A multi-antenna channel transmission method for large-scale UAV self-organizing networks

Abstract

The present invention provides a multi-antenna channel transmission method for a large-scale unmanned aerial vehicle self-organizing network, comprising the following steps: obtaining a current channel state perception range, channel state information feedback accuracy, and target communication parameters; determining an average achievable rate with feedback quantization loss based on the channel state information feedback accuracy, the channel state perception range, and the target communication parameters; determining routing quality within the channel state perception range based on the current channel state perception range and the target communication parameters; and determining whether the product of routing quality within the current channel state perception range and the average achievable rate with feedback quantization loss is maximized under constraints. If not, updating the channel state perception range and the channel state information feedback accuracy with the goal of maximizing the product of routing quality and the average achievable rate with feedback quantization loss under constraints. By implementing the present invention, average transmission delay can be reduced and transmission efficiency improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of self-organizing networks, and in particular relates to a multi-antenna channel transmission method for large-scale unmanned aerial vehicle self-organizing networks. Background Art

[0002] Large-scale intelligent unmanned swarms rely on ad hoc networking technology to provide a reliable communications backbone. Wireless ad hoc networks eliminate the need for pre-deployed infrastructure, making them flexible. Limited by the transmission capacity of single-antenna systems, traditional ad hoc networks struggle to meet the future demand for broadband transmission between large-scale unmanned nodes. Furthermore, due to severe inter-node interference, traditional ad hoc networks consume significant resources to implement access scheduling, limiting network scalability.

[0003] Multiple-Input Multiple-Output (MIMO) technology can achieve significant improvements in transmission performance. It not only provides higher point-to-point transmission rates but also reduces inter-node interference through beamforming. Therefore, MIMO is a key technology for building large-scale Ad Hoc networks. In related technologies, the asymptotic capability of multi-hop MIMO ad hoc networks has been demonstrated, and a transmission strategy selection method for MIMO ad hoc networks has been proposed, in which nodes can flexibly adjust the precoding model based on data transmission requirements. However, these studies assume that the transmitter can obtain accurate channel state information (CSI), which is difficult in practice. In the case of limited CSI feedback, a closed-form expression for the precoding performance of MIMO systems has been obtained. Based on this, a study further analyzed the impact of channel feedback loss on the transmission performance of MIMO ad hoc networks and proposed a flipping codebook design method with CSI feedback constraints to improve the precoding performance of MIMO systems.

[0004] However, ad hoc networks require multi-hop transmission, so analyzing single-hop channel feedback alone is insufficient. To achieve better routing performance, nodes in an ad hoc network must sense channel information across multiple hops. In multi-antenna ad hoc networks, to leverage the transmission advantages of MIMO systems, each node requires wide-range channel sensing and accurate feedback. This results in significant resource overhead when the network scales large. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a multi-antenna channel transmission method and device for large-scale unmanned aerial vehicle self-organizing networks to meet the needs of reducing resource overhead and improving transmission efficiency.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a multi-antenna channel transmission method for a large-scale unmanned aerial vehicle self-organizing network, comprising the following steps: obtaining a current channel state perception range, channel state information feedback accuracy, and target communication parameters; determining an average achievable rate with feedback quantization loss based on the channel state information feedback accuracy, the channel state perception range, and the target communication parameters; determining a routing quality within the channel state perception range based on the current channel state perception range and the target communication parameters; and determining whether the product of the routing quality within the current channel state perception range and the average achievable rate with feedback quantization loss is maximized under constraints. If the product is not maximized, updating the channel state perception range and the channel state information feedback accuracy with the goal of maximizing the product of the routing quality and the average achievable rate with feedback quantization loss under constraints.

[0008] Optionally, the target communication parameters include a first target communication parameter, and the routing quality within the channel state perception range is determined based on the current channel state perception range and the target communication parameters, including: determining the average number of sensed nodes based on the current channel state perception range; determining the average weight of the best path selection within the channel state perception range based on the average number of sensed nodes and the first target communication parameter; and determining the routing quality within the channel state perception range based on the average weight of the best route selection.

