Dynamic Resource Allocation Method, System and Terminal Assisted by Communication Sensing Integration

Abstract

The present invention belongs to the technical field of wireless mobile communication, and discloses a dynamic resource allocation method, system and terminal assisted by communication-sensing integration. By wireless sensing, the number of active users is obtained in real time, and according to the number of active users in the cluster and the access priorities of the users in the cluster, communication resources are adaptively allocated to maximize the resource utilization efficiency or throughput of the system. The present invention can dynamically allocate resources for each cluster according to the number of active users in the cluster, so as to always optimize the overall performance of the system. The number of active users in each cluster is dynamically changing. The present invention can accurately obtain the number of active users in the cluster through wireless sensing, so as to allocate optimal communication resources to ensure that the system always operates in an optimal state. Through simulation experiments, it is found that the present invention can more accurately capture the dynamic changes of active users in each cluster, allocate bandwidth resources more reasonably, and improve the total system throughput.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless mobile communication, and particularly relates to a communication perception integrated assisted dynamic resource allocation method, system and terminal. Background Art

[0002] At present, due to its wide coverage, large capacity, strong compatibility and other characteristics, the satellite ground relay network can effectively support the deployment of remote Internet of Things (IoT), and has broad application prospects. In such a network, how a large number of IoT nodes access the network and how resources are allocated among relays have always been a key issue. As a typical multiple access method, random access is expected to become an effective access method in the satellite ground relay network. However, with the explosive growth of IoT nodes, the resources for random access become very limited. Therefore, there is an urgent need to rationally utilize the limited wireless communication resources to support the continuously growing IoT nodes. In recent years, many scholars have proposed solutions from different perspectives to enhance the performance of random access. However, most of these works enhance the performance of random access in scenarios where the number of active users is relatively fixed, and rarely consider the dynamic changes of users. At the same time, the dynamic resource allocation among relays has also been rarely studied. Therefore, it is of great significance to study how a large number of IoT nodes access the network and how resources are allocated among relays in the satellite ground relay network. Therefore, it is urgent to design a communication perception integrated assisted dynamic resource allocation method.

[0003] Through the above analysis, the problems and defects of the prior art are as follows:

[0004] (1) With the explosive growth of satellite IoT nodes, the resources for random access become very limited, and there is an urgent need for an efficient and reasonable resource allocation method.

[0005] (2) Most of the prior art enhances the performance of random access in scenarios where the number of active users is relatively fixed, rarely considers the dynamic changes of users, and there is also little research on the dynamic resource allocation among relays. Summary of the Invention

[0006] In view of the problems existing in the prior art, the present invention provides a communication perception integrated assisted dynamic resource allocation method, system and terminal.

[0007] The present invention is implemented as follows: First, the users served by each relay form a cluster, and the users within the cluster access the system by means of random access; then the relay obtains the number of active users within the cluster in real time through wireless sensing, and obtains the optimal resource allocation coefficient within each cluster according to the number of active users in the cluster and the access priority; finally, the users within the cluster divide the best access time slots according to the allocated resources, thereby completing the optimal uplink access and achieving the maximization of the system throughput.

[0008] Furthermore, the communication-sensing integrated assisted dynamic resource allocation method further includes: for each frame, in the sensing phase, each relay senses the number of active users in the cluster through wireless sensing; each relay sends the sensing result to the satellite, and the satellite performs optimal bandwidth allocation for each cluster according to the optimal bandwidth allocation method; after receiving the optimal bandwidth allocation coefficient, each relay divides the duration of the random access phase into multiple time slots according to the allocated bandwidth; in the random access phase, each active user in the cluster performs random access in the belonging cluster according to the time slot ALOHA (SA) protocol.

[0009] Furthermore, the communication-sensing integrated assisted dynamic resource allocation method includes the following steps:

[0010] Step 1, construct a communication-sensing integrated assisted satellite-terrestrial relay network (STRN) model;

[0011] Step 2, obtain the number of active users in each cluster through wireless sensing;

[0012] Step 3, achieve optimal dynamic resource allocation according to the number of active users and the user access priority.

