CN114844538A - Millimeter wave MIMO user increment cooperative beam selection method based on wide learning - Google Patents
Millimeter wave MIMO user increment cooperative beam selection method based on wide learning Download PDFInfo
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Abstract
The invention discloses a millimeter wave MIMO user increment cooperative beam selection method based on wide learning, which comprises the following steps: aiming at the problem of downlink beam selection in a multi-point cooperation millimeter wave large-scale MIMO scene, each user collects downlink wide beam responses and transmission narrow beam responses, trains a local wide learning network, and selects beams based on predicted narrow beam responses. Further, by modeling the training problem for each user's local network as a distributed optimization problem with consistency constraints, efficient sharing of training data can be achieved with D2D communication between neighboring users. Furthermore, an incremental updating mode of the user local network in a cooperation mode is designed, and the training complexity of the network can be effectively reduced. The method fully utilizes the capability of the distributed wide learning to mine the response relation between the multi-base-station wide beam response and the transmission narrow beam response under the condition of a small sample, and can realize the low-complexity and low-overhead beam selection of the fast time-varying scene multipoint cooperative millimeter wave large-scale system.
Description
Technical Field
The invention relates to the field of wireless communication network optimization and intelligent communication, in particular to a millimeter wave MIMO user increment cooperative beam selection method based on wide learning.
Background
Due to the advantage of large bandwidth of millimeter wave frequency band, meanwhile, massive MIMO beam forming can effectively compensate millimeter wave propagation fading by using array gain, and millimeter wave massive MIMO technology is considered as one of key technologies for realizing higher spectrum efficiency of 5.5G mobile communication system. Then, since millimeter wave massive MIMO requires data transmission using narrow beams, it is very sensitive to shadowing effects existing in a channel. To solve the above problem, coordinated multipoint transmission is considered as a main mode of the millimeter wave massive MIMO system to achieve its performance gain. In a multi-point cooperative millimeter wave large-scale MIMO system, the required beam training overhead is significantly increased along with the increase of the number of cooperative base stations, and in order to reduce the training overhead, a related low-overhead beam training scheme is proposed by research based on the idea of combining traditional model driving and data driving with Machine Learning (ML).
In the existing typical ML-based solution, user uplink omnidirectional wide beam response and uplink narrow beam response samples collected at the base station side in an offline stage are utilized. Since the wide beam response from the user to multiple base stations can approximately reflect the user's location and propagation environment characteristics, the factors that determine the narrow beam response include the channel response and the pattern in the narrow beam determined by the user's location and propagation environment. By training the mapping relation between the multi-base station uplink wide beam response and the multi-base station uplink narrow beam response, the base station side can be helped to predict the uplink narrow beam response only by using the uplink wide beam response at the online stage, and corresponding beam selection is made.
The above typical ML scheme cannot be directly applied to beam selection for downlink transmission in a scenario where reciprocity between uplink and downlink of a channel is poor, such as a Frequency Division Duplex (FDD) mode. Therefore, the user side needs to complete mapping training based on the collected downlink wide beam response and downlink narrow beam response of the multiple base stations, so as to support prediction of beam selection, and feed back the prediction to the base station side.
Different from model training of a base station side, a user side needs more elaborate design due to the fact that computing power, storage capacity and interaction capacity of the user side are relatively weak, training data sharing of multiple users and complexity control of model updating and the like, and especially under a fast time-varying scene, the requirements on collection cost control of samples and real-time guarantee of model updating are higher.
Disclosure of Invention
In view of this, the present invention aims to provide a millimeter wave MIMO user increment cooperative beam selection method based on wide learning, so as to solve the problem that the current low-overhead beam training method based on wide beam response cannot be directly applied to a millimeter wave multipoint cooperative downlink transmission system without reciprocity of uplink and downlink channels, and optimize ML model design on a user side, so as to adapt to weak calculation power, storage capability and multi-user interaction capability on the user side. The method realizes the lightweight of the ML model at the user side, and simultaneously ensures the performance under the scene that a small training sample and the model need to be frequently updated.
In order to achieve the purpose, the invention adopts the following technical scheme:
a millimeter wave MIMO user incremental cooperative beam selection method based on wide learning, the method comprising:
step S1, constructing a downlink received signal receiving expression and a corresponding reachable rate expression of the downlink received signal receiving expression on the user side of the multi-point cooperative millimeter wave large-scale MIMO system; then, constructing an actual effective rate expression of the user side according to the reachable rate expression and the tracking period of the channel time variation of the system; constructing a global optimization problem model which takes the maximization of the system effectiveness and the speed as the target based on the actual effective speed expression;
step S2, constructing and initializing a local wide learning mapping network on each user side based on the wide learning architecture; taking multi-base station wide beam responses collected by each user and beam equivalent channel strength corresponding to transmission narrow beams as a local training set, wherein the multi-base station wide beam responses are taken as input features, equivalent rate index sample beams corresponding to the transmission narrow beams are taken as output, affine transformation is carried out on the input features through setting feature nodes and enhancement nodes based on a width learning framework, and the optimization problem of a network model is modeled as a weight problem for solving the affine transformation;
step S3, equivalently converting the multi-user beam selection problem into a distributed optimization problem with consistency constraint according to a distributed optimization theory, designing an iterative interactive method to train a local wide learning mapping network of each multi-user, and realizing effective sharing of a multi-user local training set, thereby reducing the requirement on the number of training samples;
step S4, introducing a collaborative incremental learning method for rapidly updating collaborative model parameters when data are newly added, and realizing a network model which is updated while being collected; performing iterative training on the data set generated in the step S2 by using the network established in the step S3;
and step S5, each user inputs the multi-base-station wide beam response collected in real time into the local mapping network to predict the beam selection of the user.
