An IRS-Assisted Ultra-Dense Network Resource Allocation Method for Capacity Coverage

Abstract

The present invention provides an IRS-assisted ultra-dense network resource allocation method for capacity coverage, belonging to the technical field of communication network resource allocation, and solving the problems of severe interference and capacity coverage holes in ultra-dense networks; it includes: S1, initializing various parameters of the system; S2, establishing a channel model and correcting the information for estimating the channel; S3, establishing a joint optimization problem and splitting the joint optimization problem into two sub-problems, sub-problem one is the optimization problem of base station-user association and subcarrier allocation, and sub-problem two is the optimization problem of IRS beam design; S4, performing user association and carrier allocation according to the network link state to achieve interference control; S5, based on the resource allocation result, performing beam design according to the interference situation, and perceptibly realizing the maximization of capacity coverage with interference; the present invention realizes interference control and the maximization of system capacity coverage, as well as the maximization of the sum of user rates within the entire communication network system.

Description

Technical Field

[0001] The invention belongs to the technical field of communication network resource allocation, is applied to ultra-dense networks, and specifically is an IRS-assisted ultra-dense network resource allocation method oriented to capacity coverage. Background Art

[0002] In order to meet the ever-increasing demand for communication network capacity, ultra-dense network (UDN) technology has gradually become the core technology supporting 5G and future networks; this technology increases the density of access points, especially in hot spots, and densifies cell deployment, thus forming an ultra-dense network. From the perspective of shortening cell size and transmission distance, it has achieved significant gains in spectrum efficiency and power efficiency, greatly improving network throughput, and enabling future networks to cope with complex and changing emerging businesses.

[0003] Compared with traditional cellular systems, ultra-dense networks are heterogeneous networks. Various densely connected devices cause stronger interference intensity and complexity, resulting in capacity coverage gaps in the network. Intelligent Reflecting Surface (IRS) is a new technology proposed for future networks. It has the characteristics of passive reflection and can control the reflection process of electromagnetic waves through low-power passive reflection units to reconfigure the wireless propagation environment. Using IRS technology to improve the performance of ultra-dense networks and make up for capacity coverage gaps caused by severe network interference has also become a current hot technology. Therefore, how to make full use of limited resources, how to effectively configure the signal propagation environment with the help of IRS, and how to propose effective interference control methods for interference in the network have become key issues that need to be urgently solved in ultra-dense networks.

[0004] In the existing results involving IRS-assisted communication network resource allocation, IRS technology is usually used to enhance the signal coverage capabilities of different communication systems. For example, for full-duplex wireless power communication networks (FD-WPCN), cognitive wireless networks (CR), semi-free transmission networks (SGF), etc., by optimizing power allocation and IRS beam design, it is demonstrated that IRS can improve the system spectrum efficiency while ensuring signal coverage; or based on the characteristics of IRS-assisted NOMA networks, the system active and passive beamforming are optimized to increase the sum of downlink rates. However, in these existing studies, there is a lack of consideration for the problem of filling the gaps in system capacity coverage, and there is no effective control of high-intensity and complex interference in ultra-dense networks.

[0005] Therefore, the prior art has the following disadvantages:

[0006] Intelligent Reflecting Surface (IRS)-aided uplink system dynamic resource allocation method, in which a scheme for dynamically selecting IRS is proposed, involving the access rules of the IRS-aided communication system and the phase shift design of IRS. The main steps of this method are as follows: (1) Specify the initial access rules of users, model the problem according to the access scheme, and introduce the concept of frames; (2) Process the established long-term dynamic optimization problem using the drift-plus-penalty algorithm based on the Lyapunov optimization framework to obtain the user access results; (3) Use the fractional programming method based on the Lagrangian duality theory to obtain the power allocation and IRS phase shift results to achieve power minimization; (4) Iterate steps (2) and (3) above to obtain the final user resource allocation results. In this method, user matching, power allocation, and IRS phase shift under the framework of the IRS-aided system are considered, but the interference between multiple access points is not considered, nor is the interference cancellation for such interference involved, which has limitations in the capacity coverage of ultra-dense networks.

[0007] Intelligent Reflecting Surface (IRS)-aided SM-NOMA system resource allocation method, in which a power allocation scheme is proposed. For the IRS-aided SM-NOMA system, dynamic user grouping is considered based on the effective channel gain of each user. The main steps of this method are as follows: (1) First, perform dynamic user grouping according to the gap in user channel gains; (2) Establish an optimization problem with the goal of maximizing the sum of system user rates, and adopt an optimization scheme that maximizes the SINR; (3) Split the optimization problem in (4) above, that is, by jointly optimizing the power allocation coefficient of users and the phase shift of IRS, the sum rate of users is maximized. In this method, the maximization of user rates under the framework of the IRS-aided system is considered, but for ultra-dense networks with severe interference, this method lacks interference management in the network.

