Adaptive modulation coding method and system based on fuzzy logic

Abstract

The invention discloses an adaptive modulation coding method and system based on fuzzy logic, and belongs to the field of wireless communication, and the system comprises a dynamic window module which dynamically adjusts the window size of a fuzzy set according to the received signal strength indication and the change trend of the packet reception rate; the input fuzzification module takes the actual received signal strength indication and the packet reception rate as input and converts the actual received signal strength indication and the packet reception rate into fuzzy variables through fuzzification processing; the fuzzy reasoning module is used for defining different membership functions, mapping continuous received signal strength indications and packet reception rate measurement values into a fuzzy set, considering membership value combinations of all RSSI and PRR fuzzy categories, calculating a weighted total score and outputting a fuzzy decision value; and the defuzzification module is used for converting into an actual modulation coding selection scheme through a defuzzification process. By adopting the method and the system, the adaptability and the response speed of the system to complex and changeable channel conditions are improved, and the accuracy and the reliability of adaptive modulation coding selection are improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a fuzzy logic-based adaptive modulation and coding method and system. Background Art

[0002] In wireless communications, the 802.11 standard has become one of the most widely used wireless local area network protocols worldwide. With the continuous advancement of wireless communications technology, particularly the pursuit of higher transmission rates, achieving terabit-per-second peak rates will become possible in the future. In complex and changing communication environments, the system's requirements for wireless resource scheduling capabilities are constantly increasing. Achieving efficient and reliable data transmission under diverse channel conditions has become a key issue.

[0003] Adaptive modulation and coding (AMC) is a key technology that selects the optimal data transmission strategy based on real-time channel quality, dynamically adjusting modulation and coding schemes to optimize system performance. The core concept of AMC is to flexibly switch between high-order and low-order modulation based on changing channel conditions, combined with different forward error correction code rates to maximize system performance. When channel quality is good, the system selects high-order modulation and weaker coding to increase the transmission rate; when channel quality is poor, the system selects low-order modulation and stronger coding to ensure reliable data transmission.

[0004] In recent years, with the rapid development of artificial intelligence, machine learning-based methods have begun to be introduced into the selection of adaptive modulation and coding (AMC), significantly improving the performance of wireless communication systems. However, most existing AMC methods lack the flexibility and adaptability to rapidly changing channel conditions and rely on large numbers of high-quality labeled samples. In practical applications, these methods can face challenges such as difficult data collection, long training times, and insufficient generalization capabilities. Summary of the Invention

[0005] The present invention aims to provide a fuzzy-logic-based adaptive modulation and coding method and system. This method, without relying on fixed lookup tables, comprehensively considers multiple measurement parameters through a fuzzy rule set and fuzzy inference process, thereby optimizing the selection of modulation and coding schemes. By incorporating fuzzy logic, this method not only improves the system's adaptability and responsiveness to complex and changing channel conditions, but also enhances the accuracy and reliability of adaptive modulation and coding selection while reducing reliance on labeled samples.

[0006] To achieve the above object, the present invention provides an adaptive modulation and coding system based on fuzzy logic, comprising:

[0007] The dynamic window module dynamically adjusts the window size of the fuzzy set based on the real-time received signal strength indicator (RSSI) and packet reception rate (PRR) change trends. If the channel quality fluctuates greatly, the window range is increased to improve the robustness of the system. If the channel quality is relatively stable, the window range is reduced to improve accuracy.

[0008] The input fuzzification module takes the actual received signal strength indicator and packet reception rate as input, converts them into fuzzy variables through fuzzification processing, and divides them into different fuzzy sets. Each set corresponds to a different received signal strength indicator and packet reception rate state. The received signal strength indicator is divided into 7 fuzzy categories, namely VrWeak, Weak, SlWeak, Medium, SlStrong, Strong, and VrStrong. The packet reception rate is divided into 5 fuzzy categories, namely VrLow, Low, Medium, High, and VrHigh.

