CN112073976B - User general grouping method in non-orthogonal multiple access based on machine learning - Google Patents
User general grouping method in non-orthogonal multiple access based on machine learning Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/14—Spectrum sharing arrangements between different networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W48/00—Access restriction; Network selection; Access point selection
- H04W48/16—Discovering, processing access restriction or access information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/30—TPC using constraints in the total amount of available transmission power
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- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention relates to a user general grouping method in non-orthogonal multiple access based on machine learning, which comprises the following steps: acquiring the transmission power budget and channel gain coefficient data of all users in a system; step 2: constructing a multi-user packet communication model; step 3: solving a multi-user packet communication model to obtain a power optimization closed solution and a user packet solution; step 4: grouping users according to the user grouping solution, and performing power control through the power optimization closed type solution to complete user general grouping. Compared with the prior art, the method has the advantages of realizing overlapping grouping of the users, improving the access quantity of the users, considering the calculation complexity and the effectiveness, and the like.
Description
Technical Field
The invention relates to the technical field of intelligent information acquisition, in particular to a user general grouping method in non-orthogonal multiple access based on machine learning.
Background
As limited spectrum resources continue to be exhausted, the problem of multiple access is challenging in 5G and future practical communication systems. On the other hand, internet of things (IoT) has become a unique set of network system, wherein various small components and sensors can autonomously generate sensing information and network traffic, and how to effectively collect sensing information of sensors distributed in a large area is a novel challenge. In the face of access requirements for large-scale users or sensors, multiple access technology is in need of improvement. The Non-orthogonal multiple access (Non-orthogonal multiple access, NOMA) technology can overlap a plurality of users in a frequency domain to multiplex frequency/time resources, so that the access quantity of the users is improved, and the Non-orthogonal multiple access technology becomes a potential technology in the future communication field, and a large number of researches show that the Non-orthogonal multiple access technology can bring performance improvement to the existing communication system.
In practical communication systems, the key to employing non-orthogonal multiple access techniques is the grouping of users. In document "On User Pairing in Uplink NOMA", the optimal user pairing set is obtained by solving the modified cost matrix by Hungarian algorithm (Hungarian). In document "Impact of User Pairing on 5G Nonorthogonal Multiple-Access Downlink Transmissions" it is proposed to arrange two users with larger channel gain differences in the same packet, and in document "Dynamic User Clustering and Power Allocation for Uplink and Downlink Non-Orthogonal Multiple Access (NOMA) Systems" this strategy is extended to the case of multiple users, resulting in a complete multi-user packet solution designed for non-orthogonal multiple access. Still other documents, such as "User Pairing for Downlink Non-Orthogonal Multiple Access Networks Using Matching Algorithm" and "Joint Sensing Duration Adaptation, user Matching, and Power Allocation for Cognitive OFDM NOMA Systems", consider the User grouping problem as a game, and the grouping is obtained by game theory.
However, existing methods for grouping users for non-orthogonal multiple access mostly assume that users can only participate in one group, and ignore the potential performance improvement of users participating in multiple groups. In fact, in most cases, because the channel gains of multiple users are similar, users of some bad channels must reduce power to reduce signal interference between users. Therefore, for access needs of large-scale users, overlapping grouping of users may be considered to further increase the effective utilization of power. On the other hand, the consideration of overlapping packets brings about combinatorial explosion, and solving the optimal overlapping packets is an NP-hard problem.
Disclosure of Invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art by providing a method for user generic grouping in non-orthogonal multiple access based on machine learning.
The aim of the invention can be achieved by the following technical scheme:
a method of user generic grouping in non-orthogonal multiple access based on machine learning, comprising:
step 1: acquiring the transmission power budget and channel gain coefficient data of all users in a system;
step 2: constructing a multi-user packet communication model;
step 3: solving a multi-user packet communication model to obtain a power optimization closed solution and a user packet solution;
step 4: grouping users according to the user grouping solution, and performing power control through the power optimization closed type solution to complete user general grouping.
Preferably, the step 2 specifically includes:
in the whole communication system, K users and a base station exist, a plurality of users form a NOMA group to share spectrum resources, the spectrum resources of each NOMA group are mutually orthogonal with the spectrum resources of other groups, and form C groups, wherein C is less than K, the transmission power budget of the users is divided evenly in the groups participated in by considering the overlapping groups of the users, and all the users are sequenced into g according to the channel gain sequence 1 ＞g 2 ＞…＞g k ＞…＞g K The conditions for enabling non-orthogonal multiple access grouping are:
wherein p is k The transmit power for the kth user; delta is the power interval between users in the pass group, which is used to ensure that the base station can perform correct decoding after receiving all signals to distinguish all user signals;
for representation of user groupings, the binary variable β k,c For indicating that the kth sensor is in the c-th group, the achievable rate R of user k in flat fading channel k,c From shannon formula c=blog (1+s/N):
wherein B is 0 Bandwidth for each block of spectrum resources; n (N) 0 Is the noise power spectral density; k is the number of users;
the objective function of the multi-user packet communication model is the maximization system and rate, and specifically comprises the following steps:
preferably, the step 3 specifically includes:
decomposing an objective function of the multi-user packet communication model into a user packet sub-objective function and a power control sub-objective function;
firstly, solving a user grouping sub-objective function by using a machine learning sub-model to obtain grouping results of K users under the input channel gain;
and then solving a power control sub-objective function by using a Lagrangian multiplier method to obtain a power optimization closed solution.
