A multi-dimensional service quality assurance method, system and device

Abstract

A multi-dimensional service quality assurance method, system and device, the method includes: using peak information freshness as a service quality assurance parameter to evaluate and adjust data freshness, and measure modeling; in the field of finite code length coding, characterizing the peak information freshness violation probability based on HARQ-IR to obtain the peak information freshness service quality assurance index; in the field of finite code length coding, characterizing the violation probability related to delay and bit error rate to obtain new delay and reliability service quality assurance indicators; according to the peak information freshness, delay and reliability service quality assurance indicators, approximately establishing a maximum supportable arrival rate model; the system and device are used to implement a multi-dimensional service quality assurance method; the present invention solves the difficult problem that the existing single service quality assurance method cannot be applied to various application scenarios and extreme and multi-dimensional performance indicators, and has the ability to establish an effective capacity universal model under the premise of high efficiency, high reliability and low delay.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a multi-dimensional service quality assurance method, system and device. Background Art

[0002] With the continuous development of the information society, the demand for wireless communication networks is getting higher and higher. Considering the complexity of transmission scenarios, diversified requirements, and multidimensional resources, new challenges have been raised in the three aspects of short code service quality assurance management, control, and optimization. The traditional single service quality assurance indicator is difficult to meet higher-level, more stringent, and more diverse performance requirements, making it increasingly complex to systematically establish a unified model of effective capacity. In view of the contradiction between the complex service scenario requirements and the extreme performance requirements faced by future wireless networks, it is crucial to model, guarantee, and analyze the key technologies for multi-dimensional heterogeneous service quality requirements. From multiple perspectives, service quality assurance technology is an important support for future ultra-reliable low-latency communication service scenarios. However, under limited resource conditions, the traditional single statistical delay service quality control theory is no longer applicable to future wireless communication services that prioritize ultra-high reliability. At the same time, unilaterally improving system reliability by extending transmission time and increasing transmission code length is no longer applicable to delay-sensitive wireless communication services. In addition, in the field of limited code length, traditional technical means of expanding and optimizing system capacity are no longer applicable to the ultra-high efficiency, ultra-large capacity, and massive communication requirements of future wireless communication services. Based on this, bottleneck problems such as the contradiction between delay control and reliability priority requirements for delay / reliability sensitive multimedia services under the premise of limited resources, and the contradiction between system capacity optimization and extreme performance requirements have arisen. How to establish a multi-dimensional short code service quality assurance system, uniformly model the effective capacity under the joint constraints of delay and reliability, and ensure the dynamic and efficient adaptation of network resources are difficult problems that need to be solved urgently.

[0003] The patent application document with publication number CN106686605B discloses "an energy-efficient statistical delay service quality assurance method in a wireless sensor network". This method obtains the optimal energy efficiency of the sensor node and the corresponding optimal power allocation scheme, and the optimal power allocation scheme can be dynamically adjusted based on the delay service quality requirements and channel conditions of the main transmitter. The shortcomings of this method are that it only considers the service quality assurance based on a single statistical delay. The traditional limited and single statistical delay service quality level division is not suitable for the new converged network architecture, and cannot provide specific customized network processing, or guarantee the ultimate service quality requirements of complex business scenarios. Therefore, the system design lacks consideration of service quality coefficients in dimensions such as reliability, resulting in a reduction in the universality of the system design, and cannot support the ultimate requirements of future wireless networks such as ultra-high speed, ultra-high reliability, and ultra-low latency.

[0004] C. Li, C. She, N. Yang, TQS Quek proposed a secure short data packet transmission power control strategy under average power constraints and queuing delay requirements in their published paper "Secure Transmission Rate of Short Packets With Queueing Delay Requirement" (IEEE Transactions on Wireless Communications, 2022, 21 (1): 203-218) to ensure ultra-low delay in secure short data packet transmission. In the field of finite code length coding, this method designs a maximum confidentiality rate model achievable for statistical delay service quality, and optimizes the transmission power under different channel states to ensure service quality. The shortcomings of this method are that it only considers the delay service quality in short data packet communication, ignoring the reliability problem faced by short packet communication, and uses the traditional effective capacity formula, which fails to solve the effective transmission problem for joint delay and reliability service quality assurance, and thus cannot fully meet the ultra-reliable low-latency requirements of future wireless communications. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to propose a multi-dimensional service quality assurance method, system and device, which, by identifying and defining new statistical multi-dimensional service quality assurance indicators, is used to solve the difficult problem that the existing single service quality assurance method cannot be applied to various application scenarios and extreme, multi-dimensional performance indicators, and provides a reliable theoretical basis and technical support for achieving the extreme requirements of future wireless networks such as ultra-large capacity, ultra-high reliability, and ultra-low latency.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A multi-dimensional service quality assurance method comprises the following steps:

