QoS-CSMA / CA improvement method for tactile service

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a haptic service-oriented QoS-CSMA / CA improvement method. According to the method, the Pareto distribution is used for modeling the data arrival process, and the time delay and throughput in the network transmission process are optimized by introducing a token bucket model and a dynamic back-off factor adjustment method; the link queue state and the delay violation condition are counted in real time, the back-off factor is dynamically adjusted, and the low-delay and high-throughput transmission performance is ensured; according to the method, a more efficient backoff mechanism and a conflict avoidance strategy are realized on the MAC layer, the statistical delay requirement of a user is met with relatively low resource consumption, and meanwhile, the network throughput is improved; according to the method, the burstiness and heavy tail characteristics of the haptic service can be efficiently processed, and reliable QoS guarantee and higher transmission efficiency are provided for a wireless communication system.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a QoS-CSMA / CA improvement method for tactile services. Background Art

[0002] The tactile internet, a next-generation ultra-low-latency, high-reliability communication technology, is widely used in scenarios such as telemedicine, autonomous driving, industrial control, and virtual reality. It places extremely high demands on network latency, jitter, and stability. However, the data arrival process for tactile services often exhibits bursty and heavy-tailed distributions, primarily due to their high-frequency, latency-sensitive interactions. Systems can generate large amounts of tactile information in a short period of time. To reduce the communication burden, compression techniques such as perceptual dead-zone coding are often used to reduce the average data rate. However, such coding methods concentrate data packets when significant tactile changes are detected, exacerbating the volatility of the instantaneous arrival rate and arrival interval. This means that large amounts of data may be generated in a short period of time, while traffic is lower during other periods, leading to increased burstiness. Furthermore, varying interaction intensities and uncertain user behavior can lead to occasional but dramatic data bursts, making the probability of extremely large data flow events far higher than the typical distribution, exhibiting a typical heavy-tailed distribution: the probability of large data packets or high arrival rates decays slowly, resulting in a thick tail. Existing wireless network protocols struggle to provide stable QoS guarantees under bursty traffic and latency constraints.

[0003] WiFi (IEEE 802.11), with its high bandwidth, low cost, and easy deployment, is well-suited for indoor network requirements and is currently one of the primary application protocols for in-vehicle and indoor networks. Furthermore, with technological advancements, modern WiFi (particularly WiFi 6 and later versions) has significantly improved latency, reliability, and bandwidth, making it capable of carrying tactile services. WiFi 6 introduces key technologies such as OFDMA, MU-MIMO, and BSS Coloring, which effectively reduce congestion, improve multi-user concurrency, and reduce transmission latency. The CSMA / CA access mechanism, a core WiFi protocol, coordinates channel access between terminals through carrier sensing, random backoff, and acknowledgment mechanisms, effectively avoiding packet collisions in traditional networks. However, the CSMA / CA mechanism has significant limitations in scenarios with bursty and heavy-tailed traffic. Bursty traffic can easily lead to a large number of terminals competing for the channel simultaneously, resulting in frequent collisions and retransmissions. The significantly increased probability of collisions triggers the exponential backoff mechanism, which increases the backoff time and increases the transmission latency of critical services. Heavy tail traffic can cause some services to occupy the channel for extended periods, leading to uneven resource allocation and high latency jitter. Furthermore, traditional CSMA / CA uses a fixed backoff strategy that struggles to cope with dynamic and complex access requirements, limiting its performance in environments with bursty traffic and heavy tail traffic.

[0004] To address these challenges, various improvements have been proposed. IEEE 802.11e differentiates service types by introducing priority queues, but its static parameter settings struggle to adapt to dynamically changing network environments. While the next-generation WiFi 6 standard utilizes advanced technologies such as OFDMA and MU-MIMO to improve throughput, its underlying architecture still relies on the traditional CSMA / CA mechanism, failing to fundamentally address the backoff delay issue. Some studies have attempted to optimize backoff strategies using machine learning algorithms, but due to their high computational complexity, large-scale deployment on resource-constrained terminal devices is difficult. Therefore, optimizing the CSMA / CA mechanism in WiFi environments to adapt it to bursty and heavy-tail traffic characteristics is crucial for ensuring QoS for tactile services.

