Millimeter wave wireless networking anti-interference method

Abstract

The invention discloses a millimeter wave wireless networking anti-interference method, which relates to the technical field of millimeter wave wireless communication networking, and comprises the following steps of: initializing configuration of networking nodes, identity authentication completion, time synchronization and basic link establishment; performing multi-dimensional channel sensing and interference detection, collecting parameters and identifying interference types; intelligent channel selection and resource allocation are carried out, and time-frequency resources are dynamically allocated; self-adaptive beam forming configuration is carried out, and beam pointing is adjusted to suppress interference; dynamic power adjustment is carried out, and transmitting power is optimized; a secure transmission mechanism, encryption verification and identity verification; real-time interference monitoring and dynamic optimization are carried out, and interference changes are responded; and reliable transmission is realized through data transmission and integrity feedback. According to the method, through the technologies of multi-dimensional sensing, beam forming, dynamic optimization and the like, efficient suppression of various interferences is realized, the transmission stability and reliability are improved, the time delay and packet loss rate are reduced, the resource utilization rate is optimized, the data security is guaranteed, and the multi-scene networking requirements are met.

Description

Technical Field

[0001] This invention relates to the field of millimeter-wave wireless communication networking technology, and in particular to a millimeter-wave wireless networking anti-interference method. Background Technology

[0002] Millimeter-wave communication, with its unique advantages in the 30-300 GHz frequency band, boasts long communication distances, abundant bandwidth resources, and strong anti-interference potential, making it one of the core technologies for medium- and long-range wireless communication. It has broad application prospects in various fields such as emergency communication, remote area communication, maritime navigation, meteorological observation, and military communication. Compared to satellite communication, millimeter-wave communication systems do not require complex and expensive space infrastructure, offering flexible deployment and lower costs. Utilizing skywave propagation and ionospheric reflection, it achieves long-distance transmission over tens of thousands of kilometers, significantly reducing the construction and maintenance costs of relay stations while enhancing the resilience of the communication system. With the increasing demand for high-speed, low-latency communication from various services, millimeter-wave wireless networking has become a key method for achieving multi-node collaborative communication and expanding coverage. Its technical performance directly determines the stability and reliability of communication services.

[0003] However, existing millimeter-wave wireless networking technologies still face numerous technical shortcomings related to anti-interference in complex electromagnetic environments, severely limiting their application effectiveness. In terms of channel sensing and interference handling, traditional networking methods often employ single-dimensional spectrum detection, which can only identify some types of interference. They lack the ability to distinguish between narrowband interference, co-channel interference, and malicious interference, and lack comprehensive evaluation of multi-dimensional parameters such as channel bandwidth, signal strength, and interference power, leading to inaccurate channel quality judgments and consequently affecting the rationality of subsequent channel selection. Regarding resource allocation and beam configuration, existing technologies mostly adopt fixed time-frequency resource allocation strategies, unable to dynamically adjust according to node communication needs and interference distribution. Furthermore, the fixed beamforming direction and width make it difficult to achieve effective null suppression in the direction of interference sources. The synergistic optimization of wide scanning angle and fast beam switching is insufficient, resulting in limited interference suppression effects. Power adjustment mechanisms are crude, often employing fixed transmit power or simple step-wise adjustments. This either leads to unstable signal transmission due to insufficient power or interference superposition due to excessive power, failing to achieve a balance between communication quality and interference control.

[0004] In terms of secure transmission and dynamic optimization, existing technologies suffer from complex encryption and verification processes, leading to increased transmission latency and difficulty in meeting the demands of low-latency services. Furthermore, inadequate identity authentication and access control mechanisms pose security risks such as identity forgery and unauthorized access. Simultaneously, the lack of a real-time and comprehensive interference monitoring mechanism results in delayed responses to interference changes, often triggering adjustment strategies only after a significant decline in communication quality. Moreover, the absence of historical interference data for trend prediction makes it impossible to proactively mitigate potential interference. The lack of network load balancing design allows some nodes to become overloaded due to concentrated service usage, exacerbating localized interference and reducing transmission efficiency. Insufficient cross-layer collaboration results in independent anti-interference strategies at the physical, data link, and network layers, hindering the formation of synergistic effects. These issues lead to poor transmission stability, high packet loss rates, and large latency fluctuations in existing millimeter-wave wireless networking systems under complex electromagnetic environments, failing to meet the stringent communication quality requirements of various services. Especially in mobile scenarios or environments with strong interference, network performance degrades significantly, limiting the large-scale promotion and application of millimeter-wave communication technology. Summary of the Invention

[0005] This invention proposes a millimeter-wave wireless networking anti-interference method to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a millimeter-wave wireless networking anti-interference method, comprising the following steps: S1: Initial configuration of network nodes. After all network nodes start up, they automatically complete identity authentication and network access registration. Time synchronization and location information interaction are achieved based on the Beidou / GPS module. Multi-beam working mode and different frequency duplex communication parameters are preset. Network topology and node communication priority are configured. Basic communication links between nodes are established. S2: Multi-dimensional channel perception and interference detection. Each node scans available channels through the spectrum perception module, collects channel parameters, and uses an algorithm that combines energy detection and feature matching to identify interference types and generate a channel quality assessment report. S3: Intelligent channel selection and resource allocation. Based on the channel quality assessment report, combined with the node communication needs and service priorities, the load balancing algorithm is used to select the optimal communication channel and allocate time and frequency resources. The resource utilization rate is improved by multiple users reusing time and frequency resources, while reserving backup channel resources in areas with severe interference. S4: Adaptive beamforming configuration, each node adjusts the beam direction and beam width according to the target node position and interference source distribution information, and suppresses interference signals by forming nulls in the direction of interference sources, and enhances the signal gain in the direction of the target. S5: Dynamic power adjustment, which adjusts the node's transmit power in real time based on channel attenuation and interference intensity, thereby adjusting the transmit EIRP value and receive gain; S6: Secure transmission mechanism, which encrypts transmitted data, verifies data integrity using integrity verification algorithms, verifies the identities of both communicating parties through an identity confirmation mechanism, and establishes a role-based access control system; S7: Real-time interference monitoring and dynamic optimization. It continuously monitors channel interference changes during data transmission. When the interference intensity exceeds the threshold, it triggers channel switching, beam adjustment, or power adjustment mechanisms. It combines historical interference data to predict interference trends and optimize communication parameters in advance. S8: Data transmission and integrity feedback. Data packets are transmitted according to a preset protocol. After the receiving node verifies the data integrity, it sends back confirmation information. When packet loss or data error occurs, a retransmission mechanism is initiated, and transmission parameters are optimized during the retransmission process.

