A method for constructing a radio spectrum map

Abstract

A method for constructing a radio spectrum map, belonging to the technical fields of wireless communication devices and computer technologies. The spectrum management center issues sensing tasks and related information. The secondary users submit applications for spectrum sensing tasks according to their own channel conditions. When the applications of the secondary users meet the contract conditions, the smart contract is automatically executed to determine the users for spectrum sensing. The sensing users upload the results to the smart contract to feedback the spectrum usage situation. The smart contract performs fusion judgment on the data based on the reputation mechanism, determines the relevant rewards for the sensing users, and broadcasts the results to each user node for verification. Regarding the interaction mechanism between the spectrum sensing performance and the incentive mechanism for sensing users in the mobile scenario, the sensing users with suitable channel conditions are preferably selected. At the same time, the sensing results of the users are recorded and stored based on the blockchain distributed ledger structure, and the immutable feature of the blockchain is used to ensure the secure feedback of the usage status of spectrum resources, thereby forming a complete, accurate and secure radio spectrum map.

Description

Technical Field

[0001] The present invention relates to a method for constructing a radio spectrum map, belonging to the technical fields of wireless communication devices and computers. Background Art

[0002] With the massive access of mobile devices to wireless communication networks, spectrum resources are becoming increasingly scarce. In the traditional approach, mobile network operators obtain the right to use the spectrum through spectrum auctions or fixed allocations, which usually results in some frequency bands being idle or having low utilization rates and cannot meet the requirements for efficient utilization of spectrum resources in the sixth-generation mobile communication (6G). In cognitive radio (CR) networks, the spectrum sharing strategy based on dynamic access provides a feasible solution for the effective utilization of spectrum resources. In order to achieve dynamic spectrum sharing without interfering with licensed users, comprehensively, accurately, and timely obtaining the usage situation of spectrum resources is one of the key tasks. However, it takes a certain amount of time from the analysis of the frequency band usage situation to the selection of users for access. For networks with high requirements for real-time performance and accuracy, such as vehicle-to-everything (V2X) systems, it is necessary to ensure that the usage situation of spectrum resources can be accurately obtained in a short period of time. Otherwise, once the information acquisition time is too long, the frequency band usage situation may change during this time, making the information previously obtained by users unreliable.

[0003] In cognitive radio networks, spectrum sharing based on dynamic access can effectively utilize spectrum resources. This technology allows secondary users to access idle spectrum when they detect that there is no signal from the primary user. However, when the licensed frequency band is occupied by the primary user, the secondary user must vacate this frequency band and look for other idle frequency bands for communication. Cooperative spectrum sensing is a basic technology for realizing spectrum sharing, which uses the diversity of the sensing space of multiple nodes to reduce the sensing cost or improve the sensing robustness. However, when users maliciously tamper with the sensing results, the performance of spectrum sensing and the security of communication will be significantly affected. How to resist malicious user attacks and ensure the accuracy and security of the cooperative sensing fusion results is a hot issue worthy of research.

[0004] As a decentralized technology, blockchain no longer requires the participation of a trusted third party. Through a chain structure and a consensus mechanism, each node in the distributed network shares data, ensuring the consistency, authenticity, and immutability of the data. However, node users in the distributed network may refuse to cooperate due to distrust relationships. Therefore, in this regard, related research has proposed a dynamic spectrum access framework based on sensing for distributed cooperative sensing, supported by blockchain. Different weights of reputation values are set for each node user, and the reputation values and sensing results of all nodes are recorded through a consensus mechanism to ensure the security and reliability of the sensing results in the absence of a fusion center, forming a low-cost dynamic spectrum sharing technical solution based on blockchain to achieve the purpose of secure access to the radio spectrum.

[0005] For a decentralized collaborative sensing platform built on blockchain, users will consume a certain amount of energy when performing sensing and uploading sensing results. Therefore, an incentive mechanism needs to be developed to encourage users to actively perform sensing tasks. In some studies, the user sensing service requirements are published through smart contracts. Only within the scope of the contract requirements can secondary users perform sensing to obtain benefits. The literature "A Secure Cooperative Spectrum Sensing Method Based on Blockchain Smart Contracts" proposed a blockchain-based spectrum sensing system that uses the characteristics of blockchain to ensure the security and correctness of the spectrum trading process. The requesters and participants of spectrum sensing can maximize their own interests through game strategies.

[0006] In some existing technical solutions, a reputation mechanism is used to determine the reliability of the node sensing results, thereby improving the accuracy of cooperative spectrum sensing. In these technical solutions, weights are usually set for each node based on the historical sensing records of users. However, these historical records are usually the results obtained when users are in a quasi-static environment or a specific environment, without considering the current channel conditions. In a mobile scenario, the user channel conditions are in a changing state, and the reference value of the historical reputation value of secondary users obtained in a quasi-static environment will decrease. Users with a high historical reputation value are likely to have poor channel conditions in a mobile state and do not have good sensing performance. The sensing performance of the nodes cannot fully meet the requirements, thus affecting the overall performance of cooperative sensing. In addition, it is necessary to consider that when the node historical records are too many, it will lead to an increase in the node computing power and time cost. And when performing sensing simulation, most use an energy-based detection algorithm, but this algorithm has poor detection performance at low signal-to-noise ratios, and the noise intensity has a great influence on the detection results. Summary of the Invention

[0007] To overcome the deficiencies of the prior art, the present invention provides a method for constructing a radio spectrum map.

[0008] A method for constructing a radio spectrum map includes a spectrum management step, a spectrum sensing step, a blockchain smart contract step, and a radio spectrum map construction step.

[0009] Step 1, the spectrum management step: The spectrum management center publishes sensing tasks and related information, and secondary users submit spectrum sensing task applications based on their own channel conditions.

[0010] Step 2, the spectrum sensing step: When the application of the secondary user meets the contract conditions, the smart contract is automatically executed to determine that the user performs spectrum sensing. The sensing user uploads the results to the smart contract to feedback the spectrum usage situation.

[0011] Step 3: Blockchain smart contract step. The smart contract makes a fusion decision on the data based on the reputation mechanism, determines the rewards related to the sensing users, and broadcasts the results to each user node for verification. The verified information data is stored in the blockchain and cannot be deleted or modified, providing a trustless and secure working environment for the cognitive radio network.

[0012] Step 4: Step of constructing a radio spectrum map. Regarding the interaction mechanism between the spectrum sensing performance and the sensing user incentive mechanism in a mobile scenario, select sensing users with suitable channel conditions, enhance the enthusiasm of secondary users for sensing while improving the sensing accuracy, and at the same time record and store the user sensing results based on the blockchain distributed ledger structure. Utilize the immutable feature of the blockchain to ensure the secure feedback of the usage status of spectrum resources, and then form a complete, accurate, and secure radio spectrum map.

[0013] The beneficial effects achieved by the present invention are as follows: A method for constructing a complete, accurate, and secure radio spectrum map is proposed. Distributed spectrum sensing is carried out based on blockchain smart contract technology to achieve real-time feedback on the usage of spectrum resources, providing convenient support for users to use spectrum resources and improving the utilization rate of spectrum resources. Under the same task budget, more secondary users with excellent channel conditions can be selected, improving the detection probability of primary user signals; and when the number of sensing users is the same, the overall sensing users can obtain higher average benefits, enhancing the enthusiasm of secondary users to participate in sensing.

