A joint optimization method for delay and cost in wireless heterogeneous networks with aggregated nodes

Abstract

The present invention belongs to the technical field of heterogeneous network resource allocation and discloses a method for jointly optimizing latency and cost in a wireless heterogeneous network with aggregation nodes. The method comprises the following steps: S1. Establishing mathematical models for various parameters in MEC task offloading based on a 5G-WiFi heterogeneous network assisted by aggregation nodes; S2. Constructing a mixed-integer nonlinear programming optimization problem based on the various mathematical models established in step S1; and S3. Solving the mixed-integer nonlinear programming optimization problem constructed in step S2. The present invention employs the aforementioned method for jointly optimizing latency and cost in a wireless heterogeneous network with aggregation nodes. This method solves the task offloading and resource management issues in a 5G-WiFi mobile edge computing network assisted by aggregation nodes through an equivalent transformation and centralized solution approach, achieving a trade-off between latency and cost. The introduction of aggregation nodes enables improved network performance.

Description

Technical Field

[0001] The present invention relates to the technical field of heterogeneous network resource allocation, and in particular to a method for jointly optimizing delay and cost in a wireless heterogeneous network with aggregation nodes. Background Art

[0002] Mobile edge computing offers new opportunities for computing-intensive applications on resource-constrained mobile devices. According to IDC, by 2025, there will be 55.9 billion connected devices, and connected IoT devices alone will generate 79.4 zettabytes of data, not to mention other data consumption. Furthermore, the growing popularity of diverse applications such as ultra-high-definition streaming, augmented reality (AR), virtual reality (VR), and intelligent autonomous driving is driving a continued increase in demand for instant information. The convergence of these two trends is leading to a massive influx of data communication and processing requests.

[0003] The explosive growth of data traffic is an inevitable problem. While mobile edge computing (MEC) offers a promising solution, when a large number of users simultaneously offload computing tasks, MEC systems can suffer from severe spectrum congestion, leading to a decline in cellular network service and quality of experience (QoE). With the deployment of IEEE-802.11-based WiFi networks, leveraging the additional bandwidth resources of wireless local area networks (WLANs) to ensure high transmission efficiency and QoE has become a viable solution. Therefore, a heterogeneous network with 5G-WiFi coexistence is a suitable solution. Both 5G and WiFi offer users excellent service quality, but their networking conditions differ. 5G offers advantages over WiFi in terms of security and service quality within operator-licensed spectrum. However, due to its commercial nature, 5G typically carries a higher per-bit data transmission cost. Compared to 5G base stations, WiFi access points offer more flexible deployment, lower per-bit cost, and greater energy efficiency, which is particularly important for battery-powered wireless devices. Therefore, how to comprehensively consider the characteristics and differences of these two networks and optimize task offloading scheduling and resource management in a two-layer heterogeneous network remains a key challenge.

[0004] Offloading 5G cellular traffic to WiFi networks plays a crucial role in alleviating the increasing burden on 5G cellular networks. However, because WiFi networks rely on the CSMA / CA contention mechanism for channel access, the large number of connected devices (TDs) and excessive traffic offloading can lead to severe access conflicts. Furthermore, WiFi access points (APs) typically only provide partial coverage, leaving some TDs outside of WiFi coverage. These TDs, unreachable by the AP, can only use the computing resources of the 5G cellular network, failing to fully utilize resources in heterogeneous networks, resulting in waste. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for jointly optimizing latency and cost in wireless heterogeneous networks with aggregation nodes. By using equivalent transformation and centralized solution, the task offloading and resource management problems in AN-assisted 5G-WiFi mobile edge computing networks are solved, achieving a trade-off between latency and cost. The introduction of AN enables the network to achieve better performance.

[0006] To achieve the above object, the present invention provides a method for jointly optimizing delay and cost in a wireless heterogeneous network with an aggregation node, comprising the following steps:

[0007] S1. Based on the 5G-WiFi heterogeneous network assisted by aggregation nodes, establish mathematical models of various parameters in MEC task offloading, including the terminal device TD communication model, the aggregation node AN communication model, the task completion delay model, and the energy consumption and business cost model;

[0008] S2. Introduce weight factors to adjust the weight between task completion delay and cost according to different requirements of network scenarios. Combine the various mathematical models established in step S1 to construct a mixed integer nonlinear programming optimization problem.