Further, in the multi-point cooperative millimeter wave massive MIMO system, it includes:
b base stations and U users, wherein the number of antennas of a single base station is M, the users configure omnidirectional antennas for signal reception, and the number of system subcarriers is K;
the user receives the cooperation downlink transmission service provided by the B base stations through an orthogonal frequency division multiple access mode;
considering the situation that a single base station uses a single beam to serve one user, and multiple users move randomly in a certain area, the base stations are set asSet of users asSet of subcarriers asThe set of subcarriers allocated to user u isThe size is recorded asSatisfy the requirement of
Recording the response of a downlink antenna domain channel from a base station b to a user u on a subcarrier k as h b,u,k Then, the modeling is as follows:
in this formula, L represents the number of scattering paths, α, of the propagation channel b,u,l Denotes the complex gain of the l-th path, f k Denotes the center frequency, τ, of the k-th subcarrier l Represents the path delay of the ith path; a (-) denotes a pilot vector of the base station multi-antenna array.
Further, for the multipoint cooperation millimeter wave massive MIMO system, the downlink received signal receiving expression and the reachable rate expression corresponding to the downlink received signal receiving expression at the user side are obtained by the following method:
considering that downlink transmission is based on typical mixed precoding, the analog beam sets of all base stations areAnd satisfy F H F=FF H =I M The base station b selects the second from FColumn(s) ofAs analogue beamsServing user u, defining an analog beamforming matrix for a plurality of base stations
The cooperative center control node collects the beam equivalent channel state information of each base station, and designs the information on the sub-carriers according to the maximum ratio transmission criterionThe numeric precoding vector of the upper service user u is:
in the formula, the first and second sets of data are represented,whereinRepresenting the response of the downlink antenna domain channel from B base stations to user u on subcarrier k.
Then the downlink received signal of the user u on the kth subcarrier is represented as:
in this formula, s u,k ~CN(0,P u ) Is a data symbol, n u,k ~CN(0,σ 2 ) For the receiver noise, σ, of user u on the k-th subcarrier 2 Is the noise power; when in useWhen the system adopts equal power distribution between users and subcarriers, the method comprises the following stepsWherein P is the total transmitting power of the base station;
correspondingly, the received signal-to-interference-and-noise ratio of user u on subcarrier k is expressed as:
the corresponding achievable rates are:
in the formula, B w Representing the system bandwidth.
Further, for the multi-point cooperative millimeter wave massive MIMO system, a global optimization problem model with the goal of maximizing system effectiveness and rate is obtained by the following method, including:
defining the tracking period of the system for channel time variation as T, namely, the system needs to perform beam training again every T to update the precoding design for data transmission; first T of each cycle r Time, the base station carries out beam training, and the rest time carries out data transmission;
thus, the actual effective rate achieved by user u is represented as:
the beam selection problem is expressed as an optimization problem that maximizes system efficiency and rate through beam selection:
since there is no interference between users accessing through OFDMA, the above problem translates into:
optimization due to the above problem requires selection of an optimal beam from a cascaded selectable analog beam space of B base stationsIn combination, the search space size is M B The conversion is thus made as follows:
a low-complexity sub-optimal solution of the original problem is obtained.
Further, the step S2 specifically includes:
to solve the problem constructed in step S1, each base stationUsing pilots in the training phaseMaking wide beamsAnd each analog beam to be selectedThe training of (2),represents the training power;
Each userBy collecting N multi-base-station wide-beam response samplesCorresponding analog wave beam equivalent rate index sample to be selected Training a local wide learning network;
the specific process is as follows:
memory usersThe wide beam response sample N collected from base station b is 1, …, with N arranged as
After data arrangement, the input of the user u local wide learning network model corresponding to the nth sample is as follows:
Then the N samples correspond to the input and output matrices as:
further, in the step S2, an algorithm based on a width learning framework is adopted to perform nonlinear transformation and affine transformation on the original feature of the wide beam response signal received by the user; the algorithm defines characteristic nodes and enhancement nodes, calculates the characteristic nodes from input data, further generates the enhancement nodes from the characteristic nodes, and then splices the two nodes together as affine transformation; based on the structure, the optimization of the model only needs to solve the weight of affine transformation;
based on a wide learning framework, the specific processing process of the input sample in the feature transformation network is as follows:
first, using input data X u Mapping to I-group feature nodesAnd J group enhanced nodesTables of F and EShowing the number of the characteristic nodes and the enhanced nodes of each group, and specifically showing that:
wherein Z u =[Z u,1 ,Z u,2 ,…,Z u,I ]A cascade matrix of characteristic nodes of group I, H u =[H u,1 ,...,H u,J ]A cascaded matrix of enhanced nodes for the J set;andrespectively representing the connection weight and the bias of the feature generation network and the feature enhancement network, and generally adopting random generation;a column vector representing elements all 1; phi (-) and xi (-) both represent linear or non-linear activation functions;
then, the user local wide learning network pair joint feature enhancement nodePerforming affine transformationsOutput of
Based on the minimum mean square error criterion and utilizing L2 norm regularization to improve the generalization performance of the network, the network weight matrix W is learned widely u The optimization problem of (a) is modeled as:
in the formula, | · luminance | | F Expressing a Frobenius norm, and lambda expresses a regularization factor;
the optimal solution is as follows:
in the formula, I IF+JE An identity matrix of dimension IF + JE is represented.