[0007] Furthermore, it also includes steps for precise location and isolation of interference sources. Multiple network nodes collaboratively collect the angle of arrival, time difference of arrival, and signal characteristics of interference signals to construct a spatial location model of the interference source. Combined with the node location information, the coordinates of the interference source are determined. For fixed interference sources, the beam pointing of surrounding nodes is adjusted to form a shielded area. For mobile interference sources, the tracking strategy is dynamically updated. The interference propagation path is blocked by a combination of beamforming null suppression and channel isolation, and the interference source information is synchronized to the entire network.

[0008] Furthermore, it also includes cross-layer collaborative anti-interference optimization steps, which integrate physical layer beamforming, data link layer channel coding and network layer routing optimization to establish cross-layer parameter mapping relationships; the physical layer feeds back channel quality parameters to the data link layer to adjust the coding rate and error correction level; the data link layer feeds back the transmission status to the network layer to optimize route selection; and the network layer adjusts the node communication topology according to the overall interference distribution.

[0009] Furthermore, in the multi-dimensional channel sensing process, an interference comprehensive assessment model is used to quantify the degree of channel interference. The calculation expression is as follows: ,in This is the comprehensive evaluation value for interference. For interference power weighting coefficients, This is the interference bandwidth weighting coefficient. The duration of the interference is the weighting factor. , The average power of the interference signal within the channel. The average power of the useful signal within the channel. To prevent interference signals from occupying bandwidth, The total available bandwidth of the channel. Duration of the interference signal This is for the statistical time window length.

[0010] Furthermore, during the adaptive beamforming configuration process, beam parameters are dynamically adjusted based on the target node's movement status. The Kalman filter algorithm is used to predict the target node's movement trajectory and adjust the beam direction in advance. Wide beam coverage is used for high-speed moving nodes, while narrow beams are used for stationary or low-speed moving nodes. The beam switching algorithm is optimized to maintain signal transmission continuity during switching.

[0011] Furthermore, the secure transmission mechanism employs a layered encryption strategy. User data is encrypted using a symmetric encryption algorithm, while the key is transmitted using an asymmetric encryption algorithm. A dynamic encryption key is generated by combining node identity identifiers and is updated periodically. Integrity verification uses a hash algorithm to generate a data verification value, and the receiving node determines whether the data has been tampered with by comparing the verification value. Identity verification uses a two-way authentication mechanism, where the communicating parties exchange identity authentication certificates containing node identifiers, permission levels, and validity periods, and verify the legitimacy of the communicating parties through the certificates. Access control is based on role-based allocation of operation permissions.

[0012] Furthermore, a power optimization model is used during dynamic power adjustment to achieve precise control of the transmit power. The calculation expression is as follows: ,in To achieve the optimal transmit power for the node, The minimum detectable power of the receiving node. This represents the total channel attenuation. For signal quality assurance factor, For the transmit antenna gain, For receiving antenna gain, For system transmission efficiency, The distance between communication nodes. This is for interference compensation power.

[0013] Furthermore, a sliding window detection algorithm is used in the real-time interference monitoring process. A fixed-length detection window is set, and the interference intensity, frequency, and type within the window are statistically analyzed in real time. When the interference assessment value within the window exceeds the set threshold three times consecutively, a first-level anti-interference response is triggered, adjusting the beam pointing and transmission power. When the interference assessment value exceeds the emergency threshold, a second-level anti-interference response is triggered, switching to a backup channel and notifying surrounding nodes to coordinate avoidance. The time, location, type, and handling measures of interference events are recorded to form an interference event database.

[0014] Furthermore, an adaptive retransmission mechanism is adopted during data transmission to dynamically adjust the number of retransmissions and the retransmission timeout based on channel quality; a data packet fragmentation transmission strategy is adopted to divide large data packets into multiple small data packets for transmission. Each data packet carries independent check information and sequence number. The receiving node reassembles the data packets according to the sequence number and retransmits missing or erroneous data packets separately.

[0015] Furthermore, it also includes a dynamic network load balancing step, which monitors the communication load and resource usage of each node in real time. When the node load exceeds the threshold, some communication services are migrated to adjacent nodes with lower loads. During the migration process, the communication link continuity is maintained, channel allocation and beam pointing are adjusted, and the network topology is optimized.

[0016] Compared with existing technologies, the beneficial effects of this invention are: In terms of core anti-interference performance, the method achieves accurate identification and comprehensive quantitative evaluation of various interference types through multi-dimensional channel sensing and interference detection, providing comprehensive data support for subsequent anti-interference strategy formulation. Adaptive beamforming configuration dynamically adjusts beam parameters based on the distribution of interference sources and the location of target nodes, forming null points in the interference direction to suppress interference signals and enhance target signal gain. Combined with wide scanning angle and fast beam switching characteristics, it significantly improves the ability to suppress dynamic interference. The precise location and isolation of interference sources achieves interference source coordinate locking through multi-node collaboration, using a combination of beam shielding and channel isolation to block interference propagation. Coupled with real-time interference monitoring and dynamic optimization mechanisms, it can quickly respond to interference changes and proactively avoid potential interference risks. The network-wide collaborative anti-interference mode further enhances the ability to respond to interference in complex environments.

[0017] In terms of resource utilization and transmission efficiency, the intelligent channel selection and resource allocation strategy makes dynamic decisions based on channel quality and service priority. Through multi-user multiplexing of time-frequency resources and load balancing design, it achieves reasonable allocation of network resources, avoids resource waste and local overload, and significantly improves resource utilization. The dynamic power adjustment mechanism optimizes the transmission power in real time according to channel attenuation and interference intensity, reducing interference superposition while ensuring communication quality, and balancing the requirements of transmission signal strength and interference control. The cross-layer collaborative anti-interference optimization integrates the technical advantages of the physical layer, data link layer, and network layer. Through parameter feedback and strategy coordination, it reduces the impact of interference superposition at each layer. Adaptive retransmission and packet fragmentation transmission strategies reduce packet loss and retransmission overhead, effectively reducing transmission latency and improving the smoothness and timeliness of data transmission.

