Perceptual link establishment and data transmission time slot allocation method for unmanned aerial vehicle cognitive communication system
Through the drone collaborative spectrum perception and particle swarm optimization algorithm, the problem of vulnerability of traditional communication networks in natural disasters is solved, efficient and reliable wireless communication services are achieved, and emergency communication is supported.
Patent Information
- Application Number
- CN202510404387.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-01
- Publication Date
- 2025-06-10
AI Technical Summary
In scenarios such as natural disasters, traditional ground communication networks are prone to damage, resulting in degradation or interruption of wireless communication services, affecting emergency rescue.
The cognitive communication system for drones is adopted, through multi-UAV cooperative spectrum perception, combined with particle swarm optimization algorithm, the perceived chain building and data transmission time slot allocation are realized, and the cognitive transmission rate is optimized.
It significantly improves the cognitive transmission rate, ensures communication reliability and flexibility in complex electromagnetic environments, and supports effective services in emergency communication scenarios.
Smart Images
Figure CN120129061A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a method for sensing link establishment and data transmission time slot allocation for an unmanned aerial vehicle (UAV) cognitive communication system, belonging to the field of wireless communication technology. Background Art
[0002] For a long time, natural disasters such as earthquakes, hurricanes, and floods have occurred frequently worldwide. Traditional terrestrial communication relies on cellular base stations and other network infrastructures, which are extremely vulnerable to damage in natural disasters, resulting in a decline or even interruption in the performance of wireless communication services in the affected areas, thus seriously affecting the process of emergency rescue. In recent years, with the rapid development of the UAV field, UAV communication technology has received extensive attention from the academic and industrial circles at home and abroad due to its unique advantages of low cost and high mobility. In addition, when natural disasters or large-scale gatherings occur, the electromagnetic environment is often complex. Spectrum sensing technology can monitor the electromagnetic environment in the affected areas in real time, improving the utilization rate of spectrum resources while minimizing interference to the terrestrial network. By combining UAV technology with spectrum sensing technology to conduct UAV cognitive communication based on cooperative spectrum sensing, the high mobility and line-of-sight characteristics of UAVs can be fully utilized to provide flexible and reliable communication services for terrestrial users, which is of great significance for emergency communication scenarios. Summary of the Invention
[0003] The present invention discloses a method for sensing link establishment and data transmission time slot allocation for an unmanned aerial vehicle (UAV) cognitive communication system. The allocation system adopted by the method includes M UAVs, a primary user, a cognitive user, and a fusion center. Considering that all nodes are equipped with a single transmitting antenna and a single receiving antenna, let represent the set of all UAVs.
[0004] The method includes the following steps:
[0005] Step 1: The UAVs respectively obtain the channel state information of the sensing link and the communication link.
[0006] Step 2: The UAVs perform spectrum sensing and report the initial sensing results to the fusion center.
[0007] Step 3: The fusion center performs a fusion decision. When the current frequency band is not occupied by the primary user, an opportunity is selected to establish a link and transmit data between a UAV and the cognitive user.
[0008] Step 4: Calculate the cognitive transmission rate of the current time slot according to the detection probability and false alarm probability.
[0009] Step 5: Construct a problem of maximizing the cognitive transmission rate, and obtain the optimal solution of time slot allocation through a heuristic iterative optimization algorithm based on particle swarm.
[0010] The UAVs respectively obtain the channel state information of the sensing link and the communication link, and report the channel state information to the fusion center;
[0011] Considering that the cognitive communication process can be divided into two stages: sensing link establishment and data transmission. Among them, in the sensing link establishment stage, each UAV uses spectrum sensing methods including but not limited to energy detection to determine whether the frequency band of the current time slot is occupied by the primary user, and reports the initial sensing result to the fusion center through the feedback link;
[0012] Based on the initial sensing results of each UAV, the fusion center uses criteria including but not limited to "AND", "OR", "the minority obeys the majority" for fusion decision-making. According to the result of the fusion decision, if the current frequency band is idle, the fusion center opportunistically selects a UAV to establish a link with the cognitive user, and data transmission is carried out after successful link establishment. If the current frequency band is occupied by the primary user, each UAV continues to perform spectrum sensing;
[0013] Calculate the detection probability and false alarm probability of the fusion decision, and use the total probability formula to obtain the cognitive transmission rate of the current time slot;
[0014] With the goal of maximizing the cognitive transmission rate, construct the problem of time slot allocation for sensing link establishment and data transmission, and design a heuristic iterative optimization algorithm based on particle swarm to obtain the optimal solution of time slot allocation.
