Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception

Abstract

The present invention relates to the field of communication technology and discloses a Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception. The method aims to solve the problems of uneven load, delay jitter, and poor reliability in existing processes. The solution mainly includes: terminals scanning APs in the Sub-6GHz and millimeter wave bands in parallel; converting the AP selection problem into a QUBO problem and constructing an objective function based on AP parameters; mapping the QUBO problem to a quantum annealing machine for solution to obtain the optimal AP set that minimizes the objective function; determining whether an IRS is detected, and if so, establishing a dual-link connection; dynamically adjusting the reflection direction of the millimeter wave signal through the IRS to focus the signal energy on the terminal; and during the transmission and reception of service data, calculating a comprehensive score based on the performance indicators of the PHY layer, MAC layer, and network layer, and triggering re-optimization based on the comprehensive score. The present invention achieves high reliability, low latency, ultra-large bandwidth, and load balancing in Wi-Fi 7 multi-link communication, and is suitable for seamless connection and ultra-high reliability service data transmission.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular to a Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception. Background Art

[0002] Wi-Fi 7's Multi-Link Operation (MLO) primarily establishes multiple independent physical links during the association phase, intelligently distributes data streams across these links for parallel transmission / reception, and reassembles them at the receiving end. This mechanism fully utilizes bandwidth resources across multiple frequency bands and significantly improves connection robustness through link redundancy.

[0003] In Wi-Fi 7's multi-link operation, the Sub-6GHz band (2.4 / 5GHz) is the core physical link, which can achieve a logical dual-link with the millimeter wave band (60GHz) through a cross-protocol collaboration solution. In the existing communication process, the terminal first scans the Sub-6GHz band, then the millimeter wave band. After scanning the wireless access points (APs) of both bands, it selects the AP with the strongest signal (highest RSSI) for the Sub-6GHz band and the AP with the largest bandwidth for the millimeter wave band based on a greedy algorithm. Then, an independent logical dual-link connection is established based on the selected APs. During the transmission and reception of service data, if millimeter wave obstruction is detected, the terminal attempts to perform beam switching on the AP side. If the switching fails, it downgrades to single-band Sub-6GHz transmission.

[0004] The existing communication process has at least the following problems: First, the APs in the Sub-6GHz band and the millimeter wave band are independently selected based on a greedy algorithm, ignoring multi-link collaboration and global optimization. In addition, the MAC layer is managed by frequency band segmentation, and the PHY layer indicators cannot be fed back to the network layer for decision-making, resulting in a lack of cross-layer and cross-band collaboration, which easily causes uneven load and delay jitter; second, it relies on the active beamforming of the millimeter wave band AP. The millimeter wave signal is easily blocked, and the millimeter wave band has poor reliability. When the terminal moves or is blocked by human body, the beam realignment requires 5-10 signaling interaction cycles. During this period, the service is completely interrupted, resulting in a decrease in bandwidth and throughput. Summary of the Invention

[0005] The present invention aims to solve the problems of uneven load, delay jitter and poor reliability in the millimeter wave frequency band in the existing Wi-Fi 7 multi-link communication process, and proposes a Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] A Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception, the method comprising:

[0008] Step 1: The terminal enables the multi-link operation function of Wi-Fi 7 and scans APs in the Sub-6 GHz and millimeter wave bands in parallel to obtain the AP list and AP parameters.

[0009] Step 2: Convert the AP selection problem into a QUBO problem and construct the objective function of the QUBO problem based on the AP parameters;

[0010] Step 3: Map the QUBO problem to the quantum annealing machine to obtain the optimal AP set that minimizes the objective function;

[0011] Step 4: Determine whether IRS is detected. If so, establish a dual-link connection based on the optimal AP set, where the terminal's control plane is connected to the Sub-6 GHz AP and the terminal's data plane is connected to the millimeter wave band AP.

[0012] Step 5: Dynamically adjust the reflection direction of the millimeter wave signal through the IRS to focus the signal energy on the terminal;

[0013] Step 6: Send and receive service data. During the service data transmission and reception process, a comprehensive score is calculated based on the performance indicators of the PHY layer, MAC layer, and network layer. If the comprehensive score is less than the score threshold, re-enter step 3.

