A dynamic spectrum allocation method for information priority protection

Abstract

The present invention relates to a dynamic spectrum allocation method for information priority protection, and belongs to the field of information technology. A dynamic spectrum allocation method for information priority protection includes a wireless network, wherein the network has multiple nodes and channels; the nodes transmit information through the channels, and the nodes are characterized in that the nodes are in a cooperative dynamic game; the nodes detect the background noise and interference power on the candidate channels through spectrum sensing technology, and receive the weight and interference price of the information being transmitted; when the closer to I average The higher the interference price, the farther away from I average The lower the interference price, the more time passes. The node that obtains the channel calculates its own interference price and announces it to the public. It also announces the weight and priority of the information being transmitted. The remaining nodes update the interference price in this time slot and announce it to the public to ensure the real-time nature of the interference price. The above process is repeated when the next time slot arrives.

Description

Technical Field

[0001] The invention relates to a dynamic spectrum allocation method for information priority protection, belonging to the field of information technology. Background Art

[0002] With the continuous development of maritime unmanned boat technology, the complexity of the tasks undertaken by unmanned boat formations is also increasing, which puts higher requirements on the performance of the formation's wireless network. Among the many measures to enhance network performance, efficient use of spectrum resources is very important.

[0003] In recent years, cognitive radio technology is considered to be one of the effective means to improve spectrum utilization. By allowing spectrum sharing between secondary users and primary users, radio spectrum can be used in a more efficient way. In the study of spectrum allocation in cognitive radio networks, Lutfa et al. transformed the determination of transmission power and rate into a convex optimization problem. Users used the "reverse water injection" method to determine the optimal transmission power, and the rate allocation was proportional to SINR. M. Ajay et al. studied the energy resource and subcarrier allocation problems of orthogonal frequency division multiple access cognitive radio networks. The transmission rate of secondary users is maximized by adjusting the energy resources of primary users and appropriate interference restrictions. Wei et al. proposed a system-level dynamic spectrum allocation scheme based on a central heterogeneous network architecture, using the corresponding matrix to evaluate the matching degree between spectrum demand and available spectrum resources, and comprehensively considering the efficiency and fairness of different cognitive radio systems. Xu et al. proposed a robust distributed power control algorithm for multi-user underlying cognitive radio networks with uncertain parameters. [5] . Under the premise of considering channel uncertainty, the interference temperature and secondary user signal-to-interference-noise ratio are used as constraints to minimize system power consumption. The concept upheld by the above-mentioned documents is that authorized users occupy the dominant position in the spectrum and provide idle frequency bands to cognitive users. This method does improve the spectrum utilization rate, but there will be a phenomenon that authorized users reserve some channels with good quality and do not release them, making these channels unable to be widely used. In addition, the dominance of the formation spectrum can be controlled by some nodes instead of some nodes. The spectrum resources are stored in a public spectrum pool. Nodes only need to use them according to the principle of self-use and release after use. Therefore, the concept upheld by the above-mentioned documents cannot be fully applicable to unmanned boat formations where nodes are not divided into primary and secondary nodes, but the algorithm for allocating spectrum and transmission power has considerable reference value.

[0004] Although the formation can ignore the primary and secondary nodes, the priority difference still needs to be considered. In actual scenarios, nodes will generate information of different importance due to complex and changing situations. In order to ensure that important information generated by any node can be transmitted quickly, it is necessary to further consider the impact of information priority on spectrum allocation based on node priority. In the study of algorithms based on priority allocation, Hoon et al. studied the spectrum sharing problem of multiple network operators. In the proposed spectrum allocation algorithm, the priority between primary services and the priority between multi-level services are incorporated into the spectrum sharing metric, and the negotiation process is proposed to adapt to emergency bandwidth requests. Gulnur et al. studied a distributed spectrum sharing mechanism based on cognitive radio user spectrum decision, priority and message mechanism. The cooperation between users relies on the message passing framework using the common control channel. The framework enables each user to retrieve the channel decision and priority of its neighbors, and all users perceive and decide the channel occupancy by themselves. Peng et al. studied the allocation problem of cognitive radio spectrum resources in the downlink of a single cellular network. Using the substrate multiplexing method, an allocation algorithm that jointly considers the priority of cognitive users and the availability of subcarrier equalizers is proposed. At the same time, according to the minimum transmission requirements of cognitive users, fair allocation of subcarriers between cognitive users is achieved. The above-mentioned literature mainly studies the priority-based resource allocation algorithm in cellular networks, but only considers user priority or service priority, and does not propose a resource allocation method that comprehensively considers the two. In addition, considering that some information needs to be quickly and reliably known to all nodes, it is necessary to provide an exclusive channel for such information. Therefore, the above-mentioned literature is not completely applicable, but it still provides research ideas for the allocation method based on information priority. Summary of the invention

