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 breaking 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 present invention belongs to the technical field of wireless communication and novel multiple access, and in particular relates to a hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting. Background Art

[0002] Future wireless communication networks will use ultra-large-scale antenna arrays and ultra-high spectrum to support the huge throughput and connectivity requirements of the network. However, the increase in the operating frequency of wireless networks and the aperture of ultra-large-scale antenna arrays has expanded the coverage of the near-field area of ​​the network, resulting in the plane wave model assumed in the original far-field research no longer being valid, and the electromagnetic wave is transformed into spherical propagation. Spherical waves introduce information in the distance dimension, allowing them to focus beam energy at a specific spatial position, that is, focusing not only by angle, but also at a specific depth along the propagation direction. Beam focusing provides the network with higher spatial degrees of freedom and channel capacity. However, due to factors such as channel estimation errors, spatial beams may not be perfectly focused, which can easily cause energy leakage.

[0003] Recent research results show that although the pre-configured beams of near-field users in the network cannot be perfectly focused, they can still serve additional near-field or far-field users to enhance the throughput and connectivity of the network. Related research mainly uses Space Division Multiple Access (SDMA) technology to manage the interference of near-field users, and then uses Non-orthogonal Multiple Access (NOMA) technology to manage the interference between near-field users and additional users. When SDMA schedules multiple users at the same time, it regards all non-target information streams as interference and directly decodes the target information stream. Excessive interference will cause the transmission rate to saturate and cannot meet the high-speed transmission requirements of users. NOMA uses power multiplexing technology at the transmitter to serve multiple users at the same time, and then uses Successive Interference Cancellation (SIC) technology at the receiver to completely decode all strong interference. Therefore, the interference management methods of SDMA and NOMA are completely non-decoding interference and completely decoding interference, respectively, which are not flexible. To this end, scholars have proposed rate splitting multiple access technology. Rate splitting multiple access technology can treat part of the interference as noise while decoding part of the interference, which has great flexibility in interference management. In addition, rate splitting multiple access can bridge SDMA and NOMA by adjusting the ratio of information splitting. However, how to utilize rate splitting multiple access technology and pre-provisioned beams to enhance the connectivity and throughput of hybrid near- and far-field networks 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, using pre-configured beams to enhance the throughput and connectivity of hybrid near- and far-field networks. However, this study used NOMA to manage interference between users, resulting in a small network throughput gain.

[0006] In 2024, Chenguang Rao et al. published "A General Analytical Framework for the Resolution of Near-Field Beamforming" in IEEE Communications Letters, in which they 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 used rate splitting multiple access technology to manage interference between users in hybrid near- and far-field networks to enhance the sum rate, but did not study how to use pre-configured beams to support more users to enhance network connectivity.

[0008] None of the above three studies on near-field communication networks considers how to simultaneously utilize rate splitting multiple access technology and pre-configured beams to enhance the throughput and connectivity of the network. Currently, there is no relevant research on hybrid near-far field networks. Summary of the invention

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

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

[0011] A hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting specifically comprises the following steps;

[0012] Step S1, establishing a channel model for far-field users and near-field users in a hybrid far-field and near-field communication scenario;

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

[0014] Step S3, using a zero-breaking algorithm to pre-configure beams of public information flows and private information flows of near-field users, so that far-field users occupy beams of near-field users for information transmission;

[0015] Step S4, establish an optimization problem with the goal of maximizing the reachability and rate, and design an iterative optimization algorithm to jointly process the beam allocation of near-field users and the transmission power allocation variables of far-field and near-field users.

[0016] As a further preferred embodiment of the hybrid near-far field communication beam and power joint optimization method based on rate splitting of the present invention, in step S1, the hybrid near-far field communication network includes a base station equipped with N antennas and K near-field users equipped with single antennas, wherein The base stations in the network use a uniform linear array with an antenna aperture size of d, so the boundary values ​​of the near-field and far-field regions are Where D = (N-1)d and λ are the antenna array aperture and the wavelength of the carrier respectively;

[0017] Assuming that the center coordinate of the uniform linear array is (0, 0) and the antenna array is deployed along the y-axis, the coordinates of the nth antenna are in The distance and angle of the kth user are denoted as d k and θ k , then the corresponding coordinate is d k =(d k cosθ k , d k sinθ 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:

[0018]

[0019] Based on the spherical wave channel model, the channel between the base station and the kth near-field user It can be modeled as:

[0020]

[0021] Among them, a(d k ,θ k ) is the near-field array steering vector; is the path loss, where f and c are the carrier frequency and the speed of light respectively; when D<<d k When the near-field array steering vector a(d k ,θ k ) into the far-field array steering vector; using the first-order Taylor expansion d k，n , the far-field channel vector It can be expressed as:

[0022]

[0023] in, are the corresponding coordinates of the far-field users; the channel between the base station and the mth additional user It can be modeled as:

[0024]

[0025] As a further preferred solution of the hybrid near-far field communication beam and power joint optimization method based on rate splitting of the present invention, in step S2, according to the coding and decoding principle of the downlink rate splitting multiple access technology, the information of the kth near-field user is split into a public part and a private part; then all the public parts are encoded into a public information stream x0; the private part of the kth near-field user is encoded into a private information stream x k ; These independent information flows are then and Linear precoding is performed.

[0026] 4. The hybrid near-far field communication beam and power joint optimization method based on rate splitting according to claim 3 is characterized in that: zero-breaking precoding is used for private information streams, that is, [p1, ..., p K ]=H(H H H) -1 F, where H = [h1, ..., h K ]and Since the public information flow depends on the decoding rate of the worst user, p0 is set to p0 = h k′ ||h k′ || -1 ,in If the mth additional user occupies the beam of the kth near-field user, the binary variable is set to b k，m =1, otherwise b k，m =0; According to the above settings, the signal transmitted by the base station can be expressed as:

[0027]

[0028] Among them, P k and P k，mis the transmission power allocation variable between the near-field user and the additional user; According to equation (5), the signal received by the kth near-field user can be obtained as in is additive Gaussian white noise; in order to decode the required information, the kth near-field user first regards the private information stream as interference to decode the public information stream, and the corresponding signal-to-interference-noise ratio is:

[0029]

[0030] in, To ensure that all near-field users can successfully decode the common information stream, At the same time, since all near-field users share the public information flow, Where R k，c is the public rate allocated to the kth near-field user; after removing the interference of the public information stream using serial interference cancellation, the near-field user starts decoding the private information stream, and the corresponding signal-to-interference-to-noise ratio is:

[0031]

[0032] Combining equations (6) and (7), we can get the transmission rate R of the kth near-field user: k =R k，c +log(1+γ k，p );

[0033] Similarly, the signal received by the mth additional user can be expressed as in is additive Gaussian white noise; assuming that the mth additional user occupies the beam of the kth near-field user, the corresponding signal-to-interference-noise ratio is:

[0034]

[0035] in, The transmission rate of the mth additional user is

[0036] As a further preferred solution of the hybrid near-far field communication beam and power joint optimization method based on rate splitting of the present invention, the sum rate of additional users is maximized under the premise of ensuring the service quality requirements of near-field users to enhance the throughput and connectivity of the network. Therefore, the optimization problem (9) can be modeled as:

[0037]

[0038] Among them, P max and R thare the maximum transmission power threshold of the base station and the service quality requirement threshold of the near-field user respectively; formula (9b) and formula (9c) are the transmission power of the base station and the service quality constraints of the near-field user respectively; formula (9d) and formula (9e) are the common rate allocation constraints of the rate splitting multiple access technology; formula (9f) limits each beam to only accept one additional user's occupancy.

[0039] As a further preferred solution of the hybrid far- and near-field communication beam and power joint optimization method based on rate splitting of the present invention, the optimization problem (9) is solved and split into two sub-problems, namely the beam allocation sub-problem and the power and common rate allocation sub-problem; then an iterative optimization algorithm is designed to solve the two sub-problems respectively.