[0009] S3. Introduce auxiliary variables to convert the target problem into a smooth optimization problem, and solve the mixed integer nonlinear programming optimization problem constructed in step S2.

[0010] Preferably, in step S1, establishing a TD communication model of the terminal device specifically includes the following steps:

[0011] S111, Class A TD communication model;

[0012] (1) Class A TD n The uplink transmission rate of the offload task through the 5G cellular network is as follows:

[0013] ;

[0014] in, The base station BS is assigned to the Class A TD n bandwidth, Indicates TD n The transmission power; Indicates TD n The channel gain between the BS and the base station, Indicates TD n The distance to the base station BS, is the path loss factor, is the background noise power in the 5G cellular network;

[0015] (2) Class A TDn The uplink transmission rate of the task offloaded through the WiFi network is as follows:

[0016] ;

[0017] in, Indicates Class A TD n The transmission rate that can be achieved after successfully seizing the WiFi channel; Represents Class A TD n and AN m The weight in channel competition, the higher the weight, the more advantage it has in channel competition; Indicates the terminal devices covered by both 5G cellular network and WiFi network, collection Indicates terminal devices that are only covered by 5G cellular networks. Represents the set of all terminal devices, labeled n; Represents the set of all aggregation nodes AN, labeled m; represents the decision variable for users other than the nth user to choose to use the WIFI network;

[0018] S112, Class B TD communication model;

[0019] (1) Class B TD n The uplink transmission rate of the offload task through the 5G cellular network is as follows:

[0020] ;

[0021] (2) Class B TD n The uplink transmission rate of the offload task to the aggregation node AN through the 5G cellular network is as follows:

[0022] ;

[0023] in, Indicates Class B TD n to AN m The channel gain, Indicates Class B TD n to AN m The distance between them, and set Conduct subsequent simulations; decision variables Indicates Class B TD n Whether to choose AN m Access to WiFi network, if Class B TD n Select AN m Connect to a WiFi network otherwise .

[0024] Preferably, in step S1, an aggregation node AN communication model is established, and the specific process is as follows:

[0025] AN m After receiving the task data transmitted by the Class B TD, it is unloaded to the edge server near the WiFi access point AP through the WiFi network and follows the CSMA / CA wireless communication model. The uplink transmission rate is as follows:

[0026] ;

[0027] in, Indicates AN m The transmission rate that can be achieved after successfully seizing the WiFi channel; The decision variable indicating whether the nth user uses the WIFI network;

[0028] To ensure that there is no congestion, for AN m The constraints are as follows:

[0029] ;

[0030] in, Indicates the ratio of tasks that Class B devices offload to Wi-Fi access points (APs). The task offload ratio does not exceed 1. Indicates TD n The total amount of data for the task.

[0031] Preferably, in step S1, a task completion delay model is established, which specifically includes the following steps:

[0032] S131, Class A TD task completion delay model, including:

[0033] (1) Class A TD n The local computing latency of some tasks is as follows:

[0034] ;

[0035] in, Represented as TD n The CPU cycle frequency, representing TD n computing power; Indicates the proportion of tasks executed locally, Indicates TD n The total computational effort of the task;

[0036] (2) Class A TD n If some tasks are offloaded to edge servers near the WiFi access point AP or base station BS for processing, the transmission delay of this part is as follows:

[0037] ;

[0038] in, Indicates the CPU cycle frequency of the edge server near the WiFi access point AP. Indicates the CPU cycle frequency of the edge server near the base station BS; therefore, Class A TD n The computation delays of some tasks at the edge are as follows:

[0039] ;

[0040] (3) In summary, Class A TD n The total task offloading latency is as follows:

[0041] ;

[0042] Class A TD n If the task offloading and task local computation are carried out in parallel, the actual total task processing delay is as follows:

[0043] ;

[0044] And Class A TD n There are strict task completion delay requirements, and the total task processing delay should not be greater than , with the following constraints:

[0045] ;

[0046] S132, Class B TD task completion delay model, including:

[0047] The tasks of Class B TD are processed in three directions in parallel, namely local computing, offloading to the WiFi access point AP through the aggregation node AN, and offloading to the base station BS for processing. n The local computation latency is as follows:

[0048] ;

[0049] Class B TD n The uplink transmission delays for offloading to the aggregation node AN and the base station BS are as follows:

[0050] ;

