Hybrid far and near field communication beam and power joint optimization method based on rate splitting

Abstract

The invention discloses a hybrid far and near field communication beam and power joint optimization method based on rate splitting, and relates to the technical field of wireless communication and novel multiple access. The designed hybrid near-far field communication beam and power joint optimization method comprises the following four steps: S1, establishing a far-field user and a near-field user channel model in a hybrid near-far field communication scene; s2, splitting data of near-field users into public information streams and private information streams by using a rate splitting multiple access technology; s3, pre-configuring beams of the public information flow and the private information flow of the near-field user by using a zero-forcing algorithm, so that the far-field user occupies the beams of the near-field user to carry out information transmission; and S4, establishing an optimization problem by taking maximization of the reachable sum rate as a target, and designing an iterative optimization algorithm to jointly process beam allocation of the near-field users and transmission power allocation variables of the near-field users and the far-field users and the near-field users. According to the invention, the throughput and connectivity of the communication network can be obviously improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of wireless communication and new type of multiple access technology, and particularly relates to a hybrid far-near field communication beam and power joint optimization method based on rate splitting. BACKGROUND

[0002] Future wireless communication networks will use super large antenna arrays and super high spectrum to support the huge throughput and connectivity requirements of the network. However, the increase of the working frequency of the wireless network and the aperture of the super large antenna array expands the coverage of the near field area of the network, resulting in that the plane wave model assumed in the original far field research no longer holds, and the electromagnetic wave turns into spherical wave propagation. The spherical wave introduces the distance dimension information, so that it can focus the beam energy at a specific spatial location, that is, not only focus by angle, but also focus along a specific depth of the propagation direction. The beam focusing provides higher spatial degrees of freedom and channel capacity for the network. However, due to factors such as channel estimation error, the spatial beam may not be perfectly focused, and energy leakage is extremely easy to cause.

[0003] Recent research results show that the pre-configured beam of the near field user in the network, although cannot be perfectly focused, can still serve additional near field or far field users to enhance the throughput and connectivity of the network. The related research mainly uses the space division multiple access (SDMA) technology to manage the interference of the near field users, and then uses the non-orthogonal multiple access (NOMA) technology to manage the interference between the near field users and the additional users. SDMA decodes the target information stream directly when scheduling multiple users at the same time, and all non-target information streams are regarded as interference. Excessive interference will cause the transmission rate to be saturated and cannot meet the high rate transmission requirements of the user. NOMA uses power multiplexing technology to serve multiple users at the same time at the sending end, and then uses the successive interference cancellation (SIC) technology to completely decode all strong interference at the receiving end. Therefore, the interference management methods of SDMA and NOMA are complete non-decoding interference and complete decoding interference respectively, and do not have flexibility. Therefore, scholars have proposed a rate splitting multiple access technology. The rate splitting multiple access technology can decode part of the interference while regarding part of the interference as noise, and has great flexibility in interference management. In addition, by adjusting the proportion of information splitting, the rate splitting multiple access technology can bridge SDMA and NOMA. However, how to use the rate splitting multiple access technology and the pre-configured beam to enhance the connectivity and throughput of the hybrid far-near field network has not been studied.

[0004] Current research status at home and abroad:

[0005] In 2023, Zhiguo Ding et al. published "NOMA-Based Coexistence of Near-Field and Far-Field Massive MIMO Communications" in IEEE Wireless Communications Letters, which utilized pre-configured beams to enhance the throughput and connectivity of hybrid near-far field networks, but this research managed inter-user interference using NOMA, resulting in a small throughput gain for the network.

[0006] In 2024, Chenguang Rao et al. published "A General Analytical Framework for the Resolution of Near-Field Beamforming" in IEEE Communications Letters, which analyzed the closed-form expression of the spatial resolution of near-field communication networks and designed precoding based on this to enhance the transmission performance of the network.

