Code rate self-adaption method and system based on Polar code

Abstract

The invention discloses a code rate self-adaption method and system based on a Polar code, and relates to the technical field of communication. The code rate self-adaption system based on the Polar code comprises a channel state data acquisition module, a code rate self-adaption module and a code rate self-adaption module, the channel monitoring module is used for acquiring and comprehensively analyzing channel state data in real time and analyzing a channel state set, the channel evaluation module is used for analyzing a channel comprehensive quality evaluation index based on the channel state set, and the code rate control module is used for adjusting a coding rate based on the channel comprehensive quality evaluation index. The channel quality can be accurately evaluated and the coding rate can be dynamically adjusted according to the comprehensive quality evaluation index of the channel, such as the signal-to-noise ratio, the bit error rate, the transmission distance, the environmental data and the receiving state of the receiving end, and the flexible self-adaptive adjustment mechanism enables the system to automatically optimize the transmission scheme according to the real-time wireless channel change, so that the transmission efficiency is improved. And the problem of possible low efficiency of a traditional static system in different environments is avoided.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and particularly to a code rate adaptation method and system based on Polar codes. Background Art

[0002] With the continuous development of wireless communication technologies, communication systems are facing increasingly complex environments and high-performance requirements. In the scenario of high-speed data transmission, how to maintain a low bit error rate, improve transmission efficiency, and enhance the reliability of the system under different channel conditions has become one of the key challenges in the field of wireless communication. Traditional channel coding methods, such as convolutional codes and Turbo codes, can work effectively in high signal-to-noise ratio environments. However, in low signal-to-noise ratio or harsh channel environments, the performance of these methods is not satisfactory, which greatly affects the efficiency and stability of wireless communication systems.

[0003] In recent years, as a new type of channel coding scheme, Polar codes have gradually become the core coding scheme in future wireless communications due to their error correction ability that theoretically approaches the Shannon limit. Polar codes achieve efficient utilization of channel resources by dividing the input bit stream into reliable and unreliable bit streams. Their greatest advantage lies in their adaptability to channel states, being able to adjust the coding method under different channel conditions to ensure communication quality.

[0004] However, due to the variability of the wireless communication environment, fluctuations in channel states often have a significant impact on communication performance. Especially under the influence of different environmental factors, the quality of the channel changes greatly.

[0005] The prior art, such as a code rate adaptation method based on Polar codes disclosed in the invention patent application with publication number: CN109032834B, includes the following steps: calculating the Bhattacharyya parameters of the bits in each storage unit, selecting the storage unit bits with lower Bhattacharyya parameters as information bits to construct the original Polar code; the constructed original Polar code passes through a Polar code encoder to obtain a coded sequence, and through Gray mapping to obtain a bit pair sequence; performing code rate adaptation design on the bit pair sequence to obtain the remaining storage codewords after puncturing; only transmitting the remaining codewords after puncturing through the MLC-type NAND flash memory storage channel; obtaining the read codewords that may be interfered by channel noise through a read operation; sending the read codewords to a Polar code adaptive decoder for initialization operation; obtaining the decoded codewords to realize the output of stored data. The present invention realizes the code rate adaptation construction design of Polar codes according to the characteristics of the MLC-type flash memory storage channel to improve the overall performance and efficiency of the storage system.

[0006] Based on the above solutions, it is found that in existing storage systems, especially those based on MLC NAND flash memory, there is generally a lack of dynamic adaptability to changes in channel conditions. As the storage density continues to increase, the number of bits stored in the storage units of MLC NAND flash memory becomes larger, resulting in an increased impact of channel noise on stored data, which in turn increases the read error rate of stored data. In this case, traditional error correction techniques, such as using conventional error correction codes, often have difficulty fully coping with the high bit error rate problems caused by unstable channel conditions. This not only reduces the reliability of the storage system but also greatly reduces the storage efficiency, leading to a high error rate and low storage efficiency. Moreover, existing technologies mainly adopt static error correction methods and coding schemes, which are usually not dynamically adjusted for real-time changes in the wireless channel during design. Specifically, it is difficult for existing technologies to optimize the coding method or adjust the code rate in real time according to different channel states. This makes the storage system prone to problems such as unstable performance and low transmission efficiency in some complex wireless environments, seriously affecting the overall performance of the storage system and the service life of the storage medium, and even easily leading to higher costs and maintenance pressures. Summary of the Invention

[0007] Aiming at the deficiencies of the prior art, the present invention provides a code rate adaptive method and system based on Polar codes, which solves the problems of the lack of channel state adaptability in the existing storage system, resulting in low storage efficiency and poor data reliability in a high-noise environment.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions.

[0009] The first aspect of the present invention provides a code rate adaptive system based on Polar codes, including:

[0010] A channel monitoring module, configured to obtain the channel state data of the current wireless channel, perform comprehensive analysis, and obtain the channel state set of the current wireless channel. The channel state data includes signal-to-noise ratio measurement values, bit error rate measurement values, transmission distance values, environmental state data within a set area, and receiving state data of the receiving end. The channel state set includes signal-to-noise ratio adjustment values, multipath change rate indices, bit error rate adjustment values, and channel attenuation indices;

[0011] A channel evaluation module, configured to perform comprehensive analysis on the channel state set of the current wireless channel to obtain the channel comprehensive quality evaluation index of the current wireless channel;

[0012] A code rate control module, configured to adjust the coding rate based on the channel comprehensive quality evaluation index of the current wireless channel.

[0013] Furthermore, the specific formula for calculating the channel comprehensive quality evaluation index of the current wireless channel is as follows:

[0014]

[0015] Among them, XzH is the channel comprehensive quality evaluation index of the current wireless channel, XzT is the signal-to-noise ratio adjustment value of the current wireless channel, λ1 is the signal-to-noise ratio evaluation coefficient stored in the database, DjB is the multipath change rate index of the current wireless channel, λ2 is the multipath change evaluation coefficient stored in the database, WmT is the bit error rate adjustment value of the current wireless channel, λ3 is the bit error rate evaluation coefficient stored in the database, XsZ is the channel attenuation index of the current wireless channel, and λ4 is the channel attenuation evaluation coefficient stored in the database.

