Millimeter wave digital information transmission efficiency optimization method

Abstract

The invention discloses a millimeter wave digital information transmission efficiency optimization method, which relates to the technical field of digital information transmission, and comprises the following steps of: pre-configuring transmission parameters, setting carrier frequency, bandwidth, power and frame structure parameters, and establishing a protocol stack adaptive to millimeter waves; real-time channel sensing is used for collecting channel characteristic parameters, and a quality report is generated after preprocessing; the modulation and coding dynamic adaptation selects a modulation and coding scheme according to channel quality; adaptive power and beam forming are carried out to adjust the transmitting power and the antenna weight; intelligent flow control allocates resources according to service priorities; the interference cooperative suppression adopts a differential strategy for different interferences; transmission synchronization optimization improves frame synchronization and carrier synchronization precision; and a receiving end performs adaptive equalization and error correction compensation on channel fading and recovers data. According to the invention, through multi-dimensional dynamic adaptation and optimization, the millimeter wave transmission efficiency and reliability are significantly improved; resource allocation and service adaptation are optimized, and service transmission requirements of different priorities are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of digital information transmission technology, and in particular to a method for optimizing the efficiency of millimeter-wave digital information transmission. Background Technology

[0002] Millimeter waves, with their ultra-wide available bandwidth, high directivity due to their short wavelength, and low latency, have become a core technology choice for high-speed digital information transmission scenarios such as 5G-Advanced, Industrial IoT, and Vehicle-to-Everything (V2X). They can meet the stringent requirements of high-definition video transmission, real-time data interaction, and remote control for transmission speed and latency. With the rapid development of the digital economy, the number of various terminal devices has surged, and the volume of business data has grown exponentially, placing higher demands on the efficiency, reliability, and adaptability of millimeter wave transmission. However, millimeter wave transmission operates at relatively high frequencies, making the signal susceptible to path loss, rain attenuation, and obstruction during propagation. The channel exhibits complex characteristics such as strong attenuation, Doppler shift, and short latency spread, while also facing problems such as co-channel interference, adjacent-channel interference, and external electromagnetic interference, severely restricting the full realization of its transmission performance.

[0003] Existing millimeter-wave digital information transmission methods suffer from numerous technical shortcomings, making them ill-suited to complex and ever-changing channel environments and diverse service requirements. Regarding transmission parameter configuration, traditional methods often employ fixed modulation and coding schemes, transmit power, and frame structure parameters, failing to dynamically adjust based on channel quality. When channel quality is good, bandwidth resources are not fully utilized to increase transmission rates; conversely, when channel quality deteriorates, insufficient parameter adaptation leads to a surge in bit error rate. Power control and beamforming technologies are not fully developed; power adjustment lacks precise channel feedback, easily resulting in power waste or received signal distortion. Beamforming's directionality and anti-interference capabilities are insufficient, and adjacent-channel interference caused by sidelobe leakage further reduces overall transmission efficiency. Flow control and transmission protocol adaptability are inadequate; transmission strategies are not dynamically adjusted based on service priority and channel status, leading to real-time and non-real-time services competing for bandwidth resources. Traditional TCP / UDP protocol parameters are difficult to match the high bandwidth and low latency characteristics of millimeter waves, easily resulting in congestion or insufficient transmission reliability.

[0004] Furthermore, existing methods suffer from limitations in channel sensing accuracy and synchronization performance. Channel parameter acquisition lacks effective preprocessing mechanisms, and outlier interference leads to inaccurate channel assessment. Frame synchronization and carrier synchronization exhibit weak anti-interference capabilities, with significant synchronization errors impacting data parsing quality. Receiver-side equalization and error correction techniques lack specificity and fail to effectively compensate for frequency-selective fading in millimeter-wave channels, resulting in poor bit error rate control. Simultaneously, the lack of effective mechanisms for channel prediction, interference co-suppression, and link redundancy backup means that the transmission system's adaptability and fault tolerance are insufficient in the face of sudden channel changes or increased interference, easily leading to transmission interruptions or significant efficiency drops. These issues collectively result in low efficiency, insufficient reliability, and limited adaptability in current millimeter-wave digital information transmission, failing to meet the demands for high-speed, low-latency, and highly reliable digital information transmission. Therefore, a comprehensive and optimized method for improving transmission efficiency is urgently needed. Summary of the Invention

[0005] This invention proposes a method for optimizing the transmission efficiency of millimeter-wave digital information to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing millimeter-wave digital information transmission efficiency, comprising the following steps: Transmission parameter pre-configuration steps: Based on the characteristics of the millimeter wave band, set the initial carrier frequency, transmission bandwidth and transmit power range, configure frame structure parameters, and establish an adapted protocol stack; Real-time channel awareness steps: Deploy a multi-antenna array and channel detection module, collect channel characteristic parameters, generate a channel quality report after preprocessing, and provide real-time feedback on channel status; Dynamic modulation and coding adaptation steps: Receive channel quality report, dynamically select modulation method and coding scheme according to mapping rules, and optimize coding structure and redundancy distribution; Adaptive power and beamforming steps: The transmit power is dynamically adjusted through a power control algorithm to maintain the signal-to-noise ratio at the receiver, and adaptive beamforming technology is activated to form a directional narrow beam; Intelligent flow control steps: Prioritize services and dynamically adjust data packet length and transmission rate based on channel quality and service type; Interference Cooperative Suppression Steps: Identify various types of interference, and use interference alignment, dynamic frequency band switching, and space-time coding techniques to suppress different types of interference in a targeted manner; Transmission synchronization optimization steps: Optimize frame synchronization and carrier synchronization mechanisms by combining correlation detection with phase-locked loop and frequency domain correction to control synchronization errors; The receiver-side adaptive equalization and error correction steps are as follows: deploy an adaptive equalizer to compensate for channel fading, perform error correction through an LDPC decoder, and feed back channel quality and bit error rate data to the transmitter.

[0007] Furthermore, it also includes a comprehensive channel quality assessment step, which constructs a channel quality assessment model using multi-dimensional parameters. The expression for the assessment model is as follows: in This is a comprehensive evaluation value for channel quality. The signal-to-noise ratio weighting coefficient is set to a value between 0.3 and 0.4. This refers to the real-time channel signal-to-noise ratio. For reference signal-to-noise ratio, This is the attenuation coefficient weighting coefficient. As the attenuation effect factor, This is the channel attenuation coefficient. This is the time delay weighting coefficient. For real-time channel coherence time, For reference coherence time, This is the Doppler frequency shift weighting coefficient. Based on the Doppler frequency shift, five channel quality levels are defined using this evaluation value, with each level corresponding to a different transmission parameter configuration scheme.

