Resource Management Method and Device for Heterogeneous Networks Based on Reconfigurable Intelligent Surfaces

Abstract

The present invention provides a resource management method and apparatus for a heterogeneous network based on a reconfigurable intelligent surface. The method includes: determining an overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS); comparing the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value; if it is less than the preset value, then the overall data rate is the target overall data rate. The performance of the heterogeneous network system is improved, and the backhaul limitation is reduced by RIS-assisted wireless backhaul.

Description

Technical Field

[0001] The present invention relates to a communication method, specifically a resource management method and apparatus for a heterogeneous network based on a reconfigurable intelligent surface. Background Art

[0002] With the rapid development of the fifth-generation (5G) wireless communication and Internet of Things (IoT) technologies, a large number of mobile devices will access the network for data services. The network will face problems of unbalanced user distribution and uneven resource utilization, which severely limit the performance of mobile devices. Small cell technology is a potential solution to solve this problem and can be used in hot spots to form a heterogeneous network HetNets. In HetNets, a traditional macro cell contains multiple micro cells, where the micro cells reuse the wireless bandwidth resources of the macro cell, and the small cell base station (SBS) can operate like a decode-and-forward relay to facilitate data transmission between users in the hot spot area and the macro cell base station (MBS). In addition, the micro cell communicates with the macro cell base station MBS through a backhaul link. However, the key problem in the backhaul link of HetNets is that it will generate additional interference through the backhaul link, thus degrading the system performance, and there is also a backhaul limitation in HetNets, that is, the data transmission rate of micro cell users cannot be greater than the data transmission rate of the backhaul link. Therefore, effective interference and resource management are crucial for HetNets.

[0003] The existing backhaul links are divided into wired backhaul and wireless backhaul. Among them, the wireless backhaul link means that the macro base station and the micro base station communicate through wireless signals. The wireless backhaul has low cost and is easy to deploy, and has more applicable scenarios, which is the best choice in the current heterogeneous network system. However, the wireless backhaul is also affected by network capacity, deployment density, required data rate, etc., and cannot alleviate the backhaul limitation, which will affect the data rate of micro cell users. And using the wireless backhaul will cause additional interference to the macro users operating on the same frequency in the system, affecting the system performance. Summary of the Invention

[0004] Aiming at the problems existing in the prior art, the present invention provides a resource management method and apparatus for a heterogeneous network based on a reconfigurable intelligent surface, which reduces the additional interference of the system to macro users operating on the same frequency and improves the system performance.

[0005] The present invention is implemented through the following technical solutions:

[0006] On the one hand, an embodiment of the present invention provides a resource management method for a heterogeneous network based on a reconfigurable intelligent surface, including:

[0007] Determine the overall data rate according to the reflection phase, transmit power allocation, and bandwidth allocation factor at the reconfigurable intelligent surface (RIS), where the RIS includes N R array elements;

[0008] Compare the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value. The previous overall data rate is obtained in the previous round of determining the overall data rate. The overall data rate includes the sum of the data rates of each small cell user equipment (SUE) and the sum of the data rates of each macro cell user (MUE) in the system. The SUE data rate includes the data rate transmitted from the small cell base station (SBS) to the SUE, and the MUE data rate includes the data rate transmitted from the macro cell base station (MBS) to the macro cell user (MUE). The MBS and K single-antenna MUEs distributed form a macro cell, and the SBS and SUE form a small cell;

[0009] If it is less than the preset value, then the overall data rate is the target overall data rate, and the preset value is the convergence threshold.

[0010] Further, the determining the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the RIS includes:

[0011] Set the transmit power allocation and bandwidth allocation factor to fixed values, and determine the target reflection phase Θ of each array element in the RIS The bandwidth allocation factor includes the ratio of the frequency bandwidth of the link between the MBS and the SBS, and the transmit power allocation includes the transmit power allocated at the macro cell base station;

[0012] Set the target reflection phase Θ and bandwidth allocation factor of the RIS to fixed values, and determine the target transmit power P. The target transmit power P includes: the transmit power p k allocated to the MUE, and the transmit power p j,0 allocated to the SBS;

[0013] Determine the target bandwidth allocation factor β according to the target transmit power P and the target reflection phase Θ of the RIS;

[0014] Determine the overall data rate according to the target reflection phase Θ, the target bandwidth allocation factor β, and the target transmit power P allocation.

