Method and device for reducing transmission delay of small data packet service in mobile communication network

Abstract

The invention provides a method and a device for reducing small data packet service transmission delay in a mobile communication network, and relates to the technical field of mobile communication. The method comprises the following steps: acquiring a time delay demand and a reliability demand of a user, and constructing a wireless communication model of an ultra-reliable low-time-delay service according to the time delay demand and the reliability demand; according to the wireless communication model, based on a random network calculation method, modeling an alpha-kappa-mu channel to obtain a delay violation probability formula under the alpha-kappa-mu channel; establishing a relationship between the time delay and the reliability according to a time delay violation probability formula; according to the time delay demand of a user, an EA3C algorithm is adopted to determine a service time delay component, frame duration, a service transmission mode, a semantic compression ratio and a bandwidth allocation result, a decision is made for a time delay reduction problem through the relationship between time delay and reliability, and a time delay quality maximum value is output, so that the service transmission time delay of a small data packet is reduced. According to the invention, the transmission delay of small data packet services in a mobile communication network can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile communications, and in particular to a method and device for reducing transmission delay of small data packet services in a mobile communication network. Background Art

[0002] With the rapid development of mobile communication technology, especially in 5G and future 6G networks, the requirements for communication performance are getting higher and higher, especially for small data packet services, which have begun to put forward extremely strict requirements on latency, requiring latency to be less than 1 millisecond. In order to meet this requirement, the communication system must minimize latency while ensuring transmission quality. Existing technical solutions mainly reduce latency by optimizing transmission protocols, increasing bandwidth, and adjusting transmission methods. However, the above methods are usually difficult to balance the flexibility and efficiency of the system, especially in a dynamically changing network environment. How to adjust the system settings in real time according to different business needs is still a technical challenge. As an emerging communication mode, semantic communication technology can significantly reduce the amount of data transmitted by extracting core semantic information from business data for transmission, thereby effectively reducing transmission latency. Semantic communication technology has been applied to data compression and efficient transmission to a certain extent, but since the semantic extraction process itself takes a certain amount of time, there are still challenges for application scenarios with extremely high latency requirements, especially small data packet transmission. Therefore, how to flexibly adjust the semantic extraction ratio under different business needs and network conditions and reduce the overall latency has become the focus of current technical research.

[0003] In the prior art, semantic communication usually reduces the amount of data by extracting all or part of the information to reduce the transmission delay. However, the delay of the semantic extraction process and the limitation of the transmission bandwidth are still the bottlenecks of its application. For example, the existing semantic compression technology does not fully consider the dynamic adjustment of the delay while ensuring the validity of the data. On the other hand, although some studies have reduced the delay through bandwidth management and frame duration optimization, they lack flexible adaptability to different system states and are difficult to be widely used in actual scenarios.

[0004] At present, the requirements for latency in the field of mobile communications, especially in 5G and future 6G networks, are becoming increasingly stringent, especially the transmission of small data packets requires a latency of less than 1 millisecond. To meet this requirement, the existing technology mainly adopts two methods: data compression and bandwidth optimization. Among them, bandwidth optimization technology reduces latency by dynamically adjusting network bandwidth and frame duration. In the existing technology, transmission is usually optimized by fixing bandwidth and frame duration, but it lacks flexibility and cannot be adjusted in real time according to network status and user needs, resulting in the inability to maximize latency optimization in some scenarios. Although semantic communication and bandwidth optimization in the existing technology can improve latency and efficiency, they have not been flexibly adjusted in actual applications, and cannot automatically select the best transmission strategy under various system conditions, resulting in insufficient performance optimization. In addition, the lack of a unified dynamic adjustment mechanism limits the optimization of latency and reliability. Summary of the invention

[0005] The prior art usually optimizes transmission by fixing bandwidth and frame duration, which lacks flexibility and cannot be adjusted in real time according to network status and user needs, resulting in the technical problem of being unable to maximize delay optimization. The embodiment of the present invention provides a method and device for reducing the transmission delay of small data packet services in a mobile communication network. The technical solution is as follows:

[0006] On the one hand, a method for reducing the transmission delay of a small data packet service in a mobile communication network is provided, the method being implemented by a device for reducing the transmission delay of a small data packet service in a mobile communication network, the method comprising:

[0007] S1. Obtain the user's latency requirements and reliability requirements, and build a wireless communication model for ultra-reliable low-latency services based on the latency requirements and reliability requirements;

[0008] S2. According to the wireless communication model of the ultra-reliable low-latency service, based on the random network calculation method, the α-κ-μ channel is modeled to obtain the delay violation probability formula under the α-κ-μ channel; according to the delay violation probability formula, the relationship between delay and reliability is established;

[0009] S3. According to the user's delay demand, the EA3C algorithm is used to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results; according to the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between the delay and reliability, a decision is made on the delay reduction problem, and the maximum delay quality is output to reduce the transmission delay of small data packet services.

[0010] Optionally, the S2 is based on a random network calculation method to model the α-κ-μ channel to obtain a delay violation probability formula under the α-κ-μ channel, including:

[0011] S21, defining a queue accumulation arrival process of services to a base station; determining an operating state of a wireless communication system according to the queue accumulation arrival process of services to a base station;

[0012] S22. Determine the dynamic change of queue length according to the operating status of the system and clarify the law of queue length change over time;

[0013] S23, using the delay violation probability as a reliability indicator, defining a situation where a delay violation occurs when the queue length exceeds a set maximum value, and obtaining an upper bound expression of the delay violation probability;

[0014] S24. Using Markov inequality and Holder inequality, the upper bound expression of the delay violation probability is converted into an expected form, and an expression related to the delay violation probability and the expectation is obtained;

[0015] S25. Based on the upper bound expression of the delay violation probability, the Poisson process is used to analyze the service arrival behavior. By assuming that the data arrival rate and packet size conform to the Poisson distribution, the characteristics of the cumulative arrival process are calculated; based on the small data packet transmission model, the behavior of the wireless channel service process is described;

[0016] S26. According to steps S21-S25, the Meijer G function is used to process the expression related to the delay violation probability and the expectation to obtain the final expression of the delay violation probability.