[0008] Furthermore, it also includes a multi-antenna cooperative transmission step, which expands the transmission link capacity through multiple-input multiple-output technology, deploys an N×M antenna array where N is the number of transmit antennas and M is the number of receive antennas, and adopts a transmission mode that combines spatial multiplexing and diversity merging. In the spatial multiplexing mode, the data stream is divided into N parallel sub-streams and transmitted simultaneously through different transmit antennas; in the diversity merging mode, different copies of the same data stream are received through multiple receive antennas, and the maximum ratio combining algorithm is used to superimpose the received signals; the transmitter dynamically switches the transmission mode based on the channel quality report, and at the same time optimizes the arrangement of the antenna array, using a uniform linear array.

[0009] Furthermore, it includes adaptive adjustment steps for transmission protocols, optimizing TCP / UDP protocol parameters based on the high bandwidth and low latency characteristics of millimeter wave transmission; for the TCP protocol, the initial value of the congestion window is adjusted to 8 to 16 data packets, the slow start threshold is set to the number of data packets corresponding to the channel bandwidth, and a delay-based congestion control algorithm is adopted to determine the network congestion status by monitoring changes in round-trip time; for the UDP protocol, a data packet checksum field and a retransmission mechanism are added. The checksum field uses the CRC32 algorithm to check the data packet payload, and the retransmission mechanism is based on the feedback confirmation information from the receiving end. If no confirmation information is received, a timeout retransmission is initiated.

[0010] Furthermore, it also includes a dynamic bandwidth allocation step, which constructs a bandwidth allocation model based on service requirements and channel conditions. The allocation model expression is as follows: in For the first Bandwidth allocation for each service Total available transmission bandwidth, For the first The priority weight of each business. For the first The bandwidth requirement rate for each service. For the first The comprehensive channel quality assessment value of each service transmission link. The total number of services transmitted simultaneously. This refers to the service sequence number. This model dynamically adjusts the bandwidth allocation ratio of each service based on channel quality, while ensuring the bandwidth requirements of high-priority services.

[0011] Furthermore, it also includes channel prediction and pre-adaptation steps. Based on historical channel quality data, a long short-term memory neural network is used to construct a channel prediction model. The input features include channel attenuation coefficient, signal-to-noise ratio, Doppler shift, and interference intensity over the past 10 to 30 cycles. The output is the predicted channel quality value for the next 5 to 15 cycles. The transmission parameters are adjusted in advance based on the prediction results.

[0012] Furthermore, it also includes a receiver noise suppression step, which involves deploying an adaptive noise canceller, collecting ambient noise signals through a reference channel, generating a cancellation signal with equal amplitude and opposite phase to the noise signal based on an adaptive filtering algorithm, and superimposing it on the original received signal at the receiver to achieve noise suppression; the noise suppression algorithm adopts the minimum mean square error criterion; and for thermal noise and phase noise in millimeter wave transmission, spectrum estimation technology is used to analyze the noise power distribution.

[0013] Furthermore, it also includes transmission link switching and redundancy backup steps. Multiple millimeter-wave transmission links and one backup microwave transmission link are pre-configured, with each link using different transmission frequency bands and routing paths. The channel quality and transmission rate of the main transmission link are monitored in real time. When the bit error rate of the main link exceeds a preset threshold or the transmission rate is lower than the service requirements for a certain period of time, the link switching mechanism is activated to quickly switch to the backup millimeter-wave link with better channel quality. When all millimeter-wave links are interfered with and cannot transmit normally, the system switches to the microwave backup link while continuously monitoring the status of the millimeter-wave links. Once the channel is restored, the system automatically switches back to the millimeter-wave link.

[0014] Furthermore, it includes data compression and transmission coordination steps. An adaptive data compression module is deployed at the transmitting end, and the corresponding compression algorithm is selected according to the data type of the service. Text data uses the LZ77 compression algorithm, image data uses the JPEG2000 compression algorithm, and video data uses the H.265 compression algorithm. The compression ratio is dynamically adjusted according to the channel quality. When the channel quality is good, a low compression ratio is used to retain more data details, and when the channel quality is poor, a high compression ratio is used to reduce the amount of data. The compressed data packet is added with compression identifier and decompression parameter information. The receiving end selects the corresponding decompression algorithm according to the identifier to quickly restore the original data.

[0015] Furthermore, it also includes transmission performance monitoring and self-optimization steps, which involve real-time collection of key performance indicators during the transmission process; establishment of a correlation analysis model between performance indicators and transmission parameters; continuous optimization of transmission parameter configuration based on reinforcement learning algorithms, using performance improvement as a reward signal to adjust configuration strategies; and regular generation of transmission performance analysis reports.

[0016] Compared with existing technologies, the beneficial effects of this invention are: This invention provides a method for optimizing millimeter-wave digital information transmission efficiency, focusing on the core modulation carrier system optimization requirements of the H04L27 / 00 classification code. Through multi-dimensional technological innovation throughout the entire process, it comprehensively solves many pain points of existing technologies, with significant core beneficial effects.

[0017] The method lays a solid foundation for millimeter-wave transmission through a transmission parameter pre-configuration step, adapting parameter settings to different service types and frequency band characteristics, clarifying the interaction logic of each layer of the protocol stack, and ensuring the initial operational stability of the transmission system. The real-time channel awareness step enables comprehensive and accurate acquisition and preprocessing of channel characteristic parameters. The generated channel quality report accurately reflects dynamic channel changes, providing a scientific basis for subsequent parameter adjustments and avoiding efficiency losses caused by blind configuration. The dynamic modulation and coding adaptation technology flexibly switches schemes according to the channel quality level. When the channel is good, it fully utilizes bandwidth to increase the transmission rate; when the channel deteriorates, it ensures transmission reliability through low-order modulation and high-redundancy coding, achieving a dynamic balance between rate and reliability.

[0018] The adaptive power and beamforming steps precisely adjust the transmit power and antenna weights, maintaining a stable signal-to-noise ratio at the receiver while effectively suppressing sidelobe interference and peripheral link interference, thus improving spectrum resource utilization. Intelligent flow control allocates resources based on service priorities, ensuring the transmission quality of real-time services. Simultaneously, variable packet lengths and fragmented transmission optimize the transmission efficiency of non-real-time services, avoiding bandwidth waste. The interference collaborative suppression step employs differentiated suppression strategies for different types of interference, significantly improving the system's anti-interference capability and enhancing transmission stability in complex interference environments.

[0019] Transmission synchronization optimization and receiver adaptive equalization error correction techniques significantly improve synchronization accuracy and data recovery quality, effectively compensate for frequency-selective fading in millimeter-wave channels, and reduce the bit error rate. Each additional step further expands the method's advantages: adaptive adjustment of the transmission protocol optimizes the compatibility of TCP / UDP protocols with millimeter-wave transmission; dynamic bandwidth allocation achieves precise and efficient resource allocation; channel prediction and pre-adaptation reduce parameter adjustment lag; noise suppression and link redundancy backup enhance the system's fault tolerance; data compression and transmission coordination reduce bandwidth requirements; and transmission performance monitoring and self-optimization ensure long-term stable system operation and continuous performance improvement.

