Greedy user scheduling method for massive MIMO low-orbit satellite communication system

Abstract

The present invention proposes a greedy user scheduling method for a massive MIMO low-orbit satellite communication system. A large-scale uniform planar antenna array is configured on the satellite base station side. The base station groups users by using the statistical channel state information of each user. Users in the same group are scheduled to use the same time-frequency resources, and users in different groups use different time-frequency resources. First, a mathematical model for user scheduling is given. The maximization of the downlink achievable average sum rate is used as the design criterion. A 0-1 indicator variable is introduced to represent the scheduling state of the user, and the sum rate maximization problem is established as a 0-1 integer programming model. The problem is divided into a large number of sub-problems using the greedy idea. In each algorithm iteration, only one user is scheduled that increases the current sum rate and maximizes the sum rate. The present invention greatly improves the transmission rate of a massive MIMO low-orbit satellite communication system.

Description

Technical Field

[0001] The invention belongs to the field of communications, and in particular relates to a greedy user scheduling method using statistical channel state information in low-orbit satellite communications using a large-scale antenna array. Background Art

[0002] In the massive MIMO low-orbit satellite communication system, the base station deploys a large-scale antenna array to serve multiple users simultaneously. The use of massive MIMO technology can effectively reduce interference between users and greatly improve the energy efficiency and spectrum efficiency of wireless communication systems. At the same time, the maximum signal-to-leakage-to-noise ratio precoding and maximum signal-to-noise ratio receiver that uses statistical channel state information can effectively avoid the difficulty of obtaining instantaneous channel state information.

[0003] The number of users within the coverage of a massive MIMO low-orbit satellite communication system is usually much greater than the number of antennas in the base station, making it impossible for the satellite to serve all users within the coverage area in the same time-frequency resource. To solve this problem, users can be scheduled so that users in the same group use the same time-frequency resources, and users in different groups use different time-frequency resources. Therefore, it is very important to design an efficient user scheduling algorithm to reasonably group users.

[0004] Common user scheduling algorithms include exhaustive search methods, which exhaust all difficult user combinations, but at the same time bring huge computational burdens, making it extremely difficult to implement in practice and only applicable to scenarios with a small number of users. In the greedy algorithm, the greedy idea is adopted so that only the choice that maximizes the current objective function is made in each iteration, which greatly reduces the complexity of the algorithm compared to the exhaustive search algorithm. Therefore, the greedy user scheduling algorithm that maximizes the sum rate only selects users with the largest sum rate in the current group to join the current group in each iteration. Compared with the exhaustive search method, the greedy method achieves better sum rate performance and significantly reduces the complexity of the algorithm, which has important practical significance. Summary of the invention

[0005] Purpose of the invention: For a low-orbit satellite communication system using massive MIMO, the present invention provides a greedy user scheduling algorithm using statistical channel state information, which improves the system's sum rate performance. Compared with the exhaustive method, the algorithm reduces the computational complexity.

[0006] Technical solution: Consider a large-scale MIMO low-orbit satellite communication system with M satellite-side base station antennas and K single-antenna users. The satellite base station 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 resources to communicate wirelessly with the satellite base station, and users scheduled in different groups use different time-frequency resources to communicate wirelessly with the satellite. The satellite base station uses the statistical channel state information of users scheduled in the same group (including the spatial angle information of each user and the average channel energy) to calculate the downlink precoding vector of each user in the group:

[0007]

[0008] in is the energy normalization coefficient such that v k With v i are the antenna array response vectors of the uniform planar array of user k and , respectively, i is the channel energy of user i, ρ k is the downlink signal-to-noise ratio of user k, I M is a unit matrix with dimension M. In the maximum signal leakage noise ratio precoding transmission system based on statistical channel state information, the downlink traversal and rate R of the spatial angle user scheduling method dl for

[0009]

[0010] Among them, G is the number of user groups, q k With q i are the transmission signal energies allocated to user k and user i, respectively, and g k is the channel gain of user k, A g is the set of users in group g, σ k is the channel noise variance of user k, is the precoding vector of user i.

