Method for optimizing communication link transmission capacity and apparatus therefor

Abstract

The present invention relates to a method for optimizing communication link transmission capacity and an optimization device for the same, wherein the method determines a communication system type according to the inclusion location of a Dynamic Metasurface Antennas (DMA) included in at least one of a transmitter and a receiver, defines at least one DMA weight matrix corresponding to the determined communication system type, defines an optimization problem function for optimizing communication link transmission capacity using a constraint on transmission power and the defined DMA weight matrix, and calculates an optimization coefficient including at least one of a transmission precoder coefficient and an optimal DMA weight matrix reconstructed from the DMA weight matrix using the defined optimization problem function.

Description

Technology Field

[0001] The present invention relates to a method for optimizing communication link transmission capacity and an optimization device for the same. Background Technology

[0002] The content described in this section merely provides background information regarding the present embodiment and does not constitute prior art.

[0003] Dynamic Metasurface Antennas (hereinafter referred to as "DMA") are antenna technologies utilizing metamaterials, which are artificial materials designed to adjust their properties for more precise control of the characteristics and behavior of electromagnetic waves. They consist of multiple microstrips, each containing multiple reconfigurable metamaterial elements. Each metamaterial element can function as a transmitting and receiving antenna, and the entire unit is typically connected to the RF (Radio Frequency) chain of a baseband device through the input / output ports of each microstrip.

[0004] Meanwhile, P2P (Point-to-Point) Multiple Input Multiple Output (MIMO) systems are one of the most promising wireless communication systems, and their use cases are increasing with the recent surge in machine-to-machine communication. However, there have been no attempts to apply DMA to MIMO systems yet. The problem to be solved

[0005] The present invention is proposed to solve the aforementioned conventional problems, and aims to provide a method for optimizing communication link transmission capacity and an optimization device for the same that can maximize the transmission speed in a DMA-based P2P MIMO system equipped with a DMA instead of a conventional metal antenna.

[0006] However, the objectives of the present invention are not limited to the above objectives, and other unmentioned objectives will be clearly understood from the description below. means of solving the problem

[0007] A method for optimizing communication link transmission capacity in a communication system including a transmitter and a receiver according to an embodiment of the present invention for achieving the purpose described above may comprise: a step of determining a communication system type according to the inclusion location of a Dynamic Metasurface Antennas (DMA) included in at least one of the transmitter and the receiver; a step of defining at least one DMA weight matrix corresponding to the determined communication system type; a step of defining an optimization problem function for optimizing communication link transmission capacity using a constraint on transmission power and the defined DMA weight matrix; and a step of calculating an optimization coefficient including at least one of a transmission precoder coefficient and an optimal DMA weight matrix reconstructed from the DMA weight matrix using the defined optimization problem function.

[0008] At this time, the determined communication system type is any one of a TX-DMA system including DMA only in the transmitter, an RX-DMA system including DMA only in the receiver, or a TRX-DMA system including DMA in both the transmitter and the receiver, and the step of defining the DMA weight matrix may further consider the determined communication system type and the communication link direction of either the uplink or the downlink, thereby defining only the transmitter's DMA weight matrix, defining only the receiver's DMA weight matrix, or defining both the transmitter's DMA weight matrix and the receiver's DMA weight matrix.

[0009] At this time, the step of calculating the optimization coefficients can be calculated by alternately and repeatedly updating the optimal solutions of the transmission precoder coefficients and the optimal DMA weight matrix until the convergence condition of the defined optimization problem function is satisfied.

[0010] At this time, the transmission precoder coefficients can be calculated according to a closed-form solution based on Singular Value Decomposition (SVD).

[0011] In an optimization device for optimizing communication link transmission capacity in a communication system including a transmitter and a receiver according to an embodiment of the present invention for achieving the purpose described above, a communication system type is determined according to the inclusion location of a Dynamic Metasurface Antennas (DMA) included in at least one of the transmitter and the receiver, at least one DMA weight matrix is ​​defined corresponding to the determined communication system type, and an optimization problem function for optimizing communication link transmission capacity is defined using a constraint on transmission power and the defined DMA weight matrix, and then an optimization coefficient including at least one of a transmission precoder coefficient and an optimal DMA weight matrix reconstructed from the DMA weight matrix can be calculated using the defined optimization problem function. Effects of the invention

[0012] According to the communication link transmission capacity optimization method and optimization device for the same of the present invention, a problem function for optimizing communication link transmission capacity is defined by considering the type of communication system according to the inclusion location of the DMA, and the transmission precoder coefficients of the transmitter and the weight matrix of the DMA are optimized and calculated according to the defined optimization problem function and applied to the communication system, thereby enabling the performance of the P2P MIMO system to be maximized.

