A wireless resource scheduling method and device for a handset direct connection satellite scenario

Abstract

The application provides a wireless resource scheduling method and device for a mobile phone direct connection satellite scene, relates to the technical field of satellite communication, and comprises the following steps: an electromagnetic map representing the corresponding relationship among the geographical position, physical resource block and ground interference power in the satellite beam coverage area is constructed; the link budget of a plurality of user terminals is calculated by combining the real-time orbit geometric parameters of the satellite and the electromagnetic map, and the signal-to-interference-and-noise ratio of each user terminal on each physical resource block is predicted; based on the predicted signal-to-interference-and-noise ratio, the proportional fair marginal gain greedy algorithm is adopted to allocate the optimal continuous frequency spectrum resource block to each user terminal; the water injection algorithm is adopted to optimize and re-distribute the total satellite transmission power on the allocated frequency spectrum resource; a final spectrum-power joint scheduling scheme is generated, and subsequent link adaptation and data transmission are guided. The application significantly improves the spectrum efficiency, throughput and connection stability of the system.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and more specifically, to a method and apparatus for wireless resource scheduling in scenarios where mobile phones are directly connected to satellites. Background Technology

[0002] In recent years, with the rapid deployment and commercial operation of Low Earth Orbit (LEO) satellite constellations, satellite communication is becoming a key technology for achieving seamless global coverage and filling gaps in terrestrial network services. Among these technologies, Direct-to-Cell (DtC) communication, as a core application scenario within the Non-Terrestrial Network (NTN) architecture, aims to enable ordinary smartphones and other terminal devices to directly access satellite networks without special modifications, and is receiving widespread attention from the industry.

[0003] In DtC communication systems, a core challenge lies in the effective utilization of spectrum resources. To achieve broad coverage and service compatibility, satellite systems typically need to coexist with existing terrestrial cellular networks in the same or adjacent frequency bands. This spectrum-sharing model inevitably leads to severe co-channel interference from terrestrial base stations and terminals. This interference exhibits significant spatiotemporal-frequency heterogeneity, meaning that the interference intensity varies greatly across different geographical locations, times, and physical resource blocks (PRBs).

[0004] Traditional wireless resource management methods, especially the resource scheduling mechanisms widely used in terrestrial cellular networks, typically rely heavily on channel quality indicators or channel state information periodically reported by user terminals. The scheduler allocates spectrum and power resources to users based on the channel quality feedback from the UE. However, directly applying such traditional scheduling methods to dynamic scenarios where mobile phones directly connect to satellites has the following drawbacks:

[0005] 1. Severely Aging Channel Information Feedback: LEO satellites move at high speeds exceeding 7 km / s, causing rapid changes in the link between satellites and ground terminals, including significant latency and Doppler effects. This results in channel quality indication information reported by user terminals being severely outdated by the time it reaches the scheduler at the satellite or ground gateway, failing to accurately reflect the current instantaneous channel conditions. Scheduling decisions based on this outdated channel quality indication information lead to resource misallocation, resulting in a significant decrease in system throughput and an increase in retransmission rate.

[0006] 2. Coarse-grained interference perception: Traditional channel quality indication (PRB) reporting is usually based on the average channel quality across the entire channel bandwidth or a relatively wide subband. This broadband averaging method masks significant differences within the spectrum, failing to distinguish between "clean PRBs" with minimal ground interference and "polluted PRBs" with severe contamination. Consequently, the scheduler lacks fine-grained frequency domain awareness, missing opportunities to optimize spectrum resources for users and thus limiting the improvement of spectrum efficiency.

[0007] 3. Rigid Power Allocation Strategy: After spectrum allocation, traditional power allocation strategies often employ equal power allocation, assigning the same transmit power to multiple PRBs allocated to the same user. This approach fails to consider the channel quality differences within the allocated resource blocks of a user, resulting in power being wasted on PRBs with poor channel conditions, while PRBs with good channel conditions fail to obtain sufficient power to maximize their transmission potential, leading to overall low power utilization efficiency. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the present invention aims to provide a wireless resource scheduling method and apparatus for mobile phone direct satellite connection scenarios. The resource scheduling mechanism for mobile phone direct satellite connection scenarios has been redesigned and optimized, which can more accurately match channel conditions, thereby significantly improving the system's spectrum efficiency and data transmission performance.