[0009] Optionally, the target communication parameters include a second target communication parameter and a third target communication parameter, and the average achievable rate with feedback quantization loss is determined based on the channel state information feedback accuracy, the channel state perception range and the target communication parameters, including: determining the time overhead of the sensing phase based on the channel state perception range and the second target communication parameter; determining the time overhead of the feedback phase based on the channel state information feedback accuracy and the third target communication parameter; determining the average achievable rate with feedback quantization loss based on the time overhead of the sensing phase and the time overhead of the feedback phase.

[0010] Optionally, the second target communication parameters include the channel information quantization length, the number of antennas for diversity transmission, the bandwidth, and the average signal-to-noise ratio of the receiving antenna. The time overhead of the sensing phase is determined based on the channel state perception range and the second target communication parameters, including: determining the average broadcast overhead of the sensing phase based on the channel state perception range and the channel information quantization length of each node; determining the sensing transmission rate based on the number of antennas for diversity transmission, the bandwidth, and the average signal-to-noise ratio of the receiving antenna; and determining the time overhead of the sensing phase based on the average broadcast overhead and the sensing transmission rate of the sensing phase.

[0011] Optionally, the third target communication parameter includes the codebook information and node computing performance of the feedback phase. The time overhead of the feedback phase is determined based on the channel state information feedback accuracy and the third target communication parameter, including: determining the time required to calculate the optimal precoding vector based on the codebook information and node computing performance of the feedback phase; determining the time required to transmit the index to the sender through the feedback channel based on the index of the optimal precoding vector and the channel state information feedback accuracy; determining the time overhead of the feedback phase based on the time required to calculate the optimal precoding vector and the time required to transmit the index to the sender through the feedback channel.

[0012] Optionally, the routing quality formula under the channel state perception range is as follows:

[0013] ;

[0014] in, , express The average weight of Indicates the sensing range The best path selection, represents the average number of nodes sensed, Indicates the channel state perception range, Indicates the number of antennas, Indicates the number of hops from source to destination. and Respectively indicate Binomial distribution parameter for the cardinality of the set of hop paths.

[0015] Optionally, the time cost calculation formula for the sensing phase is:

[0016] ;

[0017] in, , represents the average number of n hops, , K represents the number of nodes in the ad hoc network, represents the probability of connectivity between any two nodes, Indicates the channel state perception range, Indicates the quantization length of channel information, , Indicates bandwidth, represents the average signal-to-noise ratio (SNR) of each receiving antenna, Indicates the number of antennas.

[0018] Optionally, the time overhead of the feedback phase includes:

[0019] ;

[0020] in, represents the time required to calculate the optimal precoding vector, , represents the channel state information feedback accuracy, Indicates the node computing performance, that is, the time it takes for the node to calculate a complex operation. Indicates the time required for the index to be transmitted to the sender through the feedback channel. , , Indicates bandwidth, represents the average signal-to-noise ratio (SNR) of each receiving antenna, Indicates the number of antennas.

[0021] Optionally, determining an average achievable rate with feedback quantization loss based on a time overhead of the sensing phase and a time overhead of the feedback phase includes:

[0022] ;

[0023] in, , Indicates the start time, represents the time consumed in the sensing phase, represents the total time cost of the feedback phase, Indicates bandwidth, represents the average signal-to-noise ratio (SNR) of each receiving antenna, Representation matrix The maximum eigenvalue of represents the channel state information feedback accuracy, Indicates the number of antennas.

[0024] Optionally, the channel state perception range and the channel state information feedback accuracy are updated with the goal of maximizing the product of the routing quality and the average achievable rate with feedback quantization loss under constraints, including: maximizing the product of the routing quality and the average achievable rate with feedback quantization loss under constraints includes:

[0025] ;

[0026] ;

[0027]

[0028]

[0029]

[0030] in, represents the average achievable rate with feedback quantization loss, Indicates the routing quality, Indicates the channel state perception range, represents the channel state information feedback accuracy, represents a positive integer, Indicates the number of nodes in the ad hoc network. Indicates the start time, represents the time consumed in the sensing phase, Represents the total time cost of the feedback phase;

[0031] The product of maximizing the routing quality and the average achievable rate with feedback quantization loss is converted into the first optimization objective and the second optimization objective, including:

[0032] ;

[0033] ;

[0034] in, express The average weight of Indicates the sensing range The best path selection.