[0005] The performance of the CSMA / CA mechanism depends largely on the statistical characteristics of the backoff duration. The average backoff duration is called the backoff factor. Statistical delay QoS, composed of the delay bound and the delay violation probability, is another important metric. Because people perceive tactile stimulation much faster than hearing and vision, typically detecting delays within a few milliseconds, tactile services are particularly sensitive to latency. Average latency cannot effectively reflect tail performance, while statistical delay analysis can provide more definitive service guarantees, better meeting the stringent latency requirements of tactile services. Furthermore, in application scenarios such as high-frequency two-way interaction and complex multimodal feedback, tactile services also place high demands on system throughput. Tactile information is typically generated continuously at a high sampling rate. When multiple degrees of freedom or multi-point tactile feedback are involved, the data volume increases significantly. Insufficient network throughput can lead to data loss or increased compression, which in turn affects tactile resolution and stability. Therefore, in addition to ensuring low latency, maintaining sufficient throughput is equally important for tactile services. Therefore, in order to effectively reduce the delay violation probability and improve the throughput in tactile services, dynamically adjusting the backoff factor based on statistical delay QoS is an effective optimization method.

[0006] In summary, with the rapid development of the times, although there have been many improvements to the CSMA / CA protocol, in tactile services with bursty traffic and heavy tail characteristics, how to meet the diverse latency QoS requirements while providing the maximum throughput possible remains a challenge. Summary of the Invention

[0007] To overcome the above problems, the present invention provides an improved QoS-CSMA / CA method for tactile services. By analyzing the bursty characteristics of network traffic, the present invention adopts Pareto distribution to model the data packet arrival process and introduces a token bucket model to shape the arriving traffic. This method can better cope with bursty traffic and heavy-tail characteristics. Combining the effective capacity theory, the effective bandwidth under the token bucket model, and the delay violation probability, the backoff factor is dynamically adjusted to optimize the backoff process.

[0008] The purpose of the present invention is achieved through the following technical solutions:

[0009] A QoS-CSMA / CA improvement method for tactile services includes the following steps:

[0010] Step 1: Modeling the tactile service arrival process, specifically including the following:

[0011] Step 1.1: Use Pareto distribution to accurately model the burst traffic and heavy-tail characteristics of tactile services. The probability density function of the Pareto distribution is:

[0012]

[0013] in, Indicates the number of data packets arriving in a unit time slot. When the shape parameter , the mean of the Pareto distribution yes:

[0014]

[0015] According to the shape parameters and the average service arrival rate Calculate scale parameters for:

[0016]

[0017] Generate random traffic data by inverse transformation method:

[0018]

[0019] in for Uniformly distributed random numbers, Indicates a link The number of data packets arriving in a unit time slot;

[0020] Step 1.2, according to the link The delay bound and delay violation probability requirements of the link are calculated Initial QoS parameters :

[0021]

[0022] in Indicates a link The delay bound, Indicates a link The long-term delay violates the probability requirement;

[0023] Step 1.3, token bucket parameter initialization method based on burst traffic and heavy-tail distribution statistical characteristics, by calculating the mean of Pareto distribution and variance , and combined with the network load , determine the bucket capacity and token generation rate , where the initial bucket capacity is and the initial token generation rate for:

[0024]

[0025]

[0026] in represents the adjustment coefficient;

[0027]

[0028] Step 1.4: Adjust the parameters using a two-stage iterative strategy:

[0029] In the first stage, the minimum required capacity is found by gradually reducing the bucket capacity. , iteratively adjust the bucket capacity The formula is as follows:

[0030]

[0031] in represents the number of iterations, represents the reduction factor of the bucket capacity, Indicates the The bucket capacity after iterations, when the token bucket starts to drop packets, the iteration stops, thus determining the minimum bucket capacity;

[0032] In the second stage, the token generation rate is gradually reduced to find the minimum token that meets the requirements. , iteratively adjust the token generation rate The formula is as follows:

[0033]

[0034] in represents the reduction factor of the token generation rate, Indicates the The token generation rate after iterations, when the token bucket starts to drop packets, the iteration stops, thus determining the minimum token generation rate;

[0035] Step 2: Dynamic QoS awareness, including:

[0036] Step 2.1, calculate the backoff factor update period Middle Link The short-term delay violation probability requirement :

[0037]

[0038] in Indicates the backoff factor update period In the link The delay exceeds the binding The number of time slots; Indicates the total number of time slots;

[0039] Step 2.2, dynamically update the backoff factor update period QoS parameters in :

[0040]