[0051] Since Class B TD nAccess to the WiFi access point AP is through the aggregation node AN relay, so the transmission delay offloaded to the WiFi access point AP also includes the transmission delay from the aggregation node AN to the WiFi access point AP, as shown below:

[0052] ;

[0053] Finally, Class B TD n The calculation delays at different edge servers are as follows:

[0054] ;

[0055] In order to fully utilize the computing power of the edge server, Class B TD n The task offload is transmitted to different ends in parallel, so the total completion delay of the tasks to different ends is as follows:

[0056] ;

[0057] In summary, Class B TD n The actual total task processing delay is as follows:

[0058] ;

[0059] Similarly, Class B TD n There are strict task completion delay requirements, and the total task processing delay meets the following constraints:

[0060] .

[0061] Preferably, in step S1, when establishing the energy consumption and business cost model, the Class A TD n and Class B TD n The transmission costs for task offloading are as follows:

[0062] ;

[0063] in, Indicates the corresponding traffic cost incurred when using 5G cellular networks for task offloading; no additional cost is incurred when using WiFi networks for transmission.

[0064] Preferably, in step S2, a mixed integer nonlinear programming optimization problem is constructed based on the various mathematical models established in step S1. The specific process is as follows:

[0065] Introducing a weight factor , the weight between task completion delay and cost is adjusted according to different requirements of network scenarios. The objective function is the weighted sum of total task completion delay and total cost, as shown below:

[0066] ;

[0067] in, It is a parameter used to balance the different scales between task completion delay and cost;

[0068] The optimization goal is to jointly optimize the task offloading decision and the task offloading rate to minimize the weighted sum of delay and cost, while meeting the task completion delay requirement of TD and the uplink transmission rate requirement of the aggregation node AN. The optimization problem can be expressed as follows:

[0069] ;

[0070] Among them, constraints C1 and C2 indicate that the task offloading decisions of the two types of TDs are 0-1 discrete variables; constraint C3 represents the selection constraint of the task offloading decision variable of Class B TD, ensuring that each Class B TD will only select one aggregation node AN for task offloading; constraints C4 and C5 represent the strict delay constraints of Class A TD and Class B TD, respectively; constraints C6 and C7 indicate that the task offloading rate cannot exceed the upper limit; constraint C8 indicates that the uplink transmission rate of the aggregation node AN should be greater than the incoming traffic to ensure stable operation.

[0071] Preferably, in step S3, solving the mixed integer nonlinear programming optimization problem constructed in step S2 includes the following steps:

[0072] S31. Introducing auxiliary variables and , the target problem is equivalently transformed into a smooth optimization problem, then and The following conditions must be met:

[0073] ;

[0074] S32. After introducing auxiliary variables, ensure that the transformed problem is equivalent to the original problem. For type A TD, introduce the following complementary constraints:

[0075] ;

[0076] Similarly, for Class B TD, the following complementary constraints are introduced to ensure the equivalence of the problem:

[0077] ;

[0078] S33, the new equivalent smooth optimization problem is as follows:

[0079] .

[0080] Therefore, the present invention adopts the above-mentioned joint optimization method of delay and cost in wireless heterogeneous networks with aggregation nodes, and solves the task offloading and resource management problems in AN-assisted 5G-WiFi mobile edge computing networks through equivalent transformation and centralized solution, achieving a trade-off between delay and cost. The introduction of AN enables the network to achieve better performance.

[0081] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a flow chart of a method for jointly optimizing delay and cost in a wireless heterogeneous network with aggregation nodes according to the present invention;

[0083] Figure 2 This is a 5G-WiFi heterogeneous network scenario diagram based on AN assistance of the present invention. DETAILED DESCRIPTION

[0084] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0085] like Figure 1 As shown, the present invention provides a method for jointly optimizing delay and cost in a wireless heterogeneous network with an aggregation node, comprising the following steps:

[0086] S1. Based on the 5G-WiFi heterogeneous network assisted by aggregation nodes, establish mathematical models of various parameters in MEC task offloading, including the terminal device TD communication model, the aggregation node AN communication model, the task completion delay model, and the energy consumption and business cost model;

[0087] S2. Introduce weight factors to adjust the weight between task completion delay and cost according to different requirements of network scenarios. Combine the various mathematical models established in step S1 to construct a mixed integer nonlinear programming optimization problem.