[0007] In 2024, Guangyuan Zheng et al. published "Joint Hybrid Precoding and Rate Allocation for RSMA in Near-Field and Far-Field Massive MIMO Communications" in IEEE Wireless Communications Letters, which utilized rate split multiple access technology to manage inter-user interference in hybrid near-far field networks to enhance and rate, but did not study how to utilize pre-configured beams to support more users to enhance the connectivity of the network.

[0008] The above three studies on near-field communication networks do not consider how to simultaneously utilize rate split multiple access technology and pre-configured beams to enhance the throughput and connectivity of the network. There is currently no related research on hybrid near-far field networks. SUMMARY

[0009] The technical problem to be solved by the present application is to provide a hybrid near-far field communication beam and power joint optimization method based on rate splitting to solve the problems of the background art, which can significantly improve the throughput and connectivity of the communication network.

[0010] The present application adopts the following technical solutions to solve the above technical problems:

[0011] The hybrid near-far field communication beam and power joint optimization method based on rate splitting specifically includes the following steps:

[0012] Step S1, a channel model of a far-field user and a near-field user in a mixed far-near field communication scenario is established;

[0013] Step S2, data of the near-field user is split into a common information stream and a private information stream using a rate splitting multiple access technology;

[0014] Step S3, beams of the common information stream and the private information stream of the near-field user are pre-configured using a zero-forcing algorithm, so that the far-field user occupies the beams of the near-field user to transmit information;

[0015] Step S4, an optimization problem is established with the target of maximizing a sum rate, and an iterative optimization algorithm is designed to jointly process beam allocation of the near-field user and transmission power allocation variables of the far-near field users.

[0016] Compared with the prior art, the above technical scheme has the following technical effects:

[0017] The application discloses a mixed far-near field communication beam and power joint optimization method based on rate splitting, a channel model of a far-field user and a near-field user in a mixed far-near field communication scenario is established; data of the near-field user is split into a common information stream and a private information stream using a rate splitting multiple access technology; beams of the common information stream and the private information stream of the near-field user are pre-configured using a zero-forcing algorithm, so that the far-field user occupies the beams of the near-field user to transmit information; an optimization problem is established with the target of maximizing a sum rate, and an iterative optimization algorithm is designed to jointly process beam allocation of the near-field user and transmission power allocation variables of the far-near field users; the application can significantly improve the throughput and connectivity of a communication network. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, without creative labor, other drawings can also be obtained from these drawings.

[0019] Figure 1 It is a mixed far-near field communication network diagram based on the rate splitting of the present application;

[0020] Figure 2 It is a diagram simulating the relationship between the sum rate of the additional user and the minimum rate requirement of the near-field user of the present application;

[0021] Figure 3 It is a diagram simulating the relationship between the sum rate of the additional user and the number of near-field users of the present application;

[0022] Figure 4This invention simulates the relationship between the sum rate of additional users and the number of iterations. Detailed Implementation

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings:

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0025] This invention discloses a method for joint optimization of beam and power in hybrid far-field and near-field communication based on rate splitting, relating to the fields of wireless communication and novel multiple access technologies. The designed method comprises four steps: S1, establishing channel models for far-field and near-field users in a hybrid far-field and near-field communication scenario; S2, using rate splitting multiple access technology to split the data of near-field users into public and private information streams; S3, using a zero-forcing algorithm to pre-configure the beams of the public and private information streams of near-field users, so that far-field users can occupy the beams of near-field users for information transmission; S4, establishing an optimization problem with the goal of maximizing reachability and rate, and designing an iterative optimization algorithm to jointly handle the beam allocation of near-field users and the transmission power allocation variables of far-field and near-field users. This invention can significantly improve the throughput and connectivity of communication networks.

