Partial interference alignment method, device and storage medium in medium signal-to-noise ratio scenario

Abstract

The present invention discloses a partial interference alignment method, device and storage medium in a medium signal-to-noise ratio scenario. The method includes: establishing a downlink heterogeneous cellular network interference model; performing interference alignment according to the full interference alignment strategy and power constraint, and constructing a transmitter precoding matrix based on the spatial multiplexing of a predetermined user and the interference alignment of other users; performing partial interference alignment based on the transmitter power constraint, aligning the useful signal and the interference signal into two matrices respectively, and determining the transmitter precoding matrix; at the user receiver, solving the receiver matrix corresponding to the transmitter precoding matrix based on the iterative method. By partially aligning the interference, the present invention designs the macrocell precoding matrix to be orthogonal to a partial subspace of the interference channel matrix, reserves more transmission dimensions for the macrocell link, and improves the network capacity. At the same time, the interference is projected onto a partial subspace of the small cell user, which can reduce the computational overhead and simplify the solution of the receiver matrix at the user end.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless system communication, and particularly relates to a partial interference alignment method, device and storage medium in a medium signal-to-noise ratio scenario. Background Art

[0002] In recent years, great progress has been made in the research on the coexistence of macro cells and small cells through the method of interference alignment (IA). Interference alignment is a signal processing method. Its principle is to divide the signal space into two parts: the desired signal space and the interference signal space. Through precoding technology, the interference is made to overlap at the receiving end, thereby compressing the signal capacity occupied by the interference and eliminating the influence of the interference on the desired signal, so as to achieve the purpose of improving the channel capacity. Interference alignment utilizes the spatial dimension of the MIMO (Multi Input Multi Output) channel to ensure virtual interference-free transmission, which is very suitable for the full-spectrum sharing scenario of heterogeneous networks. Although small cells and macro cells operate on the same channel, they can utilize the additional spatial dimension of interference-free transmission.

[0003] Existing research results show that IA is optimal in the sense of degrees-of-freedom (DoF). In a high signal-to-noise ratio scenario, the algorithm can obtain the optimal degrees of freedom. However, it is not necessarily the optimal achievable capacity because the signal projection at the receiving end limits the spatial diversity of the system. The traditional interference alignment algorithm is a "selfless" interference management algorithm. Each user tries to minimize the interference caused by itself to the non-desired receiving end, and hardly considers its own receiving performance. Therefore, the traditional interference alignment method requires global channel state information, which is too costly if applied to an actual system, and it is very difficult to obtain a closed-form solution for interference alignment. This method can obtain the optimal solution only at high signal-to-noise ratios, and its performance drops significantly at medium signal-to-noise ratios.

[0004] Therefore, how to design a highly robust interference alignment algorithm that can achieve better achievable capacity under certain conditions is an important problem that needs to be faced in the interference alignment of heterogeneous cellular networks. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a partial interference alignment method, device and storage medium in a medium signal-to-noise ratio scenario to solve the technical problem that it is time-consuming and laborious to design an electric vehicle motor controller by using manual layout in the prior art.

[0006] The technical solutions proposed by the present invention are as follows:

[0007] A method for partial interference alignment in a medium signal-to-noise ratio scenario according to a first aspect of an embodiment of the present invention includes: establishing a downlink heterogeneous cellular network interference model; aligning small cell interference and macro cell interference at the receiver side of each small cell according to a full interference alignment strategy and power constraints, and constructing a small cell precoding matrix and a macro cell precoding matrix for full interference alignment based on spatial multiplexing of a predetermined user and interference alignment of other users; performing partial interference alignment based on transmitter power constraints, aligning useful signals and interference signals caused by macro cells into two matrices respectively, and determining a macro cell precoding matrix and a small cell precoding matrix for partial interference alignment; and solving a receiving end matrix corresponding to a transmitter end precoding matrix based on an iterative method.

[0008] Optionally, establishing a downlink heterogeneous cellular network interference model includes: determining an interference model in a downlink heterogeneous cellular network based on a macro cell and small cells; representing received signals of users in the macro cell and small cells based on a signal channel matrix and a channel interference matrix; and determining a power constraint condition in the model based on maximizing network capacity under a medium signal-to-noise ratio.

[0009] Optionally, the power constraint conditions include: the sum of the powers of the signals of each transmitter in each small cell after precoding is not greater than the total transmission power of the small cell; and the total transmission power of any small cell is less than the total transmission power of the macro cell.

[0010] Optionally, during partial interference alignment, the macro cell precoding matrix is orthogonal to a partial subspace of the channel interference matrix, the small cell precoding matrix is orthogonal to a partial subspace of the corresponding channel interference matrix for interference alignment of users in the macro cell and other small cells, and the product of the channel interference matrix and the user receiving matrix is a zero matrix.

[0011] Optionally, the partial interference alignment is to select a subspace formed by a preset number of column vectors in the small cell precoding matrix, and the preset number of column vectors corresponds to the largest singular values in the small cell link; aligning useful signals and interference signals caused by macro cells into two matrices respectively, and determining a macro cell precoding matrix and a small cell precoding matrix includes: making the useful signals orthogonal to a partial subspace of the channel interference matrix; aligning the interference caused by the macro cell link into a column vector space other than the preset number of column vectors; and representing the channel interference matrix as a block structure to determine the macro cell precoding matrix and the small cell precoding matrix.

