Multi-node ranging method and system based on code division multiple access

Abstract

The invention relates to the technical field of multi-node ranging, and particularly discloses a multi-node ranging method and system based on code division multiple access, and the method comprises the steps: S1, calculating the network complexity, obtaining the bandwidth when the network complexity exceeds a certain range, and calculating the channel capacity; s2, calculating transmission time, calculating a transmission time ratio, correcting the bandwidth based on the transmission time ratio to obtain a corrected bandwidth, and obtaining corrected time; and S3, setting the bandwidth as a modified bandwidth, sending bytes with the same number to the test nodes, obtaining actual transmission time, screening qualified nodes, presetting a byte number gradient, obtaining a ladder coefficient when the requirement is met, sorting the test nodes, retaining efficient nodes and adjusting the corresponding bandwidth as the modified bandwidth. According to the invention, the byte data transmission frequency in the multi-node ranging process can be reduced, the positioning delay is reduced, and the response speed is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-node ranging, and in particular to a multi-node ranging method and system based on code division multiple access. Background Art

[0002] Multi-node ranging involves measuring the distance or relative position between multiple nodes, such as sensors, base stations, or mobile devices, through collaboration. It is commonly used in wireless sensor networks, indoor positioning, drone fleets, the Internet of Things, and other fields. Multi-node ranging calculates the distance between nodes based on signal propagation characteristics such as time, strength, phase, or geometric relationships. Nodes interact via wireless signals, measuring information such as signal propagation time and strength, and then using a formula to convert this information into distance.

[0003] Existing multi-node ranging technologies face a bottleneck in node number. As network size expands, the complexity of inter-node communication explodes exponentially. When a network consists of n nodes, the number of communication links required for fully interconnected measurement follows a quadratic growth law of n(n-1) / 2. This not only leads to a sharp increase in communication overhead but also a significant increase in positioning latency. When the number of nodes exceeds 50, the latency for completing a full-network ranging measurement often exceeds the real-time threshold of 100 milliseconds, severely limiting timeliness. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-node ranging method and system based on code division multiple access to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The multi-node ranging method and system based on code division multiple access include the following steps:

[0007] S1: Calculating network complexity Where I represents the number of nodes in the network, N i represents the number of bytes sent by the i-th node, λ i represents the node degree of the i-th node;

[0008] When network complexity F>F sta When the bandwidth B is obtained and the channel capacity C = Blog2(1+SNR), F sta represents the preset standard complexity, and SNR represents the signal-to-noise ratio when the node transmits information;

[0009] S2: Calculate the transmission time T = D / C, where D represents the total number of bytes sent by the node. Calculate the transmission time ratio Z = T / 100. Based on the transmission time ratio Z, correct the bandwidth to obtain the corrected bandwidth BX. Repeat the above steps to obtain the corrected time TX.

[0010] S3: Set the bandwidth to the corrected bandwidth BX and send the same number of bytes to J test nodes, obtain the actual transmission time TS, and record it as a qualified node if TS ≤ TX, where the test node represents the node that receives the bytes, J represents the number of test nodes, and J = I-1.

[0011] A byte number gradient DT = ηD is preset, where the step coefficient η = 1, 2, 3, ..., and the actual transmission time is repeatedly obtained with the byte number gradient to obtain the corresponding step coefficient η when TS > TX. The test nodes are sorted in descending order according to the step coefficient η, and the top 60% of the test nodes in the sorting are recorded as efficient nodes. The efficient nodes are retained and the corresponding bandwidth is adjusted to the corrected bandwidth BX.

[0012] As a further solution of the present invention: in step S1, the out-degree λ of the i-th node is obtained. i_out and indegree λ i_in , then the node degree λ of the i-th node i =λ i_out +λ i_in .

[0013] As a further solution of the present invention: in step S1, the network complexity F≤F sta When , get the maximum value of the transmission time T at this time max , if the maximum transmission time T max <100, stop the subsequent steps.

[0014] As a further solution of the present invention: in the step S2, the bandwidth is corrected based on the transmission time ratio Z to obtain a corrected bandwidth BX=Z*B.

[0015] As a further solution of the present invention: in the step S3, if the bytes received by the test node are missing, the data transmission is recorded as invalid transmission, and the invalid transmission is not included in the subsequent calculation process.

[0016] As a further solution of the present invention: in the step S3, when there are test nodes with equal step coefficients η, the test points with smaller signal-to-noise ratios SNR are placed at the front in the sorting.