[0009] To solve the above problems, the technical solution of the present invention is as follows:

[0010] A method for wireless resource scheduling in scenarios where mobile phones directly connect to satellites includes the following steps:

[0011] Construct an electromagnetic map that characterizes the relationship between geographical location, physical resource blocks, and ground interference power within the satellite beam coverage area;

[0012] By combining real-time satellite orbital geometry parameters and electromagnetic maps, link budgets are performed for multiple user terminals to predict the signal-to-interference-plus-noise ratio (SIR) of each user terminal on each physical resource block.

[0013] Based on the predicted signal-to-interference-plus-noise ratio, a proportional fair marginal gain greedy algorithm is used to allocate the optimal continuous spectrum resource block to each user terminal.

[0014] On the already allocated spectrum resources, the water-filling algorithm is used to optimize and redistribute the total satellite transmission power;

[0015] The final spectrum-power joint scheduling scheme is generated and used to guide subsequent link adaptation and data transmission.

[0016] Preferably, in the step of constructing an electromagnetic map that characterizes the correspondence between geographical locations, physical resource blocks, and ground interference power within the satellite beam coverage area, the electromagnetic map is constructed in the following ways: measurement-driven: data is collected by deploying a ground sensor network or using a terminal with measurement capabilities, and spatial interpolation is performed on the measurement data to generate the map; or, model-driven: a professional electromagnetic simulation software is used to input a city building model, the distribution of ground interference sources, and emission parameters, and ray tracing simulation is performed to generate the map.

[0017] Prior to this, the step of combining real-time satellite orbital geometry parameters and electromagnetic maps to perform link budgeting for multiple user terminals and predict the signal-to-interference-plus-noise ratio (SIR) of each user terminal on each physical resource block specifically includes: within each scheduling cycle, for multiple user terminals within the coverage area, performing link budgeting in conjunction with real-time satellite orbital geometry parameters, and extracting the interference power of the corresponding location and physical resource block from the electromagnetic map, thereby predicting the SIR of each user terminal on each physical resource block.

[0018] Prior to this, the step of allocating the optimal continuous spectrum resource blocks to each user terminal using a proportional fair marginal gain greedy algorithm based on the predicted signal-to-interference-plus-noise ratio (SINR) specifically includes: iteratively allocating physical resource blocks to each user terminal based on the predicted SINR, and in each iteration, selecting the "user-physical resource block" allocation combination that can bring the maximum proportional fair marginal gain to the system, until all available physical resource blocks have been allocated.

[0019] Prior to this, the step of optimizing and redistributing the total satellite transmit power using a water-filling algorithm on the allocated spectrum resources specifically includes: the water-filling algorithm determines a global water level line, allocates more transmit power to physical resource blocks with good channel quality, and allocates less or no transmit power to physical resource blocks with poor channel quality, thereby maximizing the overall throughput.

[0020] Furthermore, the present invention also provides a wireless resource scheduling device for a mobile phone directly connected to a satellite scenario, comprising:

[0021] The electromagnetic map management module is used to store and provide electromagnetic map data that records the correspondence between geographical location, physical resource blocks and prior interference power;

[0022] The link budget module is used to combine satellite orbital geometry parameters with the electromagnetic map data to predict the signal-to-interference-plus-noise ratio of each user terminal in the coverage area on each physical resource block.

[0023] The resource scheduling module is used to perform joint spectrum and power allocation based on the predicted signal-to-interference-plus-noise ratio.

[0024] Preferably, the resource scheduling module includes:

[0025] The spectrum allocation unit is configured to use a marginal gain greedy algorithm to allocate the optimal spectrum resource block to the user terminal;

[0026] The power allocation unit is configured to optimize and redistribute the total satellite transmit power using a water-filling algorithm after the spectrum allocation is completed.

[0027] Preferably, the spectrum allocation unit allocates spectrum according to a proportional fair scheduling algorithm when making allocation decisions.

