Dynamic spectrum allocation method based on block chain

Abstract

The invention discloses a dynamic spectrum allocation method based on a block chain. The method comprises the following steps: constructing a spectrum resource block chain network and deploying intelligent contracts including resource registration, resource allocation, quality evaluation and use monitoring; establishing an evaluation model which comprehensively considers historical use quality, frequency band characteristics, interference constraints and other factors, and calculating a resource allocation priority of an applicant; calculating a comprehensive score based on the quality evaluation contract and automatically determining an allocation scheme; monitoring spectrum use data in real time and dynamically adjusting quality scores; through introduction of innovative designs such as a multi-dimensional evaluation index, an intelligent contract automatic verification mechanism and a self-adaptive access threshold, efficient, fair and transparent allocation of spectrum resources is realized, and long-term use efficiency is ensured through dynamic adjustment.

Description

Technical Field

[0001] The present invention belongs to the technical field of spectrum allocation, and particularly relates to a dynamic spectrum allocation method based on blockchain. Background Art

[0002] The existing spectrum resource allocation methods mainly have the following problems: First, the traditional static spectrum allocation strategy leads to low utilization rate of spectrum resources and cannot meet the growing dynamic demand; second, the centralized spectrum management method has the risk of single point of failure and efficiency bottleneck; third, there is a lack of effective trust mechanism among spectrum users, and the fairness and transparency of the allocation process cannot be guaranteed; fourth, the recording and evaluation system of spectrum usage data is imperfect, and it is difficult to achieve intelligent adjustment based on historical performance.

[0003] With the development of 5G and future 6G networks, the demand for spectrum resources in wireless communication has increased explosively, and the existing spectrum management methods have been difficult to meet the requirements of efficient, fair and secure allocation; in addition, the current spectrum evaluation model is relatively simple, and it fails to fully consider comprehensive factors such as the historical usage quality, technical ability and credit score of the applicant, resulting in low efficiency of spectrum resource allocation and lack of dynamic adjustment mechanism. Summary of the Invention

[0004] The purpose of the present invention is to provide a dynamic spectrum allocation method based on blockchain, which solves the problems of low efficiency, insufficient transparency and lack of dynamic adjustment existing in traditional spectrum management by introducing innovative designs such as multi-dimensional evaluation indicators, intelligent contract automatic verification mechanism and adaptive admission threshold.

[0005] The specific technical solutions are as follows:

[0006] A dynamic spectrum allocation method based on blockchain, the method comprising the following steps:

[0007] Step S1, constructing a spectrum resource blockchain network and deploying intelligent contracts, and the spectrum management party registers available spectrum resource information in the resource registration contract.

[0008] The intelligent contracts include: a spectrum resource registration contract, a resource allocation contract, a quality evaluation contract and a usage monitoring contract.

[0009] Step S2, establishing a spectrum resource evaluation model, considering the historical usage quality, frequency band characteristics, usage duration and interference constraint factors of the applicant, and calculating the resource allocation priority.

[0010] The frequency band characteristics include frequency band bandwidth, center frequency, geographical area and time range.

[0011] Step S3: The applicant submits resource requirements, calculates a comprehensive score based on the quality assessment contract, uses the comprehensive score as the allocation weight coefficient, and the smart contract automatically determines the spectrum allocation plan based on the weighted score result and preset rules.

[0012] Step S4: Use the monitoring contract to record spectrum usage data in real time, verify the data authenticity based on the blockchain consensus mechanism; evaluate the spectrum usage efficiency according to the monitoring data, dynamically adjust the quality score, and use the evaluation result as the basis for subsequent resource allocation.

[0013] Further, in step S2, calculating the resource allocation priority of the applicant based on the spectrum resource evaluation model includes:

[0014] Based on the normalized evaluation indicators, calculate the comprehensive priority score of the applicant: P = Q h ×M f ×I c , where Q h represents the historical usage quality of the applicant, M f represents the matching degree of the applicant's frequency band characteristics, I c represents the satisfaction degree of interference constraints;

[0015]

[0016] Among them, N s represents the cumulative number of successful uses; N t represents the total number of applications; T a represents the cumulative actual usage duration; T r represents the cumulative application duration; B r represents the application bandwidth; B a represents the available bandwidth; f c represents the application center frequency; f r represents the recommended frequency; P i represents the predicted interference power; P th represents the allowable interference threshold.