[0016] Furthermore, the environmental state data includes the regional temperature value and the regional humidity value. The specific steps to obtain the signal-to-noise ratio adjustment value of the current wireless channel are as follows: Obtain the environmental state reference data within the set area of the current wireless channel, where the environmental state reference data includes the regional temperature reference value and the regional humidity reference value; Read the signal-to-noise ratio measurement value of the current wireless channel and the environmental state data within the set area, and perform comprehensive analysis in combination with the environmental state reference data within the set area of the current wireless channel to obtain the signal-to-noise ratio adjustment value of the current wireless channel.

[0017] Furthermore, the reception state data includes the signal strength measurement value, the signal phase standard deviation, and the magnetic field strength measurement value within the set area. The specific steps to obtain the multipath change rate index of the current wireless channel are as follows: Obtain the reception reference data of the receiving end of the current wireless channel, where the reception reference data includes the received signal strength standard deviation, the signal phase change reference value, and the magnetic field strength reference value within the set area; Read the reception state data of the receiving end of the current wireless channel, and perform comprehensive analysis in combination with the reception reference data of the receiving end of the current wireless channel to obtain the multipath change rate index of the current wireless channel.

[0018] Furthermore, the specific steps to obtain the bit error rate adjustment value of the current wireless channel are: Obtain the maximum bit error rate reference value of the current wireless channel, and perform comprehensive analysis in combination with the bit error rate measurement value of the current wireless channel to obtain the bit error rate adjustment value of the current wireless channel.

[0019] Furthermore, the specific steps to obtain the channel attenuation index of the current wireless channel are as follows: Obtain the initial channel attenuation data, where the initial channel attenuation data includes the initial channel transmission attenuation coefficient and the transmission attenuation distance reference value of the wireless channel; Perform comprehensive analysis on the transmission distance value of the current wireless channel, the environmental state data and the environmental state reference data within the set area, in combination with the initial channel attenuation data, to obtain the channel attenuation index of the current wireless channel.

[0020] Furthermore, the specific formula for calculating the channel attenuation index of the current wireless channel is as follows:

[0021]

[0022] Among them, XsZ is the channel attenuation exponent of the current wireless channel, CsJ is the initial attenuation coefficient of channel transmission, CjL is the transmission distance value of the current wireless channel, CjC is the reference value of the transmission attenuation distance of the wireless channel, β is the distance attenuation adjustment coefficient stored in the database, QwD is the regional temperature value within the set area of the current wireless channel, QwC is the reference value of the regional temperature within the set area of the current wireless channel, ξ1 is the temperature attenuation adjustment coefficient stored in the database, θ1 is the temperature attenuation influence coefficient stored in the database, QsD is the regional humidity value within the set area of the current wireless channel, QsC is the reference value of the regional humidity within the set area of the current wireless channel, ξ2 is the humidity attenuation adjustment coefficient stored in the database, and θ2 is the humidity attenuation influence coefficient stored in the database.

[0023] Further, the specific steps for adjusting the coding rate based on the channel comprehensive quality evaluation index of the current wireless channel are as follows: Obtain the current coding rate, the preset minimum coding rate, and the maximum coding rate of the current wireless channel, and conduct a comprehensive analysis in combination with the channel comprehensive quality evaluation index of the current wireless channel to obtain the coding rate adjustment value of the current wireless channel. The specific formula is as follows:

[0024]

[0025] Among them, BmT is the coding rate adjustment value of the current wireless channel, BmS is the current coding rate of the current wireless channel, BmD is the preset maximum coding rate of the current wireless channel, BmX is the preset minimum coding rate of the current wireless channel, XzH is the channel comprehensive quality evaluation index of the current wireless channel, ε is the coding control factor stored in the database, ψ is the coding rate control coefficient stored in the database, and μ is the coding rate adjustment coefficient stored in the database.

[0026] Further, it also includes: a Polar code encoding module for performing Polar code encoding on the data to be transmitted based on the coding rate adjustment value of the current wireless channel; a transmission module for transmitting the encoded data to be transmitted to the receiving end; and a decoding module for performing decoding and recovery processing after receiving the encoded data to be transmitted.

[0027] The second aspect of the present invention provides a code rate adaptation method based on Polar codes, including the following steps: In the channel monitoring module, obtain the channel state data of the current wireless channel, and conduct comprehensive analysis to obtain the channel state set of the current wireless channel. The channel state data includes signal-to-noise ratio measurement values, bit error rate measurement values, transmission distance values, environmental state data within a set area, and receiving state data of the receiving end. The channel state set includes signal-to-noise ratio adjustment values, multipath change rate exponents, bit error rate adjustment values, and channel attenuation exponents;

[0028] In the channel evaluation module, conduct comprehensive analysis on the channel state set of the current wireless channel to obtain the channel comprehensive quality evaluation index of the current wireless channel;

[0029] In the code rate control module, adjust the coding rate based on the channel comprehensive quality evaluation index of the current wireless channel;

[0030] In the Polar code encoding module, perform Polar code encoding on the data to be transmitted based on the coding rate adjustment value of the current wireless channel; In the transmission module, transmit the encoded data to be transmitted to the receiving end; In the decoding module, after receiving the encoded data to be transmitted, perform decoding and recovery processing.

[0031] The present invention has the following beneficial effects:

[0032] 1. The code rate adaptation system based on Polar codes provided by the present invention can, through the channel monitoring module, channel evaluation module, and code rate control module, obtain and analyze various channel state data of the current wireless channel in real time, such as signal-to-noise ratio, bit error rate, transmission distance, environmental state, etc. Through comprehensive analysis of these data, the system can obtain the channel comprehensive quality evaluation index of the wireless channel, and then dynamically adjust the coding rate, thereby ensuring that in different channel states, the system can automatically adjust the code rate to adapt to changes in channel quality, improve the stability and efficiency of data transmission, avoid problems such as decreased transmission efficiency and increased error rate caused by changes in channel conditions in static designs, and further optimize data transmission performance in different wireless environments.