[0009] The fuzzy reasoning module effectively maps the continuous received signal strength indication and packet reception rate measurement values ​​into fuzzy sets by defining different membership functions. It calculates the weighted total score by considering the membership value combinations of all RSSI and PRR fuzzy categories and outputs the fuzzy decision value;

[0010] The defuzzification module converts the defuzzification process into the actual modulation and coding selection scheme.

[0011] Preferably, the window size is dynamically adjusted based on the real-time RSSI and PRR trends. If the channel quality fluctuates significantly, the window size is increased to improve system robustness; if the channel quality is relatively stable, the window size is reduced to improve accuracy.

[0012] For RSSI:

[0013] RSSI new =RSSI cret +α×(RSSI cret -RSSI tag );

[0014] For PRR:

[0015] PRR new =PRR cret +β×(PRR cret -PRR tag );

[0016] Among them, α and β are adjustment factors, RSSI cret 、PRR cret and RSSI tag 、PRR tagare the current value and target value of the received signal indication strength and packet reception rate respectively;

[0017] The module uses a sliding window mechanism to update RSSI and PRR measurements in real time to avoid the influence of individual outliers on module selection. The sliding window length is adjusted according to the dynamic changes in the communication environment to ensure that the communication system can respond to changes in channel conditions in a timely manner. The RSSI and PRR window lengths are initially set to 1000 and 100 respectively.

[0018] Each time a new RSSI value is obtained, it is added to the value of RSSI. new In the dynamic window:

[0019] RSSI win [RSSI cout %RSSI new ] = Curet_RSSI;

[0020] Among them, Curet_RSSI is the average indication strength of the current received signal, RSSI cout Incremented each time a new value is added;

[0021] Similarly, each time a new PRR value is obtained, it is added to the PRR size. new In the dynamic window:

[0022] PRR win [PRR cout %PRR new ] = Curet_PRR;

[0023] Among them, Curet_PRR is the average indication strength of the current received signal, PRR cout Incremented each time a new value is added;

[0024] With each update of the RSSI and PRR windows, the module recalculates the maximum and minimum values ​​of the windows to update the membership function of the fuzzy set. For RSSI window threshold updates:

[0025] RSSI min =min(RSSI win [0:RSSI new ]);

[0026] RSSI max =max(RSSI win [0:RSSI new ]);

[0027] For PRR window threshold update:

[0028] PRR min=min(PRR win [0:PRR new ]);

[0029] PRR max =max(PRR win [0:PRR new ]);

[0030] Through dynamic window design, the fuzzy rule base is also updated accordingly, ensuring that within the new window range, the module can make the optimal modulation and coding selection based on the latest RSSI and PRR values, enabling the MG-AMC method to maintain high adaptability in complex and changing wireless environments.

[0031] Preferably, different membership functions are defined in the fuzzy inference module, one for each category, to quantify the state of the channel condition. The RSSI membership function uses triangular and trapezoidal functions to represent RSSI signals at different strength levels. The triangular function is used to describe intermediate fuzzy categories, such as "Medium," "SlStong," and "SlWeak," while the trapezoidal function is used to describe boundary fuzzy categories, such as "VrWeak" and "VrStrong."

[0032] For the intermediate fuzzy category, such as the "Medium" category, its membership function is as follows, where rag is the RSSI dynamic window length:

[0033]

[0034] in,

[0035] λ1=RSSI min +0.6×rag;

[0036] λ2=RSSI min +0.8×rag;

[0037] For classes with fuzzy boundaries, such as the "VrStrong" class, the membership function is as follows:

[0038]

[0039] in,

[0040] λ1=RSSI min +0.7×rag;

[0041] λ2=RSSI min +0.9×rag.

[0042] The PRR membership function also uses triangular and trapezoidal functions to describe the degree of fuzziness at different packet reception rates. The triangular function is used to describe intermediate fuzzy categories, such as "Medium," "High," and "Low," while the trapezoidal function is used to describe boundary fuzzy categories, such as "VrHigh" and "VrLow."