[0014] Based on the interaction mechanism between the spectrum sensing performance and the cognitive user incentive mechanism in a mobile scenario, the influence of channel conditions and noise factors on the user sensing performance is analyzed, providing a reference for selecting high-sensing-performance users from a large number of wireless devices distributed in different mobile scenarios; in terms of the reputation mechanism, instead of referring to a large number of historical reputation records in a static state, a certain credibility is given to users according to the channel conditions and sensing accuracy, realizing the processing of user data and the distribution of sensing rewards, improving the accuracy of primary user signal detection and the enthusiasm of sensing users.

[0015] Generate a unique identity ID based on the user's transmitted signal characteristics and device location information, providing two-dimensional identity verification. Only user transactions that have successfully passed the identity verification by the smart contract can be packaged and written into the block, preventing malicious users from forging identities and uploading false data, and ensuring the authenticity of the spectrum sensing results.

[0016] Within a certain regional range, the present invention encourages more secondary users with excellent sensing performance to jointly provide the usage status of spectrum resources in a collaborative sensing manner, realizing real-time feedback and update of the usage of spectrum resources, providing convenient support for other users to directly obtain spectrum resources, achieving timely, reliable, and secure communication, and improving the utilization rate of spectrum resources. Brief Description of the Drawings

[0017] When considered in conjunction with the accompanying drawings and with reference to the following detailed description, the present invention can be more completely and better understood, and many of its attendant advantages will be readily appreciated. The drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The illustrative embodiments and descriptions thereof are used to explain the present invention and do not constitute an improper limitation thereof. As shown in the figures:

[0018] Figure 1 is a radio spectrum map construction system model.

[0019] Figure 2 is a schematic diagram of the feedback of the primary user presence in the radio spectrum map.

[0020] Figure 3 is one of the flowcharts of the radio spectrum map construction based on blockchain smart contracts.

[0021] Figure 4 is another flowchart of the radio spectrum map construction based on blockchain smart contracts.

[0022] Figure 5 is a comparison chart of the detection probabilities of different channels in the present invention.

[0023] Figure 6 is a comparison chart of the detection probabilities under different interferences in the present invention.

[0024] Figure 7 is a comparison chart of the number of users perceived based on reputation value in the present invention.

[0025] Figure 8 is a comparison chart of the detection probabilities based on reputation value in the present invention.

[0026] Figure 9 is a comparison chart of the average utility of the user set perceived based on reputation value in the present invention. Detailed implementation manners

[0027] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0028] Obviously, many modifications and variations made by those skilled in the art based on the purpose of the present invention fall within the protection scope of the present invention.

[0029] The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description, the meaning of "a plurality" is two or more unless otherwise specifically defined.

[0030] Unless otherwise clearly defined and limited, terms such as "installation", "connection", "linkage", "fixation" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be a direct connection or an indirect connection through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] Those skilled in the art of the present technology can understand that unless otherwise defined, all terms used herein, including technical terms and scientific terms, have the same meaning as the general understanding of those of ordinary skill in the art in the relevant field.

[0032] For the convenience of understanding the embodiments, further explanations will be given below in conjunction with [relevant content], and each embodiment does not constitute a limitation on the embodiments.

[0033] Embodiment 1: As shown in Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 、 Figure 7 、 Figure 8 and Figure 9 shown, a method for constructing a radio spectrum map includes a spectrum management step, a smart contract step, and a blockchain smart contract step.

[0034] Step 1, spectrum management step: The spectrum management center issues sensing tasks and related information. The spectrum management center issues sensing tasks and related information, and the secondary users submit applications for spectrum sensing tasks according to their own channel conditions.

[0035] Step 2, spectrum sensing step: When the application of the secondary user meets the contract conditions, the smart contract is automatically executed to determine the users for spectrum sensing. The sensing users upload the results to the smart contract to feedback the spectrum usage situation.

[0036] Step 3, blockchain smart contract step: The smart contract performs fusion judgment on the data based on the reputation mechanism, determines the relevant rewards for the sensing users, and broadcasts the results to each user node for verification. The verified information data is stored in the blockchain and cannot be deleted or modified, providing a trustless and secure working environment for the cognitive radio network.

[0037] Step 4: Steps for constructing a radio spectrum map. Regarding the interaction mechanism between spectrum sensing performance and the user incentive mechanism for sensing in a mobile scenario, select sensing users with suitable channel conditions to enhance the enthusiasm of secondary users for sensing while improving sensing accuracy. At the same time, record and store the user sensing results based on the blockchain distributed ledger structure, and use the immutable feature of the blockchain to ensure the secure feedback of the usage status of spectrum resources, thereby forming a complete, accurate, and secure radio spectrum map.

[0038] In the spectrum management step in Step 1, the following steps are further included:

[0039] Spectrum management is jointly responsible by the spectrum management center and secondary users.

[0040] For the spectrum management center: The spectrum management center issues sensing tasks. The task information includes a spectrum sensing service with a duration of T c , estimates the maximum number M of sensing nodes required according to the distribution of secondary users in the area, formulates a sensing service budget B in combination with the usage frequency η of this frequency band, and calculates as follows. At the same time, other service information such as the maximum false alarm probability and the minimum detection probability is specified.

[0041] B = f{T c , M, η}

[0042] B is the sensing task budget.

[0043] T c is the duration of the sensing task.

[0044] M is the maximum number of sensing nodes expected to be required.

[0045] η is the usage frequency of the frequency band.

[0046] The spectrum management center writes the task information into the smart contract, generates a transaction, generates a block, and broadcasts it to the whole network.

[0047] For secondary users: View the task information through the smart contract address, evaluate their own sensing performance based on the current channel conditions. When the requirements of the contract are met, that is, when it holds, submit a task application and provide information such as channel conditions and task quotes to the contract. At the same time, the user generates a radio frequency signal based on the defects of the hardware device itself and combines the user's location to generate a unique identity ID, provides two-dimensional identity verification, and generates a block for broadcasting.

[0048] where p′ f and p′ d are the false alarm probability and detection probability predicted by the secondary user based on the channel conditions when the primary user signal exists.

[0049] In the spectrum sensing step in Step 2, the following steps are further included:

[0050] First, determine the sensing users. When the smart contract receives a secondary user task application, it verifies the identity ID uploaded by the secondary user according to the trusted node identity authentication model to determine the authenticity of the user identity. For secondary users whose identity information is successfully verified, an initial reputation value is assigned according to the channel conditions, as shown in the following formula:

[0051]

[0052] C i =f(γ i ,τ i )

[0053] V i represents the initial reputation value of the i-th secondary user,

[0054] C i represents the channel condition of the i-th secondary user,

[0055] represents the average value of the channel conditions of all secondary users,

[0056] γ i represents the signal-to-noise ratio of the received signal of the i-th secondary user,

[0057] τ i represents the propagation delay of the signal reaching the i-th secondary user,

[0058] And sort the nodes in descending order to determine the sensing user set S, generate a transaction block, and broadcast it:

[0059] S={s 1(V：max) ,s 2(V：max-1) ,…,s H(V：min)}

[0060] S represents the sensing user set sorted by the initial reputation value,

[0061] H represents the number of sensing users whose identity authentication is successful,

[0062] S i represents the sensing users sorted by reputation value from high to low. For example, S 1(V：max) represents the sensing user with the highest reputation value, and s H(V:min) represents the sensing user with the lowest reputation value.