[0012] Optionally, solving for the receiver matrix corresponding to the transmitter precoding matrix based on an iterative method includes: obtaining the total interference and expected signal power received by corresponding users based on the interference covariance and signal covariance matrices of macro cell and small cell users; when the ratio of the power of the useful signal to the interference signal at the receiver is maximized, the calculation of the receiver matrix is transformed into a trace ratio optimization problem; and iteratively optimizing and solving the trace ratio optimization problem based on difference and eigenvalue decomposition to determine the receiver matrices of users in each cell.

[0013] Optionally, the partial interference alignment method in this medium signal-to-noise ratio scenario further includes: performing channel power allocation based on the channel condition to obtain the power allocation for each user; obtaining the final allocation method by summing the power allocations for each user, and when the ratio of the number of antennas of the macro cell and small cell base stations and users is fixed, performing a summation calculation based on an integration problem.

[0014] A second aspect of the embodiments of the present invention provides a partial interference alignment apparatus in a medium signal-to-noise ratio scenario, including: a model establishment module for establishing a downlink heterogeneous cellular network interference model; a first alignment module for aligning small cell interference and macro cell interference at the receiver side of each small cell according to the full interference alignment strategy and power constraint, and constructing the small cell precoding matrix and macro cell precoding matrix for full interference alignment based on the spatial multiplexing of predetermined users and the interference alignment of other users; a second alignment module for performing partial interference alignment based on the transmitter power constraint, aligning the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determining the macro cell precoding matrix and small cell precoding matrix for partial interference alignment; and a calculation module for solving for the receiver matrix corresponding to the transmitter precoding matrix based on an iterative method.

[0015] Optionally, the model establishment module specifically includes: determining an interference model in the downlink heterogeneous cellular network based on the macro cell and small cell; representing the received signals of users in the macro cell and small cell based on the signal channel matrix and channel interference matrix; and determining the power constraint condition in the model based on maximizing the network capacity under medium signal-to-noise ratio.

[0016] Optionally, the power constraint condition includes: the sum of the powers of the signals of each transmitter in each small cell after precoding is not greater than the total transmission power of the small cell; and the total transmission power of any small cell is less than the total transmission power of the macro cell.

[0017] Optionally, during partial interference alignment, the macro cell precoding matrix is orthogonal to the partial subspace of the channel interference matrix, the small cell precoding matrix is orthogonal to the partial subspace of the corresponding channel interference matrix for the interference alignment of users in the macro cell and other small cells, and the product of the channel interference matrix and the user receiver matrix is a zero matrix.

[0018] Optionally, the partial interference alignment is to select a subspace composed of a preset number of column vectors in the small cell precoding matrix, and the preset number of column vectors corresponds to the largest singular values in the small cell link; aligning the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determining the macro cell precoding matrix and the small cell precoding matrix, including: orthogonalizing the useful signal to the partial subspace of the channel interference matrix; aligning the interference caused by the macro cell link into the column vector space other than the preset number of column vectors; representing the channel interference matrix as a block structure, and determining the macro cell precoding matrix and the small cell precoding matrix.

[0019] Optionally, the calculation module specifically includes: obtaining the total interference and the expected signal power received by the corresponding user based on the interference covariance and the signal covariance matrix of the macro cell and the small cell users; when the ratio of the useful signal power to the interference signal power at the receiving end is the largest, the calculation of the receiving matrix is transformed into a trace ratio optimization problem; iteratively optimizing and solving the trace ratio optimization problem based on the difference and eigenvalue decomposition to determine the receiving matrix of each cell user.

[0020] Optionally, the partial interference alignment device in the medium signal-to-noise ratio scenario further includes: an allocation module, configured to perform channel power allocation based on the channel condition to obtain the power allocation for each user; a summation module, configured to sum according to the power allocation for each user to obtain the final allocation method, and perform summation calculation based on the integral problem when the ratio of the number of antennas of the macro cell and the small cell base stations and users is fixed.

[0021] A third aspect of the embodiments of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the partial interference alignment method in the medium signal-to-noise ratio scenario as described in the first aspect and any item of the first aspect of the embodiments of the present invention.

[0022] A fourth aspect of the embodiments of the present invention provides an electronic device, including: a memory and a processor, where the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the partial interference alignment method in the medium signal-to-noise ratio scenario as described in the first aspect and any item of the first aspect of the embodiments of the present invention.

[0023] The technical solution provided by the present invention has the following effects:

[0024] The partial interference alignment method in a medium signal-to-noise ratio scenario provided by an embodiment of the present invention establishes a downlink heterogeneous cellular network interference model; according to the full interference alignment strategy and power constraint, aligns the small cell interference and macro cell interference at the receiver side of each small cell, constructs the small cell precoding matrix and macro cell precoding matrix for full interference alignment based on the spatial multiplexing of predetermined users and the interference alignment of other users; performs partial interference alignment based on the transmitter power constraint, aligns the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determines the macro cell precoding matrix and small cell precoding matrix for partial interference alignment; solves the receiving end matrix corresponding to the transmitter precoding matrix based on the iterative method. This method determines the precoding matrices of the macro cell and small cell based on the power constraint, thereby determining the receiving matrix, achieving the effect of adaptively selecting the subspace dimension that small cell users can transmit according to the power constraint, projecting the interference onto a partial subspace rather than the entire subspace of small cell users, which can reduce the computational overhead and simplify the solution of the receiving matrix at the user end.

