot layer in real time. The operating status parameters are used as status inputs, and a multi-agent reinforcement learning model is used to dynamically adjust the resource ratio of each layer in the three-layer heterogeneous pilot pool, and the updated resource configuration parameters are output. The updated resource configuration parameters are then broadcast to the power terminal to guide the terminal's subsequent access behavior.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

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