[0018] In terms of secure transmission and network stability, the secure transmission mechanism comprehensively protects the confidentiality, integrity, and legality of data transmission through layered encryption, integrity verification, two-way identity confirmation, and role-based access control. This effectively prevents security risks such as data leakage, identity forgery, and unauthorized access, while optimizing encryption and verification processes to avoid increased transmission latency. Network node initialization configuration quickly establishes stable basic communication links through identity authentication, time synchronization, and location information exchange, improving network efficiency and node collaboration capabilities. The dynamic network load balancing process achieves a balanced distribution of load across nodes through service migration and topology optimization, reducing interference and performance degradation caused by overload of a single node, significantly improving the operational stability and long-term reliability of the entire network system.

[0019] This method possesses strong scenario adaptability, flexibly addressing diverse service requirements such as static and mobile interference, simple and complex electromagnetic environments, and low latency and high bandwidth. It demonstrates excellent performance in multiple fields including emergency communication, remote area communication, military communication, and maritime navigation. Through end-to-end anti-interference design and collaborative optimization, this method drives the transformation of millimeter-wave wireless networking technology from the traditional passive anti-interference mode to a modern mode of proactive sensing, precise response, and collaborative optimization. This significantly expands the application boundaries of millimeter-wave communication, providing stable, secure, and efficient networking solutions for various demanding wireless communication scenarios, and possesses significant technical value and practical application significance. Attached Figure Description

[0020] Figure 1 This is a flowchart of a millimeter-wave wireless networking anti-interference method proposed in this invention; Figure 2 This is a bar chart comparing the interference suppression rates of a millimeter-wave wireless networking anti-interference method proposed in this invention under different interference types. Figure 3 This is a line graph showing the transmission delay as a function of node movement speed in a millimeter-wave wireless networking anti-interference method proposed in this invention. Figure 4 This is a radar chart comparing data packet loss rates under different service priorities in a millimeter-wave wireless networking anti-interference method proposed in this invention. Figure 5 This is a scatter plot showing the relationship between network operation time and resource utilization rate of the millimeter-wave wireless networking anti-interference method proposed in this invention. Figure 6 This is a line graph comparing the network stability scores under different interference intensities for the millimeter-wave wireless networking anti-interference method proposed in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Reference Figures 1 to 6 A millimeter-wave wireless networking anti-interference method includes the following steps: S1: Initial configuration of network nodes. After all network nodes start up, they automatically complete identity authentication and network access registration. Time synchronization and location information interaction are achieved based on the Beidou / GPS module. Multi-beam working mode and different frequency duplex communication parameters are preset. Network topology and node communication priority are configured. Basic communication links between nodes are established. S2: Multi-dimensional channel perception and interference detection. Each node scans available channels in the 30 to 300 GHz millimeter wave band through the spectrum perception module, collects parameters such as channel bandwidth, signal strength, interference power, and channel attenuation, and uses an algorithm that combines energy detection and feature matching to identify interference types such as narrowband interference, co-channel interference, and malicious interference, and generates a channel quality assessment report. S3: Intelligent channel selection and resource allocation. Based on the channel quality assessment report, combined with the node communication needs and service priorities, the load balancing algorithm is used to select the optimal communication channel and allocate time and frequency resources. The resource utilization rate is improved by multiple users reusing time and frequency resources, while reserving backup channel resources in areas with severe interference. S4: Adaptive beamforming configuration. Each node adjusts the beam direction and beamwidth according to the target node location and interference source distribution information. By forming nulls in the direction of the interference source to suppress interference signals, the signal gain in the target direction is enhanced, the stability of the communication link is improved, and wide scanning angle and fast beam switching are achieved. S5: Dynamic power adjustment, which adjusts the node's transmit power in real time according to the channel attenuation and interference intensity, minimizes the transmit power while meeting communication quality requirements, reduces interference superposition caused by over-transmission, and maintains the transmit EIRP value and receive gain within the optimal range. S6: Secure transmission mechanism, which encrypts transmitted data, verifies data integrity using integrity verification algorithm, verifies the identities of both communicating parties through identity confirmation mechanism, establishes a role-based access control system, grants different access permissions to different access nodes, and optimizes encryption and verification processes to reduce transmission latency; S7: Real-time interference monitoring and dynamic optimization. It continuously monitors channel interference changes during data transmission. When the interference intensity exceeds the threshold, it triggers channel switching beam adjustment or power adjustment mechanisms. It combines historical interference data to predict interference trends and optimize communication parameters in advance. S8: Data transmission and integrity feedback. Data packets are transmitted according to a preset protocol. After the receiving node verifies the data integrity, it sends back confirmation information. If packet loss or data error occurs, a retransmission mechanism is initiated. During the retransmission process, transmission parameters are optimized to achieve reliable data transmission.

[0025] This invention also includes a step for precise location and isolation of interference sources. Multiple network nodes collaboratively collect the angle of arrival, time difference of arrival, and signal characteristics of interference signals to construct a spatial location model of the interference source. Combined with the node location information, the precise coordinates of the interference source are determined. For fixed interference sources, the beam pointing of surrounding nodes is adjusted to form a shielded area. For moving interference sources, the tracking strategy is dynamically updated. The interference propagation path is blocked by a combination of beamforming null suppression and channel isolation. At the same time, the interference source information is synchronized to the entire network to achieve network-wide collaborative anti-interference.

[0026] This invention also includes a cross-layer collaborative anti-interference optimization step, which integrates beamforming of the physical layer, channel coding of the data link layer, and routing optimization of the network layer to establish a cross-layer parameter mapping relationship. The physical layer feeds back the channel quality parameters to the data link layer to adjust the coding rate and error correction level. The data link layer feeds back the transmission status to the network layer to optimize the route selection. The network layer adjusts the node communication topology according to the overall interference distribution. Through cross-layer collaboration, the superposition of interference from each layer is reduced, thereby improving the overall anti-interference capability and transmission efficiency of the network.

[0027] In this invention, an interference comprehensive evaluation model is used to quantify the degree of channel interference during the multi-dimensional channel sensing process. The calculation expression is as follows: ,in This is the comprehensive evaluation value for interference. The interference power weighting coefficient ranges from 0.4 to 0.6. The interference bandwidth weighting coefficient ranges from 0.2 to 0.3. The weighting coefficient for the duration of interference ranges from 0.2 to 0.3. , The average power of the interference signal within the channel. The average power of the useful signal within the channel. To prevent interference signals from occupying bandwidth, The total available bandwidth of the channel. Duration of the interference signal To statistically determine the length of the time window, this quantitative assessment enables precise ranking of channel quality, providing data support for intelligent channel selection and ensuring that the interference level of the selected channel remains within a controllable range.