[0025] The partial interference alignment method in a medium signal-to-noise ratio scenario provided by an embodiment of the present invention captures the trade-off between interference avoidance at other users and spatial multiplexing at the intended user through partial interference alignment. By considering the power constraints of each transmitter to partially align the interference signals, designing the macro cell precoding matrix to be orthogonal to a partial subspace of the interference channel matrix can reserve more transmission dimensions for the macro cell link, balance interference and useful signals at each user end, and use the iterative method to solve the optimization problem to obtain the optimized receiving matrix, thereby improving the system capacity of the wireless heterogeneous network. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0027] Figure 1 is a flowchart of the partial interference alignment method in a medium signal-to-noise ratio scenario according to an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of a downlink system model according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of the transmission of a heterogeneous network with coexistence of macro cells and small cells according to an embodiment of the present invention;

[0030] Figure 4It is a structural diagram of a typical cross-layer model for coexistence of macro cells and small cells in a heterogeneous network according to an embodiment of the present invention;

[0031] Figure 5 It is a flowchart for solving soft interference using an iterative method according to an embodiment of the present invention;

[0032] Figure 6 It is a schematic diagram of power allocation according to channel fading conditions according to an embodiment of the present invention;

[0033] Figure 7 It is a structural block diagram of a partial interference alignment device in a medium signal-to-noise ratio scenario according to an embodiment of the present invention;

[0034] Figure 8 It is a structural schematic diagram of a computer-readable storage medium provided according to an embodiment of the present invention;

[0035] Figure 9 It is a structural schematic diagram of an electronic device provided according to an embodiment of the present invention. Specific embodiments

[0036] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] The terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0038] According to an embodiment of the present invention, a partial interference alignment method in a medium signal-to-noise ratio scenario is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0039] In this embodiment, a partial interference alignment method in a medium signal-to-noise ratio scenario is provided, which can be used in electronic devices such as computers, mobile phones, and tablet computers. Figure 1 It is a flowchart of the partial interference alignment method in a medium signal-to-noise ratio scenario according to an embodiment of the present invention, as Figure 1 shown. The method includes the following steps:

[0040] Step S101: Establish a downlink heterogeneous cellular network interference model.

[0041] The downlink system model is as Figure 2 shown. In the downlink heterogeneous network, due to power imbalance, the transmission power of the macro cell base station is much greater than that of the small cell base station. Since the macro cell users are geographically far from the small cell base station, the interference of the small cell base station to the macro cell users can be reasonably ignored. On the other hand, for small cell users, they need to consider both the interference generated by the macro cell base station and the interference generated by themselves and other small cell base stations.

[0042] Specifically, as Figure 3 and Figure 4 shown, the heterogeneous cellular network can be abstracted into an interference model according to the above interference situation, and the arrow represents the interference signal generated by the base station to the user. In this model, the macro cell base station and the small cell base station coexist. Its specific interference method is the open subscribe group (OSG). In addition, considering that in a large range, the interference of the small cell base station to the macro cell users can be ignored. Therefore, the macro cell users only consider the interference of the macro cell base station. On the other hand, for the interference sources of small cell users, they need to consider both the macro cell base station and the small cell base station.

[0043] At the same time, in this interference model, the received signals of users in the macro cell and the small cell are represented by a signal channel matrix and a channel interference matrix. It is assumed that the macro cell base station is equipped with N antennas and the small cell base station is equipped with M antennas. For users, they all have K antennas. The macro cell users include user 0, and the small cell users include user 1 and user 2. Then the signals received by user 0, user 1, and user 2 can be expressed as K x 1 vectors r0, r1, and r2,

[0044] r1 = H 11 V1P0s1 + H 12 V2P2s2 + H 10 V0P0s0 + n1 (1)

[0045] r2 = H 21 V1P0s1 + H 22 V2P2s2 + H 20V0P0s0 + n2 (2)

[0046] r0 = H 00 V0P0s0 + n0 (3)

[0047] where H 00 , H 11 and H 22 are the signal channel matrices of user 0, user 1, and user 2. H 10 , H 20 , H 21 , H 12 are channel interference matrices. The entries of these matrices are independent and identically distributed, and can be expressed as

[0048]

[0049] where G ij represents the small-scale fading part of the channel from the j-th base station to user i. Assuming it is fixed within a coherence time, it is a complex Gaussian distribution for G ij . D ij is the corresponding path loss part, which lasts relatively long. n0, n1, and n2 are additive white Gaussian noise vectors with variance σ 2 . The data symbol s k ∈ d k ×1 is for user k, and d k is the dimension or degrees of freedom of user k.

[0050] Traditional interference alignment techniques have no corresponding constraints on the desired signal, and power loss will occur when processing interference signals. This partial interference alignment method partially aligns interference signals by considering power constraint conditions at the transmitter. The power constraint conditions specifically include: the sum of the powers of the signals of each transmitter in each small cell after precoding is not greater than the total transmission power of that small cell. Due to the imbalance in the transmission powers of the macro cell and small cell base stations, the constraint conditions also include: the total transmission power of any small cell is less than the transmission power of the macro cell.

[0051] After determining the power constraint conditions, precoding combined with power allocation is determined. Each symbol is precoded with the precoding matrix V k , and V k is a unitary matrix responsible for the direction of the symbol. The corresponding power constraint can be expressed as:

[0052]

[0053]

[0054]

[0055] These constraints ensure that the sum of the powers of the signals from each transmitter in each cell after precoding does not exceed the total transmission power of that cell.

[0056] In addition, due to the imbalance in the transmission powers of the macro cell and small cell base stations, it is also necessary to ensure that the total transmission power of the small cell is less than that of the macro cell, that is, to satisfy p 0,max >p 1,max .