[0013] In addition, various effects other than those described above may be disclosed directly or implicitly in the detailed description according to the embodiments of the present invention to be described below. Brief explanation of the drawing

[0014] FIG. 1 is an illustrative diagram for explaining a DMA according to one embodiment of the present invention. FIG. 2 is an illustrative diagram for explaining a type of DMA communication system according to an embodiment of the present invention. FIG. 3 is a block diagram illustrating an optimization device according to one embodiment of the present invention. FIGS. 4 to 6 are exemplary diagrams illustrating an algorithm for an optimization process according to the type of communication system of the present invention. FIG. 7 is a flowchart illustrating a method for optimizing communication link transmission capacity according to an embodiment of the present invention. FIGS. 8 and FIGS. 9 are graphs illustrating the effects according to an embodiment of the present invention. Specific details for implementing the invention

[0015] Preferred embodiments that can be easily practiced by those skilled in the art to which the present invention pertains are described in detail below with reference to the attached drawings. However, in describing the operating principles of the preferred embodiments of the present invention in detail, if it is determined that a specific description of related known functions or configurations may unnecessarily obscure the essence of the present invention, such detailed description is omitted. This is intended to convey the core of the present invention more clearly without obscuring it by omitting unnecessary descriptions. Furthermore, since the present invention is susceptible to various modifications and may have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description; however, this is not intended to limit the present invention to specific embodiments, and it should be understood that it includes all modifications, equivalents, and substitutions that fall within the spirit and technical scope of the present invention.

[0016] Furthermore, the terms used in this specification are used merely to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. Additionally, terms such as “comprising” or “having” described in this specification are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0017] In addition, the present invention uses the following notation. First, scalars are indicated in lowercase italics, and vectors and matrices are indicated in lowercase and uppercase bold, respectively. and and represent the complex domain and the indicator function, respectively, and the scalar The size of It is represented as, and the phase (argument) of a complex number is It is expressed as. Matrix The element of the i-th row and j-th column is And, the determinant, trace, and inverse matrix are, respectively It can be expressed as.

[0018] Also, superscripts T, H, and * are used to represent the transpose, conjugate transpose, and complex conjugate, respectively, and vector About is the i-th element of the vector, means the Euclidean norm. The identity matrix is as, Is having as a diagonal element It is represented as a diagonal matrix, and is the modulus operator, means an operation that rounds up a given value to the nearest larger integer.

[0019] Hereinafter, a DMA according to an embodiment of the present invention will be described first.

[0020] FIG. 1 is an illustrative diagram for explaining a DMA according to an embodiment of the present invention. The DMA (10) is an antenna technology using a metamaterial, which is an artificial material designed to adjust its properties to more finely control the characteristics and operation of electromagnetic waves. It is implemented in the form of a planar array composed of a plurality of microstrips (11), each comprising radiating elements, i.e., metamaterial elements (12), arranged at intervals narrower than half a wavelength. At one end of each microstrip (11), there is an input / output port (Input / output Ports, 13) which is connected to an RF chain to input / output a transmission or reception signal, thereby allowing the number of RF chains to be naturally reduced while maintaining the same number of transmission elements.

[0021] Microstrips (11) have lossy characteristics, and as a signal passes through the microstrips (11), attenuation and phase change occur, so that it may reach each element (12) in a different form. For example, distance from the input port of the i-th microstrip during transmission The amount of attenuation experienced by a signal reaching element l located at is given by the following mathematical formula.

[0022] <Mathematical Formula 1>

[0023]

[0024] Here, and represents the attenuation coefficient and wavenumber of the i-th microstrip, respectively. The signal reaching each element can be further tuned by the configurable weights of that element. These elements operate as resonant electrical circuits, and their response is frequency-dependent. However, in a narrowband system, the response of the l-th element of the i-th microstrip can be approximated in a Lorentzian-constrained form.

[0025] <Mathematical Formula 2>

[0026]

[0027] Here, It represents an adjustable phase shift.

[0029] DMA Assuming it contains elements, here and and represent the number of microstrips and the number of radiation elements of each microstrip, respectively. The attenuation coefficients of all elements of the DMA can be collected by the diagonal matrix H as shown in the following mathematical formula.

[0030] <Mathematical Formula 3>

[0031]

[0032] The configurable weights are block diagonal matrices as shown in the mathematical formula below. It is aligned to.

[0033] <Mathematical Formula 4>

[0034]

[0035] Here, Igo am.

[0036] The present invention considers a DMA-based Point-to-Point (P2P) communication system and can analyze various DMA deployment settings in two communication directions: uplink and downlink. To this end, the DMA deployed in the transmitter in the present invention It has 1 microstrip, and each microstrip is There are dog elements, total It can have radiating elements. Similarly, the DMA placed in the receiver is It has receiving elements, where, The number of microstrips, represents the number of elements in each microstrip. The channel between the transmitter and the receiver is represented by G, which may have different dimensions depending on the direction of communication. G is primarily determined by the operating frequency and characteristics of the surrounding environment, and can be estimated using techniques specialized for DMA and then known to the transmitter.