[0020] Overall, the method of this invention is complete and logically rigorous, covering the entire process optimization from parameter configuration, channel awareness, modulation and coding, power control, traffic management, interference suppression, synchronization optimization to receiver processing. It also incorporates innovative mechanisms such as protocol adaptation, resource allocation, channel prediction, and fault-tolerant backup, which significantly improves the efficiency, reliability, and adaptability of millimeter-wave digital information transmission. It effectively addresses the challenges brought by complex channel environments and diverse service requirements, aligns with the development direction of digital information transmission technology, is applicable to various high-speed digital information transmission scenarios, and has broad application value. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of the millimeter-wave digital information transmission efficiency optimization method proposed in this invention; Figure 2 A line graph comparing transmission rates under different channel qualities; Figure 3 A bar chart showing the relationship between service priority and bandwidth allocation; Figure 4 A line graph showing the relationship between interference intensity and bit error rate; Figure 5 A line graph showing the relationship between power adjustment and signal-to-noise ratio; Figure 6 A bar chart comparing link switching delays. Detailed Implementation

[0022] 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.

[0023] 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.

[0024] 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.

[0025] Reference Figures 1 to 6 A method for optimizing the efficiency of millimeter-wave digital information transmission includes the following steps: Transmission parameter pre-configuration steps: Based on the characteristics of the millimeter-wave transmission band, set the initial carrier frequency, transmission bandwidth, and transmit power range. The carrier frequency is selected from the interference-free band in the range of 24GHz to 100GHz. The transmission bandwidth is dynamically configured from 100MHz to 2GHz according to the service type. The initial transmit power is set from 10dBm to 30dBm according to the link budget. Configure frame structure parameters, including synchronization sequence length, data block size, and pilot symbol interval. The synchronization sequence uses ZC sequence to improve synchronization accuracy. The initial data block size is set from 1024 bytes to 4096 bytes. The pilot symbol interval is set from 5ms to 20ms according to the channel coherence time. Establish a protocol stack adapted for millimeter-wave transmission, covering the physical layer modulation and coding module, the data link layer flow control module, and the network layer routing adaptation module, and clarify the data interaction format and timing requirements of each layer. Real-time channel awareness steps: Deploy a multi-antenna array and channel sounding module to collect millimeter-wave channel characteristic parameters in real time by sending sounding pilot signals. Parameter types include channel attenuation coefficient, signal-to-noise ratio, noise power spectral density, Doppler frequency shift, time delay spread, and interference source strength and frequency. The collected parameters are preprocessed using a sliding window averaging method with a window length of 10 to 20 symbol periods to remove outliers caused by transient interference. A channel quality report containing channel quality level and dynamic change trend is generated, with a report update period of 10ms to 50ms. The modulation and coding dynamic adaptation process involves receiving a channel quality report and dynamically selecting the appropriate modulation scheme and coding scheme based on a preset channel quality-modulation and coding mapping rule. When the channel quality is excellent, 64QAM or 256QAM high-order modulation is used, coupled with high-rate LDPC coding at a rate of 0.8 to 0.9. When the channel quality is medium, 16QAM modulation is used, coupled with LDPC coding at a rate of 0.6 to 0.7. When the channel quality is poor, QPSK modulation is used, coupled with low-rate LDPC coding at a rate of 0.4 to 0.5. During the coding process, the parity check matrix structure is optimized, and the coding redundancy distribution is adjusted according to the data block size to improve error correction performance. Adaptive power and beamforming steps: Based on the channel attenuation coefficient and the strength of the interference source, the transmit power is dynamically adjusted through a power control algorithm to maintain the signal-to-noise ratio at the receiver above a preset threshold. The power adjustment step size is 0.5dBm to 2dBm to prevent interference accumulation caused by excessive power and reception distortion caused by insufficient power. Adaptive beamforming technology is activated, and the phase and amplitude weights of the antenna array are adjusted through a digital beamforming algorithm to form a narrow beam pointing towards the receiver. The half-power beamwidth is controlled between 5° and 15° to suppress sidelobe energy leakage and reduce interference to surrounding links. Intelligent flow control steps: First, obtain priority information for service data, classifying services into real-time and non-real-time categories. Real-time services include voice and video communication, while non-real-time services include file transfer and data backup. Second, based on channel quality reports and service priorities, dynamically adjust data packet length and transmission rate. Real-time services are given priority in bandwidth allocation, with fixed data packet lengths ranging from 512 to 1024 bytes, and transmission rates adaptively adjusted according to channel capacity. Non-real-time services use variable-length data packets, ranging from 1024 to 8192 bytes. When the channel is congested, the transmission rate is reduced, and data packet fragmentation is initiated, with the fragment size matching the available channel bandwidth. Interference Co-suppression Steps: The channel sensing module identifies the type of interference, including co-channel interference, adjacent-channel interference, and external electromagnetic interference. An interference alignment algorithm is used to suppress co-channel interference, adjusting the spatial characteristics of the transmitted signal to superimpose and cancel the interference signals at the receiver. For adjacent-channel interference, a combination of spectrum sensing and dynamic frequency band switching is used; when the intensity of adjacent-channel interference exceeds a preset threshold, the signal is switched to a preset backup frequency band. For external electromagnetic interference, space-time coding technology is used, transmitting signal copies with different delays through multiple antennas to enhance the receiver's interference resistance. Transmission synchronization optimization steps: Optimize frame synchronization and carrier synchronization mechanisms. Frame synchronization adopts a sliding correlation detection algorithm based on training sequences. The locally generated synchronization sequence and the received sequence are correlated to determine the frame start position. The synchronization detection threshold is dynamically adjusted according to the signal-to-noise ratio. Carrier synchronization adopts a combination of phase-locked loop and frequency domain correction. The bandwidth of the phase-locked loop is set to 1kHz to 10kHz to track the carrier frequency offset. Frequency domain correction estimates the frequency deviation through pilot symbols and compensates for it. The synchronization error is controlled within 10% of the symbol period. The receiver-side adaptive equalization and error correction steps are as follows: An adaptive equalizer is deployed at the receiver, and an equalization algorithm is selected based on the channel delay spread parameter. A linear equalization algorithm is used when the delay spread is small, and a decision feedback equalization algorithm is used when the delay spread is large, to compensate for the frequency-selective fading of the millimeter-wave channel. Error correction processing is performed on the encoded data using an LDPC decoder. The decoding process employs an iterative decoding algorithm, with the number of iterations set to 5 to 20. The data recovery quality is judged based on the bit error rate of the decoded output. The channel quality information and bit error rate data from the receiver are fed back to the transmitter, providing a basis for subsequent modulation and coding adaptation power adjustment and beamforming optimization.

