Channel access method and device for vehicles in cognitive Internet of Vehicles, equipment and medium

Abstract

The invention provides a channel access method, device and equipment for vehicles in cognitive Internet of Vehicles and a medium, and belongs to the technical field of communication, the method is applied to cognitive vehicles, and the method comprises the following steps: detecting an occupation condition of a target channel at a current moment, the target channel is a channel which is determined by the cognitive vehicle based on a reinforcement learning model and is selected to access at the current moment; under the condition that the target channel is occupied by other vehicles, whether the communication distance between the cognitive vehicle and a base station associated with the target channel is larger than a set distance threshold value or not is judged, and the other vehicles comprise at least one of other cognitive vehicles and authorized vehicles; and under the condition that the communication distance between the cognitive vehicle and the base station associated with the target channel is not greater than the set distance threshold value, the cognitive vehicle accesses the target channel.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a channel access method, device, equipment and medium for vehicles in a cognitive Internet of Vehicles. Background Art

[0002] At present, the research on channel access of vehicles in cognitive Internet of Vehicles mainly focuses on the situation where a single vehicle accesses a single channel. For example, whether a vehicle can access the channel is determined based on whether the channel is idle.

[0003] However, with the increasing number of vehicles and considering the scarcity of spectrum resources, the traditional channel access method that limits a channel to only one vehicle at a time has a low utilization rate of spectrum resources, which makes it difficult for cognitive vehicle networks to meet the communication needs between vehicles under limited spectrum resources. Summary of the invention

[0004] In view of the above problems, embodiments of the present application provide a channel access method, apparatus, device and medium for vehicles in a cognitive Internet of Vehicles, so as to overcome the above problems or at least partially solve the above problems.

[0005] In a first aspect of an embodiment of the present application, a channel access method for a vehicle in a cognitive Internet of Vehicles is provided, which is applied to a cognitive vehicle, and the method includes: Detecting the occupancy of a target channel at a current moment, the target channel being: a channel selected for access at a current moment determined by the cognitive vehicle based on a reinforcement learning model; In the case where the target channel is occupied by other vehicles, determining whether the communication distance between the cognitive vehicle and the base station associated with the target channel is greater than a set distance threshold, the other vehicles including: at least one of other cognitive vehicles and authorized vehicles; When the communication distance between the cognitive vehicle and the base station associated with the target channel is not greater than the set distance threshold, the cognitive vehicle accesses the target channel.

[0006] According to a second aspect of an embodiment of the present application, a channel access device for a vehicle in a cognitive Internet of Vehicles is provided, which is applied to a cognitive vehicle, and the device includes: A first detection module is used to detect the occupancy of a target channel at a current moment, where the target channel is: a channel selected by the cognitive vehicle to access at a current moment; A first judgment module is used to judge whether the communication distance between the cognitive vehicle and the base station associated with the target channel is greater than a set distance threshold when the target channel is occupied by other vehicles, and the other vehicles include: at least one of other cognitive vehicles and authorized vehicles; The first access module is used to enable the cognitive vehicle to access the target channel when the communication distance between the cognitive vehicle and the base station associated with the target channel is not greater than the set distance threshold.

[0007] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the channel access method for a vehicle in a cognitive Internet of Vehicles described in the first aspect are implemented.

[0008] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the channel access method for a vehicle in a cognitive Internet of Vehicles as described in the first aspect are implemented.

[0009] The embodiments of the present application include the following advantages: considering the influence of the communication distance between the vehicle and the base station on the success rate and communication quality of channel access, the present application compares the communication distance between the cognitive vehicle and the base station associated with the target channel with a set distance threshold, and enables the cognitive vehicle to access the channel occupied by other vehicles when the communication distance is not greater than the set distance threshold. Thus, while ensuring the success rate and communication quality of the cognitive vehicle access channel, a channel can be accessed by multiple vehicles at the same time point, thereby improving the utilization rate of spectrum resources, and enabling the cognitive vehicle network to meet the communication needs between vehicles with limited spectrum resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0011] Figure 1 It is a flow chart of an implementation method of a channel access method of a vehicle in a cognitive Internet of Vehicles in an embodiment of the present application; Figure 2 It is a schematic diagram of a process for implementing dynamic spectrum access of multiple vehicles in an embodiment of the present application; Figure 3 is a schematic diagram of the interaction between a vehicle and a cognitive Internet of Vehicles communication environment in an embodiment of the present application; Figure 4 is a schematic diagram of a cognitive vehicle networking communication scenario in an embodiment of the present application; Figure 5It is a structural schematic diagram of a channel access device for a vehicle in a cognitive Internet of Vehicles in an embodiment of the present application; Figure 6 It is a schematic diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0012] To facilitate understanding of the technical solutions provided by the present application, the main technical concepts involved in the embodiments of the present application are briefly described below.

[0013] Frequency band: A specific frequency range of radio waves.

[0014] Channel: A further subdivision within a given frequency band. For example, it can refer to a specific frequency used to transmit data within a given frequency band.

[0015] Authorized vehicles: have priority access to and use channels associated with the authorized frequency band for data transmission.

[0016] Cognitive vehicle: Based on the dynamic spectrum access method, it dynamically accesses and uses relevant channels for data transmission. For example, the cognitive vehicle senses the channel status in real time and chooses to access the channel associated with the frequency band currently not occupied by authorized vehicles for data transmission.

[0017] In combination with the accompanying drawings, a channel access method, device, equipment and medium for a vehicle in a cognitive Internet of Vehicles provided by an embodiment of the present application are described in detail through some embodiments and their application scenarios.

