Method and system for adaptive modulation and coding transmission over a mobile channel at sea

Abstract

The application discloses a marine mobile channel adaptive coding modulation transmission method and system, relates to the technical field of wireless communication, and comprises the following steps: acquiring dynamic sensing data, spatial position and channel state information history sequence of a mobile platform; reconstructing the absolute motion velocity vector of an antenna in a reference navigation coordinate system based on the antenna installation position bias feature; extracting an effective radial velocity scalar in combination with a line-of-sight direction vector; determining the dynamic channel coherence time boundary according to the effective radial velocity scalar, performing truncation processing on the channel sequence to obtain a target prediction input sequence; generating a reference signal-to-noise ratio prediction value by using a time series prediction model; determining a nonlinear rollback compensation amount based on the angular acceleration obtained by differentiating the angular velocity, and then correcting the reference prediction value to obtain a target signal-to-noise ratio and performing physical layer resource mapping; and the application can effectively cope with the prediction failure caused by the dramatic mutation of the carrier attitude, and improve the robustness and throughput of the marine communication link.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to an adaptive coding modulation transmission method and system for maritime mobile channels. Background Technology

[0002] In maritime wireless communication scenarios, in order to cope with the ever-changing electromagnetic wave propagation environment, communication systems generally adopt adaptive modulation and coding technology. This technology aims to dynamically select the most suitable modulation order and coding rate according to the current channel quality, thereby maximizing the effective throughput of data transmission while ensuring that the bit error rate meets the requirements.

[0003] Existing conventional communication terminals typically collect historical channel feedback data and feed it into time-series prediction algorithms to predict the signal-to-noise ratio trend at the next moment. However, in real complex sea conditions, ships not only experience forward displacement during navigation but also suffer from severe rolling and pitching due to the continuous impact of swells. This rapid change in spatial attitude is amplified by the leverage effect of the mast, causing communication antennas at high positions to experience extreme spatial tangential motion. This abnormally violent oscillation causes the actual electromagnetic wave coherence time to be extremely compressed instantaneously, rendering the originally preset smooth prediction window based on translational speed invalid.

[0004] At this point, the historical channel feedback data accumulated in the early stage has actually become completely disconnected from the current physical environment and lost its relevance. If the system continues to blindly extract these lagging and distorted data for fitting, it will inevitably output blindly optimistic predictions that are seriously deviated from reality, causing the system to incorrectly allocate extremely high-order modulation methods, and ultimately causing large-scale channel congestion and regular decoding failures in normal navigation.

[0005] Therefore, how to avoid systemic prediction failures caused by drastic changes in the spatial attitude of mobile carriers has become a core technical bottleneck that urgently needs to be solved in this field. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an adaptive coding modulation transmission method and system for maritime mobile channels.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] In a first aspect, the present invention discloses an adaptive coding modulation transmission method for maritime mobile channels, comprising the following steps:

[0009] Acquire dynamic sensing data and first spatial position data of the mobile platform, second spatial position data of the target communication peer, and historical sequence of channel state information between the antenna and the target communication peer; the dynamic sensing data includes at least transient rotational angular velocity sequence, translational velocity, and transient attitude characteristics;

[0010] Based on dynamic sensing data and preset antenna installation position offset characteristics, the absolute motion velocity vector of the antenna in the reference navigation coordinate system is reconstructed.

[0011] Based on the first spatial location data and the second spatial location data, the line-of-sight direction vector of the antenna pointing to the target communication peer is determined;

[0012] Perform spatial projection processing on the absolute motion velocity vector towards the line-of-sight direction vector to extract the effective radial velocity scalar;

[0013] The dynamic channel coherence time boundary is determined based on the effective radial velocity scalar, and the historical sequence of channel state information is truncated based on the dynamic channel coherence time boundary to obtain the target prediction input sequence.

[0014] The target prediction input sequence is input into a preset time series prediction model for processing to generate a baseline signal-to-noise ratio prediction value;

[0015] Based on the transient angular acceleration scalar obtained by differential processing of the transient rotational angular velocity sequence, the nonlinear backoff compensation amount for the reference signal-to-noise ratio prediction value is determined;

[0016] The reference signal-to-noise ratio prediction value is corrected by nonlinear backoff compensation to obtain the target signal-to-noise ratio, and the modulation and coding strategy mapping of physical layer resources is performed based on the target signal-to-noise ratio.

[0017] In a second aspect, the present invention discloses a maritime mobile channel adaptive coding modulation transmission system, comprising:

[0018] The data acquisition module is used to acquire the dynamic sensing data and first spatial position data of the mobile platform, the second spatial position data of the target communication peer, and the historical sequence of channel state information between the antenna and the target communication peer; the dynamic sensing data includes at least the transient rotational angular velocity sequence, translational velocity, and transient attitude characteristics;

[0019] The motion velocity reconstruction module is used to reconstruct the absolute motion velocity vector of the antenna in the reference navigation coordinate system based on dynamic sensing data and preset antenna installation position offset characteristics.

[0020] The line-of-sight direction determination module is used to determine the line-of-sight direction vector of the antenna pointing to the target communication peer based on the first spatial position data and the second spatial position data;

[0021] The radial velocity extraction module is used to perform spatial projection processing on the absolute motion velocity vector towards the line-of-sight direction vector to extract the effective radial velocity scalar;

[0022] The channel truncation module is used to determine the dynamic channel coherence time boundary based on the effective radial velocity scalar, and to perform truncation processing on the historical sequence of channel state information based on the dynamic channel coherence time boundary to obtain the target prediction input sequence;

[0023] The signal-to-noise ratio prediction module is used to input the target prediction input sequence into a preset time-series prediction model for processing and to generate a baseline signal-to-noise ratio prediction value.

[0024] The compensation amount determination module is used to perform differential processing on the transient rotational angular velocity sequence to obtain a transient angular acceleration scalar, and to determine the nonlinear backoff compensation amount for the reference signal-to-noise ratio prediction value based on the transient angular acceleration scalar.

[0025] The correction and mapping module is used to correct the reference signal-to-noise ratio prediction value using nonlinear backoff compensation to obtain the target signal-to-noise ratio, and to perform modulation and coding strategy mapping of physical layer resources based on the target signal-to-noise ratio.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] 1. By acquiring dynamic sensing data and reconstructing the absolute motion velocity vector in combination with antenna offset characteristics, and extracting the effective radial velocity scalar using spatial projection, dynamic coherent time boundary determination and physical truncation of historical sequences based on the real physical evolution law are realized. This mechanism breaks the rigid dependence of traditional prediction models on the stable evolution of historical channels, solves the prediction distortion and decoding collapse problems caused by carrier swing, and ensures the physical consistency and accuracy of resource mapping under complex sea conditions.

[0028] 2. When the antenna movement direction is detected to be in the Doppler blind zone orthogonal to the electromagnetic wave line-of-sight propagation direction, the system actively bypasses and corrects the process by generating exemption instructions. This effectively avoids the excessive resource degradation caused by the undetectable spatial configuration in traditional solutions, thus recovering the high-order modulation bandwidth and throughput experience without loss in the safety blind spot of severe sea conditions.

[0029] 3. An asymmetric dual-track mapping strategy based on radial velocity variation trend characteristics was adopted to realize differentiated resource decisions for the channel evolution process. By calling an aggressive mapping matrix with high defense strength during the Doppler deterioration period and a conservative matrix with fast ramp-up characteristics during the environmental improvement period, the system can squeeze every bit of transmission potential in the physical environment evolution window in real time, significantly optimizing the overall spectral efficiency of the maritime communication carrier. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is the overall framework of the method in Embodiment 1 of the present invention;

[0032] Figure 2 This is an overall execution flowchart of the method in Embodiment 1 of the present invention;

[0033] Figure 3 This is the overall framework of the system in Embodiment 2 of the present invention. Detailed Implementation

[0034] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] In the field of maritime wireless communication, adaptive coding and modulation technology aims to dynamically select the appropriate modulation order and coding rate based on the current channel quality, thereby maximizing the effective throughput of data transmission. The efficient operation of this mechanism relies on accurate prediction of the signal-to-noise ratio (SNR) evolution trend, achieved by collecting historical channel feedback data and feeding it into a timing prediction algorithm for extrapolation.

