A data communication and transmission method in a wireless communication network

Abstract

The present invention relates to the field of wireless communication technologies, and discloses a data communication transmission method in a wireless communication network, including: Step 1, by deploying cognitive radio devices, receiving channel signals in the wireless communication network, extracting received signal sample data, calculating the energy value of the current channel, judging the occupancy state of the channel, and inputting the channel state as historical channel state data into a channel state prediction model to predict the channel state at a future moment; Step 2, combining the channel state predicted in Step 1 with the communication requirements of the user to determine the matching relationship between the user and the channel. By using cognitive radio technology to achieve real-time perception of the channel state, and combining with a long short-term memory network to predict the channel occupancy state at a future moment, intelligent matching of dynamic allocation of channel resources and user requirements is realized, resulting in the effects of improving the utilization rate of spectrum resources and avoiding resource waste.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and specifically to a data communication and transmission method in a wireless communication network. Background Art

[0002] With the rapid evolution of wireless communication technologies, especially the popularization and application of 5G and future 6G communication networks, the global demand for high-performance wireless data communication is increasing day by day. As the representative of current wireless communication technologies, 5G networks have the characteristics of high data rate and low latency, and are applied in the fields of the Internet of Things, cloud computing, virtual reality, and autonomous driving. However, due to the sharp increase in user equipment, the diversification of communication service types, and the explosive growth of data traffic, there are many bottlenecks in the existing wireless communication networks in terms of spectrum management, task computing, data transmission, and network load regulation, and they cannot meet the future communication requirements of intelligence, high efficiency, and low energy consumption.

[0003] The spectrum resources in a wireless communication network are limited, which is an important basis for supporting data communication and transmission. Traditional spectrum management methods mainly rely on static allocation mechanisms and cannot adapt to complex and dynamically changing network environments. In addition, due to the uncertainty of user data requirements and the dynamic changes in the wireless communication environment, how to achieve real-time perception and intelligent matching of channel resources and further improve the utilization rate of spectrum resources is an important technical problem currently faced.

[0004] In the context of the gradual popularization of edge computing technologies, the task computing and data transmission of user equipment gradually rely on edge nodes for processing. However, due to differences in the amount of task computing, the computing resources of edge nodes, and the data transmission paths, the energy consumption and latency during task offloading are significantly increased. At the same time, how to achieve joint optimization of computing energy consumption and transmission latency during task offloading to improve task execution efficiency and reduce the power consumption of network nodes is an important direction for the optimization of current wireless communication networks.

[0005] During the data communication and transmission process, path determination is an important factor determining communication efficiency. Existing technologies mostly adopt static path selection or path scheduling mechanisms based on preset rules and cannot effectively adapt to the dynamic changes in network link states. Therefore, how to dynamically segment data streams and achieve load balancing of the network and real-time optimization of transmission paths through multi-path parallel transmission is the key to solving the bottleneck of data transmission performance.

[0006] In a communication network with high traffic load, there are obvious real-time fluctuations in network link states and path quality, which easily lead to network congestion and instability of transmission paths. Traditional path scheduling algorithms usually rely on static or local optimization mechanisms and cannot perform global prediction and real-time regulation in complex dynamic network environments, resulting in a decline in data transmission efficiency and network performance.

[0007] Therefore, those skilled in the art provide a data communication transmission method in a wireless communication network to solve the above-mentioned problems. Summary of the Invention

[0008] Aiming at the deficiencies of the prior art, the present invention provides a data communication transmission method in a wireless communication network to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A data communication transmission method in a wireless communication network, including:

[0010] Step 1: By deploying cognitive radio devices, receive channel signals in the wireless communication network, extract the received signal sample data, calculate the energy value of the current channel, judge the occupancy status of the channel, and input the channel status as historical channel status data into the channel status prediction model to predict the channel status at a future moment;

[0011] Step 2: Combine the channel status predicted in Step 1 with the communication requirements of the user to determine the matching relationship between the user and the channel, dynamically allocate the available channel resources in the wireless communication network, and form a channel allocation scheme;

