TDMA communication resource allocation optimization method assisted by IRS

Abstract

The application discloses an IRS-assisted wireless relay TDMA communication resource allocation optimization method, relates to the technical field of intelligent reflecting surface application, and is used for IRS-assisted wireless relay TDMA communication, in which users close to a base station act as relays, the coverage range of the base station is effectively increased, the IRS can enhance the strength of a received signal at a receiving end, and the communication rate of a TDMA communication system is improved; in the optimization of communication resource allocation of the TDMA system, a channel model among the base station, the IRS, the relay and the remaining K users is constructed by considering the case that there is a channel error; then, a maximum minimum communication rate of the users in a worst case is taken as an objective function, a unit modulus of an IRS reflection coefficient and a time slot allocation coefficient are taken as constraints, a communication resource allocation optimization model is constructed and solved, and a communication resource time slot allocation result is obtained, so that the communication condition in an actual deployment environment is more in line with reality, and the resource allocation fairness among the users is ensured.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent reflective plane applications, and more specifically, to a method for optimizing TDMA communication resource allocation in IRS-assisted wireless relay. Background Technology

[0002] Intelligent reflecting surface (IRS) is a revolutionary new technology that can effectively improve the resource utilization of wireless communication systems and expand the communication coverage. It consists of a large number of low-cost passive reflective elements, each of which can independently adjust its reflection coefficient to change the amplitude and phase of the incident signal.

[0003] TDMA (Time Division Multiple Access) communication systems are based on time division multiple access technology, allocating different time slots to different users. They offer large communication capacity, high anti-interference and security during digital signal transmission, high spectral efficiency, and superior communication quality compared to analog communication systems. Wireless relaying is considered an important technology for achieving wide coverage. Deploying relays in a communication system allows base stations to provide communication services to remote users through multi-hop communication, thereby expanding the base station's coverage area. However, in traditional relay schemes, the long propagation path from the base station to the relay results in significant path loss, leading to low communication rates. Therefore, there is an urgent need to improve the communication performance of relay systems. IRS (Infrared Relay System) has high beamforming gain and can intelligently reconstruct the wireless propagation environment. Using IRS to assist wireless relaying can effectively enhance the quality of the communication link, thereby significantly improving the communication performance of the communication system.

[0004] In real-world communication scenarios, channel estimation errors are unavoidable due to harsh propagation environments and quantization errors; that is, the channel state information obtained by the base station is imperfect. Ignoring channel estimation errors during base station resource optimization design can lead to system performance degradation or even communication outages. Therefore, IRS can be used to assist wireless relay and TDMA network communication. To enhance the robustness of communication systems in real-world scenarios, robust system design is essential.

[0005] The existing technical literature "Q.Sun, P.Qian, W.Duan, J.Zhang, J.Wang and K.-K.Wong, "Ergodic Rate Analysis and IRS Configuration for Multi-IRS Dual-Hop DFRelaying Systems," in IEEE Communications Letters, vol.25, no.10, pp.3224-3228, Oct.2021," considers a communication system model with multiple IRS-assisted DF relays and proposes a method for maximizing the ergodic rate and an expression for the upper bound of the ergodic rate. However, this literature considers a single-user system and does not use TDMA technology. Furthermore, the proposed scheme focuses on resource allocation optimization under perfect channel state information and does not consider resource allocation optimization under imperfect channel state information. Summary of the Invention

[0006] To address the issues of current wireless communication resource allocation optimization failing to consider channel estimation errors and poor robustness of communication systems, this invention proposes a TDMA communication resource allocation optimization method with IRS-assisted wireless relay. This method effectively increases base station coverage, improves communication rates, and ensures fairness in resource allocation among users even in the presence of channel errors.

[0007] To solve the above problems, the technical solution adopted in this application is as follows:

[0008] A method for optimizing TDMA communication resource allocation in IRS-assisted wireless relay includes the following steps:

[0009] S1. Construct a TDMA communication system, including: a base station, an IRS, and K+1 users, with the first user also serving as a relay in the TDMA communication system;

[0010] S2. The communication phase between the base station and the IRS and the first user is taken as the first phase, and the communication phase between the IRS, the first user and the remaining K users is taken as the second phase. Based on TDMA technology and IRS assistance, the base station transmits information to the user.