[0013] Furthermore, the construction of the communication-sensing integrated assisted STRN model in Step 1 includes:

[0014] Analyze the STRN for the Internet of Things, where a large number of Internet of Things devices complete access to the satellite through multiple relays. When there are J relays in the STRN, the Internet of Things devices served by each relay form a cluster, so there are a total of J clusters, denoted as s1,..., s j ,..., s J ; when the access priorities of the J clusters satisfy s1 >... > s j >... > s J , the number of active devices in each cluster is modeled as a Poisson random variable. For any cluster s j , the number of active devices is denoted as m j , j ∈ [1,..., J], and satisfies m j ~ P O (λ j ), where λ j is the mean value of the Poisson distribution; divide the total bandwidth B into α1B,..., α j B,..., α J B, which are used for user information transmission in clusters s1,..., s j ,..., s J respectively, where α1,..., α j ,..., α J are bandwidth allocation coefficients, and satisfy

[0015] Furthermore, the construction of the communication-sensing integrated assisted STRN model in step one further includes:

[0016] In the random access framework structure, the random access frame is designed based on time division multiple access. The total system bandwidth is divided into J sub-bands according to the transmission requirements of each cluster; the duration of the random access frame is divided into L = L s +L RA , where L s is the duration of the sensing phase, and L RA is the duration of the random access phase; in practice, the duration allocation ratio between the sensing phase and the random access phase changes dynamically according to different service requirements; in the sensing phase, the relay senses the active users and counts the number of active users in each cluster; the relay sends the number of active users to the satellite, and the satellite adaptively allocates bandwidth resources to each cluster according to the number of active users in each cluster; each relay divides the duration of the random access phase into multiple time slots according to the allocated bandwidth; when the amount of data to be transmitted is D b bits and all users are the same; for any cluster s j , the duration of the time slot is calculated as τ j = D b / α j BR c , where R c is the data rate; the number of time slots in the random access phase is calculated as Since the bandwidths allocated to different clusters are different, the number of time slots in different clusters is different; since the allocated bandwidth changes according to the number of active users, the number of time slots in the same cluster varies for different frames; in the random access phase, considering SA-based random access, each active user randomly selects a time slot from the available time slots to transmit its data packet to the relay, and the relay forwards the data packet to the satellite; a collision occurs when two or more users in the same cluster select the same slot; all collided data packets will be lost, and the non-collided data packets will be successfully decoded.

[0017] Furthermore, the optimal dynamic resource allocation in step three includes:

[0018] The number of active users in the cluster varies for different frames. Appropriate radio resources are allocated to each cluster according to the number of active users; the average throughput of cluster s j is calculated as where represents the normalized load of cluster s j , c = BR c L RA / D b > 0, and the total throughput of the system is calculated as:

[0019]

[0020] Maximize the total system throughput by optimizing the bandwidth allocation coefficient. The optimization problem is formulated as follows:

[0021]

[0022] where the constraints describe the relationship of the bandwidth allocation coefficient and its value range, and the order of access priorities. v(·) represents the access priority function. Since the floor operation is included in G j and the access priority constraint, problem P1 is a non-convex problem. Transform the non-convex problem into a convex problem by approximation; considering the non-overloaded case, the normalized load always satisfies G j ≤1; before solving problem P1, introduce the following theorem:

[0023] When the normalized load satisfies there is Then always satisfies The theorem is easily obtained from the monotonicity of the function f(x) = x·e -x According to the theorem, the lower bound of maximizing the total system throughput is expressed as:

[0024]

[0025] where is the normalized load of cluster s after removing the floor operation j ; rewrite as where ρ j = m j / c > 0; maximize the throughput lower bound of each cluster according to the order of access priorities; for cluster s1, j = 1, the differential calculation of the lower bound with respect to α1 is:

[0026]

[0027] Let Then α1 = ρ1; when ρ1 ≤ 1, there is When ρ1 > 1, there is

[0028] Finally, the optimal bandwidth allocation coefficient of cluster s1 is expressed as:

[0029]

[0030] For cluster s j (j = 2, 3,..., J), the lower bound with respect to α jThe differential calculation is as follows:

[0031]

