CN115499053A - Genetic user scheduling method for large-scale MIMO low-orbit satellite communication system - Google Patents
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Abstract
The invention provides a genetic user scheduling method of a large-scale MIMO low-orbit satellite communication system, wherein a large-scale uniform planar antenna array is configured at a satellite base station side, the base station groups users by utilizing statistical channel state information of each user, the users in the same group are scheduled to use the same time-frequency resource, and the users in different groups use different time-frequency resources. Firstly, a mathematical model of user scheduling is given, maximized downlink reachable average sum rate is taken as a design criterion, and 0-1 indicating variables are introduced to represent the scheduling state of the user and establish the sum rate maximization problem as a 0-1 integer programming model. The problem is solved by adopting a genetic algorithm, firstly, the solution of the problem is coded into a chromosome with a determined length, and after a certain number of iterations, the offspring individual with the highest fitness, namely the optimal solution of the problem, can be found through selection, intersection and variation operations. The invention greatly improves the transmission rate of a large-scale MIMO low-orbit satellite communication system.
Description
Technical Field
The invention belongs to the field of communication, and particularly relates to a user scheduling method based on a genetic algorithm and utilizing statistical channel state information in low-orbit satellite communication adopting a large-scale antenna array.
Background
In a massive MIMO low earth orbit satellite communication system, a base station arranges a massive antenna array to serve multiple users simultaneously. By adopting the large-scale MIMO technology, the interference among users can be effectively reduced, and the energy efficiency and the spectrum efficiency of the wireless communication system are greatly improved. Meanwhile, the receiver utilizing the maximal signal-to-leakage-and-noise ratio precoding and the maximal signal-to-noise ratio of the statistical channel state information can effectively avoid the difficulty of acquiring the instantaneous channel state information.
The number of users in the coverage area of a large-scale MIMO low-earth-orbit satellite communication system is usually much larger than the number of antennas of a base station, so that a satellite cannot serve all users in the coverage area in the same time-frequency resource. In order to solve the problem, users can be scheduled, so that users scheduled in the same group use the same time-frequency resource, and users in different groups use different time-frequency resources. Therefore, designing an efficient user scheduling algorithm has a very important meaning for reasonably grouping users.
Common user scheduling algorithms include an exhaustive search method, which exhales all difficult user combinations, but brings huge computational burden, so that short-time users have huge difficulty in actual implementation and are only suitable for scenes with a small number of users. Among the greedy algorithms, the greedy method achieves better sum rate performance and reduces the complexity of the algorithm. However, the greedy algorithm has a probability of trapping a locally optimal solution, thereby causing the system and rate performance to be less than optimal. The method avoids the problem from falling into the local optimal solution, greatly improves the system sum rate performance, and has important practical significance.
Disclosure of Invention
The purpose of the invention is as follows: aiming at a low-orbit satellite communication system adopting large-scale MIMO, the invention provides a genetic user scheduling algorithm utilizing statistical channel state information, and the sum rate performance of the system is improved.
The technical scheme is as follows: considering that the number of antennas of a satellite side base station is M, and a large-scale MIMO low-orbit satellite communication system with K single-antenna users is provided, the satellite base station side utilizes the space angle information of each single-antenna user to group the users in a coverage area, the users scheduled in the same group use the same time-frequency resource to perform wireless communication with the satellite base station, and the users scheduled in different groups use different time-frequency resources to perform wireless communication with the satellite; the satellite base station side calculates the downlink precoding vector of each user in the group by using the statistical channel state information (including the space angle information and the channel average energy of each user) of the users scheduled in the same group:
wherein
Is an energy normalization coefficient such that
v k And v i Antenna array response vector, gamma, for uniform planar array of users k and k, respectively i Channel energy, p, for user i k Is the downlink signal-to-noise ratio, I, of user k M Is a unit matrix with dimension M. Downlink reachable traversal and rate R of space angle user scheduling method in maximum signal-to-leakage-and-noise ratio pre-coding transmission system based on statistical channel state information dl Is composed of
Wherein G is the number of user groups, q k And q is i The transmitted signal energy, g, allocated to user k and user i, respectively k For the channel gain of user k, A g Set of users of group g, σ k For the channel noise variance of user k,
a precoding vector for user i.