[0026] like Figure 1 The diagram illustrates a hybrid far-field and near-field communication network based on rate splitting. The designed network comprises a base station equipped with N antennas and K near-field users equipped with single antennas. Let be any integer. This invention aims to enhance network throughput and connectivity by utilizing pre-configured beams for near-field users to serve additional near-field or far-field users. If the base stations in the network employ a uniform linear array with an antenna aperture of size d, then the boundary value between the near-field and far-field regions is . Where D = (N-1)d and λ are the antenna array aperture and the carrier wavelength, respectively.

[0027] Assuming the base station's transmission antennas are placed along the positive y-axis, and the center coordinates of the uniform linear array are (0, 0), a two-dimensional coordinate system can be established based on the center position and deployment direction of the transmission antennas. According to this established two-dimensional coordinate system, the coordinates of the nth antenna are... in The distance and angle to the k-th user are denoted as d. k and θ k The user's distance and angle are defined with reference to the origin of the coordinate system, and the corresponding coordinates are d. k =(d k cosθ k d k sinθ k Based on the coordinates of the antenna and the near-field user, the distance between the nth antenna and the kth near-field user can be obtained as follows:

[0028]

[0029] Based on the spherical wave channel model, the channel between the base station and the k-th near-field user... It can be modeled as:

[0030]

[0031] Where a(d) k θ k ) is the near-field array steering vector. This is the path loss, where f and c are the carrier frequency and the speed of light, respectively. When D << d k At that time, the near-field array guiding vector a(d) k θ k This is transformed into a far-field array steering vector. A first-order Taylor expansion is then used. k，n Far-field channel vector It can be represented as:

[0032]

[0033] in These are the coordinates of the far-field user relative to the origin of the coordinate system. Therefore, the channel between the base station and the m-th additional user... It can be modeled as:

[0034]

[0035] The parameter Z represents the boundary value between the near-field and far-field regions, d m β represents the distance between the m-th additional user and the origin; m and Represents the path loss for the m-th additional user, if d m If Z is less than or equal to Z, then the path loss is β. m Otherwise take a(d m θ m )and The array response vector representing the m-th additional user, if d m If Z is less than or equal to Z, then the array response vector takes the value a(d). m θ m Otherwise, take The m-th additional user could be either a near-field user or a far-field user, depending on d. m Determine the size relationship between d and Z; if d m If the value is less than or equal to Z, the user is a near-field user; otherwise, the user is a far-field user.

[0036] Based on the encoding and decoding principle of downlink rate split multiple access (RDBMI), the information of the k-th near-field user is split into a common part and a private part; then all the common parts are encoded into a common information stream x0; the private part of the k-th near-field user is encoded into a private information stream x0. k Subsequently, these independent information flows were and Linear precoding is performed. This invention employs zero-forcing precoding for private information streams, i.e., [p1, ..., p...]. K ]=H(HH H ) -1 F, where H = [h1, ..., h K ]and Since the public information stream depends on the decoding rate of the worst user, p0 is set to p0 = h. k′ ||h k′ || -1 ,in If the m-th additional user occupies the beam of the k-th near-field user, then set the binary variable to b. k，m =1, otherwise b k，m =0; binary b k，m The variables are designed to determine the correspondence between beams and additional users, specifically which beam the m-th additional user occupies; based on the above settings, the signal transmitted by the base station is represented as:

[0037]

[0038] Among them, P k and P k，m These are the transmission power allocation variables for near-field users and additional users; This is the information required by the m-th additional user, where M is the total number of additional users. According to equation (5), the signal received by the k-th near-field user can be obtained. in It is additive white Gaussian noise; the k-th near-field user first treats the private information stream as interference to decode the public information stream, then the signal-to-interference-plus-noise ratio (SNR) of the k-th near-field user decoding the public information stream is:

[0039]