[0008] Step 1: Use the peak information freshness as a service quality assurance parameter to evaluate and adjust the data freshness, and establish a peak information freshness model;

[0009] Step 2: According to the peak information freshness model obtained in step 1, in the field of finite code length coding, based on hybrid automatic repeat request with incremental redundancy technology (HARQ-IR), the peak information freshness violation probability is characterized to obtain the peak information freshness service quality assurance indicator;

[0010] Step 3, in the field of finite code length coding, characterize the violation probability related to delay and bit error rate, and obtain new delay and reliability service quality assurance indicators;

[0011] Step 4: Based on the service quality assurance index of the peak information freshness obtained in step 2 and the service quality assurance index of the delay and reliability obtained in step 3, a maximum supportable arrival rate model, namely "ε-effective capacity", is established.

[0012] In step 1, the peak information freshness model is as follows:

[0013] T AoI (u) = T I (u-1,u)-T(u)

[0014] Among them, T AoI (u) represents the peak information freshness, T I (u-1,u) represents the time interval between the arrival of two status update packets (u-1) and u, and T(u) represents the service time of the status update packet u.

[0015] The specific process of step 2 is as follows:

[0016] Step 2.1: According to the large deviation principle, define the service quality assurance indicator θ based on the peak information freshness AoI , used to characterize the relationship between the peak information freshness threshold and the probability of violation of the peak information freshness exceeding a given threshold, and to measure the exponential decay rate of the probability of violation of the peak information freshness;

[0017] Using the Mellin transform, we can obtain the peak information freshness violation probability p AoI The upper bound of (u) is as follows:

[0018]

[0019] Among them, A th represents the peak information freshness violation threshold, n represents the code length, K(θ AoI ,u) is the kernel function used to measure the freshness of peak information, which is defined as follows:

[0020]

[0021] in, represents the Mellin transform of the arrival time interval of the transmission status update packets u-1 to u in the signal-to-noise ratio domain, It represents the Mellin transform of the cumulative service time of the transmission status update packets v to u in the signal-to-noise ratio domain, and its definition expression is as follows:

[0022]

[0023] Step 2.2, according to the peak information freshness violation probability model obtained in step 2.1, assuming that the arrival update follows a Poisson process with a rate of λ, the arrival time interval can be regarded as an exponential process with a rate of λ, and the peak information freshness violation probability p AoI The upper bound of (u) can be simplified to the following result:

[0024]

[0025] in, represents the Mellin transform of the service time of the transmission status update packet u in the signal-to-noise ratio domain, and its definition expression is as follows:

[0026]

[0027] Step 2.3, using HARQ-IR technology, in the finite code length coding field, each finite code length code word of length n is divided into L modules, each module contains symbols, namely And these modules are transmitted continuously in the subsequent time, the peak information freshness violation probability can be approximately expressed as:

[0028]

[0029] Where T represents the unit time used by each channel, ∈ l (u) represents the bit error rate of the transmission status update data packet u when transmitting the lth HARQ-IR module.

[0030] The specific process of characterizing the violation probability of the delay in step 3 and obtaining the delay service quality assurance index is as follows:

[0031] Step 3.11, construct a measurement standard for the delay service quality assurance mechanism. According to the large deviation principle, under sufficient conditions, the distribution of the process Q(t) converges to a random variable Q(∞), such that

[0032]

[0033] Among them, Q th is the captain limit; parameter θ delay is the delay-based service quality assurance indicator (θ delay >0), the delay-based service quality assurance indicator θ delay An exponential decay rate used to measure the probability of delay quality of service guarantee violation;

[0034] Step 3.12, using the Mellin transformation, effectively analyze the arrival process and service process, and then derive the delay service quality guarantee violation probability p delay The upper bound of (u) is as follows:

[0035]

[0036] Among them, D th represents the delay bound, is the kernel function used to measure the queuing delay, which is defined as follows:

[0037]

[0038] At the same time, the following stability conditions can be met:

[0039] M A(u) (1+θ delay )M S(u) (1-θ delay )<1

[0040] Among them, M A(u) (θ delay ) represents the Mellin transform of the arrival process in the signal-to-noise ratio domain, and M S(u) (θ delay ) represents the Mellin transform of the service process in the signal-to-noise ratio domain.

