A communication resource scheduling method and system based on heterogeneous carrier aggregation

Abstract

The application discloses a communication resource scheduling method and system based on heterogeneous subcarrier aggregation, and is applied to an unmanned aerial vehicle (UAV) assisted emergency communication scene. The application divides terminals into non-orthogonal multiple access groups meeting scale constraints by dynamically performing user clustering; based on the characteristics of a heterogeneous spectrum resource pool, multiple types of physical resource blocks are allocated to user groups with different channel qualities, and large bandwidth resources and high signal quality links are preferentially matched; transmission power is optimized in combination with channel states and serial interference cancellation conditions to ensure reliable decoding and minimum rate compliance within the group; by cooperatively optimizing three elements of user grouping, spectrum allocation and power control, joint decision of spectrum fragmentation integration and interference suppression is realized, and system throughput is significantly improved. The application utilizes real-time position parameters of the unmanned aerial vehicle and terminals, and adaptively changes a dynamic communication environment, thereby reducing deployment complexity while ensuring stability of emergency services.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication network resource management, in particular to a communication resource scheduling method and system based on heterogeneous subcarrier aggregation. BACKGROUND

[0002] In the unmanned aerial vehicle (UAV) assisted emergency communication system, efficient use of limited wireless resources (especially spectrum and power) is the key to improving system throughput.

[0003] With the rise of 5G and 6G technologies, the types of PRB resources have become diverse, but under the huge communication demand, PRB resources are still very scarce. In emergency scenarios, the problem of PRB resource scarcity will become more urgent. This greatly hinders users from contacting rescue personnel in time to seek help. How to reasonably manage and utilize these limited spectrum resources is not only a major challenge for the development of future communication technologies, but also an urgent demand for users in emergency scenarios.

[0004] In the unmanned aerial vehicle (UAV) assisted emergency communication system, efficient use of limited spectrum and power resources is the core challenge to improve system throughput. The existing technology has the following key bottlenecks:

[0005] 1. Spectrum resource allocation rigid problem

[0006] Although the current 5G-NR system supports multiple physical resource block configurations, in the heterogeneous spectrum fragment scenario, the traditional allocation scheme performs poorly. The average allocation strategy allocates the same bandwidth resource to all user groups, but ignores the differences in channel quality of different user groups, resulting in that the high signal quality group cannot obtain large bandwidth resources. The fixed matching strategy statically binds a specific type of resource block to a user group, which cannot dynamically aggregate discrete spectrum fragments, such as unable to integrate 100MHz, 50MHz and 25MHz resource blocks at the same time, resulting in a significant reduction in spectrum utilization.

[0007] 2. User grouping and power control split problem:

[0008] The existing non-orthogonal multiple access technology adopts a static user grouping mechanism, which only divides user clusters based on channel gain ordering, without considering the impact of spectrum allocation on intra-group interference, resulting in an increased risk of decoding failure of strong user signals. At the same time, the power allocation scheme is decoupled from the spectrum resource configuration: either fixedly using the maximum transmit power, or only optimizing the power under a single type of resource block, lacking joint modeling of spectrum aggregation bandwidth and interference cancellation constraints. The actual measurement shows that when the number of users exceeds 8, the throughput of the traditional scheme decreases by 23%.

[0009] The above limitations jointly cause three contradictions: spectrum fragmentation hinders wideband resource aggregation, limiting the available bandwidth of a single user; power control and packet strategy separation exacerbate intra-group interference, reducing the reliability of interference cancellation; and insufficient dynamic scene adaptability causes emergency communication throughput fluctuations to exceed 30%, making it difficult to guarantee the minimum rate requirement of critical services. Especially in scenarios such as real-time transmission of high-definition images, existing technologies cannot stably meet the minimum rate threshold of 838 million bits per second, seriously affecting the efficiency of emergency rescue.

[0010] In summary, there is an urgent need to develop a joint decision mechanism that can synergistically optimize user grouping, spectrum dynamic aggregation, and precise power control, maximize the total throughput of the system while ensuring the feasibility of interference cancellation and meeting the minimum rate requirement. SUMMARY

[0011] The present application provides a communication resource scheduling method and system based on heterogeneous subcarrier aggregation to overcome the shortcomings of the prior art.

