WIFI7 wireless router MLO optimization method and system and storage medium

Abstract

The invention relates to the technical field of communication, in particular to a WIFI7 wireless router MLO optimization method and system and a storage medium. The optimization method comprises the following steps: establishing MLO connection, and adding a dynamic channel evaluation link to improve a competition mechanism of a multi-band channel; in the channel competition process in the multi-band channel, the channel use condition and the congestion condition are monitored in real time; and when any channel is detected to be congested, marking the channel as a congested channel and temporarily shielding the congested channel so as to reduce channel conflicts. According to the invention, by temporarily shielding the congested channel in the MLO NSTR mode, the rollback in channel competition is reduced, so that the data transmission time delay is obviously reduced. Meanwhile, a quantitative feedback control mechanism (QFCM) dynamically adjusts the transmission parameters of the channel according to the actual transmission effect, such as transmission power and data rate, further optimizes the time delay in the transmission process, and improves the response speed of the system.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a WIFI7 wireless router MLO optimization method, system and storage medium. Background Art

[0002] The WIFI7 NSTR mode has the feature of starting and ending at the same time (called PPDUend time alignment in the 802.11be protocol). Figure 1 As shown in the figure, the most easily conceivable means of contention is the PIFS (PCF Interframe Space) mechanism used in channel bonding access. This mechanism means that after all negotiated channels are idle, channels of multiple frequency bands are merged and accessed together. At the same time, a virtual unique MLD MAC address, public packet queue, security and management parameters are used. Data packets transmitted through all links belong to the same sequence number space, which allows a terminal to transmit its data packets on one link and retransmit undelivered data packets on another link, such as Figure 2 shown.

[0003] Take a three-band wireless router as an example. Figure 3 As shown in the figure, MLO uses the 2.4 / 5 / 6G frequency bands at the same time. After winning the competition for the 6G main channel, it starts to confirm the 5G / 2.4G auxiliary channel. If both are idle, MLO is turned on to transmit data on the 6G and 5G / 2.4G frequency bands at the same time.

[0004] In this scenario, since turning on MLO will occupy a large amount of wireless bandwidth resources, and data can only be processed and forwarded after all channels are idle, the WIFI 7 wireless router will generate a large channel competition overhead and high latency. At the same time, if some channels are congested, it may cause more data retransmission. Therefore, the MLO channel competition mechanism in the current protocol has certain defects. Summary of the invention

[0005] The present invention provides a WIFI7 wireless router MLO optimization method, system and storage medium to solve the technical problem of unreasonable MLO channel competition mechanism in the current protocol.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] On the one hand, a WIFI7 wireless router MLO optimization method is provided, and the steps of the optimization method include:

[0008] Establish MLO connection and add dynamic channel evaluation link to improve the competition mechanism of multi-band channels;

[0009] During channel competition in multi-band channels, real-time monitoring of channel usage and congestion;

[0010] When any channel is detected to be congested, the channel is marked as a congested channel and temporarily blocked to reduce channel conflicts;

[0011] Perform multi-band channel merging competition. When multiple channels are detected to be idle at the same time, they are marked as idle channels, and multiple idle channels are merged to start channel competition to improve data transmission efficiency.

[0012] Manage and schedule the successfully contended channels and use available channels for data transmission and reception to avoid high latency and retransmission operations;

[0013] Implement a dynamic update mechanism to periodically evaluate and adjust the channel list based on changes in channel status to ensure real-time optimization of network performance;

[0014] Multi-link operation is used to achieve effective scheduling of channel resources to improve the wireless efficiency and network response speed of the WIFI7 wireless router.

[0015] On the other hand, a WIFI7 wireless router MLO optimization system is provided, the optimization system comprising:

[0016] Dynamic channel assessment module, used to analyze channel performance in real time, dynamically assess channel status, and provide optimization support for multi-link operations;

[0017] Channel contention optimization module, based on adaptive contention strategy, reduces contention attempts on congested channels to improve channel utilization efficiency;

[0018] The data flow intelligent scheduling module combines the channel evaluation results and terminal requirements, and uses an intelligent link allocation algorithm to dynamically allocate the data flow of each channel to improve network performance;

[0019] Predictive maintenance module, which uses machine learning to predict the future performance of channels and make channel adjustment and shielding decisions in advance to reduce network congestion and failures;

[0020] User data management module, used to manage the transmission process of user data and ensure data security;

[0021] System integration module supports integration with other network components to improve the scalability of the system.

[0022] On the other hand, a readable storage medium is provided, which stores a computer-readable program code. When the program code is read by a computer device, the above-mentioned WIFI7 wireless router MLO optimization method is executed.

[0023] The beneficial effects of the present invention are:

[0024] The present invention significantly reduces data transmission delay by temporarily shielding the congested channel in the MLO NSTR mode and reducing the backoff in channel competition. At the same time, the quantized feedback control mechanism (QFCM) dynamically adjusts the transmission parameters of the channel, such as transmission power and data rate, according to the actual transmission effect, further optimizing the delay in the transmission process and improving the response speed of the system.

[0025] By dynamically shielding the congested channels in the MLO NSTR mode and using high-quality channels for data transmission, the invalid competition and data retransmission that may occur in the existing technology when transmitting in congested channels are avoided, thereby improving the stability of the network. In addition, the intelligent link allocation algorithm dynamically adjusts the data flow allocation according to the real-time performance of the channel and the connection requirements of the terminal, reducing the transmission interruption caused by channel congestion and further enhancing the stability of the system.