[0036] However, existing technologies lack a physical boundary verification mechanism that couples macroscopic ship dynamics with microscopic electromagnetic wave propagation, making it impossible to accurately define the coherence time compression problem caused by sudden changes in antenna spatial attitude under complex sea conditions. In particular, the violent rolling and pitching caused by the impact of swells on the ship are amplified through the mast lever effect, causing the antenna at a high position to generate a large spatial tangential motion. As a result, the actual electromagnetic wave coherence time is compressed drastically in an instant, and the originally preset stable prediction window based on translational speed immediately becomes invalid, causing the historical feedback data accumulated in the early stage to be completely decoupled from the current physical environment and lose its correlation.

[0037] For example, when encountering severe sea conditions during ocean voyages, conventional communication terminals rely solely on historical channel data sampling windows that maintain a fixed ship translational speed. This fails to detect the surge in Doppler frequency shift and the sharp shortening of the effective coherence boundary caused by violent antenna swaying. Specifically, the system blindly extracts these lagging and distorted discarded data for fitting, resulting in blindly optimistic predictions that deviate significantly from reality. Consequently, the system incorrectly allocates extremely high-order modulation schemes and is unable to make accurate resource mappings based on transient physical distortions.

[0038] If the above problems are not addressed, the adaptive modulation system will continue to lose its ability to objectively predict the actual channel state under adverse sea conditions. Specifically, the failure to physically reduce the dimensions of sudden antenna attitude changes will cause the system to over-rely on outdated historical time sequences, resulting in the prediction benchmark deviating from the true electromagnetic wave coherence boundary. At the same time, the failure to correct prediction distortion will cause continuous misallocation of physical layer resources, ultimately leading to widespread channel congestion and large-scale decoding failures during routine navigation. Thus, blind prediction feedback will systematically hinder the achievement of the goal of high-throughput reliable transmission of maritime mobile channels in complex time-varying environments.

[0039] Example 1:

[0040] like Figures 1-2 As shown, the adaptive coding and modulation transmission method for maritime mobile channels includes the following steps:

[0041] Step S1: Acquire the dynamic sensing data and first spatial position data of the mobile platform, the second spatial position data of the target communication peer, and the historical sequence of channel state information between the antenna and the target communication peer; the dynamic sensing data includes at least the transient rotational angular velocity sequence, translational velocity, and transient attitude characteristics;

[0042] For ease of understanding, this embodiment uses the example of a cargo ship sailing in complex sea states (such as sea state 5, accompanied by 2 to 3 meters of swell) interacting wirelessly with a shore-based broadband base station to provide a detailed explanation of the multi-source spatiotemporal data acquisition stage required for communication. In this scenario, a communication antenna is installed at the top of the cargo ship's main mast. The system needs to overcome channel distortion caused by severe ship turbulence to maintain a highly reliable microwave communication link. In the initial stage of executing adaptive coding and modulation transmission, the system first needs to construct a cross-domain fusion global perception view, namely, acquiring the dynamic sensing data and first spatial position data of the mobile platform, the second spatial position data of the target communication peer, and the historical sequence of channel state information between the antenna and the target communication peer.

[0043] Specifically, the underlying hardware architecture of cargo ships deploys a multi-source heterogeneous sensor network, mainly including a high-precision inertial navigation system (INS), a global positioning system (GPS) receiver, and a communication baseband processing chip. Dynamics sensor data originates directly from the inertial navigation system, which typically broadcasts the ship's physical motion status periodically at high frequency via standard NMEA 2000 or RS-422 high-speed serial port protocols. In practical applications, the dynamics sensor data parsed and extracted by the system includes at least transient rotational angular velocity sequences, translational velocities, and transient attitude characteristics. Specifically, the transient rotational angular velocity sequence is represented by the three-axis angular velocity vector of the ship in the carrier coordinate system (i.e., a relative three-dimensional reference system established with the ship's center of gravity as the origin), which precisely covers the roll angular velocity, pitch angular velocity, and yaw angular velocity; the translational velocity is represented by the three-dimensional linear velocity vector of the ship's center of gravity in the local reference navigation coordinate system (usually the NED coordinate system); and the transient attitude characteristics are represented by the Euler angle set or attitude direction cosine matrix calculated from the real-time heading angle, pitch angle, and roll angle.

[0044] Meanwhile, the first spatial location data is output by the GPS receiver, specifically representing the ship's current three-dimensional coordinates of absolute longitude, latitude, and altitude. The second spatial location data comes from a pre-configured three-dimensional geographic coordinate dictionary of shore-based base stations in the system memory, or, in the case of satellite communication, from satellite ephemeris data parsed in real time from the downlink broadcast channel. In addition, the channel state information historical sequence is directly extracted from the Media Access Control (MAC) layer register of the underlying baseband of the communication, and its actual physical representation is the quantitative channel quality indicators such as signal-to-noise ratio (SNR), rank indicator (RI), and bit error rate over multiple consecutive scheduling cycles continuously fed back and buffered by the receiver.

[0045] It is worth noting that the aforementioned multi-source heterogeneous data inevitably suffers from multi-clock domain asynchrony and sampling rate barriers in real-world engineering acquisition. For example, the data update rate of an inertial navigation system is typically 100 Hz (i.e., one frame of kinematic data is output every 10 milliseconds), the data update rate of a global positioning system is typically 10 Hz, while the physical layer scheduling cycle of the communication baseband, i.e., the transmission time interval (TTI), is often as high as 1000 Hz (i.e., one frame per millisecond). To eliminate the micro-spatiotemporal misalignment caused by this multi-sensor sampling rate mismatch, the system must configure a rigorous multi-clock domain synchronization and fusion logic after acquiring the aforementioned raw data sequences. In specific implementation, the system enforces the communication baseband's transmission time interval (e.g., 1 millisecond) as the globally unique time reference grid. After capturing the raw sensor data sequences, raw positioning data sequences, and raw channel state sequences from various hardware interfaces, the system uses a zero-order hold (ZOH) mechanism to perform resampling and timestamp alignment processing on all low-frequency updated physical quantities.

[0046] Taking the alignment process of transient rotational angular velocity sequences as an example, assuming the current baseband algorithm needs to perform resource scheduling in the 100th millisecond time slot, while the last reported valid roll angular velocity data from the inertial navigation system was in the 90th millisecond, the zero-order hold will force the angular velocity state between the 90th and 99th milliseconds to remain unchanged in the system cache until the 100th millisecond when a new frame of physical sampling points derived by the inertial navigation system is received. Based on this strict time grid alignment mechanism, the system not only successfully and seamlessly interweaves isolated data streams spanning rigid body mechanics, geospatial domains, and electromagnetic physics layers onto a unified microsecond-level time axis, but also effectively avoids phase jitter and high-frequency computational noise caused by asynchronous sampling. This high-precision, fully closed-loop data acquisition and synchronization operation completely eliminates the Doppler frequency shift estimation deviation caused by the misalignment of timestamps from different sensors, thus laying a solid foundation of underlying data support for subsequent processing.