[0088] S3. Introduce auxiliary variables to convert the target problem into a smooth optimization problem, and solve the mixed integer nonlinear programming optimization problem constructed in step S2.

[0089] Example

[0090] S1. Establish a mathematical model based on the 5G-WiFi heterogeneous network assisted by aggregation nodes.

[0091] like Figure 2As shown in the figure, a 5G-WiFi heterogeneous network MEC task offloading scenario with the assistance of an aggregation node (AN) exists in the network. There is one 5G base station (5G BS) and one WiFi access point (WiFi AP). There is no cross-layer interference between the WiFi network and the 5G cellular network. Edge servers with computing capabilities are deployed near the BS and AP. Several ANs that are associated with both the WiFi network and the 5G cellular network are deployed within the coverage of the AP. The BS covers all terminal devices (TDs) in the network. The AP can only cover some TDs due to its own functional limitations. The TDs covered by both the 5G cellular network and the WiFi network are represented as Class A TDs and recorded as a set TD covered only by 5G cellular network is expressed as Class B TD and recorded as set , for the convenience of description, the set of all TDs is recorded as , labelled as n; the set of all ANs is recorded as , labelled as m. The task profile of TD is given by Indicates that Indicates TD n The total amount of data for the task, Indicates TD n The total computational effort of the task.

[0092] For all TDs, use Indicates the proportion of tasks executed locally. In order to reduce the task completion delay of Class B TD, its tasks are divided into three categories for processing, namely local execution, offloading to the edge server near the BS, and offloading to the edge server near the AP. Indicates the ratio of tasks that Class B devices offload to the AP. The task offload ratio cannot exceed 1. The constraints are as follows:

[0093] ;

[0094] For Class A TD using decision variables To represent Class A TD n Whether to choose to access the WiFi network. If the Class A TD n Select the WiFi network to connect to. otherwise For Class B TD n Using decision variables To indicate whether to select AN m Connect to the WiFi network. If the Class B TD n Select AN m Connect to a WiFi network otherwise Class B TD nTo fully utilize all network resources, some tasks must be offloaded to the WiFi network. Only one AN can be selected to access the WiFi network. The following constraints apply:

[0095] ;

[0096] S11. Establish a TD communication model.

[0097] S111, Class A TD communication model.

[0098] (1) Class A TD is in the common coverage of two networks, that is, using 5G cellular network or WiFi network for communication. The access system of 5G cellular network is based on OFDMA, and there is no interference between channels and interference between different BSs. Therefore, Class A TD n The uplink transmission rate of the offload task through the 5G cellular network is as follows:

[0099] ;

[0100] in, BS is assigned to Class A TD n bandwidth, Indicates TD n The transmission power; Indicates TD n The channel gain between the BS and Indicates TD n The distance to the BS, is the path loss factor, is the background noise power in the 5G cellular network.

[0101] (2) If all TDs accessing the WiFi network share a common channel and compete for channel access rights through CSMA / CA, then Class A TD n The uplink transmission rate of the task offloaded through the WiFi network is as follows:

[0102] ;

[0103] in, Indicates Class A TD n The transmission rate that can be achieved after successfully seizing the WiFi channel; Represents Class A TD n and AN m The weight in channel competition, the higher the weight, the more advantage it has in channel competition; represents the decision variable for users other than the nth user to choose to use the WIFI network.

[0104] S112, Class B TD communication model.

[0105] (1) Class B TD n The uplink transmission rate of the offload task through the 5G cellular network is as follows:

[0106] ;

[0107] (2) Class B TD n If the edge server near the AP is connected to the AN through the 5G cellular network and then to the AP, the Class B TD n The uplink transmission rate of the task offloaded to the AN through the 5G cellular network is as follows:

[0108] ;

[0109] in, Indicates Class B TD n to AN m The channel gain, Indicates Class B TD n to AN m The distance between them, and set Perform subsequent simulations.

[0110] S12. Establish an AN communication model.

[0111] AN m After receiving the task data transmitted by the Class B TD, it is offloaded to the edge server near the AP through the WiFi network and follows the CSMA / CA wireless communication model. The uplink transmission rate is as follows:

[0112] ;

[0113] in, Indicates AN m The transmission rate that can be achieved after successfully seizing the WiFi channel; The decision variable indicating whether the nth user uses the WIFI network.

