A signal processing method, apparatus and device in a non-terrestrial network

Abstract

This application discloses a signal processing method, apparatus, and device in a non-terrestrial network, relating to the field of communication technology. The terminal device acquires satellite orbital parameters and terminal motion state information, generates a phase rotation function, determines the sparse sampling density of the reference signal based on its phase change rate, and transmits sparse pilot signals carrying identifiers or configuration indexes when a threshold is met. After receiving the signal, the base station extracts the information to synchronize the phase rotation function, obtains sparse samples through preliminary channel estimation, and uses inverse rotation to eliminate Doppler components to obtain baseband slowly varying channel samples. It then performs time-domain low-pass interpolation and frequency-domain interpolation filtering based on the residual error estimation bandwidth consistent with the terminal device, reconstructs the full-dimensional channel response, and re-rotates it to complete signal demodulation and detection. This scheme can reduce pilot overhead and improve satellite link transmission efficiency.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a signal processing method, apparatus and device in a non-terrestrial network. Background Technology

[0002] In non-terrestrial network communications, the high-speed motion of low-Earth orbit satellites relative to ground user terminal equipment leads to a significant Doppler effect in the communication link. In typical scenarios, the Doppler frequency shift caused by relative motion can be as high as 20 ppm (e.g., about 40 kHz at a 2 GHz carrier frequency), causing the channel phase to rotate drastically over time, posing a severe challenge to channel estimation.

[0003] Existing technologies typically employ high-density demodulation reference signals (DMRS) or short-period sounding reference signals (SRS) to track rapidly changing channels. However, this dense pilot scheme suffers from the following technical drawbacks: First, excessive pilot overhead. Dense reference signals consume significant data transmission resources, substantially reducing spectral efficiency. Second, low energy efficiency. For energy-constrained satellite handheld terminals, frequent transmission of sounding reference signals drastically shortens battery life. Third, limited interpolation performance. Traditional linear interpolation or Wiener filtering, when dealing with high-frequency oscillating channels, can exhibit severe aliasing and estimation errors if there are insufficient sampling points. Summary of the Invention

[0004] This application provides a signal processing method, apparatus, and device for non-terrestrial networks. The terminal device generates a phase rotation function to determine the sparse pilot density and sends it with an identifier. The base station extracts the identifier and synchronizes the function. After inverse rotation to eliminate Doppler, the channel is reconstructed by interpolation with a uniform bandwidth. This reduces pilot overhead, ensures channel reconstruction accuracy, and improves satellite link transmission efficiency and communication quality.

[0005] In a first aspect, embodiments of this application provide a signal processing method in a non-terrestrial network, applied to a terminal device, the processing method comprising: Acquire satellite orbital parameters and motion status information of terminal equipment; Based on the orbital parameters and the motion state information, the relative radial velocity between the satellite and the terminal device is calculated, and the Doppler frequency shift time-varying function is calculated based on the relative radial velocity. A phase rotation function is generated based on the Doppler frequency shift time-varying function, and the phase rotation function characterizes the deterministic change trend of the channel phase caused by the relative motion of the satellite. The sparse sampling density of the reference signal is determined based on the phase change rate of the phase rotation function. The reference signal is mapped to a sparse time-frequency sampling location according to the sparse sampling density and sent to the base station, so that the base station performs corresponding operations based on the reference signal synchronized at the sparse time-frequency sampling location to reconstruct the channel state information.

[0006] Further, the step of calculating the relative radial velocity between the satellite and the terminal device based on the orbital parameters and the motion state information, and calculating the Doppler frequency shift time-varying function based on the relative radial velocity, and generating a phase rotation function based on the Doppler frequency shift time-varying function, includes: The position and velocity vectors of the satellite are determined based on the orbital parameters, and the position and velocity vectors of the terminal device are determined based on the motion state information. Based on the position vector and velocity vector of the satellite and the position vector and velocity vector of the terminal device, the projection of the relative velocity between the satellite and the terminal device in the radial direction is calculated to obtain the relative radial velocity; The Doppler frequency shift time-varying function is calculated based on the relative radial velocity, carrier center frequency, and electromagnetic wave propagation speed. The phase rotation function is obtained by performing time integration on the Doppler frequency shift time-varying function.

[0007] Further, determining the sparse sampling density of the reference signal based on the phase change rate of the phase rotation function includes: Calculate the phase change rate of the phase rotation function within a preset time window, where the phase change rate reflects the instantaneous change rate of the channel phase; The equivalent Doppler bandwidth is determined based on the phase change rate, and the Nyquist sampling density is calculated based on the equivalent Doppler bandwidth. Obtain the residual error estimation bandwidth, determine the sparsification coefficient based on the ratio of the equivalent Doppler bandwidth to the residual error estimation bandwidth, and downsample the Nyquist sampling density according to the sparsification coefficient to obtain the sparse sampling density, wherein the sparse sampling density is lower than the density required by the Nyquist sampling theorem for the original channel Doppler frequency shift.

[0008] Further, the step of mapping the reference signal to sparse time-frequency sampling positions according to the sparse sampling density and sending it to the base station includes: Determine whether the phase change rate of the phase rotation function satisfies the sparse sampling condition threshold; If the phase change rate satisfies the sparse sampling condition threshold, then the time-domain sampling interval is determined according to the sparse sampling density, and the time-domain sampling interval is greater than the reference signal transmission period specified in the standard protocol. At the sampling time corresponding to the time domain sampling interval, the probe reference signal is mapped to different frequency domain positions across the full bandwidth according to the frequency domain frequency hopping mode, or the demodulation reference signal is mapped to a specified symbol position according to the mode of the preceding reference signal and the additional reference signal. The mapped detection reference signal or the demodulation reference signal is sent so that the base station can synchronize the phase rotation function based on the identification information or configuration index and perform corresponding operations; The detection reference signal or the demodulation reference signal carries the identification information of the phase rotation function or the configuration index of the sparse sampling density.

[0009] Furthermore, the processing method also includes: The base station receives a reference signal transmitted by the terminal equipment according to a sparse sampling density, wherein the reference signal is a detection reference signal or a demodulation reference signal; Extract the identification information of the phase rotation function or the configuration index of the sparse sampling density from the received reference signal, and obtain the phase rotation function synchronized with the terminal device based on the identification information or the configuration index; A preliminary channel estimation is performed on the received reference signal to obtain sparsely distributed original channel estimation samples; Based on the synchronized phase rotation function, the original channel estimation sample is subjected to phase inverse rotation to obtain a baseband slow-varying channel sample that eliminates the Doppler component in the original channel. Time-frequency domain interpolation is performed on the baseband slowly varying channel samples to obtain the full-dimensional baseband channel response; A phase re-rotation operation is performed on the full-dimensional baseband channel response to recover the time-varying channel state information, so as to complete the demodulation of the reference signal and channel detection based on the channel state information.

[0010] Further, obtaining the phase rotation function synchronized with the terminal device based on the identification information or the configuration index includes: The received probe reference signal or demodulation reference signal is parsed, and the identification information of the phase rotation function or the configuration index of the sparse sampling density carried in the probe reference signal or demodulation reference signal is extracted. If the identification information of the phase rotation function is extracted, then based on the identification information, the phase rotation function consistent with that of the terminal device is called from the locally synchronized Doppler predictor; If the configuration index of the sparse sampling density is extracted, the sparse sampling density used by the terminal device is determined based on the configuration index, and the same phase rotation function generation operation as that of the terminal device is performed based on the locally stored orbit parameters and the motion state information reported by the terminal device to obtain the phase rotation function synchronized with the terminal device.

[0011] Further, the phase rotation function based on synchronization performs a phase inverse rotation operation on the original channel estimation sample to obtain a baseband slowly varying channel sample that eliminates the Doppler component in the original channel, including: An anti-rotation phase factor is generated based on the synchronized phase rotation function, wherein the anti-rotation phase factor is the complex conjugate of the phase rotation function; Multiply the original channel estimation sample by the anti-rotation phase factor to perform a phase anti-rotation operation; The baseband slowly varying channel sample is obtained by filtering the sample after inverse rotation. The phase change rate of the baseband slowly varying channel sample is lower than that of the original channel estimation sample.

