Low-delay transmission method based on 5G-A communication and inductance integration

Abstract

The invention discloses a low-delay transmission method based on 5G-A communication and inductance integration, and particularly relates to the technical field of wireless communication. According to the method, an integrated framework of deep fusion of communication and sensing signals is constructed, and resource reuse is realized through unified waveform design and cooperative processing. A dynamic resource allocation strategy is adopted, and the allocation proportion of the spectrum, the time slot and the power is jointly optimized according to the real-time network state. In data transmission, channel quality is predicted based on sensing information, and dynamic priority ranking and signaling simplification of data packets are realized through a low-delay scheduling algorithm. And meanwhile, environment and link states are analyzed by utilizing sensing data fusion, and a modulation coding and retransmission mechanism is adaptively adjusted, so that the resource efficiency is improved, and low-delay and high-reliability transmission is ensured.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a low-latency transmission method based on 5G-A integrated sensing. Background Technology

[0002] As fifth-generation mobile communication technology evolves towards fifth-generation advanced technology, the communications field is rapidly developing towards the Internet of Everything. Under this trend, the requirements for network capabilities have transcended the traditional scope of interpersonal communication, extending to high-precision perception and interaction with the physical environment. Sensing-communication integration technology has emerged as a result, becoming one of the key features of 5G-A and future sixth-generation mobile communication systems. This technology aims to deeply integrate communication and sensing functions, enabling wireless networks not only to transmit data but also to utilize communication signals to detect, locate, and identify the surrounding environment and objects. This provides integrated foundational support for low-latency, high-reliability applications such as autonomous driving, the industrial internet, and augmented reality.

[0003] However, existing integrated sensing and communication technologies still have significant shortcomings. Most solutions focus on achieving simple coexistence or time-division multiplexing of communication and sensing functions, failing to achieve true deep integration and collaborative optimization. In terms of resource allocation, communication and sensing resources often employ static or semi-static allocation strategies, lacking a mechanism for dynamic, joint optimization based on real-time service demands and channel conditions. This results in situations of sudden service disruptions or drastic environmental changes, where either sensing accuracy is sacrificed to ensure communication, or communication quality is compromised to complete the sensing task, leading to overall system inefficiency. Furthermore, traditional transmission scheduling mechanisms primarily rely on feedback information from the communication link itself, exhibiting a lag in response to channel changes and failing to effectively utilize the prior environmental information provided by sensing functions to predict link quality changes, making it difficult to meet the stringent requirements of ultra-high reliability and ultra-low latency services.

[0004] Therefore, a low-latency transmission method based on 5G-A sensing integration is proposed to address the above problems. The core issue to be solved is how to overcome the rigid resource allocation, insufficient coordination, and difficulty in further optimizing transmission latency in existing sensing integration technologies, so as to achieve efficient dynamic coordination of communication and sensing functions, and ensure extremely low latency and high reliability of data transmission in complex and ever-changing application scenarios. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a low-latency transmission method based on 5G-A sensing integration to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a low-latency transmission method based on 5G-A sensing integration, the method comprising the following steps: S1. In the 5G-A network environment, a collaborative processing mechanism integrating communication and sensing is constructed to merge and share communication transmission links and sensing and detection links, so that communication signals and sensing signals are processed simultaneously in the same frequency band and time slot. S2. Based on the integrated sensing data flow, a dynamic resource allocation strategy is adopted to adaptively adjust the allocation ratio of communication resources and sensing resources according to the real-time network status and service requirements. The resource allocation includes the joint optimization of spectrum resources, time resources and power resources. S3. During data transmission, a low-latency scheduling algorithm is introduced. This algorithm predicts channel quality changes and interference based on sensing information, prioritizes scheduling latency-sensitive service data packets, and compresses transmission latency by reducing protocol layer processing overhead and simplifying signaling interaction processes. S4. Perform sensor data fusion processing, correlate and analyze the environmental state information obtained by sensing with the communication data stream to generate transmission control parameters, which are used to dynamically adjust the modulation and coding scheme and retransmission mechanism, thereby achieving low-latency transmission while ensuring transmission reliability.