[0012] Step 3: According to the channel allocation scheme formed in Step 2, construct a network communication topology structure, model the wireless communication network as a network model including a node set and a link set, and combine the delay weights and channel allocation status of each link to determine the optimal transmission path for data communication, and dynamically determine the transmission path of user data through the path scheduling module;

[0013] Step 4: For the user tasks carried on the transmission path dynamically scheduled in Step 3, determine the offloading path of the tasks according to the computational amount of the tasks and the computational resources of the edge computing nodes, offload the tasks to the edge nodes for computational processing, and after the edge nodes complete the calculation, return the calculation results to the target user node through the network transmission path;

[0014] Step 5: According to the data and link status returned by the edge computing nodes on the transmission path in Step 4, judge the link quality, and on the premise of meeting the signal-to-noise ratio condition of the communication link, control the transmission power of the communication node, split the data stream to be transmitted into multiple data streams, and perform parallel transmission along the optimal transmission path determined in Step 3 through the multipath transmission mechanism to complete the data communication transmission in the wireless communication network.

[0015] Preferably, when calculating the energy value of the current channel in Step 1, energy detection is performed based on the received signal sample data, and the energy value is determined by the following method:

[0016]

[0017] Among them, E is the energy value of the current channel, N is the number of sampling points, and y(n) is the received signal sample data.

[0018] Preferably, when predicting the channel state at a future moment in step 1, a long short-term memory network is used as the channel state prediction model. The model is trained with historical channel state data to minimize the mean square error. The loss function of the mean square error is:

[0019]

[0020] Among them, L is the loss value of the prediction model, T is the number of time steps, is the predicted channel state, S(t) is the actual channel state, and t represents the time step.

[0021] Preferably, when dynamically allocating the available channel resources of the wireless communication network in step 2, based on the communication requirements of the user and the channel state, and aiming at maximizing the data transmission rate, the allocation relationship between the user and the channel is determined. The relationship between the data transmission rate and the signal-to-noise ratio satisfies:

[0022] R i，j = B·log2(1 + SNR i，j ),

[0023] Among them, R i，j is the transmission rate of user i on channel j, B is the channel bandwidth,

[0024] SNR i，j is the signal-to-noise ratio of user i on channel j.

[0025] Preferably, when determining the optimal transmission path for data communication in step 3, based on the network communication topology structure and the delay weights of each link, a weighted shortest path algorithm is used to determine the path to minimize the total transmission delay. The total transmission delay is expressed as:

[0026] T total = ∑ (i,j)∈P w ij ·x ij ,

[0027] Among them, P is the set of transmission paths from the source node to the destination node, (i, j) is the link from i to j in the path, w ij is the delay weight of the link (i, j), and x ij is the link usage indicator variable:

[0028] If x ij = 1, the link (i, j) is selected;

[0029] If x ijWhen it is 0, the link (i, j) is not selected;

[0030] In step 3, the dynamic scheduling of the transmission path is adjusted in combination with the congestion status of the link, and the path quality is predicted and updated in real time through a deep reinforcement learning model.

[0031] Preferably, when the task is offloaded to the edge node in step 4, the optimal task offloading path is dynamically determined according to the task calculation amount and the edge node computing resources, and the offloading time and the transmission delay are used as the optimization objectives;

[0032] In step 4, the execution time of the task offloading is determined by the task calculation amount U and the processing ability f of the edge computing node edge The execution time satisfies the following relationship:

[0033]

[0034] where T edge is the edge processing time of the task, U is the task calculation amount, and f edge is the processing ability of the edge node.

[0035] Preferably, when the task is offloaded to the edge node in step 4, in order to further reduce the energy consumption and the task execution time, an energy consumption optimization model is used to jointly optimize the task calculation amount and the transmission power, and the optimization objective is to minimize the total energy consumption E total The total energy consumption includes the computing energy consumption and the transmission energy consumption, and is expressed as:

[0036]

[0037] where E total is the total energy consumption of the task offloading, P transmit is the transmission power during the task transmission, T transmit is the data transmission time of the task, k is the energy consumption coefficient of the edge computing node, and f edge is the processing ability of the edge node, and T edge is the edge processing time of the task.