A total of K users in the beam coverage area are scheduled in G groups, R k,g Is the downlink reachable sum rate, q, of users g in the g-th user group k,g Is the power of user k in group g, q sum Is a total transmission power constraint for the downlink. Introducing a binary scheduling indication variable c in the problem k,g The relationship of a user group g of a designated user k is represented, and the values and the respective meanings of the indicating variables are
The downlink reachable traversal sum rate of the user k in the user group g is
In the user scheduling problem of the large-scale MIMO low-orbit satellite communication system, the system downlink and rate maximization problem is established as a 0-1 integer programming problem:
and is constrained
Indicating that any one user k can only be scheduled in one group at most. To simplify the analysis and feasibility in a practical system, a uniform power distribution among the users is assumed
The above-mentioned spectrum efficiency maximization problem is reduced to
A user scheduling algorithm based on a genetic algorithm firstly randomly initializes I groups of possible user scheduling combinations in sequence, and each user group comprises M randomly grouped scheduling users. Because the goal is to maximize the downlink average and rate, the average and rate of each group are calculated respectively, the average and rate are used as the fitness of each user group, a plurality of groups with higher fitness are selected for crossing, namely, a plurality of users in each group are exchanged between two parent users, and then two subgroups are generated. In addition, the user group with the maximum sum rate is selected in each iteration, the user change in the group is made on the basis of the user group, J new variant user groups generated by a plurality of the users are randomly replaced, and I-J-1 user groups are selected from the original user group set to be added. The end condition of the algorithm is that the algorithm reaches the maximum iteration number L set by the algorithm setter. After the algorithm iteration is finished, one group with the maximum output user group neutralization rate in the last iteration is selected as a final user group result of the scheduling, the genetic user scheduling algorithm is executed for G-1 times to obtain G groups, and the final scheduling results obtained by each iteration of the algorithm are different. The algorithm flow of the method is as follows:
a) Initialization parameter, user group number G, user number K, user channel direction vector v k,g Indicating variable
Precoding vectors
Set of user groups S g G = 1.. G, the encoding length of the chromosome is the maximum number of users in each groupM, cross probability α C Probability of variation beta M Terminate evolution algebra N iter ；
b) Randomly generating an initial population, randomly initializing a user group set, wherein the set comprises I possible user scheduling combinations, and calculating the average sum rate of each user group as the fitness parameter of each individual;
c) Selecting the first user groups with the highest fitness L < k in the population set for crossing, randomly selecting a plurality of crossing points in the two crossing user groups, exchanging users in the two groups, generating two new descendant user groups, and adding 1 to the current iteration number: n = n +1;
d) Randomly selecting I-J-1 user groups and user group A with highest fitness from initial user set max J variation combinations are combined to generate a brand new generation individual set;
e) If the iteration number N is less than the termination evolution algebra N and less than or equal to N gen Continuing to start the next iteration from the step b), otherwise, terminating the iteration and executing the next step;
f) Selecting the user group with the maximum fitness in the set, namely the user group with the maximum average and rate as the G-th user scheduling group result, and calculating the next user group from the step b) until G user group results are obtained.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1. the design of the downlink precoder by adopting the statistical channel state information is easier to obtain than the instantaneous channel state information, so that the reciprocity of the channel in the TDD system can be utilized, a large amount of training and feedback loads are avoided in the FDD system, and the complexity of the system is reduced.
2. The introduction of the 0-1 indication variable will create a non-0-1 integer scheduling problem with the rate-maximizing user scheduling problem, and the assumption of uniform user power allocation simplifies the problem.
