Time delay evaluation method and system, electronic equipment and computer storage medium

Abstract

The invention discloses a time delay evaluation method and system, electronic equipment and a computer storage medium, and relates to the technical field of communication, and the method comprises the steps: determining the beam alignment overhead of a terahertz self-organizing network; determining directional data transmission overhead of the terahertz self-organizing network; generating a time delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead; acquiring the current network topology of the terahertz self-organizing network; generating a current shortest delay routing table among network nodes in the current network topology; according to the current shortest delay routing table, determining the current antenna use probability of the terahertz antenna in the complete routing forwarding process, and determining the current path use probability of the path in the complete routing forwarding process; and generating the current average end-to-end time delay of the terahertz self-organizing network based on the time delay evaluation parameter, the current antenna use probability and the current path use probability. According to the invention, the determination parameters of the average end-to-end time delay are expanded, and the accuracy of time delay evaluation is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technologies, and more specifically, to a method, system, electronic device, and computer storage medium for delay evaluation. Background Art

[0002] The terahertz band is an electromagnetic wave band operating at 0.1 Thz to 10 Thz, and has characteristics such as high bandwidth, narrow beam, and low scattering. The terahertz self-organizing network is a wireless self-organizing network technology based on the terahertz band. The routing algorithm is mainly responsible for establishing, maintaining, and forwarding data in the routing table of the self-organizing network. The performance of the routing algorithm directly affects the overall network delay performance. However, due to the narrow directional beam of the terahertz self-organizing network, when transmitting data on demand to nodes in different directions, there is a certain probability that beam alignment needs to be performed, and the time overhead of a single data transmission is difficult to evaluate, resulting in a poor shortest delay routing effect for the terahertz self-organizing network.

[0003] In summary, how to accurately evaluate the delay of the terahertz self-organizing network is an urgent problem to be solved by those skilled in the art at present. Summary of the Invention

[0004] The purpose of the present application is to provide a delay evaluation method, which can, to a certain extent, solve the technical problem of how to accurately evaluate the delay of the terahertz self-organizing network. The present application also provides a delay evaluation system, an electronic device, and a computer-readable storage medium.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] A delay evaluation method, including:

[0007] Determine the beam alignment overhead of the terahertz self-organizing network;

[0008] Determine the directional data transmission overhead of the terahertz self-organizing network;

[0009] Generate a delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead;

[0010] Obtain the current network topology of the terahertz self-organizing network;

[0011] Generate the current shortest delay routing table between network nodes in the current network topology;

[0012] According to the current shortest delay routing table, determine the current antenna usage probability of the terahertz antenna in the complete routing forwarding process, and determine the current path usage probability of the path in the complete routing forwarding process;

[0013] Generate the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability.

[0014] Preferably, the generating the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability includes:

[0015] Generate the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability according to the average end-to-end delay generation formula;

[0016] The average end-to-end delay generation formula includes:

[0017] ;

[0018] Wherein, represents the average end-to-end delay; represents the number of the network node; represents the number of the network node; represents the total number of network nodes; represents the node 's terahertz antenna 's antenna usage frequency during the complete routing forwarding process; represents the path usage frequency of the path from network node to network node during the complete routing forwarding process; represents the delay evaluation parameter.

[0019] Preferably, the generating the current shortest delay routing table between network nodes in the current network topology includes:

[0020] Detect whether there is a historical average end-to-end delay at the previous moment;

[0021] In response to the non-existence of the historical average end-to-end delay at the previous moment, randomly initialize the cost of each path in the current network topology to obtain the target cost of each path;

[0022] In response to the existence of the historical average end-to-end delay at the previous moment, determine the probability value that the path does not require beam alignment; generate the target cost of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter;

[0023] Generate the current shortest delay routing table between network nodes in the current network topology according to the target cost.

[0024] Preferably, generating the target cost of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter includes:

[0025] Generating the target cost of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter according to the path cost generation formula;

[0026] The path cost generation formula includes:

[0027] ;

[0028] Wherein, represents the cost of the path from network node to network node ; represents the probability value that the path from network node to network node does not need to perform beam alignment.

[0029] Preferably, after generating the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability, it further includes:

[0030] Pruning the current network topology to obtain the pruned network topology;

[0031] Generating a pruned shortest delay routing table between network nodes in the pruned network topology;

[0032] Generating the pruned average end-to-end delay between network nodes according to the pruned shortest delay routing table;

[0033] Updating the current network topology based on the pruned network topology according to the pruned average end-to-end delay;

[0034] Judging whether to end the pruning operation;

[0035] If continuing to prune, return to execute the step of pruning the current network topology to obtain the pruned network topology;

[0036] If ending the pruning operation, determining the current shortest delay routing table of the current network topology as the target shortest delay routing table of the terahertz ad hoc network, and sending the target shortest delay routing table to each network node through the routing protocol.