[0018] First, refer to Figure 1 As shown, it is a flow chart of an implementation of a channel access method for a vehicle in a cognitive Internet of Vehicles provided in an embodiment of the present application. The channel access method for a vehicle in a cognitive Internet of Vehicles is applied to a cognitive vehicle. The method may include the following steps: Step S11: Detecting the occupancy of a target channel at the current moment, wherein the target channel is a channel selected to be accessed at the current moment determined by the cognitive vehicle based on a reinforcement learning model.

[0019] In specific implementation, the cognitive vehicle can take the maximization of performance indicators such as throughput as the optimization goal, and determine the channel (i.e., the target channel) to be selected for access at the current moment based on the reinforcement learning model. After determining the target channel, the occupancy of the target channel at the current moment is detected, that is, whether the target channel is idle. If the target channel is idle, it means that the target channel is not occupied by other vehicles; if the target channel is not idle, it means that the target channel is occupied by other vehicles.

[0020] Step S12: When the target channel is occupied by other vehicles, determine whether the communication distance between the cognitive vehicle and the base station associated with the target channel is greater than a set distance threshold, and the other vehicles include: at least one of other cognitive vehicles and authorized vehicles.

[0021] In a specific implementation, when the target channel is occupied by other vehicles (other cognitive vehicles and / or authorized vehicles), the cognitive vehicle determines the communication distance between itself and the base station associated with the target channel at the current moment based on relevant ranging technologies such as wireless pulse ranging technology, and then determines whether the communication distance between the cognitive vehicle and the base station associated with the target channel is greater than a set distance threshold.

[0022] Step S13: When the communication distance between the cognitive vehicle and the base station associated with the target channel is not greater than the set distance threshold, the cognitive vehicle accesses the target channel.

[0023] In this embodiment, the present application takes into account that in an actual communication scenario, if the cognitive vehicle is far away from the base station, the probability of it accessing the channel associated with the base station is very small or even impossible to access (that is, the success rate of channel access is low), and in this case, even if the cognitive vehicle accesses the channel, the communication quality of the cognitive vehicle will be very poor, and it will also interfere with the communication quality of other vehicles accessing the same channel.

[0024] In order to solve the aforementioned problem, the present application introduces a set distance threshold to limit the channel accessed by the cognitive vehicle by limiting the communication distance between the cognitive vehicle and the base station. By allowing the cognitive vehicle to access the channel occupied by other vehicles when the communication distance between the cognitive vehicle and the relevant base station is not greater than the set distance threshold, the success rate and communication quality of the cognitive vehicle's access to the channel can be improved, and a channel can be accessed by multiple vehicles at the same time point, thereby improving the utilization rate of spectrum resources.

[0025] As a possible implementation, when the target channel is not occupied by other vehicles, the cognitive vehicle can directly access the target channel to further improve the utilization of spectrum resources because no interference will occur between multiple vehicles accessing the same channel.

[0026] By adopting the technical solution of the embodiment of the present application, taking into account the influence of the communication distance between the vehicle and the base station on the success rate of channel access and the communication quality, the present application compares the communication distance between the cognitive vehicle and the base station associated with the target channel with the set distance threshold, and enables the cognitive vehicle to access the channel occupied by other vehicles when the communication distance is not greater than the set distance threshold. In this way, while ensuring the success rate of the cognitive vehicle accessing the channel and the communication quality, a channel can be accessed by multiple vehicles at the same time point, thereby improving the utilization rate of spectrum resources, and enabling the cognitive vehicle network to meet the communication needs between vehicles under limited spectrum resources.

[0027] As a possible implementation manner, before detecting the occupancy status of the target channel at the current moment, the method further includes: Acquire the state parameters of each channel at the current moment, the state parameters including: channel gain, duration of the cognitive vehicle transmitting data, and communication distances between each vehicle associated with the channel and between each vehicle and a base station associated with the channel; Taking the overall maximization of the total throughput associated with each cognitive vehicle including the cognitive vehicle as an optimization goal, determining the action parameters of the cognitive vehicle at the current moment through the reinforcement learning model according to the state parameters at the current moment, wherein the action parameters are used to characterize whether the cognitive vehicle selects to access a channel and the channel selected to access when selecting to access a channel; The total throughput associated with a single cognitive vehicle includes: the total throughput of the single cognitive vehicle and other vehicles (such as authorized vehicles and / or other cognitive vehicles) accessing the channel selected by the single cognitive vehicle.

[0028] In specific implementation, refer to Figure 2 The schematic diagram of the implementation process of dynamic spectrum access for multiple vehicles shown in the figure pre-establishes a cognitive vehicle network communication model including each base station, each authorized vehicle, each cognitive vehicle and each channel, and determines the coordinates of the vehicle based on the location information of each vehicle. Then, the above-mentioned optimization target is established, and the relevant indicators are calculated to determine the state parameters. Subsequently, the above-mentioned set distance threshold is combined with the reinforcement learning technology and applied to the cognitive vehicle network communication model to realize dynamic spectrum access for multiple vehicles.

[0029] It should be noted that the framework of the reinforcement learning model will consist of two parts: the vehicle and the cognitive Internet of Vehicles communication environment, which can interact and act on each other. Figure 3The schematic diagram of the interaction between the vehicle and the cognitive vehicle network communication environment shown in FIG. 1 shows that at the tth moment, the nth cognitive vehicle needs to select a channel to access. The nth cognitive vehicle first obtains the current state parameters of each channel in the cognitive vehicle network communication environment. ,in, represents the channel gain, represents the duration of the nth cognitive vehicle transmitting data, Represents the communication distance between each vehicle (including the nth cognitive vehicle) associated with the channel and between each vehicle and the base station associated with the channel.