Further, the step S3 includes:
designing a mechanism for a user to perform cooperative training according to a downlink wide beam response signal based on an ADMM algorithm so as to reduce the calculation burden of each user node, and regarding the converted distributed optimization problem as a problem that the user shares information through cooperation, specifically comprising the following steps:
if the propagation environments between the U users and the base station have similarity in space, the models of local wide learning should tend to be consistent, that is, the models are consistentI.e. joint representation of network training optimization problem for multiple users as
the shared training based on all user local training samples can be realized by solving the problems, and the performance of the wide learning method under the condition of small samples is improved;
based on the ADMM algorithm, the above problem is converted into:
the solution to this problem requires only that each user utilize locally collected training data { X } u ,Y u Performing information interaction with adjacent users through an inter-user D2D communication protocol;
the iterative solution process is as follows:
O u (t)=O u (t-1)+W u (t)-W 0 (t
in the formula, t represents iteration times, a parameter rho is a punishment coefficient for controlling and estimating consistency constraint, and rho>0; Iteratively repeating the above process t max Secondly, the distributed solution of the optimization problem is realized.
Further, the step S4 includes:
when the wireless environment changes due to the movement of users or the change of the environment, the wide learning network of each user needs to be updated in a self-adaptive manner;
defining individual usersThe newly collected training samples for adapting to the environmental changes are
Obtaining new features and enhanced nodes through feature processing of the local wide learning networkTo avoid data characteristics obtained directly for the current accumulationAnd output tag Recalculating new weight width learning weightThe incremental update is performed as follows:
definition ofWoodbury identity equation D-UBV inversed by matrix -1 =D -1 +D -1 U(B -1 -VD -1 U) -1 VD -1 ,Restated as:
in the formula, the content of the active carbon is shown in the specification,no recalculation is required for the known symmetric matrix at the time of the current update.
Further, the step S5 includes:
in the on-line execution phase, each userWide beam response of multiple base stations only using current time slotObtaining joint feature enhanced node through nonlinear feature transformation of local wide learning networkObtaining the predicted value of the analog beam index to be selected after affine transformation processingDetermining each base station according to the predicted valueAnalog beam sequence number ofAnd feeds back to the base station to enable the base station to perform subsequent downlink data transmission;
the average effective rate of each user on each subcarrier is given by:
in the formula, 2BT d Training B wide beam responses for a base stationAnd B selected analog beamsThe time spent, wherein T d Representing the time it takes for a single beam training.
Further, in the step S3, a penalty coefficient ρ of the algorithm is set to 0.001, and a regularization constraint coefficient λ is set to 2 -5 Number of interactive iterations t of the training process max =5;
In the aspect of network structure, the number of feature nodes of the wide learning network is set to 300, and the specific setting of the enhanced nodes is as follows:
when the number of local training samples is not more than 100, the number of the enhanced nodes is set to be 100, when the number of the local training samples is more than 100 and not more than 300, the number of the enhanced nodes is set to be 500, when the number of the local training samples is more than 300 and not more than 500, the number of the enhanced nodes is set to be 1000, and when the number of the local training samples is more than 500, the number of the enhanced nodes is set to be 2000;
the enhancement layer activation function is designed to Tansig in the following specific form:
the invention has the beneficial effects that:
compared with the prior art, the technical scheme of the invention has the following beneficial technical effects: the millimeter wave MIMO user increment cooperative beam selection method based on wide learning provided by the invention utilizes distributed increment updating design aiming at the wide learning model, can realize effective sharing of training data between adjacent users and low complexity updating of the local wide learning model of each user, can obviously reduce multi-user interaction overhead under the condition of no obvious performance loss compared with centralized training beam selection, and simultaneously meets the requirements on the number of local training samples of the users and the real-time property of model updating under a fast time-varying scene.
Drawings
Fig. 1 is a schematic flowchart of a millimeter wave MIMO user increment cooperative beam selection method based on wide learning in embodiment 1;
fig. 2 is a schematic view of a communication scenario of the multi-point cooperative millimeter wave massive MIMO downlink communication system provided in embodiment 1;
fig. 3 is a schematic structural diagram of a coordinated multiple point millimeter wave massive MIMO downlink communication system provided in embodiment 1;
FIG. 4 is a model diagram of the wide learning network provided in example 1;
fig. 5 is a schematic diagram illustrating a principle of a millimeter wave MIMO user increment cooperative beam selection method based on wide learning in embodiment 1;
fig. 6 and 7 are graphs comparing the performance of the millimeter wave MIMO user incremental cooperative beam selection method based on wide learning in embodiment 1 with that of the conventional method.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Example 1
Referring to fig. 1 to fig. 7, the present embodiment provides a millimeter wave MIMO user incremental cooperative beam selection method based on wide learning, where the method flow is shown in fig. 1, and the method includes the following steps:
s1, constructing a cooperative transmission millimeter wave large-scale MIMO system model under the scene of dynamic change of user positions, and establishing a multi-user beam selection optimization problem model taking the maximum system effectiveness and speed as targets;
specifically, in this embodiment, the step S1 specifically includes:
a scene is constructed and specific channel data is generated in this embodiment by employing an open source Deep MIMO data set. The data set channel data is obtained through simulation of ray tracing simulation software Wireless InState according to scene environment parameters, and the simulator can simulate hundreds of rays emitted from a transmitting end and generate channel parameters such as an arrival angle, a departure angle, time delay, path loss and the like for each propagation path reaching a receiving end.
