CN114268965B - Mobile communication network coverage rate calculation method and device based on deep learning - Google Patents
Mobile communication network coverage rate calculation method and device based on deep learning Download PDFInfo
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Abstract
The application discloses a mobile communication network coverage rate calculating method and device based on deep learning, comprising the following steps: acquisition of pilot signal transmit power for ith sub-regionAnd pilot signal transmit power for each neighbor sub-region of the ith sub-regionWherein the parameter n i Representing the number of neighbor subregions of the ith subregion; normalizing pilot signal transmitting power of the ith sub-area and the neighbor sub-area of the ith sub-area according to the following formula;wherein the method comprises the steps ofThe pilot signal maximum transmit power of the r neighbor sub-region of the i-th sub-region,the pilot signal minimum transmit power representing the r-th neighbor sub-region,representing normalized pilot signal transmitting power of the r neighbor subregion; transmitting pilot signals of each neighbor subarea of the ith subarea after normalization processingAnd (3) transmitting power to the trained deep neural network to obtain the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area.
Description
Technical Field
The present application relates to the field of mobile communications technologies, and in particular, to a method and an apparatus for calculating coverage of a mobile communications network based on deep learning.
Background
Coverage of the communication network is a problem to be considered in a real-time optimization algorithm of the pilot signal transmission power of the mobile communication network. At present, the existing mobile communication network coverage rate calculation method needs to process massive MR data, the processing time of the MR data is in direct proportion to the total amount of the MR data, when more MR data need to be processed, the required calculation amount is correspondingly increased, and the calculation efficiency is lower.
Therefore, it is necessary to provide a new method for calculating the coverage of a mobile communication network, so that the coverage of the mobile communication network can be calculated quickly and the calculation result can be ensured to have higher accuracy.
Disclosure of Invention
The embodiment of the specification provides a mobile communication network coverage rate calculating method and device based on deep learning, so that the coverage rate of a mobile communication network can be calculated quickly and the calculation result is ensured to have higher accuracy.
In order to solve the above technical problems, the embodiments of the present specification are implemented as follows:
the embodiment of the specification provides a mobile communication network coverage calculating method based on deep learning, which comprises the following steps:
step S1, obtaining pilot signal transmitting power of the ith sub-area in the areaAnd pilot signal transmit power +_ of each neighbor sub-region of the ith sub-region>q＝1,…,n i The method comprises the steps of carrying out a first treatment on the surface of the Wherein the parameter n i Representing the number of neighbor subregions of the ith subregion;
step S2, carrying out normalization processing on pilot signal transmitting power of the ith sub-area and neighbor sub-areas of the ith sub-area according to the following formula;
wherein the method comprises the steps ofMaximum transmission power of pilot signals representing the r neighbor sub-region of said i-th sub-region, and>pilot signal minimum transmit power for the r-th neighbor sub-region representing the i-th sub-region, +.>Representing the normalized pilot signal transmitting power of the r neighbor subregion of the i subregion;
step S3, inputting the pilot signal transmitting power of the ith sub-area and each neighbor sub-area of the ith sub-area after normalization processing into the trained deep neural network to obtain the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area
The deep neural network is used for calculating the communication signal coverage rate of the sub-area network formed by the sub-areas according to the pilot signal transmitting power of the sub-areas.
Preferably, the network structure of the deep neural network is specifically as follows:
the number of hidden layers of the deep neural network is m, wherein the number of neurons of the first hidden layer is N l-1 ，l＝1,2,...,m；
The connection weight of the kth neuron of the first-1 layer hidden layer and the kth neuron of the first layer hidden layer of the deep neural network is that
Output of the jth neuron of the l+1 layerCalculated from the following formula:
preferably, the activation function of the hidden layer of the deep neural network is a Sigmoid activation function.
Preferably, the activation function of the output layer of the deep neural network is a Soft-max activation function.
Preferably, a random gradient descent method SGD is adopted, and a plurality of sets of training data are obtained to train the deep neural network based on the existing mobile communication network coverage rate calculation method based on MR data; any one of the plurality of sets of training data includes a plurality of pilot signal transmit powers and a signal coverage of the plurality of pilot signal transmit powers.