[0026] This invention also includes a comprehensive channel quality assessment step, which constructs a channel quality assessment model using multi-dimensional parameters to quantify the impact of the channel on transmission efficiency, providing a precise basis for modulation and coding and power adjustment. The assessment model expression is as follows: in This is a comprehensive channel quality assessment value, ranging from 0 to 10. A higher value indicates better channel quality. The signal-to-noise ratio weighting coefficient is set to a value between 0.3 and 0.4. This refers to the real-time channel signal-to-noise ratio. For reference, the signal-to-noise ratio is set at 10dB. The attenuation coefficient weighting factor ranges from 0.25 to 0.35. The attenuation effect factor is set to 0.1 dB. -1 , The channel attenuation factor is expressed in dB. The delay weighting coefficient ranges from 0.15 to 0.25. The real-time channel coherence time is measured in milliseconds. The reference coherence time is set to 20ms. The Doppler frequency shift weighting coefficient takes values ​​from 0.05 to 0.1. The Doppler frequency shift unit is Hz. Based on this evaluation value, five channel quality levels are divided, and different levels correspond to different transmission parameter configuration schemes.

[0027] This invention also includes a multi-antenna cooperative transmission step, which expands the transmission link capacity through multiple-input multiple-output (MIMO) technology. An N×M antenna array is deployed, where N is the number of transmitting antennas and M is the number of receiving antennas. The values ​​of N and M both range from 2 to 8. A transmission mode combining spatial multiplexing and diversity merging is adopted. In spatial multiplexing mode, the data stream is divided into N parallel sub-streams, which are transmitted simultaneously through different transmitting antennas, and orthogonal coding is used to distinguish between the sub-streams. In diversity merging mode, multiple receiving antennas receive different copies of the same data stream, and a maximum ratio combining algorithm is used to superimpose the received signals, improving signal reception quality. The transmitting end dynamically switches the transmission mode based on channel quality reports. When the channel quality is good, spatial multiplexing mode is activated to increase the transmission rate; when the channel quality is poor, diversity merging mode is activated to improve transmission reliability. Simultaneously, the antenna array arrangement is optimized, using a uniform linear array to reduce hardware complexity, and the array spacing is set to 0.5 to 1 times the millimeter-wave wavelength.

[0028] This invention also includes a transmission protocol adaptive adjustment step, which optimizes TCP / UDP protocol parameters based on the high bandwidth and low latency characteristics of millimeter wave transmission. For the TCP protocol, the initial value of the congestion window is adjusted to 8 to 16 data packets, the slow start threshold is set to the number of data packets corresponding to the channel bandwidth, and a delay-based congestion control algorithm is adopted. The network congestion status is judged by monitoring the change of round-trip time, avoiding the transmission efficiency reduction caused by traditional packet loss-based congestion control. For the UDP protocol, a data packet verification field and a retransmission mechanism are added. The verification field uses the CRC32 algorithm to verify the data packet payload, and the retransmission mechanism is based on the feedback confirmation information from the receiving end. When no confirmation information is received, a timeout retransmission is initiated. The timeout time is dynamically adjusted according to 2 to 3 times the round-trip time to ensure the transmission reliability of real-time services.

[0029] This invention also includes a dynamic bandwidth allocation step, which constructs a bandwidth allocation model based on service requirements and channel conditions to achieve accurate allocation of bandwidth resources. The allocation model expression is as follows: in For the first Bandwidth allocation for each service Total available transmission bandwidth, For the first The priority weight for each business function ranges from 1 to 5; the higher the value, the higher the priority. For the first The bandwidth requirement rate for each service. For the first The comprehensive channel quality assessment value of each service transmission link. The total number of services transmitted simultaneously. Using the service sequence number as a reference, this model dynamically adjusts the bandwidth allocation ratio of each service based on channel quality while ensuring the bandwidth requirements of high-priority services, thereby reducing bandwidth resource waste and improving overall transmission efficiency.

[0030] This invention also includes channel prediction and pre-adaptation steps. A channel prediction model is constructed using a long short-term memory neural network based on historical channel quality data. The input features include channel attenuation coefficient, signal-to-noise ratio, Doppler shift, and interference intensity over the past 10 to 30 cycles. The output is a predicted channel quality value for the next 5 to 15 cycles. Based on the prediction results, transmission parameters are adjusted in advance. When the predicted channel quality will decrease, the modulation order is reduced in advance to increase coding redundancy or the transmit power is increased. When the predicted channel quality will improve, the modulation order is increased in advance to reduce coding redundancy or to adjust to a higher bandwidth, thereby reducing the lag in parameter adjustment and avoiding transmission interruptions or efficiency reductions caused by sudden channel changes.

[0031] This invention also includes a receiver noise suppression step, which involves deploying an adaptive noise canceller, collecting ambient noise signals through a reference channel, generating a cancellation signal with equal amplitude and opposite phase to the noise signal based on an adaptive filtering algorithm, and superimposing it on the original received signal at the receiver to achieve noise suppression. The noise suppression algorithm adopts the minimum mean square error criterion, with an iteration step size set to 0.001 to 0.01, and the filter order dynamically adjusted from 16 to 64 according to the noise spectrum characteristics. For thermal noise and phase noise in millimeter-wave transmission, a spectrum estimation technique is used to analyze the noise power distribution, and targeted suppression is performed on the frequency bands where noise energy is concentrated, further improving the signal-to-noise ratio at the receiver.

[0032] This invention also includes transmission link switching and redundancy backup steps. Multiple millimeter-wave transmission links and one backup microwave transmission link are pre-configured, with each link using different transmission frequency bands and routing paths. The channel quality and transmission rate of the main transmission link are monitored in real time. When the bit error rate of the main link exceeds a preset threshold or the transmission rate is lower than the service requirements for a certain period of time, a link switching mechanism is activated to quickly switch to the backup millimeter-wave link with better channel quality. The switching process uses link pre-synchronization technology to control the switching delay to within 10ms. When all millimeter-wave links are interfered with and cannot transmit normally, the system switches to the microwave backup link to ensure the continuity of basic services. At the same time, the status of the millimeter-wave links is continuously monitored, and the system automatically switches back to the millimeter-wave link after the channel is restored.

[0033] This invention also includes a data compression and transmission coordination step. An adaptive data compression module is deployed at the transmitting end, and the corresponding compression algorithm is selected according to the data type of the service. Text data uses the LZ77 compression algorithm, image data uses the JPEG2000 compression algorithm, and video data uses the H.265 compression algorithm. The compression ratio is dynamically adjusted according to the channel quality. When the channel quality is good, a low compression ratio is used to retain more data details, with the compression ratio set to 1.5:1 to 3:1. When the channel quality is poor, a high compression ratio is used to reduce the amount of data, with the compression ratio set to 3:1 to 10:1. The compressed data packet is added with compression identifiers and decompression parameter information. The receiving end selects the corresponding decompression algorithm according to the identifiers to quickly restore the original data. By reducing the amount of transmitted data through data compression, the demand for channel bandwidth is reduced, and the transmission efficiency is improved.