[0015] In step 1, The channel state information between the UAV and the primary user or the cognitive user includes two parts: path loss and small-scale fading. Specifically, the path loss can be expressed as
[0016]
[0017] where X ∈ {S, P}, S and P respectively represent the cognitive user and the primary user, β 0 represents the path loss at the reference point, α 0 represents the path loss factor, d X,m represents the distance between the m-th UAV and node X. The small-scale fading can be modeled as Rice fading and expressed as
[0018]
[0019] where K r represents the Rice factor, represents the line-of-sight component of the channel between the m-th UAV and node X, represents the non-line-of-sight component of the channel between the m-th UAV and node X.
[0020] The cognitive communication process can be divided into K time slots, and each time slot is further divided into two stages: sensing link establishment and data transmission. For the sake of convenience of representation, let denote the set of all time slots. Let the time slot length be denoted as t, and consider that the position of each UAV is approximately unchanged within one time slot. In addition, consider that in the th time slot, the durations of the sensing link establishment stage and the data transmission stage are respectively denoted as αt and (1 - α)t, where α ∈ [0, 1] represents the time slot allocation factor. In the first αt of the sensing link establishment stage, each UAV first performs spectrum sensing on whether the authorized frequency band is occupied by the primary user by using, including but not limited to, the energy detection method, and sends the initial sensing result to the fusion center for fusion decision. According to the result of the fusion decision, if the current frequency band is occupied by the primary user, each UAV continues to perform spectrum sensing. If the current frequency band is idle, the fusion center opportunistically selects a UAV to establish a link and transmit data with the cognitive user on the idle frequency band. In addition, consider reserving a duration of τ in the sensing link establishment stage for steps such as fusion decision, UAV selection, and link establishment with the cognitive user.
[0021] Let H P (k) denote whether the frequency band of the current time slot is occupied by the primary user, where H P (k) = H 0 denotes that the frequency band is idle, and H P (k) = H 1 denotes that the frequency band is occupied by the primary user. Based on the above assumptions, H P (k) is a binary random variable, and the Bernoulli distribution with parameter P a is used to model H P (k), that is, Pr(H P (k) = H 0 ) = P a , Pr(H P (k) = H 1 ) = 1 - P a .
[0022] In the sensing link establishment stage of the kth time slot, the received signal of the mth UAV can be expressed as
[0023]
[0024] where, P P represents the transmit power of the primary user, represents the channel gain between the mth UAV and the primary user in the kth time slot, n 0 (k) represents Gaussian white noise with a mean of 0 and a variance of , and θ(k) is defined as
[0025]
[0026] Among them, x P (k) represents the transmission signal of the primary user in the k-th time slot. Without loss of generality, assume that x P (k) follows a Gaussian distribution with a mean of μ P = 0 and a variance of .
[0027] Based on the expression of the received signal y P,m (k), each UAV uses spectrum sensing methods including but not limited to energy detection to determine whether the current frequency band is occupied by the primary user. Assume that f 0 is the sampling frequency of the energy detector. Then, the output statistical data of the energy detector of the m-th UAV in the k-th time slot is
[0028]
[0029] Among them, represents the number of sampling points of the energy detector, represents rounding up the variable, represents the proportion of the duration used for spectrum sensing in the sensing link establishment phase in the entire time slot. According to the central limit theorem, when the number of sampling points N is large enough, T(y) follows a Gaussian distribution. When H P (k) = H 0 , its mean is and the variance is When H P (k) = H 1 , its mean is and the variance is Therefore, the initial detection probability of the m-th UAV is
[0030]
[0031] Among them, δ represents the detection threshold of the energy detector, exp(·) represents the exponential function. To avoid serious interference to the primary user caused by cognitive transmission, it is necessary to ensure that the detection probability of spectrum sensing meets certain requirements. Therefore, without loss of generality, consider setting the target detection probability of each UAV to
[0032] Furthermore, the initial false alarm probability of the m-th UAV can be obtained, expressed as
[0033]
[0034] Among them, represents the detection threshold corresponding to the energy detector when the target detection probability of the m-th UAV is , and Q -1(·) represents the inverse function of the Q(x) function.