[0017] As a further solution of the present invention: in the step S3, if the number of test nodes J≤2, the subsequent steps are stopped.

[0018] The multi-node ranging system based on code division multiple access includes:

[0019] Judgment module: Calculate network complexity Where I represents the number of nodes in the network, Ni represents the number of bytes sent by the i-th node, λ i represents the node degree of the i-th node;

[0020] When network complexity F>F sta When the bandwidth B is obtained and the channel capacity C = Blog2(1+SNR), F sta represents the preset standard complexity, and SNR represents the signal-to-noise ratio when the node transmits information;

[0021] Calculation module: Calculate the transmission time T = D / C, where D represents the total number of bytes sent by the node. Calculate the transmission time ratio Z = T / 100. Based on the transmission time ratio Z, correct the bandwidth to obtain the corrected bandwidth BX. Repeat the above steps to obtain the corrected time TX.

[0022] Correction module: Set the bandwidth to the corrected bandwidth BX and send the same number of bytes to J test nodes, obtain the actual transmission time TS, and record the node as qualified if TS ≤ TX, where the test node represents the node that receives the bytes, J represents the number of test nodes, and J = I-1.

[0023] A byte number gradient DT = ηD is preset, where the step coefficient η = 1, 2, 3, ..., and the actual transmission time is repeatedly obtained with the byte number gradient to obtain the corresponding step coefficient η when TS > TX. The test nodes are sorted in descending order according to the step coefficient η, and the top 60% of the test nodes in the sorting are recorded as efficient nodes. The efficient nodes are retained and the corresponding bandwidth is adjusted to the corrected bandwidth BX.

[0024] Beneficial effects of the present invention: First, the present invention calculates the network complexity based on the topological results of the current network. Network complexity is an indicator that measures the comprehensive characteristics of the network system in terms of structure, function, dynamic behavior or resource requirements. The larger the scale, the higher the complexity is generally. Therefore, the more processing is required during the transmission of byte data. The transmission complexity of the byte data in the network is calculated by the specific values ​​of the number of nodes and node degrees in the network. When the network complexity is high, the channel capacity at this time needs to be calculated based on the bandwidth. The channel capacity is used to judge the speed of the current network processing data. In actual situations, there are two factors that affect the transmission of data. One is the distance between nodes, and the other is the processing speed of byte data. The processing speed of byte data is affected by bandwidth and signal-to-noise ratio. When the bandwidth is larger, the amount of data that can be processed at one time is more, so the corresponding channel capacity is larger.

[0025] Then the transmission time of the byte data is calculated. It should be noted that the present invention stipulates that the delay must be less than 100ms. Therefore, the transmission time is calculated first, and then the transmission time ratio is calculated. Then, the bandwidth is corrected according to the transmission time ratio to obtain the corrected time. Afterwards, in order to further reduce the complexity of the calculation, it is necessary to select suitable nodes from the test nodes for calculation. It is not necessary to operate on all nodes, but only some suitable nodes need to be selected for operation.

[0026] Then repeat the above steps to get the corrected time. The corrected time here is a theoretical value obtained by calculating the corrected bandwidth, not the actual value. Therefore, actual measurement is still needed. Set the bandwidth to the corrected bandwidth and send the same number of bytes to the test node, record the actual transmission time, and then compare the actual transmission time with the corrected time to screen out qualified nodes. This step is to preliminarily screen out qualified nodes. When the actual transmission time is less than the corrected time, it means that the byte data transmission at this time has preliminarily met the requirements; the subsequent screening steps can be further simplified.

[0027] Then, a byte gradient is preset. The byte gradient is used to determine the limit value of byte data transmission with the corrected bandwidth, determine the step coefficient under the limit, and then sort according to the value of the step coefficient. The test node with a higher step coefficient indicates that its stability is stronger, so it needs to be retained. In addition, in the subsequent process, when the step coefficients are equal, in order to avoid conflicts, the test node corresponding to the smaller signal-to-noise ratio is placed at the front of the sorting, thereby effectively solving the error caused by the same step coefficient, improving the accuracy of multi-node ranging, and avoiding the impact of errors on experimental accuracy. In summary, the present invention can reduce the number of byte data transmissions in the multi-node ranging process, reduce positioning delay, and improve response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below with reference to the accompanying drawings.