[0014] Furthermore, the objective function of the QUBO problem is constructed based on the AP parameters, including:

[0015] The AP parameters are normalized, and the quality factor of each AP and the coupling factor between each AP pair are calculated based on the normalized AP parameters. The objective function of the QUBO problem is constructed based on the quality factor and the coupling factor.

[0016] Furthermore, the calculation formula of the quality factor is as follows:

[0017] ;

[0018] in, Indicates the The quality factor of each AP, Indicates the The received signal strength of each AP, Indicates the The signal-to-noise-interference ratio of each AP, Indicates the The load rate of each AP, 、 and Indicates the corresponding weight coefficient.

[0019] Furthermore, the calculation formula of the coupling factor is as follows:

[0020] ;

[0021] in, Indicates the AP and The coupling factor between APs, Indicates the AP and The Euclidean distance between APs, Indicates the AP and The frequency band interference factor between APs, and Indicates the corresponding weight coefficient.

[0022] Furthermore, the objective function is as follows:

[0023] ;

[0024] in, represents the Hamiltonian, Indicates the The quality factor of each AP, Indicates the The decision variables of APs, Indicates the The decision variables of APs, Indicates the AP and The coupling factor between APs, represents the balance coefficient, Indicates the number of APs.

[0025] Furthermore, the QUBO problem is mapped to a quantum annealer for solution, including:

[0026] Step 31: setting initial annealing parameters and convergence conditions, wherein the initial annealing parameters include initial temperature and annealing time, and the convergence condition is that the current energy is less than an energy threshold;

[0027] Step 32: Run quantum annealing calculation according to the initial annealing parameters to obtain a solution for the decision variables that meets the convergence condition and minimizes the Hamiltonian, and take all APs whose decision variables are 1 as the optimal AP set;

[0028] Step 33: Verify link feasibility based on the optimal AP set. If the verification is successful, the solution ends. If the verification fails, re-enter step 32.

[0029] Furthermore, verifying link feasibility according to the optimal AP set includes:

[0030] When the load of the AP combination corresponding to the optimal AP set is less than the load threshold and the line-of-sight path or reflection path for millimeter-wave communication is verified to be available, the link feasibility verification passes.

[0031] Furthermore, the IRS dynamically adjusts the reflection direction of the millimeter wave signal, including:

[0032] Step 51: Acquire the terminal's location information, perform motion prediction on the terminal based on the terminal's motion state, and obtain a predicted terminal location;

[0033] Step 52: Calculate the phase difference that needs to be adjusted for the IRS based on the predicted terminal position, and adjust the phase of the reflector on the IRS based on the phase difference;

[0034] Step 53: Detect the beamforming error. If the beamforming error is greater than the error threshold, perform dynamic position compensation on the terminal and then re-enter step 51.

[0035] Furthermore, the calculation formula of the phase difference is as follows:

[0036] );

[0037] in, Indicates the phase difference that the IRS needs to adjust. represents the wavelength of the millimeter wave signal, represents the straight-line distance between IRS and AP, represents the straight-line distance between the IRS and the terminal, Indicates the straight-line distance between the AP and the terminal. Indicates the moving speed of the terminal. Indicates the motion prediction time.

[0038] Furthermore, a comprehensive score is calculated based on the performance indicators of the PHY layer, MAC layer, and network layer, including:

[0039] Step 61: Start a cross-layer monitoring thread to monitor the signal-to-noise-and-interference ratio and multipath delay of the PHY layer, the contention window size and HARQ retransmission rate of the MAC layer, and the delay jitter and DSCP priority of the network layer in real time.

[0040] Step 62: Determine a PHY layer score based on the signal-to-noise-and-interference ratio and multipath delay, determine a MAC layer score based on the contention window size and HARQ retransmission rate, and determine a network layer score based on the delay jitter and DSCP priority;

[0041] Step 63: Perform weighted summation on the PHY layer score, MAC layer score, and network layer score to obtain a comprehensive score.