[0017] Optionally, the components of the service delay include: queuing delay, transmission delay, frame alignment delay, semantic encoding and decoding delay, and base station and terminal processing delay;

[0018] The queuing delay is the time that a data packet waits in a buffer queue, and the queuing delay increases with the increase of load;

[0019] Wherein, the transmission delay depends on the data packet size and the transmission rate;

[0020] The frame alignment delay is the delay caused when the system must wait for the start of the next frame to transmit the data packet when the data packet arrives and the resources are ready for scheduling;

[0021] The semantic encoding and decoding delay is related to the number of cycles required for calculation, processing density and device computing power;

[0022] The base station and terminal processing delays are related to the processing capabilities of the devices themselves.

[0023] Optionally, the selection factors of the frame duration include: user's delay requirement, channel quality and data packet size;

[0024] The frame duration includes three different frame durations: 0.25 milliseconds, 0.125 milliseconds, and 0.0625 milliseconds.

[0025] Optionally, the semantic compression ratio determines the size of the data packet;

[0026] The semantic compression ratio ranges from 0 to 1. A larger value indicates a higher compression ratio and a smaller data packet.

[0027] Optionally, the transmission mode of the service is adaptive selection of bit transmission and semantic transmission.

[0028] Optionally, the S3 adopts EA3C algorithm to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation result; according to the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation result, through the relationship between delay and reliability, a decision is made on the delay reduction problem, including:

[0029] S31, initialize EA3C algorithm parameters including: Actor learning rate, Critic learning rate, discount factor and decay factor; initialize global network parameters and target network parameters; initialize global shared optimizer; start N parallel environment threads, where each thread initializes its local network parameters;

[0030] S32, each thread synchronizes parameters, copies global network parameters to local networks, initializes local environments, resets local environments, and obtains initial states;

[0031] S33, execute actions and collect experience, and select actions based on the current strategy network;

[0032] S34, execute the action, obtain the reward and the next state; store the executed action, the initialization state reward and the next state into the thread local experience sequence;

[0033] S35, update the local network and accumulate gradients; when the length of the local experience sequence reaches a certain threshold T or encounters a termination state, calculate the reward value from t to t+n, and calculate the update target from back to front: calculate the value function error and policy gradient, and calculate the total loss;

[0034] S36, update the global network parameters, use the gradient to update the global parameters, synchronize the local network parameters to the global parameters; continue to the next state, if the terminal state is not reached, continue to iterate; when all threads complete the training or reach the maximum number of iterations, end the algorithm and complete the pre-training of the network; output the pre-trained Actor network and Critic network;

[0035] S37, defining an action space and a state space according to a service delay component, a frame duration, a service transmission mode, a semantic compression ratio, and a bandwidth allocation result;

[0036] S38. Set the degree of delay completion as the reward function; according to the action space, state space and reward function, fine-tune the pre-trained Actor network and Critic network through the relationship between delay and reliability, update the network parameters, output the decision on the delay reduction problem, execute the decision, and complete resource scheduling.

[0037] On the other hand, a device for reducing the transmission delay of a small data packet service in a mobile communication network is provided, and the device is applied to a method for reducing the transmission delay of a small data packet service in a mobile communication network, and the device includes:

[0038] A first acquisition unit is used to acquire a user's latency requirement and reliability requirement, and build a wireless communication model for ultra-reliable low-latency services according to the latency requirement and reliability requirement;

[0039] A second acquisition unit is used to model the α-κ-μ channel based on the wireless communication model of the ultra-reliable low-latency service and the random network calculation method to obtain a delay violation probability formula under the α-κ-μ channel; and establish a relationship between delay and reliability based on the delay violation probability formula;

[0040] The optimization and output unit is used to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results according to the user's delay requirements and using the EA3C algorithm; based on the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between delay and reliability, make decisions on delay reduction, output the maximum delay quality, and reduce the transmission delay of small data packet services.

[0041] Optionally, the second acquiring unit is used to:

[0042] (1) defining a queue accumulation arrival process of services to a base station; and determining an operation state of a wireless communication system according to the queue accumulation arrival process of services to the base station;

[0043] (2) Determine the dynamic changes of queue length according to the operating status of the system and clarify the law of queue length changes over time;

[0044] (3) Taking the delay violation probability as the reliability indicator, we define the situation where the delay violation occurs when the queue length exceeds the set maximum value, and obtain the upper bound expression of the delay violation probability;

[0045] (4) Using Markov inequality and Holder inequality, the upper bound expression of the delay violation probability is transformed into the expected form, and the expression of the delay violation probability related to the expectation is obtained;

[0046] (5) Based on the upper bound expression of the delay violation probability, the Poisson process is used to analyze the arrival behavior. By assuming that the data arrival rate and packet size conform to the Poisson distribution, the characteristics of the cumulative arrival process are calculated; based on the small data packet transmission model, the behavior of the wireless channel service process is described;

[0047] (6) According to steps (1)-(5), the Meijer G function is used to process the expression related to the delay violation probability and the expectation to obtain the final expression of the delay violation probability.