[0037] In addition, in an environment using DMA as described above, the present invention aims to formulate an optimization problem for maximizing the transmission rate achievable by considering various DMA deployment scenarios, namely, cases where DMA is deployed only in the transmitter, cases where DMA is deployed only in the receiver, and cases where DMA is deployed in both the transmitter and the receiver.

[0039] 1) System with DMA deployed in the transmitter (TX-DMA system)

[0040] FIG. 2 is an exemplary diagram illustrating a type of DMA communication system according to an embodiment of the present invention. First, FIG. 2(a) is an exemplary diagram illustrating a TX-DMA system in which a DMA is placed in a transmitter. In the TX-DMA system, the base station (100), which is the transmitter, includes a DMA (10), and the user (200), which is the receiver, uses a standard metal antenna (20).

[0041] Looking at the downlink operation in this TX-DMA system environment, the base station (100), which is the transmitter, uses transmission power P to position at distance D To one user (200) with two traditional metal antennas (20) data streams Transmits. Data streams are independent of each other, and covariance It has. Here, is the expectation operator, and the transmitted symbol digital precorder to form It is a raw signal that must be precoded by.

[0042] Therefore, the signal received by the user (200) is equal to the following mathematical formula.

[0043] <Mathematical Formula 5>

[0044]

[0045] Here, , The mean is 0 and the variance is It refers to additive complex white Gaussian noise, and the damping coefficient matrix is defined by the aforementioned <Mathematical Equation 3>, and the weight matrix is defined by the aforementioned <Mathematical Formula 4>.

[0046] Accordingly, the capacity of the communication system of the present invention can be defined as shown in the following mathematical formula.

[0047] <Mathematical Formula 6>

[0048]

[0049] Here, is the transmit covariance matrix.

[0050] In the present invention, system capacity The goal is to find a transmit precoder F and a DMA adjustable weight matrix Q to maximize [the value]. The constraints are that the transmit power of the base station (BS) must not be exceeded, and all DMA weights must be within the Lorentzian region. The problem function for optimization under these conditions can be defined as shown in the following mathematical equation.

[0051] <Mathematical Formula 7>

[0052]

[0053] Here, the constraint (st) is and the above-mentioned <Mathematical Formula 2>.

[0054] Looking at the uplink operation in the TX-DMA system environment of the present invention, the uplink operation is such that the user (200) using the metal antenna is the transmitter, and the base station (100) using the DMA is the receiver. Due to the characteristics of the P2P (Point-to-Point) MIMO communication system of the present invention, this configuration can be performed in the same way as the downlink communication scenario described later, in which the base station (100) equipped with the metal antenna transmits a signal to the user (200) equipped with the DMA.

[0056] 2) System with DMA deployed in the receiver (RX-DMA system)

[0057] FIG. 2(b) is an example diagram illustrating an RX-DMA system in which a DMA is placed at a receiver. In the RX-DMA system, the base station (100), which is the transmitter, uses a metal antenna (20), and the user (200), which is the receiver, uses a DMA (10).

[0058] The downlink process of this RX-DMA system is, A base station (100) equipped with a metal antenna (20) To a user (200) using the power of a DMA located at distance D as a receiving antenna data streams Transmits. The data stream has the same characteristics as described above, and the digital precorder Transmitted symbols pre-encoded by It is converted to.

[0059] Afterwards, this symbol is a channel It is transmitted through. At the receiving end, all DMA elements collect the incident signal, apply the corresponding weights, and then transmit it to the corresponding RF chain through the attenuating microstrip. The received signal can be expressed by the following mathematical formula.

[0060] <Mathematical Formula 8>

[0061]

[0062] Here, Q and H are respectively and It has the dimension of, and therefore the capacity of this system can be defined as the following mathematical formula.

[0063] <Mathematical Formula 9>

[0064]

[0065] Similarly, while considering the Lorentz constraints of the base station (BS) transmit power and DMA weights, the goal is to maximize system capacity by finding a transmit precoder F and configurable weights F. The objective function for this goal, i.e., the problem function, can be expressed as shown in the following mathematical equation.

[0066] <Mathematical Formula 10>

[0067]

[0068] The constraint (st) is Wow, this is the aforementioned <Mathematical Formula 2>.

[0069] The uplink process of the RX-DMA system of the present invention involves a user (200), who is a transmitter, transmitting a signal using DMA (10), and a base station (100), which is a receiver, receiving the signal through a metal antenna (20).

[0071] 3) System with DMA deployed in transmitter and receiver (TRX-DMA system)

[0072] FIG. 2(c) is an example diagram illustrating a TRX-DMA system in which DMAs are deployed on both the transmitter and the receiver. In the TRX-DMA system, DMAs are installed on both the transmitter and the receiver, and to reflect this, indices t and r representing the transmitter and receiver are added to the configurable weight matrix Q and the attenuation coefficient matrix H of the DMA. Although the same optimization problem is derived regardless of the communication direction, the following description will focus on the downlink communication process. Additionally, adopting the same configuration as described above, the base station (100) Transmits data streams, and the user (200) receives the signal It is processed into DMA elements. As previously explained, this can be precoded to generate the following received signal.