Preferably, the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area is obtainedThereafter, the method further comprises:
step S41, presetting the critical value of the coverage rate of the communication signal as alpha, ifThen consider sub-region C i The coverage condition is not satisfied, the subarea C is processed i Marked as C i * I is the identification set of the marked sub-region; taking all marked subareas as nodes, +.>If C i * And C s * Are neighbors of each other, C i * And C s * Is communicated with, C i * And C s * There is one edge between, and record the set of all edges as E, construct graph G, wherein G= (V, E); finding all connected branches { X } in the graph G 1 ,...,X t T is the number of communication branches, and is provided with communication branch X l L=1,..the node with the lowest coverage in t is +.>Sub-area->An upward adjustment of the pilot signal transmit power by Δp;
and S42, calculating the communication signal coverage rate of all the subareas again by using a deep neural network algorithm according to the up-regulated pilot signal transmission power, marking new subareas which do not meet the coverage condition, and repeating S41 until all the subareas meet the coverage condition.
The application also provides a mobile communication network coverage rate calculating device based on deep learning, which is characterized by comprising the following components:
a pilot signal transmitting power obtaining module for obtaining pilot signal transmitting power of the ith sub-area in the areaAnd pilot signal transmit power +_ of each neighbor sub-region of the ith sub-region>q＝1,…,n i The method comprises the steps of carrying out a first treatment on the surface of the Wherein the parameter n i Representing the number of neighbor subregions of the ith subregion;
the normalization module is used for normalizing the pilot signal transmitting power of the ith sub-area and the neighbor sub-area of the ith sub-area according to the following formula;
wherein the method comprises the steps ofMaximum transmission power of pilot signals representing the r neighbor sub-region of said i-th sub-region, and>pilot signal minimum transmit power for the r-th neighbor sub-region representing the i-th sub-region, +.>Representing the normalized pilot signal transmitting power of the r neighbor subregion of the i subregion;
the communication signal coverage rate calculation module is used for inputting the pilot signal transmitting power of the ith sub-area and each neighbor sub-area of the ith sub-area after normalization processing into the trained deep neural network to obtain the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area
The deep neural network is used for calculating the communication signal coverage rate of the sub-area network formed by the sub-areas according to the pilot signal transmitting power of the sub-areas.
At least one embodiment provided in this specification can achieve the following benefits:
according to the technical scheme, on the basis of the existing communication network coverage rate calculation method, the coverage rate calculation and optimization algorithm based on the deep neural network are provided, and the running time of the new algorithm does not depend on MR data volume, so that the method is more suitable for real-time optimization of a large-scale mobile communication network, and can ensure that the calculation result has higher accuracy rate while the coverage rate of the mobile communication network is calculated rapidly.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an application scenario of a mobile communication network coverage calculating method based on deep learning according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a method for calculating coverage rate of a mobile communication network based on deep learning according to an embodiment of the present disclosure;
fig. 3 is a network structure diagram of a neural network used in a method for calculating coverage rate of a mobile communication network based on deep learning according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a mobile communication network coverage calculating device based on deep learning according to an embodiment of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of one or more embodiments of the present specification more clear, the technical solutions of one or more embodiments of the present specification will be clearly and completely described below in connection with specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without undue burden, are intended to be within the scope of one or more embodiments herein.
In order to clearly describe the technical scheme of the present embodiment, the application scenario of the technical scheme of the present embodiment will be described first, and fig. 1 is a schematic diagram of an embodiment of the present specificationA schematic diagram of an application scenario of a mobile communication network coverage rate calculation method based on deep learning is provided. As shown in fig. 1, a certain area S (such as a certain city) includes several sub-areas covered with a mobile communication network, a set B is a set formed by the sub-areas covered with the mobile communication network (the communication signal may be 2g, 3g, 4g or 5 g), and a total of L sub-areas (in this scenario, L is assumed to be equal to 11) are included in the set B, and specifically includes B 1 、b 2 、b 3 、b 4 、b 5 、b 6 、b 7 、b 8 、b 9 、b 10 And b 11 As shown in fig. 1, each sub-region b i All have a set of neighbor sub-regions N i I.e. with this sub-region b i A set of positionally adjacent subregions, subregion b 1 For illustration, subregion b 1 Neighbor sub-region N of (a) 1 Comprising sub-region b 2 、b 3 、b 4 、b 7 、b 10 And subregion b 11 . In the technical solution of this embodiment, each sub-area b i Communication base stations are installed which transmit mobile signals for user terminal devices within the sub-area. While the embodiment of the application assumes that the neighbor relation of the two sub-regions is symmetrical, i.e. if sub-region b i Is sub-region b j Then sub-region b j Also necessarily sub-region b i Is a neighbor of (c).
