CN112738883A - Method and device for determining position of air base station - Google Patents
Method and device for determining position of air base station Download PDFInfo
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- CN112738883A CN112738883A CN202011488574.XA CN202011488574A CN112738883A CN 112738883 A CN112738883 A CN 112738883A CN 202011488574 A CN202011488574 A CN 202011488574A CN 112738883 A CN112738883 A CN 112738883A
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Abstract
The invention provides a method and a device for determining the position of an air base station, wherein the method comprises the following steps: acquiring historical ground terminal quantity data of a target area, and predicting the ground terminal quantity of the target area in a target time period according to the historical ground terminal quantity data; if the number of ground terminals in the target area in the target time period is larger than a preset threshold value, acquiring the current ground terminal position information in the target area; and determining the position of the air base station according to the current ground terminal position information in the target area. According to the method for determining the position of the aerial base station, the number of the ground terminals in the target time period of the target area is predicted in advance before the service capacity of the current base station can not meet the requirements of users, and when the predicted number of the ground terminals is large and a preset threshold value is reached, the position of the aerial base station is determined according to the position information of the current ground terminals in the target area, so that the problem of communication interruption caused by untimely deployment of the aerial base station is solved.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a method and a device for determining the position of an air base station.
Background
The construction of the smart power grid is an important direction for the development and construction of electric power systems in China, but in actual construction, the problem of uncoordinated planning and construction often exists, and the situation that a local hot spot area cannot obtain good communication service occurs. On one hand, because of the construction hysteresis of the power grid communication nodes, the power grid communication nodes in the planning are not completed, and the power utilization projects in the hot spot area are early established to be in urgent need of communication; on the other hand, some hot spot areas are not hot spots in the original power grid planning, but gradually become hot spots along with the enhancement of government development intensity, such as airports, business circles and the like, the areas have blanks in the planning, and the construction of ground base stations is not kept up with the progress.
Because the aerial base station based on the unmanned aerial vehicle has the advantages of hovering capability, easiness in deployment, flexibility in action, low deployment cost and the like, the temporary communication carried out by using the aerial base station based on the unmanned aerial vehicle is regarded as an important supplementary means for a ground communication network, so that the wireless capacity and the coverage range on the ground can be effectively enhanced, and the requirements of 5G and B5G cellular mobile communication are met. When a hot spot area appears and the ground base station cannot meet the communication requirement of a user, the unmanned aerial vehicle carrying temporary base station can be arranged above the hot spot area, and the capacity of the hot spot area is enhanced.
However, at present, the deployment position of the aerial base station is determined after the hot spot area appears, a certain time is needed from the discovery of the hot spot area to the determination of the deployment position of the aerial base station, and communication interruption caused by the fact that the service capability of the ground deployment base station cannot meet the user requirement can occur in the period of time, and the problems are caused by the defect that the deployment position of the aerial base station cannot be determined in time through the prior art.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect in the prior art that the deployment position of the air base station cannot be determined in time, so as to provide a method and a device for determining the position of the air base station.
The first aspect of the present invention provides a method for determining an air base station location, including: acquiring historical ground terminal quantity data of a target area, and predicting the ground terminal quantity of the target area in a target time period according to the historical ground terminal quantity data; if the number of ground terminals in the target area in the target time period is larger than a preset threshold value, acquiring the current ground terminal position information in the target area; and determining the position of the air base station according to the current ground terminal position information in the target area.
Optionally, in the method for determining an air base station location provided by the present invention, acquiring historical data of the number of ground terminals in a target area includes: acquiring initial historical ground terminal quantity data of a plurality of areas; filtering the initial historical ground terminal quantity data according to the average value and the standard deviation of the initial historical ground terminal quantity data; grouping the initial historical ground terminal quantity data subjected to filtering processing according to the geographical position information of the plurality of areas to obtain the historical ground terminal quantity data subjected to filtering processing of the target area; and filling the filtered historical ground terminal quantity data of the target area according to the average value of the filtered historical ground terminal quantity data of the target area to obtain the historical ground terminal quantity data of the target area.
Optionally, in the method for determining the position of the air base station provided by the present invention, the step of determining the position of the air base station according to the current position information of the ground terminal in the target area includes: determining the initial position of the air base station according to the current ground terminal position information; determining the capacity of the aerial base station when the aerial base station is positioned at the initial position according to the initial position information of the aerial base station and the current ground terminal position information; and inputting the capacity of the air base station at the initial position into a reinforced learning model to determine the position of the air base station.
Optionally, in the method for determining a position of an air base station provided by the present invention, the step of determining an initial position of the air base station according to the current ground terminal position information includes: clustering the ground terminals according to the current ground terminal position information and a first preset clustering algorithm to obtain an initial clustering center; clustering the ground terminals according to the current ground terminal position information, the initial clustering center and a second preset clustering algorithm to obtain a target clustering center; and determining the target clustering center as the initial position of the aerial base station.
