Spectrum resource allocation method

Abstract

The invention discloses a spectrum resource allocation method, which comprises the following steps of: deploying a plurality of pairs of information transceivers, a passive backscatter label group and an eavesdropping user in a backscatter communication system, each passive backscattering label is equipped with an energy collection and conversion module and an information reflection module; constructing a resource allocation model meeting a reflection signal and rate maximization of secure communication; constructing a directed hypergraph based on a backscattering system and coloring the directed hypergraph; in order to solve the non-convexity problem of a resource optimization model, an auxiliary variable is introduced to remodel a resource allocation model, and eavesdropping threats are reduced from the physical level. The method provided by the invention can maximize the sum rate of the reflected signals in the system on the premise of ensuring the secure communication of each sub-channel, effectively reduces the interference among the reflected signals by dividing spectrum resources and introducing artificial noise, restrains the influence of eavesdropping users on the security of the system, and meets the requirements of the users on the quality of service.

Description

Technical Field

[0001] The present invention relates to a spectrum resource allocation method, belonging to the technical field of wireless communication. Background Art

[0002] As a product of the deep integration of new generation information technology and manufacturing industry, the industrial Internet of Things (IIoT) constructs an intelligent network system covering the entire chain of manufacturing, control, supervision, etc. by efficiently interconnecting key components such as industrial devices and sensors, and has inestimable value for improving the performance of future mobile communication systems and factory productivity. However, with the accelerating iteration of IoT technology, the application demand for ultra-dense device IoT has surged, which poses higher requirements for the flexible deployment of sensor nodes and the optimal allocation of limited communication resources.

[0003] In this context, the backscatter communication system has increasingly become the focus of research due to its unique advantages of low deployment cost and passive reflection tags. In the reflection communication network, numerous information transceivers and reflection tags share limited sub-channel resources, which leads to complex interference phenomena among reflection signals and significantly affects the overall sum rate performance of the system. Although the academic community has begun to explore optimizing the frequency reuse strategy through graph theory tools to reduce interference, unfortunately, existing research often ignores the cumulative interference effect of multiple signals on a single reflection signal in dense scenarios, and the potential threat posed by this phenomenon to communication quality cannot be ignored.

[0004] In addition, the inherent broadcast characteristic of backscatter communication makes it face severe security challenges while enjoying the improvement of transmission efficiency, especially being vulnerable to eavesdropping attacks, resulting in a sharp increase in the risks of data leakage and privacy leakage. Although various security solutions based on lightweight cryptography have been proposed currently, due to the complexity of key generation and management, the effectiveness and feasibility of these solutions encounter bottlenecks in large-scale tag deployment scenarios. Therefore, exploring physical layer security mechanisms, such as introducing artificial noise and other strategies, has become a new way to ensure the security of communication information. However, there is a trade-off problem between pursuing system sum rate improvement and ensuring communication security. How to find the best balance between resource allocation and security guarantee, meeting the requirements of efficient data transmission while ensuring the confidentiality and integrity of information transmission, is the key challenge faced by current research. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: to provide a spectrum resource allocation method, which maximizes the sum rate of reflection signals in the system on the premise of ensuring secure communication on each sub-channel, effectively reduces the interference between reflection signals by dividing spectrum resources and introducing artificial noise, and curbs the impact of eavesdropping users on system security.

[0006] The present invention adopts the following technical solutions to solve the above technical problems: A spectrum resource allocation method is applied to a backscatter system including eavesdropping users. The backscatter system includes an information transceiver, a passive reflection tag, and an eavesdropping user; there are available sub-channels in the backscatter system network; the method includes: Step 1, according to the backscatter system, construct a resource allocation model that maximizes the sum rate of reflected signals based on channel security capabilities; Step 2, construct a directed hypergraph based on the backscatter system, color the constructed directed hypergraph, and then allocate the reflected signals of each passive reflection tag to the corresponding available sub-channels; Step 3, introduce a first auxiliary variable to reshape the security capability constraints of each available sub-channel and the objective function of the resource allocation model, use quadratic transformation to solve the non-convex problem, and optimize the artificial noise ratio coefficient introduced by each passive reflection tag when reflecting signals; Step 4, introduce a second auxiliary variable to reshape the objective function of the resource allocation model, use quadratic transformation to solve the non-convex problem, and optimize the reflection coefficient of each passive reflection tag; Step 5, use the cross-iteration method to repeat steps 2 - 4 until the available sub-channel allocation results obtained in step 2, the artificial noise ratio coefficients of each passive reflection tag obtained in step 3, and the reflection coefficients of each passive reflection tag obtained in step 4 remain unchanged in two adjacent iterations, then the iteration converges. Use the available sub-channel allocation results, the artificial noise ratio coefficients of each passive reflection tag, and the reflection coefficients obtained in the last iteration to achieve spectrum resource allocation that maximizes the sum rate of reflected signals while ensuring the security capabilities of each available sub-channel.