In a real-time optimization algorithm of pilot signal transmission power of a mobile communication network, signal coverage of the communication network is a problem to be considered. At present, the existing mobile communication network coverage rate calculation method needs to process massive MR data, the processing time of the MR data is in direct proportion to the total amount of the MR data, when more MR data need to be processed, the required calculation amount is correspondingly increased, and the calculation efficiency is lower.
The coverage rate calculation and optimization algorithm based on the deep neural network are provided on the basis of the existing coverage rate calculation method. The running time of the new algorithm is independent of the MR data volume, so that the method is more suitable for real-time optimization of a large-scale mobile communication network.
The technical concept of the technical scheme of the present application is described below, and the present application obtains a large amount of input/output data of pilot channel transmission power and pilot channel signal coverage rate of each sub-area region through the existing method for calculating coverage rate of mobile communication network based on MR data (the specific technology can be seen in the patent of patent number 202111305615.1 of the present application, for example, "a method for calculating coverage rate of mobile communication network based on MR data"), and trains a deep neural network by using these data, so as to further model a complex relationship between input (i.e. pilot channel transmission power of each sub-area) and output (pilot channel signal coverage rate). Based on the trained deep neural network, a rapid calculation method of the coverage rate of the mobile communication network is provided, and finally, an optimization method of the coverage rate of the mobile communication network is provided by combining a graph theory method.
In the following, a detailed description of the technical solution of the present application is provided, as shown in fig. 2, fig. 2 is a schematic flow chart of a method for calculating a coverage rate of a mobile communication network based on deep learning according to an embodiment of the present application, as shown in fig. 2, from a program perspective, an execution subject of the flow may be a program or an application client installed on an application server.
As shown in fig. 2, the process may include the steps of:
step S202: acquiring pilot signal transmitting power of ith sub-area in areaAnd pilot signal transmit power +_ of each neighbor sub-region of the ith sub-region>q＝1,…,n i The method comprises the steps of carrying out a first treatment on the surface of the Wherein the parameter n i And representing the number of neighbor subregions of the ith subregion.
Step S204: carrying out normalization processing on pilot signal transmitting power of the ith sub-area and neighbor sub-areas of the ith sub-area according to the following formula;
wherein the method comprises the steps ofMaximum transmission power of pilot signals representing the r neighbor sub-region of said i-th sub-region, and>pilot signal minimum transmit power for the r-th neighbor sub-region representing the i-th sub-region, +.>And the normalized pilot signal transmitting power of the r neighbor subregion of the i subregion is represented.
Step S206: inputting the pilot signal transmitting power of the ith sub-area and each neighbor sub-area of the ith sub-area after normalization processing into a trained deep neural network to obtain the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area
The deep neural network is used for calculating the communication signal coverage rate of the sub-area network formed by the sub-areas according to the pilot signal transmitting power of the sub-areas.
According to the technical scheme, on the basis of the existing communication network coverage rate calculation method, the coverage rate calculation and optimization algorithm based on the deep neural network are provided, and the running time of the new algorithm does not depend on MR data volume, so that the method is more suitable for real-time optimization of a large-scale mobile communication network, and can ensure that the calculation result has higher accuracy rate while the coverage rate of the mobile communication network is calculated rapidly.
The examples of the present specification also provide some specific embodiments of the method based on the method of fig. 2, which is described below.
In an alternative embodiment, the network structure of the deep neural network is specifically as follows:
as shown in FIG. 3, the number of hidden layers of the deep neural network is m, wherein the number of neurons of the hidden layer of the first layer is N l-1 ，l＝1,2,...,m；
The connection weight of the kth neuron of the first-1 layer hidden layer and the kth neuron of the first layer hidden layer of the deep neural network is that
Output of the jth neuron of the l+1 layerCalculated from the following formula:
in an alternative embodiment technical solution, the activation function of the hidden layer of the deep neural network is a Sigmoid activation function.
In an alternative embodiment technical scheme, the activation function of the output layer of the deep neural network is a Soft-max activation function.
