A resource allocation method for a multi-hop access and backhaul integrated network

Abstract

The present application belongs to the field of resource allocation technology. The present application provides a resource allocation method for a multi-hop access and backhaul integrated network. The disclosed embodiment uses the Voronoi diagram to perform coverage division, which can estimate the access network service flow of each node before network initialization. Its computational complexity is low, and its performance is equivalent to user association in a scenario with a large number of nodes in a maximum signal-to-noise ratio manner. Taking into account the bottleneck link, it can effectively avoid the backlog of data services at individual nodes to cause congestion, and at the same time can promote the optimization algorithm to allocate more resources to the bottleneck link to achieve a higher overall network throughput. It is also considered to measure the throughput by actual transmission demand to improve the efficiency of resource allocation, and avoid the situation where some nodes occupy resources to achieve a large transmission capacity but have no transmission demand. The use of quadratic transformation to process the fractional term of power in the channel capacity formula can handle the optimization problem of multi-node and throughput maximization.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of resource allocation, and in particular to a resource allocation method for a multi-hop access and backhaul integrated network. Background Art

[0002] In Release 16, the 3GPP organization proposed the Integrated Access and Backhaul (IAB) network, which aims to replace traditional optical backhaul with wireless backhaul to reduce operators' base station deployment costs and increase base station deployment flexibility. Nodes in the IAB network are divided into two types: IAB-nodes and IAB-donors. IAB-donors are connected to the 5G core network via optical fiber and provide wireless backhaul for IAB-nodes. In a multi-hop IAB network, IAB-nodes can connect directly to IAB-donors or be relayed to IAB-donors through other IAB-nodes. Therefore, the entire network has a spanning tree topology. Considering the limited transmission resources, appropriate resources need to be allocated to each IAB-node. In the resource allocation process, first, it is necessary to resolve the node's half-duplex constraint and receiver conflicts through sub-channel allocation. Secondly, in order to improve resource utilization efficiency while suppressing cross-layer interference between non-adjacent links, node power control is required. In addition, since data aggregation in multi-hop IAB networks may lead to bottleneck links with low transmission capacity and cause congestion, in order to improve resource utilization efficiency, the transmission capacity restrictions of bottleneck links in multi-hop transmission should also be considered to reduce resource waste.

[0003] In related technologies, time slot resource allocation in a multi-hop IAB network is achieved by controlling the activation status of links. This allocation method is not only inflexible but can only avoid conflicts between adjacent links and cannot model co-channel interference between non-adjacent links. Summary of the Invention

[0004] In order to avoid the shortcomings of the existing technology, the present application provides a resource allocation method for a multi-hop access and backhaul integrated network to solve the problems in the existing technology of low flexibility of the allocation method and inability to model co-frequency interference between non-adjacent links.

[0005] According to an embodiment of the present disclosure, a resource allocation method for a multi-hop access and backhaul integrated network is provided, the method comprising:

[0006] Construct a multi-hop IAB network model;

[0007] Obtaining a network topology model, a resource model, a channel capacity model, and a throughput model based on the multi-hop IAB network model;

[0008] Constructing a joint optimization problem model of subchannel allocation and power control according to the network topology model, the resource model, the channel capacity model and the throughput model;

[0009] Decoupling the joint optimization problem model of subchannel allocation and power control into a subchannel allocation problem submodel under fixed transmit power and a power control problem submodel under fixed subchannel occupancy;

[0010] The sub-channel allocation problem sub-model under the fixed transmit power and the power control problem sub-model under the fixed sub-channel occupancy are solved to obtain an optimal resource allocation result.

[0011] Furthermore, the multi-hop IAB network model includes:

[0012] IAB Base Station Group;

[0013] The IAB base station group includes an IAB-donor node and several IAB-node nodes. The total number of IAB base station groups is N={n1,n2,...,n i ,...,n N}, n i is the i-th IAB node, the first node n1 represents the IAB-donor node, and the rest are IAB-node nodes.