[0011] S3. Considering channel estimation error, construct a channel model between the base station, IRS, relay, and the remaining K users;

[0012] S4. Solve for the user communication rates corresponding to the first stage and the second stage respectively. Based on the user communication rates corresponding to the first stage and the second stage respectively, obtain the communication rates of K+1 users after the transmission of the first stage and the second stage.

[0013] S5. Combining the channel model obtained in step S3 and the communication rates of K+1 users, calculate the minimum communication rate for K+1 users;

[0014] S6. With the objective function of maximizing the minimum communication rate of users in the worst case, and with the unit modulus of the IRS reflection coefficient and the time slot allocation coefficient as constraints, construct and solve the communication resource allocation optimization model to obtain the communication resource time slot allocation result.

[0015] Through the above technical solution, TDMA communication using IRS-assisted wireless relay, with users close to the base station acting as relays, effectively increases the coverage of the base station. The IRS can greatly enhance the strength of the received signal at the receiver, improving the communication rate of the TDMA communication system. When optimizing the communication resource allocation of the TDMA system, considering the existence of channel errors, a channel model is constructed between the base station, IRS, relays, and the remaining K users. Then, with the objective function of maximizing the minimum communication rate of users in the worst case, and with the unit modulus of the IRS reflection coefficient and the time slot allocation coefficient as constraints, a communication resource allocation optimization model is constructed and solved to obtain the communication resource time slot allocation result, which is more in line with the communication situation in the actual deployment environment and ensures the fairness of resource allocation among users.

[0016] Preferably, in the TDMA communication system, the base station and each of the K+1 users are equipped with an antenna, and the IRS has M reflection units.

[0017] Preferably, in step S2, based on TDMA technology and IRS assistance, when the base station transmits information to the user, a transmission frame is divided into 2K+1 time slots. In the first stage, the base station uses K+1 time slots to transmit the information of K+1 users to the relay. In the second stage, the relay uses the remaining K time slots to transmit the information of K users. Each of the K users uses an independent time slot when transmitting information to each user.

[0018] Preferably, the channel coefficient of the communication link from the base station to the IRS is assumed to be... The channel coefficients for the communication links from the base station to the first user and from the IRS to the first user are respectively and Ignoring the communication link between the base station and user i, where i ∈ {1, 2, ..., K}, user i is one of the K+1 users excluding the first user acting as a relay. The channel coefficient of the relay-to-IRS link is... The channel coefficients of the relay to user i and the IRS to user i communication links are respectively and Where i∈{1,2…K};

[0019] In the first stage, the reflection phase shift matrix of the IRS is φ1=diag(θ1), θ1=[q1,q2,...,q M ] H H denotes the conjugate transpose of the matrix, where, β m ∈[0,1], θ m ∈[0,2π), β m and θ m Let m and m represent the reflection amplitude and reflection phase of the m-th reflecting unit, respectively, where m∈{1,...,M};

[0020] Let |β m |=1,q m Satisfy |q m |=1, in the j-th time slot of the second stage, the IRS reflection coefficient matrix is ​​expressed as: φ 2j =diag(θ) 2j ), θ 2j =[e j1 ,e j2 ,...,e jM ] H e jm Satisfy | e jm |=1, where m∈{1,...,M}.

[0021] Preferably, in step S3, the constructed channel model between the base station, IRS, relay, and the remaining K users includes a two-path fading model corresponding to large-scale fading and a Ricean fading model corresponding to small-scale fading, specifically:

[0022]

[0023]

[0024]

[0025]

[0026] Among them, PL( BI ), PL ( B0 ), PL ( i ), PL ri The following represent the channel power gain caused by large-scale fading in the communication links from the base station to the IRS, from the base station to the first user, from the relay to user i, and from the IRS to user i, respectively. The overall expression is:

[0027] PL(d)=(λ c / (4πd)) 2 *4(sin(2πH t H r / (λ c d))) 2

[0028] Among them, H t H r , λ c d and d represent the transmitting antenna height, receiving antenna height, center subcarrier wavelength, and propagation distance, respectively; These represent the small-scale fading of the corresponding channels, modeled as a Ricean fading model.