[0007] Preferably, the dynamic resource allocation strategy specifically includes: monitoring the communication service load and sensing task priority in real time through a central control unit or distributed network nodes, dynamically dividing the available resource pool according to predefined resource allocation rules, wherein the resource allocation rules calculate the resource allocation ratio based on service type weight and latency requirements, and periodically updating the allocation parameters through a feedback mechanism to ensure the balanced execution of communication and sensing functions.

[0008] Preferably, in the resource allocation rule, the weight of the service type is determined comprehensively based on the service latency sensitivity, data rate requirements and reliability indicators, and the weight calculation process is optimized by training historical data through a machine learning model, so that the resource allocation can adapt to network changes.

[0009] Preferably, the low-latency scheduling algorithm specifically includes: predicting transmission conditions in the short term based on channel state parameters and interference maps obtained from sensing information, and adopting a priority queue management mechanism to classify service data packets according to latency thresholds. High-priority data packets are allocated more transmission opportunities and resource blocks. At the same time, the scheduling algorithm integrates physical layer and network layer information through cross-layer design to reduce scheduling decision latency.

[0010] Preferably, in the priority queue management mechanism, the delay threshold is dynamically adjusted according to the service level agreement, and the queue sorting is optimized by using a sliding window to statistically analyze real-time network performance indicators, so as to ensure the timeliness and accuracy of scheduling decisions.

[0011] Preferably, the sensing data fusion processing step specifically includes: fusing and analyzing the environmental information acquired by the sensing module, such as obstacle location, moving speed and channel attenuation characteristics, with the transmission status information in the communication data stream to generate a unified transmission control parameter set. This parameter set is used to dynamically select the modulation and coding scheme and adjust the upper limit of the number of retransmissions in order to reduce the transmission error rate and retransmission delay.

[0012] Preferably, the transmission control parameter set is optimized in real time using fuzzy logic or neural network algorithms to adapt to rapid changes in the network environment and ensure the stable operation of the integrated sensing system.

[0013] Preferably, the method further includes a communication-aware collaborative feedback mechanism, which generates collaborative control instructions by periodically collecting communication performance and sensing accuracy indicators, and uses these instructions to fine-tune resource allocation and scheduling parameters, thereby maintaining the continuity of low-latency transmission.

[0014] The technical effects and advantages of this invention are as follows: Compared to existing technologies, this invention achieves joint optimization of communication and sensing resources by constructing a dynamic resource allocation strategy that shares a deep signal processing flow between communication and sensing. This mechanism dynamically adjusts resource allocation based on real-time network conditions and service requirements, solving the problem of low resource utilization caused by static allocation. This improves the overall resource efficiency of the system in changing scenarios and achieves a highly efficient balance between communication performance and sensing accuracy.

[0015] Compared to existing technologies, this invention introduces a low-latency scheduling algorithm based on perceived information to predict channel quality, proactively applying environmentally sensed data to transmission decisions. This method utilizes perceived channel characteristics to predict interference and quality changes, prioritizes urgent data packets, and simplifies signaling procedures. This effectively reduces reliance on traditional channel feedback, shortens scheduling latency, and enhances the ability to guarantee ultra-low latency services.

[0016] Compared to existing technologies, this invention generates transmission control parameters through sensor data fusion processing, establishing an intelligent mapping between environmental states and communication strategies. This process correlates and analyzes sensing information with communication states, dynamically adjusting modulation and coding and retransmission mechanisms, enabling transmission parameters to adapt to environmental changes. This simultaneously optimizes transmission reliability and timeliness in complex scenarios, reducing performance fluctuations caused by environmental uncertainties. Attached Figure Description