[0037] Preferably, updating the current network topology based on the pruned network topology according to the pruned average end-to-end delay includes:

[0038] If the pruned average end-to-end delay is less than the current average end-to-end delay, then use the pruned network topology as the current network topology;

[0039] If the pruned network topology has a disconnected state, then keep the current network topology unchanged;

[0040] If the pruned average end-to-end delay is greater than or equal to the current average end-to-end delay and during the pruning process of the set number of times afterwards, the pruned average end-to-end delay is greater than or equal to the current average end-to-end delay, then keep the current network topology unchanged.

[0041] Preferably, pruning the current network topology to obtain the pruned network topology includes:

[0042] Generate the neighbor node status information of the current network topology;

[0043] Traverse the neighbor node status information. If the number of neighbor nodes within the terahertz antenna range of the network node being traversed is greater than the set value, then prune the random neighbor nodes within the terahertz antenna range of the network node being traversed according to the pruning probability to obtain the pruned network topology.

[0044] A delay evaluation system includes:

[0045] A first determination module, used to determine the beam alignment overhead of the terahertz ad hoc network;

[0046] A second determination module, used to determine the directional data transmission overhead of the terahertz ad hoc network;

[0047] A first generation module, used to generate a delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead;

[0048] A first acquisition module, used to acquire the current network topology of the terahertz ad hoc network;

[0049] A second generation module, used to generate the current shortest delay routing table among network nodes in the current network topology;

[0050] A third determination module, used to determine the current antenna usage probability of the terahertz antenna during the complete routing forwarding process and determine the current path usage probability of the path during the complete routing forwarding process according to the current shortest delay routing table;

[0051] A third generation module, used to generate the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability.

[0052] An electronic device includes:

[0053] A memory for storing a computer program;

[0054] A processor for implementing the steps of any of the above-mentioned delay evaluation methods when executing the computer program.

[0055] A computer-readable storage medium storing a computer program, and the computer program implements the steps of any of the above-mentioned delay evaluation methods when executed by a processor.

[0056] A delay evaluation method provided by this application includes: determining the beam alignment overhead of a terahertz ad-hoc network; determining the directional data transmission overhead of the terahertz ad-hoc network; generating a delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead; obtaining the current network topology of the terahertz ad-hoc network; generating a current shortest delay routing table between network nodes in the current network topology; determining the current antenna usage probability of a terahertz antenna during the complete routing forwarding process and determining the current path usage probability of a path during the complete routing forwarding process according to the current shortest delay routing table; generating the current average end-to-end delay of the terahertz ad-hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability. In this application, a delay evaluation parameter is generated according to the ratio of the beam alignment overhead to the directional data transmission overhead, so that the generated delay evaluation parameter is related to the beam alignment overhead and the directional data transmission overhead, thereby incorporating the beam alignment overhead and the directional data transmission overhead into the generation process of the average end-to-end delay; and it is necessary to generate the average end-to-end delay according to the current shortest delay routing table, comprehensively considering the antenna usage probability, the path usage probability, and the delay evaluation parameter, expanding the determination parameters of the average end-to-end delay, and improving the accuracy of the delay evaluation of the terahertz ad-hoc network. A delay evaluation system, an electronic device, and a computer-readable storage medium provided by this application also solve the corresponding technical problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0058] Figure 1 It is a flowchart of a delay evaluation method provided by an embodiment of the present application;

[0059] Figure 2 For node to node Schematic diagram of the beam reservation alignment process;

[0060] Figure 3 It is a flowchart of the directional shortest delay routing;

[0061] Figure 4 It is a flowchart of the shortest delay routing based on delay evaluation;

[0062] Figure 5 It is a flowchart of routing topology search;

[0063] Figure 6 It is a simulation scenario diagram of a 5-node small cluster network;

[0064] Figure 7 It is a schematic diagram of the topology search result of a 5-node small cluster;

[0065] Figure 8 It is a simulation scenario diagram of a 32-node large cluster network;

[0066] Figure 9 It is a topology search result diagram of a 32-node large cluster;

[0067] Figure 10 It is a schematic structural diagram of a delay evaluation system provided by an embodiment of the present application;

[0068] Figure 11 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0069] Figure 12 It is another schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0070] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0071] Please refer to Figure 1 , Figure 1 It is a flowchart of a delay evaluation method provided by an embodiment of the present application.

[0072] A delay evaluation method provided by an embodiment of the present application may include the following steps:

[0073] Step S101: Determine the beam alignment overhead of the terahertz ad hoc network.

[0074] Step S02: Determine the directional data transmission overhead of the terahertz ad hoc network.

[0075] Step S103: Generate a delay evaluation parameter according to the ratio of beam alignment overhead to directional data transmission overhead.

[0076] In practical applications, the end-to-end delay is a communication metric of the terahertz ad hoc network, which is mainly related to the data forwarding delay and the beam alignment time overhead. Therefore, the beam alignment overhead of the terahertz ad hoc network can be determined first, the directional data transmission overhead of the terahertz ad hoc network can be determined, and then a delay evaluation parameter can be generated according to the ratio of the beam alignment overhead to the directional data transmission overhead.