[0011] A total of K users in the beam coverage area are scheduled in G groups, R k,g is the downlink reachable sum rate of user g in the g-th user group, q k,g is the power of user k in group g, q sum is the total transmission power constraint of the downlink. In this problem, a binary scheduling indicator variable c is introduced k,g To indicate the relationship between a specified user k and a user group g, the values ​​of the indicator variables and their respective meanings are

[0012]

[0013] The downlink reachable traversal and rate of user k in user group g is

[0014]

[0015] In the user scheduling problem of massive MIMO low-orbit satellite communication system, the system downlink and rate maximization problem is established as a 0-1 integer programming problem:

[0016]

[0017] And there are constraints It shows that any user k can only be scheduled in one group at most. In order to simplify the analysis and feasibility in the actual system, it is assumed that the power distribution between users is uniform. Then the above spectrum efficiency maximization problem is simplified to

[0018]

[0019] In the greedy algorithm, the greedy idea is adopted so that in each iteration only the choice that maximizes the current objective function is made, which greatly reduces the complexity of the algorithm compared to the exhaustive search algorithm. Therefore, in each iteration of the greedy user scheduling algorithm that maximizes the sum rate, only the user who maximizes the sum rate in the current group is selected to join the current group. In the greedy user scheduling algorithm, the system's sum rate maximization problem is currently scheduled for the user group S g When selecting the i-th user, select the user k that makes the average sum rate after scheduling the largest from the candidate user set, and add user k to the current scheduling user set S only when the average sum rate after scheduling is higher than the average sum rate before scheduling g After each iteration, if a new user joins the scheduled user group, the precoding vector needs to be recalculated. The algorithm flow of this algorithm is:

[0020] a) Initialization parameters, number of user groups G, number of users K, user channel direction vector v k,g , indicator variable Precoding vector User group set S g ,g=1,...,G, let g=1;

[0021] b) Calculate each user in the current candidate user combination to join the scheduled user combination S g The sum rate after that, and select the i-th user so that the average sum rate is maximized after the user joins the scheduled combination:

[0022] i=argmax i∈K R(S g ∪i) (7)

[0023] c) If the sum rate of user i after joining the scheduled combination is greater than the sum rate before joining, that is,

[0024] R(S g ∪i)>R(S g ) (8)

[0025] Then user i is scheduled in user group S g And remove user i from the candidate user combination T:

[0026] S g =S g ∪{i}

[0027] T=T / {i} (9)

[0028] If user group S g The number of users in is less than or equal to the number of satellite antennas, that is, |S g |<=M, then return to step c) to schedule the next user; otherwise if g<G, then g=g+1, and return to step c) to schedule the next group; otherwise end the algorithm and the scheduling is completed.

[0029] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0030] 1. Statistical channel state information is used to design the downlink precoder. Compared with instantaneous channel state information, slowly changing statistical channel state information is easier to obtain, so that the reciprocity of the channel can be utilized in the TDD system, and a large amount of training and feedback load is avoided in the FDD system, reducing the complexity of the system.

[0031] 2. The introduction of 0-1 indicator variables transforms the user scheduling problem of rate maximization into a 0-1 integer programming problem, and the assumption of uniform power distribution among users simplifies the problem.

[0032] 3. The greedy idea is adopted so that only the choice that maximizes the current objective function is made in each iteration, which improves the system's sum rate performance. Compared with the exhaustive method, this algorithm reduces the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention.

[0034] Figure 2 This is a flow chart of a greedy user scheduling algorithm using statistical channel state information in an embodiment of the present invention.

[0035] Figure 3 4 is a graph comparing the sum and rate performances of different scheduling algorithms in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] The specific steps of the embodiment of the present invention are described below in conjunction with specific scenarios:

[0038] 1) Massive MIMO low-orbit satellite signal transmission model and precoding

[0039] In the massive MIMO low-orbit satellite communication system, the satellite is equipped with a large-scale antenna array to simultaneously serve a large number of single-antenna users. The satellite is equipped with a uniform planar array, which has M x With M y Antennas, M x With M y are the number of antennas in the x-axis and y-axis directions of the uniform planar array. Without loss of generality, assume that the antenna spacing in the x-axis and y-axis directions is 0.5 times the wavelength, M x With M y are all even numbers. The channel between the satellite and user k is

[0040]

[0041] Among them, P k represents the number of confidences of user k, represents the complex gain of the user, ν k,p represents the Doppler shift, τ k,p represents the transmission delay, v k,p is the antenna array response vector of the uniform planar array. Note that the channel model in equation (1) is applicable to the scenario where the position of the low-orbit satellite does not change significantly relative to user k. Therefore, it is assumed that the physical parameter of the channel P_{k}, ν k,p , τ k,p Unchanged over time.

