CN113453197B - User pairing method combining mobile prediction and dynamic power - Google Patents

User pairing method combining mobile prediction and dynamic power Download PDF

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CN113453197B
CN113453197B CN202110719617.9A CN202110719617A CN113453197B CN 113453197 B CN113453197 B CN 113453197B CN 202110719617 A CN202110719617 A CN 202110719617A CN 113453197 B CN113453197 B CN 113453197B
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CN113453197A (en
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郑键彬
黄志锋
宋晖
骆开庆
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South China Normal University
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Abstract

The invention provides a D2D pairing algorithm which takes the weighted combination of the D2D communication distance and the user SINR as a pairing index. According to the result after the two weighted combination, a better user matching point can be found for D2D communication. Meanwhile, a strategy of mobile prediction and dynamic adjustment of user transmitting power is added, overall performance is further improved, system throughput is improved, low system energy consumption is maintained, and system performance is effectively improved.

Description

User pairing method combining mobile prediction and dynamic power
Technical Field
The invention relates to the technical field of user pairing, in particular to a user pairing method combining mobile prediction and dynamic power.
Background
D2D (device to device) refers to a technique for directly exchanging information between neighboring devices in a communication network. In a communication system or a network, once a D2D communication link is established, the intervention of core equipment or intermediate equipment is not needed for data transmission, the data pressure of the core network of the communication system can be reduced, and a new way is opened for the delay-free communication of a large-scale network, the mass access of a mobile terminal and the transmission of large data.
In the D2D relay technology, the user pairing problem is the keystone of the D2D relay application. Mode selection and power control in D2D communication directly affect whether D2D communication can be effective, which can improve the spectrum efficiency of the system and reduce energy consumption. During communication, D2D users may interfere with cellular network users, and cellular users may also interfere with D2D users. By utilizing the power control technology, interference management can be effectively carried out while the system and the capacity are ensured by adjusting the power of the base station or the mobile terminal, the overall energy consumption of the system can be optimized, the network performance is enhanced, and green communication is realized. In addition, mobility prediction is applied in a wireless network, so that the robustness of the system can be improved, and the communication time delay is reduced. Although the user pairing strategy proposed in the prior art makes a better improvement on the system energy consumption, the user pairing is performed only according to a single parameter of the D2D communication distance, and the optimization of the system by dynamically adjusting the user transmission power cannot be considered.
Disclosure of Invention
Based on the technical problems, the application provides a user pairing method combining mobile prediction and dynamic power, which comprises the following steps: and dynamically adjusting the transmission power according to the path loss information of the user, and taking the weighted combination of the D2D communication distance and the SINR of the user as the pairing index of the user.
A user pairing method combining mobile prediction and dynamic power specifically comprises the following steps:
step 1: calculating the distance between the current source node (Sx, Sy) and the cache point (Cx, Cy) and the non-cache point (UCx, UCy);
and 2, step: screening out cache points and non-cache points in the communication radius of the D2D;
and step 3: calculating SINR of each point and a weighted combination index DS of the distance and the SINR;
and 4, step 4: if a terminal equipment set { T1, T2 … Ti } exists in the D2D communication distance range of the current source node and has a target cache file, selecting a terminal equipment point Ty with the maximum DS value between the source node and the cache point as an optimal matching point, namely using a D2D direct connection mode;
and 5: if no terminal equipment with a target cache file exists in the D2D communication distance range of the current source node, searching for D2D relay equipment { D1, D2 … Dj } for pairing, wherein the terminal equipment with the target cache file exists in the D2D communication distance range of the relay equipment, and selecting a relay equipment point Dy with the largest DS value between the source node and the cache point as an optimal pairing point, namely using a D2D relay mode;
step 6: if the two modes have no optimal pairing equipment, the traditional cellular mode is used for directly communicating with the base station;
and 7: and pairing the next source node according to the pairing priority sequence until all the m source nodes are paired.