[0043] For intermediate fuzzy categories, such as the "High" category, its membership function is as follows, where rag is the PRR dynamic window length:

[0044]

[0045] in,

[0046] λ1=PRR min +0.6×rag;

[0047] λ2=PRR min +0.8×rag;

[0048] For classes with fuzzy boundaries, such as the "VrLow" class, the membership function is as follows:

[0049]

[0050] in,

[0051] λ1=PRR min +0.2×rag;

[0052] λ2=PRR min +0.4×rag.

[0053] Preferably, for each possible modulation and coding scheme, a weighted total score is calculated by considering all combinations of membership values ​​of the RSSI and PRR fuzzy classes The calculation formula is as follows:

[0054]

[0055] Among them, μ RSSI 、μ PRR Indicates the current RSSI or PRR value for m r or m p The membership degree of the category, i represents the weight factor of the current modulation and coding scheme MCS, which is used to adjust the priority of different schemes;

[0056] Get all MCS scores After that, a weighted average calculation is performed to select the optimal MCS. The calculation formula is as follows:

[0057]

[0058] The optimal MCS is selected through weighted averaging across all possible MCSs. This MCS selection strategy comprehensively considers the real-time state of the channel and dynamically adjusts under varying RSSI and PRR conditions, achieving more efficient and stable data transmission.

[0059] An adaptive modulation and coding method based on fuzzy logic comprises the following steps:

[0060] S1. Initialize the RSSI window and the PRR window and fill them with initial values.

[0061] S2. When the module detects a data frame to be sent, it obtains the latest signal strength information from the received signal strength indication window and evaluates the current link quality in combination with the packet reception rate to select an appropriate modulation and coding (MCS) scheme.

[0062] S3. After obtaining the current received signal strength indicator and packet reception rate values, the received signal strength indicator and packet reception rate values ​​are fuzzified using a predefined membership function and mapped to corresponding fuzzy set categories; then, reasoning is performed on the fuzzified input according to fuzzy inference rules to calculate a score for each modulation and coding scheme;

[0063] S4. Modulating and encoding the data frame to be transmitted using the optimal modulation and coding scheme obtained through fuzzy reasoning through the communication system, and sending the data frame to the wireless channel;

[0064] S5. After the data is sent, the receiver waits for the ACK packet. If the ACK is not received within the specified time, the packet reception rate will be updated and retransmission will be attempted. If the number of retransmissions exceeds the set threshold, the retransmission will be abandoned and the next data packet will be sent.

[0065] S6. Continuously and dynamically update the received signal strength indicator and the packet reception rate window value to reflect the latest channel status.

[0066] Therefore, the present invention adopts the above-mentioned adaptive modulation and coding method and system based on fuzzy logic, which has the following beneficial effects:

[0067] (1) Improve the system's adaptability and response speed to complex and changing channel conditions;

[0068] (2) Improve the accuracy and reliability of adaptive modulation and coding selection while reducing the dependence on labeled samples;

[0069] (3) It can effectively handle complex wireless channel environments, with a system throughput of approximately 18 Mbps and a packet loss rate of approximately 1%, which are 20% higher and 35% lower than those of the traditional Minstrel algorithm respectively;

[0070] (4) Real-time and high reliability.

[0071] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 Schematic diagram of the structure of the system according to an embodiment of the present invention;

[0073] Figure 2 The RSSI in the embodiment of the present invention is min 、RSSI max Membership function diagrams of seven fuzzy sets of RSSI at -100dbm and 0dbm respectively;

[0074] Figure 3 The overall flow chart of the embodiment of the present invention is as follows: min 、PRR max The membership function diagram of the five fuzzy sets of PRR when they are 0 and 100 respectively;

[0075] Figure 4 This is a flowchart of the overall method of an embodiment of the present invention;

[0076] Figure 5 A schematic diagram of an adaptive modulation and coding method based on fuzzy logic provided in an embodiment of the present invention applied to an OFDM system;

[0077] Figure 6 Schematic diagram of packet loss rate and throughput of the method according to the embodiment of the present invention;

[0078] Figure 7 Schematic diagram of packet loss rate and throughput of other methods;

[0079] Figure 8 Schematic diagram of packet loss rate and throughput changes in different environments according to the method of an embodiment of the present invention;

[0080] Figure 9 Schematic diagram of the packet loss rate and throughput changes of the Minstrel method in different environments. DETAILED DESCRIPTION

[0081] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.