[0005] Aiming at the problem that nodes occupy channels in existing networks and affect the transmission of high-level information, a dynamic spectrum allocation method for information priority protection is provided.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] A dynamic spectrum allocation method for information priority protection includes a wireless network, wherein the network has a plurality of nodes and channels; the nodes transmit information through the channels, wherein the nodes are in a cooperative dynamic game, and the nodes determine the channel allocation order according to the information priority;

[0008] Step 1: Information priority: At the beginning of a time slot, the information priority is 1-4, and is graded according to the information generation node and delay requirements;

[0009] Step 2, the node performs a joint allocation of spectrum and power for the node A whose information transmission priority is 1. Node A detects the information transmission status of each channel and selects an idle channel or the channel where the node B with the lowest information transmission priority is located to monopolize it. If the channel originally has information of node B being transmitted, the node B transmitting information on the channel takes avoidance measures. The node to be avoided regards the remaining channels that do not transmit information of priority 1 as candidate channels. The purpose of this step is to allow the node A with priority 1 to monopolize a channel for information transmission, while other nodes B with information priority not 1 leave the channel and reselect from all channels.

[0010] Spectrum and power are jointly allocated in order to nodes with priority levels 2 to 4 for information to be transmitted: nodes detect the background noise and interference power on candidate channels through spectrum sensing technology, and receive the weight and interference price of the information being transmitted;

[0011] Step 3: Interference price, information is based on the total amount of transmission and the maximum delay requirement, and the average rate of transmission information U is obtained average , and then find the average Interference value I average , expressed as:

[0012]

[0013] Will I average Set as the threshold, the interference value I of the interference price function is redefined as:

[0014] I′=|II average | Formula 9,

[0015] The expression for the interference price is converted to:

[0016]

[0017] As I get closer average The higher the interference price, the farther away from I average The lower the interference price, the greater the interference price is. This allows node A with a priority of 1 to reuse the channel without paying an excessively high interference price when node B has a large communication margin.

[0018] The node calculates the weight and average transmission rate of the information to be transmitted, selects different transmission powers, calculates the available utility value, compares the utility values ​​of all candidate channels, selects the channel with the maximum utility value, and uses the transmission power corresponding to the maximum utility value;

[0019] Step 4: Announcement: The node that obtains the channel calculates its own interference price and announces it to the public. It also announces the weight and priority of the information being transmitted.

[0020] Step 5: The remaining nodes update the interference price in the current time slot and announce it to the public to ensure the real-time nature of the interference price.

[0021] Step 6: Repeat the above process when the next time slot arrives.

[0022] On the basis of the above technical solution, this application makes the following improvements and perfections to the above technical solution:

[0023] Furthermore, the priority is generated as follows: the short delay requirement information generated by the high priority node has a priority of 1; the short delay requirement information generated by the low priority node has a priority of 2; the long delay requirement information generated by the high priority node has a priority of 3; and the long delay requirement information generated by the low priority node has a priority of 4.

[0024] Furthermore, the node to be avoided participates in the subsequent dynamic game. If the utility value on the candidate channel is 0, the node to be avoided still selects the original channel and adjusts the transmission power to 0. After the transmission of information with priority 1 is completed, among the avoided nodes, if there is a node with a transmission information priority of 2, the maximum power is used; if there is a node with a transmission information priority of 3 or 4, the original power is restored.

[0025] Furthermore, at the beginning of a time slot, if a node needs to transmit information but has not obtained a channel, the priority of its own information to be transmitted is derived according to the priority generation method, so that the node can select a channel and power according to the priority order; if a channel has been allocated before, the previously allocated spectrum and power will continue to be used.