[0063] Until the secondary user receives feedback on the confirmation of the sensing user and then performs spectrum sensing, uploads the sensing result sequence to the blockchain network in the form of a transaction, and the miner node verifies the identity of the sensing user and pulls the sensing users with mismatched fingerprint features into the blacklist.

[0064] The sensing user combines the signal-to-noise ratio γ obtained in the mobile scenario to perform spectrum sensing and obtains the maximum detection probability of the primary user signal.

[0065]

[0066] is the detection probability at the cyclic frequency α k at the place,

[0067] Q 1 (·) is the Marcum function with 1 degree of freedom,

[0068] γ is the signal-to-noise ratio,

[0069] N is the number of FFT operation points,

[0070] G(·) is the inverse function of the incomplete gamma function,

[0071] P f is the false alarm probability.

[0072] The steps of the blockchain smart contract in step 3 also include the following steps:

[0073] First, wait for the sensing task time to expire. The smart contract obtains the sensing results of the nodes from the network for fusion processing and judges the usage status of the primary user frequency band.

[0074] Since the reputation value reflects the channel state of the sensing node, the smart contract weights its local sensing results according to the reputation value weight of the sensing user to obtain the fusion probability, which is calculated as follows:

[0075]

[0076] represents the fusion result of H sensing nodes based on the reputation value

[0077] χ represents the situation where the sensing node i judges the existence of the primary user signal during the task time,

[0078] V i represents the initial reputation value of the i-th secondary user,

[0079] At the same time, the reputation value is corrected to determine the final sensing reward that the user should receive, and the generated information is packaged into a block for global broadcast. The corrected reputation value is calculated as follows:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] β is the adjustment factor,

[0086] is the spectrum sensing accuracy of the sensing node i,

[0087] V i represents the initial credit value of the i-th secondary user,

[0088] ω i represents the credit correction factor of the sensing user i,

[0089] θ l represents the weight of the i-th sensing result,

[0090] ζ l represents the decision of the l-th sensing result,

[0091] δ represents the sensing accuracy decision threshold,

[0092] The corrected credit value comprehensively reflects the channel condition of the sensing user and the accuracy of the secondary user's sensing based on its own device performance. When the corrected credit value is high, it is considered that the sensing result of this sensing node is more accurate and reliable, and a higher sensing reward can be obtained. If the corrected credit value is low, it indicates that the sensing user's performance is not ideal in one aspect, and the obtained sensing reward will be relatively low. Since the sensing user's invocation of the smart contract to write sensing data will also incur certain expenses, when calculating the sensing user's reward, the contract needs to consider both aspects. Therefore, the sensing reward is calculated as follows:

[0093] r i = c i + p i

[0094] c i = f(V′ i , D loss )

[0095] p i = R eth · l · μ eth

[0096] r i is the sensing reward of the sensing node i,

[0097] c i is the task offer of the sensing node i,

[0098] p i is the sensing cost of the sensing node i,

[0099] D loss The equipment working loss cost is

[0100] V′ i is the reputation correction value of sensing node i,

[0101] R eth represents the data rate written to the smart contract,

[0102] l indicates the writing time.

[0103] μ eth Indicates the cost of a write operation.

[0104] The step of building a radio spectrum map in step 4 also includes the following steps:

[0105] The spectrum management center retrieves the information stored in the blockchain to obtain the spectrum usage in the area, thereby building a radio spectrum map platform. Other users in the secondary user network can directly access the platform in some way to obtain the spectrum resource usage in the area so that they can take appropriate actions, that is, access the spectrum when the channel is idle, and postpone access if the spectrum is detected to be busy.

[0106] Example 2: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 As shown, a method for constructing a radio spectrum map selects a cyclic detection method, which determines whether a primary user signal exists through cyclic spectrum distribution and reduces the impact of noise on primary signal detection.

[0107] Secondly, the influence of channel factors is considered, and the changes in secondary user perception performance in the face of different channel conditions and noise interference in mobile scenarios are analyzed, providing a basis for smart contracts to optimize secondary users with suitable channel conditions and reliable perception performance, and then make accurate judgments on the spectrum usage status.

[0108] In terms of incentives, this scheme no longer refers to a large number of historical credit records in static conditions, but interacts with the incentive mechanism based on the perceived user spectrum perception performance in the current mobile scenario, gives users a certain degree of credibility based on channel conditions and perception accuracy, and formulates an incentive mechanism based on reputation value to realize the processing of user data and the issuance of perception rewards.

[0109] Finally, when the sensing node uploads data, this solution combines the user's device characteristics and device location and other information characteristics to generate a unique user ID. Only for secondary users whose identity information has been successfully verified by the smart contract can the miners package their uploaded transactions into the block, thereby enhancing the authenticity of user information and preventing malicious nodes from forging identities, tampering with or uploading interfering data.

[0110] In the blockchain network, miners are network nodes that continuously perform hash operations to solve mathematical problems and generate proof of work, verifying and confirming transactions through computing power. The above improvements further meet the requirements for accuracy and security when building radio spectrum maps.

[0111] Example 3: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 As shown, a method for constructing a radio spectrum map is used to study dynamic spectrum sharing in combination with blockchain technology.

[0112] In mobile scenarios, secondary users with excellent perception performance within a certain area are encouraged to perceive the surrounding radio environment in a collaborative sensing manner, achieve real-time feedback on the use of spectrum resources, support blockchain-based dynamic spectrum sharing, and improve spectrum resource utilization.

[0113] Figure 1 It is a spectrum sensing application model based on blockchain, which mainly includes spectrum management unit, smart contract and secondary users.

[0114] A smart contract is a piece of code that runs on the blockchain. When the contract conditions are met, the code is automatically executed, and the verified user transactions are processed and packaged into blocks.

[0115] The spectrum management unit is used to publish sensing tasks and related information.

[0116] The secondary user is the executor of the perception task. It obtains the detection probability of the primary user signal through the spectrum sensing algorithm, makes a judgment on whether the primary user signal exists, and uploads the result to the smart contract.

[0117] Figure 2It is a schematic diagram of the feedback of the presence of the primary user in the radio spectrum map. The black squares indicate that the primary user in the area is using the authorized frequency band, and the white squares indicate that the frequency band in the area is idle and there is no primary user signal. At the same time, further combining technologies such as antenna arrays to flexibly change the antenna beam and pointing shape can achieve the reception and detection of electromagnetic waves in various frequency bands in the entire space. In the radio spectrum map, the position range of the primary user using the authorized frequency band is accurately displayed, and the distribution and usage of the spectrum in this area are more precisely reflected, providing geographical reference information for the secondary users to be connected. The black beam indicates that there is a primary user using the mission frequency band in the detection direction.

[0118] Figure 3 and Figure 4 is to Figure 1 refine the interaction between the modules in

[0119] S1. The spectrum management center publishes tasks and related information, generates a transaction to trigger the contract, and generates block type 1 for deployment to the blockchain.

