Unmanned aerial vehicle assisted symbiotic radio system capacity optimization method and related device

Abstract

The invention discloses an unmanned aerial vehicle assisted symbiotic radio system capacity optimization method and a related device, and the method comprises the steps: carrying out the channel allocation between backscattering equipment and a main user according to the number of backscattering equipment in a system, the number of main users, and the distance between the backscattering equipment and the main user; a backscatter communication capacity optimization model of the system is decomposed into a base station transmitting power optimization model, a backscatter coefficient optimization model and an unmanned aerial vehicle position optimization model, global iteration is carried out, a global optimal solution is used as a capacity optimization result of the system, and the system can be configured based on the optimization result so that the system can operate under the optimal capacity.

Description

Technical Field

[0001] The present invention relates to a method for optimizing the capacity of a symbiotic radio system assisted by an unmanned aerial vehicle and a related device, belonging to the technical field of wireless communications. Background Art

[0002] In recent years, symbiotic radio has attracted extensive attention from academia and industry due to its sharing characteristics in the spectrum and energy domains, and has been listed as one of the key candidate technologies for 6G. Ambient backscatter communication (AmBC) is a typical symbiotic radio system, in which passive IoT devices are parasitic in the main transmission system as backscatter devices (BD), using the spectrum resources and radio frequency signals of the main system (one part is used for energy harvesting, and the other part is used for backscatter communication) for communication, thus not only saving energy but also greatly increasing spectrum efficiency. In the symbiotic radio system, drones can be used as mobile receivers to receive backscatter signals. For example, in the IoT scenario, drones use symbiotic radio technology for data collection. At present, there is little research on optimizing the backscatter communication capacity in drone-assisted symbiotic radio systems. Summary of the invention

[0003] The present invention provides a UAV-assisted symbiotic radio system capacity optimization method and related devices, which solve the problems disclosed in the background technology.

[0004] According to one aspect of the present disclosure, a method for optimizing capacity of a symbiotic radio system assisted by a drone is provided, comprising:

[0005] Channel allocation between the backscatter devices and the primary users is performed according to the number of backscatter devices, the number of primary users, and the distance between the backscatter devices and the primary users in the system; wherein the system is a drone-assisted symbiotic radio system;

[0006] Based on the channel allocation results, the system's backscatter communication capacity optimization model is decomposed into base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model. The base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model are solved in an iterative manner to obtain the optimal base station transmission power, backscatter coefficient and UAV position. The channel allocation results, the optimal base station transmission power, backscatter coefficient and UAV position are used as the capacity optimization results of the system.

[0007] Further, according to the number of backscattering devices in the system, the number of primary users, and the distance between the backscattering devices and the primary users, channel allocation between the backscattering devices and the primary users is performed, including:

[0008] If the number of backscatter devices is less than or equal to the number of primary users, the KM algorithm is used to determine the primary user that matches each backscatter device based on the distance between the backscatter device and the primary user. The matched backscatter devices and the primary users share the channel.

[0009] If the number of backscatter devices is greater than the number of primary users and is less than or equal to 2 times the number of primary users, screen out the backscatter devices with the highest M receiving base station transmit power, and use the KM algorithm to determine the primary user that matches each screened backscatter device based on the distance between the screened backscatter devices and the primary users. Use the KM algorithm to determine the primary user that matches each remaining backscatter device based on the distance between the remaining backscatter devices and the primary users. The matched backscatter devices and the primary users share the channel; where M is equal to the number of primary users.

[0010] Furthermore, the system's backscatter communication capacity optimization model aims to maximize the backscatter communication capacity, and the objective function is:

[0011] ;

[0012] Constraints C1~C5 are:

[0013] ;

[0014] ;

[0015] ;

[0016] ;

[0017] ;

[0018] Where p is the base station transmission power, and are the backscatter coefficients of the kth and jth backscatter devices, respectively. u is the position of the drone. a indicates whether there is interference. a=1 indicates interference, and a=0 indicates no interference. K is the number of backscatter devices. They are the channel gains between the base station and the kth backscatter device, the drone and the kth backscatter device, the drone and the jth backscatter device, the base station and the jth backscatter device, and the base station and the mth primary user, is the internal noise power of the drone, Q is the communication rate threshold, is the total interference of the backscattering device to the primary user in a channel, is the interference of the kth backscattering device to the mth primary user, is the energy harvesting efficiency, The minimum energy required to maintain the operation of a backscatter device, is the energy collected by the kth backscattering device per unit time, is the maximum transmission power of the base station, They are the lower limit and upper limit of the drone's position respectively.

[0019] Furthermore, the objective function of the base station transmit power optimization model is:

[0020] ;

[0021] The constraints are: C1~C3;

[0022] The objective function of the backscatter coefficient optimization model is:

[0023] ;

[0024] The constraints are: C1, C2, C4;

[0025] The objective function of the drone position optimization model is:

[0026] ;

[0027] In the formula, is the coefficient, H is the fixed flight altitude of the drone, b k are the positions of k backscatter devices;

[0028] The constraint condition is: C5.