[0028] In this invention, the beam parameters are dynamically adjusted in conjunction with the target node's movement status during the adaptive beamforming configuration process. The target node's movement trajectory is predicted using a Kalman filter algorithm, and the beam pointing is adjusted in advance to reduce beam switching delay. Wide beam coverage is used for high-speed mobile nodes to improve communication continuity, while narrow beams are used for stationary or low-speed mobile nodes to improve signal gain and anti-interference capability. At the same time, the beam switching algorithm is optimized to maintain the continuity of signal transmission during switching, reduce data loss caused by switching interruptions, and improve network stability in mobile scenarios.

[0029] In this invention, a layered encryption strategy is adopted in the secure transmission mechanism. User data is encrypted using a symmetric encryption algorithm, while the key is transmitted using an asymmetric encryption algorithm. A dynamic encryption key is generated by combining the node's identity identifier, and the key is updated periodically. Integrity verification uses a hash algorithm to generate a data verification value. The receiving node confirms whether the data has been tampered with by comparing the verification value. A two-way authentication mechanism is used in the identity verification process. The communicating parties exchange identity authentication certificates, which contain the node's identifier, permission level, and validity period. The legality of the communication object is verified through certificate verification. Permission management is based on role-based allocation of operation permissions. Different roles correspond to different data access and operation permissions, preventing unauthorized nodes from obtaining sensitive information or interfering with network operation.

[0030] In this invention, a power optimization model is used to achieve precise control of the transmit power during dynamic power adjustment. The calculation expression is as follows: ,in To achieve the optimal transmit power for the node, The minimum detectable power of the receiving node. This represents the total channel attenuation. For signal quality assurance factor, For the transmit antenna gain, For receiving antenna gain, For system transmission efficiency, The distance between communication nodes. To compensate for interference power, this model is used to calculate that while ensuring communication quality, the transmission power can minimize interference to surrounding nodes, thus balancing communication reliability and the overall anti-interference performance of the network.

[0031] In this invention, a sliding window detection algorithm is used in the real-time interference monitoring process. A detection window of fixed length is set, and the interference intensity, frequency, and type within the window are statistically analyzed in real time. When the interference assessment value within the window exceeds the set threshold three times consecutively, a first-level anti-interference response is triggered, adjusting the beam pointing and transmission power. When the interference assessment value exceeds the emergency threshold, a second-level anti-interference response is triggered, switching to a backup channel and notifying surrounding nodes to coordinate avoidance. At the same time, the time, location, type, and handling measures of the interference events are recorded to form an interference event database, providing data support for subsequent interference prediction and anti-interference strategy optimization.

[0032] In this invention, an adaptive retransmission mechanism is adopted during data transmission. The number of retransmissions and the retransmission timeout are dynamically adjusted according to the channel quality. When the channel quality is good, the number of retransmissions and the timeout are reduced to improve transmission efficiency. When the channel quality is poor, the number of retransmissions is increased and the timeout is extended to improve data transmission reliability. At the same time, a data packet fragmentation transmission strategy is adopted to divide large data packets into multiple small data packets for transmission. Each data packet carries independent check information and sequence number. The receiving node reassembles the data packets according to the sequence number. Missing or erroneous data packets only need to be retransmitted individually, without retransmitting the entire large data packet, thus reducing retransmission overhead and transmission latency.

[0033] This invention also includes a dynamic network load balancing step, which monitors the communication load and resource usage of each node in real time. When the load of a node exceeds a threshold, some communication services are migrated to adjacent nodes with lower loads. During the migration process, the continuity of the communication link is maintained. The communication quality after the service migration is maintained by adjusting the channel allocation and beam pointing. At the same time, the network topology is optimized to reduce the interference aggravation and transmission efficiency reduction caused by the overload of a single node, so as to achieve a balanced distribution of network load among nodes and improve the anti-interference capability, stability and transmission efficiency of the entire millimeter wave wireless network.

[0034] The following two examples further illustrate specific embodiments of the present invention: Example 1: Anti-interference implementation of millimeter-wave wireless networking in emergency communication scenarios This embodiment is applied to the emergency rescue communication scenario in urban areas after an earthquake. It requires the rapid establishment of a temporary millimeter-wave wireless network to achieve coordinated communication between a rescue command vehicle, five portable rescue terminals, and two drone relay nodes. It is designed to cope with complex electromagnetic interference in urban ruins and ensure the reliable transmission of rescue instructions, the location of the injured, and environmental monitoring data. The network has a coverage radius of 3 kilometers and supports continuous communication under high-speed movement of mobile nodes.

[0035] The specific implementation process is as follows: During the network node initialization and configuration phase, all nodes automatically enter network mode after startup. Identity authentication is completed using pre-stored device identifiers and symmetric keys, preventing unauthorized nodes from accessing. Time synchronization is achieved based on BeiDou / GPS modules, with synchronization errors controlled within milliseconds to ensure timing consistency between nodes. Multi-beam working mode and inter-frequency full-duplex communication parameters are preset. The network topology adopts a star structure, with the rescue command vehicle as the core node and the highest priority, followed by UAV relay nodes, and portable rescue terminals with ordinary priority. Basic communication links between nodes are quickly established, and the entire initialization process takes no more than 30 seconds.

[0036] In the multi-dimensional channel perception and interference detection phase, each node scans the 30 to 300 GHz millimeter wave band through its built-in spectrum sensing module, collecting parameters such as channel bandwidth, signal strength, interference power, and channel attenuation once per second. Using an algorithm that combines energy detection and feature matching, it identifies three types of interference: narrowband interference from surrounding broadcasting equipment, co-channel interference from other rescue team communication equipment, and malicious interference from electromagnetic noise. It generates a channel quality assessment report that includes the interference level of each channel, available bandwidth, and signal stability, and pushes it to the core node in real time.

[0037] During the intelligent channel selection and resource allocation phase, the core node receives channel quality assessment reports from each node. Considering the high priority of rescue command transmission and the general need for environmental data collection, a load balancing algorithm selects three channels with the lowest interference levels. Command transmissions from the command vehicle and portable terminals are allocated to the primary channel, environmental data transmissions to the secondary channel, and one backup channel is reserved for the UAV relay node. By multiplexing time-frequency resources among multiple users, each portable terminal is allocated an independent time slice, improving resource utilization and avoiding channel congestion.