[0082] See also Figure 1 , an adaptive modulation and coding system based on fuzzy logic,

[0083] include:

[0084] The dynamic window module dynamically adjusts the window size of the fuzzy set based on the real-time received signal strength indicator RSSI and packet reception rate PRR change trends. If the channel quality fluctuates greatly, the window range is increased to improve the robustness of the system. If the channel quality is relatively stable, the window is reduced to improve accuracy.

[0085] Dynamic window design is a key component, used to dynamically adjust the value range of input parameters (Received Signal Strength Indicator RSSI and Packet Receive Rate PRR) to enhance the flexibility and response speed of the communication system and adapt to different channel conditions. The specific design is as follows:

[0086] Dynamic window size adjustment: The fuzzy set window size is dynamically adjusted based on the real-time RSSI and PRR trends. If the channel quality fluctuates significantly, the window range is increased to improve system robustness; if the channel quality is relatively stable, the window range is reduced to improve accuracy.

[0087] For RSSI:

[0088] RSSI new =RSSI cret +α×(RSSI cret -RSSI tag );

[0089] For PRR:

[0090] PRR new =PRR cret +β×(PRR cret -PRR tag );

[0091] Among them, α and β are adjustment factors, RSSI cret 、PRR cret and RSSI tag 、PRR tag are the current value and target value of the received signal indication strength and packet reception rate respectively;

[0092] The module uses a sliding window mechanism to update RSSI and PRR measurements in real time to avoid the influence of individual outliers on module selection. The sliding window length is adjusted according to the dynamic changes in the communication environment to ensure that the communication system can respond to changes in channel conditions in a timely manner. The RSSI and PRR window lengths are initially set to 1000 and 100 respectively.

[0093] Each time a new RSSI value is obtained, it is added to the value of RSSI. new In the dynamic window:

[0094] RSSI win [RSSI cout %RSSI new ] = Curet_RSSI;

[0095] Among them, Curet_RSSI is the average indication strength of the current received signal, RSSI cout Incremented each time a new value is added;

[0096] Similarly, each time a new PRR value is obtained, it is added to the PRR size. new In the dynamic window:

[0097] PRR win [PRR cout %PRR new ] = Curet_PRR;

[0098] Among them, Curet_PRR is the average indication strength of the current received signal, PRR cout Incremented each time a new value is added;

[0099] With each update of the RSSI and PRR windows, the module recalculates the maximum and minimum values ​​of the windows to update the membership function of the fuzzy set. For RSSI window threshold updates:

[0100] RSSI min =min(RSSI win [0:RSSI new ]);

[0101] RSSI max =max(RSSI win [0:RSSI new ]);

[0102] For PRR window threshold update:

[0103] PRR min =min(PRR win [0:PRR new ]);

[0104] PRR max =max(PRR win [0:PRR new ]);

[0105] Through dynamic window design, the fuzzy rule base is also updated accordingly, ensuring that within the new window range, the module can make the optimal modulation and coding selection based on the latest RSSI and PRR values, enabling the MG-AMC method to maintain high adaptability in complex and changing wireless environments.

[0106] The input fuzzification module takes the actual received signal strength indicator and packet reception rate as input, converts them into fuzzy variables through fuzzification processing, and divides them into different fuzzy sets. Each set corresponds to a different received signal strength indicator and packet reception rate state. The received signal strength indicator is divided into 7 fuzzy categories, namely VrWeak, Weak, SlWeak, Medium, SlStrong, Strong, and VrStrong. The packet reception rate is divided into 5 fuzzy categories, namely VrLow, Low, Medium, High, and VrHigh.

[0107] The fuzzy reasoning module effectively maps the continuous received signal strength indication and packet reception rate measurement values ​​into fuzzy sets by defining different membership functions. It calculates the weighted total score by considering the membership value combinations of all RSSI and PRR fuzzy categories and outputs the fuzzy decision value.