3. The genetic algorithm is adopted to solve the problem of 0-1 integer programming, so that the problem that a local optimal solution is possibly trapped in a greedy algorithm is avoided, and the sum rate performance of the system is greatly improved.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a genetic user scheduling algorithm using statistical channel state information according to an embodiment of the present invention.
Fig. 3 is a graph comparing the sum rate performance of different scheduling algorithms according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The following describes specific steps of the embodiment of the present invention with reference to specific scenarios:
1) Large-scale MIMO low-orbit satellite signal transmission model and precoding
In a large-scale MIMO low-orbit satellite communication system, a satellite is provided with a large-scale antenna array to simultaneously serve a large number of single-antenna users. The satellite is provided with a uniform planar array having M x And M y Root antenna, wherein M x And M y The number of antennas in the x-axis and y-axis directions of the uniform planar array is respectively. Without loss of generality, assume that the antenna spacing in both the x-axis and y-axis directions is 0.5 wavelength, M x And M y Are all even numbers. The channel between the satellite and user k is
Wherein, P k Representing a large number of confidences of user k,
complex gain, v, on behalf of the user k,p Representing the Doppler shift, τ k,p Representing transmission delay, v k,p An antenna array response vector that is a uniform planar array. Note that the channel model in equation (1) is applicable to scenarios where the position of the low-earth satellite does not change significantly with respect to user k, so the physical parameters P _ { k } of the channel are assumed,
ν k,p ，τ k,p is time invariant.
In downlink transmission, a time-frequency resource block of a low-earth orbit satellite simultaneously serves K single-antenna users, a user set is represented as K = {0,1., K-1}, and after a base station carries out linear precoding, a received signal of a user K belonging to K is K
Wherein the sub-carriers and symbol indices are omitted to simplify the symbols, q k Is the transmitted signal energy allocated to user k. b k Is a standardized precoding vector satisfying
s k Is the signal sent to user k, with a mean of 0 and a variance of 1.z is a radical of formula k Is additive circular symmetric complex Gaussian noise with mean of 0 and variance of sigma k I.e. z k ～CN(0,σ k )。
In downlink multi-user MIMO transmission, signal-to-leakage-noise-ratio (SLNR) is widely adopted as a convenient and efficient design criterion, and in a precoding method based on the maximum SLNR criterion, the SLNR of user k is used as a criterion for determining the maximum SLNR k Is composed of
Wherein the content of the first and second substances,
the downlink signal-to-noise ratio for user k. The precoder that maximizes the signal-to-leakage-and-noise ratio for user k is then
In the above formula, (.) * It is indicated that the conjugate operation is performed,
normalizing coefficients for energy
In the above formula, the precoder based on the maximum signal-to-leakage-and-noise ratio needs to acquire the instantaneous channel state information of the user, and it is generally difficult to acquire the instantaneous channel state information, so that the precoder based on statistical channel state information is studied, and the signal-to-leakage-and-noise ratio is rewritten as the average signal-to-leakage-and-noise ratio:
wherein
The downlink Signal-to-Noise Ratio (SNR) is the user k. So that ASLNR k The largest downlink precoding vector is
Wherein
Is an energy normalization coefficient such that
Corresponding to a maximum ASLNR of
In the maximum signal-to-leakage-and-noise ratio pre-coding transmission system based on statistical channel state information, the downlink reachable traversal and the rate of the space angle user scheduling method are as follows
A user scheduling algorithm based on a genetic algorithm firstly randomly initializes I groups of possible user scheduling combinations in sequence, and each user group comprises M randomly grouped scheduling users. Because the goal is to maximize the downlink average and rate, the average and rate of each group are respectively calculated, the average and rate is used as the fitness of each user group, a plurality of groups with higher fitness are selected for crossing, namely, a plurality of users in each group are mutually exchanged between two parent users, and then two subgroups are generated. In addition, the user group with the highest sum rate is selected in each iteration, changes of users in the group are made on the basis of the user group, J new variant user groups generated by a plurality of users in the user group are replaced randomly, and I-J-1 user groups are selected from the original user group set to be added. The end condition of the algorithm is that the algorithm reaches the maximum iteration number L set by the algorithm setter. And after the algorithm iteration is finished, selecting a group with the maximum output user group neutralization rate in the last iteration as a final user group result of the scheduling, and executing the genetic user scheduling algorithm for G-1 times to obtain G groups, wherein the final scheduling results obtained by each algorithm iteration are different. The algorithm flow of the method is as follows:
step 1: initialization parameter, user group number G, user number K, user channel direction vector v k,g Indicating variable
Precoding vectors
Set of user groups S g G =1, …, G, the code length of the chromosome is the maximum number of users M in each group, and the crossover probability α C Probability of variation beta M Terminate evolution algebra N iter ；
Step 2: randomly generating an initial population, randomly initializing a user group set, wherein the set comprises I possible user scheduling combinations, and calculating the average sum rate of each user group as the fitness parameter of each individual;
and step 3: selecting the first user groups with the highest fitness L < k in the population set for crossing, randomly selecting a plurality of crossing points in the two crossing user groups, exchanging users in the two groups, generating two new descendant user groups, and adding 1 to the current iteration number: n = n +1;
and 4, step 4: randomly selecting I-J-1 user groups and user group A with highest fitness from initial user set max And J variation combinations are combined to generate a brand-new generation individual set;
and 5: if the iteration number N is less than the termination evolution algebra N and less than or equal to N gen Continuing to start the next iteration from the step b), otherwise, terminating the iteration and executing the next step;
and 6: selecting the user group with the maximum fitness in the set, namely the user group with the maximum average and rate as the G-th user scheduling group result, and calculating the next user group from the step b) until G user group results are obtained.
Fig. 3 compares the sum rate performance of the genetic user scheduling algorithm and the semi-orthogonal user scheduling algorithm at antenna number 128. GA is a genetic user scheduling Algorithm, SA is a simulated annealing user scheduling Algorithm, SAUG is a space angle user scheduling method, semi-Orthogonal user scheduling method is adopted by Semi-Orthogonal user, and Greedy Algorithm is a Greedy user scheduling method.
Claims (8)
1. A large-scale MIMO low-orbit satellite communication system genetic user scheduling method is characterized in that a satellite base station side groups users in a coverage area, calculates downlink precoding vectors and downlink average sum rates of all groups, takes the downlink average sum rate of a maximized system as a design criterion of user scheduling, introduces 0-1 indicator variables to represent scheduling states of the users, and establishes the downlink average sum rate maximization problem as a 0-1 integer programming problem; and solving the 0-1 integer programming problem by adopting a genetic algorithm to obtain a user grouping result.
2. The method as claimed in claim 1, wherein the satellite base station side uses the spatial angle information of each single antenna user to group users in the coverage area, users scheduled in the same group use the same time frequency resource to perform wireless communication with the satellite base station, and users scheduled in different groups use different time frequency resources to perform wireless communication with the satellite.
3. The method as claimed in claim 2, wherein the satellite base station side calculates the downlink precoding vectors of the users in the group by using the statistical channel state information of the users scheduled in the same group.
4. The massive MIMO low-earth-orbit satellite communication system genetic user scheduling method as claimed in claim 3, wherein the statistical channel state information comprises spatial angle information of user k
And its average channel energy gamma k The channel uplink detection is performed or the feedback information of each user is obtained.
5. The massive MIMO low-earth-orbit satellite communication system genetic user scheduling method of claim 4, wherein the downlink precoding vector is:
wherein, (.) * It is indicated that the conjugate operation is performed,
is an energy normalization coefficient such that
v k And v i Antenna array response vectors, γ, for uniform planar arrays of user k and user i, respectively i Channel energy, p, for user i k For the downlink signal-to-noise ratio of user k, I M Is an M-dimensional unit array;
downlink average sum rate R dl Is composed of
Wherein G is the number of user groups, q k And q is i The transmitted signal energy, g, allocated to user k and user i, respectively k For the channel gain of user k, A g Set of users of group g, σ k Is the channel noise variance for user k,
a precoding vector for user i.