[0040] As a further preferred solution of the hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting of the present invention, the solution steps of the beam allocation iterative optimization algorithm are as follows:

[0041] Step A1, Lemma 1 is proposed to simplify the optimization problem;

[0042] Lemma 1 is as follows: The transmission rate of near-field users is not affected by beam allocation, that is, the rate of near-field users is independent of beam allocation; if g k′，m >g k，m , then the mth additional user prefers the beam of the k′th near-field user to the beam of the kth user, and vice versa;

[0043] Proof: Equations (6) and (7) show that the signal-to-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 decreases with g. k，m To enhance the network throughput, the mth additional user prefers the beam of the k′th near-field user, where Combining the above conclusions, Lemma 1 holds true;

[0044] Lemma 1 provides the beam allocation criterion, but multiple additional users may wish to occupy the same beam; to reduce the performance loss of some beams, the beam allocation subproblem is simplified to:

[0045]

[0046] Step A2, first introduce the matrix in 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 ],in and Given a threshold ∈th , the proposed algorithm first determines for each beam g k，m ≥∈ th Whether it can be satisfied, and then update the given threshold using the binary search method according to the result;

[0047] To facilitate the description of the algorithm flow, the following two definitions are introduced:

[0048] Definition 1: If g k，m ≥∈ th , then k is called a feasible beam for the mth additional user, and m is an optional additional user for the kth near-field user beam; the total number of feasible beams for the mth additional user is called degree, denoted by deg(m);

[0049] Definition 2: If the following conditions hold, the scheduling priority of the mth additional user is higher than that of m′;

[0050] Condition 1) deg(m)<deg(m′) or

[0051] Condition 2) deg(m)=deg(m′) and m<m′,

[0052] Among them, condition 2) is to avoid that two additional users have the same degree; similarly, deg(k) and the corresponding priority can be defined; the algorithm flow for solving beam allocation is as follows:

[0053] 1) Calculate the matrix G and initialize α min and α max , set the iteration index i = 1, the maximum tolerance error ∈ th and∈ (1) -∈ (0) >∈ th ;

[0054] 2) While |∈ (i) -∈ (i-1) |>∈ th implement;

[0055] 3) Set ∈ (i) =0.5(α min +α max ), and g = 0 M ;

[0056] 4) For Setting g k，m =0;

[0057] 5) While deg(m)≠0, implement

[0058] 6) Find the additional user m with the highest priority, and find the near-field user k with the highest priority from its neighboring nodes;

[0059] 7) Update g(j)=g k，m , j = j + 1;

[0060] 8) Update g x，y =0, where x=k or y=m;

[0061] 9) End while;

[0062] 10) Update i=i+1;

[0063] 11) If j = M;

[0064] 12) Set g k，m =0, where

[0065] 13) Update α min =min(g)≥∈ (i-1) ;

[0066] 14)else;

[0067] 15) Update α max =∈ (i-1) ;

[0068] 16) End while:

[0069] 16) Output beam allocation results

[0070] As a further preferred solution of the hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting of the present invention, the solution steps of the power and public rate allocation iterative optimization algorithm are as follows:

[0071] Step B1, Lemma 2 is proposed to construct a proxy function for the non-convex signal-to-interference-noise ratio of the decoded data of the near-field user and the additional user; wherein Lemma 2 is as follows: The constructed proxy function It is completely equivalent to the signal-to-interference-to-noise ratio, where s(p)≥0 and I(p)>0, that is,

[0072]

[0073] And the optimal solution on the right side of equation (11) is

[0074] Proof: Since 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 introduced auxiliary variable y; therefore, the optimal y can be obtained by making the first-order partial derivative equal to zero. * ,Right now y * Substituting into equation (11), we can prove that Lemma 2 holds;

[0075] Reconstruct γ using Lemma 2 k，c , γ k，p and The constructed proxy function is as follows:

[0076]

[0077] in,

[0078] Step B2, remove the minimum value symbol and restructure the subproblem as follows:

[0079]

[0080] in, and because and The coupling between variables,problem (13) is still difficult to solve directly;

[0081] Step B3, although problem (13) is difficult to solve directly, when When , we can obtain the optimal The closed-form solution of When , it can be obtained by using the convex optimization toolbox The optimal solution; Based on this, the present invention proposes a double-layer iterative optimization algorithm to solve problem (13); The specific solution algorithm is as follows:

[0082] 1) Initialization: Initialization and iteration index i = 1;

[0083] 2) While non-convergent execution;

[0084] 3) Calculate the optimal auxiliary variable according to Lemma 2

[0085] 4) Use the convex optimization toolbox to solve problem (14) and output the optimal

[0086] 5) Update i=i+1;

[0087] 6) End while;

[0088] 7) Output the suboptimal solution of network reachability and rate.