[0048] Optionally, the components of the service delay include: queuing delay, transmission delay, frame alignment delay, semantic encoding and decoding delay, and base station and terminal processing delay;

[0049] The queuing delay is the time that a data packet waits in a buffer queue, and the queuing delay increases with the increase of load;

[0050] Wherein, the transmission delay depends on the data packet size and the transmission rate;

[0051] The frame alignment delay is the delay caused when the system must wait for the start of the next frame to transmit the data packet when the data packet arrives and the resources are ready for scheduling;

[0052] The semantic encoding and decoding delay is related to the number of cycles required for calculation, processing density and device computing power;

[0053] The base station and terminal processing delays are related to the processing capabilities of the devices themselves.

[0054] Optionally, the selection factors of the frame duration include: user's delay requirement, channel quality and data packet size;

[0055] The frame duration includes three different frame durations: 0.25 milliseconds, 0.125 milliseconds, and 0.0625 milliseconds.

[0056] Optionally, the semantic compression ratio determines the size of the data packet;

[0057] The semantic compression ratio ranges from 0 to 1. A larger value indicates a higher compression ratio and a smaller data packet.

[0058] Optionally, the transmission mode of the service is adaptive selection of bit transmission and semantic transmission.

[0059] Optionally, the optimization and output unit is used to:

[0060] Initialize EA3C algorithm parameters including: Actor learning rate, Critic learning rate, discount factor, and decay factor; initialize global network parameters and target network parameters; initialize the global shared optimizer; start N parallel environment threads, where each thread initializes its local network parameters;

[0061] Each thread synchronizes parameters, copies global network parameters to local networks, initializes local environments, resets local environments, and obtains initial states;

[0062] Execute actions and collect experience, and select actions based on the current policy network;

[0063] Execute the action, obtain the reward and the next state; store the executed action, the initialization state reward and the next state in the thread-local experience sequence;

[0064] Update the local network and accumulate gradients; when the length of the local experience sequence reaches a certain threshold T or encounters a termination state, calculate the reward value from t to t+n, and calculate the update target from back to front: calculate the value function error and policy gradient, and calculate the total loss;

[0065] Update the global network parameters, use the gradient to update the global parameters, and synchronize the local network parameters to the global parameters; continue to the next state, and if the terminal state is not reached, continue to iterate; when all threads have completed training or the maximum number of iterations has been reached, end the algorithm and complete the pre-training of the network; output the pre-trained Actor network and Critic network;

[0066] Define the action space and state space based on the service delay components, frame duration, service transmission mode, semantic compression ratio, and bandwidth allocation results;

[0067] The degree of delay completion is set as the reward function. According to the action space, state space and reward function, the pre-trained Actor network and Critic network are fine-tuned through the relationship between delay and reliability, the network parameters are updated, the decision on the delay reduction problem is output, the decision is executed, and resource scheduling is completed.

[0068] On the other hand, a device for reducing the transmission delay of small data packet services in a mobile communication network is provided, and the device for reducing the transmission delay of small data packet services in the mobile communication network comprises: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the methods for reducing the transmission delay of small data packet services in the mobile communication network is implemented.

[0069] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for reducing the transmission delay of small data packet services in a mobile communication network.

[0070] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0071] The implementation of the present invention first obtains the user's delay demand and reliability demand, and builds a wireless communication model of ultra-reliable low-delay services according to the delay demand and reliability demand; secondly, according to the wireless communication model of the ultra-reliable low-delay services, based on the random network calculation method, the α-κ-μ channel is modeled to obtain the delay violation probability formula under the α-κ-μ channel; according to the delay violation probability formula, the relationship between delay and reliability is established; finally, according to the user's delay demand, the EA3C algorithm is adopted to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results; according to the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between delay and reliability, a decision is made on the delay reduction problem, and the maximum delay quality is output, so that the transmission delay of small data packet services is reduced.

[0072] The present invention enables each user to flexibly choose to extract part of the information as semantic communication transmission or direct communication transmission, can choose to use different frame durations, and can determine the transmission bandwidth. The network determines the optimal choice for each user based on a unified optimization schedule, thereby improving the overall network performance; the embodiment of the present invention combines semantic communication and direct communication, and reduces the service delay for small data packet services with delay requirements by flexibly adjusting the semantic compression ratio, frame duration, and transmission bandwidth. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0074] Figure 1 It is a flow chart of a method for reducing transmission delay of small data packet services in a mobile communication network provided by an embodiment of the present invention;

[0075] Figure 2 It is a block diagram of a device for reducing transmission delay of small data packet services in a mobile communication network provided by an embodiment of the present invention;

[0076] Figure 3It is a structural schematic diagram of a device for reducing transmission delay of small data packet services in a mobile communication network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0078] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0079] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0080] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0081] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0082] The embodiment of the present invention provides a method for reducing the transmission delay of small data packets in a mobile communication network. The method can be implemented by a device for reducing the transmission delay of small data packets in a mobile communication network. The device for reducing the transmission delay of small data packets in a mobile communication network can be a terminal or a server. Figure 1 The flowchart of the method for reducing the transmission delay of small data packet service in the mobile communication network shown in the figure, the processing flow of the method may include the following steps:

[0083] S1. Obtain the user's latency and reliability requirements, and build a wireless communication model for ultra-reliable and low-latency services based on the latency and reliability requirements.

[0084] Among them, the wireless communication model of ultra-reliable low-latency services includes: N user nodes and 1 base station, and its parameters include the delay requirement, reliability requirement, data packet size, bandwidth and power of user node i.

[0085] S2. According to the wireless communication model of ultra-reliable low-latency services, the channel is modeled based on the random network calculation method to obtain the delay violation probability formula under the channel; based on the delay violation probability formula, the relationship between delay and reliability is established.

[0086] Among them, when the channel parameters take different values, the α-κ-μ channel can cover most channel situations, including common channels such as Rayleigh channel and Ricean channel.