[0017] Further, calculate the comprehensive priority score of each applicant, sort the applicants in descending order based on the comprehensive priority score, and obtain the priority sequence of the applicants.

[0018] Further, the quality assessment contract calculates the comprehensive score and determines the spectrum allocation plan including:

[0019] Calculate the comprehensive score S of the applicant: S = P × C × T, where C is the credit score of the applicant, obtained based on the application history record, c i is the credit record of the i-th use, t iTo record time, t is the current time, λ is the time decay factor, and n is the total number of historical records.

[0020] T is the technical ability score of the applicant, R f is the radio frequency performance index score, D p is the signal processing ability score; I m is the interference control ability score; R max is the full score value of the performance index;

[0021] When S ≥ S th , and the spectrum resource constraint is satisfied, the allocation is approved; among them, the adaptive threshold S th is calculated as follows: S b is the basic threshold, N v is the current number of applications, N a is the average number of applications.

[0022] Furthermore, the method for dynamically adjusting the quality score includes:

[0023] Calculate the comprehensive spectrum utilization efficiency: Among them, D a is the actual transmitted data volume; D m is the theoretical maximum transmission capacity; T u is the actual usage duration; T p is the applied usage duration; A r is the actual coverage area; A p is the applied coverage area.

[0024] Furthermore, based on the efficiency evaluation, adjust the comprehensive score of the applicant:

[0025] Update the historical usage quality:

[0026] Update the threshold:

[0027] Among them, N is the number of allocations during the evaluation period, η t is the target efficiency value, β is the adjustment step size, and the value range is (0, 0.1); S new and S old are the comprehensive scores of the applicant after and before the update, and are the historical usage qualities of the applicant after and before the update; and are the adaptive thresholds after and before the update.

[0028] The resource allocation process in step S3 further includes:

[0029] Minimum service quality requirement setting: Q b is the basic service quality threshold,

[0030] η k is the usage efficiency in the k-th historical period, η k is the number of statistical periods;

[0031] Furthermore, control the evaluation process, T resp ≤T max and N round ≤N max ; T resp is the single-round evaluation response time, T max is the maximum allowable response time; N round is the current evaluation round; N max is the maximum number of evaluation rounds;

[0032] The intelligent contract automatic verification mechanism includes technical verification, quality verification and violation penalty mechanism;

[0033]

[0034] Among them, all parameters meet the verifiability requirements of blockchain intelligent contracts. When the technical ability score and radio frequency performance index score of the applicant are not less than the minimum thresholds T min and R min , the technical verification result V t is 1, otherwise 0; when the historical usage quality and comprehensive score of the applicant meet the minimum constraints, the quality verification V q is 1, otherwise 0; in the violation penalty mechanism Status acc , as long as one of the technical verification or quality verification is not 1, the spectrum allocation is suspended, and it is in a normal active state only when both are 1.

[0035] Furthermore, the step S4 further includes: distributed monitoring data collection D mon and zero-knowledge proof construction; D mon ={(d i ,t i ,loc i ,hash i )|i∈[1,M]}; π=ZKP(D mon ,W key ); Among them, d i is the data of the i-th monitoring node; t i is the timestamp, loc i is the geographical location information; hash iis the data hash value; M is the total number of monitoring nodes; ZKP represents zero - knowledge proof, and W key is the witness data required for the proof.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] The present invention establishes a decentralized spectrum resource management network through blockchain technology, realizing the transparency and immutability of the spectrum allocation process, and effectively solving the trust problem in traditional spectrum management; introducing a multi - dimensional evaluation model, comprehensively considering factors such as the historical usage quality of the applicant, frequency band matching degree, interference control, and technical capabilities, realizing a more fair and reasonable spectrum resource allocation; based on the automatic verification mechanism of smart contracts, reducing human intervention, improving the allocation efficiency, and at the same time ensuring the efficient utilization of spectrum resources through double guarantees of technical verification and quality verification; the dynamic adjustment mechanism can automatically update the scores and access thresholds according to the actual usage efficiency, forming a positive incentive cycle, prompting the applicant to continuously improve the spectrum usage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is the flowchart of the blockchain - based dynamic spectrum allocation method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0040] Embodiment

[0041] As Figure 1 shown, it is the flowchart of the blockchain - based dynamic spectrum allocation method of the present invention, and the method includes the following steps:

[0042] Step S1, construct a spectrum resource blockchain network and deploy a smart contract, and the spectrum management party registers the available spectrum resource information in the resource registration contract.