Specifically, considering that the total power of pilot channel transmission of any cell is increased in practical application, the signal coverage rate of the pilot channel of the mobile communication network is increased, namelyIs about->j=0, …, n. monotonically increasing function, in order to build a more reasonably realistic model, the deep neural network should be built as a monotonic neural network model. Therefore, the weight of the connection between the kth neuron of the first layer-1 and the jth neuron of the first layer is set asLet the bias of the jth neuron of the first layer be +.>The activation function of the hidden layer adopts a Sigmoid function, so that monotonicity can be kept, namely:
typically, the first layer has N l-1 Output of the (j) th neuron of the (l+1) th layerThe method comprises the following steps:
if l=1, thenFor inputting data +.>j＝0,…,n。
The matrix is used to more succinctly represent input and output: the first layer has N l-1 Neurons of the first layer and layer 1 share N l The weights of the first layer to the first layer (1) form N l-1 ×N l Is a matrix W of (2) l+1 Layer l+1 is biased to N l Vector b of x 1 l+1 . The output of the first layer is N l-1 Vector a of x 1 l The output of layer l+1 is N l Vector a of x 1 l+1 Then a l+1 The method comprises the following steps:
a l+1 ＝σ(W l+1 a l +b l+1 )
thus, the final output a m+2 The method comprises the following steps:
a m+2 ＝σ(W m+2 (σ(W m+1 (…)+b m+1 ))+b m+2 )
and then inputting a training data set, training a neural network, wherein forward propagation calculates the output of a training sample, and measuring the loss between the output and a true value of the training sample by using a cross entropy loss function J (W, b, x, y), wherein x is input data, y is the actual output of the sample, W is a weight matrix of all hidden layers and output layers, and b is the bias of all hidden layers and output layers.
J(W,b,x,y)＝-ylna m+2 -(1-y)ln(1-a m+2 )
Minimizing the loss function J (W, b, x, y) is desirable to keep the predicted output value of the training sample as close as possible to the actual output value of the sample. Iteratively optimizing W, b for each layer using a gradient descent method to minimize the loss function, noting the linear output of the first layer before activation as N l-1 Vector z of x 1 l ,z l ＝W l a l-1 +b l The output layer gradient conditions were as follows:
wherein, let:
then:
further recursion layer by layer to the upper layer, for layer i:
while
Therefore, the gradient of each layer w, b can be solved by recursion using a mathematical induction method, iterative optimization is performed until the change value of w, b is smaller than a threshold value, and the output w, b is a proper weight and bias.
In an alternative embodiment technical scheme, a random gradient descent method SGD is adopted, and a plurality of sets of training data are obtained to train the deep neural network based on the existing mobile communication network coverage rate calculation method based on MR data; any one of the plurality of sets of training data includes a plurality of pilot signal transmit powers and a signal coverage of the plurality of pilot signal transmit powers.
Then, for each cell C, using the trained, validated deep neural network i Transmitting total power according to the input pilot channelj=0,.. and calculating the signal coverage rate of the pilot channel. The specific process is as follows:
initializing:
setting the weight value of the connection between the kth neuron of the trained first layer and the jth neuron of the first layer+1 asLet the bias of the jth neuron of layer l+1 be +.>The matrix form is weight matrix from the first layer to the first layer plus 1>Bias of layer 1 +1 to N l Vector x 1>The output of layer l+1->If l=1, then->
Final output:
in an alternative embodiment, the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area is obtainedThereafter, the method further comprises:
step S41, presetting the critical value of the coverage rate of the communication signal as alpha, ifThen consider sub-region C i The coverage condition is not satisfied, the subarea C is processed i Marked as C i * I is the identification set of the marked sub-region; taking all marked subareas as nodes, +.>If C i * And C s * Are neighbors of each other, C i * And C s * Is communicated with, C i * And C s * There is one edge between, and record the set of all edges as E, construct graph G, wherein G= (V, E); finding all connected branches { X } in the graph G 1 ,...,X t T is the number of communication branches, and is provided with communication branch X l L=1,..the node with the lowest coverage in t is +.>Sub-area->An upward adjustment of the pilot signal transmit power by Δp;
and S42, calculating the communication signal coverage rate of all the subareas again by using a deep neural network algorithm according to the up-regulated pilot signal transmission power, marking new subareas which do not meet the coverage condition, and repeating S41 until all the subareas meet the coverage condition.
Meanwhile, the application also provides a mobile communication network coverage rate calculating device based on deep learning, as shown in fig. 4, the device comprises:
a pilot signal transmission power acquisition module 402, configured to acquire pilot signal transmission power of an ith sub-area in the areaAnd pilot signal transmit power +_ of each neighbor sub-region of the ith sub-region>q＝1,...,n i The method comprises the steps of carrying out a first treatment on the surface of the Wherein the parameter n i And representing the number of neighbor subregions of the ith subregion.