[0087] Among them, since the services have extremely strict delay and reliability requirements, the design of communication networks depends on accurate channel characterization. The α-κ-μ channel is a generalized wireless propagation channel, but due to the complexity of the channel, the probability density function, cumulative distribution function and moment generating function contain multiple complex and abstract hypergeometric functions, making it difficult to extend to the future 6G network.

[0088] Among them, the α-κ-μ channel contains four parameters, where α is the shape parameter that determines the shape of the distribution, κ is the shadow fading parameter that describes the shadow fading effect, μ is the scale parameter that determines the scale of the distribution, and Ω represents the average power of the signal. When the above parameters take certain specific values, the α-κ-μ channel can be simplified to a common channel, and the specific parameters are shown in Table 1.

[0089] Table 1

[0090]

[0091] Among them, the delay violation probability formula under the α-κ-μ channel provides a solid foundation for accurately analyzing the delay performance under different channel conditions. By focusing on the cumulative arrival process and cumulative service process of the queue, the Poisson distribution is used to analyze the arrival process of the service, and the finite block length coding mode is the service process of the service. Based on the random network calculus theory, the connection between delay and reliability is established.

[0092] Optionally, the specific implementation process of S2 may include S21-S26:

[0093] S21, defining a queue accumulation arrival process of services to a base station; determining an operating state of a wireless communication system according to the queue accumulation process of services to the base station;

[0094] The queue accumulation process of services to the base station includes the accumulation process of data arrival and service process. By defining the accumulation process, the system operation status is described, providing a basis for subsequent analysis.

[0095] The representation of queue length involves the balance between data arrival and service and data departure.

[0096] S22. Determine the dynamic change of queue length according to the operating status of the system and clarify the law of queue length change over time;

[0097] S23, using the delay violation probability as a reliability indicator, defining a situation where a delay violation occurs when the queue length exceeds a set maximum value, and obtaining an upper bound expression of the delay violation probability;

[0098] S24. Using Markov inequality and Holder inequality, the upper bound expression of the delay violation probability is converted into an expected form, and an expression related to the delay violation probability and the expectation is obtained;

[0099] S25. Based on the upper bound expression of the delay violation probability, the Poisson process is used to analyze the service arrival behavior. By assuming that the data arrival rate and packet size conform to the Poisson distribution, the characteristics of the cumulative arrival process are calculated; based on the small data packet transmission model, the behavior of the wireless channel service process is described;

[0100] For example, a transmission model based on channel state and data block length; the channel state is represented by an α-κ-μ channel, and the probability density function of the channel is used for analysis.

[0101] S26. According to steps S21-S25, the Meijer G function is used to process the expression related to the delay violation probability and the expectation to obtain the final expression of the delay violation probability.

[0102] Among them, the Meijer G function is used to transform complex integrals and expressions into a processable form; through simplification techniques, the expression of the Meijer G function is further simplified to adapt to different communication channel conditions and system parameters.

[0103] Among them, the final expression of the delay violation probability can adapt to different channel conditions and provide the possibility for delay analysis in complex scenarios.

[0104] The final expression of the delay violation probability can be expressed by the following formula (1):

[0105]

[0106] in,

[0107] in,

[0108] in,

[0109] Among them, ε k represents the delay violation probability, θ is a positive parameter of the random network calculus, N k Indicates the block length, represents the inverse function of the Gaussian Q function, ε c is the decoding error rate, represents the user's latency requirement, B k W k Indicates the user's bandwidth; B k represents the bandwidth of the resource block; W k represents the number of resource blocks; G represents the Meijer G function; α represents the first channel parameter; Ω represents the second channel parameter; Γ(G) represents the gamma function of G; Γ(Z) represents the gamma function of Z; where, in order to make the expression of delay violation probability clearer, Z, U, Y and C are used to replace related variables; Z represents the first replacement related variable; U represents the second replacement related variable; Y represents the third replacement related variable; C represents the fourth replacement related variable; G G Represents the power form of the Meijer G function.

[0110] According to the Meijer G function formula, by setting α=2, κ=0, μ=1, Ω=1, the Meijer G function of the Rayleigh channel is obtained, which can be expressed by the following formula (2):

[0111]

[0112] Where G' represents the Meijer G function of the Rayleigh channel;

[0113] According to the Meijer G function formula, by setting α=2, κ=0, μ=2, Ω=1, the Meijer G function of the Nakagami-m channel is obtained, which can be expressed by the following formula (3):

[0114]

[0115] Where G” represents the Meijer G function of the Nakagami-m channel.

[0116] S3. Obtain the user's delay requirements; based on the user's delay requirements, use the EA3C algorithm to determine the service delay components, frame duration, service transmission mode, semantic compression ratio, and bandwidth allocation results; based on the service delay components, service transmission mode, semantic compression ratio, frame duration, and bandwidth allocation results, make decisions on delay reduction through the relationship between delay and reliability, output the maximum delay quality, and reduce the transmission delay of small data packet services.

[0117] Optionally, the components of service delay include: queuing delay, transmission delay, frame alignment delay, semantic encoding and decoding delay, and base station and terminal processing delay;

[0118] Among them, the queuing delay is the time that the data packet waits in the buffer queue, and the queuing delay increases with the increase of load;

[0119] Among them, the transmission delay depends on the packet size and transmission rate;

[0120] Among them, frame alignment delay is the delay incurred when the system must wait for the start of the next frame to transmit the data packet when the data packet arrives and the resources are ready for scheduling;

[0121] Among them, semantic encoding and decoding latency is related to the number of cycles required for calculation, processing density, and device computing power;

[0122] Among them, the base station and terminal processing delay is related to the processing capability of the device itself.

[0123] Among them, the semantic communication method reduces the transmission delay, but increases the semantic encoding and decoding delay.

[0124] Optionally, the transmission mode of the service is adaptive selection of bit transmission and semantic transmission.