[0047] Step S2: Based on the dynamic sensing data and the preset antenna installation position offset characteristics, reconstruct the absolute motion velocity vector of the antenna in the reference navigation coordinate system;

[0048] After successfully acquiring multi-source spatiotemporal data, the core physical challenge facing the system lies in the fact that shipboard inertial navigation systems are typically installed at the ship's center of gravity or deep within a rigid deck, and their output translational velocity only represents the overall macroscopic displacement of the ship. However, the physical focus of electromagnetic wave transmission and reception in maritime microwave communication is at the top of the towering mast. When the ship experiences severe rolling in wind and waves, the lever amplification effect of the mast causes the antenna to generate a significant additional tangential motion in space. Ignoring this local displacement will directly lead to a serious deviation in the estimation of the underlying Doppler frequency shift. Therefore, the next crucial operation performed by the system is to reconstruct the absolute velocity vector of the antenna in the reference navigation coordinate system based on the dynamic sensing data acquired in the previous steps and the preset antenna installation position offset characteristics.

[0049] To achieve this cross-scale spatial physical quantity reconstruction, the system first needs to invoke a preset antenna installation position offset feature. In actual engineering deployments, this offset feature is not a dynamically calculated model, but a three-dimensional static spatial vector that has been fixed and stored in the system's non-volatile memory during the ship construction or communication equipment calibration phase. The physical meaning of this vector represents the three-dimensional geometric displacement in space from the origin of the ship's carrier coordinate system (usually the ship's center of gravity) to the equivalent phase center of the communication antenna. For example, assuming that the center of gravity of a cargo ship is located near the waterline, and the antenna is installed at the top of the mainmast, 30 meters vertically upward from the center of gravity, 2 meters horizontally offset towards the bow, and with no lateral offset, then in the carrier coordinate system with starboard-forward-upward as the positive direction, this antenna installation position offset feature can be expressed as follows: (Unit: meters). This static prior data is a crucial bridge connecting macroscopic ship motion with microscopic antenna motion.

[0050] Specifically, the reconstruction process in this embodiment follows strict rigid body kinematics theorems. The system first extracts the transient rotational angular velocity sequence from the dynamic sensing data, and then performs a vector cross product operation based on this transient rotational angular velocity sequence and the retrieved antenna installation position offset characteristics. In physics and mechanics, the cross product of the angular velocity vector and the position vector directly determines the instantaneous linear velocity at the rotation radius. Through this high-frequency cross product operation, the system can accurately calculate the tangential linear velocity vector of the antenna in the ship's own coordinate system caused by the ship's hull pitching. Continuing with the example of the 30-meter-high mast, when the cargo ship encounters transverse waves and generates a transient roll angular velocity of 0.1 radians / second, even if the ship itself is in a stationary state (translational velocity is zero), the system can still accurately extract the huge transverse tangential linear velocity of up to 3 meters / second at the antenna end through the cross product operation.

[0051] It is worth noting that the tangential linear velocity vector calculated at this point is still a relative physical quantity attached to the coordinate system of the constantly swaying ship, while the spatial propagation of electromagnetic waves and the coordinate reference of the target communication end are both established in an absolute Earth reference system. To align with the spatial reference, the system further constructs a transient attitude rotation matrix in real time, transforming the ship's coordinate system to an external reference navigation coordinate system (usually a local NE-G coordinate system), based on transient attitude characteristics (such as heading angle, roll angle, and pitch angle) contained in the dynamic sensing data. This matrix is ​​essentially an orthogonal direction cosine matrix, and its real-time update frequency is strictly synchronized with the baseband transmission time interval. Subsequently, the system uses the constructed transient attitude rotation matrix to perform coordinate system mapping processing on the previously calculated tangential linear velocity vector. This matrix multiplication operation acts like a digital gimbal, offsetting the coordinate axis deviation caused by the ship's tilt, thus obtaining the projected linear velocity vector, which is the true projection of the antenna's velocity due to swaying in absolute geographic space.

[0052] Finally, to obtain a complete picture of the antenna's motion, the system performs vector superposition processing on the calculated projected linear velocity vector and the existing translational velocity from the dynamic sensing data. Since both have now been unified to the same reference navigation coordinate system, this superposition process is a simple element-by-element addition of three-dimensional vectors, and the result is the final required absolute velocity vector. To more rigorously describe this underlying mechanism that integrates translational and rotational effects, the above series of calculations can be uniformly described by the following core three-dimensional kinematic equations: ;

[0053] In this mathematical relationship, the result calculated on the left side of the equation is... This represents the absolute velocity vector of the antenna reconstructed at the current moment; the right side of the equation... The direct translational velocity of the ship's center of gravity; Characterized by the transient attitude rotation matrix used for spatial dimensionality reduction transformation; (The part in parentheses is missing) and These correspond to the transient rotational angular velocity sequence and the preset antenna installation position offset characteristics, respectively. Through this series of closely interlocking spatial algebraic operations, the system successfully isolated the macroscopic interference of the ship hull in the algorithm's virtual space, accurately locked the absolute motion trajectory of the antenna phase center in the three-dimensional physical world, and laid the foundation for the subsequent accurate extraction of the radial effective components affecting the channel coherence characteristics.

[0054] Step S3: Based on the first spatial position data and the second spatial position data, determine the line-of-sight direction vector of the antenna pointing to the target communication peer;

[0055] After reconstructing the antenna's absolute velocity vector, the system has grasped the antenna's true trajectory in three-dimensional physical space. However, according to the Doppler effect of electromagnetic waves, only the velocity component parallel to the radio wave propagation direction truly leads to the broadening of the communication frequency and the compression of the coherence time. Since the velocity vector reconstructed in the previous step is established in a local navigation coordinate system (such as the NED coordinate system) based on the ship's current position, the system must unify the electromagnetic wave propagation direction to the same coordinate system in order to perform accurate physical quantity projection mapping in subsequent steps. Therefore, the next key operation performed by the system is to determine the line-of-sight direction vector of the antenna pointing towards the target communication endpoint based on the first and second spatial position data.

[0056] To achieve this precise spatial geometric satellite location and pointing calculation, the system incorporates a high-precision geodetic coordinate transformation rule library. In practical engineering applications, the first spatial position data (ship position) and the second spatial position data (communication counterpart position) directly obtained from the Global Positioning System (GPS) are typically expressed in spherical geodetic coordinates using longitude, latitude, and altitude (WGS-84 coordinate system). This non-linear spherical coordinate system cannot be directly used to calculate straight line Euclidean vectors in three-dimensional space. Therefore, the system first transforms the first and second spatial position data to the Geocentric-Earth-Fixed (ECEF) coordinate system. The ECEF is a three-dimensional Cartesian coordinate system with the Earth's center of mass as its origin, its X-axis pointing to the intersection of the equator and the prime meridian, and its Z-axis pointing to the geographic North Pole. By introducing the Earth's reference ellipsoidal parameters (such as the semi-major axis and the first eccentricity), the system rigorously maps the latitude, longitude, and elevation data at both ends to Cartesian three-dimensional coordinates in the ECEF coordinate system. Subsequently, by subtracting the coordinate values ​​at the antenna end from the coordinate values ​​at the communication counterpart, the absolute spatial distance vector in the ECEF coordinate system is calculated. Using the cargo ship example mentioned above, suppose the cargo ship sails to the sea area at 30 degrees north latitude and 120 degrees east longitude, and the target base station is located at a known fixed point on the coast. Through the above conversion, the system can draw a three-dimensional baseline vector in the absolute space of the earth that stretches from the ship's hull directly to the base station for tens of kilometers.