[0077] It should be noted that the beam alignment overhead and the directional data transmission overhead can be determined according to the Mac layer protocol. For example, the directional data transmission process includes data reservation -> reservation reply -> data transmission -> confirmation reply, with a total overhead of the first value in milliseconds (ms), and the additional protocol control overhead for beam alignment is the second value in ms. In addition, the terahertz ad hoc network can be a network of highly directional antennas such as millimeter-wave terahertz frequency bands.

[0078] In a specific application scenario, beam alignment can be assisted based on a low-frequency omnidirectional antenna. The process can be as follows: the node status and position information are periodically exchanged within the communication range through the low-frequency omnidirectional antenna. After the terahertz directional antenna learns the position of another node through the low-frequency omnidirectional antenna, two or three handshakes are performed to complete beam alignment and data transmission. In this way, although the time overhead is still several times or even dozens of times that of a single terahertz directional data transmission, compared with blind scanning, this solution can shorten the time overhead of beam alignment.

[0079] Step S104: Obtain the current network topology of the terahertz ad hoc network.

[0080] Step S105: Generate the current shortest-delay routing table between network nodes in the current network topology.

[0081] In practical applications, the end-to-end delay is also related to routing forwarding, and routing forwarding can be represented by a routing table. Therefore, the current network topology of the terahertz ad hoc network needs to be obtained. The current network topology is also the latest network topology of the terahertz network, which can be flexibly determined according to needs; then the current shortest-delay routing table between network nodes in the current network topology is generated to determine the average end-to-end delay of the terahertz ad hoc network according to the shortest-delay routing table.

[0082] In an exemplary embodiment, during the process of generating the current shortest delay routing table among network nodes in the current network topology, it is possible to detect whether there is a historical average end-to-end delay at the previous moment; in response to the non-existence of the historical average end-to-end delay at the previous moment, the costs of each path in the current network topology are randomly initialized to obtain the target costs of each path, so that the network nodes search for the shortest path using independent and random path cost initial values; in response to the existence of the historical average end-to-end delay at the previous moment, the probability value that a path does not require beam alignment is determined; based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter, the target costs of each path are generated; according to the target costs, the current shortest delay routing table among network nodes in the current network topology is generated.

[0083] In a specific application scenario, during the process of generating the target costs of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter, the target costs of each path can be generated based on the path cost generation formula, the historical average end-to-end delay, the probability value, and the delay evaluation parameter;

[0084] The path cost generation formula includes:

[0085] ;

[0086] wherein, represents the cost of the path from network node to network node ; represents the probability value that the path from network node to network node does not require beam alignment.

[0087] In a specific application scenario, during the process of generating the current shortest delay routing table among network nodes in the current network topology according to the target costs, shortest path algorithms in graph theory such as the Dijkstra algorithm, the Bellman-Ford algorithm, and the Floyd algorithm can be applied to generate the shortest delay routing table. Assuming the Floyd algorithm as an example, the topology relationship and the target costs of the terahertz ad hoc network are used as the algorithm input, and the shortest routing jump method between any two nodes of the global network is calculated in a dynamic programming manner. The specific process is as follows:

[0088] Suppose there is a weighted graph with N nodes, and a two-dimensional array dist[N][N] is used to represent the distances between nodes in the graph. Initially, dist[i][j] is the weight of the direct edge from node i to node j, which can be obtained by converting the target cost; if there is no direct edge, it is infinity, which can be represented by a very large number. The two-dimensional array path[N][N] records the next-hop node in the shortest path. Initially, path[i][j] = j;

[0089] In the loop iteration, for each group of (i, j, k), check whether the path from network node i to network j can be made shorter through the intermediate node k; if dist[i][k] + dist[k][j] < dist[i][j], then update dist[i][j] = dist[i][k] + dist[k][j], and at the same time update path[i][j] = path[i][k], indicating that in the shortest path from i to j, the predecessor node of j becomes the predecessor node of k in the shortest path from i to k; loop this process until all network nodes are processed. Finally, the shortest distances between any two network nodes are stored in the dist[N][N] array, and the corresponding shortest path information is stored in the path[N][N] array. Based on the dist[N][N] array and the path[N][N] array, the shortest delay routing table can be generated.

[0090] Step S106: According to the current shortest delay routing table, determine the current antenna usage probability of the terahertz antenna during the complete routing and forwarding process, and determine the current path usage probability of the path during the complete routing and forwarding process.

[0091] Step S107: Based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability, generate the current average end-to-end delay of the terahertz ad hoc network.

[0092] In practical applications, after obtaining the delay evaluation parameter and the current shortest delay routing table, the current antenna usage probability of the terahertz antenna during the complete routing and forwarding process can be determined according to the current shortest delay routing table, and the current path usage probability of the path during the complete routing and forwarding process can be determined; based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability, the current average end-to-end delay of the terahertz ad hoc network is generated.