A normalization module 404, configured to normalize pilot signal transmission powers of the ith sub-area and a neighbor sub-area of the ith sub-area according to the following formula;
wherein the method comprises the steps ofMaximum transmission power of pilot signals representing the r neighbor sub-region of said i-th sub-region, and>pilot signal minimum transmit power for the r-th neighbor sub-region representing the i-th sub-region, +.>Representing the normalized pilot signal transmitting power of the r neighbor subregion of the i subregion;
a communication signal coverage rate calculation module 406, configured to input the pilot signal transmission power of the ith sub-area and each neighbor sub-area of the ith sub-area after normalization processing to a trained deep neural network to obtain a communication signal coverage rate of a sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area
The deep neural network is used for calculating the communication signal coverage rate of the sub-area network formed by the sub-areas according to the pilot signal transmitting power of the sub-areas.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., a field programmable gate array (Field Programmable gate array, FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. The designer programs itself to "integrate" a digital system onto a single PLD without requiring the chip manufacturer to design and fabricate application specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (AdvancedBoolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmelAT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (6)
1. A mobile communication network coverage calculating method based on deep learning, which is characterized by comprising the following steps:
step S1, obtaining pilot signal transmitting power of the ith sub-area in the areaAnd pilot signal transmit power +_ of each neighbor sub-region of the ith sub-region>Wherein the parameter n i Representing the number of neighbor subregions of the ith subregion;
step S2, carrying out normalization processing on pilot signal transmitting power of the ith sub-area and neighbor sub-areas of the ith sub-area according to the following formula;
wherein the method comprises the steps ofThe pilot signal maximum transmit power of the r neighbor sub-region of the i-th sub-region,pilot signal minimum transmit power for the r-th neighbor sub-region representing the i-th sub-region, +.>Representing the normalized pilot signal transmitting power of the r neighbor subregion of the i subregion;
step S3, inputting the pilot signal transmitting power of the ith sub-area and each neighbor sub-area of the ith sub-area after normalization processing into the trained deep neural network to obtain the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area
The deep neural network is used for calculating the communication signal coverage rate of the sub-area network formed by the sub-areas according to the pilot signal transmitting power of the sub-areas;
the communication signal coverage rate of the sub-area network formed by each neighbor sub-area of the ith sub-area is obtainedThereafter, the method further comprises:
step S41, presetting the coverage of the communication signalsCritical value is alpha, ifThen consider sub-region C i The coverage condition is not satisfied, the subarea C is processed i Marked as C i * I is the identification set of the marked sub-region; taking all marked subareas as nodes, +.>If C i * And C s * Are neighbors of each other, C i * And C s * Is communicated with, C i * And C s * There is one edge between, and record the set of all edges as E, construct graph G, wherein G= (V, E); finding all connected branches { X } in the graph G 1 ,...,X t T is the number of communication branches, and is provided with communication branch X l L=1,..the node with the lowest coverage in t is +.>Sub-area->An upward adjustment of the pilot signal transmit power by Δp;
and S42, calculating the communication signal coverage rate of all the subareas again by using a deep neural network algorithm according to the up-regulated pilot signal transmission power, marking new subareas which do not meet the coverage condition, and repeating S41 until all the subareas meet the coverage condition.
2. The mobile communication network coverage calculating method based on deep learning according to claim 1, wherein the network structure of the deep neural network is specifically as follows:
the number of hidden layers of the deep neural network is m, wherein the number of neurons of the first hidden layer is N l-1 ，l＝1,2,...,m；
The connection weight of the kth neuron of the first-1 layer hidden layer and the kth neuron of the first layer hidden layer of the deep neural network is that
Output of the jth neuron of the l+1 layerCalculated from the following formula:
wherein the symbols areRepresenting the bias value corresponding to the j-th neuron of the first layer +1, and the function sigma (·) represents an activation function.
3. The deep learning-based mobile communication network coverage calculating method according to claim 2, wherein the activation function of the hidden layer of the deep neural network is a Sigmoid activation function.
4. The deep learning-based mobile communication network coverage calculating method according to claim 2, wherein the activation function of the output layer of the deep neural network is a Soft-max activation function.
5. The mobile communication network coverage calculating method based on deep learning as claimed in claim 2, wherein a random gradient descent method SGD is adopted, and a plurality of sets of training data are obtained to train the deep neural network based on the existing mobile communication network coverage calculating method based on MR data; any one of the plurality of sets of training data includes a plurality of pilot signal transmit powers and a signal coverage of the plurality of pilot signal transmit powers.
