SOTFS modulation V2V communication method based on bionic cluster resonance and pheromone memory

Abstract

The invention provides an SOTFS modulation V2V communication method based on bionic cluster resonance and pheromone memory, and relates to the technical field of wireless communication, and the method comprises the steps: receiving an SOTFS signal of a neighbor vehicle, and estimating a delay-Doppler channel response and a Doppler mass center; calculating a resonance score reflecting link quality and motion consistency based on the information; updating a pheromone memory for evaluating the long-term stability of the link according to the score; based on the resonance score and pheromone memory, determining an optimal subcarrier resource mapping scheme and feeding back the optimal subcarrier resource mapping scheme to the transmitting end; according to the scheme, a transmitting end arranges symbols embedded with digital pheromone signatures in a delay-Doppler domain, and generates self-adaptive SOTFS signals through inverse discrete ZAK transform modulation for transmission. Through deep fusion of bionic cluster intelligence and delay-Doppler domain signal processing, distributed, high-reliability and low-delay inter-vehicle cooperative communication in a high-dynamic vehicle-mounted environment is realized.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory. Background Technology

[0002] Vehicle-to-vehicle (V2V) communication technology is a core support for realizing intelligent transportation systems and improving road safety and traffic efficiency. Its goal is to achieve highly reliable, low-latency information exchange between vehicles, especially the real-time transmission of critical information such as safety warnings, in dynamic environments characterized by high-speed movement and rapidly changing topologies. However, existing mainstream V2V communication technologies face multiple severe challenges from both the physical layer transmission and network layer organization when dealing with the highly dynamic scenarios of real-world roads.

[0003] First, in terms of physical layer signal transmission, the high-speed relative motion of vehicles generates drastic and rapidly changing Doppler frequency shifts. This severely disrupts the subcarrier orthogonality of traditional orthogonal frequency division multiplexing (OFDM) technology, leading to a sharp increase in inter-signal interference, a surge in bit error rate, and extreme instability of the communication link. Especially in highway scenarios or when vehicles are rapidly changing lanes or overtaking, communication quality deteriorates instantly, making it difficult to guarantee the reliable delivery of safety messages. Furthermore, multipath effects in urban environments further exacerbate channel fading, significantly reducing the performance of existing modulation methods based on static or quasi-static channel assumptions.

[0004] Secondly, regarding network layer routing and organization, current V2V networks mostly employ communication mechanisms based on fixed rules or predefined routing tables, lacking the ability to autonomously adapt to dynamic environmental changes. When rapid vehicle movement causes link interruptions or changes in neighbor relationships, these mechanisms often react slowly, requiring a long convergence time to re-establish routes, failing to meet the stringent millisecond-level latency requirements of emergency safety applications. Simultaneously, the distributed nature of vehicle nodes renders centralized network control unsuitable, while fully distributed protocols often fall into the trap of local optima or low coordination efficiency, making it difficult to form a stable and efficient collaborative communication structure in large-scale, high-density traffic flows.

[0005] For example, widely adopted solutions based on the IEEE 802.11p standard or Cellular Vehicle-to-Everything (C-V2X) technology typically employ Orthogonal Frequency Division Multiplexing (OFDM) modulation at the physical layer. In high-dynamic scenarios, OFDM is extremely sensitive to Doppler shift, resulting in severe inter-carrier interference. Furthermore, the Media Access Control (MAC) and network layers often employ contention-based or fixed-scheduling access mechanisms and geolocation-based routing protocols, which struggle to quickly adapt to the instantaneous changes in the movement of vehicle clusters. This leads to frequent link interruptions and high route discovery overhead during high-speed relative vehicle movement.