[0014] Furthermore, the network topology model includes:

[0015] Each IAB-node selects a parent node After accessing the network, the parent node For any of the IAB nodes n i , select the IAB node n i All nodes accessing the network are the IAB nodes n i Child node n c , the IAB node n i The set of all child nodes of The IAB node n i The node set on the multi-hop transmission path to the IAB-donor node is

[0016] Furthermore, the resource model includes:

[0017] The backhaul frequency domain resources are divided into multiple orthogonal sub-channels C = {c1, c2, ... c i};

[0018] The half-duplex constraint within the IAB node, the parent node and any of the child nodes use different sub-channels for transmission;

[0019] The same parent node and all subordinate child nodes use different sub-channels for transmission;

[0020] In the process of uplink data transmission in the sub-channel, the binary matrix X is used |N|×|C| Mark subchannel allocation status, element x i,j =1 indicates the IAB base station n i Subchannel c is used i to transfer.

[0021] Furthermore, the channel capacity model includes:

[0022] The IAB node n i and its parent node The backhaul link channel capacity between

[0023] The first backhaul link channel capacity The expression is:

[0024]

[0025] Where w is the subchannel bandwidth p i,j IAB node n i In subchannel c j The transmission power on G i,j IAB node n i and its parent node In subchannel c j The path loss on the receiver is N0, the noise at the receiving end is I i,j IAB node n i The parent node In subchannel c j The interference received on i,j The expression is:

[0026]

[0027] Among them, G' k,j is the interference node n k With n i The parent node The path loss between

[0028] The IAB node n i The backhaul link channel capacity of the IAB-donor node is the second backhaul link channel capacity

[0029] The second backhaul link channel capacity The expression is:

[0030]

[0031] Where p is the set of transmission path nodes Nodes in .

[0032] Furthermore, the throughput model includes:

[0033] The throughput of each IAB node is R i , throughput R i The expression is:

[0034]

[0035] in, IAB node n i The transmission requirements consist of two parts:

[0036]

[0037] Among them, R c is the throughput of the child node, IAB node n i The sum of the throughput of the child nodes of IAB node n i The transmission requirements generated by all users within the coverage area;

[0038]

[0039] Among them, I a is the access network user arrival intensity, A i n i Coverage area.

[0040] Furthermore, the method further comprises:

[0041] When the position of the IAB node is determined, the position of the IAB node is used as a generator, and the Voronoi diagram is used to divide the plane into multiple polygons. The polygon area corresponding to each IAB node is used as the base station coverage range. i is the area of ​​the corresponding polygon.

[0042] Furthermore, the step of solving the sub-channel allocation problem sub-model under the fixed transmit power and the power control problem sub-model under the fixed sub-channel occupancy to obtain the optimal resource allocation result includes:

[0043] Fixing the transmit power of each of the sub-nodes, and solving the sub-channel allocation problem sub-model to obtain a sub-channel allocation result;

[0044] Solving the power control problem sub-model based on the sub-channel allocation result to obtain a transmit power allocation result;

[0045] The power allocation result is brought back to the power control problem sub-model for iteration until the optimal resource allocation result is obtained.

[0046] Furthermore, the step of fixing the transmit power of each of the sub-nodes and solving the sub-channel allocation problem sub-model to obtain a sub-channel allocation result includes:

[0047] The non-convex constraint x i,j ∈{0,1} is relaxed from a binary variable to a continuous variable x i,j ∈[0,1], and by adding the constraint Make x i,j The final value of is 0 or 1, then the sub-channel allocation problem sub-model is:

[0048]

[0049] Among them, x c,j For collection Any child node n in c Whether to occupy subchannel c j logo;

[0050] Using penalty terms to constrain Integrate into the objective function:

[0051]

[0052] Among them, μ is the penalty coefficient;

[0053] The sub-channel allocation problem sub-model is transformed into the difference of two convex functions. After Taylor expansion of the second term, the objective function can be transformed into a convex optimization problem model:

[0054]

[0055] in, is the second term of the difference of convex functions in the sub-channel assignment problem sub-model, is g in the tth iteration t Taylor expansion of (x), and:

[0056]

[0057] The convex optimization problem model is iteratively solved using an interior point method using a CVX solver to obtain the sub-channel allocation result.