[0012] Further, the step of performing time-frequency domain interpolation on the baseband slowly varying channel samples to obtain the full-dimensional baseband channel response includes: The baseband slowly varying channel sample is low-pass interpolated in the time domain to obtain a time-continuous channel estimate. The cutoff frequency of the low-pass interpolation is determined based on the residual error estimation bandwidth. The time-domain continuous channel estimate is interpolated or filtered in the frequency domain to complete the full-bandwidth channel sample, thus obtaining the full-dimensional baseband channel response.

[0013] In a second aspect, embodiments of this application provide a signal processing apparatus for a non-terrestrial network, comprising: The information acquisition module is used to acquire the satellite's orbital parameters and the motion status information of the terminal equipment; The function generation module is used to calculate the relative radial velocity between the satellite and the terminal device based on the orbital parameters and the motion state information, and to calculate the Doppler frequency shift time-varying function based on the relative radial velocity, and to generate a phase rotation function based on the Doppler frequency shift time-varying function. The phase rotation function characterizes the deterministic change trend of the channel phase caused by the relative motion of the satellite. The density determination module is used to determine the sparse sampling density of the reference signal based on the phase change rate of the phase rotation function. The mapping and transmission module is used to map the reference signal to a sparse time-frequency sampling position according to the sparse sampling density and transmit it to the base station, so that the base station can perform corresponding operations based on the reference signal synchronized by the sparse time-frequency sampling position to reconstruct the channel state information.

[0014] In a third aspect, embodiments of this application provide an electronic device, including: a memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the signal processing method in a non-terrestrial network as described in the first aspect.

[0015] In a fourth aspect, embodiments of this application provide a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform the signal processing method in a non-terrestrial network as described in the first aspect.

[0016] This application embodiment generates a phase rotation function based on satellite and terminal motion information by the terminal device, determines a reasonable sparse pilot density, and sends it with relevant identifiers. The base station extracts the identifiers and synchronizes the function. After inverse rotation to eliminate Doppler components, the channel is reconstructed by interpolating the bandwidth estimated using a unified residual error. By accurately capturing the dynamic characteristics of the channel to achieve pilot sparsity, pilot overhead is effectively reduced, spectrum efficiency is improved, and the terminal transmission frequency is reduced to extend battery life. At the same time, aliasing and estimation errors are avoided by synchronizing the interpolation parameters, ensuring the accuracy of channel estimation. Ultimately, the synergistic optimization of spectrum efficiency, terminal battery life, and channel estimation accuracy is achieved, adapting to the needs of low-Earth orbit satellite communication scenarios. Attached Figure Description

[0017] Figure 1 This is a flowchart of a signal processing method in a non-terrestrial network provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the generation of the phase rotation function provided in an embodiment of this application; Figure 3 This is a flowchart illustrating the determination of sparse sampling density provided in an embodiment of this application; Figure 4 This is a flowchart of sparse mapping provided in the embodiments of this application; Figure 5 This is a flowchart of reference signal processing on the base station side provided in an embodiment of this application; Figure 6 This is a flowchart illustrating the acquisition of the base station-side phase rotation function provided in an embodiment of this application; Figure 7 This is a flowchart illustrating the acquisition of baseband slowly varying channel samples provided in an embodiment of this application; Figure 8 This is a flowchart illustrating the acquisition of full-dimensional baseband channel response provided in an embodiment of this application; Figure 9 This is a structural diagram of a signal processing device in a non-terrestrial network provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but additional steps not included in the drawings may also be present. The above processes can correspond to methods, functions, procedures, subroutines, subroutines, etc.

[0019] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0020] The signal processing method, apparatus, and equipment for non-terrestrial networks provided in this application are applicable to high-dynamic communication scenarios of low-Earth orbit satellites in 5G-Advanced and 6G non-terrestrial networks. They can solve technical problems such as excessive pilot overhead, low energy efficiency, and interpolation distortion in existing dense pilot schemes. This method generates a phase rotation function based on satellite orbit parameters and the terminal device's own motion state, adaptively determines the sparse pilot density, and transmits it with an identifier. After the base station extracts the identifier and synchronizes the function, it reconstructs the full-dimensional channel state information through a closed-loop architecture of de-rotation, interpolation, and re-rotation. This achieves synergistic optimization of pilot overhead reduction and channel estimation accuracy improvement. It can be widely applied to non-terrestrial network communication devices such as satellite handheld terminals, IoT remote transmission devices, and satellite communication base stations. The aforementioned application scenarios are merely illustrative; in actual deployment, they can cover all high-dynamic communication scenarios of low-Earth orbit satellites, and this application does not limit these applications.

[0021] The signal processing method in non-terrestrial networks provided in this application has two types of execution entities. One type is terminal equipment, which refers to user equipment with satellite orbit parameter receiving capability, self-motion state acquisition capability, Doppler frequency shift calculation capability, and reference signal mapping and transmission capability, including satellite IoT terminals, handheld satellite communication devices, vehicle-mounted satellite terminals, etc. The other type is base station equipment, which refers to non-terrestrial network access equipment with reference signal receiving capability, phase rotation function synchronization capability, and channel estimation and reconstruction capability, including satellite gateway stations, ground augmentation base stations, etc. This application does not limit the specific type of base station equipment.

[0022] Figure 1 This is a flowchart illustrating a signal processing method in a non-terrestrial network according to an embodiment of this application, applied to a terminal device, such as... Figure 1 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 101: Obtain the satellite's orbital parameters and the motion status information of the terminal equipment.

[0023] Among them, the satellite's orbital parameters refer to the set of parameters describing the satellite's trajectory, including the satellite's semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly, which are used to determine the satellite's position and velocity in space; the terminal device's motion status information refers to the terminal device's spatial motion attributes, including the terminal device's position coordinates, motion speed, and motion direction, which are used to characterize the dynamic changes of the terminal device.

[0024] In one embodiment, the terminal device receives satellite orbit parameters from a satellite gateway station via a satellite broadcast channel. These orbit parameters are updated and pushed in real time by the satellite constellation system, and the terminal device stores them in its local orbit parameter cache after receiving them. The terminal device collects its own position coordinates, such as latitude, longitude, and altitude in the WGS-84 coordinate system, through its built-in Global Navigation Satellite System module, and collects its own speed and direction of motion through its inertial measurement unit. For example, the terminal device's position coordinates are (116.4°E, 39.9°N, altitude 50 meters), its speed is 30 km / h, and its direction of motion is due north. The terminal device preprocesses the received satellite orbit parameters and its own motion status information, including data format standardization and outlier removal, to ensure data accuracy and provide input for Doppler frequency shift calculation and phase rotation function generation.

[0025] Step 102: Based on the orbital parameters and the motion state information, calculate the relative radial velocity between the satellite and the terminal device, and calculate the Doppler frequency shift time-varying function based on the relative radial velocity. Generate a phase rotation function based on the Doppler frequency shift time-varying function. The phase rotation function characterizes the deterministic change trend of the channel phase caused by the relative motion of the satellite.

[0026] Among them, the relative radial velocity refers to the projection component of the relative velocity between the satellite and the terminal equipment onto the line connecting the two (line of sight), which is the core factor that generates Doppler frequency shift; the Doppler frequency shift time-varying function is the mathematical expression of how the Doppler frequency shift changes over time, characterizing the dynamic change law of the frequency shift; the phase rotation function is the phase change function generated by the accumulation of Doppler frequency shift, which can describe the deterministic rotation trend of the channel phase over time.