[0042] The beneficial effects of the present invention are as follows: the Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception provided by the present invention transforms the AP selection problem into a QUBO (quadratic unconstrained binary optimization) problem and maps it to a Hamiltonian ground state search. Compared with traditional algorithms, the quantum tunneling effect can avoid falling into local optimality and improve the solution speed when there are a large number of APs. It can quickly obtain the global optimal solution of multi-objective trade-offs, thereby avoiding load imbalance and delay jitter. By dynamically controlling the reflection phase through the intelligent transmitting surface (IRS), the channel is actively reconstructed, and the wireless environment is transformed from a random propagation medium to a programmable electromagnetic field, breaking through the obstruction limitations of traditional millimeter waves, improving the reliability of the millimeter wave frequency band, and avoiding bandwidth and throughput degradation. Through the cross-protocol layer perception decision tree to calculate the comprehensive score, the comprehensive score is triggered by re-optimization, thereby forming a closed-loop feedback mechanism, achieving real-time adaptation to the dynamic environment and reducing service interruption time. Compared with the existing single-indicator switching mechanism, the switching accuracy is improved, and cross-layer and cross-band collaboration is achieved, avoiding spectrum waste due to information islands and improving network resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception provided in an embodiment;

[0044] Figure 2 A schematic diagram of the solution process of the QUBO problem provided in the embodiment;

[0045] Figure 3 A schematic diagram of a process for dynamically adjusting the reflection direction of a millimeter wave signal by an IRS provided in an embodiment;

[0046] Figure 4 A schematic diagram of the calculation process of the comprehensive score provided in the embodiment. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment.

[0048] The technical solution of the present invention is applicable to business data transmission application scenarios in high-density mobile scenarios, especially business data transmission requiring seamless connection and ultra-high reliability.

[0049] In the current Wi-Fi 7 multi-link communication process, AP selection must simultaneously optimize throughput, interference, and latency variance, a NP-hard combinatorial optimization. Due to the limitations of classic computing power, which far exceeds the Wi-Fi control plane latency budget, suboptimal strategies such as greedy algorithms are forced to be used. However, this approach still suffers from latency jitter and is prone to falling into local optimality. Furthermore, millimeter-wave wavelengths are short and significantly attenuated by human bodies and obstacles. Current solutions rely on line-of-sight propagation, and beam switching only adjusts the transmit and receive ends, failing to change the spatial characteristics of non-line-of-sight channels. Furthermore, services are completely interrupted during beam switching, resulting in poor millimeter-wave reliability and prone to switching failures and downgrade to single-band Sub-6 GHz transmission, reducing bandwidth and throughput. Furthermore, APs are currently selected independently for each frequency band, and the MAC layer manages each frequency band separately. PHY layer metrics cannot be fed back to the network layer for decision-making, leading to a lack of cross-layer and cross-band coordination. For example, the PHY layer pursues a high SINR, but the MAC layer queue overflows but does not switch, resulting in high packet loss. When the network layer TCP is congested, the link layer is unaware, causing throughput oscillation.

[0050] Based on this, the technical solution of the present invention is proposed, which mainly includes the following steps: Step 1: The terminal starts the multi-link operation function of Wi-Fi 7, scans the APs of the Sub-6GHz and millimeter wave frequency bands in parallel, and obtains the AP list and AP parameters; Step 2: The AP selection problem is converted into a QUBO problem, and the objective function of the QUBO problem is constructed according to the AP parameters; Step 3: The QUBO problem is mapped to a quantum annealing machine for solution to obtain the optimal AP set that minimizes the objective function; Step 4: Determine whether IRS is detected. If so, establish a dual-link connection according to the optimal AP set, wherein the control plane of the terminal is connected to the Sub-6GHz AP, and the data plane of the terminal is connected to the millimeter wave band AP; Step 5: Dynamically adjust the reflection direction of the millimeter wave signal through the IRS to focus the signal energy on the terminal; Step 6: Transmit and receive service data. During the transmission and reception of service data, a comprehensive score is calculated based on the performance indicators of the PHY layer, MAC layer and network layer. If the comprehensive score is less than the score threshold, re-enter step 3.

