Decision fusion method, device and system for broadband spectrum sensing

Abstract

The invention discloses a decision fusion method, device and system for broadband spectrum sensing. The decision fusion method comprises the following steps: judging the type of a cognitive user; if the cognitive user is a reference cognitive user, keeping an original judgment symbol sequence of the cognitive user, and sending the original judgment symbol sequence to a fusion center, so that the fusion center performs fusion judgment; if the cognitive user is a non-reference cognitive user, carrying out symbol extension on an original decision symbol sequence of the cognitive user to obtain an extended decision symbol sequence; then, sequentially performing segmentation and majority voting operation on the extended decision symbol sequence to obtain a compressed decision symbol sequence, the compressed decision symbol sequence being equal to an original decision symbol sequence of a reference user in length; and finally, sending the compression judgment symbol sequence to the fusion center, so that the fusion center carries out fusion judgment. According to the invention, on the premise of ensuring the reliability of spectrum sensing, the sensing and transmission overhead of the system is obviously reduced, and the energy efficiency and realizability of cooperative spectrum sensing are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wireless communication, and particularly relates to a decision fusion method, device and system for wideband spectrum sensing. BACKGROUND

[0002] Wideband spectrum sensing is an important application direction in wireless communication, and is particularly concerned under the demand of spectrum efficient utilization of 5G and future 6G networks. Its main purpose is to realize reliable sensing of the spectrum occupancy state in a wide frequency band range under the condition of limited hardware resources, and is widely used in dynamic spectrum access, heterogeneous wireless network coexistence and intelligent wireless environment sensing, etc. which have high requirements for sensing reliability and real-time performance. In the process of wideband spectrum sensing, the system usually faces the requirements of sensing reliability and system overhead. Sensing reliability is usually guaranteed by improving the sensing algorithm or introducing multi-user cooperation; for the requirement of system overhead, in addition to the sensing algorithm itself, factors such as sampling rate, transmission overhead and fusion processing complexity also need to be considered. The present application focuses on the cooperative spectrum sensing method for reducing the system sensing and transmission overhead while meeting the requirements of spectrum sensing reliability.

[0003] The existing wideband spectrum sensing methods can be mainly divided into two categories. The first category is the compressed sensing method based on signal reconstruction, which reconstructs the wideband signal spectrum under the sub-Nyquist sampling condition by utilizing the spectrum sparsity; the second category is the compressed covariance sensing method based on statistical characteristics, which realizes spectrum sensing by restoring the second-order statistical information of the signal. However, the above two methods mostly focus on the spectrum sensing modeling under the condition of single node or ideal cooperation, and do not fully consider the engineering implementation problems such as the inconsistency of sensing information length and the limitation of transmission times in the multi-cognitive user cooperation scenario, so it is difficult to balance the sensing performance and system overhead under the condition of large-scale cooperation.

[0004] In the actual wireless network environment, the cognitive users are often constrained by hardware capability and energy consumption, and thus adopt different sub-Nyquist sampling rates to participate in cooperative spectrum sensing, resulting in the misalignment of sensing information in time and frequency dimensions, so that the fusion decision needs to be completed through multiple rounds of transmission or additional alignment operations. Meanwhile, under the conditions of parallel channels and multiple-input multiple-output fading channels, the transmission process of the sensing information is also affected by the channel state, further increasing the system overhead. With the increase of the number of cooperative cognitive users, the transmission and fusion complexity of the sensing information rapidly rises, which becomes the main bottleneck limiting the scalability of the system. Therefore, it is necessary to design a new cooperative wideband spectrum sensing mechanism to realize efficient alignment and fusion of the sensing information under the condition of heterogeneous sub-Nyquist sampling, so as to reduce the transmission overhead and improve the overall efficiency of the system. SUMMARY

[0005] In view of the above problems, the application provides a decision fusion method, device and system for wideband spectrum sensing, which performs symbol extension and compression on the local decision sequence (i.e. original decision symbol sequence) of different cognitive users, aligns the lengths of the decision symbol sequences of the cognitive users with different sampling rates, and thus significantly reduces the system sensing and transmission overheads, improves the energy efficiency and realizability of cooperative spectrum sensing under the premise of ensuring the reliability of spectrum sensing.