[0008] For the downlink of the IRS-aided NOMA communication system, there is also a resource allocation algorithm to achieve the maximization of system throughput. Jointly optimize channel allocation, NOMA user decoding order, power allocation, and IRS reflection coefficient. In this algorithm, a centralized IRS reflection coefficient design method is adopted, and all channel information in the scenario is shared; however, the dense communication nodes in ultra-dense networks result in a large amount of channel information, and usually the network cannot obtain all channel information, so this method is not applicable to ultra-dense networks. Summary of the Invention

[0009] To achieve the control of electromagnetic wave flow in ultra-dense networks through intelligent reflecting surfaces (IRS), solve the problems of severe interference caused by the dense access points in ultra-dense networks, resulting in network capacity coverage holes, and the degradation of interference management performance due to limited knowledge of channel information, the present invention proposes an IRS-assisted ultra-dense network resource allocation method for capacity coverage; this method uses a many-to-one matching model with external conditions, combines matching theory and the classic Gale-Shapley matching algorithm to complete resource allocation, realizes the maximization of interference management and system capacity coverage, and finally realizes the maximization of the sum of user rates within the entire communication network system.

[0010] The present invention adopts the following technical solutions to achieve the purpose:

[0011] An IRS-assisted ultra-dense network resource allocation method for capacity coverage, comprising the following steps:

[0012] S1. Initialize the parameters of the IRS-assisted communication system;

[0013] S2. Establish a channel model and correct the estimated channel information;

[0014] S3. With the goal of maximizing the sum of system user rates, establish a joint optimization problem and split the joint optimization problem into two sub-problems. Sub-problem one is the optimization problem of base station-user association and subcarrier allocation, and sub-problem two is the optimization problem of IRS beam design;

[0015] S4. Solve sub-problem one, perform user association and carrier allocation according to the network link state, and achieve interference management;

[0016] S5. Solve sub-problem two, based on the resource allocation result, perform beam design according to the interference situation, and perceptibly achieve the maximization of capacity coverage for interference.

[0017] Specifically, in the process of initializing the parameters of the IRS-assisted communication system in step S1, for the downlink of the IRS-assisted communication system, there are multiple communication cells in the system, as well as the IRS and users associated with each cell; each cell's base station is equipped with an IRS, and all reflection units of the IRS are placed on the base station side using centralized deployment; all base stations and ground users are equipped with single antennas; for the given positions of the base stations and IRS, the positions of the users and the phase shifts of the IRS beams are randomly generated within a given range.

[0018] Furthermore, the specific content of step S2 is as follows:

[0019] S21. Determine that the user receives the signal from the base station through the reflection of the IRS via the carrier;

[0020] S22. Calculate the received power obtained by the user, and based on the received power, obtain the channel gain of the user;

[0021] S23. Restore the incomplete signal by interpolation to obtain the channel estimation content, which contains estimation errors;

[0022] S24. Correct the estimated value based on the probability relationship between the estimated value in the channel estimation content and the channel information, so as to compensate for the estimation error;

[0023] S25. Synthesize the probability distribution relationship between the channel information and the corrected estimated value, and obtain the corrected channel coefficient based on this probability relationship and the estimated value.

[0024] Further, the specific content of step S3 is as follows:

[0025] S31. Determine the achievable rate of the user associated with the base station on the subchannel;

[0026] S32. Combine the association situation of the subchannels corresponding to the user and the base station to obtain the sum of the rates of all users covered by a base station;

[0027] S33. Establish a joint optimization problem with the goal of maximizing the sum of the system user rates;

[0028] S34. After splitting the joint optimization problem into two subproblems, alternately optimize subproblem one and subproblem two until the maximum number of iterations is reached to obtain the final optimization result.

[0029] Further, in step S31, the achievable rate of user j associated with base station m on the k-th subchannel is written as the following formula:

[0030]

[0031] In the formula, I m,j is as follows:

[0032]

[0033] I m,j represents the co-channel signal interference received by this user; p m,k is the transmission power allocated by the base station to the k-th subcarrier; h j,m,k is the channel gain from base station m to user j through subcarrier k; B C is the bandwidth of the subcarrier; the noise in the system is additive white Gaussian noise, and the noise power is σ 2 ;

[0034] In step S32, the sum of the rates of all users covered by a base station obtained, R m , is as follows:

[0035]

[0036] This formula combines the association between user j and sub-channel k corresponding to the base station.