[0051] Specifically, first, the present invention transforms the AP selection problem into a QUBO problem‌, and uses quantum annealing to map the QUBO problem into a Hamiltonian ground state search, thereby breaking through the NP-hard computing power constraint and being able to quickly obtain the global optimal solution of multi-objective trade-offs, thereby avoiding load imbalance and delay jitter; secondly, the present invention uses IRS to introduce controllable phase offset to reconstruct the channel, which can maintain the high capacity of millimeter waves in the presence of obstruction, ensure data transmission rate, and avoid bandwidth and throughput degradation; in addition, the present invention calculates a comprehensive score through a cross-protocol layer perception decision tree, triggers re-optimization through the comprehensive score, and then forms a closed-loop feedback mechanism, realizing real-time adaptation to dynamic environments. Compared with the existing single-indicator switching mechanism, the switching accuracy is improved, and cross-layer and cross-band collaboration is realized, avoiding the waste of spectrum due to information islands, and improving network resource utilization.

[0052] The technical solution of this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment. Obviously, the described embodiment is only a part of the embodiments of the present invention, rather than all the embodiments.

[0053] Figure 1 A flow chart of a Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception is shown. Figure 1 , the method comprises the following steps:

[0054] Step 1: The terminal enables the multi-link operation function of Wi-Fi 7 and scans APs in the Sub-6 GHz and millimeter wave bands in parallel to obtain the AP list and AP parameters.

[0055] It can be understood that Wi-Fi 7's multi-link operation allows terminals to monitor the Sub-6GHz band and the millimeter wave band simultaneously. Sub-6GHz has wide coverage and strong penetration, while millimeter wave has high bandwidth and low latency. In actual applications, the terminal sends a parallel scan command, configures the scan parameters for the two frequency bands respectively, and uses the hardware parallel RF chain to achieve synchronous scanning. It detects APs (access points) in the surrounding environment that support Wi-Fi 7 and belong to the same EasyMesh network. The scan obtains the AP list and AP parameters, including the received signal strength, signal-to-noise and interference ratio, and load rate of each AP.

[0056] Step 2: Convert the AP selection problem into a QUBO problem and construct the objective function of the QUBO problem based on the AP parameters.

[0057] It can be understood that AP selection is transformed into a combinatorial optimization problem, the goal of which is to minimize interference, maximize throughput, balance load, and satisfy multi-link constraints.

[0058] In this embodiment, the objective function of the QUBO problem is constructed based on the AP parameters, including: standardizing the AP parameters, calculating the quality factor of each AP and the coupling factor between each AP pair based on the standardized AP parameters, and constructing the objective function of the QUBO problem based on the quality factor and the coupling factor.

[0059] In this embodiment, the calculation formula of the quality factor is as follows:

[0060] ;

[0061] in, Indicates the The quality factor of each AP, Indicates the The received signal strength after normalization of each AP is: Indicates the The signal-to-noise-interference ratio after normalization of APs, Indicates the The load rate after normalization of APs, 、 and Indicates the corresponding weight coefficient, which is used to balance the influence of multiple AP parameters.

[0062] The calculation formula of the coupling factor is as follows:

[0063] ;

[0064] in, Indicates the AP and The coupling factor between APs, Indicates the AP and The Euclidean distance between APs. The closer the distance, the larger the value, indicating greater interference. Indicates the AP and The frequency band interference factor between APs depends on the frequency band relationship between the two APs, such as the same frequency band and the same channel, the same frequency band and adjacent channels, orthogonal frequency bands, and cooperative frequency bands. and Represents the corresponding weight coefficient, which is used to balance the influence of distance factor and frequency band interference factor. In practical applications, it can be set through experiments or network requirements.

[0065] In this embodiment, AP selection is transformed into a combinatorial optimization QUBO problem. Quantum annealing is used to map the QUBO problem into a Hamiltonian ground state search. The goal is to minimize interference, maximize throughput, balance load, and satisfy multi-link constraints. The objective function of the QUBO problem is as follows:

[0066] ;

[0067] in, represents the Hamiltonian, Indicates the The quality factor of each AP, Indicates the The decision variables of APs, Indicates the The decision variable of an AP is used to represent the AP selection state. When the decision variable is 1, it means that the corresponding AP is selected. When the decision variable is 0, it means that the corresponding AP is not selected. Indicates the AP and The coupling factor between APs, Indicates the balance coefficient, which is set through experiments or network requirements. Indicates the number of APs.

[0068] The above objective function converts the complex network optimization problem into a quantum computable QUBO form by quantifying the individual quality of APs and mutual interference.