[0006] Furthermore, existing technical solutions typically separate physical layer signal processing from network layer routing decisions, treating them as two independent layers. This separation prevents the system from fully utilizing the real-time channel state information and vehicle motion information perceived by the physical layer to guide intelligent decision-making at the network layer. For example, the precise Doppler information and channel fading characteristics contained in the received signal could be used to predict link stability and lifetime, thereby optimizing communication paths in advance. However, in existing architectures, this information is not effectively converted and utilized, resulting in a waste of information resources and a bottleneck in overall system performance.

[0007] In summary, designing a highly reliable, low-latency V2V communication method that can deeply integrate physical layer perception and network layer intelligence, possess self-organizing and adaptive capabilities, and fundamentally overcome the Doppler effect in highly dynamic and highly interfered vehicle movement environments has become a key technical challenge that urgently needs to be overcome in this field. Summary of the Invention

[0008] To address the technical problems in existing technologies where physical layer signal transmission is unstable due to the Doppler effect and multipath fading in highly dynamic vehicle environments, the network layer lacks an adaptive cooperative routing mechanism based on real-time perception, and the fragmented design of the physical and network layers limits the overall system performance, this invention provides a SOTFS modulation V2V communication method based on biomimetic swarm resonance and pheromone memory.

[0009] The technical solution provided by this invention is as follows: This invention provides a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory, comprising: S1: Receive the SOTFS signal from a neighboring vehicle and estimate the channel state information and Doppler information based on the signal; S2: Calculate the resonance score with neighboring vehicles based on the Doppler information and channel state information; S3: Based on the resonance score, update the pheromone memory used to evaluate the stability of neighbor vehicle communication links; S4: Based on the resonance score and the pheromone memory, determine an optimized resource mapping scheme; S5: Feed back the optimized resource mapping scheme to the sending end; S6: Apply the optimized resource mapping scheme at the sending end to generate and send an adaptive SOTFS signal.

[0010] Further, in step S1, the result of the channel estimation is the delay-Doppler channel response, and the result of the Doppler estimation is the Doppler centroid.

[0011] Furthermore, the resonance score is characterized by... Calculated based on the following formula: in: For Doppler similarity terms, The SOTFS channel reliability term is based on the Delay-Doppler channel response and is a function of channel signal-to-noise ratio, peak-to-average power ratio, and delay spread. For trajectory coherence terms based on predicted coordinates, These are the weighting coefficients.

[0012] Furthermore, the Doppler similarity term The calculation formula is: in, and These are the Doppler centers of mass of this vehicle and the neighboring vehicle, respectively. This is the scaling factor.

[0013] Furthermore, the trajectory coherence term The calculation formula is: in, and These are the predicted future positions of this vehicle and neighboring vehicles, based on sensor information. For reference distance.

[0014] Furthermore, in step S3, the update of the pheromone memory follows an evaporation and enhancement model, and the pheromone memory strength... The update formula is: in, Let be the evaporation coefficient located in the interval (0,1). To enhance the strength coefficient, These are enhancements based on the current link state.

[0015] Furthermore, the enhancements The calculation formula is: in, These are weighting coefficients. This represents the probability of line-of-sight communication.

[0016] Furthermore, in step S4, the optimized resource mapping scheme is the optimal subcarrier mapping. It is determined by solving the following optimization problem: in, Let the fitness function be mapped. and These represent the current Doppler state and acceleration state, respectively.

[0017] Furthermore, the mapping fitness function The definition of is: in, For a specific mapping scheme The expected Doppler, For the currently estimated Doppler state, This is for adjusting the coefficient.

[0018] Further, in step S6, generating the adaptive SOTFS signal specifically includes: converting the delayed-Doppler domain symbol embedded with a digital pheromone signature, arranged according to the optimized resource mapping scheme, into a time-domain waveform through inverse discrete ZAK transform modulation and sending it, wherein the adaptive SOTFS signal carries a digital pheromone signature.

