Star-ris assisted resource allocation method for noma coexistence radio system in hybrid communication mode

Abstract

The present application relates to a kind of mixed communication mode under the resource allocation method of simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) symbiotic radio system, belong to the field of Internet of Things.The present application includes the following steps: establishing the symbiotic radio system of STAR-RIS assisted NOMA under mixed communication mode, wherein the backscattering device (BD) in NOMA cluster adopts mixed communication mode for information transmission, STAR-RIS adopts time switching protocol to assist primary and secondary systems;Under the premise of meeting the target throughput of primary system, with the maximum throughput of secondary system BD as target, resource allocation model is constructed;Using block coordinate descent method, the resource allocation model of throughput maximization is converted into convex optimization problem;Optimization problem is solved using convex optimization toolbox, and the transmission time block of three stages, the active beam forming of base station, the reflection coefficient of STAR-RIS and BD transmission power, the transmission coefficient of STAR-RIS and BD reflection coefficient, i.e.resource allocation scheme is obtained.The present application can obtain better throughput performance, and it is proved that the fusion of mixed communication mode, STAR-RIS and NOMA technology can significantly improve the performance of symbiotic radio system.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of Internet of Things, and relates to a resource allocation method for a non-orthogonal multiple access (NOMA) symbiotic radio system assisted by a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) under a hybrid communication mode. BACKGROUND

[0002] With the rapid penetration of the Internet of Things in various fields of social production, the number of sensor nodes in the Internet of Things is showing an explosive growth. However, the scarcity of spectrum resources and the insufficient endurance of devices have become key factors restricting the sustainable development of the Internet of Things. In recent years, as a new communication paradigm, symbiotic radio is considered one of the core technologies of future Internet of Things due to its mutual sharing characteristics in the spectrum and energy domains, and high-reliable backscatter communication can be achieved through joint decoding. Symbiotic radio technology is expected to play a more important role in future communication fields.

[0003] However, the influence of double-channel fading limits the transmission performance of the symbiotic radio system. Existing researches mainly focus on improving system performance by introducing RIS technology to assist communication links or optimizing beamforming. Although these methods improve the communication quality to some extent, they still cannot fully solve the problems of resource allocation and transmission efficiency, especially in complex communication environments. SUMMARY

[0004] In view of this, the purpose of the present application is to provide a resource allocation method for a NOMA symbiotic radio system assisted by a STAR-RIS under a hybrid communication mode.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] A resource allocation method for a NOMA symbiotic radio system assisted by a STAR-RIS under a hybrid communication mode, the method comprising the following steps:

[0007] Step 1: establishing a symbiotic radio system of NOMA assisted by STAR-RIS under a hybrid communication mode, wherein the backscatter devices (BDs) in the NOMA cluster use a hybrid communication mode for information transmission, and the STAR-RIS uses a time switching protocol to assist the primary and secondary systems;

[0008] Step 2: under the premise of meeting the target throughput of the primary system, taking the maximum achievable throughput of the secondary system BD as the target, a resource allocation model is constructed;

[0009] Step 3: using the block coordinate descent method, the resource allocation model for maximizing the throughput is converted into a convex optimization problem;

[0010] Step four: solve the optimization problem by using convex optimization toolbox, and obtain the three-stage transmission time block, the active beamforming of the base station, the reflection coefficient of the STAR-RIS and the BD transmit power, the transmission coefficient of the STAR-RIS, and the BD reflection coefficient, that is, the resource allocation scheme.

[0011] Further, in step one, the STAR-RIS assisted NOMA coexistence wireless system in the hybrid communication mode includes a base station configured with N antennas, a single-antenna cooperative receiver (CR), K single-antenna BDs, and a STAR-RIS with M elements; the coverage area is divided into a reflection zone and a transmission zone according to the STAR-RIS working mode, the base station and the K BDs are located in the reflection zone of the STAR-RIS, and the CR is located in the transmission zone. Define Θr=diag{u represents an Mx1-dimensional complex matrix space, represents a unit complex vector, represents the phase shift of the mth STAR-RIS element, 1≤m≤M, [·] H represents the conjugate transpose, and the corresponding reflection coefficient matrix is Θ r =diag{u r}, diag{·} represents a diagonal matrix; similarly, the transmission coefficient vector of the STAR-RIS and its transmission coefficient matrix are and

