A wireless communication adaptive power control method based on interception probability and energy consumption

Abstract

The application discloses a wireless communication adaptive power control method based on interception probability and energy consumption, and relates to the technical field of wireless communication power control.The method comprises five steps of link data collection, interception probability model construction, energy consumption model construction, joint optimization model construction and adaptive power control.Firstly, link state information is acquired in real time;then, an interception probability model is established to accurately describe the relationship between the interception probability and various related factors;next, an energy consumption model is constructed to analyze the influence of factors such as transmission power, data transmission rate, signal-to-interference-and-noise ratio and the like on energy consumption; subsequently, a joint optimization model of the interception probability and the energy consumption is established to clearly define the objective function and the constraint condition in the model; finally, adaptive power adjustment is performed in combination with the joint optimization model and the real-time link state information to determine an optimal power strategy.Under the premise of meeting the communication quality, the method realizes low interception probability and energy consumption, and meets the safe and reliable communication demand in the communication field.

Description

Technical Field

[0001] This invention relates to the field of wireless communication power control technology, specifically a wireless communication adaptive power control method based on interception probability and energy consumption. Background Technology

[0002] In modern communication systems, the collaborative operation of ground command and unmanned aerial platforms has become a crucial technology. Ground command transmits critical data such as control commands and mission information to the unmanned aerial platform via wireless links. Leveraging their unique mobility, the unmanned aerial platform performs important tasks such as data acquisition, target location, and high-altitude monitoring. In this collaborative operation process, power control at the wireless end has become a core research area.

[0003] Traditional fixed-power control methods have significant drawbacks. When the transmission power is too high, the energy consumption of the communication equipment carried by the ground command equipment increases dramatically, resulting in a significant reduction in the equipment's endurance and severely affecting its continuous working capability. When the transmission power is too low, the unmanned launch platform may be unable to effectively receive control signals, leading to problems such as lost or delayed control commands, which prevents it from accurately executing its mission.

[0004] Most existing power control methods only consider energy consumption and reliability factors during wireless communication, neglecting signal strength control in the current electronic environment. Under specific electromagnetic conditions, the probability of signal interception increases exponentially when the signal strength exceeds a certain threshold. Therefore, to meet the requirements of communication security and stability, there is an urgent need for an innovative adaptive power control method that aims for low interception probability, low energy consumption, and satisfactory communication quality, thereby reducing the probability of signal interception and improving the security of data communication. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive power control method for wireless communication based on interception probability and energy consumption, so as to solve the problems proposed in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive power control method for wireless communication based on interception probability and energy consumption, the method comprising:

[0007] Real-time link status information is acquired; the real-time link status information is collected based on high-precision, multimodal sensing devices deployed at the communication transmitter and receiver.

[0008] A first mathematical model is constructed to describe the relationship between the interception probability and other relevant factors, including transmission power, communication frequency, distance, and interception receiver detection threshold.

[0009] Construct an energy consumption model to analyze the impact of transmit power, data transmission rate, signal-to-noise ratio, and link load on energy consumption;

[0010] A joint optimization model is determined. Based on the first mathematical model and the energy consumption model, a joint optimization model that comprehensively considers the interception probability and energy consumption is established, and the objective function and constraints in the joint optimization model are determined.

[0011] Adaptive power adjustment is performed based on a joint optimization model and real-time link state information to determine the optimal power strategy.

[0012] According to the above technical solution, obtaining real-time link status information includes:

[0013] Based on high-precision, multimodal sensing equipment deployed at the communication transmitter and receiver, the following settings are respectively configured:

[0014] The data transmission rate monitoring module accurately calculates the amount of data transmitted per unit time by marking data packet timestamps, thereby achieving real-time acquisition of the data transmission rate R.

[0015] The signal interference-to-noise ratio detection device based on the spectrum analyzer can analyze the signal power and noise power in the communication frequency band in real time.

[0016] The link load rate L monitoring unit calculates the link load in real time by comparing the current data transmission volume with the maximum carrying capacity of the link.

[0017] The communication frequency band monitoring module directly reads the frequency setting parameters of the communication equipment.

[0018] The system has a fixed data collection cycle and interfaces with various types of devices.

[0019] According to the above technical solution, the interception probability P int This refers to the intercepted signal power P received by the receiver. r Greater than or equal to the detection threshold P th The probability of.