[0007] Compared with the prior art, the present invention adopts the above technical solutions and has the following technical effects: 1. The present invention considers the system communication security and resource allocation problems in the presence of eavesdroppers. Taking the system security capabilities and rate as indicators, it solves the non-convex optimization problem by using quadratic transformation, and uses the directed hypergraph coloring algorithm for spectrum resource allocation to maximize the system sum rate on the premise of meeting the minimum security capability index.

[0008] 2. The present invention uses hypergraph theory for modeling. This method can more effectively capture and manage the mutual interference in the network, and then significantly improve the user communication quality and enhance the network data throughput through a refined frequency reuse strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is the overall layout diagram of the backscatter communication system with eavesdropping users; Figure 2It is an example diagram of various interference patterns that may exist in an undirected hyperedge; Figure 3 It is a comparison diagram of the security capabilities of the communication system before and after optimization; Figure 4 It is a diagram showing the relationship between the sum rate of the communication system and the number of available sub-channels; Figure 5 It is a diagram showing the relationship between the sum rate of the communication system and the signal-to-noise ratio (SNR); Figure 6 It is a diagram showing the relationship between the sum rate of the communication system and the number of information transceiver - reflection tags; Detailed implementation manner

[0010] The following describes in detail the implementation manner of the present invention. Examples of the implementation manner are shown in the accompanying drawings. The implementation manner described below with reference to the accompanying drawings is exemplary and is only used to explain the present invention and should not be construed as a limitation of the present invention.

[0011] The present invention provides a resource allocation method for a backscatter communication system with eavesdropping users. The backscatter communication system with eavesdropping users includes an information transceiver ( ) and a passive reflection tag ( ), and an eavesdropping user, as shown in Figure 1 . Each information transceiver includes two antennas, which are respectively used to transmit a radio frequency carrier signal and receive the reflection signal of the tag. Each passive reflection tag includes three parts: an energy harvesting module, an information acquisition module, and a reflection module. Among them, the energy harvesting module can convert the received radio frequency carrier part into DC energy to activate the information acquisition module. The information acquisition module is used to collect the information of the tag itself. The reflection module can directly reflect the received another part of the radio frequency carrier to transmit its own information. The eavesdropping user will not emit active interference but passively eavesdrop on the broadcast reflection signal.

[0012] The specific steps are as follows: Step 1: Construct a resource allocation mathematical model that maximizes the sum rate of the reflection signal based on the channel security capability; Assume that there are available sub-channels in the backscatter network. Each sub-channel is flat and static, so only the path loss of the signal is considered. The received reflection signal from can be expressed as , where is the channel gain, is the reflection signal transmitted by , and is the Gaussian white noise. However, in the backscatter network with limited spectrum resources, multiple pairs of information transceivers and reflection tags share the same channel. Therefore, in is interfered by the reflected signals from other tags. The signal-to-interference-plus-noise ratio (SINR) of the th information transceiver receiving the reflected signal can be written as: , where, represents the reflection coefficient of the th reflection tag, represents the proportion coefficient of the artificial noise injected by the th reflection tag when backhauling the signal, represents the transmission power of the radio frequency carrier sent by the th information transceiver, represents the channel gain from the th information transceiver to the th reflection tag, represents the channel gain from the th reflection tag to the th information transceiver, then represents the channel gain between the th reflection tag and the th information transceiver, is the cancellation factor according to the prior knowledge of the artificial noise, is the power of the Gaussian white noise in the channel.

[0013] In addition, it is assumed that the eavesdropper eavesdrops on all the reflected signals in the channel simultaneously. Therefore, the secrecy capacity of the th sub-channel is expressed as: , where, , , is the signal-to-interference-plus-noise ratio (SINR) of the eavesdropping user receiving the reflected signal, is the th channel gain between the reflection tag and the eavesdropping user.

