A dynamic optimization method for OBSS PD threshold in FTTR networks based on Bayesian estimation.

Abstract

This invention discloses a dynamic optimization method for the OBSS PD threshold in an FTTR network based on Bayesian estimation. This method is applied to an FTTR network comprising one master FTTR unit (MFU) and multiple slave FTTR units (SFUs). Through the synergy of SFU local distributed channel awareness and Bayesian transmission intent inference, and MFU centralized global optimization, closed-loop dynamic optimization of the OBSS PD threshold is achieved. The method includes the following steps: Step S1, system configuration and initialization; Step S2, SFU distributed channel awareness and Bayesian inference of STA transmission intent; Step S3, MFU centralized joint optimization of OBSS PD threshold and transmit power; Step S4, threshold issuance and closed-loop dynamic update. This invention relies on the fiber-optic distributed architecture of the FTTR network ("one MFU + multiple SFUs"), and achieves closed-loop dynamic optimization of the OBSS PD threshold through the synergy of SFU local distributed channel awareness and Bayesian transmission intent inference, and MFU centralized global optimization.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and indoor network optimization technology, specifically to a dynamic optimization method for OBSS PD threshold in FTTR networks based on Bayesian estimation. Background Technology

[0002] With the increasing demands for high bandwidth, low latency, and seamless whole-house coverage in home broadband, hotels, apartments, and offices, FTTR networking is gradually becoming an important solution for indoor network construction. An FTTR network typically includes a main FTTR unit (MFU) and multiple sub-FTTR units (SFUs). The MFU is connected to multiple SFUs via fiber optic links. Each SFU is deployed in different rooms or areas and provides local Wi-Fi access. This architecture enables indoor fiber optic extension to rooms, balancing high-speed backhaul and flexible coverage.

[0003] In practical applications, due to the simultaneous operation of multiple SFUs and their adjacent or overlapping coverage areas, complex wireless environments with multiple BSSs (Basic Service Sets) can easily form. In this scenario, mutual interference between different BSSs can significantly affect network performance. To improve spectrum utilization efficiency, Wi-Fi systems typically introduce the OBSS PD mechanism, which sets an overlapping basic service set preamble detection threshold to determine the device's sensitivity to signals from other BSSs, thereby affecting whether nodes back off and whether spatial multiplexing transmission is allowed.

[0004] In existing technologies, OBSS PD thresholds are typically configured with a fixed value or adjusted coarsely based on simple rules. However, the service load, terminal distribution, room structure, and interference relationships in FTTR networks are all dynamically changing, making fixed thresholds unsuitable for different scenarios. When the threshold is set too conservatively, the system may miss opportunities for spatial reuse, resulting in low spectrum utilization; when the threshold is set too aggressively, it may cause more collisions and interference, leading to decreased throughput and increased latency. Furthermore, while some existing dynamic optimization schemes attempt to introduce machine learning or other adaptive algorithms, they generally suffer from high implementation complexity, reliance on large training samples, high online inference costs, and insufficient real-time performance, making them difficult to deploy directly in real-world systems like FTTR that require rapid decision-making and stable operation. Especially in multi-SFU collaborative scenarios, without an accurate estimate of the terminal's transmission intent, adjusting the threshold solely based on instantaneous signal strength or simple statistics often fails to achieve the desired results.

[0005] Therefore, there is an urgent need for a dynamic optimization method for OBSS PD threshold in FTTR networks. This method should accurately sense terminal transmission requirements without significantly increasing system complexity, fully utilize the structural advantages of SFU distributed sensing and MFU centralized control in FTTR networks, and achieve dynamic optimization of OBSS PD threshold, thereby improving spatial reuse capability and overall network performance. Summary of the Invention

[0006] The main objective of this invention is to provide a dynamic optimization method for the OBSS PD threshold of an FTTR network based on Bayesian estimation, thereby achieving dynamic optimization of the OBSS PD threshold.

[0007] The present invention achieves the above objectives through the following technical solutions: In a first aspect, the present invention provides a method for dynamic optimization of the OBSS PD threshold in an FTTR network based on Bayesian estimation. This method is applied to an FTTR network comprising one master FTTR unit (MFU) and multiple slave FTTR units (SFU). Through the synergy of SFU local distributed channel awareness and Bayesian transmission intent inference, and MFU centralized global optimization, closed-loop dynamic optimization of the OBSS PD threshold is achieved. The method includes the following steps: Step S1, System Configuration and Initialization: The MFU configures the initial transmit power and initial OBSS PD threshold for each SFU and associated STA, initializes the Bayesian estimator and system parameters, and starts the closed-loop optimization process; Step S2, SFU Distributed Channel Awareness and STA Transmission Intent Bayesian Inference: Each SFU monitors the wireless channel status locally in each time slot. Based on the channel observation results, it uses the Bayesian estimation algorithm to calculate the queue status belief value and transmission intent belief value of each associated STA. The transmission intent belief value is compared with the preset decision threshold to predict the STA that will initiate transmission in the next time slot, generate the estimated transmission node set and report it to the MFU. Step S3: MFU centralized OBSS PD threshold and transmit power joint optimization: MFU summarizes the estimated transmission node set of the entire network, with the goal of maximizing the spectrum efficiency of the entire network, and with the feasible range of transmit power and OBSS PD threshold of each STA as constraints, establishes and solves the optimization problem to obtain the optimal transmit power of each STA. Then, based on the mapping relationship between transmit power and OBSS PD threshold, the dynamic OBSS PD threshold corresponding to each SFU is calculated. Step S4, Threshold Distribution and Closed-Loop Dynamic Update: The MFU distributes the optimized OBSS PD threshold to each corresponding SFU. The SFU applies the new threshold to perform channel evaluation and triggers the next round of optimization process when the current optimization cycle ends or when network performance fluctuations exceed preset conditions are detected.