[0035] According to the initial spectrum sensing results of each UAV, the fusion center makes a fusion decision on whether the frequency band in the current time slot is occupied by the primary user by using criteria including but not limited to "OR", "AND", "the minority obeys the majority", etc. Taking the "OR" criterion as an example, the detection probability and false alarm probability of the fusion decision can be respectively expressed as
[0036]
[0037] and
[0038]
[0039] According to the result of the fusion decision, if the current frequency band is occupied by the primary user, each UAV continues to perform spectrum sensing. If the current frequency band is idle, the fusion center opportunistically selects a UAV to establish a link and transmit data with the cognitive user in the idle frequency band. The selection criterion of the UAV can be expressed as
[0040]
[0041] where represents the channel gain between the m-th UAV and the cognitive user in the k-th time slot, and u is a UAV selected according to this criterion.
[0042] Taking to represent that the result of the fusion decision is that the current frequency band is idle, to represent that the result of the fusion decision is that the current frequency band is occupied by the primary user. Assuming that all channel state information is perfectly known, the received signal of the cognitive user can be expressed as
[0043]
[0044] where represents the channel gain between the selected UAV and the cognitive user, P U represents the transmit power of the UAV, h PS (k) represents the channel gain between the primary user and the cognitive user, and β(k) is defined as
[0045]
[0046] where, x S (k) represents the transmitted signal of the selected UAV.
[0047] Based on the expression of the received signal of the cognitive user, when and H P (k) = H 0 the transmission rate can be expressed as
[0048] R 0 R(k) = (1 - α)Blog 2 (1 + γ 0 ) (13)
[0049] Among them, B represents the cognitive transmission bandwidth, When and H P H(k) = H 1 at this time, the transmission rate can be expressed as
[0050] R 1 R(k) = (1 - α)Blog 2 (1 + γ 1 ) (14)
[0051] Among them, Therefore, according to the total probability formula, the cognitive transmission rate can be expressed as
[0052]
[0053] Taking maximizing the cognitive transmission rate as the goal and the false alarm probability of the fusion decision being less than the target false alarm probability as the constraint, construct the following time slot allocation problem
[0054]
[0055] Among them, τ represents the shortest duration required for the sensing link establishment phase, represents the target false alarm probability that needs to be guaranteed in the sensing link establishment phase. Since the optimization problem (16) is non-convex and difficult to solve directly. For this reason, combined with the penalty function method, first construct the following augmented objective function
[0056]
[0057] Among them, λ >> 1 represents the penalty coefficient.
[0058] Combining (16) and (17), transform the time slot allocation problem into the following form
[0059]
[0060] Note that problem (18) is a non-convex optimization problem defined on a closed interval. The present invention proposes a heuristic iterative optimization algorithm based on particle swarm, and the specific algorithm principle and iterative process are shown in Table 1.
[0061] Table 1 Heuristic Iterative Optimization Algorithm Based on Particle Swarm
[0062]
[0063]
[0064] In the above iterative optimization algorithm, the inner iterative function is used to obtain the current optimal position of the population, and the outer iterative function is used to determine when to terminate the iteration. When the above iterative optimization algorithm converges, the optimal position of the population is returned, which is the optimal solution to the slot allocation problem constructed by the present invention.
[0065] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method for sensing link establishment and data transmission slot allocation for an unmanned aerial vehicle (UAV) cognitive communication system described above.
[0066] A computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for sensing link establishment and data transmission slot allocation for an unmanned aerial vehicle (UAV) cognitive communication system described above is implemented.