[0035] This embodiment provides a multi-antenna channel transmission method for large-scale unmanned aerial vehicle (UAV) self-organizing networks. The method analyzes the impact of sensing range and channel state information feedback accuracy on transmission performance. Based on this impact, an optimization problem is constructed to determine the optimal sensing range and feedback accuracy for multi-hop data transmission. By adjusting the self-organizing network to this optimal sensing range and feedback accuracy, the method achieves a lower average transmission delay, reduces resource overhead, improves transmission efficiency, and maintains the transmission advantages of multi-antenna systems. Simulation results show that the method proposed in this embodiment significantly improves average transmission delay compared to traditional sensing and feedback methods.

[0036] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0038] Figure 1 This is a specific example flow chart of a multi-antenna channel transmission method for large-scale UAV self-organizing networks of the present invention;

[0039] Figure 2 Schematic diagram of time resource allocation for the MIMO ad hoc network in the present invention;

[0040] Figure 3 This is a diagram showing the relationship between the broadcast quantity and the sensing distance in the present invention;

[0041] Figure 4 A graph showing the number of nodes and average transmission delay changes under different numbers of antennas for the method proposed in an embodiment of the present invention and the traditional method;

[0042] Figure 5 This is a principle block diagram of a specific example of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components; wireless connections or wired connections. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0045] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] The embodiment of the present invention provides a multi-antenna channel transmission method for large-scale UAV self-organizing networks, such as Figure 1 As shown, including:

[0047] S101, obtaining a current channel state perception range, channel state information feedback accuracy, and target communication parameters;

[0048] S102, determining an average achievable rate with feedback quantization loss based on channel state information feedback accuracy, channel state perception range, and target communication parameters;

[0049] S103, determining the routing quality within the channel state perception range according to the current channel state perception range and the target communication parameters;

[0050] S104, judging whether the product of the routing quality under the current channel state perception range and the average achievable rate with feedback quantization loss is maximized under the constraint conditions; if not, jumping to S105; if so, ending.

[0051] S105 , with the goal of maximizing the product of routing quality and average achievable rate with feedback quantization loss under constraints, update the channel state perception range and channel state information feedback accuracy, and re-enter step S101 until maximization is achieved.

[0052] For example, in this embodiment, a A self-organizing network of nodes, each node is equipped with antennas. Assume that any two nodes and The probability of connection between , when they are connected, and The channels between dimensional square matrix Without loss of generality, assume that Each element in All follow a circularly symmetric complex Gaussian distribution, that is, When the node To the node Send a message When the node The received signal can be expressed as:

[0053] ; (1)

[0054] in, is the precoding vector, is the additive noise at the receiver. Ideally, the transmitter can obtain perfect CSI , then the point-to-point achievable rate is:

[0055] ; (2)

[0056] in, is the bandwidth, is the average signal-to-noise ratio (SNR) of each receiving antenna, is a matrix The maximum eigenvalue of express The conjugate transpose of .

[0057] However, due to limited network resources, CSI Therefore, this embodiment uses a predefined codebook for precoding. Assume that each node uses a Bit code book , the receiver will Select the optimal precoding vector To achieve:

[0058] ; (3)

[0059] Compared to traditional single-antenna ad hoc networks, the MIMO ad hoc networks considered in this embodiment of the present invention can achieve better data transmission performance through precoding. However, as can be seen from Equation (3), MIMO systems require accurate channel estimation and feedback to achieve perfect precoding design. In addition, the channel sensing distance also plays a decisive role in the routing quality of MIMO ad hoc networks. Therefore, the impact of channel sensing and feedback on system performance will be analyzed in detail.

[0060] This embodiment provides a schematic diagram of time resource allocation in a MIMO ad hoc network. Figure 2 As shown, assuming that the channel of the ad hoc network is in time The period remains unchanged, which can be regarded as a complete time slot. Then, the entire time slot is divided into three parts, corresponding to the three stages, namely the sensing stage, the feedback stage and the transmission stage. The time occupied by the three stages is expressed as 、 and .