[0029] Furthermore, the base station transmission power optimization model, the backscatter coefficient optimization model and the UAV position optimization model are solved in an iterative manner to obtain the optimal base station transmission power, backscatter coefficient and UAV position, including:

[0030] 1) Based on the backscatter coefficient and drone position obtained in the previous iteration, solve the base station transmit power optimization model to obtain the base station transmit power of this iteration;

[0031] 2) According to the base station transmission power of this iteration and the drone position obtained in the previous iteration, solve the backscatter coefficient optimization model to obtain the backscatter coefficient of this iteration;

[0032] 3) According to the base station transmission power and backscatter coefficient of this iteration, solve the drone position optimization model to obtain the drone position of this iteration;

[0033] 4) Calculate the system backscatter communication capacity of this iteration based on the channel allocation results, the base station transmit power, backscatter coefficient and drone position of this iteration;

[0034] 5) If the number of iterations corresponding to this iteration increases by 1 and reaches the maximum number of iterations, and the system backscatter communication capacity of this iteration is less than the system backscatter communication capacity of the previous iteration, the base station transmit power, backscatter coefficient and drone position of this iteration are used as the optimal base station transmit power, backscatter coefficient and drone position; if the number of iterations corresponding to this iteration increases by 1 and does not reach the maximum number of iterations, and the system backscatter communication capacity of this iteration is not less than the system backscatter communication capacity of the previous iteration, go to 1).

[0035] Furthermore, solving the base station transmission power optimization model is to use a one-dimensional search method to solve the base station transmission power optimization model;

[0036] Solving the backscattering coefficient optimization model is to solve the backscattering coefficient optimization model using the monotonicity criterion;

[0037] The UAV position optimization model is solved by using the Nelder-Mead algorithm.

[0038] According to another aspect of the present disclosure, a drone-assisted symbiotic radio system capacity optimization device is provided, comprising:

[0039] a channel allocation module, which allocates channels between the backscatter devices and the primary users according to the number of backscatter devices, the number of primary users, and the distance between the backscatter devices and the primary users in the system; wherein the system is a drone-assisted symbiotic radio system;

[0040] The iterative optimization module decomposes the system's backscatter communication capacity optimization model into base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model based on the channel allocation results, and solves the base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model in an iterative manner to obtain the optimal base station transmission power, backscatter coefficient and UAV position. The channel allocation results, the optimal base station transmission power, backscatter coefficient and UAV position are used as the system's capacity optimization results.

[0041] Furthermore, the channel allocation module is configured as follows:

[0042] If the number of backscatter devices is less than or equal to the number of primary users, the KM algorithm is used to determine the primary user that matches each backscatter device based on the distance between the backscatter device and the primary user. The matched backscatter devices and the primary users share the channel.

[0043] If the number of backscatter devices is greater than the number of primary users and is less than or equal to 2 times the number of primary users, screen out the backscatter devices with the highest M receiving base station transmit power, and use the KM algorithm to determine the primary user that matches each screened backscatter device based on the distance between the screened backscatter devices and the primary users. Use the KM algorithm to determine the primary user that matches each remaining backscatter device based on the distance between the remaining backscatter devices and the primary users. The matched backscatter devices and the primary users share the channel; where M is equal to the number of primary users.

[0044] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which stores one or more programs, the one or more programs including instructions, which, when executed by a computing device, cause the computing device to perform a drone-assisted symbiotic radio system capacity optimization method.

[0045] According to another aspect of the present disclosure, a computer device is provided, including one or more processors and one or more memories, one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a drone-assisted symbiotic radio system capacity optimization method.

[0046] The beneficial effects achieved by the present invention are as follows: the present invention performs channel allocation between backscatter devices and primary users according to the number of backscatter devices in the system, the number of primary users, and the distance between the backscatter devices and the primary users, decomposes the system's backscatter communication capacity optimization model into a base station transmission power optimization model, a backscatter coefficient optimization model, and a UAV position optimization model, and performs global iteration, taking the global optimal solution as the system's capacity optimization result, and can configure the system based on the optimization result to enable the system to operate at the optimal capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of the capacity optimization method for UAV-assisted symbiotic radio system;

[0048] Figure 2 A schematic diagram of the structure of the symbiotic radio system assisted by the drone;

[0049] Figure 3 This is a comparison chart of the maximum transmission power and system rate curves under different algorithms;

[0050] Figure 4 This is a comparison chart of backscattering coefficients under different Q when K=3;

[0051] Figure 5 This is a comparison chart of backscattering coefficients under different Q when K=5;

[0052] Figure 6This is a comparison chart of system rates under different Qs;

[0053] Figure 7 This is a comparison chart of system rates at different K and UAV flight altitudes;

[0054] Figure 8 Block diagram of a device for capacity optimization of a drone-assisted symbiotic radio system. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. It is obvious that the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0056] The relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0057] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0058] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0059] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0060] It should be noted that similar symbols and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it does not require further discussion in subsequent figures.