[0029] Figure 1 It is a flow chart of a multi-node ranging method and system based on code division multiple access of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] See also Figure 1 As shown, the present invention is a multi-node ranging method and system based on code division multiple access, comprising the following steps:

[0032] S1: Calculating network complexity Where I represents the number of nodes in the network, N i represents the number of bytes sent by the i-th node, λ i represents the node degree of the i-th node;

[0033] When network complexity F>F sta When the bandwidth B is obtained and the channel capacity C = Blog2(1+SNR), F sta represents the preset standard complexity, and SNR represents the signal-to-noise ratio when the node transmits information;

[0034] S2: Calculate the transmission time T = D / C, where D represents the total number of bytes sent by the node. Calculate the transmission time ratio Z = T / 100. Based on the transmission time ratio Z, correct the bandwidth to obtain the corrected bandwidth BX. Repeat the above steps to obtain the corrected time TX.

[0035] S3: Set the bandwidth to the corrected bandwidth BX and send the same number of bytes to J test nodes, obtain the actual transmission time TS, and record it as a qualified node if TS ≤ TX, where the test node represents the node that receives the bytes, J represents the number of test nodes, and J = I-1.

[0036] A byte number gradient DT = ηD is preset, where the step coefficient η = 1, 2, 3, ..., and the actual transmission time is repeatedly obtained with the byte number gradient to obtain the corresponding step coefficient η when TS > TX. The test nodes are sorted in descending order according to the step coefficient η, and the top 60% of the test nodes in the sorting are recorded as efficient nodes. The efficient nodes are retained and the corresponding bandwidth is adjusted to the corrected bandwidth BX.

[0037] It should be noted that the present invention proposes a method for calculating network complexity based on network topology. Network complexity, as an important indicator for measuring the comprehensive characteristics of a network system, can reflect the complexity of the network in resource allocation. In actual network environments, network scale and complexity show a significant positive correlation. With the increase in the number of nodes and the complexity of the connection relationship, network complexity usually grows exponentially. This growth characteristic requires more levels of processing and forwarding during byte data transmission, which has a direct impact on network performance. Specifically, the calculation of network complexity requires comprehensive consideration of the number of all active nodes in the network and the node degree of each node. These basic parameters can accurately quantify the path selection complexity and processing overhead that a data packet may face when transmitting in the network. When the system detects a higher network complexity, it must introduce bandwidth parameters to dynamically calculate the real-time channel capacity. This key indicator directly determines the data processing capability of the network in its current state. From a practical perspective in network engineering, the main factors affecting data transmission efficiency can be summarized into two aspects. First, the physical transmission distance factor. The physical distance between nodes directly affects signal propagation delay and attenuation. Second, the efficiency factor at the data processing level is primarily constrained by two key technical parameters: available bandwidth and channel signal-to-noise ratio. Bandwidth, as a fundamental property of a communication channel, determines the maximum amount of data that can be carried per unit time. Increasing bandwidth resources significantly increases the system's channel capacity. Meanwhile, the signal-to-noise ratio, as a key indicator of signal quality, directly affects data transmission reliability and effective throughput. The synergistic effect of these two parameters determines the network's actual processing speed for byte data. By establishing a dynamic correlation model between network complexity and channel capacity, it is possible to accurately assess and optimize network transmission performance.

[0038] When calculating the transmission time of byte data, the present invention adopts a sophisticated evaluation mechanism to ensure that network transmission performance meets strict real-time requirements. The system sets a critical delay threshold, and the transmission delay must be controlled within 100 milliseconds. This indicator can meet the stringent requirements of most real-time applications. The specific implementation process is divided into the following key steps: First, based on the current network topology and channel characteristics, the original transmission time is accurately calculated. This calculation comprehensively considers core parameters such as packet size, number of transmission path hops, and processing delay of each node. Then, an innovative indicator, the transmission time ratio, is introduced. That is, the ratio of the actual transmission time to the maximum allowable delay of 100ms. This ratio can intuitively reflect the current network load and transmission efficiency. Based on this ratio, the system will dynamically adjust the bandwidth allocation strategy and optimize the original transmission time through an intelligent bandwidth correction algorithm to obtain a corrected time value that better conforms to the actual network conditions.