[0038] Preferably, when the path quality is predicted and updated in real time through a deep reinforcement learning model in step 3, the state-action value function Q is used to optimize the path determination process, and the optimal decision of the path determination satisfies the following update relationship:

[0039]

[0040] where Q(s, a) is the value function of taking action a in state s, R is the immediate reward after executing action a, γ is the discount factor, and s ′is the next state after the current state s executes action a, a ′ is the optimal action in the next state.

[0041] Preferably, when dynamically allocating the available channel resources of the wireless communication network in step 2, by considering the user service quality constraint and the spectrum resource utilization rate, a multi-objective optimization model is adopted to dynamically allocate the channel resources. The channel conflict probability is related to the number of users and the allocation scheme, and is expressed as:

[0042]

[0043] where P conflict is the channel conflict probability, p i，j is the probability that user i has a conflict on channel j, and M is the number of users in the wireless communication network.

[0044] Preferably, when splitting the data stream into multiple data streams for multipath transmission in step 5, in order to improve the transmission reliability and delay sensitivity, the allocation ratio α i of each path is dynamically determined, satisfying the following constraints:

[0045] where α i is the data stream allocation ratio of path i, N is the number of available transmission paths, A is the total amount of data to be transmitted, R i is the data transmission rate on path i, T all is the total completion time.

[0046] The present invention provides a data communication transmission method in a wireless communication network. It has the following beneficial effects:

[0047] 1. The present invention realizes the real-time perception of the channel state through cognitive radio technology, combines the long short-term memory network to predict the channel occupancy state at future moments, realizes the intelligent matching of dynamic channel resource allocation and user requirements, and obtains the effects of improving the spectrum resource utilization rate and avoiding resource waste.

[0048] 2. The present invention establishes a joint optimization model of energy consumption and delay by comprehensively considering the task calculation amount, the computing resources of the edge node, and the transmission path situation, realizes the minimization of the computing energy consumption and transmission energy consumption of the task, and obtains the effects of reducing the energy consumption of network nodes and the task execution delay.

[0049] 3. The present invention realizes the dynamic allocation and real-time update of paths by splitting the data stream into multiple data streams and using the multipath transmission mechanism for parallel transmission, and obtains the effects of reducing the data transmission delay, balancing the network load, and improving the transmission reliability.

[0050] 4. By introducing a deep reinforcement learning model, the present invention makes real-time predictions and dynamic updates on network link status and path quality, realizes intelligent regulation of path selection and dynamic adjustment of congestion status, and achieves the effects of alleviating network congestion, improving data transmission continuity, and enhancing network performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] The present invention will be described in detail below with reference to the accompanying drawings:

[0054] Embodiment:

[0055] Please refer to the attached Figure 1 , the embodiment of the present invention provides a data communication and transmission method in a wireless communication network, including:

[0056] Step 1: By deploying cognitive radio devices, receive channel signals in the wireless communication network, extract received signal sample data, calculate the energy value of the current channel, judge the occupancy status of the channel, and input the channel status as historical channel status data into the channel status prediction model to predict the channel status at a future moment;

[0057] Step 2: Combine the channel status predicted in Step 1 with the communication requirements of the user to determine the matching relationship between the user and the channel, dynamically allocate available channel resources in the wireless communication network, and form a channel allocation scheme;

[0058] Step 3: According to the channel allocation scheme formed in Step 2, construct a network communication topology structure, model the wireless communication network as a network model including a node set and a link set, and combine the delay weights and channel allocation status of each link to determine the optimal transmission path for data communication, and dynamically determine the transmission path of user data through the path scheduling module;

[0059] Step 4: For the user tasks carried on the transmission path dynamically scheduled in Step 3, determine the task offloading path according to the computational amount of the task and the computational resources of the edge computing nodes, offload the task to the edge node for computational processing, and after the edge node completes the calculation, return the calculation result to the target user node through the network transmission path;

[0060] Step 5: According to the data and link status returned by the edge computing nodes on the transmission path in Step 4, judge the link quality. On the premise of meeting the signal-to-noise ratio condition of the communication link, control the transmission power of the communication nodes, split the data stream to be transmitted into multiple data streams, and perform parallel transmission along the optimal transmission path determined in Step 3 through the multi-path transmission mechanism to complete the data communication transmission in the wireless communication network.