[0037] Furthermore, in step S33, a matrix X of size N BS ×N user ×N sub is used to represent the relationship between base station-user association and sub-channel allocation; if x m,j,k = 1, it means that the j-th user is allocated to the k-th sub-channel of the m-th base station, otherwise x m,j,k = 0;

[0038] The established joint optimization problem is as follows:

[0039]

[0040] s.t. (1) x m,j,k ≤ a m,j

[0041]

[0042]

[0043] (4) I m,j ≤ I th

[0044] (5) θ n ∈ [0, 2π)

[0045] (6) x m,j,k , a m,j ∈ {0, 1}

[0046] Among them, the sets of base stations, users, and IRSs are respectively represented as M = [M 1 … M m … M NBS , J = [J 1 … J j … J Nuser , and R = [R 1 … R r … R NIRS ; the carrier set is represented as k = [K 1 … K k … K Nsub ; the unit set of each reflecting surface is N = [N 1 … N n … N Nelement .

[0047] Further, in step S4, a three-dimensional matrix X is used to store the matching situation among the three groups of communication elements of base stations, user associations, and subcarriers. At the same time, the GS matching algorithm with modified bilateral preferences is used to solve sub-problem 1 of base station-user association and subcarrier allocation, specifically including:

[0048] S41. Define the user set, base station set, and sub-channel set, and determine the matching rules;

[0049] S42. Determine the matching preference on the user side, focusing on the base station-sub-channel units with high transmission rates, and select the units with larger channel gains;

[0050] S43. Determine the matching preference of the base station-sub-channel set;

[0051] S44. Determine the preference of the sub-channel pair for each base station-sub-channel unit.

[0052] Further, in step S43, a preference matrix Γ is defined algorithmically j,(m,k) , which is used to describe the influence of other units (i, (t, k)) on (j, (m, k)), as shown in the following formula:

[0053]

[0054] When , it is considered that (j 1 , (m 1 , k)) is more suitable to be matched with the base station-sub-channel unit (m, k) than (j 2 , (m 2 , k));

[0055] When , it is considered that (j 2 , (m 2 , k)) is more suitable to be matched with the base station-sub-channel unit (m, k) than (j 1 , (m 1 , k));

[0056] In step S44, for sub-channel k, the following is used:

[0057]

[0058] to measure the preference of this channel and the unit (m, k). Specifically, if:

[0059]

[0060] then it is considered that base station m is more suitable to be matched with k 1 ; if:

[0061]

[0062] Then it is considered that base station m is more suitable for k 2 to match.

[0063] Furthermore, in step S5, a method combining fractional optimization transformation based on Lagrangian variation and semidefinite relaxation is adopted to solve sub-problem two of IRS beam design, specifically including:

[0064] S51. Perform Lagrangian transformation on the original problem optimization objective and update α for this round of iteration (i) ;

[0065] S52. Transform the original problem optimization objective and interference constraint conditions;

[0066] S53. Perform fractional optimization transformation on the original problem optimization objective and determine ε for this round of iteration (i) , and on this basis, organize the problem form;

[0067] S54. Transform the interference constraint, and finally obtain the transformed form of the IRS beam design sub-problem, solve the transformed sub-problem, and obtain the optimization result.

[0068] Furthermore, in step S54, the transformed form of the IRS beam design sub-problem is as follows:

[0069]

[0070] For the above form of the sub-problem, a method of semidefinite relaxation is adopted for solution, and at the same time, the optimization result is obtained with the help of CVX.

[0071] To sum up, due to the adoption of this technical solution, the beneficial effects of the present invention are as follows:

[0072] 1. The present invention is a resource allocation method in an IRS-assisted ultra-dense network, which establishes a system model of an IRS-assisted ultra-dense network, and can perceptually realize interference control for the characteristic of strong interference in the ultra-dense network. The resource allocation is completed by adopting a many-to-one matching model with external conditions, combining matching theory and the classical Gale-Shapley matching algorithm to realize interference control. The interference situation of users in each cell is measured by the way of interference ranking to divide the strong and weak interference user types, and the interference received by strong interference users is restricted to optimize the beam design. The association between ground users and base stations, the downlink carrier allocation of base stations, and the IRS beamforming are jointly optimized to maximize the system capacity coverage.

[0073] 2. In view of the complex channel conditions and limited information acquisition in dense networks, the present invention corrects the information of cascaded channel estimation for the scenario assisted by IRS. Based on this, the user-base station association, carrier allocation, and beam design of IRS in the system are optimized, and finally the maximization of the sum of user rates in the entire communication system is achieved.