[0073] <Mathematical Formula 11>

[0074]

[0075] Here, , , , and .... Consequently, the capacity of this system is equal to the following mathematical formula.

[0076] <Mathematical Formula 12>

[0077]

[0078] In the structures of Figures 2(a) and 2(b), only two optimizable parameters were addressed, whereas in the case of (c), the goal is to optimize three variables: the transmitting precoder F and the DMA weights in the transmitter. and DMA weights at the receiver System capacity including This is intended to satisfy power and Lorentz constraints while maximizing, and the objective (problem) function can be defined as shown in the following mathematical equation.

[0079] <Mathematical Formula 13>

[0080]

[0081] Here, the constraint (st) is and <Mathematical Formula 2>.

[0082] In the TRX-DMA system, both the transmitter and the receiver are equipped with DMA (10), and since this is a P2P (Point-to-Point) scenario, the uplink communication has a structure in which the transmitter uses DMA to transmit the signal and the receiver also uses DMA to receive the signal. That is, in the TRX-system, the downlink process and the uplink process can be performed identically.

[0083] Thus, the present invention is for optimizing the transmission capacity of a communication link in a communication system including a transmitter and a receiver, wherein the communication link can perform the optimization of the transmission capacity by considering an uplink and a downlink. At this time, as described above, the uplink process in a TX-DMA system and the downlink operation in an RX-DMA system can be performed identically, and the uplink process in an RX-DMA system and the downlink operation in a TX-DMA system can be performed identically.

[0084] Therefore, the optimization process described below can be divided into a downlink process in a TX-DMA system, a downlink process in an RX-DMA system, and an uplink / downlink process in a TRX-DMA system, and the problem functions for this can be defined as P(1), P(2), and P(3), respectively, as described above.

[0085] In addition, the problem function for optimization of the present invention is difficult to solve due to the deep interrelationships between optimizable parameters and the somewhat peculiar Lorentz constraint of DMA weights. However, the present invention derives the proposed algorithm by utilizing the unique structure and form exchangeability of matrices Q and H, thereby providing a closed-form solution for each optimizable parameter.

[0086] Before specifically describing the optimization process of the present invention, the optimization device for performing the optimization process of the present invention will be described with reference to FIG. 3.

[0087] FIG. 3 is a block diagram illustrating an optimization device according to one embodiment of the present invention.

[0088] Referring to FIG. 3, an optimization device (30) according to one embodiment of the present invention may be configured to include a memory (31) and a processor (32).

[0089] In one embodiment, the memory (31) may store information of any form generated or determined by the processor (32). The memory (31) of the present invention may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, a magnetic disk, and an optical disk. Additionally, the memory (31) of the present invention may be implemented in the form of web storage. The description of the memory described above is merely an example and is not limited thereto.

[0090] The processor (32) performs overall processing according to an embodiment of the present invention and may be composed of one or more cores and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU).

[0091] The processor (32) of the present invention can read a computer program stored in memory (31) and perform data processing according to one embodiment of the present invention. That is, the processor (32) of the present invention can determine a communication system type according to the inclusion location of a DMA included in at least one of a transmitter and a receiver, define at least one DMA weight matrix corresponding to the determined communication system type, define an optimization problem function for optimizing communication link transmission capacity using constraints on transmission power and the defined DMA weight matrix, and perform processing for the entire process of calculating an optimization coefficient including at least one of a transmission precoder coefficient and an optimal DMA weight matrix reconstructed from the DMA weight matrix using the defined optimization problem function.

[0092] The optimization device (30) including the processor (32) of the present invention may, for example, be a base station (BS) in FIG. 2. The base station (100) in this case is a terminal node of a network that communicates directly with the user (200), and may be replaced by terms such as fixed station, Node B, eNode B (eNB), and Access Point (AP). However, depending on the implementation method, the operation of the present invention may be performed by an upper node of the base station, or the user (200) may perform the operation as the optimization device (30).

[0093] Hereinafter, an optimization process according to one embodiment of the present invention will be described in more detail with reference to FIGS. 4 to 6.

[0094] FIGS. 4 to 6 are exemplary diagrams illustrating an algorithm for an optimization process according to the type of communication system of the present invention, illustrating an optimization algorithm for each of the defined problem functions P(1), P(2), and P(3) of the present invention. Since the optimizable variables in each of the three formalized problems of the problem functions P(1), P(2), and P(3) of the present invention are deeply interrelated, an Alternating Optimization (AO) method is adopted to perform a process of reaching convergence by iteratively optimizing each variable one at a time. The following description shows that by optimizing each variable individually, a closed-loop solution can be developed for the variables of each problem, and consequently, this can lead to an algorithm that exhibits high overall performance.

[0096] A. Proposed solution for problem P(1)

[0097] First, let's explain the optimization process of problem P(1).