[0093] In an exemplary embodiment, during the process of generating the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability, the current average end-to-end delay of the terahertz ad hoc network can be generated according to the average end-to-end delay generation formula based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability;

[0094] The average end-to-end delay generation formula includes:

[0095] ;

[0096] Wherein, represents the average end-to-end delay; represents the number of the network node; represents the number of the network node; Represents the total number of network nodes; Represents a node 's terahertz antenna The frequency of antenna usage during the complete routing and forwarding process; Represents the path from network node to network node The frequency of path usage during the complete routing and forwarding process; Represents the delay evaluation parameter.

[0097] It should be noted that assuming the path cost from network node to network node is , and there are nodes in the ad-hoc network, then the average end-to-end delay can be defined as:

[0098] ;

[0099] As Figure 2 shown, assume that any node in the terahertz ad-hoc network generates data with equal probability and sends it to other nodes. Node will transmit data to nodes within one-hop range. Node has a total of neighbor nodes within the coverage of this terahertz antenna . Because the directional beam is extremely narrow, almost every neighbor in each direction has to adjust the beam direction, otherwise alignment cannot be completed. That is, node has different beam directions under this beam range. The ratio of the beam alignment overhead to the directional data transmission time overhead is . Then the expected path cost of this data transmission is:

[0100] ;

[0101] Among them represents the probability that node does not need to perform beam alignment when sending data to node . The former item represents the path cost when the beams of the two nodes do not need to be aligned, represents the path cost that requires beam alignment. To obtain the path cost , it is necessary to obtain the probability matrix of not requiring beam alignment for any path , when the usage frequency of each link can be obtained according to the routing table of the network when all nodes send data packets with equal probability. For example, when node a sends a data packet to node d and passes through b and c respectively, the link frequencies of links a->b, b->c, and c->d are all incremented by 1. At the same time, the usage times of the antennas of these nodes in this direction are also incremented by 1. Finally, by dividing the usage frequency of this link by the usage frequency of the antenna of the receiving node, the probability of not requiring alignment can be obtained. Therefore, the probability matrix is related to the input routing table, and during the data sending process, the sending node knows the location of the receiving node, but the beam direction of the receiving node remains at the direction of the previous transmission. Therefore, it is necessary to determine based on the usage frequency of the receiving node's antenna and can be expressed as: That is,

[0102] ;

[0103] where is the usage frequency of the terahertz antenna of the receiving node during the complete routing forwarding process, is the frequency of use of the path -> during the complete routing forwarding process; by reverse deduction according to the above process, the relationship between the final average end-to-end delay and the routing table input can be obtained:

[0104] .

[0105] In an exemplary embodiment, to present a low-delay routing scheme, the present application can also incorporate the time overhead of beam alignment into a part of the path overheads of each path in the network topology, and on this basis, evaluate the average end-to-end delay, update the path overheads, and calculate the routing table. Among them, the pruning search of the routing topology is an important influencing factor for exploring the solution space. Without this process, the routing scheme will quickly converge to a locally optimal solution. Through the pruning and backtracking iteration of the routing topology, an adaptive search loop and chain structure of the routing topology can be realized, and finally the shortest-delay terahertz ad hoc network routing can be obtained. That is, after generating the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability, the terahertz ad hoc network can also be processed through pruning operations based on the shortest-delay routing table and the average end-to-end delay, so that the delay and routing of the terahertz ad hoc network meet the requirements. The overall logic of this process can be as Figure 3 and Figure 4As shown, that is, the current network topology can be pruned to obtain the pruned network topology; generate a pruned shortest delay routing table among network nodes in the pruned network topology; generate a pruned average end-to-end delay among network nodes according to the pruned shortest delay routing table; update the current network topology based on the pruned network topology according to the pruned average end-to-end delay; determine whether to end the pruning operation, and the conditions for ending the pruning operation can be that the number of loops is sufficient or the average end-to-end delay converges and drops to a stable value, etc.; if pruning continues, return to execute the step of pruning the current network topology to obtain the pruned network topology; if the pruning operation ends, determine the current shortest delay routing table of the current network topology as the target shortest delay routing table of the terahertz ad hoc network, and send the target shortest delay routing table to each network node through a routing protocol to prevent abnormal situations such as packet loss caused by routing loops and ensure the stability and reliability of network data transmission.

[0106] In a specific application scenario, to prevent the shortest delay routing algorithm process from falling into a local optimum and accelerate the convergence speed of the routing algorithm, the present application implements a heuristic routing topology search algorithm based on network pruning and backtracking. This algorithm mainly has two key points:

[0107] 1) Prune the network topology randomly with a probability of λ to reduce the local topology structure with frequent short-hop beam alignments in the ad hoc network. At this time, the more neighbor nodes within the same terahertz antenna range, the greater the pruning probability.

