Block chain enabled battlefield distributed frequency spectrum allocation architecture and method

Abstract

The invention provides a block chain enabled battlefield distributed frequency spectrum allocation architecture and method, and belongs to the field of battlefield electromagnetic resource spectrum management. The architecture comprises a tail end node, a negotiation node and an auditing node, and the tail end node is used for transmitting frequency consumption preference to a superior negotiation node in an asymmetric encryption mode; the negotiation node is used for collecting and summarizing the frequency demand of the tail end node in the combat cluster and uploading the frequency demand of the combat cluster to the auditing node; and the auditing node is used for performing admission authentication on the tail end node participating in frequency allocation and performing frequency resource planning based on a distributed frequency matching algorithm so as to complete the consensus of a frequency allocation scheme. According to the invention, based on a many-to-one matching theory, rapid and efficient distribution of frequency spectrum requirements of frequency equipment is realized.

Description

Technical Field

[0001] The present invention belongs to the field of electromagnetic spectrum management, and specifically is a blockchain-enabled battlefield distributed frequency spectrum allocation architecture and method. Technical Background

[0003] At present, the battlefield electromagnetic environment is very complex. It not only includes military countermeasure equipment such as radar, communication, navigation and electronic countermeasures, but also includes a wealth of civilian electromagnetic equipment. After the combat units are deployed, the demand for spectrum resources in each frequency band increases sharply, and frequency conflicts with military frequency weapons and equipment may occur at any time. At present, the frequency allocation method is basically carried out in the form of centralized allocation, resulting in low spectrum resource utilization, long spectrum planning time, and no dynamic adjustment capability. Therefore, frequency-using equipment in the battlefield environment requires an efficient spectrum allocation method. Summary of the invention

[0004] In view of the above-mentioned deficiencies of the existing centralized electromagnetic spectrum management, the purpose of the present invention is to provide a blockchain-enabled battlefield distributed frequency spectrum allocation architecture in combination with the improved many-to-one matching theory, so as to solve the problems of low spectrum allocation efficiency and lack of dynamics in the current battlefield electromagnetic spectrum management mode.

[0005] The technical solution for achieving the purpose of the present invention is: a blockchain-enabled battlefield distributed frequency spectrum allocation architecture, including: end nodes, negotiation nodes and audit nodes, wherein the end nodes are used to transmit frequency preferences to the upper-level negotiation nodes through asymmetric encryption; the negotiation nodes are used to collect and summarize the frequency requirements of the end nodes in the combat cluster, and report the frequency requirements of the combat cluster to the audit node; the audit nodes are used to perform access authentication on the end nodes participating in the frequency allocation and perform frequency resource planning based on the distributed frequency matching algorithm to complete the consensus on the frequency allocation plan.

[0006] The present invention also proposes a blockchain-enabled battlefield distributed frequency spectrum allocation method, including:

[0007] Step 1: Model the mathematical model of the smart contract at the blockchain contract layer. The end node transmits the frequency preference to the upper-level negotiation node through asymmetric encryption. The negotiation node summarizes the frequency preference of the end nodes of the combat cluster to which it belongs and reports it to the review node.

[0008] Step 2: After receiving the frequency preference reported by the negotiation node, the review node performs frequency resource planning based on the distributed frequency matching algorithm;

[0009] Step 3: When all negotiation nodes complete frequency allocation, the review node initiates a consensus process on the frequency allocation plan for all negotiation nodes. After all negotiation nodes reach a consensus on the frequency allocation plan, a spectrum block is generated and the frequency allocation plan is attached to the end of the spectrum blockchain to form a spectrum block.