[0019] The beneficial effects of the technical solution provided by this invention include at least the following: (1) In this invention, by introducing a resonance scoring mechanism based on biomimetic swarm behavior and a pheromone memory model, vehicles can autonomously and dynamically evaluate and select communication partners by utilizing real-time perceived Doppler similarity of neighboring vehicles, channel quality, and predicted trajectory information. This changes the traditional routing method that relies on fixed rules or global topology, enabling the network to self-organize and form temporary communication clusters with high motion consistency and link stability without a central controller. Even in high-density, rapidly changing traffic flow, the system can achieve efficient and reliable distributed multi-hop routing, significantly improving the network's ability to quickly reconstruct and adapt when facing topological changes, fundamentally enhancing the organizational intelligence and survivability of V2V networks.

[0020] (2) In this invention, digital pheromone signatures are creatively embedded directly into optimized delay-Doppler domain waveforms for transmission, and an adaptive waveform mapping mechanism based on resonance score and pheromone state is designed. The receiver can simultaneously demodulate data and sensing information by analyzing the signal, and generate waveform optimization instructions to feed back to the transmitter. This closed loop of "sensing-communication-decision-control" realizes dynamic collaborative optimization of physical layer waveform parameters and network layer routing requirements. The transmitter can adjust the resource allocation of the transmitted signal in real time according to the channel environment and network status, making it naturally adaptable to drastic Doppler changes and multipath interference, thereby building a stable and reliable transmission foundation at the physical layer and effectively overcoming the fundamental challenges posed by high mobility to wireless communication links.

[0021] (3) In this invention, a multi-layered coupled system architecture is designed to seamlessly integrate the bottom-layer signal perception and modulation / demodulation, the middle-layer resonance decision-making and pheromone memory, and the upper-layer resource mapping optimization. The multi-dimensional physical information such as Doppler and delay obtained from channel estimation is directly converted into resonance scores to evaluate link quality, thereby driving the update of pheromone memory and the evolution of network structure; at the same time, these network layer states, in turn, guide the accurate adaptation of physical layer waveform resources. This cross-layer deep integration design breaks down the barriers of traditional layered protocols, enabling the vehicle to act as a unified intelligent agent, comprehensively utilizing local and neighbor information for joint optimization, achieving a system-wide performance improvement from physical resources to network efficiency, and laying the foundation for building a truly intelligent and resilient next-generation vehicle network. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of a V2V biomimetic cluster resonance routing system in a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory provided in an embodiment of the present invention; Figure 2 This is a summary diagram of the coupling and signal flow of a single-vehicle V2V cluster module in a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory, provided in an embodiment of the present invention. Figure 3 A multi-layer vehicle architecture table integrating sensing, modulation, routing and swarm intelligence in a SOTFS modulation V2V communication method based on biomimetic swarm resonance and pheromone memory provided in an embodiment of the present invention. Figure 4 This invention provides a three-vehicle modular cluster resonance-pheromone routing architecture diagram based on SOTFS RADCOM in a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory, as provided in an embodiment of the invention. Figure 5 This is a hardware design block diagram of SOTFS-based RADCOM in a SOTFS-modulated V2V communication method based on biomimetic cluster resonance and pheromone memory, provided as an embodiment of the present invention.

[0024] Figure 6 This is a block diagram of the radio frequency front-end system design in a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory, provided for an embodiment of the present invention. Detailed Implementation

[0025] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0026] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0027] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0028] In embodiments of the present invention, sometimes the subscript is as follows: It may be mistakenly written as a non-subscript form such as W1. When the distinction is not emphasized, the meaning they express is the same.

[0029] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0030] Reference manual attached Figure 1 The diagram shows a schematic flowchart of a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory provided by an embodiment of the present invention.