[0041] Step 3: Estimation of effective bandwidth based on the token bucket model, where the effective bandwidth The calculation uses the following formula:

[0042]

[0043] in, is the curve that regulates the arrival process, , Indicates time slot;

[0044] Step 4: Dynamically adjust the link backoff factor, specifically including:

[0045] Step 4.1, calculation of effective link capacity:

[0046] A two-state Markov chain model is established based on the service process. In the Markov chain model, state 1 represents the link The transmission rate is 1 and the status 0 indicates a link The transmission rate is 0; the duration of the fallback process and the transmission duration of the link are respectively subject to the mean and an exponential distribution with mean 1; the transition rate matrix for:

[0047]

[0048] Based on the backoff factor and QoS parameters ,link The effective capacity is:

[0049]

[0050] in, Indicates a link The average arrival rate of

[0051] Step 4.2, Parameters and Adaptive update of:

[0052] The sub-gradient algorithm is used to find the parameters required to calculate the optimal back-off factor:

[0053]

[0054]

[0055] in, and Indicates that in the sub-gradient algorithm The Lagrange multiplier of the iteration, is a variation of the backoff factor, , transforming the non-convex optimization problem into a convex optimization problem, Indicates a link Effective bandwidth, and Indicates the step size of the corresponding Lagrange multiplier, setting and To ensure fast convergence of the sub-gradient method;

[0056] Step 4.3, taking into account the current network throughput, queue status and QoS requirements, the optimal backoff factor is obtained by solving to achieve the best balance between transmission efficiency and QoS guarantee, where the backoff factor The calculation is as follows:

[0057] First, calculate it by the following formula :

[0058]

[0059] in, , Indicates a link In the The backoff factor adjusts the amount of data successfully transmitted within a period;

[0060] Then pass Get the optimal backoff factor;

[0061] In step 4.4, the backoff time is calculated as follows:

[0062]

[0063] in, Indicates the length of a time slot, is a random variable that follows an exponential distribution, with a mean and a backoff factor related;

[0064] Step 5: Real-time monitoring and dynamic scheduling of link transmission status; specifically including:

[0065] Step 5.1: At the beginning of each time slot, the queue length of each link is updated in real time based on the number of newly arrived packets and successfully transmitted packets. The queue status update formula for each time slot is:

[0066]

[0067] in, Indicates the queue length at the end of the previous time slot, Indicates a link In the time slot The queue length, Indicates the amount of data that was successfully transmitted and removed from the queue in the current time slot. Ensure that the queue length does not become negative;

[0068] Step 5.2, delay violation judgment condition:

[0069] when hour, The value of is increased by 1, otherwise it remains unchanged. Indicates a link The average arrival rate, Indicates a link The delay exceeds the binding Number of time slots;

[0070] In step 5.3, the system sorts the queue lengths of each link in descending order in each time slot to determine the priority. It then determines in sequence whether each link satisfies both the backoff timer reaching zero and the channel being idle. If so, transmission begins immediately. Otherwise, the system continues counting down and waiting until the transmission conditions are met.

[0071] 2. The QoS-CSMA / CA improvement method for tactile services according to claim 1, wherein the step 5.2 is performed by accumulating the number of delay violations of the link in the current parameter adjustment period. , estimates the actual delay violation probability of the link, reflects the delay violation of the current link and provides feedback for subsequent parameter adjustments.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The present invention aims to solve the problem of handling burst traffic and heavy-tail characteristics in tactile services, and proposes a dynamic backoff factor adjustment method based on the probability of delay violation. By analyzing the burst characteristics of network traffic, the present invention adopts Pareto distribution to model the data packet arrival process, and introduces a token bucket model to shape the arriving traffic, which can better cope with burst traffic and heavy-tail characteristics. It combines the effective capacity theory, the effective bandwidth under the token bucket model, and the probability of delay violation to dynamically adjust the backoff factor and optimize the backoff process. Compared with the traditional fixed backoff mechanism, the present invention can effectively avoid the problems of long backoff time and low throughput in tactile services, and maintain the smooth operation of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings used in the description of the embodiments of the present invention. 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 the contents of the embodiments of the present invention and these drawings without paying any creative work.

[0075] Figure 1 This is the QoS-CSMA / CA flow chart.

[0076] Figure 2 For the current setting , , (unit: ), the delay violation probability box plot of the present invention.

[0077] Figure 3 For the current setting , , (unit: ), the measured average arrival and service rates of each link, and the evolution of the average arrival and service rates of link 1.