[0067] Compared with the prior art, the advantages of the present invention are as follows:
[0068] First, for the UAV cognitive communication scenario, a multi-UAV cooperative spectrum sensing method is adopted, and the sensing result of whether the current frequency band is occupied by the primary user is more accurate. When the number of UAV nodes participating in cooperative spectrum sensing is greater than or equal to 2, the detection probability of the fusion decision can be guaranteed to be higher than 99%. Second, the present invention significantly improves the cognitive transmission rate by optimizing the slot allocation in the cognitive transmission process. Under the parameters set in the embodiments of the present invention, compared with the traditional equal slot allocation scheme, the cognitive transmission rate of the proposed scheme is increased by more than 70%. BRIEF DESCRIPTION OF THE DRAWINGS
[0069] Figure 1 It is a system model diagram of the method for sensing link establishment and data transmission slot allocation for an unmanned aerial vehicle (UAV) cognitive communication system proposed by the present invention.
[0070] Figure 2 It is a flowchart of the steps in the embodiments of the present invention.
[0071] Figure 3 It is a graph showing the relationship between the transmission power of the UAV and the cognitive transmission rate in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0072] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the system model diagram, the flowchart of the steps, and the simulation result diagram. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the protection scope of the present invention.
[0073] Embodiment: Refer to Figures 1 - 3, the present invention discloses a method for sensing link establishment and data transmission time slot allocation for an unmanned aerial vehicle (UAV) cognitive communication system. The system includes M UAVs, a primary user, a cognitive user, and a fusion center. It is considered that all nodes are equipped with a single transmit antenna and a single receive antenna. For the sake of convenience of representation, let represent the set of all UAVs. Specifically, the method includes the following steps:
[0074] The UAVs respectively obtain the channel state information of the sensing link and the communication link, and report the channel state information to the fusion center;
[0075] It is considered that the cognitive communication process can be divided into two stages: sensing link establishment and data transmission. Among them, in the sensing link establishment stage, each UAV uses a spectrum sensing method including but not limited to energy detection method to sense whether the frequency band of the current time slot is occupied by the primary user, and reports the initial sensing result to the fusion center through the feedback link;
[0076] Based on the initial sensing results of each UAV, the fusion center uses criteria including but not limited to "AND", "OR", "the minority obeys the majority", etc. for fusion decision. According to the result of the fusion decision, if the current frequency band is idle, the fusion center opportunistically selects a UAV to establish a link with the cognitive user, and data transmission is carried out after successful link establishment. If the current frequency band is occupied by the primary user, each UAV continues to perform spectrum sensing;
[0077] Calculate the detection probability and false alarm probability of the fusion decision, and use the total probability formula to obtain the cognitive transmission rate of the current time slot;
[0078] With the goal of maximizing the cognitive transmission rate, a sensing link establishment and data transmission time slot allocation problem is constructed, and a heuristic iterative optimization algorithm based on particle swarm is designed to obtain the optimal solution of the time slot allocation.
[0079] The channel state information between the th UAV and the primary user or the cognitive user includes two parts: path loss and small-scale fading. Specifically, the path loss can be expressed as
[0080]
[0081] where X ∈ {S, P}, S and P respectively represent the cognitive user and the primary user, β 0 represents the path loss at the reference point, α 0 represents the path loss factor, and d X,m represents the distance between the mth UAV and the node X. The small-scale fading can be modeled as Rice fading and is expressed as
[0082]
[0083] Among them, K r represents the Rice factor, represents the line-of-sight component of the channel between the m-th unmanned aerial vehicle (UAV) and node X, represents the non-line-of-sight component of the channel between the m-th UAV and node X.