[0029] Preferably, in step S3, the following is defined: k∈{1,...,K}, then:

[0030]

[0031]

[0032] Let h be the direct link from the base station to the relay. d The cascaded link g0 from the base station to the IRS to the relay, and the direct link h from the relay to user k. k and the cascaded link g from the relay to the IRS to the user k k There is a channel error; the actual channel model is as follows:

[0033]

[0034]

[0035] k∈{0,...,K}

[0036] Among them, h d g0, h k and g k For the actual channel coefficients, and The channel coefficients estimated for the base station and relay, Δh d , Δg0, Δh k and Δg k To address the channel estimation error, a bounded CSI error model is used to describe the channel error. The channel error is then modeled as follows:

[0037] ||Δh d ||≤ε d ||Δg0||≤ε g0

[0038] ||Δh k ||≤ε k ||Δg k ||≤ε gk

[0039] k∈{0,...,K}

[0040] Where ||| denotes the 2-norm operation, ε d ε g0 ε k and ε gk This represents the radius of the uncertainty range corresponding to the channel error.

[0041] Preferably, in the i-th time slot of the first stage, the signal received by the relay is:

[0042]

[0043] Where n0 represents a mean of 0 and a variance of . Gaussian white noise signal; P t s represents the maximum transmission power of the base station. i Let be the data symbols transmitted from the base station to user i, which follow an independent and identically distributed circular symmetric complex Gaussian distribution with mean 0 and variance 1. In the first stage, the communication rate of user i is:

[0044]

[0045] Among them, t 1i This indicates the time slot used by the base station when transmitting information for user i in the first phase;

[0046] In the j-th time slot of the second phase, the signal received by the relay is represented as:

[0047]

[0048] Where, n j This indicates that the mean is 0 and the variance is 0. Gaussian white noise signal;

[0049] After two stages of transmission, the communication rate for the first user is:

[0050] R0 = R0 1

[0051] In the second phase, the communication rate of user j is:

[0052]

[0053] Among them, t 2j P represents the time slot used by the base station to transmit information to user j in the second phase; t s represents the maximum transmission power of the base station. i Let be the data symbols transmitted from the base station to user i, which follow an independent and identically distributed circular symmetric complex Gaussian distribution with mean 0 and variance 1;

[0054] After two stages of transmission, the communication rate for the remaining K users is:

[0055]

[0056] Preferably, the process of determining the minimum communication rate for K+1 users by combining the channel model obtained in step S3 and the communication rates of K+1 users is as follows:

[0057] Let set Λ i ={(Δh) i ,Δg i )|‖Δh i ||≤ε i ,‖Δg i ||≤ε gi}, i∈{1,...,K}, set Λ0={(Δh d ,Δg0)|‖Δh d ||≤ε d ,‖Δg0‖≤ε g0 In the case of channel uncertainty, the worst-case communication rate of user k can be expressed as:

[0058]

[0059] In the worst-case scenario, the communication rate of the first user is:

[0060]

[0061] Preferably, the constructed communication resource allocation optimization model is as follows:

[0062]

[0063] |θ 1,ii |=1,|θ 2j,ii |=1,i=1,…M,j=1,…K (1b)

[0064]

[0065] t 1j ≥0, j=0…K(1d)

[0066] t 2j ≥0, j=1…K(1e)

[0067] The first constraint (1b) represents the unit mode constraint of the IRS reflection coefficient; the second constraint (1c) represents the time slot allocation coefficient.

[0068] Preferably, the communication resource allocation optimization model is a non-convex optimization model, and slack variables are introduced during the solution process.