[0027] In one embodiment, the terminal device calculates the satellite's position and velocity vectors at the current moment and within a preset future time period based on the acquired satellite orbit parameters using an orbital dynamics model; simultaneously, it determines its own position and velocity vectors based on its own motion state information; the terminal device calculates the relative velocity vector between the satellite and itself, projects this relative velocity vector onto the line-of-sight direction between the two to obtain the relative radial velocity; and the terminal device then calculates the relative radial velocity based on the relative radial velocity, the carrier center frequency (e.g., 2 GHz), and the electromagnetic wave propagation speed (speed of light c = 3 × 10⁻⁶). 8 The Doppler frequency shift time-varying function is calculated using the Doppler frequency shift calculation formula (m / s). The terminal device performs time integration on the Doppler frequency shift time-varying function to obtain the phase rotation function. This phase rotation function accurately reflects the deterministic change trend of the channel phase caused by the relative motion between the satellite and the terminal. The phase rotation function is: ,in It is a time-varying function of Doppler frequency shift.

[0028] Step 103: Determine the sparse sampling density of the reference signal based on the phase change rate of the phase rotation function.

[0029] Among them, the phase change rate refers to the amount of phase change of the phase rotation function per unit time, reflecting how fast the instantaneous phase of the channel changes; the sparse sampling density refers to the sampling distribution density of the reference signal on the time-frequency resources, which is lower than the sampling density required by the Nyquist sampling theorem for the original channel Doppler frequency shift, thus reducing the resource occupation of the reference signal.

[0030] In one embodiment, the terminal device selects a preset time window, calculates the phase change of the phase rotation function within that window, and thus obtains the phase change rate. Based on the phase change rate, it determines the equivalent Doppler bandwidth, which is positively correlated with the phase change rate; the faster the phase change rate, the larger the equivalent Doppler bandwidth. The terminal device calculates the Nyquist sampling density based on the equivalent Doppler bandwidth to ensure the minimum density requirement for distortion-free sampling. The terminal device obtains a preset residual error estimation bandwidth, which is determined based on the system's allowed channel estimation error. It calculates the ratio of the equivalent Doppler bandwidth to the residual error estimation bandwidth to obtain the sparsity coefficient. The terminal device downsamples the Nyquist sampling density based on the sparsity coefficient to obtain a sparse sampling density lower than the Nyquist sampling density. For example, if the Nyquist sampling density is 10 sampling points per millisecond and the sparsity coefficient is 0.2, then the sparse sampling density is 2 sampling points per millisecond, achieving a sparse configuration of the reference signal.

[0031] Step 104: Map the reference signal to the sparse time-frequency sampling position according to the sparse sampling density and send it to the base station, so that the base station can perform corresponding operations based on the reference signal synchronized by the sparse time-frequency sampling position to reconstruct the channel state information.

[0032] Among them, the sparse time-frequency sampling position refers to the distribution position of the reference signal in the time and frequency domains determined according to the sparse sampling density; the reference signal includes the sounding reference signal (SRS) and the demodulation reference signal (DMRS), which are used by the base station for channel estimation and state detection; the identification information refers to the feature information used to uniquely identify the phase rotation function; the configuration index refers to the preset index value corresponding to the sparse sampling density, which is used by the base station to synchronize relevant configuration parameters.

[0033] In one embodiment, the terminal device determines whether the phase change rate of the phase rotation function meets the sparse sampling condition threshold. If the threshold is met, the terminal device determines the time-domain sampling interval based on the sparse sampling density. This time-domain sampling interval is greater than the reference signal transmission period specified in the standard protocol. For example, if the standard protocol specifies that the reference signal transmission period is 1ms, the time-domain sampling interval after sparse sampling is 5ms. Optionally, at the sampling time corresponding to the time-domain sampling interval, the terminal device maps the probe reference signal to different frequency domain positions across the entire bandwidth according to the frequency-domain frequency hopping mode to ensure that the reference signal covers the entire frequency band, or maps the demodulated reference signal to a specified symbol position according to the pattern of pre-reference signal and additional reference signal. The terminal device carries the identification information of the phase rotation function or the configuration index of the sparse sampling density in the probe reference signal or demodulated reference signal and sends it to the base station through the satellite communication link. The base station can synchronize the phase rotation function based on the identification information or configuration index, and then perform channel reconstruction operation.

[0034] As described above, by sequentially executing information acquisition, phase rotation function generation, sparse sampling density determination, and reference signal mapping transmission, a complete Doppler prediction and sparse pilot transmission process can be formed on the terminal side. Based on orbit and motion information, adaptive sparsity of the pilot can be achieved, which can reduce reference signal overhead and terminal transmission power consumption while ensuring channel reconfigurability, and at the same time provide a foundation for channel reconfiguration on the base station side.

[0035] Figure 2 This is a flowchart illustrating the generation of the phase rotation function provided in an embodiment of this application, such as... Figure 2 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 201: Determine the position vector and velocity vector of the satellite based on the orbital parameters, and determine the position vector and velocity vector of the terminal device based on the motion state information.

[0036] In one embodiment, the terminal device analyzes the satellite orbital parameters, extracts key parameters such as the satellite's semi-major axis, eccentricity, and orbital inclination, inputs these parameters into the orbital dynamics model, and calculates the satellite's position vector in three-dimensional space by combining the current timestamp. With velocity vector Simultaneously, the terminal device analyzes its own motion state information and extracts its position coordinates. With speed of movement This converts the data into position and velocity vectors in a unified coordinate system, ensuring that the vector data of the satellite and the terminal equipment are in the same spatial coordinate system.

[0037] Step 202: Based on the position vector and velocity vector of the satellite and the position vector and velocity vector of the terminal device, calculate the projection of the relative velocity between the satellite and the terminal device in the radial direction to obtain the relative radial velocity.

[0038] In one embodiment, the terminal device calculates the difference between the satellite position vector and the terminal device position vector to obtain the relative position vector. Normalize the relative position vector to obtain the unit vector in the line-of-sight direction. The terminal device calculates the difference between the satellite velocity vector and the terminal device's velocity vector to obtain the relative velocity vector. The relative velocity vector is multiplied by the unit vector in the line-of-sight direction to obtain the projection of the relative velocity in the radial direction, i.e., the relative radial velocity. This speed is the direct cause of the Doppler shift.

[0039] Step 203: Calculate the Doppler frequency shift time-varying function based on the relative radial velocity, carrier center frequency, and electromagnetic wave propagation speed.

[0040] In one embodiment, the terminal device obtains the carrier center frequency configured by the system. ( =2GHz) and the electromagnetic wave propagation speed c (c=3×10) 8 m / s), using the Doppler frequency shift calculation formula Calculate the time-varying function of Doppler frequency shift, where The relative radial velocity varies with time. The terminal device substitutes the real-time calculated relative radial velocity into the formula to obtain the Doppler frequency shift value at different times, forming a time-varying Doppler frequency shift function. This function can dynamically reflect the time variation law of the Doppler frequency shift. For example, when the relative radial velocity is 300 m / s, the Doppler frequency shift = 300 × 2 × 10⁻⁶ m / s. 9 / (3×10 8 =2000Hz.

[0041] Step 204: Integrate the Doppler frequency shift time-varying function over time to obtain the phase rotation function.

[0042] In one embodiment, the terminal device determines the integration time range from reference time t=0 to the current time t, and applies the Doppler frequency shift time-varying function. Perform time integration, the integration formula is as follows: The phase rotation function is calculated by numerical integration or analytical integration. This function can characterize the cumulative change of the channel phase over time and reflect the deterministic change trend of the channel phase caused by the relative motion between the satellite and the terminal.

[0043] The above-mentioned method calculates the relative radial velocity using the satellite and terminal position vectors and velocity vectors, and further generates a Doppler frequency shift time-varying function and a phase rotation function, which can accurately describe the deterministic phase change law of non-terrestrial network channels. Modeling the high dynamic Doppler effect as a predictable phase function can effectively eliminate the estimation difficulties caused by rapid channel changes and improve the rationality and accuracy of subsequent sparse sampling and channel reconstruction.

[0044] Figure 3 This is a flowchart of the sparse sampling density determination provided in the embodiments of this application, such as... Figure 3 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 301: Calculate the phase change rate of the phase rotation function within a preset time window. The phase change rate reflects the instantaneous change speed of the channel phase.