[0084] The cognitive communication process can be divided into K time slots, and each time slot is further divided into two stages: sensing and link establishment, and data transmission. For the sake of convenience of representation, let represent the set of all time slots. Let the time slot length be denoted as t, and consider that the position of each UAV is approximately unchanged within one time slot. In addition, consider that in the -th time slot, the durations of the sensing and link establishment stage and the data transmission stage are respectively denoted as αt and (1 - α)t, where α ∈ [0, 1] represents the time slot allocation factor. In the first αt of the sensing and link establishment stage, each UAV first performs spectrum sensing on whether the licensed frequency band is occupied by the primary user by using, including but not limited to, the energy detection method, and sends the initial sensing result to the fusion center for fusion decision. According to the result of the fusion decision, if the current frequency band is occupied by the primary user, each UAV continues to perform spectrum sensing. If the current frequency band is idle, the fusion center opportunistically selects a UAV to establish a link and transmit data with the cognitive user in the idle frequency band. In addition, consider reserving a duration of τ in the sensing and link establishment stage for steps such as fusion decision, UAV selection, and link establishment with the cognitive user.
[0085] Let H P (k) represent whether the frequency band of the current time slot is occupied by the primary user, where H P (k) = H 0 represents that the frequency band is idle, and H P (k) = H 1 represents that the frequency band is occupied by the primary user. Based on the above assumptions, H P (k) is a binary random variable, and a Bernoulli distribution with parameter P a is used to model H P (k), that is, Pr(H P (k) = H 0 ) = P a , Pr(H P (k) = H 1 ) = 1 - P a .
[0086] In the sensing and link establishment stage of the k-th time slot, the received signal of the m-th UAV can be expressed as
[0087]
[0088] Among them, P PDenote the transmit power of the primary user, Denote the channel gain between the m-th UAV and the primary user in the k-th time slot, n 0 (k) represents Gaussian white noise with a mean of 0 and a variance of , and θ(k) is defined as
[0089]
[0090] where x P (k) represents the transmit signal of the primary user in the k-th time slot. Without loss of generality, assume that x P (k) follows a Gaussian distribution with a mean μ P = 0 and a variance of .
[0091] Based on the expression of the received signal y P,m (k), each UAV uses, including but not limited to, the energy detection method to perform spectrum sensing on whether the current frequency band is occupied by the primary user. Assume that f 0 is the sampling frequency of the energy detector. Then, the output statistical data of the energy detector of the m-th UAV in the k-th time slot is
[0092]
[0093] where represents the number of sampling points of the energy detector, represents rounding up the variable, represents the proportion of the duration used for spectrum sensing in the sensing link establishment phase in the entire time slot. According to the central limit theorem, when the number of sampling points N is large enough, T(y) follows a Gaussian distribution. When H P (k) = H 0 , its mean is and the variance is When H P (k) = H 1 , its mean is and the variance is Therefore, the initial detection probability of the m-th UAV is
[0094]
[0095] where δ represents the detection threshold of the energy detector, exp(·) represents the exponential function. To avoid serious interference to the primary user caused by cognitive transmission, it is necessary to ensure that the detection probability of spectrum sensing meets certain requirements. Therefore, without loss of generality, consider setting the target detection probability of each UAV to
[0096] Furthermore, the initial false alarm probability of the m-th UAV can be obtained and is expressed as
[0097]
[0098] Among them, represents the target detection probability of the m-th unmanned aerial vehicle (UAV), and is the detection threshold corresponding to the energy detector when Q -1 (·) represents the inverse function of the Q(x) function.
[0099] Based on the initial spectrum sensing results of each UAV, the fusion center makes a fusion decision on whether the frequency band in the current time slot is occupied by the primary user by using criteria including but not limited to "OR", "AND", "the minority obeys the majority", etc. Taking the "OR" criterion as an example, the detection probability and false alarm probability of the fusion decision can be respectively expressed as
[0100]
[0101] and
[0102]
[0103] According to the result of the fusion decision, if the current frequency band is occupied by the primary user, each UAV continues spectrum sensing. If the current frequency band is idle, the fusion center opportunistically selects a UAV to establish a link and transmit data with the cognitive user in the idle frequency band. The selection criterion of the UAV can be expressed as
[0104]
[0105] Among them, represents the channel gain between the k-th time slot and the m-th UAV and the cognitive user, and u is a UAV selected according to this criterion.