[0120] S2. The secondary user views the task information through the smart contract address, evaluates its own sensing performance, and when meeting the contract requirements, applies for the task, and at the same time attaches the user's unique identity ID, generates block type 2 for broadcasting.

[0121] S3. The smart contract verifies the digital identity ID of the secondary user, assigns a certain initial reputation value to the secondary user with successful identity verification, determines the set of sensing users in combination with the task quotation and sensing accuracy of the secondary user, and generates block type 3.

[0122] S4. The secondary user receives the feedback, performs spectrum sensing, uploads the sensing results to the blockchain network, and the miner nodes verify the identity of the sensing user, and package the transactions that pass the verification to generate block type 4 for broadcasting.

[0123] S5. Wait until the time limit of the sensing task expires. The smart contract obtains the sensing information of the nodes from the network, performs fusion processing on the data based on the reputation mechanism, judges the usage status of the primary user frequency band, corrects the reputation value, determines the sensing reward for the user, and packages the relevant results to generate block type 5 for network-wide broadcasting.

[0124] S6. The spectrum management center retrieves the spectrum usage information in the area from the blockchain storage, and constructs a radio spectrum map platform based on this. Other users in the secondary user network can directly access the platform in a certain way to obtain the spectrum resource usage situation in this area, so as to take appropriate actions, that is, perform spectrum access when the channel is idle, and postpone access if the spectrum is detected to be busy.

[0125] The following experimental results are obtained through computer simulation. In the following simulation, the main user signal parameters are set as follows: the carrier frequency is 2 GHz, the signal bandwidth is 20 MHz, the number of subcarriers of the OFDM signal is 64, and the subcarriers are modulated by QPSK.

[0126] Example 4: As Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 shown, a method for constructing a radio spectrum map. In a wireless communication system, the main user signal usually experiences reflection, refraction, or diffraction, etc. before reaching the secondary user.

[0127] In addition, due to the mobility of the main / secondary users themselves, according to the Doppler effect in physics, there will be a Doppler frequency shift at the receiving end. These signals with the same information will have corresponding changes in the arrival time and signal strength at the receiving end due to different channels.

[0128] If x(t) is the main user signal and f c is the high-frequency carrier, the channel response of each path can be expressed as:

[0129]

[0130]

[0131] where, α n (t) is the time-varying attenuation of the signal amplitude of different paths, determined by path loss and shadow fading,

[0132] ψ n (t) is the phase change of the nth multipath signal, affected by the initial phase, time delay, and Doppler,

[0133] τ n (t) is the signal path transmission delay of different paths,

[0134] is the Doppler phase shift of different paths.

[0135] Therefore, when the main user signal adopts OFDM technology for multi-carrier modulation, each channel can be approximately regarded as narrow-band fading. The received signal y i (t) of the ith sensing user is:

[0136]

[0137] y i$(t)$ is the received signal of the $i$-th sensing user,

[0138] $h$ n $(t)$ is the $n$-th channel response,

[0139] $N(t)$ is the number of multipath channels,

[0140] $x(t)$ is the primary user signal,

[0141] $n$ i $(t)$ is the noise interference received by the $i$-th sensing user.

[0142] In a mobile scenario, a cyclic detection algorithm is adopted. For a specific cyclic frequency detection, if the actual wireless environment signal-to-noise ratio of the sensing user is $\gamma$, its maximum detection probability can be calculated as:

[0143]

[0144] is the detection probability at the cyclic frequency $\alpha$ k at,

[0145] $Q$ 1 $(\cdot)$ is the Marcum function with 1 degree of freedom,

[0146] $\gamma$ is the signal-to-noise ratio,

[0147] $N$ is the FFT operation point,

[0148] $G(\cdot)$ is the inverse function of the incomplete gamma function,

[0149] $P$ f is the false alarm probability.

[0150] By performing sensing analysis on the received signals under three different channel models in 3GPP, the spectrum sensing performance of the sensing user is obtained.

[0151] Table 1 shows the three channel models and related parameters in the 3GPP protocol.

[0152] Figure 3 and Figure 4 shows the sensing results of the sensing user for the primary user signal under different channel models.

[0153] From Figure 5 it can be seen that when the primary user is present, the primary user signal can be detected almost 100% under the AWGN channel (i.e., no fading). However, when the false alarm probability is set to be small, such as $p$ fWhen ρ = 0.2, when the primary signal passes through the EPA and ETU multipath fading channels, the detection probability of the primary user signal by the sensing user will be reduced to about 86% and 82% respectively; when the signal passes through the EVA multipath fading channel, the detection probability is significantly reduced to about 61%, and the detection performance drops significantly.

[0154] In addition, the frequent electronic ignition of vehicles and the partial discharge of high-voltage equipment in power stations will generate strong burst pulse interference, seriously interfering with the performance of communication systems and greatly reducing the reliability of wireless communication systems. Electromagnetic radiation pulse noise is a non-stationary signal with randomly occurring short pulses and high-amplitude pulses. The Middleton Class A noise model describes a type of electromagnetic interference commonly encountered in mobile communications. The probability density function of the Middleton Class A noise model is defined as:

[0155]

[0156]

[0157] where A represents the pulse index, which is the product of the average number of pulses generated per unit time and the average pulse duration; T represents the ratio of the power of the Gaussian component to the power of the pulse component in the interference; by adjusting the values of the two parameters A and T, different types of electromagnetic interference noise can be fitted.

[0158] m is the noise component. When m = 0, it represents the Gaussian white noise component, and when m > 0, it represents the pulse component in the noise.

[0159] σ 2 is the sum of the powers of the pulse noise and the Gaussian noise, that is, the total noise power of the system.

[0160] In the fading channel, the noise distribution is a random process. For the detection probability of the primary user signal in the fading channel, it is usually obtained by integrating the probability density distribution function of the noise, expressed as:

[0161]

[0162] is the detection probability of the primary user signal.

[0163] Q m (·) is the Marcum Q function.

[0164] γ is the signal-to-noise ratio at the receiving end.

[0165] λ is the decision threshold.

[0166] f(x) is the probability density distribution function of the noise in the fading channel.

[0167] In the simulation, it is assumed that A = 0.1 and T = 0.1. Referring to the fading channel parameters in EVA, the sensing performance of the sensing users is analyzed under two different environmental noise models, namely AWGN and Middleton Class A electromagnetic pulse noise. From Figure 6 It can be seen that when the signal-to-noise ratio is only 10 dB, the influence of AWGN noise on signal detection is significantly stronger, and the signal detection probability under this interference is lower than that under Middleton Class A electromagnetic pulse noise interference. However, as the signal-to-noise ratio increases, such as SNR = 20 dB, the influence of Middleton Class A electromagnetic pulse noise on signal detection gradually increases, resulting in a gradual decrease in the detection probability of the sensing node, which is lower than the detection probability under AWGN. When the received primary user signal is very strong (for example, SNR = 30 dB), the detection probabilities are both close to 100% under the AWGN and Middleton Class A noise models, and the influence of noise on the detection of the primary user signal is very low.

[0168] In summary, in the mobile communication scenario, the fading channel type, noise type, and noise intensity distribution will all affect the detection result of the primary user signal. Therefore, in order to accurately and reliably reflect the usage of the primary user's authorized frequency band, it is necessary to consider the influence of the channel and interference on the user's sensing performance.

