A multi-repeater cooperative transmission method for improving transmission rate

Abstract

The present invention relates to a multi-repeater cooperative transmission method for improving transmission rate, comprising the following steps: S1, initializing the minimum transmission rate requirement γ of local cell users m , initialize the upper limit F of the amplification conversion multiple of the local cell repeater, and propose the optimal beamforming strategy for the local cell, with minimizing the total power of the cell base station as the optimization goal, and taking the minimum transmission rate requirement of all cell users as the constraint condition for joint optimization, S2, relax the expression in S1 into a relaxation problem, and use a one-dimensional search algorithm on the feasible domain of F to find the optimal solution P(F), so that the value of P(F) is minimized; the present invention has the advantage of improving the transmission rate.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless communications, and in particular relates to a multi-repeater cooperative transmission method for improving transmission rate. Background Art

[0002] A repeater is a co-frequency amplification device, a radio transmission relay device that enhances the signal during wireless communication transmission. It is composed of antennas, RF duplexers, low noise amplifiers, mixers, electrically adjustable attenuators, filters, power amplifiers and other components or modules, including uplink and downlink amplification links. Its working principle is: the downlink signal of the base station is received into the repeater by the forward antenna (donor antenna), the useful signal is amplified by the low noise amplifier, the noise signal in the signal is suppressed, and the signal-to-noise ratio (S / N) is improved; then it is down-converted to an intermediate frequency signal, filtered by a filter, amplified by the intermediate frequency, and then up-converted to a radio frequency by frequency shifting, amplified by a power amplifier, and transmitted to the mobile station by the backward antenna (retransmission antenna); at the same time, the uplink signal of the mobile station is received by the backward antenna, and processed by the uplink amplification link along the opposite path: that is, it is transmitted to the base station through the low noise amplifier, down converter, filter, intermediate amplifier, up converter, power amplifier, so as to achieve two-way communication between the base station and the mobile station.

[0003] Repeater cooperative communication is a technology that uses repeaters to increase user capacity. The basic idea of ​​this technology is to use repeaters in the system for cooperative communication and construct a "virtual multi-antenna array" to obtain a new type of spatial diversity gain, namely cooperative diversity, thereby improving the service quality of the communication system and supporting the specific capacity of high-speed users. However, when multiple repeaters are used for cooperative transmission, due to the overlap of their transmission range or frequency band, mutual interference will occur, resulting in signal collision and attenuation, which limits the transmission rate of the communication network. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a multi-repeater cooperative transmission method for improving the transmission rate.

[0005] The technical solution of the present invention is as follows:

[0006] A multi-repeater cooperative transmission method for improving transmission rate comprises the following steps;

[0007] S1. Initialize the minimum transmission rate requirement of local cell users γ k , initialize the local cell repeater amplification conversion factor F, and propose the optimal beamforming strategy for the local cell, with minimizing the total power of the cell base station as the optimization goal, and taking the minimum transmission rate requirement of all cell users as the constraint condition for joint optimization. The expression of this optimization problem is as follows:

[0008]

[0009] Where W is the total power of the base station, R k is the transmission rate of user k, γ k is the minimum transmission rate of user k;

[0010] S2, relax the expression in S1 into a relaxed problem, and use a one-dimensional search algorithm on the feasible domain of F to find the optimal solution P(F) so that the value of P(F) is minimized;

[0011] S3, substitute P(F) in S2 into the relaxed problem and solve the relaxed problem, thereby obtaining the optimal solution A of the relaxed problem;

[0012] S4. When the rank of A is 1, the optimal solution A of the relaxation problem is the optimal solution of the expression S1. By performing eigenvalue decomposition on A, the optimal beamforming strategy of the local cell in the optimization problem and the corresponding base station power tr(w) can be obtained;

[0013] S5. When the rank of A is not 1, after performing eigenvalue decomposition on A, an advanced randomization method is used to scale the optimization problem. By optimizing the scaling problem, an approximate solution A of the optimization problem is obtained. * , where the more candidate beamforming vectors there are, the closer the approximate solution is to the optimal solution of the optimization problem.