[0012] Further, in step two, under the premise of meeting the main system target throughput, the resource allocation model is constructed to maximize the achievable throughput of the secondary system BD, which is configured as:

[0013]

[0014] C6:t1+t2+t3≤T,t1,t2,t3≥0

[0015] C7:t R =t1,t T =t2+t3

[0016]

[0017] where max represents the maximum value of the objective function, respectively represent the total throughput of the BD and the total throughput of the base station in the three transmission stages, the superscript of indicates that the throughput value takes the sum operation, and the subscript corresponds to the BD, i={1,2,3}, the superscript i corresponds to the i-th stage of transmission, where the subscript corresponds to the main system base station; t = [t1, t2, t3] is the transmission time block of the three phases, the subscript i corresponds to the three phases of transmission, w is the base station transmit beamforming vector, a is the BD reflection coefficient, P b is the BD transmit power, the subscript corresponds to the BD; C1 represents the minimum limit of the achievable throughput of the main system, C2 represents the QoS constraint when the BD of the secondary system is decoded, SINR of BD k in phase i, the superscript corresponds to the corresponding transmission phase, and the subscript corresponds to BD k, represents the SINR threshold for BD k to decode c k , the superscript min represents the minimum value of the decoding SINR; C3 represents the maximum limit of the total transmit power of the base station , the superscript max represents the maximum value, and the subscript corresponds to the main system base station; C4 requires that the energy collected by the BD in the first two phases must be greater than the energy required for the BD to implement active communication in the third phase, P b,k represents the transmit power of BD k, and are the energy collected by the BDs in phase 1 and phase 2, respectively, the subscript corresponds to the corresponding BD, and the superscript corresponds to the corresponding transmission phase; C5 represents the non-negative constraint of the transmit power of the BD when performing active communication in phase three; C6 is the time constraint of the three phases, where T represents the entire system working time block; C7 is the reflection and transmission time allocation factor of STAR-RIS and the respective time proportion of the three phases, where the reflection and transmission times of STAR-RIS are t R and t T ; C8 is the reflection coefficient constraint of the BD, a i,k represents the reflection coefficient of BD k at phase i; C9 is the reflection and transmission phase shift constraint of STAR-RIS, and represent the phase shift of STAR-RIS at the mth reflection unit and transmission unit, respectively.

[0018] Further, in step three, in the solving process of the resource allocation model for throughput maximization, the following steps are included:

[0019] a. For the transmission time block optimization of the three phases, P1 is transformed into the following optimization problem, which can be directly solved.

[0020]

[0021] s.t.C1,C4,C6,C7

[0022] b. For the base station active beamforming optimization, the optimization problem transformed from P1 is as follows:

[0023]

[0024] s.t.C1,C2,C3,C4

[0025] P1-2 is still a non-convex optimization problem, wherein the objective function is a non-convex function, and C1 and C2 are non-convex constraints.

[0026] c. For STAR-RIS reflection coefficient and BD transmit power optimization, the optimization problem converted by P1 is as follows:

[0027]

[0028] s.t.C1,C2,C4,C5,C9

[0029] d. For STAR-RIS transmission coefficient matrix optimization, the optimization problem converted by P1 is as follows:

[0030]

[0031] s.t.C1,C2,C9

[0032] e. For BD reflection coefficient optimization, the optimization problem converted by P1 is as follows:

[0033]

[0034] s.t.C1,C2,C4,C8

[0035] Further, in step four, the optimization problem has been converted into a convex optimization problem, and the optimization problem can be directly solved by using a convex optimization (CVX) tool to obtain the time slot between the NOMA clusters, the reflection coefficient of the backscatter device and the transmit power of the full duplex, that is, to obtain the resource allocation scheme.