[0169] Table 1 3GPP multipath fading channel model

[0170]

[0171] Example 5: Example 1: As Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 shown, a method for constructing a radio spectrum map. In the simulation scenario, it is assumed that the task budget for spectrum sensing is 300, there are 10 secondary users, and the 7th secondary user is a malicious user. The effective sensing radius of each secondary user is 300 m. The moving speeds of these 10 secondary users are 120, 40, 15, 85, 20, 55, 60, 70, 50, 95 km / h respectively. For secondary users with speeds between 0 and 20 km / h, the EPA fading channel model is used for primary user signal detection; for secondary users with speeds between 20 and 60 km / h, the ETU fading channel model is used; for secondary users with speeds between 60 and 120 km / h, the EVA channel model is used. The noise is AWGN.

[0172] When 10 secondary users apply for sensing tasks, their sensing results are as Figure 7As shown in the figure. Under the same budget, more sensing users can be selected based on the reputation value algorithm. During the process of selecting sensing users, malicious users obtain a higher reputation value by falsely reporting the channel conditions. However, due to their uploading of incorrect data, from Figure 8 It can be seen that this leads to a decrease in the detection probability of the primary user after fusion processing. However, as the task budget increases, the number of selected sensing users increases, the proportion of malicious users decreases, the impact on the fusion decision of the primary user signal decreases, and the accuracy of spectrum sensing is improved. Moreover, even if there are malicious users, as long as the proportion of honest users is guaranteed to be large enough, the detection result obtained by fusing based on the reputation value is higher than that without using reputation value fusion. In addition, when there is no malicious user interference, the detection probability of the primary user obtained by fusing the sensing results based on the reputation value is significantly higher than that without using the reputation value mechanism.

[0173] In the literature "Incentive Mechanisms for Crowdsensing: Crowdsourcing with Smartphones," the bid of the i-th secondary user (C i ) is determined by the sensing time (t i ) and the task budget (B), as follows:

[0174]

[0175] c i represents the bid of the i-th secondary user,

[0176] t i represents the sensing time of the i-th secondary user,

[0177] N represents the total number of sensing users,

[0178] B represents the task budget.

[0179] The sensing cost (p i ) of the user is only related to the sensing time and the cost per unit time, and the calculation formula is:

[0180] p i = ε·t i

[0181] p i represents the sensing cost of the i-th secondary user,

[0182] t i represents the sensing time of the i-th secondary user,

[0183] ε represents the cost per unit time.

[0184] Such as Figure 9As shown, when the number of perceived users is the same, the reputation value-based incentive algorithm can ensure that the overall perceived users obtain a higher average utility, and as the number of perceived users increases, the improvement of the average utility will tend to be stable.

[0185] A method for constructing a radio spectrum map cross-layer optimizes the interaction mechanism between the accuracy of spectrum sensing in a mobile scenario and the perception user incentive mechanism, and combines blockchain smart contract technology to achieve dynamic spectrum sharing based on blockchain.

[0186] The main part of this solution mainly includes a spectrum management unit, a smart contract, and secondary users. The smart contract is a piece of code running on the blockchain. When the contract conditions are met, the code automatically executes to complete transactions between users, generates blocks and stores them in the blockchain. The data stored on the blockchain after verification cannot be deleted or modified, providing a trustless and secure working environment for cognitive radio networks. The spectrum management unit is used to publish sensing tasks and related information. Secondary users are the executors of sensing tasks. They obtain the detection probability of the primary user signal through spectrum sensing algorithms, make a judgment on the existence of the primary user signal, and upload the results to the smart contract. All information will be synchronously recorded on the blockchain. The blockchain smart contract performs a fusion judgment on the sensing results based on the reputation mechanism and determines the relevant rewards for the sensing users.

[0187] S1. The spectrum management center publishes tasks and related information, generating a transaction to trigger the contract.

[0188] The spectrum management unit publishes a spectrum sensing service with a duration of T c According to the distribution of secondary users in this area, the maximum number M of required sensing nodes is estimated. At the same time, combined with the usage frequency

[0189] η of this frequency band, the budget B of the sensing service is formulated as shown in Equation (1), and the maximum false alarm probability p f and the minimum detection probability p d and other service information are written into the smart contract, generating a transaction and broadcasting it to the whole network, generating block type 1.

[0190] B = f{T c , M, η} (1)

[0191] B is the budget of the sensing task,

[0192] T c is the duration of the sensing task,

[0193] M is the maximum number of required sensing nodes estimated,

[0194] η is the usage frequency of the frequency band,

[0195] S2. The secondary user views the task information through the smart contract address, evaluates its own sensing performance based on the current channel conditions, and when the contract requirements are met, that is when it holds, a task application is made.

[0196] Among them, p' f and p' d are the false alarm probability and detection probability predicted by the secondary user according to the channel conditions when the primary user signal exists.

[0197] The secondary user triggers the contract by making a task application, provides information such as channel conditions and task quotes to the contract. At the same time, the user generates a radio frequency signal based on the defects of its own hardware device and combines it with the user's location to generate a unique identity ID, provides two-dimensional identity verification, and generates a block type 2 for broadcasting.

[0198] S3. The smart contract verifies the identity ID uploaded by the secondary user according to the identity verification model trained by actual trusted user nodes to determine the authenticity of the user identity. For the secondary user whose identity information verification is successful, the smart contract assigns a certain initial reputation value according to the channel conditions, as shown in the following formula (2),

[0199]

[0200] C i = f(γ i , τ i )

[0201] V i represents the initial reputation value of the i-th secondary user,

[0202] C i represents the channel situation of the i-th secondary user,

[0203] represents the average value of the channel situations of all secondary users,

[0204] γ i represents the signal-to-noise ratio of the received signal of the i-th secondary user,

[0205] t i represents the propagation delay of the signal reaching the i-th secondary user,

[0206] and sorts them in descending order to determine the sensing user set S, and generates a block type 3 for broadcasting:

[0207] S = {s 1(V：max) , s 2(V：max-1) ,..., s H(V：min)}

[0208] S represents the sensing user set after sorting the initial reputation values,

[0209] H represents the number of perceived users who have successfully authenticated,

[0210] S i represents the perceived users sorted by reputation value from high to low. For example, S 1(V：max) represents the perceived user with the highest reputation value, and s H(V:min) represents the perceived user with the lowest reputation value.

[0211] In a blockchain network, a miner is a network node that continuously performs hash operations to solve mathematical puzzles and generates a proof of work, and validates and confirms transactions through computing power.

[0212] A miner refers to an individual who operates a dedicated computer device to perform specific operations and solve mathematical puzzles to obtain the right to generate a block. Therefore, a miner can also be regarded as a network node.

[0213] S4. The secondary user receives feedback to trigger the contract, performs spectrum sensing, uploads the spectrum sensing result sequence to the network in the form of a transaction. The miner node verifies the identity of the perceived user, pulls the perceived users with mismatched fingerprint features into the blacklist, and packages the verified transactions to generate block type 4.