[0032] Let Then there is α j = ρ j When There is When There is When And There is The optimal bandwidth allocation coefficient of the final cluster s j Is expressed as:

[0033]

[0034] Another object of the present invention is to provide a communication-aware integrated-assisted dynamic resource allocation system applying the communication-aware integrated-assisted dynamic resource allocation method. The communication-aware integrated-assisted dynamic resource allocation system includes:

[0035] An STRN model construction module for constructing a communication-aware integrated-assisted STRN model;

[0036] An active user number acquisition module for obtaining the number of active users in each cluster through wireless sensing;

[0037] A dynamic resource allocation module for adaptively allocating communication resources according to the number of active users in the cluster and the access priorities of the users within the cluster, thereby maximizing the resource utilization efficiency or throughput of the system.

[0038] Another object of the present invention is to provide a computer device. The computer device includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the communication-aware integrated-assisted dynamic resource allocation method.

[0039] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the communication-aware integrated-assisted dynamic resource allocation method.

[0040] Another object of the present invention is to provide an information data processing terminal for implementing the communication-aware integrated-assisted dynamic resource allocation system.

[0041] Combined with the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solution to be protected by the present invention from the following aspects:

[0042] First, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving such problems, closely combining with the technical solution to be protected by the present invention, as well as the results and data during the R & D process, etc., analyze in detail and profoundly how the technical solution of the present invention solves the technical problems and the creative technical effects brought after solving the problems. The specific description is as follows:

[0043] Communication-sensing integration is a key technology in the 6th generation mobile communication. The dynamic resource allocation method assisted by communication-sensing integration proposed by the present invention relates to the key technology in the field of wireless mobile communication. The traditional static resource allocation scheme will lead to unbalanced resource allocation among clusters and cannot optimize the overall system performance. The present invention proposes a dynamic resource allocation method, which can dynamically allocate resources to each cluster according to the number of active users in the cluster, so as to always optimize the overall performance of the system. Specifically, for each frame, in the sensing phase, each relay first senses the number of active users in the cluster through wireless sensing. Then, each relay sends the sensing result to the satellite, and the satellite performs optimal bandwidth allocation for each cluster according to the optimal bandwidth allocation method. After receiving the optimal bandwidth allocation coefficient, each relay divides the duration of the random access phase into multiple time slots according to the allocated bandwidth. In the random access phase, each active user in the cluster performs random access in its affiliated cluster according to the SA protocol.

[0044] The present invention discloses a dynamic resource allocation method assisted by communication-sensing integration, which can effectively improve the utilization efficiency and throughput of wireless communication resources in a satellite-ground relay network. In a satellite-ground relay network, multiple users form a cluster centered around the relay serving them, and the number of active users in each cluster is always dynamically changing. In this scenario, the traditional static resource allocation method is not always optimal. To make full use of communication resources, it is necessary to accurately obtain the number of active users in each cluster and then allocate appropriate communication resources to each cluster. For this purpose, the present invention proposes a dynamic resource allocation method assisted by communication-sensing integration, which first obtains the accurate number of active users in each cluster through wireless sensing, and then allocates appropriate communication resources according to the number of active users in the cluster and the access priority of the users in the cluster. From the perspective of the long-term access process, the number of active users in each cluster is always dynamically changing, and the present invention can always accurately obtain the number of active users in the cluster through wireless sensing, so as to allocate optimal communication resources to ensure that the system always operates in an optimal state.

[0045] Second, regarding the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by the present invention are specifically described as follows:

[0046] Based on the main problems existing in the background technology and the limitations of the existing solutions, the present invention proposes a dynamic resource allocation method assisted by communication and sensing integration, aiming to obtain the number of active users in real time through wireless sensing, and then adaptively allocate reasonable communication resources according to the number of active users and the user access priority, so as to maximize the resource utilization efficiency or throughput of the system.