[0036] The beneficial effects of the present application are as follows:

[0037] In the face of the higher requirements of the future Internet of Things on spectrum efficiency, energy efficiency and communication quality, the present application fuses NOMA and STAR-RIS technologies in a symbiotic radio system, and designs a STAR-RIS assisted NOMA symbiotic radio system resource allocation method in a hybrid communication mode. Under the condition of meeting the target throughput constraint of the primary system, a non-convex optimization function subject to multiple conditions is constructed to maximize the total throughput of BDs. The function is converted into a solvable convex optimization problem by using successive convex approximation (SCA), variable substitution and semi-positive relaxation method; finally, based on the idea of alternating iteration, the joint optimization of three stages of transmission time block, active beamforming of base station, reflection coefficient of STAR-RIS and BD transmission power, transmission coefficient of STAR-RIS and BD reflection coefficient is realized. The results show that compared with other four traditional methods, the throughput of the present application can be increased by 14.36%, 43.43%, 67.78% and 439.69% respectively, which proves that the fusion of the three technologies can significantly improve the performance of the symbiotic radio system. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The flow chart of the resource allocation method of the present application is shown in the figure.

[0039] Figure 2 The overall framework diagram of the STAR-RIS assisted NOMA symbiotic radio system in a hybrid communication mode of the present application is shown in the figure.

[0040] Figure 3 The convergence characteristic curve of the total throughput of BDs of the present application is shown in the figure.

[0041] Figure 4 The curve of the total throughput of BDs at the base station transmission power of the present application and the comparative method is shown in the figure. DETAILED DESCRIPTION

[0042] Please refer to Figures 1-4 , a STAR-RIS assisted NOMA symbiotic radio system resource allocation method in a hybrid communication mode.

[0043] The present application provides a STAR-RIS assisted NOMA symbiotic radio system resource allocation method in a hybrid communication mode, as shown in the figure. Figure 1 The method comprises the following steps:

[0044] Step 1: Establish a STAR-RIS assisted NOMA symbiotic radio system in a hybrid communication mode, wherein the BDs in the NOMA cluster use a hybrid communication mode for information transmission, and the STAR-RIS uses a time switching protocol to assist the primary and secondary systems.

[0045] Step two: Under the premise of meeting the target throughput of the primary system, a resource allocation model is constructed to maximize the throughput of the secondary system BDs;

[0046] Step three: The resource allocation model for maximizing throughput is converted into a convex optimization problem by using the block coordinate descent method.

[0047] Step four: The optimization problem is solved using a convex optimization toolbox to obtain the transmission time block, active beamforming of the base station, reflection coefficient of STAR-RIS, BD transmit power, transmission coefficient of STAR-RIS, and BD reflection coefficient in the three stages, i.e., the resource allocation scheme.

[0048] Further, in step one, as Figure 2 The system includes a base station with N antennas, a single-antenna cooperative receiver (CR), K single-antenna BDs, and a STAR-RIS with M elements; the coverage area is divided into reflection and transmission zones according to the STAR-RIS operating mode, the base station and K BDs are located in the reflection zone of STAR-RIS, and the CR is located in the transmission zone. Define as the reflection coefficient vector of STAR-RIS, where is the phase shift of the mth STAR-RIS element, 1≤m≤M, and the corresponding reflection coefficient matrix is Θ r = diag{u r}; Similarly, the transmission coefficient vector of STAR-RIS and its transmission coefficient matrix are and

[0049] The transmission process is divided into three stages. First, in the first two stages when the primary system's licensed spectrum is busy, the BDs perform backscattering communication and form a symbiotic relationship with the primary system. In stage one, STAR-RIS improves energy harvesting through reflection mode, and in stage two, it assists the primary and secondary systems through transmission mode. Then, in the third stage when the spectrum is idle, STAR-RIS assists the active communication of BDs through transmission mode.

[0050] When STAR-RIS works in T mode, the channel from the base station to CR through STAR-RIS is denoted as The subscript corresponds to the primary system, i.e. The backscattering channel from BD k to CR through STAR-RIS is denoted as The subscript corresponds to the secondary system, i.e. When STAR-RIS works in R mode, the channel from the base station to BD k through STAR-RIS is denoted as The subscript corresponds to the primary system, i.e. where the channel coefficients from the base station to CR, BD k, and STAR-RIS are The subscript k corresponds to BD k; STAR-RIS to BD k, CR channel coefficients are h The subscript r corresponds to the reflection region, and t corresponds to the transmission region; the channel coefficients of BD k to CR, STAR-RIS are h c,k , Where k ∈ {1,...,K}.