[0020] According to the above technical solution, when calculating the probability of interception, it is assumed that the received signal power P r It follows a log-normal distribution;

[0021] The interception probability P int It can be represented as

[0022]

[0023] Where φ is the cumulative distribution function of the standard normal distribution, erfc is the complementary error function, and σ is the standard deviation of shadowing fading, which measures the degree of fluctuation in the received signal power.

[0024] Among them, the received signal power P r Calculation of received signal power using the free-space propagation model:

[0025] P r =P t +G t +G r -L path

[0026] Among them, P t It is the transmission power; G t and G r These are the gains of the transmitting antenna and the interceptor receiver antenna, respectively; L path It is free space path loss, calculated based on the distance between the transmitter and the intercepting receiver, and the signal frequency;

[0027] L path =32.45+20log 10 d+20log 10 f

[0028] Where d is the distance between the transmitter and the intercepting receiver; f is the signal frequency.

[0029] According to the above technical solution, during the operation of the wireless communication system, the transmission power P is obtained respectively. t The data transmission rate R, signal-to-interference-to-noise ratio (SINR), link load rate L, and number of data retransmissions K are taken as the transmit power P. t The value P t1 The energy consumption model is formed by mathematically modeling the data transmission rate R1, the signal-to-interference-plus-noise ratio SINR1, the link load rate L1, and the number of data retransmissions K.

[0030] According to the above technical solution, it specifically includes:

[0031] Based on P t1 Modeling the impact of energy consumption: When the power amplifier is operating, the energy consumption is related to the transmit power P. t This exhibits a nonlinear relationship. As the transmit power increases, the power amplifier efficiency changes, leading to a nonlinear increase in energy consumption. A quadratic term, denoted as αP, is introduced to describe this nonlinear relationship. t1 2 ;

[0032] Modeling based on the joint effect of R1 and SINR1: ​​When the data transmission rate R increases, more complex modulation and coding methods are required. At the same time, in order to ensure the reliability of the signal during transmission, the signal interference noise ratio SINR needs to be increased simultaneously, thereby increasing energy consumption. Therefore, the model parameter β is introduced to reflect the joint effect of the two on energy consumption, denoted as βR1×SINR1.

[0033] Based on L1 and P t1 Joint impact modeling: When the link load rate is high, the device needs to work continuously to handle more data transmission tasks and requires higher power to maintain communication quality. Therefore, the model parameter γ is introduced to reflect the combined impact of the two on energy consumption, denoted as γL1×P. t1 ;

[0034] Modeling the impact of retransmission count K on energy consumption: The number of retransmissions K is determined by the link bit error rate (BER). The higher the BER, the more retransmissions are required. Introducing the model parameter δ, we construct the retransmission energy consumption term δK×P. t1 ;

[0035] The number of retransmissions K can be calculated using the bit error rate model.

[0036] Constructing an energy consumption model:

[0037] E = αP t1 2 +βR1×SINR1+γL1×P t1 +δK×P t1

[0038] Among them, the model parameters α, β, γ, and δ can be obtained by performing energy consumption tests on different types of communication equipment under various typical communication scenarios and fitting them using the least squares method. E refers to the energy consumption output value.

[0039] According to the above technical solution, determining the objective function and constraints in the joint optimization model includes:

[0040] Determine the objective function: A weighted summation method is used to balance the two objectives of interception probability and energy consumption, establishing the objective function F:

[0041] F=ω1P int +ω2E norm

[0042] Among them, P int E refers to the probability of interception. norm The energy consumption index refers to the balance; ω1 and ω2 are weighting coefficients.

[0043] Wherein, E norm The calculations include:

[0044]

[0045] Among them, E min With E max These refer to the lower limit and upper limit of energy consumption set by the system, respectively.

[0046] Clearly define the constraints:

[0047] SINR constraint: To ensure communication quality, SINR must be greater than a set threshold value. th Let the power of the interference signal be P. j The noise power is N, according to have to Among them, P r This refers to the received signal power;

[0048] Energy Constraint: Energy consumption must be within a given range, i.e., E min ≤αP t1 2 +βR1×SINR1+γL1×P t1 +δK×P t1 ≤E max ;

[0049] Retransmission count constraint: K≤K max , where K max The maximum number of retransmissions set by the system;

[0050] Transmit power non-negativity constraint: P t1 ≥0;

[0051] Weight constraints: ω1+ω2=1,ω1,ω2∈[0,1].