[0038] During the adaptive beamforming configuration phase, the UAV relay node dynamically adjusts its beam direction and beamwidth based on the real-time location information of the command vehicle and portable terminal, using its built-in positioning module to acquire coordinate data. For high-speed mobile portable terminals, wide beam coverage ensures communication continuity; for stationary command vehicles, narrow beams enhance signal gain. Null points are formed in the direction of identified interference sources to suppress interference signal strength, enabling wide scanning angles and rapid beam switching. The beam switching response time is no more than 10 milliseconds, ensuring uninterrupted communication for mobile nodes.

[0039] During the dynamic power adjustment phase, each node adjusts its transmission power in real time based on channel attenuation and interference intensity. Command vehicles and UAV relay nodes, due to their longer transmission distances, have their transmission power appropriately increased to maintain the transmission EIRP value within the optimal range. Portable rescue terminals, with shorter transmission distances, have their transmission power minimized to reduce interference aggregation, while ensuring the receiving gain meets communication requirements and avoiding rapid battery depletion due to excessive power.

[0040] During the secure transmission phase, sensitive data such as rescue instructions and casualty locations are processed using the AES symmetric encryption algorithm. The encryption key is transmitted using the RSA asymmetric encryption algorithm, and a dynamic key is generated by combining it with the node's identity identifier, automatically updated hourly. Integrity verification uses the SHA-256 hash algorithm to generate a data verification value; the receiving node compares the verification value to confirm that the data has not been tampered with. A two-way authentication mechanism is used during identity verification; both communicating parties exchange digital certificates containing node identifiers, permission levels, and validity periods, verifying each other's legitimacy through certificate verification. Access control is divided into three levels: command, rescue, and observation. The command level can issue instructions and modify parameters; the rescue level can upload data and receive instructions; and the observation level can only receive public data, preventing unauthorized nodes from obtaining sensitive information or interfering with network operation.

[0041] In the real-time interference monitoring and dynamic optimization phase, a sliding window detection algorithm is employed, setting a 10-second detection window to statistically analyze interference intensity, frequency, and type within the window. When the interference assessment value exceeds a set threshold three times consecutively within the window, a Level 1 anti-interference response is triggered, adjusting the beam pointing and transmit power. When the interference assessment value exceeds an emergency threshold, a Level 2 anti-interference response is triggered, switching to a backup channel and notifying surrounding nodes to coordinate avoidance. Simultaneously, the time, location, type, and handling measures of interference events are recorded, forming an interference event database. Combined with historical data, interference trends are predicted, communication parameters are optimized in advance, and repeated interference is avoided.

[0042] During the data transmission and integrity feedback phase, data packets are transmitted according to a pre-set emergency communication protocol. Large data packets are divided into 1KB smaller packets, each carrying independent checksum information and a sequence number. The receiving node reassembles the packets according to their sequence numbers. Missing or erroneous packets only need to be retransmitted individually, without retransmitting the entire large data packet. If packet loss or data errors occur, an adaptive retransmission mechanism is activated. When channel quality is good, the number of retransmissions is 2, with a timeout of 50 milliseconds; when channel quality is poor, the number of retransmissions increases to 4, and the timeout is extended to 100 milliseconds, ensuring reliable data transmission.

[0043] During the precise location and isolation phase of the interference source, the command vehicle, drones, and portable terminals collaboratively collected the angle of arrival, time difference of arrival, and signal characteristics of the interference signal to construct a spatial location model of the interference source. Combined with the location information of each node, the interference source was determined to be a damaged broadcast base station 500 meters away from the network area. For this fixed interference source, the beam pointing of three surrounding nodes was adjusted to form a shielded area. The interference propagation path was blocked by a combination of beamforming null suppression and channel isolation. Simultaneously, the interference source information was synchronized to the entire network, achieving network-wide collaborative anti-interference.

[0044] In the cross-layer collaborative anti-interference optimization phase, beamforming from the physical layer, channel coding from the data link layer, and routing optimization from the network layer are integrated to establish a cross-layer parameter mapping relationship. The physical layer feeds back channel quality parameters to the data link layer. When the channel quality is good, higher-order coding is used to improve the transmission rate; when the channel quality is poor, lower-order coding is used to enhance error correction capability. The data link layer feeds back the transmission status to the network layer. When the packet loss rate of a certain link is high, the route selection is optimized and switched to a more stable transmission path. The network layer adjusts the node communication topology according to the overall interference distribution, reducing the superposition of interference from each layer through cross-layer collaboration.

[0045] During the dynamic network load balancing phase, the communication load and resource usage of each node are monitored in real time. When the load of the command vehicle node exceeds the threshold, some environmental data reception and storage services are migrated to the UAV relay node with a lower load. During the migration, the continuity of the communication link is maintained. The communication quality after the service migration is maintained by adjusting the channel allocation and beam pointing. At the same time, the network topology is optimized to reduce the increased interference and decreased transmission efficiency caused by the overload of a single node, so as to achieve a balanced distribution of network load among the nodes.

[0046] Table 1: Comparison of Network Performance in Emergency Communication Scenarios

[0047] Table 1 clearly demonstrates the significant advantages of the method of this invention in emergency communication scenarios. Traditional millimeter-wave networking methods lack multi-dimensional interference detection and precise suppression techniques. When faced with complex electromagnetic interference, their interference suppression effect is poor, resulting in high data packet loss rates, large transmission delay fluctuations, and easy communication interruptions when mobile nodes move rapidly. This makes it difficult to guarantee network stability to meet emergency rescue needs. This invention, through multi-dimensional channel sensing, adaptive beamforming, precise interference source localization, and cross-layer collaboration, effectively suppresses various types of interference, controls data packet loss rates to an extremely low level, maintains stable transmission delays within a low range, ensures strong communication continuity for mobile nodes, and exhibits excellent network stability. It can guarantee reliable transmission of command instructions and rescue data in the complex environment of emergency rescue, providing communication support for the efficient conduct of rescue work.

[0048] Example 2: Anti-interference implementation of multi-node millimeter-wave wireless networking in remote mountainous areas This embodiment is applied to a meteorological monitoring and environmental data collection scenario in remote mountainous areas. The network includes one central station node, six fixed monitoring nodes, and two mobile patrol nodes, with a coverage radius of 5 kilometers. The fixed monitoring nodes are responsible for collecting data such as temperature, humidity, and air quality, while the mobile patrol nodes are responsible for mobile monitoring and data supplementation. The interference mainly comes from civilian communication equipment in surrounding villages and electromagnetic noise from the mountains and forests. It is necessary to ensure the long-term stable operation of the network and efficient data transmission.