Optionally, in the method for determining the position of the air base station provided by the present invention, the air base station is a millimeter wave air base station, and the step of determining the capacity of the air base station at the initial position according to the initial position information of the air base station and the current ground terminal position information includes: according to initial position information of the millimeter wave air base station and current position information of the ground terminal, line-of-sight link loss and non-line-of-sight link loss from the ground terminal to the millimeter wave air base station and antenna gain in a millimeter wave transmission process are determined; acquiring environmental parameters of the current position of the millimeter wave air base station, and determining the line-of-sight link probability and the non-line-of-sight link probability from the ground terminal to the millimeter wave air base station according to the environmental parameters and the initial position of the millimeter wave air base station; obtaining the total path loss from the ground terminal to the millimeter wave air base station according to the line-of-sight link probability and the non-line-of-sight link probability and the corresponding line-of-sight link loss and the non-line-of-sight link loss; determining the total signal-to-noise ratio of a receiver when the millimeter wave air base station is at the initial position according to the antenna gain and the total path loss of each ground terminal; and determining the capacity of the millimeter wave air base station at the initial position according to the total signal-to-noise ratio of the receiver.
Optionally, in the method for determining a position of an air base station provided by the present invention, the step of obtaining a total path loss from the ground terminal to the millimeter wave air base station according to the line-of-sight link probability and the non-line-of-sight link probability and the corresponding line-of-sight link loss and the non-line-of-sight link loss includes:
wherein the content of the first and second substances,
representing the line-of-sight link loss from the ground terminal i to the millimeter wave air base station,
representing the non-line-of-sight link loss from the ground terminal i to the millimeter wave air base station,
ρ is a fixed path loss given by ρ 32.4+20log (f), f is the frequency of the millimeter wave carried by the millimeter wave air base station, xLLognormal random variable, representing shadowing effects in line-of-sight link scenarios, xNIs a lognormal random variable representing the shadowing effect in a non-line-of-sight link scenario, alphaLoSThe representation is the path loss exponent, alpha, in the line-of-sight link scenarioNLosRepresenting path loss exponent, d, in non-line-of-sight link scenariosiIs the distance of a ground terminal i from an air base station, PLoSiIs the line-of-sight link probability, PNLoS, of the ground terminal i and the millimeter wave air base stationiThe probability of the non-line-of-sight link between the ground terminal i and the millimeter wave air base station,
a and b are environment-dependent parameters,
represents the elevation angle from the ground terminal i to the millimeter wave air base station, h represents the height of the initial position, xiRepresenting the horizontal distance, PNLoS, of the vertical projection of the ground terminal i from the millimeter wave air base station on the groundi＝1-PLoSi。
Optionally, in the method for determining a position of an air base station provided by the present invention, determining a total signal-to-noise ratio of a receiver when the millimeter wave air base station is at an initial position according to an antenna gain and a total path loss of each ground terminal includes:
where SNR represents the receiver overall signal-to-noise ratio, GaDenotes the antenna gain, Ga＝Gi_mainGr_main，Gi_mainRepresenting the main lobe gain, G, of the millimeter wave air base stationr_mainRepresenting the sidelobe gain, P, of the millimeter wave sky base stationtIs the transmission power of the millimeter wave air base station, sigma2Is noise, PLiRepresenting the overall path loss from the ground terminal i to the millimeter wave air base station.
A second aspect of the present invention provides an apparatus for determining an aerial base station position, including: the ground terminal quantity prediction module is used for acquiring historical ground terminal quantity data of the target area and predicting the ground terminal quantity of the target area in a target time period according to the historical ground terminal quantity data; the current ground terminal position information acquisition module is used for acquiring current ground terminal position information in the target area if the number of ground terminals in the target area in the target time period is greater than a preset threshold value; and the base station position determining module is used for determining the position of the air base station according to the current ground terminal position information in the target area.
A third aspect of the present invention provides a computer apparatus comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform the method of aerial base station position determination as provided in the first aspect of the invention.
A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for causing a computer to execute the method for determining an air base station position according to the first aspect of the present invention.
The technical scheme of the invention has the following advantages:
1. according to the method for determining the position of the aerial base station, the number of the ground terminals in the target time period of the target area is predicted in advance before the service capacity of the current base station can not meet the requirements of users, and when the predicted number of the ground terminals is large and a preset threshold value is reached, the position of the aerial base station is determined according to the position information of the current ground terminals in the target area, so that the problem of communication interruption caused by untimely deployment of the aerial base station is solved.
2. According to the aerial base station position determining method provided by the invention, after the initial ground terminal quantity data is obtained, the initial ground terminal quantity data is filtered, then the filtered data is filled, so that the historical ground terminal quantity data of the target area is obtained, abnormal data in the initial ground terminal quantity data is filtered through filtering, and the integrity of the data is ensured through filling.
3. According to the method for determining the position of the air base station, the capacity of the air base station is combined with reinforcement learning after the initial position of the air base station is determined according to the current ground terminal position information, so that the reinforcement learning model can determine the position of the air base station according to the capacity of the initial position, the flexibility of deployment of the air base station is improved, and the effectiveness of communication supplement of the air base station is improved.