[0017] Figure 1 This is a system framework diagram of the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1: As attached Figure 1 The method shown is a low-latency transmission method based on 5G-A sensing integration, the method comprising the following steps: S1. Construct a collaborative processing mechanism integrating communication and sensing in a 5G-A network environment. This mechanism includes a signal fusion framework and a collaborative processing unit. The signal fusion framework generates a composite signal waveform that simultaneously carries communication data and sensing detection functions through a unified signal generation and modulation module. The waveform uses precoding technology to ensure the orthogonality or non-orthogonal superposition of communication and sensing signals in the time domain, frequency domain, and spatial domain. The collaborative processing unit is deployed at the network access point or user equipment side. It contains a joint signal processor, which uses digital signal processing algorithms to synchronously demodulate and separate the received composite signal, extracting the communication data stream and sensing parameter information. The sensing parameter information includes, but is not limited to, target distance, speed, and angle. This collaborative processing mechanism achieves hardware resource reuse of the communication and sensing links by sharing a local oscillator and radio frequency front end, thereby reducing system overhead and signal processing latency.

[0020] S2. Based on the integrated sensing data flow, a dynamic resource allocation strategy is adopted. The execution entity of this strategy is a centralized controller or distributed scheduling node in the network, which maintains a real-time updated network status information database, which includes communication traffic volume, sensing task priority, channel quality indication, and interference noise level. The dynamic resource allocation process first predicts the channel change trend in the short term based on the environmental information obtained by the sensing task. Then, based on this prediction result and the current communication service requirements, convex optimization or heuristic algorithms are used to solve the optimal or suboptimal solution of resource allocation, and the total system bandwidth and time resource blocks and communication functions and sensing functions are dynamically divided. in, Constraints: This indicates the amount of resources allocated to communication functions (which can be a proportion of bandwidth, time slots, or power).

[0021] This indicates the amount of resources allocated to the sensing function.

[0022] This indicates the total amount of available resources in the system.

[0023] This represents the minimum amount of resources required to guarantee basic communication service quality.

[0024] This represents the minimum amount of resources required to ensure basic sensing accuracy.

[0025] ( ) indicates the amount of resources The achievable communication data rate function.

[0026] ( ) indicates the amount of resources The achievable perception performance metrics (such as ranging accuracy or resolution) function.

[0027] w c Weighting factors representing communication performance.

[0028] w s The weighting factor represents the perceived performance.

[0029] λ represents the total delay D total The penalty factor.

[0030] D total This represents the total transmission delay estimated by the system.

[0031] This formula clarifies the mathematical optimization objective of the dynamic resource allocation strategy. Its core innovation lies in incorporating communication performance (R... c ), perception performance (P) s ) and total system delay (D total ) through weighting factor (w c , w s The optimization of communication and sensing resources (λ) is performed jointly within a single utility function, rather than considering individual indicators in isolation. By solving this constrained optimization problem, a dynamic and adaptive allocation of communication and sensing resources under total constraints can be achieved. This maximizes the overall system efficiency while ensuring the lower limits of both performance parameters, which is the foundation for realizing low-latency transmission integrating communication and sensing.

[0032] The allocation ratio is not fixed, but is adaptively adjusted according to a preset utility function. This utility function comprehensively considers three key performance indicators: communication rate, sensing accuracy, and total system latency. The allocation of power resources adopts the water-filling algorithm or its variants to achieve dynamic optimization of the total system power while meeting the communication signal-to-interference-plus-noise ratio requirements and the sensing signal-to-noise ratio threshold.

[0033] S3. During data transmission, a low-latency scheduling algorithm is introduced. The core of this algorithm is a prediction model based on enhanced sensing information. This model uses the real-time sensing data obtained in step S1 (such as multipath component delay, Doppler frequency shift, and interference source location) to construct short-time channel impulse response prediction and interference map. Based on this prediction result, the scheduler dynamically prioritizes the queue of data packets to be transmitted. The prioritization rule is based on the delay budget of the data packets, the size of the data packets, and the service type weight factor. For latency-sensitive service data packets, the system allocates specific time-frequency resource blocks with lower transmission latency and may use unauthorized scheduling or configuration authorization mode to skip part of the scheduling request process; Meanwhile, the scheduling algorithm directly interacts with the physical layer channel state information and the network layer queue state information through cross-layer design, which simplifies the scheduling signaling interaction process of the media access control layer and adopts a single hybrid automatic retransmission request mechanism instead of multiple retransmissions to reduce the confirmation waiting time.