[0078] Figure 4 For the current setting , , (unit: ), the network's delay performance.

[0079] Figure 5 For the current setting , and the statistical delay QoS requirement for the first 30% of links is , ,When other links have no QoS constraints, the average throughput change trend under fixed network scale. DETAILED DESCRIPTION

[0080] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0081] Example 1

[0082] An improved method for the CSMA / CA protocol based on latency QoS for tactile services uses token bucket flow control and real-time statistics of link queue status and latency violations to dynamically adjust the backoff factor. This method can meet diverse latency QoS requirements in a distributed manner without providing signaling overhead for traffic information from other devices and scheduling information from access points (APs). This method optimizes the performance of the traditional CSMA / CA protocol in network environments with bursty and heavy-tailed traffic. The method flow chart of the present invention is shown in the figure below. Figure 1 As shown, the method includes the following steps:

[0083] Step 1: The data arrival process of tactile services often exhibits bursty and heavy-tailed distribution characteristics, making the traditional uniform traffic assumption ineffective in describing these characteristics. Therefore, this invention employs a Pareto distribution (not limited to a Pareto distribution) to model the packet arrival process of tactile services. A separate token bucket model is constructed for each link, and the bucket capacity and token generation rate are initialized based on the statistical characteristics of bursty traffic and heavy-tailed distribution. The token bucket parameters are gradually reduced until the bucket begins to drop packets, thereby determining the minimum token bucket parameters. By adjusting the token generation rate and bucket capacity, it is possible to effectively address common bursty and heavy-tailed traffic issues in networks, ensuring stable and efficient network operation.

[0084] The tactile service arrival process modeling includes the following:

[0085] (1) In this invention, the Pareto distribution is used to accurately model the burst traffic and heavy-tail characteristics in the tactile service (not limited to the Pareto distribution). Compared with the traditional Poisson distribution, the Pareto distribution can more accurately characterize the common burst traffic characteristics and heavy-tail characteristics in actual networks. The probability density function of the Pareto distribution is:

[0086]

[0087] in, Indicates the amount of data packets arriving in a unit time slot (i.e., the amount of upper-layer service data flowing to the wireless transmission queue within a time slot length). When the shape parameter , the mean of the Pareto distribution yes:

[0088]

[0089] According to the shape parameters and the average service arrival rate Calculate scale parameters for:

[0090]

[0091] In order to ensure that the traffic modeling is consistent with the actual network conditions, the generation of packet arrival volume adopts the inverse transformation method, which is an efficient and reliable random number generation method. This method first generates a uniformly distributed random number Then, the inverse function of the Pareto distribution is used to transform the data to obtain the required bursty and heavy-tailed data. This implementation method is computationally efficient and ensures that the statistical characteristics of the generated data are consistent with the theoretical model.

[0092] Generate random traffic data by inverse transformation method:

[0093]

[0094] in for Uniformly distributed random numbers, Indicates a link The number of data packets arriving in a unit time slot;

[0095] In the present invention, the inverse transformation method is used to generate random traffic data that conforms to the Pareto distribution. This method can effectively reflect different traffic demands, sudden traffic, and heavy-tail characteristics.

[0096] (2) According to the link The delay bound and delay violation probability requirements of the link are calculated Initial QoS parameters :

[0097]

[0098] in Indicates a link The delay bound, Indicates a link The long-term delay violates the probability requirement;

[0099] (3) A token bucket parameter initialization method based on the statistical characteristics of burst traffic and heavy-tailed distribution is proposed. and variance , and combined with the network load , determine the bucket capacity and token generation rate , where the initial bucket capacity is and the initial token generation rate for:

[0100]

[0101]

[0102] in represents the adjustment coefficient, Indicates network load;

[0103] Pareto distribution statistics:

[0104]

[0105]

[0106] (4) A two-stage iterative strategy is used to adjust the parameters:

[0107] In the first stage, the minimum required capacity is found by gradually reducing the bucket capacity. In the second stage, the token generation rate is gradually reduced to find the minimum token that meets the requirements. This phased approach avoids mutual interference between parameters. The iterative adjustment of token bucket parameters is as follows:

[0108]

[0109]

[0110] in represents the number of iterations, and Indicates the reduction factor of the token bucket parameters, Indicates the The bucket capacity after iterations, Indicates the The token generation rate after iterations is stopped when the token bucket starts to drop data packets, thereby determining the minimum token bucket parameters. The purpose is to find the minimum token bucket parameters that meet business needs without packet loss, achieve effective traffic control, minimize the resource overhead required for traffic control, and ensure stable and reliable business performance.