[0051] Further, in step two, the process of constructing a resource allocation model that maximizes the throughput of the BDs includes:

[0052] Considering the symbiotic primary and secondary systems, let the periods of the primary and secondary system symbols be T s and T c , respectively, and T c = LT S , satisfying L » 1, » represents much greater than. Let the primary system symbol be s(l), representing the lth primary symbol within a single symbol period of the BD, and the base station transmit beamforming vector be w, then the equivalent transmitted signal of the base station is ws(l), l ∈ {1,...,L}.

[0053] In the first stage, the BD performs backscattering communication. In the lth primary symbol period within a single symbol period of the BD, the backscattered signal of BD k can be represented as Where α 1,k ∈ [0,1] is the reflection coefficient of BD k in stage one, c k represents the transmission symbol of BD k. Correspondingly, the energy collected by BD k is:

[0054]

[0055] Where η k ∈ (0,1) represents the energy collection efficiency of BD k.

[0056] The received signal at CR is:

[0057]

[0058] Where, represents the additive white Gaussian noise at CR, which is subject to 0 mean and variance .

[0059] In CSR, the equivalent channel for decoding s(l) can be represented as Thus, for the primary system, the SINR for decoding s(l) is:

[0060]

[0061] Therefore, the achievable throughput of the primary system in the first stage is:

[0062]

[0063] where log2(·) denotes the logarithm function with base 2, E[·] denotes the expectation, and the variable c k .

[0064] For L » 1, the achievable throughput at CR for s(l) is approximately:

[0065]

[0066] For the secondary system, CR employs a successive interference cancellation (SIC) technique, which first decodes the primary signal and then removes the successfully decoded primary signal. Thus, the intermediate decoded signal corresponding to L primary symbols in a symbol period of the secondary system can be expressed as follows, where [·] T denotes the transpose.

[0067]

[0068] where s = [s(1), s(1),..., s(L)] T is the symbol vector of the primary system, and n c = [n c (1), n c (2),..., n c (L)] T denotes the intermediate noise vector corresponding to the SIC decoding process.

[0069] According to the SIC principle, it is assumed that c i is decoded before c j (i < j). By decoding c k according to the maximal ratio combining technique, the corresponding SINR for CR decoding c k is:

[0070]

[0071] Thus, the total throughput of the first stage of BD is:

[0072]

[0073] In the second stage, BD performs backscatter communication. The signal backscattered by BD k can be expressed as Correspondingly, the energy collected by BD k is:

[0074]

[0075] The signal received at CR is:

[0076]

[0077] where s(l) is decoded s The equivalent channel for s(l) is Thus, the SINR for decoding s(l) for the primary system is

[0078]

[0079] Correspondingly, if L » 1, the achievable throughput at s(l) for the CR can be approximated as

[0080]

[0081] For the secondary system, SIC is also employed at the CR, and the primary system symbols are decoded first, and then the successfully decoded symbols are removed. The intermediate signal for the second stage decoding is where s(l) is decoded

[0082]

[0083] Similar to the first stage, the SINR for decoding c k is

[0084]

[0085] Thus, the total throughput for the second stage BD is

[0086]

[0087] In the third stage, the BD occupies the licensed band for information transmission, and performs active communication. For this purpose, the received signal at the CR is

[0088]

[0089] where P b,k is the transmit power of the BD k, and needs to satisfy the following condition:

[0090]

[0091] The SINR for decoding c k is

[0092]

[0093] Since the decoding order for uplink NOMA does not affect the total throughput achievable by all BDs, the total throughput achievable by the BDs in the secondary system in the third stage is

[0094]

[0095] The objective of the present application is to maximize the achievable throughput of the secondary system BDs under the premise of meeting the main system target throughput by jointly optimizing the three-stage transmission time block t, base station active beamforming, STAR-RIS reflection coefficient and BD transmit power P b , STAR-RIS transmission coefficient, and BD reflection coefficient α, the resource optimization problem can be described as:

[0096]

[0097] C6: t1 + t2 + t3 ≤ T, t1, t2, t3 ≥ 0

[0098] C7: t R = t1, t T = t2 + t3

[0099]

[0100] Wherein, C1 represents the minimum limit of the main system achievable throughput; C2 represents the QoS constraint when the secondary system BD decodes, wherein represents the SNR threshold of BD k decoding c k ; C3 represents that the maximum limit of the total power of the base station transmission is C4 requires that the energy collected by the BD in the first two stages must be greater than the energy required for the active communication of the BD in the third stage; C5 represents the non-negative constraint of the transmission power of the BD when performing active communication in the third stage; C6 is the time constraint of the three stages; C7 is the relationship between the reflection and transmission time allocation factor of the STAR-RIS and the respective time proportion of the three stages; C8 is the reflection coefficient constraint of the BD; C9 is the reflection and transmission phase shift constraint of the STAR-RIS.