[0052] According to the above technical solution, the adaptive power adjustment includes:

[0053] The communication link status information is used as the environmental input, and the power adjustment strategy is used as the action; the communication link status information includes the transmission rate, signal-to-interference-to-noise ratio, link load rate, distance between the transmitter and the intercepting receiver, and signal frequency; the power adjustment strategy includes the magnitude of increasing or decreasing the transmission power;

[0054] In the initial state, the interception probability, energy consumption output value and objective function value are calculated. Then, a power adjustment strategy is randomly selected, and the interception probability, energy consumption output value and objective function value are calculated after the power adjustment. The strategy advantage is evaluated based on whether each parameter meets the constraint conditions after the power adjustment and the changing trend of the objective function in the two tests.

[0055] The power adjustment strategy is set as follows: under various constraints, when the objective function is less than the objective function of the previous time slot, the strategy direction is maintained and adjustment continues; when the objective function is greater than the objective function of the previous time slot, the strategy is adjusted in the opposite direction; when the constraints are not met, the strategy is adjusted in the opposite direction directly.

[0056] According to the above technical solution, in a dynamic environment, the status information of the communication link is updated in real time, the interception probability, energy consumption output value and objective function value are continuously calculated in a loop, and the strategy is continuously adjusted to ensure that the communication system achieves adaptive power control.

[0057] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention can significantly reduce the probability of interception in wireless communication systems by acquiring the communication link status in real time and adjusting the transmission power according to the interception probability model, ensuring that the signal strength is always within a low risk range for interception. Actual testing shows that compared with a fixed-power communication system without this invention, this invention can reduce the interception probability by 25.21%, greatly improving communication security. Simultaneously, it significantly reduces energy consumption. The constructed energy consumption model comprehensively considers the impact of factors such as transmission power, data transmission rate, signal-to-noise ratio, and link load on energy consumption, achieving reasonable energy consumption control. While meeting the requirements of communication quality and low interception probability, it avoids increased energy consumption caused by excessive transmission power. Actual testing shows that compared with a fixed communication system without this invention, energy consumption is reduced by 44.06%, effectively improving the platform's continuous operating capability. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the steps of an adaptive power control method for wireless communication based on interception probability and energy consumption according to the present invention.

[0059] Figure 2 This is a schematic diagram of iterative data in the process of adjusting the transmit power of a wireless communication adaptive power control method based on interception probability and energy consumption according to the present invention. Detailed Implementation

[0060] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0061] Example: Figures 1-2 As shown, the present invention provides a technical solution, a wireless communication adaptive power control method based on interception probability and energy consumption, the method comprising:

[0062] Real-time link status information is acquired; the real-time link status information is collected based on high-precision, multimodal sensing devices deployed at the communication transmitter and receiver.

[0063] A first mathematical model is constructed to describe the relationship between the interception probability and other relevant factors, including transmission power, communication frequency, distance, and interception receiver detection threshold.

[0064] Construct an energy consumption model to analyze the impact of transmit power, data transmission rate, signal-to-noise ratio, and link load on energy consumption;

[0065] A joint optimization model is determined. Based on the first mathematical model and the energy consumption model, a joint optimization model that comprehensively considers the interception probability and energy consumption is established, and the objective function and constraints in the joint optimization model are determined.

[0066] Adaptive power adjustment is performed based on a joint optimization model and real-time link state information to determine the optimal power strategy.

[0067] According to the above technical solution, obtaining real-time link status information includes:

[0068] Based on high-precision, multimodal sensing equipment deployed at the communication transmitter and receiver, the following settings are respectively configured:

[0069] The data transmission rate monitoring module accurately calculates the amount of data transmitted per unit time by marking data packet timestamps, thereby achieving real-time acquisition of the data transmission rate R.

[0070] The signal interference-to-noise ratio detection device based on the spectrum analyzer can analyze the signal power and noise power in the communication frequency band in real time.

[0071] The link load rate L monitoring unit calculates the link load in real time by comparing the current data transmission volume with the maximum carrying capacity of the link.

[0072] The communication frequency band monitoring module directly reads the frequency setting parameters of the communication equipment.

[0073] The system has a fixed data collection cycle and interfaces with various types of devices.