Further, before calculating the distance between the current source node (Sx, Sy) and the cache point (Cx, Cy), the non-cache point (UCx, UCy), the base station, the method comprises:
step 1.1: acquiring and loading trajectory data, generating the trajectory data by using a mobile scene generation tool Bonnmotion software based on JAVA, dividing the first 90% of trajectory original data into training data, taking the last 10% of trajectory original data as test data, standardizing the training data to have zero mean and unit variance, and standardizing the test data by using the same parameters as the training data during prediction;
step 1.2: defining a long-time and short-time memory LSTM network structure, creating an LSTM regression network,
step 1.3: training an LSTM network model by adopting a deep learning toolbox in MATLAB
Deep Learning Toolbox, training the LSTM network using a training option specified by a model training function, train network;
step 1.4: predicting and generating a predicted trajectory: defining a prediction function, predicting by using a trained LSTM model, taking original 1-100 s of data, putting the data into the prediction function to predict horizontal and vertical coordinates in 101-105 s, and storing the data into a prediction track matrix position _ pre (100, 2); taking data of 1-105 s as horizontal and vertical coordinates of sample prediction 106-110 s, and so on to obtain a complete prediction track matrix.
Further, in step 1.2, the implicit unit of the LSTM layer is designated as 200, the solver is set as a random optimization method of adaptive momentum and performs 200 rounds of training, the gradient threshold is set as 1, the initial learning rate is designated as 0.005, and the learning rate is reduced by multiplying by a factor of 0.2 after 125 rounds of training.
Further, in step 3, the SINR calculation for the user includes the following steps:
step 3.1, calculating the path loss of the user: the adopted path loss model is a micro-urban model according to an international telecommunication union radio communication group ITU-R, and the path loss model of D2D user i is as follows:
Figure BDA0003136037760000021
where D is the distance between D2D users in m, fc is the carrier frequency of the system in GHz;
the path loss model for cellular user j is as follows:
Figure BDA0003136037760000022
where r is the distance of the cellular user from the base station in m;
step 3.2, the user receives the calculation of power, and records the transmitting power of the D2D user i as
Figure BDA0003136037760000031
The path loss of the receiving user at a distance D2D of
Figure BDA0003136037760000032
Antenna _ Gain is Antenna Gain, shading is shadow fading; the received power at the receiving end of each D2D user
Figure BDA0003136037760000033
The calculation is as follows:
Figure BDA0003136037760000034
the same holds for the transmission power of cellular user j
Figure BDA0003136037760000035
Path loss of cellular user j from base station is
Figure BDA0003136037760000036
The received power of the base station side
Figure BDA0003136037760000037
The calculation is as follows:
Figure BDA0003136037760000038
step 3.3, the interference of the user is calculated, and the transmitting power of the cellular user j is recorded as
Figure BDA0003136037760000039
The path loss from the cellular subscriber to the D2D receiver is noted as
Figure BDA00031360377600000310
Calculating the co-channel interference of cellular users suffered by D2D users by adopting a path loss model of the D2D users
Figure BDA00031360377600000311
Comprises the following steps:
Figure BDA00031360377600000312
let the transmit power of D2D user i be noted
Figure BDA00031360377600000313
The path loss from the D2D subscriber to the cellular subscriber's receiver is noted as
Figure BDA00031360377600000314
The co-channel interference of D2D user to cellular user is calculated by using path loss model of cellular user
Figure BDA00031360377600000315
Comprises the following steps:
Figure BDA00031360377600000316
further, SINR calculation for users, assuming D2D user i multiplexes resources for cellular user j, D2D user
Figure BDA00031360377600000317
Represented by the following formula, where N is the thermal noise power:
Figure BDA00031360377600000318
for the same reason, of cellular subscriber j
Figure BDA00031360377600000319
Comprises the following steps:
Figure BDA00031360377600000320
further, step 3 further comprises calculating a transmit power,
P(i)＝min{Pmax,P0(i)+10*log10M(i)+α*PL+Δmcs(i)+f(Δi)}
wherein the meanings of the symbols are as follows:
Pmax: maximum transmit power of the user terminal;
P0(i) the method comprises the following steps Cell-specific or user terminal-specific parameters including target SINR, interference level, i.e. allocating a resource block for each user in the cell;