[0012] To achieve the above invention purposes, the technical solutions adopted by the present application are as follows:

[0013] A communication resource scheduling method based on heterogeneous subcarrier aggregation is used in a UAV-assisted communication system, the method comprising the following steps:

[0014] User clustering: divide the user set N = {1, 2,..., N} into multiple non-orthogonal multiple access (NOMA) groups I = {1, 2,..., I}, where each NOMA group satisfies the constraint condition:

[0015] (each NOMA group contains at least two users),

[0016] (each user belongs to only one NOMA group),

[0017] where ε n,i is a binary variable indicating whether user n belongs to NOMA group i;

[0018] Spectrum allocation: allocate physical resource blocks (PRBs) in the spectrum resource pool Q = {Q1, Q2,..., Q K} to each NOMA group, where Q k represents the set of the kth type of PRB, k ∈ K = {1, 2,..., K}, and the allocation strategy is defined by the binary variable α i,k,x :

[0019] α i,k,x = 1 indicates that the xth PRB of the kth type is allocated to NOMA group i, and satisfies the constraint:

[0020] (Each PRB is assigned to only one NOMA group),

[0021] (The number of PRBs for each NOMA group does not exceed the upper limit Ω max );

[0022] Power optimization: optimize the transmit power of each user to maximize the total throughput where the throughput r n of user n is defined as:

[0023]

[0024] where p n represents the user transmit power, g n represents the channel gain, σ 2 represents the noise power, p D represents the minimum power difference for SIC decoding, r min represents the minimum throughput requirement, B k represents the bandwidth of the kth type of PRB, X k represents the number of resource blocks of the kth type of PRB, N i represents the user set within group i, |N i | represents the total number of users within the group.

[0025] The constraints need to be satisfied:

[0026] (The user transmit power does not exceed the maximum limit),

[0027] (SIC decoding constraint),

[0028] (minimum throughput requirement);

[0029] Overall optimization goal: maximize the total throughput between users and UAVs by jointly optimizing {ε n,i}, {p n}, {α i,k,x}.

[0030] Further, the user clustering step specifically includes:

[0031] According to the channel power gain between the user and the UAV sort the users in descending order;

[0032] The sorted users are assigned to NOMA groups in turn, wherein the user with the highest channel gain is assigned to the first position in the group, and the sorting of users in each NOMA group satisfies that the channel gain is from high to low, so as to minimize the intra-group interference;

[0033] The grouping rule satisfies that when the number of users is N, the user grouping manner is:

[0034] For j∈[1,N], if jI≤|N|, the users {(j-1)I+1,...,jI} are assigned to the jth position of the NOMA group {1,...,I};

[0035] Otherwise, the remaining users {(j-1)I+1,...,|N|} are assigned to the jth position of the NOMA group {1,...,|N|-(j-1)I}.

[0036] Further, the step of spectrum allocation specifically comprises:

[0037] The total signal-to-interference-and-noise ratio (SINR) of each NOMA group is calculated;

[0038] The NOMA groups are sorted in descending order of total SINR;

[0039] The NOMA group with the highest SINR is preferentially assigned to a PRB type with a larger bandwidth, and the assignment order is: starting from the PRB type with the largest bandwidth (B k value), until the number of PRBs of the group reaches Ω max or there is no PRB available for assignment;

[0040] wherein the bandwidth B k of the PRB type k satisfies the total bandwidth consistency, and the assignment strategy ensures efficient aggregation of spectrum resources.

[0041] Further, the step of power optimization is implemented by using the Lagrange multiplier method, comprising:

[0042] A Lagrange function L is constructed, and multipliers λ n , μ n , and ψ n are introduced

[0043]

[0044] wherein W i represents the total bandwidth of the NOMA group i, which is calculated from the spectrum allocation result; p n represents the transmission power of the user n; g n represents the channel gain from the user n to the UAV; σ2 represents the Gaussian white noise power spectral density; |N i | represents the total number of users in the NOMA group i; and j∈Ni denotes the user index of the user n; p max denotes the maximum transmit power of a single user; φ denotes the minimum throughput requirement r min denotes the converted SINR threshold; p D denotes the minimum power difference required for SIC decoding.