[0026] By introducing the adaptive channel quality management model (ACQM), the best channel is evaluated and selected for transmission in real time, thus improving the efficiency of data transmission. The time-division preemption algorithm (TDP) dynamically adjusts the timing of channel competition according to the contention priority (PS) of each channel, reduces the contention conflict of multi-band channels, and improves the utilization efficiency of channel resources. The intelligent link allocation algorithm minimizes the average delay (D avg ) and maximize data throughput (T avg ), further improving the overall transmission efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of the WiFi 7 protocol frame alignment principle.

[0028] Figure 2 This is a schematic diagram of WiFi 7 MLO NSTR mode.

[0029] Figure 3 This is a schematic diagram of WiFi 7 channel competition.

[0030] Figure 4 The process of the WIFI7 wireless router MLO optimization method in one embodiment of the present invention is as follows Figure 1 .

[0031] Figure 5 The process of the WIFI7 wireless router MLO optimization method in one embodiment of the present invention is as follows Figure 2 . DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0034] The present invention provides the following preferred embodiments:

[0035] Embodiment 1

[0036] In order to solve the high latency and frequent retransmission problems caused by the unreasonable channel competition mechanism of the WIFI7 wireless router in the MLO (Multi-Link Operation) mode, this embodiment proposes a WIFI7 wireless router MLO optimization method, which optimizes the establishment and maintenance process of the MLO connection, and significantly improves the wireless efficiency and network response speed of the system by adding a dynamic channel evaluation link, real-time monitoring of channel usage and congestion, marking congested channels and temporarily shielding them, merging idle channels for competition, managing and scheduling successfully competed channels, periodically evaluating and adjusting the channel list, and using multi-link operation to achieve effective scheduling of channel resources.

[0037] like Figure 4 and Figure 5 As shown, the steps of the optimization method include:

[0038] S100, establish an MLO connection and add a dynamic channel evaluation link to improve the competition mechanism of multi-band channels.

[0039] S200, during channel contention in a multi-band channel, monitor channel usage and congestion in real time.

[0040] S300: When any channel is detected to be congested, the channel is marked as a congested channel and is temporarily shielded to reduce channel conflicts.

[0041] S400, performing multi-band channel merging competition. When it is detected that multiple channels are idle at the same time, they are marked as idle channels, and multiple idle channels are merged to start channel competition to improve data transmission efficiency.

[0042] S500: Manage and schedule the successfully contended channels, and use the available channels to send and receive data to avoid high delay and retransmission operations.

[0043] S600. Implement a dynamic update mechanism to periodically evaluate and adjust the channel list according to changes in channel status to ensure real-time optimization of network performance.

[0044] S700. Use multi-link operation to achieve effective scheduling of channel resources to improve the wireless efficiency and network response speed of the WIFI7 wireless router.

[0045] Establish MLO connection and add dynamic channel evaluation link to improve the competition mechanism of multi-band channels;

[0046] During channel competition in multi-band channels, real-time monitoring of channel usage and congestion;

[0047] When any channel is detected to be congested, the channel is marked as a congested channel and temporarily blocked to reduce channel conflicts;

[0048] Perform multi-band channel merging competition. When multiple channels are detected to be idle at the same time, they are marked as idle channels, and multiple idle channels are merged to start channel competition to improve data transmission efficiency.

[0049] Manage and schedule the successfully contended channels, and use available channels for data transmission and reception to avoid high latency and retransmission operations, thus improving network performance;

[0050] Implement a dynamic update mechanism to periodically evaluate and adjust the channel list based on changes in channel status to ensure real-time optimization of network performance;

[0051] Multi-link operation is used to achieve effective scheduling of channel resources to improve the wireless efficiency and network response speed of the WIFI7 wireless router.

[0052] When establishing an MLO connection, this embodiment adds a dynamic channel evaluation link. Specifically, when the terminal device establishes an MLO connection with the WIFI7 wireless router, the router will start a dynamic channel evaluation module to analyze the performance of each frequency band channel in real time. It should be understood that the dynamic channel evaluation module can evaluate the current state of the channel based on real-time data, providing an accurate basis for subsequent channel competition and scheduling. Through this link, the channel with the best performance can be selected at the beginning of the connection, thereby improving the efficiency and stability of the initial connection.

[0053] Furthermore, in the competition process of multi-band channels, the WIFI7 wireless router of this embodiment monitors the usage and congestion of the channels in real time. Specifically, the router collects key parameters such as the flow rate, data packet arrival time and delay of the channel in real time through the channel status sensors installed on each frequency band channel. These parameters are analyzed by the internal data processing module to calculate the congestion probability of the channel. It can be understood that the real-time monitoring mechanism can detect channel congestion in time and provide timely data support for subsequent channel adjustments.

[0054] Furthermore, when any channel is detected to be congested, the present embodiment marks the channel as a congested channel and temporarily shields it to reduce channel conflicts. Specifically, when the channel status sensor detects that the probability of congestion in a certain channel is high and exceeds a preset threshold, the router will immediately mark the channel as a congested channel and temporarily stop data transmission on the channel. It should be understood that by temporarily shielding the congested channel, invalid competition attempts can be avoided, channel conflicts can be reduced, and the competition success rate and data transmission efficiency of other channels can be improved.