[0045] In one embodiment, the terminal device presets a time window length, which is determined based on channel variation characteristics, for example, a preset time window of 5ms; the terminal device selects multiple consecutive preset time windows and calculates the initial phase value of the phase rotation function within each window. and the end phase value The rate of phase change within each window is calculated using the formula ΔΦ / Δt, where ΔΦ = - This represents the phase change value. The window length is used to smooth the phase change rate of multiple windows, remove outliers, and obtain the final phase change rate, which reflects the instantaneous change rate of the channel phase.

[0046] Step 302: Determine the equivalent Doppler bandwidth based on the phase change rate, and calculate the Nyquist sampling density based on the equivalent Doppler bandwidth.

[0047] In one embodiment, the terminal device determines the equivalent Doppler bandwidth based on the mapping relationship between the phase change rate and the equivalent Doppler bandwidth. The mapping relationship can be obtained through system preset or experimental calibration. For example, when the phase change rate is 100π rad / ms, the equivalent Doppler bandwidth is 5 kHz. The terminal device calculates the Nyquist sampling density according to the Nyquist sampling theorem. The Nyquist sampling frequency is twice the equivalent Doppler bandwidth, thus obtaining the Nyquist sampling density. That is, when the equivalent Doppler bandwidth is 5 kHz, the Nyquist sampling frequency is 10 kHz. If the time period of the reference signal is 1 ms, the Nyquist sampling density is 10 sampling points per millisecond, ensuring distortion-free sampling of the Doppler frequency shift.

[0048] Step 303: Obtain the residual error estimation bandwidth, determine the sparsification coefficient based on the ratio of the equivalent Doppler bandwidth to the residual error estimation bandwidth, and downsample the Nyquist sampling density according to the sparsification coefficient to obtain the sparse sampling density.

[0049] In one embodiment, the terminal device obtains the residual error estimation bandwidth from the system configuration. This bandwidth is determined based on the channel estimation error allowed by the system, assuming the residual error estimation bandwidth is 500Hz. The ratio of the equivalent Doppler bandwidth to the residual error estimation bandwidth is calculated to obtain the sparsity coefficient. For example, if the equivalent Doppler bandwidth is 5kHz and the residual error estimation bandwidth is 500Hz, then the sparsity coefficient is 0.1. The terminal device multiplies the Nyquist sampling density by the sparsity coefficient to obtain the sparse sampling density. For example, if the Nyquist sampling density is 10 sampling points per millisecond and the sparsity coefficient is 0.1, then the sparse sampling density is 1 sampling point per millisecond. This sparse sampling density is lower than the Nyquist sampling density, which can reduce the resource occupation of the reference signal while ensuring that the channel estimation error is within the allowable range.

[0050] The above method determines the equivalent Doppler bandwidth based on the phase change rate and calculates the sparsification coefficient by estimating the bandwidth using the residual error. This method can obtain the optimal reference signal density while meeting the channel estimation accuracy constraints. It can significantly sparsify high-density pilots, reduce time-frequency resource consumption, and ensure that the interpolation reconstruction is free of aliasing and distortion, thus achieving synergistic optimization of overhead and performance.

[0051] Figure 4 This is a flowchart of the sparse mapping provided in the embodiments of this application, such as... Figure 4 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 401: Determine whether the phase change rate of the phase rotation function satisfies the sparse sampling condition threshold.

[0052] In one embodiment, the terminal device obtains a preset sparse sampling condition threshold, which is determined based on factors such as system performance requirements and channel estimation accuracy requirements. Let's say the sparse sampling condition threshold is 80π rad / ms. The terminal device compares the calculated phase change rate with this threshold. If the phase change rate is less than or equal to the threshold, it indicates that the channel phase change is relatively smooth, meeting the sparse sampling condition, and the reference signal can be sparsified. If the phase change rate is greater than the threshold, it indicates that the channel phase change is too fast, and sparse sampling may lead to excessive channel estimation errors. In this case, the terminal device does not perform sparse sampling and transmits the reference signal using the sampling density specified by the standard protocol.

[0053] Step 402: If the phase change rate satisfies the sparse sampling condition threshold, then the time-domain sampling interval is determined according to the sparse sampling density, and the time-domain sampling interval is greater than the reference signal transmission period specified in the standard protocol.

[0054] In one embodiment, if the phase change rate meets the sparse sampling condition threshold, the terminal device calculates the time-domain sampling interval based on the sparse sampling density. The time-domain sampling interval is inversely proportional to the sparse sampling density; the lower the sparse sampling density, the larger the time-domain sampling interval. When the sparse sampling density is 1 sampling point per millisecond, the time-domain sampling interval is 1 ms. If the sparse sampling density is 1 sampling point per 5 milliseconds, the time-domain sampling interval is 5 ms. This time-domain sampling interval is greater than the reference signal transmission period specified in the standard protocol. If the standard protocol specifies that the reference signal transmission period is 1 ms, the time-domain sampling interval after sparse sampling is 5 ms, thereby extending the reference signal transmission period and reducing the number of reference signal transmissions.

[0055] Step 403: At the sampling time corresponding to the time domain sampling interval, the probe reference signal is mapped to different frequency domain positions of the full bandwidth according to the frequency domain frequency hopping mode, or the demodulation reference signal is mapped to the specified symbol position according to the mode of the pre-reference signal and the additional reference signal.

[0056] Among them, the frequency-domain frequency hopping mode refers to mapping the probe reference signal to different physical resource block groups, subcarrier groups, or comb positions within the full bandwidth of the system according to a preset frequency hopping sequence at different sparse sampling transmission times. This allows the probe reference signal transmitted at multiple times to traverse and cover the entire transmission bandwidth, achieving full-bandwidth channel detection. The pre-reference signal and supplementary reference signal mode refers to the demodulation reference signal adopting a segmented mapping structure. The demodulation reference signal is configured at the beginning symbol position of the transmission time slot to form the pre-reference signal, and configured at the middle symbol position of the transmission time slot to form the supplementary reference signal, which is used for initial channel estimation and time-varying channel tracking.

[0057] In one embodiment, for the sounding reference signal (SRS), the terminal device maps the reference signal to different frequency domain positions across the full bandwidth at the sampling time corresponding to the time domain sampling interval using a frequency domain frequency hopping mode. The frequency hopping mode can be random frequency hopping, sequential frequency hopping, etc. Optionally, the terminal device maps the SRS signal sequentially to 10 different frequency domain subcarrier positions within the 2GHz-2.01GHz bandwidth at a 10ms time domain sampling interval to ensure that the reference signal covers the entire frequency band and provides the base station with a full-bandwidth channel estimation basis. For the demodulation reference signal (DMRS), the terminal device maps it to a specified symbol position according to the pattern of a pre-reference signal and an additional reference signal. A pre-reference DMRS is configured at the first symbol position of each time slot, and an additional DMRS is configured at the seventh symbol position to ensure that the base station can accurately obtain channel state information during data demodulation.

[0058] Step 404: Send the mapped detection reference signal or the demodulation reference signal so that the base station can synchronize the phase rotation function and perform corresponding operations based on the identification information or configuration index.

[0059] In one embodiment, the terminal device adds identification information of the phase rotation function or configuration index of the sparse sampling density to the mapped probe reference signal or demodulated reference signal. For example, the identification information can be the feature code of the phase rotation function, and the configuration index is a preset index value corresponding to the sparse sampling density. For example, a configuration index of 001 corresponds to a sparse sampling density of 1 sampling point per millisecond. The terminal device sends the mapped reference signal to the base station through a satellite communication link. After receiving the reference signal, the base station can extract the identification information or configuration index to synchronize the phase rotation function and sparse sampling density of the terminal device, and then perform operations such as channel estimation, phase inversion rotation, and interpolation reconstruction to realize channel state information reconstruction.

[0060] As described above, by judging the sparsity condition and extending the time-domain sampling interval, and combining frequency-domain frequency hopping or pre-addition mode to complete the reference signal mapping, full bandwidth coverage and channel tracking can be achieved with the lowest pilot overhead. The sparse pilot, together with the synchronization flag transmission, enables the base station to quickly obtain the phase model, reducing the transmission frequency without sacrificing channel estimation performance.