[0069] Step 3: Map the QUBO problem to the quantum annealing machine to obtain the optimal AP set that minimizes the objective function.

[0070] See also Figure 2 In this embodiment, step 3 specifically includes the following steps:

[0071] Step 31: setting initial annealing parameters and convergence conditions, wherein the initial annealing parameters include initial temperature and annealing time, and the convergence condition is that the current energy is less than an energy threshold.

[0072] In this embodiment, the initial temperature T0 = 100 mK, the annealing time τ = 50 μs, and the energy threshold ΔE = 0.05 eV.

[0073] Step 32: Run quantum annealing calculation according to the initial annealing parameters to obtain a solution of the decision variables that meets the convergence condition and minimizes the Hamiltonian, and take all APs whose decision variables are 1 as the optimal AP set.

[0074] In practical applications, after mapping the QUBO matrix to quantum bits, an annealing process is performed. Quantum annealing uses the quantum tunneling effect to escape the local optimal solution. It can efficiently solve NP-hard combinatorial optimization problems within 20μs and obtain the optimal AP set that meets the convergence conditions and minimizes the Hamiltonian. Compared with traditional heuristic algorithms, quantum annealing converges faster in high-dimensional combinatorial space.

[0075] Step 33: Verify link feasibility based on the optimal AP set. If the verification is successful, the solution ends. If the verification fails, re-enter step 32.

[0076] In this embodiment, link feasibility is verified based on the optimal AP set, including: when the load of the AP combination corresponding to the optimal AP set is less than a load threshold, and the line-of-sight path or reflection path for millimeter-wave communication is verified to be available, the link feasibility verification is passed. The load threshold can be set through experiments or network requirements. For example, the link feasibility verification is passed when the load of the AP combination is less than 80% and the line-of-sight path or reflection path for millimeter-wave communication is available.

[0077] Step 4: Determine whether IRS is detected. If so, establish a dual-link connection based on the optimal AP set, where the control plane of the terminal is connected to the Sub-6GHz AP and the data plane of the terminal is connected to the millimeter wave band AP.

[0078] The intelligent reflecting surface (IRS) is made of programmable metamaterials and can reconstruct channels by adjusting unit phase offsets. This embodiment establishes a dual-link connection after detecting the IRS. The control plane uses the sub-6 GHz frequency band, that is, signaling is transmitted through sub-6 GHz APs to achieve high reliability, and the data plane uses the millimeter wave frequency band, that is, data is transmitted through millimeter wave APs to achieve high throughput.

[0079] Step 5: Dynamically adjust the reflection direction of the millimeter wave signal through the IRS to focus the signal energy on the terminal.

[0080] It can be understood that IRS can dynamically reconstruct the millimeter wave propagation path to overcome the blocking problem and high path loss of millimeter wave propagation, thereby solving the obstruction problem. Figure 3 , specifically including the following steps:

[0081] Step 51: Acquire the location information of the terminal, perform motion prediction on the terminal in combination with the motion state of the terminal, and obtain the predicted terminal location.

[0082] In practical applications, the current position of the terminal can be obtained through the control plane (Sub-6GHz link) (through AoA, ToF and other methods), and the terminal's moving speed can be estimated through the inertial measurement unit data or historical position information reported by the terminal. , and then according to the moving speed of the terminal at the current time point , predicting the future time Displacement of the rear terminal.

[0083] Step 52: Calculate the phase difference that needs to be adjusted for the IRS based on the predicted terminal position, and adjust the phase of the reflection unit on the IRS based on the phase difference.

[0084] Ideally, the length of the millimeter-wave signal's path from the AP to the IRS and then to the terminal (UB) is equal to the length of the signal's direct path from the AP to the terminal. This allows the signals to coherently superimpose at the terminal, creating a wave-like superposition and stable interference. However, in real-world applications, the terminal moves, and the signal needs to be focused to a predicted location. Therefore, the phase difference between the ideal reflection path and the direct path—the phase difference that the IRS needs to adjust—must be calculated using the following formula:

[0085] );

[0086] in, Indicates the phase difference that the IRS needs to adjust. represents the wavelength of the millimeter wave signal, represents the straight-line distance between IRS and AP, represents the straight-line distance between the IRS and the terminal, Indicates the straight-line distance between the AP and the terminal. Indicates the moving speed of the terminal. Indicates the motion prediction time.