[0074] 3. Through the present invention, the system capacity of the entire network can be effectively improved; compared with traditional random resource allocation, the present invention performs certain interference management in the user association and subcarrier allocation stages, effectively improving the system capacity; compared with the system without IRS assistance, the present invention realizes interference management through IRS beam design, which further improves the system capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is a schematic flowchart of the method of the present invention;

[0076] Figure 2 is a schematic diagram of the system model involved in the present invention;

[0077] Figure 3 is a schematic diagram of performance comparison under different resource allocation schemes;

[0078] Figure 4 is a schematic diagram of performance comparison using interference management and effective signal enhancement technologies;

[0079] Figure 5 is a schematic diagram of performance comparison of different channel estimation corrections. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0081] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0082] Embodiment 1

[0083] As Figure 1 shown, an IRS-assisted ultra-dense network resource allocation method for capacity coverage includes the following steps:

[0084] S1. Initialize each parameter of the IRS-aided communication system;

[0085] S2. Establish a channel model and correct the information for estimating the channel;

[0086] S3. With the goal of maximizing the sum of system user rates, establish a joint optimization problem and split the joint optimization problem into two sub-problems. Sub-problem one is the optimization problem of base station-user association and subcarrier allocation, and sub-problem two is the optimization problem of IRS beam design;

[0087] S4. Solve sub-problem one, perform user association and carrier allocation according to the network link state, and achieve interference control;

[0088] S5. Solve sub-problem two, and based on the resource allocation result, perform beam design according to the interference situation, and perceptibly achieve the maximization of capacity coverage for interference.

[0089] This embodiment will explain in detail the implementation process of this method and the calculation parameters involved in sequence.

[0090] Step S1. Initialize each parameter of the system.

[0091] For the downlink of the IRS-aided communication system, there are multiple communication cells in the system, as well as the IRS and users associated with each cell, as Figure 2 shown. Each cell's base station is equipped with an IRS. Since the IRS has no signal amplification / regeneration ability, in order to reduce the impact of distance path loss on its service range, a centralized deployment is adopted to place all reflection units on the base station side. All base stations and ground users are equipped with single antennas. For the given positions of the base station and the IRS, the positions of the users and the phase shifts of the IRS beams are randomly generated within a given range.

[0092] Step S2. Establish a channel model and correct the information for estimating the channel, as follows.

[0093] Step S21: Each base station in the system is associated with an IRS containing N reflection units to improve the communication quality of users. The signal received by the user is the superposition of two signals from the base station and the IRS. Among them, the signal received by user j from base station m through the reflection of the IRS via carrier k is:

[0094]

[0095] In the formula, α 1n and α 2n are the path losses of the signal entering and leaving the nth reflection unit of the IRS respectively, ε 1 and ε 2 are the phase shifts of the signal entering and leaving this reflection unit, and xm,k The signal transmitted by base station m through carrier k is \(s_{m,k}(t)\).

[0096] Step S22: There is also a channel gain \(h_{m,j}^{\mathrm{LoS}}\) for the direct link from base station m to user j. d Therefore, the received power obtained by this user can be expressed as:

[0097] \(P_{j}^{\mathrm{LoS}}\) rj =\(\vert h_{m,j}^{\mathrm{LoS}}\Theta_{g}+h_{m,j}^{\mathrm{NLoS}}\vert^{2}P_{m,k}\) r \(\Theta_{g}\) d \(\vert\) 2 \(\cdot P_{m,k}\) tm,k

[0098] where \(h_{m,j}^{\mathrm{NLoS}}\) is a vector of size \(1\times N\) composed of r each reflecting element; \(g\) is a vector of size \(N\times1\) composed of each reflecting element; \(\Theta\) is a diagonal matrix of size \(N\times N\) composed of each reflecting element, and in \(\Theta\), \(\theta_{n}\) is the phase shift adjustment made by each reflecting element to the signal, and beam design is completed through phase shift adjustment. n

[0099] Then the channel gain of this user is:

[0100] \(h_{m,j}=h_{m,j}^{\mathrm{LoS}}\Theta g + h_{m,j}^{\mathrm{NLoS}}\) m,j,k \(h_{m,j}\) rm \(\Theta\) m \(g\) m \(+\) dm,j

[0101] Step S23: In a ultra-dense scenario with complex channel information, the ability to obtain channel information is limited. An interpolation method is adopted to restore the incomplete signal and obtain the channel estimation content. The result obtained by such estimation often has an estimation error \(\epsilon\). m,j,k That is:

[0102]

