CN-121997725-A - Unmanned aerial vehicle resource optimization method and device based on outage probability space modeling

CN121997725ACN 121997725 ACN121997725 ACN 121997725ACN-121997725-A

Abstract

The invention provides an unmanned aerial vehicle resource optimization method and device based on interrupt probability space modeling, wherein the method comprises the steps of constructing a line-of-sight probability model by adopting a parameterized S-shaped curve function based on a pitch angle; the unmanned aerial vehicle resource optimization method comprises the steps of constructing a large-scale fading model according to the sight distance probability model and the safety margin, constructing a small-scale fading model based on a Lese factor and a pitch angle, constructing an interruption probability space according to the large-scale fading model and the small-scale fading model, determining an unmanned aerial vehicle reachable area according to the interruption probability space, and carrying out unmanned aerial vehicle resource optimization based on the interruption probability space in the unmanned aerial vehicle reachable area. The method can effectively improve the reliability and the resource utilization efficiency of the unmanned aerial vehicle relay communication system in a complex urban environment.

Inventors

WANG SHENYU
QUAN SIPING
ZHANG FAN
WANG FENG
WANG LIHUI
JIANG BIN
WU RONG
DAI YONGDONG
Wang Maofei
ZHOU BIN
HOU YE
WANG SHUHENG
JU LING
LIAO XIAOYUN

Assignees

国网江苏省电力有限公司泰州供电分公司
国网江苏省电力有限公司
东南大学

Dates

Publication Date: 20260508
Application Date: 20260112

Claims (10)

1. The unmanned aerial vehicle resource optimization method based on interrupt probability space modeling is characterized by comprising the following steps of: constructing a vision range probability model by adopting a parameterized S-shaped curve function based on the pitch angle; Constructing a large-scale fading model according to the line-of-sight probability model and the safety margin; establishing a small-scale fading model based on the Lees factor and the pitch angle; establishing an outage probability space according to the large-scale fading model and the small-scale fading model, and determining an accessible area of the unmanned aerial vehicle according to the outage probability space; and in the unmanned aerial vehicle reachable area, performing unmanned aerial vehicle resource optimization based on the outage probability space.
2. The method of claim 1, wherein constructing the line-of-sight probability model using a parameterized S-curve function based on pitch angle comprises: Based on environmental factors, fitting to obtain an offset parameter for controlling the horizontal position of the S-shaped curve, an amplitude parameter for controlling the probability of the S-shaped curve at the horizontal position and a slope parameter for controlling the steepness of the S-shaped rise; And constructing an S-shaped curve function according to the pitch angle, the offset parameter, the amplitude parameter and the slope parameter, and obtaining the sight distance probability model.
3. The method of claim 1, wherein constructing a large scale fading model from the line-of-sight probability model and a safety margin comprises: Calculating free space path loss according to the communication distance between the unmanned aerial vehicle and the source node, the carrier frequency and the speed of light; Establishing a pitch angle-related standard deviation function, and calculating a first average extra loss under the condition of visual range and a second average extra loss under the condition of non-visual range according to the standard deviation function and the safety margin; probability weighted summation is carried out on the first average extra loss and the second average extra loss based on the sight distance probability model, and total average extra loss is obtained; And summing the free space path loss and the total average additional loss to obtain the large-scale fading model.
4. A method according to claim 3, wherein the standard deviation function is constructed using an exponential model based on pre-fitted relevant environmental parameters and the pitch angle.
5. The method of claim 1, wherein establishing a small scale fading model based on the rice factor and the pitch angle comprises: constructing a relation function of a rice factor and a pitch angle; Fitting Marcum-Q scale adjustment polynomials and shape adjustment polynomials in the function by using a polynomial approximation method according to the relation function to obtain the small-scale fading model; Wherein the Marcum-Q function is an exponential function with respect to the scale and shape adjustment polynomials.
6. The method of claim 5, wherein constructing an outage probability space from the large-scale fading model and the small-scale fading model comprises: calculating total path loss according to the large-scale fading model; Substituting the total path loss and the rice factor in the small-scale fading model into the Marcum-Q function to construct a monotonic interruption probability model; and constructing an outage probability space according to the single-hop outage probability model and the outage probability constraint condition.
7. The method of claim 6, wherein determining a drone reachable area from the outage probability space comprises: calculating interrupt probability according to the single-hop interrupt probability model for each point in a preset space; And determining an area formed by points with the outage probability meeting the outage probability constraint condition as an unmanned plane reachable area.
8. The method of claim 6, wherein performing unmanned aerial vehicle resource optimization based on the outage probability space comprises: Establishing a signal-to-noise ratio model of a two-hop link; Taking the time allocation factor, the bandwidth allocation factor and the power allocation factor as optimization variables, and establishing an objective function according to the single-hop interrupt probability model; setting the time distribution factor, the bandwidth distribution factor and the power distribution factor value range and taking the unmanned aerial vehicle power constraint coefficient value range as constraint conditions; and solving the objective function according to the two-hop link signal-to-noise ratio model to obtain a resource optimization result of the unmanned aerial vehicle.
9. Unmanned aerial vehicle resource optimization device based on outage probability space modeling, characterized by comprising: The visual range modeling module is used for constructing a visual range probability model by adopting a parameterized S-shaped curve function based on the pitch angle; The large-scale modeling module is used for constructing a large-scale fading model according to the line-of-sight probability model and the safety margin; The small-scale modeling module is used for establishing a small-scale fading model based on the Lees factor and the pitch angle; The outage probability modeling module is used for constructing an outage probability space according to the large-scale fading model and the small-scale fading model, and determining an accessible area of the unmanned aerial vehicle according to the outage probability space; and the optimization module is used for carrying out unmanned aerial vehicle resource optimization based on the outage probability space in the reachable area of the unmanned aerial vehicle.
10. An electronic device comprising a processor and a memory, the memory storing a plurality of instructions, the processor configured to read the instructions and perform the method of any of claims 1-8.