[0043] In the specific implementation process, the execution of these steps needs to be based on a sound system architecture. The entire system can adopt a layered design, including an infrastructure layer, a blockchain network layer, a smart contract layer, a business logic layer, and an application interface layer. The infrastructure layer mainly includes physical servers, storage devices, and network devices; the blockchain network layer is responsible for maintaining the distributed ledger and the consensus mechanism; the smart contract layer realizes the automatic execution of business rules; the business logic layer processes the specific processes of spectrum resource management; the application interface layer provides a unified access entry for various users. The layers of the system communicate through standardized interfaces to ensure the security and efficiency of data flow. To ensure the reliability of the system, a sound monitoring and alarm mechanism and disaster recovery plan also need to be established, including functions such as node status monitoring, performance metric collection, and automatic fault switching.

[0044] When constructing a blockchain network for spectrum resources, it is necessary to select a underlying blockchain architecture suitable for the characteristics of spectrum management, such as Hyperledger Fabric or Ethereum Enterprise Edition, to support the requirements of high-throughput and low-latency transaction processing. The network nodes include spectrum management agency nodes, operator nodes, regulatory agency nodes, and public verification nodes, forming a distributed ledger system jointly maintained by multiple parties. The deployment of smart contracts adopts a step-by-step iterative method. First, the basic version is deployed, and after security audits and functional verifications, advanced functions are gradually added. The information registered by the spectrum management party in the resource registration contract includes multi-dimensional parameters such as frequency band number, frequency range, bandwidth, geographical coverage, usage period, interference protection requirements, power limit, and priority policy. These parameters are stored on the blockchain in the form of structured data, and at the same time, a unique identifier is generated for subsequent allocation and tracking.

[0045] The smart contracts include: a spectrum resource registration contract, a resource allocation contract, a quality assessment contract, and a usage monitoring contract.

[0046] Step S2, establish a spectrum resource evaluation model, consider the historical usage quality of the applicant, frequency band characteristics, usage duration, and interference constraint factors, and calculate the resource allocation priority.

[0047] The frequency band characteristics include frequency band bandwidth, center frequency, geographical area, and time range.

[0048] The implementation of the evaluation model needs to consider the requirements of various actual scenarios. In the mobile communication scenario, the historical usage quality can be subdivided into multiple dimensions, including channel quality indicator (CQI) statistics, resource block utilization rate, service completion rate, etc. When evaluating the frequency band characteristics, the influence of terrain and landform needs to be considered, and a propagation loss prediction model can be established by combining geographical information system (GIS) data. The usage duration evaluation can adopt a machine learning-based demand prediction model to analyze the historical usage pattern and predict future resource requirements.

[0049] Interference constraint evaluation can establish a graph - theory - based interference model, modeling all potential interference sources as nodes of an interference graph, and optimizing frequency allocation through a graph - coloring algorithm. Meanwhile, the evaluation model can also introduce an adaptive weight adjustment mechanism to dynamically adjust the weights of various evaluation factors according to the network load conditions.

[0050] Bandwidth characteristics can be further divided into two types: continuous bandwidth and discrete bandwidth. Appropriate allocation strategies are selected for different application scenarios. For example, for high - definition video transmission services, continuous bandwidth is preferentially allocated; for Internet of Things applications, discrete bandwidth allocation can be accepted. The selection of the center frequency needs to consider the radio frequency characteristics of the device, including parameters such as receiver sensitivity and transmitter linearity.

[0051] The division of geographical regions can adopt a multi - level grid model, dividing the coverage area into grids of different sizes to achieve flexible spatial reuse. The planning of the time range needs to consider the time - varying characteristics of the service, and a refined management mechanism based on time slots can be established. In addition, the propagation characteristics of the frequency band need to be considered, such as multipath effects and diffraction losses in complex urban environments.

[0052] In step S3, the applicant submits resource requirements, calculates a comprehensive score based on the quality assessment contract, and uses the comprehensive score as the allocation weight coefficient. The smart contract automatically determines the spectrum allocation plan based on the weighted score result and preset rules.

[0053] A hierarchical evaluation structure can be adopted. First, score the basic indicators, such as device compliance and historical credit records; then evaluate the technical ability indicators, including radio frequency device performance and interference control ability; finally, evaluate the business requirement indicators, such as bandwidth utilization and service quality requirements. The fuzzy - logic evaluation method can be introduced during the scoring process to handle the uncertainty of the indicators.