[0055] Furthermore, when performing multi-band channel merging competition, when multiple channels are detected to be idle at the same time, this embodiment marks these channels as idle channels, and merges multiple idle channels to start channel competition. Specifically, when the real-time monitoring module finds that multiple channels are idle, these channels will be marked as idle channels, and through the channel merging competition module, these channels will compete as a whole. It should be understood that the channel merging competition mechanism can make full use of all available resources and improve the efficiency of data transmission when multiple channels are idle at the same time.

[0056] Furthermore, the successfully competed channels are managed and scheduled, and available channels are used for data transmission and reception to avoid high latency and retransmission operations and improve network performance. Specifically, when the channel merging competition module successfully competes for available channels, the information of these channels will be passed to the channel management and scheduling module. This module dynamically adjusts the allocation of channels according to the real-time status of the channel and the needs of the terminal device to ensure efficient data transmission and low latency. It can be understood that through dynamic management and scheduling, the transmission capacity of each channel can be maximized, unnecessary retransmission operations can be reduced, and the performance of the entire network can be improved.

[0057] Furthermore, this embodiment implements a dynamic update mechanism to periodically evaluate and adjust the channel list according to changes in channel status to ensure real-time optimization of network performance. Specifically, the router will start a channel evaluation and update process at regular intervals (such as 5 minutes). During this process, the dynamic channel evaluation module re-evaluates the current status of all channels and updates the channel list based on the evaluation results. It should be understood that the dynamic update mechanism can continuously adjust the channel list over time to ensure that the system is always in the best state and improve the long-term stability and performance of the network.

[0058] Furthermore, this embodiment uses multi-link operation to achieve effective scheduling of channel resources to improve the wireless efficiency and network response speed of the WIFI7 wireless router. Specifically, when multiple channels are available at the same time, the multi-link operation module dynamically allocates data streams according to the connection requirements of the terminal device and the real-time performance of the channel. In this way, data can be transmitted simultaneously on different channels to improve the concurrency and efficiency of data transmission. It can be understood that multi-link operation not only improves wireless efficiency, but also enhances the response speed of the network, so that the terminal device can receive data faster and improve the user experience.

[0059] Furthermore, in the actual operation process, the optimization strategy in this embodiment is implemented through the following steps: First, when the terminal device establishes an MLO connection with the WIFI7 wireless router, the dynamic channel evaluation module is started, the performance of each channel is evaluated, and the optimal channel is selected for initial connection. Secondly, the channel status sensor monitors the channel usage and congestion in real time, and transmits the data to the channel management and scheduling module. When any channel is detected to be congested, the channel management and scheduling module marks the channel as a congested channel and temporarily blocks it to avoid invalid competition. At the same time, when it is detected that multiple channels are idle at the same time, the channel merging competition module merges these channels to compete for multi-band channels to ensure the efficiency of data transmission. Through periodic channel evaluation and updating, the system can continuously optimize the channel list and improve network performance.

[0060] The benefit of this embodiment is that, through the MLO optimization method in this embodiment, the channel competition mechanism of the WIFI7 wireless router in the multi-link operation mode is improved, the network delay is reduced, the invalid competition and data retransmission are reduced, and the wireless efficiency and network response speed of the system are improved. At the same time, through real-time monitoring and dynamic update mechanisms, the system can continuously optimize the channel status to ensure long-term efficient operation. The benefit of this embodiment is that through comprehensive optimization strategies and dynamic management, the performance of the WIFI7 wireless router is improved, providing users with a better network experience.

[0061] Embodiment 2

[0062] In order to solve the high latency and frequent retransmission problems caused by the unreasonable multi-band channel competition mechanism of the WIFI7 wireless router in the MLO (Multi-Link Operation) mode, this embodiment further optimizes the dynamic channel evaluation link and improves the channel competition mechanism based on the Adaptive Channel Quality Management Model (ACQM). The ACQM model combines the Channel Quality Indicator (CQI) and the Channel Availability Rate (CAR), and calculates an Overall Competition Score (OCS) for each channel through a self-optimization algorithm to reflect the current state of the channel. The specific calculation formula is:

[0063] Where i represents the i-th channel; μ CQI and σ CQI are the mean and standard deviation of all channel quality indexes, respectively, which are used to standardize the channel quality. w is the weight factor for adjusting the influence of channel availability on the overall score. CAR is the channel availability. log(CAR i +1) is to transform the channel availability to increase the discrimination of low availability channels.

[0064] Furthermore, when establishing an MLO connection, the router will start the ACQM model for channel evaluation. It should be understood that the ACQM model can more accurately evaluate the performance of each channel by monitoring the quality and availability of the channel in real time, so as to select the optimal channel at the beginning of the connection. Specifically, the channel quality index (CQI) reflects the transmission quality of the channel, including signal strength, interference, etc.; the channel availability rate (CAR) reflects the proportion of the channel that has been idle in the recent period of time. By integrating these two indicators, the ACQM model can comprehensively consider the transmission quality of the channel and its availability, and calculate a comprehensive contention score (OCS) for each channel, so as to better guide channel contention.

[0065] Furthermore, there are two key parts in the calculation formula of the channel comprehensive competition score (OCS): the normalization of the channel quality index (CQI) and the logarithmic transformation of the channel availability rate (CAR). The normalization eliminates the dimensional differences between different channels by subtracting the mean and dividing by the standard deviation, making the evaluation of channel quality more objective. The logarithmic transformation increases the distinction of low-availability channels in the comprehensive competition score by processing the channel availability, avoiding the excessive suppression of low-availability channels by high-availability channels, and making the channel evaluation more balanced.