4. The aerial base station position determining device predicts the number of the ground terminals of the target area in the target time period in advance before the service capability of the current base station can not meet the requirement of a user, and determines the position of the aerial base station according to the current ground terminal position information in the target area when the predicted number of the ground terminals is large and a preset threshold value, so that the problem of communication interruption caused by untimely deployment of the aerial base station is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1-5 are flowcharts of specific examples of methods for determining the position of an airborne base station according to embodiments of the present invention;
FIG. 6 is a schematic block diagram of a specific example of an airborne base station position determining apparatus in an embodiment of the present invention;
fig. 7 is a schematic block diagram of a specific example of a computer device in the embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the technical features related to the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
An embodiment of the present invention provides a method for determining an air base station location, as shown in fig. 1, including:
step S10: and acquiring historical ground terminal quantity data of the target area.
In a specific embodiment, the target area may be any area where a ground terminal exists, such as an airport, a business district, and a residential area, and the ground terminal is any device that needs to transmit data through a base station, for example, a device such as a mobile phone and a computer with a communication function. The historical ground terminal quantity data of the target area refers to the quantity of the ground terminals in the target area in different time periods.
Step S20: and predicting the number of ground terminals of the target area in the target time period according to the historical data of the number of the ground terminals. The target time period may be the next half year, a year, etc.
In a specific embodiment, the number of ground terminals in the target time period of the target area can be predicted by a differential Autoregressive Integrated Moving Average (ARIMA), where ARIMA is composed of three parts, i.e., Autoregressive (AR), differential (I), and Moving Average (MA). AR is used to predict future values by linear combination of past values, MA is used to predict future values by extracting the influence of past values, and I represents a differential time series to smooth it.
In the embodiment of the invention, the formula for predicting the number of ground terminals in the target time period of the target area through ARIMA is as follows:
wherein y istRepresenting the predicted result of the target time period t, aiIs the AR parameter, θjIs the MA parameter,. epsilontIs a random error term and p, q are the order of AR and MA, respectively. In a specific embodiment, for p and q, after acquiring historical ground terminal quantity data, drawing data, observing whether the data is a stationary time sequence, performing differential calculation on a non-stationary time sequence to obtain a stationary time sequence, then respectively obtaining an autocorrelation coefficient ACF and a partial autocorrelation coefficient PACF of the stationary time sequence, obtaining an optimal hierarchy p and an order q by analyzing an autocorrelation graph and a partial autocorrelation graph, and finally obtaining an optimal ARIMA model, wherein for AR parameters and MA parameters, auto.
Step S30: and judging whether the number of the ground terminals of the target area in the target time period is greater than a preset threshold, if so, executing the steps S40 and S50, and if not, not executing any operation.
In a specific embodiment, the preset threshold may be determined according to the number of terminals supportable by the base station in the current target area, for example, the preset threshold may be set to 85% of the number of terminals supportable by the base station in the current target area, and if the predicted number of ground terminals of the target area in the target time period is greater than 85% of the number of terminals supportable by the base station in the current target area, it is determined that the number of ground terminals of the target area in the target time period is greater than the preset threshold, which means that there is a problem that communication is interrupted due to the fact that the service capability of the current base station cannot meet the user requirement in the target time period, so that steps S40 and S50 need to be performed to determine the position of the aerial base station, and implement the deployment work of the aerial base.
Step S40: and acquiring the current ground terminal position information in the target area. The current ground terminal location information within the target area may be two-dimensional coordinate information of all terminals within the current target area.
Step S50: and determining the position of the air base station according to the current ground terminal position information in the target area.
Since the relative position of the terminal in the target area and the air base station has a certain influence on the capacity of the air base station, in order to increase the capacity of the air base station as much as possible, the method for determining the position of the air base station provided in the embodiment of the present invention combines the current position information of the ground terminal in the target area when calculating the position of the air base station.
According to the method for determining the position of the aerial base station, the number of the ground terminals in the target time period of the target area is predicted in advance before the service capacity of the current base station cannot meet the requirements of users, and when the predicted number of the ground terminals is large and a preset threshold value is reached, the position of the aerial base station is determined according to the position information of the current ground terminals in the target area, so that the problem of communication interruption caused by untimely deployment of the aerial base station is solved.
In an optional embodiment, in the method for determining an air base station location provided in the embodiment of the present invention, as shown in fig. 2, the step S10 specifically includes:
step S11: initial historical ground terminal quantity data of a plurality of areas is obtained.
Step S12: and filtering the initial historical ground terminal quantity data according to the average value and the standard deviation of the initial historical ground terminal quantity data.
The purpose of filtering the initial historical ground terminal quantity data is to filter abnormal values in the initial historical ground terminal quantity data, and when the data are subjected to normal distribution, the probability of being not more than 3 sigma away from the average value is P (| x-mu |) according to the definition of the normal distribution>3σ)<And 0.003, which belongs to the extremely small probability event, and when the number of terrestrial terminals in a certain time period is more than 3 σ from the average value of the initial historical number of terrestrial terminals data, the number of terrestrial terminals is determined to be an abnormal value. Where μ and σ are the mean and standard deviation, respectively, based on the historical data set { x1,x2,…,xm}, can be calculated by:
step S13: and grouping the initial historical ground terminal quantity data subjected to filtering processing according to the geographical position information of the plurality of areas to obtain the historical ground terminal quantity data subjected to filtering processing of the target area.