[0214] The perceived user combines the signal-to-noise ratio γ obtained in the mobile scenario to perform spectrum sensing and obtains the maximum detection probability of the primary user signal

[0215]

[0216] is the detection probability at the cyclic frequency α k at this point,

[0217] Q 1 (·) is the Marcum function with 1 degree of freedom,

[0218] γ is the signal-to-noise ratio,

[0219] N is the number of FFT operation points,

[0220] G(·) is the inverse function of the incomplete gamma function,

[0221] P f is the false alarm probability.

[0222] S5. Wait until the time limit of the sensing task expires. The smart contract obtains the sensing information of the nodes from the network for fusion processing, determines the usage status of the primary user frequency band, and at the same time corrects the reputation value to determine the final sensing reward that the user deserves, and packages the generated information into block type 5 for network-wide broadcast.

[0223] Since the reputation value reflects the channel state of the sensing node, the smart contract weights its local sensing result according to the reputation value weight of the sensing user to obtain the fusion probability, as shown in Equation (4):

[0224]

[0225] Indicates the fusion result of H sensing nodes based on the reputation value

[0226] χ represents the judgment of whether the primary user signal exists by the sensing node i during the task time,

[0227] V i Represents the initial reputation value of the i-th secondary user.

[0228] Therefore, the sensing accuracy of the sensing node i can be expressed as:

[0229]

[0230] Is the spectrum sensing accuracy of the sensing node i.

[0231] In the incentive algorithm introduced in this solution, the reputation value is an index to evaluate the channel condition performance of secondary users in a mobile scenario. It reflects the credibility of secondary users detecting the existence of primary user signals in the current mobile environment. Secondary users with better channel conditions will have relatively higher reputation values, indicating that the detection results of these secondary users are more reliable. However, the initial reputation value given by the smart contract to users only reflects the influence of the channel in the mobile scenario and does not consider the influence of the performance of the sensing user's own device on the sensing result. Therefore, in order to more accurately reflect the reliability of the sensing performance of the sensing user, it is necessary to correct the reputation value by combining the actual sensing accuracy of the sensing user.

[0232] For the i-th sensing user, the smart contract analyzes the effectiveness of the L times of sensing performed by the user during the task time, as shown in Equation (6). For the L times of sensing tasks, the sensing accuracy weight θ is expanded in chronological order from near to far and satisfies Equation (7)

[0233]

[0234] ζ l Represents the judgment of the l-th sensing result,

[0235] Represents the l-th sensing accuracy,

[0236] δ represents the sensing accuracy judgment threshold.

[0237]

[0238] θ lRepresents the weight of the l-th sensing result.

[0239] Therefore, the reputation correction factor of the sensing user i Can be expressed as:

[0240]

[0241] ω i Represents the reputation correction factor of the sensing user i,

[0242] ζ l Represents the decision of the l-th sensing result,

[0243] θ l Represents the weight of the l-th sensing result.

[0244] Finally, the initial reputation value is corrected according to the sensing accuracy and reputation correction factor of the sensing node this time:

[0245]

[0246] V′ i Is the reputation correction value

[0247] β is the adjustment factor,

[0248] Is the spectrum sensing accuracy of the sensing node i,

[0249] V i Represents the initial reputation value of the i-th secondary user,

[0250] ω i Represents the reputation correction factor of the sensing user i.

[0251] The corrected reputation value comprehensively reflects the channel condition of the sensing user and the accuracy of the secondary user's sensing based on its own device performance. When the corrected reputation value is high, it is considered that the sensing result of this sensing node is more accurate and reliable, and a higher sensing reward can be obtained. If the corrected reputation value is low, it indicates that the sensing user's performance in one of these aspects is not ideal, and the obtained sensing reward will be relatively low. In addition, since the sensing user's calling of the smart contract to write sensing data will also incur certain expenses, when the contract calculates the sensing user's remuneration, it needs to consider both aspects.

[0252] If R eth Represents the data rate written into the smart contract. l represents the writing duration,

[0253] eth ·l, the cost required to generate the sensing report is: ​

[0254] The perceived cost is determined by the corrected reputation value and equipment loss, and is denoted by c.

[0255] c = f(V′, D loss ) (10)

[0256] c is the perceived cost,

[0257] D loss is the equipment working loss cost,

[0258] V′ is the reputation correction value of the sensing node i,

[0259] Therefore, the reward r of the sensing node i i can be expressed as:

[0260] r i = c i + p i (11)

[0261] r i is the sensing reward of the sensing node i,

[0262] c i is the task quotation of the sensing node i,

[0263] p i is the sensing cost of the sensing node i.

[0264] S6. The spectrum management center retrieves the blockchain-stored information to obtain the spectrum usage in the area, and constructs a radio spectrum map platform based on this. Other users in the secondary user network can directly access this platform in a certain way to obtain the spectrum resource usage in this area, so as to take appropriate actions, that is, perform spectrum access when the channel is idle, and postpone access if the spectrum is detected to be busy.

[0265] Example 4: As Figure 1 , Figure 2 shown, a method for constructing a radio spectrum map adopts distributed spectrum sensing based on smart contracts. In a certain area, by motivating more secondary users with excellent sensing performance to jointly maintain and update the usage status of spectrum resources in a collaborative sensing manner, real-time feedback on the usage of spectrum resources is achieved, providing convenient support for other users to directly obtain the spectrum resource usage, realizing timely, reliable, and secure communication, and improving the utilization rate of spectrum resources.

[0266] Based on the interaction mechanism between spectrum sensing performance and cognitive user incentive mechanism in the mobile scenario, cross-layer optimization is achieved through the spectrum blockchain to achieve the purpose of selecting sensing users with suitable channel conditions.

[0267] This solution mainly includes a spectrum management unit, a smart contract, and secondary users.

[0268] The spectrum management unit is used to issue sensing tasks and related information.

[0269] Secondary users are the executors of sensing tasks. They obtain the detection probability of the primary user signal through spectrum sensing algorithms, make a judgment on whether the primary user signal exists, upload the results to the smart contract, and synchronously record all information on the chain.

[0270] A smart contract is a piece of code running on the blockchain. When the contract conditions are met, the code is automatically executed to complete transactions between users. The blockchain smart contract performs a fusion judgment on data based on a reputation mechanism, determines the relevant rewards for sensing users, and broadcasts the results to each user node for verification. The verified information data is stored in the blockchain and cannot be deleted or modified, providing a trustless and secure working environment for cognitive radio networks.

[0271] In a wireless communication system, there are usually certain obstacles between mobile communication users, such as urban buildings, forests, etc. This causes the primary user signal to experience multipath effects such as reflection, refraction, or diffraction before reaching the secondary user. At the same time, due to the mobility of the primary / secondary users themselves, according to the Doppler effect in physics, there is a Doppler frequency shift at the receiving signal end. These signals with the same information will have different arrival times and signal intensities at the receiving end due to different channels. Therefore, sensing users need to consider factors such as the attenuation of the primary user signal, channel delay, and Doppler frequency shift, and analyze the received signals under three different channel models under 3GPP to obtain the changes in spectrum sensing performance under different channel conditions.

[0272] In addition, frequent electronic ignition of vehicles and partial discharge of high-voltage equipment in power stations will generate strong burst pulse interference, seriously interfering with the performance of the communication system and greatly reducing the reliability of the wireless communication system. Therefore, considering the existence of electromagnetic interference, the Gaussian white noise model is no longer suitable for simulating the real-world radio frequency (RF) noise environment. Therefore, at the receiving end, sensing users need to consider the influence of electromechanical (vehicle) noise when analyzing the received signal.