[0087] Step 53: Detect the beamforming error. If the beamforming error is greater than the error threshold, perform dynamic position compensation on the terminal and then re-enter step 51.

[0088] As can be understood, since the terminal is constantly moving, the above steps need to be performed periodically to dynamically adjust the phase. In actual applications, after the IRS is configured, the AP transmits a pilot signal, and the terminal measures and feeds back the received signal strength or signal-to-noise ratio, calculating the beamforming error. If the beamforming error exceeds the error threshold, the dynamic compensation mechanism is triggered, the terminal is repositioned, and the process returns to step 51 for recalculation.

[0089] The above steps adjust the phase of the reflecting unit on the IRS based on the calculated phase difference, so that the wavefront of the reflected signal is superimposed in phase at the predicted terminal position, enabling the IRS to dynamically track the terminal position and continuously focus the millimeter wave signal energy on the mobile terminal, thereby solving the problems of obstruction and path loss, significantly improving the reliability of millimeter wave communications, ensuring data transmission rate, and avoiding the decline of bandwidth and throughput.

[0090] Step 6: Send and receive service data. During the service data transmission and reception process, a comprehensive score is calculated based on the performance indicators of the PHY layer, MAC layer, and network layer. If the comprehensive score is less than the score threshold, re-enter step 3.

[0091] See also Figure 4 In this embodiment, the comprehensive score is calculated based on the performance indicators of the PHY layer, MAC layer, and network layer, including the following steps:

[0092] Step 61: Start a cross-layer monitoring thread to detect in real time the signal-to-noise-and-interference ratio and multipath delay of the PHY layer, the contention window size and HARQ retransmission rate of the MAC layer, and the delay jitter and DSCP priority of the network layer.

[0093] Step 62: Determine a PHY layer score based on the signal-to-noise-and-interference ratio and the multipath delay, determine a MAC layer score based on the contention window size and the HARQ retransmission rate, and determine a network layer score based on the delay jitter and the DSCP priority.

[0094] Step 63: Perform weighted summation on the PHY layer score, MAC layer score, and network layer score to obtain a comprehensive score.

[0095] In practical applications, the signal-to-noise-interference ratio (SNR) and multipath delay of the PHY layer can be normalized. For example, the normalized curve corresponding to the SNR is linear growth, and the normalized curve corresponding to the multipath delay is exponential decay. The normalized SNR and multipath delay are then weighted and summed to obtain the PHY layer score. The contention window size score and HARQ retransmission rate score of the MAC layer can be determined separately according to the corresponding scoring algorithm, and the contention window size score and HARQ retransmission rate score are weighted and summed to obtain the MAC layer score. Similarly, the delay jitter score and DSCP priority score of the network layer can be determined separately according to the corresponding scoring algorithm, and the delay jitter score and DSCP priority score are weighted and summed to obtain the network layer score. Finally, the PHY layer score, MAC layer score, and network layer score are weighted and summed to obtain the comprehensive score. The weights can be set experimentally or based on network requirements. For example, the weight corresponding to the PHY layer score is set to 0.35, the weight corresponding to the MAC layer score is set to 0.25, and the weight corresponding to the network layer score is set to 0.4.

[0096] After calculating the comprehensive score, if the comprehensive score is less than the score threshold, step 3 is re-entered, and re-optimization is triggered by the comprehensive score, thereby forming a closed-loop feedback mechanism, achieving real-time adaptation to the dynamic environment. Compared with the existing single-indicator switching mechanism, it improves switching accuracy and realizes cross-layer and cross-band collaboration, avoiding spectrum waste due to information islands and improving network resource utilization.