[0033] 2. The rate adaptation system based on Polar codes provided by the present invention not only considers the direct influence of the channel state, but also combines environmental parameters such as temperature, humidity, and receiving state data at the receiving end such as signal strength, signal phase standard deviation, magnetic field strength, etc., further improving the sensitivity to changes in the wireless channel. Through the comprehensive analysis of these external environmental factors, the system can more accurately evaluate and adjust the signal quality, such as through signal-to-noise ratio adjustment, bit error rate adjustment, and channel attenuation exponent adjustment, enhancing the anti-interference ability of the system in harsh environments. Especially in areas with severe multipath effects, the system can effectively reduce signal distortion, ensure high reliability and transmission accuracy of data, and significantly improve the adaptability in complex wireless environments and the reliability of data transmission.

[0034] 3. The rate adaptation system based on Polar codes provided by the present invention adaptively adjusts the coding rate according to the comprehensive channel quality evaluation index. While ensuring the transmission quality, the system can avoid performance waste caused by setting the coding rate too high or too low. By combining the comprehensive analysis of the current coding rate, the highest and lowest coding rates, and the channel quality index, the system dynamically adjusts the coding rate, achieving efficient rate control. This adaptive design not only optimizes the transmission efficiency, but also reduces unnecessary energy consumption and hardware pressure. Especially in the case of good signal quality, it can reduce power consumption and extend the service life of the device. In addition, the automatic adjustment feature enables the system to adjust and optimize according to environmental changes without frequent manual intervention, reducing the operation cost and enhancing the economy and sustainability of the system.

[0035] 4. The rate adaptation method based on Polar codes provided by the present invention can accurately evaluate the channel quality and dynamically adjust the coding rate according to the comprehensive channel quality evaluation index by real-time acquiring and comprehensively analyzing channel state data such as signal-to-noise ratio, bit error rate, transmission distance, environmental data, and receiving state at the receiving end. This flexible adaptive adjustment mechanism enables the system to automatically optimize the transmission scheme according to real-time changes in the wireless channel, avoiding the inefficiency problems that may occur in traditional static systems in different environments. In an environment with large changes in wireless channel conditions, such as high interference or long-distance transmission, the system can respond in real-time and adjust the transmission strategy, thereby enhancing the transmission stability of the system in complex and changing environments. This method not only improves the reliability of data transmission, but also ensures that the system can provide stable performance in diverse application scenarios, enhancing the overall system's adaptive ability and environmental adaptability.

[0036] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1Block diagram of the code rate adaptive system based on Polar code of the present invention.

[0038] Figure 2 Specific step flowchart for obtaining the multipath change rate index of the current wireless channel in the code rate adaptive system based on Polar code of the present invention.

[0039] Figure 3 Flowchart of the code rate adaptive method based on Polar code of the present invention. Detailed implementation manners

[0040] Please refer to Figure 1 , an embodiment of the present invention provides a code rate adaptive system based on Polar code, including: a channel monitoring module, configured to obtain the channel state data of the current wireless channel, and perform comprehensive analysis to obtain the channel state set of the current wireless channel, where the channel state data includes signal-to-noise ratio measurement values (which can be measured and obtained by a receiving end device in the wireless channel using signal and noise measurement tools), bit error rate measurement values (which can be calculated by comparing the transmitted data at the receiving end), transmission distance values (i.e., the actual transmission distance between the transmitting end and the receiving end, which can be measured by a measuring device or based on a global positioning system), environmental state data within a set area, and receiving state data of the receiving end, and the channel state set includes signal-to-noise ratio adjustment values, multipath change rate indices, bit error rate adjustment values, and channel attenuation indices; a channel evaluation module, configured to perform comprehensive analysis on the channel state set of the current wireless channel to obtain the channel comprehensive quality evaluation index of the current wireless channel; and a code rate control module, configured to adjust the coding rate based on the channel comprehensive quality evaluation index of the current wireless channel.

[0041] The specific formula for calculating the channel comprehensive quality evaluation index of the current wireless channel is as follows:

[0042]

[0043] Wherein, XzH is the channel comprehensive quality evaluation index of the current wireless channel, XzT is the signal-to-noise ratio adjustment value of the current wireless channel, λ1 is the signal-to-noise ratio evaluation coefficient stored in the database, DjB is the multipath change rate index of the current wireless channel, λ2 is the multipath change evaluation coefficient stored in the database, WmT is the bit error rate adjustment value of the current wireless channel, λ3 is the bit error rate evaluation coefficient stored in the database, XsZ is the channel attenuation index of the current wireless channel, and λ4 is the channel attenuation evaluation coefficient stored in the database.

[0044] It should be noted that the specific steps for obtaining the signal-to-noise ratio evaluation coefficient λ1, multipath change evaluation coefficient λ2, bit error rate evaluation coefficient λ3, and channel attenuation evaluation coefficient λ4 stored in the database are as follows: These coefficients are obtained by analyzing the state of the current channel based on the channel data and environmental parameters stored in the database. First, obtain the signal measurement data (such as signal-to-noise ratio, bit error rate, etc.) of the current wireless channel, and conduct a comprehensive analysis in combination with the environmental data (such as temperature, humidity, etc.) of the set area; then, calculate the corresponding evaluation coefficients by analyzing factors such as multipath change, signal strength, and attenuation; finally, store these coefficients in the database as reference values for channel evaluation to facilitate subsequent channel quality evaluation and coding rate adjustment.

[0045] Among them, the specific implementation example of calculating the channel comprehensive quality evaluation index of the current wireless channel is as follows. The following parameters are available:

[0046] The signal-to-noise ratio adjustment value of the current wireless channel is approximately: 1.245.

[0047] The signal-to-noise ratio evaluation coefficient stored in the database is approximately: 1.538.

[0048] The multipath change rate index of the current wireless channel is approximately: 0.762.