[0273] To address the issue of the enthusiasm of secondary users to participate in tasks, a certain credibility is assigned to users based on the channel conditions in the current mobile scenario, and an incentive mechanism based on reputation values is formulated to achieve the processing of user data and the distribution of sensing rewards.

[0274] In a blockchain network, the reputation value is an indicator for evaluating the channel condition performance of secondary users in a mobile scenario. It reflects the credibility of secondary users detecting the presence of primary user signals in the current mobile environment. Secondary users with superior channel conditions have relatively higher reputation values. However, the initial reputation value assigned to users by the smart contract only reflects the channel impact in the mobile scenario and does not consider the influence of the performance of the sensing users' own devices on the sensing results. To more accurately reflect the reliability of the sensing performance of sensing users, the reputation value is thus corrected in combination with the actual sensing accuracy of the sensing users.

[0275] The corrected reputation value comprehensively reflects the channel conditions of the sensing users and the sensing accuracy of secondary users based on their own device performance. When the corrected reputation value is high, it is considered that the sensing results of this sensing node are more accurate and reliable, and higher sensing rewards can be obtained. Otherwise, it indicates that the sensing user's performance is not ideal in one aspect, and the obtained sensing rewards will be relatively low.

[0276] When secondary users upload transactions, a unique user identity ID is generated in combination with user device characteristics and device location information. Only secondary users whose identity information is successfully verified by the smart contract will have their uploaded transactions packaged and written into the block by miners, preventing malicious nodes from forging identities to tamper with or write interference data.

[0277] A method for constructing a radio spectrum map based on a smart contract, which supports dynamic spectrum sharing. Using the radio spectrum map, the usage situation of spectrum resources can be obtained in real time, and the utilization of spectrum resources can be realized more safely, accurately and economically.

[0278] Based on the interaction mechanism between spectrum sensing performance and the sensing user incentive mechanism in the mobile scenario, cross-layer optimization of the spectrum blockchain is realized. In the mobile scenario, sensing users with suitable channel conditions are preferred, which improves the sensing accuracy while enhancing the enthusiasm of secondary users for sensing.

[0279] Considering the influence of different channel conditions under 3GPP on sensing performance and the influence of electromechanical (vehicle) noise, and aiming at various interference factors (radio channel fading, electromagnetic interference) existing in the mobile scenario, the sensing performance of cognitive users is analyzed.

[0280] Considering the influence of the channel state information of the environment where the sensing node is located and the performance of the user's own device on the user's reward, and aiming at the problem of the enthusiasm of sensing users to participate in tasks, a reputation incentive mechanism determined based on channel conditions and sensing accuracy is analyzed.

[0281] A unique identity ID is generated using the radio frequency fingerprint information formed by the user's transmitted signal and the device location information, providing two-dimensional identity verification to prevent malicious users from forging identities and uploading false data, ensuring the authenticity of the spectrum sensing results.

[0282] Adopt distributed spectrum sensing based on smart contracts. Within a certain area range, encourage more secondary users with excellent sensing performance to jointly maintain and update the usage status of spectrum resources in a collaborative sensing manner, achieve real-time feedback on the usage of spectrum resources, provide convenient support for other users to directly obtain the usage of spectrum resources, realize timely, reliable, and secure communication, and improve the utilization rate of spectrum resources.

[0283] A method for constructing a radio spectrum map, based on the interaction mechanism between spectrum sensing performance and cognitive user incentive mechanism in a mobile scenario, realizes cross-layer optimization through a spectrum blockchain, and achieves the purpose of selecting sensing users with suitable channel conditions.

[0284] It includes a spectrum management unit, a smart contract, and secondary users.

[0285] The spectrum management unit is used to publish sensing tasks and related information.

[0286] Secondary users are the executors of sensing tasks. They obtain the detection probability of the primary user signal through a spectrum sensing algorithm, make a judgment on whether the primary user signal exists, and upload the result to the smart contract, synchronously recording all information on the chain.

[0287] A smart contract is a piece of code running on a blockchain. When the contract conditions are met, the code automatically executes to complete transactions between users.

[0288] The blockchain smart contract performs fusion judgment on data based on a reputation mechanism, determines the relevant rewards for sensing users, and broadcasts the result to each user node for verification. The verified information data is stored in the blockchain and cannot be deleted or modified, providing a trustless and secure working environment for cognitive radio networks.

[0289] In a wireless communication system, there are usually certain obstacles between mobile communication users, such as urban buildings, forests, etc. This causes the primary user signal to experience multipath effects such as reflection, refraction, or diffraction before reaching the secondary user. At the same time, due to the mobility of the primary / secondary users themselves, according to the Doppler effect in physics, there is a Doppler frequency shift at the receiving signal end. These signals with the same information will have different arrival times and signal intensities at the receiving end due to different channels. Therefore, sensing users need to consider factors such as the attenuation of the primary user signal, channel delay, and Doppler frequency shift, and analyze the received signals under three different channel models under 3GPP to obtain the changes in spectrum sensing performance under different channel conditions.

[0290] In addition, due to the frequent electronic ignition of vehicles and the partial discharge of high-voltage equipment in power stations, strong burst pulse interference will be generated, seriously interfering with the performance of communication systems and greatly reducing the reliability of wireless communication systems. Therefore, considering the existence of electromagnetic interference, the Gaussian white noise model is no longer applicable to simulate the radio frequency (RF) noise environment in the real world. Therefore, at the receiving end, when the sensing user analyzes the received signal, the impact of electromechanical (vehicle) noise needs to be considered.

[0291] Regarding the issue of the enthusiasm of secondary users to participate in tasks, a certain credibility is given to users based on the channel conditions in the current mobile scenario, and an incentive mechanism based on reputation value is formulated to implement the processing of user data and the distribution of sensing rewards. In the blockchain network introduced in this application, the reputation value is an indicator for evaluating the performance of the channel conditions of secondary users in the mobile scenario. It reflects the credibility of secondary users detecting the presence of primary user signals in the current mobile environment. Secondary users with superior channel conditions have relatively higher reputation values. However, the initial reputation value given to users by the smart contract only reflects the impact of the channel in the mobile scenario and does not consider the impact of the performance of the sensing user's own equipment on the sensing results. In order to more accurately reflect the reliability of the sensing performance of the sensing user, the reputation value is corrected in combination with the actual sensing accuracy of the sensing user. The corrected reputation value comprehensively reflects the channel conditions of the sensing user and the sensing accuracy of the secondary user based on its own equipment performance. When the corrected reputation value is relatively high, it is considered that the sensing result of this sensing node is more accurate and reliable, and a higher sensing reward can be obtained. Otherwise, it indicates that the sensing user's performance is not ideal in one aspect, and the obtained sensing reward will be relatively low.

[0292] When secondary users upload transactions, a unique user identity ID is generated in combination with user equipment characteristics and equipment location information. Only secondary users whose identity information is successfully verified by the smart contract will have their uploaded transactions packaged and written into the block by the miner, preventing malicious nodes from forging identities to tamper with or write interference data.