[0057] However, the geocentric coordinate system is referenced to the Earth's center, which differs significantly in spatial rotation angle from the local navigation coordinate system characterizing the antenna's transient velocity (typically with the ship's center of mass as its origin and its tangent plane tangent to the Earth's surface). To eliminate this reference frame barrier, the system further constructs a local tangent plane coordinate transformation matrix based on the first spatial position data (i.e., the ship's current latitude and longitude). This matrix is ​​essentially an orthogonal direction cosine matrix containing two sets of rotation operations: it mathematically "translates and rotates" the absolute reference frame of the Earth's center of mass to the geographic tangent plane of the ship's current position using the sine and cosine values ​​of the ship's current longitude and latitude. This specific matrix structure is adopted because it can losslessly transform global macroscopic vectors into local microscopic vectors from the observer's perspective.

[0058] Next, the system uses the constructed local tangent plane coordinate transformation matrix to perform coordinate system mapping multiplication on the absolute spatial distance vector obtained in the previous step, thereby seamlessly transforming it to the local navigation coordinate system to obtain the local distance vector. At this point, this local distance vector and the antenna absolute motion velocity vector reconstructed in the previous step are in the same Northeast-East (NED) mathematical space. In order to completely eliminate the numerical interference of the physical distance between the two ends on the directional attributes and purely extract the angular features of electromagnetic wave propagation, the system finally performs modulus normalization processing on the obtained local distance vector. In specific implementation, the system calculates the three-dimensional Euclidean norm of the local distance vector (i.e., the straight-line physical distance between the two points) and divides the three orthogonal components of the local distance vector by the norm. Through a series of translations, rotations, and normalization scaling of the above spatial coordinates, the system finally outputs a three-dimensional unit vector with a strict length of 1, which is the line-of-sight direction vector required in this embodiment. This vector acts like an invisible laser guide line drawn for the antenna in a virtual algorithmic space, marking the core physical axis of the radio wave directly hitting the base station in the current transient state, thus paving the way for the next step of stripping the effective radial velocity through vector inner product.

[0059] Step S4: Perform spatial projection processing on the absolute motion velocity vector towards the line-of-sight direction vector to extract the effective radial velocity scalar;

[0060] In the preliminary steps, the system has already constructed the antenna's motion vector in absolute three-dimensional space and the line-of-sight geometric direction of electromagnetic wave propagation. Based on the first principles of the Doppler effect, only the relative velocity component parallel to the radio wave propagation direction can truly cause the broadening of the communication frequency and lead to a sharp compression of the channel coherence time. Therefore, at this stage, the system performs spatial projection processing on the absolute motion velocity vector towards the line-of-sight direction vector to extract the effective radial velocity scalar.

[0061] Specifically, to reduce the macroscopic three-dimensional spatial kinematic characteristics to a direct influencing factor of microscopic electromagnetic wave frequency offset, the system performs a spatial vector inner product operation on the absolute motion velocity vector and the line-of-sight direction vector, and determines the absolute value of the inner product result as the effective radial velocity scalar. This physical process can be achieved through the core mathematical formula: Describe it, in which Characterizing the effective radial velocity scalar, The vector representing absolute motion velocity. This represents the line-of-sight unit vector that has been normalized to its modulus. Since the line-of-sight vector has a length of 1, the geometric essence of this inner product operation is to extract the absolute projection length of the antenna's actual velocity onto the communication radio frequency ray. For example, assuming the reconstructed absolute velocity vector of the antenna is 10 m / s, if the spatial angle between its direction of motion and the line-of-sight vector is 60 degrees, then according to the law of cosines, the effective radial velocity scalar calculated is 5 m / s. If this angle is close to 90 degrees, that is, the antenna's direction of motion is almost completely perpendicular to the electromagnetic wave propagation direction, then the inner product projection result will approach zero.

[0062] Step S5: Determine the dynamic channel coherence time boundary based on the effective radial velocity scalar, and perform truncation processing on the historical sequence of channel state information based on the dynamic channel coherence time boundary to obtain the target prediction input sequence;

[0063] In traditional communication systems, time-series prediction models typically employ a fixed-length sliding window to capture historical sequences of channel state information. However, based on fundamental electromagnetic principles, when a mobile platform (such as a ship) experiences severe radial and tangential motion due to wind and waves, the channel fading rate increases dramatically. The physical timescale characterizing channel features—the coherence time—is compressed instantaneously. At this point, outdated historical data exceeding the coherence time boundary not only loses its guiding significance for future channel states but also becomes a source of strong noise, polluting the prediction model's input space and causing severe distortion in the prediction results. Therefore, the next crucial step in the system's operation is to determine the dynamic channel coherence time boundary based on the effective radial velocity scalar and then truncate the historical sequence of channel state information based on this dynamic coherence time boundary to obtain a high-purity target prediction input sequence.

[0064] The system first needs to call and establish a series of preset physical and system constants from the underlying configuration library. Specifically, the system obtains preset communication carrier wavelength, anti-zero constant, transmission time interval, and maximum historical prediction window length. Among them, the communication carrier wavelength is uniquely determined by the center frequency used by the current radio frequency front end, representing the basic physical scale of space electromagnetic waves; the anti-zero constant is an extremely small positive floating-point number (e.g., set to 10 to the power of -6), which is introduced into the underlying system architecture because when the ship is completely stationary or in an extremely orthogonal state where the antenna movement direction is absolutely perpendicular to the line-of-sight direction, the effective radial velocity will approach zero. This constant can prevent program crashes caused by division-by-zero overflow from a software engineering perspective; the transmission time interval represents the minimum scheduling time grid of the baseband media access control layer (e.g., the common 1 millisecond); and the maximum historical prediction window length is a safety upper limit set based on the maximum computing capacity and computing power overhead of the neurons or state machines inside the subsequent time series prediction model (e.g., preset to 50 time slots), used to limit endless data backtracking when the sea state is extremely stable.

[0065] After configuring the above parameters, the system immediately enters the physical boundary calculation process. First, the system calculates the transient physical coherence time based on the preset communication carrier wavelength and the current transient effective radial velocity scalar. This calculation process follows the Clarke-Doppler spectrum evolution law in classical wireless communication, that is, coherence time is inversely proportional to Doppler frequency shift. Subsequently, in order to convert the continuous physical time into a discrete mathematical step size that can be directly processed by the digital baseband, the system performs a floor operation on the quotient of the calculated transient physical coherence time and the preset transmission time interval, thereby obtaining the theoretically usable number of coherent time slots. Furthermore, in order to prevent the number of coherent time slots from expanding infinitely under extreme ideal conditions such as absolute stillness and thus breaking through the system's computing power bottleneck, the system rigorously compares the preset maximum historical prediction window length with the above number of coherent time slots, and finally determines the minimum value of the two as the dynamic channel coherence time boundary.

[0066] Specifically, the system determines the boundary according to the following mathematical expression:

[0067] ;

[0068] In this expression, This represents the number of time slots corresponding to the coherent time boundary of the dynamic channel; this value is a dynamically updated positive integer. This indicates the preset maximum historical prediction window length; Indicates the communication carrier wavelength; This represents the effective radial velocity scalar extracted in the preceding step; Indicates a constant that is protected against being divided by zero; Indicates the transmission time interval, while This means performing the integer down operation.

[0069] To facilitate understanding of this microscopic truncation process, let's take an ocean-going cargo ship using the 6 GHz band (corresponding to a communication carrier wavelength of approximately 0.05 meters) as an example. Assume the baseband transmission time interval is 1 millisecond, and the preset maximum historical prediction window length is 50 time slots. When the ship rolls violently in severe sea conditions, resulting in an effective radial velocity scalar as high as 5 m / s, the system calculates the transient physical coherence time to be approximately 4.23 milliseconds according to the above formula. After quoting this time interval and rounding down, the number of coherent time slots is 4. Since 4 is much smaller than the maximum prediction window length of 50, the system shrinks the dynamic channel coherence time boundary to 4 time slots. Based on this extremely stringent physical boundary, the system then performs retention and discard filtering on the historical sequence of channel state information that has already been time-aligned in the previous steps, according to the time length represented by this boundary. Specifically, the system retains only the 4 channel state feedback samples closest to the current time in the buffer queue, while discarding all 46 earlier samples as expired and invalid data. Through this hard truncation mechanism based on underlying physical characteristics, the system successfully eliminated non-stationary time-varying noise contained in long-tailed historical data, and output target prediction input sequences with absolute strong correlation, thus laying a clean data foundation for the next step of the time series prediction model to output true and reliable signal-to-noise ratio predictions.