[0103] Step S24: According to the probability relationship between the channel estimation value and the channel information, the estimation value is corrected to make up for the estimation error. For the cascaded channel assisted by IRS and the passive characteristics of IRS, the method of directly estimating the cascaded channel can avoid introducing additional hardware and energy overhead, that is:

[0104]

[0105] where

[0106]

[0107] Since the channel information of the direct channel and the cascaded channel is imperfect, therefore:​

[0108]

[0109]

[0110] where and are the estimated values of the incomplete CIS for the channel, respectively, and ε d and ε a are the estimation errors that follow CSCG. Then:

[0111]

[0112] where

[0113]

[0114] Then:

[0115]

[0116] where, from the lack of correlation of the channel estimation error, we can obtain:

[0117]

[0118] Combining the above probability distribution relationship between the channel information and its estimated value, a corrected channel coefficient is obtained based on this probability relationship and the estimated value.

[0119] Step S3: With the goal of maximizing the sum of the system user rates, a joint optimization problem is established for solution.

[0120] Step S31: The rate that user j associated with base station m can achieve on the k-th subchannel can be written as:

[0121]

[0122] In the formula, I m,j is as follows:

[0123]

[0124] I m,j represents the co-channel signal interference received by this user; p m,k is the transmission power allocated by the base station to the k-th subcarrier; h j,m,k is the channel gain from base station m to user j through subcarrier k, and the channel gain model will be elaborated in detail in the next subsection; B C is the bandwidth of the subcarrier; the noise in the system is additive white Gaussian noise, and the noise power is δ 2 .

[0125] Step S32: Based on the association between the user and the subchannels corresponding to the base station, the sum of the rates of all users covered by a base station can be obtained as follows:

[0126]

[0127] Step S33: Establish a joint optimization problem; use a matrix X of size N BS ×N user ×N sub to represent the relationship between base station-user association and subchannel allocation. If x m,j,k = 1, it means that the j-th user is allocated to the k-th subchannel of the m-th base station; otherwise, x m,j,k = 0.

[0128] Establish an optimization problem with the maximum sum of user rates, and the problem is as follows:

[0129]

[0130] s.t. (1) x m,j,k ≤ a m,j

[0131]

[0132]

[0133]

[0134]

[0135] (6) x m,j,k , a m,j ∈ {0, 1}

[0136] where the sets of base stations, users, and IRSs are represented as M = [M 1 … M m … M NBS , J = [J 1 … J j … J Nuser , and R = [R 1 … R r … R NIRS ; the carrier set is represented as K = [K 1 … K k … K Nsub ; the unit set of each reflecting surface is N = [N 1 … N n … N Nelemeny .

[0137] Constraint (1) represents the maximum received power constraint. Specifically, the user selects the base station that can obtain the maximum received power to access the network, where a m,j characterizes the association relationship between the base station and the user;

[0138] Constraint (2) means that within each time slot, each terrestrial user can only occupy one terrestrial link subchannel;

[0139] Constraint (3) means that within each time slot, each link subchannel of the base station can only allow one terrestrial user to use;

[0140] Constraint (4) means that the interference of users within each cell should be controlled within the threshold, where I th =media{I m,j}j∈Nuser m That is, the median in I m,j .

[0141] Step S34: Split the problem into two sub-problems, namely the optimization problem of the association between the base station and the user and subcarrier allocation in Step S4 and the optimization problem of IRS beam design in Step S5. These two sub-problems are in different time dimensions in actual solution. Jointly optimize user association and carrier allocation based on the initialized beam design result, and then solve the beam design based on the results of user association and carrier allocation. Alternately optimize Step S4 and Step S5 until the maximum number of iterations is reached to obtain the final result.

[0142] Step S4: Perform user association and carrier allocation according to the network link state to achieve interference control.

[0143] Use a three-dimensional matrix X to store the matching situation among the three groups of communication elements of the association between the base station and the user and subcarriers. And there is an interactive relationship among users, base stations and subcarriers. Adopt the idea of the Gale-Shapely (GS) matching algorithm with modified bilateral preferences to solve the resource allocation sub-problem.

[0144] Step S41: Define three sets J, M, K, representing users, base stations and subchannels respectively. Jointly consider the base station and subchannels, and construct the base station-subchannel set D = M×K, where (m,k) represents a unit of the base station-subchannel set. For j∈J and (m,k)∈D, the matching rule is that η satisfies the following three points:

[0145] · η(j)∈D and η(j)≤1;

[0146] · η(m,k)∈J and |η(m,k)|≤1;

[0147] · If and only if j∈η(m,k), η(j)∈(m,k).