[0045] By solving and partial derivative equations, the optimal power allocation is obtained:

[0046] If B' = S, C' = S, then

[0047] If B' ≠ S, C' = S, then

[0048] and

[0049] If B' = S, C' ≠ S, then

[0050] and

[0051] Further, the method is applied to an emergency communication scenario, wherein the UAV coordinates are defined as (x M , y M , H), the user coordinates are defined as (x n , y n ), the Euclidean distance between the user and the UAV is and the channel gain g n is calculated based on the distance.

[0052] Further, the bandwidth B k of the kth type of PRB in the spectrum resource pool satisfies: B1 > B2 > B3 (for example, B1 = 100 MHz, B2 = 50 MHz, and B3 = 25 MHz), and the total bandwidth of each type of PRB is consistent.

[0053] Further, in the constraint r n ≥ r min , the minimum throughput requirement r min = 8.38 × 10 8 bps, and the power difference threshold p D in the SIC constraint = 10 dBm.

[0054] The application further discloses a communication resource scheduling system based on heterogeneous subcarrier aggregation, which can be used to implement the above-mentioned communication resource scheduling method, and specifically comprises:

[0055] The user clustering module is configured to divide a user set into a plurality of non-orthogonal multiple access groups, ensure that each group contains at least two users and each user belongs to only one group, and record the grouping relationship through a binary grouping identification variable;

[0056] The spectrum aggregation module is configured to allocate different types of physical resource blocks to each user group from a heterogeneous spectrum resource pool, preferentially allocate large bandwidth resource blocks to high signal quality groups, and ensure that each resource block is allocated only once and the number of single-group resource blocks does not exceed an upper limit;

[0057] The power optimization module is configured to optimize the transmission power based on the channel gain of the user and the unmanned aerial vehicle, calculate the user throughput under the premise of meeting the power upper limit, the serial interference cancellation decoding condition and the minimum rate requirement;

[0058] The cooperative decision processor is connected and coordinates the user clustering module, the spectrum aggregation module and the power optimization module, jointly optimizes the grouping strategy, the spectrum allocation scheme and the power control instruction, to maximize the total throughput of the system, and outputs the scheduling instruction to the unmanned aerial vehicle communication base station;

[0059] The deployment scene interface is configured to receive the unmanned aerial vehicle coordinates and user position information in real time, and calculate the channel gain parameters between the user and the unmanned aerial vehicle.

[0060] The application also discloses a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the above-mentioned communication resource scheduling method.

[0061] Compared with the prior art, the application has the following advantages:

[0062] 1. By designing dynamic aggregation of different types of physical resource blocks, discrete spectrum fragments are integrated into available wide frequency band resources. The system intelligently allocates resource blocks with different bandwidths according to the signal quality of the user group, breaking the limitation of static binding of resource types and user groups in traditional schemes. By utilizing the combinability of heterogeneous bandwidth resource blocks, the system effectively adapts to the multi-granularity spectrum scenarios of 5G-NR system, and solves the problem of low utilization rate of fragmented spectrum.

[0063] 2. In the user grouping stage, the power control strategy is optimized synchronously to ensure the decoding reliability of strong user signals in the non-orthogonal multiple access group. By predicting the influence of spectrum allocation on inter-group interference, combined with the serial interference cancellation constraint condition, the risk of signal aliasing is actively avoided. This mechanism significantly reduces the communication interruption probability in high-density user scenarios and ensures the stability of critical business transmission.

[0064] 3. The joint optimization architecture of packet, spectrum and power is adopted to break through the limitation of isolated decision of each link in traditional resource scheduling. The global optimal scheduling scheme is generated by the real-time integration of channel state, location information and resource pool characteristics by the cooperative processor. This integrated decision mode avoids the conflict of subsystem goals and fundamentally improves the upper limit of system throughput.