[0030] The cognitive vehicle obtains the state parameters corresponding to each channel at present. Input to the reinforcement learning model, the reinforcement learning model is based on the input state parameters and the optimization goal according to the strategy Determine action parameters The cognitive vehicle then outputs the action parameters based on the reinforcement learning model. Determine whether to select an access channel, and if so, determine the selected access channel.

[0031] Among them, the action parameters ;Strategy For the state space and action space The related mapping function describes the mapping relationship between state parameters and action parameters. The reinforcement learning model will continuously learn (i.e. update the strategy) based on the interaction between the vehicle and the environment. ), to ensure that it can determine the optimal action parameters for the optimization goal.

[0032] Exemplarily, the action parameter It can be expressed as follows:

[0033] in, , W represents the number of channels in the cognitive vehicle network communication model, Equal to 0 means not selecting any channel access. Not equal to 0 means selecting access channel and selecting access channels.

[0034] It is understandable that at different time periods, each cognitive vehicle will take different actions (characterized by action parameters), and all possible actions constitute the action space , so in the above example the action space Size After the cognitive vehicle performs a channel access operation based on the action parameters (such as choosing not to access a channel or choosing to access a certain channel), the channel access operation will interact and act with the environment, such as changing the state parameters related to the environment from (corresponding to the environmental state at the tth moment) becomes (corresponding to the state of the environment at the t+1th moment). The reinforcement learning model will accordingly convert the reward at the t+1th moment into Feedback to the cognitive vehicle.

[0035] Subsequently, the interaction and actions between the vehicle and the environment will continue in a similar manner as described above, namely: ,in, represents the state parameters at each moment, Indicates the action parameters at each moment, Represents the reward value at each moment.

[0036] In this implementation mode, the present application improves the optimization target in order to identify the impact of the channel access action of the cognitive vehicle on the overall throughput of each vehicle rather than just identifying the impact on the throughput associated with a single cognitive vehicle (i.e., avoiding the cognitive vehicle from interfering with the throughput optimization of other cognitive vehicles in order to maximize its own associated throughput). Specifically, the overall maximization of the sum of the throughput associated with each cognitive vehicle including the cognitive vehicle (such as maximization of the average value or maximization of the weighted sum, etc.) is taken as the optimization target, and then the reinforcement learning model is used to determine the globally optimal action parameters based on the optimization target, thereby optimizing the overall throughput of each vehicle in the cognitive Internet of Vehicles communication scenario (or the cognitive Internet of Vehicles communication model established based on the scenario).

[0037] As a possible implementation manner, the channel gain in the state parameter includes: the channel gain of the useful signal of the cognitive vehicle, the channel gain of the useful signal of other vehicles accessing the channel selected by the cognitive vehicle, and the channel gain of the interference signal from any one of the cognitive vehicle and other vehicles accessing the channel selected by the cognitive vehicle to another vehicle; The channel gain of the useful signal of a single vehicle is determined by the following formula:

[0038] in, represents the channel gain of the useful signal of the single vehicle, represents the transmission power of the single vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the communication distance between the single vehicle and the base station associated with the channel, Represents the path loss exponent, usually between 2 and 4; The channel gain of the interference signal from the mth vehicle to the nth vehicle among the cognitive vehicle and other vehicles accessing the channel selected by the cognitive vehicle is determined by the following formula:

[0039] in, represents the channel gain of the interference signal from the mth vehicle to the nth vehicle, represents the transmission power of the mth vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the communication distance between the mth vehicle and the nth vehicle, Represents the path loss exponent, usually between 2 and 4.

[0040] In this embodiment, based on the Rayleigh fading model, the channel gains of the cognitive vehicle itself and other vehicles connected to the channel selected by the cognitive vehicle are determined, and the determined channel gains are used as the channel gains required to be included in the state parameters, so that the reinforcement learning model can more accurately analyze the transmission capacity of each vehicle-related signal in the channel based on the channel gains included in the input state parameters, thereby helping to improve the optimization effect of the reinforcement learning model on the overall throughput.

[0041] Optionally, taking the overall maximization of the sum of the throughputs associated with each cognitive vehicle including the cognitive vehicle as the optimization target includes: taking the average value of the cumulative discount reward values ​​of each cognitive vehicle including the cognitive vehicle as the optimization target, and the cumulative discount reward value of a single cognitive vehicle is determined by the following formula:

[0042] in, represents the cumulative discount reward value of the single cognitive vehicle; represents the discount factor corresponding to the t-1th moment; represents the reward value of the single cognitive vehicle at the t+1th moment, and the reward value is determined according to the sum of the throughputs associated with the single cognitive vehicle at the t+1th moment.

[0043] It is understandable that in the cognitive vehicle network communication scenario, the environment has no terminal state, and the total reward (also called cumulative reward or long-term reward) will be infinite, so the above moment ). On this basis, this application introduces a discount factor To flexibly control the weight of long-term rewards, ,when When it is close to 1, the reinforcement learning model pays more attention to long-term rewards, and when As it approaches 0, the current reward becomes more important in the total reward.

[0044] In specific implementation, the present application maps the sum of the throughput between the single cognitive vehicle and other vehicles connected to the channel selected by the single cognitive vehicle at a single moment to the reward value (also called the instant reward value) obtained by the cognitive vehicle at the single moment. In addition, in addition to the instant reward, the long-term cumulative reward should also be considered, because only by ensuring the stability of the long-term reward in the cognitive vehicle network communication scenario can the long-term optimization effect of the overall throughput be ensured. Therefore, the present application takes the maximization of the average value of the cumulative discounted reward value of each cognitive vehicle including the cognitive vehicle as the optimization goal, so as to ensure the stability of the long-term reward by optimizing the average cumulative reward of the cognitive vehicle.