[0014] Furthermore, the expression of the optimization problem in step S1 is equivalent to a quadratically constrained quadratic programming (QCQP) problem, and according to the monotonicity of the logarithmic function, the conditional constraint formula is transformed into a function related to the signal to interference noise ratio.

[0015] Further, the relaxation process of relaxing the expression of the optimization problem into a relaxation problem in S2 includes: using a semi-positive definite relaxation algorithm to relax the QCQP problem into a linear programming (LP) problem, where a new variable is introduced

[0016] in and They are w k , and The conjugate transposed matrix of , the quadratic term in the expression of the optimization problem is expressed as a semi-positive definite matrix (PSD), and the non-convex condition that the matrix rank is 1 is discarded, so that the QCQP problem is relaxed to an LP problem, where the solution of the LP problem is a lower bound in the expression of the optimization problem. The standard form of rewriting this LP problem into a semi-definite programming (SDP) problem is as follows:

[0017]

[0018] W≥0

[0019] in,

[0020] Furthermore, the steps for obtaining the feasible domain of F in S2 are:

[0021] S21, converting the semidefinite programming (SDP) problem expression into a single variable optimization problem about a variable F, and assigning a value to F in the semidefinite programming (SDP) problem expression;

[0022] S22. Use convex optimization tools to solve and obtain the feasible domain of F: 1≤F≤F max Among them, F max is the maximum magnification of the repeater;

[0023] The expression of the optimization problem is transformed into the problem of optimizing the optimization problem in S1 on the feasible domain of F. The expression is as follows:

[0024] minP(F)

[0025] 1≤F≤F max .

[0026] Furthermore, the advanced randomization method in step S5 is specifically a Gaussian randomization method, and when the rank of the solution A of the relaxation problem is not 1, the steps for obtaining the optimal solution are:

[0027] S51. For the approximate solution A * Perform eigenvalue decomposition, and the expression after decomposition is:

[0028] S52: The beamforming vector in step S51 is multiplied by a Gaussian vector v t Adjust, where t represents the tth candidate vector and v t Following a Gaussian distribution with a mean of 0 and a variance of 1, the adjusted beamforming vector is:

[0029]

[0030] S53, for the candidate vector set p m After selection and optimization, the optimal solution is finally obtained, and the formula is:

[0031]

[0032] Further, through Solving the formula in step S53 can obtain the optimal solution to this problem. And the optimization problem beamforming vector is obtained in, is the beamforming vector of the local cell base station.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention can optimize the coverage and transmission quality of the cooperative transmission system by appropriately adjusting the total power of the base station, improve the reliability and correctness of the data, and take into account the energy consumption and cost of the system;

[0035] 2. The present invention uses a semi-definite relaxation algorithm and a Gaussian randomization method to solve the power minimization problem. Under the premise of ensuring the user service quality, the total power of the base station is minimized through a beamforming strategy to improve the power of the base station.

[0036] In summary, the present invention has the advantage of improving the transmission rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a diagram of collaborative transmission of multiple repeaters in a single cell. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0039] A multi-repeater cooperative transmission method for improving transmission rate, the model of its application scenario is as follows Figure 1 As shown:

[0040] In this scenario, the base station of the cellular cell is equipped with N t antennas, and there are K single-antenna users in the cell, and there are three users configured with N r Repeater with 1 antenna. Assuming that all channels in this scenario are quasi-static channels, the channel status information of all users in the local cell and the repeater can be obtained through a reliable feedback link;

[0041] Depend on Figure 1 It can be seen that in the downlink repeater cooperative communication, the data required by user k is transmitted cooperatively by the base station and two repeaters that are closer to the user. The direct link between repeater m and the user cannot be used to transmit data information due to severe channel fading.