[0040] wherein, P0 represents the transmission power allocated by the base station for the common information stream; b 0，m is a binary variable indicating whether the mth extra user occupies the beam of the common information stream, b 0，m = 1 if the mth extra user occupies the beam of the common information stream, otherwise b 0，m = 0. P 0，m and P k，m represent the transmission power allocated by the base station for the mth extra user on the beam of the common information stream and the beam of the kth near-field user private information stream, respectively; to ensure that all near-field users can successfully decode the common information stream, it can be obtained that Meanwhile, since all near-field users share the common information stream, it can be obtained that wherein R k，c is the common rate allocated for the kth near-field user; after removing the interference of the common information stream by using the serial interference cancellation, the near-field user starts to decode the private information stream, and the signal-to-interference-and-noise ratio thereof is:

[0041]

[0042] In combination with equations (6) and (7), the transmission rate R k of the kth near-field user can be obtained as: k，c + log (1 + γ k，p ).

[0043] Similarly, the signal received by the mth extra user can be expressed as: wherein is additive white Gaussian noise. Assuming that the mth extra user occupies the beam of the kth near-field user, the signal-to-interference-and-noise ratio of the mth extra user decoding the information stream is:

[0044]

[0045] wherein Therefore, the transmission rate of the mth extra user is:

[0046] The main objective of the present application is to maximize the sum rate of the extra users under the premise of guaranteeing the quality of service requirements of the near-field users, so as to enhance the throughput and connectivity of the network, and therefore the optimization problem can be modeled as:

[0047]

[0048] wherein P k，m represents the transmission power allocated by the base station for the mth extra user on the beam of the kth near-field user private information stream; Rk is the transmission rate of the kth near-field user; P max and R th are the maximum transmission power threshold of the base station and the quality of service requirement threshold of the near-field user, respectively.(9b) and (9c) are the transmission power of the base station and the quality of service constraint of the near-field user, respectively;(9d) and (9e) are the common rate allocation constraints of the rate splitting multiple access technology;(9f) limits that each beam can only accept one additional user.

[0049] Beam and power joint optimization algorithm design: The optimization problem (9) modeled has discreteness and the constraint condition is highly non-convex, and it is difficult to directly obtain the optimal beam and power allocation. To solve the optimization problem (9), the present application splits it into two sub-problems, namely a beam allocation sub-problem and a power and common rate allocation sub-problem; and then designs an iterative optimization algorithm to solve the two sub-problems respectively.

[0050] The solving steps of the beam allocation iterative optimization algorithm are as follows:

[0051] Step 1, Lemma 1 is proposed to simplify the optimization problem. Lemma 1 is as follows:

[0052] Lemma 1: The transmission rate of the near-field user is not affected by the beam allocation, that is, the rate of the near-field user is independent of the beam allocation; if g k′，m > g k，m , the mth additional user prefers the beam of the k' th near-field user to the beam of the kth user, and vice versa.

[0053] Proof: Equations (6) and (7) show that the signal-to-interference-and-noise ratio of the near-field user depends on the power allocation, but is independent of the beam allocation when the energy allocation is fixed. Similarly, equation (8) shows that the transmission rate of the additional user increases with the increase of g k，m . Therefore, to enhance the throughput of the network, the mth additional user prefers the beam of the k' th near-field user, where In combination with the above conclusion, it can be concluded that Lemma 1 is established.

[0054] Lemma 1 provides the criteria for beam allocation, but multiple additional users may expect to occupy the same beam. To reduce the performance loss of part of the beams, the beam allocation sub-problem is simplified as:

[0055]

[0056] Step 2, first introduce the matrix where Then an accelerated binary search algorithm is proposed to solve the optimization problem (10). The search interval of the algorithm is [α min , α max ], where and Given a threshold ∈ th , the proposed algorithm first determines whether it can satisfy for each beam g k，m ≥ ∈ th , then updates the given threshold by using the bisection search method according to the result. For the convenience of describing the algorithm process, the present application introduces the following two definitions:

[0057] Definition 1: If g k，m ≥ ∈ th , it is called that k is a feasible beam of the mth extra user, and m is the optional extra user of the kth near-field user beam. The total number of feasible beams of the mth extra user is called degree, denoted as deg(m).