[0069] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0070] This invention proposes an optimization method for TDMA communication resource allocation using IRS-assisted wireless relay. By using IRS-assisted wireless relay in TDMA communication, and employing users closer to the base station as relays, the coverage of the base station is effectively increased. The IRS can significantly enhance the signal strength received at the receiver, improving the communication rate of the TDMA system. When optimizing the communication resource allocation of the TDMA system, the existence of channel errors is considered. A channel model is constructed between the base station, IRS, relays, and the remaining K users. Then, with the objective function of maximizing the minimum communication rate of users in the worst-case scenario, and with the unit modulus of the IRS reflection coefficient and the time slot allocation coefficient as constraints, a communication resource allocation optimization model is constructed and solved to obtain the communication resource time slot allocation result, which better reflects the communication situation in the actual deployment environment and ensures the fairness of resource allocation among users. Attached Figure Description

[0071] Figure 1 A flowchart illustrating the TDMA communication resource allocation optimization method for IRS-assisted wireless relay proposed in Embodiment 1 of the present invention;

[0072] Figure 2 This diagram illustrates the structure of the TDMA communication system proposed in Embodiment 1 of the present invention.

[0073] Figure 3 This diagram illustrates the variation of the minimum user communication rate with channel uncertainty in the TDMA communication system proposed in Embodiment 3 of the present invention.

[0074] Figure 4 This diagram illustrates the variation of the minimum user communication rate with transmit power in the TDMA communication system proposed in Embodiment 3 of the present invention.

[0075] Figure 5 This graph shows the variation of the minimum user communication rate with the number of IRS reflection units in the TDMA communication system proposed in Embodiment 3 of the present invention. Detailed Implementation

[0076] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0077] To better illustrate this embodiment, some parts of the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions;

[0078] It is understandable to those skilled in the art that some well-known details may be omitted from the accompanying drawings.

[0079] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments;

[0080] The positional relationships depicted in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0081] Example 1

[0082] This embodiment proposes a method for optimizing TDMA communication resource allocation in IRS-assisted wireless relay. The flowchart of this method is shown below. Figure 1 It includes the following steps:

[0083] S1. Construct a TDMA communication system, including: a base station, an IRS, and K+1 users, with the first user also serving as a relay in the TDMA communication system;

[0084] like Figure 2 As shown, in the case of channel uncertainty, consider a downlink TDMA communication system with IRS-assisted wireless relay. The base station transmits information to users located far from the base station through a multi-hop method. See [link to relevant documentation]. Figure 2 The system includes a shore base station, an IRS, and K+1 users. The first user, User 0, also acts as a relay. In this embodiment, it is assumed that the base station and each user are equipped with an antenna, and the IRS has M reflection elements.

[0085] S2. The communication phase between the base station and the IRS and the first user is taken as the first phase, and the communication phase between the IRS, the first user and the remaining K users is taken as the second phase. Based on TDMA technology and IRS assistance, the base station transmits information to the user.

[0086] S3. Considering channel estimation error, construct a channel model between the base station, IRS, relay, and the remaining K users;

[0087] S4. Solve for the user communication rates corresponding to the first stage and the second stage respectively. Based on the user communication rates corresponding to the first stage and the second stage respectively, obtain the communication rates of K+1 users after the transmission of the first stage and the second stage.

[0088] S5. Combining the channel model obtained in step S3 and the communication rates of K+1 users, calculate the minimum communication rate for K+1 users;

[0089] S6. With the objective function of maximizing the minimum communication rate of users in the worst case, and with the unit modulus of the IRS reflection coefficient and the time slot allocation coefficient as constraints, construct and solve the communication resource allocation optimization model to obtain the communication resource time slot allocation result.

[0090] Example 2

[0091] Based on TDMA technology and IRS assistance, when a base station transmits information to a user, a transmission frame is divided into 2K+1 time slots. In the first stage, the base station uses K+1 time slots to transmit the information of K+1 users to the relay. In the second stage, the relay uses the remaining K time slots to transmit the information of K users. Each of the K users uses an independent time slot when transmitting information.

[0092] In this embodiment, the channel coefficient of the communication link from the base station to the IRS is assumed to be... The channel coefficients for the communication links from the base station to the first user and from the IRS to the first user are respectively and The distance between the base station and user i is relatively long. Due to severe path loss over long distances, the communication link between the base station and user i is not considered in the actual implementation. i∈{1,2…K}, and user i is one of the K+1 users excluding the first user acting as a relay. The channel coefficient of the relay-to-IRS link is... The channel coefficients of the relay to user i and the IRS to user i communication links are respectively and Where i∈{1,2…K};