[0061] Symbiotic Radio (SR) is a new type of communication that allows devices to share spectrum and receivers. Its basic system consists of three nodes: a primary transmitter (such as a base station), a primary receiver (here defined as a primary user), and a backscatter device. In a symbiotic radio system, the primary transmitter sends its primary information to the primary receiver through transmit beamforming, while enabling the backscatter device to send its own information to the primary receiver. The backscatter device modulates its own information on the primary signal from the primary transmitter by changing its reflection coefficient, thereby achieving low-power communication. This design allows the backscatter device to share not only the same spectrum but also the same receiver with the primary system. An important application scenario of the symbiotic radio system is the Internet of Things (IoT), especially passive IoT devices. A drone-assisted symbiotic radio system is a symbiotic radio system in which a drone is used as a mobile receiver to receive backscatter signals. For example, in the IoT scenario, drones use symbiotic radio technology for data collection.

[0062] The present disclosure provides a method for optimizing the capacity of a drone-assisted symbiotic radio system, which aims to obtain the optimal capacity of the system by constructing and solving a backscatter communication capacity optimization model of a drone-assisted symbiotic radio system, and is applicable to the field of wireless communication technology. The optimization method can be executed by an optimization device, which can be a terminal device or a server. Among them, the terminal device can include but is not limited to a mobile phone, a computer, a smart wearable device, a smart vehicle-mounted device, etc., which is not limited by the embodiments of the present application; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, big data and artificial intelligence platforms, etc., which is not limited by the embodiments of the present application. Optionally, the optimization method can also be collaboratively executed by multiple electronic devices with computing power. For the sake of convenience, the subsequent embodiments are described as being executed by the optimization device.

[0063] See also Figure 1 , Figure 1 1 is a flow chart of a method for optimizing the capacity of a symbiotic radio system assisted by a drone provided in an embodiment of the present application. The method can be performed by an optimization device. The method can at least include the following steps S101 to S102:

[0064] Step S101, performing channel allocation between backscatter devices and primary users according to the number of backscatter devices, the number of primary users, and the distance between the backscatter devices and the primary users in the system; wherein the system is a drone-assisted symbiotic radio system.

[0065] Figure 2The figure is an example of a symbiotic radio system assisted by a drone. The system may include one drone (i.e., the UAV in the figure), a base station, K backscatter devices on the bottom surface, and M primary users (PUs). The primary transmission system consists of the base station and M primary users, and the secondary transmission system consists of the drone and K backscatter devices on the ground. The base station sends signals to the M primary users in an OMA manner. The backscatter device is equivalent to a secondary user parasitic on the primary transmission system, which uses the RF signal of the primary transmission system to transmit backscatter information, and at the same time shares the channel of the primary user to send signals to the drone.

[0066] Assume that the base station transmits a signal x, the M primary users and K backscattering devices on the ground will receive the signal sent by the base station, and the backscattering devices will use the received signals for energy collection and backscattering communication respectively. Define the position of the kth backscattering device on the ground as b k (Specifically, the location coordinates), the location of the mth primary user is e m (Specifically, the position coordinates), the drone flies at a fixed height H, so the position of the drone (specifically, the two-dimensional horizontal coordinates) is set to , then the distance between the UAV and the kth backscattering device can be expressed as , the channel gain is set to ;in, are coefficients, specifically the coefficients of the large-scale fading channel, is the large-scale fading channel index corresponding to the air-ground user.

[0067] As a receiver, the drone not only receives the backscatter signals from K backscatter devices, but also receives the direct link signal from the base station. Assuming that the transmission power of the base station is p, the signal y received by the drone can be obtained. UAV for:

[0068] ;

[0069] In the formula, is the channel gain between the base station and the kth backscattering device, is the backscatter coefficient of the kth backscatter device, c k is the signal that the kth backscattering device needs to reflect, is the channel gain between the UAV and the kth backscattering device, is the channel gain between the drone and the base station, is the distance between the UAV and the base station, and n is the internal noise signal of the UAV.

[0070] The K backscattering devices on the ground use part of the received signal from the base station for energy collection. Then the energy E collected by the kth backscattering device per unit time is kIt is expressed as:

[0071] ;

[0072] In the formula, is the energy collection efficiency.

[0073] Since the backscatter device is equivalent to a secondary user parasitic in the main transmission system, it uses the RF signal of the main transmission system to transmit backscatter information and share the channel of the main user. Therefore, in order to reduce the interference of channel sharing, it is necessary to optimize the channel sharing matching problem between the main user and the secondary user, that is, to optimize the channel allocation between the backscatter device and the main user.

[0074] In some embodiments, the number of backscatter devices and the number of primary users are compared, and different optimizations are performed according to the comparison, which may be specifically as follows:

[0075] S101-01) If the number of backscatter devices is less than or equal to the number of primary users, the KM algorithm is used to determine the primary user that matches each backscatter device based on the distance between the backscatter device and the primary user, and the matching backscatter devices and the primary users share the channel.

[0076] If K≤M, K backscatter devices share the channel with M primary users respectively, and different backscatter devices occupy different channels. Therefore, there is at most one primary user and one backscatter device in a channel. The backscatter device interferes with the primary user, which can be expressed as:

[0077] ;

[0078] In the formula, is the interference of the kth backscattering device to the mth primary user, p is the base station transmit power, is the channel gain between the kth backscatter device and the mth primary user, is the large-scale fading channel index corresponding to the terrestrial user.