[0065] 4. The channel gain is calculated based on the dynamic position of the unmanned aerial vehicle and the user in real time to automatically respond to the fast-moving emergency communication environment. The system continuously adjusts the resource allocation strategy to offset the influence of terrain changes and link fluctuations under the premise of ensuring the minimum transmission rate. Its adaptability can meet the reliable transmission demand of sudden high-load services and improve the emergency rescue communication support capability.

[0066] 5. The user clustering, spectrum aggregation and power optimization functions are modularly packaged to form a standardized resource scheduling logic. The module interface is compatible with the mainstream unmanned aerial vehicle communication platform parameter protocol to support plug-and-play system integration. This design greatly reduces the technical threshold and operation and maintenance cost of deploying a cooperative communication system in a heterogeneous base station network. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 is a flow chart of the communication resource scheduling method of the embodiment of the present application;

[0068] Figure 2 is a user grouping schematic diagram of the embodiment of the present application;

[0069] Figure 3 is a multi-spectrum aggregation schematic diagram of the embodiment of the present application;

[0070] Figure 4 is a total throughput change diagram of the CRS-HSA algorithm and the ABS algorithm under different NOMA groups of the embodiment of the present application;

[0071] Figure 5 is a total throughput comparison diagram of the CRS-HSA algorithm, the ABS algorithm and the CSFP algorithm under different numbers of users of the embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the following will further describe the present application according to the drawings and examples.

[0073] As Figure 1As shown, the application provides a communication resource scheduling method based on heterogeneous carrier aggregation. Through the allocation of spectrum resources and the optimization of user transmit power, the total throughput of users is maximized. Different types of PRB resources are fully scheduled to each NOMA group, and under the premise that the user transmit power is not higher than the maximum transmit power, the interference between NOMA group users is reduced, and the throughput maximization between users and UAVs is realized.

[0074] I. System model

[0075] The user set is defined as N={1, 2,..., N}. The user coordinates are defined as (x n ,y n ), and the UAV coordinates are defined as (x M ,y M ,H). d n is the Euclidean distance between the user and the UAV.

[0076]

[0077] In order to obtain greater communication bandwidth and higher spectrum efficiency, users are divided into multiple NOMA groups. According to the principle of uplink NOMA technology, user tasks are transmitted in a non-orthogonal manner on the frequency spectrum, so that users in the same NOMA group can share spectrum resources. The set of NOMA groups is defined as I={1, 2,..., I}. W i represents the communication bandwidth of the ith NOMA group.

[0078] Define a binary variable ε n,i n represents whether user n belongs to NOMA group i, i.e.

[0079]

[0080] According to the grouping rules of users, it is necessary to satisfy

[0081]

[0082] (3a) represents that each NOMA group contains at least two users, and (3b) represents that each user can only belong to one NOMA group. Therefore, the user set belonging to the ith NOMA group is represented as

[0083]

[0084] The uplink NOMA system selects to decode users according to the channel power gain. Specifically, when multiple users send signals to the UAV through the uplink at the same time, the signals of users with weak channel gain are decoded first. The user with the strongest channel gain will be interfered by other users. For example, in the ith NOMA group, users are sorted according to the channel power gain from the highest to the lowest. The signal of the nth user is interfered by the signals of the remaining users in the group, i.e., the (n+1), (n+2),..., |N i | users. The SINR of user n communicating with the UAV can be represented as

[0085]

[0086] p n represents the transmit power of the nth user, σ 2 represents the noise power, and the channel gain is the same as in the third chapter.

[0087] Since different PRB types have different bandwidths, the number of PRBs required to achieve the same total bandwidth is also different. To meet the communication needs of the NOMA group, different PRB types are aggregated to improve the spectrum utilization efficiency in the case of limited PRB resources. K types of PRBs are considered, and the total bandwidth of each type of PRB is consistent. The type of PRB is represented by the set K = {1, 2,..., K}. The bandwidth and number of the kth type of PRB are represented as B k ,X k . At the same time, Q k is defined to represent the set of the kth type of PRB, represented as

[0088]

[0089] Therefore, the spectrum resource pool is

[0090] Q = {Q1, Q2,..., Q K}. (7)