[0057] After designing the precoding matrix V k in the above manner, the achievable rate of user k can be expressed as

[0058]

[0059] where W k is the receiving matrix of user k, and Q k is

[0060]

[0061] Since this method requires the maximum sum capacity rather than the maximum degrees of freedom under medium signal-to-noise ratio conditions. Therefore, the goal is to maximize the achievable capacity of the network under power constraint conditions, and the formula is:

[0062]

[0063] subject to p1≤p 1,max ,p2≤p 2,max ,p0≤p 0,max (11)

[0064] where R0, R1, and R2 are the achievable rates of user 0, user 1, and user 2 respectively.

[0065] Step S102: According to the full interference alignment strategy and power constraint, align the small cell interference and macro cell interference at the receiver side of each small cell, and construct the small cell precoding matrix and macro cell precoding matrix for full interference alignment based on the spatial multiplexing of the predetermined users and the interference alignment of other users.

[0066] For small cell users, user 1 and user 2, there are cross small cell interference and macro cell base station interference. According to the traditional full interference alignment strategy, align the cross small cell interference and macro cell interference at the receiver side of each small cell, that is, expressed by the following formula:

[0067] span(H 10 V0)=span(H 12 V2) (12)

[0068] span(H 20V0) = span(H 21 V1) (13)

[0069] Equation (1) and Equation (2) respectively represent aligning the interference received at User 1 from Transmitter 0 and Transmitter 2; aligning the interference received at User 2 from Transmitter 0 and Transmitter 1.

[0070] For the full interference alignment strategy, a precoding matrix V k ∈ M×d k is designed to eliminate interference and extract the symbols to be used. Take d k = K. The estimator of the data symbol is

[0071]

[0072] After designing the anti-interference alignment matrix, the achievable rate of User k can be expressed as

[0073]

[0074] Specifically, when designing the macrocell and small cell precoding matrices, combined with power allocation, the precoding vector is decomposed into two parts, which respectively represent the spatial utilization of the predetermined user and the interference alignment of other users. Among them, in the constructed macrocell precoding matrix, the useful signal is made orthogonal to a partial subspace of the interference channel matrix, reserving more transmission dimensions for the macrocell link. When constructing the small cell precoding matrix, the interference from the macrocell and other small cell users is eliminated, and it is aligned with the interference of the macrocell link to other small cell users. For the precoding matrix and receiving matrix of traditional full interference alignment, the corresponding receiving matrix needs to satisfy that the product of the precoding matrix of other base stations, the channel interference matrix, and the user receiving matrix is a zero matrix. Step S103: Perform partial interference alignment based on the transmitter power constraint, align the useful signal and the interference signal caused by the macrocell into two matrices respectively, and determine the macrocell precoding matrix and the small cell precoding matrix. Due to the imbalance in the transmission powers of the macrocell base station and the small cell base station, the macrocell precoding matrix is made orthogonal to the partial subspace of the channel interference matrix, which can reserve more transmission dimensions for the macrocell link and improve the system capacity. The small cell precoding matrix is made orthogonal to the partial subspace of the corresponding channel interference matrix for the interference alignment of the macrocell and other small cell users. At the same time, according to the traditional full interference alignment strategy, the product of the channel interference matrix and the user receiving matrix is set to a zero matrix.

[0075] Specifically, in order to optimize the sum capacity of the system, the partial interference alignment is to select a subspace composed of a preset number of column vectors in the small cell precoding matrix, and the preset number of column vectors corresponds to the largest singular value in the small cell link; specifically, a subspace composed of the column vectors of the first d columns in the small cell precoding matrix can be selected. Align the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determine the macro cell precoding matrix and the small cell precoding matrix, including: making the useful signal orthogonal to the partial subspace of the channel interference matrix; aligning the interference caused by the macro cell link into the column vector space other than the preset number of column vectors; representing the channel interference matrix in a block structure, and determining the macro cell precoding matrix and the small cell precoding matrix.

[0076] Step S104: Solve the receiver matrix corresponding to the transmitter precoding matrix based on the iterative method. Specifically, when solving, first obtain the total interference and the expected signal power received by the user by using the interference covariance and the signal covariance matrix of the cell users. When the power ratio of the useful signal and the interference signal at the receiver is the largest, the performance of the receiving system is the best. Use this idea to establish a trace ratio optimization problem, and solve it through an iterative optimization program. Give a threshold according to the specific performance requirements of the system. When the step size is less than the threshold, stop the iteration, and solve the receiver matrices of each cell user, and the column vectors are orthogonal to each other. For the corresponding precoding matrix, update it by the method opposite to the above.

[0077] The partial interference alignment method in the medium signal-to-noise ratio scenario provided by the embodiments of the present invention establishes a downlink heterogeneous cellular network interference model; according to the full interference alignment strategy and power constraints, align the small cell interference and the macro cell interference at each small cell receiver side, and construct the small cell precoding matrix and the macro cell precoding matrix for full interference alignment based on the spatial multiplexing of the predetermined user and the interference alignment of other users; perform partial interference alignment based on the transmitter power constraint, align the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determine the macro cell precoding matrix and the small cell precoding matrix for partial interference alignment; solve the receiver matrix corresponding to the transmitter precoding matrix based on the iterative method. This method determines the precoding matrices of the macro cell and the small cell based on the power constraint, thereby determining the receiver matrix, thus achieving the effect of adaptively selecting the subspace dimension that the small cell users can transmit according to the power constraint, projecting the interference onto a partial subspace of the small cell users instead of the entire subspace, which can reduce the computational overhead and simplify the solution of the receiver matrix at the user end.