[0042] Repeater m in the cell receives the transmission signal from the base station for:

[0043]

[0044] in, is the channel status information between the cell base station and the repeater m; is the beamforming matrix from the base station to the repeater m; The mean is 0 and the variance is The additive Gaussian noise of the received signal at the repeater end.

[0045] Repeater m transmits a signal to user j for:

[0046]

[0047] in, is the transmitting end beamforming matrix from repeater m to user j in the cell; is the amplification and forwarding matrix of repeater m.

[0048] The received signal of the kth user is:

[0049]

[0050] in, is the channel state information between the local cell base station and the local user k; and are the channel status information between cooperative repeater 1, cooperative repeater 2, repeater m and user k respectively; s k ,s j are the transmission signals from the base station to users k and j respectively; w are the transmission signals from cooperative repeater 1 and cooperative repeater 2 to user k, and the transmission signal from repeater m to user j (including the case where user j=k); j , w k is the beamforming vector used by the base station to send signals to user k and user j; n k To obey the mean of 0, is the variance of the additive Gaussian white noise of the signal received by user k.

[0051] From the above formula, the user's received signal-to-noise ratio can be deduced as:

[0052]

[0053] in and are the channel matrices between cooperative repeater 1, cooperative repeater 2 and user k, and They are and The conjugate transposed matrix of ; and are the beamforming vectors of cooperative repeater 1 and cooperative repeater 2 respectively.

[0054] It can be concluded that the user's transmission rate is: R k =log2(1+SINR k )

[0055] Based on the above content, the important indicator of user transmission rate in the single-cell multi-repeater cooperative transmission system is obtained. The speed of user transmission rate determines the speed of data transmission, affects the user experience, and also affects the performance and efficiency of the cooperative transmission system. On the other hand, the total power of the base station is one of the important factors for the cooperative transmission system to ensure data transmission. It determines the transmission range and quality of the wireless signal, thereby affecting the user's transmission rate and data reliability.

[0056] In view of the cooperative transmission of repeaters in the above single-cell scenario, a multi-repeater cooperative transmission method to improve the transmission rate is proposed:

[0057] S1. Initialize the minimum transmission rate requirement for local cell users γ m , initialize the local cell repeater amplification conversion factor F, and propose the optimal beamforming strategy for the local cell, with minimizing the total power of the cell base station as the optimization goal, and taking the minimum transmission rate requirement of all cell users as the constraint condition for joint optimization. The expression of this optimization problem is as follows:

[0058]

[0059] Where W is the total power of the base station, R k is the transmission rate of user k, γ k is the minimum transmission rate of user k;

[0060] The expression of the optimization problem is equivalent to a quadratic constrained quadratic programming (QCQP) problem. According to the monotonicity of the logarithmic function, the conditional constraint formula is transformed into a function related to the signal-to-interference-noise ratio.

[0061] S2, relax the expression in S1 into a relaxed problem, and use a one-dimensional search algorithm on the feasible domain of F to find the optimal solution P(F) so that the value of P(F) is minimized;

[0062] The relaxation process of relaxing the expression of the optimization problem into a relaxation problem includes: using a semi-positive definite relaxation algorithm to relax the QCQP problem into a linear programming (LP) problem, where a new variable is introduced

[0063]

[0064] By expressing the quadratic term in the optimization problem formula (5) as a semi-positive definite matrix (PSD) and discarding the non-convex condition that the matrix rank is 1, the original QCQP problem can be relaxed into an LP problem. The solution of this relaxed LP problem is a lower bound of the optimization problem. The standard form of rewriting this LP problem into a semi-definite programming (SDP) problem is as follows:

[0065]

[0066]

[0067] W≥0

[0068] in,

[0069] The steps to obtain the feasible region of F are:

[0070] S21, converting the semidefinite programming (SDP) problem expression into a single variable optimization problem about a variable F, and assigning a value to F in the semidefinite programming (SDP) problem expression;