[0058] Definition 2: If the following conditions are met, the scheduling priority of the mth extra user is higher than m'.

[0059] Condition 1) deg(m) < deg(m') or

[0060] Condition 2) deg(m) = deg(m') and m < m',

[0061] Wherein, condition 2) is to avoid two extra users having the same degree. Similarly, deg(k) and the corresponding priority can be defined. The algorithm process for solving the beam allocation is as follows:

[0062] 1) Calculate the matrix G, initialize α min and α max , set the iteration index i = 1, the maximum tolerance error ∈ th and ∈ (1) - ∈ (0) > ∈ th ;

[0063] 2) While | ∈ (i) - ∈ (i-1) | > ∈ th , execute

[0064] 3) Set ∈ (i) = 0.5(α min + α max ), and g = 0 M ;

[0065] 4) Set g k，m = 0 for ;

[0066] 5) While deg(m) ≠ 0, wherein execute

[0067] 6) Find the extra user m with the highest priority and find the near-field user k with the highest priority from its neighbor nodes;

[0068] 7) Update g(j) = g k，m , j = j + 1;

[0069] 8) Update g x，y = 0, where x = k or y = m;

[0070] 9) End while;

[0071] 10) Update i = i + 1;

[0072] 11) If j = M

[0073] 12) Set g k，m = 0, where

[0074] 13) Update a min = min(g) ≥ ∈ (i-1) ;

[0075] 14) else;

[0076] 15) Update a max = ∈ (i-1) ;

[0077] 16) End while;

[0078] 16) Output the beam allocation result

[0079] In the above solving process, are two sets, respectively used to record the allocation results of beams and the set of extra users who have allocated beams. g = 0 M is a zero vector of dimension M, used for the update of a min in line 13, which can accelerate the convergence rate. g(j) = g k,m represents the size of the jth element in the vector g is updated to g k，m .

[0080] The solving steps of the power and common rate allocation iterative optimization algorithm are as follows:

[0081] Step 1, Lemma 2 is proposed to construct a surrogate function for the non-convex signal-to-interference noise ratio of near-field users and extra users decoding data. Lemma 2 is as follows:

[0082] Lemma 2: Let s(p) and I(p) represent the numerator and denominator of some arbitrary function, where s(p) and I(p) represent functions of s and I with respect to variable p, provided that s(p) ≥ 0 and I(p) > 0, then the proxy function can be constructed by introducing an auxiliary variable y and it can be verified that the constructed proxy function satisfies:

[0083]

[0084] and the optimal solution on the right side of equation (11) is

[0085] Proof: By I(p) > 0 and the logarithmic function is a monotonically increasing function, it can be obtained that f(y, p) is a concave function with respect to the auxiliary variable y introduced. Therefore, the optimal y can be obtained by the first-order partial derivative being equal to zero * , i.e. Substitute y * into equation (11) to verify that Lemma 2 is correct.

[0086] Replace s(p) and I(p) in Lemma 2 with the numerators and denominators of formula (6), formula (7) and formula (8) to obtain the proxy functions of formula (6), formula (7) and formula (8) as follows:

[0087]

[0088] where y k , and are auxiliary variables introduced for the reconstruction of the near-field user to decode the public information stream, the near-field user to decode the private information stream and the mth additional user to decode the information stream, respectively; P includes all power allocation variables, i.e. P = {P0, P1,..., P K , P 1，1 ,..., P K，M}

[0089] Step 2, remove the minimum value symbol and reconstruct the sub-problem as:

[0090]

[0091] where, and Due to the coupling between the variables and , problem (13) is still difficult to solve directly.