[0061] Sensing phase: In each At the beginning, the channel sensing phase is introduced to collect channel information in a hop-by-hop manner. In this phase, when the sensing range is Each node will send its The preamble sequence and CSI of the hop neighbors are the basis of MIMO precoding and multi-hop routing in ad hoc networks.

[0062] Feedback phase: After the sensing phase, each node can estimate the exact channel matrix from its 1-hop neighbors based on the received pilot sequence When a node needs to receive data, it will and codebook Determine the optimal encoding vector . The index will be fed back to the emitter.

[0063] Transmission phase: Finally, in the data transmission phase, the source will determine the best route to the destination based on the CSI results in the sensing phase, and then send the data to the next hop according to the precoding index obtained in the feedback phase, as shown in Formula (1).

[0064] from Figure 2 As can be seen, the sensing range during the sensing phase affects the routing quality of multi-hop data transmission, while the feedback accuracy (codebook size) during the feedback phase affects the quality of point-to-point data transmission. However, increasing the CSI sensing range and channel feedback accuracy both incurs greater resource overhead, resulting in reduced transmission resources. Therefore, in resource-constrained ad hoc networks, it is necessary to jointly determine the optimal sensing range and feedback accuracy based on the network scale and transmission environment to achieve optimal transmission.

[0065] Therefore, this embodiment solves the impact of the channel state perception range and the channel state information feedback accuracy on the routing quality and the average achievable rate with feedback quantization loss within the channel state perception range, with the goal of maximizing the product of the routing quality and the average achievable rate with feedback quantization loss, and adjusts the channel state perception range and the channel state information feedback accuracy to achieve better transmission.

[0066] It should be noted that, in this embodiment, the target communication parameters may include the channel information quantization length, the number of antennas for diversity transmission, the bandwidth and the average signal-to-noise ratio of the receiving antenna, the codebook information in the feedback phase, and the node computing performance, etc.

[0067] In step S102, the average achievable rate with feedback quantization loss is determined based on the channel state information feedback accuracy, the channel state perception range and the target communication parameters, including: determining the time overhead of the sensing phase based on the channel state perception range and the second target communication parameter; determining the time overhead of the feedback phase based on the channel state information feedback accuracy and the third target communication parameter; and determining the average achievable rate with feedback quantization loss based on the time overhead of the sensing phase and the time overhead of the feedback phase.

[0068] Among them, the second target communication parameters include the channel information quantization length, the number of antennas for diversity transmission, the bandwidth, and the average signal-to-noise ratio of the receiving antenna. According to the channel state perception range and the second target communication parameters, the time overhead of the sensing phase is determined, including: determining the average broadcast overhead of the sensing phase according to the channel state perception range and the channel information quantization length of each node; determining the sensing transmission rate according to the number of antennas for diversity transmission, the bandwidth, and the average signal-to-noise ratio of the receiving antenna; and determining the time overhead of the sensing phase according to the average broadcast overhead and the sensing transmission rate of the sensing phase.

[0069] Specifically, without losing generality, we take node 1 as an example. In the sensing phase, each node in the network first broadcasts a pilot sequence, and node 1 can The pilot sequence estimates the exact signal Due to the multi-antenna system, the transmit and receive channels of node 1 are not completely reciprocal. Therefore, it is assumed that node 1 only obtains its own receive channel from its 1-hop neighbor through a single broadcast, but does not know the transmit channel to its 1-hop neighbor, that is, the current sensing range is 0.5 hop.

[0070] As shown in formula (2), is the determining factor of the achievable rate. Therefore, each node will calculate all the channels obtained For example, at node 1, we get The maximum eigenvalue of , and then quantize them into bits for further transmission. Then, all nodes will add the quantized characteristic values ​​of their 1-hop neighbors to the broadcast data packet for a second broadcast. In this way, node 1 not only obtains the transmission channel information of its 1-hop neighbors from itself, but also obtains the receiving channel information of its 2-hop neighbors from its 1-hop neighbors. At this time, the sensing range of node 1 is 1.5 hops. The relationship between the number of broadcasts and the sensing distance is as follows Figure 3 As shown, multiple broadcasts result in a larger sensing range.