[0047] Thirdly, as the creative auxiliary evidence of the claims of the present invention, it is also reflected in the following important aspects:

[0048] (1) The expected benefits and commercial values after the transformation of the technical solution of the present invention are:

[0049] The main application background of the present invention is the satellite ground relay network. The dynamic resource allocation problem in this network is solved through communication and sensing integration. The expected benefits after the transformation of the technical solution of the present invention are closely related to the maturity of the current development of the satellite ground relay network. At present, the satellite ground relay network has moved from theory to application, and has broad application prospects in the uplink access scenario of Internet of Things nodes in remote areas. After the transformation of the technical invention results, it can be applied to the uplink access and optimal resource allocation of large-scale satellite Internet of Things, improving the resource utilization efficiency and system throughput.

[0050] (2) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been successful in solving:

[0051] In the satellite ground relay network for the Internet of Things, the main technical problem faced by the existing technology is the dynamic optimization and utilization problem of limited communication resources. The existing technology mainly conducts optimal resource allocation in the scenario where the number of active users is relatively fixed within a period of time. However, in actual applications, the number of active users always changes dynamically, which poses a huge challenge to the optimization and utilization of limited communication resources. The present invention uses communication and sensing integration to obtain the dynamic change characteristics of active users in real time, and dynamically allocates limited resources according to the obtained results and user access priority. From the perspective of long-term access time, the method proposed by the present invention can effectively improve the resource utilization efficiency. Brief Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments of the present invention. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0053] Figure 1 It is the flowchart of the dynamic resource allocation method assisted by communication and sensing integration provided by the embodiment of the present invention;

[0054] Figure 2 Schematic diagram of a communication-awareness integrated auxiliary STRN model provided by an embodiment of the present invention;

[0055] Figure 3 1 is a schematic diagram of a random access frame structure based on time division multiple access provided by an embodiment of the present invention;

[0056] Figure 4 Schematic diagram of the relationship between throughput and the average number of active users in cluster 1 provided by an embodiment of the present invention;

[0057] Figure 5 Schematic diagram of the relationship between throughput and the average number of active users in cluster 2 provided by an embodiment of the present invention;

[0058] Figure 6 Schematic diagram of the relationship between throughput and the average number of active users in cluster 3 provided by an embodiment of the present invention;

[0059] Figure 7 It is a schematic diagram of performance comparison between the traditional SA, ISAC-assisted SA and the proposed solution provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] In response to the problems existing in the prior art, the present invention provides a dynamic resource allocation method, system and terminal assisted by integrated communication and perception. The present invention is described in detail below with reference to the accompanying drawings.

[0062] 1. Explanatory Examples In order to enable those skilled in the art to fully understand how to implement the present invention, this section provides an illustrative example that expands upon the technical solutions of the claims.

[0063] Traditional static resource allocation schemes result in unbalanced resource allocation between clusters and fail to optimize overall system performance. Embodiments of the present invention propose a dynamic resource allocation method that dynamically allocates resources to each cluster based on the number of active users within the cluster, thereby consistently optimizing overall system performance. Specifically, for each frame, during the sensing phase, each relay first senses the number of active users within the cluster through wireless sensing. Each relay then transmits the sensing result to the satellite, which then allocates optimal bandwidth to each cluster based on an optimal bandwidth allocation method. After receiving the optimal bandwidth allocation coefficient, each relay divides the duration of the random access phase into multiple time slots based on the allocated bandwidth. During the random access phase, each active user within the cluster performs random access within its cluster according to the SA protocol.

[0064] As shown in Figure 1 the figure, the dynamic resource allocation method assisted by communication and sensing integration provided by the embodiment of the present invention includes the following steps:

[0065] S101, constructing a communication and sensing integration assisted STRN model;

[0066] S102, obtaining the number of active users in each cluster through wireless sensing;

[0067] S103, realizing optimal dynamic resource allocation according to the number of active users and user access priorities.