[0078] In one embodiment, for the interference model established above, to achieve full interference alignment, it is assumed that the constraint is N - 2K > K. Since it is difficult to obtain the precoding and receiving matrices for maximizing the system sum capacity, as an alternative to finding the optimal closed-form solution, this problem is solved by means of partial interference alignment with power constraints. At the same time, an iterative method is used to calculate the receiving matrix, and the interference and signals are balanced by considering the power imbalance in the Hetnet model to further optimize the network and capacity.

[0079] First, for the full interference alignment strategy, in the macro cell link, for the transmitter of user 0, the precoding matrix should include the interference to the small cell users. Thus, the macro cell precoding matrix V0 is represented by the product of two sub-precoding matrices in the following formula.

[0080] V0 = V0 (1) V0 (2) (16)

[0081] where V0 (1) is for interference avoidance of small cell users, while V0 (2) is for maximizing the multiplexing gain of macro cell users.

[0082] The interference channels from macro cell user 0 to small cell users 1 and 2 are represented by the following formula:

[0083]

[0084] where H 210 ∈ 2K×N, H 10 , H 20 ∈ K×N, let represent the singular value decomposition of the interference channel matrix from macro cell user 0 to small cell users 1 and 2, where V H210 ∈ N×N. The first part V0 (1) of the macro cell precoding matrix is represented as the left 2K orthogonal columns of V H210 , that is

[0085]

[0086] This can eliminate the interference to users 1 and 2. It can be seen that the macro cell sacrifices a certain degree of freedom in the downlink transmission in exchange for the anti-interference ability of small cell users.

[0087] After obtaining V0 (1) , then determine V0 (2) . Let the equivalent macro cell link channel matrix be Then let Then V0 (2) is represented by the following formula

[0088]

[0089] The corresponding receiving matrix is

[0090]

[0091] For the small cell link, the same method can be used to determine the precoding matrix. For the transmitter of User 1, the precoding matrix should include the interference to Small Cell User 2, so as to obtain the corresponding precoding matrix.

[0092] Specifically, the small cell precoding matrix is determined in the following manner:

[0093] Let be the singular value decomposition of the interference channel matrix from Small Cell User 1 to User 2, where By selecting the appropriate V H21 of the min(K,M-K) columns, the first part V1 in the precoding matrix can be determined (1) . The V1 determined in this way (1) can eliminate the interference caused to User 2.

[0094] After obtaining V1 (1) , then determine V1 (2) . When determining, the equivalent small cell link channel matrix can be written as Let V1 (2) can be expressed as

[0095]

[0096] The corresponding receiving matrix is

[0097]

[0098] The rationality of the precoding design of the full interference alignment method lies in that the null space of H 210 and the null space of H 00 are not likely to be related.

[0099] In one embodiment, in order to optimize the sum capacity of the system, the interference signals are partially aligned by considering the power constraints of each transmitter, rather than considering full interference alignment. This can be called hard interference alignment. Without loss of generality, assume that the degree of freedom of User k is d k <K, k = 1, 2.

[0100] After projecting the precoding vector V0 onto the null space of H 210 , the downlink transmission of the macro cell BS will sacrifice a certain degree of freedom.

[0101] In the present invention, V is selected during partial alignmentH11 The subspace spanned by the first d1 column vectors, which corresponds to the largest d1 singular values in the small cell link. Then, in order to align the interference caused by the macro cell link into the remaining K - d1 dimensions, the interference channel matrix and are represented in a block structure,

[0102]

[0103]

[0104] where is divided into and is divided into and

[0105] Due to the power imbalance between the transmit powers of macro cell base station 0 and small cell base stations 1 and 2, the precoding matrix V0 can be designed to be orthogonal to the and partial subspaces. In this way, more transmission dimensions can be reserved for the macro cell link.

[0106] To align the interference caused by the macro link into the d1 + d2 dimensions, the precoding matrix V0 (1) must satisfy:

[0107]

[0108] Let Let be 's d1 columns, then V0 (1) can be obtained by projecting onto the null space:

[0109]

[0110] It should be noted that when selecting partial columns as precoding vectors, is inconsistent. When selecting the complete columns, it is identical. So the precoding matrix is valid here.

[0111] When determining the small cell precoding matrix V1, it should eliminate the interference to user 2. To align its interference with the remaining K - d2 dimensions of the macro cell link to user 2, the channel matrix is represented as It can be represented in the following block structure,

[0112]

[0113] where is divided into and

[0114] Then V1 (1) can be obtained by projecting onto the null space of, and the equivalent macrocell link channel matrix can be written as Let V1 (2) can be expressed as

[0115]

[0116] The corresponding receive matrix is

[0117]

[0118] Then the small cell precoding matrix in partial interference alignment can be expressed as

[0119]

[0120] The precoding matrix determined by the above formula can balance the multiplexing gain of the macrocell link and the interference avoidance of small cell users. It also adaptively selects the dimension that the small cell link can transmit according to the power constraint of the system, and flexibly utilizes the interference alignment method.

[0121] In one embodiment, in order to improve the hard interference alignment algorithm, the following soft interference alignment algorithm is adopted. This soft interference alignment method no longer strictly calibrates the interference at the transmitter, but balances the interference and the useful signal at each user end to improve the sum capacity of the system.