[0045] It should be noted that the reinforcement learning model mainly controls the performance of the system by designing a reward function. In the embodiment of the present application, the reinforcement learning model aims to learn a strategy to maximize the expected cumulative discounted reward value.

[0046] Specifically, the reinforcement learning model is implemented by Value Optimization Strategy ,and The value and the state parameter obtained by the cognitive vehicle at the current moment (i.e., the tth moment) and action parameters is closely related and can be written as . The value can be approximately equal to the state parameter Based on the action parameters Expected total reward for a cognitive vehicle performing a channel access maneuver.

[0047] The reinforcement learning model chooses Update the strategy with the action parameter with the largest value Then update it with the new strategy value, and repeat this process until The value converges to the optimal value Once you get , we can find the optimal strategy . For example, The iteration formula of the value can be expressed as follows:

[0048] in, represents the learning rate, represents the discount factor, Indicates the maximum value at the next moment (i.e., at the t+1th moment) value.

[0049] In specific implementation, you can use Value table to store the obtained value, The size of the value table is , represents the size of the action space, represents the size of the state space. As the state-action space increases, The size of the value table will increase significantly. In the channel access problem of multiple cognitive vehicles communicating, the state space is large and uncertain, so it is necessary to allow the cognitive vehicle and the environment to fully interact to obtain the most accurate state.

[0050] As a possible implementation method, in order to make the cognitive vehicle fully understand the channel occupancy in the cognitive vehicle network communication scenario (that is, to make the cognitive vehicle and the environment fully interact), a greedy algorithm can be used to make the cognitive vehicle select The probability of the action parameter with the largest value is , and with probability From the action space The reinforcement learning model then selects the action parameters randomly at each moment based on the selection method of the action parameters mentioned above. The value and strategy are iteratively updated to gradually converge to the optimal strategy.

[0051] Exemplarily, based on the above-mentioned selection method of action parameters, the action parameters at the t+1th moment can be determined by the following formula:

[0052] in, Indicates that the state parameter Next Select The action parameter with the largest value , is a decimal number between 0 and 1. The corresponding probability is situation, The corresponding probability is situation, Represents the action space The action parameters are randomly selected in .

[0053] As a possible implementation, the reward value of the single cognitive vehicle at a single moment is determined by the following steps: When the single cognitive vehicle selects to access a channel at the single moment, and the communication distance between the single cognitive vehicle and a base station associated with the selected access channel is not greater than the set distance threshold, a reward value of the single cognitive vehicle at the single moment is determined by a first reward function, and the first reward function is expressed as follows:

[0054] When the single cognitive vehicle selects to access a channel at the single moment, and the communication distance between the single cognitive vehicle and a base station associated with the selected access channel is greater than the set distance threshold, a reward value of the single cognitive vehicle at the single moment is determined by a second reward function, and the second reward function is expressed as follows:

[0055] In the case where the single cognitive vehicle does not select an access channel at the single moment, a reward value of the single cognitive vehicle at the single moment is determined by a third reward function, and the third reward function is expressed as follows:

[0056] in, represents the reward value of the single cognitive vehicle at the single moment, represents the throughput of the single cognitive vehicle at the single moment, represents the throughput of other vehicles accessing the channel selected by the single cognitive vehicle at the single moment.

[0057] In this embodiment, the present application uses whether the cognitive vehicle selects to access a channel and whether the channel selected to access satisfies the distance constraint (i.e., whether the communication distance between the associated base station and the cognitive vehicle is not greater than the set distance threshold) as the basis for situation classification, and designs reward functions corresponding to the above-mentioned different situations in combination with the sum of throughputs associated with the single cognitive vehicle at a single moment (i.e., the sum of throughputs between the single cognitive vehicle and other vehicles accessing the channel selected to access by the single cognitive vehicle at a single moment).

[0058] It can be understood that the reinforcement learning model is improved based on the reward functions corresponding to the above different situations, that is, the reinforcement learning model determines the instantaneous reward at each moment according to the improved reward function (that is, the first reward function, the second reward function and the third reward function), and then obtains the reward for updating the optimization strategy. The total reward (i.e. value), so that the improved reinforcement learning model can take the overall throughput as the optimization goal under the premise of satisfying the distance constraint, continuously update the strategy and output the globally optimal action parameters, thereby achieving long-term optimization of the overall throughput, channel access success rate and communication quality of each cognitive vehicle in the cognitive Internet of Vehicles communication scenario.

[0059] For example, refer to Figure 4 The schematic diagram of the cognitive Internet of Vehicles communication scenario shown in the figure includes a situation where an authorized vehicle and a cognitive vehicle simultaneously access the same channel for communication, and the corresponding cognitive Internet of Vehicles communication model includes: 1 base station, W authorized vehicles, L cognitive vehicles and W channels.