[0101] For P1, first, by block coordinate descent method, it is transformed into the following five sub-optimization problems:

[0102] a. Three-stage transmission time block optimization, P1 is transformed into the following optimization problem, which can be directly solved.

[0103]

[0104] s.t.C1,C4,C6,C7

[0105] b. Base station active beamforming optimization, the optimization problem transformed by P1 is as follows:

[0106]

[0107] s.t.C1,C2,C3,C4

[0108] where the objective function is non-convex with respect to w. First, the objective function is converted into a form that is easy to handle, define the base station active beamforming matrix satisfying W > 0 and Rank(W) = 1, where Rank(·) denotes the rank of a matrix. For the convenience of subsequent analysis, define Further define The subscript k corresponds to BD k. Since only the first two stages involve the active beamforming vector w in the objective function, the objective function is rewritten as

[0109]

[0110] where Tr(·) denotes the trace of a matrix. The objective function is still non-convex, for the ease of handling, the SCA method is adopted. By introducing the relaxation variables l = [l i ] and i = 1, 2, i corresponds to transmission stage i. Thus, we have

[0111]

[0112]

[0113] where exp(·) denotes the exponential function.

[0114] The first-order Taylor approximation is adopted, i.e.,

[0115] Further, we have

[0116]

[0117] Thus, the objective function is re-expressed as

[0118]

[0119] Further, for C1, the throughput of the main system can be re-expressed as

[0120]

[0121] where where the superscript denotes the conjugate transpose, and the subscript is mainly used for differentiation.

[0122] For C4, let Then the sum of the energy collected by BD in the first two stages is

[0123]

[0124] Through the above conversion, P1-2 can be converted to

[0125]

[0126] C10: Rank(W) = 1

[0127] C11: C11-1, C11-2, C11-3, C11-4

[0128] where C10 is still a non-convex constraint; after removing the rank 1 constraint in P1-2', the problem can be relaxed as a semidefinite programming (SDP) problem. If the optimal solution W * with rank 1 can be obtained by singular value decomposition W* = w*(w*) H solving w, otherwise, a suboptimal solution of P1-2' is obtained by the method of Gaussian randomization, and the superscript denotes the optimal value.

[0129] c. STAR-RIS reflection coefficients and BD transmit power optimization, the optimization problem of P1 transformation is as follows:

[0130]

[0131] s.t. C1, C2, C4, C5, C9

[0132] where the objective function and C1 are non-convex. For subsequent processing, according to the reflection coefficient vector satisfies

[0133] For the objective function, we have using the trace operation of the matrix and the inner product operation of the vector, it is transformed into a matrix form Tr(Q brk1 U r ), where and satisfies U r ≥ 0 and Rank(U r ) = 1, where the subscript r represents the reflection region, and b corresponds to the BD in the secondary system.

[0134] Since the STAR-RIS reflection coefficient and the BD transmit power optimization are not involved in phase two, the objective function can be rewritten as follows:

[0135]

[0136] where the objective function is still non-convex, and the SCA method is also used. By introducing the relaxation variables q1 = [q 1,1 ,..., q 1,K-1 ] and φ1 = [φ 1,1 ,..., φ 1,K-1 ], the subscript corresponds to the BD k, then for k, k ∈ {1,..., K-1} we have:

[0137]

[0138] Similarly, using first-order Taylor approximation, we have

[0139]

[0140] Therefore, the objective function can be rewritten as

[0141]

[0142] Then, for C1, similar treatment can be applied in the objective function. First, we express the first term of as where Thus, the first term can be rewritten as Tr(Q cr1 U r ), where the second term of cr2 U r ) can be rewritten as Tr(Q Therefore, the throughput of the main system is

[0143]

[0144] Similarly, C4 can be rewritten as

[0145]

[0146] According to the above transformation, P1-3 becomes P1-3' as shown.