[0111] Step 2: Dynamic QoS perception method, which decomposes the long-term QoS constraints into a series of parameter update cycle progressive QoS constraints. By real-time statistics of link queue status and delay violations, the parameter update cycle progressive QoS requirements are recursively updated in each parameter update cycle. In order to better characterize the strictness of the current delay service quality requirements, the present invention introduces a progressive QoS parameter. Based on the large deviation theory, by analyzing the tail probability of the queue length or queuing delay distribution, it is concluded that the parameter is jointly determined by the delay bound and the delay violation probability in the current cycle. When the delay violation probability is low or the delay bound is small, the corresponding QoS parameter will increase, indicating that the current cycle has more stringent requirements for delay control. Dynamically adjust the QoS parameters in each cycle according to the delay bound and the delay violation probability to better meet the QoS requirements of tactile services; specifically including:

[0112] (1) Calculate the parameter (set backoff factor) update period Middle Link The short-term delay violation probability requirement :

[0113]

[0114] in Indicates the parameter (backoff factor) update cycle In the link The delay exceeds the binding The number of time slots, through the link Local measurement of the transmitter; Represents the total number of time slots; obviously, Can be based on its The local measurements are updated recursively in each parameter update cycle;

[0115] (2) Dynamic update parameter update cycle QoS parameters in :

[0116] Calculate the delay violation probability requirement during the parameter update cycle , and then calculate the QoS parameters using the following formula :

[0117]

[0118] This dynamic update process can adapt to changes in the network environment in real time, avoid the inefficiency or instability caused by fixed parameter settings, and improve the network's latency guarantee capability and overall performance stability in tactile services.

[0119] Step three, estimate the effective bandwidth based on the token bucket model, find appropriate parameters by dynamically adjusting the token generation rate and bucket capacity, and then estimate the effective capacity of the link in combination with the QoS requirements. Unlike traditional static estimation methods based on average rate or instantaneous bandwidth, the present invention fully considers the burstiness and heavy-tail distribution characteristics of traffic, uses the token bucket model to shape and rate control burst traffic, adjusts the token bucket parameters according to real-time traffic conditions, and ensures that the token bucket parameters match the actual traffic demand, thereby achieving dynamic estimation of the effective bandwidth of the link. Specifically, the present invention dynamically adjusts the token generation rate and bucket capacity by statistically analyzing the status of each link of the tactile service, balances the token generation rate with the actual traffic demand, and then estimates the effective bandwidth of the current link under the delay QoS constraint in combination with the QoS requirements of the service. This not only improves the accuracy of the effective bandwidth estimation, but also reduces the impact of burst traffic and heavy-tail traffic on network performance, thereby improving the delay stability and throughput performance of the overall network.

[0120] Specifically include:

[0121] (1) A token bucket model-based effective bandwidth estimation method is proposed. This method takes into account the statistical characteristics of burst traffic and heavy-tailed distribution, and can more accurately estimate the actual bandwidth resources required by the network. The calculation adopts the following improved formula:

[0122]

[0123] in, is the curve that regulates the arrival process, , is the token generation rate, Indicates time slot, Indicates the token bucket capacity;

[0124] Step 4: Dynamic adjustment method of link backoff factor, based on real-time statistics of link queue status and delay violation and QoS parameters within parameter update cycle , combined with the effective bandwidth and effective capacity theory, dynamically adjust the backoff factor and transmission strategy to optimize channel occupancy and improve transmission stability. During operation, the backoff factor Dynamic optimization is performed through the gradient descent method to balance latency and transmission efficiency under normal load; when queue congestion or increased probability of latency violation is detected, the system automatically reduces To extend the back-off time; in the light load state, Change to 0 to avoid unnecessary backoff delays.