[0014] In summary, the resource allocation mathematical model for maximizing the sum rate of the reflected signals based on the channel security capacity is configured as: , where, is the total number of information transceiver and passive reflection tag pairs in the system, is the energy conversion power of the radio frequency carrier signal received by the th reflection tag pair, is the minimum activation power required by the th reflection tag, is the minimum confidentiality capability standard to be met; the constraint C1 represents the guarantee that each reflection tag can be successfully activated, the constraint C2 represents the QoS guarantee for the transmitted reflected signals in each sub-channel to meet the secure link, and the constraints C3 and C4 are the non-negativity restrictions on the reflection coefficient and the artificial noise ratio factor respectively.

[0015] Step 2: Construct a directed hypergraph based on the backscatter communication system; Resource allocation using the directed hypergraph coloring algorithm can be divided into two stages: 1) Construct a directed hypergraph model according to the interference relationship between the reflected signals; 2) Color the constructed directed hypergraph, and then allocate the spectrum resources of the system; When constructing the directed hypergraph model, the reflected signals are regarded as the nodes of the hypergraph, and then the directed hyperedges are constructed according to the independent interference and cumulative interference between the reflected signals. By introducing the directed hyperedges, the directed hypergraph model can effectively distinguish the interference and being interfered relationships between the reflected signals, and can avoid the waste of resources caused by repeated retrieval. When constructing the directed hyperedges, first judge the independent interference. When the reflected signal satisfies construct ; where represents the transmission power of the radio frequency carrier sent by the th information transceiver, represents the channel gain between the th information transceiver and the th reflection tag, then represents the channel gain between the th reflection tag and the th information transceiver, is the Gaussian white noise power in the channel. Then judge the cumulative interference. When the reflected signal satisfies construct ; When judging the situation where the th reflected signal is used as the node with cumulative interference, all the nodes that have formed independent interference on the th reflected signal will be preferentially excluded.

[0016] As Figure 2 shown, in the traditional undirected hypergraph model, it is impossible to accurately distinguish the interference and being interfered relationships of the 4 nodes (①, ②, ③, and ④) in the hyperedge. For example, there may be a situation where the signals of nodes ①, ②, and ③ cause cumulative interference to the signal of node ④, or the signals of nodes ①, ②, and ④ cause cumulative interference to the signal of node ③, etc. The traditional undirected hypergraph interference model cannot accurately distinguish the interfered node and the interfering node, and there are multiple combinations possible. Therefore, the present invention introduces a directed hypergraph model. In the directed hypergraph model, the nodes in the directed hyperedge are divided into an interference set and a being interfered set. As shown, where belongs to the being interfered set, while Belonging to the interference set, the direction is set to .

[0017] When coloring the constructed directed hypergraph, an improved coloring algorithm is used to solve the problem that the number of colors is not limited in traditional greedy coloring, and it can be better applied to communication systems with limited spectrum resources. The specific improved rules are as follows: a. Each node corresponds to one color; b. A total of colors can be used, where is the number of available sub-channels; c. Nodes within the same hyper-edge should be colored with different colors. When coloring, first color the vertex with the highest "degree", and then "delete" the colored nodes. Subsequently, other nodes are colored according to the interference relationship. Among them, the "degree" of a certain node refers to the number of reflected signals affected by this node as an interference signal; "deleting" a certain node means deleting all hyper-edges containing this node from the hyper-edge set, deleting this node from the node set, and updating the sub-hypergraph of the remaining nodes and hyper-edges.

[0018] Step 3. Since each sub-channel is independent of each other and the introduced artificial noise does not interfere with the signals in other channels, the security capacity problem of the system can be split into independent sub-problems, and artificial noise is introduced into independent sub-channels respectively to ensure the security capacity of the system, and the quadratic transformation is used to solve the non-convex problem, that is, the proportional coefficient of the introduced artificial noise is set for the reflection label; In the resource allocation mathematical model for maximizing the sum rate of reflected signals based on channel security capacity, since the numerator and denominator are linear polynomials, the objective function is a non-convex problem with respect to the variable . For this reason, an auxiliary variable is introduced to reshape the security capacity constraint and the objective function expression, and the changed formula can be written as: , where , .