[0092] Step 3, although problem (13) is difficult to solve directly, when is fixed, the optimal can be obtained by Lemma 2; when The optimal solution of (13) can be obtained by using the convex optimization toolbox. Based on this, the present application proposes a double-layer iterative optimization algorithm to solve problem (13). The solving algorithm is as follows:

[0093] 1) Initialization: initialize and iteration index i = 1;

[0094] 2) While not convergent, execute;

[0095] 3) According to Lemma 2, calculate the optimal auxiliary variable

[0096] 4) Solve problem (14) using the convex optimization toolbox, and output the optimal

[0097] 5) Update i = i + 1;

[0098] 6) End while;

[0099] 7) Output the suboptimal solution of network reachability and rate.

[0100] Simulation results: The simulation parameters are set as follows: The coverage range of the proposed hybrid far-field communication network is 120 meters, wherein K = 8 near-field users and M = 8 additional users are independently and randomly distributed in the entire area. The working frequency of the network is f c = 30 GHz, the number of transmission antennas equipped by the base station is N = 127, the maximum transmission power of the base station is P max = 30 dBm, the background noise is σ 2 = -80 dBm, the communication rate requirement of the user is R th = 0.8 bp / Hz; the wavelength λ = 0.01 meters, and the antenna aperture The Rayleigh distance is meters.

[0101] Figure 2 is a schematic diagram of the present application simulating the relationship between the rate of the additional user and the minimum rate requirement of the near-field user; at the same time, the simulation diagram provides a performance comparison between the transmission scheme proposed by the present application and the spatial division multiple access technology and the beam random allocation. The simulation results show that the transmission scheme proposed by the present application is significantly better than the beam random allocation method, which proves the effectiveness of the beam allocation algorithm proposed by the present application. Compared with the spatial division multiple access technology, the transmission scheme proposed by the present application has certain advantages in improving the network rate, and these advantages benefit from the flexible co-channel interference management capability of the rate splitting multiple access technology.

[0102] Figure 3is a schematic diagram of the relationship between the sum rate of extra users and the number of near-field users simulated by the present application; the results show that as the number of near-field users increases, the sum rate of extra users increases. This is because as the number of near-field users increases, the mixed far-near field network can serve more extra users. However, compared with the scheme proposed in the present application, the increase of the beam random allocation scheme is relatively slow.

[0103] Figure 4 is a schematic diagram of the relationship between the sum rate of extra users and the number of iterations simulated by the present application. The results show that the algorithm converges after about 20 iterations. In addition, as the number of near-field users increases, the number of iterations required by the algorithm remains basically stable.

[0104] Those skilled in the art can understand that the above description is only preferred examples of the application and is not used to limit the application, although the application is described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions recorded in the foregoing examples or make equivalent replacements for part of the technical features. Any modification, equivalent replacement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application. All technical features in the embodiments can be freely combined according to actual needs.

[0105] Finally, it should be noted that: the above description is only the preferred embodiment of the present application and is not used to limit the present application, although the present application is described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments or make equivalent replacements for part of the technical features, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for joint optimization of mixed far and near field communication beams and power based on rate splitting, characterized in that: Specifically comprising the following steps: Step S1, establishing a channel model of far-field users and near-field users in a mixed far-field and near-field communication scenario; Step S2, using a rate-splitting multiple access technology to split data of the near-field users into a common information stream and a private information stream; Step S3, pre-configuring beams of the common information stream and the private information stream of the near-field users by using a zero-forcing algorithm, so that the far-field users occupy the beams of the near-field users to transmit information; Step S4, establishing an optimization problem with the goal of maximizing achievable sum rate, and designing an iterative optimization algorithm to jointly process beam allocation of the near-field users and transmission power allocation variables of the far-field and near-field users.