[0098] To optimize problem P(1), the optimization process of F for a fixed Q is performed. Constraints of problem P(1) Since F is included in it, the optimization of F is more complex, but since F is not physically constrained, the constraints are temporary It can be replaced with. After optimizing the equivalent problem induced by this, the optimal precoder is scaled to the original power constraint It can satisfy this. According to this method, given Q, the optimization of F is transformed into a standard capacity maximization problem in P2P MIMO communication, and its optimal solution can be given by eigenmode transmission.

[0099] Specifically, if we apply truncated singular value decomposition (SVD) to the composite matrix GHQ is. Here, and are the truncated left and right unitary matrices, respectively, and is a truncated diagonal matrix, the diagonal elements of which are nonnegative and sorted in descending order. Also, the maximum number of data streams that a base station (BS) can transmit. And, through this, the optimal transmission covariance matrix can be defined by the following mathematical formula.

[0100] <Mathematical Formula 14>

[0101]

[0102] Here, represents the optimal power allocated to the i-th data stream through a water-filling policy, and in particular, the power allocation value of the i-th data stream can be given by the following mathematical formula.

[0103] <Mathematical Formula 15>

[0104]

[0105] Here, It is generally called the water level, and It is selected to satisfy the conditions. Also, Is It refers to the element located at the i-th row and j-th column of the matrix.

[0106] To explain the optimization process of Q for a fixed F, in order to optimize Q, the structure and form of the matrix H and Q in the aforementioned <Equation 5> can be transformed as shown in the following equation.

[0107] <Mathematical Formula 16>

[0108]

[0109] This transformation plays an important role in the optimization of Q, and through this, the diagonal matrix and block diagonal matrix The positions of can be swapped. Therefore, <Equation 5> can be transformed into the following equation.

[0110] <Mathematical Formula 17>

[0111]

[0112] Through this, system capacity It can be defined as shown in the following mathematical formula.

[0113] <Mathematical Formula 18>

[0114]

[0115] since, If expressed in the form of eigenvalue decomposition (EVD), that is and, here is a matrix It is based on the fact that it is a unitary matrix, and this This is because it has positive semi-definiteness. Is It is a diagonal matrix containing the eigenvalues ​​of, and the corresponding eigenvectors are Another decomposition of Q, which is collected in and is essential for further simplifying the aforementioned <Equation 18>, applies the Lorenz constraint partitioning technique as shown in the following equation.

[0116] <Mathematical Formula 19>

[0117]

[0118] Here, And, ..., and substituting this into <Mathematical Equation 18>, If set as such, the following mathematical formula can be derived.

[0119] <Mathematical Formula 20>

[0120]

[0121] In addition, to simplify the notation Defined as such, and expressing GET as the sum of the column and row products of the included matrices, i.e. It is expressed as. Here, represents the i-th column of matrix G, and represents the i-th row of matrix T. Through this, the aforementioned <Equation 20> can be redefined as the following equation.

[0122] <Mathematical Formula 21>

[0123]

[0124] After extracting the optimizable weights of the DMA as individual terms from the original matrix form using the aforementioned <Mathematical Equation 21>, in order to solve joint optimization The problem can be solved by optimizing only the specific n-th weight while keeping the other weights fixed. The objective function for this can be defined as shown in the following mathematical equation.

[0125] <Mathematical Formula 22>

[0126]

[0127]

[0128] Here, and (b) is It is due to. Set to, and If set to , (b) of the above-described <Mathematical Formula 22> can be expressed as follows.

[0129] <Mathematical Formula 23>

[0130]

[0131] procession is positive definite and is a full-rank matrix. On the other hand, is a strict rank-one matrix, which is and rank when this is not 0 This is because it is 1. These properties play an important role in the problem optimization process, and first, Since this is a full-rank matrix, its inverse exists, so the above-mentioned <Equation 23> It can be calculated. Also, the second term is the optimization variable Since it is a constant independent of, it can be ignored. Consequently, the optimization problem can be defined as shown in the following mathematical formula.

[0132] <Mathematical Formula 24>

[0133]

[0134] The constraint (st) at this time is am.

[0135] In addition, the present invention relates to a matrix and We will utilize several additional properties of, first Since this is a positive definite matrix, the objective function The third term of is is. Also, in the form of Eigenvalue Decomposition (EVD), i.e. It can be expressed as, and at this time, of silver It is a matrix of dimension, and is a single non-zero eigenvalue ( It is a diagonal matrix with ) and this rank( This is because )=1. By applying this transformation It can be re-expressed in a new form such as the following mathematical formula.

[0136] <Mathematical Formula 25>

[0137]

[0138]

[0139] Here, Is From the definition of, it can be seen that it is a Hermitian matrix. is a matrix It is the n-th column of, and is a matrix It is the n-th row of, and Considering that this diagonal contains only one non-zero element, the above-mentioned <Mathematical Formula 25> It can be defined as shown in the following mathematical formula.