[0054] The process of formulating the allocation plan can adopt a multi - objective optimization algorithm, considering objectives such as spectrum efficiency, fairness, and system revenue simultaneously. Genetic algorithms or particle swarm optimization algorithms can be used to search for the optimal allocation plan. To improve decision - making efficiency, a plan library can be established to store the optimal allocation plans in typical scenarios for quick response.

[0055] In step S4, the monitoring contract is used to record the spectrum usage data in real - time, verify the data authenticity based on the blockchain consensus mechanism; evaluate the spectrum usage efficiency according to the monitoring data, dynamically adjust the quality score, and use the evaluation result as the basis for subsequent resource allocation.

[0056] The specific implementation of the monitoring system can adopt a distributed sensing network architecture and deploy multi-level monitoring nodes. Fixed monitoring stations can be equipped with high-performance spectrum analyzers to achieve broadband signal monitoring; mobile monitoring vehicles can perform area patrol tasks; portable monitoring devices are used for refined monitoring of key areas. Data collection adopts a hierarchical compression strategy, with edge nodes performing preliminary data compression and central nodes performing in-depth data analysis.

[0057] To ensure the credibility of the monitoring data, a multi-source data cross-verification mechanism can be adopted. For example, associate spectrum monitoring data with user service data, device operation logs, etc. for correlation analysis to promptly detect abnormal situations. The efficiency evaluation can establish a multi-dimensional evaluation system, including utilization indicators in the frequency dimension, time dimension, and space dimension. The dynamic score adjustment adopts an incremental update method to avoid drastic fluctuations in the score.

[0058] In step S2, calculating the resource allocation priority of the applicant based on the spectrum resource evaluation model includes:

[0059] Based on the normalized evaluation indicators, calculate the comprehensive priority score of the applicant: P = Q h ×M f ×I c , where Q h represents the historical usage quality of the applicant, M f represents the matching degree of the applicant's frequency band characteristics, and I c represents the satisfaction degree of interference constraints;

[0060]

[0061] Among them, N s represents the cumulative number of successful uses; N t represents the total number of applications; T a represents the cumulative actual usage duration; T r represents the cumulative application duration; B r represents the applied bandwidth; B a represents the available bandwidth; f c represents the applied center frequency; f r represents the recommended frequency; P i represents the predicted interference power; P th represents the allowable interference threshold; The calculation of the historical usage quality Qh can adopt a weighted moving average method, giving higher weights to recent usage records. Multiple evaluation periods can be set, such as daily, weekly, and monthly evaluations, and the final score can be comprehensively calculated based on the performance at different time scales. The calculation of the matching degree of frequency band characteristics Mf needs to consider the actual performance parameters of the device, such as carrier aggregation ability, duplex mode, etc.

[0062] The evaluation of the interference constraint satisfaction Ic can introduce a scenario-based interference model; for example, in indoor scenarios, wall attenuation and multipath effects are considered; in outdoor scenarios, environmental factors such as weather conditions and terrain undulations need to be considered; for dynamically changing interference environments, an adaptive interference estimation algorithm can be used to adjust the interference prediction model in real time.

[0063] For example, for the usage ratio Ns / Nt, the minimum statistical sample number requirement can be set to avoid evaluation bias caused by too few samples. The calculation of time utilization Ta / Tr can introduce time granularity parameters and select appropriate statistical periods according to different business types. The exponential decay term in the frequency matching calculation can be optimized for calculation efficiency by using a table lookup method.

[0064] The bandwidth matching calculation can take into account the discrete characteristics of the bandwidth. For non-continuous bandwidth, the segmented calculation method can be used. The calculation of the interference power ratio Pi / Pth needs to take into account the measurement error. The concept of confidence interval can be introduced, and the probability statistics method can be used to deal with uncertainty. In order to improve the calculation efficiency, a parameter pre-calculation table can be established to calculate the results in advance for common parameter combinations.

[0065] Calculate the comprehensive priority score of each applicant, sort the applicants in descending order based on the comprehensive priority score, and obtain the priority sequence of the applicants. In order to avoid priority solidification, a random perturbation factor can be introduced to introduce moderate randomness between applicants with similar scores. At the same time, a priority decay mechanism can be set up, and applicants who have continuously obtained high priority will have their priorities appropriately reduced in subsequent applications to promote fairness in resource allocation.