[0028] Compared with existing technologies, the method of this invention uses a priori 3D electromagnetic map as the interference information source, combines real-time satellite orbit geometry to perform link budgeting to predict the signal-to-interference-plus-noise ratio (SIR / NDR) of each user in each physical resource block (PRB), and then employs a two-stage joint optimization algorithm, first using a greedy strategy based on proportional fair marginal gain for spectrum allocation and then using an algorithm based on the water-filling principle for power redistribution. This invention improves the spectral efficiency, system throughput, and transmission robustness of mobile phone direct-to-satellite baseband systems, and solves key performance degradation problems caused by channel information aging, coarse interference perception granularity, and rigid power allocation, providing technical support for intelligent spectrum access and resource management for future large-scale low-Earth orbit constellations. Attached Figure Description

[0029] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0030] Figure 1 This is a macroscopic application scenario diagram of the present invention;

[0031] Figure 2 This is a flowchart of the wireless resource scheduling method for mobile phones directly connected to satellites in the present invention.

[0032] Figure 3 This is a schematic diagram of the electromagnetic map structure in this invention;

[0033] Figure 4 This is a schematic diagram of the PF-MUGS algorithm in this invention;

[0034] Figure 5 This is a schematic diagram of the water injection algorithm in this invention;

[0035] Figure 6 This is a structural block diagram of the wireless resource scheduling device for mobile phone direct satellite connection scenarios according to the present invention. Detailed Implementation

[0036] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the scope of protection of the present invention.

[0037] Specifically, typical macroscopic application scenarios of the present invention are as follows: Figure 1 As shown, in this scenario, the satellite provides direct service to DTC users far from terrestrial network coverage, while the terrestrial base station provides communication for local cellular users. When both operate on the same or adjacent frequency bands, co-channel interference may occur, thus requiring effective interference coordination and resource optimization allocation. To address the challenges in this scenario, this invention proposes a wireless resource scheduling method for direct satellite connection scenarios, such as... Figure 2 As shown, the method includes the following steps:

[0038] S1: Construct an electromagnetic map that represents the relationship between geographical location, physical resource blocks and ground interference power within the satellite beam coverage area;

[0039] Specifically, a three-dimensional electromagnetic map covering the satellite communication service area is acquired or constructed. This electromagnetic map records prior interference power information on a predetermined geographic location grid and physical resource blocks (PRBs). For example... Figure 3 As shown in the figure, all are The grid matrix, corresponding to The electromagnetic map shows the communication service coverage area, and the shades of the color represent the magnitude of the interference power (in dBm). Logically, this electromagnetic map constitutes a three-dimensional interference power distribution map. Through this map, the distribution pattern of interference power of different PRBs in geographic space can be observed intuitively, providing basic data for subsequent signal-to-interference-plus-noise ratio prediction and resource allocation optimization.

[0040] The methods for constructing the electromagnetic map include, but are not limited to:

[0041] 1. Measurement-driven: Long-term data acquisition is carried out by deploying a ground sensor network or using terminals with measurement capabilities, and spatial interpolation is performed on the measurement data to generate the data.

[0042] 2. Model-driven: Using professional electromagnetic simulation software, inputting urban building models, the distribution of ground interference sources (such as ground base stations), and emission parameters, ray tracing simulation is performed to generate the electromagnetic map. Maintaining the electromagnetic map is a dynamic process; it can be adapted to slow changes in the interference environment by incorporating a small number of real-time reported interference measurements and using machine learning models for online calibration and updates of the prior map.

[0043] S2: Combining real-time satellite orbital geometry parameters and electromagnetic maps, perform link budgeting for multiple user terminals and predict the signal-to-interference-plus-noise ratio of each user terminal on each physical resource block;

[0044] Specifically, for multiple user terminals within the coverage area, link budgeting is performed using real-time satellite orbital geometry parameters, and the interference power of the corresponding location and PRB is extracted from the electromagnetic map to predict the signal-to-interference-plus-noise ratio (SINR) of each user terminal on each PRB. In a specific embodiment, within each scheduling cycle, for the user set within the service area, the geometric parameters of each user are calculated using the real-time satellite orbit solution and beam pointing, and a three-state link visualization model is established accordingly. This model completes the evaluation of large / small-scale losses and co-channel interference plus noise, ultimately obtaining the instantaneous predicted SINR on each physical resource block.

[0045] S3: Based on the predicted signal-to-interference-plus-noise ratio, the optimal continuous spectrum resource block is allocated to each user terminal using a proportional fair marginal gain greedy algorithm.