[0125] Specifically include:

[0126] (1) Calculation of effective link capacity:

[0127] A two-state Markov chain model is established based on the service process. In the Markov chain model, state 1 represents the link The transmission rate is 1 and the status 0 indicates a link The transmission rate is 0; in the present invention, the duration of the fallback process and the transmission duration of the link are respectively subject to the mean and an exponential distribution with mean 1; the transition rate matrix for:

[0128]

[0129] Based on the backoff factor and QoS parameters ,link The effective capacity is:

[0130]

[0131] in, Indicates a link The average arrival rate of

[0132] (2) Parameters and Adaptive update of:

[0133] Use the sub-gradient algorithm to find the parameters required to calculate the optimal backoff factor and :

[0134]

[0135]

[0136] in, and Indicates that in the sub-gradient algorithm The Lagrange multiplier of the iteration, It is a deformation of the backoff factor. In order to better solve the optimization problem, let , transforming the non-convex optimization problem into a convex optimization problem, Indicates a link Effective bandwidth, , and Indicates the step size of the corresponding Lagrange multiplier, setting and To ensure fast convergence of the sub-gradient method;

[0137] (3) A new backoff factor calculation method is proposed. Taking into account the current network throughput, queue status and QoS requirements, the optimal backoff factor is obtained by solving it to achieve the best balance between transmission efficiency and QoS guarantee. The calculation is as follows:

[0138] First, calculate it by the following formula :

[0139]

[0140] in, , Indicates a link In the The first parameter is the amount of data successfully transmitted within the backoff factor adjustment period;

[0141] Then pass Get the optimal backoff factor;

[0142] (4) Backoff time calculation (used for subsequent QoS-CSMA / CA backoff):

[0143] In the CSMA / CA mechanism, to reduce channel access conflicts, a node delays transmission by a backoff period when it detects a busy channel. Traditional CSMA / CA (such as IEEE 802.11) uses a binary exponential backoff mechanism. To improve network performance, the optimal backoff factor is studied to optimize the distribution of backoff times, thereby reducing conflicts, increasing throughput, and reducing latency.

[0144] The backoff time is calculated as follows:

[0145]

[0146] in, Indicates the length of a time slot, is a random variable that follows an exponential distribution, with a mean and a backoff factor related;

[0147] The backoff time is used to initialize the backoff timer. During the backoff process, the backoff timer will be decremented only when the channel remains idle within a backoff time slot. The node will start sending only when the backoff timer reaches zero and the channel remains idle. The backoff time is calculated using an exponential distribution and By Converting this into the expected backoff time allows for adaptive matching of collision probability with network load, effectively reducing the probability of network collisions. This dynamic adjustment method can cope with sudden high-load situations in the network, reducing collisions and latency, and improving channel utilization.

[0148] Step 5: Real-time monitoring and dynamic scheduling of link transmission status. A multi-dimensional network monitoring system is established to collect real-time statistics on link queue status and latency violations, automatically triggering parameter reconfiguration when performance indicators exceed thresholds. By monitoring network status in real time, traffic control policies are dynamically adjusted, prioritizing data transmission requests on highly loaded links based on current link loads. This improves overall network efficiency and ensures stable communication under varying conditions.

[0149] Specifically include:

[0150] (1) An efficient queue status update method is designed. At the beginning of each time slot, the queue length of each link is updated in real time based on the number of newly arrived packets and successfully transmitted packets. This method has a small computational cost but can accurately reflect the current congestion status of the network. The queue status update formula for each time slot is:

[0151]

[0152] in, Indicates the queue length at the end of the previous time slot, Indicates a link In the time slot The queue length, Indicates the amount of newly arrived data in the current time slot, Indicates the amount of data that was successfully transmitted and removed from the queue in the current time slot. Ensure that the queue length does not become negative;

[0153] (2) Delay violation judgment conditions:

[0154] when hour, The value of is increased by 1, otherwise it remains unchanged. Indicates a link The average arrival rate, Indicates a link The delay exceeds the binding Number of time slots;

[0155] Compare the current queue length with the theoretical maximum allowed queue length (calculated based on the arrival rate and delay threshold) to quickly determine whether a delay violation has occurred.

[0156] By accumulating the number of link delay violations in the current parameter adjustment period ,estimate the actual delay violation probability of the link, reflecting the delay violation of the current link to provide feedback for subsequent parameter adjustments and help dynamically optimize the transmission strategy;

[0157] (3) The system sorts the queue lengths of each link in descending order in each time slot to determine the priority (the longer the queue length, the higher the priority). It then sequentially determines whether each link meets the conditions of "backoff timer reaches zero and channel is idle" at the same time. If so, transmission begins immediately; otherwise, it continues counting down and waiting until the transmission conditions are met.