[0006] In order to achieve the above technical purposes and achieve the above technical effects, the application is implemented by the following technical solutions:

[0007] In the first aspect, the application provides a decision fusion method for wideband spectrum sensing, which is applied to a cognitive user and includes the following steps:

[0008] judging the type of the cognitive user;

[0009] if the cognitive user is a reference cognitive user, keeping the original decision symbol sequence of the reference cognitive user and sending the original decision symbol sequence to a fusion center, so that the fusion center performs fusion decision, the length of the original decision symbol sequence of the reference cognitive user is , , is the down-sampling rate of the reference cognitive user, is the total number of sub-bands;

[0010] if the cognitive user is a non-reference cognitive user, performing symbol extension on the original decision symbol sequence of the non-reference cognitive user to obtain an extended decision symbol sequence, the length of the extended decision symbol sequence is , then performing segmentation and majority voting operations on the extended decision symbol sequence in sequence to obtain a compressed decision symbol sequence, the length of the compressed decision symbol sequence is , and finally sending the compressed decision symbol sequence to the fusion center, so that the fusion center performs fusion decision.

[0011] In combination with the first aspect, optionally, the generation method of the extended decision symbol sequence includes:

[0012] for the non-reference cognitive user , repeating the original decision symbol sequence of the non-reference cognitive user times to obtain an extended decision symbol sequence , the length of the extended decision symbol sequence is , wherein is the down-sampling rate of the non-reference cognitive user .

[0013] In combination with the first aspect, optionally, the generation method of the compressed decision symbol sequence includes:

[0014] equally dividing the extended decision symbol sequence into each segment has a length of ;

[0015] performing a majority operation based on the decision symbols at the th position in each segment to obtain a compressed decision symbol sequence th position in each segment, the majority operation comprising: if the decision symbol at the th position in each segment satisfies a preset condition, the compressed decision symbol sequence th position in each segment is 1, otherwise the decision symbol at the th position in the compressed decision symbol sequence is 0. th position in each segment th position in each segment th position in each segment th position in each segment

[0016] In combination with the first aspect, optionally, a mathematical expression of the preset condition is:

[0017] , , denotes the th segment, denotes the decision symbol at the th position in the th segment.

[0018] In a second aspect, the present application provides a decision fusion method for wideband spectrum sensing, applied to a fusion center, comprising:

[0019] receiving an original decision symbol sequence uploaded by a reference cognitive user, the original decision symbol sequence of the reference cognitive user having a length of , , is a downsampling rate of the reference cognitive user, is a total number of subbands;

[0020] receiving a compressed decision symbol sequence uploaded by a non-reference cognitive user; the compressed decision symbol sequence is generated by the non-reference cognitive user by performing the following steps: performing symbol extension on an original decision symbol sequence thereof to obtain an extended decision symbol sequence, the extended decision symbol sequence having a length of ; then, performing segmentation and majority operation on the extended decision symbol sequence in sequence to obtain the compressed decision symbol sequence, the compressed decision symbol sequence having a length of ;

[0021] performing fusion decision based on the original decision symbol sequence uploaded by the reference cognitive user and the compressed decision symbol sequence uploaded by the non-reference cognitive user.

[0022] With reference to the second aspect, optionally, the method for generating the extended decision symbol sequence comprises:

[0023] to a non-reference cognitive user repeating the original decision symbol sequence of the non-reference cognitive user to obtain an extended decision symbol sequence , the length of the extended decision symbol sequence is , wherein is a down-sampling rate of the non-reference cognitive user .

[0024] With reference to the second aspect, optionally, the method for generating the compressed decision symbol sequence comprises:

[0025] dividing the extended decision symbol sequence into segments, each segment having a length of ;

[0026] performing a majority voting operation based on the decision symbol at the th position in each segment to obtain a decision symbol at the th position in the compressed decision symbol sequence , the majority voting operation comprising: if the decision symbol at the th position in each segment satisfies a preset condition, then the decision symbol at the th position in the compressed decision symbol sequence is 1, otherwise the decision symbol at the th position in the compressed decision symbol sequence is 0.