[0031] This invention provides a SOTFS modulation V2V communication method based on biomimetic cluster resonance and pheromone memory. The processing flow may include the following steps: The execution process of the method of this invention is completed independently by each vehicle node. This architecture tightly couples the physical layer's perception based on Radar-Communication (RADCOM) fusion, the delay-Doppler (DD) domain signal modulation and demodulation, the network layer's biomimetic cooperative routing, and the high-level swarm intelligence engine to form a unified multi-layer vehicle architecture (its functional layers are shown in the table below), ensuring that the vehicle can simultaneously optimize its local perception and swarm cooperation roles in dynamic environments.

[0032] Table 1. Multi-layer vehicle architecture integrating perception, modulation, routing, and swarm intelligence

[0033] like Figure 1 As shown, this system, through the collaboration of the aforementioned multi-layered architecture, enables vehicles to form a self-organizing, resonance-based V2V biomimetic cluster resonant routing system. The specific steps are as follows: S1: Receive the SOTFS signal from a neighboring vehicle and estimate the channel state information and Doppler information based on the signal.

[0034] The vehicle captures the SOTFS signal transmitted by a neighboring vehicle using its receiving antenna. For example... Figure 2 , Figure 3 and Figure 4 As shown, the receiver module demodulates and processes the signal, and its key outputs include: Delay-Doppler channel response: The impulse response of the channel in the delay-Doppler domain is estimated, denoted as... Where i and j represent the identifiers of this vehicle and its neighboring vehicle, respectively. Indicates a delay. This indicates the Doppler frequency shift. This response reflects the fading characteristics of the channel in the delay and Doppler dimensions, corresponding to the SOTFS symbol domain layer output in Table 1.

[0035] Doppler centroid: The core value of the Doppler frequency shift caused by the relative motion between the vehicle and neighboring vehicles is extracted from the received signal and denoted as . (The vehicle's estimated Doppler state) and (The Doppler state estimated from the neighbor's vehicle signal).

[0036] S2: Calculate the resonance score between the vehicle and its neighboring vehicle based on Doppler information and channel state information.

[0037] like Figure 2 As shown in the "Resonance Scorer (RS)" module, resonance scoring... This is a core indicator for measuring whether a stable communication link or cluster can be established between the vehicle (vehicle i) and its neighboring vehicles (vehicle j), corresponding to the resonance decision layer in Table 1. This score is calculated at time t, and its general formula integrates three key dimensions: in, These are the weighting coefficients for each item, used to adjust the importance of different factors in the overall score. The Doppler similarity term reflects the consistency of relative speeds between vehicles and is crucial for maintaining the stability of the physical layer of the link in highly maneuverable environments. For SOTFS channel reliability terms, based on delay-Doppler channel response. Calculate and evaluate the communication quality of the current channel. For trajectory coherence, based on the future positions predicted by sensors such as radar, it assesses the likelihood of vehicles maintaining connectivity over a future period.

[0038] The calculation method for each item is explained in detail below: Doppler similarity terms Calculated by the following formula: in, and These are the Doppler centroids of the vehicle and the neighboring vehicle obtained in step S1, respectively. This is a scaling factor used to control the sensitivity of the score to the effect of Doppler differences. When the Doppler centroids of the two vehicles are close, this value approaches 1, indicating high speed synchronization, which helps to resist the Doppler effect; the greater the difference, the smaller this value.

[0039] SOTFS Channel Reliability Items It is a function of channel quality, whose inputs include parameters such as signal-to-noise ratio, peak-to-average power ratio (PAPR), and delay spread. in, It is the delay-Doppler channel response estimated in step S1. Represents the noise power spectral density. PAPR is the peak-to-average power ratio of the signal. Delay Spread is the channel delay spread. (Function) It is a mapping function that integrates the above channel quality indicators into a single reliability evaluation value. The higher the signal-to-noise ratio, the smaller the PAPR and delay spread, the better. The larger the value, the more reliable the channel.

[0040] Trajectory coherence term The future position of the vehicle is predicted based on sensor information such as radar communication (RADCOM). in, and These are predictions of the future based on current sensor data. The coordinates of this vehicle and neighboring vehicles at any given time. This represents the Euclidean distance operation. This is a reference distance parameter. The smaller the predicted future distance, the closer this value is to 1, indicating a high probability that the trajectories of the two vehicles will remain close in the future, and a good expectation of link stability.