[0061] Figure 5 This is a flowchart of the reference signal processing on the base station side provided in the embodiments of this application, such as... Figure 5 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 501: The base station receives a reference signal sent by the terminal device according to the sparse sampling density, wherein the reference signal is a detection reference signal or a demodulation reference signal.

[0062] Among them, the probe reference signal (SRS) is a reference signal sent by the terminal equipment to the base station for probing the channel quality; the demodulation reference signal (DMRS) is a reference signal sent by the terminal equipment to the base station along with the data signal for data demodulation; and the sparse sampling density is the reference signal sampling density determined by the terminal equipment based on the phase change rate of the phase rotation function.

[0063] In one embodiment, the base station receives a reference signal sent by a terminal device via a satellite receiving link. The reference signal is configured and sent by the terminal device according to a sparse sampling density and includes a probe reference signal or a demodulated reference signal. The base station preprocesses the received reference signal, including operations such as signal filtering, synchronization detection, and noise suppression, to remove interference and noise from the signal and extract effective reference signal components, providing signal input for subsequent operations such as identifier extraction and channel estimation.

[0064] Step 502: Extract the identification information of the phase rotation function or the configuration index of the sparse sampling density from the received reference signal, and obtain the phase rotation function synchronized with the terminal device based on the identification information or the configuration index.

[0065] In one embodiment, the base station parses the preprocessed reference signal to extract the identification information of the phase rotation function or the configuration index of the sparse sampling density carried in the signal. If the identification information of the phase rotation function is extracted, the base station calls the phase rotation function consistent with that of the terminal device from the locally synchronized Doppler predictor based on the identification information. The Doppler predictor stores multiple phase rotation functions and their corresponding identification information to ensure that the base station can accurately obtain the synchronized phase rotation function. If the configuration index of the sparse sampling density is extracted, the base station determines the sparse sampling density used by the terminal device based on the configuration index, and obtains relevant data from the locally stored satellite orbit parameters and the motion state information reported by the terminal device. It then performs the same phase rotation function generation operation as the terminal device, including relative radial velocity calculation, Doppler frequency shift time-varying function generation, time integration operation, etc., to obtain the phase rotation function synchronized with the terminal device, ensuring that the phase rotation function of the base station and the terminal device are completely consistent.

[0066] Step 503: Perform preliminary channel estimation on the received reference signal to obtain sparsely distributed original channel estimation samples.

[0067] Preliminary channel estimation refers to the process of making a preliminary estimate of the channel characteristics based on the received reference signal; the original channel estimation sample refers to the channel estimation value obtained at sparse time-frequency sampling locations, which is sparsely distributed.

[0068] In one embodiment, the base station uses algorithms such as least squares estimation and linear minimum mean square error estimation to perform preliminary channel estimation on the received reference signal; exemplarily, the least squares (LS) estimation algorithm is used, according to the formula... Perform preliminary channel estimation, where Let be the value of the received sparse reference signal at the k-th time-frequency sampling position. Let be the value of the local reference signal sequence at the k-th time-frequency sampling position. The original channel estimation sample for the k-th time-frequency sampling position is obtained by the base station only at sparse time-frequency sampling positions because the reference signal is transmitted according to sparse sampling density, forming a sparsely distributed original channel estimation sample. For example, the base station obtains the original channel estimation sample at a time-domain sampling interval of 5 milliseconds and 10 frequency-domain hopping positions. These samples are sparsely distributed and cannot directly reflect the channel state in all dimensions.

[0069] Step 504: Perform a phase inverse rotation operation on the original channel estimation sample based on the synchronized phase rotation function to obtain a baseband slow-varying channel sample with the Doppler component removed from the original channel.

[0070] Among them, the phase inverse rotation operation refers to the process of using the conjugate complex number of the phase rotation function to perform phase compensation on the original channel estimation sample; the baseband slow-varying channel sample refers to the channel estimation sample after eliminating the Doppler frequency shift component, whose phase change rate is reduced and exhibits slow-varying characteristics.

[0071] In one embodiment, the base station generates an inverse phase rotation factor based on a synchronized phase rotation function, where the inverse phase rotation factor is the conjugate complex number of the phase rotation function, i.e. , where Φ(t) is the phase rotation function and j is the imaginary unit; the base station multiplies the sparsely distributed original channel estimation samples with the anti-rotation phase factor to perform an anti-phase rotation operation, canceling the phase rotation caused by Doppler frequency shift in the original channel; the samples after anti-rotation processing are low-pass filtered to remove residual high-frequency noise and interference, resulting in a baseband slow-varying channel sample. The phase change rate of this sample is lower than that of the original channel estimation sample, exhibiting slow-varying characteristics.

[0072] Step 505: Perform time-frequency domain interpolation on the baseband slowly varying channel sample to obtain the full-dimensional baseband channel response.

[0073] Among them, time-frequency domain interpolation processing refers to performing interpolation operations on sparse baseband slowly varying channel samples in the time and frequency domains to complete the channel samples in all dimensions; full-dimensional baseband channel response refers to channel response information covering all time-frequency resources, which can comprehensively reflect the channel characteristics.

[0074] In one embodiment, the base station performs low-pass interpolation on the baseband slowly varying channel samples in the time domain. The interpolation algorithm can be linear interpolation, polynomial interpolation, spline interpolation, etc. The cutoff frequency of the interpolation is determined based on the bandwidth estimated by the residual error to ensure that the interpolated time-domain signal has no aliasing distortion, thus obtaining a continuous channel estimate in the time domain. The base station then performs interpolation or filtering on the continuous channel estimate in the frequency domain to complete the channel samples of the full bandwidth. For example, a linear interpolation algorithm is used to complete the channel response values ​​of all frequency domain subcarriers to obtain the full-dimensional baseband channel response.

[0075] Step 506: Perform a phase re-rotation operation on the full-dimensional baseband channel response to recover the time-varying channel state information, so as to complete the reference signal demodulation and channel detection based on the channel state information.

[0076] Among them, phase re-rotation operation refers to multiplying the full-dimensional baseband channel response with the phase rotation function to recover the Doppler frequency shift component in the original channel; time-varying channel state information refers to complete channel information that can reflect the channel's characteristics as it changes over time.

[0077] In one embodiment, the base station device retrieves the full-dimensional baseband channel response from the local channel reconstruction database. The phase re-rotation factor is generated by the synchronized phase rotation function Φ(t). The phase rerotation operation is performed by multiplying the full-dimensional baseband channel response by the phase rerotation factor. ,in, To achieve the final channel response, the time-varying characteristics caused by Doppler frequency shift in the original channel are restored, and complete time-varying channel state information is obtained. Based on this time-varying channel state information, the base station performs demodulation of the reference signal and channel detection, compensates for channel transmission distortion, and improves the reliability of signal transmission. For example, the base station uses the time-varying channel state information to perform equalization processing on the received data signal, eliminates the effects of channel fading and Doppler frequency shift, and achieves accurate data demodulation and signal detection.

[0078] The above-mentioned process, through receiving sparse pilot signals, synchronizing phase functions, inverse rotation, interpolation, and re-rotation, enables the recovery of high-precision time-varying channels with low pilot overhead. This process makes full use of Doppler prior information, avoids aliasing distortion caused by traditional direct interpolation, and improves demodulation and detection performance in high-dynamic scenarios of non-terrestrial networks.

[0079] Figure 6 This is a flowchart illustrating the acquisition of the base station-side phase rotation function provided in an embodiment of this application, as shown below. Figure 6 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 601: Parse the received probe reference signal or demodulation reference signal, and extract the identification information of the phase rotation function or the configuration index of the sparse sampling density carried in the probe reference signal or demodulation reference signal.

[0080] In one embodiment, the base station performs signal parsing on the received probe reference signal or demodulation reference signal to extract additional information fields carried in the signal. The base station identifies and extracts the identification information of the phase rotation function or the configuration index of the sparse sampling density from the additional information fields. The identification information can be a code of a preset length, and the configuration index is a preset value corresponding to the sparse sampling density. If the identification information is extracted, it indicates that the terminal device has uniformly identified the phase rotation function, and the base station can directly call the corresponding function through the identification. If the configuration index is extracted, it indicates that the base station needs to determine the sparse sampling density based on the index and generate a synchronized phase rotation function on its own.