[0066] The quality assessment contract calculates the comprehensive score and determines the spectrum allocation plan including:

[0067] Calculate the applicant's comprehensive score S: S = P × C × T, where C is the applicant's credit score, which is obtained based on the application history. c i is the credit record used for the i-th time, t i is the recording time, t is the current time, λ is the time decay factor, and n is the total number of historical records.

[0068] T is the applicant's technical capability score, R f The RF performance index score, D p Score for signal processing capability; I m Score for interference control ability; R max is the full score of the performance indicator;

[0069] When S ≥ S th , and when the spectrum resource constraints are met, the allocation is approved; among them, the adaptive admission threshold Sth The calculation method is as follows: S b is the basic threshold value, N c is the current number of applications, N a is the average number of applications.

[0070] Taking the dynamic spectrum allocation in 5G networks as an example, a specific calculation process is as follows: Suppose an operator applies to use the 3.5 GHz frequency band for 5G network deployment. The operator's historical records show that in the past year, it applied to use the spectrum 100 times, successfully used it 90 times, the cumulative application duration was 1000 hours, and the actual usage was 900 hours. Then the historical usage quality Qh = 0.9×0.9 = 0.81. In terms of the frequency band characteristic matching degree, the applied bandwidth is 100 MHz, the available bandwidth is 80 MHz, the applied center frequency is 3550 MHz, and the recommended frequency is 3500 MHz. Then Mf≈0.786. The predicted interference power is -110 dBm, and the allowable interference threshold is -90 dBm. Then the interference constraint satisfaction degree Ic≈0.778. The comprehensive priority score P = ≈0.495. If the operator's credit record in the past 12 months has an average of 0.85 and the technical ability score is 0.92, then the final comprehensive score S = ≈0.387; this score needs to be compared with the current admission threshold to determine whether to approve the allocation.

[0071] The method for dynamically adjusting the quality score includes:

[0072] Calculating the comprehensive spectrum utilization efficiency: where D a is the actual data transmission volume; D m is the theoretical maximum transmission capacity; T u is the actual usage duration; T p is the applied usage duration; A r is the actual coverage area; A p is the applied coverage area.

[0073] Based on the efficiency evaluation, adjusting the comprehensive score of the applicant:

[0074] Updating the historical usage quality:

[0075] Updating the admission threshold:

[0076] where N is the number of allocations within the evaluation period, η t is the target efficiency value, β is the adjustment step size, and the value range is (0, 0.1); S new and S old are the comprehensive scores of the applicant after and before the update, and is the historical usage quality of the applicant before and after the update; and is the adaptive access threshold before and after the update.

[0077] The resource allocation process in step S3 further includes:

[0078] Setting the minimum service quality requirement: Q b is the basic service quality threshold,

[0079] η k is the usage efficiency in the k-th historical period, η k is the number of statistical periods;

[0080] Controlling the evaluation process, T resp ≤T max , N round ≤N max ; T resp is the single-round evaluation response time, T max is the maximum allowed response time; N round is the current evaluation round; N max is the maximum evaluation round;

[0081] The intelligent contract automatic verification mechanism includes technical verification, quality verification, and violation penalty mechanism;

[0082]

[0083]

[0084] Among them, all parameters meet the verifiability requirements of blockchain intelligent contracts. When the technical ability score and radio frequency performance index score of the applicant are not less than the minimum thresholds T min and R min the technical verification result V t is 1, otherwise 0; when the historical usage quality and comprehensive score of the applicant meet the minimum constraints, the quality verification V q is 1, otherwise 0; in the violation penalty mechanism Status acc as long as one of the technical verification or quality verification is not 1, the spectrum allocation is suspended, and it is in a normal active state only when both are 1.

[0085] Step S4 further includes: Distributed monitoring data collection D mon and zero-knowledge proof construction; D m0n ={(d i ,t i ,loc i ,hash i) | {i ∈ [1, M]}; π = ZKP(D mon , W key ); where d i is the data of the i-th monitoring node; t i is the timestamp, loc i is the geographical location information; hash i is the data hash value; M is the total number of monitoring nodes; ZKP represents zero - knowledge proof, and W key is the witness data required for the proof.