[0061] Benefits of Step 1: Realize the real-time perception of the channel state through cognitive radio technology, accurately judge the occupancy of the current channel, ensure the availability of data transmission, introduce a channel state prediction model, effectively predict the future channel occupancy state, plan channel resources in advance, avoid the problem of resource waste caused by static channel allocation, and provide data support for subsequent dynamic channel resource allocation;

[0062] Benefits of Step 2: Combine user requirements with the channel state to achieve dynamic allocation of channel resources, improve the utilization rate of spectrum resources. The dynamic nature of the channel allocation scheme enables the network to adapt to real-time communication requirements, meet the transmission requirements of different users at different time periods, provide an efficient channel allocation mechanism, improve the network transmission rate, and alleviate the resource conflict problem in the communication process;

[0063] Benefits of Step 3: By constructing the network communication topology structure, comprehensively describe the communication relationship between network nodes and links, provide a basis for path scheduling, consider the delay weight of the link and the channel allocation status, determine the optimal transmission path for data communication, effectively reduce the transmission delay, adopt a dynamic path scheduling module, and adjust the path in real time according to the link state, reduce network congestion, and improve the stability and reliability of data transmission;

[0064] Benefits of Step 4: Through the deployment of edge computing nodes, dynamically offload the task computation amount to appropriate edge nodes, reduce the computing burden of terminal devices, combine the computing resources of edge nodes, dynamically determine the optimal task offloading path, effectively improve the task processing efficiency, shorten the computing delay of tasks, reduce the response time of data return, and meet application scenarios with high real-time requirements;

[0065] Benefits of Step 5: By judging the link quality, dynamically control the transmission power of communication nodes, effectively reduce network energy consumption, meet the requirements of green communication, split the data stream to be transmitted into multiple data streams, and perform parallel transmission through the multi-path transmission mechanism, further improve the data transmission rate and network bandwidth utilization rate. In multi-path transmission, the dynamic allocation and real-time update of paths enhance the reliability and stability of data transmission, reduce network congestion and data loss situations.

[0066] When calculating the energy value of the current channel in Step 1, energy detection is performed based on the received signal sample data, and the energy value is determined as follows:

[0067]

[0068] where E is the energy value of the current channel, N is the number of sampling points, and y(n) is the received signal sample data.

[0069] When predicting the channel state at future moments in Step 1, a long short-term memory network is used as the channel state prediction model. The model is trained with historical channel state data to minimize the mean squared error, and the loss function of the mean squared error is:

[0070]

[0071] where L is the loss value of the prediction model, T is the number of time steps, is the predicted channel state, S(t) is the actual channel state, and t represents the time step.

[0072] In Step 1, energy detection is performed through the received signal sample data, which can real-time judge the occupancy state of the current channel and determine whether the channel is idle. The calculation of the specific energy value is based on the cumulative energy of the received signal sample data, effectively avoiding the complex channel estimation process, and having the advantages of simple calculation and strong real-time performance. In addition, the energy detection method is applicable to most channel environments, can achieve fast detection under low signal-to-noise ratio conditions, and ensure the accuracy and reliability of channel state perception.

[0073] By using a long short-term memory network as the channel state prediction model, it can make full use of historical channel state data, capture the long-term dependence relationship of the channel state, and predict the channel state at future moments. The long short-term memory network model trains and optimizes the channel state by minimizing the mean squared error loss function, thereby improving the accuracy of the prediction results. The prediction results provide accurate data support for subsequent channel resource allocation, realize the advance planning and dynamic scheduling of channel resources, and further improve the communication efficiency of the network.