[0039] In order to improve computing efficiency and reduce system overhead, the present invention adopts an intelligent node selection strategy. This method no longer requires traversal calculations of all network nodes, but instead uses an efficient screening algorithm to identify key nodes from a set of test nodes for targeted calculations. This selective calculation mechanism greatly reduces the complexity of the algorithm while ensuring the accuracy of the calculation results. Specifically, the node selection process will comprehensively consider factors such as the node's connectivity, historical load data, and geographical location to ensure that the selected nodes can fully represent the transmission characteristics of the entire network. Through this optimization, the system can significantly improve computing efficiency while ensuring calculation accuracy, and is particularly suitable for real-time performance evaluation and optimization in large-scale complex network environments.

[0040] After calculating the corrected time, the system enters the verification and screening phase. Corrected time is a theoretical transmission time calculated based on the corrected bandwidth. However, due to numerous uncontrollable factors in actual network environments, there may be discrepancies between the theoretical value and actual transmission performance. Therefore, the present invention has designed a rigorous experimental verification process: first, the network bandwidth is dynamically adjusted to the calculated corrected bandwidth value, then the same amount of test bytes are sent to pre-selected test nodes, and the actual end-to-end transmission time is recorded using a high-precision timer. This measurement process uses multiple sampling and averaging to ensure data stability and reliability.

[0041] After obtaining the actual transmission time, the system compares and analyzes it with the theoretical correction time to perform preliminary node screening. When a node's actual transmission time is less than the theoretical correction time, it indicates that the node's transmission performance under the current network conditions has met the expected requirements and is marked as qualified. These qualified nodes will enter the next stage of the optimization process, while unqualified nodes will be temporarily excluded or marked as candidates for optimization. This screening mechanism has a dual advantage: on the one hand, it significantly reduces the number of nodes required for subsequent calculations, reducing system overhead; on the other hand, it ensures that subsequent optimization efforts focus on nodes that truly meet basic performance requirements, improving overall efficiency.

[0042] It's worth noting that initial screening is only the first quality checkpoint in the entire optimization process. Nodes that pass this stage, while meeting basic transmission requirements, still undergo subsequent evaluation before being finalized as optimal transmission path nodes. This phased screening strategy ensures both the quality of network optimization and the efficient use of computing resources, making it particularly suitable for real-time performance optimization in dynamically changing network environments.

[0043] The present invention evaluates the extreme performance of network transmission by presetting a byte gradient. This gradient adopts a progressive data volume design and can systematically detect the maximum stable transmission capacity of each node under different load conditions. On the basis of correcting the bandwidth, the transmission critical point of each node is accurately determined by gradually increasing the amount of test data, thereby calculating the core indicator reflecting the stability of the node - the step coefficient. This coefficient not only reflects the throughput performance of the node under extreme load, but also includes a comprehensive evaluation of its anti-interference ability and transmission reliability. The node sorting mechanism based on the step coefficient adopts a dual judgment standard. First, the main sorting is performed according to the level of the step coefficient, and those nodes that can still maintain excellent performance in a high-pressure environment are prioritized. When encountering nodes with the same step coefficient, the signal-to-noise ratio is introduced as an auxiliary criterion, so that those nodes that can still achieve the same performance level in a poor communication environment are given higher priority. This design not only solves the conflict problem in traditional sorting, but also taps into potential high-quality nodes. The entire evaluation process uses an intelligent gradient testing strategy to quickly lock in high-quality nodes with the least number of tests, avoiding the waste of resources caused by redundant data transmission. This method significantly improves system response speed while maintaining accurate ranging, making it particularly suitable for large-scale networking scenarios with stringent real-time requirements, such as the Internet of Things and edge computing. The innovation of this method lies in combining extreme performance testing with an intelligent sorting algorithm. Using the step coefficient as a comprehensive metric, it accurately assesses node transmission capacity, providing a reliable basis for decision-making in network optimization.

[0044] In another preferred embodiment of the present invention, the out-degree λ of the i-th node is obtained. i_out and indegree λ i_in , then the node degree λ of the i-th node i =λ i_out +λ i_in .

[0045] It's worth noting that in network topology analysis, a node's connectivity characteristics are reflected not only in its ability to receive data but also in its ability to forward data, represented by its in-degree and out-degree, respectively. The necessity of this bidirectional measurement stems from the interactive nature of modern networks, where nodes often simultaneously play multiple roles as data receivers, processors, and forwarders. Specifically, a node's in-degree reflects its load pressure as a data aggregation point, while its out-degree reflects its distribution capabilities as a data source. The dynamic balance between the two jointly determines the node's actual influence on the network's information flow. The system can more accurately identify key hub nodes that are active in both data transmission and reception. These nodes often play a decisive role in the transmission efficiency and stability of the overall network.