[0027] With reference to the second aspect, optionally, the mathematical expression of the preset condition is:

[0028] , , denotes the th segment, denotes the decision symbol at the th position in the th segment.

[0029] Thirdly, the present application provides a decision fusion device for wideband spectrum sensing, comprising: a fusion center, and a plurality of cognitive users in communication with the fusion center;

[0030] Each cognitive user is configured to perform the decision fusion method of any one of the first aspect;

[0031] ​The fusion center fuses the uploaded original decision symbol sequences of the benchmark cognitive users and the uploaded compressed decision symbol sequences of the non-benchmark cognitive users to make a fusion decision.

[0032] In a fourth aspect, the application provides a decision fusion system for wideband spectrum sensing, comprising a storage medium and a processor.

[0033] The storage medium is used for storing instructions.

[0034] The processor is used for operating according to the instructions to execute the method according to any one of the first aspect or the second aspect.

[0035] Compared with the prior art, the application has the following beneficial effects:

[0036] The application unifies the decision symbol sequences of the cognitive users with different sampling rates into the same length through the expansion and compression mechanism , reduces the total transmission round, and significantly reduces the system overhead.

[0037] Different cognitive users can adopt different down-sampling rates, and still can align the symbol quantity through the scheme provided by the application, improve the flexibility and scalability of the system, and be conducive to maintaining stable sensing performance under low overhead conditions. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor, wherein:

[0039] Figure 1 is a flow chart of the decision fusion method for wideband spectrum sensing provided by an embodiment of the application;

[0040] Figure 2 is an architecture diagram of the decision fusion device for wideband spectrum sensing provided by an embodiment of the application;

[0041] Figure 3 is a resource allocation example diagram provided by an embodiment of the application;

[0042] Figure 4 is a detection performance comparison diagram provided by an embodiment of the application. DETAILED DESCRIPTION

[0043] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0044] In addition, if the present application embodiments involve the description of "first", "second", etc., the description of "first", "second", etc. is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can be explicitly or implicitly included at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it. When the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope of the present application.

[0045] Embodiment 1

[0046] The present application provides a decision fusion method for wideband spectrum sensing, which is suitable for parallel channel and multiple-input multiple-output (MIMO) fading channel scenarios, and is applied to cognitive users, comprising:

[0047] judging the type of the cognitive user;

[0048] If the cognitive user is a reference cognitive user, the original decision symbol sequence of the reference cognitive user is kept and sent to the fusion center, so that the fusion center makes a fusion decision. The length of the original decision symbol sequence of the reference cognitive user is , , is the down-sampling rate of the reference cognitive user, is the total number of sub-bands; the original decision symbol sequence is obtained by the reference cognitive user locally under sub-sampling condition;

[0049] If the cognitive user is a non-reference cognitive user, the original decision symbol sequence of the non-reference cognitive user is extended to obtain an extended decision symbol sequence. The length of the extended decision symbol sequence is . Then, the extended decision symbol sequence is segmented and majority-voted in sequence to obtain a compressed decision symbol sequence. The length of the compressed decision symbol sequence is . Finally, the compressed decision symbol sequence is sent to the fusion center, so that the fusion center makes a fusion decision.

[0050] In the above scheme, multiple subbands of broadband signals are sensed in a multi-cognitive user collaborative scenario. Through expansion and compression mechanisms, the decision symbol sequences of cognitive users with heterogeneous sampling rates (i.e., under different downsampling rate conditions) are unified to the same length. This reduces the total number of transmission rounds, significantly decreasing system overhead. Furthermore, different cognitive users can use different downsampling rates while still aligning the number of decision symbols using this method, improving system flexibility and scalability, and helping to maintain stable sensing performance under low-overhead conditions.

[0051] In one specific embodiment of the present invention, the method for generating the extended decision symbol sequence includes:

[0052] For non-benchmark cognitive users Repeat its original decision symbol sequence Next, we obtain the extended decision symbol sequence. Extended decision symbol sequence The length is ,in, For non-benchmark cognitive users downsampling rate.