[0046] Specifically, the spectrum allocation step is performed by iteratively allocating PRBs to each user terminal based on the predicted signal-to-interference-plus-noise ratio (SINR). In each iteration, the “user-PRB” allocation combination that can bring the maximum proportional fair marginal gain to the system is selected until all available PRBs are allocated.

[0047] The core objective of this step is to provide user sets with [something] within a scheduling period (TTI). Allocate the available set of Physical Resource Blocks (PRBs) This invention employs the Proportional Fair Marginal Utility Greedy Scheduler (PF-MUGS) algorithm. The core principle of the PF-MUGS algorithm is as follows: Figure 4 As shown, its operation is based on the benefit assessment of the proportional fairness criterion and a greedy multi-user resource selection mechanism. In each iteration, the algorithm selects the action that brings the maximum power factor (PF) gain, and gradually allocates consecutive power factor (PRB) to different users until all spectrum resources are allocated.

[0048] The design concept of this invention employs an efficient greedy heuristic algorithm. Its core logic lies in decomposing the global, one-time allocation decision into a series of sequential, micro-level allocation decisions. At each step, the algorithm does not aim for a distant global optimum, but rather shortsightedly selects the smallest allocation action (i.e., allocating a PRB) that brings the maximum marginal utility to the system. This utility is precisely defined by a proportional fairness metric, which, by dividing the instantaneous throughput gain (numerator) by the user's historical average throughput (denominator), naturally achieves a delicate balance between maximizing the system's instantaneous performance and ensuring long-term service fairness among users. Through a series of carefully selected locally optimal decisions, the algorithm gradually constructs a globally high-quality suboptimal solution.

[0049] The PF-MUGS algorithm is an iterative process. In each iteration, it evaluates all possible "minimum allocation actions" and executes the optimal one.

[0050] To perform accurate and fair spectrum allocation, the Proportional Fair Marginal Utility Greedy Scheduler (PF-MUGS) algorithm relies on a well-defined set of input information. The fundamental input to this algorithm is the predicted signal-to-interference-plus-noise ratio (SINR) matrix, denoted as... It provides the scheduler with information on each active user within the current scheduling period. In each available physical resource block (PRB) High-fidelity channel quality prediction is performed. To achieve proportional fairness, the algorithm must also receive a vector as input, which records the historical service level of each user and represents the user's historical average throughput. It is for every user Before the current scheduling decision ( The algorithm provides quantitative data on resource acquisition at any given time. Furthermore, to prioritize urgent retransmission requests and ensure link reliability and low latency, the algorithm also requires a hybrid automatic repeat request state vector as input. The elements in this vector identify which users currently have data packets to retransmit, allowing the scheduler to assign a priority weight to the utility calculations of these users. After processing this input information through its iterative greedy decision-making process, the PF-MUGS algorithm ultimately generates a deterministic spectrum allocation scheme as its core output. This output is specifically represented as a set of user resource allocations. , where each independent set It precisely includes the information allocated to the user. The indexes of that set of consecutive PRBs. These output resource block sets strictly satisfy mutual exclusion, that is, for any two different users... and , there must be Furthermore, they are collectively complete, ensuring that all available spectrum resources are allocated.

[0051] The Proportional Fair Marginal Utility Greedy Scheduler (PF-MUGS) algorithm is the core algorithm designed in this invention to address the complex resource allocation challenges in direct satellite communication between mobile phones. This algorithm cleverly transforms a computationally intractable NP-hard combinatorial optimization problem into a series of deterministic locally optimal decisions that can be completed in polynomial time. By introducing the metric of "proportional fair marginal utility," it identifies the "user-PRB" allocation action that contributes the most to the overall system utility in each iteration. Through this iterative greedy construction process, the PF-MUGS algorithm can generate a highly optimized spectrum allocation scheme that conforms to practical engineering constraints (such as PRB continuity) while maintaining low computational complexity.