[0140] <Mathematical Formula 26>

[0141]

[0142] Next, an arbitrary matrix and for and If given, the above-mentioned <Mathematical Formula 26> It can be simplified as shown in the following mathematical formula.

[0143] <Mathematical Formula 27>

[0144]

[0145]

[0146]

[0147]

[0148] Here, is a complex number Represents the real part (real park) of, where (c1) and (c2) are respectively and It is due to.

[0149] Since the logarithmic function is a monotonically increasing function, the problem P(1-n) of <Equation 24> with a simplified objective function (c2) has the same constraints ( Under It is identical to the problem of maximizing, and the optimal solution can be given in the following proposition 1.

[0150] proposition1: If This unit rank matrix If is the unique non-zero eigenvalue, the optimal solution to problem P(1-n) is given by the following mathematical formula.

[0151] <Mathematical Formula 28>

[0152]

[0153] If you prove this, Considering , in order to maximize this, After differentiating with respect to , equal to 0, that is Set to. If you solve it, We obtain , and therefore the optimal solution is as described in <Equation 28> above.

[0154] Nevertheless, matrix This may not always be diagonalizable, and in such cases It occurs when, and in such cases All that satisfy is the optimal solution to problem P(1-n). Consequently, the general optimal solution to problem P(1-n) is given by the following mathematical formula.

[0155] <Mathematical Formula 29>

[0156]

[0157] FIG. 4 is an algorithm for the entire process of finding the optimal solution for each optimizable parameter of problem P(1) in the TX-DMA system of the present invention as described above, and the algorithm can achieve the best performance by utilizing individual optimization update strategies. When channel information, attenuation coefficients of each DMA element, and transmit and noise power are input, this algorithm randomly initializes the weights of each DMA element based on the above-described <Equation 2>, iteratively optimizes the DMA weights using the transmit precoder and alternating optimization (AO) method, and when convergence is reached, outputs the optimized transmit covariance matrix and the reconstructed DMA weight matrix.

[0158] The dominant computational complexity of the TX DMA algorithm is P(1). and To calculate each, = and having It is expressed as. And represents the number of iterations of the outer AO loop.

[0160] B. Proposed solution for problem P(2)

[0161] Below, we will explain the optimization process of problem P(2).

[0162] If we look at problems P(1) and P(2), the difference between these two problems is matrices It is the shape of and the change in receiver noise statistics in <Equation 8>. As a result, an inverse matrix term called the noise covariance matrix is ​​added in <Equation 9>, and thus, the DMA weight matrix Optimization can be complicated. To solve this problem, the present invention, as described below, and To find the optimal solution for alternately, an alternating optimization (AO) technique is applied, similar to problem P(1).

[0163] First, F optimization is performed for a fixed Q. When Q is fixed, the transmission precoder F optimization process is similar to that described earlier, but the matrix for performing Singular Value Decomposition (SVD) and the transmission power constraints are different. Specifically, we can express the inverse matrix term appearing in the objective function of problem P(2) as shown in the following equation using Eigenvalue Decomposition (EVD).

[0164] <Mathematical Formula 30>

[0165]

[0166] Here, and are each and It has the same properties as, and also If you apply the same logic as... ... holds, and now, by substituting this eigenvalue decomposition (EVD) into Equation 9> and applying the standard mathematical transformation of the determinant, It can be expressed in the form of the following mathematical formula.

[0167] <Mathematical Formula 31>

[0168]

[0169] This ultimately means that the Singular Value Decomposition (SVD) that must be performed to find the optimal transmitting precoder is This means that it must be done for the composite matrix of. In the following part, Q is optimized and It is adjusted to satisfy the conditions, and thus the eigenvalue decomposition (EVD) of the inverse matrix term of the above-described <Equation 30> is required only when Q is randomly initialized in the first iteration of the algorithm. Nevertheless, it is not required in subsequent iterations.

[0170] To explain the optimization of Q for a fixed F, when F is fixed, the problem of finding Q that maximizes P(2) is similar to the problem of finding an analog combiner in a hybrid MIMO system. Therefore, in the present invention, the left unitary matrix is ​​selected from the singular value decomposition (SVD) of the effective channel. That is, Q is sought such that QH approximates the left unitary matrix from the SVD of channel G in terms of the Frobenius norm, and there is a possibility of deriving a suboptimal solution of Q due to the matrix shape of Q and the strong coupling between Q and H.

[0171] To solve this problem, the present invention utilizes the property that the optimal left unitary matrix always transforms the inverse term of <Equation 9> into the identity matrix. Design an O matrix that satisfies [the equation]. In this case, thanks to the structure of Q and H, the form of O is the identity matrix. It is identical to, and therefore, the number of variables to be considered in the optimization process It can be simplified into diagonal elements. In other words, find Q and set the matrix O such that all diagonal elements are 1 as in the following mathematical formula.