[0008] Optionally, in step S2, the SFU calculates the queue state belief value of each STA using a Bayesian estimation algorithm, specifically including: Each SFU continuously monitors the radio channel status within its BSS at the time slot level. The physical layer sensing results are divided into three categories of observation events: ① If the channel is observed to be idle, then ② If successfully received from the associated STA j The data packet, ③ If the channel is observed to be busy, then ; SFU based on current observations Calculate the likelihood probability of the queue state corresponding to the observation for each associated STA k. ;in, For time slots t At that time, with SFU m Associated STA k The queue state belief value represents the probability that the current queue state is not empty, and is also expressed as... ; For STA k The actual queue state is also represented as ,in This is an indicator function that determines whether a specified condition is true or false. If the condition is true, the function outputs the value 1; if the condition is false, the function outputs the value 0. for t The actual number of packets in the time queue; SFU uses a Bayesian update formula, combining the calculated likelihood probability with historical queue state belief values, to update the queue state belief values ​​of each STA in the current time slot in real time: ; In the formula, and The queue state of STA k corresponds to the current observation. The likelihood probability.

[0009] Optionally, the likelihood probability is calculated as follows: The formula for calculating likelihood probability depends on the type of observation. , and The differences are different; the STA's own average transmission probability must be considered. Transmission intent belief values ​​of other STAs SFU m STA in k The likelihood probabilities are as follows: 1) When hour: ; 2) When hour: when hour: ; when hour: ; 3) When hour: ; in, Indicate Remove from set element, For SFU m The set of neighboring STA nodes; Indicates the first m A set of STAs associated with each SFU.

[0010] Optionally, the transmission intention belief value of the STA in the current time slot mentioned in step S2 is calculated by the following formula: ; in, Indicates the average transmission probability; Transmit the transmission intent belief value of each STA With preset decision threshold In comparison, if the value exceeds the threshold, it is predicted that the STA will initiate transmission in the next time slot; otherwise, it is predicted that no transmission will occur. ; in, This represents the estimated number of nodes that will transmit; summing these together yields the estimated set of transmitting nodes. .

[0011] Optionally, step S2 also includes an update step for the average transmission probability. The SFU uses an exponential smoothing method, combining the statistical results of the entire network channel observations with the current transmission intention belief value. Update the average transmission probability of the STA. Used for Bayesian inference in the next time slot: ; in, It is a smoothing factor adjusted in a single step. These are step size factors adjusted in a single instance, and are all preset fixed parameters. This indicates the weight assigned to each frequency based on actual channel observations in the current time slot, indicating whether the channel is idle, successfully transmitted, or busy.

[0012] Optionally, the establishment of the optimization problem in step S3 includes: MFU maximizes the prediction of the transmission node set. The overall spectral efficiency of all STAs is used as the objective function, and the feasible range of transmit power and OBSS / PD thresholds for each STA is used as constraints to establish an optimization problem; the optimization objective is to maximize the long-term average spectral efficiency. , is represented as: ; in, BSS m Inside STA k The number of successfully received messages changes over time. M For the number of SFUs, For SFU m STA in k The signal-to-interference-plus-noise ratio is expressed as: ; in, , indicating STA i In the time slot t Is it currently sending to its associated SFU? n Transmitting data STA i Data transmission is performed, and vice versa; For SFU m Associated STA k In t Gaussian white noise power in time slots; For STA k to SFU m Channel gain; For SFU m Associated STA k The transmission power.

[0013] Optionally, solving the optimization problem in step S3 includes: The SCA algorithm with two-layer iteration is used to solve the problem: the outer iteration uses a first-order Taylor expansion to linearly approximate the disturbance term in the objective function at the current MAC power iteration point, thus transforming the original non-convex optimization problem into a convex approximation subproblem; Optimizing OBSS PD threshold using the SCA algorithm and transmission power : Set constraints, including threshold ranges. and power range ;according to Optimize the target Rewritten as: ; The outer iteration transforms the original non-convex problem into a convex approximate subproblem using the SCA strategy, with the capacity expression... Rewritten as: ; in, Indicates interference power; In the outer iteration, the objective function is... At the current power point By performing a first-order Taylor expansion, the problem is approximated as a linear function, thus transforming the original problem into a convex optimization subproblem. At the current iteration point The expression for the first-order Taylor expansion of the disturbance is: ; in, yes In the j The first-order Taylor approximation of the next iteration, then the outermost... j The convex approximation subproblem of the next iteration is expressed as: ; in, Indicates the first j Next iteration node The channel capacity is a first-order Taylor approximation; this problem is a convex optimization problem, and the projection gradient ascent method is used in the inner iteration to update the power value to ensure that the constraint conditions are met until the algorithm converges; For the convex approximation subproblem generated by the outer iteration, the inner iteration uses the projection gradient ascent method to update the transmit power values ​​of each STA. At the same time, the projection operation ensures that the power values ​​meet the preset upper and lower limits. The inner iteration continues until the algorithm converges or reaches the maximum number of iterations, and the optimal transmit power of each STA after global optimization is output. The projected gradient ascent method is stated as follows: for Each node All right The power derivative of a node is calculated as follows: ; but The gradient descent update of transmit power can be expressed as: ; in, This indicates the power update step size. For power projection operations, it is expressed as the updated power value strictly satisfying... Constraints; when the number of iterations reaches the upper limit.E When, or when the difference between the power of the current iteration result and the power of the previous iteration result does not exceed the threshold. The inner loop terminates, resulting in optimized power.