[0071] S22. Use convex optimization tools to solve and obtain the feasible domain of F: 1≤F≤F max , where F max is the maximum magnification of the repeater;

[0072] The expression of the optimization problem is transformed into the problem of optimizing the optimization problem in S1 on the feasible domain of F. The expression is as follows:

[0073] minP(F)

[0074] 1≤F≤F max ;

[0075] S3, substitute P(F) in S2 into the relaxed problem and solve the relaxed problem, thereby obtaining the optimal solution A of the relaxed problem;

[0076] S4. When the rank of A is 1, the optimal solution A of the relaxation problem is the optimal solution of the expression S1. By performing eigenvalue decomposition on A, the optimal beamforming strategy of the local cell in the optimization problem and the corresponding base station power tr(w) can be obtained;

[0077] S5. When the rank of A is not 1, after performing eigenvalue decomposition on A, an advanced randomization method is used to scale the optimization problem. By optimizing the scaling problem, an approximate solution A of the optimization problem is obtained. *, where the more candidate beamforming vectors there are, the closer the approximate solution finally obtained is to the optimal solution of the optimization problem;

[0078] The advanced randomization method in step S5 is specifically a Gaussian randomization method, and when the rank of the solution A of the relaxed problem is not 1, the steps for obtaining the optimal solution are:

[0079] S51. For the approximate solution A * Perform eigenvalue decomposition, and the expression after decomposition is: λ1,λ2,…,λ L A * The L eigenvalues ​​of ;

[0080] S52: The beamforming vector in step S51 is multiplied by a Gaussian vector v t Adjust, where t represents the tth candidate vector and v t Following a Gaussian distribution with a mean of 0 and a variance of 1, the adjusted beamforming vector is:

[0081]

[0082] S53, for the candidate vector set p m After selection and optimization, the optimal solution is finally obtained, and the formula is:

[0083]

[0084] In this embodiment, by Solving the formula in step S53 can obtain the optimal solution to this problem. And the optimization problem beamforming vector is obtained in, is the beamforming vector of the local cell base station. At this time, if the vector rank is 1, it is the optimal solution under the optimization problem. When the rank is not 1, the suboptimal solution obtained by the Gaussian randomization method is obtained. As the number of candidate beamforming vectors increases, the result obtained will be closer and closer to the optimal solution after the relaxation of the optimization problem. The above steps can optimize the coverage and transmission quality of the cooperative transmission system by appropriately adjusting the total power of the base station, improve the reliability and correctness of the data, and take into account the energy consumption and cost of the system. The semi-positive relaxation algorithm and the Gaussian randomization method are used to solve the power minimization problem. Under the premise of ensuring the quality of user service, the beamforming strategy is used to minimize the total power of the base station and improve the power of the base station.