[0093] In the first stage, the reflection phase shift matrix of the IRS is φ1=diag(θ1), θ1=[q1,q2,...,q M ] H H denotes the conjugate transpose of the matrix, where, β m ∈[0,1], θ m ∈[0,2π), β m and θ m Let m and m represent the reflection amplitude and reflection phase of the m-th reflecting unit, respectively, where m∈{1,...,M};

[0094] To achieve the maximum reflection power gain, let |β m |=1,q m Satisfy |q m |=1, in the j-th time slot of the second stage, the IRS reflection coefficient matrix is ​​expressed as: φ 2j =diag(θ) 2j ), θ 2j =[e j1 ,e j2 ,...,e jM ] H e jm Satisfy | e jm |=1, where m∈{1,...,M}.

[0095] In this embodiment, the channel model constructed between the base station, IRS, relay, and the remaining K users includes a two-path fading model corresponding to large-scale fading and a Ricean fading model corresponding to small-scale fading, specifically:

[0096]

[0097]

[0098]

[0099]

[0100] Among them, PL(d BI ), PL(d B0 ), PL(d i ), PL(d ri The following represent the channel power gain caused by large-scale fading in the communication links from the base station to the IRS, from the base station to the first user, from the relay to user i, and from the IRS to user i, respectively. The overall expression is:

[0101] PL(d)=(λ c / (4πd)) 2 *4(sin(2πH t H r / (λ c d))) 2

[0102] Among them, H t H r , λ c d and d represent the transmitting antenna height, receiving antenna height, center subcarrier wavelength, and propagation distance, respectively; and These represent the small-scale fading of the corresponding channels, modeled as a Ricean fading model.

[0103] definition: k∈{1,...,K}, then:

[0104]

[0105]

[0106] Since it is almost impossible for a base station to obtain perfect channel state information, let h be the direct link from the base station to the relay. d The cascaded link g0 from the base station to the IRS to the relay, and the direct link h from the relay to user k. k and the cascaded link g from the relay to the IRS to the user k k There is a channel error; the actual channel model is as follows:

[0107]

[0108]

[0109] k∈{0,...,K}

[0110] Among them, h d g0, h k and g k For the actual channel coefficients, and The channel coefficients estimated for the base station and relay, Δh d , Δg0, Δh k and Δg k To address the channel estimation error, a bounded CSI error model is used to describe the channel error. The channel error is then modeled as follows:

[0111] ||Δh d ||≤ε d ||Δg0||≤ε g0

[0112] ||Δh k ||≤ε k ||Δg k ||≤ε gk

[0113] k∈{0,...,K}

[0114] Where ||| denotes the 2-norm operation, ε d ε g0 ε k and ε gk This represents the radius of the uncertainty range corresponding to the channel error.

[0115] In this embodiment, in the i-th time slot of the first stage, the baseband signal transmitted by the base station to the relay can be expressed as: P t s represents the maximum transmission power of the base station. i Let s be the data symbols transmitted by the base station to user i, and s i The signal follows an independent, identically distributed, circularly symmetric complex Gaussian distribution with mean 0 and variance 1. After receiving information from all users, the relay decodes the information of each user one by one, and re-encodes and transmits the information of user i in the second stage, where i∈{1,...,K}. In the j-th time slot (j∈{1,2…K}) of the second stage, the baseband signal transmitted by the relay can be expressed as... P r This represents the maximum transmit power of the relay.

[0116] In the i-th time slot of the first phase, the signal received by the relay is:

[0117]

[0118] Where n0 represents a mean of 0 and a variance of . Gaussian white noise signal; P t s represents the maximum transmission power of the base station. i Let be the data symbols transmitted from the base station to user i, which follow an independent and identically distributed circular symmetric complex Gaussian distribution with mean 0 and variance 1. In the first stage, the communication rate of user i is:

[0119]

[0120] Among them, t 1i This indicates the time slot used by the base station when transmitting information for user i in the first phase;

[0121] In the j-th time slot of the second phase, the signal received by the relay is represented as:

[0122]

[0123] Where, n j This indicates that the mean is 0 and the variance is 0. Gaussian white noise signal;

[0124] After two stages of transmission, the communication rate for the first user is:

[0125] R0 = R0 1

[0126] In the second phase, the communication rate of user j is:

[0127]