[0108] To accurately describe the channel state information for RSSI and PRR, separate membership functions are defined. Each category has a corresponding membership function that quantifies the channel condition. The RSSI membership function uses triangular and trapezoidal functions to represent RSSI signals at different strength levels. The triangular function is used to describe intermediate, fuzzy categories such as "Medium," "SlStong," and "SlWeak," while the trapezoidal function is used to describe boundary-fuzzy categories such as "VrWeak" and "VrStrong."

[0109] For the intermediate fuzzy category, such as the "Medium" category, its membership function is as follows, where rag is the RSSI dynamic window length:

[0110]

[0111] in,

[0112] λ1=RSSI min +0.6×rag;

[0113] λ2=RSSI min +0.8×rag;

[0114] For classes with fuzzy boundaries, such as the "VrStrong" class, the membership function is as follows:

[0115]

[0116] in,

[0117] λ1=RSSI min +0.7×rag;

[0118] λ2=RSSI min +0.9×rag;

[0119] Assuming RSSI min 、RSSI max are -100dBm and 0dBm respectively, then the membership function diagrams of the seven fuzzy sets of RSSI are as follows: Figure 2 shown.

[0120] The PRR membership function also uses triangular and trapezoidal functions to describe the degree of fuzziness at different packet reception rates. The triangular function is used to describe intermediate fuzzy categories, such as "Medium," "High," and "Low," while the trapezoidal function is used to describe boundary fuzzy categories, such as "VrHigh" and "VrLow."

[0121] For intermediate fuzzy categories, such as the "High" category, its membership function is as follows, where rag is the PRR dynamic window length:

[0122]

[0123] in,

[0124] λ1=PRR min +0.6×rag;

[0125] λ2=PRR min +0.8×rag;

[0126] For classes with fuzzy boundaries, such as the "VrLow" class, the membership function is as follows:

[0127]

[0128] in,

[0129] λ1=PRR min +0.2×rag;

[0130] λ2=PRR min +0.4×rag.

[0131] Assuming PRR min 、PRR max are 0 and 100 respectively, then the membership function diagrams of the five fuzzy sets of PRR are as follows Figure 3 shown.

[0132] For each possible modulation and coding scheme, a weighted total score is calculated by considering all combinations of membership values ​​of the RSSI and PRR fuzzy classes. The calculation formula is as follows:

[0133]

[0134] Among them, μ RSSI 、μ PRR Indicates the current RSSI or PRR value for m r or m p The membership degree of the category, i represents the weight factor of the current modulation and coding scheme MCS, which is used to adjust the priority of different schemes;

[0135] Get all MCS scores After that, a weighted average calculation is performed to select the optimal MCS. The calculation formula is as follows:

[0136]

[0137] The optimal MCS is selected through weighted averaging across all possible MCSs. This MCS selection strategy comprehensively considers the real-time state of the channel and dynamically adjusts under varying RSSI and PRR conditions, achieving more efficient and stable data transmission.

[0138] The defuzzification module converts the defuzzification process into the actual modulation and coding selection scheme.

[0139] like Figure 4 , a fuzzy logic based adaptive modulation and coding method (MG-AMC), comprising the following steps:

[0140] S1. Initialize the RSSI window and the PRR window and fill them with initial values.

[0141] S2. When the module detects a data frame to be sent, it obtains the latest signal strength information from the received signal strength indication window and evaluates the current link quality in combination with the packet reception rate to select an appropriate modulation and coding (MCS) scheme.

[0142] S3. After obtaining the current received signal strength indicator and packet reception rate values, the received signal strength indicator and packet reception rate values ​​are fuzzified using a predefined membership function and mapped to corresponding fuzzy set categories; then, reasoning is performed on the fuzzified input according to fuzzy inference rules to calculate a score for each modulation and coding scheme;

[0143] S4. Modulating and encoding the data frame to be transmitted using the optimal modulation and coding scheme obtained through fuzzy reasoning through the communication system, and sending the data frame to the wireless channel;

[0144] S5. After the data is sent, the receiver waits for the ACK packet. If the ACK is not received within the specified time, the packet reception rate will be updated and retransmission will be attempted. If the number of retransmissions exceeds the set threshold, the retransmission will be abandoned and the next data packet will be sent.