[0014] Optionally, the mapping relationship between the transmit power and the OBSS PD threshold in step S3 is as follows: Based on the optimized STA transmit power, the dynamic OBSS PD threshold for each SFU is calculated according to the following mapping formula. At the same time, ensure that the threshold constraints are within the range allowed by the standard: ; in, These represent the minimum and maximum values ​​of the OBSS / PD threshold, respectively. For reference transmission power, This represents the actual transmit power of the STA.

[0015] In a second aspect, the present invention provides a computer device / apparatus / system, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0016] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. This invention achieves highly accurate inference of STA transmission intentions, providing a reliable real-time basis for threshold optimization. Based on a Bayesian estimation inference mechanism, this invention fully aligns with the statistical characteristics of Wi-Fi random channel contention. Simulation results show that the node transmission state estimation accuracy remains stable above 70%, reaching approximately 88% when a single BSS contains 5 STAs. Furthermore, the estimation accuracy is unaffected by fluctuations in packet arrival rate, demonstrating strong scenario robustness. This fundamentally solves the problem of existing solutions lacking accurate state basis for threshold adjustment.

[0018] 2. Significantly improves the spectral efficiency and spatial multiplexing capability of FTTR networks. This invention achieves precise interference control and reasonable scheduling of parallel transmissions through accurate transmission intent inference and joint optimization of transmit power and OBSS PD threshold. Simulation results show that, regardless of the increase in the number of STAs or the improvement in packet arrival rate, the overall network spectral efficiency of this invention is significantly better than the traditional fixed OBSS PD threshold scheme, greatly improving network throughput and access capability in dense Wi-Fi deployment scenarios.

[0019] 3. Significantly reduces algorithm computational overhead, meeting real-time deployment requirements. This invention eliminates the need for the large amounts of offline training data and high-computing-power online iteration overhead required by existing machine learning / reinforcement learning schemes. Bayesian inference can be performed in real time by SFU on a local low-computing-power chip. The SCA algorithm has fast convergence speed and low computational complexity, and can complete the optimization solution within a standard Wi-Fi time slot period, fully adapting to the real-time deployment requirements of FTTR networks.

[0020] 4. It fully leverages the architectural advantages of FTTR networks, making it highly practical for engineering implementation. This invention is deeply adapted to the native architecture of FTTR, which combines "master-slave distributed + low-latency fiber optic transmission," achieving synergy between distributed sensing and centralized optimization. Furthermore, the overall solution is fully compatible with mainstream Wi-Fi standards such as IEEE 802.11ax / 11be, requiring only a firmware upgrade to deploy on existing FTTR devices without significant modifications to the hardware architecture. This demonstrates its strong practical value and promising prospects for widespread adoption. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall process of the dynamic optimization method for OBSS PD threshold of FTTR network based on Bayesian estimation of the present invention. Figure 2 This is a schematic diagram of a typical FTTR network deployment scenario of the present invention; Figure 3 This is a flowchart illustrating the execution of the core algorithm of this invention; Figure 4 The figure shows the simulation results of how the transmission node estimation accuracy of the present invention changes with the number of STAs and the data arrival rate. Figure 5 shows the simulation results comparing the spectral efficiency of the proposed scheme and the fixed threshold scheme; where, Figure 5A and Figure 5B The system spectral efficiency is shown as a function of the number of STAs per SFU. Km and packet arrival rate λ The diagram shows the changes under three different conditions. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments provided by this invention without inventive effort are within the scope of protection of this invention.

[0023] Obviously, the accompanying drawings described below are merely some examples or embodiments of the present invention. Those skilled in the art can apply the present invention to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this invention, modifications to design, manufacturing, or production based on the technical content disclosed in this invention are merely conventional technical means and should not be construed as insufficient disclosure of the present invention.

[0024] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention may be combined with other embodiments without conflict.