[0046] In another preferred embodiment of the present invention, the network complexity F≤F sta When , get the maximum value of the transmission time T at this timemax , if the maximum transmission time T max <100, stop the subsequent steps.

[0047] It is understandable that when the network complexity is low, in order to reduce unnecessary operations and optimize the process, a preliminary judgment is needed. If the maximum transmission time T max <100, indicating that the data transmission speed is fast enough, the delay is small, and the network complexity F≤F sta , it indicates that the current network structure is not complicated, so the impact of the network structure on data byte data transmission can be ignored.

[0048] In another preferred embodiment of the present invention, the bandwidth is corrected based on the transmission time ratio Z to obtain a corrected bandwidth BX=Z*B.

[0049] It should be noted that according to the formula, the larger the transmission time ratio Z, the larger the bandwidth needs to be adjusted. When the bandwidth becomes larger, the channel capacity becomes larger, and the time used for byte transmission will become shorter, so that it meets the requirement of less than 100ms.

[0050] In another preferred embodiment of the present invention, if there are missing bytes received by the test node, this data transmission is recorded as invalid transmission, and the invalid transmission is not included in the subsequent calculation process.

[0051] It should be noted that if the test node receives missing bytes, the data transmission will be recorded as invalid and will not be included in the subsequent calculation process. Because data loss may occur during the data transmission process, in order to address the impact of unstable factors, it is necessary to remove the transmission records with missing bytes.

[0052] In another preferred embodiment of the present invention, when there are test nodes with equal step coefficients η, the test points with smaller signal-to-noise ratios SNR are placed at the front in the sorting.

[0053] It is understandable that in response to the possibility of identical step coefficients in network performance evaluations, when multiple test nodes exhibit exactly the same step coefficient in the byte gradient test, the system will further analyze the real-time signal-to-noise ratio indicators of each node and prioritize nodes with lower signal-to-noise ratios in higher positions. Under the same step coefficient conditions, nodes with lower signal-to-noise ratios are actually in a more severe transmission environment, but they can still maintain the same transmission stability as nodes in high-quality environments, which proves that the nodes have better physical layer anti-interference capabilities and link adaptation mechanisms. Through this sorting principle, the system can identify nodes that can still maintain stable transmission under adverse conditions. These nodes often have stronger environmental adaptability and more reliable transmission quality. This sorting method not only solves the sorting conflict problem caused by the same step coefficient in traditional evaluations, but more importantly, it discovers high-quality nodes that may be overlooked in conventional evaluations, thereby significantly improving the scientific nature and accuracy of the entire node selection system.

[0054] In another preferred embodiment of the present invention, if the number of test nodes J≤2, the subsequent steps are stopped.

[0055] It is worth noting that when the number of test nodes is too small, the adjustment using the method described in the present invention is meaningless and therefore needs to be stopped.

[0056] The multi-node ranging system based on code division multiple access includes:

[0057] Judgment module: Calculate network complexity Where I represents the number of nodes in the network, N i represents the number of bytes sent by the i-th node, λ i represents the node degree of the i-th node;

[0058] When network complexity F>F sta When the bandwidth B is obtained and the channel capacity C = Blog2(1+SNR), F sta represents the preset standard complexity, and SNR represents the signal-to-noise ratio when the node transmits information;

[0059] Calculation module: Calculate the transmission time T = D / C, where D represents the total number of bytes sent by the node. Calculate the transmission time ratio Z = T / 100. Based on the transmission time ratio Z, correct the bandwidth to obtain the corrected bandwidth BX. Repeat the above steps to obtain the corrected time TX.

[0060] Correction module: Set the bandwidth to the corrected bandwidth BX and send the same number of bytes to J test nodes, obtain the actual transmission time TS, and record the node as qualified if TS ≤ TX, where the test node represents the node that receives the bytes, J represents the number of test nodes, and J = I-1.

[0061] A byte number gradient DT = ηD is preset, where the step coefficient η = 1, 2, 3, ..., and the actual transmission time is repeatedly obtained with the byte number gradient to obtain the corresponding step coefficient η when TS > TX. The test nodes are sorted in descending order according to the step coefficient η, and the top 60% of the test nodes in the sorting are recorded as efficient nodes. The efficient nodes are retained and the corresponding bandwidth is adjusted to the corrected bandwidth BX.