[0108] 2) Random pruning may lead to an increase in the average end-to-end delay or a state where the network cannot be fully connected. At this time, it is necessary to backtrack to the previous routing topology relationship.

[0109] To facilitate the understanding of the routing topology search algorithm of the present application, assume that the network topology relationship G is a g×g matrix, corresponding to the routing topology of N nodes and other nodes. 1 indicates that the two-point routing connection is reachable, and 0 indicates that the two nodes are not routable. In the ad hoc network, there are a total of N nodes, and each node has m antennas. The neighbor node status H within the different antenna ranges of each node in the ad hoc network is an N×m matrix, and the row represents the node number , and the column represents the number of neighbor nodes within the th terahertz antenna range. Then the algorithm process is as follows:

[0110] Save the network topology relationship G matrix and the neighbor node status matrix H before pruning;

[0111] Traverse the H matrix. If node 's If the number of neighbor nodes within a terahertz antenna range is greater than 1, randomly prune the neighbors within the antenna range with probability λ, update the network topology relationships G' and H'´, and end the traversal process;

[0112] Re-evaluate the average end-to-end delay and path overhead of the terahertz ad-hoc network. If the average end-to-end delay decreases, continue to execute the above two steps under this network topology for DFS depth search; if the average end-to-end delay increases, and during the subsequent set number of pruning searches, the average end-to-end delay fails to be less than the backtracking point, then perform state backtracking; if the routing topology becomes disconnected, immediately perform state backtracking; if the average end-to-end becomes smaller, the pruning is effective, and save G' and H' as G and H. The implementation process of this algorithm is as Figure 4 shown.

[0113] In a specific application scenario, during the process of updating the current network topology based on the pruned average end-to-end delay and the pruned network topology, in response to the pruned average end-to-end delay being less than the current average end-to-end delay, use the pruned network topology as the current network topology; in response to the pruned network topology having a disconnected state, keep the current network topology unchanged; in response to the pruned average end-to-end delay being greater than or equal to the current average end-to-end delay and during the subsequent set number of pruning processes, the pruned average end-to-end delay is greater than or equal to the current average end-to-end delay, keep the current network topology unchanged, otherwise, use the pruned network topology as the current network topology.

[0114] In a specific application scenario, during the process of pruning the current network topology to obtain the pruned network topology, the neighbor node status information of the current network topology can be generated, and this neighbor node status information can be represented by a matrix; traverse the neighbor node status information. If the number of neighbor nodes within the terahertz antenna range of the network node being traversed is greater than the set value, prune the random neighbor nodes within the terahertz antenna range of the network node being traversed according to the pruning probability. For example, according to the pruning probability of to prune the random neighbor nodes within the terahertz antenna range of the network node being traversed to obtain the pruned network topology.

[0115] It should be noted that the generation principles of all types of parameters such as the shortest delay routing table and average end-to-end delay in this application are the same as those described in this application. For example, the generation principle of the pruned shortest delay routing table is the same as that of the current shortest delay routing table, and the generation principle of the pruned average end-to-end delay is the same as that of the current average end-to-end delay, etc.

[0116] In this way, the present application integrates the above-mentioned average end-to-end delay evaluation algorithm, shortest path algorithm, and routing topology search algorithm, realizing the shortest delay routing calculation of the terahertz ad-hoc network based on topological position and neighbor information. To verify the performance of the present application, research can be carried out with the help of OPNET simulation software. Assume that the simulation scenario of a 5-node small cluster network is as Figure 6 shown. OPNET is a professional software widely used in network simulation and modeling, supporting the modeling of all levels of communication networks and enabling accurate simulation of network performance in specific scenarios. To verify the algorithm performance, the embodiments construct two application scenarios of terahertz ad-hoc networks with 5 nodes and 32 nodes in the OPNET simulation software, and use an omnidirectional low-frequency antenna to assist the communication architecture of the terahertz ad-hoc network. Considering that when data is transmitted in an omnidirectional low-frequency network, the time overhead of assisting beam alignment fluctuates with the network load, the simulation experiment sets three load scenarios with the ratio of the data transmission time overhead that requires or does not require assisting beam alignment as 3:1, 10:1, and 20:1 for simulation.

[0117] The delay performance of the terahertz ad-hoc network is mainly characterized by theoretical delay and simulation delay. The theoretical delay is the average end-to-end delay estimated by the shortest delay routing algorithm, while the simulation delay is the data transmission delay based on the operation of the MAC protocol, and also includes the random backoff under the CSMA mechanism and the maintenance time overhead of the MAC protocol, which is closer to the true performance of the routing algorithm. By comparing the theoretical delay and the simulation delay, the performance of the algorithm in different scenarios can be evaluated more comprehensively and accurately. The simulation comparison process is as follows:

[0118] a) Routing performance simulation of small cluster terahertz ad-hoc network: In Figure 6 the 5-node small cluster terahertz ad-hoc network, each node has two terahertz antennas, and the single-antenna coverage range is 90°, enabling data transmission in the first and third quadrants. In Figure 7 , before topology pruning, the antennas of all nodes are in a connectable state. After calculation according to the shortest delay routing algorithm, the actually used routing topology is the solid line, and the remaining dotted lines are the pruned topology relationships.