[0128] Among them, t 2j P represents the time slot used by the base station to transmit information to user j in the second phase; t s represents the maximum transmission power of the base station. i Let be the data symbols transmitted from the base station to user i, which follow an independent and identically distributed circular symmetric complex Gaussian distribution with mean 0 and variance 1;

[0129] After two stages of transmission, the communication rate for the remaining K users is:

[0130]

[0131] Combining the channel model obtained in step S3 and the communication rates of K+1 users, the process of calculating the minimum communication rate for K+1 users is as follows:

[0132] Let set Λ i ={(Δh)i ,Δg i )|||Δh i ||≤ε i ,‖Δg i ||≤ε gi}, i∈{1,...,K}, set Λ0={(Δh d ,Δg0)|‖Δh d ||≤ε d ,‖Δg0‖≤ε g0 In the case of channel uncertainty, the worst-case communication rate of user k can be expressed as:

[0133]

[0134] In the worst-case scenario, the communication rate of the first user is:

[0135]

[0136] In this embodiment, considering the presence of channel errors, to ensure fairness for maritime users, the communication resource allocation optimization model is constructed by jointly optimizing the time slot allocation coefficients and the IRS reflection coefficients to maximize the communication rate of the minimum user in the worst-case scenario.

[0137]

[0138] |θ 1,ii |=1,|θ 2j,ii |=1,i=1,…M,j=1,…K (1b)

[0139]

[0140] t 1j ≥0, j=0…K (1d)

[0141] t 2j ≥0, j=1…K (1e)

[0142] The first constraint (1b) represents the unit mode constraint of the IRS reflection coefficient; the second constraint (1c) represents the time slot allocation coefficient.

[0143] The communication resource allocation optimization model is a non-convex optimization model. Since the optimization variables are tightly coupled together in the objective function (1a), coupled with the non-convex unit modulus constraint (1b) and the uncertainty of the channel, the constructed problem is a non-convex problem that is difficult to solve directly.

[0144] This model is a difficult non-convex problem. To solve the objective function, a relaxation variable λ is introduced. The model after introducing the relaxation variable can be rewritten as follows:

[0145]

[0146]

[0147]

[0148] λ≥0(2d)

[0149] (1b)-(1e)(2e)

[0150] The rewritten model is defined as an optimization problem (P2). Optimization problem (P2) is still a difficult non-convex problem, the most difficult part of which is the infinite number of inequality constraints (2d) and (2c). In order to transform constraints (2d) and (2c) into easily manageable constraints, the following corollary is required:

[0151]

[0152] Therefore, in set Λ0, |h d +1 T The following inequalities hold for g0:

[0153]

[0154]

[0155] when When, the inequality on the left side holds the equality sign, when When, the inequality on the right side of the above equation takes the equality sign, where θ1 * This indicates the conjugate operation on vector θ1.

[0156] Similarly, we have:

[0157]

[0158] Where j = 1, ..., K. Based on the two inequalities above, constraints (2b) and (2c) can be relaxed to the following constraints:

[0159]

[0160]

[0161] From inequality (2f), it can be seen that λ can change with... As increases, similarly, λ in inequality (2g) can increase with . As θ increases, θ1 and θ... 1i The closed-form solution to the optimal solution is:

[0162]

[0163] After relaxing constraints (2b) and (2c) above, the optimization problem (P2) can be transformed into:

[0164]

[0165]

[0166]

[0167] (2d)(1b)-(1e)(3e)

[0168] The optimization problem (P3) is about {t} 1j}、{t 2j The joint convex function of} and λ.

[0169] make:

[0170]

[0171]

[0172] From the optimization problem (P3), we can see that when (P3) reaches its optimal value, the following equation holds:

[0173] t 10 R′0=t 11 R′0=…=t 1K R′0=t 21 R′1=t 22 R′2=…=t 2K R′ K =λ

[0174]

[0175] From the above two equations, the closed-form solution to the optimization problem (P3) regarding time slot allocation is:

[0176]

[0177] Example 3

[0178] The following simulation experiment compares the method proposed in this application with existing solutions. Table 1 shows the English-Chinese comparison of the comparative solutions used in the simulation.