[0071] Since the overhead of broadcasting only the pilot sequence is small, the overhead of the first broadcast can be ignored. The amount of data required for the second broadcast depends on the amount of data per node. The number of 1-hop neighbors and the channel information quantization length , that is, the amount of data for the second broadcast is . Similarly, The amount of data required for this broadcast is , the corresponding perception range is The relationship between the number of steps, broadcasts, and the relationship between broadcast overhead and sensing range is shown in Table 1.

[0072] Table 1. Correspondence between broadcast number and overhead

[0073]

[0074] When the CSI sensing range of each node is n hops, the total overhead is the first The sum of the broadcasts, that is:

[0075] ; (4)

[0076] For analysis purposes, this embodiment calculates the average broadcast overhead, such as:

[0077] ; (5)

[0078] The average number of single hops is . Then, the average number of n hops is approximately:

[0079] ; (6)

[0080] Assume that the transmission rate of the broadcast channel used for CSI sensing is , then the time consumed in the sensing phase is:

[0081] (7)

[0082] The third target communication parameter includes the codebook information and node computing performance of the feedback phase. The time overhead of the feedback phase is determined based on the channel state information feedback accuracy and the third target communication parameter, including: determining the time required to calculate the optimal precoding vector based on the codebook information and node computing performance of the feedback phase; determining the time required for the index to be transmitted to the sender through the feedback channel based on the index of the optimal precoding vector and the channel state information feedback accuracy; and determining the time overhead of the feedback phase based on the time required to calculate the optimal precoding vector and the time required to transmit the index to the sender through the feedback channel.

[0083] Specifically, the time overhead of the feedback phase consists of two parts. The first part is the node’s response to the codebook and the real-time channel. Time required to calculate the optimal precoding vector , and the other part is the time required for the index of the vector to be transmitted to the sender through the feedback channel .

[0084] As mentioned earlier, The bit is used for CSI feedback in the feedback phase. The feedback transmission rate is used The transmission time can be calculated by In addition, a larger codebook will also introduce more computation and selection time for each node. The dimension of , codebook The dimension of , so the calculation time It can be quantified as:

[0085] ; (8)

[0086] in is the time it takes for a node to calculate a complex operation. Then the total time cost of the feedback phase is:

[0087] (9)

[0088] For CSI sensing transmission rate and feedback transmission rate , this embodiment uses the following method to solve:

[0089] In order to achieve stable transmission of sensing information and feedback information, this embodiment adopts diversity transmission in the sensing stage and feedback stage. antennas, nodes Slave nodes The received signal can be expressed as:

[0090] ; (10)

[0091] in, yes The transpose of , therefore, the average SNR can be expressed as:

[0092] ; (11)

[0093] in, is a matrix The Frobenius norm of ,have:

[0094] ; (12)

[0095] Therefore, CSI sensing transmission rate and feedback transmission rate It can be calculated as follows:

[0096] ; (13)

[0097] The above results in the time overhead of the sensing phase and the time overhead of the feedback phase. Based on the time overhead of the two phases, the average achievable rate with feedback quantization loss is determined. Specifically:

[0098] According to formula (2), when a singular value decomposition-based precoding scheme is adopted, the achievable rate of complete CSI feedback can be given by the following formula, taking into account the resource overhead of the sensing stage and the feedback stage:

[0099] (14)

[0100] Among them, T0 represents the starting time, T1 represents the time consumed in the sensing phase, T2 represents the total time overhead in the feedback phase, and W represents the bandwidth. represents the average signal-to-noise ratio (SNR) of each receiving antenna, Representation matrix The maximum eigenvalue of .

[0101] With limited feedback resources In the case of , the average achievable rate with feedback quantization loss can be expressed as:

[0102] ;(15)

[0103] Among them, α is the coefficient, that is:

[0104] ; (16)

[0105] In step S103, the routing quality within the channel state perception range is determined based on the current channel state perception range and the target communication parameters. The specific process includes: determining the average number of sensed nodes based on the current channel state perception range; determining the average weight of the best path selection within the channel state perception range based on the average number of sensed nodes and the first target communication parameters; and determining the routing quality within the channel state perception range based on the average weight of the best route selection.