[0053] The above scheme provides a specific method for generating extended decision symbol sequences, which can extend the original decision symbol sequences of non-baseline cognitive users.

[0054] In one specific embodiment of the present invention, the method for generating the compressed decision symbol sequence includes:

[0055] The extended decision symbol sequence is divided into equal parts. Segments, each segment is [length missing] ;

[0056] Based on the first in each paragraph The decision symbols at each position are used to perform a majority vote, resulting in a compressed sequence of decision symbols. The Middle The decision symbol at position n, the majority voting operation includes: if the nth position in each segment... If the decision symbols at each position satisfy the preset conditions, then the decision symbol sequence is compressed. No. Judgment symbol at each position If it is 1, otherwise the compressed number is 1. Judgment symbol at each position It is 0.

[0057] The above scheme provides a specific method for generating compressed decision symbol sequences, which can compress extended decision symbol sequences of non-baseline cognitive users, and ultimately unify the decision symbol sequences of cognitive users with heterogeneous sampling rates to the same length through extension and compression mechanisms. This reduces the total number of transmission rounds, significantly decreasing system overhead. Furthermore, different cognitive users can use different downsampling rates while still aligning the number of symbols using this method, improving system flexibility and scalability, and helping to maintain stable sensing performance under low-overhead conditions.

[0058] In one specific embodiment of the present invention, the mathematical expression of the preset condition is:

[0059] , .

[0060] Example 2

[0061] This invention provides a decision fusion method for broadband spectrum awareness, applied to a fusion center, comprising:

[0062] Receive the original decision symbol sequence uploaded by the benchmark cognitive user. The length of the original decision symbol sequence uploaded by the benchmark cognitive user is [length missing]. , , The downsampling rate is used as the baseline for understanding users. This represents the total number of sub-bands.

[0063] Receive compressed decision symbol sequences uploaded by non-baseline cognitive users; the compressed decision symbol sequences are generated by the non-baseline cognitive users by performing the following steps: performing symbol expansion on their original decision symbol sequences to obtain extended decision symbol sequences, the length of which is... Then, the extended decision symbol sequence is sequentially segmented and subjected to majority voting to obtain a compressed decision symbol sequence, the length of which is... ;

[0064] The decision is fused based on the original decision symbol sequence uploaded by the benchmark cognitive user and the compressed decision symbol sequence uploaded by the non-benchmark cognitive user.

[0065] In one specific embodiment of the present invention, the method for generating the extended decision symbol sequence includes:

[0066] For non-benchmark cognitive users Repeat its original decision symbol sequence Next, we obtain the extended decision symbol sequence. Extended decision symbol sequence The length is ,in, For non-benchmark cognitive users downsampling rate.

[0067] In one specific embodiment of the present invention, the method for generating the compressed decision symbol sequence includes:

[0068] The extended decision symbol sequence is divided into equal parts. Segments, each segment is [length missing] ;

[0069] Based on the first in each paragraph The decision symbols at each position are used to perform a majority vote, resulting in a compressed sequence of decision symbols. The Middle The decision symbol at position n, the majority voting operation includes: if the nth position in each segment... If the decision symbols at each position satisfy the preset conditions, then the decision symbol sequence is compressed. No. Judgment symbol at each position If it is 1, otherwise the compressed number is 1. Judgment symbol at each position It is 0.

[0070] In one specific embodiment of the present invention, the mathematical expression of the preset condition is:

[0071] , .

[0072] Example 3

[0073] This invention provides a decision fusion device for broadband spectrum sensing, such as... Figure 2 As shown, it includes: a fusion center, and several cognitive users communicating with the fusion center;

[0074] Each cognitive user is configured to perform the decision fusion method described in any one of Embodiment 1;

[0075] The fusion center performs fusion decisions based on the original decision symbol sequence uploaded by the benchmark cognitive user and the compressed decision symbol sequence uploaded by the non-benchmark cognitive user.

[0076] The specific working process of the decision fusion device in this embodiment of the invention will be described in detail below with reference to a specific implementation method.