The data set can simulate a scenarized real channel environment, rather than a statistical channel model commonly used in past theoretical analysis. The constructed DeepMIMO channel can capture the dependency relationship of environment, materials and the positions of a transmitter and a receiver, and is widely applied to millimeter wave and large-scale MIMO research in industry and academia. The DeepMIMO data set is based on an outdoor street scene of 18 base stations and 100 multi-user location points, and generates a DeepMIMO channel data set allowing customization according to a group of parameters which can be adjusted by researchers, such as the number of antennas, the number of OFDM subcarriers, the number of channel paths and the like. The 'O1' scene in the Deep MIMO dataset is horizontally a main street 600m long (along the Y-axis) and 40m wide (along the X-axis), with a total of 12 base stations, 6 on each side. The user position grid is positioned along the main street, and is 550m long and 35m wide. Starting from the top right, 15m from the street start, and ending at the bottom left, 35m from the street end, the grid includes 2751 lines, from R1 to R2751. Wherein, each row has 181 user positions, the distance between every two adjacent user positions is 20cm, and the receiving height of each position user antenna is constant to be 2 m.
In this embodiment, for a multi-point cooperative millimeter wave large-scale MIMO downlink system, it is assumed that there are B base stations and U users, the number of antennas of a single base station is M, the users configure omnidirectional antennas for signal reception, and the number of subcarriers of the system is K.
Multiple users receive the coordinated downlink transmission service provided by the B base stations through an Orthogonal Frequency Division Multiple Access (OFDMA) mode.
Consider again the typical case where a single base station serves one user with a single beam, and multiple users move randomly within a certain area. Recording a set of base stations asSet of users asSet of subcarriers asThe set of subcarriers allocated to user u isThe size is recorded asSatisfy the requirement of
The response of a downlink antenna domain channel from a base station b to a user u on a subcarrier k is recorded as h b,u,k The modeling can be as follows:
in this formula, L represents the number of scattering paths, α, of the propagation channel b,u,l Denotes the complex gain of the l-th path, f k Denotes the center frequency, τ, of the k-th subcarrier l Represents the path delay of the ith path; a (-) denotes a pilot vector of the base station multi-antenna array.
If the base station uses a Uniform rectangular planar antenna array (UPA) with half-wavelength array element spacing, the number of antennas in the y and z directions in the yz plane is W and H, i.e., M is WH. The steering vector for the UPA array can be expressed as:
in this formula, θ b,u,l Phi and phi b,u,l Respectively, the azimuth angle and the downtilt angle of the incident array signal, an
Considering that downlink transmission is based on typical mixed precoding, the analog beam sets of all base stations areSuch as a standard Discrete Fourier Transform (DFT) matrix, satisfying F H F=FF H =I M The base station b selects the second from FColumn(s) ofAs analogue beamsServing user u, defining an analog beamforming matrix for a plurality of base stations
The cooperative center control node can collect the Channel State Information (CSI) of each base station, and design the CSI on a subcarrier according to the Maximum Ratio Transmission (MRT) criterionThe numeric precoding vector of the upper service user u isWhereinWhereinRepresenting the response of the downlink antenna domain channel of B base stations to user u on subcarrier k.
The downlink received signal of the user u on the k-th subcarrier can be represented as:
in this formula, s u,k ~CN(0,P u ) Is a data symbol, n u,k ~CN(0,σ 2 ) For the receiver noise, σ, of user u on the k-th subcarrier 2 Is the noise power; when in useWhen the system adopts equal power distribution between users and subcarriers, the method comprises the following stepsWhere P is the total transmit power of the base station.
Accordingly, the received Signal-to-Noise-and-Interference-Ratio (SINR) of user u on subcarrier k can be expressed as:
the corresponding achievable rates are:
in the formula, B w Representing the system bandwidth.
The base station needs to acquire a certain CSI for designIn a massive MIMO system, a beam training mode is usually adopted, and a large amount of pilot/time overhead is consumed for complete beam training. Defining the tracking period of the system for channel time variation as T, i.e. the system needs to perform beam training again every T to update the precoding design for data transmission. First T of each cycle r And in time, the base station performs beam training, and the rest time is used for data transmission.
Thus, the actual effective rate available to user u can be expressed as:
the beam selection problem can be expressed as an optimization problem that maximizes the system efficiency and rate through beam selection:
since there is no interference between users accessing through OFDMA, the above problem can be translated into
Since optimization of the above problem requires selection of the optimal beam combination from the cascaded alternative simulated beam space of the individual B base stations, the search space size is M B Practical systems tend to be difficult to withstand; the conversion may be made as follows,
a low-complexity sub-optimal solution of the original problem is obtained.
For the problem after conversion, if the base station side can obtain the equivalent CSI information of the complete beam domain
It is possible to maximizec b,u Representing a userEquivalent rate index available under base station b. But due to the large overhead of beam training time required, i.e.Smaller, may result in poor overall performance. How to ensure c b,u On the premise of no obvious loss, the beam training time T is reduced as much as possible r And becomes the key to solve the problem.
More specifically, in the present embodiment, as shown in fig. 2, a 36m × 40m area covered by rows R1166 to R1366 in a Deep MIMO main street is selected for a communication scene, sample data at positions 201 × 181 ═ 36281 are collected in total, and the distance between adjacent position points is 20 cm. The system works in a millimeter wave frequency band, the carrier frequency is 60GHz, the working bandwidth is 300MHz, the number of base stations is 3, an 8 multiplied by 4 uniform planar array is configured, the number of cooperative users is 2, and all the antennas are configured with omnidirectional single antennas. Both mobile users access the base station through the OFDMA mode and can exchange information through the D2D protocol. Consider two mobile users no more than 15m apart during the collaboration period.