[0066] Furthermore, in order to achieve the real-time and accuracy of the adaptive channel quality management model (ACQM), this embodiment uses a high-performance channel state sensor and a data processing module. The channel state sensor can collect key parameters such as channel traffic, packet arrival time and delay in real time. The data processing module is based on a high-performance processor and advanced algorithms, and can quickly process and analyze the collected data to calculate the channel's comprehensive competition score (OCS).

[0067] The benefit of this embodiment is that by introducing the adaptive channel quality management model (ACQM) and the comprehensive contention score (OCS), the WIFI7 wireless router can more accurately evaluate the performance of multi-band channels and select the optimal channel during connection establishment and channel competition, thereby significantly reducing network latency, reducing invalid contention and data retransmission, and improving the overall performance and stability of the system.

[0068] Embodiment 3

[0069] In order to solve the accuracy and real-time problems of real-time monitoring of channel usage and congestion of WIFI7 wireless router in MLO (Multi-Link Operation) mode, this embodiment further optimizes the channel monitoring method and adopts a channel state statistical analysis method based on a time sliding window. Specifically, by setting a time window, at the end of each time window, the time interval of data packet arrival on each channel is statistically analyzed to calculate the channel congestion probability (Channel Congestion Probability, CCP). The specific calculation formula is:

[0070] Among them, N i (t) represents the number of packets received at time t, || represents the absolute value symbol, and CCPi reflects the possibility of congestion of the i-th channel in the last T seconds. It can be understood that during the channel competition process, the WIFI7 wireless router of this embodiment collects the packet arrival time interval of each channel in real time through the channel state sensor, and transmits these data to the channel monitoring module. At the end of each time window, the channel monitoring module performs statistical analysis on the packet arrival time interval and calculates the channel congestion probability (CCP). It should be understood that through the statistical analysis method based on the time sliding window, the router can detect the congestion of the channel in time and provide accurate data support for subsequent channel adjustments.

[0071] Furthermore, the calculation formula of the channel congestion probability (CCP) compares the arrival of packets at different time points in the channel and reflects the degree of congestion of the channel by calculating the average of the absolute value difference. This calculation method can not only capture the instantaneous congestion of the channel, but also analyze the long-term trend of the channel, thereby providing a more comprehensive evaluation result. Specifically, when the number of packet arrivals in a channel fluctuates greatly in a short period of time, its congestion probability will increase significantly; conversely, if the number of packet arrivals is relatively stable, the congestion probability is low.

[0072] Furthermore, the channel monitoring module of this embodiment not only calculates the congestion probability of the channel, but also can dynamically adjust the channel's competition strategy according to the congestion probability. When the congestion probability of the channel is high, the monitoring module will promptly notify the channel management and scheduling module to adjust the channel's usage strategy, such as temporarily shielding the channel or lowering the channel's competition priority. In this way, the router can take measures at the early stage of channel congestion to avoid invalid competition attempts, reduce channel conflicts, and improve the efficiency and stability of data transmission.

[0073] The benefit of this embodiment is that by introducing a channel status statistical analysis method based on a time sliding window, the WIFI7 wireless router can monitor the channel congestion more accurately and timely, thereby improving the efficiency of channel competition and management.

[0074] Embodiment 4

[0075] In order to solve the timing and rationality issues of the WIFI7 wireless router marking the channel as a congested channel and temporarily shielding it in MLO (Multi-Link Operation) mode, this embodiment further optimizes the channel shielding mechanism. Specifically, when the congestion probability (CCP) of the channel exceeds the preset threshold and fails to obtain the channel use right after multiple consecutive competition attempts, the channel will be marked as a congested channel and temporarily shielded. The shielding time is calculated as:

[0076] t shield =α·t idle +β, where α and β are preset parameters, t idle Is the length of time that the channel is idle.

[0077] During the channel monitoring process, the WIFI7 wireless router of this embodiment collects the data packet arrival time interval of each channel in real time through the channel status sensor, and transmits these data to the channel monitoring module. The channel monitoring module calculates the congestion probability (CCP) of the channel at the end of each time window. When the congestion probability exceeds the preset threshold, the monitoring module marks the channel as a congested channel. It should be understood that the setting of this threshold needs to be adjusted according to the actual network environment and application requirements to ensure the accuracy of the marking.

[0078] Furthermore, after marking the congested channel, the router will make multiple consecutive contention attempts to verify whether the channel is really in a congested state. Specifically, when the router fails to obtain the right to use the channel in multiple consecutive time windows, it will further confirm the congestion of the channel. If it is confirmed that the channel is indeed in a congested state, the router will temporarily block the channel to avoid invalid contention attempts. It can be understood that this multiple verification mechanism can improve the accuracy of channel shielding and avoid resource waste caused by misjudgment.

[0079] Furthermore, in the calculation formula of the shielding time, the settings of the parameters α and β need to be adjusted according to the characteristics of the actual channel and network requirements. idle The shielding time is the length of time that the channel is idle. By associating the shielding time with the length of the channel idle time, the router can re-enable the channel in time after the channel is restored, thereby improving resource utilization. Specifically, when the channel is idle for a long time, the shielding time will also be increased accordingly to ensure that the channel will rejoin the competition after it has recovered to a stable state.