[0074] Specifically, high-precision, multi-modal equipment is deployed at both the communication transmitter and receiver (unmanned aerial platform) to collect data at 100ms intervals. This equipment can monitor various key information in real time: a data transmission rate monitoring module calculates the transmission rate by marking data packet timestamps; a signal-to-interference-plus-noise ratio (SINR) detection device based on a spectrum analyzer acquires the SINR, including the interference signal power P. j The noise power N; the link load rate monitoring unit compares the current data transmission volume with the maximum carrying capacity of the link to obtain the link load rate L; the communication frequency band monitoring module reads the frequency setting parameters of the communication equipment to determine the signal frequency f, and obtains the distance d between the transmitter and the intercepting receiver through the positioning system and related sensors.

[0075] Taking a specific implementation example to collect data, the data collected at the initial moment is: P t =20dBm, R=10Mbps, L=0.6, f=1000MHz, d=8km, P j=-100dBm, N=-120dBm.

[0076] According to the above technical solution, when calculating the probability of interception, it is assumed that the received signal power P r It follows a log-normal distribution;

[0077] The interception probability P int It can be represented as

[0078]

[0079] Where φ is the cumulative distribution function of the standard normal distribution, erfc is the complementary error function, and σ is the standard deviation of shadowing fading, which measures the degree of fluctuation in the received signal power.

[0080] Among them, the received signal power P r Calculation of received signal power using the free-space propagation model:

[0081] P r =P t +G t +G r -L path

[0082] Among them, P t It is the transmission power; G t and G r These are the gains of the transmitting antenna and the interceptor receiver antenna, respectively; L path It is free space path loss, calculated based on the distance between the transmitter and the intercepting receiver, and the signal frequency;

[0083] L path =32.45+20log 10 d+20log 10 f

[0084] Where d is the distance between the transmitter and the intercepting receiver; f is the signal frequency.

[0085] Specifically, set the intercept receiver detection threshold P. th = -80dBm, transmit antenna gain G t =5dBi, intercepted receiver antenna gain G r =3dBi, shadow fading standard deviation σ=4. In each iteration, according to the free space path loss formula L... path (dB)=32.45+20log 10 d+20log 10 f Calculate path loss L path .

[0086] Next, by P r=P t +G t +G r -L path Calculate the received signal power P r Assume that P is at this point. t =20dBm, then P r =20dBm+5dBi+3dBi-110.51dB=-82.51dBm.

[0087] Then, according to the interception probability formula Calculate the interception probability P using the standard normal distribution table int .at this time

[0088] According to the above technical solution, during the operation of the wireless communication system, the transmission power P is obtained respectively. t The data transmission rate R, signal-to-interference-to-noise ratio (SINR), link load rate L, and number of data retransmissions K are taken as the transmit power P. t The value P t1 The energy consumption model is formed by mathematically modeling the data transmission rate R1, the signal-to-interference-plus-noise ratio SINR1, the link load rate L1, and the number of data retransmissions K.

[0089] According to the above technical solution, it specifically includes:

[0090] Based on P t1 Modeling the impact of energy consumption: When the power amplifier is operating, the energy consumption is related to the transmit power P. t This exhibits a nonlinear relationship. As the transmit power increases, the power amplifier efficiency changes, leading to a nonlinear increase in energy consumption. A quadratic term, denoted as αP, is introduced to describe this nonlinear relationship. t1 2 ;

[0091] Modeling based on the joint effect of R1 and SINR1: ​​When the data transmission rate R increases, more complex modulation and coding methods are required. At the same time, in order to ensure the reliability of the signal during transmission, the signal interference noise ratio SINR needs to be increased simultaneously, thereby increasing energy consumption. Therefore, the model parameter β is introduced to reflect the joint effect of the two on energy consumption, denoted as βR1×SINR1.

[0092] Based on L1 and P t1 Joint impact modeling: When the link load rate is high, the device needs to work continuously to handle more data transmission tasks and requires higher power to maintain communication quality. Therefore, the model parameter γ is introduced to reflect the combined impact of the two on energy consumption, denoted as γL1×P. t1 ;

[0093] Modeling the impact of retransmission count K on energy consumption: The number of retransmissions K is determined by the link bit error rate (BER). The higher the BER, the more retransmissions are required. Introducing the model parameter δ, we construct the retransmission energy consumption term δK×P. t1 ;

[0094] The number of retransmissions K can be calculated using the bit error rate model.

[0095] Constructing an energy consumption model:

[0096] E = αP t1 2 +βR1×SINR1+γL1×P t1 +δK×P t1

[0097] Among them, the model parameters α, β, γ, and δ can be obtained by performing energy consumption tests on different types of communication equipment under various typical communication scenarios and fitting them using the least squares method. E refers to the energy consumption output value.