[0074] When dynamically allocating the available channel resources of the wireless communication network in Step 2, based on the communication requirements of users and the channel state, and in accordance with the goal of maximizing the data transmission rate, the allocation relationship between users and channels is determined. The relationship between the data transmission rate and the signal-to-noise ratio satisfies:

[0075] R i，j =B·log2(1+SNR i，j ),

[0076] where R i，j is the transmission rate of user i on channel j, and B is the channel bandwidth,

[0077] SNR i，j is the signal-to-noise ratio of user i on channel j.

[0078] When the present invention dynamically allocates available channel resources of a wireless communication network, it comprehensively considers the communication requirements of users and the current channel state to ensure that channel resources can be reasonably allocated to users and meet the communication requirements of different users. Compared with the traditional static channel allocation method, the dynamic allocation mechanism can make adjustments according to the changes in the real-time network environment, effectively avoid resource waste, and further improve the utilization rate of spectrum resources.

[0079] This step determines the allocation relationship between users and channels by correlating the user data transmission rate with the channel signal-to-noise ratio and aiming at maximizing the data transmission rate. Allocate more user resources on channels with higher signal-to-noise ratios, make full use of the transmission capabilities of high-quality channels, improve the data transmission rate of users, and improve communication efficiency.

[0080] When determining the optimal transmission path for data communication in step 3, based on the network communication topology structure and the delay weights of each link, a weighted shortest path algorithm is used to determine the path to minimize the total transmission delay, and the total transmission delay is expressed as:

[0081] T total = ∑ (i,j)∈P w ij ·x ij ,

[0082] where P is the set of transmission paths from the source node to the destination node, (i, j) is the i→j link in the path, w ij is the delay weight of link (i, j), and x ij is the link usage indicator variable:

[0083] If x ij = 1, link (i, j) is selected;

[0084] If x ij = 0, link (i, j) is not selected;

[0085] The dynamic scheduling of the transmission path in step 3 is adjusted in combination with the congestion state of the link, and the path quality is predicted and updated in real time through a deep reinforcement learning model.

[0086] The present invention determines the optimal data transmission path by using a weighted shortest path algorithm through the network communication topology structure and the delay weights of each link, aiming at minimizing the total transmission delay. In the optimization process of link weights, the real-time state of the link is comprehensively considered to effectively reduce the transmission time of data in the network, improve communication efficiency, and meet application scenarios with high requirements for low latency.

[0087] During the data transmission process, the state of the network link fluctuates dynamically, which easily leads to path congestion. The present invention dynamically schedules the path by combining the congestion state of the link, adjusts the data transmission path in real time, avoids the use of congested links, improves the transmission stability of network data, and ensures the balanced distribution of network load.

[0088] The present invention introduces a deep reinforcement learning model in the dynamic scheduling of the transmission path. By real-time monitoring the network link state and dynamically predicting the path quality, it realizes the intelligent path selection. Through the self-learning and real-time update mechanism of reinforcement learning, it can quickly find the optimal path in a complex network environment, adapt to the dynamic changes of the link state, and effectively improve the continuity of data transmission and network performance.

[0089] Through the intelligent decision-making and dynamic optimization of the path scheduling module, the utilization rate of network resources is further improved. At the same time, the dynamic determination and real-time update of the optimal path ensure the transmission stability of data communication, reduce the impact brought by the failure or performance degradation of the transmission path, and enhance the robustness of the network.

[0090] When the task is offloaded to the edge node in step 4, according to the task computation amount and the computing resources of the edge node, the optimal task offloading path is dynamically determined, and the sum of the offloading time and the transmission delay is used as the optimization objective;

[0091] When the task is offloaded to the edge node in step 4, according to the task computation amount and the computing resources of the edge node, the optimal task offloading path is dynamically determined, and the sum of the offloading time and the transmission delay is used as the optimization objective;

[0092] The execution time of the task offloading in step 4 is determined by the task computation amount U and the processing capacity f of the edge computing node edge and satisfies the following relationship:

[0093]

[0094] where, T edge is the edge processing time of the task, U is the task computation amount, and f edge is the processing capacity of the edge node.