[0080] The benefit of this embodiment is that by optimizing the channel shielding mechanism, the WIFI7 wireless router can more reasonably manage congested channels, reduce invalid competition attempts, reduce channel conflicts, and improve the efficiency and stability of data transmission. At the same time, by dynamically adjusting the shielding time, it ensures that the channel can rejoin the competition in time after recovery, thereby improving resource utilization.

[0081] Embodiment 5

[0082] In order to solve the efficiency and fairness issues of multi-band channel merging competition in the MLO (Multi-Link Operation) mode of the WIFI7 wireless router, this embodiment further optimizes the channel merging competition mechanism and adopts the Time Division Preemption Algorithm (TDP). The TDP algorithm dynamically adjusts the timing of each channel competition according to the competition priority (Priority Score, PS) of each channel. The specific calculation formula is:

[0083] Among them, w1 and w2 are adjustment weights, OCS i and CCP i They are the comprehensive channel contention score and channel congestion probability, PS i Reflects the contention priority of the i-th channel.

[0084] During the channel competition process, the WIFI7 wireless router of this embodiment monitors the usage and congestion of each channel in real time through the channel status sensor, and transmits these data to the channel management and scheduling module. The channel management and scheduling module calculates a competition priority (PS) for each channel based on the channel comprehensive competition score (OCS) calculated by the adaptive channel quality management model (ACQM) and the channel congestion probability (CCP) calculated by the channel status statistical analysis method based on the time sliding window. It should be understood that through the dynamic adjustment of the competition priority (PS), the router can more reasonably select channels in multi-band channel competition and improve the efficiency and fairness of competition.

[0085] Furthermore, the time-division preemption algorithm (TDP) dynamically adjusts the timing of channel contention according to the contention priority (PS) of the channel. Specifically, when multiple channels are idle at the same time, the router will select the channel with the highest priority for contention according to the contention priority (PS). If the contention fails, the contention priority is recalculated according to the updated channel status, and a new channel with the highest priority is selected for contention. In this way, the router can use idle channels more efficiently, avoid invalid contention attempts, and improve the efficiency and stability of data transmission.

[0086] Furthermore, the channel management and scheduling module in this embodiment is not only responsible for the calculation of competition priority, but also for the allocation and management of channel resources. When the channel merging competition module successfully competes for an available channel, the scheduling module will dynamically adjust the allocation of the channel according to the real-time status of the channel and the needs of the terminal device to ensure the high efficiency and low latency of data transmission. It can be understood that through dynamic management and scheduling, the router can maximize the transmission capacity of each channel, reduce unnecessary retransmission operations, and further improve the overall performance of the system.

[0087] The benefit of this embodiment is that by introducing the time-sharing preemption algorithm (TDP) and dynamically adjusting the channel competition priority, the WIFI7 wireless router can compete for multi-band channels more efficiently and fairly, avoid invalid competition attempts and channel conflicts, and improve the efficiency and stability of data transmission. At the same time, through dynamic management and scheduling, the optimal utilization of channel resources is ensured.

[0088] Embodiment 6

[0089] In order to solve the high delay and packet loss rate problems in the channel management and scheduling process of the WIFI7 wireless router in the MLO (Multi-Link Operation) mode, this embodiment further optimizes the channel management and scheduling mechanism and introduces the Quantitative Feedback Control Mechanism (QFCM). The QFCM mechanism dynamically adjusts the transmission parameters of each channel, such as transmission power and data rate, according to the actual transmission effect, and optimizes the channel parameters by minimizing the weighted sum of transmission delay and packet loss rate. The specific calculation formula is:

[0090] min{w3·D+w4·L}, where w3 and w4 are adjustment weights used to balance transmission delay and packet loss rate.

[0091] After successfully competing for an available channel, the WIFI7 wireless router of this embodiment monitors the effect of data transmission in real time, including transmission delay and packet loss rate, through the channel management and scheduling module. Specifically, the channel management and scheduling module calculates the weighted sum of transmission delay and packet loss rate based on the real-time monitored data, thereby evaluating the transmission effect of the current channel. It should be understood that through this real-time monitoring and evaluation mechanism, the router can promptly discover problems in the channel during transmission and provide accurate data support for subsequent parameter adjustments.

[0092] Furthermore, the channel management and scheduling module dynamically adjusts the transmission parameters of each channel, such as transmission power and data rate, according to the weighted sum of transmission delay and packet loss rate. Specifically, when the transmission delay is high, the scheduling module will reduce the transmission power of the channel to reduce interference and improve transmission quality; when the packet loss rate is high, the scheduling module will reduce the data rate to ensure reliable transmission of data packets. In this way, the router can continuously optimize channel parameters during data transmission to improve transmission effect and network performance.

[0093] The benefit of this embodiment is that by introducing a quantitative feedback control mechanism (QFCM) and dynamically adjusting channel parameters, the WIFI7 wireless router can more effectively manage successfully contested channels, reduce high latency and packet loss rates, and improve the reliability and efficiency of data transmission.

[0094] Embodiment 7

[0095] In order to solve the accuracy and timeliness issues of channel performance evaluation and shielding decision-making in the MLO (Multi-Link Operation) mode of the WIFI7 wireless router, this embodiment further optimizes the dynamic update mechanism and introduces a channel performance prediction model based on machine learning. The model uses historical channel status data as input to predict the performance of each channel in the future, thereby providing support for channel evaluation and shielding decisions. Specifically, the channel performance prediction model uses a deep autoencoder algorithm, which can automatically adjust the parameters of the prediction model without human intervention to ensure the accuracy of the prediction.