[0049] The multipath change evaluation coefficient stored in the database is approximately: 0.812.

[0050] The bit error rate adjustment value of the current wireless channel is approximately: 0.005.

[0051] The bit error rate evaluation coefficient stored in the database is approximately: 0.25.

[0052] The channel attenuation index of the current wireless channel is approximately: 0.586.

[0053] The channel attenuation evaluation coefficient stored in the database is approximately: 1.197.

[0054] Substitute the above data into the specific formula for calculating the channel comprehensive quality evaluation index of the current wireless channel respectively, and we get:

[0055] The channel comprehensive quality evaluation index of the current wireless channel = (1 + ((((1.245) 1.538 *(0.762) 0.812 ) - ((0.005) 0.25 *(0.586) 1.197 )) / ((((1.245) 1.538 *(0.762) 0.812 ) + ((0.005) 0.25 *(0.586) 1.197 ))))1 / (1.538+0.812+0.25+1.197) ≈1.164。

[0056] Specifically, the environmental state data includes the regional temperature value (which can be measured and obtained through a temperature sensor) and the regional humidity value (which can be measured and obtained through a humidity sensor). The specific steps to obtain the signal-to-noise ratio adjustment value of the current wireless channel are as follows: Obtain the environmental state reference data within the set area of the current wireless channel. The environmental state reference data includes the regional temperature reference value (usually obtained by analyzing historical environmental data or experimental data) and the regional humidity reference value (also obtained by analyzing historical environmental data or experimental data); Read the measured signal-to-noise ratio value of the current wireless channel and the environmental state data within the set area, and conduct comprehensive analysis in combination with the environmental state reference data within the set area of the current wireless channel to obtain the signal-to-noise ratio adjustment value of the current wireless channel.

[0057] The specific formula for calculating the signal-to-noise ratio adjustment value of the current wireless channel is as follows:

[0058] Among them, XzT is the signal-to-noise ratio adjustment value of the current wireless channel, XzC is the measured signal-to-noise ratio value of the current wireless channel, QwD is the regional temperature value within the set area of the current wireless channel, QwC is the regional temperature reference value within the set area of the current wireless channel, α1 is the signal-temperature adjustment coefficient stored in the database (i.e., the adjustment coefficient of temperature on the signal-to-noise ratio), δ1 is the signal-temperature influence coefficient stored in the database (i.e., the influence coefficient of temperature on the signal-to-noise ratio), QsD is the regional humidity value within the set area of the current wireless channel, QsC is the regional humidity reference value within the set area of the current wireless channel, α2 is the signal-humidity adjustment coefficient stored in the database (i.e., the adjustment coefficient of humidity on the signal-to-noise ratio), and δ2 is the signal-humidity influence coefficient stored in the database (i.e., the influence coefficient of humidity on the signal-to-noise ratio).

[0059] It should be explained that the specific steps to obtain the signal-temperature adjustment coefficient α1 and the signal-humidity adjustment coefficient α2 stored in the database are as follows: These adjustment coefficients are evaluated based on the influence of the actual temperature and humidity changes of the wireless channel on the signal quality. First, obtain the temperature and humidity data in the area where the current channel is located and compare and analyze them with the historical environmental data to obtain the specific influence of temperature and humidity on the signal quality. Then, through statistical analysis of these data, calculate the influence degree of temperature and humidity on the channel, and finally obtain the signal-temperature adjustment coefficient and the signal-humidity adjustment coefficient. These coefficients reflect the actual adjustment effect of environmental temperature and humidity changes on the signal quality.

[0060] The specific steps for obtaining the signal temperature influence coefficient δ1 and the signal humidity influence coefficient δ2 stored in the database are as follows: These influence coefficients are obtained through comprehensive analysis of the influence of channel temperature and humidity on the signal. First, collect and store the temperature and humidity data of multiple regions, and combine with the channel state data to analyze the specific influence of temperature and humidity on the signal quality. Then, use models or empirical formulas to calculate the signal temperature influence coefficient and the signal humidity influence coefficient. These coefficients reflect the degree of signal attenuation in different temperature and humidity environments. Finally, store these coefficients in the database for subsequent signal adjustment and optimization.

[0061] In this implementation plan, by precisely considering the influence of temperature and humidity on the wireless channel, the signal quality and transmission efficiency are optimized. First, by obtaining and analyzing the temperature and humidity data in the region in real time and combining with the signal-to-noise ratio measurement value, the system can dynamically adjust the signal-to-noise ratio to ensure the stability of transmission under different environmental conditions. The introduction of the signal temperature adjustment coefficient and the signal humidity adjustment coefficient enables the system to respond flexibly to environmental changes. Through these coefficients, the system can quantify the influence of environmental factors on the signal quality and adjust the signal processing strategy accordingly. By collecting historical environmental data and comparing it with the current conditions, it can ensure optimized transmission effects in various environments. This temperature and humidity-based adjustment mechanism not only improves the adaptability of the wireless communication system in practical applications but also significantly reduces signal attenuation caused by environmental changes, reduces the bit error rate, thereby improving the reliability and efficiency of data transmission. At the same time, the signal temperature influence coefficient and the signal humidity influence coefficient provide a more refined adjustment basis, enabling precise control of the degree of signal attenuation, thus enhancing the overall performance of the system. Finally, the calculation and storage processes of all these adjustment coefficients and influence coefficients can provide data support for subsequent channel optimization, system upgrade, and actual operation, ensuring the long-term operation of the system in complex environments and ultimately enhancing the economy and sustainability of the wireless communication system.