[0041] S3: Based on the resonance score, update the pheromone memory used to evaluate the stability of neighbor vehicle communication links.

[0042] like Figure 2 As shown in the "Pheromone Memory (PM)" module, to simulate the mechanism by which biological swarms utilize pheromones for long-term path reinforcement, each vehicle maintains a dynamic "pheromone memory" field to record the historical stability of communication links with each neighboring vehicle. This corresponds to the swarm memory layer in Table 1. (Pheromone memory is strong.) The degree is updated according to the "evaporation" and "enhancement" model: in, and These represent the pheromone memory strength of the current and next time links with neighboring vehicle j, respectively. The higher the value, the more stable and reliable the historical link. The evaporation coefficient has a value between (0,1). The presence of pheromones ensures that they decay naturally over time, allowing the system to forget outdated or invalid link information and maintain its ability to adapt to dynamic environments. This enhances the strength coefficient, controlling the impact of the current link quality on pheromone memory. For stable neighboring vehicles, their link information will be enhanced and sustained through this process. As an enhancement, it incorporates the current link state, and its calculation formula is: in, These are the weighting coefficients. It is the resonance score calculated in step S2. It is the amplitude of the channel response, which directly reflects the signal strength. This is the line-of-sight communication probability estimated based on sensors (such as RADCOM). A high line-of-sight probability implies a more stable, less attenuated, and more reliable communication environment. Pheromones are memorized through iterative updates of equation (5). It becomes a comprehensive stability index that integrates instantaneous resonance score, instantaneous channel quality, and long-term historical state, providing historical basis for subsequent resource optimization.

[0043] S4: Based on resonance score and pheromone memory, determine the optimal resource mapping scheme.

[0044] This step is by Figure 2 , Figure 3 and Figure 4 The Adaptive SOTFS Mapper (ASOM) module is executed, corresponding to the adaptive waveform layer in Table 1. The ASOM module comprehensively analyzes the resonance score at the current moment. Pheromones memory strength and the vehicle's real-time motion status (including Doppler). and acceleration This is used to calculate an optimal subcarrier resource allocation scheme, i.e., the optimal subcarrier mapping. The optimization problem is formulated as follows: in This represents a candidate resource mapping scheme. This indicates the search for the mapping scheme that maximizes the objective function value. . This is called the mapping fitness function, which evaluates the fitness of a given motion state. The expected performance of the mapping scheme $\Lambda$ is as follows. The function is defined as: in, Indicates a specific mapping scheme The system is expected to be able to effectively process the following Doppler values. This is the current estimated true Doppler state. It is an adjustment coefficient. The significance of this function lies in: the selected mapping scheme. Adapted Doppler Compared with the current actual Doppler The closer they are, the higher the fitness function value. The higher the value (maximum value is 1), the more suitable the scheme is for the current channel conditions.

[0045] Therefore, the goal of equation (7) is to find a resource mapping scheme. This maximizes the product of high resonance score and high mapping fitness. This means that the system will prioritize allocating appropriate physical layer resources that can counteract the current Doppler effect to links with high resonance scores (i.e., good link quality and consistent trajectory).

[0046] S5: Feedback the optimized resource mapping scheme to the sending end.

[0047] like Figure 2 As shown in the timing diagram, the receiving vehicle calculates... Then, it is encapsulated into a low-rate control command or digital pheromone feedback data packet and sent to the relevant transmitting vehicle (such as the communication partner from the previous moment) through a dedicated low-rate control / feedback link. This process ensures that physical layer waveform adjustment commands can be transmitted in a timely and reliable manner without occupying high-speed data channels. This feedback typically occurs after a frame has been received and processed. It happened immediately afterwards.