α: a cell-specific path loss compensation coefficient, PL being the path loss of the downlink reference signal measured by the user terminal;
Δmcs(i) the method comprises the following steps The power offset determined by the modulation and coding scheme is calculated as shown in the following formula:
Δmcs(i)＝10*log10(2MPR(i)-Ks-1)
wherein MPR (i) is the current modulation and coding mode, Ks is a cell level parameter, values of 0 and 1.25 are obtained through RRC radio resource control layer signaling, Ks value is 0, that is, the power compensation is not performed on the current modulation and coding mode of the user terminal,
f (Δ i): function f (i) performs power control based on Δ i, based on which the user isiAdjusts the transmit power as shown in the following equation:
f(Δi)＝Δi[dBm]
at this time,. DELTA.iHas a value of ΔiE { -1,0, +1 }; the base station dynamically adjusts part of parameters of the user terminal and sends corresponding power control commands (TPC) to the user terminal, and the user terminal correspondingly adjusts the transmitting power after receiving the TPC, thereby achieving the purpose of adjusting the power.
Further, if the current SINRi>SINRhighThe channel quality of the users is better, and the transmitting power can be properly reduced under the condition of ensuring the throughput, so that the power consumption of the user equipment is reduced on one hand, the interference to other users can be reduced on the other hand, and the transmitting power is adjusted downwards when the users receive the command of the transmitting power control command; deltai-1 dBm; if the current SINR of the useri<SINRlowThe interference to the user is large and the channel quality is poor, in order to ensure the quality of D2D communication, the transmit power of the user can be appropriately increased, when the user receives a TPC command, the transmit power is adjusted up, and Δ i is +1 dBm; for SINRi(SINRi≤SINRhighAnd SINRi≥SINRlow) The channel condition of the user is good, the interference to other users is not very large, the transmitting power of the user can not be adjusted greatly, and the TPC command is determined by the following formula:
Figure BDA0003136037760000041
according to the above parameter settings, the power control formula of the D2D user can be simplified as follows:
P(i)＝min{Pmax,P0(i)+α*PL+f(Δi)}
further, the DS pairing algorithm includes:
the user terminal equipment selects the best one from three modes of cellular mode, D2D direct communication and D2D relay communication for communication, the priority of D2D direct communication is the highest, the D2D relay communication is the next time, the cellular mode is the last choice, the best paired user is selected by combining the D2D communication Distance and the user SINR weight into an index DS (Distance-SINR), and the formula of weight combination is as follows:
DS＝a*Distance+(1-a)*SINR
wherein a is a weighted combination coefficient, and a is 0.1-0.9; distance is the Distance between users, and after Min-Max normalization processing is carried out on SINR, weighting combination is carried out. And selecting the user with the largest DS value for pairing by calculating the DSs of different users in the communication range of the D2D.
By using the user pairing optimization algorithm, the energy consumption of the system can be effectively reduced, higher system throughput is realized, and the performance of the D2D communication network can be further improved after the scheme of LSTM mobile prediction and dynamic user transmission power adjustment is added.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a system model diagram of a user pairing algorithm of the present invention;
FIG. 2 is a flow chart of the present invention for dynamically adjusting transmit power;
FIG. 3 is a flow chart of the DS pairing algorithm of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the detailed embodiment of the user pairing algorithm is as follows:
the system model is set to the cellular network model of only one base station. The mobile terminals are randomly distributed in the communication range of the base station, and the motion direction (0-2 pi) and the motion speed (0-5 m/s) both meet the random walk model. In the system model, there are N users Ui (i ═ 1,2, …, N), and each user may store a cache file or request a desired file from other users or a base station. Assuming that the number of users having cached files among the N users is M, the other N-M users can obtain the required files through different file request methods. And the user exchanges information with the base station once every delta t.