[0062] Specifically, as Figure 2As shown, the received status data includes the signal strength measurement value (which is measured by the receiver at the receiving end. The receiver uses a radio wave receiving device to capture the signal sent from the transmitting end and then calculates the signal strength), the standard deviation of the signal phase (obtained through statistical analysis of the fluctuations of the signal phase. The receiver at the receiving end measures the phase of the received signal and samples multiple times, and calculates the deviation of these phase values), and the magnetic field strength measurement value within the set area (obtained through measurement by a magnetic field sensor). The specific steps to obtain the multipath change rate index of the current wireless channel are as follows: Obtain the received reference data of the receiving end of the current wireless channel. The received reference data includes the standard deviation of the received signal strength (obtained through statistical analysis of multiple signal strength measurements), the reference value of the signal phase change (obtained through measurement and analysis of the signal phase changes under different times, locations, and environmental conditions), and the reference value of the magnetic field strength within the set area (which can be obtained through historical environmental data and measurements); Read the received status data of the receiving end of the current wireless channel and perform comprehensive analysis in combination with the received reference data of the receiving end of the current wireless channel to obtain the multipath change rate index of the current wireless channel.

[0063] Among them, the specific formula for calculating the multipath change rate index of the current wireless channel is as follows:

[0064]

[0065] Among them, DjB is the multipath change rate index of the current wireless channel, JqB is the standard deviation of the received signal strength of the receiving end of the current wireless channel, JsQ is the measured value of the received signal strength of the receiving end of the current wireless channel, ω1 is the received signal strength adjustment coefficient stored in the database, XxB is the standard deviation of the signal phase of the receiving end of the current wireless channel, XbC is the reference value of the signal phase change of the receiving end of the current wireless channel, ω2 is the signal phase adjustment coefficient stored in the database, CqZ is the measured value of the magnetic field strength within the set area of the receiving end of the current wireless channel, CqC is the reference value of the magnetic field strength within the set area of the receiving end of the current wireless channel, and ω3 is the magnetic field strength adjustment coefficient stored in the database.

[0066] It should be explained that the specific steps for obtaining the received signal strength adjustment coefficient ω1, signal phase adjustment coefficient ω2, and magnetic field strength adjustment coefficient ω3 stored in the database are as follows: First, the system measures the signal strength, signal phase, and magnetic field strength data of the current receiving end, and analyzes them in combination with the environmental factors in the set area. These data are used to evaluate the changes in the signal under different environments. Then, by comparing the historical data with the current measurement results, the adjustment coefficients of the signal strength, phase, and magnetic field strength are calculated. This process involves a comprehensive evaluation of environmental parameters such as temperature, humidity, and other physical conditions to ensure that these adjustment coefficients can reflect the specific impact of environmental changes on signal quality. Finally, all the calculated adjustment coefficients are stored in the database for subsequent applications to ensure that the system can adjust the signal processing strategy according to different signal conditions and environmental conditions.

[0067] In this implementation plan, by comprehensively considering the adjustment coefficients of the received signal strength, signal phase, and magnetic field strength, as well as their correlation with environmental factors, the system can accurately reflect the impact of environmental changes on signal quality, thereby optimizing signal transmission and processing. In wireless communication, the quality of the signal is affected by various factors such as multipath effects, signal attenuation, and interference. Especially in complex environments, traditional signal processing methods often struggle to effectively handle these dynamic changes. By combining environmental factors such as temperature and humidity and adjusting the adjustment coefficients of signal strength, phase, and magnetic field strength in real time, the system can respond more flexibly and precisely to different signal states and environmental changes. Through the comparative analysis of historical data and real-time measurements, the system can calculate the degree of impact of the environment on signal quality and adjust relevant parameters accordingly to achieve the best signal transmission effect. This dynamic adjustment mechanism ensures that under different environmental conditions, the stability and transmission efficiency of the signal can be guaranteed, reducing the bit error rate and improving the anti-interference ability of the signal. In addition, all adjustment coefficients are stored in the database to ensure the continuous optimization and precise control of the signal processing process, providing data support and technical guarantee for the long-term reliable operation of the wireless communication system, thereby enhancing the overall performance and sustainability of the system.

[0068] Specifically, the specific steps for obtaining the bit error rate adjustment value of the current wireless channel are as follows: Obtain the maximum reference value of the bit error rate of the current wireless channel, and conduct a comprehensive analysis in combination with the measured value of the bit error rate of the current wireless channel to obtain the bit error rate adjustment value of the current wireless channel.

[0069] The specific formula for calculating the bit error rate adjustment value of the current wireless channel is as follows:

[0070]

[0071] Among them, WmT is the error rate adjustment value of the current wireless channel, π is the pi, which takes the value of 3.14 in this embodiment, CwM is the error rate measurement value of the current wireless channel, WmC is the maximum reference value of the error rate of the current wireless channel, and η is the error rate adjustment coefficient stored in the database.

[0072] It should be explained that the specific steps for obtaining the error rate adjustment coefficient η stored in the database are as follows: First, by real-time monitoring the error rate data of the wireless channel, combining historical records and current environmental conditions (such as temperature, humidity, etc.), statistical analysis is carried out on the channel error rate. Then, according to the comparison between the measurement result of the error rate and the set maximum error rate standard value, the adjustment range of the error rate is determined. Then, a mathematical model or empirical formula is used to calculate the error rate adjustment coefficient, which reflects the specific impact of the change in the channel error rate on the transmission process. Finally, this adjustment coefficient is stored in the database as the basis for adjusting the coding strategy and optimizing the transmission efficiency in the subsequent signal transmission process.

[0073] In this implementation scheme, a dynamic adjustment mechanism based on real-time data and environmental factors is provided to help optimize the error rate performance of the wireless channel. By obtaining the maximum reference value and real-time measurement value of the error rate of the wireless channel, and combining the error rate adjustment coefficient, the system can timely evaluate and adjust the signal transmission strategy to ensure the efficiency and stability of data transmission. In a wireless communication system, the error rate is one of the key indicators for evaluating system performance. Especially in the case of large environmental changes, the channel error rate is often affected by various factors, such as temperature, humidity, etc. Through comprehensive analysis of these influencing factors, the system can automatically adjust the signal coding strategy under different environmental conditions, thereby avoiding the negative impact of high error rates on system performance. By comparing historical data and real-time measurement results, the error rate adjustment coefficient can accurately reflect the change in channel quality and optimize the coding rate and transmission strategy of the system in real time, reducing errors in transmission and improving the efficiency and reliability of data transmission. In addition, all adjustment coefficients are stored in the database, providing a flexible coping strategy for the system to ensure the best performance in different environments. This method effectively improves the robustness, anti-interference ability and long-term stability of the wireless communication system, and enhances the adaptability and sustainability of the system.