[0089] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0090] The present invention discloses a hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting, establishes a channel model of far-field users and near-field users in a hybrid far-field and near-field communication scenario; uses rate splitting multiple access technology to split the data of near-field users into a public information flow and a private information flow; uses a zero-breaking algorithm to pre-configure the beams of the public information flow and the private information flow of the near-field users so that the far-field users occupy the beams of the near-field users for information transmission; establishes an optimization problem with the goal of maximizing the reachability and rate, and designs an iterative optimization algorithm to jointly process the beam allocation of near-field users and the transmission power allocation variables of far-field and near-field users; the present invention can significantly improve the throughput and connectivity of the communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0092] Figure 1 It is a schematic diagram of a hybrid far-field and near-field communication network based on rate splitting proposed in the present invention;

[0093] Figure 2 The present invention simulates the relationship between the sum rate of additional users and the minimum rate requirement of near-field users;

[0094] Figure 3 is a schematic diagram of the relationship between the sum rate of additional users and the number of near-field users simulated by the present invention;

[0095] Figure 4 It is a schematic diagram of the relationship between the sum rate and the number of iterations simulated by the present invention for additional users. DETAILED DESCRIPTION

[0096] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0097] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The present invention is described in detail below according to the drawings and preferred embodiments, and the purpose and effect of the present invention will become clearer. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0098] The present invention discloses a hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting, which relates to the field of wireless communication and new multiple access technology. The designed hybrid far-field and near-field communication beam and power joint optimization method includes four steps: S1, establishing a channel model for far-field users 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 information streams and private information streams; S3, using a zero-breaking algorithm to pre-configure the beams of the public information stream and the private information stream of the near-field user, so that the far-field user occupies the beam of the near-field user for information transmission; S4, establishing an optimization problem with the goal of maximizing the reachability and rate, and designing an iterative optimization algorithm to jointly process the beam allocation of near-field users and the transmission power allocation variables of far-field and near-field users. The present invention can significantly improve the throughput and connectivity of the communication network.

[0099] like Figure 1 As shown in FIG. 1 , a schematic diagram of a hybrid near-far field communication network based on rate splitting is depicted. The designed hybrid near-far field communication network includes a base station equipped with N antennas and K near-field users equipped with single antennas, where The present invention aims to use the beams pre-configured for near-field users to serve additional near-field or far-field users to enhance the throughput and connectivity of the network. The base stations in the network use a uniform linear array with an antenna aperture size of d. The boundary value of the near-field and far-field regions is Where D = (N-1)d and λ are the antenna array aperture and the wavelength of the carrier respectively.

[0100] Assuming that the center coordinate of the uniform linear array is (0, 0) and the antenna array is deployed along the y-axis, the coordinates of the nth antenna are in The distance and angle of the kth user are denoted as d k and θ k , then the corresponding coordinate is d k =(d k cosθ k , d k sinθ 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:

[0101]

[0102] Based on the spherical wave channel model, the channel between the base station and the kth near-field user It can be modeled as:

[0103]

[0104] where a(d k ,θ k) is the near-field array steering vector. is the path loss, where f and c are the carrier frequency and the speed of light, respectively. k When the near-field array steering vector a(d k ,θ k ) is transformed into a far-field array steering vector. Using the first-order Taylor expansion d k，n , the far-field channel vector It can be expressed as:

[0105]

[0106] in are the corresponding coordinates of the far-field users. Therefore, the channel between the base station and the mth additional user is It can be modeled as:

[0107]

[0108] According to the coding and decoding principle of downlink rate split multiple access technology, the information of the kth near-field user is split into a public part and a private part; then all the public parts are encoded into a public information stream x0; the private part of the kth near-field user is encoded into a private information stream x k ; These independent information flows are then and The present invention adopts zero-breaking precoding for private information streams, that is, [p1, ..., p K ]=H(H H H) -1 F, where H = [h1, ..., h K ]and Since the public information flow depends on the decoding rate of the worst user, p0 is set to p0 = h k′ ||h k′ || -1 ,in If the mth additional user occupies the beam of the kth near-field user, the binary variable is set to b k，m =1, otherwise b k，m =0. According to the above settings, the signal transmitted by the base station can be expressed as:

[0109]