[0086] Taking the 5G network construction in a provincial capital city as an example to illustrate the specific implementation effect of the present invention. This city faced multiple challenges in the initial stage of 5G network construction: severe spectrum resource contention among the three major operators in the core business district, low spectrum allocation efficiency, with an average approval cycle exceeding 15 days, difficulty in real - time monitoring of usage data and the existence of illegal usage phenomena. At the same time, the spectrum resource utilization rate was unbalanced, with congestion in some areas and idle in some areas.

[0087] After adopting the method of the present invention, first, a blockchain network jointly participated by the provincial communication administration, the three major operators, and 5 third - party monitoring agencies was constructed. 30 fixed monitoring stations and 5 mobile monitoring vehicles were deployed in the core business district. The spectrum management party registered frequency band resources such as 3.5 GHz (3400 - 3600 MHz), 2.6 GHz (2515 - 2675 MHz), and 4.9 GHz (4800 - 4900 MHz) in the resource registration contract.

[0088] Taking a certain operator's application for 100 MHz bandwidth of 3.5 GHz frequency band resources as an example, based on its historical data in the past 6 months for evaluation: This operator has applied 200 times in total, successfully used 180 times, with an actual usage duration of 4200 hours and an applied usage duration of 4500 hours. Accordingly, the historical usage quality Qh = 0.84 is calculated. In the calculation of frequency band characteristic matching degree, considering factors such as the applied bandwidth of 100 MHz, available bandwidth of 80 MHz, applied center frequency of 3550 MHz, and recommended frequency of 3500 MHz, the matching degree Mf = 0.786 is obtained. The predicted interference power is - 110 dBm, and the allowable interference threshold is - 90 dBm. The interference constraint satisfaction degree Ic = 0.778 is calculated.

[0089] Further combining the operator's credit records such as on - time payment (0.95), compliant usage (0.90), and complaint handling (0.85) in the past 12 months, using the time - decay factor λ = 0.1, the credit score C = 0.88 is calculated. In terms of technical ability assessment, the radio frequency performance index of this operator is 0.92, the signal processing ability is 0.95, and the interference control ability is 0.88. Accordingly, the technical ability score T = 0.88 is obtained. Finally, the comprehensive score S = 0.397 is calculated.

[0090] The effect evaluation was carried out three months after the system deployment: the actual transmitted data volume of the operator in the approved frequency band was 850 TB (the theoretical maximum capacity was 1000 TB), the actual usage duration was 2160 hours (the planned usage duration was 2200 hours), and the actual coverage area was 45 square kilometers (the planned coverage area was 50 square kilometers). Based on this, the spectrum utilization efficiency η = 0.892 was calculated, showing a good level of resource utilization.

[0091] From the overall effect, the application of the present invention has achieved remarkable results: the spectrum allocation approval cycle has been shortened from 15 days to an average of 2.5 days, the resource utilization rate has been increased by 35%, and the system throughput has been increased by 28%; the satisfaction of the applicant has been increased by 40%, the disputes over resource allocation have been reduced by 65%, and the complaint handling time has been shortened by 70%; the whole process is traceable, the illegal usage rate has been decreased by 85%, and the supervision efficiency has been increased by 60%; the congestion situation during the peak period has been reduced by 45%, the resource waste rate has been decreased by 38%, and the system response ability has been increased by 50%.

[0092] The above embodiments fully prove that the present invention realizes the automation, transparency and dynamic optimization of spectrum resource allocation through the combination of blockchain technology and smart contracts, and effectively solves the problems such as low efficiency and lack of fairness in spectrum resource management. This method is not only theoretically feasible, but also can achieve remarkable technical effects and economic benefits in practical applications, and has good popularization and application value.

[0093] The specific embodiments described above further elaborate on the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic spectrum allocation method based on blockchain, characterized in that: The method comprises the following steps: Step S1: construct a spectrum resource blockchain network and deploy smart contracts. The spectrum manager registers the available spectrum resource information in the resource registration contract. The smart contracts include: spectrum resource registration contract, resource allocation contract, quality assessment contract and usage monitoring contract; Step S2, establishing a spectrum resource evaluation model, taking into account the applicant's historical usage quality, frequency band characteristics, usage duration, and interference constraints, and calculating resource allocation priorities; The frequency band characteristics include frequency band bandwidth, center frequency, geographical area and time range; Step S3: The applicant submits resource requirements, and a comprehensive score is calculated based on the quality assessment contract. The comprehensive score is used as an allocation weight coefficient, and the smart contract automatically determines the spectrum allocation plan based on the weighted score result and preset rules. Step S4, use the monitoring contract to record spectrum usage data in real time, and verify the authenticity of the data based on the blockchain consensus mechanism; evaluate the spectrum usage efficiency based on the monitoring data, dynamically adjust the quality score, and use the evaluation results as the basis for subsequent resource allocation.