In a specific embodiment, the initial historical ground terminal quantity data acquired in the earlier stage is data of a plurality of regions, and in order to perform accurate analysis on each region, the initial historical ground terminal quantity data subjected to filtering processing is grouped according to the geographical position information and the acquisition time of each initial historical ground terminal quantity data in the embodiment of the present invention: u ═ U1,U2,…,Ui,…,UkIn which U isiIndicating the ith group of data having the same time and location. And then acquiring filtered historical ground terminal quantity data of the target area according to the geographical position information of the target area to be researched.
Step S14: and filling the filtered historical ground terminal quantity data of the target area according to the average value of the filtered historical ground terminal quantity data of the target area to obtain the historical ground terminal quantity data of the target area.
When the abnormal value is deleted in step S13, there is a problem that data is missing, and it is difficult to accurately predict the number of ground terminals using incomplete data, and therefore, it is necessary to fill in the filtered data. In the embodiment of the present invention, a Conditional Mean Interpolation (CMI) is used to process missing values in data, and specifically, the mean value of each group is calculated as follows:
wherein X represents the number of the filtered historical ground terminals in one group, | UiI represents UiThe size of (2). After filling the corresponding missing values with the average values of each group, the interference can be eliminated and a more accurate prediction result can be obtained.
In an optional embodiment, in the method for determining an air base station location provided in the embodiment of the present invention, as shown in fig. 3, the step S50 specifically includes:
step S51: and determining the initial position of the air base station according to the current ground terminal position information.
In a specific embodiment, the aerial base station is established to meet the communication requirements of more ground terminals, so that the terminals in the target area can be clustered according to the current ground terminal position information, a clustering center is determined, and the clustering center is determined as the initial position of the aerial base station.
Step S52: and determining the capacity of the air base station when the air base station is positioned at the initial position according to the initial position information of the air base station and the current ground terminal position information.
Step S53: and inputting the capacity of the air base station at the initial position into a reinforced learning model to determine the position of the air base station. In the embodiment of the invention, the capacity of the air base station at the initial position is input into the reinforcement learning model, so that the position of the air base station is determined, and the aim is to maximize the capacity of the air base station.
Illustratively, the reinforcement learning model is obtained by training with the capacity of the air base station at the current position and the capacity of the position at the last moment as rewards and with the highest capacity as an optimization target.
The reinforcement learning is to continuously explore in a given scene through the intelligent agent to obtain the information of the environment state, meanwhile, the environment can feed back an award value to the intelligent agent according to the action taken by the intelligent agent, and the intelligent agent continuously explores and learns to the optimal decision to obtain the maximum long-term award. In the reinforcement learning model (Deep Q Network, DNQ) in this embodiment, a millimeter wave air base station is trained as an agent, and training is performed to complete a task of maximizing system capacity by adjusting the position of the millimeter wave air base station.
The specific reinforcement learning model is modeled as follows:
agent (Agent): the millimeter wave air base station can be regarded as a single intelligent agent, when a task starts, the millimeter wave air base station selects an action according to an e-greedy strategy, then the environment sends the next state, and rewards are returned to the intelligent agent. The agent updates its knowledge with the reward returned by the environment, evaluating the last action. This cycle continues until the air base station task ends.
State (State): the state set is the current position of the base station in the air, i.e., S { (x, y) }.
Action (Action): the action set is the movable direction of the base station in the air, i.e. a ═ five options of forward 10 meters, backward 10 meters, left 10 meters, right 10 meters, hover.
Reward (Reward): the instant prize is set as the system capacity difference between the current time and the last time, and is expressed as: r ═ Ccapacity(t+tδ)-Ccapacity(t), wherein tδIs the time difference between the current time and the previous time.
The main process of the DQN algorithm is as follows:
step1, initializing an experience playback pool D with the capacity of N; and (4) the intelligent agent is enabled to explore the environment, the experience pool is accumulated to a certain degree, and a batch of samples are randomly extracted for training.
Step2, initializing a Q network and a neural network parameter omega thereof; initializing a target Q network and its neural network parameters omega-。
DQN consists of two networks, a Q network and a target Q network, which are identical in structure but differ in parameters. The Q network uses the latest parameters, whereas the parameters of the target Q network are used several iterations. Q (S, A; omega) represents the output of the current Q network and is used for evaluating the value function of the action pair of the current state; q (S, A; omega)-) Representing the output of the target Q network, the parameters of the target Q network may be updated according to a loss function: and copying the parameters of the Q network to the target Q network after a certain number of iterations. The purpose of the target Q network is to improve the stability of the algorithm, because the target Q value is kept unchanged for a period of time, and the correlation between the current Q value and the target Q value is reduced to a certain extent.