[0074] Specifically, the specific steps for obtaining the channel attenuation index of the current wireless channel are as follows: Obtain the initial channel attenuation data, which includes the initial transmission attenuation coefficient of the channel and the reference value of the transmission attenuation distance of the wireless channel; comprehensively analyze the transmission distance value of the current wireless channel, the environmental state data and the environmental state reference data within the set area, combined with the initial channel attenuation data, to obtain the channel attenuation index of the current wireless channel.

[0075] Among them, the initial attenuation coefficient of channel transmission represents an initial or reference description of channel attenuation under a reference environment (i.e., standard temperature and humidity, reference distance, etc.), and can be understood as "the attenuation starting point in an ideal environment and at the reference distance".

[0076] The reference value of the transmission attenuation distance of a wireless channel is usually a fixed distance selected in an experiment or theoretical model. At this distance, a "reference" signal power or attenuation value can be measured or defined. It serves as a standard for comparing the signal attenuation levels at different actual transmission distances. For example, in the free-space propagation model, 1 meter can be selected as the reference distance, and the signal strength at 1 meter is used as the reference.

[0077] The specific formula for calculating the channel attenuation exponent of the current wireless channel is as follows:

[0078]

[0079] Among them, XsZ is the channel attenuation exponent of the current wireless channel, CsJ is the initial attenuation coefficient of channel transmission, CjL is the transmission distance value of the current wireless channel, CjC is the reference value of the transmission attenuation distance of the wireless channel, β is the distance attenuation adjustment coefficient stored in the database (i.e., the adjustment coefficient of transmission distance for channel attenuation), QwD is the regional temperature value within the set area of the current wireless channel, QwC is the reference regional temperature value within the set area of the current wireless channel, ξ1 is the temperature attenuation adjustment coefficient stored in the database (i.e., the adjustment coefficient of temperature for channel attenuation), θ1 is the temperature attenuation influence coefficient stored in the database (i.e., the influence coefficient of temperature on channel attenuation), QsD is the regional humidity value within the set area of the current wireless channel, QsC is the reference regional humidity value within the set area of the current wireless channel, ξ2 is the humidity attenuation adjustment coefficient stored in the database (i.e., the adjustment coefficient of humidity for channel attenuation), and θ2 is the humidity attenuation influence coefficient stored in the database (i.e., the influence coefficient of humidity on channel attenuation).

[0080] It should be explained that the specific steps for obtaining the distance attenuation adjustment coefficient β stored in the database are as follows: First, the system analyzes the attenuation pattern of the signal with distance by obtaining the actual transmission distance data of the wireless channel and combining the statistical information on signal attenuation in historical data. Then, through experiments or simulations on the signal strength under different distance conditions, the adjustment amplitude of the distance attenuation coefficient is determined and dynamically adjusted according to environmental changes. Finally, this adjustment coefficient is stored in the database for subsequent channel optimization and transmission efficiency improvement.

[0081] The specific steps for obtaining the temperature attenuation adjustment coefficient ξ1 and the humidity attenuation adjustment coefficient ξ2 stored in the database are as follows: Obtained by analyzing the influence of temperature and humidity on signal transmission. First, through experimental measurement of signal strength under different temperature and humidity conditions, collect the corresponding signal attenuation data and compare it with historical environmental data. Then, apply statistical methods or physical models to calculate the influence amplitude of temperature and humidity on the signal, and obtain the temperature attenuation adjustment coefficient and the humidity attenuation adjustment coefficient. Finally, these coefficients are stored in the database for use in subsequent channel evaluation and optimization processes.

[0082] The specific steps for obtaining the temperature attenuation influence coefficient θ1 and the humidity attenuation influence coefficient θ2 stored in the database are as follows: First, through comprehensive measurement of the signal strength change under different temperature and humidity conditions, combined with the specific data of the channel environment, evaluate the influence of temperature and humidity on signal quality. Then, use these data to calculate the specific influence of temperature change and humidity change on signal attenuation, and obtain the temperature attenuation influence coefficient and the humidity attenuation influence coefficient. Finally, based on these calculation results, store the temperature attenuation influence coefficient and the humidity attenuation influence coefficient in the database to provide a basis for subsequent channel optimization and transmission adjustment.

[0083] In this implementation plan, by precisely considering the influence of channel attenuation and environmental changes on the signal, the transmission quality and efficiency of the wireless channel are optimized. Specifically, the calculation of the channel attenuation exponent, the temperature attenuation adjustment coefficient, the humidity attenuation adjustment coefficient, and their influence coefficients enables the system to dynamically adjust the transmission strategy to ensure the best signal quality under different environmental conditions. In wireless communication, signal attenuation is a key factor, especially in long-distance transmission and complex environments. By obtaining environmental parameters such as transmission distance, temperature, and humidity in real time, combined with historical data and real-time measurement, the system can accurately evaluate the degree of signal attenuation and make optimization adjustments in a timely manner. This dynamic adjustment mechanism based on environmental changes can effectively improve the stability of the signal in different environments, reduce the error rate caused by attenuation, and improve the transmission efficiency. In addition, by storing these coefficients in the database, the system can achieve intelligent and adaptive channel optimization, avoiding the limitations of traditional static models that are difficult to cope with real-time changes. Finally, this method not only improves the transmission performance and stability of the wireless channel, but also provides continuous data support and technical guarantee for channel optimization during long-term operation, ensuring the reliability and sustainability of the system.

[0084] Specifically, the specific steps for adjusting the coding rate based on the channel comprehensive quality evaluation index of the current wireless channel are as follows: Obtain the current coding rate, the preset minimum coding rate, and the maximum coding rate of the current wireless channel, and conduct comprehensive analysis in combination with the channel comprehensive quality evaluation index of the current wireless channel to obtain the coding rate adjustment value of the current wireless channel.