[0096] First, when the initial connection is established, the WIFI7 wireless router in this embodiment collects the historical data of each channel in real time through the channel status sensor, including key parameters such as signal strength, interference, packet arrival time interval, and packet loss rate. These data are stored in the built-in storage module of the router as input data for the channel performance prediction model. It should be understood that the accumulation and storage of historical data is the basis of the prediction model. Through the analysis of a large amount of historical data, the model can more accurately capture the performance change law of the channel.

[0097] Furthermore, the Deep Autoencoder algorithm is used to process these historical data. Specifically, the algorithm learns the high-dimensional feature representation of the channel state data through the encoding and decoding process of a multi-layer neural network, and predicts the channel performance in the future based on these features. It should be understood that the Deep Autoencoder can not only process high-dimensional data, but also automatically adjust the parameters of the model to maintain the accuracy of the prediction under different network environments. Through this machine learning method, the router can predict the congestion probability and transmission quality of the future channel, providing a scientific basis for subsequent channel evaluation and shielding decisions.

[0098] Furthermore, the output data of the channel performance prediction model is passed to the channel evaluation and shielding decision module. Based on the prediction results, this module dynamically evaluates the status of the current channel and decides whether a channel needs to be shielded. Specifically, when the model predicts that a channel will be highly congested in the future, the router will mark the channel as a potential congested channel in advance and lower its priority in subsequent competitions. If it fails to compete for many consecutive times, the channel will be temporarily shielded. It can be understood that this prediction-based shielding decision mechanism can avoid channel congestion in advance, reduce invalid competition attempts, and improve the stability of the overall system.

[0099] The benefit of this embodiment is that by introducing a channel performance prediction model based on machine learning, the WIFI7 wireless router can more accurately and timely evaluate and manage the performance of multi-band channels, make channel adjustment and shielding decisions in advance, thereby significantly reducing network congestion and failures and improving system stability and performance. At the same time, the self-adjustment ability of the deep autoencoder algorithm enables the model to maintain the accuracy of predictions in different network environments, further improving the intelligence level of the system.

[0100] Embodiment 8

[0101] In order to solve the efficiency and fairness issues of channel resource scheduling of WIFI7 wireless routers in MLO (Multi-Link Operation) mode, this embodiment further optimizes the scheduling mechanism of channel resources and introduces an intelligent link allocation algorithm. This algorithm dynamically adjusts the data flow allocation between channels in different frequency bands according to the real-time performance of the channel and the connection requirements of the terminal to improve network performance. Specifically, the goal of the intelligent link allocation algorithm is to minimize the average delay D of the entire system. avg , and maximize the data throughput T avg , find the optimal channel configuration solution through iteration. The specific calculation formula is:

[0102]

[0103] Among them, D avg (n) is the average delay of the system at the nth iteration; T avg (n) is the average data throughput of the system at the nth iteration; D ij (n) and L ij (n) are the data transmission delay and packet loss rate of the i-th channel and the j-th terminal at the n-th iteration; f and g are the harmonic functions in the scheduling algorithm, which are used to convert the delay and packet loss rate into applicable scheduling indicators. ij (n) and R ij (n) are the data rate and transmission power of the two, respectively, and the weight ij It is the scheduling weight that is dynamically adjusted according to the connection quality; N is the total number of connection pairs.

[0104] In the channel resource scheduling process, the WIFI7 wireless router of this embodiment collects channel performance data and terminal connection demand data in real time through the channel state sensor and the terminal connection management module. The channel state sensor can collect key parameters such as channel flow, data packet arrival time, delay and packet loss rate with high accuracy. The terminal connection management module is responsible for recording the connection requirements of each terminal, including data transmission volume, service quality requirements, etc. These data are transmitted to the data processing module for real-time analysis and processing.

[0105] Furthermore, the data processing module calculates the connection quality between each channel and the terminal based on the collected channel performance data and terminal connection demand data, and dynamically adjusts the scheduling weight according to the connection quality. It should be understood that the dynamic adjustment of the scheduling weight can ensure the fair distribution of data streams between different channels, avoid excessive burden on a certain channel, and improve the resource utilization of the entire system. Specifically, when the connection quality of a channel is high, its scheduling weight will also increase accordingly, so that it will be given priority in the scheduling process.

[0106] Furthermore, the intelligent link allocation algorithm searches for the optimal channel configuration scheme in an iterative manner. In each iteration, the algorithm adjusts the data flow allocation between the channel and the terminal according to the average delay and average data throughput of the current system. Specifically, the algorithm first calculates the average delay and average data throughput of the current system, and then converts these indicators into applicable scheduling indicators based on the harmonic functions f and g. By continuously adjusting the channel configuration scheme, the algorithm can gradually optimize the overall performance of the system. It can be understood that this iterative optimization method can adapt to changes in the network environment and ensure the optimal utilization of channel resources.

[0107] Embodiment 9

[0108] In order to solve the problem of multi-module collaborative work in the MLO (Multi-Link Operation) mode of the WIFI7 wireless router, this embodiment proposes a WIFI7 wireless router MLO optimization system. The system includes a dynamic channel evaluation module, a channel competition optimization module, a data flow intelligent scheduling module, a predictive maintenance module, a user data management module and a system integration module. These modules work closely together to provide a complete set of optimization solutions and improve the stability and performance of the system.