Step3 loops through round epamode ═ 1,2, …, M:
step3.1 initializes the state set S;
step3.2 loops through step ═ 1,2, …, T:
step3.2.1 adopts an action strategy A by using an epsilon-greedy strategy;
step3.2.2 executing the action A, calculating the cost reward R of the millimeter wave air base station for taking the action A under the state S, and obtaining the planning state S' of the next moment by the millimeter wave air base station;
step3.2.3 stores the samples (S, A, R, S') in an empirical replay pool D;
step3.2.4 Using uniformly randomly sampled samples Minibatch in an empirical replay pool, the target Q value, y, was calculatedi＝R+γ·maxAQ(S′,A；ω-) Wherein, yiFor the target Q value, R is the reward function, γ is the Discount factor (γ (Discount Rate) value is between 0 and 1, indicating the importance of future return relative to the current return, when γ is 0, it is equivalent to only considering immediate return and not considering long-term return, when γ is 1, long-term return and immediate return are equally important). Updating Q network parameter ω to reduce loss function yi-Q(S,A；ω)]2。
Step3.2.5 Per IntervalC steps updates parameters of base station planning target Q network, namely omega-I.e. copying the neural network parameter ω of the Q network to the neural network parameter ω of the target Q network-。
By the aid of the pre-established reinforcement learning model, the position of the unmanned aerial vehicle bearing the millimeter wave air base station can be adjusted according to the capacity of the position at each moment, the change of the capacity difference between the previous moment and the current moment is used as a reward (reward) of the reinforcement learning model, and the reward obtained is larger when the capacity difference is large. The unmanned aerial vehicle bearing the millimeter wave aerial base station can select actions (the actions are randomly selected according to the probability of belonging to the group, and the actions capable of obtaining the maximum reward are selected according to the probability of belonging to the group) according to the strategy of belonging to the group-greedy at the next moment, if the capacity of the position is higher than that of the current position, the unmanned aerial vehicle bearing the millimeter wave aerial base station has higher probability to select the action, and through multiple iterations, the unmanned aerial vehicle bearing the millimeter wave aerial base station can move to the position with the maximum capacity, so that dynamic deployment is realized, and the optimal communication effect is obtained.
According to the method for determining the position of the air base station, the capacity of the air base station is combined with reinforcement learning, so that the reinforcement learning model can determine the position of the air base station according to the capacity of the current position, the flexibility of deployment of the air base station is improved, and the effectiveness of communication supplement of the air base station is improved.
In the embodiment of the present invention, the simulation parameters of the reinforcement learning model are shown in table 1.
TABLE 1
According to the method for determining the position of the air base station, provided by the embodiment of the invention, after the initial position of the air base station is determined according to the current ground terminal position information, the capacity of the air base station is combined with reinforcement learning, so that a reinforcement learning model can determine the position of the air base station according to the capacity of the initial position, the deployment flexibility of the air base station is improved, and the effectiveness of the air base station in communication supplement is increased.
In an alternative embodiment, as shown in fig. 4, in the method for determining an air base station location provided in the embodiment of the present invention, the step S51 specifically includes:
step S511: and clustering the ground terminals according to the current ground terminal position information and a first preset clustering algorithm to obtain an initial clustering center.
In the embodiment of the present invention, the first preset clustering algorithm is a Self-organizing mapping algorithm (SOM), the SOM algorithm is an artificial neural network algorithm with visualization and unsupervised features, can simulate the feature of human brain processing signals, performs competitive learning, has high adaptive learning capability and robustness, is suitable for initial cluster analysis of complex samples, specifically clusters ground terminals according to current ground terminal position information and the SOM algorithm, and obtains an initial clustering center, where the step of obtaining the initial clustering center includes:
1) and (5) initializing the weight value. For the weight vector W connecting the input node to the jth output nodej(j ═ 1,2, …, p) is assigned a random number, and the initial number of cycles is set.
2) SOM initial clustering. Position information X for each ground terminalN(N-1, 2, …, N), W is first calculated according to the following equationjWinner weight vector W ingAnd XNThe distance of (c):
then, define Ng(t) is the neighborhood of the winner, the unit g is the winner, the connection weight vector corresponding to each unit in the neighborhood is connected with XiAnd (4) closing. And repeating the training times until the network is stable, and finishing initial clustering of the samples according to the response of the output nodes. The learning equation is as follows:
in the formula: eta (t) is the learning rate of the t time and decreases progressively as the training times increase;
the input of the ith input node of the Nth sample; w is aijIs a connection weight between the ith input node and the jth output node, where j ∈ NN(t)。
3) The clustering center Z obtained by applying the SOM algorithm is equal to (Z)1，Z2，…，ZK) As the initial center.
Step S512: and clustering the ground terminals according to the current ground terminal position information, the initial clustering center and a second preset clustering algorithm to obtain a target clustering center.