[0010] Compared with the prior art, the present invention has the following significant advantages: 1. The present invention deploys blockchain into battlefield spectrum resource management, realizing decentralized, high-fault-tolerant, and highly secure spectrum allocation of spectrum data in battlefield environments; 2. The present invention realizes data protection of frequency-using equipment and low-complexity sharing strategy design through a distributed blockchain architecture, thereby improving spectrum allocation efficiency, meeting battlefield frequency requirements, and improving combat mobility; 3. The present invention is based on the many-to-one matching theory to realize rapid and efficient allocation of spectrum requirements for frequency-using equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a dynamic distributed battlefield spectrum allocation framework diagram enabled by blockchain.

[0012] Figure 2 It is a flow chart of distributed battlefield spectrum resource allocation.

[0013] Figure 3 It is a comparison of the time performance of different spectrum management methods.

[0014] Figure 4 It is a comparison of satisfaction between centralized and distributed spectrum allocation.

[0015] Figure 5 It is a comparison of the time overhead of centralized and distributed spectrum allocation.

[0016] Figure 6 It is the distribution of satisfaction of combat units for centralized and distributed spectrum allocation. DETAILED DESCRIPTION

[0018] The following is combined with Figure 1 , Attachment Figure 2 The present invention is further described.

[0019] A blockchain-enabled battlefield distributed frequency spectrum allocation architecture, including:

[0020] End nodes, negotiation nodes, and review nodes. In the battlefield spectrum resource allocation architecture, the frequency-using equipment is the end node. Each frequency-using equipment forms several combat clusters according to the combat organization. The negotiation node is responsible for reporting the frequency demand of each combat cluster. The review node summarizes the frequency demand of each combat cluster and then reviews the frequency demand and allocates frequency resources. The schematic diagram of its allocation architecture is shown in the attached figure. Figure 1 Each node in this framework has the following functions:

[0021] End nodes: All frequency-using equipment participating in joint operations that have been certified by the audit nodes are end nodes. During the operational planning stage, end nodes submit their own frequency usage requirements based on their respective frequency usage preferences and determine consensus and no disputes through consensus protocols within the combat cluster.

[0022] Negotiation node: Negotiation node is responsible for reporting the frequency demand of each combat cluster. Negotiation node can be the head node of the combat cluster, or it can be designated by the head node of the combat cluster, or it can be selected by consensus by the end nodes in the combat cluster based on the geographical location. Negotiation node is mainly responsible for collecting and summarizing the frequency demand of the end nodes in the combat cluster, and reporting the frequency demand of the combat cluster to the review node.

[0023] Audit node: The audit node is mainly responsible for the access authentication of the end nodes participating in the frequency allocation, and generates a dedicated public / private key pair for them to prevent malicious nodes and enemy nodes from joining the blockchain system. At the same time, the audit node is also responsible for the maintenance of smart contracts. Smart contracts can be regarded as a piece of script code that can be automatically executed. For each frequency demand submitted to the spectrum blockchain, the spectrum blockchain will automatically run the smart contract to ensure that the newly submitted frequency demand is efficiently and reasonably met on the basis of the existing frequency allocation results (spectrum blockchain) to the greatest extent. Smart contracts can be regarded as frequency allocation algorithms in this article, and the audit node must ensure that they are efficient and executable in the entire frequency allocation process.

[0024] A blockchain-enabled battlefield distributed frequency spectrum allocation method forms a new frequency allocation block through a frequency allocation algorithm and adds it to the spectrum blockchain, so that each node of the battlefield distributed frequency allocation architecture can download the required allocation data anytime and anywhere. This process is planned to be completed through the following steps.