[0147]

[0148] C13: Rank(U r ) = 1

[0149] C14: C14-1, C14-2

[0150] where C13 is non-convex and can be relaxed using SDR technique. After removing C13, P1-3' can be solved by convex optimization tool CVX. Note that the solution may not satisfy the rank-one constraint, in which case the solution of * can be similar to that of w

[0151] d. STAR-RIS transmission coefficient matrix optimization, the optimization problem of P1 transformation is as follows:

[0152] ​​​​

[0153] s.t.C1,C2,C9

[0154] where the objective function and C1are non-convex. Similar to the reflection coefficient vector optimization, define the transmission coefficient vector Then we have Thus, in the objective function can be rewritten as Tr(Q btk1 U t ), where satisfies U t ≥ 0 and Rank(U t ) = 1; where The subscript t denotes the transmission zone, and b corresponds to the BD in the sub-system. In addition, since the first stage does not involve the transmission coefficient vector of STAR-RIS, the objective function can be rewritten as follows.

[0155]

[0156] However, the above equation is still non-convex, so the SCA method is used. Introduce the relaxation variables q2= [q 2,1 ,...,q 2,K-1 ] and φ2= [φ 2,1 ,...,φ 2,K-1 ], for any k∈{1,...,K-1} have:

[0157]

[0158] Similarly, using the first-order Taylor approximation, we have:

[0159]

[0160] The objective function can be represented as:

[0161]

[0162] The third and fourth terms of can be represented as Tr(Q ct1 U t ) and Tr(Q ct2 U t ), respectively, where

[0163] where the subscript c corresponds to the main system. Therefore, the throughput of the main system can be represented as:

[0164]

[0165] According to the above conversion, P1-4 becomes P1-4'.

[0166]

[0167] C16: Rank(U t ) = 1

[0168] C17: C17-1, C17-2

[0169] Among them, C16 in P1-4' is non-convex, which can also be relaxed by SDR technology, and the solution is The method is similar to solving w * in P1-2'; after removing C16, P1-4' can be solved by convex optimization tool CVX.

[0170] e. BD reflection coefficient optimization, the optimization problem of P1 conversion is as follows:

[0171]

[0172] s.t. C1, C2, C4, C8

[0173] Among them, the objective function and C1 are non-convex; similarly, the SCA method is used to process the problem.

[0174] For the objective function, introduce the relaxation variable u = [u i ] and ψ = [ψ i ], where u i = [u i,1 ,..., u i,K-1 ], ψ i = [ψ i,1 ,..., ψ i,K-1 ], i = 1, 2. Thus, for any k ∈ {1,..., K-1} we have:

[0175]

[0176] Similarly, using the first-order Taylor approximation, we get respectively:

[0177]

[0178] Among them, represents the feasible point of α 1,k at the mth iteration when the SCA method is used to process.

[0179] In the objective function, since only stages one and two involve variables, the objective function is rewritten as:

[0180]

[0181] For non-convex constraint condition C1, the lower bound of is used for approximation. The lower bound of main system throughput is:

[0182]

[0183] wherein, the superscript represents the lower bound value of the main system throughput.

[0184] According to the above conversion, P1-5 becomes P1-5′.

[0185]

[0186] C2, C4, C8

[0187] C18: C18-1, C18-2, C18-3, C18-4

[0188] This problem is a convex optimization problem, and can be effectively solved by using the CVX tool.

[0189] Embodiment

[0190] This example combines simulation to describe the application effect of the present application in detail.

[0191] This embodiment verifies the convergence and effectiveness of the method through simulation results. Considering a three-dimensional space coordinate model, the base station, CR, STAR-RIS and BD1, 2 are located at (0, 0, 10), (0, 50, 0), (0, 10, 10), (0, 1, 0), (0, -1, 0) (unit: meters) respectively. The path loss exponents from the base station to the CR, the base station to the BD and the BD to the CR are set to 3.8, 3 and 2.8 respectively, and other path loss exponents are 2. Other parameters are set as follows, K = 2, L = 128, T = 1, N = 6, M = 40, η = 0.8, ε0 = -30 dB, κ = 3 dB.