[0148] Step S42: Determine the preference relationship based on the sets J and D in S41. First, determine the user-side matching preference. Since the user is unknown about the content of the files transmitted by the base stations, the user's preference focuses on the base station-subchannel units with a larger transmission rate, that is, select the units with a larger channel gain.

[0149] Step S43: Determine the matching preference of the base station-subchannel set. Due to co-channel interference, there is an externality in this matching game. The base station-subchannel unit (m,k) may be affected by other matching units, and each matching result of (j,(m,k)) will also be affected by other units. Define a preference matrix Γ in the algorithm j,(m,k) to describe the influence of other units (i,(t,k)) on (j,(m,k)):

[0150]

[0151] When it is considered that (j 1 ,(m 1 ,k)) is more suitable to be matched with the base station-subchannel unit (m,k) than (j 2 ,(m 2 ,k));

[0152] When it is considered that (j 2 ,(m 2 ,k)) is more suitable to be matched with the base station-subchannel unit (m,k) than (j 1 ,(m 1 ,k)).

[0153] Step S44: Determine the preference of the subchannel pair for each base station-subchannel unit. For subchannel k, use:

[0154]

[0155] to measure the preference of this channel and the unit (m,k). Specifically, if:

[0156]

[0157] then it is considered that base station m is more suitable to be matched with k 1 ; if:

[0158]

[0159] then it is considered that base station m is more suitable to be matched with k 2 .

[0160] Step S5: Based on the resource allocation result in step S4, beam design is carried out according to the interference situation of users in each cell, and the capacity coverage is maximized in a perceptible way of interference.

[0161] A distributed beam design strategy is adopted among various IRSs to maximize the sum of the rates of all users in the cell. The users are sorted according to the interference they receive and divided into strong-interference users and weak-interference users, and interference control is carried out for users of different interference types. For different parameter environments, a unified method is needed to distinguish between strong and weak interference users. Therefore, the users with the top 50% interference power among the covered users are regarded as strong-interference users, and the remaining users are weak-interference users.

[0162] A method combining fractional optimization transformation based on Lagrangian variation and semidefinite relaxation (SDR) is used to solve the optimization problem.

[0163] Step S51: Perform Lagrangian transformation on the original problem optimization objective and update α for this round of iteration. (i) 。

[0164] For the sum-rate expression in the optimization objective, introduce slack variables:

[0165] α = [α 1 …α j …α Nuser

[0166] Using Lagrangian variation, the optimization objective of the original problem, the sum of the rates of all users, is expressed as:

[0167]

[0168] This expression is a concave function for α = [α 1 …α j …α Nuser . Then when the objective is maximized, through It can be obtained that:

[0169]

[0170] Here, the subscript of α m,j represents the value corresponding to user j associated with base station m. Therefore, in this round of iteration, the optimization objective can be expressed as:

[0171]

[0172] Among them,

[0173] Step S52: Transform the original problem optimization objective and interference constraint conditions.

[0174] Let​ The product of the channel gain and power is expressed as:

[0175] h m,j,k ·P m,j =|h rm,j Θ m g m +h dm,j | 2 ·P m,j =(b m,j +θ H ·a m,j ) 2

[0176] The optimization problem is expressed as:

[0177]

[0178] The interference constraint condition is expressed as:

[0179]

[0180] Step S53: Perform a fractional optimization transformation on the original problem's optimization objective and determine ε for this round of iteration (i) 。

[0181] Introduce the slack variable ε = [ε 1 …ε j …ε Nuser , and the problem form can be sorted out as:

[0182]

[0183] This expression is a concave function for ε = [ε 1 …ε j …ε Nuser . Then, when the objective is maximized, through it can be obtained that:

[0184]

[0185] Under ε for this round of iteration, the problem is finally sorted out into the following form:

[0186] f 3 (θ) = θ H Uθ + 2Re{θ H ·v} + C

[0187]

[0188]

[0189]

[0190] Step S54: Deform the interference constraint:

[0191]

[0192] Among them,

[0193]

[0194] Finally, the optimization problem for the beam design sub - problem is deformed into:

[0195]

[0196] For the above - mentioned form of the sub - problem, the semi - definite relaxation method is used for solution, and the optimization result is obtained with the help of CVX.

[0197] Embodiment 2

[0198] On the basis of Embodiment 1, to reflect the beneficial effects of the proposed technology, the gain of the method of this embodiment on the performance of the ultra - dense network is shown. At the same time, the gain of the interference management and control based on intelligent reflecting surface - assisted ultra - dense network on the network capacity is shown, and a comparison is made with the network capacity of the traditional method. In the simulation experiment scenario of the method of this embodiment, 5 base stations are deployed in a 400m×400m site, each base station is equipped with an IRS for assistance, and the IRS is deployed at the base station end in a centralized deployment manner. The specific location parameters are shown in Table 1 below; there are 100 sub - carrier resources; users are randomly and uniformly distributed in the site; the urban micro - cell loss model is used to measure the channel condition, and Rayleigh fading is used to describe the small - scale fading.