[0110] Where P k and P k，m is the transmission power allocation variable between the near-field user and the additional user. According to equation (5), the signal received by the kth near-field user is in is additive Gaussian white noise. In order to decode the desired information, the kth near-field user first treats the private information stream as interference to decode the public information stream, and the corresponding signal-to-interference-noise ratio is:

[0111]

[0112] in To ensure that all near-field users can successfully decode the common information stream, there should be At the same time, since all near-field users share the public information flow, Where R k，c is the public rate allocated to the kth near-field user. After removing the interference of the public information stream using serial interference cancellation, the near-field user starts decoding the private information stream, and the corresponding signal-to-interference-to-noise ratio is:

[0113]

[0114] Combining equations (6) and (7), we can get the transmission rate R of the kth near-field user: k =R k，c +log(1+γ k，p ).

[0115] Similarly, the signal received by the mth additional user can be expressed as in is additive Gaussian white noise. Assuming that the mth additional user occupies the beam of the kth near-field user, the corresponding signal-to-interference-noise ratio is:

[0116]

[0117] in Therefore, the transmission rate of the mth additional user is

[0118] The main goal of the present invention is to maximize the sum rate of additional users under the premise of ensuring the service quality requirements of near-field users to enhance the throughput and connectivity of the network. Therefore, the optimization problem can be modeled as:

[0119]

[0120] Where P max and R th are the maximum transmission power threshold of the base station and the service quality requirement threshold of the near-field user. (9b) and (9c) are the transmission power constraints of the base station and the service quality constraints of the near-field user, respectively; (9d) and (9e) are the common rate allocation constraints of the rate splitting multiple access technology; (9f) limits each beam to only accept one additional user.

[0121] Design of beam and power joint optimization algorithm: The optimization problem (9) modeled is discrete and the constraints are highly non-convex, making it difficult to directly obtain the optimal beam and power allocation. To solve the optimization problem (9), the present invention divides it into two sub-problems, namely the beam allocation sub-problem and the power and public rate allocation sub-problem; then an iterative optimization algorithm is designed to solve the two sub-problems respectively.

[0122] The solution steps of the beam allocation iterative optimization algorithm are as follows:

[0123] Step 1, Lemma 1 is proposed to simplify the optimization problem. Lemma 1 is as follows:

[0124] Lemma 1: The transmission rate of near-field users is not affected by beam allocation, that is, the rate of near-field users is independent of beam allocation; if g k′，m >g k，m , then the mth additional user prefers the beam of the k′th near-field user to the beam of the kth user, and vice versa.

[0125] Proof: Equations (6) and (7) show that the signal-to-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 decreases with g. k，m Therefore, to enhance the network throughput, the mth additional user prefers the beam of the kth near-field user, where Combining the above conclusions, Lemma 1 holds.

[0126] Lemma 1 provides the criterion for beam allocation, but multiple additional users may wish to occupy the same beam. To reduce the performance loss of partial beams, the beam allocation subproblem is simplified to:

[0127]

[0128] Step 2, first introduce the matrix in 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 ],in and Given a threshold ∈ th , the proposed algorithm first determines for each beam g k，m ≥∈ th Whether it can be satisfied, and then update the given threshold using the binary search method according to the result. To facilitate the description of the algorithm flow, the present invention introduces the following two definitions:

[0129] Definition 1: If g k，m ≥∈ th, then k is called a feasible beam for the mth additional user, and m is an optional additional user for the kth near-field user beam. The total number of feasible beams for the mth additional user is called degree, denoted by deg(m).

[0130] Definition 2: If the following conditions hold, the scheduling priority of the mth additional user is higher than m′.

[0131] Condition 1) deg(m)<deg(m′) or

[0132] Condition 2) deg(m)=deg(m′) and m<m′,

[0133] Among them, condition 2) is to avoid two additional users having the same degree. Similarly, deg(k) and the corresponding priority can be defined. The algorithm flow for solving beam allocation is as follows:

[0134] 1) Calculate the matrix G and initialize α min and α max , set the iteration index i = 1, the maximum tolerance error ∈ th and∈ (1) -∈ (0) >∈ th ;

[0135] 2) While |∈ (i) -∈ (i-1) |>∈ th implement

[0136] 3) Set ∈ (i) =0.5(α min +α max ), and g = 0 M ;

[0137] 4) For Setting g k，m =0;