[0158] Dynamic scheduling prioritizes traffic based on queue length in descending order. The dynamic scheduling algorithm prioritizes traffic based on queue length in descending order. This prioritizes high-load links, reducing the risk of queue overflow while ensuring fairness across links.

[0159] Step 5: Through real-time monitoring and dynamic sorting of link queue status, combined with backoff time and queue priority sorting, effective scheduling decisions are made within the current cycle. At the same time, real-time link status feedback is provided for the dynamic adjustment of subsequent backoff factors, thereby improving overall transmission efficiency and adaptive performance.

[0160] Example 2

[0161] Set the parameter values ​​as shown in Table 1;

[0162] Table 1. Simulation parameters

[0163]

[0164] When setting , , (unit: ), the delay violation probability box plot of the present invention is as follows Figure 2 As shown, the evolution of the measured average arrival and service rates of each link and the average arrival and service rate of link 1 is shown in Figure 3 As shown, the network delay performance under the same conditions is as follows Figure 4 shown. Figure 5 The average throughput variation trend of the present invention under different loads is shown, where the average arrival rate of each link is set to The average throughput in the network first increases and then slows down as the load increases. Simulation results show that in the face of burst traffic and heavy-tail characteristics in tactile services, the present invention can ensure low-latency and high-throughput transmission performance by dynamically adjusting the backoff factor.

[0165] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the scope of protection of the present invention is not limited to the specific details of the above embodiments. Within the technical concept of the present invention, any technician familiar with the technical field can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention. These simple variations all fall within the scope of protection of the present invention.

Claims

1. A QoS-CSMA / CA improvement method for tactile services, characterized in that: The following steps are involved: Step 1: Modeling the tactile service arrival process: Pareto distribution is used to model the burst traffic and heavy-tail characteristics of tactile services; initial QoS parameters are calculated based on the link delay bound and delay violation probability requirements; token bucket parameter initialization method based on the statistical characteristics of burst traffic and heavy-tail distribution is used to determine the bucket capacity by calculating the mean and variance of the Pareto distribution and combining it with the network load. and token generation rate ; The parameters are adjusted using a two-stage iterative strategy: in the first stage, the minimum value is found by gradually reducing the bucket capacity. In the second stage, the token generation rate is gradually reduced to find the minimum ; Step 2: Dynamic QoS perception: Calculate the probability of short-term delay violation of the link during the backoff factor update period; dynamically update the QoS parameters during the backoff factor update period: Step 3: Estimation of effective bandwidth based on the token bucket model; Step 4: Dynamic adjustment of link backoff factor: Calculate the effective capacity of the link; parameter and Adaptive update; taking into account the current network throughput, queue status and QoS requirements, the optimal backoff factor is obtained; Calculate backoff time; Step 5: Real-time monitoring and dynamic scheduling of link transmission status: At the beginning of each time slot, the queue length of each link is updated in real time based on the number of newly arrived packets and successfully transmitted packets; Determine the delay violation judgment conditions; sort the queue lengths of each link in descending order in each time slot, and determine whether each link meets the backoff timer reset and channel idle conditions at the same time. If so, transmit immediately; otherwise, continue counting down and waiting until the transmission conditions are met.

2. The QoS-CSMA / CA improvement method for tactile services according to claim 1, characterized in that: Step 1: Modeling the tactile service arrival process, specifically including the following: Step 1.1: Use Pareto distribution to accurately model the burst traffic and heavy-tail characteristics of tactile services. The probability density function of the Pareto distribution is: ； in, Indicates the number of data packets arriving in a unit time slot. When the shape parameter , the mean of the Pareto distribution yes: ； According to the shape parameters and the average service arrival rate Calculate scale parameters for: ； Generate random traffic data by inverse transformation method: ； in for Uniformly distributed random numbers, Indicates a link The number of data packets arriving in a unit time slot; Step 1.2, according to the link The delay bound and delay violation probability requirements of the link are calculated Initial QoS parameters : ； in Indicates a link The delay bound, Indicates a link The long-term delay violates the probability requirement; Step 1.3, token bucket parameter initialization method based on burst traffic and heavy-tail distribution statistical characteristics, by calculating the mean of Pareto distribution and variance , and combined with the network load , determine the bucket capacity and token generation rate , where the initial bucket capacity is and the initial token generation rate for: ； ； in represents the adjustment coefficient; ； Step 1.4: Adjust the parameters using a two-stage iterative strategy: In the first stage, the minimum required capacity is found by gradually reducing the bucket capacity. , iteratively adjust the bucket capacity The formula is as follows: ； in represents the number of iterations, represents the reduction factor of the bucket capacity, Indicates the The bucket capacity after iterations, when the token bucket starts to drop packets, the iteration stops, thus determining the minimum bucket capacity; In the second stage, the token generation rate is gradually reduced to find the minimum token that meets the requirements. , iteratively adjust the token generation rate The formula is as follows: ； in represents the reduction factor of the token generation rate, Indicates the The token generation rate after iterations is calculated. The iteration is stopped when the token bucket starts to drop packets, thereby determining the minimum token generation rate.