[0070] Step S6: Input the target prediction input sequence into the preset time series prediction model for processing to generate a baseline signal-to-noise ratio prediction value;

[0071] After the preliminary physical boundary truncation and filtering, the system has successfully removed outdated data that has become invalid due to exceeding the coherence time, obtaining a target prediction input sequence with extremely high timeliness and physical relevance. At this point, the system needs to transform these clean historical slices into a prediction of the channel state for the next frame, which is the core operation of inputting the target prediction input sequence into a preset time-series prediction model to generate a baseline signal-to-noise ratio prediction value.

[0072] To achieve mathematical deduction from discrete historical sequences to future continuous states, a time-series prediction model trained with deep learning is pre-deployed within the system. In practical applications, considering that the pre-truncation mechanism can cause the length of the input sequence to fluctuate drastically with sea conditions, this pre-defined time-series prediction model does not employ traditional fully connected neural networks or convolutional architectures that require a fixed input dimension. Instead, it is specifically constructed as a dynamic recursive architecture based on Long Short-Term Memory (LSTM). Specifically, the model's input layer is configured to receive variable-length one-dimensional time-series features, with the feature dimension corresponding to the signal-to-noise ratio scalar in the channel state information. The intermediate hidden layer consists of multiple cascaded memory units, integrating forget gates, input gates, and output gates, specifically designed to capture Markov evolution dependencies in the time-series data. The model's output layer is a linear fully connected layer used to map the high-dimensional time-series features extracted from the hidden layer back to a single scalar in physical space, its physical meaning being the predicted signal-to-noise ratio within the next transmission time interval. The system uses a long short-term memory network as the core prediction engine because its recursive characteristics naturally support the ingestion of variable-length sequences. This forms a perfect software engineering coupling with the coherent time boundary that dynamically expands and contracts with the transient radial velocity in the preceding steps, ensuring that the model can continuously and stably extract the channel fading trend under different historical window lengths.

[0073] Before deploying the time-series prediction model to a real ship for online inference, the system needs to complete the model's pre-training in the offline phase, specifically including the following steps:

[0074] Training sample construction: A massive amount of continuous signal-to-noise ratio (SNR) real observation sequences were extracted from the historical sea trial database. To accommodate dynamically varying truncated inputs during the online phase, a random sliding window was used to truncate the inputs to a length that was within a certain range. The subsequences between these subsequences are used as feature inputs for training the model, and the real signal-to-noise ratio scalar of the next time slot immediately following the subsequence is used as the ground truth label.

[0075] Hidden State Initialization and Prevention of Sequence Discontinuity: For batch training of sequences of variable length, the system employs masking or padding techniques to align the tensor dimensions, and initializes the LSTM cell states before the start of each independent channel coherence period. and hidden state Reset to zero vector to prevent cross-cycle temporal feature contamination.

[0076] Loss function and parameter optimization: Mean squared error (MSE) is used as the loss function to quantify the residual between the predicted signal-to-noise ratio of the model output and the supervision label. The Adam optimizer is combined with the backpropagation time series algorithm (BPTT) to iteratively update the network weights of the forget gate, input gate, and fully connected layer until the loss function on the validation set converges to the preset accuracy threshold, thus obtaining the time series prediction model.

[0077] In the specific online inference process, the system first formats the truncated target prediction input sequence into a tensor sequence arranged in chronological order. As this sequence is input into the time-series prediction model slot by slot, the hidden states within the model are continuously updated with the progression of data. Since noise and lag components in the input sequence have been forcibly cut off by the physical boundaries of the preceding sequence, the Long Short-Term Memory (LSTM) network no longer needs to expend extra attention to identify long-tailed invalid data. Instead, it focuses its forget gate and input gate weights entirely on the channel energy slippage or ramp-up slope within the current short coherence window. When the last time step data in the target prediction input sequence passes through the hidden layer, the model passes the final hidden vector, which accumulates all current valid Markov states, to the output layer. The output layer performs a linear dot product and bias addition on this vector using the pre-trained regression weight matrix, ultimately outputting a specific floating-point value.

[0078] To illustrate this extrapolation process more intuitively, let's assume that under extremely severe sea conditions, the dynamic channel coherence boundary calculated in the preliminary steps has only four time slots. The system then feeds the most recent four milliseconds' actual signal-to-noise ratio (SNR) feedback values, such as 15.2 dB, 14.8 dB, 14.1 dB, and 13.2 dB, into the Long Short-Term Memory (LSTM) network as the target prediction input sequence. The model, through its internal state machine, accurately captures this steep fading gradient, which decreases by nearly 1 dB per millisecond. After linear regression mapping in the output layer, it directly outputs a predicted value of 12.1 dB. This result is the baseline SNR prediction value generated by the system at this stage. It's important to note that this prediction value is called a "baseline" because it only reflects a mathematical extrapolation based on historical stable evolution trends and does not consider sudden changes in physical structural stress caused by external wind and waves. Therefore, this baseline SNR prediction value will serve as crucial intermediate base data and will be transferred to the next processing stage for nonlinear physical correction based on transient angular acceleration.

[0079] Step S7: Based on the transient angular acceleration scalar obtained by differential processing of the transient rotational angular velocity sequence, determine the nonlinear backoff compensation amount for the reference signal-to-noise ratio prediction value;

[0080] The target prediction input sequence extracted in the preliminary steps is essentially historical observation data. Any prediction algorithm based on historical time series (such as long short-term memory networks or autoregressive models) inevitably carries inertial lag characteristics. In actual maritime navigation, when a ship encounters sudden waves or performs an emergency collision avoidance maneuver with a large rudder angle, the ship body instantly generates a huge rotational acceleration. This nonlinear mutation induced by external forces will cause a violent multipath structure collapse and a surge in Doppler frequency shift in the underlying communication channel. However, since the baseband receiver has not yet completed the decoding feedback within the current transmission time interval, the time series prediction model cannot "foresee" this instantaneous physical disaster from the historical sequence, and will therefore output a blindly optimistic baseline signal-to-noise ratio prediction value. If this prediction value is directly used for resource scheduling, it will inevitably lead to the complete corruption of a large number of high-order modulation data packets at the physical layer. Therefore, the system further determines the nonlinear backoff compensation amount for the baseline signal-to-noise ratio prediction value based on the transient angular acceleration scalar obtained by differential processing of the transient rotational angular velocity sequence, thereby constructing a "physical feedforward risk firewall".

[0081] To implement this compensation mechanism, the system needs to extract two key static calibration parameters from the underlying configuration register in advance: the preset acceleration reference constant and the penalty gain coefficient. Specifically, the acceleration reference constant is a physical threshold obtained through statistical analysis of large amounts of sea trial data, representing the upper limit of normal angular acceleration that the ship can tolerate under normal, stable sea conditions. The penalty gain coefficient is a scalar variable that maps a dimensionless physical degradation index to communication radio frequency energy units (decibels, dB), and its value is usually strongly correlated with the difference in signal-to-noise ratio demodulation threshold between two adjacent modulation orders (such as 16QAM and 64QAM) in the baseband system. These parameters are configured and distributed during the factory calibration or initial power-on of the ship's communication equipment and are securely stored in the system's local memory in the form of key-value pairs.