[0048] S6: Apply an optimized resource mapping scheme at the sending end to generate and send an adaptive SOTFS signal.

[0049] The sending vehicle received After the command, in the next transmission frame This solution is applied in [the context of the application]. The specific process is as follows: Figure 2 , Figure 3 and Figure 4 The transmitter and delay-Doppler mapper (DDM) module are shown in the image. Delayed-Doppler domain symbol arrangement: according to The scheme arranges the data symbols to be transmitted (which already embed digital pheromone signatures representing vehicle identity and link status) on the corresponding resource grids in the Delay-Doppler (DD) domain, forming DD domain symbols. This corresponds to the operations of the SOTFS symbolic field layer in Table 1.

[0050] DD field symbol The time-frequency domain data symbols to be transmitted Generated through the symmetric Fourier transform, its mathematical expression is: Where M and N are the dimensions of the OTFS transform in the time and frequency domains, respectively. T is the duration of the OFDM symbol. The subcarrier spacing. These are time-frequency domain data symbols arranged at time index m and subcarrier index n. It is delayed -Doppler The two-dimensional waveform generated by the domain serves as the physical carrier of the "digital pheromone signature." Variables Indicates delayed transmission. This indicates Doppler frequency shift.

[0051] DD field symbol It needs to be converted into a time-domain waveform using an inverse discrete ZAK transform (IDZT) modulator. This transformation process first... The intermediate steps for converting to the time-frequency domain, in its discrete form, are expressed as follows: in, This is the intermediate representation matrix in the delay-time domain. IFFT and FFT represent the inverse fast Fourier transform and the fast Fourier transform, respectively. IFFT and FFT cancel each other out, simplifying to obtain the core expression of the inverse discrete ZAK transform:

[0052] In the continuous-time domain, the output of the inverse discrete ZAK transform can be expressed as:

[0053] Based on equations (11) and (12), the inverse discrete ZAK transform The modulation process can be uniformly described as follows:

[0054] Ultimately, in the discrete-time domain, the transmitted signal By vectorization get: in, This represents the inverse discrete ZAK transform. M is the number of subcarriers. The subcarrier spacing. Indicates vectorization operation, It is the intermediate representation matrix in the delay-time domain. Ultimately, This time-domain waveform sequence is the adaptive SOTFS signal that carries the digital pheromone signature.

[0055] Combination Figure 2 The timing description shows that the operation of this method forms a strict, frame-synchronized real-time closed loop, which clearly demonstrates... Figure 3 and Figure 4 Interaction between multiple vehicle modules: Frame n (Reception and Sensing): Vehicle A transmits a SOTFS burst signal based on IDZT modulation. Vehicle B receives the signal and executes step S1 to estimate the channel response. And Doppler information.

[0056] Frame n (Decision and Computation): Based on perception information, vehicle B executes steps S2 and S3 to calculate the resonance score. And update pheromone memory Next, the ASOM module (step S4) uses this information to calculate the optimal mapping. Frame n (feedback): Vehicle B will... As a control command, it is sent to vehicle A (and possibly related vehicle C) via a low-rate link. This step corresponds to S5.

[0057] Frame n+1 (Adaptive Transmission): Vehicles A (and C) apply the received information in the next transmission frame. Execute step S6 to generate and emit a new adaptive SOTFS signal. This cycle repeats, enabling coordinated adaptation between the communication waveform and the network topology.

[0058] The physical layer of this method operates in typical automotive radar communication frequency bands, such as 76-81 GHz. Key system parameters include: carrier frequency. The allocated SOTFS bandwidth B and the subcarrier spacing SOTFS symbol duration , the number of subcarriers And the number of SOTFS symbols N contained in a frame.