Step S2, each user utilizes the collected multi-base-station wide beam responses and the beam equivalent channel intensity corresponding to the transmission narrow beam to construct a local training set; and taking the multi-base-station wide beam response as an input characteristic, taking an equivalent rate index sample beam corresponding to each transmission narrow beam as an output, and initializing the user local wide learning mapping network.
Specifically, in the present embodiment, the step S2 includes:
research has proved that the wide beam transmitted by the base station can help the user to sense the environmental characteristic information between the user and the transmitting base station, and an implicit user position representation can be obtained by using the wide beam responses of a plurality of base stations.
Since the beam selection is determined by the beam domain equivalent CSI, and the CSI information is directly related to the user position, the mapping relation between the wide beam response of the multiple base stations and the beam selection can be judged.
On the other hand, although different users are served by different subcarriers, the narrow beam response strength of the channel can be judged to be independent of the subcarrier frequency according to the channel model. Therefore, as for the problem constructed in step S1, a feasible solution is that each base stationUsing pilots in the training phase(for simplicity, this may be a pilot sequence in practice) for broad beam formationAnd each analog beam to be selectedThe training of (2),representing the training power.
After pilot frequency matching, the user can obtain each wave beam responseThe estimation of (d) is:
each userBy collecting N multi-base-station wide-beam response samplesCorresponding analog wave beam equivalent rate index sample to be selected A local wide learning network is trained.
The specific process is as follows:
memory usersThe wide beam response sample N collected from base station b is 1, …, with N arranged as The response samples of the beam to be selected are
All the wide beam response samples collected from the B base stations are arranged intoCorresponding to-be-selected beam response sample arrangement
After data arrangement, the input of the user u local wide learning network model corresponding to the nth sample is as follows:
Then the N samples correspond to the input and output matrices as:
more specifically, in the embodiment of the present invention, in order to better fit the network, the training data set is subjected to a maximum normalization process, and all inputs of the local wide learning network model of user u are divided by a constant scaling factor Δ of amplitude norm Defined as:
for the output of the local wide learning network model, response samples of each beam to be selected of each base station b are obtained by a user uRespectively carrying out normalization processing, and operating as follows:
specifically, in the embodiment of the invention, each user local wide learning network has 2BK u Each input corresponds to K u Amplitude and phase of wide beam response to B base stations on the sub-carrier; each user local wide learning network has BM outputs corresponding to the equivalent rate indexes of each analog beam to be selected of B base stations. The simulation divides the 36281 location points into a training set and a testing set, the ratio is 8:2, and the sizes are 29024 and 7256 respectively, and the training set and the testing set are used by two cooperative users.
Specifically, in the embodiment of the invention, the original feature of the wide beam response signal received by the user is subjected to nonlinear transformation and affine transformation by adopting an algorithm based on a width learning framework. The algorithm defines feature nodes and enhancement nodes, calculates the feature nodes from input data, further generates the enhancement nodes from the feature nodes, and then splices the two nodes together as affine transformation. Based on the structure, the optimization of the model only needs to solve the weight of affine transformation.
More specifically, based on the wide learning framework, the specific processing procedure of the input sample in the feature transformation network is as follows:
first, input data X is utilized u Mapping to I-group feature nodesAnd J group enhanced nodesF and E respectively represent the number of the characteristic nodes and the enhanced nodes of each group, and are specifically represented as follows:
wherein Z u =[Z u,1 ,Z u,2 ,…,Z u,I ]Is a group I characteristicCascade matrix of symbolic nodes, H u =[H u,1 ,...,H u,J ]A cascaded matrix of enhanced nodes for the J set;andrespectively representing the connection weight and the bias of the feature generation network and the feature enhancement network, and generally adopting random generation;a column vector representing elements all of 1; both φ (-) and ξ (-) represent linear or nonlinear activation functions.
Then, the user local wide learning network pair joint feature enhancement nodePerforming affine transformationsOutput of
Based on the minimum mean square error criterion and utilizing L2 norm regularization to improve the generalization performance of the network, the network weight matrix W is learned widely u The optimization problem of (a) can be modeled as:
in the formula, | · luminance | | F Expressing a Frobenius norm, and lambda expresses a regularization factor; the optimal solution is as follows:
in the formula, I IF+JE Representing an identity matrix of dimension IF + JE.
Step S3, equivalently converting the multi-user beam selection problem into a distributed optimization problem with consistency constraint according to a distributed optimization theory, designing an iterative interactive method to train a local wide learning mapping network of each multi-user, and realizing effective sharing of a multi-user local training set, thereby reducing the requirement on the number of training samples;
specifically, in this implementation, a mechanism for a user to perform cooperative training according to a downlink wide beam response signal is designed based on an ADMM algorithm to reduce the computational burden of each user node, and a converted distributed optimization problem is regarded as a problem that the user shares information through cooperation, which specifically includes the following steps:
if the propagation environments between the U users and the base station have similarity in space, the models of the local wide learning should tend to be consistent, that is, the models of the local wide learning should tend to be consistentThat is, the network training optimization problem of multiple users can be jointly expressed as
by solving the problems, shared training based on all user local training samples can be realized, and the performance of the wide learning method under the condition of small samples is improved.
However, the problem needs to be solved based on the wide beam characteristic transformation a of all users and the corresponding analog beam response Y, and the corresponding actual system for information interaction between users is difficult to bear. The following converts the above problem into, based on the ADMM algorithm:
The solution to this problem requires only that each user utilize locally collected training data { X } u ,Y u And a small amount of information interaction with neighboring users is performed through an inter-user D2D communication protocol.