[0192] In this embodiment, the proposed STAR-RIS assisted NOMA coexisting radio system resource allocation method in the mixed communication mode is compared with Figure 3 It can be seen that the convergence characteristics of the total throughput of the BD of the present method. In the figure, the total throughput of the BD tends to be stable after about 2 iterations, which indicates that the BD can obtain stable throughput after 2 iterations of the iteration algorithm; at the same time, with the increase of the number of base station antennas or the number of STAR-RIS units, the total throughput of the BD shows an upward trend.

[0193] Compared with the traditional method, from Figure 4The performance of the method of the present invention and four traditional methods in terms of BD throughput increase trend and difference was compared. The method of the present invention is always better than other traditional methods at all power levels. Compared with traditional method one, the method of the present invention reasonably allocates the time of stage one, so that the BD can collect more energy, thereby increasing the duration of stage three, and thus improving the throughput of the subsystem; compared with traditional method two, the method of the present invention uses the NOMA scheme to achieve active communication in stage three, thereby improving the throughput of the subsystem; compared with traditional method three, the phase shift of STAR-RIS is optimized to improve the BD communication environment, thereby significantly improving the throughput of the subsystem; and traditional method four does not have the active communication stage of stage three, resulting in a relatively small total throughput of the BD, and its trend with power change is not obvious.

[0194] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred implementation cases, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A resource allocation method for non-orthogonal multiple access (NOMA) symbiotic radio system assisted by simultaneously transmissive and reflective reconfigurable smart surface (STAR-RIS) in hybrid communication mode, characterized by: The following steps are involved: Step 1: Establish a NOMA symbiotic radio system assisted by STAR-RIS in hybrid communication mode, where the backscatter device (BD) in the NOMA cluster adopts hybrid communication mode for information transmission, and STAR-RIS uses a time-switched protocol to assist the primary and secondary systems; the NOMA symbiotic radio system assisted by STAR-RIS in hybrid communication mode includes a base station with N antennas, a single-antenna cooperative receiver (CR), K single-antenna BDs, and a STAR-RIS with M units; According to the STAR-RIS working mode, the coverage area is divided into a reflection area and a transmission area. The base station and K BDs are located in the reflection area of ​​STAR-RIS, and the CR is located in the transmission area. represents the reflection coefficient vector of STAR-RIS, where the subscript r represents the STAR-RIS reflection area, represents the M×1 dimensional complex matrix space, represents a unit complex vector, represents the phase shift of the m-th STAR-RIS element, 1≤m≤M, [·] H Represents conjugate transpose, and the corresponding reflection coefficient matrix is ​​Θ r =diag{u r }, diag{·} represents a diagonal matrix; the transmission coefficient vector and transmission coefficient matrix of STAR-RIS are and Step 2: Under the premise of meeting the target throughput of the primary system, the resource allocation model is constructed with the goal of maximizing the achievable throughput of the secondary system BD. Under the premise of meeting the target throughput of the primary system, the resource allocation model is constructed with the goal of maximizing the achievable throughput of the secondary system BD. The resource allocation model is configured as follows: Among them, max means that the objective function takes the maximum value, Respectively represent the total BD throughput and the total base station throughput in the three transmission stages, The superscript of represents the throughput value sum operation, and the subscript corresponds to BD. i={1,2,3}, where superscript i corresponds to the i-th stage of transmission, The subscripts of correspond to the main system base station; t = [t1, t2, t3] is the transmission time block of the three phases, the subscript i corresponds to the three phases of transmission, w is the base station transmit beamforming vector, α is the BD reflection coefficient, P b is the BD transmission power, the subscript corresponds to BD; C1 represents the minimum limit of the throughput that can be achieved by the main system, The target throughput of the main system, the superscript min indicates the minimum value of the base station throughput; C2 represents the QoS constraint during subsystem BD decoding. is the signal-to-interference-plus-noise ratio (SINR) of BD k in phase i, the superscript corresponds to the corresponding transmission phase, and the subscript corresponds to BD k, Indicates BD k decoding c k The SINR threshold, the superscript min indicates the minimum value of the decoded SINR; C3 indicates the maximum limit of the total base station transmission power is The superscript max indicates the maximum value, and the subscript corresponds to the main system base station; C4 requires that the energy collected by BD in the first two stages must be greater than the energy required for BD to achieve active communication in the third stage, P b,k Expressed as the transmit power of BD k, and are the energies collected by BDs in phase 1 and phase 2, respectively. The subscripts correspond to the corresponding BDs, and the superscripts correspond to the corresponding transmission phases. C5 represents the non-negative constraint on the transmission power of BDs when performing active communication in phase 3. C6 represents the time constraint of the three phases, where T represents the working time block of the entire system. C7 represents the relationship between the reflection and transmission time allocation factor of STAR-RIS and the time proportion of each of the three phases, where the reflection and transmission times of STAR-RIS are t and t, respectively. R and t T ; C8 is the reflection coefficient constraint of BD, α i,k represents the reflection coefficient of BD k at stage i; C9 is the reflection and transmission phase shift constraint for STAR-RIS, and denote the phase shift of the mth reflection unit and transmission unit of STAR-RIS respectively; Step 3: Use the block coordinate descent method to transform the throughput maximization resource allocation model into a convex optimization problem; Step 4: Use the convex optimization toolbox to solve the optimization problem and obtain the transmission time blocks for the three phases, the base station's active beamforming, the STAR-RIS reflection coefficient and BD transmit power, the STAR-RIS transmission coefficient, and the BD reflection coefficient, thus obtaining the resource allocation solution.