[0138] 5) While deg(m)≠0, implement

[0139] 6) Find the additional user m with the highest priority, and find the near-field user k with the highest priority from its neighboring nodes;

[0140] 7) Update g(j)=g k，m , j = j + 1;

[0141] 8) Update g x，y =0, where x=k or y=m;

[0142] 9) End while;

[0143] 10) Update i=i+1;

[0144] 11) If j = M

[0145] 12) Set g k，m =0, where

[0146] 13) Update α min =min(g)≥∈ (i-1) ;

[0147] 14)else;

[0148] 15) Update α max =∈ (i-1) ;

[0149] 16) End while:

[0150] 16) Output beam allocation results

[0151] The solution steps of the iterative optimization algorithm for power and public rate allocation are as follows:

[0152] Step 1, Lemma 2 is proposed to construct a proxy function for the non-convex signal-to-interference-noise ratio of the decoded data of the near-field user and the additional user. Lemma 2 is as follows:

[0153] Lemma 2: The proxy function constructed by the present invention It is completely equivalent to the signal-to-interference-to-noise ratio, where s(p)≥0 and I(p)>0, that is,

[0154]

[0155] And the optimal solution on the right side of equation (11) is

[0156] Proof: Since 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 introduced auxiliary variable y. Therefore, the optimal y can be obtained by making the first-order partial derivative equal to zero. * ,Right now y * Substituting into equation (11), we can prove that Lemma 2 holds.

[0157] Reconstruct γ using Lemma 2 k，c , γ k，p and The constructed proxy function is as follows:

[0158]

[0159] in

[0160] Step 2, remove the minimum symbol and restructure the subproblem as follows:

[0161]

[0162] in, and because and Due to the coupling between variables, problem (13) is still difficult to solve directly.

[0163] Step 3: Although problem (13) is difficult to solve directly, when When , we can obtain the optimal The closed-form solution of When , it can be obtained by using the convex optimization toolbox The optimal solution. Based on this, the present invention proposes a two-layer iterative optimization algorithm to solve problem (13). The specific solution algorithm is as follows:

[0164] 1) Initialization: Initialization and iteration index i = 1;

[0165] 2) While non-convergent execution;

[0166] 3) Calculate the optimal auxiliary variable according to Lemma 2

[0167] 4) Use the convex optimization toolbox to solve problem (14) and output the optimal

[0168] 5) Update i=i+1;

[0169] 6) End while;

[0170] 7) Output the suboptimal solution of network reachability and rate.

[0171] Simulation results: Simulation parameter settings: The coverage range of the proposed hybrid near-far field communication network is 120 meters, where K = 8 near-field users and M = 8 additional users are independently and randomly distributed throughout the area. The operating frequency of the network is f c =30GHz, the number of transmission antennas equipped at the base station is N=127, and the maximum transmission power of the base station is P max =30dBm, background noise is σ 2 =-80dBm, the user's communication rate requirement is R th =0.8bp / Hz; wavelength λ = 0.01m, antenna aperture Rayleigh distance rice.

[0172] Figure 2 The present invention simulates the relationship between the sum rate of additional users and the minimum rate requirement of near-field users; at the same time, the simulation diagram provides a performance comparison between the transmission scheme proposed in the present invention and the space division multiple access technology and beam random allocation. The simulation results show that the transmission scheme proposed in the present invention is significantly better than the beam random allocation method, which proves the effectiveness of the beam allocation algorithm proposed in the present invention. Compared with the space division multiple access technology, the transmission scheme proposed in the present invention has certain advantages in improving the network sum rate, and these advantages benefit from the flexible co-channel interference management capability of the rate splitting multiple access technology.

[0173] Figure 3 The present invention simulates the relationship between the sum rate of additional users and the number of near-field users; the results show that as the number of near-field users increases, the sum rate of additional users increases accordingly. This is because as the number of near-field users increases, the hybrid near-far field network can serve more additional users. However, compared with the scheme proposed by the present invention, the increase of the beam random allocation scheme is relatively slow.

[0174] Figure 4 The present invention simulates the relationship between the sum rate of additional users and the number of iterations. The results show that the proposed algorithm can converge 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.

[0175] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention should be included in the protection scope of the invention. All technical features in this embodiment can be freely combined according to actual needs.