[0015] Further, the setting the transmit power allocation and bandwidth allocation factor of the RIS to fixed values and determining the target reflection phase Θ of each array element in the RIS includes: including:

[0016] Determine the MUE data rate as the overall data rate according to the fixed transmit power allocation and bandwidth allocation factor;

[0017] While fixing the reflection phases of the other N R -1 array elements, traverse all possible values of the first array element to determine the phase that maximizes the overall data rate;

[0018] While fixing the other N R -1 array elements and the reflection phases of the array elements traverse all possible values of the second array element to determine the phase that maximizes the overall data rate;

[0019] And so on, determine the target reflection phase Θ of each array element in each RIS ;

[0020] Furthermore, the step of setting the reflection phase Θ and bandwidth allocation factor of the RIS as fixed values and determining the target transmit power P includes:

[0021] Using the Lagrange multiplier method and the Karush-Kuhn-Tucker (KKT) conditions to determine the optimal value of the transmit power p k allocated to the MUE, and the optimal value of the transmit power p j,0 allocated to the SBS;

[0022] Furthermore, the step of setting the reflection phase Θ and transmit power P of the RIS as fixed values and determining the target bandwidth allocation factor β includes:

[0023] According to the fixed transmit power allocation and RIS phase, determine the bandwidth allocation factor β according to the minimum feasible value within the feasible region under the fronthaul constraint;

[0024] Furthermore, before determining the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS), it further includes:

[0025] Initialize the reflection phase, bandwidth allocation factor, and transmit power allocation at the RIS, and obtain the initialized overall data rate;

[0026] On the other hand, an embodiment of the present invention provides a resource management device for a heterogeneous network based on a reconfigurable intelligent surface, including:

[0027] An acquisition module, configured to determine the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS), where the reconfigurable intelligent surface (RIS) includes N R array elements;

[0028] A comparison module, configured to compare the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value. The previous overall data rate is the one obtained in the previous round when determining the overall data rate. The overall data rate includes the sum of the data rates of each SUE in the system and the sum of the data rates of each macro cell user MUE. The SUE data rate includes the data rate transmitted from the small cell base station SBS to the SUE, and the MUE data rate includes the data rate transmitted from the macro cell base station MBS to the macro cell user MUE. The macro cell base station MBS configured with N T antennas and K single-antenna macro cell users MUE distributed therein form a macro cell, and the small cell base station SBS and the SUE form a small cell;

[0029] A determination module, configured to, if it is less than the preset value, set the overall data rate as the target overall data rate, where the preset value is a convergence threshold.

[0030] Further, the obtaining module is specifically configured to set the transmit power allocation and the bandwidth allocation factor as fixed values, and determine the target reflection phase Θ of each element in the RIS . The bandwidth allocation factor includes the ratio of the frequency bandwidth of the link between the MBS and the SBS, and the transmit power allocation includes the transmit power allocated at the MBS; set the target reflection phase Θ and the bandwidth allocation factor of the RIS as fixed values, and determine the target transmit power P. The target transmit power P includes: the transmit power p k allocated to the MUE, and the transmit power p j,0 allocated to the SBS; determine the target bandwidth allocation factor β according to the target transmit power P and the target reflection phase Θ of the RIS; determine the overall data rate according to the allocation of the target reflection phase Θ, the target bandwidth allocation factor β, and the target transmit power P.

[0031] Further, the obtaining module is specifically configured to determine the MUE data rate as the overall data rate according to the fixed transmit power allocation and bandwidth allocation factor; while fixing the reflection phases of the other N R -1 elements, traverse all possible values of the first element to determine the phase that maximizes the overall data rate; while fixing the reflection phases of the other N R -1 elements and the element , traverse all possible values of the second element to determine the phase that maximizes the overall data rate; and so on, to determine the target reflection phase Θ of each element in the RIS .

[0032] Further, the obtaining module is specifically configured to use the Lagrange multiplier method and the Karush-Kuhn-Tucker (KKT) conditions to determine the optimal value of the transmit power p allocated to the MUE and the optimal value of the transmit power p allocated to the SBS. k of, and the optimal value of the transmit power p allocated to the SBS. j,0 of.

[0033] Further, the obtaining module is specifically configured to determine the bandwidth allocation factor β according to the transmit power allocation and the RIS phase being fixed values and the minimum feasible value within the feasible region under the backhaul constraint.

[0034] Further, the obtaining module is further configured to initialize the reflection phase, the bandwidth allocation factor, and the transmit power allocation at the RIS and obtain the initialized overall data rate.

[0035] Compared with the prior art, the present invention has the following beneficial technical effects:

[0036] The embodiment of the present invention provides a method for determining the overall data rate based on the reflection phase, the bandwidth allocation factor, and the transmit power allocation at a reconfigurable intelligent surface (RIS). The reconfigurable intelligent surface (RIS) includes N R array elements; comparing the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value. The previous overall data rate is obtained in the previous round of determining the overall data rate. The overall data rate includes the sum of the data rates of each SUE and the sum of the data rates of each macrocell user (MUE). The SUE data rate includes the data rate transmitted from the small cell base station (SBS) to the SUE, and the MUE data rate includes the data rate transmitted from the macrocell base station (MBS) to the MUE. The MBS configured with N T antennas and K single-antenna MUEs distributed form a macrocell, and the SBS and the SUE form a small cell; if it is less than the preset value, the overall data rate is the target overall data rate, and the preset value is the convergence threshold. The system performance is improved, and the backhaul limitation is reduced through RIS-assisted wireless backhaul. The present invention comprehensively considers an overall data rate optimization method for RIS passive beamforming (i.e., reflection phase), system bandwidth allocation, MBS transmit power allocation, and wireless backhaul limitation, and proposes a scheme for alternately optimizing bandwidth allocation, transmit power allocation, and passive beamforming on the RIS to solve the above problems. This method can significantly improve the system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic flowchart of a resource management method for a heterogeneous network based on a reconfigurable intelligent surface according to an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of a communication system according to an embodiment of the present invention;

[0039] Figure 3 is a schematic diagram of the CDF curve of the MUE data rate according to an embodiment of the present invention;

[0040] Figure 4 is a schematic diagram of the performance of the overall user data rate with different numbers of MUEs according to an embodiment of the present invention;

[0041] Figure 5 is a schematic diagram of the performance of the overall user data rate with different numbers of RIS elements according to an embodiment of the present invention;

[0042] Figure 6 is a resource management device for a heterogeneous network based on a reconfigurable intelligent surface according to an embodiment of the present invention. Detailed implementation manners

[0043] The present invention will be further described in detail below in conjunction with specific embodiments, which are explanations rather than limitations of the present invention.

[0044] As Figure 1 shown, the resource management method for a heterogeneous network based on a reconfigurable intelligent surface provided by an embodiment of the present invention includes the following steps:

[0045] Step 101: Determine the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface RIS.

[0046] The reconfigurable intelligent surface RIS in this embodiment includes N R array elements;

[0047] Figure 2 is a schematic diagram of a communication system according to an embodiment of the present invention. As shown in FIG. 2, the communication system of this embodiment is a two-tier HetNet, where one macro cell is covered by S micro cells, and there is an MBS equipped with N T antennas at the center of the macro cell. There are K single-antenna macro cell users MUEs evenly distributed in the macro cell. Assume N T >> S and N T >> K. A device with N RReconfigurable intelligent surface (RIS) with [number of] array elements. In each small cell, there is a small cell base station (SBS) at the center and a randomly distributed small cell user equipment (SUE). Both the SBS and the SUE are equipped with an antenna. The SBS operates in a similar manner to a decode-and-forward relay and communicates with the macro cell base station (MBS) via a wireless backhaul. In the present invention, orthogonal bandwidth division is adopted in the access link and the wireless backhaul link to avoid self-interference at the SBS. Herein, the link between the MBS and the SBS is referred to as the wireless backhaul link, and the link between the macro cell user equipment (MUE) and the MBS or between the SUE and its associated SBS is referred to as the access link. The ratio of the frequency bandwidth allocated to the wireless backhaul link is β. Then the remaining 1 - β is the proportion of the bandwidth of the access link. To further reduce the interference involved, the HetNet operates in the reverse time-division duplex (TDD) mode, where the downlink transmission time slot of the macro cell is the uplink transmission time slot of the small cell, and vice versa.

[0048] Transmission from the MBS to the MUE: For the signal transmission from the MBS to the MUE, the received signal at the MUE is interfered by the uplink transmission of the SUE in the reverse TDD Ethernet. Generally, the transmit power of a mobile terminal is much lower than that of a base station. The signal transmission from the MBS to the MUE can ignore the cross-layer interference from the SUE. Therefore, the received signal at the k-th MUE from the MBS can be written as

[0049] y k =(g k +h k ΘF)x+u k (1)

[0050] where represents the channel state information (CSI) from the MBS to the k-th MUE, represents the CSI from the RIS to the k-th MUE, represents the CSI from the MBS to the RIS. represents the passive beamforming matrix at the RIS, represents the reflection coefficient, where is the phase shift of the r-th array element, b is the discrete phase resolution of the RIS array elements, and i is an integer. Where is the signal transmitted from the MBS to the MUE. Where p k , s k and w k are the power, information symbol, and precoding vector transmitted from the MBS to the k-th MUE, respectively. Assume that s k is an independent random variable with zero mean and unit variance. represents the additive white Gaussian noise (AWGN) at the k-th MUE.