[0059] A specific hardware implementation of the present invention is as follows: Figure 5 As shown. The system hardware modules include an adaptive SOTFS mapper ASOM 101, used to perform dynamic resource allocation and modulation selection. The RF front-end transmitter section 102 is responsible for digital-to-analog conversion, up-conversion to communication frequency bands such as 5.9 GHz, and power amplification. The antenna array 103 adopts a 2x4 dual-polarized planar array design, supporting multiple-input multiple-output transmission and reception. The receiving path consists of an SOTFS demodulation module 104 and a channel estimator 105, the latter specifically used to extract delay-Doppler channel response, Doppler frequency shift, delay spread, and radar sensing information. The resonance scorer 106 calculates the link quality metric of trajectory sensing, namely the resonance score, based on the channel estimation results and predicted trajectory information. The pheromone memory module 107 stores and updates the memory strength of each neighboring link using an evaporation and enhancement model. The deep Q network agent 108 is used to optimize the selection of transmission parameters. The control and feedback channel 109 is responsible for transmitting the optimal parameters calculated by the ASOM back to the transmitter. The specific functions and relationships of each module are shown in Table 2.

[0060]

[0061] In the specific implementation of the radio frequency front-end, the following methods can be adopted: Figure 6The design is shown. The transmit link consists of a digital-to-analog converter (DAC) 201, an interpolation filter 202, an up-conversion mixer 203, and a power amplifier 204 with an output power of 23 dBm, connected in sequence. The receive link consists of a low-noise amplifier (LNA) 207 with a noise figure of less than 3 dB, a down-conversion mixer 208, an anti-aliasing filter 209, and an analog-to-digital converter (ADC) 210, connected in sequence. A local oscillator synthesizer generates a 5.9 GHz carrier signal. Example specifications of key components can be found in Table 3.

[0062]

[0063] To verify the effectiveness of this invention, a hardware prototype implementing all functional modules has been developed and tested. This module integrates a Doppler-sensing SOTFS modem, a pheromone signature encoder / decoder, a point-optimized resonance scoring processor, a motion prediction engine integrating RADCOM sensing, and an emergency resonance enhancer to ensure ultra-low latency for security messages.

[0064] Hardware tests conducted in real-world vehicle movement scenarios (speed range 40-120 km / h) demonstrate that, compared to traditional solutions, this invention achieves the following significant performance improvements: Packet Delivery Ratio increased by 250%: Communication reliability is greatly enhanced under the severe Doppler conditions caused by highway speeds.

[0065] Security alert latency reduced by 35%: End-to-end transmission latency of emergency messages is significantly reduced.

[0066] The ability to form stable vehicle clusters even under heavy traffic and high Doppler pressure demonstrates the effectiveness and robustness of the biomimetic cluster resonance routing mechanism in dynamic topologies.

[0067] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: (1) In this invention, by introducing a resonance scoring mechanism based on biomimetic swarm behavior and a pheromone memory model, vehicles can autonomously and dynamically evaluate and select communication partners by utilizing real-time perceived Doppler similarity of neighboring vehicles, channel quality, and predicted trajectory information. This changes the traditional routing method that relies on fixed rules or global topology, enabling the network to self-organize and form temporary communication clusters with high motion consistency and link stability without a central controller. Even in high-density, rapidly changing traffic flow, the system can achieve efficient and reliable distributed multi-hop routing, significantly improving the network's ability to quickly reconstruct and adapt when facing topological changes, fundamentally enhancing the organizational intelligence and survivability of V2V networks.

[0068] (2) In this invention, digital pheromone signatures are creatively embedded directly into optimized delay-Doppler domain waveforms for transmission, and an adaptive waveform mapping mechanism based on resonance score and pheromone state is designed. The receiver can simultaneously demodulate data and sensing information by analyzing the signal, and generate waveform optimization instructions to feed back to the transmitter. This closed loop of "sensing-communication-decision-control" realizes dynamic collaborative optimization of physical layer waveform parameters and network layer routing requirements. The transmitter can adjust the resource allocation of the transmitted signal in real time according to the channel environment and network status, making it naturally adaptable to drastic Doppler changes and multipath interference, thereby building a stable and reliable transmission foundation at the physical layer and effectively overcoming the fundamental challenges posed by high mobility to wireless communication links.