[0119] To compare the performance effects of routing algorithms, the present embodiment compares the proposed shortest delay routing algorithm, ring-based routing algorithm, and Dijkstra routing algorithm under different network loads. As shown in Table 1, the shortest delay routing algorithm proposed in this patent achieves a performance improvement of 8.4% - 38.6% in terms of theoretical delay and 12.4% - 31.1% in terms of simulation delay.

[0120] Table 1 Comparison results of delay performance of 5-node small cluster network

[0121]

[0122] b) Simulation of the routing performance of a large cluster of terahertz ad hoc networks: In Figure 8 a 32-node large cluster of terahertz ad hoc networks, each node has four terahertz antennas, and the single-antenna coverage range is 90°, enabling data transmission within the entire plane. As Figure 9 shown, before topology pruning, the antennas of nodes with a short distance interval are in a connectable state, and the antennas of nodes with a long distance interval are in a non-connectable state. After calculating according to the shortest delay routing algorithm, the actually used routing topology is the yellow solid line, and the remaining dotted lines are the pruned topology relationships. After multiple cyclic iterations, there are two characteristics in the node routing selection of the large cluster ad hoc network: 1. There are as few neighbor nodes as possible within the range of the same terahertz antenna. 2. The routing topology is evenly distributed within the range of the four terahertz antennas as much as possible. These two characteristics strongly prove the effectiveness of the routing topology search algorithm, which can effectively optimize the network topology structure and improve the performance of the terahertz ad hoc network.

[0123] To compare the performance effects of routing algorithms, in this embodiment, the proposed shortest delay routing algorithm, the ring-based routing algorithm, and the Dijkstra routing algorithm are compared under different network loads. As shown in Table 2, the shortest delay routing algorithm proposed in this application has a performance improvement of 6.6% - 55.1% in terms of theoretical delay and 4.7% - 58.9% in terms of simulation delay.

[0124] Table 2 Comparison results of the network delay performance of a 32-node large cluster

[0125]

[0126] A delay evaluation method provided by this application determines the beam alignment overhead of a terahertz ad-hoc network; determines the directional data transmission overhead of the terahertz ad-hoc network; generates a delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead; obtains the current network topology of the terahertz ad-hoc network; generates the current shortest delay routing table among network nodes in the current network topology; determines the current antenna usage probability of the terahertz antenna during the complete routing and forwarding process according to the current shortest delay routing table, and determines the current path usage probability of the path during the complete routing and forwarding process; generates the current average end-to-end delay of the terahertz ad-hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability. In this application, a delay evaluation parameter is generated according to the ratio of the beam alignment overhead to the directional data transmission overhead, so that the generated delay evaluation parameter is related to the beam alignment overhead and the directional data transmission overhead, thereby incorporating the beam alignment overhead and the directional data transmission overhead into the generation process of the average end-to-end delay; and it is necessary to generate the average end-to-end delay based on the current shortest delay routing table, comprehensively considering the antenna usage probability, the path usage probability, and the delay evaluation parameter, which expands the determination parameters of the average end-to-end delay and improves the accuracy of the delay evaluation of the terahertz ad-hoc network.

[0127] Please refer to Figure 10 , Figure 10 which is a schematic structural diagram of a delay evaluation system provided by an embodiment of this application.

[0128] A delay evaluation system provided by an embodiment of this application may include:

[0129] The first determination module 101 is used to determine the beam alignment overhead of the terahertz ad-hoc network;

[0130] The second determination module 102 is used to determine the directional data transmission overhead of the terahertz ad-hoc network;

[0131] The first generation module 103 is used to generate a delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead;

[0132] The first acquisition module 104 is used to obtain the current network topology of the terahertz ad-hoc network;

[0133] The second generation module 105 is used to generate the current shortest delay routing table among network nodes in the current network topology;

[0134] The third determination module 106 is used to determine the current antenna usage probability of the terahertz antenna during the complete routing and forwarding process according to the current shortest delay routing table, and determine the current path usage probability of the path during the complete routing and forwarding process;

[0135] The third generation module 107 is configured to generate the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability.

[0136] For a delay evaluation system provided by an embodiment of the present application, the third generation module may include:

[0137] The first generation unit is configured to generate the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability according to the average end-to-end delay generation formula;

[0138] The average end-to-end delay generation formula includes:

[0139] ;

[0140] Wherein, represents the average end-to-end delay; represents the number of the network node; represents the number of the network node; represents the total number of network nodes; represents the node 's terahertz antenna usage frequency during the complete routing forwarding process; represents the path from network node to network node usage frequency during the complete routing forwarding process; represents the delay evaluation parameter.