[0049] The specific implementation process is as follows: During the initial configuration phase of the network nodes, after all nodes start up, they complete identity authentication through pre-stored device identifiers and encryption algorithms. The GPS module achieves time synchronization, with synchronization errors controlled within milliseconds. Multi-beam working mode and inter-frequency full-duplex communication parameters are preset. The network topology adopts a tree structure, with the central station node as the root node, fixed monitoring nodes as child nodes, and mobile patrol nodes as leaf nodes. The central station node has the highest communication priority, followed by the fixed monitoring nodes. After each node completes network access registration, a basic communication link is established between nodes. Backup communication links are reserved between fixed monitoring nodes to improve network redundancy.

[0050] In the multi-dimensional channel sensing and interference detection phase, each node periodically scans available channels within the 30-300 GHz millimeter-wave band using a spectrum sensing module, collecting parameters such as channel bandwidth, signal strength, interference power, and channel attenuation every 30 seconds. An algorithm combining energy detection and feature matching is used to identify interference types such as co-channel interference from civilian communication equipment in surrounding villages and malicious interference from natural electromagnetic noise in mountainous areas. A channel quality assessment report, including channel quality score, interference type, and interference intensity, is generated and uploaded to the central station node for aggregation.

[0051] During the intelligent channel selection and resource allocation phase, the central station node, based on the channel quality assessment reports reported by each node and considering node communication needs and service priorities, uses a load balancing algorithm to allocate a dedicated communication channel to each fixed monitoring node. Mobile patrol nodes dynamically select communication channels based on the channel quality of their area. Resource utilization is improved by using multi-user multiplexing of time-frequency resources. In areas surrounding villages with severe interference, two backup channels are reserved for relevant nodes to ensure rapid switching in case of increased interference.

[0052] During the adaptive beamforming configuration phase, fixed monitoring nodes adjust their beam pointing based on the central station's location, employing narrow beams to enhance signal gain and anti-interference capabilities. Mobile patrol nodes, combining their own movement status with the location of target communication nodes, predict their movement trajectory using a Kalman filter algorithm, adjusting their beam pointing in advance to reduce beam switching delay. For identified interference sources, each node adjusts its beam to form null points, suppressing interference signals and achieving wide scanning angles and rapid beam switching, meeting the communication needs of mobile patrol nodes during low-speed movement.

[0053] During the dynamic power adjustment phase, each node adjusts its transmit power in real time based on channel attenuation and interference intensity. In mountainous areas where channel attenuation is significant and the transmission distance between fixed monitoring nodes and the central station is long, the transmit power is appropriately increased to ensure effective signal coverage. Mobile patrol nodes are closer to fixed nodes or the central station, so the transmit power is minimized while meeting communication quality requirements, and the transmit EIRP value and receive gain are maintained within the optimal range to reduce interference to surrounding nodes.

[0054] In the secure transmission mechanism phase, a layered encryption strategy is adopted. User data such as meteorological and environmental data are encrypted using the AES symmetric encryption algorithm, while the encryption key is transmitted using the RSA asymmetric encryption algorithm. A dynamic encryption key is generated by combining node identity identifiers and is automatically updated every 2 hours. Integrity verification uses the SHA-256 hash algorithm to generate a data verification value. The receiving node confirms whether the data has been tampered with by comparing the verification value. A two-way authentication mechanism is used in the identity verification process. The communicating parties exchange digital certificates containing node identifiers, permission levels, and validity periods. The legitimacy of the communicating parties is verified through certificate verification. Access management is based on role-based allocation of operation permissions, divided into management level, collection level, and access level. The management level can configure parameters and modify permissions, the collection level can upload data and receive instructions, and the access level can only query public data, preventing unauthorized nodes from obtaining sensitive information or interfering with network operation.

[0055] In the real-time interference monitoring and dynamic optimization phase, a sliding window detection algorithm is employed, setting a 30-second detection window to statistically analyze interference intensity, frequency, and type within the window. When the interference assessment value exceeds a set threshold three times consecutively within the window, a Level 1 anti-interference response is triggered, adjusting the beam pointing and transmit power. When the interference assessment value exceeds an emergency threshold, a Level 2 anti-interference response is triggered, switching to a backup channel and notifying surrounding nodes to coordinate avoidance. Simultaneously, the time, location, type, and handling measures of interference events are recorded, forming an interference event database. This database provides data support for subsequent interference prediction and anti-interference strategy optimization, and combined with historical interference data, interference trends are predicted to optimize communication parameters in advance.

[0056] During the data transmission and integrity feedback phase, data packets are transmitted according to the preset meteorological monitoring communication protocol. The large collected data packets are divided into 512-byte smaller data packets for transmission, each carrying independent checksum information and a sequence number. The receiving node reassembles the data packets according to their sequence numbers. Missing or erroneous data packets only need to be retransmitted individually, eliminating the need to retransmit the entire large data packet, thus reducing retransmission overhead and transmission latency. An adaptive retransmission mechanism is employed: when channel quality is good, the number of retransmissions and timeout time are reduced to improve transmission efficiency; when channel quality is poor, the number of retransmissions is increased and the timeout time is extended to improve data transmission reliability.

[0057] In the precise location and isolation phase of the interference source, the central station and fixed monitoring nodes collaboratively collect the angle of arrival, time difference of arrival, and signal characteristics of the interference signal to construct a spatial location model of the interference source. Combined with the location information of each node, the interference source is determined to be civilian communication equipment in surrounding villages and electromagnetic noise sources from the mountains and forests. For fixed civilian communication equipment interference sources, the beam pointing of two surrounding fixed monitoring nodes is adjusted to form a shielded area. For moving electromagnetic noise sources, the tracking strategy is dynamically updated, and the interference propagation path is blocked by a combination of beamforming null suppression and channel isolation. Simultaneously, the interference source information is synchronized to the entire network, achieving network-wide collaborative anti-interference.