[0077] Step 1: Initialization, such as Figure 1 As shown in the image.

[0078] In this step, let the set of all cognitive users be denoted as . The total number of sub-bands is .

[0079] In this step, record the first... ( The downsampling rate used by each cognitive user was ) The locally available original decision sequence consists of a series of decision symbols, and the number of decision symbols contained in the original decision sequence is... , The extended decision symbol sequence for each cognitive user is initialized as follows: Initialize the compressed decision symbol sequence for each cognitive user as follows: All sets are empty.

[0080] Step 2: Identify the baseline cognitive user, such as Figure 1 As shown in the image.

[0081] In this step, from the set Select one user as the baseline cognitive user, denoted as . Its downsampling rate is .

[0082] In this step, the number of decision symbols that the baseline cognitive user needs to upload is determined. This length serves as the uniform decision symbol length that all other non-benchmark cognitive users ultimately need to align with.

[0083] Step 3: Perform symbolic expansion for each non-baseline cognitive user, such as... Figure 1 As shown in the image.

[0084] In this step, for the set Every non-baseline cognitive user in the system, excluding the baseline cognitive user. Perform the following operations:

[0085] Repeat its original decision symbol sequence This, so that the length of the expanded decision symbol sequence is... The extended decision symbol sequence is denoted as Here, it is ensured that the extended sequence length is consistent for all cognitive users (all are...). ).

[0086] In this step, the original decision symbol sequence of the baseline cognitive user is preserved.

[0087] Step 4: Perform symbol compression on the extended decision symbol sequence, such as... Figure 1 As shown in the image.

[0088] In this step, for the set Every non-baseline cognitive user in the system, excluding the baseline cognitive user. Perform the following operations:

[0089] Extend the decision symbol sequence Divided equally Segments, each segment is [length missing] .

[0090] In this step, a majority vote is performed on the symbols at the same position in all segments: if the first... The symbols at each position satisfy , Then compress the decision symbol sequence The Middle Judgment symbol at each position =1 (i.e.) Otherwise, compress the decision symbol sequence. The Middle Judgment symbol at each position =0 (i.e.) ).

[0091] In this step, the final length is obtained as compressed decision symbol sequence .

[0092] In this step, the original decision symbol sequence of the baseline cognitive user is preserved.

[0093] Step 5: Upload the sequence of decision symbols with uniform length, such as... Figure 1 As shown in the image.

[0094] In this step, all cognitive users sequentially send the original decision symbol sequence or the compressed decision symbol sequence to the fusion center via time-division multiple access. .

[0095] In this step, the fusion center receives the original decision symbol sequence and compressed decision symbol sequence of uniform length uploaded by all cognitive users. Then, decision-making and integration are carried out.

[0096] exist Figure 3 An example is provided to illustrate the decision fusion apparatus for broadband spectrum awareness proposed in this embodiment of the invention. In this example, the decision fusion apparatus for broadband spectrum awareness has 10 cognitive users, and the downsampling rates of each cognitive user are as follows: Broadband signals are divided into Each cognitive user makes a decision on the broadband signal locally based on its corresponding downsampling rate. In this embodiment, a cognitive user with a downsampling rate of 2 is selected as the baseline cognitive user, and its downsampling rate is denoted as: Therefore, the length of the judgment symbol sequence generated by the benchmark cognitive user is: .

[0097] Each cognitive user generates a binary decision sequence of different lengths based on their respective downsampling rate. For example, a cognitive user with a downsampling rate of 3 generates a decision symbol sequence of length 840. Since the decision symbol sequences of different users are of different lengths, they cannot be directly fused together, so alignment processing of the decision symbol sequences is required.

[0098] For each non-baseline cognitive user, a symbol expansion operation is performed on their generated original decision symbol sequence. For example, for a cognitive user with a downsampling rate of 3, the length of their original decision symbol sequence is 840. This decision symbol sequence is repeated 3 times, thereby expanding the decision sequence into an expanded sequence of length 2520. Through this expansion operation, the length of the decision symbol sequence for all cognitive users is uniformly expanded to 2520.