Claims

1. A Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception, characterized by: The method comprises: Step 1: The terminal enables the multi-link operation function of Wi-Fi 7 and scans APs in the Sub-6 GHz and millimeter wave bands in parallel to obtain the AP list and AP parameters. Step 2: Convert the AP selection problem into a QUBO problem and construct the objective function of the QUBO problem based on the AP parameters; Step 3: Map the QUBO problem to the quantum annealing machine to obtain the optimal AP set that minimizes the objective function; Step 4: Determine whether IRS is detected. If so, establish a dual-link connection based on the optimal AP set, where the terminal's control plane is connected to the Sub-6 GHz AP and the terminal's data plane is connected to the millimeter wave band AP. Step 5: Dynamically adjust the reflection direction of the millimeter wave signal through the IRS to focus the signal energy on the terminal; Step 6: Send and receive service data. During the service data transmission and reception process, a comprehensive score is calculated based on the performance indicators of the PHY layer, MAC layer, and network layer. If the comprehensive score is less than the score threshold, the process re-enters step 3. The objective function of the QUBO problem is constructed based on the AP parameters, including: Normalize the AP parameters, calculate the quality factor of each AP and the coupling factor between each AP pair based on the normalized AP parameters, and construct the objective function of the QUBO problem based on the quality factor and coupling factor; The quality factor is calculated as follows: ； in, Indicates the The quality factor of each AP, Indicates the The received signal strength of each AP, Indicates the The signal-to-noise-interference ratio of each AP, Indicates the The load rate of each AP, 、 and represents the corresponding weight coefficient; The coupling factor is calculated as follows: ； in, Indicates the AP and The coupling factor between APs, Indicates the AP and The Euclidean distance between APs, Indicates the AP and The frequency band interference factor between APs, and represents the corresponding weight coefficient; The objective function is as follows: ； in, represents the Hamiltonian, Indicates the The quality factor of each AP, Indicates the The decision variables of APs, Indicates the The decision variables of APs, Indicates the AP and The coupling factor between APs, represents the balance coefficient, Indicates the number of APs.

2. The Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception according to claim 1 is characterized in that: Mapping the QUBO problem to a quantum annealer for solution includes: Step 31: setting initial annealing parameters and convergence conditions, wherein the initial annealing parameters include initial temperature and annealing time, and the convergence condition is that the current energy is less than an energy threshold; Step 32: Run quantum annealing calculation according to the initial annealing parameters to obtain a solution for the decision variables that meets the convergence condition and minimizes the Hamiltonian, and take all APs whose decision variables are 1 as the optimal AP set; Step 33: Verify link feasibility based on the optimal AP set. If the verification is successful, the solution ends. If the verification fails, re-enter step 32.

3. The Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception according to claim 2 is characterized in that: Verifying link feasibility according to the optimal AP set includes: When the load of the AP combination corresponding to the optimal AP set is less than the load threshold and the line-of-sight path or reflection path for millimeter-wave communication is verified to be available, the link feasibility verification passes.

4. The Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception according to claim 1 is characterized in that: The IRS dynamically adjusts the reflection direction of the millimeter wave signal, including: Step 51: Acquire the terminal's location information, perform motion prediction on the terminal based on the terminal's motion state, and obtain a predicted terminal location; Step 52: Calculate the phase difference that needs to be adjusted for the IRS based on the predicted terminal position, and adjust the phase of the reflector on the IRS based on the phase difference; Step 53: Detect the beamforming error. If the beamforming error is greater than the error threshold, perform dynamic position compensation on the terminal and then re-enter step 51.

5. The Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception according to claim 4 is characterized in that: The calculation formula of the phase difference is as follows: )； in, Indicates the phase difference that the IRS needs to adjust. represents the wavelength of the millimeter wave signal, represents the straight-line distance between IRS and AP, represents the straight-line distance between the IRS and the terminal, Indicates the straight-line distance between the AP and the terminal. Indicates the moving speed of the terminal. Indicates the motion prediction time.

6. The Wi-Fi 7 collaborative networking method based on quantum optimization and cross-layer perception according to claim 1 is characterized in that: A comprehensive score is calculated based on the performance indicators of the PHY layer, MAC layer, and network layer, including: Step 61: Start a cross-layer monitoring thread to monitor the signal-to-noise-and-interference ratio and multipath delay of the PHY layer, the contention window size and HARQ retransmission rate of the MAC layer, and the delay jitter and DSCP priority of the network layer in real time. Step 62: Determine a PHY layer score based on the signal-to-noise-and-interference ratio and multipath delay, determine a MAC layer score based on the contention window size and HARQ retransmission rate, and determine a network layer score based on the delay jitter and DSCP priority; Step 63: Perform weighted summation on the PHY layer score, MAC layer score, and network layer score to obtain a comprehensive score.

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