[0068] As a preferred embodiment, the dynamic resource allocation method assisted by communication and sensing integration provided by the embodiment of the present invention specifically includes the following steps:

[0069] 1. Communication and sensing integration random access architecture

[0070] In the construction of the network model, as Figure 2 shown in the figure, the embodiment of the present invention considers a satellite-oriented STRN, in which a large number of Internet of Things devices complete access to the satellite through multiple relays. The embodiment of the present invention assumes that there are J relays in the considered STRN, and the Internet of Things devices served by each relay can form a cluster. Therefore, there are a total of J clusters, which can be denoted as s1,..., s j ,..., s J . Assume that the access priorities of the J clusters satisfy s1 >... > s j >... > s J , and the number of active devices in each cluster is modeled as a Poisson random variable. For any cluster sj, the number of active devices can be expressed as m j , j ∈ [1,..., J], and satisfies m j ~P O (λ j ), where λ j is the mean value of the Poisson distribution. To avoid interference between clusters, the embodiment of the present invention divides the total bandwidth B into α1B,..., α j B,..., α J B, which are used for user information transmission in clusters s1,..., s j ,..., s J respectively. Among them, α1,..., α j ,..., α J are bandwidth allocation coefficients, and satisfy

[0071] In the random access framework structure, the random access frame is designed based on time division multiple access. The total system bandwidth is divided into J sub-bands according to the transmission requirements of each cluster, which can avoid mutual interference between clusters. As Figure 3 shown, the duration of the random access frame is divided into L = L s + L RA , where L s is the duration of the sensing phase, and L RA is the duration of the random access phase. In practical applications, the duration allocation ratio between the sensing phase and the random access phase can be dynamically adjusted according to different service requirements. In the sensing phase, the relay first senses the active users and counts the number of active users in each cluster, and then the relay sends the number of active users to the satellite. The satellite adaptively allocates bandwidth resources to each cluster according to the number of active users in each cluster. Subsequently, each relay divides the duration of the random access phase into multiple time slots according to the allocated bandwidth. The embodiment of the present invention assumes that the amount of data to be transmitted is D b bits, and it is assumed that all users are the same. For any cluster s j , the duration of the time slot can be calculated as τ j = D b / α j BR c , where R c is the data rate. The number of time slots in the random access phase can be calculated as It should be noted that since the bandwidths allocated to different clusters are different, the number of time slots in different clusters may be different. In addition, since the allocated bandwidth changes according to the number of active users, the number of time slots in the same cluster varies for different frames. In the random access phase, SA-based random access is considered, that is, each active user randomly selects a time slot from the available time slots to transmit its data packet to the relay, and then the relay forwards the data packet to the satellite. When two or more users in the same cluster select the same slot, a collision occurs. All collided data packets will be lost, and non-collided data packets will be successfully decoded.

[0072] 2. Optimal Dynamic Resource Allocation

[0073] The number of active users in a cluster varies from frame to frame. If a fixed resource allocation scheme is adopted, it may lead to unbalanced resource allocation between clusters. Therefore, the embodiment of the present invention needs to allocate appropriate wireless resources to each cluster according to the number of active users. The average throughput of cluster s j can be calculated as where represents the normalized load of cluster s j , c = BR c L RA / D b > 0. The total throughput of the system can be calculated as:

[0074]

[0075] The goal of the embodiments of the present invention is to maximize the total system throughput by optimizing the bandwidth allocation coefficient. The optimization problem can be formulated as:

[0076]

[0077] where the constraints describe the relationship of the bandwidth allocation coefficients, their value ranges, and the order of access priorities, and v(·) represents the access priority function. Since the floor operation is included in G j and the access priority constraints, problem P1 is a non-convex problem. To solve this problem, the embodiments of the present invention transform it into a convex problem by approximation. Here, the non-overloaded case is considered, that is, the normalized load always satisfies G j ≤1. Before solving problem P1, a theorem 1 is introduced as follows:

[0078] Theorem 1: When the normalized load satisfies there is Then always satisfies This theorem can be easily obtained from the monotonicity of the function f(x) = x·e -x According to Theorem 1, the embodiments of the present invention need to maximize the lower bound of the total system throughput, which can be expressed as:

[0079]

[0080] where is the normalized load of cluster s j after removing the floor operation. For convenience of representation, is rewritten as where ρ j = m j / c > 0. After removing the floor operation, due to the access priority constraints, problem P2 still cannot be directly solved. To maximize the lower bound of the total system throughput under the access priority constraints, the embodiments of the present invention need to maximize the lower bound of the throughput of each cluster in the order of access priorities. For cluster s1, that is, j = 1, the differential of the lower bound with respect to α1 can be calculated as:

[0081]

[0082] Let Then α1 = ρ1. When ρ1 ≤ 1, there is When ρ1 > 1, there is

[0083] Finally, the optimal bandwidth allocation coefficient of cluster s1 can be expressed as:

[0084]

[0085] For cluster s j (j=2, 3, ..., J), lower bound About α j The differential of can be calculated as:

[0086]

[0087] Similarly, let Then we have α j =ρ j ,when Sometimes, there are when have when Head. Sometimes, there are Finally, cluster s j The optimal bandwidth allocation coefficient can be expressed as:

[0088]

[0089] Ultimately, the present invention obtains the optimal bandwidth allocation coefficient for each cluster, which consistently maximizes the lower bound of the total system throughput. The optimal solution to problem P2 shows that the optimal coefficient for each cluster is related to the number of active users within the cluster. The more active users, the more bandwidth that needs to be allocated.

[0090] Traditional static resource allocation schemes result in unbalanced resource allocation between clusters and fail to optimize overall system performance. Embodiments of the present invention propose a dynamic resource allocation method that dynamically allocates resources to each cluster based on the number of active users within the cluster, thereby consistently optimizing overall system performance. Specifically, for each frame, during the sensing phase, each relay first senses the number of active users within the cluster through wireless sensing. Each relay then transmits this sensing result to the satellite, which then allocates optimal bandwidth to each cluster based on an optimal bandwidth allocation method. After receiving the optimal bandwidth allocation coefficients, each relay divides the duration of the random access phase into multiple time slots based on the allocated bandwidth. During the random access phase, each active user within the cluster performs random access within its cluster using the slotted ALOHA protocol.

[0091] The dynamic resource allocation system assisted by communication and sensing integration provided by the embodiments of the present invention includes: an STRN model construction module for constructing an STRN model assisted by communication and sensing integration; an active user number acquisition module for obtaining the number of active users in each cluster through wireless sensing; and a dynamic resource allocation module for adaptively allocating communication resources according to the number of active users in the cluster and the access priorities of the users within the cluster, so as to maximize the resource utilization efficiency or throughput of the system.

[0092] II. Application embodiments. In order to prove the creativity and technical value of the technical solution of the present invention, this part is an application embodiment of the technical solution of the claims on specific products or related technologies.

[0093] This application embodiment elaborates on applying the dynamic resource allocation method assisted by communication and sensing integration proposed by the present invention to a satellite-ground relay network for the Internet of Things, which can solve the problem of optimizing resource allocation during the uplink access of large-scale satellite Internet of Things nodes and improve resource utilization efficiency and network capacity.

[0094] III. Evidence of the related effects of the embodiments. The embodiments of the present invention have achieved some positive effects during the research and development or use process, and indeed have great advantages compared with the prior art. The following content is described in combination with data, charts, etc. in the test process.

[0095] Simulation verification: The embodiments of the present invention verified the performance of the proposed solution through computer simulation. Assume that the satellite is a LEO satellite with an orbital altitude of 500 kilometers, the system bandwidth is B = 200 kHz, and the number of relays is J = 3, that is, users can be divided into 3 clusters. Assume that the relays are randomly distributed within the satellite coverage area, and the number of active Internet of Things users in each cluster is generated based on a Poisson distribution.

[0096] Figure 4 Shows the relationship between the throughput and the average number of active users λ1 in cluster 1, and the average number of active users in clusters 2 and 3 is set to λ2 = λ3 = 50. It can be seen from the figure that the throughput of cluster 1 almost increases linearly with the increase of λ1. When λ1 is small, the throughputs of clusters 2 and 3 remain unchanged with the increase of λ1. As λ1 increases, the throughput of cluster 3 first decreases, and then the throughput of cluster 2 decreases. This can be explained as follows: when λ1 is small or medium, the bandwidth resources are sufficient to support all active users, while when λ1 is large, more bandwidth resources need to be allocated to support the active users in cluster 1 at this time, and the bandwidth resources allocated to the active users in clusters 2 and 3 are reduced, resulting in a decrease in throughput.