[0114] If an AN has a good channel gain for Class B TD, a large number of Class B TD will be unloaded through the AN. However, the AN often does not have a strong enough load performance. m To ensure that there is no congestion, the constraints are as follows:

[0115] ;

[0116] S13. Establish a task completion delay model.

[0117] Since the task results returned from the edge server are often very small, the transmission delay of the task result return is ignored in the task processing delay. Since the task partial offloading methods of Class A TD and Class B TD are different, the composition of task processing delay is also different.

[0118] S131. Class A TD task completion delay model.

[0119] (1) Class A TD n The local computing latency of some tasks is as follows:

[0120] ;

[0121] in, Represented as TD n The CPU cycle frequency, representing TD n computing power.

[0122] (2) In addition to local computing tasks, Class A TD n You can choose to offload the remaining part to the edge server near the AP or BS for processing. The transmission delay of this part is as follows:

[0123] ;

[0124] in, Indicates the CPU cycle frequency of the edge server near the AP. Indicates the CPU cycle frequency of the edge server near the BS; therefore, Class A TD n The computation delays of some tasks at the edge are as follows:

[0125] ;

[0126] (3) In summary, Class A TD n The total task offloading latency is as follows:

[0127] ;

[0128] Class A TD n If the task offloading and task local computation are carried out in parallel, the actual total task processing delay is as follows:

[0129] ;

[0130] And Class A TD n There are strict task completion delay requirements, and the total task processing delay should not be greater than , with the following constraints:

[0131] ;

[0132] S132. Class B TD task completion delay model.

[0133] The tasks of Class B TD are processed in three directions in parallel, namely local computing, offloading to AP through AN, and offloading to BS for processing. n The local computation latency is as follows:

[0134] ;

[0135] Class B TD n The uplink transmission delays for offloading to the AN and to the BS are as follows:

[0136]

[0137] Since Class B TD n Accessing the AP is done through the AN relay. Therefore, the transmission delay offloaded to the AP also includes the transmission delay from the AN to the AP, as shown below:

[0138]

[0139] Finally, Class B TD n The calculation delays at different edge servers are as follows:

[0140] ;

[0141] In order to fully utilize the computing power of the edge server, Class B TD n The task offload is transmitted to different ends in parallel, so the total completion delay of the tasks to different ends is as follows:

[0142] ;

[0143] In summary, Class B TD n The actual total task processing delay is as follows:

[0144] ;

[0145] Similarly, Class B TD n There are strict task completion delay requirements, and the total task processing delay should not be greater than , with the following constraints:

[0146] ;

[0147] S14. Establish an energy consumption and business cost model.

[0148] First, the business fee cost is TDn The traffic transmission cost of using 5G cellular network for task offloading will generate the corresponding traffic cost expressed as ($ / bit), there is no additional cost when using WiFi network transmission. Therefore, Class A TD n and Class B TD n The transmission costs for task offloading are as follows:

[0149] ;

[0150] S2. Construct a mixed integer nonlinear programming (MINLP) optimization problem based on the mathematical models established in step S1.

[0151] Introducing a weight factor , the weight between task completion delay and cost is adjusted according to different requirements of network scenarios. The objective function is the weighted sum of total task completion delay and total cost, as shown below:

[0152] ;

[0153] in, It is a parameter used to balance the different scales between task completion delay and cost.

[0154] The optimization goal is to jointly optimize the task offloading decision and the task offloading rate to minimize the weighted sum of delay and cost, while meeting the task completion delay requirement of TD and the uplink transmission rate requirement of AN. The optimization problem can be expressed as follows:

[0155] ;

[0156] Constraints C1 and C2 indicate that the task offloading decisions for the two types of TDs are discrete 0-1 variables; constraint C3 represents a one-or-one constraint on the task offloading decision variable for Class B TDs, ensuring that each Class B TD selects only one AN for task offloading; constraints C4 and C5 represent strict latency constraints for Class A and Class B TDs, respectively; constraints C6 and C7 indicate that the task offloading rate cannot exceed an upper limit; and constraint C8 states that the AN's uplink transmission rate must be greater than the incoming traffic to ensure stable operation. Both the objective function and the constraints contain highly coupled nonconvex terms and are nonsmooth, making this optimization problem a nonsmooth mixed integer nonlinear programming (MINLP) problem.

[0157] S3. Solve the MINLP joint optimization problem constructed in step S2.