[0058] Furthermore, the step of solving the power control problem sub-model based on the sub-channel allocation result to obtain a transmit power allocation result includes:

[0059] Based on the sub-channel allocation result, the sub-channel allocation matrix X is fixed. |N|×|C| , noise and channel gain, the expression of the power control problem sub-model is:

[0060]

[0061] An auxiliary variable is introduced, and a quadratic transformation is used to process the fractional term of the power in the first backhaul link channel capacity, so as to convert the power control problem sub-model into a multi-fractional programming problem model.

[0062] Iteratively solving the multi-fractional programming problem model to obtain the transmit power allocation result;

[0063] Among them, the first backhaul link channel capacity The calculation formula is rewritten as:

[0064]

[0065] Where A i,j =p i,j G i,j , B i,j =I i,j +N0,

[0066] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0067] In the embodiments of the present disclosure, through the resource allocation method of the above-mentioned multi-hop access and backhaul integrated network, first, the Voronoi diagram is used to perform coverage division, and the access network service flow of each node can be estimated before the network is initialized. The computational complexity is low, and the performance is equivalent to user association in a maximum signal-to-noise ratio manner in a scenario with a large number of nodes. Secondly, the bottleneck link is taken into account, which can effectively avoid the backlog of data services at individual nodes to cause congestion, and at the same time can promote the optimization algorithm to allocate more resources to the bottleneck link to achieve a higher overall network throughput. Then, it is also considered to measure the throughput by the actual transmission demand to improve the efficiency of resource allocation, and avoid the situation where some nodes occupy resources to achieve a large transmission capacity but have no transmission demand. Finally, the use of quadratic transformation to process the fractional term of power in the channel capacity formula can handle the optimization problem of multi-node and throughput maximization. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0069] Figure 1 A diagram showing the steps of a resource allocation method for a multi-hop access and backhaul integrated network in an exemplary embodiment of the present disclosure;

[0070] Figure 2 A schematic diagram illustrating a multi-hop IAB network system model in an exemplary embodiment of the present disclosure is shown;

[0071] Figure 3 A flowchart showing a resource allocation process in an exemplary embodiment of the present disclosure is shown;

[0072] Figure 4 A flow chart showing alternate iterative solutions to power allocation results in an exemplary embodiment of the present disclosure is shown;

[0073] Figure 5 A schematic diagram showing a network topology in a simulation scenario in an exemplary embodiment of the present disclosure is shown;

[0074] Figure 6 A comparison diagram showing curves of network throughput versus number of nodes for the present method and a comparative method in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0075] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0076] In addition, the accompanying drawings are merely schematic illustrations of embodiments of the present disclosure and are not necessarily drawn to scale. Like reference numerals in the figures represent like or similar parts, and thus repeated descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically separate entities.

[0077] This example embodiment provides a resource allocation method for a multi-hop access backhaul integrated network. Figure 1 As shown in , the resource allocation method for the multi-hop access and backhaul integrated network may include: steps S100 to S500.

[0078] Step S100: constructing a multi-hop IAB network model;

[0079] Step S200: obtaining a network topology model, a resource model, a channel capacity model, and a throughput model based on the multi-hop IAB network model;

[0080] Step S300: constructing a joint optimization problem model of subchannel allocation and power control according to the network topology model, the resource model, the channel capacity model, and the throughput model;

[0081] Step S400: decoupling the joint optimization problem model of subchannel allocation and power control into a sub-model of subchannel allocation problem under fixed transmit power and a sub-model of power control problem under fixed subchannel occupancy;

[0082] Step S500: solving the sub-channel allocation problem sub-model under the fixed transmit power and the power control problem sub-model under the fixed sub-channel occupancy to obtain an optimal resource allocation result.