[0019] Step 4. Similarly, since each sub-channel is independent of each other, when optimizing the reflection coefficient, it can be split into independent sub-problems, and the reflection labels in independent sub-channels are discussed respectively, and the quadratic transformation is used to solve the non-convex problem to optimize the reflection coefficient of each reflection label; Similarly, in the resource allocation mathematical model for maximizing the sum rate of reflected signals based on channel security capacity, the objective function is also a non-convex problem with respect to the variable , and by introducing an auxiliary variable The reshaped objective function expression can be rewritten as: , where , .

[0020] Step 5: In a cross-iterative manner, repeat Steps 2 to 4 until the channel allocation results in Step 2, the artificial noise ratio coefficient results of each reflection tag in Step 3, and the reflection coefficient results of each reflection tag in Step 4 remain unchanged in two adjacent iterations, then the optimization result converges. At this time, the backscatter communication system achieves the maximization of the reflection signal sum rate while ensuring the security capabilities of each sub-channel.

[0021] As Figure 3 shown, by introducing artificial noise, the security capabilities of each sub-channel in the communication system can all reach the minimum security standard, effectively curbing the threat to the system security from eavesdropping users and meeting the user requirements for service quality.

[0022] As Figure 4 , 5 and 6 shown, simulations are respectively carried out on the relationships between the system sum rate and the number of available sub-channels, SNR, and the number of information transceiver-reflection tags. The results show that the use of the directed hypergraph coloring algorithm has a significant improvement compared with the use of the graph coloring algorithm. Especially in the case of denser device deployment, higher SNR, and limited spectrum resources, the use of the directed hypergraph coloring algorithm can bring a sum rate increase of nearly .

[0023] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the foregoing spectrum resource allocation method are implemented.

[0024] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the foregoing spectrum resource allocation method are implemented.

[0025] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0026] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or in a plurality of blocks.

[0027] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or in a plurality of blocks.

[0028] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or in a plurality of blocks.

[0029] The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the present invention.

Claims

1. A spectrum resource allocation method, applied to a backscattering system including an eavesdropping user, the backscattering system comprising For information transceivers and passive reflective tags, as well as an eavesdropping user; backscatter system network exists Available sub-channels; characterized in that, The method comprises: Step 1, according to the backscattering system, construct a resource allocation model based on channel security capability and rate maximization of reflected signals; Step 2, constructing a directed hypergraph based on the backscattering system, and coloring the constructed directed hypergraph, thereby allocating the reflected signal of each passive reflective tag to the corresponding available sub-channel; Step 3: introduce the first auxiliary variable to reshape the security capability constraints of each available sub-channel and the objective function of the resource allocation model, use the quadratic change to solve the non-convex problem, and optimize the artificial noise ratio coefficient introduced by each passive reflective tag when reflecting the signal; Step 4, introducing the second auxiliary variable to reshape the objective function of the resource allocation model, using quadratic changes to solve non-convex problems, and optimizing the reflection coefficient of each passive reflective tag; Step 5, repeating steps 2 to 4 in a cross-iteration manner until the available sub-channel allocation results obtained in step 2, the artificial noise proportional coefficient of each passive reflective tag obtained in step 3, and the reflection coefficient of each passive reflective tag obtained in step 4 remain unchanged in two adjacent iterations. The iteration converges, and the available sub-channel allocation results, the artificial noise proportional coefficient and the reflection coefficient of each passive reflective tag obtained in the last iteration are used to achieve spectrum resource allocation that maximizes the reflected signal and rate while ensuring the safety capability of each available sub-channel.

2. The spectrum resource allocation method according to claim 1, characterized in that: In the backscattering system, each information transceiver includes two antennas respectively used for sending radio frequency carrier signals and receiving reflected signals of passive reflective tags corresponding to the information transceiver; each passive reflective tag includes an energy collection module, an information collection module and a reflection module, wherein the energy collection module is used to convert a part of the received radio frequency carrier signal into direct current energy and use the direct current energy to activate the information collection module; the information collection module is used to collect information of the passive reflective tag itself; and the reflection module is used to transmit the information of the passive reflective tag itself by reflecting another part of the received radio frequency carrier signal to the information transceiver.