[0058] In the cross-layer collaborative anti-interference optimization phase, beamforming at the physical layer, channel coding at the data link layer, and routing optimization at the network layer are integrated to establish a cross-layer parameter mapping relationship. The physical layer feeds back channel quality parameters to the data link layer, adjusting to higher-order coding to increase transmission rate when channel attenuation is small, and adjusting coding rate and error correction level to enhance anti-interference capability when channel attenuation is large. The data link layer feeds back transmission status to the network layer, optimizing route selection and switching to backup links when the transmission efficiency of a certain link is low. The network layer adjusts node communication topology according to the overall interference distribution, reducing the superposition of interference from each layer through cross-layer collaboration, thereby improving the overall anti-interference capability and transmission efficiency of the network.

[0059] During the dynamic load balancing phase, the communication load and resource usage of each node are monitored in real time. When the load of the central station node exceeds the threshold, some data storage and preliminary processing services are migrated to two fixed monitoring nodes with lower loads. Communication link continuity is maintained during the migration process. Communication quality after migration is maintained by adjusting channel allocation and beam pointing. Simultaneously, the network topology is optimized to reduce interference and transmission efficiency degradation caused by overload of a single node, achieving a balanced distribution of network load among nodes and ensuring long-term stable network operation.

[0060] Table 2: Comparison of Network Performance in Remote Mountainous Areas

[0061] Table 2 data fully demonstrates the application value of the method of this invention in remote mountainous scenarios. Traditional millimeter-wave networking methods suffer from limited interference suppression capabilities, generally low data transmission reliability, and low utilization rates due to unreasonable resource allocation in complex channel environments in remote mountainous areas. Long-term operation is prone to stability issues due to uneven load or interference accumulation, and service migration is easily interrupted. This invention achieves excellent interference suppression effects, extremely high data transmission reliability, and significantly improved resource utilization through intelligent channel selection, dynamic power adjustment, cross-layer collaborative optimization, and load balancing technologies. It can operate stably for a long time to meet the long-term monitoring needs of mountainous areas, and the service migration process is smooth and uninterrupted. This method is adapted to the channel characteristics and interference environment of remote mountainous areas, providing a stable and efficient networking solution for scenarios such as meteorological monitoring and environmental data acquisition, solving many pain points of traditional methods in remote areas.

[0062] refer to Figure 2 This bar chart visually demonstrates the suppression advantages of this invention under various interference scenarios. Traditional millimeter-wave networking methods rely solely on single-spectrum detection methods, resulting in weak identification and suppression capabilities for different types of interference. Under mixed interference, the suppression rate is less than 40%, failing to meet the anti-interference requirements of scenarios such as emergency communication and monitoring in remote mountainous areas. This invention, through multi-dimensional channel sensing and interference detection technology, accurately identifies the characteristics and intensity of various interferences. Combined with adaptive beamforming null suppression and precise location and isolation of interference sources, it dynamically adjusts the anti-interference scheme for different interference types, achieving a stable suppression rate of over 89% for all types of interference. Especially in emergency communication scenarios, the suppression rate reaches 94% for co-channel interference, effectively blocking interference from other rescue equipment and ensuring the clarity of command transmission. This solves the core problems of poor interference suppression and low scenario adaptability of traditional methods.

[0063] refer to Figure 3 The line graph clearly reflects the low-latency advantage of this invention in mobile scenarios. Traditional millimeter-wave networking methods suffer from slow beam switching response. As node movement speed increases, the beam cannot track the target in time, leading to a significant increase in transmission latency. At high speeds of 40 km / h, the latency exceeds 60 ms, which cannot meet the communication needs of mobile nodes such as portable terminals and drones in emergency rescue. This invention predicts the node's movement trajectory using a Kalman filter algorithm and adjusts the beam direction in advance, controlling the beam switching response time to within 10 milliseconds. Even with high-speed node movement, the latency increases only slightly, reaching only 12 ms at 40 km / h. Simultaneously, packet fragmentation and adaptive retransmission mechanisms reduce the latency caused by packet loss and retransmission, ensuring continuous and low-latency communication for mobile nodes, adapting to the needs of dynamic scenarios such as emergency communication and mobile patrols in mountainous areas.

[0064] refer to Figure 4This radar chart comprehensively demonstrates the transmission reliability of this invention under different service scenarios. Traditional millimeter-wave networking methods lack differentiated resource allocation and anti-interference strategies, resulting in packet loss rates exceeding 10% for emergency priority services, which can easily lead to the loss of critical instructions during emergency rescue and cause serious consequences. This invention dynamically allocates time-frequency resources based on service priorities, prioritizing emergency and high-priority services to occupy high-quality channels. Combined with secure transmission mechanisms and adaptive retransmission strategies, the packet loss rate for all types of services is controlled within 1%. Even for ordinary log data monitored in mountainous areas, the packet loss rate is only 0.55%, ensuring complete data collection and transmission. This differentiated packet loss control strategy not only ensures the absolute reliability of core services but also takes into account the transmission efficiency of ordinary services, solving the problems of high packet loss rates and insufficient service priority adaptation in traditional methods.

[0065] refer to Figure 5 This scatter plot highlights the resource utilization advantages of this invention during long-term operation. Traditional millimeter-wave networking methods employ fixed time-frequency resource allocation strategies. As operating time increases, some nodes become overloaded while others become idle, leading to a continuous decline in resource utilization. After 9 hours, the utilization rate is only 40%, resulting in significant resource waste. This is especially problematic in long-term monitoring scenarios in mountainous areas, where inefficient resource utilization increases equipment energy consumption and maintenance costs. This invention, through load balancing algorithms and dynamic resource allocation strategies, adjusts the time-frequency resource usage of each node in real time. Even after 9 hours of operation, the resource utilization rate remains above 81%. Furthermore, backup channel reservation and cross-layer collaborative optimization further improve resource utilization efficiency, avoiding the utilization rate decline caused by resource allocation imbalances in traditional methods. This makes it suitable for resource requirements in scenarios such as long-term networking in remote mountainous areas and continuous emergency communication.

[0066] refer to Figure 6 This line graph fully demonstrates the stable operation capability of this invention under strong interference environments. Traditional millimeter-wave networking methods lack real-time interference monitoring and dynamic optimization mechanisms. When the interference intensity increases, the network is prone to problems such as link interruption and parameter mismatch. Under 50dB strong interference, the stability score is only 40 points, which cannot guarantee continuous operation in scenarios such as emergency communication and mountain monitoring. This invention monitors interference changes in real time through a sliding window detection algorithm. When the interference intensity exceeds the threshold, it quickly triggers response strategies such as beam adjustment and channel switching. Combined with historical data, it optimizes parameters in advance. Even under 50dB strong interference, the stability score still reaches 90 points. At the same time, network load balancing and cross-layer collaborative optimization further enhance the network resilience, ensuring that the network stability always remains above 90 points in complex electromagnetic environments, solving the core pain points of weak anti-interference capability and poor stability of traditional methods.