[0025] Step 1: Based on the analysis of blockchain architecture and its key technologies, the mathematical model of smart contracts is modeled at the blockchain contract layer. Assume that in the allocation of battlefield spectrum resources of size L×L, there are M combat clusters participating in the operation, and the combat cluster set U={u1,u2,...,u M}, for combat cluster u i , 1≤i≤M, where the number of terminal nodes in the combat cluster u is the total number of terminal nodes is In a battlefield environment, available frequency resources are divided into discrete frequency points K = {k1, k2, ..., k s}. Terminal node C directly involved in combat operations iGenerate frequency requirements based on specific tasks. In actual joint operations, in order to ensure the frequency requirements of each terminal node, multiple frequency resources are usually applied for to prevent emergencies. Therefore, in the spectrum division of this solution, the terminal node applies for frequency points based on the principle of "one main, two backup and three frequencies". i Generate multiple frequency preferences, the frequency preference table is: K i ={k i1 ,k i2 ,..k ij ..,k iL}, where L represents the terminal node C i The number of applied frequencies, ki j Represents the terminal node C i Apply for k j At the same time, in the battlefield environment, different terminal nodes have different emergency conditions when performing tasks, so the priority of the terminal nodes is different. Define R i C i The frequency priority of R i ={1,2,3} represents high, medium and low priorities respectively. In this solution, the smaller the priority value, the higher the priority of the terminal node. The proportion of high-priority terminal nodes is ε=N h / N,N h is the number of high-priority end nodes, N is the total number of end nodes. In frequency allocation, the audit node will give priority to high-priority end nodes in allocating frequencies. The end node transmits the frequency preference to the upper negotiation node D through asymmetric encryption. i , negotiation node D i Summarize the frequency usage preferences of the terminal nodes of the combat cluster and then report them to the review node.

[0026] Step 2: After the audit node receives the frequency preference reported by the negotiation node, it starts frequency resource planning based on the distributed Gale-Shapley (D-GS) frequency matching algorithm, as shown in the attached figure. Figure 2 As shown:

[0027] (1) In joint operations, each combat group submits the time t according to the frequency demand. m Different. According to the negotiation node submission time t m In chronological order, the review node divides the frequency usage of the combat cluster that first submits the demand, and defines the frequency allocation matrix X N×S , where the frequency allocation matrix Represents the terminal node C n Whether it matches with frequency k, if C n Matching with frequency point k, then on the contrary

[0028] (2) Sort the terminal nodes in the cluster according to their priorities, traverse the terminal nodes in the combat cluster from high to low in order of priority, and read the terminal node C i Frequency preference table header frequency k i1 , and the frequency point k i1 From the end node C i Remove from the frequency preference table;

[0029] (3) Determine whether there is frequency interference. If there is no interference, set the frequency point k i1 Assigned to the end node C i If there is interference, the priority of the two end nodes with interference is determined, and the frequency point k is i1 Assigned to the terminal node with the highest priority.

[0030] The specific method for determining whether there is frequency interference is:

[0031] In this scheme, only the same-frequency type of frequency interference is considered, and the frequency reuse condition is used as the frequency interference model based on the distance threshold. According to the distance reuse matrix D TH (N×N) determines whether frequency reuse is satisfied. The specific form is:

[0032]

[0033] in, The terminal node C m and C n The distance reuse threshold between the end nodes C m and C n When applying for the same frequency k, if the actual distance between nodes Less than the corresponding distance reuse threshold When , it is determined that the two nodes will cause interference. When two end nodes share the same frequency band, serious frequency interference will occur. i1 Assigned to the terminal node with the highest priority.

[0034] (4) According to the frequency allocation plan X N×S Calculate the frequency satisfaction of the current combat cluster and generate the initial frequency scheme P0. The frequency satisfaction within the combat cluster is defined as follows:

[0035]

[0036] Among them, R i The terminal node C i The priority of L is C i The length of the frequency preference table, For C iThe position in the frequency preference table.

[0037] (5) Traverse the terminal nodes in the combat cluster until the frequency demand set of the terminal node is empty, or the frequency satisfaction of the current combat cluster reaches the threshold SAT TH When the frequency allocation to the current combat cluster is stopped, the frequency allocation to the current combat cluster will be stopped. In the context of large-scale joint operations, frequency resources are scarce, and there may even be an extreme situation of "no frequency to allocate", which leads to a decrease in the overall frequency satisfaction of the combat cluster. TH The definition is as follows: the number of nodes that have completed frequency allocation in the combat cluster / the total number of nodes in the combat cluster.