[0106] Taking as the result that the fusion decision is that the current frequency band is idle, as the result that the fusion decision is that the current frequency band is occupied by the primary user. Assuming that all channel state information is perfectly known, the received signal of the cognitive user can be expressed as
[0107]
[0108] Among them, represents the channel gain between the selected UAV and the cognitive user, P U represents the transmission power of the UAV, h PS (k) represents the channel gain between the primary user and the cognitive user, and β(k) is defined as
[0109]
[0110] Among them, x S(k) represents the transmission signal of the selected UAV.
[0111] Based on the expression of the received signal by the cognitive user, when and H P (k) = H 0 the transmission rate can be expressed as
[0112] R 0 (k) = (1 - α)Blog 2 (1 + γ 0 ) (13)
[0113] where B represents the cognitive transmission bandwidth, When and H P (k) = H 1 the transmission rate can be expressed as
[0114] R 1 (k) = (1 - α)Blog 2 (1 + γ 1 ) (14)
[0115] where, Therefore, according to the total probability formula, the cognitive transmission rate can be expressed as
[0116]
[0117] With the goal of maximizing the cognitive transmission rate and the constraint that the false alarm probability of the fusion decision is less than the target false alarm probability, the following time slot allocation problem is constructed
[0118]
[0119] where τ represents the shortest duration required for the sensing link establishment phase, represents the target false alarm probability that needs to be guaranteed during the sensing link establishment phase. Since the optimization problem (16) is non-convex and difficult to solve directly. Therefore, combined with the penalty function method, the following augmented objective function is first constructed
[0120]
[0121] where λ >> 1 represents the penalty coefficient.
[0122] Combining (16) and (17), the time slot allocation problem is transformed into the following form
[0123]
[0124] Noting that problem (18) is a non-convex optimization problem defined on a closed interval, the present invention proposes a heuristic iterative optimization algorithm based on particle swarm. The specific algorithm principle and iterative process are shown in Table 1.
[0125] Table 1 Heuristic Iterative Optimization Algorithm Based on Particle Swarm
[0126]
[0127]
[0128] In the above iterative optimization algorithm, the inner iterative function is used to obtain the current optimal position of the population, and the outer iterative function is used to determine when to terminate the iteration. When the above iterative optimization algorithm converges, the optimal position of the population is returned, which is the optimal solution to the slot allocation problem constructed by the present invention.
[0129] To verify the effectiveness of the slot allocation method proposed by the present invention, an example of the present invention is simulated and implemented on a computer using MATLAB language. The specific simulation parameter settings are as follows:
[0130] In the simulation, it is set that there are M = 12 unmanned aerial vehicles (UAVs) evenly distributed in the area, and the flight height H of the UAVs is 40 meters. The distance between the primary user and the cognitive user is set to 60 meters. The Rice channel is used to model the channels between each node, with the Rice factor K r = 10, the path loss factor α 0 = 2.2, the path loss β at a reference distance of 1 meter 0 = 10 -3 , the channel noise power The transmit signal-to-noise ratio of the primary user is set to 78 dB. The duration t of each time slot is set to 10 ms, where the reserved time τ for the sensing and link establishment phase is 1 ms, which is used for steps such as fusion decision-making, UAV selection, and link establishment with the cognitive user. The spectrum idle probability P a = 0.7, the target initial detection probability At this time, as long as the number of nodes participating in spectrum sensing is not less than 2, the detection probability P of the fusion decision can be guaranteed d ≥ 99%. The target false alarm probability of the fusion decision is set to The transmission signal bandwidth B = 20 MHz, and the sampling frequency f of the energy detector 0 = 40 MHz. In the particle swarm algorithm, the number of particles N in the population is set p = 30, the initial fitness value is f 0 = 0, and the convergence accuracy of the algorithm is ε = 10 -2 . According to the above parameter settings, the Monte Carlo simulation of the optimization scheme proposed by the present invention is carried out. When the sensing delay t c ≤ 2 ms, the simulation results of the corresponding cognitive transmission rate are asFigure 3 As shown in Figure 3 It can be seen that as the transmission power of the UAV increases, the cognitive transmission rates of both schemes show an upward trend. Compared with the traditional equal time-slot allocation scheme, the time-slot allocation scheme proposed in the present invention significantly improves the cognitive transmission rate.