3. The QoS-CSMA / CA improvement method for tactile services according to claim 2, characterized in that: Step 2: Dynamic QoS awareness, including the following: Step 2.1, calculate the backoff factor update period Middle Link The short-term delay violation probability requirement : ； in Indicates the backoff factor update period In the link The delay exceeds the binding The number of time slots; Indicates the total number of time slots; Step 2.2, dynamically update the backoff factor update period QoS parameters in : 。 4. The QoS-CSMA / CA improvement method for tactile services according to claim 3, characterized in that: Step 3: Effective bandwidth estimation based on the token bucket model, where the effective bandwidth The calculation uses the following formula: ； in, is the curve that regulates the arrival process, , Indicates a time slot.

5. The QoS-CSMA / CA improvement method for tactile services according to claim 4, characterized in that: Step 4: Dynamically adjust the link backoff factor, including the following: Step 4.1, calculation of effective link capacity: A two-state Markov chain model is established based on the service process. In the Markov chain model, state 1 represents the link The transmission rate is 1 and the status 0 indicates a link The transmission rate is 0; the duration of the fallback process and the transmission duration of the link are respectively subject to the mean and an exponential distribution with mean 1; the transition rate matrix for: ； Based on the backoff factor and QoS parameters ,link The effective capacity is: ； in, Indicates a link The average arrival rate of Step 4.2, Parameters and Adaptive update of: The sub-gradient algorithm is used to find the parameters required to calculate the optimal back-off factor: ； ； in, and Indicates that in the sub-gradient algorithm The Lagrange multiplier of the iteration, is a variation of the backoff factor, , transforming the non-convex optimization problem into a convex optimization problem, Indicates a link Effective bandwidth, and Indicates the step size of the corresponding Lagrange multiplier, setting and To ensure fast convergence of the sub-gradient method; Step 4.3, taking into account the current network throughput, queue status and QoS requirements, the optimal backoff factor is obtained by solving to achieve the best balance between transmission efficiency and QoS guarantee, where the backoff factor The calculation is as follows: First, calculate it by the following formula : ； in, , Indicates a link In the The backoff factor adjusts the amount of data successfully transmitted within a period; Then pass Get the optimal backoff factor; In step 4.4, the backoff time is calculated as follows: ； in, Indicates the length of a time slot, is a random variable that follows an exponential distribution, with a mean and a backoff factor related.

6. The QoS-CSMA / CA improvement method for tactile services according to claim 5, characterized in that: Step 5: Real-time monitoring and dynamic scheduling of link transmission status; specifically, the following: Step 5.1: At the beginning of each time slot, the queue length of each link is updated in real time based on the number of newly arrived packets and successfully transmitted packets. The queue status update formula for each time slot is: ； in, Indicates the queue length at the end of the previous time slot, Indicates a link In the time slot The queue length, Indicates the amount of data that was successfully transmitted and removed from the queue in the current time slot. Ensure that the queue length does not become negative; Step 5.2, delay violation judgment condition: when hour, The value of is increased by 1, otherwise it remains unchanged. Indicates a link The average arrival rate, Indicates a link The delay exceeds the binding Number of time slots; In step 5.3, the system sorts the queue lengths of each link in descending order in each time slot to determine the priority. It then determines in sequence whether each link satisfies both the backoff timer reaching zero and the channel being idle. If so, transmission begins immediately. Otherwise, the system continues counting down and waiting until the transmission conditions are met.

7. The QoS-CSMA / CA improvement method for tactile services according to claim 6, characterized in that: In step 5.2, the number of delay violations of the link in the current parameter adjustment period is accumulated. , estimates the actual delay violation probability of the link, reflects the delay violation of the current link and provides feedback for subsequent parameter adjustments.

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