The following four methods are used for requesting the file by the user: firstly, when a file requested by a user exists in the cache space of the user, the file can be directly acquired from the cache space of the user, and the method has no transmission delay and no energy consumption; secondly, when the file requested by the user does not exist in the cache space of the user but exists in the cache space of another user within the communication range of the D2D, the D2D direct connection mode can be used, and a communication link can be established between the two; thirdly, when the file requested by the user exists in the buffer space of another user outside the communication range of the D2D, the relay mode of the D2D can be used for searching for idle relay nodes in the range and establishing communication links among the three; fourthly, when the files required by the user do not exist in the buffer spaces of other users, the cellular mode is used for directly communicating and linking with the base station.
The trajectory is a sequence with spatio-temporal information, and the cyclic neural network can be used to learn the context of the trajectory features, thereby predicting the next position of the trajectory. The long-short time memory network LSTM is an improved recurrent neural network, can solve the problem that the RNN gradient of the recurrent neural network disappears or explodes due to the increase of the number of layers of the neural network, and can remember information for a longer time compared with the RNN. Therefore, the LSTM is used as a main body of the model, and the relation between the input track training features and the labels of the next positions is learned by constructing a multi-layer network structure, so that the positions which are possibly reached in the future are predicted. The model structure mainly comprises an LSTM layer, a Dropout layer and a full connection layer, and an activation function is used for obtaining a prediction result.
The user pairing algorithm is realized as follows:
construction of a movement prediction model:
and acquiring and loading track data. Trajectory data are generated by using Bonnmotion software, the first 90% of trajectory original data are divided into training data, and the second 10% of trajectory original data are used as test data. To obtain a better fit and prevent training divergence, the training data is normalized to have zero mean and unit variance, and the test data is normalized using the same parameters as the training data at the time of prediction.
A long-term memory LSTM regression network is created. The hidden cell of the LSTM layer is designated 200 and the solver is set to Adam and performs 200 rounds of training. To prevent gradient explosions, the gradient threshold is set to 1. An initial learning rate of 0.005 is specified, which is reduced by multiplying by a factor of 0.2 after 125 rounds of training.
The LSTM network model is trained. Training of the LSTM model the LSTM network was trained using Deep Learning Toolbox using training options specified by the rainNet.
And predicting and generating a predicted trajectory. Defining a predict function, predicting by using a trained LSTM model, taking original 1-100 s of data, putting the data into the predict function to predict horizontal and vertical coordinates in 101-105 s, and storing the horizontal and vertical coordinates in a predicted trajectory matrix position _ pre (100, 2). And then taking data of 1-105 s as horizontal and vertical coordinates of sample prediction 106-110 s, and so on to obtain a complete prediction track matrix. The position of each user in the subsequent process is given by the prediction matrix.
Calculation of parameters related to dynamic transmit power adjustment:
the use of D2D communication in cellular networks not only reduces the energy consumption of the whole system, but also effectively improves the throughput of the whole system, but also causes serious signal interference to the system during communication in this case. Under the condition, the receiving end receives real-time information and dynamically adjusts the transmitting power of the user end, so that the energy consumption of the system can be further optimized and the overall network performance can be enhanced on the basis of ensuring higher system throughput.
Wherein the calculation of the transmission power
P(i)＝min{Pmax,P0(i)+10*log10M(i)+α*PL+Δmcs(i)+f(Δi)}
In the formula, PmaxIs the maximum transmit power of the user terminal. Defined herein as 20 dBm.
P0(i) The method comprises the following steps Is a cell-specific or user terminal-specific parameter (including target SINR, interference level, etc.), which is taken to be-50 dBm here.
M (i): the value of m (i) is set to 1 for the number of resource blocks allocated to users in the cell, i.e., one resource block is allocated to each user in the cell.
α: is a cell-specific path loss compensation factor that depends on the magnitude of the "partial power control". According to the 3GPP standard, α has a value range of {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}, where α has a value of 0.8.
PL: is the path loss of the downlink reference signal measured by the user terminal.