[0158] S31. Due to the non-smoothness of the task completion delay model in the objective function, the problem cannot be solved directly, so the auxiliary variable is introduced and , the target problem is equivalently transformed into a smooth optimization problem, then the introduced and The following conditions must be met:

[0159] ;

[0160] S32. Since TD processes tasks locally and remotely in parallel, the task completion delay described depends on the larger of the local and edge sides. To ensure that the transformed problem is equivalent to the original problem after introducing auxiliary variables, the following complementary constraints are introduced for Class A TD:

[0161] ;

[0162] Similarly, for Class B TD, the following complementary constraints are introduced to ensure the equivalence of the problem:

[0163] ;

[0164] S33, the new equivalent smooth optimization problem is as follows:

[0165] ;

[0166] Based on the above process, the BARON solver is used to obtain the global optimal solution to evaluate the performance and advantages of the proposed scheme in MEC task offloading.

[0167] Therefore, the present invention adopts the above-mentioned joint optimization method of delay and cost in wireless heterogeneous networks with aggregation nodes, and solves the task offloading and resource management problems in AN-assisted 5G-WiFi mobile edge computing networks through equivalent transformation and centralized solution, achieving a trade-off between delay and cost. The introduction of AN enables the network to achieve better performance.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for joint optimization of delay and cost in wireless heterogeneous networks with aggregation nodes, characterized in that: The following steps are involved: S1. Based on the 5G-WiFi heterogeneous network assisted by aggregation nodes, establish mathematical models of various parameters in MEC task offloading, including the terminal device TD communication model, the aggregation node AN communication model, the task completion delay model, and the energy consumption and business cost model; S2. Introduce weight factors and adjust the weight between task completion delay and cost according to different network scenario requirements. Combined with the various mathematical models established in step S1, a mixed integer nonlinear programming optimization problem is constructed. The specific process is as follows: Introducing a weight factor , the weight between task completion delay and cost is adjusted according to different requirements of network scenarios. The objective function is the weighted sum of total task completion delay and total cost, as shown below: ； in, It is a parameter used to balance the different scales between task completion delay and cost; The optimization goal is to jointly optimize the task offloading decision and the task offloading rate to minimize the weighted sum of delay and cost, while meeting the task completion delay requirement of TD and the uplink transmission rate requirement of the aggregation node AN. The optimization problem can be expressed as follows: ； Among them, constraints C1 and C2 indicate that the task offloading decisions of the two types of TDs are 0-1 discrete variables; constraint C3 is the one-or-the-other constraint of the task offloading decision variable of Class B TD, ensuring that each Class B TD will only select one aggregation node AN for task offloading; constraints C4 and C5 are strict delay constraints for Class A TD and Class B TD, respectively; constraints C6 and C7 indicate that the task offloading rate cannot exceed the upper limit; constraint C8 indicates that the uplink transmission rate of the aggregation node AN must be greater than the incoming traffic to ensure stable operation. S3. Introduce auxiliary variables to convert the target problem into a smooth optimization problem, and solve the mixed integer nonlinear programming optimization problem constructed in step S2.

2. The method for joint optimization of delay and cost in a wireless heterogeneous network with an aggregation node according to claim 1, characterized in that: In step S1, a TD communication model of a terminal device is established, specifically including the following steps: S111, Class A TD communication model; (1) Class A TD n The uplink transmission rate of the offload task through the 5G cellular network is as follows: ； in, The base station BS is assigned to the Class A TD n bandwidth, Indicates TD n The transmission power; Indicates TD n The channel gain between the BS and the base station, Indicates TD n The distance to the base station BS, is the path loss factor, is the background noise power in the 5G cellular network; (2) Class A TD n The uplink transmission rate of the task offloaded through the WiFi network is as follows: ； in, Indicates Class A TD n The transmission rate that can be achieved after successfully seizing the WiFi channel; Represents Class A TD n and AN m The weight in channel competition, the higher the weight, the more advantage it has in channel competition; Indicates the terminal devices covered by both 5G cellular network and WiFi network, collection Indicates terminal devices that are only covered by 5G cellular networks. Represents the set of all terminal devices, labeled n; Represents the set of all aggregation nodes AN, labeled m; represents the decision variable for users other than the nth user to choose to use the WIFI network; S112, Class B TD communication model; (1) Class B TD n The uplink transmission rate of the offload task through the 5G cellular network is as follows: ； (2) Class B TD n The uplink transmission rate of the offload task to the aggregation node AN through the 5G cellular network is as follows: ； in, Indicates Class B TD n to AN m The channel gain, Indicates Class B TD n to AN m The distance between them, and set Conduct subsequent simulations; decision variables Indicates Class B TD n Whether to choose AN m Access to WiFi network, if Class B TD n Select AN m Connect to a WiFi network otherwise .