[0097] Figure 5Shows the relationship between the throughput and the average number of active users λ2 in cluster 2. The average number of active users in clusters 1 and 3 is set to λ1 = λ3 = 50. It can be seen from the figure that the throughput of cluster 2 almost increases linearly with the increase of λ2. The throughput of cluster 1 is not affected by λ2, while the throughput of cluster 3 remains unchanged first and then decreases with the increase of λ2. This is because cluster 1 has the highest access priority and is first allocated sufficient bandwidth resources to support the active users. In addition, the access priority of cluster 2 is higher than that of cluster 3, and more bandwidth resources are allocated to support the growing active users in cluster 2, resulting in a decrease in the throughput of cluster 3.

[0098] Figure 6 Shows the throughput and the average number of active users λ3 in cluster 3. The average number of active users in clusters 1 and 2 is set to λ1 = λ2 = 50. Obviously, with the increase of λ3, the throughputs of clusters 1 and 2 remain unchanged. This is because the access priorities of clusters 1 and 2 are higher than that of cluster 3. The throughput of cluster 3 first increases and then decreases with the increase of λ3. This is because the remaining bandwidth resources are not sufficient to support all users in cluster 3, resulting in more collisions and reduced throughput.

[0099] Figure 7 Shows the comparison of the total system throughput of traditional SA, ISAC-assisted SA, and the proposed scheme. The settings of the two benchmark schemes are as follows: 1) Conventional SA: A frame consists only of a random access phase with equally divided fixed number of time slots, and the bandwidth resources of each cluster are always the same and fixed from frame to frame. 2) ISAC-assisted SA: A frame consists of a sensing phase and a random access phase, and only the random access phase is equally divided into a fixed number of time slots, and the bandwidth resources of each cluster are always the same and fixed frame by frame. Different from these two benchmark schemes, the proposed scheme can dynamically allocate bandwidth resources for each cluster according to the number of active users in the cluster. Note that in this simulation, the average number of active users in the cluster is randomly selected from [10, 150], which makes the number of active users in the cluster in each frame random. It is obvious from the figure that the total system throughput of the proposed scheme is about 15.5% higher than that of traditional SA and about 26.4% higher than that of ISAC-assisted SA. The throughput of ISAC-assisted SA is lower than that of traditional SA because the sensing phase of ISAC occupies a part of the time duration, resulting in a reduction in the number of time slots and throughput. By comparing the embodiments of the present invention, it is found that although the sensing phase will occupy a part of the time duration, the present invention can more accurately capture the dynamic changes of active users in each cluster, which can enable the present invention to allocate bandwidth resources more reasonably and improve the total system throughput.

[0100] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated designed hardware. Those of ordinary skill in the art can understand that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and their modules of the present invention can be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software such as firmware.