Δmcs(i) The method comprises the following steps The power offset determined by the modulation and coding scheme is calculated as shown in the following formula:
Δmcs(i)＝10*log10(2MPR(i)-Ks-1)
wherein, MPR (i) is the current modulation and coding mode, Ks is a cell level parameter, the values are 0 and 1.25 through RRC wireless resource control layer signaling, Ks value is 0, namely, the power compensation is not carried out on the current modulation and coding mode of the user terminal,
f (Δ i): function f (i) performs power control based on Δ i, based on which the user isiAdjusts the transmit power by an absolute value of (a):
f(Δi)＝Δi[dBm]
at this time,. DELTA.iHas a value of ΔiE { -1,0, +1 }; the base station dynamically adjusts part of parameters of the user terminal and sends corresponding power control commands (TPC) to the user terminal, and the user terminal correspondingly adjusts the transmitting power after receiving the TPC, thereby achieving the purpose of adjusting the power.
According to the requirement of China Mobile test, SINR >25dB is an excellent point, SINR ═ 16-25 dB is an excellent point, SINR ═ 11-15 dB is a middle point, SINR ═ 3-10 dB is a poor point, and SINR ≦ 3dB is an extremely poor point. The upper limit SINRhigh and the lower limit SINRlow of the SINR used herein are 25dB and 11dB, respectively.
If the current SINRi is greater than SINRhigh. The channel quality of such users is better, and the transmission power can be properly reduced under the condition of ensuring the throughput, so that the power consumption of the user equipment is reduced on one hand. On the other hand, the interference to other users can be reduced. When the user receives the TPC command, the transmission power is adjusted downwards. Δ i ═ 1 dBm; if the current SINRi of the user is less than SINRlow. The interference to the user is larger, the channel quality is worse, and in order to ensure the quality of D2D communication, the transmission power of the user can be increased appropriately. When the user receives the TPC command, the transmission power is adjusted up. Δ i ═ 1 dBm.
For SINRi(SINRi≤SINRhighAnd SINRi≥SINRlow) The channel condition of the user is good, the interference to other users is not very large, the transmitting power of the user can not be adjusted greatly, and the TPC command is determined by the following formula:
Figure BDA0003136037760000071
according to the above parameter settings, the power control formula of the D2D user can be simplified as follows:
P(i)＝min{Pmax,P0(i)+α*PL+f(Δi)}
path loss calculation for the user:
the path loss model employed herein is a micro-urban model according to ITU-R. The path loss model for D2D user i is:
Figure BDA0003136037760000081
where D is the distance between D2D users in m and fc is the carrier frequency of the system in GHz.
The path loss model for cellular user j is as follows:
Figure BDA0003136037760000082
where r is the distance of the cellular user from the base station in m.
The calculation of the user acceptance power comprises the following steps:
let the transmit power of D2D user i be noted
Figure BDA0003136037760000083
The path loss of the receiving user at a distance D2D of
Figure BDA0003136037760000084
Antenna _ Gain is Antenna Gain, shading is shadow fading; the received power at the receiving end of each D2D user
Figure BDA0003136037760000085
The calculation is as follows:
Figure BDA0003136037760000086
the same holds for the transmission power of cellular user j
Figure BDA0003136037760000087
Path loss of cellular user j from base station is
Figure BDA0003136037760000088
The received power of the base station side
Figure BDA0003136037760000089
The calculation is as follows:
Figure BDA00031360377600000810
the interference calculation of the user comprises the following steps:
the transmit power of cellular user j is noted
Figure BDA00031360377600000811
The path loss from the cellular subscriber to the D2D receiver is noted as
Figure BDA00031360377600000812
Figure BDA00031360377600000813
The calculation uses the path loss model of D2D user, D2DCo-channel interference experienced by cellular subscribers
Figure BDA00031360377600000814
Comprises the following steps:
Figure BDA00031360377600000815
let the transmit power of D2D user i be noted
Figure BDA00031360377600000816
The path loss from the D2D subscriber to the cellular subscriber's receiver is noted as
Figure BDA00031360377600000817
The co-channel interference of D2D user to cellular user is calculated by using path loss model of cellular user
Figure BDA00031360377600000818
Comprises the following steps:
Figure BDA00031360377600000819
the SINR calculation of the user comprises the following steps:
assuming D2D user i multiplexes resources for cellular user j, then D2D user
Figure BDA00031360377600000820
Represented by the following formula, where N is the thermal noise power:
Figure BDA00031360377600000821
similarly, of cellular subscriber j
Figure BDA00031360377600000822
Comprises the following steps:
Figure BDA00031360377600000823
the flow chart for dynamically adjusting the transmission power is shown in fig. 2, and includes the following steps:
firstly, the current path loss, the receiving power and the co-channel interference of the users are calculated according to the distance between the D2D users.