[0125] Among them, semantic transmission compresses data packets, which can save bandwidth and improve anti-interference capabilities under low signal-to-noise ratio conditions. However, semantic transmission introduces encoding and decoding delays, which is not friendly to users with extremely strict delay indicators. Therefore, this application can flexibly select the transmission method of the service according to the user's delay requirements and channel conditions.

[0126] Optionally, factors for selecting the frame duration include: user latency requirements, channel quality, and data packet size;

[0127] The frame duration includes three different frame durations: 0.25 milliseconds, 0.125 milliseconds, and 0.0625 milliseconds.

[0128] In a feasible implementation, for delay-sensitive services, the traditional 1ms frame duration cannot meet the user's delay requirements. A larger frame duration seriously increases the size of the frame alignment delay; choosing a small frame duration will lead to frequent signaling overhead and reduce spectrum efficiency. The factors that affect the selection of frame duration mainly include: the user's delay requirements, channel quality and data packet size; when the user's delay requirements are strict, a smaller frame duration is preferred; control signaling contains scheduling authorization and related information for decoding, etc.; when the channel quality deteriorates, the control signaling overhead will be larger; and the size of the data packet will directly affect the user's transmission delay, and large data packets tend to use a larger frame duration to complete the transmission. Consider the user's delay requirements, channel quality and data packet size, and select a frame duration size for the user. In this application, the frame duration can be selected from three different frame durations: 0.25 milliseconds, 0.125 milliseconds and 0.0625 milliseconds.

[0129] The calculation process of semantic coding delay can be expressed by the following formula (4):

[0130]

[0131] in, represents the computational latency of semantic encoding and decoding, represents the number of computing cycles required for data recovery; β represents the processing density of the computing terminal; and f represents the processing power of the computing resources, such as CPU frequency.

[0132] Since there is a correlation between semantic information recovery and data packet size, the calculation cycle required for data recovery at the receiving end can be expressed by the following formula (5):

[0133]

[0134] Where: v k represents the size of the data packet; a1, a2 and a3 are constant parameters obtained through simulation; when the constraints of the algorithm are not met, the reward function is 0.

[0135] Optionally, the semantic compression ratio determines the size of the data packet;

[0136] The semantic compression ratio ranges from 0 to 1. A larger value indicates a higher compression ratio and a smaller data packet.

[0137] In a feasible implementation, the semantic compression ratio determines the size of the data packet, which will directly affect the size of the transmission delay. At the same time, the size of the semantic compression ratio determines the number of calculation cycles required for semantic compression, which will also affect the encoding and decoding delay of the semantic part. Therefore, the application can flexibly select the semantic compression ratio according to the user's delay requirements and channel conditions.

[0138] In a feasible implementation manner, regarding the allocation of bandwidth, the total amount of transmission bandwidth in the system is certain, all users use this transmission bandwidth together, and the bandwidth occupied by each user cannot overlap; in this application, since different frame durations are involved, the size of the resource block should be fully considered when allocating bandwidth; the allocation of bandwidth in the communication system involves time-frequency resources. When the frame duration of the resource block is determined, the size of the corresponding frequency domain has also been determined. The frequency domain sizes of resource blocks with different frame durations are different. The larger the time domain, the smaller the frequency domain.

[0139] In a feasible implementation, the present application adopts the EA3C algorithm to obtain the option that maximizes the delay quality of the network. The EA3C algorithm takes the degree of delay completion as the optimization target. The specific degree of delay completion is represented by the difference between the actual calculated delay and the delay requirement divided by the delay requirement. The constraints of the EA3C algorithm include: (1) the delay violation probability calculated by the random network calculation meets the reliability requirements; (2) the delay is less than the user's maximum delay tolerance limit; (3) the user's bandwidth is less than the total system bandwidth.

[0140] Among them, the method proposed in this application takes the user's choices in each round, including: the transmission mode of the service, the semantic compression ratio, the frame duration and the bandwidth allocation result, as the strategy input into the strategy space, and selects the operation from each state and evaluates the selected operation in real time by evaluating the expected benefits of taking a specific action in a given state, and selects the user's choice in the next round based on the evaluation results.

[0141] S6. According to the service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, the EA3C algorithm is used to make decisions on delay reduction through the relationship between delay and reliability, and the maximum delay quality is output; according to the maximum delay quality, the transmission delay of small data packet services is reduced.

[0142] Among them, the EA3C algorithm asynchronous advantage actor-critic algorithm is an algorithm widely used in the field of reinforcement learning. It combines the policy gradient method and the learning of the value function to approximate the solution to the Markov decision process problem. It is a method mastered by technical personnel in this field and will not be further elaborated in this application.

[0143] Optionally, S3 uses EA3C algorithm to determine service delay components, frame duration, service transmission mode, semantic compression ratio, and bandwidth allocation results; based on the service delay components, service transmission mode, semantic compression ratio, frame duration, and bandwidth allocation results, and through the relationship between delay and reliability, a decision is made on the delay reduction problem, including:

[0144] S31, initialize EA3C algorithm parameters including: Actor learning rate, Critic learning rate, discount factor and decay factor; initialize global network parameters and target network parameters; initialize global shared optimizer; start N parallel environment threads, where each thread initializes its local network parameters;

[0145] S32, each thread synchronizes parameters, copies global network parameters to local networks, initializes local environments, resets local environments, and obtains initial states;

[0146] S33, execute actions and collect experience, and select actions based on the current strategy network;

[0147] S34, execute the action, obtain the reward and the next state; store the executed action, the initialization state reward and the next state into the thread local experience sequence;