Description

Unmanned aerial vehicle resource optimization method and device based on outage probability space modeling Technical Field The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle resource optimization method and device based on interrupt probability space modeling. Background With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly widely applied in the field of communication relay. The unmanned aerial vehicle plays an important role in scenes such as emergency communication, cellular network supplementation, data acquisition of the Internet of things and the like by virtue of the high flexibility and the sight distance transmission advantage of the unmanned aerial vehicle. However, the conventional unmanned aerial vehicle relay communication system faces serious challenges in a complex urban environment. Patent document CN120151995A proposes a method for controlling power of an unmanned aerial vehicle auxiliary relay system based on statistical knowledge, wherein the probability line-of-sight link condition between the unmanned aerial vehicle and a ground node, and large-scale and small-scale fading effects are considered, however, the existing line-of-sight probability model, large-scale fading model and small-scale fading model have the following defects: The existing line-of-sight probability model has high complexity, for example, the ITU recommendation model needs detailed building parameters, depends on specific scene configuration, lacks generality and adaptability, and is difficult to accurately predict channel states in different urban environments. Most studies use a simplified free space path loss model or rayleigh fading model, which does not accurately reflect the actual characteristics of the space-to-ground channel. Nonlinear relation between pitch angle and shadow fading variance is not fully considered in traditional large-scale fading modeling, and huge fluctuation of path loss in a low elevation area is ignored, so that the path loss estimation is inaccurate, and the system performance evaluation is influenced. In the prior art, a Rayleigh channel model is generally adopted to carry out small-scale fading modeling, but obvious line-of-sight components exist in unmanned aerial vehicle communication, and the Rayleigh model assumes no direct path and is seriously inconsistent with the actual channel characteristics. Although some studies employ rice channels, there is a lack of accurate modeling of rice factor versus spatial geometry. The above drawbacks severely limit the reliability, coverage and resource utilization efficiency of the unmanned aerial vehicle relay communication system in complex urban environments. Particularly in high-density urban areas, the performance degradation of the traditional method is more obvious due to building shielding and multipath effects. Disclosure of Invention The invention provides an unmanned aerial vehicle resource optimization method and device based on interrupt probability space modeling, which can effectively improve the reliability and resource utilization efficiency of an unmanned aerial vehicle relay communication system in a complex urban environment. An unmanned aerial vehicle resource optimization method based on interrupt probability space modeling comprises the following steps: constructing a vision range probability model by adopting a parameterized S-shaped curve function based on the pitch angle; Constructing a large-scale fading model according to the line-of-sight probability model and the safety margin; establishing a small-scale fading model based on the Lees factor and the pitch angle; establishing an outage probability space according to the large-scale fading model and the small-scale fading model, and determining an accessible area of the unmanned aerial vehicle according to the outage probability space; and in the unmanned aerial vehicle reachable area, performing unmanned aerial vehicle resource optimization based on the outage probability space. Further, constructing a line-of-sight probability model based on the pitch angle by using a parameterized S-shaped curve function, including: Based on environmental factors, fitting to obtain an offset parameter for controlling the horizontal position of the S-shaped curve, an amplitude parameter for controlling the probability of the S-shaped curve at the horizontal position and a slope parameter for controlling the steepness of the S-shaped rise; And constructing an S-shaped curve function according to the pitch angle, the offset parameter, the amplitude parameter and the slope parameter, and obtaining the sight distance probability model. Further, constructing a large-scale fading model according to the line-of-sight probability model and the safety margin, including: Calculating free space path loss according to the communication distance between the unmanned a