[0038] When allocating combat clusters, this step will focus on providing guarantees for the frequency requirements of high-priority terminal nodes. When the negotiation node submits a frequency application to the review node, the optimization goals of minimizing frequency interference and maximizing frequency satisfaction are achieved. The D-GS algorithm is used to solve the following optimization problem to obtain a frequency resource planning solution.

[0039]

[0040] Step 3: When all negotiation nodes have completed frequency allocation, the audit node initiates a frequency allocation plan X to all negotiation nodes. N×S After all negotiation nodes reach a consensus on the frequency allocation plan, a spectrum block is generated and the frequency allocation plan X is N×S Attached to the end of the spectrum blockchain, forming spectrum block L. When the end node is disturbed or the current frequency allocation plan cannot meet the demand, the end node needs to make a request to the negotiation node to adjust the frequency strategy within the combat cluster. The negotiation node D i After receiving the frequency adjustment request from the terminal node in the combat cluster, download the current spectrum block L from the spectrum blockchain and obtain the current frequency plan X N×S , adjust the current frequency usage plan according to the frequency coordination rules and the frequency usage priority of the end node, update the spectrum block after the adjustment, and form a new block L'. If other low-priority end nodes need to give up frequency usage, the negotiation node will also notify the low-priority end node of this adjustment information in an asymmetric encrypted manner.

[0041] The present invention can provide an evolutionary idea for the current battlefield electromagnetic spectrum management architecture, or be directly applied to the organization and implementation of electromagnetic spectrum management in future battlefield operations.

[0042] Frequency allocation time overhead is an important indicator of the frequency allocation algorithm, especially in large-scale joint operations. The battlefield environment has a highly dynamic characteristic of changing rapidly. In the actual battlefield spectrum resource allocation, the tasks and scales handled by different combat clusters have highly dynamic characteristics. Therefore, the frequency application submitted has high uncertainty and randomness. In joint operations, each combat cluster has a high frequency demand time t m , submit the demand, assuming that the cluster starts timing and calculates the time overhead when submitting the frequency demand, that is, t1 = 0. At the same time, the frequency allocation time T is related to the number of terminal nodes N, the algorithm complexity o and the number of combat clusters m, that is, T = f T (n,m,o). Figure 3 In the figure, the time performance comparison of different spectrum management methods is compared. As can be seen from the figure, after the last negotiation node submits the frequency demand, the static centralized method will unify all M combat clusters for frequency allocation, while the dynamic distributed method allocates frequencies immediately after each negotiation node submits the frequency demand in the form of "on a first-come, on-the-spot". It seems that the dynamic distributed method only saves the final frequency allocation time. In fact, this "on a first-come, on-the-spot" mode means that clusters that submit frequency demands earlier have a greater probability of being allocated better frequency resources, thereby encouraging users to submit frequency demands as soon as possible; secondly, clusters can use frequency resources immediately after being allocated them, that is, "on a first-come, on-the-spot". Clusters that have not submitted frequency demands do not affect the use of allocated frequency resources. Therefore, compared with the static centralized frequency allocation mode, the dynamic distributed frequency allocation will greatly speed up the frequency allocation of the battlefield.

[0043] Different from the centralized strategy where all end nodes report their needs for unified spectrum allocation, the distributed spectrum allocation has low complexity, high energy efficiency and good dynamic characteristics, which improves the utilization of spectrum resources. Finally, in order to verify the advantages of this method, simulation experiments are used to illustrate it.

[0044] Comparison algorithms:

[0045] 1. Exhaustive search algorithm: Exhaustive search (ES) enumerates all possible matches and finds the allocation combination with the maximum satisfaction as the theoretical upper limit of satisfaction in the allocation problem.

[0046] 2. Grey Wolf Optimizer (GWO): Grey Wolf Optimizer is a novel swarm intelligence algorithm that is superior to other heuristic algorithms in solving problems such as resource allocation. It is widely used in optimization problems such as resource allocation and path planning. GWO is applied in dynamic distributed and static centralized spectrum modes, namely Distributed GWO algorithm (D-GWO) and Centralized GWO algorithm (C-GWO).