[0083] The above-mentioned resource allocation method for an integrated multi-hop access and backhaul network first uses a Voronoi diagram for coverage partitioning, allowing estimation of access network traffic at each node before network initialization. This method has low computational complexity and performs equivalently to user association in a scenario with a large number of nodes using a maximum signal-to-noise ratio. Secondly, bottleneck links are taken into account, effectively preventing data traffic from accumulating at individual nodes and causing congestion. This also encourages the optimization algorithm to allocate more resources to bottleneck links, thereby achieving higher overall network throughput. Furthermore, the throughput is measured by actual transmission demand to improve resource allocation efficiency, avoiding situations where some nodes occupy resources to achieve a large transmission capacity but have no transmission demand. Finally, the use of a quadratic transform to process the fractional term related to power in the channel capacity formula can address the optimization problem of multiple nodes and throughput maximization.

[0084] Below, we will refer to Figures 1 to 6 Each part of the resource allocation method for the multi-hop access and backhaul integrated network in this exemplary embodiment is described in more detail.

[0085] In steps S100 to S300, in view of the coverage division requirements of the multi-hop IAB network, the Voronoi diagram is used to divide the coverage range according to the location of the IAB nodes, and the transmission requirements generated by the access network are estimated. In view of the need to improve resource utilization in the multi-hop IAB network, a sub-channel multiplexing rule is constructed considering half-duplex constraints and receiving end collisions, and an interference model between nodes is constructed under the sub-channel multiplexing rule. In view of the characteristics of multiple relay backhauls in the multi-hop IAB network, the bottleneck capacity in the multi-hop backhaul path is considered to measure the backhaul capability of the node. In the model for measuring node throughput, the actual transmission requirements and the backhaul capability of the node are taken into account. In this way, an optimization model with maximization and throughput as the goal is constructed (i.e., a joint optimization problem model of sub-channel allocation and power control).

[0086] Specifically, such as Figure 2 As shown, this application considers a multi-hop IAB network in a single connection scenario, in which there is a base station as an IAB-donor node connected to the core network through a wired network, and the remaining base stations are IAB-node nodes without wireless backhaul. The set of these IAB base stations is denoted as N = {n1, n2, ... n i}, where n i represents the i-th IAB base station in the set, the first base station n1 represents the IAB-donor node, and the rest are IAB-node nodes.

[0087] Network topology model:

[0088] Each IAB-node needs to select a parent node to access the network during the access process. The parent node can be an IAB-donor or an IAB-node that has already accessed the network. i The parent node is denoted as Multiple IAB-nodes can choose the same parent node to access, and all selected nodes n i The nodes that access are all n i Child node, node n i The set of child nodes is denoted as n i A connection can be established with IAB-donor via multi-hop relays, where all nodes in the multi-hop path are n i The upstream nodes of

[0089] Resource Model:

[0090] To implement OFDMA multiple access, the backhaul frequency domain resources are divided into multiple orthogonal sub-channels, denoted as C = {c1, c2, ... c iSince the IAB node needs to receive data from its child nodes while transmitting to the parent node, the half-duplex constraint within the IAB node means that the parent node and any child node cannot reuse the same sub-channel for transmission. In addition, to avoid collisions between signals from multiple child nodes at the receiving end, all child nodes under the same parent node cannot reuse the same sub-carrier. In addition, the IAB node n i It can occupy multiple sub-channels to transmit data uplink to its parent node Binary matrix X |N|×|C| Used to mark the subchannel allocation status, where the element x i,j =1 indicates node n i Subchannel c is used i to transfer.

[0091] Channel capacity model:

[0092] Node n i and its parent node The backhaul link channel capacity between

[0093] IAB networks usually have self-interference, same-layer interference, and cross-layer interference. In the sub-channel allocation process, this application only restricts parent and child nodes and sibling nodes mounted under the same parent node from sharing the same sub-channel. There is no limit on the number of times each sub-channel can be reused. Therefore, all nodes that reuse the same sub-channel will affect each other. i and its parent node The backhaul link channel capacity between It can be expressed as:

[0094]

[0095] Where w is the subchannel bandwidth p i,j For node n i In subchannel c j The transmission power on G i,j For node n i and its parent node In subchannel c j The path loss on the receiver is N0, the noise at the receiving end is I i,j For node n i The parent node In subchannel c j Interference received on:

[0096]

[0097] where G' k,j is the interference node n k With n i The parent node The path loss between them.