[0034] This invention also includes transmission performance monitoring and self-optimization steps. Key performance indicators during transmission are collected in real time, including transmission rate, bit error rate, round-trip time, packet loss rate, and bandwidth utilization. A correlation analysis model between performance indicators and transmission parameters is established to identify key parameters affecting transmission efficiency. Transmission parameter configuration is continuously optimized based on reinforcement learning algorithms, using performance improvement as a reward signal to adjust the configuration strategies of parameters such as modulation and coding scheme power value, beamforming weight, and bandwidth allocation ratio. Transmission performance analysis reports are generated periodically to record performance changes before and after parameter optimization, providing data support for subsequent algorithm iterations and parameter adjustments. This enables the transmission system to adapt to different application scenarios and channel environments, maintaining high transmission efficiency over the long term.

[0035] The following two examples further illustrate the specific implementation of this system: Example 1: Millimeter-wave transmission scenario in an industrial IoT smart manufacturing workshop This embodiment is applied to a smart manufacturing workshop, which requires high-speed digital information transmission between multiple industrial robots, high-definition monitoring equipment, sensors and control center. The service types include real-time control commands, high-definition video streams and sensor data acquisition. The transmission distance is 50-200 meters. There are electromagnetic interference and obstruction scenarios in the workshop. The method of this invention optimizes the transmission efficiency and ensures the transmission rate and reliability.

[0036] I. Implementation Details of Core Steps Transmission parameter pre-configuration steps: Based on the workshop environment and business requirements, a 60GHz interference-free frequency band is selected as the carrier frequency. The transmission bandwidth is dynamically configured according to the service type: 100MHz for control command transmission, 1GHz for high-definition video stream transmission, and 500MHz for sensor data transmission. The initial transmit power is set to 20dBm according to the link budget. Frame structure parameters are configured: the synchronization sequence uses a ZC sequence with a length of 128 points, the initial data block size is set to 2048 bytes, and the pilot symbol interval is set to 10ms according to the channel coherence time. A protocol stack adapted for millimeter-wave transmission is established. The physical layer integrates a modulation and coding module supporting QPSK, 16QAM, 64QAM, and 256QAM modulation and LDPC coding. A flow control module is deployed at the data link layer, and a routing adaptation module is configured at the network layer. The frame format and timing synchronization requirements for data interaction at each layer are clearly defined.

[0037] Real-time channel awareness steps: Deploy a 4×4 multi-antenna array and channel sounding module. By sending orthogonal frequency division multiplexing (OFDM) sounding pilot signals, collect real-time data on channel attenuation coefficient, signal-to-noise ratio, noise power spectral density, Doppler shift, delay spread, and the intensity and frequency of electromagnetic interference sources from workshop equipment. Preprocess the collected parameters using a sliding window averaging method, with a window length of 15 symbol periods. Remove transient interference anomalies caused by motor start-up and shutdown. Generate a channel quality report containing five channel quality levels and their dynamic trends. The report is updated every 20ms and transmitted to the control center via industrial Ethernet.

[0038] Modulation and coding dynamic adaptation steps: Receive channel quality reports and select a scheme according to preset mapping rules. When the channel quality level is excellent, the high-definition video stream uses 256QAM modulation with LDPC encoding at a rate of 0.85; when the level is medium, sensor data uses 16QAM modulation with LDPC encoding at a rate of 0.65; when the level is poor, control commands use QPSK modulation with LDPC encoding at a rate of 0.45. During encoding, the column redistribution of the parity check matrix is ​​adjusted according to the data block size, the redundant distribution of control command data blocks is optimized to improve real-time error correction capability, and the balanced redundancy of video stream data blocks ensures transmission rate.

[0039] Adaptive power and beamforming steps: Based on the channel attenuation coefficient and the intensity of electromagnetic interference sources, the transmit power is dynamically adjusted using a proportional-integral-differential power control algorithm with a power adjustment step size of 1 dBm to maintain the signal-to-noise ratio at the receiver above 15 dB. Adaptive beamforming technology is then activated, using a digital beamforming algorithm to adjust the phase and amplitude weights of the antenna array to form a narrow beam pointing towards the receiver. The half-power beamwidth is controlled at 8° to suppress sidelobe energy leakage and reduce interference to the transmission links of adjacent devices.

[0040] Intelligent traffic control steps: Obtain service priority information, with control commands having the highest priority, high-definition video streams having medium priority, and sensor data having general priority. Real-time control commands prioritize bandwidth resource allocation, with a fixed data packet length of 512 bytes and a transmission rate adaptively adjusted according to channel capacity; high-definition video streams use fixed-length 1024-byte data packets; sensor data uses variable-length data packets, ranging from 1024 to 4096 bytes. When the channel is congested, the transmission rate is reduced and fragmented transmission is initiated, with the fragment size matching the available channel bandwidth.

[0041] Interference Cooperative Suppression Steps: The interference type is identified by the channel sensing module. For co-channel interference generated by workshop equipment, an interference alignment algorithm is used to adjust the spatial characteristics of the transmitted signal so that the interference signals are superimposed and canceled at the receiving end. When the intensity of adjacent channel interference exceeds -80dBm, the signal is switched to the preset 61GHz spare frequency band. For external electromagnetic interference, space-time coding technology is used to transmit signal copies with different delays through four transmitting antennas to enhance the interference resistance capability of the receiving end.

[0042] Transmission synchronization optimization steps: Frame synchronization employs a sliding correlation detection algorithm based on training sequences. A locally generated ZC synchronization sequence is correlated with the received sequence, and the synchronization detection threshold is dynamically adjusted according to the signal-to-noise ratio. Carrier synchronization uses a combination of phase-locked loop (PLL) and frequency domain correction. The PLL bandwidth is set to 5kHz to track carrier frequency offset. Frequency domain correction estimates and compensates for frequency deviation using pilot symbols, keeping the synchronization error within 8% of the symbol period.

[0043] Receiver-side adaptive equalization and error correction steps: An adaptive equalizer is deployed at the receiver. For sensor data transmission with relatively small latency spread, a linear equalization algorithm is used; for high-definition video stream transmission with larger latency spread, a decision feedback equalization algorithm is used to compensate for channel frequency-selective fading. Error correction is performed using an LDPC decoder, with 10 iterations. Data recovery quality is judged based on the decoded output bit error rate. The receiver transmits channel quality information and bit error rate data to the transmitter via a feedback link, providing a basis for subsequent parameter optimization.