[0041] The violation probability of the bit error rate in step 3 is characterized to obtain the reliability service quality assurance index. The specific process is:

[0042] Step 3.21, construct a measurement standard for the reliability service quality assurance mechanism, systematically describe the attenuation of the bit error probability ∈(u) as the code length n increases. According to the large deviation principle, when the coding rate is lower than the channel capacity, the reliability service quality assurance indicator characterizes the exponential decay rate of the reliability service quality violation probability (i.e., bit error rate), which is defined as follows:

[0043]

[0044] Where ∈(u) indicates the bit error rate of the state update data packet u. The above reliability service quality assurance indicator characterization formula indicates that the bit error rate increases at an exponential rate θ error The attenuation, which measures the stringency of the statistical reliability service quality guarantee, increases with the increase of the code length n. Correspondingly, for a given coding rate, the bit error rate decreases exponentially when the code length n tends to infinity, that is,

[0045] ∈(u)≤exp(-nθ error )

[0046] Step 3.22, the service quality assurance index based on reliability can be obtained by definition, which is specifically defined as follows:

[0047] θ error = sup{E0[ρ,Px (x)]-ρR * (u)}

[0048] Among them, R * (u) is the maximum encoding rate for the state update packet u, ρ∈[0,1] is the Lagrange multiplier parameter, P x (x) represents the probability density function (PDF) of the transmitted signal vector x, E0[ρ,P x (x)] is defined as the cumulative generating function (CGF), which is expressed as follows:

[0049]

[0050] Where γ(u) represents the signal-to-interference-noise ratio of the system.

[0051] In step 4, under the premise of constructing the peak information freshness, delay and reliability combined with service quality guarantee constraints, the maximum supportable arrival rate model, namely "ε-effective capacity", EC ε (θ) is approximately expressed as:

[0052]

[0053] In step 3.22, by applying Jensen's inequality, we can express E0[ρ,P x (x)], accurately modeling statistical reliability service quality assurance indicators under high signal-to-noise ratio conditions.

[0054] A multi-dimensional service quality assurance system, comprising:

[0055] The peak information freshness module uses the peak information freshness as a service quality assurance parameter to evaluate and adjust data freshness, and establishes a peak information freshness model by modeling its measurement;

[0056] A peak information freshness service quality assurance indicator module, based on the peak information freshness model obtained by the peak information freshness module, characterizes the peak information freshness violation probability based on hybrid automatic repeat request with incremental redundancy technology (HARQ-IR) in the field of finite code length coding, and obtains the peak information freshness service quality assurance indicator;

[0057] The delay and reliability service quality assurance indicator module characterizes the violation probability of delay and bit error rate in the field of finite code length coding, and obtains the delay and reliability service quality assurance indicators;

[0058] The arrival rate model module establishes the maximum supportable arrival rate model, namely "ε-effective capacity", based on the service quality assurance index of peak information freshness and the service quality assurance index of delay and reliability.

[0059] A multi-dimensional service quality assurance device, comprising:

[0060] Memory: used for storing a computer program of the multi-dimensional service quality assurance method;

[0061] Processor: used to implement a multi-dimensional service quality assurance method when executing the computer program.

[0062] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a multi-dimensional service quality assurance method can be implemented.

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] First, the present invention fills the gaps in traditional service quality assurance performance indicators. In response to the current lack of research on service quality assurance indicators such as non-zero bit error rate and information freshness, it re-identifies and defines a performance indicator system for multi-dimensional service quality assurance for short codes, constructs a multi-dimensional service quality assurance method, and realizes measurement modeling of information freshness, delay and reliability, effectively reflecting the diverse and extreme performance indicators in special application scenarios, and supporting end-to-end multiple application scenarios in complete future wireless networks.

[0065] Second, in the field of finite code length coding, the statistical multi-dimensional service quality assurance mechanism developed by the present invention measures the exponential decay rates of multiple service quality assurance violation probabilities, and asymptotically formulates corresponding analysis and measurement models for ultra-reliable low-latency communication performance indicators, including bit error rate, peak information freshness violation probability and delay violation probability exponential function. It analyzes the interdependence, coupling and constraint relationships between multi-dimensional service quality assurance performance indicators, expands the supported application scenarios, and supports the establishment of a comprehensive performance assurance mechanism for ultra-reliable low-latency communications in the future.

[0066] Third, the present invention defines a new unified "ε-effective capacity" theory, which balances the peak information freshness, queuing delay and transmission bit error rate in short code transmission through a joint control mechanism of peak information freshness, delay and reliability, and proposes a universal model of effective capacity for multi-dimensional heterogeneous service quality assurance, laying a theoretical foundation for realizing adaptive resource regulation.

[0067] In summary, the present invention aims at the field of limited code length transmission, uses hybrid automatic repeat request and incremental redundancy technology, and establishes a multi-dimensional statistical service quality assurance mechanism for information freshness, delay and reliability; innovatively defines a new "ε-effective capacity" theory for peak information freshness, delay and reliability, and proposes a universal model of effective capacity for multi-dimensional heterogeneous service quality assurance, laying a theoretical foundation for ensuring dynamic and efficient adaptation of network resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A diagram of the system model constructed for the present invention.