[0122] Specifically, the total interference received by user k from the interference signals and noise of other cell base stations is:

[0123]

[0124] where Q k is the interference covariance matrix of user k.

[0125] The expected signal power of the desired base station at user k is:

[0126]

[0127] where

[0128]

[0129] is the signal covariance matrix of user k.

[0130] By balancing interference and the useful signal, when the power ratio of the useful signal to the interference signal at the receiving end is maximized, the performance of the receiving matrix is optimal at this time. Therefore, for each cell user, the following optimization problem is solved.

[0131]

[0132] This is a standard trace ratio problem, which can be solved by an iterative optimization program. The obtained solution has column vectors that are unit vectors and orthogonal to each other.

[0133] Denote A k = R k + Q k , then the optimization problem is equivalent to:

[0134]

[0135] where

[0136] Specifically, when solving the optimization problem in an iterative manner, first calculate the trace ratio λ from the previous receiving matrix n .

[0137] Then, according to the obtained λ n , update the receiving matrix

[0138]

[0139] It can be solved by the eigenvalue decomposition method. Select the column vectors corresponding to the largest singular eigenvalue d to obtain:

[0140] W k = V k (:, 1:d k ) (37)

[0141] Similarly, for the small cell precoding matrix V k , it can also be updated in the same way.

[0142] In one embodiment, the iterative algorithm for solving the soft interference alignment receiving matrix is specifically implemented by the process shown in Figure 5 as follows:

[0143] Step 1. Initialize the receiving matrix W k as a randomly selected orthogonal matrix column;

[0144] Step 2. For n = 1, 2,..., N max ,

[0145] 1) Calculate the ratio λ from the previous receiving matrix n

[0146]

[0147] where R k is the signal covariance matrix of user k, and A k = R k + Q k , where Q k is the interference signal covariance matrix.

[0148] 2) Calculate The difference problem formula of

[0149]

[0150] 3) Solve the problem through eigenvalue decomposition:

[0151]

[0152] 4) Reshape the column vectors of

[0153] Step 3. Update to

[0154] ε is a given appropriate threshold, and the iteration is terminated when the condition is satisfied.

[0155] In one embodiment, the partial interference alignment method in a medium signal-to-noise ratio scenario further includes: performing channel power allocation based on the channel condition to obtain the power allocation for each user; summing according to the power allocation for each user to obtain the final allocation method, and when the ratio of the number of antennas of the macro cell and small cell base stations and users is fixed, performing a summation calculation based on the integral problem.

[0156] Specifically, in order to improve the system capacity, from the perspective of power allocation, the users can be sorted according to the channel condition, and less power is allocated to the channels with poor channel conditions to determine the specific power allocation for each user, and then summing them can obtain the final allocation method. This can reduce the waste of power and further improve the system capacity and performance. When summing, when the number of antennas of the cell base station and users is very large and the ratio is fixed, the summation problem can be transformed into an integral form. When the number of antennas approaches infinity, the eigenvalues of the channel matrix gradually converge, so as to obtain the eigenvalue distribution of the channel matrix in the high-dimensional case, that is, the channel condition, and then perform power allocation for the channel.

[0157] In one embodiment, the different power requirements of small cell users are sorted and summed from the perspective of power allocation to reduce power waste and improve the total system capacity. Specifically, it is implemented using the following formula:

[0158] The water level β can be obtained from the power constraint: ​

[0159]

[0160] In the finite case, the power p corresponding to the nth singular value 2,n can be expressed as

[0161]

[0162] as The eigenvalue represents the channel fading amplitude. The smaller the eigenvalue, the greater the fading. Therefore, the worse the channel condition, the smaller the allocated power. If the channel fading is too large, no power is allocated to this channel. The schematic diagram is as Figure 6 shown.

[0163] where β can be written as:

[0164]

[0165] d is the user dimension, and σ 2 is the variance of additive white Gaussian noise.

[0166] It should be noted that the above formula utilizes the diversity of singular values. First, the specific total power is not given. Then, the users are sorted according to the power allocation to determine the power requirements of each specific user. Summing them up gives the final total power requirement. In this way, the waste of power can be reduced, and the system capacity and performance can be further improved.

[0167] where d can be expressed as the sum of indicator functions:

[0168]

[0169] When then otherwise

[0170] Specifically, the asymptotic transmission dimension users of the small cell are defined as follows:

[0171]

[0172] Here, an approximation is made. When the transmission dimension of the system is very high, the summation can be written in integral form. The empirical eigenvalue distribution almost converges to the deterministic limiting eigenvalue distribution is the corresponding probability density function, which can be determined by the Marcenko - Pastur method.

[0173]

[0174] where Meanwhile, the density function has non-zero values in {{0} ∪ {[a, b]}}.

[0175] When the number of antennas tends to infinity, the eigenvalues of the random matrix converge to the distribution. Let f(λ) be a function of the parameter λ. Then, if the eigenvalues fall within the interval λ ∈ (a, b) and the distribution converges to g(λ j ), the average sum of f(λ j ) converges as the dimension increases.

[0176]

[0177] The partial interference alignment method in the medium signal-to-noise ratio scenario provided by the embodiments of the present invention captures the trade-off between interference avoidance at other users and spatial multiplexing at the intended user through partial interference alignment. By partially aligning the interference signals considering the power constraints of each transmitter and designing the macrocell precoding matrix to be orthogonal to a partial subspace of the interference channel matrix, more transmission dimensions can be reserved for the macrocell link. Meanwhile, the interference and useful signals are balanced at each user end, and an iterative method is used to solve the optimization problem to obtain the optimized receiving matrix, thereby improving the system capacity of the wireless heterogeneous network.