[0044] Additional steps implemented: The comprehensive channel quality assessment quantifies channel quality through multi-dimensional parameters, providing precise data for modulation coding and power adjustment; multi-antenna cooperative transmission employs a 4×4 antenna array, activating spatial multiplexing mode to improve video stream transmission rate when the channel is good, and activating diversity merging mode to ensure reliable control command transmission when the channel is poor; the transmission protocol adaptively adjusts and optimizes TCP protocol parameters, with the initial congestion window set at 12 data packets; dynamic bandwidth allocation allocates bandwidth according to service priority and channel quality; channel prediction uses a long short-term memory neural network to predict channel quality for the next 10 cycles; receiver noise suppression deploys a 32nd-order adaptive noise canceller; transmission link switching is configured with two millimeter-wave links and one microwave backup link; the data compression module uses the H.265 compression algorithm for high-definition video streams, achieving a compression ratio of 2:1 when the channel is good and 8:1 when the channel is poor; transmission performance monitoring collects indicators in real time and optimizes parameter configuration based on reinforcement learning algorithms.

[0045] Table 1 Comparison of Transmission Performance in Industrial IoT Workshops

[0046] Table 1 clearly demonstrates the advantages of this invention in industrial IoT scenarios. Traditional transmission methods use fixed parameter configurations, which cannot adapt to the complex channels and multi-service requirements of workshops. They suffer from low transmission rates, high control command error rates, bandwidth utilization of less than 60%, and poor interference suppression and synchronization performance. This invention accurately captures channel changes through real-time channel sensing, fully utilizes bandwidth resources through dynamic adaptation of modulation coding and power, significantly improves anti-interference capabilities through multi-antenna cooperation and interference suppression technology, and reduces bit error rate and synchronization errors through synchronization optimization and receiver processing. The average transmission rate is increased to 820Mbps, the control command error rate is reduced to an extremely low level, and the bandwidth utilization reaches 92%, effectively meeting the high-speed and reliable transmission requirements of smart manufacturing workshops.

[0047] Example 2: High-speed mobile millimeter-wave transmission scenario in vehicle-to-everything (V2X) This embodiment is applied to the Internet of Vehicles scenario to realize the transmission of digital information between high-speed vehicles and between vehicles and roadside units. The services include real-time control commands for autonomous driving, high-definition road condition video, vehicle status data, and map update data. The vehicle speed is 0-120km / h, and there are Doppler frequency shift and multipath interference. The method of this invention optimizes the transmission efficiency and ensures the stability and reliability of transmission under high-speed movement.

[0048] I. Implementation Details of Core Steps Transmission parameter pre-configuration steps: Select the 28GHz band as the carrier frequency, which is suitable for the propagation characteristics of high-speed mobile scenarios. Configure the transmission bandwidth according to the service: 200MHz for autonomous driving control commands, 2GHz for high-definition road condition video, and 800MHz for vehicle status data. Set the initial transmit power to 25dBm. Configure frame structure parameters: the synchronization sequence adopts a ZC sequence with a length of 256 points, the initial data block size is set to 1024 bytes, and the pilot symbol interval is set to 8ms according to the channel coherence time. Establish the protocol stack: the physical layer supports multiple modulation and coding schemes, the data link layer deploys flow control and link management modules, and the network layer configures a dynamic routing adaptation module, clarifying the data interaction format and timing requirements of each layer.

[0049] Real-time channel awareness steps: Deploy a 2×4 multi-antenna array and channel sounding module, transmit high-frequency sounding pilot signals, and collect in real time channel attenuation coefficient, signal-to-noise ratio, noise power spectral density, Doppler shift, delay spread, and interference intensity and frequency from surrounding vehicles and base stations. Preprocess parameters using a sliding window averaging method with a window length of 12 symbol periods to remove outliers caused by sudden interference, generate a report containing 5 channel quality levels, update every 15ms, and transmit to vehicle terminals and roadside units via a dedicated vehicle-to-everything (V2X) link.

[0050] The modulation and coding dynamic adaptation process involves selecting a scheme based on the received channel quality report. When channel quality is excellent, high-definition road condition video uses 64QAM modulation with an LDPC code rate of 0.8; when the quality is medium, vehicle status data uses 16QAM modulation with an LDPC code rate of 0.7; and when the quality is poor, autonomous driving control commands use QPSK modulation with an LDPC code rate of 0.5. During encoding, the parity check matrix structure is adjusted according to the data block size. Control command data blocks are reinforced with redundancy to improve error correction capabilities, while video data blocks are optimized for encoding efficiency to ensure transmission rate.

[0051] Adaptive power and beamforming steps: Based on the channel attenuation coefficient and interference intensity, the transmit power is dynamically adjusted using an adaptive power control algorithm with a power adjustment step size of 1.5 dBm to maintain the signal-to-noise ratio at the receiver above 12 dB. Adaptive beamforming technology is then activated, using a fast digital beamforming algorithm to adjust the phase and amplitude weights of the antenna array, forming a narrow beam pointing towards the receiver. The half-power beamwidth is controlled at 12° to quickly track the high-speed moving receiver and suppress sidelobe interference.

[0052] Intelligent traffic control steps: Prioritize services, with autonomous driving control commands having the highest priority, high-definition traffic video having medium priority, and map update data having general priority. Control commands are allocated bandwidth preferentially, with a fixed data packet length of 768 bytes, and the transmission rate is adaptively adjusted according to channel capacity; high-definition traffic video uses fixed-length 1024-byte data packets; map update data uses variable-length data packets, ranging from 2048 to 8192 bytes, and the transmission rate is reduced and fragmented during channel congestion.

[0053] Interference Cooperative Suppression Steps: Identify the type of interference; suppress co-channel interference from surrounding vehicles using an interference alignment algorithm; when adjacent channel interference intensity exceeds -75dBm, switch to the 29GHz backup frequency band; external electromagnetic interference is handled using space-time coding technology, transmitting signal copies with different delays through two transmitting antennas to enhance the receiver's interference resistance.

[0054] Transmission synchronization optimization steps: Frame synchronization employs a sliding correlation detection algorithm based on training sequences. The locally generated synchronization sequence is correlated with the received sequence to determine the frame start position, and the synchronization detection threshold is dynamically adjusted according to the signal-to-noise ratio. Carrier synchronization uses a combination of phase-locked loop (PLL) and frequency domain correction. The PLL bandwidth is set to 8kHz to track the carrier offset caused by Doppler frequency shift due to high-speed movement. Frequency domain correction estimates and compensates for the deviation using pilot symbols, keeping the synchronization error within 9% of the symbol period.