2. The method according to claim 1, characterized in that In step 3, the process of solving the resource allocation model for maximizing throughput includes the following steps: a. Optimizing the transmission time blocks in three stages, P1 is transformed into the following optimization problem, which can be solved directly: stC1,C4,C6,C7 b. For base station active beamforming optimization, P1 is transformed into the following optimization problem: stC1,C2,C3,C4 Among them, for w, the objective function is non-convex; first, the objective function is converted into a form that is easy to handle and the base station active beamforming matrix is ​​defined as Satisfy W>0 and Rank(W)=1, where Rank(·) represents the rank of the matrix; adopt the successive convex approximation (SCA) method, by introducing the slack variable l=[l i ]and i=1,2, the subscript represents the stage i, and the first-order Taylor approximation is used to complete the processing of the objective function, and the problem is relaxed into a semi-definite programming problem; c. For the optimization of STAR-RIS reflection coefficient and BD transmission power, P1 is transformed into the following optimization problem: stC1,C2,C4,C5,C9 Among them, the objective function and C1 are non-convex; in order to facilitate subsequent processing, according to the reflection coefficient vector satisfy For the objective function, Converted into matrix form Tr(Q brk1 U r ), where Tr(·) represents the trace of the matrix, |·| 2 represents taking the square of the complex modulus, And satisfy U r ≥0 and Rank(U r )=1; Then, the SCA method is used to complete the processing of the objective function; for C1, similar processing in the objective function can be used; The first term can be re-expressed as Tr(Q cr1 U r ),in The second term can be re-expressed as Tr(Q cr2 U r ),in The subscript r represents the reflection area, and b corresponds to the BD in the subsystem. The problem is relaxed into a semi-definite programming problem. d. For the STAR-RIS transmission coefficient matrix optimization, P1 is transformed into the following optimization problem: stC1,C2,C9 Among them, the objective function and C1 are non-convex; similar to the optimization of the reflection coefficient vector, the transmission coefficient vector is defined Then there is In the objective function Can be rewritten as Tr(Q btk1 U t ),in Meet U t ≥0 and Rank(U t )=1; in The subscript t represents the transmission area, and b corresponds to the BD in the subsystem. Then, the SCA method is used to relax the problem into a semi-positive programming problem. e. For BD reflection coefficient optimization, P1 is transformed into the following optimization problem: stC1,C2,C4,C8 Among them, the objective function and C1 are non-convex; the SCA method is also used to deal with this problem; the slack variable u=[u i ] and ψ=[ψ i ], where u i =[u i,1 ,...,u i,K-1 ],ψ i =[ψ i,1 ,...,ψ i,K-1 ], i=1,2, the subscript represents the stage i, and the first-order Taylor approximation is used to relax the problem into a semi-definite programming problem.

3. The method according to claim 1, wherein: In step 4, the optimization problem has been transformed into a convex optimization problem. The convex optimization (CVX) tool can be used to solve the optimization problem directly. The three-stage transmission time blocks, the base station's active beamforming, the STAR-RIS reflection coefficient and BD transmit power, the STAR-RIS transmission coefficient, and the BD reflection coefficient are obtained, thus obtaining the resource allocation solution.

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