[0082] Entering the specific real-time computing pipeline, the system first needs to extract core features characterizing the severity of the sudden change from the dynamic sensing dimension. The system extracts the transient rotational angular velocity sequence (a continuous time sequence containing a three-dimensional spatial angular velocity vector) obtained in the previous steps. This sequence is first input into a preset low-pass filter (such as a Butterworth filter or a moving average window) to filter out high-frequency mechanical vibration noise from the hull. Then, the smoothed sequence undergoes first-order time-difference processing (or numerical differentiation) along the time axis, thereby calculating the transient angular acceleration vector sequence. Next, to eliminate directional interference and purely quantify the intensity of the sudden change, the system calculates the L2 norm magnitude of this transient angular acceleration vector (i.e., the spatial Euclidean distance), thus reducing its dimension from a three-dimensional vector to a single transient angular acceleration scalar. This scalar accurately characterizes the magnitude of the rotational stress generated by the impact of external waves on the hull at the current instant.

[0083] Considering that the distortion probability of communication channels often exhibits a non-linear, "avalanche-like" increase with physical acceleration, the system abandons the simple linear proportional penalty. Specifically, the system calculates the quotient of the transient angular acceleration scalar and a preset acceleration reference constant. This normalization process eliminates scale differences caused by different ship types, yielding a dimensionless target exponent. Then, using the natural constant (i.e., the mathematical constant e, approximately 2.718) as the base and this target exponent as the exponent, the system performs an exponential transition operation to calculate the baseline defense factor. Finally, the system calculates the product of this baseline defense factor and a preset penalty gain coefficient, ultimately outputting a non-linear backoff compensation amount for the reference signal-to-noise ratio prediction.

[0084] To more clearly illustrate this cross-domain coupling process, assume a ship's preset acceleration baseline constant is 0.5 radians / second squared, and the penalty gain coefficient is set to 1.5 dB. During normal navigation in calm waters, the ship's transient angular acceleration scalar approaches zero, and the calculated target exponent also approaches zero. At this point, the zero power of the natural constant approaches 1, and the backoff compensation generated by the system is at an extremely low baseline level, not interfering with the normal prediction of the time series model. However, when a large lateral wave suddenly impacts the hull, causing the transient angular acceleration scalar to surge instantaneously to 1.0 radians / second squared, the system's calculated target exponent reaches 2.0. After exponential calculation, the baseline defense factor surges to approximately 7.389 (i.e., the square of the natural constant). The system further multiplies this factor by the penalty gain coefficient of 1.5, instantly generating a nonlinear backoff compensation of approximately 11.08 dB. This extremely aggressive compensation amount is like installing a highly sensitive "damper" between the physical environment and the information network. It can directly lower the system's expectations by a huge negative bias at the moment when the baseband algorithm is blindly optimistic due to historical lag. This forces the system to switch to the most robust low-order modulation strategy in advance, fundamentally realizing a deep architecture design that uses deterministic physical feedforward to combat unknown channel mutations.

[0085] Step S8: Correct the reference signal-to-noise ratio prediction value using the nonlinear backoff compensation amount to obtain the target signal-to-noise ratio, and perform modulation and coding strategy mapping of physical layer resources based on the target signal-to-noise ratio;

[0086] After obtaining the nonlinear backoff compensation amount characterizing the physical abrupt stress, the system enters the final decision-making stage of physical layer resource scheduling. Specifically, the system performs a difference operation between the baseline signal-to-noise ratio prediction value output in the previous step and the calculated nonlinear backoff compensation amount, thereby obtaining the target signal-to-noise ratio after physical risk hedging. In this embodiment, the derivation of the target signal-to-noise ratio satisfies the following core mathematical expression:

[0087] ;

[0088] In this expression, This indicates the target signal-to-noise ratio of the final output. This represents the baseline signal-to-noise ratio prediction value output by the time series prediction model. This represents the preset penalty gain coefficient. Represents the transient angular acceleration scalar. This represents the preset acceleration baseline constant. This formula suppresses the potential for undue optimism in time-series models when facing sudden sea conditions, providing a secure data foundation for subsequent resource mapping.

[0089] It is worth noting that traditional adaptive coding and modulation schemes often perform resource degradation or prediction window resets solely based on the severity of the ship's rolling, which easily falls into a geometrical "over-defense" trap. When a ship rolls violently in severe sea conditions, but the normal vector of the rolling plane happens to be parallel to the direction of the shore-based or satellite base station, the high-speed oscillation of the antenna does not substantially damage the actual channel quality. To completely avoid the throughput waste caused by such misjudgments, the system is configured with a set of false positive immunity judgment rules based on orthogonal decoupling. In practical application scenarios, the system first calculates the L2 norm of the transient rotational angular velocity sequence in the aforementioned dynamic sensing data, using it as an objective basic physical quantity characterizing the current severity of the ship's rolling. Subsequently, the system introduces two key preset judgment thresholds: a first preset threshold for defining the severity of sea conditions (e.g., a severe rolling judgment benchmark of 0.15 radians / second), and a second preset threshold for defining Doppler distortion tolerance (e.g., a radial velocity critical exemption benchmark of 0.5 meters / second).

[0090] When the L2 norm of the calculated transient rotational angular velocity sequence exceeds a first preset threshold, and the effective radial velocity scalar is less than or equal to a second preset threshold, a false positive exemption command is generated. The physical essence of this logic is that the system confirms the ship is currently experiencing extremely severe turbulence, but the antenna's violent tangential motion is precisely within the "Doppler blind zone" orthogonal to the electromagnetic wave propagation direction. Therefore, this command will act as a high-priority control signal to intervene in subsequent channel processing pipelines. Specifically, based on the false positive exemption command, the system stops truncating the historical channel state information sequence and correcting the baseline signal-to-noise ratio (SNR) prediction value, directly determining the historical channel state information sequence as the target prediction input sequence, and directly determining the baseline SNR prediction value as the target SNR. This means the system actively bypasses the original punishment and defense mechanisms used to cope with severe sea conditions. Through this immune exemption mechanism deeply integrated with three-dimensional spatial geometry, it not only accurately captures truly fatal radial Doppler distortion but also salvages valuable high-order modulation bandwidth and high-speed throughput experience for the communication link without loss in the safety blind spot of stormy seas.

[0091] After calculating the target signal-to-noise ratio (SNR), the system immediately performs a physical layer resource modulation and coding scheme (MCS) mapping based on the target SNR. Traditional communication terminals typically only have a static mapping lookup table (LUT) built in, but the rolling motion of ships at sea exhibits strong physical asymmetry—the physical destructive force when the hull violently cuts into the waves is drastically different from the subsequent slow recovery period. Based on this, this system pre-builds an asymmetric dual-track mapping rule base in the underlying memory, containing a preset first modulation and coding scheme mapping matrix and a preset second modulation and coding scheme mapping matrix. Both matrices store multiple discrete modulation orders (such as QPSK, 16QAM, 64QAM, etc.) and a SNR decision threshold strictly bound to each modulation order in a key-value pair data structure. To prevent the system from frequently switching modulation orders near the critical threshold and causing a "ping-pong effect," both matrices are configured with state switching protection intervals for hysteresis judgment. Specifically, to address deteriorating trends, the first modulation and coding strategy mapping matrix is ​​configured with a wider first-state switching protection interval; while to quickly restore throughput when the environment improves, the second modulation and coding strategy mapping matrix is ​​configured with a narrower second-state switching protection interval. That is, the system physically limits the value of the first-state switching protection interval to be greater than the value of the second-state switching protection interval. Furthermore, in terms of internal threshold settings, the signal-to-noise ratio (SNR) decision threshold for backing down to lower-order modulation in the first modulation and coding strategy mapping matrix is ​​set to be significantly higher than the SNR decision threshold for backing down to lower-order modulation in the second modulation and coding strategy mapping matrix, thus giving the first matrix a more "aggressive" degradation defense characteristic and a more "conservative" upgrade characteristic.