[0095] When the task is offloaded to the edge node in step 4, in order to further reduce the energy consumption and task execution time, an energy consumption optimization model is used to jointly optimize the task computation amount and transmission power, and the optimization objective is to minimize the total energy consumption E total , and the total energy consumption includes computing energy consumption and transmission energy consumption, which is expressed as:

[0096]

[0097] where, E total is the total energy consumption of the task offloading, and Ptransmit is the transmission power during the task transmission process, T transmit is the data transmission time of the task, k is the energy consumption coefficient of the edge computing node, f edge is the processing capacity of the edge node, T edge is the edge processing time of the task.

[0098] By comprehensively considering the task computation volume and the computing resources of the edge nodes, the present invention dynamically determines the optimal task offloading path to ensure that the task is executed on the edge nodes with stronger computing capabilities. The task execution time is dynamically matched by the task computation volume and the processing capacity of the edge nodes, and the computing resources are reasonably allocated, minimizing the execution delay of the task to the greatest extent, improving the computing and processing efficiency, and meeting the requirements for low-latency tasks.

[0099] During the task offloading process, the present invention introduces an energy consumption optimization model, comprehensively considers the task computation volume and the transmission power, and jointly minimizes the computing energy consumption and the transmission energy consumption. By dynamically adjusting the transmission power and the task execution path, balancing the computing resources and the transmission energy consumption, the total energy consumption of the network nodes during the task offloading process is further reduced, enhancing the energy-saving performance of the system.

[0100] During the task offloading process, the present invention simultaneously optimizes the task transmission time and the computing execution time to ensure that the task is computed and fed back within the shortest time. In the transmission stage, by controlling the transmission power during the task transmission process, the transmission energy consumption and the task delay are balanced; in the computing stage, the task computation is quickly completed through the efficient processing capacity of the edge nodes, realizing the fast response and execution of the task.

[0101] By dynamically offloading the task to the edge nodes for processing, the computing burden of the terminal device is effectively reduced, the energy consumption and the temperature of the device are decreased, and the service life of the terminal device is prolonged. At the same time, the terminal device can use more resources for other key functions, improving the efficiency and performance of the overall system.

[0102] By dynamically determining the task offloading path and combining the real-time joint optimization of energy consumption and delay, the present invention can flexibly adapt to the dynamic changes of the network environment and the node computing resources. In the case of high load or network state fluctuations, the optimal offloading path can be effectively determined to ensure the efficient execution of the task.

[0103] When the path quality is predicted and updated in real time through the deep reinforcement learning model in step 3, the state-action value function Q is used to optimize the path determination process, and the optimal decision of path determination satisfies the following update relationship:

[0104]

[0105] Among them, Q(s, a) is the value function of taking action a in state s, R is the immediate reward after executing action a, γ is the discount factor, s ′ is the next state after the current state s executes action a, and a ′ is the optimal action in the next state.

[0106] By introducing a deep reinforcement learning model in Step 3 and using the state-action value function for real-time prediction and optimization of path quality, the present invention achieves the following key advantages:

[0107] By using the reinforcement learning model to monitor the link state in real time and dynamically predict the path quality, the optimality and real-time nature of path determination are guaranteed.

[0108] In the case of dynamic changes in the network state, it can quickly adjust the path decision, avoid congested links, and improve the accuracy of path selection.

[0109] Reinforcement learning continuously optimizes the path decision through a self-learning mechanism, improving network transmission efficiency and system performance.

[0110] Optimizing the path based on the state-action value function reduces data transmission delay and meets the requirements of low-latency applications.

[0111] It has strong robustness and anti-interference ability in a complex network environment, ensuring the continuity and reliability of data transmission.

[0112] In summary, through the introduction of a deep reinforcement learning model in Step 3, intelligent prediction and real-time optimization of path decision are achieved, effectively reducing data transmission delay, alleviating network congestion, and improving the stability and reliability of data communication, providing an intelligent and efficient path scheduling solution for data transmission in wireless communication networks.