[0091] A binary variable α i,k,x is defined to represent the PRB allocation strategy, represented as

[0092]

[0093] The throughput between user n and the UAV is represented as

[0094]

[0095] II. Problem modeling

[0096] By jointly optimizing the user transmit power and spectrum resource allocation strategy, the throughput maximization between users and UAVs is achieved, which can be expressed as

[0097]

[0098]

[0099] Constraint (10b) represents that the transmit power of a user cannot be greater than the maximum transmit power. Constraint (10c) represents that the user power in each NOMA group satisfies the successive interference cancellation (SIC) constraint, where p D is the minimum power difference required to distinguish the decoded signal from the remaining undecoded message signal. Constraint (10d) ensures that the throughput of users in each NOMA group meets the minimum throughput requirement r min . Constraint (10e) specifies that there are at least two users in one NOMA group. Constraint (10f) specifies that one user can only belong to one NOMA group. Constraint (10g) represents that each PRB can only be allocated to one NOMA group. Constraint (10h) specifies that the number of PRBs for each NOMA group cannot exceed the upper limit of the number of PRBs max .

[0100] The joint optimization of integer variables and continuous variables poses a huge computational challenge, making it difficult to directly solve the optimization problem. To solve this problem, the optimization problem is decomposed into multiple sub-problems, including user clustering and spectrum allocation problems, user transmit power optimization problems.

[0101] III. Problem optimization

[0102] (1) User clustering and spectrum allocation sub-problem

[0103] To maximize the total throughput of users, users are grouped according to the channel gain between users, and PRB resources are allocated in these NOMA groups using a spectrum allocation method. This optimization sub-problem is expressed as

[0104]

[0105] To save spectrum resources, the present invention proposes a user clustering algorithm that divides users into multiple NOMA groups with controllable low levels. First, arrange the users in descending order of their channel power gain. Then, starting from the first user, allocate users to each NOMA group. Users with stronger channel gain will occupy a more forward position in each NOMA group, thereby minimizing interference to users with weaker channel power gain.

[0106] Figure 2For a five-user group example, here the five users are divided into two NOMA groups. First, the users are sorted according to the channel gain, with the user having the highest channel power gain being placed first. Among the 5 users, user A has the highest channel power gain and is assigned to the first position in NOMA group 1. User B has the second highest channel power gain after A and is assigned to the first position in NOMA group 2. Then, since there is no third NOMA group, user C is assigned to the second position in NOMA group 1, and similarly, user D is assigned to the second position in NOMA group 2. Finally, user E, which has the lowest channel power gain, is assigned to the last position in NOMA group 1. By using this method, the interference of the user with a stronger channel power gain on the user with a weaker channel power gain can be mitigated, thereby improving the total system throughput.

[0107] To determine the value of a i,k,x , a PRB allocation algorithm based on the total SINR of a NOMA group is proposed. The algorithm calculates the throughput of each NOMA group without considering the channel bandwidth and ranks them in descending order, and then assigns a sufficient number of PRBs to each NOMA group to ensure that the constraint (11d) is met. Then, under the condition that the inter-user interference is controllable, according to the size of the throughput of each NOMA group, the NOMA group with a larger throughput will be preferentially allocated PRBs with a larger bandwidth, so that the user group with a better SINR condition can obtain a larger communication bandwidth until the NOMA has Ω max PRBs or there are no more PRBs to allocate. This algorithm can allow the NOMA group with a better SINR condition to obtain a larger communication bandwidth while ensuring that the inter-user interference within each NOMA group is small. The proposed spectrum aggregation process is as follows Figure 3 .

[0108] (2) Transmit power optimization subproblem

[0109] After user grouping and spectrum optimization, PRBs are allocated to all NOMA groups from the resource pool. The bandwidth allocated to NOMA group i is denoted by W i . For NOMA group i, the power allocation problem is described as

[0110]

[0111] To solve the above problem, the Lagrange multiplier method is used to optimize the power within the NOMA group. According to the inequality constraint in (12), the Lagrange multipliers λ n , μ n , and ψ n are introduced.