[0098] Specifically, the energy consumption model parameters are set as α = 0.05, β = 0.01, γ = 0.03, and δ = 0.01. The signal-to-interference-to-noise ratio is calculated. retransmissions per second According to the energy consumption model E=αP t1 2 +βR1×SINR1+γL1×P t1 +δK×P t1 Calculate the energy consumption. The result is E = 0.05 × 20⁻⁶. 2 +0.01×10×17.44+0.03×0.6×20+0.02×0.02×20+0.01×0.02×20=22.1080

[0099] According to the above technical solution, determining the objective function and constraints in the joint optimization model includes:

[0100] Determine the objective function: A weighted summation method is used to balance the two objectives of interception probability and energy consumption, establishing the objective function F:

[0101] F=ω1P int +ω2E norm

[0102] Among them, P int E refers to the probability of interception. norm The energy consumption index refers to the balance; ω1 and ω2 are weighting coefficients.

[0103] Wherein, E norm The calculations include:

[0104]

[0105] Among them, E min With E max These refer to the lower limit and upper limit of energy consumption set by the system, respectively.

[0106] Clearly define the constraints:

[0107] SINR constraint: To ensure communication quality, SINR must be greater than a set threshold value. th Let the power of the interference signal be P. j The noise power is N, according to have to Among them, P r This refers to the received signal power;

[0108] Energy Constraint: Energy consumption must be within a given range, i.e., E min ≤αP t1 2 +βR1×SINR1+γL1×P t1 +δK×P t1 ≤E max ;

[0109] Retransmission count constraint: K≤K max , where K max The maximum number of retransmissions set by the system;

[0110] Transmit power non-negativity constraint: P t1 ≥0;

[0111] Weight constraints: ω1+ω2=1,ω1,ω2∈[0,1].

[0112] Specifically, set the SINR threshold value SINR th =10dB, lower limit of energy consumption E min =0, energy consumption limit E max =30, retransmission limit K max =30, with weight coefficients ω1=0.5 and ω2=0.5 in the initial iteration, and can be dynamically adjusted later according to the interception probability and the degree to which energy consumption deviates from the ideal value.

[0113] The objective function is The calculated P int Substitute the value of E into the objective function to calculate F. When P int =0.2652, E=22.1080,

[0114] Signal-to-interference-to-noise ratio Energy consumption E min ≤E=22.1080≤Emax K = 0.02 <K max Transmit power P t =20≥0, weights ω1=0.5, ω2=0.5 all satisfy the constraints.

[0115] According to the above technical solution, the adaptive power adjustment includes:

[0116] The communication link status information is used as the environmental input, and the power adjustment strategy is used as the action; the communication link status information includes the transmission rate, signal-to-interference-to-noise ratio, link load rate, distance between the transmitter and the intercepting receiver, and signal frequency; the power adjustment strategy includes the magnitude of increasing or decreasing the transmission power;

[0117] In the initial state, the interception probability, energy consumption output value and objective function value are calculated. Then, a power adjustment strategy is randomly selected, and the interception probability, energy consumption output value and objective function value are calculated after the power adjustment. The strategy advantage is evaluated based on whether each parameter meets the constraint conditions after the power adjustment and the changing trend of the objective function in the two tests.

[0118] The power adjustment strategy is set as follows: under various constraints, when the objective function is less than the objective function of the previous time slot, the strategy direction is maintained and adjustment continues; when the objective function is greater than the objective function of the previous time slot, the strategy is adjusted in the opposite direction; when the constraints are not met, the strategy is adjusted in the opposite direction directly.

[0119] The status information of the communication link (R, SINR, L, d, f, P) j As an environmental input, the power adjustment strategy is set to adjust in increments of at least 0.2 dBm within the range of -1 dBm to 1 dBm.

[0120] The initial power adjustment strategy is selected to increase the transmit power by 1 dBm (i.e., P). t =21dBm). Recalculate relevant parameters: Assuming d = 8km and f = 1000MHz, calculate L. path =32.45+20log 10 8+20log 10 1000≈110.51dB;

[0121] P r =21dBm+5dBi+3dBi-110.51dB=-82.51dBm;

[0122]

[0123] At this time, R = 10 Mbps, calculate... L = 0.6, retransmissions per second Then E = 0.05 × 21 2 +0.01×10×18.44+0.03×0.6×21+0.01×0.01×21=24.2741.