[0113] When dynamically allocating available channel resources in the wireless communication network in Step 2, by considering the user service quality constraint and spectrum resource utilization rate, a multi-objective optimization model is adopted to dynamically allocate channel resources. The channel conflict probability is related to the number of users and the allocation scheme, and is expressed as:

[0114]

[0115] Among them, P conflict is the channel conflict probability, p i，j is the probability that user i has a conflict on channel j, and M is the number of users in the wireless communication network.

[0116] When splitting the data stream into multiple data streams for multi-path transmission in Step 5, to improve the reliability and delay sensitivity of transmission, the allocation ratio α i of each path is dynamically determined, satisfying the following constraints:

[0117]

[0118] where α i is the data flow allocation ratio of path i, N is the number of available transmission paths, A is the total amount of data to be transmitted, R i is the data transmission rate on path i, and T all is the total completion time.

[0119] Through the dynamic channel resource allocation in step 2 and the dynamic allocation of multi-path data flow in step 5, the present invention achieves the following key advantages:

[0120] By dynamically allocating channel resources through a multi-objective optimization model, the probability of channel conflict is reduced, and the utilization efficiency of spectrum resources is improved.

[0121] The data flow is split and transmitted in parallel through multiple paths, reducing the impact of a single-link failure and enhancing the stability of data transmission.

[0122] By dynamically allocating the traffic ratio of each path, high-quality links are determined, effectively reducing the transmission delay and meeting the requirements of delay-sensitive applications.

[0123] Through the dynamic allocation of traffic in the multi-path mechanism, the network load is balanced, the network bandwidth utilization rate is enhanced, and the continuity and efficiency of data transmission are ensured.

[0124] In summary, steps 2 and 5, through the combination of resource optimization and path dynamic allocation, solve the problems of low utilization rate of spectrum resources, poor reliability of data transmission, high transmission delay, and unbalanced network load, and provide an efficient, stable, and low-delay wireless communication network data transmission solution.

[0125] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data communication transmission method in a wireless communication network, characterized in that: include: Step 1: Deploy cognitive radio equipment to receive channel signals in the wireless communication network, extract received signal sample data, calculate the energy value of the current channel, determine the occupancy state of the channel, and input the channel state as historical channel state data into the channel state prediction model to predict the channel state at a future time. Step 2: Combining the channel state predicted in step 1 with the user's communication needs, determining the matching relationship between the user and the channel, dynamically allocating available channel resources in the wireless communication network, and forming a channel allocation plan; Step 3: According to the channel allocation scheme formed in step 2, a network communication topology is constructed, and the wireless communication network is modeled as a network model including a node set and a link set. The optimal transmission path for data communication is determined by combining the delay weight and channel allocation status of each link, and the transmission path for user data is dynamically determined through a path scheduling module. Step 4: Determine the offloading path of the user task carried on the transmission path after dynamic scheduling in step 3 according to the computing amount of the task and the computing resources of the edge computing node, and offload the task to the edge node for computing and processing. After the edge node completes the calculation, the calculation result is returned to the target user node through the network transmission path; Step 5: According to the data and link status returned by the edge computing node on the transmission path in step 4, the link quality is judged, and under the premise of meeting the signal-to-noise ratio condition of the communication link, the transmission power of the communication node is controlled, and the data stream to be transmitted is divided into multiple data streams, and the data streams are transmitted in parallel along the optimal transmission path determined in step 3 through the multi-path transmission mechanism to complete the data communication transmission in the wireless communication network.

2. A method for data communication transmission in a wireless communication network according to claim 1, characterized in that: When calculating the energy value of the current channel in step 1, energy detection is performed based on the received signal sample data, and the energy value is determined in the following manner: Wherein, E is the energy value of the current channel, N is the number of sampling points, and y(n) is the received signal sample data.

3. The method for data communication transmission in a wireless communication network according to claim 1, characterized in that: When predicting the channel state at a future time in step 1, a long short-term memory network is used as a channel state prediction model. The model is trained by historical channel state data to minimize the mean square error. The loss function of the mean square error is: Among them, L is the loss value of the prediction model, T is the number of time steps, is the predicted channel state, S(t) is the actual channel state, and t represents the time step.