[0051] Then, the signal-to-interference-plus-noise ratio (SINR) at the k-th MUE can be calculated by the following formula:

[0052]

[0053] where I inter-user,k is the interference between users,

[0054] To mitigate the interference between users among MUEs, zero-forcing (ZF) precoding is adopted at the MBS in the present invention. The cascaded signal A at the MUE is A = (G + HΦF). Where

[0055] Therefore, the zero-forcing coding matrix is

[0056]

[0057] Let be the precoding matrix of the k-th MUE,

[0058] The present invention normalizes the zero-forcing precoding vector to

[0059]

[0060] Then, the SINR of the k-th user can be simplified to In this way, the data rate of the k-th user can be obtained:

[0061]

[0062] Transmission from the MBS to the SBS: For the wireless backhaul link from the MBS to the SBS, the received signal at each SBS includes the transmitted signal from the MBS and the reflected signal from the RIS. The received signal at the j-th SBS is

[0063] z j,0 =(g j,0 +h j,0 ΘF)x0 + u j,0 (6)

[0064] where represents the CSI from the MBS to the j-th SBS, represents the CSI from the RIS to the j-th SBS. Where is the signal transmitted by the MBS to the SBS, where p j,0 , s j,0 and w j,0 are the power, information symbol, and precoding vector transmitted by the MBS to the j-th SBS, respectively. Assume s j,0Independent random variables with zero mean and unit variance. Represents the additive white Gaussian noise (AWGN) at the j-th SBS.

[0065] The SINR at the j-th SBS can be expressed as:

[0066]

[0067] Similarly, assume that zero-forcing precoding is used at the MBS to eliminate the backhaul interference between the backhaul links. The cascaded signal at the SBS is A0 = (G0 + H0ΘF). Where

[0068] Therefore, the zero-forcing coding matrix can be obtained as

[0069]

[0070] Let be the precoding matrix of the j-th SBS,

[0071] In this example, the zero-forcing precoding vector is normalized to

[0072]

[0073] Then the SINR of the j-th SBS can be simplified to In this way, the data rate of the backhaul link of the j-th SBS can be obtained:

[0074]

[0075] From the SBS to the SUE: For the transmission from the SBS to the SUE, the signal of the reflection path through the RIS is not considered because the RIS does not work in the downlink time slot of the microcell. In addition, since the transmission power of the mobile terminal is usually much lower than that of the base station, the cross-layer interference from the mobile terminal can be ignored. Therefore, for the downlink transmission from the SBS to the SUE, the interference of each SUE is mainly composed of the co-layer interference of the downlink transmission from other microcells. The received signal of the SUE in the j-th microcell can be expressed as

[0076]

[0077] where, l j,m is the CSI from the m-th SBS to the SUE in the j-th microcell, p m,m and x m,m are the transmission power and data from the m-th SBS to its associated SUE respectively, Represents the additive white Gaussian noise (AWGN) at the j-th SUE.

[0078] The SINR at the SUE in the j-th small cell can be written as:

[0079]

[0080] When the SUE utilizes 1-β portion of the frequency resources, the data rate of the SUE in the j-th small cell can be expressed as

[0081] R j,j =(1-β)B log2(1 + Γ j,j ) (13)

[0082] For HetNets with wireless backhaul links, the data rate of the SUE in the j-th small cell is limited by the wireless backhaul capacity of its associated SBS

[0083] R j,j ≤R j,0 (14)

[0084] Considering the passive beamforming, bandwidth allocation, transmit power allocation, and wireless backhaul limitations at the RIS, the present invention formulates the problem of maximizing the overall data rate of all users, where the overall data rate of all users in the HetNets can be calculated by the following formula:

[0085]

[0086] Considering the passive beamforming, bandwidth partitioning, transmit power allocation, and wireless backhaul limitations at the RIS, the present invention formulates the problem of maximizing the overall data rate of all users, and the optimization problem can be expressed as

[0087]

[0088] s.t is the constraint condition, where (16a) is the constraint on the phase of the RIS elements, (16b) is the constraint corresponding to the bandwidth resource allocation factor, (16c) is the wireless backhaul capacity limitation, and (16d) limits that the total transmit power of the MBS cannot exceed its maximum power limit. The variables in the optimization problem in (16) are coupled and non-convex. The joint optimization problem is difficult to solve directly. Therefore, the present invention develops an effective scheme to iteratively solve this problem.

[0089] This example first decomposes the problem of maximizing the overall data rate of all users into three sub-problems, namely passive beamforming, transmit power allocation, and bandwidth allocation, then formulates corresponding schemes to solve these three sub-problems, and finally iteratively executes the proposed alternating optimization scheme to solve the original problem.

[0090] Specifically, determining the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS) includes:

[0091] Set the transmit power allocation and bandwidth allocation factor to fixed values, and determine the target reflection phase Θ of each element in the RIS The bandwidth allocation factor includes the ratio of the frequency bandwidth of the link between the MBS and the SBS, and the transmit power allocation includes the transmit power allocated at the macro cell base station;

[0092] For example, according to the fixed transmit power allocation and bandwidth allocation factor, determine the MUE data rate as the overall data rate;

[0093] While fixing the reflection phases of the other N R -1 elements, traverse all possible values of the first element to determine the phase that maximizes the overall data rate;

[0094] While fixing the reflection phases of the other N R -1 elements and the element traverse all possible values of the second element to determine the phase that maximizes the overall data rate;

[0095] And so on, determine the target reflection phase Θ of each element in the RIS

[0096] Specifically, in this example, first fix the bandwidth allocation factor β and the allocated power P at the macro base station, and optimize the sub-problem of the passive beamforming matrix. From the overall data rate of all users in (15), it can be seen that when β and P are fixed, the overall data rate of the SUE becomes a constant. Therefore, in the case of fixed β and P, for the passive beamforming at the RIS, the problem of maximizing the overall data rate of all users in (16) is equivalent to

[0097]

[0098] where A0 = (1 - β)B.