[0199] Table 1 Distribution location parameter table of each communication node

[0200]

[0201] Figure 3 For the performance comparison of the resource allocation method of this embodiment, random resource allocation, and resource allocation without reflecting surface, it can be concluded that the system capacity increases continuously as the number of users in this area increases, that is, as the access terminals become more and more dense. This shows that through the method of this embodiment, the system capacity of the entire network can be effectively improved; compared with the traditional random resource allocation, the method of this embodiment effectively improves the system capacity by performing certain interference management and control in the user association and sub - carrier allocation stages; compared with the system without IRS assistance, the method of this embodiment effectively improves the system capacity by realizing interference management and control through IRS beam design.

[0202] Figure 4For the comparison of the capacity coverage gain effects of interference control and signal enhancement respectively in dense scenarios, it can be seen that for ultra-dense networks, as the number of users in this space continues to increase and the user density becomes too large, the performance of the method of this embodiment that adopts interference control to improve the system capacity is better than that of the technology of effective signal enhancement. For the ultra-dense network targeted by the method of this embodiment, the main reason for the decline in network communication performance is that users are overly interfered, and there is an upper limit on the effective signal power. Therefore, the performance of the technology of enhancing the effective signal will reach the upper bound and decline at a certain user density. Instead, the use of interference control technology can better enhance the system capacity.

[0203] Figure 5 For the comparison of the results of the method of this embodiment (correcting the channel estimation results) and the uncorrected results when the channel information is not fully known, taking the complete knowledge of all channel information as the upper bound of its performance, it can be concluded that as the number of users increases, generally the performance of the method of this embodiment is better than that of the uncorrected performance. For ultra-dense networks, when the user density is relatively dense (the number of users is between 200 and 500), the method of this embodiment has a certain advantage in improving the system capacity compared with the uncorrected technology; however, when the user density is too large, the channel conditions in the system are too complex, resulting in performance degradation, and the performance of the uncorrected and the method of this embodiment gradually converges and approaches the upper bound. Therefore, the method of this embodiment can achieve better improvement in system capacity for relatively dense scenarios.

Claims

1. An IRS-assisted ultra-dense network resource allocation method for capacity coverage, characterized in that, it includes the following steps: S1. Initialize the parameters of the IRS-assisted communication system; S2. Establish a channel model and correct the information of the estimated channel; S3. With the goal of maximizing the sum of system user rates, establish a joint optimization problem and split the joint optimization problem into two sub-problems. Sub-problem one is the optimization problem of base station-user association and sub-carrier allocation, and sub-problem two is the optimization problem of IRS beam design; S4. Solve sub-problem one, perform user association and carrier allocation according to the network link state, and achieve interference control; S5. Solve sub-problem two. Based on the resource allocation result, perform beam design according to the interference situation, and perceptibly realize the maximization of capacity coverage for interference; In step S5, the method of combining fractional optimization transformation based on Lagrangian variation with semi-definite relaxation is used to solve sub-problem two of IRS beam design, specifically including: S51. Perform Lagrangian transformation on the optimization objective of the original problem and update the slack variable α for this round of iteration (i) , where i represents the current iteration round; S52. Transform the original problem optimization objective and interference constraint conditions; S53. Perform a fractional optimization transformation on the original problem optimization objective and determine the slack variable ε for this round of iteration. (i) , and on this basis, organize the problem form; S54. Transform the interference constraint, and finally obtain the transformed form of the IRS beam design sub-problem, solve the transformed sub-problem, and obtain the optimization result.

2. An IRS-assisted ultra-dense network resource allocation method for capacity coverage according to claim 1, characterized in that: During the process of initializing the IRS-assisted communication system parameters in step S1, for the downlink of the IRS-assisted communication system, there are multiple communication cells in the system and the IRS and users associated with each cell; each cell's base station is equipped with an IRS, and all reflection units of the IRS are placed on the base station side by centralized deployment; all base stations and ground users are equipped with single antennas; for the given positions of the base station and IRS, the positions of users and the phase shifts of IRS beams are randomly generated within a given range.