[0069] Figure 2 A diagram of a joint resource scheduling model for short code service quality assurance constructed for the present invention.

[0070] Figure 3 It is the technical route and working principle diagram of the present invention.

[0071] Figure 4 This is a simulation diagram of the simulation experiment 1 of the present invention: the relationship between the peak information freshness violation probability and the peak information freshness service quality assurance index.

[0072] Figure 5 This is a simulation diagram of simulation experiment 2 of the present invention: the relationship between the peak information freshness violation probability and the code length n.

[0073] Figure 6 This is a simulation diagram of simulation experiment 3 of the present invention: the relationship between the delay violation probability and the delay service quality assurance index.

[0074] Figure 7 This is a simulation diagram of simulation experiment 4 of the present invention: the relationship between bit error rate and reliability service quality assurance index.

[0075] Figure 8 This is a simulation diagram of simulation experiment 5 of the present invention: the relationship between “ε-effective capacity” and code length n. DETAILED DESCRIPTION

[0076] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0077] Aiming at the field of limited code length transmission, the present invention uses hybrid automatic repeat request and incremental redundancy technology (HARQ-IR) to propose a multi-dimensional statistical service quality assurance mechanism. Specifically, a measurement model based on information freshness is designed; a service quality index with limited peak information freshness is characterized using the hybrid automatic repeat request and incremental redundancy technology protocol; a set of new basic statistical service quality indicators are developed within the limited code length range, including delay-based service quality indicators and bit error rate-based service quality indicators in limited code length transmission; a multi-dimensional statistical service quality assurance scheme is evaluated and analyzed; a new unified "ε-effective capacity" formula is defined, which accurately and quantitatively depicts the modeling framework of joint statistical service quality assurance for peak information freshness, delay and reliability, and a dynamic joint control scheme is designed.

[0078] like Figure 1 As shown, the steps of the present invention are as follows:

[0079] Step 1: Use the peak information freshness as a service quality assurance parameter to evaluate and adjust the data freshness, and establish a peak information freshness model;

[0080] The peak information freshness model is as follows:

[0081] T AoI (u) = T I (u-1,u)-T(u)

[0082] Among them, T AoI (u) represents the peak information freshness, T I (u-1,u) represents the time interval between the arrival of two status update packets (u-1) and u, and T(u) represents the service time of the status update packet u.

[0083] Step 2, using finite length coding (FBC) and hybrid automatic repeat request and incremental redundancy technology to characterize the peak information freshness violation probability;

[0084] Step 2.1: Quantitatively characterize the statistical service quality assurance technology for information freshness. According to the large deviation principle, define the service quality assurance index θ based on the peak information freshness. AoI , characterizes the relationship between the peak information freshness threshold and the probability of peak information freshness violation exceeding the given threshold, measures the exponential decay rate of the peak information freshness violation probability, and aims to alleviate the tail violation behavior associated with the peak information freshness indicator. The peak information freshness violation probability quantitatively represents the tail behavior of the peak information freshness and can be compared with the delay and bit error rate violation probabilities.

[0085] Using the Mellin transform, we can obtain the peak information freshness violation probability p AoI The upper bound of (u) is as follows:

[0086]

[0087] Among them, A th represents the peak information freshness violation threshold, n represents the code length, K(θ AoI ,u) is the kernel function used to measure the freshness of peak information, which is defined as follows:

[0088]

[0089] in, represents the Mellin transform of the arrival time interval of the transmission status update packets u-1 to u in the signal-to-noise ratio domain, It represents the Mellin transform of the cumulative service time of the transmission status update packets v to u in the signal-to-noise ratio domain, and its definition expression is as follows:

[0090]

[0091] The above peak information freshness violation probability model fully describes the probability bounds of peak information freshness violation in the wireless communication network modeling framework driven by information freshness. This result is helpful to compare with the delay and bit error rate violation probabilities, so as to establish a multi-dimensional service quality assurance system based on information freshness, delay and bit error rate.

[0092] Step 2.2, based on the above model, assuming that the arrival update follows a Poisson process with rate λ, the arrival time interval can be regarded as an exponential process with rate λ, so the peak information age violation probability p AoI The upper bound of (u) can be simplified to the following result:

[0093]

[0094] in, represents the Mellin transform of the service time of the transmission status update packet u in the signal-to-noise ratio domain, and its definition expression is as follows:

[0095]

[0096] Step 2.3, using HARQ-IR technology, in the finite code length domain, each finite code length code word of length n is divided into L modules, each module contains symbols, namely And these modules are transmitted continuously in the subsequent time. The peak information freshness violation probability can be approximately expressed as:

[0097]

[0098] Where T represents the unit time used by each channel, ∈ l (u) represents the bit error rate of the transmission status update data packet u when transmitting the lth HARQ-IR module.