[0025] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "a," "an," "an," "the," and similar words used in this invention do not indicate quantity limitation and may indicate singular or plural. The terms "comprising," "including," "having," and any variations thereof used in this invention are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms "connected," "linked," "coupled," and similar words used in this invention are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "A plurality" used in this invention refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships may exist; for example, "A and / or B" can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects have an "or" relationship. The terms "first," "second," and "third" used in this invention are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0026] This invention provides a wireless parameter adaptive optimization method for Fiber To The Room (FTTR) networks. Specifically, it is a dynamic optimization method for the OBSS PD (Overlapping Basic Service Set Preamble Detect) threshold of FTTR networks based on Bayesian estimation, belonging to Wi-Fi spatial multiplexing control, anti-interference optimization, and multi-access point collaborative scheduling technology.

[0027] This method aims to solve the following technical problems: 1. This solution addresses the problem that existing solutions cannot accurately characterize the contention characteristics of Wi-Fi random channels and cannot reliably infer the transmission intentions of stations (STAs), resulting in a lack of accurate state basis for threshold optimization. It enables real-time inference of STA transmission intentions with low overhead and high accuracy.

[0028] 2. To address the issues of high computational complexity, reliance on large amounts of training data, and insufficient real-time performance of existing dynamic optimization schemes, this paper proposes an OBSS PD threshold optimization scheme with low computational overhead, fast convergence speed, and real-time deployment capability.

[0029] 3. To address the issue that existing solutions fail to adapt to the advantages of the native FTTR architecture, a mechanism for deep collaboration between distributed sensing and centralized optimization is constructed to achieve joint optimization of OBSS PD threshold and transmit power, thereby maximizing the overall spectrum efficiency and spatial reuse capability of the FTTR network.

[0030] 4. It solves the problem that fixed threshold solutions cannot adapt to dynamic network environments and have poor robustness, and achieves closed-loop dynamic threshold adjustment that can adaptively adapt to different network scales and service loads, while being compatible with existing mainstream Wi-Fi standards, reducing the difficulty of engineering implementation.

[0031] like Figure 1 As shown, the Bayesian estimation-based dynamic optimization method for OBSS PD threshold in FTTR networks of this invention relies on the fiber-optic distributed architecture of "1 MFU + multiple SFUs" in FTTR networks. Through the synergy of SFU local distributed channel awareness and Bayesian transmission intent inference, and MFU centralized global optimization, it achieves closed-loop dynamic optimization of the OBSS PD threshold. The complete technical solution includes four core steps, forming a periodic closed-loop optimization process, as follows: Step S1: System configuration and initialization.

[0032] This step is fundamental to the method's execution and is centrally executed by the MFU. The specific process is as follows: 1. Network configuration: such as Figure 2 The diagram shows the construction of an FTTR network, with the core architecture including one MFU and... MEach SFU and MFU is connected to all SFUs via fiber optic links. Each SFU is associated with a set of STAs to form the basic service set for the corresponding room.

[0033] 2. Basic Parameter Preset: The MFU automatically scans all access SFUs via the fiber optic link layer discovery protocol and assigns them a unique physical address index; it also configures the initial transmit power for all STAs in the network. Initial OBSS PD threshold The initial values ​​adopt conservative default values ​​conforming to the IEEE 802.11 standard and are constrained to the upper and lower limits allowed by the standard. and Inside.

[0034] 3. Algorithm and System Parameter Initialization: The MFU initializes the built-in Bayesian estimator with the queue state belief values ​​for each STA. Average transmission probability Assign initial values; set transmission intent determination thresholds. Smoothing factor of Bayesian update With step size factor Set the global optimization cycle. T The minimum signal-to-interference-plus-noise ratio (SINR) threshold required for successful demodulation, and the Bernoulli process parameters for data arrival. At the same time, configure the basic parameters of the Media Access Control (MAC) layer, including contention window range, inter-frame interval, and slot length.

[0035] 4. Process Startup: After all parameter configurations are completed, the MFU starts the periodic optimization timer, the system enters a stable operating state, and the closed-loop optimization process begins.

[0036] Step S2: SFU distributed channel awareness and STA transmission intent Bayesian inference.

[0037] This step is executed independently by each SFU locally, completing channel awareness and transmission intent inference on a time-slot basis. It is the core basis for threshold optimization. Figure 3 As shown, the specific sub-steps are as follows: 1. Real-time acquisition of local channel status.

[0038] Each SFU continuously monitors the radio channel status within its BSS at the time slot level. The physical layer sensing results are divided into three categories of observation events: ① If the channel is observed to be idle, ② If successfully received from the associated STA j data packets, ③ If the channel is observed to be busy, .

[0039] 2. Likelihood probability calculation.

[0040] SFU based on current observations Calculate the likelihood probability of each associated STA k corresponding to the queue state (empty or non-empty) for that observation. , For time slots t At that time, with SFU m Associated STA k The queue state belief value represents the probability that the current queue state is not empty, and can also be expressed as... ; For STA k The actual queue state can also be represented as ,in This is an indicator function that determines whether a specified condition is true or false. If the condition is true, the function outputs the value 1; if the condition is false, the function outputs the value 0. for t The actual number of packets in the time queue. The calculation formula depends on the observation type ( , , The difference is that the STA's own average transmission probability must be considered. Transmission intent belief values ​​of other STAs SFU m STA in k The likelihood probability is specifically: 1) When hour: ; 2) When hour: when hour: ; when hour: ; 3) When hour: ; in, Indicate Remove from set element, For SFU m The set of neighboring STA nodes, which includes those that will directly affect the SFU when transmitting signals. mSTA nodes that have a significant impact on channel observation results; Indicates the first m A set of STAs associated with each SFU.