4. The method for data communication transmission in a wireless communication network according to claim 1, characterized in that: When dynamically allocating available channel resources of the wireless communication network in step 2, the allocation relationship between users and channels is determined based on the communication requirements of users and channel status and in accordance with the goal of maximizing the data transmission rate, and the relationship between the data transmission rate and the signal-to-noise ratio satisfies: R i，j =B·log2(1+SNR i，j ), Among them, R i，j is the transmission rate of user i on channel j, B is the channel bandwidth, SNR i，j is the signal-to-noise ratio of user i on channel j.

5. The method for data communication transmission in a wireless communication network according to claim 1, characterized in that: When determining the optimal transmission path for data communication in step 3, a weighted shortest path algorithm is used to determine the path based on the network communication topology and the delay weight of each link to minimize the total transmission delay, which is expressed as: T total =∑ (i,j)∈P w ij ·x ij , Where P is the set of transmission paths from the source node to the destination node, (i, j) is the link from i to j in the path, and w ij is the delay weight of link (i, j), x ij Use indicator variables for links: If x ij =1, link (i, j) is selected; If x ij = 0, link (i, j) is not selected; The dynamic scheduling of the transmission path in step 3 is adjusted in combination with the congestion status of the link, and the path quality is predicted and updated in real time through a deep reinforcement learning model.

6. The method for data communication transmission in a wireless communication network according to claim 1, characterized in that: When the task is unloaded to the edge node in step 4, the optimal task unloading path is dynamically determined according to the task computing amount and the edge node computing resources, and the unloading time and the transmission delay are taken as the optimization target; The execution time of the task offloading in step 4 is determined by the task computation amount U and the processing capacity f of the edge computing node. edge It is determined that the execution time satisfies the following relationship: Among them, T edge is the edge processing time of the task, U is the task computation amount, and f edge The processing power of the edge node.

7. A method for data communication transmission in a wireless communication network according to claim 6, characterized in that: When the task is offloaded to the edge node in step 4, in order to further reduce energy consumption and task execution time, the energy consumption optimization model is used to jointly optimize the task's computational workload and transmission power. The optimization goal is to minimize the total energy consumption E total , the total energy consumption includes computing energy consumption and transmission energy consumption, expressed as: Among them, E total is the total energy consumption of task offloading, P transmit is the transmission power during the task transmission process, T transmit is the data transmission time of the task, k is the energy consumption coefficient of the edge computing node, and f edge is the processing capacity of the edge node, T edge Processing time for the edge of the task.

8. The method for data communication transmission in a wireless communication network according to claim 5, characterized in that: When the path quality is predicted and updated in real time by the deep reinforcement learning model in step 3, the path determination process is optimized by using the state-action value function Q. The optimal decision for path determination satisfies the following update relationship: Among them, Q(s, a) is the value function of taking action a in state s, R is the immediate reward after performing action a, γ is the discount factor, s′ is the next state after performing action a in the current state s, and a′ is the optimal action in the next state.

9. The method for data communication transmission in a wireless communication network according to claim 4, characterized in that: When dynamically allocating available channel resources of the wireless communication network in step 2, by considering user service quality constraints and spectrum resource utilization, a multi-objective optimization model is adopted to dynamically allocate channel resources. The channel conflict probability is related to the number of users and the allocation scheme, which is expressed as: Among them, P conflict is the channel collision probability, p i，j is the probability of collision between user i and channel j, and M is the number of users in the wireless communication network.

10. The method for data communication transmission in a wireless communication network according to claim 7, characterized in that: When the data stream is divided into multiple data streams for multi-path transmission in step 5, in order to improve the reliability and delay sensitivity of transmission, the allocation ratio α of each path is dynamically determined. i , satisfying the following constraints: Among them, α i is the data flow allocation ratio of path i, N is the number of available transmission paths, A is the total amount of data to be transmitted, and R i is the data transmission rate on path i, T all is the total completion time.

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