[0060] In the cognitive vehicle networking communication model, Authorized vehicles and A cognitive vehicle is connected to the Take the target channel selected by a cognitive vehicle as an example. Respectively Authorized vehicles, cognitive vehicle and The throughput of the cognitive vehicle at the current time t, then The reward function value obtained by the cognitive vehicle at the current time t It is related to the following two situations: Case 1: When trying to access the target channel If the communication distance between a cognitive vehicle and the relevant base station is not greater than the set distance threshold, it can be considered that the cognitive vehicle successfully accesses the channel. The reward function value obtained by each cognitive vehicle at the current time t is determined by the first reward function to generate positive feedback, that is:

[0061] Case 2: When trying to access the target channel When the communication distance between a cognitive vehicle and the relevant base station is greater than the set distance threshold, it can be considered that the cognitive vehicle fails to access the channel. The reward function value obtained by each cognitive vehicle at the current time t is determined by the second reward function to generate negative feedback, that is:

[0062] It is understandable that if The action parameters of the cognitive vehicle based on the output of the reinforcement learning model do not select any channel access at the current moment. The reward function value obtained by a cognitive vehicle at the current time t is associated with the following situation 3: Case 3: If The first cognitive vehicle does not select any channel to access. The reward function value obtained by each cognitive vehicle at the current time t is determined by the third reward function, that is:

[0063] Furthermore, the The cumulative discount reward value obtained by each cognitive vehicle can be determined by the following formula:

[0064] in, Indicates the The cumulative discount reward value of each cognitive vehicle; represents the discount factor corresponding to the t-1th moment; Indicates the A reward value of a cognitive vehicle at the t+1th moment, wherein the reward value is determined using the improved reward function (ie, the first reward function, the second reward function, and the third reward function).

[0065] Optionally, the throughput of a single vehicle is determined by the following steps: In the case where the single vehicle is an authorized vehicle, determining the signal to interference noise ratio of the single vehicle by using a first formula; In the case where the single vehicle is a cognitive vehicle, determining a signal to interference noise ratio of the single vehicle by a second formula; Determine the throughput of the single vehicle according to the signal to interference noise ratio of the single vehicle and the bandwidth of the channel. For example, the throughput of the single vehicle may be determined according to the Shannon theorem. The first formula is expressed as follows:

[0066] in, represents the signal-to-interference-noise ratio of the authorized vehicle, represents the transmission power of the authorized vehicle, represents the channel gain of the useful signal of the authorized vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the total interference caused to the authorized vehicle by all cognitive vehicles accessing the same channel as the authorized vehicle, and N1 represents the number of all cognitive vehicles accessing the same channel as the authorized vehicle; The second formula is expressed as follows:

[0067] in, represents the signal-to-interference-noise ratio of the cognitive vehicle, represents the transmission power of the cognitive vehicle, represents the channel gain of the useful signal of the cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the transmission power of the authorized vehicle accessing the same channel as the cognitive vehicle, represents the channel gain of the interference signal from the authorized vehicle to the cognitive vehicle, represents the total interference caused to the cognitive vehicle by other cognitive vehicles accessing the same channel as the cognitive vehicle, and N2 represents the number of all cognitive vehicles accessing the channel.

[0068] For example, the above Figure 4 Taking the cognitive vehicle networking communication model corresponding to the cognitive vehicle networking communication scenario shown in FIG. 1 as an example, the acquisition method of the above-mentioned state parameters and throughput is illustrated. The cognitive vehicle networking communication model includes: 1 base station, Authorized vehicles, cognitive vehicles and channels.

[0069] The first part of cognitive vehicle networking communication model Authorized vehicles and A cognitive vehicle is connected to the The target channel selected by the cognitive vehicle is The state parameters of each channel for a cognitive vehicle can be expressed as follows:

[0070] in, represents the channel gain at the current time t; represents each vehicle at the current time t (i.e. Authorized vehicles, cognitive vehicle and The communication distance between each cognitive vehicle) and between each vehicle and a base station associated with the target channel; Indicates The duration of data transmission by a cognitive vehicle.

[0071] It is understandable that at different time periods, the environmental state observed by each cognitive vehicle (characterized by state parameters) will be different, and these possible states constitute the state space , in this example the state space size is .

[0072] for , which is based on The location coordinates of the authorized vehicles , No. The position coordinates of the cognitive vehicle and The position coordinates of the cognitive vehicle The calculation is as follows: At the current time t The communication distance between the authorized vehicle and the base station associated with the target channel ( ) can be expressed as follows:

[0073] At the current time t The communication distance between the cognitive vehicle and the base station associated with the target channel ( ) can be expressed as follows:

[0074] At the current time t Authorized vehicle and The communication distance between cognitive vehicles ( ) can be expressed as follows:

[0075] At the current time t The communication distance between the cognitive vehicle and the base station associated with the target channel ( ) can be expressed as follows:

[0076] At the current time t Authorized vehicle and The communication distance between cognitive vehicles ( ) can be expressed as follows:

[0077] At the current time t A cognitive vehicle and The communication distance between cognitive vehicles ( ) can be expressed as follows:

[0078] for , which contains the current time t Channel gain of the useful signal of the authorized vehicle It can be expressed as follows:

[0079] in, Indicates The transmission power of each authorized vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates The communication distance between an authorized vehicle and the base station associated with the channel, Represents the path loss exponent.

[0080] for , which contains the current time t Channel gain of the useful signal of a cognitive vehicle It can be expressed as follows:

[0081] in, Indicates The transmission power of each cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates The communication distance between a cognitive vehicle and the base station associated with the channel, Represents the path loss exponent.

[0082] for , which contains the current time t Authorized vehicles to Channel gain of interference signals from cognitive vehicles It can be expressed as follows:

[0083] in, Indicates The transmission power of each authorized vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates Authorized vehicle and The communication distance between cognitive vehicles, Represents the path loss exponent.

[0084] for , which contains the current time t Channel gain of the useful signal of a cognitive vehicle It can be expressed as follows:

[0085] in, Indicates The transmission power of each cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates The communication distance between a cognitive vehicle and the base station associated with the channel, Represents the path loss exponent.