[0062] It is worth noting that

[0063] The above is a detailed description of an embodiment of the present invention. However, the content is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A multi-node ranging method based on code division multiple access, characterized in that: The following steps are involved: S1: Calculating network complexity Where I represents the number of nodes in the network, N i represents the number of bytes sent by the i-th node, λ i represents the node degree of the i-th node; When network complexity F>F sta When the bandwidth B is obtained and the channel capacity C = B log2 (1 + SNR), F sta represents the preset standard complexity, and SNR represents the signal-to-noise ratio when the node transmits information; S2: Calculate the transmission time T = D / C, where D represents the total number of bytes sent by the node. Calculate the transmission time ratio Z = T / 100. Based on the transmission time ratio Z, correct the bandwidth to obtain the corrected bandwidth BX. Repeat the above steps to obtain the corrected time TX. S3: Set the bandwidth to the corrected bandwidth BX and send the same number of bytes to J test nodes, obtain the actual transmission time TS, and record it as a qualified node if TS ≤ TX, where the test node represents the node that receives the bytes, J represents the number of test nodes, and J = I-1. A byte number gradient DT = ηD is preset, where the step coefficient η = 1, 2, 3, ..., and the actual transmission time is repeatedly obtained with the byte number gradient to obtain the corresponding step coefficient η when TS > TX. The test nodes are sorted in descending order according to the step coefficient η, and the top 60% of the test nodes in the sorting are recorded as efficient nodes. The efficient nodes are retained and the corresponding bandwidth is adjusted to the corrected bandwidth BX.

2. The multi-node ranging method based on code division multiple access according to claim 1, characterized in that: In step S1, the out-degree λ of the i-th node is obtained. i_out and indegree λ i_in , then the node degree λ of the i-th node i =λ i_out +λ i_in .

3. The multi-node ranging method based on code division multiple access according to claim 1, characterized in that: In the step S1, the network complexity F≤F sta When , get the maximum value of the transmission time T at this time max , if the maximum transmission time T max <100, stop the subsequent steps.

4. The multi-node ranging method based on code division multiple access according to claim 1, characterized in that: In the step S2, the bandwidth is corrected based on the transmission time ratio Z to obtain a corrected bandwidth BX=Z*B.

5. The multi-node ranging method based on code division multiple access according to claim 1, characterized in that: In step S3, if the bytes received by the test node are missing, the data transmission is recorded as invalid transmission, and the invalid transmission is not included in the subsequent calculation process.

6. The multi-node ranging method based on code division multiple access according to claim 1, characterized in that: In step S3, when there are test nodes with the same step coefficient η, the test point with the smaller signal-to-noise ratio SNR is placed at the front in the sorting.

7. The multi-node ranging method based on code division multiple access according to claim 1, characterized in that: In step S3, if the number of test nodes J≤2, the subsequent steps are stopped.

8. A multi-node ranging system based on code division multiple access, characterized in that: include: Judgment module: Calculate network complexity Where I represents the number of nodes in the network, N i represents the number of bytes sent by the i-th node, λ i represents the node degree of the i-th node; When network complexity F>F sta When the bandwidth B is obtained and the channel capacity C = B log2 (1 + SNR), F sta represents the preset standard complexity, and SNR represents the signal-to-noise ratio when the node transmits information; Calculation module: Calculate the transmission time T = D / C, where D represents the total number of bytes sent by the node. Calculate the transmission time ratio Z = T / 100. Based on the transmission time ratio Z, correct the bandwidth to obtain the corrected bandwidth BX. Repeat the above steps to obtain the corrected time TX. Correction module: Set the bandwidth to the corrected bandwidth BX and send the same number of bytes to J test nodes, obtain the actual transmission time TS, and record the node as qualified if TS ≤ TX, where the test node represents the node that receives the bytes, J represents the number of test nodes, and J = I-1. A byte number gradient DT = ηD is preset, where the step coefficient η = 1, 2, 3, ..., and the actual transmission time is repeatedly obtained with the byte number gradient to obtain the corresponding step coefficient η when TS > TX. The test nodes are sorted in descending order according to the step coefficient η, and the top 60% of the test nodes in the sorting are recorded as efficient nodes. The efficient nodes are retained and the corresponding bandwidth is adjusted to the corrected bandwidth BX.

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