In the embodiment of the invention, the second preset clustering algorithm is a K-means algorithm, the K-means algorithm is an objective function dividing method adopting sample Euclidean distance as a similarity evaluation index, and the K-means algorithm is a typical non-hierarchical clustering algorithm based on distance. The K-means principle is simple, the running speed is high, but the position selection of the initialized mass center has great influence on the final clustering result and the running time, if the selection is only completely random, the clustering result is not ideal and the algorithm convergence is slow, so that in the embodiment of the invention, before the K-means algorithm is used for clustering, the initial clustering center is obtained through the SOM algorithm. Specifically, the step of clustering by the K-means algorithm includes:
firstly, calculating the value of the sum J (C) of the squares of the distances from all samples to the cluster centers of the categories where the samples are located, and dividing each ground terminal to the nearest category center, wherein the same category center is one category.
In the formula: u. ofmnBeing binary variables, umn1 denotes the n-thIndividual ground terminals belonging to class m, umn0 means not belonging to this class; d (c)m,xn) The distance from the ground terminal to the category clustering center of the ground terminal; c. CmIs a clustering center; x is the number ofnAnd other ground terminal data in the class.
Then updating the clustering centers, and updating the centers c of the K classes according to the division result calculated in the previous step, the least square method and the Lagrange principlemUntil the convergence condition is satisfied.
And obtaining a final clustering result, wherein the total number of the clusters is k. Each cluster is allocated with a millimeter wave air base station, and the millimeter wave air base station provides communication service for the power grid terminal equipment in the cluster. The initial position of each millimeter wave air base station is the center c of the corresponding clusterm。
Step S513: and determining the target clustering center as the initial position of the aerial base station.
In an optional embodiment, in the method for determining a position of an air base station according to the embodiment of the present invention, the air base station is a millimeter wave air base station, as shown in fig. 5, where the step S52 specifically includes:
s521: according to initial position information of the millimeter wave air base station and current position information of the ground terminal, line-of-sight link loss and non-line-of-sight link loss from the ground terminal to the millimeter wave air base station and antenna gain in a millimeter wave transmission process are determined;
illustratively, according to the initial position information of the millimeter wave air base station and the position information of the ground terminal, the distance between the ground terminal i and the millimeter wave air base station, that is, the distance between the ground terminal i and the millimeter wave air base station can be obtained
xiThe horizontal distance from the ground terminal i to the millimeter wave air base station in the vertical projection on the ground is represented by h, and the height of the millimeter wave air base station is represented by h.
According to the distance between the ground terminal i and the millimeter wave air base station, the line-of-sight link loss and the non-line-of-sight link loss can be obtained through the following formulas:
wherein the content of the first and second substances,
representing the line-of-sight link loss from the ground terminal i to the millimeter wave air base station,
represents the non-line-of-sight link loss of the ground terminal i to the millimeter wave air base station, rho is the fixed path loss given by rho being 32.4+20log (f), f is the frequency of the millimeter wave loaded by the millimeter wave air base station, χLLognormal random variable, representing shadowing effects in line-of-sight link scenarios, xNIs a lognormal random variable representing the shadowing effect in a non-line-of-sight link scenario, alphaLoSThe representation is the path loss exponent, alpha, in the line-of-sight link scenarioNLoSRepresenting path loss exponent, d, in non-line-of-sight link scenariosiFor the distance of the ground terminal i from the aerial base station, i.e.
In the examples of the present invention, αLoSHas a value of 2, alphaNLoSHas a value of 3.3, std (χ)L) Has a value of 5.2, std (χ)N) The value of (A) was 7.2.
Antenna arrays are arranged on the millimeter wave air base station and the ground receiver to form directional beams, and therefore antenna gains are also obtained in the signal transmission process. When the assumed communication is established, the antennas on the millimeter wave air base station and the ground electric terminal can be adjusted in angle and aligned with each other, and then the antenna gain is the product of the main lobe gain and the side lobe gain of the transmitting end:
Ga＝Gi_mainGr_main，
Gi_mainrepresenting the millimeter wave air basisMain lobe gain of a station, Gr_mainThe intrinsic parameters of the base station antenna when the sidelobe gain, the main lobe gain and the sidelobe gain of the millimeter wave aerial base station are expressed are related to the specific model, and illustratively, the main lobe gain is 10dB, and the sidelobe gain is-10 dB.
S522: acquiring environmental parameters of the current position of the millimeter wave air base station, and determining the line-of-sight link probability and the non-line-of-sight link probability from the ground terminal to the millimeter wave air base station according to the environmental parameters and the initial position of the millimeter wave air base station;
the probability of the line-of-sight link from the ground terminal to the millimeter wave air base station is determined according to the environmental parameters and the current position information of the millimeter wave air base station, and can be obtained by the following formula:
wherein, PLoSiIs the line-of-sight link probability of the ground terminal i and the millimeter wave air base station,
the elevation angle of the ground terminal i to the millimeter wave air base station is shown, h is the height of the initial position, in a specific embodiment, the height of the initial position can be determined to be 100 meters, and a and b are parameters depending on the environment, such as a high-rise urban environment (a-27.23; b-0.08), a dense urban environment (a-12.08; b-0.11), and a suburban environment (a-4.88; b-0.43).