[0042] In downlink transmission, a time-frequency resource block of a low-orbit satellite simultaneously serves K single-antenna users. The user set is represented by K = {0, 1, ..., K-1}. After the base station performs linear precoding, the received signal of user k∈K is

[0043]

[0044] The subcarrier and symbol subscripts are omitted to simplify the notation, qk is the transmitted signal energy allocated to user k. k is a standardized precoding vector that satisfies s k is the signal sent to user k, with a mean of 0 and a variance of 1. k is an additive circularly symmetric complex Gaussian noise with a mean of 0 and a variance of σ k , that is, z k ~CN(0,σ k ).

[0045] In downlink multi-user MIMO transmission, the signal-to-leakage-plus-noise-ratio (SLNR) is widely used as a convenient and efficient design criterion. In the precoding method based on the maximum SLNR criterion, the SLNR of user k is k for

[0046]

[0047] in, is the downlink signal-to-noise ratio of user k. Then, the precoder that maximizes the signal-to-noise ratio of user k is

[0048]

[0049] In the above formula, (·) * represents the conjugate operation, is the energy normalization coefficient In the above formula, the precoder based on the maximum signal leakage and noise ratio needs to obtain the instantaneous channel state information of the user, but it is usually difficult to obtain the instantaneous channel state information. Therefore, the precoder based on statistical channel state information is studied, and the signal leakage and noise ratio is rewritten as the average signal leakage and noise ratio:

[0050]

[0051] in is the downlink signal-to-noise ratio (SNR) of user k. k The maximum downlink precoding vector is

[0052]

[0053] in is the energy normalization coefficient such that The corresponding maximum ASLNR is

[0054]

[0055] In the maximum signal-leakage-to-noise ratio precoding transmission system based on statistical channel state information, the downlink traversal and rate of the spatial angle user scheduling method are:

[0056]

[0057] In the greedy algorithm, the greedy idea is adopted to make R dl In each iteration, only the choice that maximizes the current objective function is made, which greatly reduces the complexity of the algorithm compared to the exhaustive search algorithm. Therefore, in each iteration of the greedy user scheduling algorithm that maximizes the sum rate, only the user who maximizes the sum rate in the current group is selected to join the current group. In the greedy user scheduling algorithm, the system's sum rate maximization problem is currently scheduled user group S g When selecting the i-th user, select the user k that makes the average sum rate after scheduling the largest from the candidate user set, and add user k to the current scheduling user set S only when the average sum rate after scheduling is higher than the average sum rate before scheduling g After each iteration, if a new user joins the scheduled user group, the precoding vector needs to be recalculated. The algorithm flow of this algorithm is:

[0058] Step 1: Initialize parameters, user group number G, user number K, user channel direction vector v k,g , indicator variable Precoding vector User group set S g ,g=1,...,G, let g=1;

[0059] Step 2: Calculate the number of users in the current candidate user combination to join the scheduled user combination S g The sum rate after , and select the th user so that the average sum rate is maximized after the user joins the scheduled i combination:

[0060] i=argmax i∈K R(S g ∪i) (9)

[0061] Step 3: If the sum rate of user i after joining the scheduled combination is greater than the sum rate before joining, that is,

[0062] R(S g ∪i)>R(S g ) (10)

[0063] Then the user is scheduled in user group S g And remove user i from the candidate user combination T:

[0064] S g =S g ∪{i}

[0065] T=T / {i} (11)

[0066] If user group S g The number of users in is less than or equal to the number of satellite antennas, that is, |S g |<=M, then return to step 2 to schedule the next user; otherwise, if g<G, then g=g+1, and return to step 2 to schedule the next group; otherwise, the algorithm ends and the scheduling is completed.