[0124] calculate

[0125] Since the signal-to-interference-to-noise ratio (SINR), power consumption (E), and retransmission count (K) all meet the constraints, the objective function F = 0.581018, which is greater than the objective function of the previous time slot (0.501049). Therefore, the next time slot employs a reverse adjustment strategy, reducing the transmit power by 1 dBm (i.e., P). t =20dBm).

[0126] According to the above technical solution, in a dynamic environment, the status information of the communication link is updated in real time, the interception probability, energy consumption output value and objective function value are continuously calculated in a loop, and the strategy is continuously adjusted to ensure that the communication system achieves adaptive power control.

[0127] like Figure 2 The diagram shows the iterative process of continuously adjusting the transmission power to find a solution that satisfies the constraints and makes the objective function F reach its optimal solution.

[0128] from Figure 2 As can be seen, with the increase of the number of iterations, the transmit power, interception probability, energy consumption, and objective function value gradually stabilize, indicating that the algorithm gradually converges to a better power control strategy, achieving a lower interception probability and energy consumption while meeting communication quality requirements. Test data shows that, compared to a fixed power (P) method, this invention... t The method (=20dBm) reduces the interception probability by 0.2650-0.0129=25.21% and reduces energy consumption. In practical applications, the weight coefficients ω1 and ω2 can be dynamically adjusted according to specific scenarios and needs. If the energy consumption is close to the ideal low level, but the interception probability is still high, the weight of ω1 can be appropriately increased to make the algorithm more focused on reducing the interception probability.

[0129] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wireless communication adaptive power control method based on interception probability and energy consumption, characterized in that: The method includes: Real-time link status information is acquired; the real-time link status information is collected based on high-precision, multimodal sensing devices deployed at the communication transmitter and receiver. A first mathematical model is constructed to describe the relationship between the interception probability and other relevant factors, including transmission power, communication frequency, distance, and interception receiver detection threshold. Construct an energy consumption model to analyze the impact of transmit power, data transmission rate, signal-to-noise ratio, and link load on energy consumption; A joint optimization model is determined. Based on the first mathematical model and the energy consumption model, a joint optimization model that comprehensively considers the interception probability and energy consumption is established, and the objective function and constraints in the joint optimization model are determined. Adaptive power adjustment is performed based on a joint optimization model and real-time link state information to determine the optimal power strategy.

2. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 1, characterized in that: The acquisition of real-time link status information includes: Based on high-precision, multimodal sensing equipment deployed at the communication transmitter and receiver, the following settings are respectively configured: The data transmission rate monitoring module accurately calculates the amount of data transmitted per unit time by marking data packet timestamps, thereby achieving real-time acquisition of the data transmission rate R. The signal interference-to-noise ratio detection device based on the spectrum analyzer can analyze the signal power and noise power in the communication frequency band in real time. The link load rate L monitoring unit calculates the link load in real time by comparing the current data transmission volume with the maximum carrying capacity of the link. The communication frequency band monitoring module directly reads the frequency setting parameters of the communication equipment. The system has a fixed data collection cycle and interfaces with various types of devices.

3. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 1, characterized in that: The interception probability P int This refers to the intercepted signal power P received by the receiver. r Greater than or equal to the detection threshold P th The probability of.

4. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 3, characterized in that: When calculating the probability of interception, assume the received signal power P r It follows a log-normal distribution; The interception probability P int It can be represented as Where φ is the cumulative distribution function of the standard normal distribution, erfc is the complementary error function, and σ is the standard deviation of shadowing fading, which measures the degree of fluctuation in the received signal power. Among them, the received signal power P r Calculation of received signal power using the free-space propagation model: P r =P t +G t +G r -L path Among them, P t It is the transmission power; G t and G r These are the gains of the transmitting antenna and the interceptor receiver antenna, respectively; L path It is free space path loss, calculated based on the distance between the transmitter and the intercepting receiver, and the signal frequency; L path =32.45+20log 10 d+20log 10 f Where d is the distance between the transmitter and the intercepting receiver; f is the signal frequency.

5. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 1, characterized in that: During the operation of the wireless communication system, the transmit power P is obtained respectively. t The data transmission rate R, signal-to-interference-to-noise ratio (SINR), link load rate L, and number of data retransmissions K are taken as the transmit power P. t The value P t1 The energy consumption model is formed by mathematically modeling the data transmission rate R1, the signal-to-interference-plus-noise ratio SINR1, the link load rate L1, and the number of data retransmissions K.

6. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 5, characterized in that: Specifically, it includes: Based on P t1 Modeling the impact of energy consumption: When the power amplifier is operating, the energy consumption is related to the transmit power P. t This exhibits a nonlinear relationship. As the transmit power increases, the power amplifier efficiency changes, leading to a nonlinear increase in energy consumption. A quadratic term, denoted as αP, is introduced to describe this nonlinear relationship. t1 2 ; Modeling based on the joint effect of R1 and SINR1: ​​When the data transmission rate R increases, more complex modulation and coding methods are required. At the same time, in order to ensure the reliability of the signal during transmission, the signal interference noise ratio SINR needs to be increased simultaneously, thereby increasing energy consumption. Therefore, the model parameter β is introduced to reflect the joint effect of the two on energy consumption, denoted as βR1×SINR1. Based on L1 and P t1 Joint impact modeling: When the link load rate is high, the device needs to work continuously to handle more data transmission tasks and requires higher power to maintain communication quality. Therefore, the model parameter γ is introduced to reflect the combined impact of the two on energy consumption, denoted as γL1×P. t1 ; Modeling the impact of retransmission count K on energy consumption: The number of retransmissions K is determined by the link bit error rate (BER). The higher the BER, the more retransmissions are required. Introducing the model parameter δ, we construct the retransmission energy consumption term δK×P. t1 ; The number of retransmissions K can be calculated using the bit error rate model. Constructing an energy consumption model: E = AP t1 2 +βR1×SINR1+γL1×P t1 +δK×P t1 Among them, the model parameters α, β, γ, and δ can be obtained by performing energy consumption tests on different types of communication equipment under various typical communication scenarios and fitting them using the least squares method. E refers to the energy consumption output value.

7. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 6, characterized in that: The determination of the objective function and constraints in the joint optimization model includes: Determine the objective function: A weighted summation method is used to balance the two objectives of interception probability and energy consumption, establishing the objective function F: F=ω1P int +ω2E norm Among them, P int E refers to the probability of interception. norm The energy consumption index refers to the balance; ω1 and ω2 are weighting coefficients. Wherein, E norm The calculations include: Among them, E min With E max These refer to the lower limit and upper limit of energy consumption set by the system, respectively. Clearly define the constraints: SINR constraint: To ensure communication quality, SINR must be greater than a set threshold value. th Let the power of the interference signal be P. j The noise power is N, according to have to Among them, P r This refers to the received signal power; Energy Constraint: Energy consumption must be within a given range, i.e., E min ≤αP t1 2 +βR1×SINR1+γL1×P t1 +δK×P t1 ≤E max ; Retransmission count constraint: K≤K max , where K max The maximum number of retransmissions set by the system; Transmit power non-negativity constraint: P t1 ≥0; Weight constraints: ω1+ω2=1,ω1,ω2∈[0,1].

8. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 1, characterized in that: The adaptive power adjustment includes: The communication link status information is used as the environmental input, and the power adjustment strategy is used as the action; the communication link status information includes the transmission rate, signal-to-interference-to-noise ratio, link load rate, distance between the transmitter and the intercepting receiver, and signal frequency; the power adjustment strategy includes the magnitude of increasing or decreasing the transmission power; In the initial state, the interception probability, energy consumption output value and objective function value are calculated. Then, a power adjustment strategy is randomly selected, and the interception probability, energy consumption output value and objective function value after power adjustment are calculated. The advantage of the strategy is evaluated based on whether each parameter meets the constraint conditions after power adjustment and the changing trend of the objective function in the two tests. The power adjustment strategy is set as follows: under various constraints, when the objective function is less than the objective function of the previous time slot, the strategy direction is maintained and adjustment continues; when the objective function is greater than the objective function of the previous time slot, the strategy is adjusted in the opposite direction; when the constraints are not met, the strategy is adjusted in the opposite direction directly.

9. The adaptive power control method for wireless communication based on interception probability and energy consumption according to claim 1, characterized in that: Also includes: In a dynamic environment, the status information of the communication link is updated in real time, and the interception probability, energy consumption output value and objective function value are continuously calculated in a loop. The strategy is constantly adjusted to ensure that the communication system achieves adaptive power control.

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