[0099] After symbol expansion, symbol compression is performed on the expanded decision symbol sequence for each cognitive user. The expanded decision symbol sequence of length 2520 is evenly divided into... There are segments, each segment having a length of . Then, for each position Perform symbol compression operation based on majority voting rules, i.e., if Then the compressed first Each decision symbol takes a value of 1, otherwise 0. Through this compression operation, each cognitive user ultimately obtains a compressed decision symbol sequence of length 1260 on each subband, consistent with the baseline cognitive user.

[0100] Finally, the reference user and all non-reference users send the original decision symbol sequence and the compressed decision symbol sequence to the fusion center via time-division multiple access. The fusion center receives the uniform-length decision symbol sequences uploaded by all users, performs the corresponding operations, and conducts decision fusion.

[0101] This example demonstrates that, in the method proposed in this invention, each cognitive user only needs to upload a compressed decision symbol sequence of length 1260, which reduces the number of symbols uploaded per user by 50% compared to the conventional method that requires uploading a decision symbol sequence of length 2520, significantly reducing the communication overhead in the cooperative spectrum sensing system. Figure 4 This is a schematic diagram illustrating the detection performance of a fusion center using different fusion rules for spectrum sensing in an embodiment of the present invention, with the result of symbol expansion and compression (with EC prefix), compared to the detection performance of a fusion center without symbol expansion and compression under the same fusion rules. Figure 4 In the middle, the vertical axis P D Represents the detection probability; the horizontal axis P FEC LLR: Represents the detection result using the log-likelihood ratio test algorithm with sign expansion and compression processing; EC ZF+Max-log: Represents the detection result using the zero-forcing filter + Max-log detection algorithm with sign expansion and compression processing; EC ZF+CV: Represents the detection result using the zero-forcing filter + Chair-Varshney detection algorithm with sign expansion and compression processing; ECWL,0: Represents the detection result using the generalized linear estimation algorithm with sign expansion and compression processing, with the suffix 0 indicating that the bias coefficient is within the assumed... The calculation is performed under the following conditions. EC WL,1: This represents the detection result using the generalized linear estimation algorithm with sign expansion and compression processing. The suffix 1 indicates that the bias coefficient is calculated under the assumption that... The calculations are performed as follows: LLR: Represents the detection result using the log-likelihood ratio test algorithm without sign expansion and compression (with EC prefix). ZF+Max-log: Represents the detection result using the zero-forcing filter + Max-log detection algorithm without sign expansion and compression; ZF+CV: Represents the detection result using the zero-forcing filter + Chair-Varshney detection algorithm without sign expansion and compression; WL,0: Represents the detection result using the generalized linear estimation algorithm without sign expansion and compression, with the suffix 0 indicating that the bias coefficient is within the assumption... The calculation is performed under the following conditions. WL,1: represents the detection result using the generalized linear estimation algorithm without sign expansion and compression. The suffix 1 indicates that the bias coefficient is calculated under the assumption that... The calculations are then performed. It should be noted that the voting operations used in symbol expansion and compression may introduce some differences at the statistical decision level, but... Figure 4 As shown, this difference has a relatively small impact on the final decision result of the fusion center in a statistical sense. Therefore, this invention can maintain spectrum sensing performance comparable to existing solutions while reducing communication overhead, and has good engineering application value.

[0102] Example 4

[0103] This invention provides a decision fusion system for broadband spectrum sensing, including a storage medium and a processor;

[0104] The storage medium is used to store instructions;

[0105] The processor is configured to operate according to the instructions to execute the method according to any one of Embodiment 1 or Embodiment 2.

[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0111] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A decision fusion method for broadband spectrum sensing, characterized in that, Applied to cognitive users, including: Determine the type of user you are aware of; If the cognitive user is the baseline cognitive user, then its original decision symbol sequence is preserved and sent to the fusion center so that the fusion center can make a fusion decision. The length of the baseline cognitive user's original decision symbol sequence is... , , The downsampling rate is used as the baseline for cognitive users. This represents the total number of sub-bands. If the cognitive user is a non-baseline cognitive user, then its original decision symbol sequence is symbolically expanded to obtain an expanded decision symbol sequence, the length of which is... Then, the extended decision symbol sequence is sequentially segmented and subjected to majority voting to obtain a compressed decision symbol sequence, the length of which is... Finally, a compressed decision symbol sequence is sent to the fusion center so that the fusion center can make a fusion decision.