[0067] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A millimeter-wave wireless networking anti-interference method, characterized in that, Includes the following steps: S1: Initial configuration of network nodes. After all network nodes start up, they automatically complete identity authentication and network access registration. Time synchronization and location information interaction are achieved based on the Beidou / GPS module. Multi-beam working mode and different frequency duplex communication parameters are preset. Network topology and node communication priority are configured. Basic communication links between nodes are established. S2: Multi-dimensional channel perception and interference detection. Each node scans available channels through the spectrum perception module, collects channel parameters, and uses an algorithm that combines energy detection and feature matching to identify interference types and generate a channel quality assessment report. S3: Intelligent channel selection and resource allocation. Based on the channel quality assessment report, combined with the node communication needs and service priorities, the load balancing algorithm is used to select the optimal communication channel and allocate time and frequency resources. The resource utilization rate is improved by multiple users reusing time and frequency resources, while reserving backup channel resources in areas with severe interference. S4: Adaptive beamforming configuration, each node adjusts the beam direction and beam width according to the target node position and interference source distribution information, and suppresses interference signals by forming nulls in the direction of interference sources, and enhances the signal gain in the direction of the target. S5: Dynamic power adjustment, which adjusts the node's transmit power in real time based on channel attenuation and interference intensity, thereby adjusting the transmit EIRP value and receive gain; S6: Secure transmission mechanism, which encrypts transmitted data, verifies data integrity using integrity verification algorithms, verifies the identities of both communicating parties through an identity confirmation mechanism, and establishes a role-based access control system; S7: Real-time interference monitoring and dynamic optimization. It continuously monitors channel interference changes during data transmission. When the interference intensity exceeds the threshold, it triggers channel switching, beam adjustment, or power adjustment mechanisms. It combines historical interference data to predict interference trends and optimize communication parameters in advance. S8: Data transmission and integrity feedback. Data packets are transmitted according to a preset protocol. After the receiving node verifies the data integrity, it sends back confirmation information. When packet loss or data error occurs, a retransmission mechanism is initiated, and transmission parameters are optimized during the retransmission process.

2. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, It also includes steps for precise location and isolation of interference sources. Multiple network nodes work together to collect the angle of arrival, time difference of arrival, and signal characteristics of interference signals to construct a spatial location model of the interference source. Combined with the node location information, the coordinates of the interference source are determined. For fixed interference sources, the beam pointing of surrounding nodes is adjusted to form a shielded area. For mobile interference sources, the tracking strategy is dynamically updated. The interference propagation path is blocked by a combination of beamforming null suppression and channel isolation. The interference source information is synchronized to the entire network.

3. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, It also includes cross-layer collaborative anti-interference optimization steps, which integrate physical layer beamforming, data link layer channel coding and network layer routing optimization to establish cross-layer parameter mapping relationships; the physical layer feeds back channel quality parameters to the data link layer to adjust coding rate and error correction level; The data link layer feeds back the transmission status to the network layer to optimize routing; the network layer adjusts the node communication topology based on the overall interference distribution.

4. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, In the multi-dimensional channel sensing process, an interference comprehensive assessment model is used to quantify the degree of channel interference. The calculation expression is as follows: ,in This is the comprehensive evaluation value for interference. For interference power weighting coefficients, This is the interference bandwidth weighting coefficient. The duration of the interference is the weighting factor. , The average power of the interference signal within the channel. The average power of the useful signal within the channel. To prevent interference signals from occupying bandwidth, The total available bandwidth of the channel. Duration of the interference signal This is for the statistical time window length.

5. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, During adaptive beamforming configuration, beam parameters are dynamically adjusted based on the target node's movement status. The Kalman filter algorithm is used to predict the target node's movement trajectory and adjust the beam direction in advance. Wide beam coverage is used for high-speed moving nodes, while narrow beams are used for stationary or low-speed moving nodes. The beam switching algorithm is optimized to maintain signal transmission continuity during switching.

6. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, The secure transmission mechanism employs a layered encryption strategy. User data is encrypted using a symmetric encryption algorithm, while the key is transmitted using an asymmetric encryption algorithm. A dynamic encryption key is generated by combining the node's identity identifier and is updated periodically. Integrity verification uses a hash algorithm to generate a data verification value, and the receiving node determines whether the data has been tampered with by comparing the verification value. Identity verification adopts a two-way authentication mechanism, where the two communicating parties exchange identity authentication certificates containing node identifiers, permission levels, and validity periods, and verify the legitimacy of the communicating parties through the certificates; permission management assigns operation permissions based on roles.

7. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, During dynamic power regulation, a power optimization model is used to achieve precise control of the transmit power. The calculation expression is as follows: ,in To achieve the optimal transmit power for the node, The minimum detectable power of the receiving node. This represents the total channel attenuation. For signal quality assurance factor, For the transmit antenna gain, For receiving antenna gain, For system transmission efficiency, The distance between communication nodes. This is for interference compensation power.

8. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, The real-time interference monitoring process employs a sliding window detection algorithm, setting a fixed-length detection window to statistically analyze the interference intensity, frequency, and type within the window in real time. When the interference assessment value exceeds the set threshold three times consecutively within the window, a Level 1 anti-interference response is triggered, adjusting the beam pointing and transmission power. When the interference assessment value exceeds the emergency threshold, a Level 2 anti-interference response is triggered, switching to a backup channel and notifying surrounding nodes to coordinate avoidance. The time, location, type, and handling measures of interference events are recorded to form an interference event database.

9. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, An adaptive retransmission mechanism is used during data transmission to dynamically adjust the number of retransmissions and the retransmission timeout based on channel quality. A data packet fragmentation transmission strategy is adopted to divide large data packets into multiple small data packets for transmission. Each data packet carries independent check information and sequence number. The receiving node reassembles the data packets according to the sequence number and retransmits missing or erroneous data packets separately.

10. The millimeter-wave wireless networking anti-interference method according to claim 1, characterized in that, It also includes a dynamic network load balancing step, which monitors the communication load and resource usage of each node in real time. When the node load exceeds the threshold, some communication services are migrated to adjacent nodes with lower load. During the migration process, the communication link continuity is maintained, channel allocation and beam pointing are adjusted, and the network topology is optimized.

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