The probability of the corresponding non-line-of-sight link between the ground terminal i and the millimeter wave air base station is as follows:
PNLoSi＝1-PLoSi。
s523, obtaining the total path loss from the ground terminal to the millimeter wave air base station according to the line-of-sight link probability and the non-line-of-sight link probability and the corresponding line-of-sight link loss and the non-line-of-sight link loss;
for example, according to the line-of-sight link probability and the non-line-of-sight link probability and the corresponding line-of-sight link loss and non-line-of-sight link loss, the manner of obtaining the total path loss from the ground terminal to the millimeter wave air base station may be determined by the following formula:
wherein PLiRepresenting the total path loss, PLoS, from the ground terminal i to the millimeter wave air base stationiExpressed as the line-of-sight link probability of the ground terminal i with the millimeter wave air base station,
represents the line-of-sight link loss, PNLoS, from the ground terminal i to the millimeter wave air base stationiRepresenting the probability of a non-line-of-sight link between the ground terminal i and the millimeter wave air base station,
representing the non-line-of-sight link loss of the ground terminal i to the millimeter wave air base station.
S524: determining the total signal-to-noise ratio of the receiver of the millimeter wave air base station at the initial position according to the antenna gain and the total path loss of each ground terminal;
illustratively, according to the antenna gain and the total path loss of each ground terminal, the total signal-to-noise ratio of the receiver for determining that the millimeter wave air base station is at the initial position may be obtained by the following formula:
s525: and determining the capacity of the millimeter wave air base station at the initial position according to the total signal-to-noise ratio of the receiver.
Illustratively, the capacity is an important index of the communication capability of the communication system and the terminal device, and according to the signal-to-noise ratio formula, the capacity when the millimeter wave air base station is at the current position can be deduced as follows:
Ccapacity＝B log2(1+SNR)；
wherein, CcapacityRepresenting the capacity of the millimeter wave air base station at the initial position, B being the channel bandwidth, and SNR representing the total signal-to-noise ratio of the receiver.
In any of the above embodiments, simulation parameters of the millimeter wave transmission process are shown in table 2 below.
TABLE 2
| Parameter | Value |
| Frequency | 28GHz |
| Bandwidth | 2GHz |
| Transmit Power | 1W |
| αLoS, |
2,3.3 |
| std(χL),std(χN) | 5.2,7.2 |
| Region of Interest | 100×100m2 |
| Height of UAV | 100m |
| Overload thresholdτ | 85％ |
An embodiment of the present invention further provides an apparatus for determining a location of an air base station, as shown in fig. 6, including:
the ground terminal quantity predicting module 10 is configured to obtain historical ground terminal quantity data of the target area, and predict the ground terminal quantity of the target area in the target time period according to the historical ground terminal quantity data, for details, see the description of step S10 and step S20, which is not described herein again.
If the number of ground terminals in the target area within the target time period is greater than the preset threshold, the current ground terminal position information obtaining module 20 is configured to obtain current ground terminal position information in the target area, and the details are described in the above description of step S30 and step S40, and are not repeated here.
The base station position determining module 30 is configured to determine the position of the air base station according to the current ground terminal position information in the target area, for details, see the description of step S50 above, and are not described herein again.
The aerial base station position determining device provided by the embodiment of the invention predicts the number of the ground terminals in the target time period in the target area in advance before the service capability of the current base station can not meet the requirement of a user, and determines the position of the aerial base station according to the current ground terminal position information in the target area when the predicted number of the ground terminals is large and a preset threshold value, thereby avoiding the problem of communication interruption caused by untimely deployment of the aerial base station.
A further embodiment of the present invention provides a computer device, as shown in fig. 7, the computer device mainly includes one or more processors 61 and a memory 62, and fig. 7 takes one processor 61 as an example.
The computer device may further include: an input device 63 and an output device 64.
The processor 61, the memory 62, the input device 63 and the output device 64 may be connected by a bus or other means, and fig. 7 illustrates the connection by a bus as an example.
The processor 61 may be a Central Processing Unit (CPU). The Processor 61 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory 62 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the airborne base station position determining apparatus, and the like. Further, the memory 62 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 62 optionally includes memory located remotely from the processor 61, and these remote memories may be connected to the over-the-air base station position determining device via a network. The input device 63 may receive a calculation request (or other numeric or character information) input by a user and generate a key signal input relating to the airborne base station position determining device. The output device 64 may include a display device such as a display screen for outputting the calculation result.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer-readable storage medium stores computer-executable instructions, where the computer-executable instructions may execute the method for determining an airborne base station location in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. An over-the-air base station position determination method, comprising:
acquiring historical ground terminal quantity data of a target area, and predicting the ground terminal quantity of the target area in a target time period according to the historical ground terminal quantity data;
if the number of the ground terminals in the target area in the target time period is larger than a preset threshold value, acquiring the current ground terminal position information in the target area;
and determining the position of the air base station according to the current ground terminal position information in the target area.