[0172] <Mathematical Formula 32>

[0173]

[0174] Here, and and represent the n-th configurable weight and attenuation coefficient of the m-th microstrip, respectively; to simplify notation, when optimizing elements of a specific microstrip, the index of that microstrip is omitted, i.e. and In addition, the present invention applies a Lorentzian constraint partitioning technique, through which <Equation 32> for a specific microstrip can be simplified by transforming it as follows.

[0175] <Mathematical Formula 33>

[0176]

[0177] Here, represents the imaginary part of the complex number x, and is a vector containing attenuation constants for all elements of the m-th microstrip. Of the aforementioned <Equation 33> at, Due to the coupling between them, it is difficult to solve this problem using a joint optimization method. Therefore, the present invention adopts a method of obtaining the solution for each individual weight in a closed form, i.e., a closed-form solution, by applying an alternating optimization (AO) technique; specifically, a specific When optimizing, the following mathematical formula is used.

[0178] <Mathematical Formula 34>

[0179]

[0180]

[0181] According to trigonometric rules, the above-mentioned <Equation 34> It provides two solutions, and among them, the solution that maximizes the system capacity must be selected. Each Once a closed-loop solution for is derived, the design of the optimization technique of the present invention is completed. This technique is a method for solving RX-DMA, a system in which a DMA is deployed at a receiver, and Algorithm 2 for this purpose is exemplified in FIG. 5. Algorithm 2 uses the same input values ​​as Algorithm 1 in FIG. 4 and randomly generates the DMA weights of the receiver to form the matrix required to optimize the transmit covariance matrix. Constitutes.

[0182] After that, the receiver's DMA weights are iteratively adjusted using the Alternating Optimization (AO) method, and when the overall algorithm reaches convergence, the optimized transmit covariance matrix and DMA weights are output.

[0183] The mathematical expression specified as 34b in FIG. 5 is of <Mathematical Equation 34> And, the mathematical expression specified as 33a is of <Mathematical Equation 33> am.

[0184] In addition, the total computational complexity of Algorithm 2 It causes. Here, is of P(2) It is the complexity of calculating, and is the number of iterations that occurred in steps 6 to 12 of Algorithm 2.

[0186] C. Proposed solution for problem P(3)

[0187] Below, the optimization process of problem P(3) will be explained. Unlike problems P(1) and P(2), problem P(3) includes three optimizable parameters, and the constraint of <Equation 13> With the addition of this, the process of designing the optimal solution becomes more complex. Despite this complexity, a method to find a solution to problem P(3) can be explored using the procedure described above. As with problems P(1) and P(2), the Alternating Optimization (AO) technique is used to optimize one parameter at a time, while the remaining variables are fixed in their previously updated state.

[0188] Fixed first during the subsequent process and To explain the optimization process of F for , since F is coupled with other variables, the existing strategy replacing the transmit power constraint is applied as is, and the composite matrix as described above Constituting, here, and silver , in other words It is derived from the eigenvalue decomposition (EVD) of the noise covariance matrix. Matrix By performing Singular Value Decomposition (SVD) on , the optimal as in <Equation 14> You can find it. Also, Q , H If substituted, the explanation related to <Equation 31> can be applied in the same way.

[0189] Fixed and for F The optimization process of is, F and If it is fixed, The subproblem of finding is similar to what was explained earlier, and therefore, Q , H By replacing it with, steps 5 through 13 of Algorithm 2 can be used directly for Q optimization.

[0190] Fixed and for F The optimization process of is the optimal To find, It can be redefined as shown in the following mathematical formula.

[0191] <Mathematical Formula 35>

[0192]

[0193] The second log term of the aforementioned <Mathematical Equation 35> is the optimization variable Since it is a constant independent of, it can be omitted in subsequent processes. Next, and By applying a method that exchanges the shape and position, an equivalent expression as shown in the following mathematical formula can be obtained.

[0194] <Mathematical Formula 36>

[0195]

[0196] Here, Is It is a diagonal matrix of, and Is It is a block diagonal matrix of size, and its elements are determined by <Equation 16>. In addition, as described above By optimizing The goal is to set it up to satisfy the conditions. Accordingly, <Equation 36> can be further simplified as shown in the following equation.

[0197] <Mathematical Formula 37>

[0198]

[0199] Here, am.

[0200] is now of <Equation 18> It can be confirmed that it has a form similar to that, and therefore, by applying the procedure to optimize Q similar to the process explained earlier It can be optimized.

[0201] The procedure for solving problem P(3) is sequentially organized in Algorithm 3, which operates based on a system using DMA at both the transmitter and the receiver, and iteratively applies proposition 1 to find the transmit DMA weights and before applying Algorithm 2 to find the covariance matrix and the receive DMA weights and It can be randomly initialized.

[0202] In this process, the worst-case computational complexity is And, and Each of P(3) and It is the complexity for calculating.

[0203] The optimization process according to the embodiments of the present invention has been described above.