[0148] S35, update the local network and accumulate gradients; when the length of the local experience sequence reaches a certain threshold T or encounters a termination state, calculate the reward value from t to t+n, and calculate the update target from back to front: calculate the value function error and policy gradient, and calculate the total loss;

[0149] S36, update the global network parameters, use the gradient to update the global parameters, synchronize the local network parameters to the global parameters; continue to the next state, if the terminal state is not reached, continue to iterate; when all threads complete the training or reach the maximum number of iterations, end the algorithm and complete the pre-training of the network; output the pre-trained Actor network and Critic network;

[0150] S37, defining an action space and a state space according to a service delay component, a frame duration, a service transmission mode, a semantic compression ratio, and a bandwidth allocation result;

[0151] Among them, the action space is defined as a = {a1, a2, a3, a4};

[0152] Among them, a1 is the transmission mode decision; a1=0 indicates semantic transmission, a1=1 indicates bit transmission; a2 is the frame duration; a3 is the number of resource blocks, i.e., bandwidth; and a4 is the semantic compression rate.

[0153] In a feasible implementation, after each step of the algorithm is executed, the network status is updated and stored in the experience pool, and the state space is represented as s = {s1, s2, s2, s4, s5}; where s1 is the available resources, including the idle bandwidth and available power at the current moment; S2 is the channel quality, which has an important influence on the selection of transmission mode and bandwidth quantity; S3 is the user demand, including latency and reliability requirements; S4 is the number of constraint violations, which is used to quantify the negative impact of the action on the reward; S5 is the action selected at the previous moment.

[0154] S38. Set the degree of delay completion as the reward function; according to the action space, state space and reward function, fine-tune the pre-trained Actor network and Critic network through the relationship between delay and reliability, update the network parameters, output the decision on the delay reduction problem, execute the decision, and complete resource scheduling.

[0155] The decision is executed, the maximum value of the delay quality is output, and the transmission delay of the small data packet service is reduced according to the maximum value of the delay quality.

[0156] The implementation of the present invention first obtains the user's delay demand and reliability demand, and builds a wireless communication model of ultra-reliable low-delay services according to the delay demand and reliability demand; secondly, according to the wireless communication model of the ultra-reliable low-delay services, based on the random network calculation method, the α-β-μ channel is modeled to obtain the delay violation probability formula under the α-κ-μ channel; according to the delay violation probability formula, the relationship between delay and reliability is established; finally, according to the user's delay demand, the EA3C algorithm is adopted to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results; according to the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between delay and reliability, a decision is made on the delay reduction problem, and the maximum delay quality is output, so that the transmission delay of small data packet services is reduced.

[0157] The present invention enables each user to flexibly choose to extract part of the information as semantic communication transmission or direct communication transmission, can choose to use different frame durations, and can determine the transmission bandwidth. The network determines the optimal choice for each user based on a unified optimization schedule, thereby improving the overall network performance; the embodiment of the present invention combines semantic communication and direct communication, and reduces the service delay for small data packet services with delay requirements by flexibly adjusting the semantic compression ratio, frame duration, and transmission bandwidth.

[0158] Figure 2 The present invention is a block diagram of a device for reducing the transmission delay of a small data packet service in a mobile communication network according to an exemplary embodiment. The device is used in a method for reducing the transmission delay of a small data packet service in a mobile communication network. Figure 2 The device includes a first acquisition unit 210, a second acquisition unit 220 and an optimization and output unit 230. Wherein:

[0159] A first acquisition unit 210 is used to acquire a user's latency requirement and reliability requirement, and build a wireless communication model for ultra-reliable low-latency services according to the latency requirement and reliability requirement;

[0160] The second acquisition unit 220 is used to model the α-κ-μ channel based on the wireless communication model of the ultra-reliable low-latency service and the random network calculation method to obtain a delay violation probability formula under the α-κ-μ channel; and establish a relationship between delay and reliability according to the delay violation probability formula;

[0161] The optimization and output unit 230 is used to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results according to the user's delay requirements and using the EA3C algorithm; based on the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, and through the relationship between delay and reliability, make decisions on delay reduction, output the maximum delay quality, and reduce the transmission delay of small data packet services.

[0162] Optionally, the second acquiring unit 220 is configured to:

[0163] (1) defining a queue accumulation arrival process of services to a base station; and determining an operation state of a wireless communication system according to the queue accumulation process of services to the base station;

[0164] (2) Determine the dynamic changes of queue length according to the operating status of the system and clarify the law of queue length changes over time;

[0165] (3) Taking the delay violation probability as the reliability indicator, we define the situation where the delay violation occurs when the queue length exceeds the set maximum value, and obtain the upper bound expression of the delay violation probability;

[0166] (4) Using Markov inequality and Holder inequality, the upper bound expression of the delay violation probability is transformed into the expected form, and the expression of the delay violation probability related to the expectation is obtained;

[0167] (5) Based on the upper bound expression of the delay violation probability, the Poisson process is used to analyze the service arrival behavior. By assuming that the data arrival rate and packet size conform to the Poisson distribution, the characteristics of the cumulative arrival process are calculated; based on the communication model, the behavior of the wireless communication system service process is described;

[0168] (6) According to steps (1)-(5), the Meijer G function is used to process the expression related to the delay violation probability and the expectation to obtain the final expression of the delay violation probability.

[0169] Optionally, the components of the service delay include: queuing delay, transmission delay, frame alignment delay, semantic encoding and decoding delay, and base station and terminal processing delay;

[0170] The queuing delay is the time that a data packet waits in a buffer queue, and the queuing delay increases with the increase of load;

[0171] Wherein, the transmission delay depends on the data packet size and the transmission rate;

[0172] The frame alignment delay is the delay caused when the system must wait for the start of the next frame to transmit the data packet when the data packet arrives and the resources are ready for scheduling;

[0173] The semantic encoding and decoding delay is related to the number of cycles required for calculation, processing density and device computing power;

[0174] The base station and terminal processing delays are related to the processing capabilities of the devices themselves.