2. The blockchain-based dynamic spectrum allocation method according to claim 1, characterized in that: In step S2, calculating the resource allocation priority of the applicant based on the spectrum resource evaluation model includes: Based on the normalized evaluation indicators, calculate the applicant's comprehensive priority score: P = Q h ×M f ×I c , where Q h Indicates the historical usage quality of the applicant, M f Indicates the matching degree of the applicant’s frequency band characteristics, I c represents the interference constraint satisfaction; Among them, N s Indicates the cumulative number of successful uses; N t Indicates the total number of applications; T a Indicates the actual cumulative usage time; T r Indicates the cumulative application time; B r Indicates bandwidth application; B a Indicates the available bandwidth; f c Indicates the application center frequency; f r Indicates the recommended frequency; P i represents the predicted interference power; P th Indicates the allowed interference threshold; The comprehensive priority score of each applicant is calculated, and the applicants are sorted in descending order based on the comprehensive priority scores to obtain a priority sequence of the applicants.

3. The blockchain-based dynamic spectrum allocation method according to claim 2, characterized in that: The quality assessment contract calculates the comprehensive score and determines the spectrum allocation plan including: Calculate the applicant's comprehensive score S: S = P × C × T, where C is the applicant's credit score, which is obtained based on the application history. c i is the credit record used for the i-th time, t i is the recording time, t is the current time, λ is the time decay factor, and n is the total number of historical records; T is the applicant's technical capability score, R f The RF performance index score, D p Score for signal processing capability; I m Score for interference control ability; R max is the full score of the performance indicator; When S ≥ S th , and when the spectrum resource constraints are met, the allocation is approved; among them, the adaptive admission threshold S th The calculation method is: S b is the basic threshold value, N c is the current number of applications, N a is the average number of applications.

4. The blockchain-based dynamic spectrum allocation method according to claim 3, characterized in that: The method for dynamically adjusting the quality score includes: Calculate the comprehensive spectrum utilization efficiency: Among them, D a is the actual amount of data transmitted; D m is the theoretical maximum transmission capacity; T u is the actual usage time; T p A is the duration of application; r is the actual coverage; A p To apply for coverage; Adjust the applicant's comprehensive score based on efficiency evaluation: Update history usage quality: Update access threshold: Where N is the number of allocations within the evaluation period, η t is the target efficiency value, β is the adjustment step length, and its value range is (0, 0.1); S new and S old The comprehensive score of the applicant before and after the update. and The historical usage quality of the applicant before and after the update; and These are the adaptive admission thresholds before and after the update.

5. The blockchain-based dynamic spectrum allocation method according to claim 3, characterized in that: The resource allocation process in step S3 also includes: Minimum quality of service requirement settings: Q b is the basic service quality threshold, η k is the utilization efficiency of the kth historical period, η k is the number of statistical cycles; Control the evaluation process, T resp ≤T max , N round ≤N max ; T resp is the single round evaluation response time, T max is the maximum allowed response time; N round is the current evaluation round; N max is the maximum number of evaluation rounds; The automatic verification mechanism of smart contracts includes technical verification, quality verification and violation penalty mechanism; Among them, all parameters meet the verifiability requirements of blockchain smart contracts, and the applicant's technical capability score and RF performance index score are not less than the minimum threshold T min and R min When the technical verification result V t is 1, otherwise it is 0; if the applicant's historical usage quality and comprehensive score meet the minimum constraints, the quality verification V q 1, otherwise 0; Violation penalty mechanism Status acc In the process, as long as one of the technical verification or quality verification is not 1, the spectrum allocation will be suspended. It is a normal active state only when both are 1.

6. The method according to claim 1, characterized in that The step S4 also includes: distributed monitoring data collection D mon and zero-knowledge proof construction; D mon ={(d i ,t i ,loc i ,hash i )|i∈[1,M]};π=ZKP(D mon ,W key ), where d i is the data of the i-th monitoring node; t i is the timestamp, loc i is the geographic location information; hash i is the data hash value; M is the total number of monitoring nodes; ZKP represents zero-knowledge proof, W key Witness data required for proof.

Images