[0099] Then, obtain the passive beamforming that can maximize the overall data rate of all users through a local search method. Specifically, fix the phase shift values of the other N R -1 elements, and then for each element of the present invention, traverse all possible values in the discrete feasible set and select the best value that maximizes the overall data rate. Then use this value to optimize the phase shift of another element until all elements are optimized.​

[0100] Set the target reflection phase Θ and bandwidth allocation factor of the RIS to fixed values, and determine the target transmit power P, where the target transmit power P includes: the transmit power p allocated to the MUE k , and the transmit power p allocated to the SBS j,0 ;

[0101] For example, the Lagrange multiplier method and the Karush - Kuhn - Tucker (KKT) conditions are used to determine the optimal value of the transmit power p allocated to the MUE k , and the optimal value of the transmit power p allocated to the SBS j,0 .

[0102] Specifically, according to the optimal value of Θ and the fixed value of β obtained in the previous section, optimize the allocated transmit power P of the MUE and SBS. The problem of allocating the transmit power with β and Θ fixed can be simplified to

[0103]

[0104] It can be seen from the overall data rate of all users in (15) that the overall data rate of the SUE is independent of the variable P to be optimized for a given β. Therefore, the problem in (18) is equivalent to

[0105]

[0106] (19) The optimization problem is convex. This problem can be effectively solved using standard convex optimization techniques. In this example, the Lagrange multiplier method and the Karush - Kuhn - Tucker (KKT) conditions are used to find its closed - form solution. Specifically, in this example, the optimal values of p k and p j,0 are

[0107]

[0108]

[0109] where

[0110] According to the target transmit power P and the target reflection phase Θ of the RIS, determine the target bandwidth allocation factor β;

[0111] For example, according to the fixed values of the transmit power allocation and the RIS phase, determine the bandwidth allocation factor β based on the minimum feasible value within the feasible region under the backhaul constraint.

[0112] Specifically, optimize the bandwidth allocation factor β based on the optimal values of Θ and P in the previous part. Based on the values of Θ and P, the bandwidth division problem can be written as

[0113]

[0114] Note that the objective function in (16) is a monotonically decreasing function of β. Therefore, the maximum value of the overall user data rate can be obtained with the minimum value of β within its feasible region. Specifically, according to the constraints in (16b), this example can obtain Combining the constraints in (16b) and (16c), this example can obtain Therefore, the optimal β is

[0115] Determine the overall data rate according to the target reflection phase Θ, the target bandwidth allocation factor β, and the target transmit power allocation.

[0116] In summary, the solutions proposed for the corresponding three sub-problems in this example will be alternately iterated to finally solve the problem in (16). Since the overall data rate increases during the iteration process, the convergence of the algorithm can be guaranteed.

[0117] Step 102: Compare the overall data rate with the previous overall data rate and determine whether the comparison result is less than a preset value.

[0118] The previous overall data rate in this embodiment is obtained in the previous round of determining the overall data rate. The overall data rate includes the sum of the data rates of each SUE and the sum of the data rates of each MUE in the system. The SUE data rate includes the data rate transmitted from the SBS to the SUE, and the MUE data rate includes the data rate transmitted from the MBS to the MUE. The MBS configured with N T antennas and K single-antenna MUEs distributed form a macro cell, and the SBS and SUE form a micro cell;

[0119] Step 103: If it is less than the preset value, then the overall data rate is the target overall data rate.

[0120] In this embodiment, if it is not less than the preset value, return to step 101 for a new round of iteration. The preset value is the convergence threshold.

[0121] In this implementation, by determining the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface RIS, the reconfigurable intelligent surface RIS includes N Rarray elements; comparing the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value, where the previous overall data rate is obtained in the previous round of determining the overall data rate, and the overall data rate includes the sum of the data rates of each SUE and the sum of the data rates of each MUE. The SUE data rate includes the data rate transmitted from the SBS to the SUE, and the MUE data rate includes the data rate transmitted from the MBS to the MUE. The MBS configured with N T The MBS with N antennas and K single-antenna MUEs distributed form a macro cell, and the SBS and SUE form a micro cell; if it is less than the preset value, the overall data rate is the target overall data rate, and the preset value is the convergence threshold. The system performance is improved, and the backhaul limitation is reduced through RIS-assisted wireless backhaul. The present invention comprehensively considers the overall data rate optimization method of RIS passive beamforming (i.e., reflection phase), MBS bandwidth allocation and transmit power allocation, and wireless backhaul limitation, and proposes a scheme for alternately optimizing bandwidth allocation, transmit power allocation, and passive beamforming on the RIS to solve the above problems. This method can significantly improve the system performance.