[0081] Step 602: If the identification information of the phase rotation function is extracted, then based on the identification information, the phase rotation function consistent with that of the terminal device is called from the locally synchronized Doppler predictor.

[0082] In one embodiment, the base station maintains a locally synchronized Doppler predictor, which stores multiple phase rotation functions, each function corresponding to a unique identifier, and is kept synchronized with the phase rotation function of the terminal device. The base station matches the extracted identifier with the phase rotation function identifier stored in the Doppler predictor. After finding a matching phase rotation function, the base station calls the phase rotation function to ensure that the phase rotation function used by the base station is consistent with that of the terminal device, thus providing a basis for phase compensation for phase anti-rotation operation.

[0083] Step 603: If the configuration index of the sparse sampling density is extracted, the sparse sampling density used by the terminal device is determined based on the configuration index, and the same phase rotation function generation operation as that of the terminal device is performed based on the locally stored orbit parameters and the motion state information reported by the terminal device to obtain the phase rotation function synchronized with the terminal device.

[0084] In one embodiment, the base station, based on the extracted configuration index, queries a preset mapping table between the configuration index and the sparse sampling density to determine the sparse sampling density used by the terminal device. The base station obtains satellite orbit parameters from local storage and motion state information of the terminal device from the signaling reported by the terminal device. Following the same generation logic as the terminal device, the base station sequentially performs operations such as position vector and velocity vector calculation, relative radial velocity calculation, Doppler frequency shift time-varying function generation, and time integration operation to generate a phase rotation function. After generation, the base station verifies the function to ensure that it is synchronized with the phase rotation function of the terminal device. Verification methods can include phase value comparison and trend comparison to ensure the accuracy of subsequent phase anti-rotation operations.

[0085] As mentioned above, by extracting identifiers or configuring indexes to synchronize phase rotation functions, it is possible to ensure that the base station and terminal equipment use completely consistent Doppler prediction models. A unified phase model is a prerequisite for realizing phase inversion and reliable channel reconstruction, which can significantly improve the consistency of channel estimation and reduce system transmission errors.

[0086] Figure 7 This is a flowchart of the baseband slowly varying channel sample acquisition provided in the embodiments of this application, such as... Figure 7 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 701: Generate an anti-rotation phase factor based on the synchronized phase rotation function, wherein the anti-rotation phase factor is the conjugate complex number of the phase rotation function.

[0087] In one embodiment, the base station acquires a phase rotation function Φ(t) synchronized with the terminal device; the base station performs a conjugate operation on this phase rotation function to generate an inverse phase rotation factor, the expression of which is: This anti-rotation phase factor can cancel the phase rotation caused by Doppler frequency shift in the original channel estimation sample, providing a key factor for phase anti-rotation operation.

[0088] Step 702: Multiply the original channel estimation sample by the anti-rotation phase factor to perform a phase anti-rotation operation.

[0089] In one embodiment, the base station obtains sparsely distributed raw channel estimation samples. With anti-rotation phase factor Perform point-by-point multiplication, where This represents the sampling time of the k-th original sample. Through this multiplication operation, the phase rotation component in the original channel estimation sample is canceled out, resulting in an intermediate sample free from the Doppler shift effect. The intermediate sample initially removed the time-varying phase components caused by the relative motion between the satellite and the terminal.

[0090] Step 703: Filter the inverse-rotation processed samples to obtain the baseband slow-varying channel samples. The phase change rate of the baseband slow-varying channel samples is lower than the phase change rate of the original channel estimation samples.

[0091] In one embodiment, the base station uses a low-pass filter to filter the intermediate samples after inverse rotation. Filtering is performed, with the cutoff frequency of the low-pass filter determined based on the bandwidth estimated from the residual error. This filtering process removes high-frequency noise and residual Doppler components from the intermediate samples, yielding the baseband slowly varying channel samples. The phase change rate of this baseband slow-varying channel sample is significantly reduced, lower than that of the original channel estimation sample, exhibiting stable slow-varying characteristics. This facilitates time-frequency domain interpolation, effectively improving interpolation accuracy and reducing channel estimation errors.

[0092] As described above, by using phase inversion and low-pass filtering to transform the fast-changing channel into a baseband slow-changing channel sample, the channel change rate can be reduced, making subsequent interpolation more stable and accurate. After eliminating the Doppler dominant component, the channel exhibits slow-changing characteristics, which can improve interpolation accuracy and reduce estimation noise, thereby improving the final channel recovery quality.

[0093] Figure 8 This is a flowchart of the full-dimensional baseband channel response acquisition provided in the embodiments of this application, such as... Figure 8 As shown, the signal processing method in this non-terrestrial network includes the following steps: Step 801: Perform low-pass interpolation on the baseband slow-varying channel sample in the time domain to obtain a time-continuous channel estimate. The cutoff frequency of the low-pass interpolation is determined based on the residual error estimation bandwidth.

[0094] In one embodiment, the base station selects an interpolation algorithm for the baseband slowly varying channel samples. Time-domain low-pass interpolation is performed. The interpolation algorithm can be selected according to system performance requirements and is not limited here. For example, a cubic spline interpolation algorithm is used. The cutoff frequency of the low-pass interpolation is set, which is equal to the residual error estimation bandwidth, to ensure that the interpolated time-domain signal is free from aliasing distortion. The base station performs sparsely distributed baseband slowly varying channel samples. Interpolation is performed to complete the channel estimates for all time-domain moments, resulting in a time-continuous channel estimate. This estimate can reflect the continuous variation characteristics of the channel in the time domain.

[0095] Step 802: Interpolate or filter the time-domain continuous channel estimate in the frequency domain to complete the full-bandwidth channel sample and obtain the full-dimensional baseband channel response.

[0096] In one embodiment, the base station performs time-domain continuous channel estimation. A Fourier transform is performed to convert the data to the frequency domain, and processing is then performed in the frequency domain. If the frequency domain samples are sparse, an interpolation algorithm is used to complete the channel samples across the entire bandwidth. The interpolation algorithm can be linear interpolation, Lagrange interpolation, etc., and is not limited here. For example, a linear interpolation algorithm is used to complete the channel response values ​​for all frequency domain subcarriers. If there is noise interference in the frequency domain, a frequency domain filtering algorithm is used to filter the channel estimate, remove noise interference, and improve the smoothness of the channel response. Through frequency domain interpolation or filtering, the channel samples across the entire bandwidth are completed, resulting in the full-dimensional baseband channel response. , where t is time and f is frequency, representing that the response covers all time-frequency resources.

[0097] As described above, time-domain low-pass interpolation and frequency-domain completion based on residual error estimation bandwidth can reconstruct a smooth channel in all dimensions under sparse sample conditions; the unified bandwidth constraint ensures that the interpolation is free from aliasing and excessive smoothing, providing a channel response basis for phase re-rotation and signal demodulation.

[0098] Figure 9 This is a structural diagram of a signal processing device in a non-terrestrial network provided in an embodiment of this application. The device is configured to execute the signal processing method in a non-terrestrial network provided in the above embodiment, and has corresponding functional modules and beneficial effects for executing the method. Figure 9 As shown, the device specifically includes: The information acquisition module 901 is used to acquire the satellite's orbital parameters and the motion status information of the terminal equipment; The function generation module 902 is used to calculate the relative radial velocity between the satellite and the terminal device based on the orbital parameters and the motion state information, and to calculate the Doppler frequency shift time-varying function based on the relative radial velocity, and to generate a phase rotation function based on the Doppler frequency shift time-varying function. The phase rotation function characterizes the channel phase deterministic change trend caused by the relative motion of the satellite. The density determination module 903 is used to determine the sparse sampling density of the reference signal based on the phase change rate of the phase rotation function. The mapping and transmitting module 904 is used to determine the sparse sampling density of the reference signal based on the phase change rate of the phase rotation function.