[0034] S4. Perform sensor data fusion processing; this step is completed in the sensor integrated processing center, which contains a data fusion engine; the engine receives the sensing environment status information (including target outline, motion trajectory, channel scattering characteristics) from S1 and the real-time transmission status information (including bit error rate, throughput, and data packet arrival rate) from the communication link. The fusion engine uses Kalman filtering or particle filtering algorithms to track and filter the sensed information to eliminate measurement noise, and uses data correlation technology to perform causal correlation analysis between the sensed environmental changes and communication link performance fluctuations. Based on this correlation analysis, a set of transmission control parameters is generated. This set of parameters is directly input to the adaptive coding and modulation module and the hybrid automatic repeat request management module of the communication link. The adaptive coding and modulation module dynamically selects the modulation order and coding rate according to the fused channel quality estimate, while the hybrid automatic repeat request management module dynamically adjusts the maximum number of retransmissions and the retransmission timer setting, thereby achieving joint optimization of transmission reliability and delay.

[0035] The dynamic resource allocation strategy specifically includes: the central control unit or distributed network nodes periodically collecting network performance measurement reports through built-in monitoring agents; the measurement indicators of communication service load include uplink and downlink data volume, number of active users, and average queue length; and the priority of the sensing tasks is predefined or dynamically negotiated by the type of sensing application (such as high-precision positioning and environmental imaging) and its service quality requirements. The predefined resource allocation rules are stored in the control unit in the form of a policy form, which defines the initial allocation weights of communication resources and sensing resources under different business scenarios (such as ultra-reliable low-latency communication scenarios, enhanced mobile broadband scenarios, and large-scale machine-type communication scenarios). The dynamic partitioning process employs an online control algorithm based on Lyapunov optimization theory. This algorithm decomposes the long-term resource allocation constraint optimization problem into a series of independent single-slot optimization problems. Within each scheduling interval, the resource allocation ratio is calculated based on real-time data in the current network state information database. The feedback mechanism collects system performance indicators (such as communication latency and sensing update rate) after resource allocation through the control channel and compares them with preset target values. The resulting deviation signal is used to adjust the weight parameters in the strategy form or the control variables of the optimization algorithm, forming a closed-loop control to ensure a balance between high communication speed and high sensing accuracy.

[0036] In the resource allocation rules, the process of determining the weight of business types involves a multi-attribute decision analysis module. This module constructs a feature vector for each business type (e.g., remote control, augmented reality, video surveillance). The vector dimensions include latency sensitivity (measured by the maximum allowable latency), data rate requirement (measured by the minimum guaranteed bit rate), and reliability index (measured by the packet error rate). The weight factor of each dimension is assigned subjectively or objectively using the analytic hierarchy process or the entropy weight method. Then, the comprehensive weight value of each business type is calculated using a weighted summation or approximation of the ideal solution ranking method; the machine learning model adopts a supervised learning paradigm, and the training dataset consists of historical network operation data, including historical business load, resource allocation decisions and corresponding system performance results; The model structure can be either a deep neural network or a support vector machine. Its input is the current network state features, and its output is the optimized business type weight prediction value. By periodically retraining with the latest data, the weight calculation process can adapt to changes in network traffic patterns and the emergence of new businesses.

[0037] The low-latency scheduling algorithm specifically includes: the channel state parameters and interference map obtained based on sensing information, and the calculation of key indicators from the original sensing data through a parameter extraction module, including the expected signal strength, the main interference signal strength, the channel coherence time, and the channel coherence bandwidth; The prediction of transmission conditions in the short future period is made using an autoregressive model or a long short-term memory network model, and extrapolation is used to predict the channel quality indication and interference level in the slot in the next few time slots. The priority queue management mechanism adopts a multi-level feedback queue structure, which contains multiple sub-queues. Each sub-queue corresponds to a priority level and a corresponding scheduling strategy. Data packets are classified into different sub-queues according to their service type identifier and the dynamic latency threshold under the current network conditions. The dynamic latency threshold is calculated in real time by the scheduler based on the overall network load and service combination. in, Variable description: This represents the scheduling priority score of the k-th data packet; the higher the score, the higher the priority.