[0099] By applying the HARQ-IR protocol, an analysis of the asymptotic peak information freshness violation probability is provided. Due to the dynamic characteristics of channel fading, the closed-form expression of the peak information freshness violation probability becomes complicated, so an asymptotic representation under high signal-to-noise ratio conditions can be used to perform the analysis in a more convenient way. This simplification helps to understand the system behavior more clearly while still capturing its essential characteristics.

[0100] Step 3, in the finite code length domain, characterize the violation probability related to delay and bit error rate, and develop a new set of service quality assurance performance indicators with bounded statistical delay and bit error rate;

[0101] Step 3.11, quantitatively characterize the statistical service quality assurance technology for delay, and construct a measurement standard for the delay service quality assurance mechanism, so as to facilitate the explanation of the design ideas of multi-source service delay service quality assurance. According to the large deviation principle, under sufficient conditions, the distribution of the process Q(t) converges to a random variable Q(∞), such that

[0102]

[0103] Among them, Q th is the captain limit; parameter θ delay It is called the delay-based service quality assurance indicator (θ delay >0), which is used to measure the exponential decay rate of the probability of delay quality of service guarantee violation; θ delay The larger the corresponding fading rate, the faster the system can provide a strict delay service quality guarantee requirement. delay The smaller the corresponding fading rate is, the slower it is, indicating that the system provides a looser requirement for the quality of service provided by the system. delay When it gradually approaches infinity, it means that the system cannot tolerate any delay, which corresponds to a very strict delay service quality assurance constraint.

[0104] Step 3.12, using the Mellin transform, effectively analyze the arrival process and service process, and then derive the delay violation probability p delay The upper bound of (u) is as follows:

[0105]

[0106] Among them, D th represents the delay bound, is the kernel function used to measure the queuing delay, which is defined as follows:

[0107]

[0108] At the same time, the following stability conditions can be met:

[0109] M A(u) (1+θ delay )M S(u) (1-θ delay )<1

[0110] Among them, M A(u) (θ delay ) represents the Mellin transform of the arrival process in the signal-to-noise ratio domain, and M S(u) (θ delay ) represents the Mellin transform of the service process in the signal-to-noise ratio domain.

[0111] Step 3.21, quantitatively characterize the reliability-oriented statistical service quality assurance technology. In the field of finite code length, a measurement standard for the reliability-oriented service quality assurance mechanism is constructed to systematically describe the attenuation of the bit error probability ∈(u) as the code length n increases. According to the large deviation principle, when the coding rate is lower than the channel capacity, the service quality assurance indicator based on the bit error rate characterizes the exponential decay rate of the reliability service quality violation probability (i.e., the bit error rate), which is defined as follows:

[0112]

[0113] Where ∈(u) indicates the bit error rate of the state update packet u. The above formula indicates that the bit error rate increases at an exponential rate θ. error The attenuation, which measures the stringency of the statistical reliability service quality guarantee, increases with the increase of the code length n. Correspondingly, for a given coding rate, the bit error rate decreases exponentially as the code length n tends to infinity, that is,

[0114] ∈(u)≤exp(-nθ error )

[0115] Step 3.22, the service quality assurance indicator based on bit error rate can be obtained by definition, which is specifically defined as follows:

[0116]

[0117] Among them, R * (u) is the maximum encoding rate for the state update packet u, ρ∈[0,1] is the Lagrange multiplier parameter, P x (x) represents the probability density function (PDF) of the transmitted signal vector x, E0[ρ,P x (x)] is defined as the cumulative generating function (CGF), which is expressed as follows:

[0118]

[0119] Where γ(u) represents the signal-to-interference-noise ratio of the system.

[0120] The most challenging part of the closed-form expression of the reliability service quality assurance indicator obtained by the above formula involves solving the E0[ρ,P x (x)], we can express E0[ρ,P asymptotically by applying Jensen’s inequality. x (x)]. In addition, the statistical reliability service quality assurance indicators can be accurately modeled under high signal-to-noise ratio conditions.

[0121] Step 4: In order to support ultra-reliable low-latency communication under the multiple statistical service quality assurance of peak information freshness, delay and information reliability, the "ε-effective capacity" is innovatively proposed, and the "ε-effective capacity" joint control scheme for peak information freshness, delay and reliability is designed. Different from the traditional single delay-oriented service quality assurance, the short code service quality assurance theoretical system establishes the maximum supportable arrival rate model, namely the "ε-effective capacity" EC, under the premise of constructing the joint constraints of peak information freshness, delay and reliability. ε The unified mathematical expression of (θ) is:

[0122]

[0123] It can be seen from the above formula that, unlike the effective capacity of traditional systems, "ε-effective capacity" is not only related to the peak information freshness and delay service quality assurance indicators, but also a function of limited code length and bit error rate (i.e., system reliability). The system can guarantee multi-dimensional service quality requirements ranging from low timeliness and low reliability to high timeliness and high reliability, and conduct joint dynamic control to more accurately evaluate the system's carrying capacity, including the comprehensive impact of factors such as channel conditions, service priorities, and interference, providing a more scientific basis for resource allocation for different services.