[0099] As described above, by acquiring the satellite's orbital parameters and the motion status information of the terminal equipment, an input data source can be provided for calculating the relative motion between the satellite and the terminal. By calculating the relative radial velocity, the Doppler frequency shift time-varying function, and generating a phase rotation function, the deterministic phase change trend of the channel caused by the high-speed relative motion of the satellite can be accurately characterized, transforming the highly dynamic and rapidly changing channel into a predictable and compensable structured channel. By determining the sparse sampling density of the reference signal based on the phase change rate through the density determination module, the reference signal transmission density can be adaptively reduced while ensuring channel reconfigurability, thereby reducing pilot overhead and terminal power consumption. By completing the time-frequency mapping of the reference signal according to the sparse sampling density and sending it to the base station, the base station can complete high-precision channel reconstruction based on the sparse reference signal and the synchronous phase rotation function. The above scheme can significantly reduce the reference signal resource occupation and terminal transmission power consumption in high-dynamic scenarios of non-terrestrial networks, while avoiding interpolation aliasing and Doppler distortion in traditional channel estimation, thus improving channel estimation accuracy and signal demodulation performance.

[0100] In one embodiment, the function generation module 902 is specifically used for: The position and velocity vectors of the satellite are determined based on the orbital parameters, and the position and velocity vectors of the terminal device are determined based on the motion state information. Based on the position vector and velocity vector of the satellite and the position vector and velocity vector of the terminal device, the projection of the relative velocity between the satellite and the terminal device in the radial direction is calculated to obtain the relative radial velocity; The Doppler frequency shift time-varying function is calculated based on the relative radial velocity, carrier center frequency, and electromagnetic wave propagation speed. The phase rotation function is obtained by performing time integration on the Doppler frequency shift time-varying function.

[0101] In one embodiment, the density determination module 903 is specifically used for: Calculate the phase change rate of the phase rotation function within a preset time window, where the phase change rate reflects the instantaneous change rate of the channel phase; The equivalent Doppler bandwidth is determined based on the phase change rate, and the Nyquist sampling density is calculated based on the equivalent Doppler bandwidth. Obtain the residual error estimation bandwidth, determine the sparsification coefficient based on the ratio of the equivalent Doppler bandwidth to the residual error estimation bandwidth, and downsample the Nyquist sampling density according to the sparsification coefficient to obtain the sparse sampling density.

[0102] In one embodiment, the mapping sending module 904 is specifically used for: Determine whether the phase change rate of the phase rotation function satisfies the sparse sampling condition threshold; If the phase change rate satisfies the sparse sampling condition threshold, then the time-domain sampling interval is determined according to the sparse sampling density, and the time-domain sampling interval is greater than the reference signal transmission period specified in the standard protocol. At the sampling time corresponding to the time domain sampling interval, the probe reference signal is mapped to different frequency domain positions across the full bandwidth according to the frequency domain frequency hopping mode, or the demodulation reference signal is mapped to a specified symbol position according to the mode of the preceding reference signal and the additional reference signal. The mapped probe reference signal or demodulation reference signal is transmitted so that the base station can synchronize the phase rotation function and perform corresponding operations based on the identification information or configuration index.

[0103] In one embodiment, after the mapping transmission module 904, the base station is specifically used for: The base station receives a reference signal transmitted by the terminal equipment according to a sparse sampling density, wherein the reference signal is a detection reference signal or a demodulation reference signal; Extract the identification information of the phase rotation function or the configuration index of the sparse sampling density from the received reference signal, and obtain the phase rotation function synchronized with the terminal device based on the identification information or the configuration index; A preliminary channel estimation is performed on the received reference signal to obtain sparsely distributed original channel estimation samples; Based on the synchronized phase rotation function, the original channel estimation sample is subjected to phase inverse rotation to obtain a baseband slow-varying channel sample that eliminates the Doppler component in the original channel. Time-frequency domain interpolation is performed on the baseband slowly varying channel samples to obtain the full-dimensional baseband channel response; A phase re-rotation operation is performed on the full-dimensional baseband channel response to recover the time-varying channel state information, so as to complete the demodulation of the reference signal and channel detection based on the channel state information.

[0104] The base station is also used to: parse the received probe reference signal or demodulation reference signal, and extract the identification information of the phase rotation function or the configuration index of the sparse sampling density carried in the probe reference signal or demodulation reference signal; If the identification information of the phase rotation function is extracted, then based on the identification information, the phase rotation function consistent with that of the terminal device is called from the locally synchronized Doppler predictor; If the configuration index of the sparse sampling density is extracted, the sparse sampling density used by the terminal device is determined based on the configuration index, and the same phase rotation function generation operation as that of the terminal device is performed based on the locally stored orbit parameters and the motion state information reported by the terminal device to obtain the phase rotation function synchronized with the terminal device.

[0105] The base station is also configured to: generate an anti-rotation phase factor based on the synchronized phase rotation function, wherein the anti-rotation phase factor is the complex conjugate of the phase rotation function; Multiply the original channel estimation sample by the anti-rotation phase factor to perform a phase anti-rotation operation; The baseband slowly varying channel sample is obtained by filtering the sample after inverse rotation. The phase change rate of the baseband slowly varying channel sample is lower than that of the original channel estimation sample.

[0106] The base station is also used to: perform low-pass interpolation on the baseband slowly varying channel sample in the time domain to obtain a time-continuous channel estimate, wherein the cutoff frequency of the low-pass interpolation is determined based on the residual error estimation bandwidth; The time-domain continuous channel estimate is interpolated or filtered in the frequency domain to complete the full-bandwidth channel sample, thus obtaining the full-dimensional baseband channel response.

[0107] It is worth noting that in the above embodiments of the signal processing device in non-terrestrial networks, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not configured to limit the protection scope of the embodiments of this application.

[0108] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 10 As shown, the electronic device includes a processor 1001, a memory 1002, an input device 1003, and an output device 1004.

[0109] The number of processors 1001 can be one or more. Figure 10Taking a processor 1001 as an example; the processor 1001, memory 1002, input device 1003, and output device 1004 can be connected via a bus or other means. Figure 10 Taking a bus connection as an example, the memory 1002, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the signal processing method in the non-terrestrial network in this embodiment of the application. The processor 1001 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 1002, that is, it implements the aforementioned signal processing method in the non-terrestrial network, including the acquisition of satellite orbit parameters and motion state information on the terminal side, generation of phase rotation functions, determination of sparse sampling density, and transmission of reference signal mapping, as well as the reception of reference signals, synchronization of phase rotation functions, channel estimation and reconstruction, etc., on the base station side. The input device 1003 can be configured to receive input satellite orbit parameters, terminal motion state information, system configuration parameters, and other digital or character information, and generate key signal inputs related to the user settings and function control of the device. The output device 1004 can be configured to send reference signals, signaling messages, and other output information, and display the device's working status, configuration parameters, and other display content. This electronic device, through the combination of hardware and software, realizes signal processing functions in non-terrestrial networks, can adapt to the needs of high-dynamic communication scenarios of low-orbit satellites, reduce pilot overhead, and improve channel estimation accuracy and signal transmission reliability.

[0110] This application also provides a storage medium for storing computer-executable instructions. When executed by a computer processor, these computer-executable instructions are used to perform all steps of the signal processing method in the non-terrestrial network described in the above embodiments. The storage medium includes non-volatile storage media such as flash memory, hard disk, optical disk, and USB flash drive. The stored program instructions can be called and executed by the terminal device processor or the base station device processor. In specific execution, the processor executes program instructions to perform the following operations: acquire the satellite's orbital parameters and the motion state information of the terminal equipment; calculate the relative radial velocity between the satellite and the terminal equipment based on the orbital parameters and motion state information, generate a Doppler frequency shift time-varying function, and then obtain the phase rotation function; determine the sparse sampling density of the reference signal according to the phase change rate of the phase rotation function; map the reference signal to the sparse time-frequency sampling position according to the sparse sampling density and send it to the base station; after receiving the reference signal, the base station extracts the identification information or configuration index and synchronizes the phase rotation function; performs preliminary channel estimation on the reference signal to obtain the original channel estimation sample; performs phase inversion operation to obtain the baseband slowly varying channel sample; performs time-frequency domain interpolation processing on the baseband slowly varying channel sample to obtain the full-dimensional baseband channel response; performs phase re-rotation operation to restore the time-varying channel state information and complete the demodulation of the reference signal and channel detection. This storage medium provides a persistent storage carrier for the method of this application, facilitating program distribution, device pre-installation, and system upgrades, ensuring stable execution of the method on different hardware platforms, and meeting the standardized deployment and maintenance requirements of non-terrestrial network systems.