[0101] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A dynamic resource allocation method assisted by communication and sensing integration, characterized in that The dynamic resource allocation method assisted by communication and sensing integration includes the following steps: Step 1, construct a communication and sensing integration assisted STRN model; Step 2, obtain the number of active users in each cluster through wireless sensing; Step 3, achieve optimal dynamic resource allocation according to the number of active users and the user access priority; The construction of the communication and sensing integration assisted STRN model in Step 1 includes: Analyze the satellite ground intermediate network STRN for the Internet of Things, where a large number of Internet of Things devices access the satellite through multiple relays; when there are J relays in the STRN, the Internet of Things devices served by each relay form a cluster, so there are J clusters in total, denoted as s1, …, s j , …, s J ; when the access priorities of the J clusters satisfy s1 > … > s j > … > s J , the number of active devices in each cluster is modeled as a Poisson random variable; for any cluster s j , the number of active devices is denoted as m j , j ∈ [1, …, J], and satisfies m j ~P O (λ j ), where λ j is the mean of the Poisson distribution; divide the total bandwidth B into α1B, …, α j B, …, α J B, which are used for user information transmission within clusters s1, …, s j , …, s J , where α1, …, α j , …, α J are bandwidth allocation coefficients, and satisfy The optimal dynamic resource allocation in Step 3 includes: The number of active users in a cluster varies with frames, and appropriate radio resources are allocated to each cluster according to the number of active users; cluster s j The average throughput of is calculated as where represents the normalized load of cluster s j and c = BR c L RA / D b > 0, the total throughput of the system is calculated as: Maximize the total system throughput by optimizing the bandwidth allocation coefficient, and the optimization problem is formulated as: where R c is the data rate, D b represents the amount of data to be transmitted, L RA is the duration of the RA phase; the constraint describes the relationship of the bandwidth allocation coefficient and its value range, the order of access priorities, and v(·) represents the access priority function; since the floor operation is included in G j and the access priority constraint, problem P1 is a non-convex problem; the non-convex problem is transformed into a convex problem by approximation; considering the non-overloaded case, the normalized load always satisfies G j ≤ 1; before solving problem P1, the following theorem is introduced: The normalized load satisfies When there is Then Always satisfies The theorem is easily obtained from the monotonicity of the function f(x) = x·e -x According to the theorem, the lower bound of maximizing the total system throughput is expressed as: where is the normalized load of cluster s after removing the floor operation; j Let be rewritten as where ρ j = m j / c > 0; Maximize the lower bound of the throughput of each cluster in the order of access priority; For cluster s1, j = 1, the lower bound The differential calculation of with respect to α1 is: Let Then we have α1 = ρ1; when ρ1 ≤ 1, we have When ρ1 > 1, we have Finally, the optimal bandwidth allocation coefficient of cluster s1 is expressed as: For cluster s j (j = 2, 3, ..., J), the lower bound with respect to α j is calculated by differentiation as follows: Let Then there is α j = ρ j When there is When there is When and there is The optimal bandwidth allocation coefficient of the final cluster s j is expressed as:

2. The dynamic resource allocation method assisted by communication and sensing integration according to claim 1, characterized in that The construction of the communication and sensing integration assisted STRN model in Step 1 further includes: In the RA framework structure, the RA frame is designed based on time division multiple access; the total system bandwidth is divided into J sub - bands according to the transmission requirements of each cluster; the duration of the RA frame is divided into L = L s +L RA , where L s is the duration of the sensing phase. In practice, the duration allocation ratio between the sensing phase and the RA phase changes dynamically according to different service requirements; in the sensing phase, the relay senses the active users and counts the number of active users in each cluster; the relay sends the number of active users to the satellite, and the satellite adaptively allocates bandwidth resources to each cluster according to the number of active users in each cluster; each relay divides the duration of the RA phase into multiple time slots according to the allocated bandwidth; when the amount of data to be transmitted is D b bits and all users are the same; for any cluster s j , the duration of the time slot is calculated as τ j =D b / α j BR c , where B is the bandwidth; the number of time slots in the RA phase is calculated as Since the bandwidths allocated to different clusters are different, the number of time slots in different clusters is different; since the allocated bandwidth changes according to the number of active users, the number of time slots in the same cluster varies for different frames; in the RA phase, considering SA - based RA, each active user randomly selects a time slot from the available time slots to transmit its data packet to the relay, and the relay forwards the data packet to the satellite; a collision occurs when two or more users in the same cluster select the same slot; all collided data packets will be lost, and non - collided data packets will be successfully decoded.

3. A communication and sensing integrated assisted dynamic resource allocation system applying the communication and sensing integrated assisted dynamic resource allocation method according to any one of claims 1 to 2, characterized in that, The dynamic resource allocation system assisted by communication and sensing integration includes: An STRN model construction module for constructing a communication and sensing integration assisted STRN model; An active user number acquisition module for obtaining the number of active users in each cluster through wireless sensing; A dynamic resource allocation module for adaptively allocating communication resources according to the number of active users in the cluster and the access priority of the users in the cluster, thereby maximizing the resource utilization efficiency or throughput of the system.

4. A computer device, characterized in that, The computer device includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the dynamic resource allocation method assisted by communication and sensing integration according to any one of claims 1 to 2.

5. A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the steps of the dynamic resource allocation method assisted by communication and sensing integration according to any one of claims 1 to 2.

6. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the dynamic resource allocation system assisted by communication and sensing integration according to claim 3.

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