[0085] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-repeater cooperative transmission method for improving transmission rate, characterized in that: The following steps are involved: S1. Initialize the minimum transmission rate requirement for local cell users γ k , initialize the local cell repeater amplification conversion factor F, and propose the optimal beamforming strategy for the local cell, with minimizing the total power of the cell base station as the optimization goal, and taking the minimum transmission rate requirement of all cell users as the constraint condition for joint optimization. The expression of this optimization problem is as follows: Where W is the total power of the base station, R k is the transmission rate of user k, γ k is the minimum transmission rate of user k; S2, relax the expression in S1 into a relaxation problem, and use a one-dimensional search algorithm on the feasible domain of F to find the optimal solution P(F), so that the value of P(F) is minimized, and the expression of the optimization problem in step S1 is equivalent to a quadratic constrained quadratic programming (QCQP) problem. According to the monotonicity of the logarithmic function, the conditional constraint formula is converted into a function related to the signal-to-interference-noise ratio; The relaxation process of relaxing the expression of the optimization problem into a relaxed problem includes: using the semi-positive definite relaxation algorithm to relax the QCQP problem into a linear programming (LP) problem, where the new variable is introduced in h k , and They are w k , and The conjugate transposed matrix, w k is the beamforming vector used by the base station to send signals to user k, and It means the beamforming vector from the base station to cooperative repeater 1 and cooperative repeater 2. is the beamforming vector from repeater m to user j, and is the beamforming vector between cooperative repeater 1 and user k and the beamforming vector between cooperative repeater 2 and user k, and are the channel state information between cooperative repeater 1, cooperative repeater 2 and user k respectively; and These are the amplification and forwarding matrices of cooperative repeater 1 and cooperative repeater 2. is the channel matrix between the base station and repeater m, and is the channel matrix between the base station and cooperative repeater 1 and cooperative repeater 2. is the channel matrix between repeater m and user k; h k is the channel matrix between the base station and user k; f mr is the forwarding amplification matrix of repeater station m, represents the beamforming vector from the base station to the repeater m, where P mr represents the second amplification and forwarding matrix of repeater m; The quadratic term in the expression of the optimization problem is expressed as a semi-positive definite matrix (PSD), and the non-convex condition that the matrix rank is 1 is discarded, so that the QCQP problem is relaxed to an LP problem, where the solution of the LP problem is a lower bound in the expression of the optimization problem. The standard form of rewriting this LP problem into a semi-definite programming (SDP) problem is as follows: W≥0 in, Γ represents the constant vector on the right side of the constraint; S3, substitute P(F) in S2 into the relaxed problem and solve the relaxed problem, thereby obtaining the optimal solution A of the relaxed problem; S4. When the rank of A is 1, the optimal solution A of the relaxation problem is the optimal solution of the expression S1. By performing eigenvalue decomposition on A, the optimal beamforming strategy of the local cell in the optimization problem and the corresponding base station power tr(w) can be obtained; S5. When the rank of A is not 1, after performing eigenvalue decomposition on A, an advanced randomization method is used to scale the optimization problem. By optimizing the scaling problem, an approximate solution A of the optimization problem is obtained. * , where the more candidate beamforming vectors there are, the closer the approximate solution finally obtained is to the optimal solution of the optimization problem; The advanced randomization method is specifically a Gaussian randomization method, and when the rank of the solution A of the relaxed problem is not 1, the steps for obtaining the optimal solution are: S51. For the approximate solution A * Perform eigenvalue decomposition, and the expression after decomposition is: λ1,λ2,…,λ L A * The L eigenvalues ​​of ; S52: The beamforming vector in step S51 is multiplied by a Gaussian vector v t Adjust, where t represents the tth candidate vector and v t Following a Gaussian distribution with a mean of 0 and a variance of 1, the adjusted beamforming vector is: S53, for the candidate vector set p m After selection and optimization, the optimal solution is finally obtained, and the formula is: minp m U m is the feature matrix; It's U m The conjugate transposed matrix of ; is the variance of the additive Gaussian white noise of the signal received by user k, w j is the beamforming vector used by the base station to send signals to user j.

2. A multi-repeater cooperative transmission method for improving transmission rate according to claim 1, characterized in that: The steps for obtaining the feasible domain of F in S2 are: S21, converting the semidefinite programming (SDP) problem expression into a single variable optimization problem about a variable F, and assigning a value to F in the semidefinite programming (SDP) problem expression; S22. Use convex optimization tools to solve and obtain the feasible domain of F: 1≤F≤F max , where F max is the maximum magnification of the repeater; The expression of the optimization problem is transformed into the problem of optimizing the optimization problem in S1 on the feasible domain of F. The expression is as follows: minP(F) 1≤F≤F max 。 3. A multi-repeater cooperative transmission method for improving transmission rate according to claim 2, characterized in that: pass Solving the formula in step S53 can obtain the optimal solution to this problem. And the optimization problem beamforming vector is obtained in, is the beamforming vector of the local cell base station.

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