[0041] 3. Belief value update and transmission intention prediction based on Bayesian criteria.

[0042] First, the queue state belief value is updated. SFU uses the Bayesian update formula, combining the calculated likelihood probability with the historical queue state belief value, to update the queue state belief value of each STA in the current time slot in real time: ; In the formula, and The queue state of STA k corresponds to the current observation. The likelihood probability.

[0043] Next, the transmission intent probability is calculated. Based on the updated queue state belief value and combined with the STA's average transmission probability, the STA's transmission intent belief value in the current time slot is calculated: ; in, This represents the average transmission probability.

[0044] Finally, transmission node prediction is performed, and the transmission intention belief values ​​of each STA are calculated. With preset decision threshold In comparison, if the value exceeds the threshold, it is predicted that the STA will initiate transmission in the next time slot; otherwise, it is predicted that no transmission will occur. ; in, This represents the estimated nodes that will transmit data. Summarizing these results yields the estimated set of transmitting nodes. The SFU reports the generated estimated set of transmitting nodes to the MFU via the fiber optic link. This set will be used in the following successive convex approximation (SCA) algorithm to optimize the OBSS PD threshold and transmit power.

[0045] 4. Average transmission probability update and information reporting.

[0046] SFU employs an exponential smoothing method, combining network-wide channel observation statistics with current transmission intent belief values. Update the average transmission probability of the STA. Used for Bayesian inference in the next time slot: ; in, It is a smoothing factor adjusted in a single step. These are step size factors adjusted in a single instance, and are all preset fixed parameters. This indicates the weight assigned to each frequency based on actual channel observations in the current time slot, indicating whether the channel is idle, successfully transmitted, or busy.

[0047] Step S3: Joint optimization of MFU centralized OBSS PD threshold and transmit power.

[0048] This step is executed centrally by the MFU, and is optimized based on the estimated set of transmission nodes reported by all SFUs in the network, with the goal of maximizing the overall network spectrum efficiency. The specific sub-steps are as follows: 1. Optimize problem establishment.

[0049] MFU maximizes the prediction of the transmission node set. The overall spectral efficiency of all STAs is used as the objective function, and an optimization problem is established with constraints on the feasible range of transmit power and OBSS PD threshold for each STA. The optimization objective is to maximize the long-term average spectral efficiency. , is represented as: ; in, BSS m Inside STA k The number of successfully received messages changes over time. M For the number of SFUs, For SFU m STA in k The signal-to-interference-plus-noise ratio is expressed as: ; in, , indicating STA i In the time slot t Is it currently sending to its associated SFU? n Transmitting data STA i Data transmission is performed, and vice versa; For SFU m Associated STA k In t Gaussian white noise power in time slots; For STA k to SFU m Channel gain; For SFU m Associated STA k The transmission power.

[0050] 2. Transformation of non-convex problems based on SCA.

[0051] For the aforementioned non-convex optimization problem, a two-layer iterative SCA algorithm is adopted to solve it: the outer iteration uses a first-order Taylor expansion to linearly approximate the disturbance term in the objective function at the current MAC power iteration point, thus transforming the original non-convex optimization problem into a convex approximation subproblem.

[0052] Optimizing OBSS PD threshold using the SCA algorithm and transmission power Set constraints, including threshold ranges. and power range .according to Optimize the target It can be rewritten as: ; The outer iteration transforms the original non-convex problem into a convex approximation subproblem through the SCA strategy.

[0053] Specifically, capacity expression It can be rewritten as: ; in, This represents the interference power. In the outer iteration, the objective function... At the current power point By performing a first-order Taylor expansion, the problem is approximated as a linear function, thus transforming the original problem into a convex optimization subproblem. At the current iteration point The expression for the first-order Taylor expansion of the disturbance is: ; in, yes In the j The first-order Taylor approximation of the next iteration, then the outermost... j The convex approximation subproblem of the next iteration can be expressed as: ; in, Indicates the first j Next iteration node The channel capacity is given by the first-order Taylor approximation. This is a convex optimization problem. The inner iterations use the projected gradient ascent method to update the power value, ensuring that the constraints are met, until the algorithm converges.

[0054] 3. Solving the convex part problem.

[0055] For the convex approximation subproblem generated by the outer iteration, the inner iteration uses the projection gradient ascent method to update the transmit power values ​​of each STA. At the same time, the projection operation ensures that the power values ​​meet the preset upper and lower limits. The inner iteration continues until the algorithm converges or reaches the maximum number of iterations, and the optimal transmit power of each STA after global optimization is output.

[0056] The projected gradient ascent method can be expressed as follows: for Each node All right The power derivative of a node is calculated as follows: ; but The gradient descent update of transmit power can be expressed as: ; in, This indicates the power update step size. For power projection operations, it can be stated that the updated power value strictly satisfies... Constraints. When the maximum number of iterations is reached. E When, or when the difference between the power of the current iteration result and the power of the previous iteration result does not exceed the threshold. The inner loop terminates, resulting in optimized power.