3. The method for joint optimization of delay and cost in a wireless heterogeneous network with an aggregation node according to claim 2, characterized in that: In step S1, the aggregation node AN communication model is established. The specific process is as follows: AN m After receiving the task data transmitted by the Class B TD, it is unloaded to the edge server near the WiFi access point AP through the WiFi network and follows the CSMA / CA wireless communication model. The uplink transmission rate is as follows: ； in, Indicates AN m The transmission rate that can be achieved after successfully seizing the WiFi channel; The decision variable indicating whether the nth user uses the WIFI network; To ensure that there is no congestion, for AN m The constraints are as follows: ； in, Indicates the ratio of tasks that Class B devices offload to Wi-Fi access points (APs). The task offload ratio does not exceed 1. Indicates TD n The total amount of data for the task.

4. The method for joint optimization of delay and cost in a wireless heterogeneous network with an aggregation node according to claim 1, characterized in that: In step S1, a task completion delay model is established, which specifically includes the following steps: S131, Class A TD task completion delay model, including: (1) Class A TD n The local computing latency of some tasks is as follows: ； in, Represented as TD n The CPU cycle frequency, representing TD n computing power; Indicates the proportion of tasks executed locally, Indicates TD n The total computational effort of the task; (2) Class A TD n If some tasks are offloaded to edge servers near the WiFi access point AP or base station BS for processing, the transmission delay of this part is as follows: ； in, Indicates the CPU cycle frequency of the edge server near the WiFi access point AP. Indicates the CPU cycle frequency of the edge server near the base station BS; therefore, Class A TD n The computation delays of some tasks at the edge are as follows: ； (3) In summary, Class A TD n The total task offloading latency is as follows: ； Class A TD n If the task offloading and task local computation are carried out in parallel, the actual total task processing delay is as follows: ； The total task processing delay should not be greater than , with the following constraints: ； S132, Class B TD task completion delay model, including: The tasks of Class B TD are processed in three directions in parallel, namely local computing, offloading to the WiFi access point AP through the aggregation node AN, and offloading to the base station BS for processing. n The local computation latency is as follows: ； Class B TD n The uplink transmission delays for offloading to the aggregation node AN and the base station BS are as follows: ； Since Class B TD n Access to the WiFi access point AP is through the aggregation node AN relay, so the transmission delay offloaded to the WiFi access point AP also includes the transmission delay from the aggregation node AN to the WiFi access point AP, as shown below: ； Finally, Class B TD n The calculation delays at different edge servers are as follows: ； In order to fully utilize the computing power of the edge server, Class B TD n The task offload is transmitted to different ends in parallel, so the total completion delay of the tasks to different ends is as follows: ； In summary, Class B TD n The actual total task processing delay is as follows: The total task processing delay satisfies the following constraints: 。 5. The method for joint optimization of delay and cost in a wireless heterogeneous network with an aggregation node according to claim 1, characterized in that: In step S1, when establishing the energy consumption and business cost model, the Class A TD n and Class B TD n The transmission costs for task offloading are as follows: ； in, Indicates the corresponding traffic cost incurred when using 5G cellular networks for task offloading; no additional cost is incurred when using WiFi networks for transmission.

6. The method for joint optimization of delay and cost in a wireless heterogeneous network with an aggregation node according to claim 1, characterized in that: In step S3, the mixed integer nonlinear programming optimization problem constructed in step S2 is solved, including the following steps: S31. Introducing auxiliary variables and , the target problem is equivalently transformed into a smooth optimization problem, then and The following conditions must be met: ； S32. After introducing auxiliary variables, ensure that the transformed problem is equivalent to the original problem. For type A TD, introduce the following complementary constraints: ； Similarly, for Class B TD, the following complementary constraints are introduced to ensure the equivalence of the problem: ； S33, the new equivalent smooth optimization problem is as follows: 。

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