[0109] Specifically, the dynamic channel assessment module is used to analyze channel performance in real time, dynamically evaluate channel status, and provide optimization support for multi-link operations. Specifically, the module collects key parameters such as channel signal strength, interference, packet arrival time, and packet loss rate in real time through channel status sensors. It should be understood that the dynamic channel assessment module can not only provide real-time channel performance data, but also calculate an integrated competition score (OCS) for each channel through a self-optimization algorithm, thereby more accurately evaluating the current status of the channel.

[0110] Furthermore, the channel contention optimization module reduces contention attempts on congested channels based on an adaptive contention strategy to improve channel utilization efficiency. Specifically, the module dynamically adjusts the contention priority of the channel based on the channel's comprehensive contention score (OCS) and channel congestion probability (CCP) to ensure that the optimal channel is selected in multi-band channel competition. It should be understood that the channel contention optimization module can reduce invalid contention attempts, reduce channel conflicts, and improve the efficiency and stability of data transmission.

[0111] Furthermore, the data stream intelligent scheduling module combines the channel evaluation results and terminal requirements, and uses an intelligent link allocation algorithm to dynamically allocate the data stream of each channel to improve network performance. Specifically, the module dynamically adjusts the allocation of channels according to the real-time status of the channel and the connection requirements of the terminal to ensure efficient data transmission and low latency. It can be understood that the data stream intelligent scheduling module can maximize the transmission capacity of each channel and reduce unnecessary retransmission operations, thereby improving the performance of the entire network.

[0112] Furthermore, the predictive maintenance module predicts the future performance of the channel through machine learning, and makes channel adjustment and shielding decisions in advance to reduce network congestion and failures. Specifically, the module uses a channel performance prediction model based on a deep autoencoder to predict the performance of each channel in the future. It should be understood that the predictive maintenance module can detect potential channel congestion in advance, so as to take measures to avoid network failures and improve the stability and reliability of the system.

[0113] Furthermore, the user data management module is used to manage the transmission process of user data and ensure data security. Specifically, this module is responsible for the encryption, decryption, identity authentication and access control of user data to ensure the security and privacy protection of data during transmission. It should be understood that the addition of the user data management module enables the system to not only efficiently transmit data, but also ensure the security of data, thereby meeting the user's requirements for network security.

[0114] Furthermore, the system integration module supports integration with other network components to improve the scalability of the system. Specifically, the module can be seamlessly integrated with network devices such as switches, firewalls, intrusion detection systems, etc. to achieve unified management and optimization of the network. It can be understood that the introduction of the system integration module enables the WIFI7 wireless router to better adapt to complex network environments and improve the scalability and flexibility of the system.

[0115] The benefit of this embodiment is that, through modular system design, the WIFI7 wireless router can more efficiently evaluate and manage channel performance in multi-link operation, reduce competition attempts on congested channels, optimize the scheduling of data flows, predict channel performance in advance and make maintenance decisions, ensure the secure transmission of user data, and support integration with other network components. This modular design not only improves the stability of the system, but also enhances the scalability of the system, providing users with more comprehensive and efficient network services.

[0116] Embodiment 10

[0117] In order to solve the problem of software system update and maintenance of WIFI7 wireless router in MLO (Multi-Link Operation) mode, this embodiment proposes a readable storage medium storing computer-readable program code. When the program code is read by a computer device, the MLO optimization method in this embodiment can be executed, thereby improving the stability of the system.

[0118] Specifically, the readable storage medium in this embodiment is used to store the program code of the MLO optimization algorithm. Specifically, the storage medium uses a high-speed, large-capacity NVMe SSD, which has the characteristics of high read and write speed and low latency. Through this storage medium, the router can quickly load and run the optimization algorithm to ensure the efficient operation of the system. It should be understood that the use of high-speed storage media can significantly improve the response speed and execution efficiency of the software system.

[0119] Furthermore, the program code includes a series of optimization algorithms and models, including an adaptive channel quality management model (ACQM), a channel status statistical analysis method based on a time sliding window, a channel shielding mechanism, a time-sharing preemption algorithm (TDP), and an intelligent link allocation algorithm. These algorithms and models can run independently or work together through a modular design to provide a complete optimization solution. Specifically, the adaptive channel quality management model is used to evaluate the channel's overall contention score (OCS), the channel status statistical analysis method is used to calculate the channel congestion probability (CCP), the channel shielding mechanism is used to temporarily shield the channel during high congestion, the time-sharing preemption algorithm is used to dynamically adjust the channel's contention priority, and the intelligent link allocation algorithm is used to optimize the scheduling of data streams. It should be understood that the combination of these algorithms and models can comprehensively cover all aspects of channel management and scheduling and improve the overall performance of the system.

[0120] The above embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A WIFI7 wireless router MLO optimization method, characterized in that: The optimization method comprises the following steps: Establish MLO connection and add dynamic channel evaluation link to improve the competition mechanism of multi-band channels; During channel competition in multi-band channels, real-time monitoring of channel usage and congestion; When any of the channels is detected to be congested, the channel is marked as a congested channel and is temporarily shielded to reduce channel conflicts; Perform multi-band channel merging competition. When multiple channels are detected to be idle at the same time, they are marked as idle channels, and multiple idle channels are merged to start channel competition to improve data transmission efficiency; Manage and schedule the successfully contended channels, and use available channels for data transmission and reception to avoid high latency and retransmission operations; Implement a dynamic update mechanism to periodically evaluate and adjust the channel list based on changes in channel status to ensure real-time optimization of network performance; Multi-link operation is used to achieve effective scheduling of channel resources to improve the wireless efficiency and network response speed of the WIFI7 wireless router.