[0178] The embodiments of the present invention also provide a partial interference alignment device in the medium signal-to-noise ratio scenario, as Figure 7 shown. The device includes:

[0179] A model establishment module for establishing a downlink heterogeneous cellular network interference model; for specific content, refer to the corresponding part of the above method embodiment, which will not be elaborated here.

[0180] A first alignment module for aligning small cell interference and macrocell interference at the small cell receiver side according to the full interference alignment strategy and power constraints, and constructing the small cell precoding matrix and the macrocell precoding matrix for full interference alignment based on the spatial multiplexing of the predetermined user and the interference alignment of other users; for specific content, refer to the corresponding part of the above method embodiment, which will not be elaborated here.

[0181] A second alignment module for performing partial interference alignment based on the transmitter power constraint, aligning the useful signal and the interference signal caused by the macrocell into two matrices respectively, and determining the macrocell precoding matrix and the small cell precoding matrix for partial interference alignment; for specific content, refer to the corresponding part of the above method embodiment, which will not be elaborated here.

[0182] A calculation module for solving the receiving end matrix corresponding to the transmitter precoding matrix based on the iterative method. For specific content, refer to the corresponding part of the above method embodiment, which will not be elaborated here.

[0183] The partial interference alignment device in a medium signal-to-noise ratio scenario provided by an embodiment of the present invention determines the precoding matrices of the macro cell and the small cell based on power constraints, thereby determining the receiving matrix, and thus achieves the effect of adaptively selecting the subspace dimension that small cell users can transmit according to power constraints. The interference is projected onto a partial subspace rather than the entire subspace of small cell users, which can reduce the computational overhead and simplify the solution of the receiving matrix at the user end.

[0184] For the functional description of the partial interference alignment device in a medium signal-to-noise ratio scenario provided by an embodiment of the present invention, refer to the description of the partial interference alignment method in the above embodiment in detail for the medium signal-to-noise ratio scenario.

[0185] In one embodiment, the model establishment module specifically includes: determining the interference model in the downlink heterogeneous cellular network based on the macro cell and the small cell; representing the received signals of users in the macro cell and the small cell based on the signal channel matrix and the channel interference matrix; determining the power constraint conditions in the model based on maximizing the network capacity under medium signal-to-noise ratio.

[0186] In one embodiment, the power constraint conditions include: the sum of the powers of the signals of each transmitter in each small cell after precoding is not greater than the total transmission power of the small cell; the total transmission power of any one small cell is less than the total transmission power of the macro cell.

[0187] In one embodiment, during partial interference alignment, the macro cell precoding matrix is orthogonal to the partial subspace of the channel interference matrix, and the small cell precoding matrix is orthogonal to the partial subspace of the corresponding channel interference matrix for interference alignment of users in the macro cell and other small cells. The product of the channel interference matrix and the user receiving matrix is a zero matrix.

[0188] In one embodiment, the partial interference alignment is to select the subspace composed of a preset number of column vectors in the small cell precoding matrix, and the preset number of column vectors corresponds to the largest singular values in the small cell link; align the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determine the macro cell precoding matrix and the small cell precoding matrix, including: making the useful signal orthogonal to the partial subspace of the channel interference matrix; aligning the interference caused by the macro cell link into the column vector space other than the preset number of column vectors; representing the channel interference matrix as a block structure, and determining the macro cell precoding matrix and the small cell precoding matrix.

[0189] In one embodiment, the calculation module specifically includes: obtaining the total interference and the expected signal power received by the corresponding users based on the interference covariance and the signal covariance matrices of the macro cell and small cell users; when the power ratio of the useful signal and the interference signal at the receiving end is the largest, the calculation of the receiving matrix is transformed into a trace ratio optimization problem; iteratively optimizing and solving the trace ratio optimization problem based on difference and eigenvalue decomposition to determine the receiving matrices of the users in each cell.

[0190] In one embodiment, the partial interference alignment device in the medium signal-to-noise ratio scenario further includes: an allocation module, configured to perform channel power allocation based on the channel condition to obtain the power allocation for each user; a summation module, configured to sum according to the power allocation for each user to obtain the final allocation method, and when the ratio of the number of antennas of the macro cell and the small cell base stations and users is fixed, perform summation calculation based on the integral problem.

[0191] The embodiment of the present invention also provides a storage medium, such as Figure 8 shown, on which a computer program 601 is stored. When the instruction is executed by a processor, it implements the steps of the partial interference alignment method in the medium signal-to-noise ratio scenario in the above embodiment. Audio and video stream data, feature frame data, interaction request signaling, encrypted data, and preset data sizes are also stored on the storage medium. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0192] Those skilled in the art can understand that to implement all or part of the processes in the above embodiment methods, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0193] The embodiment of the present invention also provides an electronic device, such as Figure 9 shown. The electronic device may include a processor 51 and a memory 52. The processor 51 and the memory 52 can be connected through a bus or other means. Figure 9 Taking the connection through the bus as an example.

[0194] The processor 51 may be a Central Processing Unit (CPU). The processor 51 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above types of chips.

[0195] The memory 52, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the corresponding program instructions / modules in the embodiments of the present invention. The processor 51 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 52, that is, implements the partial interference alignment method in the medium signal-to-noise ratio scenario in the above method embodiments.