[0057] 4. OBSS PD threshold mapping.

[0058] Based on the optimized STA transmit power, the dynamic OBSS PD threshold for each SFU is calculated using the following mapping formula, while ensuring that the threshold constraint is within the standard allowable range of -82dBm to -62dBm: ; in, These represent the minimum and maximum values ​​of the OBSS PD threshold, typically -82dBm and -62dBm, respectively. For reference transmit power (typically 21 dBm in the case of a single antenna), and This represents the actual transmit power of the STA.

[0059] Step S4: Threshold issuance and closed-loop dynamic update.

[0060] The MFU encapsulates the optimized OBSS PD thresholds for each SFU into downlink control frames and sends them to the corresponding SFUs via fiber optic links. Upon receiving the control frames, the SFU directly modifies the Clear Channel Assessment (CCA) sensitivity register of the Wi-Fi chip's baseband processor via its internal bus. The physical layer immediately applies the new OBSS PD thresholds, selectively ignoring or avoiding OBSS interference signals. The SFU continuously monitors the packet reception success rate and overall network throughput of its BSS. If it detects fluctuations in overall network throughput exceeding a preset threshold... The system automatically sends an interrupt request to the MFU, triggering the system to immediately jump to step S2 to re-execute the optimization; if there is no abnormality, it will automatically enter the closed-loop optimization process of the next cycle after the current optimization cycle T ends.

[0061] In the core closed-loop process of this invention, the four steps of S1 system initialization, S2 Bayesian transmission intent inference, S3 centralized SCA optimization, and S4 threshold issuance and closed-loop update are essential core components. The absence of any one of these steps will prevent the realization of the core objective of dynamic threshold optimization for OBSS PD in this invention. The smoothing factor, step size factor, and transmission intent decision threshold estimated by Bayes can be adapted and adjusted according to the network scale and service load characteristics of the actual deployment scenario without affecting the implementation of the core method. The inner iterative solution method of the SCA algorithm, besides the projection gradient ascent method, can replace other convex optimization algorithms such as the interior point method and the conjugate gradient method, all of which can achieve efficient solutions to convex subproblems. The global optimization period T can be dynamically adjusted according to the speed of channel changes in the actual network. In scenarios with rapid channel changes, the period can be shortened; in scenarios with relatively stable channels, the period can be extended to reduce computational overhead.

[0062] The core method of this invention can be extended to a fully distributed optimization scheme, that is, the centralized optimization step of MFU is eliminated, and each SFU estimates the transmission node set information through fiber optic links, and completes the distributed optimization solution of OBSS PD threshold locally. This scheme can reduce the computational pressure of MFU and is suitable for ultra-large-scale FTTR networking scenarios.

[0063] To evaluate the Bayesian estimation theory To assess the performance of the prediction algorithm, this embodiment uses accuracy as an indicator, defined as the percentage of STA nodes whose estimated transmission conditions match the actual transmission conditions out of the total number of STA nodes. Simulation results are as follows: Figure 4 As shown, the algorithm's accuracy in estimating node transmission states is generally above 70%, and this accuracy improves with the increase in the number of STAs per SFU, reaching approximately 88% accuracy when each SFU contains 5 STAs. The increased number of STAs enhances the information content of the SFU's channel state observations while reducing the uncertainty caused by a small number of STAs, making the estimated belief values ​​closer to the true values. Furthermore, the algorithm dynamically adjusts its belief values ​​to adapt to different network traffic conditions, rather than directly relying on packet arrival rates. Therefore, the algorithm's estimation accuracy remains largely unaffected by different packet arrival rates, and the trends and values ​​of the curves are consistent, demonstrating the good robustness of the algorithm.

[0064] Figure 5A and Figure 5B The system spectral efficiency was shown respectively.R ave varies with the number of STAs under each SFU Km and packet arrival rate λ The changes are as follows in three scenarios: 1) In a fully contentious channel scenario, a centralized SCA algorithm is used; 2) In a random channel contention scenario, a distributed Bayesian estimation algorithm is used first, followed by a centralized SCA algorithm; 3) With fixed OBSS / PD, all STAs adopt... A reference OBSS / PD threshold of 62 dBm was used. Simulation results show that, in all three cases, regardless of the OBSS / PD setting scheme, the spectral efficiency... R The spectrum of ave (average frequency range) showed a trend of first increasing and then decreasing. This is because when the service load is light, the spectrum is idle, and as the packet arrival rate or the number of STAs increases, R Ave increases; when the load is too heavy, channel conflicts and interference intensify, leading to... R AVE performance decreases. At the same time, it can be seen that although spectral efficiency in random channel contention scenarios will decrease due to… The estimation error is somewhat lower than that of the spectral efficiency under a fully contentious channel scenario, but it is still superior to the spectral efficiency under a fixed OBSS PD threshold scheme, regardless of whether the packet arrival rate or the number of STAs increases. The simulation results above demonstrate the effectiveness and robustness of the method of this invention.