[0122] In this embodiment, the method of Embodiment 1 is simulated and evaluated through simulation. This embodiment considers a macro cell HetNets containing multiple micro cells. The MBS is located at the center of the macro cell, and the coordinate value is (0m, 0m). The radius of the macro cell is set to 250m. There are S micro cells and K uniformly distributed MUEs in the macro cell. The SBS is located at the center of each micro cell, and the radius of the micro cell is set to 10m. A SUE is randomly distributed in each micro cell. A RIS with a coordinate value of (0m, 10m) is placed near the MBS. The discrete phase resolution bit of the RIS element is set to b = 3. The noise is set to -80dBm. For the channel model, the small-scale fading in this embodiment adopts the Rician fading channel model, and for the large-scale fading, the spatial propagation path loss in this embodiment is set to

[0123]

[0124] where C0 = -30dB is the path loss at the reference distance D0 = 1m, d represents the link distance, and κ represents the path loss exponent. In this example, κ = 2.2 is set in the MBS-RIS channel, κ = 2.8 is set in the RIS-SBS and RIS-MUE channels, and κ = 3.5 is set in the MBS-SBS, MBS-MUE, and SBS-SUE channels. The maximum transmit power of the MBS is 43dBm, and the maximum transmit power of the SBS is 33dBm.

[0125] Figure 4Shows the cumulative distribution function (CDF) performance of the MUE data rate when K = 10, S = 1 and K = 10, S = 4, where the legends "BW-TXpower-opt", "proposed" and "w / o RIS" represent the schemes of optimizing bandwidth allocation and transmit power allocation and random phase configuration at the RIS, the scheme proposed by the present invention, and the reference scheme of bandwidth division optimization and equal power allocation, respectively, where there is no RIS involved in the system. From Figure 3 It can be seen that compared with the reference algorithm without RIS, after adding RIS to the system, the CDF curve shifts to the right, which indicates that adding RIS helps to significantly improve the user data rate. In addition, the CDF curve after alternately optimizing the bandwidth, transmit power and passive beamforming at the RIS is located on the far right, which indicates that the method proposed by the present invention performs the best among these schemes.

[0126] Figure 5 Shows the performance of the overall user data rate for different numbers of MUEs when N t = 16. It can be seen from the figure that the overall data rate increases with the increase in the number of MUEs, and adding RIS to the HetNet can improve the overall user data rate of the system. Moreover, as the number of MUEs increases, the performance gain will be greater. In addition, as the number of microcells increases, the overall data rate also increases, and in the case of only one microcell, the increasing trend will be greater.

[0127] Figure 6 Provides the performance of the overall user data rate when S = 1 and S = 2 with different numbers of RIS elements. It can be seen from the figure that as the number of RIS elements changes from 2 3 to 2 8 the overall user data rate increases. And as the number of elements increases, the growth trend of the overall data rate becomes faster.

[0128] The present invention first decomposes the problem of maximizing the overall data rate of all users into three sub-problems, namely passive beamforming, transmit power allocation and bandwidth allocation, then formulates corresponding schemes to solve these three sub-problems, and finally iteratively executes the proposed alternating optimization scheme to solve the original problem.

[0129] First, the present invention fixes the bandwidth allocation factor and the allocated power at the macro base station, and then obtains the reflection phase Θ of the RIS elements that can maximize the sum rate through the local search method. Then, according to the phase obtained in this round and the fixed bandwidth allocation factor, the closed-form solution of the allocated power at the macro base station is found using the Lagrange multiplier method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, the bandwidth allocation factor is optimized based on the optimal values of Θ and P obtained in the previous part. Based on the values of Θ and P, and the backhaul constraint, the bandwidth allocation factor that satisfies the backhaul constraint can be obtained. Then, the next round of iteration is performed according to the optimization result of this round until the overall data rate of all users converges.

[0130] Figure 6 This is a resource management device for a heterogeneous network based on a reconfigurable intelligent surface according to an embodiment of the present invention. The device includes: an acquisition module 61, a comparison module 62, and a determination module 63, where

[0131] The acquisition module 61 is configured to determine the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS). The reconfigurable intelligent surface (RIS) includes N R array elements;

[0132] The comparison module 62 is configured to compare the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value. The previous overall data rate is obtained in the previous round of determining the overall data rate. The overall data rate includes the sum of the data rates of each small cell user equipment (SUE) and the sum of the data rates of each macro user equipment (MUE). The SUE data rate includes the data rate transmitted from the small cell base station (SBS) to the cell user SUE. The MUE data rate includes the data rate transmitted from the macro cell base station (MBS) to the MUE. The macro cell base station (MBS) configured with N T antennas and the distributed K single-antenna MUEs form a macro cell, and the SBS and the cell user SUE form a small cell;

[0133] The determination module 63 is configured to, if it is less than the preset value, set the overall data rate as the target overall data rate, and the preset value is the convergence threshold.