[0047] In order to better compare the performance differences between different algorithms, the simulation results are normalized. Figure 4 , 5 A comparison of satisfaction and time overhead under centralized and distributed spectrum allocation is given, with the number of end nodes / available frequency points ranging from 50% to 150%, of which the number of fixed frequency points is 2000. For ease of comparison, the frequency satisfaction and time overhead are normalized.

[0048] Attached Figure 4 Figure 3 is the changing trend of frequency satisfaction with the end node / available frequency ratio. It can be seen that the frequency satisfaction increases with the improvement of the end node / available frequency ratio. From the perspective of frequency satisfaction, the difference between D-GS and C-GWO and D-GWO is not big and can be tolerated.

[0049] Attached Figure 5 The exhaustive search algorithm has the highest time complexity, which is in line with expectations. Compared with C-GWO, the time overhead of D-GS is reduced by more than one order of magnitude to only about 8% of C-GWO. Even compared with D-GWO, the time overhead of D-GS is reduced by 4 to 8 times.

[0050] From the above analysis, it can be seen that distributed battlefield frequency allocation can greatly improve the efficiency of battlefield frequency allocation when the frequency satisfaction basically meets the requirements; and the use of fast matching algorithm under the distributed frequency allocation framework can further improve the efficiency of battlefield spectrum allocation, greatly shorten the time of battlefield frequency allocation, and adapt to the ever-changing information battlefield.

[0051] Attached Figure 6The distribution comparison of the satisfaction of combat clusters under centralized and distributed spectrum allocation is given, and the frequency satisfaction status of ten combat clusters U1~U10 is given. It can be seen that the overall frequency satisfaction of C-GWO is relatively high and average, and there is no phenomenon of "favoritism"; in D-GWO, the frequency satisfaction of the combat cluster that reports the frequency demand first is relatively high, and the frequency satisfaction of the combat cluster that reports the frequency demand later is relatively low, and the frequency satisfaction of the combat cluster that reports the frequency demand last is even less than 10%, indicating that the order of reporting the frequency demand has a great influence on the frequency satisfaction; in D-GS, although the frequency satisfaction is still not as good as that of C-GWO, the overall frequency satisfaction has been greatly improved. Due to the addition of the satisfaction threshold, the frequency satisfaction of the combat cluster that reports the frequency demand last has increased from less than 10% in D-GWO to about 40%.

[0052] The present invention considers the distributed deployment of end nodes in the battlefield environment, and proposes a distributed battlefield frequency allocation process enabled by blockchain. The spectrum demand generated in a certain area is distributed to the nodes that manage spectrum resources in this area, and the asymmetric encryption in the blockchain is used to realize the sharing of spectrum data between nodes. The spectrum is dynamically and real-time allocated to the needs of the end nodes, which realizes the rapid allocation of spectrum resources and meets the satisfaction level of most end nodes.

Claims

1. A blockchain-enabled battlefield distributed frequency spectrum allocation architecture, characterized in that: include: An end node, a negotiation node and an audit node, wherein the end node is used to transmit the frequency preference to the upper negotiation node by asymmetric encryption; The negotiation node is used to collect and summarize the frequency requirements of the terminal nodes within the combat cluster, and report the frequency requirements of the combat cluster to the review node; the review node is used to perform access authentication on the terminal nodes participating in the frequency allocation and to perform frequency resource planning based on the distributed frequency matching algorithm to reach a consensus on the frequency allocation plan.