6. A mobile communications network coverage computing device based on deep learning, the device comprising:
a pilot signal transmitting power obtaining module for obtaining pilot signal transmitting power of the ith sub-area in the areaAnd pilot signal transmit power +_ of each neighbor sub-region of the ith sub-region>Wherein the parameter n i Representing the number of neighbor subregions of the ith subregion;
the normalization module is used for normalizing the pilot signal transmitting power of the ith sub-area and the neighbor sub-area of the ith sub-area according to the following formula;
wherein the method comprises the steps ofThe pilot signal maximum transmit power of the r neighbor sub-region of the i-th sub-region,pilot signal minimum transmit power for the r-th neighbor sub-region representing the i-th sub-region, +.>Representing the normalized pilot signal transmitting power of the r neighbor subregion of the i subregion;
communication signal coverage rate calculation moduleA block, configured to input the pilot signal transmitting power of the ith sub-area and each neighbor sub-area of the ith sub-area after normalization processing to a trained deep neural network to obtain a communication signal coverage rate of a sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-area
The device also comprises a transmitting power up-regulating module for obtaining the communication signal coverage rate of the sub-area network formed by the ith sub-area and each neighbor sub-area of the ith sub-areaThereafter, the following processing is performed:
presetting the critical value of the coverage rate of the communication signal as alpha, ifThen consider sub-region C i The coverage condition is not satisfied, the subarea C is processed i Marked as C i * I is the identification set of the marked sub-region; taking all marked subareas as nodes, +.>If C i * And C s * Are neighbors of each other, C i * And C s * Is communicated with, C i * And C s * There is one edge between, and record the set of all edges as E, construct graph G, wherein G= (V, E); finding all connected branches { X } in the graph G 1 ,...,X t T is the number of communication branches, and is provided with communication branch X l L=1,..the node with the lowest coverage in t is +.>Sub-area->An upward adjustment of the pilot signal transmit power by Δp;
the device further comprises a covering condition unsatisfied marking module, a covering condition determining module and a covering condition determining module, wherein the covering condition determining module is used for determining the covering condition of the pilot signal according to the received signal, and the covering condition determining module is used for determining the covering condition of the pilot signal according to the covering condition of the pilot signal;
the deep neural network is used for calculating the communication signal coverage rate of the sub-area network formed by the sub-areas according to the pilot signal transmitting power of the sub-areas.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016115394A1 (en) * | 2015-01-16 | 2016-07-21 | Hughes Network Systems, Llc | Method and apparatus for beam selection for a multibeam multi-satellite communications system |
| CN108834100A (en) * | 2018-06-15 | 2018-11-16 | 九江学院 | A kind of efficient broadcast routing method |
| CN111226254A (en) * | 2017-07-10 | 2020-06-02 | 埃奈迪斯公司 | Reliability of network maps |
| CN111882041A (en) * | 2020-07-31 | 2020-11-03 | 国网重庆市电力公司电力科学研究院 | Power grid attack detection method and device based on improved RNN (neural network) |
| CN112770398A (en) * | 2020-12-18 | 2021-05-07 | 北京科技大学 | Far-end radio frequency end power control method based on convolutional neural network |
| CN113706333A (en) * | 2021-08-10 | 2021-11-26 | 国网电子商务有限公司 | Method and system for automatically generating topology island of power distribution network |
-
2021
- 2021-12-21 CN CN202111575058.5A patent/CN114268965B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016115394A1 (en) * | 2015-01-16 | 2016-07-21 | Hughes Network Systems, Llc | Method and apparatus for beam selection for a multibeam multi-satellite communications system |
| CN111226254A (en) * | 2017-07-10 | 2020-06-02 | 埃奈迪斯公司 | Reliability of network maps |
| CN108834100A (en) * | 2018-06-15 | 2018-11-16 | 九江学院 | A kind of efficient broadcast routing method |
| CN111882041A (en) * | 2020-07-31 | 2020-11-03 | 国网重庆市电力公司电力科学研究院 | Power grid attack detection method and device based on improved RNN (neural network) |
| CN112770398A (en) * | 2020-12-18 | 2021-05-07 | 北京科技大学 | Far-end radio frequency end power control method based on convolutional neural network |
| CN113706333A (en) * | 2021-08-10 | 2021-11-26 | 国网电子商务有限公司 | Method and system for automatically generating topology island of power distribution network |
Non-Patent Citations (1)
| Title |
|---|
| 无线Ad Hoc网络功率控制透明传输调度算法;马晓雷，刘元安，高锦春，沈雅琴;北京邮电大学学报;-;第-卷(第05期);全文 * |
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