The iterative solution process is as follows:
O u (t)=O u (t-1)+W u (t)-W 0 (t)
in the formula, t represents iteration times, a parameter rho is a punishment coefficient for controlling and estimating consistency constraint, and rho>0。 Iteratively repeating the above process t max Second, distributed solving of the above optimization problem can be achieved.
More specifically, in the embodiment of the present invention, as shown in fig. 4, an ADMM algorithm based on a wide learning framework is adopted to perform weight update in user cooperation. As shown in FIG. 5, the user cooperation interaction process continuously loops until the set interaction iteration number t is reached max . In the embodiment of the invention, the penalty coefficient rho of the algorithm is set to be 0.001, and the regularization constraint coefficient lambda is set to be 2 -5 Number of interactive iterations t of the training process max 5. In the aspect of a network structure, the number of characteristic nodes of the wide learning network is set to 300, the number of enhanced nodes is flexibly set according to the change of the number of local training samples, the taken parameters are all configurations capable of obtaining better performance, and the specific settings are as follows: when the number of local training samples is not more than 100, the number of the enhanced nodes is set to 100, when the number of the local training samples is more than 100 and not more than 300, the number of the enhanced nodes is set to 500, when the number of the local training samples is more than 300 and not more than 500, the number of the enhanced nodes is set to 1000, and when the number of the local training samples is more than 500, the number of the enhanced nodes is set to 2000. Regardless of the nonlinear activation function of the feature layer, the enhancement layer activation function is designed as Tansig in the following specific form:
and step S4, introducing a cooperative incremental learning method, wherein each user can quickly update the local learning network by using a small amount of collected new samples when the environment changes, and effective tracking of the multi-user beam selection on the fast time-varying environment is realized.
Specifically, in this embodiment, when the wireless environment changes due to the movement of the user or the environment itself, the wide learning network of each user needs to be updated adaptively. Defining individual usersThe newly collected training samples for adapting to the environmental changes are Obtaining new features and enhanced nodes through feature processing of the local wide learning network To avoid data characteristics obtained directly for the current accumulationAnd output tagRecalculating new weight width learning weight(the main calculated amount isThe inversion operation of (d) in the following manner.
Definition ofWoodbury identity equation D-UBV inversed by matrix -1 =D -1 +D -1 U(B -1 -VD -1 U) -1 VD -1 In a clear view of the above, it is known that,restated as:
in the formula, the first step is that,no recalculation is required for the known symmetric matrix at the time of the current update.The calculation only involvingInversion of the dimensional symmetric matrix ifThe computational overhead of the incremental update method described above is much less than the direct inversion method.
In the embodiment of the present invention, it should be noted that, because real and effective samples in an actual communication scenario are difficult to obtain, incremental learning mainly considers the condition that the number of samples collected by a user is limited, and makes full use of the advantage of an incremental user cooperation network on small data volume. The number of local sample data sets used for training is sequentially increased from 50 to 1000.
Step S5: each user inputs the multi-base-station wide beam response collected in real time into a local mapping network to predict the self beam selection.
As shown in FIG. 3, the phases are executed online, with each user executingWide beam response of multiple base stations only using current time slot Obtaining joint feature enhanced node through nonlinear feature transformation of local wide learning networkObtaining the predicted value of the analog beam index to be selected after affine transformation processingBased on the predicted values, each base station can be determinedAnalog beam sequence number ofAnd feeds back to the base station to enable the base station to perform subsequent downlink data transmission.
The average effective rate of each user on each subcarrier is given by:
in the formula, 2BT d Training B wide beam responses for a base stationAnd B selected analog beamsThe time spent, wherein T d Representing the time it takes for a single beam training. In the embodiment of the invention, T is set to be 1ms, and T d Set to 0.01 ms.
The embodiment of the invention utilizes fig. 6 to compare the user effective rate curves of the designed user incremental cooperative beam selection method based on the wide learning with the user side centralized beam selection method based on the wide learning, the user side fully distributed beam selection method based on the wide learning, the user side centralized beam selection method based on the deep learning and the traditional exhaustive beam scanning search method. The Genie-aid curve in the figure represents an ideal situation, and the base station side does not need any training overhead, namely T r The optimal beam selection can be made in the case of 0, representing an upper rate performance limit. The Basline curve is set to the average effective rate obtained by using the traditional exhaustive beam scanning search method for beam training. In the designed user increment cooperative beam selection method based on wide learning, t is set max 5. As can be seen from fig. 6, the wide learning-based user-side fully distributed beam selection method has poor performance, but is superior to the deep learning-based user-side centralized beam selection method, and particularly in the case of a small number of samples, the advantage is more obvious. This is because the wide learning network in this case is easier to get a reasonable tradeoff of training sample number versus model complexity. Compared with the traditional exhaustive beam scanning search method, when the total training sample number is less than 100, the wide learning-based user-side fully distributed waveThe beam selection method can also achieve performance advantages with an average effective rate improvement of about 7.5%. The above results indicate that the wide learning network is suitable for the beam selection problem concerned by this patent as a typical lightweight learning network. Further, when the number of local training samples is increased to 400, the designed wide learning-based user incremental cooperative beam selection method has the beneficial average effective rate of 6.529bps/Hz, which is improved by about 6.9% compared with the wide learning-based user-side fully-distributed beam selection method and is improved by about 29% compared with the traditional exhaustive beam scanning search method, and has obvious performance improvement; and with the increase of the number of training samples, the performance approaches to the user side centralized beam selection method based on the wide learning. Further, the incremental updating mechanism designed by the invention achieves almost the same performance as the method based on wide learning direct retraining, and the effectiveness is proved.