2. A blockchain-enabled battlefield distributed frequency spectrum allocation method, characterized in that: include: Step 1: Model the mathematical model of the smart contract at the blockchain contract layer. The end node transmits the frequency preference to the upper-level negotiation node through asymmetric encryption. The negotiation node summarizes the frequency preference of the end nodes of the combat cluster to which it belongs and reports it to the review node. Step 2: After receiving the frequency preference reported by the negotiation node, the review node performs frequency resource planning based on the distributed frequency matching algorithm; Step 3: When all negotiation nodes complete frequency allocation, the review node initiates a consensus process on the frequency allocation plan for all negotiation nodes. After all negotiation nodes reach a consensus on the frequency allocation plan, a spectrum block is generated and the frequency allocation plan is attached to the end of the spectrum blockchain to form a spectrum block.

3. The blockchain-enabled battlefield distributed frequency spectrum allocation method according to claim 1 is characterized in that: The specific method of modeling the mathematical model of smart contracts at the blockchain contract layer is: In the L×L battlefield spectrum resource allocation, there are M combat clusters participating in the operation, and the combat cluster set U = {u1,u2,...,u M }, Combat Cluster u i The number of terminal nodes is n i , 1≤i≤M, the total number of terminal nodes is The available frequency resources in the battlefield environment are divided into discrete frequency points K = {k1, k2, ..., k s }; End Node C i Generate multiple frequency preferences, C i The frequency preference table of K is: i ={k i1 ,k i2 ,..k ij ..,k iL }, where L represents the terminal node C i The number of applied frequencies, k ij Represents the terminal node C i Application K j Frequency; Set the end node C i Frequency priority R i , where R i ={1,2,3} represents high, medium and low priorities respectively. The smaller the priority value, the higher the priority of the terminal node.

4. The blockchain-enabled battlefield distributed frequency spectrum allocation method according to claim 2 is characterized in that: The specific method for frequency resource planning based on the distributed frequency matching algorithm is as follows: Step 21: Submit time t according to the frequency demand of the negotiated node m In order, the review node divides the frequency for the combat cluster that submits the demand first, and defines the frequency allocation matrix X N×S ,in Represents the terminal node C n Whether it matches with frequency k, if C n Matching with frequency point k, then on the contrary 1≤k≤S,1≤n≤N; Sort the end nodes in the cluster by priority; Step 22: Sort the end nodes in the cluster by priority and read the end node C with the highest priority. i Frequency preference table header frequency k i1 , and the frequency point k i1 From the end node C i Remove from the frequency preference table; Step 23: Determine whether there is frequency interference. If there is no interference, set the frequency point k i1 Assigned to the end node C i If there is interference, the priority of the two end nodes with interference is determined, and the frequency point k is i1 Assigned to the terminal node with high priority; Step 24: Calculate the frequency satisfaction of the current combat cluster; Step 25: Repeat steps 22 to 24 for the next priority end node until the frequency demand set of the end node is empty, or the frequency satisfaction of the current combat cluster reaches the threshold, stop allocating frequency to the current combat cluster, and obtain a frequency allocation plan.

5. The blockchain-enabled battlefield distributed frequency spectrum allocation method according to claim 4 is characterized in that: The specific method for determining whether there is frequency interference is: Construct the distance reuse matrix D TH (N×N), the specific form is: in, The terminal node C m and C n The distance reuse threshold between them; When the end node C m and C n When applying for the same frequency k, if the actual distance between nodes Less than the corresponding distance reuse threshold When , it is determined that the two end nodes will cause interference.

6. The blockchain-enabled battlefield distributed frequency spectrum allocation method according to claim 4 is characterized in that: Combat Cluster i Frequency satisfaction is specifically: Among them, R i The terminal node C i The priority of L is C i The length of the frequency preference table, At the end node C i The position in the frequency preference table, Represents the terminal node C i Whether it matches with frequency k, n i For combat cluster u i The number of terminal nodes is n i .

7. The blockchain-enabled battlefield distributed frequency spectrum allocation method according to claim 4 is characterized in that: The frequency satisfaction threshold SAT TH The definition is as follows: the number of nodes that have completed frequency allocation in the combat cluster / the total number of nodes in the combat cluster.

Images