[0293] As described above, the embodiments of the present invention have been described in detail. However, as long as it does not substantially deviate from the inventive points and effects of the present invention, there can be many variations, which are obvious to those skilled in the art. Therefore, such variations are also all included in the protection scope of the present invention.

Claims

1. A method for constructing a radio spectrum map, characterized in that , it includes a spectrum management step, a spectrum sensing step, a blockchain smart contract step, and a step for constructing a radio spectrum map. Step 1, the spectrum management step: The spectrum management center issues sensing tasks and relevant information, and the secondary users submit applications for spectrum sensing tasks according to their own channel conditions. Step 2, the spectrum sensing step: When the application of the secondary user meets the contract conditions, the smart contract is automatically executed to determine the users for spectrum sensing. The sensing users upload the results to the smart contract to update the spectrum usage situation. Step 3, the blockchain smart contract step: Based on the reputation mechanism, the data is fused and judged to determine the relevant rewards for the sensing users, and the results are broadcast to each user node for verification. The verified information data is stored in the blockchain and cannot be deleted or modified, providing a trustless and secure working environment for the cognitive radio network. Step 4, the step for constructing a radio spectrum map: Regarding the interaction mechanism between the spectrum sensing performance and the sensing user incentive mechanism in the mobile scenario, select the sensing users with suitable channel conditions to enhance the sensing accuracy and the enthusiasm of the secondary users for sensing. At the same time, based on the blockchain distributed ledger structure, record and store the sensing results of the users, and use the immutable feature of the blockchain to ensure the secure feedback of the spectrum resource usage status, thereby forming a complete, accurate, and secure radio spectrum map. In the spectrum sensing step of Step 2, it further includes the following steps: First, determine the sensing users. When the smart contract receives the task application from the secondary user, it verifies the identity ID uploaded by the secondary user according to the trusted node identity verification model to determine the authenticity of the user identity. For the secondary users with successful identity information verification, assign them an initial reputation value according to the following formula: C i = f(γ i , τ i ) V i represents the initial credit value of the i-th secondary user, C i Indicates the channel condition of the i-th secondary user, Represents the average value of all sub-user channel conditions, γ i represents the signal-to-noise ratio of the received signal of the i-th secondary user receiver, τ i represents the propagation delay of the signal arriving at the i-th secondary user, And sort the nodes in descending order to determine the set S of sensing users, generate a transaction block, and broadcast it: S = {s 1(V:max) , s 2(V：max-1) ,..., s H(V:min)} S represents the set of sensing users sorted by the initial reputation value. H represents the number of sensing users with successful identity verification. s i represents the perceived users sorted by credibility value from high to low, s 1(V:max) represents the perceived user with the highest credibility value, s H(V:min) represents the perceived user with the lowest credibility value Until the secondary user receives the feedback on the confirmation of the sensing users and then conducts spectrum sensing, upload the sensing result sequence to the blockchain network in the form of a transaction. The miner node verifies the identity of the sensing users and pulls the sensing users with mismatched fingerprint features into the blacklist. The sensing users combine the signal-to-noise ratio γ obtained in the mobile scenario to perform spectrum sensing to obtain the primary Maximum detection probability of household signals is the cycle frequency, α k is the detection probability at Q 1 (·) is the Marcum function with 1 degree of freedom, γ is the signal-to-noise ratio. N is the FFT operation point. G(·) is the inverse function of the incomplete gamma function. P f is the false alarm probability.

2. A method for constructing a radio spectrum map according to claim 1, characterized in that , in the spectrum management step of Step 1, it further includes the following steps: The spectrum management is jointly responsible by the spectrum management center and the secondary users. Spectrum Management Center Party: The Spectrum Management Center issues sensing tasks, and the task information includes a spectrum sensing service with a duration of T c . Based on the distribution of secondary users in the area, the maximum number of sensing nodes M required is estimated. The sensing service budget B is formulated in combination with the frequency band used by the communication frequency currently used by the user. The calculation is as follows. At the same time, the maximum false alarm probability and the minimum detection probability and other service information are specified B = f{T c , M, η} B is the sensing task budget. T c To sense the task duration M is the maximum number of sensing nodes expected to be required. η is the frequency of use of the frequency band. The spectrum management center writes the task information into the smart contract, generates a transaction, generates a block, and broadcasts it to the whole network. Secondary user side: View the task information through the smart contract address, evaluate its own sensing performance based on the current channel conditions, and when the contract requirements are met, that is When it is established, submit a task application to provide the channel conditions and task quotation information to the contract. At the same time, the user generates a radio frequency signal based on the hardware device's own defects and combines the user's location to generate a unique identity ID, generates a block and broadcasts it. where p′ f and p′ d are the false alarm probability and the detection probability predicted by the secondary user according to the channel conditions when the primary user signal exists.

3. A method for constructing a radio spectrum map according to claim 1, characterized in that , in the blockchain smart contract step of Step 3, it further includes the following steps: First, after waiting for the sensing task time to expire, the smart contract obtains the sensing results of the nodes from the network for fusion processing and determines the usage status of the main user frequency band. Since the reputation value reflects the channel status of the sensing node, the smart contract performs weighted processing on its local sensing results according to the perceived user’s reputation value weight to obtain the fusion probability, which is calculated as follows: Indicates the fusion result of H sensing nodes based on the reputation value. χ i Indicates the situation of the sensing node i judging the existence of the primary user signal during the task time V i represents the initial credit value of the i-th secondary user, At the same time, the reputation value is modified to determine the perceived reward that the user ultimately deserves, and the generated information is packaged into blocks for network-wide broadcasting. The modified reputation value is calculated as follows: β is the adjustment factor, For the spectrum sensing accuracy of sensing node i, V i represents the initial credit value of the i-th secondary user, ω i Indicates the reputation correction factor of the perceived user i θ l represents the weight of the l-th sensing result ζ l represents the l-th perception result decision δ represents the perception accuracy decision threshold, The corrected reputation value comprehensively reflects the channel conditions of the perception user and the accuracy of the user's perception based on the performance of its own equipment. When the corrected reputation value is high, it is considered that the perception result of the perception node is more accurate and reliable, and a higher perception reward is obtained. If the corrected reputation value is low, it means that the perception user's performance in one aspect is not ideal, and the perception reward obtained will be relatively low. Since the perception user calls the smart contract to write the perception data, it will also incur certain expenses. Therefore, when calculating the perception user's reward, the contract needs to consider both aspects. Therefore, the perception reward is calculated as follows: r i = c i + p i c i = f(V' i , D loss ) p i = R eth · l · μ eth r i is the sensing reward for sensing node i c i is the task quotation for sensing node i p i is the sensing cost of sensing node i D loss is the operating loss cost of the device V′ i is the reputation correction value of sensing node i R eth represents the data rate written to the smart contract l indicates the writing time. μ eth represents the cost of a write operation.

4. A method for constructing a radio spectrum map according to claim 2, Features ,The step of building a radio spectrum map in step 4 also includes the following steps: the spectrum management center retrieves the ,blockchain storage information to obtain the spectrum usage in the area, thereby building a radio spectrum map ,platform. Other users in the secondary user network directly access the ,platform in some way to obtain the spectrum resource usage in the ,area, so as to take appropriate actions, i.e., spectrum access when the channel is idle, and ,access is postponed if the spectrum is detected to be busy.

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