Decision fusion method, device and system for wideband spectrum sensing

Abstract

The application discloses a decision fusion method, device and system for wideband spectrum sensing. The decision fusion method comprises judging the type of cognitive user. If the cognitive user is a reference cognitive user, the original decision symbol sequence of the cognitive user is kept and sent to a fusion center so that the fusion center makes a fusion decision. If the cognitive user is a non-reference cognitive user, the original decision symbol sequence of the cognitive user is extended to obtain an extended decision symbol sequence. Then, the extended decision symbol sequence is subjected to segmentation and majority voting operation in sequence to obtain a compressed decision symbol sequence, wherein the compressed decision symbol sequence has the same length as the original decision symbol sequence of the reference user. Finally, the compressed decision symbol sequence is sent to the fusion center so that the fusion center makes a fusion decision. The application can ensure the reliability of spectrum sensing, significantly reduce the system sensing and transmission overhead, and improve the energy efficiency and realizability of cooperative spectrum sensing.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication, and specifically relates to a decision fusion method, apparatus and system for broadband spectrum sensing. Background Technology

[0002] Wideband spectrum sensing is an important application area in wireless communication, especially attracting attention due to the need for efficient spectrum utilization in 5G and future 6G networks. Its main purpose is to achieve reliable sensing of spectrum occupancy status over a wide bandwidth under limited hardware resources. It is widely used in fields with high requirements for sensing reliability and real-time performance, such as dynamic spectrum access, coexistence of heterogeneous wireless networks, and intelligent wireless environment sensing. In wideband spectrum sensing, the system typically faces requirements for both sensing reliability and system overhead. Sensing reliability is usually ensured by improving sensing algorithms or introducing multi-user cooperation; regarding system overhead, in addition to the sensing algorithm itself, factors such as sampling rate, transmission overhead, and fusion processing complexity must also be considered. This invention focuses on a cooperative spectrum sensing method that reduces system sensing and transmission overhead while meeting the reliability requirements of spectrum sensing.

[0003] Existing broadband spectrum sensing methods can be broadly categorized into two main types. The first type is compressed sensing methods based on signal reconstruction, which reconstruct the broadband signal spectrum under sub-Nyquist sampling conditions by utilizing spectral sparsity. The second type is compressed covariance sensing methods based on statistical properties, which achieve spectrum sensing by recovering the second-order statistical information of the signal. However, both of these methods primarily focus on spectrum sensing modeling under single-node or ideal collaborative conditions, failing to adequately consider engineering implementation challenges such as inconsistent sensing information lengths and limited transmission times in multi-awareness user collaborative scenarios. Therefore, they struggle to balance sensing performance and system overhead under large-scale collaborative conditions.

[0004] In real-world wireless network environments, cognitive users, constrained by hardware capabilities and energy consumption, often employ different sub-Nyquist sampling rates for cooperative spectrum sensing. This leads to misalignment issues in the time and frequency dimensions of the sensed information, requiring multiple rounds of transmission or additional alignment operations to complete the fusion decision. Furthermore, under parallel channel and MIMO fading channel conditions, the transmission of sensed information is also affected by channel states, further exacerbating system overhead. As the number of cooperative cognitive users increases, the complexity of sensed information transmission and fusion rises rapidly, becoming a major bottleneck limiting system scalability. Therefore, it is necessary to design a novel cooperative broadband spectrum sensing mechanism to achieve efficient alignment and fusion of sensed information under heterogeneous sub-Nyquist sampling conditions, thereby reducing transmission overhead and improving overall system efficiency. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a decision fusion method, apparatus, and system for broadband spectrum sensing. By symbol expansion and compression of the local decision sequences (i.e., the original decision symbol sequences) of different cognitive users, the lengths of the decision symbol sequences of cognitive users with different sampling rates are aligned. This significantly reduces system sensing and transmission overhead while ensuring the reliability of spectrum sensing, thereby improving the energy efficiency and feasibility of cooperative spectrum sensing.

[0006] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0007] In a first aspect, the present invention provides a decision fusion method for broadband spectrum awareness, applied to cognitive users, comprising:

[0008] Determine the type of user you are aware of;

[0009] 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.

[0010] 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.

[0011] In conjunction with the first aspect, optionally, the method for generating the extended decision symbol sequence includes:

[0012] 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 The downsampling rate.

[0013] In conjunction with the first aspect, optionally, the method for generating the compressed decision symbol sequence includes:

[0014] The extended decision symbol sequence is divided into equal parts. Segments, each segment is [length missing] ;

[0015] 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.

[0016] In conjunction with the first aspect, optionally, the mathematical expression for the preset condition is:

[0017] , , Indicates the first part, Indicates the first Duan Di The decision symbol for each position.

[0018] Secondly, the present invention provides a decision fusion method for broadband spectrum sensing, applied in a fusion center, comprising:

[0019] 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.

[0020] 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... ;

[0021] 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.

[0022] In conjunction with the second aspect, optionally, the method for generating the extended decision symbol sequence includes:

[0023] 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 The downsampling rate.

[0024] In conjunction with the second aspect, optionally, the method for generating the compressed decision symbol sequence includes:

[0025] The extended decision symbol sequence is divided into equal parts. Segments, each segment is [length missing] ;

[0026] 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.

[0027] In conjunction with the second aspect, optionally, the mathematical expression for the preset condition is:

[0028] , , Indicates the first part, Indicates the first Duan Di The decision symbol for each position.

[0029] Thirdly, the present invention provides a decision fusion device for broadband spectrum awareness, comprising: a fusion center, and a plurality of cognitive users communicating with the fusion center;

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

[0031] 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.

[0032] Fourthly, the present invention provides a decision fusion system for broadband spectrum sensing, including a storage medium and a processor;

[0033] The storage medium is used to store instructions;

[0034] The processor is configured to operate according to the instructions to perform the method according to any one of the first or second aspects.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] This invention unifies the decision symbol sequences of cognitive users with heterogeneous sampling rates to the same length through expansion and compression mechanisms. This reduces the total number of transmission rounds and significantly reduces system overhead.

[0037] Different cognitive users can use different downsampling rates, but the number of symbols can still be aligned using the scheme proposed in this invention, which improves the flexibility and scalability of the system and helps maintain stable perception performance under low overhead conditions. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0039] Figure 1 This is a flowchart of a decision fusion method for broadband spectrum sensing provided in one embodiment of the present invention;

[0040] Figure 2 This is an architectural diagram of a decision fusion device for broadband spectrum sensing provided in one embodiment of the present invention;

[0041] Figure 3 This is a resource allocation example diagram provided in one embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram showing a comparison of detection performance provided in one embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0044] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0045] Example 1

[0046] This invention provides a decision fusion method for broadband spectrum awareness, applicable to parallel channel and multiple-input multiple-output (MIMO) fading channel scenarios, and applied to cognitive users, including:

[0047] Determine the type of user you are aware of;

[0048] 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. The total number of subbands; the original decision symbol sequence was obtained locally by the benchmark cognitive user under subsampling conditions;

[0049] 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.

[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 The 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 cognitive 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 The 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 is preserved for the baseline cognitive user.

[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 is preserved for the baseline cognitive user.

[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 as a reference. 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. 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 The downsampling rate; 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.

2. The decision fusion method for broadband spectrum sensing according to claim 1, 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.

3. 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; 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 The downsampling rate; 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 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 1-2; 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.

6. 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 1-2 or 3-4.

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