[0065] In summary, this invention proposes a dynamic optimization method for the OBSS PD threshold of an FTTR network based on Bayesian estimation. This method mainly includes: 1. A distributed STA transmission intent inference mechanism based on Bayesian estimation is proposed. The transmission intent of the STA can be accurately inferred by only the three types of channel states observable by the local physical layer of the SFU: idle, successful reception, and collision. It does not require obtaining complete channel state information, fully adapts to the statistical characteristics of random channel contention in Wi-Fi, and has high inference accuracy and strong robustness.

[0066] 2. A collaborative architecture of "distributed sensing and inference + centralized global optimization" was constructed, which fully leverages the advantages of low-latency transmission and master-slave unit collaboration in FTTR networks, while taking into account the real-time performance of local channel sensing and the optimality of global optimization, and achieving joint optimization of OBSS PD threshold and transmit power.

[0067] 3. The two-layer iterative SCA algorithm is used to solve the non-convex optimization problem, which transforms the complex problem of maximizing non-convex spectral efficiency into a convex optimization subproblem that can be solved efficiently. It has low computational complexity, fast convergence speed, and does not require a large amount of offline training data, which fully meets the requirements of real-time deployment of FTTR network.

[0068] 4. A complete periodic closed-loop dynamic optimization mechanism has been formed, which can adaptively adapt to changes in different network scales, service loads and channel environments, while being compatible with existing IEEE 802.11 series standards. No major modifications to the hardware architecture are required, making it highly feasible for engineering implementation.

[0069] Furthermore, the present invention also provides a computer device / apparatus / system, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0070] The present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or block diagrams.

[0072] These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more blocks in a block diagram.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, thereby providing steps for implementing the functions specified in one or more flowcharts and / or one or more blocks in a block diagram.

[0074] It should be noted that, depending on the implementation needs, the various steps / components described in this invention can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0075] Those skilled in the art will readily understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamically optimizing the OBSS PD threshold of an FTTR network based on Bayesian estimation, characterized in that, This method, applied to FTTR networks comprising one master FTTR unit (MFU) and multiple slave FTTR units (SFU), achieves closed-loop dynamic optimization of the OBSS (On-Board Detection and Detection) threshold through the synergy of SFU local distributed channel awareness and Bayesian transmission intent inference, and MFU centralized global optimization. The method includes the following steps: Step S1, System Configuration and Initialization: The MFU configures the initial transmit power and initial OBSS PD threshold for each SFU and associated STA, initializes the Bayesian estimator and system parameters, and starts the closed-loop optimization process; Step S2, SFU Distributed Channel Awareness and STA Transmission Intent Bayesian Inference: Each SFU monitors the wireless channel status locally in each time slot. Based on the channel observation results, it uses the Bayesian estimation algorithm to calculate the queue status belief value and transmission intent belief value of each associated STA. The transmission intent belief value is compared with the preset decision threshold to predict the STA that will initiate transmission in the next time slot, generate the estimated transmission node set and report it to the MFU. Step S3: MFU centralized OBSS PD threshold and transmit power joint optimization: MFU summarizes the estimated transmission node set of the entire network, with the goal of maximizing the spectrum efficiency of the entire network, and with the feasible range of transmit power and OBSS PD threshold of each STA as constraints, establishes and solves the optimization problem to obtain the optimal transmit power of each STA. Then, based on the mapping relationship between transmit power and OBSS PD threshold, the dynamic OBSS PD threshold corresponding to each SFU is calculated. Step S4, Threshold Distribution and Closed-Loop Dynamic Update: The MFU distributes the optimized OBSS PD threshold to each corresponding SFU. The SFU applies the new threshold to perform channel evaluation and triggers the next round of optimization process when the current optimization cycle ends or when network performance fluctuations exceed preset conditions are detected.

2. The method of claim 1, wherein the method is based on Bayesian estimation of FTTR network OBSS PD threshold dynamic optimization. In step S2, the SFU calculates the queue state belief value of each STA using a Bayesian estimation algorithm, specifically including: Each SFU continuously monitors the radio channel status within its BSS at the time slot level. The physical layer sensing results are divided into three categories of observation events: ① If the channel is observed to be idle, then ② If successfully received from the associated STA j The data packet, ③ If the channel is observed to be busy, then ; SFU based on current observations Calculate the likelihood probability of the queue state corresponding to the observation for each associated STA k. ;in, For time slots t At that time, with SFU m Associated STA k The queue state belief value represents the probability that the current queue state is not empty, and is also expressed as... ; For STA k The actual queue state is also represented as ,in This is an indicator function that determines whether a specified condition is true or false. If the condition is true, the function outputs the value 1; if the condition is false, the function outputs the value 0. for t The actual number of packets in the time queue; SFU uses a Bayesian update formula, combining the calculated likelihood probability with historical queue state belief values, to update the queue state belief values ​​of each STA in the current time slot in real time: ； In the formula, and The queue state of STA k corresponds to the current observation. The likelihood probability.

3. The method for dynamic optimization of OBSS PD threshold in FTTR network based on Bayesian estimation according to claim 2, characterized in that, The likelihood probability is calculated as follows: The formula for calculating likelihood probability depends on the type of observation. , and The differences are different; the STA's own average transmission probability must be considered. Transmission intent belief values ​​of other STAs SFU m STA in k The likelihood probabilities are as follows: 1) When hour: ； 2) When hour: when hour: ； when hour: ； 3) When hour: ； in, Indicate Remove from set element, For SFU m The set of neighboring STA nodes; Indicates the first m A set of STAs associated with each SFU.