[0134] In this embodiment, the overall data rate is determined according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS); the overall data rate is compared with the previous overall data rate to determine whether the comparison result is less than a preset value; if it is less than the preset value, the overall data rate is set as the target overall data rate. The performance of the heterogeneous network system is improved, and the backhaul constraint is reduced through RIS-assisted wireless backhaul.

[0135] Based on the above embodiments, the obtaining module 61 is specifically configured to set the transmit power allocation and bandwidth allocation factor to fixed values, and determine each element in the RIS 's target reflection phase Θ, where the bandwidth allocation factor includes the ratio of the frequency bandwidth of the link between the MBS and the SBS, and the transmit power allocation includes the transmit power allocated at the macro cell base station; set the target reflection phase Θ and the bandwidth allocation factor of the RIS to fixed values, and determine the target transmit power P, where the target transmit power P includes: the transmit power p k allocated to the MUE, and the transmit power p j,0 allocated to the SBS; determine the target bandwidth allocation factor β according to the target transmit power P and the target reflection phase Θ of the RIS; determine the overall data rate according to the target reflection phase Θ, the target bandwidth allocation factor β, and the target transmit power P allocation.

[0136] Based on the above embodiments, the obtaining module 61 is specifically configured to determine the MUE data rate as the overall data rate according to the fixed transmit power allocation and bandwidth allocation factor; while fixing the reflection phases of the other N R - 1 elements, traverse all possible values of the first element to determine the phase that maximizes the overall data rate; while fixing the reflection phases of the other N R - 1 elements and the element 's reflection phase, traverse all possible values of the second element to determine the phase that maximizes the overall data rate; and so on, to determine the target reflection phase Θ of each element in each RIS.

[0137] Based on the above embodiments, the obtaining module 61 is specifically configured to use the Lagrange multiplier method and the Karush - Kuhn - Tucker (KKT) conditions to determine the optimal value of the transmit power p k allocated to the MUE, and the optimal value of the transmit power p j,0 allocated to the SBS.

[0138] Based on the above embodiments, the obtaining module 61 is specifically configured to determine the bandwidth allocation factor β according to the fixed transmit power allocation and the RIS phase, based on the minimum feasible value within the feasible region under the backhaul constraint;

[0139] Further, based on the above embodiments, the obtaining module 61 is further configured to initialize the reflection phase, bandwidth allocation factor, and transmit power allocation at the RIS, and obtain the initialized overall data rate.

[0140] The working principle and technical effects of a resource management device for a heterogeneous network based on a reconfigurable intelligent surface provided by an embodiment of the present invention are similar to those of the above method, and will not be elaborated here.

Claims

1. A resource management method for a heterogeneous network based on reconfigurable intelligent surfaces, characterized in that, Including: Determine the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS), where the RIS includes N R array elements; Compare the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value. The previous overall data rate is obtained in the previous round of determining the overall data rate. The overall data rate includes the sum of the data rates of each small cell user (SUE) and the sum of the data rates of each macro cell user (MUE). The SUE data rate includes the data rate transmitted from the small cell base station (SBS) to the SUE, and the MUE data rate includes the data rate transmitted from the macro cell base station (MBS) to the MUE. The MBS and K single-antenna MUEs distributed therein form a macro cell, and the SBS and the SUE form a small cell; If it is less than the preset value, then the overall data rate is the target overall data rate, and the preset value is the convergence threshold; Wherein, determining the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS) includes: Set the transmit power allocation and bandwidth allocation factors to fixed values, and determine the target reflection phase Θ of each array element in the RIS The bandwidth allocation factor includes the ratio of the frequency bandwidth of the link between the MBS and the SBS, and the transmit power allocation includes the transmit power allocated at the macro cell base station; Set the target reflection phase Θ and bandwidth allocation factor of the RIS to fixed values, and determine the target transmit power P, where the target transmit power P includes: the transmit power p allocated to the MUE k , and the transmit power p allocated to the SBS j,0 ; Determine the target bandwidth allocation factor β according to the target reflection phase Θ and the target transmit power P of the RIS; Determine the overall data rate according to the target reflection phase Θ, the target bandwidth allocation factor β, and the target transmit power allocation; 2. The resource management method for a heterogeneous network based on reconfigurable intelligent surfaces according to claim 1, characterized in that, Setting the transmission power allocation and bandwidth allocation factor β of the RIS to a fixed value and determining the target reflection phase Θ of each array element in the RIS includes: ​ Determine the MUE data rate as the overall data rate according to the fixed transmit power allocation and bandwidth allocation factor; While fixing the reflection phases of the other N R - 1 array elements, the first array element is traversed through all possible values to determine the phase that maximizes the overall data rate; While fixing the reflection phases of the other N R - 1 array elements and the said array element at the same time, the second array element is traversed through all possible values to determine the phase that maximizes the overall data rate; ​ And so on, to determine the target reflection phase Θ of each array element in each RIS .