[0055] Receiver-side adaptive equalization and error correction steps: An adaptive equalizer is deployed at the receiver. A linear equalization algorithm is used for vehicle status data transmission, while a decision feedback equalization algorithm is used for high-definition road condition video transmission to compensate for channel frequency-selective fading. Error correction is performed using an LDPC decoder with 15 iterations, and data recovery quality is judged based on the bit error rate. The receiver feeds back channel quality information and bit error rate data to the transmitter, providing a basis for subsequent parameter optimization.

[0056] Additional implementation steps: A comprehensive channel quality assessment quantifies channel quality, providing a basis for parameter adjustment; multi-antenna cooperative transmission uses a 2×4 antenna array, activating spatial multiplexing mode to improve transmission rate when the channel is good, and activating diversity merging mode to ensure reliability when the channel is poor; the transmission protocol adaptively adjusts and optimizes UDP protocol parameters, adding a CRC32 check field and retransmission mechanism; dynamic bandwidth allocation allocates bandwidth according to service priority and channel quality; channel prediction uses a long short-term memory neural network to predict channel quality for the next 8 cycles; receiver noise suppression deploys a 48th-order adaptive noise canceller; transmission link switching is configured with 3 millimeter-wave links and 1 microwave backup link; the data compression module uses the H.265 compression algorithm for high-definition traffic video, achieving a compression ratio of 3:1 when the channel is good and 10:1 when the channel is poor; transmission performance monitoring collects indicators in real time, and parameter configuration is optimized based on reinforcement learning algorithms.

[0057] Table 2 Comparison of High-Speed ​​Mobile Transmission Performance of Vehicle-to-Everything (V2X) Networks

[0058] Table 2 highlights the application value of this invention in high-speed mobile scenarios of the Internet of Vehicles (IoV). Traditional transmission methods struggle to cope with Doppler shift and multipath interference caused by high-speed movement, resulting in low transmission rates, high error rates in autonomous driving commands, prolonged link switching, weak Doppler immunity, and insufficient reliability. This invention captures channel changes under high-speed movement through real-time channel sensing, dynamically adapts modulation coding and power to channel fluctuations, rapidly tracks targets through multi-antenna cooperation and beamforming, enhances anti-interference capabilities through interference suppression technology, shortens latency through link switching and redundancy backup, and reduces the error rate through receiver processing. The average transmission rate is increased to 950 Mbps, the error rate of autonomous driving commands is significantly reduced, the link switching latency is shortened to 8 ms, the Doppler shift immunity is significantly enhanced, and the link reliability reaches 98%, fully meeting the high-reliability and low-latency transmission requirements of the IoV under high-speed mobile conditions.

[0059] Reference Figure 2 This figure visually demonstrates the speed advantage of this invention under different channel quality conditions. Traditional methods use fixed modulation and coding schemes and power configurations, resulting in a sharp drop in speed when channel quality deteriorates, reaching only 100 Mbps in extremely poor channels. This invention accurately assesses channel conditions through real-time channel awareness. Its dynamic modulation and coding adaptation technology employs 256QAM high-order modulation and high-code-rate coding when the channel is excellent, fully utilizing bandwidth resources; when the channel deteriorates, it automatically switches to QPSK modulation and low-code-rate coding to ensure a basic transmission rate. Adaptive power control and beamforming technology further improve signal reception quality, maintaining a speed of 250 Mbps even in extremely poor channels. Data shows that the dynamic adaptation mechanism of this invention effectively copes with channel fluctuations, significantly improving transmission rates in different scenarios and meeting the demands of high-speed data transmission.

[0060] Reference Figure 3 This figure illustrates the advantages of the intelligent flow control proposed in this invention. Traditional bandwidth allocation methods lack priority differentiation, with the highest priority services receiving only 30% of the bandwidth, easily leading to real-time service congestion. This invention, through a service priority division mechanism, increases the bandwidth of the highest priority services to 45%, ensuring the transmission needs of critical services such as autonomous driving control commands and industrial real-time control. The dynamic bandwidth allocation technology adjusts the allocation ratio in real time based on channel quality and service requirements. Low-priority services automatically reduce bandwidth usage when the channel is congested, avoiding resource waste. Data shows that the bandwidth allocation strategy of this invention is more reasonable, meeting the low-latency requirements of high-priority services while improving overall bandwidth utilization and optimizing the performance of concurrent multi-service transmission.

[0061] Reference Figure 4This figure highlights the interference suppression capability of this invention. Traditional methods have weak anti-interference technology, with a bit error rate of 1% at an interference intensity of -50dBm, which cannot meet the requirements of high-reliability transmission. This invention uses interference cooperative suppression technology, employing an interference alignment algorithm for co-channel interference, triggering dynamic frequency band switching for adjacent channel interference, and enhancing resistance to external electromagnetic interference through space-time coding. Adaptive power control and beamforming technology further improve signal anti-interference performance, achieving a bit error rate of only 2×10 at an interference intensity of -50dBm. -4 Data shows that the interference suppression mechanism of this invention significantly reduces the bit error rate and can maintain high-reliability transmission even in environments with strong interference, making it suitable for applications in complex electromagnetic environments.

[0062] Reference Figure 5 This figure illustrates the advantages of the adaptive power control of this invention. Traditional methods lack precise channel feedback in power adjustment, resulting in a signal-to-noise ratio (SNR) of only 14 dB at 30 dBm power, leading to power waste and weak anti-interference capabilities. This invention, based on a real-time channel-aware power control algorithm, dynamically adjusts the transmit power according to channel attenuation and interference intensity. At 10 dBm power, the SNR reaches 12 dB, a 50% improvement over traditional methods; at 30 dBm power, the SNR further increases to 22 dB, significantly enhancing signal reception quality. Adaptive beamforming technology further improves the SNR by optimizing antenna weights, enhancing signal directivity and anti-interference capabilities. Data shows that the power control mechanism of this invention is more efficient, improving the SNR while reducing power consumption, and optimizing transmission performance and energy efficiency.

[0063] Reference Figure 6 This figure illustrates the advantages of the link switching and redundancy backup of this invention. Traditional switching mechanisms are slow, with switching delays reaching 30ms when the primary link fails, easily leading to transmission interruptions. This invention, through link pre-synchronization technology, establishes a backup link synchronization state in advance, eliminating the need for resynchronization during switching, achieving a switching delay of only 8ms even under primary link interference scenarios. Dynamic link monitoring and fast switching algorithms, combined with multi-antenna cooperative transmission technology, further reduce switching delay to 6ms in high-mobility scenarios. The redundancy backup mechanism ensures rapid switching to the backup link in the event of a link failure, guaranteeing transmission continuity. Data shows that the link switching mechanism of this invention significantly reduces latency, improves the reliability and fault tolerance of the transmission system, and is suitable for scenarios with high dynamic and high-reliability transmission requirements.