3. An IRS-assisted ultra-dense network resource allocation method for capacity coverage according to claim 1, characterized in that, The specific content of step S2 is as follows: S21. Determine that the user receives the signal from the base station through the reflection of the IRS via the carrier; S22. Calculate the received power obtained by the user, and obtain the channel gain of the user according to the received power; S23. Use interpolation to restore the incomplete signal to obtain the channel estimation content, and there is an estimation error in this content; S24. According to the probability relationship between the estimated value in the channel estimation content and the channel information, correct the estimated value to make up for the estimation error; S25. Synthesize the probability distribution relationship between the channel information and the corrected estimated value, and obtain the corrected channel coefficient according to this probability relationship and the estimated value.

4. An IRS-assisted ultra-dense network resource allocation method for capacity coverage according to claim 1, characterized in that, The specific content of step S3 is as follows: S31. Determine the achievable rate of the user associated with the base station on the sub-channel; S32. Combine the association situation of the sub-channels corresponding to the user and the base station to obtain the sum of the rates of all users covered by one base station; S33. With the goal of maximizing the sum of system user rates, establish a joint optimization problem; S34. After splitting the joint optimization problem into two sub-problems, alternately optimize sub-problem one and sub-problem two until the maximum number of iterations is reached to obtain the final optimization result.

5. A method for IRS-assisted ultra-dense network resource allocation for capacity coverage according to claim 4, wherein, in step S31, the achievable rate of user j associated with base station m on the k-th sub-channel is written as the following formula: where I m,j is as follows: I m,j represents the co-channel signal interference received by the user; p m,k is the transmission power allocated by the base station to the k-th subcarrier; h j,m,k is the channel gain from base station m to user j through subcarrier k; B C is the bandwidth of the subcarrier; the noise in the system is additive white Gaussian noise, and the noise power is σ 2 ; In step S32, the sum R of the user rates of all users covered by a base station obtained m is as follows: This formula combines the association situation of user j and the corresponding sub-channel k of the base station.

6. A method for IRS-assisted ultra-dense network resource allocation for capacity coverage according to claim 5, wherein, In step S33, a matrix X of size N BS ×N user ×N sub is used to represent the relationship between base station-user association and sub-channel allocation; if x m,j,k = 1, it means that the j-th user is allocated to the k-th sub-channel of the m-th base station, otherwise x m,j,k = 0; the established joint optimization problem is as follows: s.t. (1) x m,j,k ≤a m,j (6)x m,j,k ,a m,j ∈{0,1} Among them, the sets of base stations, users, and IRSs are respectively represented as \(M = [M 1 \cdots M m \cdots M NBS , J = [J 1 \cdots J j \cdots J Nuser , and \(R = [R 1 \cdots R r \cdots R NIRS ; the set of carriers is represented as \(K = [K 1 \cdots K k \cdots K Nsub ; the set of units of each reflecting surface is \(N = [N 1 \cdots N n \cdots N Nelement .

7. A method for IRS-assisted ultra-dense network resource allocation for capacity coverage according to claim 6, wherein: in step S4, a three-dimensional matrix X is used to store the matching situation among the three communication elements of base station-user association and sub-carriers; at the same time, the Gale-Shapley (GS) matching algorithm with modified bilateral preferences is used to solve sub-problem one of base station-user association and sub-carrier allocation, specifically including: S41. Define the user set, base station set, and sub-channel set, and determine the matching rules; S42. Determine the matching preference on the user side, focusing on the base station-sub-channel units with high transmission rates, and select the units with channel gain greater than the preset threshold; S43. Determine the matching preference of the base station-sub-channel set; S44. Determine the preference of the sub-channel pair for each base station-sub-channel unit.

8. A method for IRS-assisted ultra-dense network resource allocation for capacity coverage according to claim 7, wherein: In step S43, a preference matrix Γ is defined algorithmically j,(m,k) , which is used to describe the influence of other units (i, (t, k)) on (j, (m, k)), as shown in the following formula: When it is considered that (j 1 , (m 1 , k)) is more suitable for matching with the base station - sub - channel unit (m, k) than (j 2 , (m 2 , k)); When , it is considered that (j 2 , (m 2 , k)) is more suitable for matching with the base station - sub - channel unit (m, k) than (j 1 , (m 1 , k)); in step S44, for sub-channel k, use: to measure the preference of this channel and the (m,k) unit, specifically, if: Then it is considered that base station m is more suitable for matching with k 1 ; if: Then it is considered that base station m is more suitable for matching with k 2 Match.

9. A method for IRS-assisted ultra-dense network resource allocation for capacity coverage according to claim 1, wherein: in step S54, the deformed form of the IRS beam design sub-problem is as follows: For the form of the above sub-problem, the method of semi-definite relaxation is used for solution, and the optimization result is obtained with the help of CVX.

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