[0196] The memory 52 may include a program storage area and a data storage area. Among them, the program storage area can store an operating device and application programs required for at least one function; the data storage area can store data created by the processor 51, etc. In addition, the memory 52 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 52 may optionally include a memory remotely set relative to the processor 51, and these remote memories can be connected to the processor 51 through a network. Examples of the above networks include, but are not limited to, the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.

[0197] The one or more modules are stored in the memory 52 and, when executed by the processor 51, execute the partial interference alignment method in the medium signal-to-noise ratio scenario in the Figures 1-6 illustrated embodiments.

[0198] For specific details of the above electronic device, reference can be made to the Figures 1 to 6 corresponding relevant descriptions and effects in the illustrated embodiments, which will not be elaborated here.

[0199] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A partial interference alignment method in a medium signal-to-noise ratio scenario, characterized in that Including: Establishing a downlink heterogeneous cellular network interference model; According to the full interference alignment strategy and power constraint, aligning small cell interference and macro cell interference at the receiver side of each small cell, and constructing a small cell precoding matrix and a macro cell precoding matrix for full interference alignment based on the spatial multiplexing of a predetermined user and the interference alignment of other users; Performing partial interference alignment based on the transmitter power constraint, aligning the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determining the macro cell precoding matrix and the small cell precoding matrix during partial interference alignment; Solving the receiver matrix corresponding to the transmitter precoding matrix based on the iterative method; Establishing a downlink heterogeneous cellular network interference model, including: Determining the interference model in the downlink heterogeneous cellular network based on the macro cell and the small cell; Representing the received signals of users in the macro cell and the small cell based on the signal channel matrix and the channel interference matrix; Determining the power constraint condition in the model based on maximizing the network capacity under medium signal-to-noise ratio.

2. The partial interference alignment method in a medium signal-to-noise ratio scenario according to claim 1, wherein The power constraint condition includes: the sum of the powers of the signals of each transmitter in each small cell after precoding is not greater than the total transmission power of the small cell; the total transmission power of any small cell is less than the total transmission power of the macro cell.

3. The partial interference alignment method in a medium signal-to-noise ratio scenario according to claim 1, wherein, During partial interference alignment, the macro cell precoding matrix is orthogonal to the partial subspace of the channel interference matrix, the small cell precoding matrix is orthogonal to the partial subspace of the corresponding channel interference matrix of the interference alignment of the users in the macro cell and other small cells, and the product of the channel interference matrix and the user receiver matrix is a zero matrix.

4. The partial interference alignment method in a medium signal-to-noise ratio scenario according to claim 1, wherein The partial interference alignment is to select the subspace composed of a preset number of column vectors in the small cell precoding matrix, and the preset number of column vectors corresponds to the largest singular values in the small cell link; Aligning the useful signal and the interference signal caused by the macro cell into two matrices respectively, and determining the macro cell precoding matrix and the small cell precoding matrix, including: Orthogonalizing the useful signal to the partial subspace of the channel interference matrix; Aligning the interference caused by the macro cell link into the column vector space other than the preset number of column vectors; Representing the channel interference matrix as a block structure, and determining the macro cell precoding matrix and the small cell precoding matrix.

5. The partial interference alignment method in a medium signal-to-noise ratio scenario according to claim 1, wherein Solving the receiver matrix corresponding to the transmitter precoding matrix based on the iterative method, including: Obtaining the total interference and the expected signal power received by the corresponding users based on the interference covariance and the signal covariance matrix of the macro cell and small cell users; When the power ratio of the useful signal and the interference signal at the receiver is the largest, the calculation of the receiver matrix is transformed into a trace ratio optimization problem; Performing iterative optimization to solve the trace ratio optimization problem based on the difference and eigenvalue decomposition, and determining the receiver matrices of the users in each cell.

6. The partial interference alignment method in a medium signal-to-noise ratio scenario according to claim 1, characterized in that, It also includes: Performing channel power allocation based on the channel condition to obtain the power allocation for each user; Obtaining the final allocation method by summing the power allocations of each user. When the ratio of the number of antennas of the macro cell and small cell base stations and users is fixed, performing the summation calculation based on the integral problem.

7. A partial interference alignment device in a medium signal-to-noise ratio scenario, characterized in that, Including: A model establishment module for establishing a downlink heterogeneous cellular network interference model; A first alignment module, configured to align small-cell interference and macro-cell interference at each small-cell receiver side according to a full interference alignment strategy and power constraints, and construct a small-cell precoding matrix and a macro-cell precoding matrix for full interference alignment based on spatial multiplexing of a predetermined user and interference alignment of other users; A second alignment module, configured to perform partial interference alignment based on transmitter power constraints, align useful signals and interference signals caused by macro-cells into two matrices respectively, and determine a macro-cell precoding matrix and a small-cell precoding matrix for partial interference alignment; A calculation module, configured to solve a receiver matrix corresponding to a transmitter precoding matrix based on an iterative method; The model establishment module specifically includes: determining an interference model in a downlink heterogeneous cellular network based on a macro-cell and a small-cell; representing received signals of users in the macro-cell and the small-cell based on a signal channel matrix and a channel interference matrix; and determining a power constraint condition in the model based on maximizing network capacity under medium signal-to-noise ratio.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the partial interference alignment method in the medium signal-to-noise ratio scenario according to any one of claims 1-6.

9. An electronic device, characterized in that, Including: A memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the partial interference alignment method in the medium signal-to-noise ratio scenario according to any one of claims 1-6 by executing the computer instructions.

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