[0079] The above channel allocation problem can be regarded as a typical bipartite graph. In order to reduce the co-channel interference as much as possible, the principle of matching each backscatter device and the primary user is that the distance between the two is as large as possible. First, the distance between each backscatter device and each primary user can be calculated and marked on the bipartite graph. This distance can be regarded as the weight of the edge. Therefore, this bipartite graph problem can be regarded as the optimal matching problem of a weighted bipartite graph, which can be solved by the Kuhn-Munkres (KM) algorithm to obtain the allocation result.

[0080] S101-02) If the number of backscatter devices is greater than the number of primary users and less than or equal to twice the number of primary users, screen out the M backscatter devices with the highest transmission powers received from the base station. According to the distances between the screened-out backscatter devices and the primary users, use the KM algorithm to determine the primary users matched by each screened-out backscatter device. According to the distances between the remaining backscatter devices and the primary users, use the KM algorithm to determine the primary users matched by each remaining backscatter device. The matched backscatter devices and primary users share the channel; where M is equal to the number of primary users.

[0081] If M < K ≤ 2M, sort the K backscatter devices in descending order according to the received transmission powers from the base station, and then select the top M backscatter devices. Calculate the distances from the M backscatter devices to each primary user in turn. Therefore, this is also a weighted bipartite graph optimal matching problem, which is solved using the KM algorithm. Then, use the same matching method to achieve the optimal matching of the remaining K - M backscatter devices with the M primary users. In this case, there is at most 1 primary user and 2 backscatter devices in one channel, and the primary user is interfered by 2 backscatter devices, denoted as .

[0082] Denote the set of K backscatter devices as U, the set of M primary users as V. Take the backscatter devices and primary users as vertices, the connection line between the two as an edge, the set of edges as E, and the weight of the edge as w, specifically the distance between the backscatter device and the primary user. The steps to solve the channel optimal matching problem using the KM algorithm can be as follows:

[0083] S101-10) Given a weighted bipartite graph , construct a weight matrix W; where the elements in W are the weights of the edges.

[0084] S101-11) Establish two label arrays L U and L V , corresponding to the labels of the vertex sets U and V respectively. The initial values of the labels are set to the maximum weights of the corresponding vertices.

[0085] S101-12) By constructing an initial feasible matching (which may be an empty matching), the initial matching can be found using the greedy algorithm or other methods.

[0086] S101-13) Repeat the execution: use the augmenting path method of the Hungarian algorithm to improve the matching; if an improved matching scheme is found during the process of finding the augmenting path, update the labels; adjust the current matching according to the augmenting path and update the matching edges; until there are no more augmenting paths.

[0087] S101-14) Output the optimal one-to-one matching between the backscatter device and the primary user.

[0088] After implementing the channel optimal matching problem, there are at most three users (one primary user and two secondary users) in one channel, otherwise there are only two users (one primary user and one secondary user). Through such a matching method, each primary user can have at least one backscatter device sharing the channel with it, increasing the spectrum utilization rate.

[0089] Step S102: Based on the channel allocation result, decompose the backscatter communication capacity optimization model of the system into a base station transmit power optimization model, a backscatter coefficient optimization model, and a UAV position optimization model. Use an iterative method to solve the base station transmit power optimization model, the backscatter coefficient optimization model, and the UAV position optimization model to obtain the optimal base station transmit power, backscatter coefficient, and UAV position. Take the channel allocation result, the optimal base station transmit power, backscatter coefficient, and UAV position as the capacity optimization result of the system.

[0090] The UAV uses SIC technology to decode the information sent by the backscatter device. When K ≤ M, since the signal strength received by the UAV from the base station is much greater than the backscatter signal strength, the UAV first decodes the signal from the base station, then subtracts this signal from the received signal, and finally decodes the reflected signal from the backscatter device.

[0091] Therefore, the signal-to-noise ratio corresponding to the k-th backscatter device decoded by the UAV is:

[0092] ;

[0093] The normalized communication capacity is:

[0094]

[0095] where is the power of the internal noise of the UAV.

[0096] When M < K ≤ 2M, two backscatter devices sharing the same primary user channel use NOMA to send information to the UAV. The UAV uses SIC technology to first decode the direct signal from the base station with higher power, then subtract this signal from the received signal, and finally decode the remaining reflected signal. The signal-to-noise ratio is expressed as:

[0097] ;

[0098] The normalized backscatter communication capacity is expressed as

[0099] ;

[0100] In the formula, is the channel gain between the base station and the jth backscattering device, is the backscatter coefficient of the jth backscatter device, is the channel gain between the UAV and the jth backscatter device, a indicates whether there is interference, a=1 indicates interference, that is, when the UAV adopts non-orthogonal multiple access technology (NOMA), it uses the successive interference cancellation technology (SIC) to decode the signal. When decoding two backscatter devices in the same channel, when the UAV decodes the device with higher power, there will be co-channel interference from the device with lower power, a=0 indicates no interference, that is, when the UAV decodes the device with lower power, the device with higher power will not interfere with it.