Then, based on the parameters, the SINR of the user and the set SINR are calculatedhige、SINRlowThe comparison is made and the TPC command is executed.
If the current SINRi>SINRhighIndicates good channel quality, ΔiTaking-1 dBm, properly reducing the transmitting power of the user, reducing energy consumption, re-executing the step 1, and calculating the path loss and SINR of the user; if the current SINR of the useri<SINRlowIndicates poor channel quality, ΔiTaking +1dBm, properly improving the transmitting power of the user, executing the step 1 again, and calculating the path loss and SINR of the user; if the SINR isi≤SINRhighAnd SINRi≥SINRlowThe channel quality is better, ΔiTaking 0dBm, it is not necessary to make large adjustments to its transmit power. Finally, the calculation is ended.
DS pairing algorithm flow, as shown in fig. 3: at time t, assuming that there is no prediction model, the interaction interval time Δ t is 1s, and after adding the movement prediction, Δ t is 5s, that is, every 5s the user reports the position information of the past 5s to the base station, so the number of information interactions K in the same time will be 1/5 when there is no prediction, (t, t + Δ t ] the position of each user is predicted by the movement model.
The method comprises the steps of pairing source nodes of a request file through a DS user pairing algorithm, firstly calculating the Distance between a current source node i and a cache point j, a non-cache point q and a Base station Base, screening out cache points and non-cache points in the communication radius of D2D, then calculating SINR of each point, executing a TPC instruction to carry out dynamic power control, and then calculating an index DS (Distance-SINR) of weighted combination of the communication Distance of D2D and the SINR of a user according to the following formula:
DS＝a*Distance+(1-a)*SINR
if a terminal equipment set { T1, T2 … Ti } exists in the D2D communication distance range of the current source node and has a target cache file, selecting a terminal equipment point Ty with the maximum DS value with the cache point as the optimal matching point, namely using a D2D direct connection mode; if no terminal equipment with the target cache file exists in the range of Rd2D of the current source node, finding the D2D relay equipment { D1, D2 … Dj } to pair. A terminal device with a target cache file exists in the D2D communication distance range of the relay device, and a relay device point Dy with the largest DS value between non-cache points is selected as an optimal matching point, namely a D2D relay mode is used; if there is no best paired device in either of the above two modes, then the conventional cellular mode is used to communicate directly with the base station. And finally, pairing the next source node according to the pairing priority sequence until all the m source nodes are paired.
All or part of the flow of the method of the embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a processor, to instruct related hardware to implement the steps of the embodiments of the methods. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (8)

1. A user pairing method combining mobile prediction and dynamic power is characterized in that:
step 1: calculating the distance between the current source node (Sx, Sy) and the cache point (Cx, Cy) and the non-cache point (UCx, UCy);
step 2: screening out cache points and non-cache points in the communication radius of the D2D;
and step 3: calculating SINR of each point and a weighted combination index DS of the distance and the SINR;
and 4, step 4: if a terminal equipment set { T1, T2 … Ti } exists in the D2D communication distance range of the current source node and a target cache file exists, selecting a terminal equipment point Ty with the maximum DS value between the source node and the cache point as an optimal pairing point, namely using a D2D direct connection mode;
and 5: if no terminal equipment with a target cache file exists in the D2D communication distance range of the current source node, searching for D2D relay equipment { D1, D2 … Dj } for pairing, wherein the terminal equipment with the target cache file exists in the D2D communication distance range of the relay equipment, and selecting a relay equipment point Dy with the largest DS value between the source node and the cache point as an optimal pairing point, namely using a D2D relay mode;
step 6: if the two modes have no optimal pairing equipment, the traditional cellular mode is used for directly communicating with the base station;
and 7: and pairing the next source node according to the pairing priority sequence until all the m source nodes are paired.