[0052] S4: On the already allocated spectrum resources, the water-filling algorithm is used to optimize and redistribute the total satellite transmission power;

[0053] Specifically, the Water-Filling algorithm is used to optimize and redistribute the total satellite transmit power on the allocated spectrum resources. After determining a unique spectrum allocation scheme in step S3, the goal of this step is to optimize and redistribute the power of all allocated PRBs under the constraint of the total satellite transmit power. This problem can be solved using the classic Water-Filling algorithm, the physical principle of which is as follows: Figure 5 As shown in the figure. This algorithm maximizes overall throughput by determining a global watermark and allocating more transmit power to PRBs with good channel quality, while allocating less or no transmit power to PRBs with poor channel quality.

[0054] The power allocation algorithm based on the water-filling principle is a key optimization method employed in this invention to achieve refined management of satellite launch power after spectrum resource allocation is determined. This algorithm provides an optimal solution to the classic problem of maximizing the total capacity of multiple channels under limited total power conditions. By intuitively analogizing the complex optimization problem to filling a container with uneven bottom with water, its core lies in calculating the equivalent noise level of each allocated PRB and iteratively finding a globally unique water level line. This ensures that power resources are not allocated evenly or arbitrarily, but rather preferentially tilted according to the intrinsic quality of each channel. Ultimately, PRBs with higher channel quality receive more power, while PRBs with lower quality receive less or even no power.

[0055] S5: Generate the final spectrum-power joint scheduling scheme and guide subsequent link adaptation and data transmission.

[0056] Specifically, through the above-mentioned refined spectrum and power allocation steps, the method of the present invention can perform refined configuration of wireless resources at both the macro (between users) and micro (within users) levels, and finally generate a spectrum-power joint scheduling scheme, which is used to guide subsequent link adaptation and data transmission, thereby maximizing system performance in the complex mobile phone direct satellite communication environment and completing the dynamic resource management closed loop of the system.

[0057] The intelligent scheduling method based on electromagnetic maps proposed in this invention fundamentally solves the core drawbacks of traditional scheduling mechanisms in scenarios where mobile phones directly connect to satellites by replacing reactive channel feedback with predictive interference priors. This method not only consistently improves system spectral efficiency, but more importantly, it demonstrates the potential to continuously expand its performance advantages in future, more complex, and diverse satellite-ground integrated network architectures. It provides a proven and feasible technical path for building an efficient, reliable, and highly adaptable next-generation satellite communication resource management system.

[0058] Furthermore, the present invention also provides a wireless resource scheduling device for scenarios where mobile phones are directly connected to satellites, such as... Figure 6 As shown, the device includes:

[0059] Electromagnetic map management module 1 is used to store and provide electromagnetic map data that records the correspondence between geographical location, physical resource blocks (PRBs) and prior interference power;

[0060] Link budget module 2 is used to combine satellite orbital geometry parameters with the electromagnetic map data to predict the signal-to-interference-plus-noise ratio (SINR) of each user terminal in the coverage area on each PRB.

[0061] Resource scheduling module 3 is used to perform joint spectrum and power allocation based on the predicted signal-to-interference-plus-noise ratio (SINR). The resource scheduling module includes:

[0062] The spectrum allocation unit 4 is configured to use a marginal gain greedy algorithm to allocate the optimal spectrum resource block to the user terminal. When making allocation decisions, the spectrum allocation unit allocates according to a proportional fairness (PF) scheduling algorithm, which comprehensively considers the marginal increment of spectrum efficiency and the user's historical throughput.

[0063] Power allocation unit 5 is configured to optimize and redistribute the total satellite transmit power using a water-filling algorithm after the spectrum allocation is completed.