[0101] In order to obtain the maximum capacity, the system backscatter communication capacity optimization model can be constructed with the maximum backscatter communication capacity as the goal, where the objective function can be expressed as:

[0102] ;

[0103] Constraints C1~C5 are:

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] Where K is the number of backscatter devices, They are the channel gains between the base station and the kth backscattering device, the drone and the kth backscattering device, the drone and the jth backscattering device, the base station and the jth backscattering device, and the base station and the mth primary user. Q is the communication rate threshold, generally Q=3. is the total interference of the backscattering device to the primary user in a channel, The minimum energy required to maintain the operation of a backscatter device, is the maximum transmission power of the base station, are the lower limit and upper limit of the drone position respectively. Constraint C1 is the QoS requirement of the primary user, that is, the communication rate of each primary user is greater than Q, constraint C2 is the energy constraint of the backscattering device in unit time, constraint C3 is that the base station transmit power meets the maximum transmit power constraint, constraint C4 is the backscatter coefficient constraint, and constraint C5 is the drone position constraint, that is, the drone position can only be within the square area formed by all primary users on the ground with the base station as the origin.

[0110] When the channel allocation result is determined, the objective function of the above model can be further converted to:

[0111] ;

[0112] In addition to the above-mentioned channel allocation, system capacity optimization also includes base station transmission power, backscatter coefficient and UAV position optimization. Therefore, the above model can be decomposed into base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model.

[0113] exist Given the above, the objective function of the base station transmit power optimization model can be expressed as:

[0114] ;

[0115] The constraints are: C1~C3.

[0116] Similarly, the objective function of the backscatter coefficient optimization model can be expressed as:

[0117] ;

[0118] The constraints are: C1, C2, C4;

[0119] Will and Substituting the original objective function, the objective function of the UAV position optimization model can be expressed as:

[0120] ;

[0121] Where H is the fixed flight altitude of the UAV, b k are the positions of k backscatter devices;

[0122] The constraint condition is: C5.

[0123] For the optimization of base station transmit power, in some embodiments, a classification discussion method can be used to solve the optimal solution of the transmit power subproblem in two cases where the number of backscatter devices is less than or equal to / greater than the number of primary users. Specifically, a one-dimensional search method is used to solve the base station transmit power optimization model. The process can be as follows:

[0124] When \(K\leq M\), that is, when \(a = 0\), the objective function is a monotonically increasing function with respect to the optimization variable \(p\). Therefore, the optimal transmit power at this time is the maximum value of \(p\); according to C2 and C3, the value range of \(p\) can be obtained as , so by using the one-dimensional search method, when the optimization variable \(p\) satisfies , the maximum value of \(p\) that satisfies C1 is found and can be denoted as , and at this time ;

[0125] When \(M < K\leq 2M\), the objective function is the sum of two parts, that is, there is interference from co-channel backscatter devices (2 backscatter devices share one channel) and no interference (1 backscatter device shares one channel). Therefore, the objective function is rewritten as:

[0126] ;

[0127] In the formula, is the channel gain between the base station and the \(i\)-th backscatter device, is the backscattering coefficient of the \(i\)-th backscatter device, is the channel gain between the UAV and the \(i\)-th backscatter device, is the channel gain between the base station and the \(l\)-th backscatter device, is the backscattering coefficient of the \(l\)-th backscatter device, is the channel gain between the UAV and the \(l\)-th backscatter device; the first term in the formula is the part with interference, and the second term is the part without interference. According to the mathematical properties of the logarithmic function, the fraction in the first term is changed into an integer as follows:

[0128] ;

[0129] Combining the above two formulas, the objective function can be transformed into:

[0130] ;

[0131] In the formula, the second term is a monotonically increasing function with respect to the optimization variable \(p\). Taking the derivative of in the first term with respect to \(p\), we can get:

[0132] ;

[0133] From the above formula, it can be seen that the first term of the right side with respect to the optimization variable \(p\) is also a monotonically increasing function. Therefore, the objective function with respect to the optimization variable \(p\) is a monotonically increasing function, that is, the maximum value restricted by the constraint conditions.

[0134] From C2 and C3, the value range of \(p\) can be obtained as Finally, the optimization variable p is satisfied by one-dimensional search method. Find the maximum value of p that satisfies C1, which can be written as ,at this time .

[0135] In summary, the optimal solution to the base station transmit power subproblem can be expressed as:

[0136] .

[0137] For backscatter coefficient optimization, in some embodiments, the backscatter coefficient optimization model may be solved using a monotonicity criterion, and the specific process may be as follows:

[0138] By observing the objective function of the backscatter coefficient optimization model, it can be found that the objective function changes with the optimization variable is a monotonically increasing function, so it can be reduced by C2 and C4 The value range of , combined with the QoS constraints of the primary user, the optimal solution of the backscatter coefficient can be calculated as:

[0139] ;

[0140] In the formula, is the channel gain between the j-th backscattering device and the m-th primary user.

[0141] For the optimization of the drone position, in some embodiments, the Nelder-Mead algorithm may be used to solve the drone position optimization model. The specific process may be as follows:

[0142] S102-10) Initial simplex, select multiple initial points of the objective function to form an initial simplex containing n1+1 points (n1 is the dimension of the variable).