[0204] With reference to the flowchart of FIG. 7, the method for optimizing communication link transmission capacity performed in the optimization device (30) illustrated in FIG. 3 according to an embodiment of the present invention is described as follows: the optimization device (30) of the present invention determines the type of communication system according to the location of DMA inclusion (S100). That is, the optimization device (30) of the present invention can determine the type of communication system as any one of a TX-DMA system that includes DMA only in the transmitter, an RX-DMA system that includes DMA only in the receiver, or a TRX-DMA system that includes DMA in both the transmitter and the receiver.

[0205] And, the optimization device (30) of the present invention defines an MA weight matrix corresponding to the determined communication system type (S110). That is, in the case of a TX-DMA system, a DMA weight matrix for optimizing the transmitter DMA is defined, in the case of an RX-DMA system, a DMA weight matrix for optimizing the receiver DMA is defined, and in the case of a TRX-DMA system, a transmitter DMA weight matrix and a receiver DMA weight matrix for optimizing the transmitter DMA and receiver DMA, respectively are defined.

[0206] Subsequently, the optimization device (30) of the present invention defines optimization problem functions P(1), P(2) and P(3) by considering the defined DMA weight matrix and constraints on the transmission power (S120), and calculates optimization coefficients using the optimization problem functions (S130). At this time, the optimization coefficients include the transmission precoder coefficients and the optimal DMA weight matrix reconstructed from the DMA weight matrix, and the optimization coefficients can be calculated by optimizing alternately according to the AO method.

[0207] Referring to FIGS. 8 and 9 regarding the performance evaluation of the present invention through the communication link transmission capacity optimization method according to the embodiment of the present invention, it can be confirmed that performance increases as the number of microstrips of the transmitter including DMA increases, thereby reducing hardware complexity and lowering power consumption.

[0208] The method for optimizing communication link transmission capacity according to an embodiment of the present invention has been described above.

[0209] The communication link transmission capacity optimization method of the present invention as described above may be provided in the form of a computer-readable medium suitable for storing computer program instructions and data.

[0210] Such computer-readable recording media may include program instructions, data files, data structures, etc., either individually or in combination, and include all types of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks); magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM (Read Only Memory), RAM (Random Access Memory), and flash memory.

[0211] In addition, computer-readable recording media are distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner. Furthermore, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers skilled in the art to which the present invention pertains.

[0212] Although preferred embodiments illustrating the technical concept of the present invention have been described and illustrated above, the present invention is not limited to the configuration and operation as illustrated and described, and those skilled in the art will understand that numerous changes and modifications can be made to the present invention without departing from the scope of the technical concept. Accordingly, all such appropriate changes and modifications and equivalents should be considered to be within the scope of the present invention. Explanation of the symbols

[0213] 10: DMA 20: Metal antenna 30: Optimizer 31: Memory 32: Processor 100: Transmitter 200: Receiver

Claims

Claim 1 A method for optimizing communication link transmission capacity in a communication system including a transmitter and a receiver, comprising: a step of determining a communication system type according to the inclusion location of a Dynamic Metasurface Antennas (DMA) included in at least one of the transmitter and the receiver; a step of defining at least one DMA weight matrix corresponding to the determined communication system type; a step of defining an optimization problem function for optimizing communication link transmission capacity using a constraint on transmission power and the defined DMA weight matrix; and a step of calculating an optimization coefficient including at least one of a transmission precoder coefficient and an optimal DMA weight matrix reconstructed from the DMA weight matrix using the defined optimization problem function. Claim 2 A method for optimizing communication link transmission capacity according to claim 1, wherein the determined communication system type is any one of a TX-DMA system including DMA only in the transmitter, an RX-DMA system including DMA only in the receiver, and a TRX-DMA system including DMA in both the transmitter and the receiver, and the step of defining the DMA weight matrix is ​​characterized by further considering the determined communication system type and the communication link direction of either the uplink or the downlink, defining only the transmitter's DMA weight matrix, defining only the receiver's DMA weight matrix, or defining both the transmitter's DMA weight matrix and the receiver's DMA weight matrix. Claim 3 A communication link transmission capacity optimization method according to claim 1, wherein the step of calculating the optimization coefficients is characterized by calculating the optimal solutions of the transmission precoder coefficients and the optimal DMA weight matrix by alternately updating them until the convergence condition of the defined optimization problem function is satisfied. Claim 4 A method for optimizing communication link transmission capacity according to claim 1, wherein the transmission precoder coefficients are calculated according to a closed-form solution based on Singular Value Decomposition (SVD). Claim 5 An optimization device for optimizing communication link transmission capacity in a communication system including a transmitter and a receiver, characterized by determining a communication system type based on the inclusion location of a Dynamic Metasurface Antennas (DMA) included in at least one of the transmitter and the receiver, defining at least one DMA weight matrix corresponding to the determined communication system type, defining an optimization problem function for optimizing communication link transmission capacity using a constraint on transmission power and the defined DMA weight matrix, and calculating an optimization coefficient including at least one of a transmission precoder coefficient and an optimal DMA weight matrix reconstructed from the DMA weight matrix using the defined optimization problem function.

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