[0112]

[0113] wherein, The set of vectors P is denoted as The user transmit power vector is denoted as

[0114] The Lagrange multipliers are denoted as: The solution set S is denoted as

[0115] The partial derivatives of the Lagrangian function L with respect to the variables n , μ n , ψ n are taken and set to zero to obtain the following system of equations:

[0116]

[0117] In a NOMA group of size |N i |, the Lagrange multiplier method introduces (3|N i |-1) Lagrange multipliers, which solve three sets of constraints: maximum transmit power constraint, minimum throughput constraint, and SIC constraint. Meanwhile, another three sets of Lagrange multipliers are defined to simplify the description:

[0118] A' = S - A, B' = S - B, C' = S - C.

[0119]

[0120] When all elements in the solution set S come from set A, we have B' = S, C' = S, and the transmit power of all users in the NOMA group satisfies equation (15a). When the elements in the solution set S contain elements in set B, we have B' ≠ S, C' = S, and the |N i |th user satisfies equation (15b), and the rest of the users satisfy equation (15a). When the elements in the solution set S contain elements in set C, we have B' = S, C' ≠ S, where the |N i |th user satisfies equation (15c), and the rest of the users satisfy equation (15a). The lemma 1 is obtained, for a NOMA user group of size |N i |, the optimal transmit power of the users is:

[0121] Lemma 1: if B' = S, C' = S:

[0122]

[0123] Lemma 2: if B' ≠ S, C' = S:

[0124]

[0125] Lemma 3: if B' = S, C' ≠ S:

[0126]

[0127] Based on the established lemmas, it can be observed that in the uplink NOMA system targeting at maximizing the total user throughput, power control is only necessary for the user under the worst channel condition. All other users can transmit with the maximum transmission power. Table 1 summarizes the proposed algorithm.

[0128] Table 1

[0129]

[0130]

[0131] IV. Simulation and Results Analysis

[0132] In the simulation, the total bandwidth of each PRB is set to be uniform size, and the number of the first type of PRB is taken as the standard. The proposed CRS-HSA algorithm is compared with the following benchmark algorithms: (1) Average Bandwidth Scheduling (ABS) algorithm: the PRB resources are uniformly allocated to each NOMA group. The user clustering and power allocation algorithms are the same as those of the CRS-HSA algorithm; (2) Communication Scheduling with Fixed Power (CSFP) algorithm: the transmission power of the user is set to p max The user clustering algorithm and PRB allocation algorithm are consistent with those of the CRS-HSA algorithm. The simulation parameters involved and their values are shown in Table 2

[0133] Table 2 Simulation Parameters

[0134]

[0135] Figure 4To compare the total throughput of the CRS-HSA algorithm and the ABS algorithm with the increase of the number of NOMA groups, the number of users is set to 20, and the number of the first PRB is set to 10. When the users are divided into 4 NOMA groups, the maximum throughput is achieved. When the number of NOMA groups exceeds 4, the throughput no longer increases significantly. The reason is that the high concentration of users in a single NOMA group will cause significant interference, which has an adverse effect on the throughput. When the number of users in each NOMA group decreases, the interference between users also decreases. However, due to the limited number of available PRBs, it is difficult for NOMA groups to obtain additional PRB resources. Since the total bandwidth is limited, the total throughput no longer changes significantly. When the number of NOMA groups is 4, the total throughput of the CRS-HSA algorithm is 16% higher than that of the ABS algorithm. It can be verified that the total throughput of the proposed CRS-HSA algorithm is better than that of the ABS algorithm.

[0136] Figure 5 To compare the total throughput of the CRS-HSA, ABS, and CSFP algorithms under different user numbers and different maximum transmission powers. With the increase of the number of users, the throughput of all algorithms will improve. At the same time, a larger maximum transmission power helps to achieve higher total throughput. Compared with the ABS algorithm, the CRS-HSA algorithm can allocate PRBs more reasonably according to different channel gain conditions and increase the communication bandwidth. When the maximum transmission power is 24dBm and the number of users is 10, the total throughput of the CRS-HSA algorithm is 8% higher than that of the ABS algorithm. Compared with the CSFP algorithm, the CRS-HSA algorithm can use the Lagrange multiplier method to optimize user transmission power and reduce interference within the NOMA group to increase the total throughput. When the maximum transmission power is 24dBm and the number of users is 10, the total throughput of the CRS-HSA algorithm is 11% higher than that of the CSFP algorithm.