[0143] S102-11) Calculate the objective function value of each vertex, sort and find the best point (maximum value) and the worst point (minimum value).

[0144] S102-12) Based on the geometric center of the optimal point and other points, calculate the reflection point of the worst point and replace the worst point (if the reflection point performs better). If the reflection point is obviously better, expand it; otherwise, if the reflection point is not as good as the original simplex, shrink it. If all steps do not improve the simplex, shrink the simplex as a whole.

[0145] S102-13) Repeat the above steps until the convergence condition is met.

[0146] In the above step S102, the base station transmission power optimization model, the backscatter coefficient optimization model and the drone position optimization model are solved in an iterative manner to obtain the optimal base station transmission power, backscatter coefficient and drone position. The specific process may include:

[0147] 1) Based on the backscatter coefficient and drone position obtained in the previous iteration, solve the base station transmit power optimization model to obtain the base station transmit power for this iteration.

[0148] At the beginning, the base station transmission power, backscatter coefficient, drone position, the number of outer loop iterations (that is, the number of iterations to obtain the optimal base station transmission power, backscatter coefficient and drone position), the number of Nelder-Mead algorithm iterations and accuracy, etc. are initialized.

[0149] 2) According to the base station transmission power of this iteration and the drone position obtained in the previous iteration, solve the backscatter coefficient optimization model to obtain the backscatter coefficient of this iteration.

[0150] 3) According to the base station transmission power and backscatter coefficient of this iteration, solve the drone position optimization model to obtain the drone position of this iteration.

[0151] 4) Calculate the system backscatter communication capacity of this iteration based on the channel allocation results, the base station transmit power, backscatter coefficient and drone position of this iteration.

[0152] 5) If the number of iterations corresponding to this iteration increases by 1 and reaches the maximum number of iterations, and the system backscatter communication capacity of this iteration is less than the system backscatter communication capacity of the previous iteration, the base station transmit power, backscatter coefficient and drone position of this iteration are used as the optimal base station transmit power, backscatter coefficient and drone position; if the number of iterations corresponding to this iteration increases by 1 and does not reach the maximum number of iterations, and the system backscatter communication capacity of this iteration is not less than the system backscatter communication capacity of the previous iteration, go to 1).

[0153] Furthermore, based on the above method, the optimal system capacity can be obtained, and the system can be configured accordingly through this capacity, that is, the system can be configured according to the channel allocation result, the optimal base station transmission power, the backscatter coefficient and the drone position, so that the system can operate at the optimal capacity.

[0154] The above method allocates channels between backscatter devices and primary users according to the number of backscatter devices, the number of primary users, and the distances between backscatter devices and primary users in the system. It decomposes the backscatter communication capacity optimization model of the system into a base station transmit power optimization model, a backscatter coefficient optimization model, and a UAV position optimization model, and performs global iteration. The globally optimal solution is used as the capacity optimization result of the system, and the system can be configured based on the optimized results, that is, the system is configured according to the channel allocation result, the optimal base station transmit power, the backscatter coefficient, and the UAV position, so that the system can operate at the optimal capacity.

[0155] To verify the above method, the above method is compared with three algorithms; among them, Algorithm 1: The position of the UAV is fixed above the geometric center of the backscatter devices, and the channel allocation, transmit power, and backscatter coefficient are optimized according to the above method; Algorithm 2: The channel allocation of the backscatter devices is not optimized, and the channels of secondary users and primary users are randomly matched, and the UAV position, transmit power, and backscatter coefficient are optimized according to the above method; Algorithm 3: The transmit power of the base station and the backscatter coefficient of the backscatter devices are optimized, and the UAV position and channel allocation are not optimized.

[0156] See Figure 3 , in the case of K = 3, the maximum transmit power and system rate curves obtained by the above method, Algorithm 1, Algorithm 2, and Algorithm 3 are compared.

[0157] It can be seen from the figure that the backscatter rates of several algorithms increase with the increase of the maximum transmit power, and the above method is superior to the other three algorithms in terms of backscatter communication rate performance. This is because the position of the UAV is fixed, and the receiving position of the UAV is above the geometric center of the backscatter devices, resulting in an increase in interference between the UAV and the backscatter devices and a decrease in the backscatter communication rate, so the backscatter rate is lower than the above method. The channel random allocation algorithm will increase the co-channel interference, so the backscatter communication rate will decrease accordingly. By observing the two curves of fixed UAV position and random channel allocation, it can be found that the channel allocation has a greater impact on the objective function. Therefore, it is necessary to reasonably allocate channels to reduce interference in actual transmission. Algorithm 3 does not consider channel allocation and UAV position optimization, and when other parameters remain the same, the backscatter communication system and rate are the smallest.

[0158] In the cases of K ≤ M and M < K ≤ 2M, the graph of the optimal solution of the backscatter coefficient of the backscatter devices changing with the QoS of the primary transmission system. By observing Figure 4 and Figure 5 it can be found that as the Q of the primary transmission system increases, the backscatter coefficient of the ground backscatter devices will decrease, because when the backscatter coefficient decreases, the interference to the primary users will also decrease, so the communication rate of the primary users will increase accordingly.