[0086] for , which contains the current time t Authorized vehicles to Channel gain of interference signals from cognitive vehicles It can be expressed as follows:

[0087] in, Indicates The transmission power of each authorized vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates Authorized vehicle and The communication distance between cognitive vehicles, Represents the path loss exponent.

[0088] for , which contains the current time t Cognitive vehicles to The channel gain of the interference signal of the authorized vehicle It can be expressed as follows:

[0089] in, Indicates The transmission power of each cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates Authorized vehicle and The communication distance between cognitive vehicles, Represents the path loss exponent.

[0090] for , which contains the current time t Cognitive vehicles to Channel gain of interference signals from cognitive vehicles It can be expressed as follows:

[0091] in, Indicates The transmission power of each cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates Authorized vehicle and The communication distance between cognitive vehicles, Represents the path loss exponent.

[0092] for , which contains the current time t Cognitive vehicles to Channel gain of interference signals from cognitive vehicles It can be expressed as follows:

[0093] in, Indicates The transmission power of each cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, Indicates A cognitive vehicle and The communication distance between cognitive vehicles, Represents the path loss exponent.

[0094] Furthermore, according to Shannon's theorem, Throughput of authorized vehicles , No. Throughput of cognitive vehicles and Throughput of cognitive vehicles It can be expressed as follows:

[0095]

[0096]

[0097] in, Indicates the bandwidth of the channel; The signal-to-interference-noise ratio of the authorized vehicle , No. Signal-to-Interference-Noise Ratio of a Cognitive Vehicle and Signal-to-Interference-Noise Ratio of a Cognitive Vehicle It can be expressed as follows:

[0098]

[0099]

[0100] in, Respectively represent Authorized vehicles, A cognitive car, The transmission power of each cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel. All cognitive vehicles connected to the current channel have Total interference caused by authorized vehicles , other cognitive vehicles connected to the current channel Total interference caused by cognitive vehicles , other cognitive vehicles connected to the current channel Total interference caused by cognitive vehicles , Respectively represent Authorized vehicles, A cognitive car, The channel gain of the useful signal of the cognitive vehicle, Indicates Authorized vehicles to The channel gain of the interference signal of the cognitive vehicle, Indicates Authorized vehicles to The channel gain of the interference signal of the cognitive vehicle, Indicates Cognitive vehicles to The channel gain of the interference signal of the authorized vehicle, Indicates Cognitive vehicles to The channel gain of the interference signal of the authorized vehicle, Indicates Cognitive vehicles to The channel gain of the interference signal of the cognitive vehicle, Indicates Cognitive vehicles to The channel gain of the interference signal of a cognitive vehicle.

[0101] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the order of the actions described, because according to the embodiments of the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0102] Second, Figure 5 1 is a schematic diagram of a structure of a channel access device for a vehicle in a cognitive Internet of Vehicles embodiment of the present application. The channel access device for a vehicle in a cognitive Internet of Vehicles is applied to a cognitive vehicle, and the device includes: The first detection module 100 is used to detect the occupancy of the target channel at the current moment, where the target channel is: the channel selected by the cognitive vehicle to access at the current moment; The first judgment module 200 is used to judge whether the communication distance between the cognitive vehicle and the base station associated with the target channel is greater than a set distance threshold when the target channel is occupied by other vehicles, and the other vehicles include: at least one of other cognitive vehicles and authorized vehicles; The first access module 300 is configured to enable the cognitive vehicle to access the target channel when the communication distance between the cognitive vehicle and the base station associated with the target channel is not greater than the set distance threshold.

[0103] By adopting the technical solution of the embodiment of the present application, taking into account the influence of the communication distance between the vehicle and the base station on the success rate of channel access and the communication quality, the present application compares the communication distance between the cognitive vehicle and the base station associated with the target channel with the set distance threshold, and enables the cognitive vehicle to access the channel occupied by other vehicles when the communication distance is not greater than the set distance threshold. In this way, while ensuring the success rate of the cognitive vehicle accessing the channel and the communication quality, a channel can be accessed by multiple vehicles at the same time point, thereby improving the utilization rate of spectrum resources, and enabling the cognitive vehicle network to meet the communication needs between vehicles under limited spectrum resources.

[0104] Optionally, the device further comprises: The parameter acquisition module 410 is used to respectively acquire the state parameters of each channel at the current moment, wherein the state parameters include: channel gain, duration of the cognitive vehicle transmitting data, and communication distances between each vehicle associated with the channel and between each vehicle and the base station associated with the channel; A model decision module 420 is used to take the overall maximization of the total throughput associated with each cognitive vehicle including the cognitive vehicle as an optimization goal, and determine the action parameters of the cognitive vehicle at the current moment through the reinforcement learning model according to the state parameters at the current moment, wherein the action parameters are used to characterize whether the cognitive vehicle selects to access a channel, and the channel selected to access when selecting to access a channel; The total throughput associated with a single cognitive vehicle includes: the total throughput of the single cognitive vehicle and other vehicles accessing the channel selected by the single cognitive vehicle to access.

[0105] Optionally, the model decision module 420 is further used to: maximize the average value of the cumulative discount reward values ​​of each cognitive vehicle including the cognitive vehicle as the optimization target, and the cumulative discount reward value of a single cognitive vehicle is determined by the following formula:

[0106] in, represents the cumulative discount reward value of the single cognitive vehicle; represents the discount factor corresponding to the t-1th moment; represents the reward value of the single cognitive vehicle at the t+1th moment, and the reward value is determined according to the sum of the throughputs associated with the single cognitive vehicle at the t+1th moment.