More preferably, the power control sub-objective function is specifically:
wherein B is 0 Bandwidth for each block of spectrum resources; n (N) 0 Is the noise power spectral density; p is p k The actual transmit power for user k; n is the total number of users in the group, n is less than or equal to K.
More preferably, the solving method of the user grouping sub-objective function specifically comprises the following steps:
firstly, using a hypergraph model to represent groups of users as an associated matrix with the size of K multiplied by K, wherein rows and columns of the matrix respectively represent the users and groups in which the users are positioned;
and then predicting the general packet with the performance gain according to the channel gain input of the user by using a machine learning sub-model, wherein the general packet with the performance gain is specifically: and calibrating a corresponding user grouping result with performance gain for each input channel gain, and training in a supervised learning mode to finally obtain the user grouping result with the performance gain.
More preferably, the machine learning sub-model is specifically a random forest sub-model.
More preferably, the random forest sub-model uses 10 independent decision trees to obtain the final classification result in a voting mode.
More preferably, the machine learning sub-model is specifically a support vector machine sub-model.
More preferably, the support vector machine sub-model is specifically a support vector machine model with a radial odd function as a nonlinear kernel.
More preferably, the objective function of the machine learning sub-model is a cross entropy function.
Compared with the prior art, the invention has the following advantages:
1. realizing overlapping grouping of user grouping: the invention firstly proposes that Xu Chongdie grouping is allowed in the non-orthogonal multiple access user grouping, namely, the user or the equipment can participate in a plurality of groups simultaneously to communicate, and a plurality of non-zero elements can exist in each row in the incidence matrix, so that the transmitting power of the user can be fully utilized, and the communication capacity of the system is further improved.
2. Improving the access quantity of users: the user general grouping method in the invention allows the user to carry out overlapped grouping, can further improve the user access quantity in the 5G system, and can meet the large-scale access requirement.
3. And the calculation complexity and the effectiveness are considered: the user general grouping method in the invention utilizes a machine learning method to determine the user grouping, can select a mature random forest or a support vector machine to realize the user grouping, and can consider the calculation complexity and the effectiveness.
Drawings
FIG. 1 is a flow chart of a user general packet method according to the present invention;
FIG. 2 is a schematic diagram of a structure for predicting user groupings using a machine learning sub-model in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
A method for user generic grouping in non-orthogonal multiple access based on machine learning, the flow of which is shown in fig. 1, comprising:
step 1: acquiring the transmission power budget and channel gain coefficient data of all users in a system;
step 2: constructing a multi-user packet communication model;
the method comprises the following steps: in the whole communication system, K users and a base station exist, a plurality of users form a NOMA group to share spectrum resources, the spectrum resources of each NOMA group are mutually orthogonal with the spectrum resources of other groups, and form C groups, wherein C is less than K, the transmission power budget of the users is divided evenly in the groups participated in by considering the overlapping groups of the users, and all the users are sequenced into g according to the channel gain sequence 1 ＞g 2 ＞…＞g k ＞…＞g K The conditions for enabling non-orthogonal multiple access grouping are:
wherein p is k The transmit power for the kth user; delta is the power interval between users in the pass group, which is used to ensure that the base station can perform correct decoding after receiving all signals to distinguish all user signals;
for representation of user groupings, the binary variable β k,c For indicating that the kth sensor is in the c-th group, the achievable rate R of user k in flat fading channel k,c From shannon formula c=blog (1+s/N):
wherein B is 0 Bandwidth for each block of spectrum resources; n (N) 0 Is the noise power spectral density; k is the number of users;
the objective function of the multi-user packet communication model is the maximization system and rate, and specifically comprises the following steps:
step 3: solving a multi-user packet communication model to obtain a power optimization closed solution and a user packet solution;
because the original problem is an NP-hard problem, the objective function of the multi-user packet communication model is decomposed into a user packet sub-objective function and a power control sub-objective function;
firstly, solving a user grouping sub-objective function by using a machine learning sub-model to obtain grouping results of K users under the input channel gain;
and then solving a power control sub objective function by using a Lagrangian multiplier method to obtain a power optimization closed solution, wherein the problem is a convex problem, so that the Lagrangian function can be solved for the original problem, then a first-order derivative function is solved for each variable, and whether the power optimization closed solution meets the KKT optimal solution condition is checked, so that the power optimization closed solution can be obtained.
The power control sub-objective function is specifically:
wherein B is 0 Bandwidth for each block of spectrum resources; n (N) 0 Is the noise power spectral density; p is p k The actual transmit power for user k; n is the total number of users in the group, n is less than or equal to K.