[0098] Considering that the backhaul capacity in multi-hop backhaul is not only determined by the parent node, the transmission capacity bottleneck of the entire transmission path needs to be considered when evaluating the node backhaul capacity. i The channel capacity of the return link between the node n1 and the donor node is denoted as Its capacity is determined by the transmission path set The minimum backhaul capacity of all upstream nodes in is determined by:

[0099]

[0100] That is, in multi-hop backhaul, the actual backhaul capability of a node is limited by the transmission capacity bottleneck of its upstream link.

[0101] Throughput Model:

[0102] The throughput of each node is denoted as R i , determined by its transmission requirements and backhaul capabilities:

[0103]

[0104] in Represents node n i The transmission requirements consist of two parts:

[0105]

[0106] The second part is n i The amount of data actually uploaded by all child nodes is the sum of the throughput of all child nodes. n i The transmission requirements generated by all users within the coverage area are uploaded to n i , Determined by the cell coverage area and user service arrival intensity:

[0107]

[0108] Among them I a is the access network user arrival intensity, A i n i Coverage area. When the base station location is determined, the base station location is used as the generator and the Voronoi diagram is used to divide the plane into multiple polygons. The polygon area corresponding to each base station is the base station coverage area. i is the area of ​​the corresponding polygon.

[0109] In steps S400 and S500, in a multi-hop IAB network, the IAB-donor, as a control node, has global information and is responsible for allocating resources x for uplink transmission on the backhaul link to all |N| ​​IAB-nodes. i,j And the transmit power p on each subchannel i,j In order to maximize the network throughput, the problem of maximizing the network throughput (i.e., the joint optimization problem model of subchannel allocation and power control) can be expressed as:

[0110]

[0111] Among them, constraint 1 indicates that the sum of the transmit power on all subchannels is subject to the maximum transmit power limit; constraint 2 takes into account the half-duplex constraint and the child node cannot occupy the same subchannel as the parent node; constraint 3 prevents multiple child nodes occupying the same subchannel from causing collisions at the parent node's receiving end.

[0112] This optimization problem is a mixed integer linear programming problem. This application first ignores the interference processing subchannel allocation problem under the condition of fixed power. Then, based on the obtained subchannel allocation results, interference management is performed through power control to determine the transmit power of each node on different subchannels. The obtained power allocation results are then brought back to the subchannel allocation problem for iteration until the performance does not improve or the maximum number of iterations is reached to obtain the final solution. The processing flow is as follows: Figure 3 shown.

[0113] Subchannel allocation problem (i.e., subchannel allocation problem submodel):

[0114] When the transmission power of each node is determined, the network throughput can be determined by the sub-channel allocation result. In this paper, we firstly set x i,j ∈{0,1} is relaxed from a binary variable to a continuous variable x i,j ∈[0,1], by adding constraints to force x i,j The final value of is 0 or 1, then the subchannel allocation problem can be described as follows:

[0115]

[0116] Constraint 4 can be treated as a penalty term and integrated into the objective function:

[0117]

[0118] Where μ takes a larger value, the optimization problem is transformed into the difference between two convex functions. After Taylor expansion of the second term, the objective function can be transformed into a standard convex optimization problem:

[0119]

[0120] in is the second term of the difference of convex functions in the original optimization problem, is g in the tth iteration t Taylor expansion of (x):

[0121]

[0122] This problem can be solved iteratively using the interior point method using the CVX solver.

[0123] Power control problem (i.e., power control problem submodel)

[0124] In the subchannel allocation matrix X |N|×|C| When the channel gain, noise, and backhaul capability of each node are determined by its transmit power on each subchannel, the power control problem can be described as follows:

[0125]

[0126] Constraint 1 indicates that the sum of the transmit power of each IAB node on all sub-channels does not exceed the maximum transmit power limit.

[0127] Considering the channel capacity The expression contains information about p i,j The objective function is the sum of the channel capacities of multiple nodes, so the problem is transformed into a Sum-of-Ratios Fractional Programming problem through quadratic transform.