[0092] After clarifying the internal structure of the dual-track matrix, the system enters the dynamic track switching and query process. The system first acquires the effective radial velocity scalar within a continuous sampling period (e.g., the past ten milliseconds). After removing pulse flypoint noise, it performs first-order time derivative calculation on this continuous effective radial velocity scalar sequence to obtain the radial velocity change rate characteristic. The physical significance of this characteristic lies in revealing whether the current antenna's Doppler distortion is in a "rapid deterioration" or "deceleration" phase. When the radial velocity change rate characteristic is positive, it indicates that the radial tangential velocity is surging, and the system immediately invokes the first modulation and coding strategy mapping matrix, which has high defensive strength. Conversely, when the radial velocity change rate characteristic is negative, it indicates that the swaying kinetic energy is decaying, the channel is stabilizing, and the system invokes the second modulation and coding strategy mapping matrix, which facilitates rapid ramp-up.

[0093] After successfully invoking the corresponding mapping matrix, the system performs the final matching query operation. The system reads multiple discrete modulation orders and their associated signal-to-noise ratio (SNR) decision thresholds from the invoked mapping matrix, and compares the calculated target SNR with each of these SNR decision thresholds. The system traverses the vast candidate set, rigorously selecting the modulation and coding strategy corresponding to the highest modulation and coding order that the target SNR can satisfy, and outputs this as the target modulation and coding strategy instruction to the baseband physical layer for final symbol modulation.

[0094] For example, suppose the system calculates a target signal-to-noise ratio (SNR) of 14.5 dB. In the system's preset rule base, the first modulation and coding strategy mapping matrix specifies a minimum SNR threshold of 15.0 dB for maintaining 16QAM, while the second modulation and coding strategy mapping matrix specifies a threshold of only 13.5 dB. When the ship is accelerating into a large wave (radial velocity change rate characteristic is positive), the system calls the first matrix. Since 14.5 dB fails to meet the stringent requirement of 15.0 dB, the system decisively downgrades the target modulation and coding strategy instruction to a lower-order QPSK, thereby ensuring the absolute survival rate of data packets during severe moments. Conversely, when the ship is in the righting phase (radial velocity change rate characteristic is negative), the system calls the second matrix. At this time, 14.5 dB successfully surpasses the lenient threshold of 13.5 dB, and the system continues to maintain high-order 16QAM transmission. Through this asymmetric dimensionality reduction mapping mechanism based on calculus trend perception, the system not only bids farewell to traditional conservative scheduling, but also reveals the ultimate throughput potential of each bit amidst turbulent times.

[0095] In summary, this invention addresses the mismatch between drastic fluctuations in the physical environment and the blindness of algorithm scheduling in maritime communication by constructing a coupling mechanism from macroscopic dynamic states to microscopic physical layer resources. Specifically, the system utilizes the spatial projection of the antenna's absolute motion vector along the line-of-sight direction to eliminate orthogonal components unrelated to electromagnetic wave propagation, avoiding bandwidth waste caused by Doppler distortion false positives. Simultaneously, by introducing a nonlinear backoff compensation based on transient angular acceleration, it breaks the dependence of traditional time-series prediction models on historical stationary data, endowing the system with immediate defense capabilities against physical abrupt changes. Furthermore, combined with an asymmetric dual-track mapping rule base, the system achieves aggressive protection during channel degradation and rapid ramp-up during stable periods, ensuring the absolute self-consistency of the resource mapping strategy under three-dimensional spatial geometry and nonlinear dynamic constraints. Through the synergistic effect of the above solutions, this invention not only significantly improves the effective throughput of communication links under complex sea conditions but also maintains the robustness of the communication baseline under extreme physical shocks, providing objective and precise technical support for high-reliability transmission of maritime broadband networks in harsh dynamic environments.

[0096] Example 2:

[0097] like Figure 3 As shown, a maritime mobile channel adaptive coding and modulation transmission system includes:

[0098] The data acquisition module is used to acquire the dynamic sensing data and first spatial position data of the mobile platform, the second spatial position data of the target communication peer, and the historical sequence of channel state information between the antenna and the target communication peer; the dynamic sensing data includes at least the transient rotational angular velocity sequence, translational velocity, and transient attitude characteristics;

[0099] The motion velocity reconstruction module is used to reconstruct the absolute motion velocity vector of the antenna in the reference navigation coordinate system based on dynamic sensing data and preset antenna installation position offset characteristics.

[0100] The line-of-sight direction determination module is used to determine the line-of-sight direction vector of the antenna pointing to the target communication peer based on the first spatial position data and the second spatial position data;

[0101] The radial velocity extraction module is used to perform spatial projection processing on the absolute motion velocity vector towards the line-of-sight direction vector to extract the effective radial velocity scalar;

[0102] The channel truncation module is used to determine the dynamic channel coherence time boundary based on the effective radial velocity scalar, and to perform truncation processing on the historical sequence of channel state information based on the dynamic channel coherence time boundary to obtain the target prediction input sequence;

[0103] The signal-to-noise ratio prediction module is used to input the target prediction input sequence into a preset time-series prediction model for processing and to generate a baseline signal-to-noise ratio prediction value.

[0104] The compensation amount determination module is used to perform differential processing on the transient rotational angular velocity sequence to obtain a transient angular acceleration scalar, and to determine the nonlinear backoff compensation amount for the reference signal-to-noise ratio prediction value based on the transient angular acceleration scalar.

[0105] The correction and mapping module is used to correct the reference signal-to-noise ratio prediction value using nonlinear backoff compensation to obtain the target signal-to-noise ratio, and to perform modulation and coding strategy mapping of physical layer resources based on the target signal-to-noise ratio.

[0106] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0107] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0108] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An adaptive coding modulation transmission method for maritime mobile channels, characterized in that, It is applied to a communication device installed on a mobile platform, the mobile platform being equipped with an antenna, and the method includes: The system acquires the dynamic sensing data and first spatial position data of the mobile platform, the second spatial position data of the target communication peer, and the historical sequence of channel state information between the antenna and the target communication peer; the dynamic sensing data includes at least the transient rotational angular velocity sequence, translational velocity, and transient attitude characteristics. Based on the dynamic sensing data and the preset antenna installation position offset characteristics, the absolute motion velocity vector of the antenna in the reference navigation coordinate system is reconstructed. Based on the first spatial location data and the second spatial location data, the line-of-sight direction vector of the antenna pointing to the target communication peer is determined; Perform spatial projection processing on the absolute motion velocity vector and the line-of-sight direction vector to extract the effective radial velocity scalar; The dynamic channel coherence time boundary is determined based on the effective radial velocity scalar, and the channel state information historical sequence is truncated based on the dynamic channel coherence time boundary to obtain the target prediction input sequence. The target prediction input sequence is input into a preset time-series prediction model for processing to generate a baseline signal-to-noise ratio prediction value; Based on the transient angular acceleration scalar obtained by differential processing of the transient rotational angular velocity sequence, the nonlinear backoff compensation amount for the reference signal-to-noise ratio prediction value is determined; The reference signal-to-noise ratio prediction value is corrected using the nonlinear backoff compensation amount to obtain the target signal-to-noise ratio, and the modulation and coding strategy mapping of physical layer resources is performed based on the target signal-to-noise ratio.