4. The method for dynamic optimization of OBSS PD threshold in FTTR network based on Bayesian estimation according to claim 2, characterized in that, The transmission intent belief value of the STA in the current time slot mentioned in step S2 is calculated by the following formula: ； in, Indicates the average transmission probability; Transmit the transmission intent belief value of each STA With preset decision threshold In comparison, if the value exceeds the threshold, it is predicted that the STA will initiate transmission in the next time slot; otherwise, it is predicted that no transmission will occur. ； in, This represents the estimated number of nodes that will transmit; summing these together yields the estimated set of transmitting nodes. .

5. The method for dynamic optimization of OBSS PD threshold in FTTR network based on Bayesian estimation according to claim 2, characterized in that, Step S2 also includes an update step for the average transmission probability. SFU uses an exponential smoothing method, combining the statistical results of the entire network channel observation with the current transmission intention belief value. Update the average transmission probability of the STA. Used for Bayesian inference in the next time slot: ； in, It is a smoothing factor adjusted in a single step. These are step size factors adjusted in a single instance, and are all preset fixed parameters. This indicates the weight assigned to each frequency based on actual channel observations in the current time slot, indicating whether the channel is idle, successfully transmitted, or busy.

6. The method for dynamic optimization of OBSS PD threshold in FTTR network based on Bayesian estimation according to claim 4, characterized in that, The establishment of the optimization problem in step S3 includes: MFU maximizes the prediction of the transmission node set. The overall spectral efficiency of all STAs is used as the objective function, and the feasible range of transmit power and OBSS / PD thresholds for each STA is used as constraints to establish an optimization problem; the optimization objective is to maximize the long-term average spectral efficiency. , represented as: ； in, BSS m Inside STA k The number of successfully received messages changes over time. M For the number of SFUs, For SFU m STA in k The signal-to-interference-plus-noise ratio is expressed as: ； in, , indicating STA i In the time slot t Is it currently being sent to its associated SFU? n Transmitting data STA i Data transmission is performed, and vice versa; For SFU m Associated STA k In t Gaussian white noise power in time slots; For STA k to SFU m Channel gain; For SFU m Associated STA k The transmission power.

7. The method for dynamic optimization of OBSS PD threshold in FTTR network based on Bayesian estimation according to claim 6, characterized in that, Solving the optimization problem in step S3 includes: The SCA algorithm with two-layer iteration is used to solve the problem: the outer iteration uses a first-order Taylor expansion to linearly approximate the disturbance term in the objective function at the current MAC power iteration point, thus transforming the original non-convex optimization problem into a convex approximation subproblem; Optimizing OBSS PD threshold using the SCA algorithm and transmission power : Set constraints, including threshold ranges. and power range ;according to Optimize the target Rewritten as: ； The outer iteration transforms the original non-convex problem into a convex approximate subproblem using the SCA strategy, with the capacity expression... Rewritten as: ； in, Indicates interference power; In the outer iteration, the objective function is... At the current power point By performing a first-order Taylor expansion, the problem is approximated as a linear function, thus transforming the original problem into a convex optimization subproblem. At the current iteration point The expression for the first-order Taylor expansion of the disturbance is: ； in, yes In the j The first-order Taylor approximation of the next iteration, then the outermost... j The convex approximation subproblem of the next iteration is expressed as: ； in, Indicates the first j Next iteration node The channel capacity is a first-order Taylor approximation; this problem is a convex optimization problem, and the projection gradient ascent method is used in the inner iteration to update the power value to ensure that the constraint conditions are met until the algorithm converges; For the convex approximation subproblem generated by the outer iteration, the inner iteration uses the projection gradient ascent method to update the transmit power values ​​of each STA. At the same time, the projection operation ensures that the power values ​​meet the preset upper and lower limits. The inner iteration continues until the algorithm converges or reaches the maximum number of iterations, and the optimal transmit power of each STA after global optimization is output. The projected gradient ascent method is expressed as follows: for Each node All right The power derivative of a node is calculated as follows: ； but The gradient descent update of transmit power is expressed as: ； in, This indicates the power update step size. For power projection operations, it is expressed as the updated power value strictly satisfying... Constraints; when the number of iterations reaches the upper limit. E When, or when the difference between the power of the current iteration result and the power of the previous iteration result does not exceed the threshold. The inner loop terminates, resulting in optimized power.

8. The method for dynamic optimization of OBSS PD threshold in FTTR network based on Bayesian estimation according to claim 1, characterized in that, The mapping relationship between the transmit power and the OBSS PD threshold in step S3 is as follows: Based on the optimized STA transmit power, the dynamic OBSSPD threshold for each SFU is calculated according to the following mapping formula. At the same time, ensure that the threshold constraints are within the range allowed by the standard: ； in, These represent the minimum and maximum values ​​of the OBSS / PD threshold, respectively. For reference transmission power, This represents the actual transmit power of the STA.

9. A computer device / equipment / system, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 8.

Images