[0124] like Figure 2 As shown in the figure, a joint resource scheduling model diagram for multi-dimensional service quality assurance is constructed. By analyzing the wireless channel quality and multi-dimensional service quality assurance indicators, the short packet retransmission technology is used to greatly improve the system reliability, ensure the link transmission quality, and efficiently guarantee high-reliability, low-latency and large-capacity communications.

[0125] Figure 3 The technical route and working principle of the present invention are explained. By using a variety of theoretical tools such as information theory and probability theory, a short code multi-dimensional service quality assurance system is established to support the establishment of a unified model and control mechanism of "ε-effective capacity".

[0126] The effect of the present invention is further described below in conjunction with simulation experiments:

[0127] 1. Simulation experiment conditions:

[0128] The hardware platform of the simulation experiment of the present invention is: the processor is an Intel Pentium CPU, the main frequency is 3.3GHz, and the memory is 8GB.

[0129] The software platform of the simulation experiment of the present invention is: Windows 10 operating system and Matlab 2016a. The antenna gain used in the simulation experiment of the present invention is 20dBi, and the transmission power is between 10dBm and 50dBm.

[0130] 2. Simulation content and results analysis:

[0131] Figure 4 It shows that the freshness of a given peak information violates the threshold A th In the case of peak information freshness, the service quality assurance index θ AoI As the value of increases, the probability of peak information freshness violation decreases. This observation confirms the reliability and accuracy of the peak information freshness service quality assurance indicator. It is worth noting that the large peak information freshness service quality assurance indicator determines the lower bound of the probability of peak information freshness violation, while the small peak information freshness bounded service quality assurance indicator determines the upper bound of the probability of peak information freshness violation.

[0132] Figure 5 It shows that the freshness of a given peak information violates the threshold A th and the peak information freshness service quality assurance index θ AoI In the case of A, the probability of peak information freshness violation increases as the code length n increases. At the same time, a larger peak information freshness violation threshold leads to a smaller peak information freshness violation probability. This is because as A th As it increases, the constraints of the model based on the peak information freshness become more relaxed, thus achieving a smaller probability of peak information freshness violation. In addition, the peak information freshness service quality assurance index θ AoI When increasing from 0.01 to 0.05, the probability of peak information freshness violation decreases.

[0133] Figure 6 The delay violation probability and the delay service quality guarantee index θ are explained delay The relationship between th In the case of , as the delay service quality assurance index increases, the delay violation probability decreases. Therefore, a smaller delay service quality assurance index determines the upper bound of the delay violation probability, while a larger delay service quality assurance index determines the lower bound of the delay violation probability. At the same time, the larger delay limit D th This results in a smaller delay violation probability. This is because as D th As increases, the delay-based constraints of the model become more relaxed, thus achieving a smaller delay violation probability.

[0134] Depend on Figure 7 It can be seen that in the non-asymptotic region, the reliability service quality assurance index characterization formula is effective for the bit error rate and the reliability service quality assurance index θ error The relationship between. Figure 7It shows that when the code length n is 500, 700, 900 and 1100 respectively, the reliability service quality assurance indicator shows an indicator attenuation pattern relative to the bit error rate, which confirms the accuracy of the definition of the reliability service quality assurance indicator.

[0135] Figure 8 It shows the relationship between “ε-effective capacity” and code length n. It proves that under a given delay service quality guarantee index θ delay and the maximum number of HARQ-IR transmission modules L, the “ε-effective capacity” is an increasing function of the finite code length. Figure 8 In the figure, the dotted line indicates the effective capacity in the ideal model, that is, the relationship between the effective capacity of the traditional channel and the delay service quality guarantee index when the finite code length n approaches infinity and the bit error rate approaches 0. When the delay service quality guarantee index is larger, the corresponding "ε-effective capacity" decay rate is higher. This phenomenon indicates that the model of the present invention can guarantee more stringent delay service quality requirements; and when the delay service quality guarantee index is smaller, the corresponding "ε-effective capacity" decay rate is lower, indicating that the model of the present invention can only meet relaxed delay service quality requirements. When the code length increases infinitely, the "ε-effective capacity" approaches the channel effective capacity. In addition, Figure 8 It means that reducing the maximum number of HARQ-IR transmission modules L can achieve a higher "ε-effective capacity". This is because reducing L leads to a decrease in the number of HARQ-IR retransmissions, thereby increasing the maximum achievable coding rate and increasing the "ε-effective capacity".