2. The method of claim 1, wherein obtaining historical ground terminal quantity data for a target area comprises:
acquiring initial historical ground terminal quantity data of a plurality of areas;
filtering the initial historical ground terminal quantity data according to the average value and the standard deviation of the initial historical ground terminal quantity data;
grouping the initial historical ground terminal quantity data subjected to filtering processing according to the geographic position information of the plurality of areas to obtain the historical ground terminal quantity data subjected to filtering processing of the target area;
and filling the filtered historical ground terminal quantity data of the target area according to the average value of the filtered historical ground terminal quantity data of the target area to obtain the historical ground terminal quantity data of the target area.
3. The method of claim 1, wherein the step of determining the position of the air base station according to the current position information of the ground terminal in the target area comprises:
determining the initial position of the air base station according to the current ground terminal position information;
determining the capacity of the aerial base station when the aerial base station is positioned at the initial position according to the initial position information of the aerial base station and the current ground terminal position information;
and inputting the capacity of the air base station at the initial position into a reinforcement learning model, and determining the position of the air base station.
4. The method of claim 3, wherein the step of determining the initial position of the airborne base station based on the current ground terminal position information comprises:
clustering the ground terminals according to the current ground terminal position information and a first preset clustering algorithm to obtain an initial clustering center;
clustering the ground terminals according to the current ground terminal position information, an initial clustering center and a second preset clustering algorithm to obtain a target clustering center;
determining the target cluster center as an initial position of the airborne base station.
5. The method according to claim 3, wherein the air base station is a millimeter wave air base station, and the step of determining the capacity of the air base station at the initial position according to the initial position information of the air base station and the current ground terminal position information comprises:
according to the initial position information of the millimeter wave air base station and the current position information of the ground terminal, determining the line-of-sight link loss and the non-line-of-sight link loss from the ground terminal to the millimeter wave air base station and the antenna gain in the millimeter wave transmission process;
acquiring environmental parameters of the current position of the millimeter wave air base station, and determining the line-of-sight link probability and the non-line-of-sight link probability from the ground terminal to the millimeter wave air base station according to the environmental parameters and the initial position of the millimeter wave air base station;
obtaining the total path loss from the ground terminal to the millimeter wave air base station according to the line-of-sight link probability and the non-line-of-sight link probability and the corresponding line-of-sight link loss and the non-line-of-sight link loss;
determining the total signal-to-noise ratio of a receiver when the millimeter wave air base station is at the initial position according to the antenna gain and the total path loss of each ground terminal;
and determining the capacity of the millimeter wave air base station at the initial position according to the total signal-to-noise ratio of the receiver.
6. The air base station position determining method of claim 5, wherein the step of obtaining the total path loss from the ground terminal to the millimeter wave air base station according to the line-of-sight link probability and the non-line-of-sight link probability and the corresponding line-of-sight link loss and the non-line-of-sight link loss comprises:
wherein the content of the first and second substances,
representing the line-of-sight link loss from the ground terminal i to the millimeter wave air base station,
representing the non-line-of-sight link loss from the ground terminal i to the millimeter wave air base station,
ρ is a fixed path loss given by ρ 32.4+20log (f), f is a frequency of a millimeter wave loaded in the millimeter wave air base station, χLLognormal random variable, representing shadowing effects in line-of-sight scenes, χNIs a lognormal random variable representing the shadowing effect in a non-line-of-sight link scenario, alphaLoSThe representation is the path loss exponent, alpha, in the line-of-sight link scenarioNLoSRepresenting path loss exponent, d, in non-line-of-sight link scenariosiIs the distance of a ground terminal i from an air base station, PLoSiIs the line-of-sight link probability, PNLoS, of the ground terminal i and the millimeter wave air base stationiThe probability of the non-line-of-sight link between the ground terminal i and the millimeter wave air base station,
a and b are environment-dependent parameters,
represents the elevation angle from the ground terminal i to the millimeter wave air base station, h represents the height of the initial position, xiRepresenting the horizontal distance, PNLoS, of the vertical projection of the ground terminal i from the millimeter wave air base station on the groundi＝1-PLoSi。
7. The method of claim 6, wherein determining the total signal-to-noise ratio of the receiver when the millimeter wave air base station is at the initial position according to the antenna gain and the total path loss of each ground terminal comprises:
where SNR represents the receiver overall signal-to-noise ratio, GaDenotes the antenna gain, Ga＝Gi_mainGr_main，Gi_mainRepresenting the main lobe gain, G, of the millimeter wave air base stationr_mainRepresenting the sidelobe gain, P, of the millimeter wave sky base stationtIs the transmission power of the millimeter wave air base station, sigma2Is noise, PLiRepresenting the overall path loss from the ground terminal i to the millimeter wave air base station.
8. An over-the-air base station position determining apparatus, comprising:
the ground terminal quantity prediction module is used for acquiring historical ground terminal quantity data of a target area and predicting the ground terminal quantity of the target area in a target time period according to the historical ground terminal quantity data;
the current ground terminal position information acquisition module is used for acquiring current ground terminal position information in the target area if the number of ground terminals in the target area in the target time period is greater than a preset threshold value;
and the base station position determining module is used for determining the position of the air base station according to the current ground terminal position information in the target area.
9. A computer device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform the method of aerial base station position determination according to any of claims 1-7.
10. A computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of airborne base station location determination according to any of claims 1-7.
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