[0128] Specifically, the channel capacity calculation formula is rewritten as:

[0129]

[0130] Among them A i,j =p i,j G i,j , B i,j =I i,j +N0, When the auxiliary variable y is fixed, the maximization objective function is a convex problem about power. Therefore, the auxiliary variable y and the power p can be iterated alternately to optimize and solve the maximization problem of the objective function. The specific algorithm flow is as follows Figure 4 shown.

[0131] In a specific embodiment, the scenario is as follows Figure 5As shown, the distribution of base stations in the simulated area is randomly scattered according to the two-bit Poisson point process, where the central node n1 is the IAB-donor and the remaining nodes are IAB-nodes. They are associated with each other with the maximum signal-to-noise ratio to form a spanning tree IAB network topology. The communication frequency band and path loss parameter configuration adopt the configuration in the existing method (i.e., the comparison method). The comparison scheme adopts the joint optimization scheme of subchannel allocation and power control proposed in the existing method, changes the number of subchannels, the number of IAB nodes, and the user arrival intensity, and repeats each scenario independently 100 times to take the average value as the result to observe the changes in network throughput.

[0132] Figure 6 The figure shows the trend of network throughput as it changes with the number of IAB cells. The solid blue, black, and red lines represent the network throughput trends of the proposed solution with 8, 12, and 16 subchannels, respectively, while the dashed line represents the comparative solution. As can be seen from the figure, as the number of nodes increases, the service range of the IAB network expands, and the network throughput of both the proposed solution and the comparative solution improves. The proposed solution consistently outperforms the comparative solution in the same scenario.

[0133] Simulation results show that compared with the comparison method, the proposed method has improved throughput performance in scenarios with different numbers of nodes and sub-channels, verifying the effectiveness of the proposed method.

[0134] The above-mentioned resource allocation method for an integrated multi-hop access and backhaul network first uses a Voronoi diagram for coverage partitioning, allowing estimation of access network traffic at each node before network initialization. This method has low computational complexity and performs equivalently to user association in a scenario with a large number of nodes using a maximum signal-to-noise ratio. Secondly, bottleneck links are taken into account, effectively preventing data traffic from accumulating at individual nodes and causing congestion. This also encourages the optimization algorithm to allocate more resources to bottleneck links, thereby achieving higher overall network throughput. Furthermore, the throughput is measured by actual transmission demand to improve resource allocation efficiency, avoiding situations where some nodes occupy resources to achieve a large transmission capacity but have no transmission demand. Finally, the use of a quadratic transform to process the fractional term related to power in the channel capacity formula can address the optimization problem of multiple nodes and throughput maximization.

[0135] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly indicate the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0136] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0137] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A resource allocation method for a multi-hop access and backhaul integrated network, characterized in that: The method includes: Construct a multi-hop IAB network model; Obtaining a network topology model, a resource model, a channel capacity model, and a throughput model based on the multi-hop IAB network model; Constructing a joint optimization problem model of subchannel allocation and power control according to the network topology model, the resource model, the channel capacity model and the throughput model; Decoupling the joint optimization problem model of subchannel allocation and power control into a subchannel allocation problem submodel under fixed transmit power and a power control problem submodel under fixed subchannel occupancy; Solving the sub-channel allocation problem sub-model under the fixed transmit power and the power control problem sub-model under the fixed sub-channel occupancy to obtain an optimal resource allocation result; specifically comprising: Non-convex constraints Relax binary variables into continuous variables and by adding the constraint make The final value of is 0 or 1, then the sub-channel allocation problem sub-model is: Among them, the elements Indicates the IAB base station Subchannels are used To transmit, For collection Any child node in Whether to occupy the sub-channel The logo, is the parent node, is the throughput; Using penalty terms to constrain Integrate into the objective function: in, is the penalty coefficient; The sub-channel allocation problem sub-model is transformed into the difference of two convex functions. After Taylor expansion of the second term, the objective function can be transformed into a convex optimization problem model: in, is the second term of the difference of convex functions in the sub-channel assignment problem sub-model, For the On Iteration Taylor expansion of , and: Iteratively solving the convex optimization problem model using an interior point method using a CVX solver to obtain the sub-channel allocation result; Based on the sub-channel allocation result, the sub-channel allocation matrix is ​​fixed. , noise and channel gain, the expression of the power control problem sub-model is: An auxiliary variable is introduced, and a quadratic transformation is used to process the fractional term of the power in the first backhaul link channel capacity, so as to convert the power control problem sub-model into a multi-fractional programming problem model. Iteratively solving the multi-fractional programming problem model to obtain the transmit power allocation result; Among them, the first backhaul link channel capacity The calculation formula is rewritten as: Where, , , , Subchannel bandwidth IAB node In the subchannel The transmission power on IAB node and its parent node In the subchannel Road damage on is the noise at the receiving end, IAB node The parent node In the subchannel The interference received.