[0145] S6. Continuously and dynamically update the received signal strength indicator and the packet reception rate window value to reflect the latest channel status.

[0146] This embodiment builds an actual test platform based on the openwifi development board, and compares the performance with traditional methods in various interference environments. The hardware platform openwifi required for the method is an ARM+FPGA dual-core software-defined radio design based on zynq and AD9361. Compared with commercial wifi, openwifi implements an open source full-stack wifi system. By using the openwifi development board ant-sdr, the adaptive modulation and coding method based on fuzzy logic is deployed and tested in an actual physical environment, and its performance is compared with that of traditional modulation and coding methods. In the experiment, the 802.11 standard based on orthogonal frequency division multiplexing (OFDM) technology is used to implement the adaptive modulation and coding mechanism. OFDM is widely used in modern wireless communication systems due to its efficient spectrum utilization and ability to resist multipath interference. Figure 5 The fuzzy logic-based adaptive modulation and coding method provided in this embodiment is applied to an OFDM system. Two OpenWiFi development boards, ant-sdr, serve as the transmitter and receiver, respectively. The transmitter is responsible for the data modulation and coding portion of the OFDM-AMC system, while the receiver is responsible for data demodulation and decoding, as well as feedback on relevant channel parameters (received signal strength indicator RSSI and packet reception rate PRR). The fuzzy logic-based adaptive modulation and coding method provided in this embodiment is implemented in the driver layer of the OpenWiFi processing system (PS side). The transmitter uses fuzzy logic to calculate the current optimal modulation and coding scheme based on the real-time RSSI and PRR information fed back by the receiver, thereby achieving adaptive modulation and coding under different channel conditions.

[0147] Comparative experiments were conducted between MG-AMC and traditional methods in different scenarios. Taking into account factors such as space closure and interference sources, the experiment selected three typical test environments: laboratory, corridor and playground. The packet loss rate and throughput of MG-AMC and other methods were tested in each environment. The traditional methods selected the classic Minstrel method, RSSI-AMC method and PRR-AMC method, among which the RSSI-AMC method and PRR-AMC method are hard decision AMC methods based on actual signal strength and packet reception rate, respectively. The transmission performance test was carried out in the above three environments. Each group of experiments was repeated 5 times, 1000 data packets were transmitted each time, and the average packet loss rate and throughput were taken; the packet loss rate and throughput of MG-AMC and other methods are shown as follows. Figure 6 、 Figure 7The method is tested for robustness against MG-AMC and Minstrel method, and 10 rounds of packet loss and throughput performance test are carried out in three environments, 5000 data packets are transmitted and received in each round, Figure 8 、 Figure 9 The change of packet loss rate and throughput of the two methods in different environments is shown.

[0148] Therefore, the application adopts the above-mentioned adaptive modulation and coding method and system based on fuzzy logic, combines the received signal strength indication and the packet reception rate, two important channel quality indicators, and dynamically adjusts the modulation and coding scheme by using fuzzy logic to improve the transmission efficiency of the wireless communication system under dynamic channel conditions. The application builds an actual test platform based on the openwifi development board and compares the performance with the traditional method in various interference environments. Compared with the traditional adaptive modulation and coding method, the packet loss rate of the application is reduced by 35%, and the system throughput is increased by 20%, which verifies the effectiveness and feasibility of the application in dealing with complex wireless channel environment.