The method for solving the user grouping sub-objective function is shown in fig. 2, and specifically comprises the following steps:
firstly, using a hypergraph model to represent groups of users as an associated matrix with the size of K multiplied by K, wherein rows and columns of the matrix respectively represent the users and groups in which the users are positioned;
and then predicting the general packet with the performance gain according to the channel gain input of the user by using a machine learning sub-model, wherein the general packet with the performance gain is specifically: and calibrating a corresponding user grouping result with performance gain for each input channel gain, and training in a supervised learning mode to finally obtain the user grouping result with the performance gain.
The machine learning submodel in the embodiment is specifically a random forest submodel or a support vector machine submodel, and when the random forest submodel is selected, 10 independent decision trees can be adopted to obtain a final classification result in a voting mode; when the support vector machine submodel is selected, a support vector machine model with a radial odd function as a nonlinear kernel can be adopted as the random learning submodel of the embodiment.
The objective function of the machine learning sub-model in this embodiment is a cross entropy function.
Step 4: grouping users according to the user grouping solution, and performing power control through the power optimization closed type solution to complete user general grouping.
The invention is mainly oriented to the scene of data acquisition in an actual 5G communication/Internet of things system, wherein the problem of multiple access of users needs to overcome the mutual channel interference, and reasonable grouping of the users can effectively relieve the mutual interference. In addition, the proposed user grouping method innovatively allows overlapping groupings, i.e., users can participate in multiple group communications simultaneously, in a generic user grouping format. The accuracy can be effectively improved by determining the user grouping by using the machine learning method, so that the total throughput of the communication system is further improved under the condition of low computational complexity.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. A method for user generic grouping in non-orthogonal multiple access based on machine learning, comprising:
step 1: acquiring the transmission power budget and channel gain coefficient data of all users in a system;
step 2: constructing a multi-user packet communication model;
step 3: solving a multi-user packet communication model to obtain a power optimization closed solution and a user packet solution;
step 4: grouping users according to the user grouping solution, and performing power control through the power optimization closed solution to complete user general grouping of user overlapping grouping;
the step 2 specifically comprises the following steps:
in the whole communication system, K users and a base station exist, the multiple users form a NOMA group to share spectrum resources, the spectrum resources of each NOMA group are mutually orthogonal with the spectrum resources of other groups, and form C groups, wherein C is less than K, the transmission power budget of the users is divided equally in the groups participated in by the users considering the overlapping groups of the users, and all the users are sequenced into g according to the channel gain sequence 1 ＞g 2 ＞…＞g k ＞…＞g K The conditions for enabling non-orthogonal multiple access grouping are:
wherein p is k The transmit power for the kth user; delta is the power interval between users in the same group, which is used to ensure that the base station can correctly decode after receiving all signals to distinguish all user signals;
table for user groupingShown, the binary variable beta k,c For indicating that the kth sensor is in the c-th group, the achievable rate R of user k in flat fading channel k,c From shannon formula c=blog (1+s/N):
wherein B is 0 Bandwidth for each block of spectrum resources; n (N) 0 Is the noise power spectral density; k is the number of users;
the objective function of the multi-user packet communication model is the maximization system and rate, and specifically comprises the following steps:
the step 3 specifically comprises the following steps:
decomposing an objective function of the multi-user packet communication model into a user packet sub-objective function and a power control sub-objective function;
firstly, solving a user grouping sub-objective function by using a machine learning sub-model to obtain grouping results of K users under the input channel gain;
then solving a power control sub-objective function by using a Lagrangian multiplier method to obtain a power optimization closed solution;
the power control sub-objective function specifically comprises:
wherein B is 0 Bandwidth for each block of spectrum resources; n (N) 0 Is the noise power spectral density; p is p k The actual transmit power for user k; n is the total number of users in the group, n is less than or equal to K; p is a vector that refers to all p_k.
2. The method for grouping users in non-orthogonal multiple access based on machine learning according to claim 1, wherein the method for solving the user grouping sub-objective function is specifically as follows:
firstly, using a hypergraph model to represent groups of users as an associated matrix with the size of K multiplied by K, wherein rows and columns of the matrix respectively represent the users and groups in which the users are positioned;
and then predicting the general packet with the performance gain according to the channel gain input of the user by using a machine learning sub-model, wherein the general packet with the performance gain is specifically: and calibrating a corresponding user grouping result with performance gain for each input channel gain, and training in a supervised learning mode to finally obtain the user grouping result with the performance gain.
3. The method of claim 1, wherein the machine learning sub-model is a random forest sub-model.
4. A machine learning based method of user generic grouping in non-orthogonal multiple access according to claim 3, wherein the random forest sub-model uses 10 independent decision trees to voting to obtain the final classification result.
5. The method of claim 1, wherein the machine learning sub-model is a support vector machine sub-model.
6. The method of claim 5, wherein the support vector machine sub-model is a support vector machine model with radial odd function as nonlinear kernel.
7. The method of claim 1, wherein the objective function of the machine learning sub-model is a cross entropy function.
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| CN107949061A (en) * | 2017-11-28 | 2018-04-20 | 重庆邮电大学 | Multi-user's group technology based on non-orthogonal multiple system |
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