[0038] This represents the remaining delay budget for the k-th data packet.

[0039] This indicates the data rate required by the service to which the k-th data packet belongs.

[0040] This indicates the predicted channel quality indicator obtained by the above prediction algorithm.

[0041] This represents the current estimated bit error rate.

[0042] The weighting factor represents the latency sensitivity of the k-th data packet.

[0043] This represents the weighting factor for the throughput requirement of the k-th data packet.

[0044] This represents the weighting factor for the error sensitivity of the k-th data packet.

[0045] This function is the core decision-making mechanism of the low-latency scheduling algorithm. Its innovation lies in allocating the remaining latency budget of the data packet (…). As a key denominator, it ensures that urgent data packets receive higher priority; at the same time, it innovatively introduces predicted channel quality (…). ) and current link status ( This allows scheduling decisions to consider not only service requirements but also channel conditions, enabling intelligent scheduling that prioritizes high-rate or error-prone data transmission when the channel is good, and prioritizes time-sensitive data transmission when the channel is poor. This optimizes latency and reliability at the system level. Packets in high-priority queues have priority in scheduling and may be assigned more robust modulation and coding schemes and more frequent transmission opportunities (e.g., shorter transmission intervals). The cross-layer design is reflected in the fact that the scheduler can directly access the instantaneous channel status information report provided by the physical layer and the service flow queue status information provided by the network layer. Therefore, when making scheduling decisions, it can comprehensively consider channel conditions and data urgency, avoiding the additional delay caused by inter-layer information transmission in the traditional layered protocol stack.

[0046] In the priority queue management mechanism, the dynamic adjustment of the delay threshold is based on the service level agreement negotiated with the network core network or service server, which specifies the maximum allowable end-to-end delay and jitter range for different service data streams. The scheduler internally maintains a latency budget manager, which tracks the latency consumed by each data stream and dynamically adjusts its priority in the queue based on the remaining latency budget. The sliding window statistical technique is used to calculate real-time network performance metrics, including average scheduling latency, packet drop rate, and channel utilization. The size of the sliding window can be configured according to network stability requirements. The algorithm for optimizing queue ordering is based on these real-time metrics. For example, when the average scheduling delay exceeds a certain threshold, the system will automatically increase the priority weight of all delay-sensitive services, or when the channel quality is generally poor, it will temporarily relax the delay requirements of some non-critical services to ensure the timely transmission of critical services.

[0047] The specific steps of the sensor data fusion processing include: the environmental information acquired by the sensing module is obtained through a sensing signal processing pipeline, which includes three stages: signal detection, parameter estimation, and target tracking. Finally, structured environmental description data is output, including the three-dimensional coordinates of obstacles, motion velocity vectors, radar cross-sections, and multipath component parameters (delay, angle, gain) of the channel. The transmission status information in the communication data stream is provided by the performance monitoring unit of the communication link, including bit error rate, block error rate, signal-to-interference-plus-noise ratio, and throughput; The fusion analysis is performed in the data fusion engine. The engine first timestamps and spatially registers the perceived environment description data and the communication transmission status data. Then, it uses correlation analysis or causal reasoning algorithms (such as Granger causality test) to establish a mapping relationship model between environmental features (such as the presence of strong reflectors in a specific direction) and communication performance indicators (such as a sudden drop in signal strength in that direction). The process of generating a unified set of transmission control parameters is based on the above mapping model. For the current perceived environmental state, the model is queried to obtain the expected channel impact, and then the recommended transmission parameters are derived. The dynamic selection of modulation and coding scheme is achieved through a lookup table method or online calculation. The table or calculation function takes the fused equivalent channel quality estimate as input and outputs the optimal modulation order and coding rate pair. The adjustment of the upper limit of the number of retransmissions is determined based on the channel stability predicted by the sensing information. For scenarios where the channel changes rapidly, the maximum number of retransmissions should be appropriately reduced to avoid excessive retransmission delays. For scenarios where the channel is stable, the number of retransmissions can be maintained or appropriately increased to improve reliability.