[0085] Among them, the specific formula for calculating the coding rate adjustment value of the current wireless channel is as follows:

[0086]

[0087] Among them, BmT is the coding rate adjustment value of the current wireless channel, BmS is the current coding rate of the current wireless channel, BmD is the preset maximum coding rate of the current wireless channel, BmX is the preset minimum coding rate of the current wireless channel, XzH is the channel comprehensive quality evaluation index of the current wireless channel, ε is the coding control factor stored in the database, which is used to prevent the denominator from being 0, ψ is the coding rate control coefficient stored in the database, and μ is the coding rate adjustment coefficient stored in the database.

[0088] It should be explained that the specific acquisition steps of the coding rate control coefficient ψ and the coding rate adjustment coefficient μ stored in the database are as follows: First, by analyzing the historical wireless channel state data and combining the transmission efficiency and error rate under different channel quality conditions, the corresponding coding rate control coefficients are calculated. These coefficients reflect the maximum coding rate that the channel can withstand and its variation law in different wireless environments. Then, according to the quality evaluation of the real-time channel, the current coding rate adjustment coefficient is adjusted by using the coding rate adjustment algorithm to optimize the transmission efficiency of the signal. This process combines the channel state data, environmental changes, and transmission objectives. Finally, these control and adjustment coefficients are stored in the database for the system to dynamically adjust and optimize the coding rate.

[0089] In this implementation plan, by dynamically adjusting the coding rate, it is ensured that the wireless channel can achieve the best transmission efficiency and performance under different environmental conditions. Specifically, the system dynamically adjusts the coding rate by comprehensively analyzing the channel comprehensive quality evaluation index, the current coding rate, and the preset coding rate range to ensure the stability and efficiency of signal transmission. In wireless communication, the fluctuation of channel quality will lead to a decrease in transmission efficiency, especially in an environment with large channel attenuation or strong noise. The traditional fixed coding rate often fails to meet the actual requirements. By introducing the coding rate control coefficient and the coding rate adjustment coefficient, the system can automatically optimize the coding rate according to the real-time evaluated channel quality, thereby improving the transmission efficiency and reducing the error rate. These coefficients are obtained by analyzing historical data and comparing real-time measurement results, ensuring the accuracy and flexibility of coding rate adjustment. In addition, all adjustment coefficients are stored in the database, providing continuous data support for subsequent system optimization. By this method, the system can not only maintain stable performance in a complex environment but also effectively cope with environmental changes, improve the overall communication quality and reliability, and ensure the efficient operation of the wireless communication system.

[0090] Specifically, it further includes: a Polar code encoding module, configured to perform Polar code encoding on data to be transmitted based on the encoding rate adjustment value of the current wireless channel; a transmission module, configured to transmit the encoded data to be transmitted to the receiving end (of the current wireless channel); and a decoding module, configured to perform decoding and recovery processing after receiving the encoded data to be transmitted.

[0091] In this embodiment, through a complete signal processing flow, it is ensured that the wireless communication system can efficiently and reliably transmit data under different channel conditions. First, the Polar code encoding module performs data encoding according to the encoding rate adjustment value of the current wireless channel to optimize the transmission efficiency of the signal and minimize the bit error rate. Due to its superior error correction ability, the Polar code can improve the robustness of the system in an environment with low signal-to-noise ratio and ensure the smooth transmission of data in an unstable channel. Secondly, the transmission module is responsible for transmitting the encoded data to the receiving end, and by real-time evaluating the wireless channel conditions, the stability and efficiency of data transmission can be ensured. Finally, the decoding module further improves the reliability of the data by performing decoding and recovery on the received data and can effectively handle errors caused by channel noise or attenuation. The entire process ensures that the wireless communication system can provide efficient and high-quality communication services in various environments by dynamically adjusting the encoding rate and optimizing the signal processing strategy. Through this modular design, the system not only improves the transmission efficiency but also enhances the adaptability and anti-interference ability of the system, and can maintain stable communication quality in a complex environment, thereby improving the overall performance and long-term stability of the wireless communication system.

[0092] Please refer to Figure 3 , an embodiment of the present invention provides a code rate adaptation method based on Polar codes, including the following steps: In the channel monitoring module, obtain the channel state data of the current wireless channel and perform comprehensive analysis to obtain the channel state set of the current wireless channel. The channel state data includes the signal-to-noise ratio measurement value, the bit error rate measurement value, the transmission distance value, the environmental state data within the set area, and the receiving state data of the receiving end. The channel state set includes the signal-to-noise ratio adjustment value, the multipath change rate index, the bit error rate adjustment value, and the channel attenuation index; In the channel evaluation module, perform comprehensive analysis on the channel state set of the current wireless channel to obtain the channel comprehensive quality evaluation index of the current wireless channel; In the code rate control module, adjust the encoding rate based on the channel comprehensive quality evaluation index of the current wireless channel; In the Polar code encoding module, perform Polar code encoding on the data to be transmitted based on the encoding rate adjustment value of the current wireless channel; In the transmission module, transmit the encoded data to be transmitted to the receiving end (of the current wireless channel); In the decoding module, perform decoding and recovery processing after receiving the encoded data to be transmitted.

[0093] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0094] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A rate adaptive system based on Polar code, characterized in that: include: A channel monitoring module is used to obtain the channel state data of the current wireless channel and perform comprehensive analysis to obtain the channel state set of the current wireless channel, wherein the channel state data includes a signal-to-noise ratio measurement value, a bit error rate measurement value, a transmission distance value, environmental state data in a set area, and receiving state data of the receiving end, and the channel state set includes a signal-to-noise ratio adjustment value, a multipath change rate index, a bit error rate adjustment value, and a channel attenuation index; A channel evaluation module is used to comprehensively analyze the channel state set of the current wireless channel to obtain a channel comprehensive quality evaluation index of the current wireless channel; The code rate control module is used to adjust the coding rate based on the channel comprehensive quality evaluation index of the current wireless channel.