[0136] A multi-dimensional service quality assurance system, comprising:

[0137] The peak information freshness module uses the peak information freshness as a service quality assurance parameter to evaluate and adjust data freshness, and establishes a peak information freshness model by modeling its measurement;

[0138] A peak information freshness service quality assurance indicator module, based on the peak information freshness model obtained by the peak information freshness module, characterizes the peak information freshness violation probability based on the hybrid automatic repeat request with incremental redundancy technology (HARQ-IR) in the field of finite code length coding, and obtains the peak information freshness service quality assurance indicator;

[0139] The delay and reliability service quality assurance indicator module characterizes the violation probability of delay and bit error rate in the field of finite code length coding, and obtains the delay and reliability service quality assurance indicators;

[0140] The arrival rate model module establishes the maximum supportable arrival rate model, namely "ε-effective capacity", based on the service quality assurance index of peak information freshness and the service quality assurance index of delay and reliability.

[0141] A multi-dimensional service quality assurance device, comprising:

[0142] Memory: used for storing a computer program of the multi-dimensional service quality assurance method;

[0143] Processor: used to implement a multi-dimensional service quality assurance method when executing the computer program.

[0144] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a multi-dimensional service quality assurance method can be implemented.

Claims

1. A multi-dimensional service quality assurance method, characterized in that: The following steps are involved: Step 1: Use the peak information freshness as a service quality assurance parameter to evaluate and adjust the data freshness, and establish a peak information freshness model; Step 2: According to the peak information freshness model obtained in step 1, in the field of finite code length coding, based on hybrid automatic repeat request with incremental redundancy technology (HARQ-IR), the peak information freshness violation probability is characterized to obtain the peak information freshness service quality assurance indicator; Step 3, in the field of finite code length coding, characterize the violation probability of delay and bit error rate, and obtain delay and reliability service quality assurance indicators; Step 4: Based on the service quality assurance index of the peak information freshness obtained in step 2 and the service quality assurance index of the delay and reliability obtained in step 3, a maximum supportable arrival rate model, namely "ε-effective capacity", is established. Through the joint control mechanism of peak information freshness, delay and reliability, the peak information freshness, queuing delay and transmission bit error rate in short code transmission are balanced to obtain the maximum supportable arrival rate and realize low-delay and high-reliability transmission.

2. A multi-dimensional service quality assurance method according to claim 1, characterized in that: In step 1, the peak information freshness model is as follows: T AoI (u)=T I (u-1,u)-T(u) Among them, T AoI (u) represents the peak information freshness, T I (u-1,u) represents the time interval between the arrival of two status update packets (u-1) and u, and T(u) represents the service time of the status update packet u.

3. A multi-dimensional service quality assurance method according to claim 1, characterized in that: The specific process of step 2 is as follows: Step 2.1: According to the large deviation principle, define the service quality assurance indicator θ based on the peak information freshness AoI , used to characterize the relationship between the peak information freshness threshold and the probability of violation of the peak information freshness exceeding a given threshold, and to measure the exponential decay rate of the probability of violation of the peak information freshness; Using the Mellin transform, we can obtain the peak information freshness violation probability p AoI The upper bound of (u) is as follows: Among them, A th represents the peak information freshness violation threshold, n represents the code length, K(θ AoI ,u) is the kernel function used to measure the freshness of peak information, which is defined as follows: Among them, T I (u-1,u) represents the time interval between the arrival of two status update packets (u-1) and u. represents the Mellin transform of the arrival time interval of the transmission status update packets u-1 to u in the signal-to-noise ratio domain, It represents the Mellin transform of the cumulative service time of the transmission status update packets v to u in the signal-to-noise ratio domain, and its definition expression is as follows: Step 2.2, according to the peak information freshness violation probability model obtained in step 2.1, assuming that the arrival update follows a Poisson process with a rate of λ, the arrival time interval can be regarded as an exponential process with a rate of λ, and the peak information freshness violation probability p AoI The upper bound of (u) can be simplified to the following result: in, represents the Mellin transform of the service time of the transmission status update packet u in the signal-to-noise ratio domain, and its definition expression is as follows: Step 2.3, using HARQ-IR technology, in the finite code length coding field, each finite code length code word of length n is divided into L modules, each module contains symbols, namely And these modules are transmitted continuously in the subsequent time, the peak information freshness violation probability can be approximately expressed as: Where T represents the unit time used by each channel, ∈ l (u) represents the bit error rate of the transmission status update data packet u when transmitting the lth HARQ-IR module.