3. The spectrum resource allocation method according to claim 1, characterized in that: The resource allocation model for maximizing the reflected signal and rate based on the channel security capability is specifically as follows: No. Transceiver Receive from Passive reflective tags When the reflected signal of a passive reflective tag is interfered by the reflected signal from other passive reflective tags, Signal-to-interference-noise ratio of received reflected signal It is expressed as: ， in, Respectively represent The reflection coefficient of a passive reflective tag, Indicates The artificial noise ratio coefficient injected by a passive reflective tag when returning the signal, Respectively represent The transmission power of the radio frequency carrier signal sent by a transceiver, Indicates The information transceiver to the The channel gain of a passive reflective tag, Indicates Passive reflective tag to the The channel gain of each transceiver, Indicates The information transceiver to the The channel gain of a passive reflective tag, Indicates A passive reflective tag and The channel gain between the information transceivers is is the elimination factor based on the prior knowledge of artificial noise, is the Gaussian white noise power in the channel; Set the eavesdropping user to eavesdrop on the Subchannel All reflected signals in The confidentiality of the subchannel It is expressed as: ， in, , , Indicates Passive reflective tags The reflected signal is divided into sub-channels, is the signal-to-interference-noise ratio of the reflected signal received by the eavesdropping user, , For the The channel gain between a passive reflective tag and the eavesdropping user; The resource allocation model based on the reflected signal and rate maximization of the channel security capability is as follows: ， in, is the rate of reflected signals in the backscatter system, For the The rate of reflected signals from passive reflective tags, For the The energy conversion power of a passive reflective tag to the received RF carrier signal is For the The minimum activation power required for a passive reflective tag is, is the minimum confidentiality capability standard that needs to be met; constraint C1 is the guarantee that each passive reflective tag can be successfully activated, constraint C2 is the QoS guarantee that the reflected signal transmitted in each available subchannel meets the security link, and constraints C3 and C4 are non-negative restrictions on the reflection coefficient and artificial noise proportional coefficient respectively.

4. The spectrum resource allocation method according to claim 3, characterized in that: The construction process of the directed hypergraph is as follows: The reflected signals of the passive reflective tags are used as nodes of the directed hypergraph, and directed hyperedges are constructed according to the independent interference and cumulative interference between the reflected signals; When constructing a directed hyperedge, we first determine the independent interference between any two nodes. The reflected signal of a passive reflective tag satisfies When constructing a directed hyperedge ,in, Indicates The reflection coefficient of a passive reflective tag, Indicates The artificial noise ratio coefficient injected by a passive reflective tag when returning the signal, Indicates The transmission power of the radio frequency carrier signal sent by a transceiver, Indicates The information transceiver to the The channel gain of a passive reflective tag, Indicates Passive reflective tag to the The channel gain of each transceiver, Indicates A passive reflective tag and The channel gain between the information transceivers is is the signal-to-interference-noise ratio threshold of independent interference; Then determine the cumulative interference between nodes, that is, when the The reflected signal of a passive reflective tag satisfies When constructing a directed hyperedge ,in, Indicates A passive reflective tag and The channel gain between the information transceivers is is the signal-to-interference-noise ratio threshold of the cumulative interference; In judging When the reflected signal of the passive reflective tag is used as the node with cumulative interference, all nodes that have been interfered with by the first The reflected signals of the passive reflective tags form independent interfering nodes.

5. The spectrum resource allocation method according to claim 4, characterized in that: The above is the coloring of the constructed directed hypergraph, specifically: The constructed directed hypergraph is colored using the improved greedy coloring algorithm. The coloring rules are as follows: a. Each node corresponds to a color; b. The available colors are c. The nodes in the same directed hyperedge are colored with different colors; Each time you color, get the degree of all nodes in the current node set, color the node with the highest degree, delete the colored nodes, and then proceed to the next coloring; The degree of a node refers to the number of reflected signals affected by the node as an interference signal; Deleting a colored node refers to deleting the colored node from the node set and deleting all directed hyperedges containing the colored node from the directed hyperedge set.

6. The spectrum resource allocation method according to claim 5, characterized in that: The specific process of step 3 is as follows: The objective function of the resource allocation model is relative to This is a non-convex problem, introducing auxiliary variables Reshape the security capability constraints of each available sub-channel and the objective function of the resource allocation model, the expression is as follows: ， in, For the The sum rate of the reflected signals in the sub-channels, , .

7. The spectrum resource allocation method according to claim 6, characterized in that: The specific process of step 4 is as follows: The objective function of the resource allocation model is relative to This is a non-convex problem, introducing auxiliary variables Reshape the objective function of the resource allocation model, the expression is as follows: ， in, , .

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the spectrum resource allocation method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the spectrum resource allocation method according to any one of claims 1 to 7 are implemented.

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