6. The large-scale MIMO low-earth-orbit satellite communication system genetic user scheduling method as claimed in claim 1, wherein 0-1 indication variable is introduced to represent scheduling state of user to establish downlink average and rate maximization problem as 0-1 integer programming problem, and the specific steps comprise:
a total of K users in the beam coverage area are scheduled in G groups, R k,g For the downlink sum rate, q, of user k in the g-th user group k,g As power of users in group g, q sum A total transmission power constraint for the downlink; binary scheduling indicator variable c k,g Representing the relationship of a group g of specified users k, indicating the values and respective meanings of variables as
User k in user group g has a downlink sum rate of
Wherein q is k,g And q is i,g The transmitted signal energies, g, allocated to user k and user i in group g, respectively k,g For the channel gain, σ, of user k in group g k,g For the channel noise variance of user k in group g,
and with
The precoding vectors for user i and user k in group g, respectively.
The downlink average and rate maximization problem is established as a 0-1 integer programming problem:
is constrained to
Indicating that any one user k can only be scheduled in one group at most.
7. The large-scale MIMO low-earth-orbit satellite communication system genetic user scheduling method according to claim 4, wherein a genetic algorithm is adopted to solve the 0-1 integer programming problem, and the specific steps comprise: firstly, sequentially and randomly initializing possible user scheduling combinations of I groups, wherein each user group comprises M randomly grouped scheduling users; respectively calculating the downlink average and the downlink rate of each group, taking the downlink average and the downlink rate as the fitness of each user group, and selecting a plurality of groups with high fitness for crossing, namely generating two subgroups after the groups exchange a plurality of users in each group between two parent users; selecting a user group with the largest downlink average and speed in each iteration, changing users in the group on the basis of the user group, randomly replacing a plurality of users in the group to generate J new varied user groups, and selecting I-J-1 user groups from an original user group set to join; the end condition of the algorithm is that the algorithm reaches a preset maximum iteration number L; after the algorithm iteration is finished, one group with the largest downlink average and speed in the output user groups in the last iteration is selected as a final user group result of the scheduling, the genetic user scheduling algorithm is executed for G-1 times to obtain G groups, and the final scheduling results obtained by each algorithm iteration are different.
8. The large-scale MIMO low-earth-orbit satellite communication system genetic user scheduling method according to claim 7, wherein the algorithm flow of the genetic algorithm is as follows:
a) Initialization parameter, user group number G, user number K, user channel direction vector v k,g Indicating variable
Precoding vectors
Set of user groups S g G = 1.. G, the encoding length of the chromosome is the maximum number of users M in each group, and the cross probability α C Probability of variation beta M Terminate evolution algebra N iter ；
b) Randomly generating an initial population, randomly initializing a user group set, wherein the set comprises I possible user scheduling combinations, and calculating the average sum rate of each user group as the fitness parameter of each individual;
c) Selecting the first user groups with the highest fitness L < k in the population set for crossing, randomly selecting a plurality of crossing points in the two crossing user groups, exchanging users in the two groups, generating two new descendant user groups, and adding 1 to the current iteration number: n = n +1;
d) Randomly selecting I-J-1 user groups and user group A with highest fitness from initial user set max And J variation combinations are combined to generate a brand-new generation individual set;
e) If the iteration number i is less than the termination evolution algebra N and less than or equal to N gen Continuing to start the next iteration from the step b), otherwise, terminating the iteration and executing the next step;
f) Selecting the user group with the maximum fitness in the set, namely the user group with the maximum average and rate as the G-th user scheduling group result, and calculating the next user group from the step b) until G user group results are obtained.
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