[0107] Optionally, the model decision module 420 is further configured to perform the following steps: When the single cognitive vehicle selects to access a channel at the single moment, and the communication distance between the single cognitive vehicle and a base station associated with the selected access channel is not greater than the set distance threshold, a reward value of the single cognitive vehicle at the single moment is determined by a first reward function, and the first reward function is expressed as follows:

[0108] When the single cognitive vehicle selects to access a channel at the single moment, and the communication distance between the single cognitive vehicle and a base station associated with the selected access channel is greater than the set distance threshold, a reward value of the single cognitive vehicle at the single moment is determined by a second reward function, and the second reward function is expressed as follows:

[0109] In the case where the single cognitive vehicle does not select an access channel at the single moment, a reward value of the single cognitive vehicle at the single moment is determined by a third reward function, and the third reward function is expressed as follows:

[0110] in, represents the reward value of the single cognitive vehicle at the single moment, represents the throughput of the single cognitive vehicle at the single moment, represents the throughput of other vehicles accessing the channel selected by the single cognitive vehicle at the single moment.

[0111] Optionally, the channel gain in the state parameter includes: the channel gain of the useful signal of the cognitive vehicle, the channel gain of the useful signal of other vehicles accessing the channel selected by the cognitive vehicle, and the channel gain of the interference signal from any one of the cognitive vehicle and other vehicles accessing the channel selected by the cognitive vehicle to another vehicle; The channel gain of the useful signal of a single vehicle is determined by the following formula:

[0112] in, represents the channel gain of the useful signal of the single vehicle, represents the transmission power of the single vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the communication distance between the single vehicle and the base station associated with the channel, represents the path loss exponent; The channel gain of the interference signal from the mth vehicle to the nth vehicle among the cognitive vehicle and other vehicles accessing the channel selected by the cognitive vehicle is determined by the following formula:

[0113] in, represents the channel gain of the interference signal from the mth vehicle to the nth vehicle, represents the transmission power of the mth vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the communication distance between the mth vehicle and the nth vehicle, Represents the path loss exponent.

[0114] Optionally, the device further comprises: A first processing module 510, configured to determine the signal to interference noise ratio of the single vehicle by using a first formula when the single vehicle is an authorized vehicle; A second processing module 520, configured to determine the signal to interference and noise ratio of the single vehicle by using a second formula when the single vehicle is a cognitive vehicle; A third processing module 530 is used to determine the throughput of the single vehicle according to the signal to interference noise ratio of the single vehicle and the bandwidth of the channel; The first formula is expressed as follows:

[0115] in, represents the signal-to-interference-noise ratio of the authorized vehicle, represents the transmission power of the authorized vehicle, represents the channel gain of the useful signal of the authorized vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the total interference caused to the authorized vehicle by all cognitive vehicles accessing the same channel as the authorized vehicle, and N1 represents the number of all cognitive vehicles accessing the same channel as the authorized vehicle; The second formula is expressed as follows:

[0116] in, represents the signal-to-interference-noise ratio of the cognitive vehicle, represents the transmission power of the cognitive vehicle, represents the channel gain of the useful signal of the cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the transmission power of the authorized vehicle accessing the same channel as the cognitive vehicle, represents the channel gain of the interference signal from the authorized vehicle to the cognitive vehicle, represents the total interference caused to the cognitive vehicle by other cognitive vehicles accessing the same channel as the cognitive vehicle, and N2 represents the number of all cognitive vehicles accessing the channel.

[0117] Optionally, the device further comprises: The second access module 600 is used for the cognitive vehicle to access the target channel when the target channel is not occupied by other vehicles.

[0118] It should be noted that the device embodiment is similar to the method embodiment, so the description is relatively simple, and the relevant parts can be referred to the method embodiment.

[0119] The present application also provides an electronic device, referring to Figure 6 , Figure 6Schematic diagram of an electronic device according to an embodiment of the present application. Figure 6 As shown, the electronic device 100 includes: a memory 110 and a processor 120. The memory 110 and the processor 120 are connected via a bus communication. A computer program is stored in the memory 110. The computer program can be run on the processor 120 to implement the steps in the channel access method of the vehicle in the cognitive Internet of Vehicles disclosed in the embodiment of the present application.

[0120] An embodiment of the present application also provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the channel access method for a vehicle in the cognitive Internet of Vehicles as disclosed in the embodiment of the present application is implemented.

[0121] The embodiment of the present application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the channel access method for a vehicle in the cognitive Internet of Vehicles as disclosed in the embodiment of the present application.

[0122] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0123] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, systems, devices, storage media, and program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0128] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0129] The above is a detailed introduction to the channel access method, device, equipment and medium for vehicles in a cognitive Internet of Vehicles provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A channel access method for a vehicle in a cognitive vehicle network, characterized in that: Applied to cognitive vehicles, the method comprises: Detecting the occupancy of a target channel at a current moment, the target channel being: a channel selected for access at a current moment determined by the cognitive vehicle based on a reinforcement learning model; In the case where the target channel is occupied by other vehicles, determining whether the communication distance between the cognitive vehicle and the base station associated with the target channel is greater than a set distance threshold, the other vehicles including: at least one of other cognitive vehicles and authorized vehicles; When the communication distance between the cognitive vehicle and the base station associated with the target channel is not greater than the set distance threshold, the cognitive vehicle accesses the target channel.