[0179] Table 1

[0180]

[0181] Figure 3This graph illustrates the variation of the minimum user communication rate with channel uncertainty in a TDMA communication system. The horizontal axis represents channel uncertainty; a higher value indicates greater channel uncertainty. Table 1 also provides the variation curves for several comparative schemes. The boundary of channel error is defined as... like Figure 3 As shown, the number of IRS reflective units M = 64, derived from... Figure 3 The following conclusions can be drawn:

[0182] (1) The minimum user rate of all schemes decreases as the channel uncertainty δ increases, because as δ increases, the channel becomes more uncertain, and the user's communication rate decreases in the worst case.

[0183] (2) The communication rate of the proposed algorithm is greater than that of other schemes within the range of δ variation, which proves the superiority of the proposed algorithm.

[0184] (3) As δ increases, the performance of the proposed algorithm only decreases slightly, proving the robustness of the method proposed in this patent.

[0185] (4) The proposed scheme performs better than the “Same-phase” scheme because the proposed scheme has more degrees of freedom for optimization.

[0186] (5) The performance comparison between the proposed scheme and the "Same-time" and "Rand w / o T" schemes shows that optimizing the time slot allocation coefficient can effectively improve system performance.

[0187] (6) The minimum communication rate of the system users in the proposed scheme is higher than that of the "Without-IRS" and "Rand w / T" schemes, which proves the effectiveness of deploying IRS and optimizing IRS reflection coefficient in improving the system communication rate.

[0188] In summary, deploying an IRS and allocating time slots in a wireless relay system can effectively improve the performance of the communication system, and also demonstrates the robustness of the robust optimization method proposed in this application.

[0189] Figure 4 This embodiment presents a graph showing the variation of the minimum user communication rate with transmit power in the TDMA communication system. Table 1 shows the corresponding curves for several comparative schemes. Figure 4 The following conclusions can be drawn:

[0190] (1) All schemes increase linearly with the increase of transmission power, because increasing transmission power will enhance the strength of the received signal, thereby improving the communication rate of the system.

[0191] (2) The method proposed in this application is superior to other comparative schemes.

[0192] Figure 5 The graph shows the variation of the minimum user communication rate with the number of IRS reflection units in a TDMA communication system, and Table 1 shows the corresponding variation curves for several comparative schemes, where δ = 0.01. Figure 5 The following conclusions can be drawn:

[0193] (1) Except for the “Without-IRS” scheme, the minimum user rate of the system increases with the increase of the number of IRS reflection units. This is because with the increase of the number of IRS reflection units, the IRS can provide greater beamforming gain, which can more effectively enhance the strength of the received signal, thereby improving the user's communication rate.

[0194] (2) The performance gain of the proposed scheme relative to the "Without-IRS" scheme increases with the increase of the number of IRS reflection units, proving the effectiveness of IRS beamforming gain in improving the system communication rate.

[0195] (3) The performance of the method proposed in this application is better than that of other comparative schemes, which shows that optimizing the IRS phase and optimizing the time slot allocation can effectively improve the performance of the relay system.

[0196] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing TDMA communication resource allocation in IRS-assisted wireless relay, characterized in that, Includes the following steps: S1. Construct a TDMA communication system, including: a base station, an IRS, and K+1 users, with the first user also serving as a relay in the TDMA communication system; S2. The communication phase between the base station and the IRS and the first user is taken as the first phase, and the communication phase between the IRS, the first user and the remaining K users is taken as the second phase. Based on TDMA technology and IRS assistance, the base station transmits information to the user. S3. Considering channel estimation error, construct a channel model between the base station, IRS, relay, and the remaining K users; S4. Solve for the user communication rates corresponding to the first stage and the second stage respectively. Based on the user communication rates corresponding to the first stage and the second stage respectively, obtain the communication rates of K+1 users after the transmission of the first stage and the second stage. S5. Combining the channel model obtained in step S3 and the communication rates of K+1 users, calculate the minimum communication rate for K+1 users; S6. With the objective function of maximizing the minimum communication rate of users in the worst case, and with the unit modulus of the IRS reflection coefficient and the time slot allocation coefficient as constraints, construct and solve the communication resource allocation optimization model to obtain the communication resource time slot allocation result.

2. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 1, characterized in that, In a TDMA communication system, the base station and each of the K+1 users are equipped with an antenna, and the IRS has M reflection units.

3. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 2, characterized in that, In step S2, based on TDMA technology and IRS assistance, when the base station transmits information to the user, a transmission frame is divided into 2K+1 time slots. In the first stage, the base station uses K+1 time slots to transmit the information of K+1 users to the relay. In the second stage, the relay uses the remaining K time slots to transmit the information of K users. Each of the K users uses an independent time slot when transmitting information to each user.

4. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 3, characterized in that, Let the channel coefficient of the communication link from the base station to the IRS be... The channel coefficients for the communication links from the base station to the first user and from the IRS to the first user are respectively and Without considering the communication link between the base station and user i, User i is one of the K+1 users, excluding the first user acting as a relay. The channel coefficient of the relay-to-IRS link is... relay to user IRS to user The channel coefficients of the communication link are respectively and ,in, ; In the first stage, the reflection phase shift matrix of the IRS is: , , Denotes the conjugate transpose of a matrix, where, , , , and They represent the first The reflection amplitude and reflection phase of each reflecting unit, where, ; make , satisfy In the second phase The time slot, IRS reflection coefficient matrix is ​​expressed as: , , satisfy ,in, .

5. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 4, characterized in that, In step S3, the constructed channel model between the base station, IRS, relay, and the remaining K users includes a two-path fading model corresponding to large-scale fading and a Ricean fading model corresponding to small-scale fading, specifically: ， ， in, , , , These represent the connection from the base station to the IRS, from the base station to the first user, and from the relay to the user, respectively. IRS to user In a communication link, the overall expression for the channel power gain caused by large-scale fading is: in, , These represent the transmitting antenna height, receiving antenna height, center subcarrier wavelength, and propagation distance, respectively. , , and These represent the small-scale fading of the corresponding channels, modeled as a Ricean fading model.

6. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 5, characterized in that, In step S3, the following is defined: , ,but: Assume a direct link from the base station to the relay. Cascading links from base station to IRS to relay Relay to user direct link and relay to IRS to user Cascaded links There is a channel error; the actual channel model is as follows: in, For the actual channel coefficients, , , and Channel coefficients estimated for base stations and relays. , , and To address the channel estimation error, a bounded CSI error model is used to describe the channel error. The channel error is then modeled as follows: in Represents the L2 norm operation. , , and This represents the radius of the uncertainty range corresponding to the channel error.

7. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 6, characterized in that, In the i-th time slot of the first phase, the signal received by the relay is: in, This indicates that the mean is 0 and the variance is 0. Gaussian white noise signal; This is the maximum transmission power of the base station. Let be the data symbols transmitted from the base station to user i, which follow an independent and identically distributed circular symmetric complex Gaussian distribution with mean 0 and variance 1. In the first stage, the communication rate of user i is: ，i in, This indicates the time slot used by the base station when transmitting information for user i in the first phase; In the second phase Time slot, user The received signal is represented as: in, This indicates that the mean is 0 and the variance is 0. Gaussian white noise signal; After two stages of transmission, the communication rate for the first user is: In the second phase, users The communication rate is: in, This indicates that the relay is for the user in the second phase. The time slot used when transmitting information; This is the maximum transmit power of the relay; After two stages of transmission, the communication rate for the remaining K users is: 。 8. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 7, characterized in that, Combining the channel model obtained in step S3 and the communication rates of K+1 users, the process of calculating the minimum communication rate for K+1 users is as follows: Let set ,gather In the case of channel uncertainty, the worst-case communication rate of user k is expressed as: In the worst-case scenario, the communication rate of the first user is: 。 9. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 8, characterized in that, The constructed communication resource allocation optimization model is as follows: (1a) (1b) (1c) （1d） （1e） The first constraint (1b) represents the unit mode constraint of the IRS reflection coefficient; the second constraint (1c) represents the time slot allocation coefficient.

10. The TDMA communication resource allocation optimization method for IRS-assisted wireless relay according to claim 9, characterized in that, The communication resource allocation optimization model is a non-convex optimization model, and slack variables are introduced during the solution process.

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