[0149] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. An adaptive modulation and coding system based on fuzzy logic, characterized in that: include: The dynamic window module dynamically adjusts the window size of the fuzzy set according to the real-time received signal strength indicator RSSI and packet reception rate PRR change trend; The input fuzzification module takes the actual RSSI and packet reception rate as input, converts them into fuzzy variables through fuzzification processing, and divides them into different fuzzy sets. Each set corresponds to a different RSSI and packet reception rate state. The RSSI is divided into seven fuzzy categories: VrWeak, Weak, SlWeak, Medium, SlStrong, Strong, and VrStrong; the packet reception rate is divided into five fuzzy categories: VrLow, Low, Medium, High, and VrHigh. The fuzzy reasoning module maps the continuous RSSI and PRR measurements into fuzzy sets by defining different membership functions, calculates the weighted total score by considering all membership value combinations of RSSI and PRR fuzzy categories, and outputs the fuzzy decision value; The defuzzification module converts the defuzzification process into the actual modulation and coding selection scheme.

2. The adaptive modulation and coding system based on fuzzy logic according to claim 1, characterized in that: The contents of the dynamic window module are as follows: For RSSI: RSSI new =RSSI cret +α×(RSSI cret -RSSI tag ); For PRR: RRP new =PRR cret +β×(PRR cret -PRR tag ); Among them, α and β are adjustment factors, RSSI cret 、PRR cret and RSSI tag 、PRR tag are the current value and target value of the received signal indication strength and packet reception rate respectively; The window lengths of RSSI and PRR are initially set to 1000 and 100; Each time a new RSSI value is obtained, it is added to the value of RSSI. new In the dynamic window: RSSI win [RSSI cout %RSSI new ]=Curet_RSSI; Among them, Curet_RSSI is the average indication strength of the current received signal, RSSI cout Incremented each time a new value is added; Similarly, each time a new PRR value is obtained, it is added to the PRR size. new In the dynamic window: RRP win [PRR cout %PRR new ]=Curet_PRR; Among them, Curet_PRR is the average indication strength of the current received signal, PRR cout Incremented each time a new value is added; With each update of the RSSI and PRR windows, the maximum and minimum values ​​of the windows are recalculated to update the membership function of the fuzzy set. For the RSSI window threshold update: RSSI min =min(RSSI win [0:RSSI new ]); RSSI max =max(RSSI win [0:RSSI new ]); For PRR window threshold update: RRP min =min(PRR win [0:PRR new ]); RRP max =max(PRR win [0:PRR new ]); Through dynamic window design, the fuzzy rule base is also updated accordingly.

3. The adaptive modulation and coding system based on fuzzy logic according to claim 2, characterized in that: For each modulation and coding scheme, a weighted total score is calculated by considering all combinations of membership values ​​of RSSI and PRR fuzzy classes The calculation formula is as follows: Among them, μ RSSI 、μ PRR Indicates the current RSSI or PRR value for m r or m p The membership degree of the category, i represents the weight factor of the current modulation and coding scheme MCS, which is used to adjust the priority of different schemes; Get all MCS scores After that, a weighted average calculation is performed to select the optimal MCS. The calculation formula is as follows:

4. An adaptive modulation and coding method based on fuzzy logic, applied to an adaptive modulation and coding system based on fuzzy logic according to any one of claims 1 to 3, characterized in that: The following steps are involved: S1. Initialize the RSSI window and the PRR window and fill them with initial values. S2. When the module detects a data frame to be sent, it obtains the latest signal strength information from the received signal strength indication window and evaluates the current link quality in combination with the packet reception rate to select an appropriate modulation and coding (MCS) scheme. S3, after obtaining the current received signal strength indicator and packet reception rate value, fuzzifying the received signal strength indicator and packet reception rate value through a predefined membership function and mapping them to corresponding fuzzy set categories; Then the fuzzified input is reasoned according to the fuzzy inference rules to calculate the score of each modulation and coding scheme; S4. Modulating and encoding the data frame to be transmitted using the optimal modulation and coding scheme obtained through fuzzy reasoning through the communication system, and sending the data frame to the wireless channel; S5. After the data is sent, the receiver waits for the ACK packet. If the ACK is not received within the specified time, the packet reception rate will be updated and retransmission will be attempted. If the number of retransmissions exceeds the set threshold, the retransmission will be abandoned and the next data packet will be sent. S6. Continuously and dynamically update the received signal strength indicator and the packet reception rate window value to reflect the latest channel status.

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