2. The maritime mobile channel adaptive coding modulation transmission method according to claim 1, characterized in that: The calculation process of the absolute motion velocity vector includes: Based on the transient rotational angular velocity sequence and the antenna installation position offset feature, a vector cross product operation is performed to obtain the tangential linear velocity vector of the antenna in the carrier coordinate system; Based on the transient attitude characteristics, a transient attitude rotation matrix is ​​constructed that transforms the carrier coordinate system to the reference navigation coordinate system; The transient attitude rotation matrix is ​​used to perform coordinate system mapping on the tangential linear velocity vector to obtain the projected linear velocity vector. The projection linear velocity vector and the translational velocity are superimposed to obtain the absolute motion velocity vector.

3. The maritime mobile channel adaptive coding modulation transmission method according to claim 1, characterized in that: The process of determining the line-of-sight direction vector includes: The first spatial location data and the second spatial location data are converted to the geocentric-ground-fixed coordinate system, and the absolute spatial distance vector in the geocentric-ground-fixed coordinate system is calculated. Based on the first spatial location data, a local tangent plane coordinate transformation matrix is ​​constructed, and the absolute spatial distance vector is transformed to the local navigation coordinate system using the local tangent plane coordinate transformation matrix to obtain the local distance vector; The local distance vector is normalized to obtain the line-of-sight direction vector.

4. The maritime mobile channel adaptive coding modulation transmission method according to claim 1, characterized in that: Perform spatial projection processing on the absolute motion velocity vector towards the line-of-sight direction vector to extract the effective radial velocity scalar, including: Perform a spatial vector inner product operation on the absolute motion velocity vector and the line-of-sight direction vector, and determine the absolute value of the inner product operation result as the effective radial velocity scalar; When the L2 norm modulus of the transient rotational angular velocity sequence is calculated to be greater than a first preset threshold, and the effective radial velocity scalar is less than or equal to a second preset threshold, a false positive exemption instruction is generated. Based on the false positive exemption instruction, the truncation processing of the channel state information historical sequence and the correction processing of the reference signal-to-noise ratio prediction value are stopped. The channel state information historical sequence is directly determined as the target prediction input sequence, and the reference signal-to-noise ratio prediction value is directly determined as the target signal-to-noise ratio.

5. The maritime mobile channel adaptive coding modulation transmission method according to claim 1, characterized in that: The dynamic channel coherence time boundary is determined based on the effective radial velocity scalar, and the channel state information history sequence is truncated based on the dynamic channel coherence time boundary to obtain the target prediction input sequence, including: The transient physical coherence time is calculated based on the preset communication carrier wavelength and the effective radial velocity scalar. The quotient of the transient physical coherence time and the preset transmission time interval is rounded down to obtain the number of coherent time slots; The maximum historical prediction window length is compared with the number of coherent time slots, and the minimum value of the two is determined as the dynamic channel coherent time boundary. Based on the time length represented by the dynamic channel coherence time boundary, the retention and discard filtering of the time-aligned channel state information historical sequence is performed to obtain the target prediction input sequence. The dynamic channel coherence time boundary satisfies the following mathematical expression: ； in, This indicates the number of time slots corresponding to the coherence time boundary of the dynamic channel. This indicates the preset maximum historical prediction window length. Indicates the communication carrier wavelength. Represents the effective radial velocity scalar. Indicates the prevention of zero constant, Indicates the transmission time interval.

6. The maritime mobile channel adaptive coding modulation transmission method according to claim 1, characterized in that: The calculation process for the target signal-to-noise ratio includes: The target exponent is obtained by calculating the quotient of the transient angular acceleration scalar and the preset acceleration reference constant. The bottom-line defense factor is calculated using the natural constant as the base and the target exponent as the exponent. The nonlinear backoff compensation amount is obtained by multiplying the bottom-line defense factor with the preset penalty gain coefficient. The target signal-to-noise ratio is obtained by performing a difference operation between the predicted baseline signal-to-noise ratio and the nonlinear backoff compensation amount.

7. The maritime mobile channel adaptive coding modulation transmission method according to claim 6, characterized in that: The target signal-to-noise ratio satisfies the following mathematical expression: ； in, Indicates the target signal-to-noise ratio. This represents the baseline signal-to-noise ratio prediction value. Indicates the penalty gain coefficient. Represents the transient angular acceleration scalar. This represents the acceleration reference constant.

8. The maritime mobile channel adaptive coding modulation transmission method according to claim 1, characterized in that: Based on the target signal-to-noise ratio, the modulation and coding strategy mapping of physical layer resources is performed, including: The effective radial velocity scalar within a continuous sampling period is obtained, and the first-order time derivative is calculated on the continuous effective radial velocity scalar to obtain the radial velocity change rate characteristics. When the radial velocity change rate characteristic is positive, a preset first modulation and coding strategy mapping matrix is ​​invoked, and the first modulation and coding strategy mapping matrix is ​​configured with a first state switching protection interval. When the radial velocity change rate characteristic is negative, a preset second modulation and coding strategy mapping matrix is ​​invoked. The second modulation and coding strategy mapping matrix is ​​configured with a second state switching protection interval, and the first state switching protection interval is greater than the second state switching protection interval. In the first modulation and coding strategy mapping matrix or the second modulation and coding strategy mapping matrix, a matching query is performed based on the target signal-to-noise ratio, and the target modulation and coding strategy instruction is output.

9. The maritime mobile channel adaptive coding modulation transmission method according to claim 8, characterized in that: Based on the target signal-to-noise ratio, a matching query is performed, and a target modulation and coding strategy instruction is output, including: Get the multiple discrete modulation orders contained in the mapping matrix of the call, and the signal-to-noise ratio decision threshold bound to each modulation order; Wherein, the signal-to-noise ratio decision threshold for backing back to lower-order modulation in the first modulation and coding strategy mapping matrix is ​​higher than the signal-to-noise ratio decision threshold for backing back to lower-order modulation in the second modulation and coding strategy mapping matrix. The target signal-to-noise ratio is compared with the signal-to-noise ratio decision threshold in the called mapping matrix. The modulation and coding strategy corresponding to the highest order that the target signal-to-noise ratio can satisfy is selected and used as the target modulation and coding strategy instruction.

10. A maritime mobile channel adaptive coding and modulation transmission system, characterized in that: Using the maritime mobile channel adaptive coding and modulation transmission method as described in any one of claims 1-9, comprising: The data acquisition module is used to acquire the dynamic sensing data and first spatial position data of the mobile platform, the second spatial position data of the target communication peer, and the historical sequence of channel state information between the antenna and the target communication peer; the dynamic sensing data includes at least the transient rotational angular velocity sequence, translational velocity, and transient attitude characteristics; The motion velocity reconstruction module is used to reconstruct the absolute motion velocity vector of the antenna in the reference navigation coordinate system based on the dynamic sensing data and the preset antenna installation position offset characteristics. The line-of-sight direction determination module is used to determine the line-of-sight direction vector of the antenna pointing to the target communication peer based on the first spatial position data and the second spatial position data; The radial velocity extraction module is used to perform spatial projection processing on the absolute motion velocity vector towards the line-of-sight direction vector to extract the effective radial velocity scalar. The channel truncation module is used to determine the dynamic channel coherence time boundary based on the effective radial velocity scalar, and to perform truncation processing on the historical sequence of channel state information based on the dynamic channel coherence time boundary to obtain the target prediction input sequence; The signal-to-noise ratio prediction module is used to input the target prediction input sequence into a preset time-series prediction model for processing and to generate a baseline signal-to-noise ratio prediction value. The compensation amount determination module is used to perform differential processing on the transient rotational angular velocity sequence to obtain a transient angular acceleration scalar, and to determine the nonlinear backoff compensation amount for the reference signal-to-noise ratio prediction value based on the transient angular acceleration scalar. The correction and mapping module is used to correct the reference signal-to-noise ratio prediction value using the nonlinear backoff compensation amount to obtain the target signal-to-noise ratio, and to perform modulation and coding strategy mapping of physical layer resources based on the target signal-to-noise ratio.

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