[0141] For a delay evaluation system provided by an embodiment of the present application, the second generation module may include:

[0142] The first detection unit is configured to detect whether there is a historical average end-to-end delay at the previous moment; in response to the absence of the historical average end-to-end delay at the previous moment, randomly initialize the overhead of each path in the current network topology to obtain the target overhead of each path; in response to the existence of the historical average end-to-end delay at the previous moment, determine the probability value that the path does not need to perform beam alignment; generate the target overhead of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter;

[0143] The second generation unit is configured to generate the current shortest delay routing table between network nodes in the current network topology according to the target overhead.

[0144] For a delay evaluation system provided by an embodiment of the present application, the first detection unit may be configured to: generate the target overhead of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter according to the path overhead generation formula;

[0145] The path cost generation formula includes:

[0146] ;

[0147] Wherein, represents the cost of the path from network node to network node ; represents the probability value that the path from network node to network node does not require beam alignment.

[0148] A delay evaluation system provided by an embodiment of the present application may further include:

[0149] A first pruning module, configured to prune the current network topology after a third generation module generates the current average end-to-end delay of the terahertz ad-hoc network based on delay evaluation parameters, the current antenna usage probability, and the current path usage probability, to obtain a pruned network topology;

[0150] A fourth generation module, configured to generate a pruned shortest delay routing table between network nodes in the pruned network topology;

[0151] A fifth generation module, configured to generate a pruned average end-to-end delay between network nodes according to the pruned shortest delay routing table;

[0152] A first update module, configured to update the current network topology based on the pruned average end-to-end delay and the pruned network topology;

[0153] A first determination module, configured to determine whether to end the pruning operation; if the pruning continues, return to execute the step of pruning the current network topology to obtain a pruned network topology; if the pruning operation ends, determine the current shortest delay routing table of the current network topology as the target shortest delay routing table of the terahertz ad-hoc network, and send the target shortest delay routing table to each network node through a routing protocol.

[0154] For a delay evaluation system provided by an embodiment of the present application, the first update module may include: in response to the pruned average end-to-end delay being less than the current average end-to-end delay, using the pruned network topology as the current network topology; in response to the pruned network topology having a disconnected state, keeping the current network topology unchanged; in response to the pruned average end-to-end delay being greater than or equal to the current average end-to-end delay and in the subsequent set number of pruning processes, the pruned average end-to-end delay is greater than or equal to the current average end-to-end delay, keeping the current network topology unchanged.

[0155] For a delay evaluation system provided by an embodiment of the present application, the first pruning module may include:

[0156] A third generation unit, configured to generate neighbor node status information of the current network topology;

[0157] A first pruning unit, configured to traverse the neighbor node status information. If the number of neighbor nodes within the terahertz antenna range of a network node during traversal is greater than a set value, randomly prune neighbor nodes within the terahertz antenna range of the traversed network node according to a pruning probability to obtain a pruned network topology.

[0158] This application also provides an electronic device and a computer-readable storage medium, both of which have corresponding effects of a delay evaluation method provided in an embodiment of this application. Please refer to Figure 11 , Figure 11 is a schematic structural diagram of an electronic device provided in an embodiment of this application.

[0159] An electronic device provided in an embodiment of this application includes a memory 201 and a processor 202. A computer program is stored in the memory 201, and when the processor 202 executes the computer program, the steps of the delay evaluation method described in any of the foregoing embodiments are implemented.

[0160] Please refer to Figure 12 , in another electronic device provided in an embodiment of this application, may further include: an input port 203 connected to the processor 202, configured to transmit an externally input command to the processor 202; a display unit 204 connected to the processor 202, configured to display the processing result of the processor 202 to the outside; a communication module 205 connected to the processor 202, configured to implement communication between the electronic device and the outside. The display unit 204 may be a display panel, a laser scanning display, etc.; the communication methods adopted by the communication module 205 include but are not limited to Mobile High-Definition Link (MHL), Universal Serial Bus (USB), High-Definition Multimedia Interface (HDMI), wireless connections: Wireless Fidelity (WiFi), Bluetooth communication technology, low-power Bluetooth communication technology, and communication technology based on IEEE802.11s.

[0161] A computer-readable storage medium provided in an embodiment of this application stores a computer program, and when the computer program is executed by a processor, the steps of the delay evaluation method described in any of the foregoing embodiments are implemented.

[0162] The computer-readable storage medium involved in this application includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROM (Compact Disc Read-Only Memory), or any other form of storage medium well-known in the technical field.

[0163] For the description of the relevant parts in a delay evaluation system, an electronic device, and a computer-readable storage medium provided in the embodiments of this application, please refer to the detailed description of the corresponding parts in a delay evaluation method provided in the embodiments of this application, which will not be elaborated here. In addition, the parts of the above technical solutions provided in the embodiments of this application that are the same as the corresponding technical solutions in the prior art in terms of implementation principles are not described in detail to avoid excessive elaboration.