[0137] In another embodiment of the present application, a communication resource scheduling system based on heterogeneous subcarrier aggregation is provided, which can be used to implement the above-mentioned communication resource scheduling method, and specifically includes:

[0138] A user clustering module configured to divide a user set into a plurality of non-orthogonal multiple access groups, ensure that each group contains at least two users and each user belongs to only one group, and record the grouping relationship through a binary grouping identification variable;

[0139] A spectrum aggregation module configured to allocate different types of physical resource blocks to each user group from a heterogeneous spectrum resource pool, preferentially allocate large bandwidth resource blocks to high signal quality groups, and ensure that each resource block is allocated only once and the number of single-group resource blocks does not exceed the upper limit;

[0140] Power optimization module: configured to optimize the transmission power based on the channel gain between the user and the UAV, and to calculate the user throughput under the premise of meeting the power upper limit, serial interference cancellation decoding condition and minimum rate requirement;

[0141] Cooperative decision processor: connected and coordinated with the user clustering module, the spectrum aggregation module and the power optimization module, jointly optimizing the grouping strategy, the spectrum allocation scheme and the power control instruction to maximize the system total throughput, and outputting the scheduling instruction to the UAV communication base station;

[0142] Deployment scenario interface: configured to receive the UAV coordinates and the user position information in real time, and to calculate the channel gain parameters between the user and the UAV according to the information.

[0143] In another embodiment of the present application, a terminal device is provided, which comprises a processor and a memory, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement a corresponding method process or a corresponding function. The processor in the embodiment of the present application can be used for the operation of the communication resource scheduling method.

[0144] In still another embodiment, a computer program product, preferably a computer-readable storage medium (Memory) is provided, which is a memory device of a terminal device, for storing programs and data. It should be understood that the computer-readable storage medium here can include an internal storage medium of the terminal device, and of course can also include an expansion storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores an operating system of the terminal. Moreover, one or more instructions adapted to be loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory.

[0145] The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the communication resource scheduling method in the above embodiments; the one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0146] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0147] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0148] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0150] Those skilled in the art will realize that the embodiments described herein are for illustrative purposes only and that various modifications and changes in light thereof will be apparent to those skilled in the art without departing from the scope and spirit of the application. It is therefore understood that this application can encompass all such modifications and changes as fall within the scope of the appended claims.

Claims

1. A method for communication resource scheduling based on heterogeneous carrier aggregation, used in a UAV-assisted communication system, characterized in that, The method comprises the following steps: User clustering: partitioning a set of users into a plurality of non-orthogonal multiple access (NOMA) groups where each NOMA group satisfies a constraint condition: where each NOMA group satisfies a constraint condition: ， ， wherein is a binary variable indicating whether user n belongs to NOMA group i; Spectrum allocation: Physical resource blocks (PRBs) in a spectrum resource pool are allocated to NOMA groups, where Q k denotes the set of k-th type of PRBs, k e , and the allocation strategy is defined by a binary variable : = 1 indicates that the xth PRB of the kth type is allocated to the NOMA group i and satisfies the constraint: ， ， wherein denotes the upper limit of the number of PRBs per NOMA group; The spectrum allocation step specifically comprises: Calculate the total signal-to-interference-and-noise ratio (SINR) of each NOMA group; Sort the NOMA groups in descending order of total SINR; The NOMA group with the highest SINR is preferentially allocated a PRB type with a larger bandwidth, and the allocation order is: starting from the PRB type with the largest bandwidth, until the number of the group PRBs reaches or no allocable PRB; wherein the bandwidth of PRB type k satisfies the total bandwidth consistency, and the allocation strategy ensures efficient aggregation of spectrum resources; Power optimization: optimize the transmit power p of each user n , to maximize the total throughput where the throughput r of user n is defined as: n ​ ， The constraint needs to be met: ， ， ， where p n denotes the transmit power of user n, p j denotes the transmit power of user j, g n denotes the channel gain from user n to the UAV, g j denotes the channel gain from user j to the UAV, σ 2 denotes the noise power, p D denotes the minimum power difference for SIC decoding, r min denotes the minimum throughput requirement, B k denotes the bandwidth of the kth PRB, X k denotes the number of resource blocks of the kth PRB, N i denotes the user set within group i, N i denotes the total number of users within the group; j e N i denotes the user index after user n within the group, p max denotes the maximum transmit power for single user; Overall optimization goal: maximize the total throughput between users and UAVs by joint optimization }, {p n}, { } 2. The method of claim 1, wherein, The user clustering step specifically comprises: According to a channel power gain between a user and a UAV Ranking users in descending order; Assign the sorted users to the NOMA groups in turn, with the user having the highest channel gain being assigned to the first position in the group, and the order of the users in each NOMA group satisfying the condition that the channel gain decreases from high to low, so as to minimize the intra-group interference; The grouping rule satisfies: when the number of users is N, the user grouping mode is: For , if , then assign the user to the position of the NOMA group ; Otherwise, the remaining users are assigned to the jth position of the NOMA group .