[0064] 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 method for optimizing the transmission efficiency of millimeter-wave digital information, characterized in that, Includes the following steps: Transmission parameter pre-configuration steps: Based on the characteristics of the millimeter wave band, set the initial carrier frequency, transmission bandwidth and transmit power range, configure frame structure parameters, and establish an adapted protocol stack; Real-time channel awareness steps: Deploy a multi-antenna array and channel detection module, collect channel characteristic parameters, generate a channel quality report after preprocessing, and provide real-time feedback on channel status; Dynamic modulation and coding adaptation steps: Receive channel quality report, dynamically select modulation method and coding scheme according to mapping rules, and optimize coding structure and redundancy distribution; Adaptive power and beamforming steps: The transmit power is dynamically adjusted through a power control algorithm to maintain the signal-to-noise ratio at the receiver, and adaptive beamforming technology is activated to form a directional narrow beam; Intelligent flow control steps: Prioritize services and dynamically adjust data packet length and transmission rate based on channel quality and service type; Interference Cooperative Suppression Steps: Identify various types of interference, and use interference alignment, dynamic frequency band switching, and space-time coding techniques to suppress different types of interference in a targeted manner; Transmission synchronization optimization steps: Optimize frame synchronization and carrier synchronization mechanisms by combining correlation detection with phase-locked loop and frequency domain correction to control synchronization errors; The receiver-side adaptive equalization and error correction steps are as follows: deploy an adaptive equalizer to compensate for channel fading, perform error correction through an LDPC decoder, and feed back channel quality and bit error rate data to the transmitter.

2. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes a comprehensive channel quality assessment step, which constructs a channel quality assessment model using multi-dimensional parameters. The expression for the assessment model is as follows: in This is a comprehensive evaluation value for channel quality. The signal-to-noise ratio weighting coefficient is set to a value between 0.3 and 0.

4. This refers to the real-time channel signal-to-noise ratio. For reference signal-to-noise ratio, This is the attenuation coefficient weighting coefficient. As the attenuation effect factor, This is the channel attenuation coefficient. This is the time delay weighting coefficient. For real-time channel coherence time, For reference coherence time, This is the Doppler frequency shift weighting coefficient. Based on the Doppler frequency shift, five channel quality levels are defined using this evaluation value, with each level corresponding to a different transmission parameter configuration scheme.

3. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes a multi-antenna cooperative transmission step, which expands the transmission link capacity through multiple-input multiple-output technology, deploys an N×M antenna array where N is the number of transmitting antennas and M is the number of receiving antennas, and adopts a transmission mode that combines spatial multiplexing and diversity merging. In the spatial multiplexing mode, the data stream is divided into N parallel sub-streams and transmitted simultaneously through different transmitting antennas; in the diversity merging mode, different copies of the same data stream are received through multiple receiving antennas, and the maximum ratio combining algorithm is used to superimpose the received signals. The transmitter dynamically switches transmission modes based on channel quality reports, while optimizing the antenna array arrangement by using a uniform linear array.

4. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes adaptive adjustment steps for the transmission protocol, optimizing TCP / UDP protocol parameters based on the high bandwidth and low latency characteristics of millimeter wave transmission; for the TCP protocol, the initial value of the congestion window is adjusted to 8 to 16 data packets, the slow start threshold is set to the number of data packets corresponding to the channel bandwidth, and a delay-based congestion control algorithm is adopted to judge the network congestion status by monitoring changes in round-trip time; for the UDP protocol, a data packet checksum field and a retransmission mechanism are added. The checksum field uses the CRC32 algorithm to check the data packet payload, and the retransmission mechanism is based on the feedback confirmation information from the receiving end. If no confirmation information is received, a timeout retransmission is initiated.

5. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes a dynamic bandwidth allocation step, which constructs a bandwidth allocation model based on service requirements and channel conditions. The allocation model expression is as follows: in For the first Bandwidth allocation for each service Total available transmission bandwidth, For the first The priority weight of each business. For the first The bandwidth requirement rate for each service. For the first The comprehensive channel quality assessment value of each service transmission link. The total number of services transmitted simultaneously. This refers to the service sequence number. This model dynamically adjusts the bandwidth allocation ratio of each service based on channel quality, while ensuring the bandwidth requirements of high-priority services.

6. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes channel prediction and pre-adaptation steps. Based on historical channel quality data, a long short-term memory neural network is used to construct a channel prediction model. The input features include the channel attenuation coefficient, signal-to-noise ratio, Doppler shift and interference intensity of the past 10 to 30 cycles. The output is the channel quality prediction value for the next 5 to 15 cycles. The transmission parameters are adjusted in advance based on the prediction results.

7. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes a receiver noise suppression step, which involves deploying an adaptive noise canceller, collecting ambient noise signals through a reference channel, generating a cancellation signal with equal amplitude and opposite phase to the noise signal based on an adaptive filtering algorithm, and superimposing it on the original received signal at the receiver to achieve noise suppression; the noise suppression algorithm adopts the minimum mean square error criterion; and for thermal noise and phase noise in millimeter wave transmission, spectrum estimation technology is used to analyze the noise power distribution.

8. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes transmission link switching and redundancy backup steps, pre-configuring multiple millimeter-wave transmission links and one backup microwave transmission link, with each link using different transmission frequency bands and routing paths; real-time monitoring of the channel quality and transmission rate of the main transmission link, and when the bit error rate of the main link exceeds a preset threshold or the transmission rate is lower than the service requirements for a certain period of time, the link switching mechanism is activated to quickly switch to the backup millimeter-wave link with better channel quality; when all millimeter-wave links are interfered with and cannot transmit normally, the system switches to the microwave backup link, while continuously monitoring the status of the millimeter-wave links, and automatically switching back to the millimeter-wave link after the channel is restored.

9. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes data compression and transmission coordination steps. An adaptive data compression module is deployed at the transmitting end, and the corresponding compression algorithm is selected according to the data type of the service. The LZ77 compression algorithm is used for text data, the JPEG2000 compression algorithm is used for image data, and the H.265 compression algorithm is used for video data. The compression ratio is dynamically adjusted according to the channel quality. When the channel quality is good, a low compression ratio is used to retain more data details, and a high compression ratio is used to reduce the amount of data when the channel quality is poor. The compressed data packet is added with compression identifier and decompression parameter information. The receiving end selects the corresponding decompression algorithm according to the identifier to quickly restore the original data.

10. The method for optimizing millimeter-wave digital information transmission efficiency according to claim 1, characterized in that, It also includes transmission performance monitoring and self-optimization steps, real-time collection of key performance indicators during the transmission process; establishment of a correlation analysis model between performance indicators and transmission parameters; continuous optimization of transmission parameter configuration based on reinforcement learning algorithms, using performance improvement as a reward signal to adjust the configuration strategy; Generate transmission performance analysis reports regularly.

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