[0176] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting, characterized by: The specific steps include: Step S1, establishing a channel model for far-field users and near-field users in a hybrid far-field and near-field communication scenario; Step S2, using rate splitting multiple access technology to split the data of the near-field user into a public information flow and a private information flow; Step S3, using a zero-breaking algorithm to pre-configure beams of public information flows and private information flows of near-field users, so that far-field users occupy beams of near-field users for information transmission; Step S4, establish an optimization problem with the goal of maximizing the reachability and rate, and design an iterative optimization algorithm to jointly process the beam allocation of near-field users and the transmission power allocation variables of far-field and near-field users.

2. The hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting according to claim 1 is characterized by: In step S1, the hybrid near-far field communication network includes a base station equipped with N antennas and K near-field users equipped with single antennas, wherein The base stations in the network use a uniform linear array with an antenna aperture size of d, so the boundary values ​​of the near-field and far-field regions are Where D = (N-1)d and λ are the antenna array aperture and the wavelength of the carrier respectively; Assuming that the center coordinate of the uniform linear array is (0, 0) and the antenna array is deployed along the y-axis, the coordinates of the nth antenna are in The distance and angle of the kth user are denoted as d k and θ k , then the corresponding coordinate is d k =(d k cosθ k , d k sinθ 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 It can be modeled as: Among them, a(d k ,θ k ) is the near-field array steering vector; is the path loss, where f and c are the carrier frequency and the speed of light respectively; when D<<d k When the near-field array steering vector a(d k ,θ k ) into the far-field array steering vector; using the first-order Taylor expansion d k，n , the far-field channel vector It can be expressed as: in, are the corresponding coordinates of the far-field users; the channel between the base station and the mth additional user It can be modeled as:

3. The hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting according to claim 2 is characterized by: In step S2, according to the coding and decoding principle of the downlink rate split multiple access technology, the information of the kth near-field user is split into a public part and a private part; then all the public parts are encoded into a public information stream x0; the private part of the kth near-field user is encoded into a private information stream x k ; These independent information flows are then and Linear precoding is performed.

4. The hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting according to claim 3 is characterized by: For private information streams, zero-breaking precoding is used, that is, [p1, ..., p K ]=H(H H H) -1 F, where H = [h1, ..., h K ]and Since the public information flow depends on the decoding rate of the worst user, p0 is set to p0 = h k′ ||h k′ || -1 ,in If the mth additional user occupies the beam of the kth near-field user, the binary variable is set to b k，m =1, otherwise b k，m =0; According to the above settings, the signal transmitted by the base station can be expressed as: Among them, P k and P k，m is the transmission power allocation variable between the near-field user and the additional user; According to equation (5), the signal received by the kth near-field user can be obtained as in is additive Gaussian white noise; in order to decode the required information, the kth near-field user first regards the private information stream as interference to decode the public information stream, and the corresponding signal-to-interference-noise ratio is: in, To ensure that all near-field users can successfully decode the common information stream, At the same time, since all near-field users share the public information flow, Where R k，c is the public rate allocated to the kth near-field user; after removing the interference of the public information stream using serial interference cancellation, the near-field user starts decoding the private information stream, and the corresponding signal-to-interference-to-noise ratio is: Combining equations (6) and (7), we can get the transmission rate R of the kth near-field user: k =R k，c +log(1+γ k，p ); Similarly, the signal received by the mth additional user can be expressed as in is additive Gaussian white noise; assuming that the mth additional user occupies the beam of the kth near-field user, the corresponding signal-to-interference-noise ratio is: in, The transmission rate of the mth additional user is 5. The hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting according to claim 4 is characterized by: Under the premise of ensuring the service quality requirements of near-field users, the sum rate of additional users is maximized to enhance the throughput and connectivity of the network. Therefore, the optimization problem (9) can be modeled as: Among them, P max and R th are the maximum transmission power threshold of the base station and the service quality requirement threshold of the near-field user respectively; formula (9b) and formula (9c) are the transmission power of the base station and the service quality constraints of the near-field user respectively; formula (9d) and formula (9e) are the common rate allocation constraints of the rate splitting multiple access technology; formula (9f) limits each beam to only accept one additional user's occupancy.

6. The hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting according to claim 5 is characterized by: Solve the optimization problem (9) and split it into two sub-problems, namely the beam allocation sub-problem and the power and common rate allocation sub-problem; then design an iterative optimization algorithm to solve the two sub-problems respectively.