2. The method according to claim 1, characterized in that: Before detecting the occupancy of the target channel at the current moment, the method further includes: Acquire the state parameters of each channel at the current moment, the state parameters including: channel gain, duration of the cognitive vehicle transmitting data, and communication distances between each vehicle associated with the channel and between each vehicle and a base station associated with the channel; Taking the overall maximization of the total throughput associated with each cognitive vehicle including the cognitive vehicle as an optimization goal, determining the action parameters of the cognitive vehicle at the current moment through the reinforcement learning model according to the state parameters at the current moment, wherein the action parameters are used to characterize whether the cognitive vehicle selects to access a channel and the channel selected to access when selecting to access a channel; The total throughput associated with a single cognitive vehicle includes: the total throughput of the single cognitive vehicle and other vehicles accessing the channel selected by the single cognitive vehicle to access.

3. The method according to claim 2, characterized in that The overall maximization of the sum of the throughputs associated with each cognitive vehicle including the cognitive vehicle as the optimization goal includes: maximizing the average value of the cumulative discount reward values ​​of each cognitive vehicle including the cognitive vehicle as the optimization goal, and the cumulative discount reward value of a single cognitive vehicle is determined by the following formula: in, represents the cumulative discount reward value of the single cognitive vehicle; represents the discount factor corresponding to the t-1th moment; represents the reward value of the single cognitive vehicle at the t+1th moment, and the reward value is determined according to the sum of the throughputs associated with the single cognitive vehicle at the t+1th moment.

4. The method according to claim 3, characterized in that The reward value of a single cognitive vehicle at a single moment is determined by the following steps: When the single cognitive vehicle selects to access a channel at the single moment, and the communication distance between the single cognitive vehicle and a base station associated with the selected access channel is not greater than the set distance threshold, a reward value of the single cognitive vehicle at the single moment is determined by a first reward function, and the first reward function is expressed as follows: When the single cognitive vehicle selects to access a channel at the single moment, and the communication distance between the single cognitive vehicle and a base station associated with the selected access channel is greater than the set distance threshold, a reward value of the single cognitive vehicle at the single moment is determined by a second reward function, and the second reward function is expressed as follows: In the case where the single cognitive vehicle does not select an access channel at the single moment, a reward value of the single cognitive vehicle at the single moment is determined by a third reward function, and the third reward function is expressed as follows: in, represents the reward value of the single cognitive vehicle at the single moment, represents the throughput of the single cognitive vehicle at the single moment, represents the throughput of other vehicles accessing the channel selected by the single cognitive vehicle at the single moment.

5. The method according to claim 2, characterized in that: The channel gains in the state parameters include: channel gains of useful signals of the cognitive vehicle, channel gains of useful signals of other vehicles accessing the channel selected by the cognitive vehicle, and channel gains of interference signals from any one of the cognitive vehicle and other vehicles accessing the channel selected by the cognitive vehicle to another vehicle; The channel gain of the useful signal of a single vehicle is determined by the following formula: in, represents the channel gain of the useful signal of the single vehicle, represents the transmission power of the single vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the communication distance between the single vehicle and the base station associated with the channel, represents the path loss exponent; The channel gain of the interference signal from the mth vehicle to the nth vehicle among the cognitive vehicle and other vehicles accessing the channel selected by the cognitive vehicle is determined by the following formula: in, represents the channel gain of the interference signal from the mth vehicle to the nth vehicle, represents the transmission power of the mth vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the communication distance between the mth vehicle and the nth vehicle, Represents the path loss exponent.

6. The method according to claim 2, characterized in that The throughput of a single vehicle is determined by the following steps: In the case where the single vehicle is an authorized vehicle, determining the signal to interference noise ratio of the single vehicle by using a first formula; In the case where the single vehicle is a cognitive vehicle, determining a signal to interference noise ratio of the single vehicle by a second formula; Determining the throughput of the single vehicle according to the signal to interference noise ratio of the single vehicle and the bandwidth of the channel; The first formula is expressed as follows: in, represents the signal-to-interference-noise ratio of the authorized vehicle, represents the transmission power of the authorized vehicle, represents the channel gain of the useful signal of the authorized vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the total interference caused to the authorized vehicle by all cognitive vehicles accessing the same channel as the authorized vehicle, and N1 represents the number of all cognitive vehicles accessing the same channel as the authorized vehicle; The second formula is expressed as follows: in, represents the signal-to-interference-noise ratio of the cognitive vehicle, represents the transmission power of the cognitive vehicle, represents the channel gain of the useful signal of the cognitive vehicle, represents the bandwidth of the channel, represents the power spectral density of the background noise in the channel, represents the transmission power of the authorized vehicle accessing the same channel as the cognitive vehicle, represents the channel gain of the interference signal from the authorized vehicle to the cognitive vehicle, represents the total interference caused to the cognitive vehicle by other cognitive vehicles accessing the same channel as the cognitive vehicle, and N2 represents the number of all cognitive vehicles accessing the channel.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: When the target channel is not occupied by other vehicles, the cognitive vehicle accesses the target channel.

8. A channel access device for a vehicle in a cognitive vehicle network, characterized in that: Applied to cognitive vehicles, the device comprises: A first detection module is used to detect the occupancy of a target channel at a current moment, where the target channel is: a channel selected by the cognitive vehicle to access at a current moment; A first judgment module is used to judge whether the communication distance between the cognitive vehicle and the base station associated with the target channel is greater than a set distance threshold when the target channel is occupied by other vehicles, and the other vehicles include: at least one of other cognitive vehicles and authorized vehicles; The first access module is used to enable the cognitive vehicle to access the target channel when the communication distance between the cognitive vehicle and the base station associated with the target channel is not greater than the set distance threshold.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the channel access method for a vehicle in a cognitive connected vehicle network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the channel access method for a vehicle in a cognitive Internet of Vehicles as described in any one of claims 1 to 7 is implemented.

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