[0067] Figure 3 The proposed greedy user scheduling algorithm and semi-orthogonal user scheduling algorithm are compared in terms of sum rate performance when the number of antennas is 128. The simulation results show that the sum rate of the system varies with the input signal-to-noise ratio when the number of base station antennas is 128 and the number of users is 400. The simulation results show that the proposed greedy user scheduling algorithm with maximum sum rate has better sum rate performance than the SAUG method.

Claims

1. A greedy user scheduling method for a massive MIMO low-orbit satellite communication system, characterized in that: The satellite base station grouped the users in the coverage area, calculated the downlink precoding vector and downlink average sum rate of each group, and used the maximization of the system downlink average sum rate as the design criterion for user scheduling. A 0-1 indicator variable was introduced to represent the user's scheduling status, and the downlink average sum rate maximization problem was established as a 0-1 integer programming problem. A greedy algorithm was used to solve the 0-1 integer programming problem, and the 0-1 integer programming problem was divided into several sub-problems. In each algorithm iteration, only one user was scheduled that increased the current sum rate and maximized the sum rate. The satellite base station 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 resources to communicate wirelessly with the satellite base station, and users scheduled in different groups use different time-frequency resources to communicate wirelessly with the satellite. Introducing a 0-1 indicator variable to represent the user's scheduling status, the downlink average and rate maximization problem is established as a 0-1 integer programming problem. The specific steps include: A total of K users in the beam coverage area are scheduled in G groups, R k,g is the downlink sum rate of user k in the g-th user group, q sum is the total transmission power constraint of the downlink; the binary scheduling indicator variable c k,g Indicates the relationship between a specified user k and a user group g. The values ​​of the indicator variables and their respective meanings are The downlink sum rate of user k in user group g is Among them, q k,g With q i,g are the transmission signal energies allocated to user k and user i in group g, respectively. k,g is the channel gain of user k in group g, σ k,g is the channel noise variance of user k in group g, and are the precoding vectors of user i and user k in group g, respectively, k,g is the user channel direction vector; The downlink average and rate maximization problem is formulated as a 0-1 integer programming problem: constraint This indicates that any user k can be scheduled in at most one group; The satellite base station side calculates the downlink precoding vector of each user in the group using the statistical channel state information of the users scheduled in the same group; The statistical channel state information includes the spatial angle information of user k and its average channel energy, which is obtained by uplink channel detection or through feedback information of each user.

2. According to claim 1, a greedy user scheduling method for a massive MIMO low-orbit satellite communication system is characterized in that: The downlink precoding vector is: in,(·) * represents the conjugate operation, is the energy normalization coefficient such that v k With v i are the antenna array response vectors of the uniform planar array for user k and user i, respectively, i is the channel energy of user i, ρ k is the downlink signal-to-noise ratio of user k, I M is an M-dimensional unit matrix, where M is the number of base station antennas on the satellite side; Downlink average sum rate R dl for G is the number of user groups, q k With q i are the transmission signal energies allocated to user k and user i, respectively, and g k is the channel gain of user k, A g is the set of users in group g, σ k is the channel noise variance of user k, is the precoding vector of user i.

3. According to claim 1, a greedy user scheduling method for a massive MIMO low-orbit satellite communication system is characterized in that: The algorithm flow of the greedy algorithm is: a) Initialization parameters, number of user groups G, number of users K, user channel direction vector v k,g , indicator variable Precoding vector User group set S g ,g=1,...,G, let g=1; b) Calculate each user in the current candidate user combination to join the scheduled user combination S g The downlink sum rate after , and select the i-th user so that the average downlink sum rate is maximized after the user joins the scheduled combination: i=argmaxx ∈K R(S g ∪i) c) If the average downlink rate of user i after joining the scheduled combination is greater than the average downlink rate before joining, that is, R(S g ∪i)>R(S g ) Then user i is scheduled in user group S g And remove user i from the candidate user combination T: S g =S g ∪{i} T=T / {i} If user group S g The number of users in is less than or equal to the number of satellite antennas, that is, |S g |<=M, then return to step c) to schedule the next user; otherwise if g<G, then g=g+1, and return to step c) to schedule the next group; otherwise end the algorithm and the scheduling is completed.

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