2. The WIFI7 wireless router MLO optimization method as claimed in claim 1, characterized in that: The added dynamic channel evaluation link is based on an adaptive channel quality management model, which integrates the channel quality index and the channel availability, and calculates a comprehensive competition score for each channel through a self-optimization algorithm to reflect the current state of the channel. The calculation formula of the comprehensive competition score is: Where i represents the i-th channel; μ CQI and σ CQI are the mean and standard deviation of all channel quality indexes, respectively, which are used to standardize the channel quality. w is the weight factor for adjusting the influence of channel availability on the overall score. CAR is the channel availability. log(CAR i +1) is to transform the channel availability to increase the discrimination of low availability channels.

3. The WIFI7 wireless router MLO optimization method as claimed in claim 2, characterized in that: The real-time monitoring of channel usage and congestion adopts a channel state statistical analysis method based on a time sliding window. By setting a time window, at the end of each time window, a statistical analysis is performed on the time interval of data packets arriving on each channel to calculate the channel congestion probability. The calculation formula of the channel congestion probability is: Among them, N i (t) represents the number of packets received at time t, || represents the absolute value sign, and CCPi reflects the possibility of congestion on the i-th channel in the last T seconds.

4. The WIFI7 wireless router MLO optimization method as claimed in claim 3, characterized in that: The channel is marked as a congested channel and the channel congestion probability CCP based on a certain channel is temporarily shielded. i If the number of users exceeds the preset threshold and fails to obtain the channel right after multiple consecutive competition attempts, the channel is considered a congested channel and is temporarily blocked for a period of time t. shield The calculation formula is: t shield =α·t idle +β, where α and β are preset parameters, t idle Is the length of time that the channel is idle.

5. The WIFI7 wireless router MLO optimization method as claimed in claim 3, characterized in that: The multi-band channel merging competition adopts a time-sharing preemption algorithm, and dynamically adjusts the timing of each channel competition according to the competition priority of each channel. The calculation formula of the competition priority is: Among them, w1 and w2 are adjustment weights, OCS i and CCP i They are the comprehensive channel contention score and channel congestion probability, PS i Reflects the contention priority of the i-th channel.

6. The WIFI7 wireless router MLO optimization method as claimed in claim 1, characterized in that: The management and scheduling of the successfully contended channels introduces a quantitative feedback control mechanism, and dynamically adjusts the transmission parameters of each channel according to the actual transmission effect. The quantitative feedback control mechanism optimizes the channel parameters by minimizing the weighted sum of the transmission delay D and the packet loss rate L. The specific calculation formula is: min{w3·D+w4·L}, where w3 and w4 are adjustment weights used to balance transmission delay and packet loss rate.

7. The WIFI7 wireless router MLO optimization method as claimed in claim 1, characterized in that: The dynamic update mechanism is implemented based on a channel performance prediction model of machine learning. The channel performance prediction model uses historical channel status data as input to predict the performance of each channel in the future for channel evaluation and shielding decisions. The channel performance prediction model adopts a deep autoencoder algorithm.

8. The WIFI7 wireless router MLO optimization method as claimed in claim 1, characterized in that: The method of utilizing multi-link operation to realize effective scheduling of channel resources uses an intelligent link allocation algorithm to dynamically adjust the data flow allocation between channels of different frequency bands according to the real-time performance of the channel and the connection requirements of the terminal. The goal of the intelligent link allocation algorithm is to minimize the average delay D of the entire system. avg , while maximizing data throughput T avg , the intelligent link allocation algorithm searches for the optimal channel configuration solution in an iterative manner, and the iterative update calculation formula is: Among them, D avg (n) is the average delay of the system at the nth iteration; T avg (n) is the average data throughput of the system at the nth iteration; D ij (n) and L ij (n) are the data transmission delay and packet loss rate of the i-th channel and the j-th terminal at the n-th iteration; f and g are the harmonic functions in the scheduling algorithm, which are used to convert the delay and packet loss rate into applicable scheduling indicators. ij (n) and R ij (n) are the data rate and transmission power of the two, respectively, and the weight ij It is the scheduling weight that is dynamically adjusted according to the connection quality; N is the total number of connection pairs.

9. A WIFI7 wireless router MLO optimization system, characterized in that: The optimization system comprises: Dynamic channel assessment module, used to analyze channel performance in real time, dynamically assess channel status, and provide optimization support for multi-link operations; Channel contention optimization module, based on adaptive contention strategy, reduces contention attempts on congested channels to improve channel utilization efficiency; The data flow intelligent scheduling module combines the channel evaluation results and terminal requirements, and uses an intelligent link allocation algorithm to dynamically allocate the data flow of each channel to improve network performance; Predictive maintenance module, which uses machine learning to predict the future performance of channels and make channel adjustment and shielding decisions in advance to reduce network congestion and failures; User data management module, used to manage the transmission process of user data and ensure data security; System integration module supports integration with other network components to improve the scalability of the system.

10. A computer-readable storage medium storing a computer-readable program code, characterized in that: When the program code is read by a computer device, the WIFI7 wireless router MLO optimization method as described in any one of claims 1 to 8 is executed.

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