[0164] It should also be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0165] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A delay evaluation method, characterized in that, Including: Determine the beam alignment overhead of the terahertz ad-hoc network; Determine the directional data transmission overhead of the terahertz ad-hoc network; Generate a delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead; Obtain the current network topology of the terahertz ad-hoc network; Generate a current shortest delay routing table between network nodes in the current network topology; According to the current shortest delay routing table, determine the current antenna usage probability of the terahertz antenna during the complete routing forwarding process, and determine the current path usage probability of the path during the complete routing forwarding process; Based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability, generate the current average end-to-end delay of the terahertz ad-hoc network.

2. The method according to claim 1, characterized in that, The generating the current average end-to-end delay of the terahertz ad-hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability includes: According to the average end-to-end delay generation formula, generate the current average end-to-end delay of the terahertz ad-hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability; The average end-to-end delay generation formula includes: ； Among them, represents the average end-to-end delay; represents the number of the network node; represents the number of the network node; represents the total number of the network nodes; represents the node 's terahertz antenna 's antenna usage frequency during the complete routing and forwarding process; represents the path usage frequency of the path from the network node to the network node during the complete routing and forwarding process; represents the delay evaluation parameter.

3. The method according to claim 2, wherein The generating the current shortest delay routing table between network nodes in the current network topology includes: Detect whether there is a historical average end-to-end delay at the previous moment; In response to the non-existence of the historical average end-to-end delay at the previous moment, randomly initialize the overhead of each path in the current network topology to obtain the target overhead of each path; In response to the existence of the historical average end-to-end delay at the previous moment, determine the probability value that the path does not require beam alignment; based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter, generate the target overhead of each path; According to the target overhead, generate the current shortest delay routing table between network nodes in the current network topology.

4. The method according to claim 3, characterized in that The generating the target overhead of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter includes: According to the path overhead generation formula, generate the target overhead of each path based on the historical average end-to-end delay, the probability value, and the delay evaluation parameter; The path overhead generation formula includes: ； Among them, represents the cost of the path from network node to network node ; represents the probability value that the path from network node to network node does not require beam alignment.

5. The method according to claim 4, wherein After generating the current average end-to-end delay of the terahertz ad-hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability, further includes: Prune the current network topology to obtain a pruned network topology; Generate a pruned shortest delay routing table between network nodes in the pruned network topology; According to the pruned shortest delay routing table, generate the pruned average end-to-end delay between network nodes; According to the pruned average end-to-end delay, update the current network topology based on the pruned network topology; Judge whether to end the pruning operation; If continue to prune, return to execute the step of pruning the current network topology to obtain a pruned network topology; If the pruning operation is ended, the current shortest-delay routing table of the current network topology is determined as the target shortest-delay routing table of the terahertz ad hoc network, and the target shortest-delay routing table is sent to each network node through a routing protocol.

6. The method according to claim 5, wherein The updating of the current network topology based on the pruned network topology according to the pruned average end-to-end delay includes: In response to the pruned average end-to-end delay being less than the current average end-to-end delay, the pruned network topology is used as the current network topology; In response to the pruned network topology being in a disconnected state, the current network topology remains unchanged; In response to the pruned average end-to-end delay being greater than or equal to the current average end-to-end delay and in the subsequent pruning processes for a set number of times, the pruned average end-to-end delay is greater than or equal to the current average end-to-end delay, the current network topology remains unchanged.

7. The method according to claim 5, characterized in that The pruning of the current network topology to obtain the pruned network topology includes: Generating neighbor node status information of the current network topology; Traversing the neighbor node status information. If the number of neighbor nodes within the terahertz antenna range of a network node during traversal is greater than a set value, random neighbor nodes within the terahertz antenna range of the traversed network node are pruned according to a pruning probability to obtain the pruned network topology.

8. A time delay evaluation system, characterized in that, Including: A first determination module for determining the beam alignment overhead of the terahertz ad hoc network; A second determination module for determining the directional data transmission overhead of the terahertz ad hoc network; A first generation module for generating a delay evaluation parameter according to the ratio of the beam alignment overhead to the directional data transmission overhead; A first acquisition module for acquiring the current network topology of the terahertz ad hoc network; A second generation module for generating the current shortest-delay routing table between network nodes in the current network topology; A third determination module for determining the current antenna usage frequency of the terahertz antenna during the complete routing forwarding process and the current path usage frequency of the path during the complete routing forwarding process according to the current shortest-delay routing table; A third generation module for generating the current average end-to-end delay of the terahertz ad hoc network based on the delay evaluation parameter, the current antenna usage probability, and the current path usage probability.

9. An electronic device, characterized in that, Including: A memory for storing a computer program; A processor for implementing the steps of the delay evaluation method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer program stored in the computer-readable storage medium, when executed by the processor, implements the steps of the delay evaluation method according to any one of claims 1 to 7.

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