[0175] Optionally, the selection factors of the frame duration include: user's delay requirement, channel quality and data packet size;

[0176] The frame duration includes three different frame durations: 0.25 milliseconds, 0.125 milliseconds, and 0.0625 milliseconds.

[0177] Optionally, the semantic compression ratio determines the size of the data packet;

[0178] The semantic compression ratio ranges from 0 to 1. A larger value indicates a higher compression ratio and a smaller data packet.

[0179] Optionally, the transmission mode of the service is adaptive selection of bit transmission and semantic transmission.

[0180] Optionally, the optimization and output unit 230 is used to:

[0181] Initialize EA3C algorithm parameters including: Actor learning rate, Critic learning rate, discount factor, and decay factor; initialize global network parameters and target network parameters; initialize the global shared optimizer; start N parallel environment threads, where each thread initializes its local network parameters;

[0182] Each thread synchronizes parameters, copies global network parameters to local networks, initializes local environments, resets local environments, and obtains initial states;

[0183] Execute actions and collect experience, and select actions based on the current policy network;

[0184] Execute the action, obtain the reward and the next state; store the executed action, the initialization state reward and the next state in the thread-local experience sequence;

[0185] Update the local network and accumulate gradients; when the length of the local experience sequence reaches a certain threshold T or encounters a termination state, calculate the reward value from t to t+n, and calculate the update target from back to front: calculate the value function error and policy gradient, and calculate the total loss;

[0186] Update the global network parameters, use the gradient to update the global parameters, and synchronize the local network parameters to the global parameters; continue to the next state, and if the terminal state is not reached, continue to iterate; when all threads have completed training or the maximum number of iterations has been reached, end the algorithm and complete the pre-training of the network; output the pre-trained Actor network and Critic network;

[0187] Define the action space and state space based on the service delay components, frame duration, service transmission mode, semantic compression ratio, and bandwidth allocation results;

[0188] The degree of delay completion is set as the reward function. According to the action space, state space and reward function, the pre-trained Actor network and Critic network are fine-tuned through the relationship between delay and reliability, the network parameters are updated, the decision on the delay reduction problem is output, the decision is executed, and resource scheduling is completed.

[0189] The implementation of the present invention first obtains the user's delay demand and reliability demand, and builds a wireless communication model of ultra-reliable low-delay services according to the delay demand and reliability demand; secondly, according to the wireless communication model of the ultra-reliable low-delay services, based on the random network calculation method, the α-κ-μ channel is modeled to obtain the delay violation probability formula under the α-κ-μ channel; according to the delay violation probability formula, the relationship between delay and reliability is established; finally, according to the user's delay demand, the EA3C algorithm is adopted to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results; according to the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between delay and reliability, a decision is made on the delay reduction problem, and the maximum delay quality is output, so that the transmission delay of small data packet services is reduced.

[0190] The present invention enables each user to flexibly choose to extract part of the information as semantic communication transmission or direct communication transmission, can choose to use different frame durations, and can determine the transmission bandwidth. The network determines the optimal choice for each user based on a unified optimization schedule, thereby improving the overall network performance; the embodiment of the present invention combines semantic communication and direct communication, and reduces the service delay for small data packet services with delay requirements by flexibly adjusting the semantic compression ratio, frame duration, and transmission bandwidth.

[0191] Figure 3 is a schematic diagram of a structure of a device for reducing transmission delay of small data packets in a mobile communication network provided by an embodiment of the present invention, such as Figure 3 As shown, the device for reducing the transmission delay of small data packet services in the mobile communication network may include the above Figure 2Optionally, the device 310 for reducing transmission delay of small data packet service in a mobile communication network may include a first processor 2001 .

[0192] Optionally, the device 310 for reducing transmission delay of small data packet services in a mobile communication network may further include a memory 2002 and a transceiver 2003 .

[0193] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0194] Combine the following Figure 3 The components of the device 310 for reducing the transmission delay of small data packets in a mobile communication network are specifically introduced as follows:

[0195] The first processor 2001 is the control center of the device 310 for reducing the transmission delay of small data packets in the mobile communication network, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0196] Optionally, the first processor 2001 can execute various functions of the device 310 for reducing transmission delay of small data packet services in a mobile communication network by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0197] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0198] In a specific implementation, as an embodiment, the device 310 for reducing the transmission delay of small data packet services in a mobile communication network may also include multiple processors, such as Figure 3The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0199] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0200] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0201] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0202] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 3 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0203] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0204] It should be noted that Figure 3 The structure of the device 310 for reducing transmission delay of small data packets in the mobile communication network shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0205] In addition, the technical effect of the device 310 for reducing the transmission delay of small data packets in a mobile communication network can refer to the technical effect of the method for reducing the transmission delay of small data packets in a mobile communication network described in the above method embodiment, and will not be repeated here.

[0206] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0207] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0208] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0209] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0210] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0211] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0212] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0213] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0214] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0215] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0216] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0217] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0218] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for reducing transmission delay of small data packets in a mobile communication network, characterized in that: The method comprises: S1. Obtain the user's latency requirements and reliability requirements, and build an ultra-reliable low-latency service wireless communication model based on the latency requirements and reliability requirements; S2. According to the ultra-reliable low-latency service wireless communication model, based on the random network calculation method, the α-κ-μ channel is modeled to obtain the delay violation probability formula under the α-κ-μ channel; according to the delay violation probability formula, the relationship between delay and reliability is established; S3. According to the user's delay demand, the EA3C algorithm is used to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results; according to the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between the delay and reliability, a decision is made on the delay reduction problem, and the maximum delay quality is output to reduce the transmission delay of small data packet services.