3. The resource management method for a heterogeneous network based on reconfigurable intelligent surfaces according to claim 2, characterized in that, The setting the reflection phase Θ and the bandwidth allocation factor β of the RIS to fixed values to determine the target transmit power P includes: According to the Lagrange multiplier method and the Karush-Kuhn-Tucker (KKT) conditions, determine the optimal value of the transmit power \(p\) allocated to the MUE, and the optimal value of the transmit power \(p\) allocated to the SBS. k j,0 ​ 4. The resource management method for a heterogeneous network based on reconfigurable intelligent surfaces according to claim 3, characterized in that, The setting the reflection phase Θ and the transmit power allocation of the RIS to fixed values to determine the target bandwidth allocation factor β includes: Determine the bandwidth allocation factor β according to the fixed transmit power allocation and RIS phase, and the minimum feasible value within the feasible region under the backhaul constraint; 5. The resource management method for a heterogeneous network based on reconfigurable intelligent surfaces according to claims 1-4, characterized in that, Before determining the overall data rate according to the reflection phase, bandwidth allocation factor, and transmit power allocation at the reconfigurable intelligent surface (RIS), it further includes: Initialize the reflection phase, bandwidth allocation factor, and transmit power allocation at the RIS, and obtain the initialized overall data rate; 6. A resource management device for a heterogeneous network based on reconfigurable intelligent surfaces, characterized in that, Including: An acquisition module, configured to determine an overall data rate according to a reflection phase, a transmit power allocation, and a bandwidth allocation factor at a reconfigurable intelligent surface (RIS), where the reconfigurable intelligent surface (RIS) includes N R array elements; A comparison module for comparing the overall data rate with the previous overall data rate to determine whether the comparison result is less than a preset value. The previous overall data rate is obtained in the previous round of determining the overall data rate. The overall data rate includes the sum of the data rates of each small cell user (SUE) and the sum of the data rates of each macro cell user (MUE). The SUE data rate includes the data rate transmitted from the small cell base station (SBS) to the SUE, and the MUE data rate includes the data rate transmitted from the macro cell base station (MBS) to the MUE. The MBS and K single-antenna MUEs distributed therein form a macro cell, and the SBS and the SUE form a small cell; A determination module for, if it is less than the preset value, the overall data rate is the target overall data rate, and the preset value is the convergence threshold; Among them, the obtaining module is specifically configured to set the transmit power allocation and the bandwidth allocation factor to fixed values, and determine the target reflection phase Θ of each element in the RIS The bandwidth allocation factor includes the ratio of the frequency bandwidth of the link between the MBS and the SBS, and the transmit power allocation includes the transmit power allocated at the macro cell base station; set the target reflection phase Θ and the bandwidth allocation factor of the RIS to fixed values, and determine the target transmit power P. The target transmit power P includes: the transmit power p k allocated to the MUE, and the transmit power p j,0 allocated to the SBS; determine the target bandwidth allocation factor β according to the target transmit power P and the target reflection phase Θ of the RIS; determine the overall data rate according to the target reflection phase Θ, the target transmit power P allocation, and the target bandwidth allocation factor β.

7. The resource management device for a heterogeneous network based on reconfigurable intelligent surfaces according to claim 6, characterized in that, The obtaining module is specifically configured to determine that the MUE data rate is the overall data rate according to the fact that the transmit power allocation and the bandwidth allocation factor are fixed values; while fixing the reflection phases of the other N R - 1 array elements, traverse all possible values of the first array element to determine the phase that maximizes the overall data rate; while fixing the other N R - 1 array elements and the reflection phase of the array element traverse all possible values of the second array element to determine the phase that maximizes the overall data rate; and so on, to determine the target reflection phase Θ of each array element in each RIS. ​ 8. The resource management device for a heterogeneous network based on reconfigurable intelligent surfaces according to claim 6, characterized in that, The obtaining module is specifically configured to determine the optimal value of the transmission power p k allocated to the MUE and the optimal value of the transmission power p j,0 allocated to the SBS by using the Lagrange multiplier method and the Karush-Kuhn-Tucker (KKT) conditions; and is configured to determine the bandwidth allocation factor β according to the minimum feasible value within the feasible region under the backhaul constraint, with the transmission power allocation and the RIS phase being fixed values.

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