2. The decision fusion method for broadband spectrum sensing according to claim 1, characterized in that: The method for generating the extended decision symbol sequence includes: For non-benchmark cognitive users Repeat its original decision symbol sequence Next, we obtain the extended decision symbol sequence. The extended decision symbol sequence The length is ,in, For non-benchmark cognitive users downsampling rate.

3. The decision fusion method for broadband spectrum sensing according to claim 1, characterized in that: The method for generating the compressed decision symbol sequence includes: The extended decision symbol sequence is divided into equal parts. Segments, each segment is [length missing] ; Based on the first in each paragraph The decision symbols at each position are used to perform a majority vote, resulting in a compressed sequence of decision symbols. The Middle The decision symbol at position n, the majority voting operation includes: if the nth position in each segment... If the decision symbols at each position satisfy the preset conditions, then the decision symbol sequence is compressed. No. Judgment symbol at each position If it is 1, otherwise the compressed number is 1. Judgment symbol at each position It is 0.

4. The decision fusion method for broadband spectrum sensing according to claim 3, characterized in that: The mathematical expression for the preset condition is: , , Indicates the first part, Indicates the first Duan Di The decision symbol for each position.

5. A decision fusion method for broadband spectrum sensing, characterized in that, Applications in fusion centers include: Receive the original decision symbol sequence uploaded by the benchmark cognitive user. The length of the original decision symbol sequence uploaded by the benchmark cognitive user is [length missing]. , , The downsampling rate is used as the baseline for cognitive users. This represents the total number of sub-bands. Receive compressed decision symbol sequences uploaded by non-baseline cognitive users; the compressed decision symbol sequences are generated by the non-baseline cognitive users by performing the following steps: performing symbol expansion on their original decision symbol sequences to obtain extended decision symbol sequences, the length of which is... Then, the extended decision symbol sequence is sequentially segmented and subjected to majority voting to obtain a compressed decision symbol sequence, the length of which is... ; The decision is fused based on the original decision symbol sequence uploaded by the benchmark cognitive user and the compressed decision symbol sequence uploaded by the non-benchmark cognitive user.

6. The decision fusion method for broadband spectrum sensing according to claim 5, characterized in that: The method for generating the extended decision symbol sequence includes: For non-benchmark cognitive users Repeat its original decision symbol sequence Next, we obtain the extended decision symbol sequence. Extended decision symbol sequence The length is ,in, For non-benchmark cognitive users downsampling rate.

7. The decision fusion method for broadband spectrum sensing according to claim 5, characterized in that: The method for generating the compressed decision symbol sequence includes: The extended decision symbol sequence is divided into equal parts. Segments, each segment is [length missing] ; Based on the first in each paragraph The decision symbols at each position are used to perform a majority vote, resulting in a compressed sequence of decision symbols. The Middle The decision symbol at position n, the majority voting operation includes: if the nth position in each segment... If the decision symbols at each position satisfy the preset conditions, then the decision symbol sequence is compressed. No. Judgment symbol at each position If it is 1, otherwise the compressed number is 1. Judgment symbol at each position It is 0.

8. The decision fusion method for broadband spectrum sensing according to claim 7, characterized in that: The mathematical expression for the preset condition is: , , Indicates the first part, Indicates the first Duan Di The decision symbol for each position.

9. A decision fusion device for broadband spectrum sensing, characterized in that, include: A fusion center, and several cognitive users communicating with the fusion center; Each cognitive user is configured to perform the decision fusion method as described in any one of claims 1-4; The fusion center performs fusion decisions based on the original decision symbol sequence uploaded by the benchmark cognitive user and the compressed decision symbol sequence uploaded by the non-benchmark cognitive user.

10. A decision fusion system for broadband spectrum sensing, characterized in that, Including storage media and processor; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1-4 or 5-8.

Images