[0048] During the real-time optimization of the transmission control parameter set, the fuzzy logic algorithm defines the membership functions of input variables (such as the fused channel quality level, service priority, and historical transmission success rate) and output variables (such as modulation and coding scheme level and retransmission number suggestion), and constructs a fuzzy rule base (e.g., IF channel quality IS poor AND service priority IS high THEN use low-order modulation AND increase retransmission number); the inference engine performs fuzzification, rule matching, and fuzzy inference based on the current precise input value, and finally outputs precise control parameter values ​​through defuzzification; The neural network algorithm employs a pre-trained multilayer perceptron or recurrent neural network model. This model takes the real-time collected network state vector (containing fused environmental and communication indicators) as input and directly outputs the optimized transmission control parameter values. The training objective of the neural network is to minimize a comprehensive loss function that simultaneously considers transmission delay, reliability, and spectral efficiency. Through online learning or incremental learning, the optimization algorithm can adapt to non-stationary changes in network topology, service characteristics, and propagation environment.

[0049] The communication sensing collaborative feedback mechanism specifically includes: the periodically collected communication performance indicators are provided by the performance measurement unit on the user equipment and the service base station, including end-to-end latency, packet loss rate, and achievable throughput; The perception accuracy indicators are calculated by the perception performance evaluation unit, including target positioning error, velocity estimation variance, and false alarm probability; these indicators are transmitted to the integrated sensing management entity through a dedicated feedback channel. The management entity runs a collaborative optimization controller, which compares the collected performance indicators with preset performance target thresholds and calculates the performance deviation. Based on the magnitude and direction of the deviation, it applies a proportional-integral-derivative control algorithm or a reinforcement learning strategy to generate collaborative control commands. The fine-tuning of resource allocation parameters includes dynamically adjusting the weight coefficients of the utility function or the calculation benchmark value of the resource allocation ratio in step S2. The fine-tuning of scheduling parameters includes adjusting the scheduling weight of the priority queue, the scaling factor of the dynamic delay threshold, or the confidence threshold of the prediction model in step S3. This feedback loop operates at fixed time intervals or in an event-triggered manner to ensure that the system can continuously adapt to internal and external changes and maintain stable low-latency transmission performance.

[0050] Example 2 The detailed workflow of the low-latency transmission method based on 5G-A sensing integration described in this invention is as follows.

[0051] The workflow of this invention begins with generating and transmitting a fused signal that simultaneously carries communication data and sensing detection functions at a 5G-A network node; after the signal is reflected by the target or environment, it is captured by the receiving end and simultaneously demodulated and separated through a joint signal processing mechanism to extract the original communication data stream and sensing information containing parameters such as target distance and speed; Subsequently, based on the real-time monitored network service load, sensing task priority, and the initially acquired channel status, the system uses a dynamic resource allocation algorithm to calculate the optimal allocation ratio of communication and sensing functions in terms of spectrum, time, and power resources within the current time slot and then performs the allocation. During the data transmission phase, the low-latency scheduler sorts the data packets to be transmitted according to the predicted channel quality and interference spectrum based on the dynamically calculated priority, prioritizes the allocation of resources to latency-sensitive services, and uses a simplified signaling process for rapid scheduling. Data packets are transmitted on a link consisting of adaptive coding modulation and a hybrid automatic repeat request mechanism. The modulation and coding scheme and retransmission parameters are controlled in real time by a sensor data fusion engine. This engine dynamically outputs the optimal transmission parameters by analyzing the correlation model between sensory information and communication status. Throughout the transmission process, periodic performance metrics are collected and fed back to the resource allocation and scheduling module, forming a closed-loop control that continuously fine-tunes system parameters to maintain low-latency, high-performance transmission.