[0106] Specifically, in an Ad hoc network, the quality of multi-hop routing depends on the sensing range of the node. In this embodiment, the As an approximation of the delay of each hop. For each element, dimensional square matrix , The maximum eigenvalue of Obeys Tracy-Widom (TW) type 2 distribution, whose mean is , the variance is ,Right now:

[0107] ; (17)

[0108] in, is a random variable following TW 2 distribution. Therefore, formula (17) can be rewritten as:

[0109] ; (18)

[0110] Then, the single-hop transmission delay can be expressed as:

[0111] ; (19)

[0112] The total amount of information to be sent is set to 1. Taylor expansion at , formula (19) can be approximated as:

[0113] ; (20)

[0114] Ignoring the second term on the right, it can be approximated as following the parameter An exponentially distributed random variable, that is: The probability density function (PDF) is:

[0115] ; (twenty one)

[0116] Here, let the set of all paths between source and destination be:

[0117] ; (twenty two)

[0118] in, Is a Since the weight (delay) of each hop follows an exponential distribution, any The weight of the jump path The obedience parameter is and The gamma distribution is:

[0119] ; (twenty three)

[0120] in, Indicates the path The total multi-hop weight of . Then, The cumulative distribution function (CDF) can be expressed as:

[0121] ; (twenty four)

[0122] in, yes The factorial of . is the path with the minimum total multi-hop weight, i.e., .So, The CDF of can be calculated as:

[0123] ; (25)

[0124] in, is a collection The cardinality of .

[0125] definition is the path from source to destination with minimum multi-hop weight, which can be expressed as:

[0126] ; (26)

[0127] therefore, The CDF of is:

[0128] ; (27)

[0129] In order to avoid the influence of source and destination, it is necessary to obtain the average multi-hop weight of the network, which can be done by:

[0130] ; (28)

[0131] Since it is difficult to calculate the formula (28) about , this embodiment is approximately:

[0132] ; (29)

[0133] When there is a When jumping paths, this means that in the remaining Among the nodes sequentially connected nodes as relay nodes, and Links connect them sequentially. Therefore, yes:

[0134] ; (30)

[0135] It's obvious. It follows a binomial distribution with parameters and The expected value can be obtained by the following formula:

[0136] ; (31)

[0137] Then, formula (29) can be rewritten as:

[0138] ; (32)

[0139] In practical applications, it is difficult to obtain the global CSI of all nodes, and the source can only be based on the sensing range obtained in the sensing stage. The sensing range under the conditions shown in this embodiment is The best path selection is According to formula (6), when the sensing range is When , the average number of sensed nodes is approximately ,in Represents the rounding function. Nodes can form at most Jump paths, assuming that these paths are within the sensing range of the source. By following the same logic as in Equation (32). The average weight is:

[0140] ; (33)

[0141] In this embodiment, since the weight of the total multi-hop path represents the transmission delay, it is desirable to find the path with the minimum weight. In order to combine the routing performance with the point-to-point transmission performance, the sensing range is set to The routing quality is defined as:

[0142] .

[0143] In steps S104 and S105, the goal of this embodiment is to obtain the best CSI feedback accuracy. and perception range It is worth noting that since only a few bits are sufficient to accurately reflect the CSI of a link, the sensing overhead Therefore, it can be considered that is a constant. The optimal problem can be written as:

[0144] (34)

[0145] ; (34a)

[0146] (34b)

[0147] (34c)

[0148] (34d)

[0149] Among them, the optimal problem is to obtain the optimal cost and reward in the sensing phase and the feedback phase. and Constraints (34a) and (34b) ensure that is a positive integer describing the number of hops, and the maximum number of hops does not exceed the network size. Constraint (34c) ensures that is an integer number of bits. Finally, constraint (34d) implies that the time overhead of the sensing phase and the feedback phase cannot exceed the stable period of the channel.