[0159] Figure 6 The curve in it is the curve of the system rate changing with the number of backscatter devices. It can be seen from the figure that as the number of ground backscatter devices increases, the system rate will also increase accordingly. Therefore, increasing the number of ground secondary users can increase the backscatter communication rate. And when the QoS of the primary transmission system rises, the backscatter communication sum rate will decrease. Combining Figure 4 and Figure 5 it can be found that this is because when the value of Q becomes larger, the backscatter coefficient will decrease. Therefore, when the other parameters remain unchanged, the backscatter communication rate will decrease. In addition, observing Figure 6 it is found that there is a turning point in the curve when K = 4. When K ≤ 4, the growth rate is relatively fast, and when K > 4, the growth rate slows down. This is because when K ≤ M, adding one more backscatter device will not increase the interference between backscatter devices, while when M < K ≤ 2M, adding one more backscatter device will bring co-channel interference between backscatter devices, so that the objective function will grow more slowly.

[0160] Figure 7 shows the curves of the system rate changing with the flight altitude of the UAV in two cases of K ≤ M and M < K ≤ 2M. It can be seen from the figure that the increase in the flight altitude of the UAV will reduce the backscatter communication rate. This is because when the flight altitude becomes larger, the distance between the UAV and the backscatter device becomes larger, resulting in an increase in the interference between the UAV and the backscatter device. Therefore, the backscatter communication rate decreases.

[0161] Figure 8 is a block diagram of the UAV-assisted coexisting radio system capacity optimization device of the present disclosure. Figure 8 The embodiment of is a virtual device that can be loaded and executed by a computer device, and the computer device may include the above optimization device. Figure 8 The device of / may include a channel allocation module and an iterative optimization module. When used to execute the above coexisting radio system capacity optimization method, it can:

[0162] The channel allocation module is used to perform channel allocation between the backscatter device and the primary user according to the number of backscatter devices, the number of primary users in the system, and the distance between the backscatter device and the primary user; wherein, the system is a UAV-assisted coexisting radio system.

[0163] In some embodiments, the channel allocation module is further configured as follows: if the number of backscatter devices is less than or equal to the number of primary users, the KM algorithm is used to determine the primary user that matches each backscatter device based on the distance between the backscatter device and the primary user, and the matched backscatter device and the primary user share a channel; if the number of backscatter devices is greater than the number of primary users and is less than or equal to 2 times the number of primary users, the backscatter devices with the highest M receiving base station transmit power are screened out, and the KM algorithm is used to determine the primary user that matches each screened backscatter device based on the distance between the screened backscatter device and the primary user, and the KM algorithm is used to determine the primary user that matches each remaining backscatter device based on the distance between the remaining backscatter devices and the primary user, and the matched backscatter devices and the primary user share a channel; wherein, M is equal to the number of primary users.

[0164] The iterative optimization module is used to decompose the system's backscatter communication capacity optimization model into a base station transmission power optimization model, a backscatter coefficient optimization model and a UAV position optimization model based on the channel allocation results, and solve the base station transmission power optimization model, the backscatter coefficient optimization model and the UAV position optimization model in an iterative manner to obtain the optimal base station transmission power, backscatter coefficient and UAV position, and use the channel allocation results, the optimal base station transmission power, backscatter coefficient and UAV position as the system's capacity optimization results.

[0165] The above-mentioned device allocates channels between backscatter devices and main users according to the number of backscatter devices, the number of main users and the distance between the backscatter devices and the main users in the system, decomposes the backscatter communication capacity optimization model of the system into a base station transmission power optimization model, a backscatter coefficient optimization model and a drone position optimization model, and performs global iteration, and takes the global optimal solution as the capacity optimization result of the system. The system can be configured based on the optimization result, that is, the system is configured according to the channel allocation result, the optimal base station transmission power, the backscatter coefficient and the drone position, so that the system can operate at the optimal capacity.

[0166] The present disclosure also relates to a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform a drone-assisted symbiotic radio system capacity optimization method.

[0167] The present disclosure also relates to a computer device, comprising one or more processors and one or more memories, one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for executing a drone-assisted symbiotic radio system capacity optimization method.

[0168] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A UAV-assisted symbiotic radio system capacity optimization method, characterized in that: include: Channel allocation between the backscatter devices and the primary users is performed according to the number of backscatter devices, the number of primary users, and the distance between the backscatter devices and the primary users in the system; wherein the system is a drone-assisted symbiotic radio system; Based on the channel allocation results, the system's backscatter communication capacity optimization model is decomposed into base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model. The base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model are solved in an iterative manner to obtain the optimal base station transmission power, backscatter coefficient and UAV position. The channel allocation results, the optimal base station transmission power, backscatter coefficient and UAV position are used as the capacity optimization results of the system.

2. The method according to claim 1, characterized in that According to the number of backscatter devices in the system, the number of primary users, and the distance between the backscatter devices and the primary users, the channel allocation between the backscatter devices and the primary users is performed, including: If the number of backscatter devices is less than or equal to the number of primary users, the KM algorithm is used to determine the primary user that matches each backscatter device based on the distance between the backscatter device and the primary user. The matched backscatter devices and the primary users share the channel. If the number of backscatter devices is greater than the number of primary users and is less than or equal to 2 times the number of primary users, screen out the backscatter devices with the highest M receiving base station transmit power, and use the KM algorithm to determine the primary user that matches each screened backscatter device based on the distance between the screened backscatter devices and the primary users. Use the KM algorithm to determine the primary user that matches each remaining backscatter device based on the distance between the remaining backscatter devices and the primary users. The matched backscatter devices and the primary users share the channel; where M is equal to the number of primary users.