[0052] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A low-latency transmission method based on 5G-A sensing integration, characterized in that, The method includes the following steps: S1. In the 5G-A network environment, a collaborative processing mechanism integrating communication and sensing is constructed to merge and share communication transmission links and sensing and detection links, so that communication signals and sensing signals are processed simultaneously in the same frequency band and time slot. S2. Based on the integrated sensing data flow, a dynamic resource allocation strategy is adopted to adaptively adjust the allocation ratio of communication resources and sensing resources according to the real-time network status and service requirements. The resource allocation includes the joint optimization of spectrum resources, time resources and power resources. S3. During data transmission, a low-latency scheduling algorithm is introduced. This algorithm predicts channel quality changes and interference based on sensing information, prioritizes scheduling latency-sensitive service data packets, and compresses transmission latency by reducing protocol layer processing overhead and simplifying signaling interaction processes. S4. Perform sensor data fusion processing, and analyze the correlation between the environmental state information obtained by sensing and the communication data stream to generate transmission control parameters, which are used to dynamically adjust the modulation and coding scheme and retransmission mechanism, thereby achieving low-latency transmission while ensuring transmission reliability.

2. The low-latency transmission method based on 5G-A sensing integration according to claim 1, characterized in that, The dynamic resource allocation strategy specifically includes: real-time monitoring of communication service load and sensing task priority through a central control unit or distributed network nodes; dynamic division of available resource pools according to predefined resource allocation rules; wherein the resource allocation rules calculate the resource allocation ratio based on service type weight and latency requirements; and periodically updating allocation parameters through a feedback mechanism to ensure balanced execution of communication and sensing functions.

3. The low-latency transmission method based on 5G-A sensing integration according to claim 2, characterized in that, In the resource allocation rules, the weight of the service type is determined comprehensively based on the service latency sensitivity, data rate requirements and reliability indicators, and the weight calculation process is optimized by training historical data through a machine learning model, so that the resource allocation can adapt to network changes.

4. The low-latency transmission method based on 5G-A sensing integration according to claim 1, characterized in that, The low-latency scheduling algorithm specifically includes: predicting transmission conditions in the short term based on channel state parameters and interference maps obtained from sensing information, and adopting a priority queue management mechanism to classify service data packets according to latency thresholds. High-priority data packets are allocated more transmission opportunities and resource blocks. At the same time, the scheduling algorithm integrates physical layer and network layer information through cross-layer design to reduce scheduling decision latency.

5. The low-latency transmission method based on 5G-A sensing integration according to claim 4, characterized in that, In the priority queue management mechanism, the latency threshold is dynamically adjusted according to the service level agreement, and the queue sorting is optimized by using a sliding window to collect real-time network performance indicators, so as to ensure the timeliness and accuracy of scheduling decisions.

6. The low-latency transmission method based on 5G-A sensing integration according to claim 1, characterized in that, The specific steps of the sensor data fusion processing include: fusing and analyzing the environmental information acquired by the sensing module, such as obstacle positions, movement speed and channel attenuation characteristics, with the transmission status information in the communication data stream to generate a unified transmission control parameter set. This parameter set is used to dynamically select the modulation and coding scheme and adjust the upper limit of the number of retransmissions in order to reduce the transmission error rate and retransmission delay.

7. The low-latency transmission method based on 5G-A sensing integration according to claim 6, characterized in that, The transmission control parameter set is optimized in real time using fuzzy logic or neural network algorithms to adapt to rapid changes in the network environment and ensure the stable operation of the integrated sensing system.

8. The low-latency transmission method based on 5G-A sensing integration according to claim 1, characterized in that, The method also includes a communication-aware collaborative feedback mechanism, which generates collaborative control commands by periodically collecting communication performance and sensing accuracy indicators to fine-tune resource allocation and scheduling parameters, thereby maintaining the continuity of low-latency transmission.

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