2. The method of claim 1, wherein: In step S1, the hybrid near-far field communication network comprises a base station equipped with N antennas and K near field users each equipped with a single antenna, wherein is an arbitrary integer; the base station in the network adopts a uniform linear array with an antenna aperture size of d, and the boundary value of the near field and far field regions is where D = (N - 1)d and λ is the wavelength of the antenna array aperture and the carrier respectively; The transmission antenna of the base station is placed along the positive half axis of the y axis, and the center coordinates of the uniform linear array are (0, 0), so a two-dimensional coordinate system can be established according to the center position and the deployment direction of the transmission antenna; according to the established two-dimensional coordinate system, the coordinates of the nth antenna are wherein The distance and angle of the kth user are respectively denoted as d k and θ k , wherein the distance and angle of the user are defined with the origin of the coordinate system as the reference, so the corresponding coordinates are d k = (d k cos θ k , d k sin θ k ); according to the coordinates of the antenna and the near-field user, the distance between the nth antenna and the kth near-field user is: Based on the spherical wave channel model, the channel between the base station and the kth near-field user is modeled as: where a(d k , θ k ) is the near-field array response vector; is the path loss, where f and c are the carrier frequency and the speed of light, respectively; when D << d k , the near-field array steering vector a(d k , θ k ) transforms into the far-field array steering vector; using a first-order Taylor expansion d k，n , the far-field channel vector is expressed as: wherein, is the coordinate of the far-field user relative to the coordinate origin; the channel between the base station and the mth additional user is modeled as: In equation (4), parameter Z represents the boundary value between near-field and far-field region, d m represents the distance between the mth extra user and the coordinate origin; β m and represents the path loss of the mth extra user, if d m is less than or equal to Z, the path loss takes β m , otherwise takes a(d m , θ m ) and represents the array response vector of the mth extra user, if d m is less than or equal to Z, the array response vector takes a(d m , θ m ), otherwise takes The mth extra user can be a near-field user or a far-field user, which needs to be determined according to the size relationship between d m and Z; if d m is less than or equal to Z, it is a near-field user, otherwise it is a far-field user.

3. The method of claim 2, wherein: In step S2, according to the coding principle of the downlink rate splitting multiple access technology, the information of the kth near-field user is split into a common part and a private part; then all the common parts are coded into a common information stream x0; the private part of the kth near-field user is coded into a private information stream xk; subsequently, the mutually independent information streams are linearly precoded. k and ​​ 4. The method of claim 3, wherein: For the private information stream, a zero-forcing precoding is adopted, i.e., [p1,...,p K ] = H(H H H) -1 F, where H = [h1,...,h K ] and Since the public information stream depends on the decoding rate of the worst user, p0is set as p0= h k′ ||h k′ || -1 , where If the m-th extra user occupies the k-th near-field user's beam, the binary variable is set as b k，m = 1, otherwise b k，m ; the binary b k，m variable aims to determine the correspondence between the beam and the extra user, to determine which beam is occupied by the m-th extra user. According to the above setting, the signal transmitted by the base station is represented as: where P k and P k，m are the transmission power allocation variables for the near-field users and the extra users, respectively; is the information required by the mth extra user, and M is the total number of extra users. According to equation (5), the signal received by the kth near-field user is where is additive white Gaussian noise. The kth near-field user first regards the private information stream as interference to decode the common information stream. The signal-to-interference-plus-noise ratio (SINR) of the kth near-field user decoding the common information stream is wherein, P0represents the transmission power allocated by the base station for the common information stream; b 0，m is a binary variable indicating whether the mthextra user occupies the beam of the common information stream, b 0，m = 1 if the mthextra user occupies the beam of the common information stream, otherwise b 0，m = 0; P 0，m and P k，m represent the transmission power allocated by the base station for the mthextra user on the beam of the common information stream and the kthnear-field user on the beam of the private information stream, respectively; to ensure that all near-field users successfully decode the common information stream, it can be obtained that Meanwhile, since all near-field users share the common information stream, it can be obtained that wherein R k，c is the common rate allocated for the kthnear-field user; after removing the interference of the common information stream by using the serial interference cancellation, the near-field user starts to decode the private information stream, and the signal-to-interference-and-noise ratio is: Combining equations (6) and (7), the transmission rate Rkof the kth near-field user can be obtained as k = Rk= R k，c + log(l + γk); (8) k，p ) ; (9) Similarly, the signal received by the mth extra user is denoted as where is an additive white Gaussian noise; assuming that the mth extra user occupies the beam of the kth near-field user, the signal-to-interference-plus-noise ratio (SINR) of the mth extra user is wherein, the transmission rate of the mth additional user is 5. The method of claim 4, wherein: Under the premise of guaranteeing quality of service requirements of the near-field users, the sum rate of the additional users is maximized to enhance the throughput and connectivity of the network, so the optimization problem (9) is modeled as: where P k，m represents the transmission power allocated by the base station for the m-th additional user on the beam of the k-th near-field user private information stream; R k is the transmission rate of the k-th near-field user; P max and R th are the maximum transmission power threshold of the base station and the quality of service requirement threshold of the near-field user, respectively; formula (9b) and formula (9c) are the transmission power and the quality of service constraint of the near-field user, respectively; formula (9d) and formula (9e) are the common rate allocation constraints of the rate-splitting multiple access technology; and formula (9f) limits that each beam can only accept one additional user occupation.