7. The hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting according to claim 6 is characterized by: The solution steps of the beam allocation iterative optimization algorithm are as follows: Step A1, Lemma 1 is proposed to simplify the optimization problem; Lemma 1 is as follows: The transmission rate of near-field users is not affected by beam allocation, that is, the rate of near-field users is independent of beam allocation; if g k′，m >g k，m , then the mth additional user prefers the beam of the k′th near-field user to the beam of the kth user, and vice versa; Proof: Equations (6) and (7) show that the signal-to-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 decreases with g. k，m To enhance the network throughput, the mth additional user prefers the beam of the k′th near-field user, where Combining the above conclusions, Lemma 1 holds true; Lemma 1 provides the beam allocation criterion, but multiple additional users may wish to occupy the same beam; to reduce the performance loss of some beams, the beam allocation subproblem is simplified to: Step A2, first introduce the matrix in Then 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 ],in and Given a threshold ∈ th , the proposed algorithm first determines for each beam g k，m ≥∈ th Whether it can be satisfied, and then update the given threshold using the binary search method according to the result; To facilitate the description of the algorithm flow, the following two definitions are introduced: Definition 1: If g k，m ≥∈ th , then k is called a feasible beam for the mth additional user, and m is an optional additional user for the kth near-field user beam; the total number of feasible beams for the mth additional user is called degree, denoted by deg(m); Definition 2: If the following conditions hold, the scheduling priority of the mth additional user is higher than that of m′; Condition 1) deg(m)<deg(m′) or Condition 2) deg(m)=deg(m′) and m<m′, Among them, condition 2) is to avoid that two additional users have the same degree; similarly, deg(k) and the corresponding priority can be defined; the algorithm flow for solving beam allocation is as follows: 1) Calculate the matrix G and initialize α min and α max , set the iteration index i = 1, the maximum tolerance error ∈ th and∈ (1) -∈ (0) >∈ th ; 2) While |∈ (i) -∈ (i-1) |>∈ th implement; 3) Set ∈ (i) =0.5(α min +α max ), and g = 0 M ; 4) For Setting g k，m =0; 5) While deg(m)≠0, implement 6) Find the additional user m with the highest priority, and find the near-field user k with the highest priority from its neighboring nodes; 7) Update g(j)=g k，m , j = j + 1; 8) Update g x，y =0, where x=k or y=m; 9) End while; 10) Update i=i+1; 11) If j = M; 12) Set g k，m =0, where 13) Update α min =min(g)≥∈ (i-1) ; 14)else; 15) Update α max =∈ (i-1) ; 16) End while; 16) Output beam allocation results 8. The hybrid far-field and near-field communication beam and power joint optimization method based on rate splitting according to claim 6 is characterized by: The solution steps of the iterative optimization algorithm for power and public rate allocation are as follows: Step B1, Lemma 2 is proposed to construct a proxy function for the non-convex signal-to-interference-noise ratio of the decoded data of the near-field user and the additional user; wherein Lemma 2 is as follows: The constructed proxy function It is completely equivalent to the signal-to-interference-to-noise ratio, where s(p)≥0 and I(p)>0, that is, And the optimal solution on the right side of equation (11) is Proof: Since 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 introduced auxiliary variable y; therefore, the optimal y can be obtained by making the first-order partial derivative equal to zero. * ,Right now y * Substituting into equation (11), we can prove that Lemma 2 holds; Reconstruct γ using Lemma 2 k，c , γ k，p and The constructed proxy function is as follows: in, Step B2, remove the minimum value symbol and restructure the subproblem as follows: in, and because and The coupling between variables,problem (13) is still difficult to solve directly; Step B3, although problem (13) is difficult to solve directly, when When , we can obtain the optimal The closed-form solution of When , it can be obtained by using the convex optimization toolbox The optimal solution; Based on this, the present invention proposes a double-layer iterative optimization algorithm to solve problem (13); The specific solution algorithm is as follows: 1) Initialization: Initialization and iteration index i = 1; 2) While non-convergent execution; 3) Calculate the optimal auxiliary variable according to Lemma 2 4) Use the convex optimization toolbox to solve problem (14) and output the optimal 5) Update i=i+1; 6) End while; 7) Output the suboptimal solution of network reachability and rate.

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