2. The method for reducing transmission delay of small data packets in a mobile communication network according to claim 1, characterized in that: The S2 is based on a random network calculation method to model the α-κ-μ channel and obtain a delay violation probability formula under the α-κ-μ channel, including: S21, defining a queue accumulation arrival process of services to a base station; determining an operating state of a wireless communication system according to the queue accumulation arrival process of services to a base station; S22. Determine the dynamic change of queue length according to the operating status of the system and clarify the law of queue length change over time; S23, using the delay violation probability as a reliability indicator, defining a situation where a delay violation occurs when the queue length exceeds a set maximum value, and obtaining an upper bound expression of the delay violation probability; S24. Using Markov inequality and Holder inequality, the upper bound expression of the delay violation probability is converted into an expected form, and an expression related to the delay violation probability and the expectation is obtained; S25. Based on the upper bound expression of the delay violation probability, the Poisson process is used to analyze the service arrival behavior. By assuming that the data arrival rate and packet size conform to the Poisson distribution, the characteristics of the cumulative arrival process are calculated; based on the small data packet transmission model, the behavior of the wireless channel service process is described; S26. According to steps S21-S25, the Meijer G function is used to process the expression related to the delay violation probability and the expectation to obtain the final expression of the delay violation probability.

3. The method for reducing transmission delay of small data packets in a mobile communication network according to claim 1, characterized in that: The components of the service delay include: queuing delay, transmission delay, frame alignment delay, semantic encoding and decoding delay, and base station and terminal processing delay; The queuing delay is the time that a data packet waits in a buffer queue, and the queuing delay increases with the increase of load; Wherein, the transmission delay depends on the data packet size and the transmission rate; The frame alignment delay is the delay caused when the system must wait for the start of the next frame to transmit the data packet when the data packet arrives and the resources are ready for scheduling; The semantic encoding and decoding delay is related to the number of cycles required for calculation, processing density and device computing power; The base station and terminal processing delays are related to the processing capabilities of the devices themselves.

4. The method for reducing transmission delay of small data packets in a mobile communication network according to claim 1, characterized in that: The factors for selecting the frame duration include: user's delay requirement, channel quality and data packet size; The frame duration includes three different frame durations: 0.25 milliseconds, 0.125 milliseconds, and 0.0625 milliseconds.

5. The method for reducing transmission delay of small data packets in a mobile communication network according to claim 1, characterized in that: The semantic compression ratio determines the size of the data packet; The semantic compression ratio ranges from 0 to 1. A larger value indicates a higher compression ratio and a smaller data packet.

6. The method for reducing transmission delay of small data packets in a mobile communication network according to claim 1, characterized in that: The transmission mode of the service is adaptive selection of bit transmission and semantic transmission.

7. The method for reducing transmission delay of small data packets in a mobile communication network according to claim 1, characterized in that: The S3 adopts the EA3C algorithm to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results; according to the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between delay and reliability, a decision is made on the delay reduction problem, including: S31, initialize EA3C algorithm parameters including: Actor learning rate, Critic learning rate, discount factor and decay factor; initialize global network parameters and target network parameters; initialize global shared optimizer; start N parallel environment threads, where each thread initializes its local network parameters; S32, each thread synchronizes parameters, copies global network parameters to local networks, initializes local environments, resets local environments, and obtains initial states; S33, execute actions and collect experience, and select actions based on the current strategy network; S34, execute the action, obtain the reward and the next state; store the executed action, the initialization state reward and the next state into the thread local experience sequence; S35, update the local network and accumulate gradients; when the length of the local experience sequence reaches a certain threshold T or encounters a termination state, calculate the reward value from t to t+n, and calculate the update target from back to front: calculate the value function error and policy gradient, and calculate the total loss; S36, update the global network parameters, use the gradient to update the global parameters, synchronize the local network parameters to the global parameters; continue to the next state, if the terminal state is not reached, continue to iterate; when all threads complete the training or reach the maximum number of iterations, end the algorithm and complete the pre-training of the network; output the pre-trained Actor network and Critic network; S37, defining an action space and a state space according to a service delay component, a frame duration, a service transmission mode, a semantic compression ratio, and a bandwidth allocation result; S38. Set the degree of delay completion as the reward function; according to the action space, state space and reward function, fine-tune the pre-trained Actor network and Critic network through the relationship between delay and reliability, update the network parameters, output the decision on the delay reduction problem, execute the decision, and complete resource scheduling.

8. A device for reducing transmission delay of small data packets in a mobile communication network, the device for reducing transmission delay of small data packets in a mobile communication network is used to implement the method for reducing transmission delay of small data packets in a mobile communication network as claimed in any one of claims 1 to 7, characterized in that: The device comprises: A first acquisition unit is used to acquire a user's latency requirement and reliability requirement, and build a wireless communication model for ultra-reliable low-latency services according to the latency requirement and reliability requirement; A second acquisition unit is used to model the α-κ-μ channel according to the wireless communication model of the ultra-reliable low-latency service based on a random network calculation method, and obtain a delay violation probability formula under the α-κ-μ channel; according to the delay violation probability formula, establish a relationship between delay and reliability; The optimization and output unit is used to determine the service delay components, frame duration, service transmission mode, semantic compression ratio and bandwidth allocation results according to the user's delay requirements and using the EA3C algorithm; based on the service delay components, service transmission mode, semantic compression ratio, frame duration and bandwidth allocation results, through the relationship between delay and reliability, make decisions on delay reduction, output the maximum delay quality, and reduce the transmission delay of small data packet services.

9. A device for reducing transmission delay of small data packets in a mobile communication network, characterized in that: The device for reducing the transmission delay of small data packet services in the mobile communication network includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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