[0064] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A wireless resource scheduling method for mobile phone direct satellite connection scenarios, characterized in that, The method includes the following steps: Construct an electromagnetic map that characterizes the relationship between geographical location, physical resource blocks, and ground interference power within the satellite beam coverage area; By combining real-time satellite orbital geometry parameters and electromagnetic maps, link budgets are performed for multiple user terminals to predict the signal-to-interference-plus-noise ratio (SIR) of each user terminal on each physical resource block. Based on the predicted signal-to-interference-plus-noise ratio (SIR), a proportional fairness marginal utility greedy scheduler algorithm is used to allocate optimal continuous spectrum resource blocks to each user terminal. Specifically, this includes: iteratively allocating physical resource blocks to each user terminal based on the predicted SIR; in each iteration, selecting the "user-physical resource block" allocation combination that brings the maximum proportional fairness marginal gain to the system, until all available physical resource blocks are allocated; in each decision step, the proportional fairness marginal utility greedy scheduler algorithm selects the minimum allocation action that brings the maximum marginal utility to the system, i.e., allocating one physical resource block. Utility is defined by a proportional fairness metric, which is calculated by dividing the instantaneous throughput gain by the user's historical average throughput. The input to the proportional fairness marginal utility greedy scheduler algorithm is the predicted SIR matrix, denoted as... It provides the scheduler with information on each active user within the current scheduling period. In each available physical resource block For high-fidelity channel quality prediction, the algorithm also receives a vector recording the historical service level of each user as input, which represents the user's historical average throughput. It is for every user The algorithm provides a quantitative basis for the resource acquisition situation prior to the current scheduling decision. It also requires a hybrid automatic repeat request state vector as input. The elements in this vector identify which users currently have data packets to retransmit, allowing the scheduler to assign a priority weight to the utility calculations of these users. After processing this input information through its iterative greedy decision-making process, the proportionally fair marginal utility greedy scheduler algorithm ultimately generates a deterministic spectrum allocation scheme as its output, which is represented as a set of user resource allocations. , where each independent set Includes allocation to users That set of consecutive PRB indexes, for any two different users and , there must be Furthermore, they are collectively complete, ensuring that all available spectrum resources are allocated; On the already allocated spectrum resources, the water-filling algorithm is used to optimize and redistribute the total satellite transmission power; The final spectrum-power joint scheduling scheme is generated and used to guide subsequent link adaptation and data transmission.

2. The wireless resource scheduling method for mobile phone direct satellite connection scenarios according to claim 1, characterized in that, In the step of constructing an electromagnetic map that characterizes the correspondence between geographical locations, physical resource blocks, and ground interference power within the satellite beam coverage area, the electromagnetic map is constructed in the following ways: Measurement-driven: Data is acquired through the deployment of a ground-based sensor network or by utilizing measurement-capable terminals, and spatial interpolation is performed on the measurement data to generate the desired output; or Model-driven: Using professional electromagnetic simulation software, input the city building model, the distribution of ground interference sources and emission parameters, and perform ray tracing simulation generation.

3. The wireless resource scheduling method for mobile phone direct satellite connection scenarios according to claim 1, characterized in that, The step of combining real-time satellite orbital geometry parameters and electromagnetic maps to perform link budgeting for multiple user terminals and predict the signal-to-interference-plus-noise ratio (SIR) of each user terminal on each physical resource block specifically includes: within each scheduling cycle, for multiple user terminals within the coverage area, performing link budgeting based on real-time satellite orbital geometry parameters, and extracting the interference power of the corresponding location and physical resource block from the electromagnetic map, thereby predicting the SIR of each user terminal on each physical resource block.

4. The wireless resource scheduling method for mobile phone direct satellite connection scenarios according to claim 1, characterized in that, The step of optimizing and redistributing the total satellite transmission power using a water-filling algorithm on the allocated spectrum resources specifically includes: the water-filling algorithm maximizes the overall throughput by determining a global water level.

5. A wireless resource scheduling device for a mobile phone direct satellite connection scenario, used to perform the method according to any one of claims 1 to 4, characterized in that, The device includes: The electromagnetic map management module is used to store and provide electromagnetic map data that records the correspondence between geographical location, physical resource blocks and prior interference power; The link budget module is used to combine satellite orbital geometry parameters with the electromagnetic map data to predict the signal-to-interference-plus-noise ratio of each user terminal in the coverage area on each physical resource block. The resource scheduling module is used to perform joint spectrum and power allocation based on the predicted signal-to-interference-plus-noise ratio.

6. The wireless resource scheduling device for mobile phone direct satellite connection scenarios according to claim 5, characterized in that, The resource scheduling module includes: The spectrum allocation unit is configured to use a marginal gain greedy algorithm to allocate the optimal spectrum resource block to the user terminal; The power allocation unit is configured to optimize and redistribute the total satellite transmit power using a water-filling algorithm after the spectrum allocation is completed.

7. The wireless resource scheduling device for mobile phone direct satellite connection scenarios according to claim 6, characterized in that, The spectrum allocation unit uses a proportional fair marginal utility greedy scheduler algorithm to make allocation decisions.

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