Fig. 7 shows a variation relationship between the beam selection success probability of the user incremental cooperative beam selection method based on the wide learning and the number of user cooperative iterations, and compared with the centralized method based on the wide learning and the fully distributed method based on the wide learning, it can be seen that when the number of samples of the local input data set is 500, the beam selection success rate of the user incremental cooperative beam selection method based on the wide learning increases with the increase of the number of interactive iterations among the cooperative users, and compared with the fully distributed method, the method has an obvious advantage that the beam selection accuracy is improved by about 6%, and when the number of iterations is 5, an effect close to the centralized method has been obtained.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A millimeter wave MIMO user increment cooperative beam selection method based on wide learning is characterized by comprising the following steps:
step S1, constructing a downlink received signal receiving expression and a corresponding reachable rate expression of the downlink received signal receiving expression on the user side of the multi-point cooperative millimeter wave large-scale MIMO system; then, constructing an actual effective rate expression of the user side according to the reachable rate expression and the tracking period of the channel time variation of the system; constructing a global optimization problem model which takes the maximization of the system effectiveness and the speed as the target based on the actual effective speed expression;
step S2, constructing and initializing a local wide learning mapping network on each user side based on the wide learning architecture; taking multi-base station wide beam responses collected by each user and beam equivalent channel strength corresponding to transmission narrow beams as a local training set, wherein the multi-base station wide beam responses are taken as input features, equivalent rate index sample beams corresponding to the transmission narrow beams are taken as output, affine transformation is carried out on the input features through setting feature nodes and enhancement nodes based on a width learning framework, and the optimization problem of a network model is modeled as a weight problem for solving the affine transformation;
step S3, equivalently converting the multi-user beam selection problem into a distributed optimization problem with consistency constraint according to a distributed optimization theory, designing an iterative interactive method to train a local wide learning mapping network of each multi-user, and realizing effective sharing of a multi-user local training set, thereby reducing the requirement on the number of training samples;
step S4, introducing a collaborative incremental learning method for rapidly updating collaborative model parameters when data are newly added, and realizing a network model which is updated while being collected; performing iterative training on the data set generated in the step S2 by using the network established in the step S3;
and step S5, each user inputs the multi-base-station wide beam response collected in real time into the local mapping network to predict the beam selection of the user.
2. The wide learning based millimeter wave MIMO user incremental cooperative beam selection method according to claim 1, wherein in the multi-point cooperative millimeter wave massive MIMO system, it comprises:
b base stations and U users, wherein the number of antennas of a single base station is M, the users configure omnidirectional antennas for signal reception, and the number of system subcarriers is K;
the user receives the cooperation downlink transmission service provided by the B base stations through an orthogonal frequency division multiple access mode;
considering the situation that a single base station uses a single beam to serve one user, and multiple users move randomly in a certain area, the base stations are set asSet of users asSet of subcarriers asThe set of subcarriers allocated to user u isThe size is recorded asSatisfy the requirement of
The response of a downlink antenna domain channel from a base station b to a user u on a subcarrier k is recorded as h b,u,k Then, the modeling is as follows:
in this formula, L represents the scattering of the propagation channelNumber of paths, α b,u,l Denotes the complex gain of the l-th path, f k Denotes the center frequency, τ, of the k-th subcarrier l Represents the path delay of the ith path; α (-) denotes a steering vector of the base station multi-antenna array.
3. The method for selecting the millimeter wave MIMO user increment cooperative beam based on the wide learning as claimed in claim 2, wherein for the multi-point cooperative millimeter wave massive MIMO system, the downlink received signal receiving expression and the reachable rate expression corresponding thereto at the user side are obtained by:
considering that downlink transmission is based on typical mixed precoding, the analog beam sets of all base stations areAnd satisfy F H F=FF H =I M The base station b selects the second from FColumn(s) ofAs analogue beamsServing user u, defining an analog beamforming matrix for a plurality of base stations
The cooperative center control node collects the beam equivalent channel state information of each base station, and designs the information on the sub-carriers according to the maximum ratio transmission criterionThe numeric precoding vector of the upper service user u is:
in the formula, the first and second sets of data are represented,whereinRepresenting the response of the downlink antenna domain channel from the B base stations to the user u on the subcarrier k;
then the downlink received signal of the user u on the kth subcarrier is represented as:
in this formula, s u,k ~CN(0,P u ) Is a data symbol, n u,k ~CN(0,σ 2 ) For the receiver noise, σ, of user u on the k-th subcarrier 2 Is the noise power; when in useWhen the system adopts equal power distribution between users and subcarriers, the method comprises the following stepsWherein P is the total transmitting power of the base station;
correspondingly, the received signal-to-interference-and-noise ratio of user u on subcarrier k is expressed as:
the corresponding achievable rates are:
in the formula, B w Representing the system bandwidth.
4. The wide learning-based millimeter wave MIMO user incremental cooperative beam selection method according to claim 3, wherein for the multi-point cooperative millimeter wave massive MIMO system, a global optimization problem model with the goal of maximizing system efficiency and rate is obtained by the following method, comprising:
defining the tracking period of the system for channel time variation as T, namely, the system needs to perform beam training again every T to update the precoding design for data transmission; first T of each cycle r Time, the base station carries out beam training, and the rest time carries out data transmission;
thus, the actual effective rate achieved by user u is represented as:
the beam selection problem is expressed as an optimization problem that maximizes the system efficiency and rate through beam selection:
since there is no interference between users accessing through OFDMA, the above problem translates into:
since optimization of the above problem requires selection of the optimal beam combination from the cascaded alternative simulated beam space of the individual B base stations, the search space size is M B The conversion is thus made as follows:
a low-complexity sub-optimal solution of the original problem is obtained.