6. The method of claim 5, wherein: Solving the optimization problem (9), it is split into two sub-problems, i.e., a beam allocation sub-problem and a power and common rate allocation sub-problem; then an iterative optimization algorithm is designed to solve the two sub-problems respectively.

7. The method of claim 6, wherein: The solving steps of the iterative optimization algorithm of the beam allocation sub-problem are as follows: Step A1, proposing Lemma 1 to simplify the optimization problem; The transmission rate of a near-field user is not affected by the beam allocation, i.e., the rate of a near-field user is independent of the beam allocation; if g k′，m > g k，m then the mthextra user prefers the beam of the k' near-field user over the beam of the kthuser, and vice versa; The beam allocation sub-problem is simplified as: Step A2, introducing a matrix where An accelerated binary search algorithm is proposed to solve the binary variables in the optimization problem (10); the search interval of the algorithm is [α min , α max ], where and Given a threshold ∈ th , the proposed algorithm first determines whether it is satisfied for each beam g k，m ≥ ∈ th , and updates the given threshold according to the result using the binary search method.

8. The method of claim 6, wherein: The solving steps of the iterative optimization algorithm of the power and common rate allocation sub-problem are as follows: Step B1, propose Lemma 2 to construct a proxy function for the non-convex SINR of near-field users and extra users decoding data; let s(p) and I(p) represent the numerator and denominator of an arbitrary function, where s(p) and I(p) represent s and I as functions of variable p, as long as s(p) ≥ 0 and I(p) > 0, a proxy function can be constructed by introducing an auxiliary variable y And it can be verified that the constructed proxy function satisfies: and the optimal solution on the right side of equation (11) is Proof: From I(p) > 0 and the fact that the logarithmic function is a monotonically increasing function, it can be shown that f(y, p) is a concave function in the auxiliary variable y introduced. Therefore, the optimum y is obtained by setting the first derivative equal to zero * i.e. Substituting y * into equation (11) proves Lemma 2. Substitute s(p) and I(p) in Lemma 2 into the numerators and denominators of formula (6), formula (7) and formula (8), and the surrogate functions of formula (6), formula (7) and formula (8) are: wherein y k , and are auxiliary variables introduced for the reconstruction of the near-field user-decoded common information stream, the near-field user-decoded private information stream and the m-th extra user-decoded information stream, respectively; P collects all the power allocation variables, i.e. P = {P0, P1,..., P K , P 1，1 , ..., P K，M}. Step B2, removing the minimum value symbol, and reconstructing the sub-problem as: where and Due to and The problem (13) is still difficult to solve directly due to the coupling between variables. Step B3, problem (13) is difficult to solve directly, but when fixing , the optimal can be obtained by Lemma 2; when fixing , the optimal solution of can be obtained by the convex optimization toolbox. Based on this, the present application solves problem (13) by using a double-layer iterative optimization algorithm.

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