[0111] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments provided herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A signal processing method in a non-terrestrial network, applied to a terminal device, characterized in that, The processing method includes: Acquire satellite orbital parameters and motion status information of terminal equipment; Based on the orbital parameters and the motion state information, the relative radial velocity between the satellite and the terminal device is calculated, and the Doppler frequency shift time-varying function is calculated based on the relative radial velocity. A phase rotation function is generated based on the Doppler frequency shift time-varying function, and the phase rotation function characterizes the deterministic change trend of the channel phase caused by the relative motion of the satellite. The sparse sampling density of the reference signal is determined based on the phase change rate of the phase rotation function. The reference signal is mapped to a sparse time-frequency sampling location according to the sparse sampling density and sent to the base station, so that the base station performs corresponding operations based on the reference signal synchronized at the sparse time-frequency sampling location to reconstruct the channel state information.

2. The method of claim 1, wherein, The process of calculating the relative radial velocity between the satellite and the terminal device based on the orbital parameters and the motion state information, and calculating the Doppler frequency shift time-varying function based on the relative radial velocity, and generating a phase rotation function based on the Doppler frequency shift time-varying function, includes: The position and velocity vectors of the satellite are determined based on the orbital parameters, and the position and velocity vectors of the terminal device are determined based on the motion state information. Based on the position vector and velocity vector of the satellite and the position vector and velocity vector of the terminal device, the projection of the relative velocity between the satellite and the terminal device in the radial direction is calculated to obtain the relative radial velocity; The Doppler frequency shift time-varying function is calculated based on the relative radial velocity, carrier center frequency, and electromagnetic wave propagation speed. The phase rotation function is obtained by performing time integration on the Doppler frequency shift time-varying function.

3. The method of claim 1, wherein, Determining the sparse sampling density of the reference signal based on the phase change rate of the phase rotation function includes: Calculate the phase change rate of the phase rotation function within a preset time window, where the phase change rate reflects the instantaneous change rate of the channel phase; The equivalent Doppler bandwidth is determined based on the phase change rate, and the Nyquist sampling density is calculated based on the equivalent Doppler bandwidth. Obtain the residual error estimation bandwidth, determine the sparsification coefficient based on the ratio of the equivalent Doppler bandwidth to the residual error estimation bandwidth, and downsample the Nyquist sampling density according to the sparsification coefficient to obtain the sparse sampling density, wherein the sparse sampling density is lower than the density required by the Nyquist sampling theorem for the original channel Doppler frequency shift.

4. The method of claim 1, wherein, The step of mapping the reference signal to sparse time-frequency sampling positions according to the sparse sampling density and sending it to the base station includes: Determine whether the phase change rate of the phase rotation function satisfies the sparse sampling condition threshold; If the phase change rate satisfies the sparse sampling condition threshold, then the time-domain sampling interval is determined according to the sparse sampling density, and the time-domain sampling interval is greater than the reference signal transmission period specified in the standard protocol. At the sampling time corresponding to the time domain sampling interval, the probe reference signal is mapped to different frequency domain positions across the full bandwidth according to the frequency domain frequency hopping mode, or the demodulation reference signal is mapped to a specified symbol position according to the mode of the preceding reference signal and the additional reference signal. The mapped detection reference signal or the demodulation reference signal is sent so that the base station can synchronize the phase rotation function based on the identification information or configuration index and perform corresponding operations; The detection reference signal or the demodulation reference signal carries the identification information of the phase rotation function or the configuration index of the sparse sampling density.

5. The method of signal processing in a non-terrestrial network of claim 4, wherein, The processing method further includes: The base station receives a reference signal transmitted by the terminal equipment according to a sparse sampling density, wherein the reference signal is a detection reference signal or a demodulation reference signal; Extract the identification information of the phase rotation function or the configuration index of the sparse sampling density from the received reference signal, and obtain the phase rotation function synchronized with the terminal device based on the identification information or the configuration index; A preliminary channel estimation is performed on the received reference signal to obtain sparsely distributed original channel estimation samples; Based on the synchronized phase rotation function, the original channel estimation sample is subjected to phase inverse rotation to obtain a baseband slow-varying channel sample that eliminates the Doppler component in the original channel. Time-frequency domain interpolation is performed on the baseband slowly varying channel samples to obtain the full-dimensional baseband channel response; A phase re-rotation operation is performed on the full-dimensional baseband channel response to recover the time-varying channel state information, so as to complete the demodulation of the reference signal and channel detection based on the channel state information.

6. The treatment method according to claim 5, characterized in that, The step of obtaining the phase rotation function synchronized with the terminal device based on the identification information or the configuration index includes: The received probe reference signal or demodulation reference signal is parsed, and the identification information of the phase rotation function or the configuration index of the sparse sampling density carried in the probe reference signal or demodulation reference signal is extracted. If the identification information of the phase rotation function is extracted, then based on the identification information, the phase rotation function consistent with that of the terminal device is called from the locally synchronized Doppler predictor; If the configuration index of the sparse sampling density is extracted, the sparse sampling density used by the terminal device is determined based on the configuration index, and the same phase rotation function generation operation as that of the terminal device is performed based on the locally stored orbit parameters and the motion state information reported by the terminal device to obtain the phase rotation function synchronized with the terminal device.

7. The treatment method of claim 5, wherein, The phase rotation function based on synchronization performs a phase inverse rotation operation on the original channel estimation sample to obtain a baseband slowly varying channel sample with Doppler components removed from the original channel, including: An anti-rotation phase factor is generated based on the synchronized phase rotation function, wherein the anti-rotation phase factor is the complex conjugate of the phase rotation function; Multiply the original channel estimation sample by the anti-rotation phase factor to perform a phase anti-rotation operation; The baseband slowly varying channel sample is obtained by filtering the sample after inverse rotation. The phase change rate of the baseband slowly varying channel sample is lower than that of the original channel estimation sample.

8. The processing method of claim 5, wherein, The step of performing time-frequency domain interpolation on the baseband slowly varying channel samples to obtain the full-dimensional baseband channel response includes: The baseband slowly varying channel sample is low-pass interpolated in the time domain to obtain a time-continuous channel estimate. The cutoff frequency of the low-pass interpolation is determined based on the residual error estimation bandwidth. The time-domain continuous channel estimate is interpolated or filtered in the frequency domain to complete the full-bandwidth channel sample, thus obtaining the full-dimensional baseband channel response.

9. A signal processing apparatus in a non-terrestrial network, characterized by, include: The information acquisition module is used to acquire the satellite's orbital parameters and the motion status information of the terminal equipment; The function generation module is used to calculate the relative radial velocity between the satellite and the terminal device based on the orbital parameters and the motion state information, and to calculate the Doppler frequency shift time-varying function based on the relative radial velocity, and to generate a phase rotation function based on the Doppler frequency shift time-varying function. The phase rotation function characterizes the deterministic change trend of the channel phase caused by the relative motion of the satellite. The density determination module is used to determine the sparse sampling density of the reference signal based on the phase change rate of the phase rotation function. The mapping and transmission module is used to map the reference signal to a sparse time-frequency sampling position according to the sparse sampling density and transmit it to the base station, so that the base station can perform corresponding operations based on the reference signal synchronized by the sparse time-frequency sampling position to reconstruct the channel state information.

10. An electronic device, comprising: include: Memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the signal processing method in a non-terrestrial network as described in any one of claims 1-8.

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