5. The method for selecting millimeter wave MIMO user increment cooperative beam based on wide learning according to claim 4, wherein the step S2 specifically comprises:
to solve the problem constructed in step S1, each base stationUsing pilots in the training phaseFor making wide beamAnd each analog beam to be selectedThe training of (2),represents the training power;
each userBy collecting N multiple base station wide beam response samplesCorresponding analog wave beam equivalent rate index sample to be selected Training a local wide learning network;
the specific process is as follows:
memory usersThe wide beam response sample N collected from base station b is 1, …, with N arranged as
After data arrangement, the input of the user u local wide learning network model corresponding to the nth sample is as follows:
Then the N samples correspond to the input and output matrices as:
6. the millimeter wave MIMO user increment cooperative beam selection method based on wide learning according to claim 5, wherein in the step S2, an algorithm based on a wide learning framework is adopted to perform nonlinear transformation and affine transformation on an original feature of a wide beam response signal received by a user; the algorithm defines characteristic nodes and enhancement nodes, calculates the characteristic nodes from input data, further generates the enhancement nodes from the characteristic nodes, and then splices the two nodes together as affine transformation; based on the structure, the optimization of the model only needs to solve the weight of affine transformation;
based on a wide learning framework, the specific processing process of the input sample in the feature transformation network is as follows:
first, using input data X u Mapping to I-group feature nodesAnd J group enhanced nodesF and E respectively represent the number of the characteristic nodes and the enhanced nodes of each group, and are specifically represented as follows:
wherein Z u =[Z u,1 ,Z u,2 ,…,Z u,I ]A cascade matrix of characteristic nodes of group I, H u =[H u,1 ,...,H u,J ]A cascaded matrix of enhanced nodes for the J set;andrespectively representing the connection weight and the bias of the feature generation network and the feature enhancement network, and generally adopting random generation;a column vector representing elements all 1; phi (-) and xi (-) both represent linear or non-linear activation functions;
then, the user local wide learning network pair joint feature enhancement nodePerforming affine transformationsOutput of
Based on the minimum mean square error criterion, the network generalization performance is improved by utilizing L2 norm regularization, and the network weight matrix W is learned widely u The optimization problem of (a) is modeled as:
in the formula, | · luminance | | F Expressing a Frobenius norm, and lambda expresses a regularization factor;
the optimal solution is as follows:
in the formula, I IF+JE Representing an identity matrix of dimension IF + JE.
7. The millimeter wave MIMO user incremental cooperative beam selection method based on wide learning according to claim 6, wherein the step S3 comprises:
designing a mechanism for a user to perform cooperative training according to a downlink wide beam response signal based on an ADMM algorithm so as to reduce the calculation burden of each user node, and regarding the converted distributed optimization problem as a problem that the user shares information through cooperation, specifically comprising the following steps:
if the propagation environments between U users and the base station have similarity in space, the models of local wide learning should tend to be consistent, i.e. W u →W,I.e. network training for multiple usersThe optimization problem is jointly expressed as:
the shared training based on all user local training samples can be realized by solving the problems, and the performance of the wide learning method under the condition of small samples is improved;
based on the ADMM algorithm, the above problem is converted into:
the solution to this problem requires only that each user utilize locally collected training data { X } u ,Y u Performing information interaction with adjacent users through an inter-user D2D communication protocol;
the iterative solution process is as follows:
O u (t)=O u (t-1)+W u (t)-W 0 (t)
8. The millimeter wave MIMO user increment cooperative beam selection method based on wide learning of claim 7, wherein the step S4 includes:
when the wireless environment changes due to the movement of users or the change of the environment, the wide learning network of each user needs to be updated in a self-adaptive manner;
defining individual usersThe newly collected training samples for adapting to the environmental changes are
Obtaining new features and enhanced nodes through feature processing of the local wide learning networkTo avoid data characteristics obtained directly for the current accumulationAnd output tag Recalculating new weight width learning weightThe incremental update is performed as follows:
definition ofWoodbury identity equation D-UBV inversed by matrix -1 =D -1 +D -1 U(B -1 -VD -1 U) -1 VD -1 ,Restated as:
9. The millimeter wave MIMO user increment cooperative beam selection method based on wide learning of claim 8, wherein the step S5 includes:
in the on-line execution phase, each userWide beam response of multiple base stations only using current time slotGo through the bookJoint feature enhancement node obtained by nonlinear feature transformation of terrain learning networkObtaining the predicted value of the analog beam index to be selected after affine transformationDetermining each base station according to the predicted valueAnalog beam sequence number ofAnd feeds back to the base station to enable the base station to perform subsequent downlink data transmission;
the average effective rate of each user on each subcarrier is given by:
10. The wide-learning-based millimeter wave MIMO user incremental cooperative beam selection method according to claim 9, wherein in the step S3, a penalty coefficient p of the algorithm is set to 0.001, and a regularization constraint coefficient λ is set to 2 -5 Number of interactive iterations t of the training process max =5;
In the aspect of network structure, the number of feature nodes of the wide learning network is set to 300, and the specific setting of the enhanced nodes is as follows:
when the number of local training samples is not more than 100, the number of the enhanced nodes is set to be 100, when the number of the local training samples is more than 100 and not more than 300, the number of the enhanced nodes is set to be 500, when the number of the local training samples is more than 300 and not more than 500, the number of the enhanced nodes is set to be 1000, and when the number of the local training samples is more than 500, the number of the enhanced nodes is set to be 2000;
the enhancement layer activation function is designed to Tansig in the following specific form:
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