2. Method according to claim 1, comprising, before calculating the distances between the current source node (Sx, Sy) and the cache points (Cx, Cy), the non-cache points (UCx, UCy), the base station:
step 1.1: acquiring and loading trajectory data, generating the trajectory data by using a mobile scene generation tool Bonnmotion software based on JAVA, dividing the first 90% of trajectory original data into training data, taking the last 10% of trajectory original data as test data, standardizing the training data to have zero mean and unit variance, and standardizing the test data by using parameters the same as those of the training data during prediction;
step 1.2: defining a long-time memory LSTM network architecture, and creating an LSTM regression network;
step 1.3: training an LSTM network model, wherein the LSTM model is trained by adopting a Deep Learning Toolbox in MATLAB and training the LSTM network by using a training option specified by a model training function trainNet;
step 1.4: predicting and generating a predicted trajectory: defining a prediction function, predicting by using a trained LSTM model, taking original 1-100 s of data, putting the data into the prediction function to predict horizontal and vertical coordinates in 101-105 s, and storing the data into a prediction track matrix position _ pre (100, 2); taking data of 6-105 s as horizontal and vertical coordinates of sample prediction 106-110 s, and so on to obtain a complete prediction track matrix.
3. The method of claim 2, step 1.2, the implicit element of the LSTM layer is designated 200, the solver is set to a stochastic optimization method of adaptive momentum and 200 rounds of training are performed, the gradient threshold is set to 1, an initial learning rate of 0.005 is designated, and the learning rate is reduced by multiplying by a factor of 0.2 after 125 rounds of training.
4. The method of claim 1, wherein in step 3, the SINR calculation of the user comprises the steps of: step 3.1, calculating the path loss of the user: the adopted path loss model is a micro-urban model according to an international telecommunication union radio communication group ITU-R, and the path loss model of D2D user i is as follows:
PLi D2D＝-41+40*log10(d)+30*log10(fc)
where D is the distance between D2D users in m and fc is the carrier frequency of the system in GHz;
the path loss model for cellular user j is as follows:
PLj CELL＝-71.9+37.6*log10(r)+26*log10(fc/5.0)
where r is the distance of the cellular user from the base station in m;
step 3.2, the user receives the calculation of power, and the transmitting power of the D2D user i is recorded as TPi D2DThe path loss of the receiving subscriber at distance D2D is PLi D2DAntenna _ Gain is Antenna Gain, shadowing is shadow fading; the received power RP at the receiving end of each D2D useri D2DThe calculation is as follows:
RPi D2D＝TPi D2D-PLi D2D+Antenna_Gain-shadowing
the same holds for the transmission power of cellular user j as TPj CELLPath loss of cellular user j from base station is PLj CELLThen the received power RP of the base station endj CELLThe calculation is as follows:
RPj CELL＝TPj CELL-PLj CELL+Antenna_Gain-shadowing
step 3.3, the interference of the user is calculated, and the transmitting power of the cellular user j is recorded as TPj CELLThe path loss from the cellular subscriber to the receiving end of D2D is denoted as PLj CELL-D2D,PLj CELL-D2DCalculating the co-channel interference I of cellular users suffered by D2D users by adopting a path loss model of the D2D usersi CELLComprises the following steps:
Ii CELL＝TPj CELL-PLj CELL-D2D
let the transmit power of D2D user i be TPi D2DThe path loss from the D2D user to the receiving end of the cellular user is denoted as PLi D2D -CELL，PLi D2D-CELLThe calculation adopts a path loss model of a cellular user, and the same frequency of a D2D user suffered by the cellular userInterference Ij D2DComprises the following steps:
Ij D2D＝TPi D2D-PLi D2D-CELL
step 3.4, SINR calculation of users, assuming D2D user i multiplexes resources of cellular user j, SINR of D2D useri D2DExpressed as follows, where N is the thermal noise power:
Figure FDA0003522559470000021
similarly, SINR of cellular user jj CELLComprises the following steps:
Figure FDA0003522559470000022
5. the method of claim 4, step 3 further comprising calculating a transmit power,
P(i)＝min{Pmax,P0(i)+10*log10M(i)+α*PL+Δmcs(i)+f(Δi)}
wherein the meanings of the symbols are as follows:
Pmax: maximum transmit power of the user terminal;
P0(i) the method comprises the following steps Cell-specific or user terminal-specific parameters including target SINR, interference level, that is, one resource block is allocated to each user in the cell;
α: a cell-specific path loss compensation coefficient, PL being the path loss of the downlink reference signal measured by the user terminal;
Δmcs(i) the method comprises the following steps The power offset determined by the modulation and coding scheme is calculated as shown in the following formula:
Δmcs(i)＝10*log10(2MPR(i)-Ks-1)
wherein, mpr (i) is the current modulation and coding mode, Ks is a cell level parameter, which is known by RRC radio resource control layer signaling to take values of 0 and 1.25, and Ks takes value of 0, that is, the power compensation is not performed on the current modulation and coding mode of the user terminal;
f (Δ i): function f (i) performs power control based on Δ i, based on which the user isiAdjusts the transmit power by an absolute value of (a):
f(Δi)＝Δi[dBm]
at this time deltaiHas a value of ΔiE { -1,0, +1 }; the base station dynamically adjusts part of parameters of the user terminal and sends corresponding power control command TPC command to the user terminal, and the user terminal adjusts the transmitting power correspondingly after receiving TPC, thereby achieving the purpose of adjusting the power.
6. The method of claim 5, wherein the current SINR is determinedi>SINRhighThe channel quality of the users is better, and the transmitting power can be properly reduced under the condition of ensuring the throughput, so that the power consumption of the user equipment is reduced on one hand, the interference to other users can be reduced on the other hand, and the transmitting power is adjusted downwards when the users receive the command of the transmitting power control command; deltai-1 dBm; if the current SINR of the useri<SINRlowThe interference to the user is large and the channel quality is poor, in order to ensure the quality of D2D communication, the transmit power of the user can be appropriately increased, when the user receives a TPC command, the transmit power is adjusted up, and Δ i is +1 dBm; for SINRi(SINRi≤SINRhighAnd SINRi≥SINRlow) The channel condition of the user is good, the interference to other users is not very large, the transmitting power of the user can not be adjusted greatly, and the TPC command is determined by the following formula:
Figure FDA0003522559470000031
according to the above parameter settings, the power control formula of the D2D user can be simplified as follows:
P(i)＝min{Pmax,P0(i)+α*PL+f(Δi)}。
7. the method of claim 1, the DS pairing algorithm comprising:
the user terminal equipment selects the best one from three modes of cellular mode, D2D direct communication and D2D relay communication for communication, the priority of D2D direct communication is the highest, the D2D relay communication is the next time, the cellular mode is the last choice, the best paired user is selected by combining the D2D communication Distance and the user SINR weight into an index DS (Distance-SINR), and the formula of weight combination is as follows:
DS＝a*Distance+(1-a)*SINR
wherein a is a weighted combination coefficient, and a is 0.1-0.9; the Distance represents the Distance between users, after Min-Max normalization processing is carried out on SINR, weighting combination is carried out, and the users with the largest DS value are selected for pairing by calculating the DS of different users in the D2D communication range.
8. A readable storage medium, comprising a program or instructions for performing the method of any of claims 1 to 7 when the program or instructions are run on a computer.
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