3. The method according to claim 1, characterized in that The system's backscatter communication capacity optimization model aims to maximize the backscatter communication capacity, and the objective function is: ； Constraints C1~C5 are: ； ； ； ； ； Where p is the base station transmission power, and are the backscatter coefficients of the kth and jth backscatter devices, respectively. u is the position of the drone. a indicates whether there is interference. a=1 indicates interference, and a=0 indicates no interference. K is the number of backscatter devices. They are the channel gains between the base station and the kth backscatter device, the drone and the kth backscatter device, the drone and the jth backscatter device, the base station and the jth backscatter device, and the base station and the mth primary user, is the internal noise power of the drone, Q is the communication rate threshold, is the total interference of the backscattering device to the primary user in a channel, is the interference of the kth backscattering device to the mth primary user, is the energy harvesting efficiency, The minimum energy required to maintain the operation of a backscatter device, is the energy collected by the kth backscattering device per unit time, is the maximum transmission power of the base station, They are the lower limit and upper limit of the drone's position respectively.

4. The method according to claim 3, characterized in that The objective function of the base station transmit power optimization model is: ； The constraints are: C1~C3; The objective function of the backscatter coefficient optimization model is: ； The constraints are: C1, C2, C4; The objective function of the drone position optimization model is: ； In the formula, is the coefficient, H is the fixed flight altitude of the drone, b k are the positions of k backscatter devices; The constraint condition is: C5.

5. The method according to claim 1, characterized in that The base station transmission power optimization model, backscatter coefficient optimization model and drone position optimization model are solved iteratively to obtain the optimal base station transmission power, backscatter coefficient and drone position, including: 1) Based on the backscatter coefficient and drone position obtained in the previous iteration, solve the base station transmit power optimization model to obtain the base station transmit power of this iteration; 2) According to the base station transmission power of this iteration and the drone position obtained in the previous iteration, solve the backscatter coefficient optimization model to obtain the backscatter coefficient of this iteration; 3) According to the base station transmission power and backscatter coefficient of this iteration, solve the drone position optimization model to obtain the drone position of this iteration; 4) Calculate the system backscatter communication capacity of this iteration based on the channel allocation results, the base station transmit power, backscatter coefficient and drone position of this iteration; 5) If the number of iterations corresponding to this iteration increases by 1 and reaches the maximum number of iterations, and the system backscatter communication capacity of this iteration is less than the system backscatter communication capacity of the previous iteration, the base station transmit power, backscatter coefficient and drone position of this iteration are used as the optimal base station transmit power, backscatter coefficient and drone position; if the number of iterations corresponding to this iteration increases by 1 and does not reach the maximum number of iterations, and the system backscatter communication capacity of this iteration is not less than the system backscatter communication capacity of the previous iteration, go to 1).

6. The method according to claim 5, characterized in that Solving the base station transmission power optimization model is to use a one-dimensional search method to solve the base station transmission power optimization model; Solving the backscattering coefficient optimization model is to solve the backscattering coefficient optimization model using the monotonicity criterion; The UAV position optimization model is solved by using the Nelder-Mead algorithm.

7. UAV-assisted symbiotic radio system capacity optimization device, characterized in that: include: a channel allocation module, which allocates channels between the backscatter devices and the primary users according to the number of backscatter devices, the number of primary users, and the distance between the backscatter devices and the primary users in the system; wherein the system is a drone-assisted symbiotic radio system; The iterative optimization module decomposes the system's backscatter communication capacity optimization model into base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model based on the channel allocation results, and solves the base station transmission power optimization model, backscatter coefficient optimization model and UAV position optimization model in an iterative manner to obtain the optimal base station transmission power, backscatter coefficient and UAV position. The channel allocation results, the optimal base station transmission power, backscatter coefficient and UAV position are used as the system's capacity optimization results.

8. The device according to claim 7, characterized in that The channel allocation module is configured as follows: If the number of backscatter devices is less than or equal to the number of primary users, the KM algorithm is used to determine the primary user that matches each backscatter device based on the distance between the backscatter device and the primary user. The matched backscatter devices and the primary users share the channel. If the number of backscatter devices is greater than the number of primary users and is less than or equal to 2 times the number of primary users, screen out the backscatter devices with the highest M receiving base station transmit power, and use the KM algorithm to determine the primary user that matches each screened backscatter device based on the distance between the screened backscatter devices and the primary users. Use the KM algorithm to determine the primary user that matches each remaining backscatter device based on the distance between the remaining backscatter devices and the primary users. The matched backscatter devices and the primary users share the channel; where M is equal to the number of primary users.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes any one of the methods of claims 1 to 6.

10. A computer device, characterized in that: include: One or more processors and one or more memories, one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in claims 1 to 6.

Images