4. A multi-dimensional service quality assurance method according to claim 1, characterized in that: The specific process of characterizing the violation probability of the delay in step 3 and obtaining the delay service quality assurance index is as follows: Step 3.11, construct a measurement standard for the delay service quality assurance mechanism. According to the large deviation principle, under sufficient conditions, the distribution of the process Q(t) converges to a random variable Q(∞), such that: Among them, Q th is the captain limit; parameter θ delay is the delay-based service quality assurance indicator (θ delay >0), the delay-based service quality assurance indicator θ delay An exponential decay rate used to measure the probability of delay quality of service guarantee violation; Step 3.12, using the Mellin transformation, effectively analyze the arrival process and service process, and then derive the delay service quality guarantee violation probability p delay The upper bound of (u) is as follows: Among them, D th represents the delay bound, is the kernel function used to measure the queuing delay, which is defined as follows: At the same time, the following stability conditions can be met: M A(u) (1+θd elay )M S(u) (1-θ delay )<1 Among them, M A(u) (θ delay ) represents the Mellin transform of the arrival process in the signal-to-noise ratio domain, and M S(u) (θ delay ) represents the Mellin transform of the service process in the signal-to-noise ratio domain.

5. A multi-dimensional service quality assurance method according to claim 1, characterized in that: The violation probability of the bit error rate in step 3 is characterized to obtain the reliability service quality assurance index. The specific process is: Step 3.21, construct a measurement standard for the reliability service quality assurance mechanism to describe the attenuation of the bit error probability ∈(u) as the code length n increases. According to the large deviation principle, when the coding rate is lower than the channel capacity, the reliability service quality assurance indicator characterizes the reliability service quality violation probability, that is, the exponential decay rate of the bit error rate, which is defined as follows: Where ∈(u) indicates the bit error rate of the state update data packet u. The above reliability service quality assurance indicator characterization formula indicates that the bit error rate increases at an exponential rate θ error The attenuation, which measures the stringency of the statistical reliability service quality guarantee, increases with the increase of the code length n. Correspondingly, for a given coding rate, the bit error rate decreases exponentially when the code length n tends to infinity, that is, ∈(u)≤exp(-nθ error ) Step 3.22, the service quality assurance index based on reliability can be obtained by definition, which is specifically defined as follows: i error =sup[E0[ρ,P x (x)]-ρR * (u)} Among them, R * (u) is the maximum coding rate of the state update packet u, ρ∈[0,1] is the Lagrange multiplier parameter, P x (x) represents the probability density function (PDF) of the transmitted signal vector x, E0[ρ,P x (x)] is defined as the cumulative generating function (CGF), which is expressed as follows: Where γ(u) represents the signal-to-interference-noise ratio of the system.

6. A multi-dimensional service quality assurance method according to claim 1, characterized in that: In step 4, under the premise of constructing the peak information freshness, delay and reliability combined with service quality guarantee constraints, the maximum supportable arrival rate model, namely "ε-effective capacity", EC ε (θ) is approximately expressed as: Among them, θ delay is the delay-based service quality assurance indicator (θ delay >0), p AoI (u) represents the peak information freshness violation probability, ∈(u) represents the bit error rate of the state update packet u, n represents the code length, R * (u) represents the maximum coding rate of state update packet u.

7. A multi-dimensional service quality assurance method according to claim 5, characterized in that: By applying Jensen's inequality, we can express E0[ρ,P x (x)], accurately modeling statistical reliability service quality assurance indicators under high signal-to-noise ratio conditions.

8. A multi-dimensional service quality assurance system based on the method according to any one of claims 1 to 7, characterized in that: include: The peak information freshness module uses the peak information freshness as a service quality assurance parameter to evaluate and adjust data freshness, and establishes a peak information freshness model by modeling its measurement; A peak information freshness service quality assurance indicator module, based on the peak information freshness model obtained by the peak information freshness module, characterizes the peak information freshness violation probability based on the hybrid automatic repeat request with incremental redundancy technology (HARQ-IR) in the field of finite code length coding, and obtains the peak information freshness service quality assurance indicator; The delay and reliability service quality assurance indicator module characterizes the violation probability of delay and bit error rate in the field of finite code length coding, and obtains the delay and reliability service quality assurance indicators; The arrival rate model module establishes the maximum supportable arrival rate model, namely "ε-effective capacity", based on the peak information freshness service quality assurance index and the delay and reliability service quality assurance index. Through the joint control mechanism of peak information freshness, delay and reliability, it balances the peak information freshness, queuing delay and transmission bit error rate in short code transmission, obtains the maximum supportable arrival rate, and realizes low-delay and high-reliability transmission.

9. A multi-dimensional service quality assurance device, characterized in that: include: Memory: used to store a computer program of a multi-dimensional service quality assurance method according to any one of claims 1 to 7; Processor: used to implement a multi-dimensional service quality assurance method as described in any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it can implement a multi-dimensional service quality assurance method as described in any one of claims 1-7.

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