[0150] Due to optimization problems It is difficult to solve, so it can be updated sequentially in an iterative manner and According to formula (34), given and , can be obtained from the following optimization problem:

[0151] ; (35)

[0152] Similarly, given , optimal It can be determined by the following optimization problem:

[0153] (36)

[0154] This embodiment provides a multi-antenna channel transmission method for large-scale unmanned aerial vehicle self-organizing networks. It analyzes the impact of the sensing range and channel state information feedback accuracy on transmission performance, and constructs an optimization problem based on the impact of the sensing range and channel state information feedback accuracy on transmission performance. It determines the optimal sensing range and feedback accuracy for multi-hop data transmission, and adjusts the self-organizing network based on the optimal sensing range and feedback accuracy. This can achieve a lower average transmission delay, reduce resource overhead, improve transmission efficiency, and ensure the transmission advantages of the multi-antenna system. Simulation results Figure 4 It shows that compared with the traditional sensing and feedback method, the average transmission delay of the method proposed in this embodiment is significantly improved.

[0155] The present application also provides an electronic device, such as Figure 5 As shown, a processor 501 and a memory 502 , wherein the processor 501 and the memory 502 may be connected via a bus or other means.

[0156] The processor 501 may be a central processing unit (CPU). The processor 501 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of these chips.

[0157] Memory 502, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the multi-antenna channel transmission method for a large-scale unmanned aerial vehicle self-organizing network in the embodiments of the present invention. The processor executes the non-transitory software programs, instructions, and modules stored in the memory to perform various processor functions and data processing.

[0158] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 502 may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] The one or more modules are stored in the memory 502 and when executed by the processor 501, perform the following steps: Figure 1 The multi-antenna channel transmission method for large-scale drone self-organizing networks in the illustrated embodiment.

[0160] For details of the above electronic equipment, please refer to Figure 1 The corresponding descriptions and effects in the embodiments shown can be understood and will not be repeated here.

[0161] This embodiment also provides a computer storage medium storing computer-executable instructions capable of executing the multi-antenna channel transmission method for a large-scale unmanned aerial vehicle self-organizing network in any of the aforementioned method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the storage medium may also include a combination of the aforementioned types of memory.

[0162] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A multi-antenna channel transmission method for large-scale unmanned aerial vehicle self-organizing networks, characterized in that: include: Obtain the current channel state perception range, channel state information feedback accuracy, and target communication parameters; Determine an average achievable rate with feedback quantization loss based on channel state information feedback accuracy, channel state perception range, and target communication parameters; Determine the routing quality within the current channel state perception range based on the target communication parameters; Determine whether the product of the routing quality within the current channel state perception range and the average achievable rate with feedback quantization loss is maximized under the constraints. If not, update the channel state perception range and channel state information feedback accuracy with the goal of maximizing the product of the routing quality and the average achievable rate with feedback quantization loss under the constraints. The goal is to maximize the product of routing quality and average achievable rate with feedback quantization loss under constraints, and update the channel state perception range and channel state information feedback accuracy, including: The objective is to maximize the product of routing quality and average achievable rate with feedback quantization loss under constraints including: ； ； in, represents the average achievable rate with feedback quantization loss, Indicates the routing quality, Indicates the channel state perception range, represents the channel state information feedback accuracy, represents a positive integer, Indicates the number of nodes in the ad hoc network. Indicates the start time, represents the time consumed in the sensing phase, Represents the total time cost of the feedback phase; The product of maximizing the routing quality and the average achievable rate with feedback quantization loss is converted into the first optimization objective and the second optimization objective, including: ； ； in, express The average weight of Indicates the sensing range The best path selection.

2. The multi-antenna channel transmission method for large-scale UAV self-organizing networks according to claim 1 is characterized in that: The target communication parameter includes a first target communication parameter. Determining the routing quality within the channel state perception range according to the current channel state perception range and the target communication parameter includes: Determine the average number of sensed nodes based on the current channel state sensing range; Determining an average weight for optimal path selection within the channel state sensing range based on an average number of sensed nodes and a first target communication parameter; The routing quality within the channel state perception range is determined based on the average weight of the best routing selection.