2. The resource allocation method for a multi-hop access backhaul integrated network according to claim 1, characterized in that: The multi-hop IAB network model includes: IAB Base Station Group; The IAB base station group includes an IAB-donor node and several IAB-node nodes. All the IAB base station groups are , For the i IAB nodes, the first node Indicates the IAB-donor node, and the rest are IAB-node nodes.

3. The resource allocation method for a multi-hop access backhaul integrated network according to claim 2, characterized in that: The network topology model includes: Each IAB-node selects a parent node After accessing the network, the parent node For any of the IAB nodes , select the IAB node All nodes accessing the network are the IAB nodes Child nodes of , the IAB node The set of all child nodes of , the IAB node The set of transmission path nodes on the multi-hop transmission path to the IAB-donor node is .

4. The resource allocation method for a multi-hop access backhaul integrated network according to claim 3, characterized in that: The resource model includes: The backhaul frequency domain resources are divided into multiple orthogonal sub-channels ; The half-duplex constraint within the IAB node, the parent node and any of the child nodes use different sub-channels for transmission; The same parent node and all subordinate child nodes use different sub-channels for transmission; In the process of uplink data transmission in the sub-channel, the binary matrix is ​​used Mark subchannel allocation status, element Indicates the IAB base station Subchannels are used to transfer.

5. The resource allocation method for a multi-hop access backhaul integrated network according to claim 4, characterized in that: The channel capacity model includes: The IAB node and its parent node The backhaul link channel capacity between ; The first backhaul link channel capacity The expression is: in, Subchannel bandwidth IAB node In the subchannel The transmission power on IAB node and its parent node In the subchannel Road damage on is the noise at the receiving end, IAB node The parent node In the subchannel The interference received on The expression is: in, Interference node and The parent node The path loss between The IAB node The backhaul link channel capacity of the IAB-donor node is the second backhaul link channel capacity ; The second backhaul link channel capacity The expression is: in, p Transmission path node set Nodes in .

6. The resource allocation method for a multi-hop access backhaul integrated network according to claim 5, characterized in that: The throughput model includes: The throughput of each IAB node is , throughput The expression is: in, IAB node The transmission requirements consist of two parts: in, is the throughput of the child node, IAB node The sum of the throughput of the child nodes of IAB node The transmission requirements generated by all users within the coverage area; in, is the access network user arrival intensity, for Coverage area.

7. The resource allocation method for a multi-hop access and backhaul integrated network according to claim 6, characterized in that: The method further includes: When the position of the IAB node is determined, the position of the IAB node is used as a generator, and the Voronoi diagram is used to divide the plane into multiple polygons. The polygon area corresponding to each IAB node is used as the base station coverage range. is the area of ​​the corresponding polygon.

8. The resource allocation method for a multi-hop access and backhaul integrated network according to claim 7, characterized in that: The step of solving the sub-channel allocation problem sub-model under the fixed transmit power and the power control problem sub-model under the fixed sub-channel occupancy to obtain the optimal resource allocation result includes: Fixing the transmit power of each of the sub-nodes, and solving the sub-channel allocation problem sub-model to obtain a sub-channel allocation result; Solving the power control problem sub-model based on the sub-channel allocation result to obtain a transmit power allocation result; The power allocation result is brought back to the power control problem sub-model for iteration until the optimal resource allocation result is obtained.

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