3. The method of claim 1, wherein, The power optimization step is implemented by using the Lagrange multiplier method, comprising: A Lagrangian function L is constructed, introducing a multiplier Processing constraints: ， wherein ; W i denotes the total bandwidth of NOMA group i, calculated by the spectrum allocation result; p n denotes the transmit power of user n; g n denotes the channel gain from user n to the UAV; σ 2 denotes the Gaussian white noise power spectral density; |N i | denotes the total number of users within the NOMA group i; j∈N i denotes the user index after user n in the group; p max denotes the maximum transmit power of a single user; p D denotes the minimum power difference required for SIC decoding; The optimal power allocation is obtained by solving the partial derivative equations n = 0 and the partial derivative equations. If then , If then and ; If then and ; wherein ; solution set denoted as .

4. The method of claim 1, wherein The method applies to emergency communication scenarios, wherein the UAV coordinates are defined as , the user coordinates are defined as , the Euclidean distance between the user and the UAV is , and the channel gain is calculated based on the distance.

5. The method of claim 1, wherein, a bandwidth B of the kth type of PRB in the spectrum resource pool k satisfying: B1>B2>B3, and the total bandwidth of each type of PRB is consistent.

6. The method for scheduling communication resources according to claim 1, wherein, The constraint r n ≥ In the minimum throughput requirement =8.38 bps, and the power difference threshold in the SIC constraint =10dBm.

7. A communication resource scheduling system based on heterogeneous carrier aggregation, characterized in that, The system can be used to implement the communication resource scheduling method according to any one of claims 1 to 6, specifically comprising: The user clustering module is configured to divide the user set into multiple non-orthogonal multiple access (NOMA) groups, ensure that each group contains at least two users and each user belongs to only one group, and record the grouping relationship through a binary grouping identification variable; The spectrum aggregation module is configured to allocate different types of physical resource blocks to each user group from a heterogeneous spectrum resource pool, preferentially allocate large bandwidth resource blocks to high signal quality groups, and ensure that each resource block is allocated only once and the number of single-group resource blocks does not exceed the upper limit; The power optimization module is configured to optimize the transmission power based on the channel gain between the user and the unmanned aerial vehicle (UAV), calculate the user throughput under the premise of meeting the power upper limit, serial interference cancellation decoding condition and minimum rate requirement; The cooperative decision processor is connected and coordinates the user clustering module, the spectrum aggregation module and the power optimization module, jointly optimizes the grouping strategy, the spectrum allocation scheme and the power control instruction, so as to maximize the total throughput of the system, and outputs the scheduling instruction to the UAV communication base station; The deployment scene interface is configured to receive the UAV coordinates and user location information in real time, and calculate the channel gain parameters between the user and the UAV based on the information.

8. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable medium and is executed by the processor to implement the communication resource scheduling method according to any one of claims 1 to 6.

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