3. The multi-antenna channel transmission method for large-scale UAV self-organizing networks according to claim 1 is characterized in that: The target communication parameters include a second target communication parameter and a third target communication parameter. An average achievable rate with feedback quantization loss is determined based on the channel state information feedback accuracy, the channel state perception range, and the target communication parameters, including: Determine the time overhead of the sensing phase according to the channel state perception range and the second target communication parameter; Determining a time overhead of a feedback phase based on a channel state information feedback accuracy and a third target communication parameter; Based on the time overhead of the sensing phase and the time overhead of the feedback phase, the average achievable rate with feedback quantization loss is determined.

4. The multi-antenna channel transmission method for large-scale UAV self-organizing networks according to claim 3 is characterized in that: The second target communication parameters include the channel information quantization length, the number of antennas for diversity transmission, the bandwidth, and the average signal-to-noise ratio of the receiving antenna. Based on the channel state perception range and the second target communication parameters, the time overhead of the sensing phase is determined, including: The average broadcast overhead of the sensing phase is determined based on the channel state perception range of each node and the quantized length of the channel information; Determine the sensing transmission rate based on the number of antennas for diversity transmission, bandwidth, and the average signal-to-noise ratio of the receiving antennas; The time cost of the sensing phase is determined based on the average broadcast cost and the sensing transmission rate during the sensing phase.

5. The multi-antenna channel transmission method for large-scale UAV self-organizing networks according to claim 4 is characterized in that: The third target communication parameter includes codebook information and node computing performance in the feedback phase. The time overhead of the feedback phase is determined based on the channel state information feedback accuracy and the third target communication parameter, including: Determine the time required to calculate the optimal precoding vector based on the codebook information and node computing performance in the feedback phase; Determine the time required for the index to be transmitted to the sender through the feedback channel based on the index of the optimal precoding vector and the feedback accuracy of the channel state information; The time overhead of the feedback phase is determined based on the time required to calculate the optimal precoding vector and the time required to transmit the index to the sender through the feedback channel.

6. The multi-antenna channel transmission method for large-scale UAV self-organizing networks according to claim 2, characterized in that: The routing quality formula under the channel state perception range is as follows: ； in, , express The average weight of Indicates the sensing range The best path selection, represents the average number of nodes sensed, Indicates the channel state perception range, Indicates the number of antennas, Indicates the number of hops from source to destination. and Respectively indicate Binomial distribution parameter for the cardinality of the set of hop paths.

7. The multi-antenna channel transmission method for large-scale UAV self-organizing networks according to claim 3 is characterized in that: The time cost calculation formula of the sensing phase is: ； in, , represents the average number of n hops, , K represents the number of nodes in the ad hoc network, represents the probability of connectivity between any two nodes, Indicates the channel state perception range, Indicates the quantization length of channel information, , Indicates bandwidth, represents the average signal-to-noise ratio (SNR) of each receiving antenna, Indicates the number of antennas.

8. The multi-antenna channel transmission method for large-scale UAV self-organizing networks according to claim 3 is characterized in that: The time cost of the feedback phase includes: ； in, represents the time required to calculate the optimal precoding vector, , represents the channel state information feedback accuracy, Indicates the node computing performance, that is, the time it takes for the node to calculate a complex operation. Indicates the time required for the index to be transmitted to the sender through the feedback channel. , , Indicates bandwidth, represents the average signal-to-noise ratio (SNR) of each receiving antenna, Indicates the number of antennas.

9. A multi-antenna channel transmission method for large-scale unmanned aerial vehicle self-organizing networks according to claim 7 or 8, characterized in that: Based on the time overhead of the sensing phase and the time overhead of the feedback phase, the average achievable rate with feedback quantization loss is determined, including: ； in, , Indicates the start time, represents the time consumed in the sensing phase, represents the total time cost of the feedback phase, Indicates bandwidth, represents the average signal-to-noise ratio (SNR) of each receiving antenna, Representation matrix The maximum eigenvalue of represents the channel state information feedback accuracy, Indicates the number of antennas.

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