2. The code rate adaptive system based on Polar code according to claim 1, characterized in that: The specific formula for calculating the channel comprehensive quality evaluation index of the current wireless channel is as follows: Among them, XzH, XzT, DjB, WmT, and XsZ are the comprehensive channel quality evaluation index, signal-to-noise ratio adjustment value, multipath change rate index, bit error rate adjustment value, and channel attenuation index of the current wireless channel respectively, and λ1, λ2, λ3, and λ4 are the signal-to-noise ratio evaluation coefficient, multipath change evaluation coefficient, bit error rate evaluation coefficient, and channel attenuation evaluation coefficient stored in the database respectively.

3. The rate adaptive system based on Polar code according to claim 1, characterized in that: The environmental status data includes regional temperature value and regional humidity value. The specific steps of obtaining the signal-to-noise ratio adjustment value of the current wireless channel are as follows: Acquire environmental state parameter data within the current wireless channel setting area, wherein the environmental state parameter data includes regional temperature parameter values ​​and regional humidity parameter values; The signal-to-noise ratio measurement value of the current wireless channel and the environmental status data in the set area are read, and a comprehensive analysis is performed in combination with the environmental status parameter data in the set area of ​​the current wireless channel to obtain the signal-to-noise ratio adjustment value of the current wireless channel.

4. The rate adaptive system based on Polar code according to claim 1, characterized in that: The receiving state data includes a signal strength measurement value, a signal phase standard deviation, and a magnetic field strength measurement value within a set area. The specific steps for obtaining the multipath change rate index of the current wireless channel are as follows: Acquire receiving parameter data of the receiving end of the current wireless channel, wherein the receiving parameter data includes a standard deviation of the received signal strength, a signal phase change parameter value, and a magnetic field strength parameter value within a set area; The receiving state data of the receiving end of the current wireless channel is read, and combined with the receiving parameter data of the receiving end of the current wireless channel, a comprehensive analysis is performed to obtain the multipath change rate index of the current wireless channel.

5. The code rate adaptive system based on Polar code according to claim 1, characterized in that: The specific steps of obtaining the bit error rate adjustment value of the current wireless channel are: obtaining the maximum parameter value of the bit error rate of the current wireless channel, and performing comprehensive analysis in combination with the bit error rate measurement value of the current wireless channel to obtain the bit error rate adjustment value of the current wireless channel.

6. The rate adaptive system based on Polar code according to claim 3, characterized in that: The specific steps for obtaining the channel attenuation index of the current wireless channel are as follows: Acquire initial channel attenuation data, wherein the initial channel attenuation data includes an initial attenuation coefficient of channel transmission and a transmission attenuation distance parameter value of the wireless channel; The transmission distance value of the current wireless channel, the environmental status data in the set area, the environmental status parameter data, and the initial data of the channel attenuation are comprehensively analyzed to obtain the channel attenuation index of the current wireless channel.

7. The rate adaptive system based on Polar code according to claim 6, characterized in that: The specific formula for calculating the channel attenuation index of the current wireless channel is as follows: Among them, XsZ is the channel attenuation index of the current wireless channel, CsJ is the initial attenuation coefficient of channel transmission, CjC is the transmission attenuation distance parameter value of the wireless channel, CjL, QwD, QwC, QsD, QsC are the transmission distance value of the current wireless channel, the regional temperature value in the set area, the regional temperature parameter value, the regional humidity value, the regional humidity parameter value respectively, β, ξ1, θ1, ξ2, θ2 are the distance attenuation adjustment coefficient, temperature attenuation adjustment coefficient, temperature attenuation influence coefficient, moisture attenuation adjustment coefficient, moisture attenuation influence coefficient stored in the database respectively.

8. The rate adaptive system based on Polar code according to claim 1, characterized in that: The specific steps for adjusting the coding rate based on the channel comprehensive quality assessment index of the current wireless channel are as follows: The current coding rate, the preset minimum coding rate, and the maximum coding rate of the current wireless channel are obtained, and a comprehensive analysis is performed in combination with the channel comprehensive quality evaluation index of the current wireless channel to obtain the coding rate adjustment value of the current wireless channel. The specific formula is as follows: Among them, BmT, BmS, BmD, BmX, and XzH are the coding rate adjustment value of the current wireless channel, the current coding rate, the preset maximum coding rate, the minimum coding rate, and the channel comprehensive quality assessment index respectively, and ε, ψ, and μ are the coding control factor, coding rate control coefficient, and coding rate adjustment coefficient stored in the database respectively.

9. The rate adaptive system based on Polar code according to claim 1, characterized in that: Also includes: A Polar code encoding module, used for performing Polar code encoding of data to be transmitted based on a coding rate adjustment value of a current wireless channel; A transmission module, used for transmitting the encoded data to be transmitted to a receiving end; The decoding module is used to perform decoding and recovery processing after receiving the encoded data to be transmitted.

10. A rate adaptation method based on Polar code, using the rate adaptation system based on Polar code according to any one of claims 1 to 9, characterized in that: The following steps are involved: In the channel monitoring module, the channel state data of the current wireless channel is obtained, and a comprehensive analysis is performed to obtain the channel state set of the current wireless channel, wherein the channel state data includes a signal-to-noise ratio measurement value, a bit error rate measurement value, a transmission distance value, environmental state data in a set area, and receiving state data of the receiving end, and the channel state set includes a signal-to-noise ratio adjustment value, a multipath change rate index, a bit error rate adjustment value, and a channel attenuation index; In the channel evaluation module, a channel state set of the current wireless channel is comprehensively analyzed to obtain a channel comprehensive quality evaluation index of the current wireless channel; In the code rate control module, the coding rate is adjusted based on the channel comprehensive quality assessment index of the current wireless channel; In the Polar code encoding module, Polar code encoding of the data to be transmitted is performed based on the coding rate adjustment value of the current wireless channel; In the transmission module, the encoded data to be transmitted is transmitted to the receiving end; In the decoding module, after receiving the encoded data to be transmitted, decoding and recovery processing is performed.

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