[0131] The above is only one implementation manner of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.
Claims
1. A method for sensing link establishment and data transmission time slot allocation for unmanned aerial vehicle cognitive communication system, characterized in that: The distribution system adopted by the method includes M drones, a master user, a cognitive user and a fusion center, and the method includes the following steps: Step 1: The UAV obtains the channel state information of the perception link and the communication link respectively. Step 2: The drones perform spectrum sensing to determine whether the current frequency band is occupied by the primary user and report the initial sensing results to the fusion center. Step 3: The fusion center makes a fusion decision according to the "or" principle. According to the decision result, when the current frequency band is not occupied by the primary user, the drone continues to sense the spectrum. When the current frequency band is not occupied by the primary user, it will choose a drone to establish a link and transmit data with the cognitive user. Step 4: Calculate the cognitive transmission rate of the current time slot based on the detection probability and false alarm probability of the fusion decision. Step 5: Taking the false alarm probability of the fusion decision detection probability as a constraint, construct the cognitive transmission rate maximization problem, and obtain the optimal solution of time slot allocation through the heuristic iterative optimization algorithm based on particle swarm.
2. The method for sensing link establishment and data transmission time slot allocation for unmanned aerial vehicle cognitive communication system according to claim 1 is characterized in that: In step 1, The channel state information between the UAV and the primary user or cognitive user includes two parts: path loss and small-scale fading. Specifically, the path loss is expressed as Where X∈{S,P}, S and P represent cognitive users and primary users respectively, β0 represents the path loss at the reference point, α0 represents the path loss factor, and d X,m represents the distance between the mth UAV and node X. The small-scale fading is modeled by Rice fading and is expressed as Among them, K r represents the Rice factor, represents the line-of-sight component of the channel between the mth UAV and node X, Represents the non-line-of-sight component of the channel between the mth UAV and node X.
3. The method for sensing link establishment and data transmission time slot allocation for unmanned aerial vehicle cognitive communication system according to claim 2 is characterized in that: The cognitive communication process is divided into K time slots, and each time slot is further divided into two stages: perception link establishment and data transmission. Represents the set of all time slots, and denotes the time slot length as t. Consider In a time slot, the duration of the perception link establishment phase and the data transmission phase are recorded as αt and (1-α)t respectively, where α∈[0,1] represents the time slot allocation factor. In the perception link establishment phase of the first αt, each UAV first uses the energy detection method to perform spectrum perception on whether the authorized frequency band is occupied by the primary user, and sends the initial perception result to the fusion center for fusion judgment. According to the result of the fusion judgment, if the current frequency band is occupied by the primary user, each UAV continues to perform spectrum perception. If the current frequency band is idle, the fusion center will select a UAV to establish a link and transmit data with the cognitive user in the idle frequency band. Let H P (k) indicates whether the frequency band of the current time slot is occupied by the primary user, where H P (k) = H0 means the frequency band is idle, H P (k) = H1 indicates that the frequency band is occupied by the primary user. Based on the above assumption, H P (k) is a binary random variable with parameter P a The Bernoulli distribution of H P (k) Modeling, that is, Pr(H P (k)=H0)=P a 、Pr(H P (k) = H1) = 1-P a , In the sensing and link building phase of the kth time slot, the received signal of the mth UAV is expressed as Among them, P P represents the transmit power of the primary user, represents the channel gain between the mth UAV and the primary user in the kth time slot, n0(k) represents the mean value of 0 and the variance of Gaussian white noise, θ(k) is defined as Among them, x P (k) represents the transmission signal of the primary user in the kth time slot, assuming x P (k) obeys the mean μ P =0, variance Gaussian distribution, Based on the received signal y P,m (k), each UAV uses energy detection method to sense whether the current frequency band is occupied by the primary user. Let f0 be the sampling frequency of the energy detector. Then the energy detector output statistics of the mth UAV in the kth time slot are in, represents the number of sampling points of the energy detector, Indicates that the variable is rounded up. It indicates the proportion of the time used for spectrum sensing in the sensing link establishment phase to the entire time slot. When the number of sampling points N is large enough, T(y) obeys Gaussian distribution. P When (k)=H0, its mean is The variance is When H P When (k) = H1, its mean is The variance is The initial detection probability of the mth drone is Where δ represents the detection threshold of the energy detector, exp(·) represents the exponential function, setting the target detection probability of each drone to The initial false alarm probability of the mth UAV is obtained and expressed as in, It means the detection probability of the mth UAV target is The detection threshold corresponding to the energy detector, Q -1 (·) represents the inverse function of Q(x) function.