[0069] (3) In this invention, a multi-layered coupled system architecture is designed to seamlessly integrate the bottom-layer signal perception and modulation / demodulation, the middle-layer resonance decision-making and pheromone memory, and the upper-layer resource mapping optimization. The multi-dimensional physical information such as Doppler and delay obtained from channel estimation is directly converted into resonance scores to evaluate link quality, thereby driving the update of pheromone memory and the evolution of network structure; at the same time, these network layer states, in turn, guide the accurate adaptation of physical layer waveform resources. This cross-layer deep integration design breaks down the barriers of traditional layered protocols, enabling the vehicle to act as a unified intelligent agent, comprehensively utilizing local and neighbor information for joint optimization, achieving a system-wide performance improvement from physical resources to network efficiency, and laying the foundation for building a truly intelligent and resilient next-generation vehicle network.

[0070] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0071] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0072] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0073] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0074] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory, characterized in that, Comprising: S1: receiving SOTFS signals from neighbor vehicles, and estimating channel state information and Doppler information based on the signals; S2: calculating resonance scores between the vehicle and the neighbor vehicles based on the Doppler information and the channel state information; S3: updating pheromone memory for evaluating stability of communication links with the neighbor vehicles based on the resonance scores; S4: determining an optimized resource mapping scheme based on the resonance scores and the pheromone memory; S5: feeding back the optimized resource mapping scheme to a transmitting end; S6: applying the optimized resource mapping scheme at the transmitting end to generate and transmit adaptive SOTFS signals.

2. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 1, characterized in that, In step S1, the result of the channel estimation is a delay-Doppler channel response, and the result of the Doppler estimation is a Doppler centroid.

3. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 2, characterized in that, the resonance score is calculated based on the following formula: wherein: is a Doppler similarity term, is an SOTFS channel reliability term based on the delay-Doppler channel response, which is a function of channel signal-to-noise ratio, peak-to-average power ratio, and delay spread; is a trajectory coherence term based on predicted coordinates, is a weighting coefficient.

4. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 3, the Doppler similarity term is calculated by the formula: wherein, and Doppler centroid of the own vehicle and the neighbor vehicle, respectively, is a scaling factor.

5. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 3, characterized in that, The trajectory coherence term The formula for calculating is: wherein, and are the positions of the ego vehicle and the neighbor vehicle at a future time instant predicted based on the sensor information, respectively, is a reference distance.

6. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 1, characterized in that, In step S3, the updating of the pheromone memory follows the evaporation and reinforcement model, and the updating formula of the pheromone memory strength is: wherein, is an evaporation coefficient located in the interval (0, 1), is an enhancement strength coefficient, is an enhancement term based on the current link state.

7. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 6, characterized in that, the enhancement The calculation formula is: wherein, is a weighting factor, is a line-of-sight communication probability.

8. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 1, characterized in that, In step S4, the optimized resource mapping scheme is an optimal subcarrier mapping which is determined by solving the following optimization problem: wherein, is a mapping fitness function, and are the current Doppler state and acceleration state, respectively.

9. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 8, characterized in that, The mapping fitness function is defined as: in, For a specific mapping scheme The expected Doppler, For the currently estimated Doppler state, This is for adjusting the coefficient.

10. The SOTFS modulated V2V communication method based on bionic swarm resonance and pheromone memory according to claim 1, characterized in that, In step S6, the generation of the adaptive SOTFS signals specifically comprises: converting delay-Doppler domain symbols arranged according to the optimized resource mapping scheme and embedded with digital pheromone signatures into time domain waveforms through inverse discrete ZAK transform modulation and transmitting, the adaptive SOTFS signals carrying digital pheromone signatures.

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