4. The method for sensing link establishment and data transmission time slot allocation for unmanned aerial vehicle cognitive communication system according to claim 1 is characterized in that: In step 3, based on the initial spectrum sensing results of each drone, the fusion center uses the "or", "and", and "minority obeys majority" criteria to make a fusion decision on whether the frequency band of the current time slot is occupied by the primary user; among them, the detection probability and false alarm probability of the fusion decision based on the "or" criterion are expressed as and According to the result of the fusion judgment, if the current frequency band is occupied by the primary user, each drone continues to perform spectrum sensing. If the current frequency band is idle, the fusion center will select a drone to establish a link and transmit data with the cognitive user in the idle frequency band. The selection criteria of the drone are expressed as in, represents the channel gain between the mth UAV and the cognitive user in the kth time slot, and u is a UAV selected according to this criterion.
5. The method for sensing link establishment and data transmission time slot allocation for unmanned aerial vehicle cognitive communication system according to claim 1 is characterized in that: by Indicates that the fusion decision result is that the current frequency band is idle. Indicates that the fusion decision result is that the current frequency band is occupied by the primary user. Assuming that all channel state information is perfectly known, the received signal of the cognitive user is expressed as in, represents the channel gain between the selected UAV and the cognitive user, P U represents the transmitting power of the UAV, h PS (k) represents the channel gain between the primary user and the cognitive user, and β(k) is defined as Among them, x S (k) represents the transmission signal of the selected UAV, Based on the expression of cognitive user receiving signal, when And H P When (k) = H0, the transmission rate is expressed as R0(k)=(1-α)Blog2(1+γ0) (13) Where B represents the cognitive transmission bandwidth, when And H P When (k) = H1, the transmission rate is expressed as R1(k)=(1-α)Blog2(1+γ1) (14) in, Therefore, according to the total probability formula, the cognitive transmission rate is expressed as 6. The method for sensing link establishment and data transmission time slot allocation for unmanned aerial vehicle cognitive communication system according to claim 1 is characterized in that: In step 5, with the goal of maximizing the cognitive transmission rate and the constraint that the false alarm probability of the fusion decision is less than the target false alarm probability, the following time slot allocation problem is constructed: Among them, τ represents the shortest duration required for the perception link establishment phase, It represents the target false alarm probability that needs to be guaranteed in the perception link building stage. Combined with the penalty function method, the following augmented objective function is first constructed Among them, λ>>1 represents the penalty coefficient, Combining (16) and (17), the time slot allocation problem is transformed into the following form: Note that problem (18) is a non-convex optimization problem defined on a closed interval. A heuristic iterative optimization algorithm based on particle swarm is proposed. The specific algorithm principle and iterative process are shown in Table 1. Table 1 Heuristic iterative optimization algorithm based on particle swarm In the above-mentioned iterative optimization algorithm, the inner iterative function is used to obtain the current optimal position of the population, and the outer iterative function is used to determine when to terminate the iteration. When the above-mentioned iterative optimization algorithm converges, it returns the optimal position of the population, which is the optimized solution to the time slot allocation problem constructed in this application.
7. An electronic device 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, it implements a perception link establishment and data transmission time slot allocation method for a UAV cognitive communication system as described in any one of claims 1 to 6.
8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instruction is executed by the processor, a perception link establishment and data transmission time slot allocation method for a UAV cognitive communication system is implemented as described in any one of claims 1-6.