[0026] Furthermore, a dynamic spectrum allocation model is constructed in the wireless network. The dynamic spectrum allocation model adopts a complete information dynamic game. The game model is expressed as:

[0027] G= <N,{S i} i∈N ,{u i} i∈N > Formula 1,

[0028] In the formula, N is the set of players in the game, S i is the strategy space of player i, u i represents the utility function of the i-th player; the ultimate goal of the game is to achieve Nash equilibrium; Nash equilibrium refers to For The optimal choice of player i under the condition is

[0029]

[0030] The Nash equilibrium to be achieved in the network is an ideal situation in which each node selects the appropriate channel and power to maximize its communication rate while ensuring that information is transmitted according to priority.

[0031] Furthermore, the weight of the information is based on two indicators: the total amount of information S and the maximum transmission delay requirement T. At the same time, in order to reflect the importance of the maximum transmission delay requirement between the two, the information weight is defined as:

[0032]

[0033] In the formula, α is a constant coefficient. When nodes have different priorities, the corresponding α values ​​will also be different. For high-priority nodes, α is 30; for low-priority nodes, α is 10.

[0034] The channel selected by node k is denoted as The destination of node k is denoted as d. The transmission power on express, The data rate of node k can be described as:

[0035]

[0036] In the formula, h jk represents the channel gain from the transmitter of node j to the receiver of node k, h kd represents the channel gain from the transmitter of node k to the receiver of node d;

[0037] The algorithm objectives to be achieved by each node after selecting the appropriate channel and power are:

[0038]

[0039] Since the ADP algorithm has the excellent property of quickly converging to the global optimal solution, this algorithm is introduced to design the utility function.

[0040] Furthermore, the algorithm utility function increases the transmission cost of high-priority information, so the node chooses to reduce power; reduces the transmission cost of low-priority information, so the node will choose to increase power to ensure timely transmission of high-priority information, and allocates channel communication capacity on demand. The specific method is:

[0041]

[0042] Where U acquire is the communication rate that the node can obtain, U averageis the average transmission rate of transmitted information. For nodes transmitting high-priority information, it is the minimum transmission rate.

[0043] This application first analyzes the traditional ADP (Asynchronous Distributed Pricing, ADP) algorithm, improves the definition of interference prices in the algorithm, and overcomes the defect of higher pricing when the node has more communication margin. Then, the improved ADP algorithm is used to design the utility function, and the information weight is added to it, and a dynamic spectrum allocation algorithm based on information priority is proposed. The node selects the spectrum and power according to the game results, and increases the throughput while ensuring that the information can be reliably transmitted according to the delay requirements. Finally, in terms of throughput and reliability, the allocation algorithm based on information priority is simulated and compared with the allocation algorithm based on node priority, which verifies the effectiveness of the algorithm of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of the wireless communication network topology of the unmanned boat formation at sea;

[0045] Figure 2 The simulation results of throughput obtained by two allocation methods are given when the background noise is 100mW;

[0046] Figure 3 The sum of data transmission achieved by each node in 100 time slots for the two algorithms is given;

[0047] Figure 4 Comparison of the total amount of data transmission for information with the same priority level;

[0048] Figure 5 Comparison of algorithm packet loss;

[0049] Figure 6 Comparison of total packet loss of nodes;

[0050] Figure 7 Comparison of the total amount of packet loss for information of different priorities;

[0051] Figure 8 Iterate the update graph for node channel selection. DETAILED DESCRIPTION

[0052] The following embodiments, in conjunction with the accompanying drawings, are only intended to illustrate the technical solutions described in the claims and are not intended to limit the protection scope of the claims.

[0053] A dynamic spectrum allocation method for information priority protection includes a wireless network, wherein the network has a plurality of nodes and channels; the nodes transmit information through the channels,

[0054] 1. Building a dynamic spectrum allocation model

[0055] The key to improving spectrum utilization in competitive wireless networks is whether nodes can correctly predict the behavior of other individuals to select adaptive channels to reduce or avoid conflicts. As one of the methods to predict individual behavior, game theory can adapt to the dynamic changes of individual decision-making. This application adopts a complete information dynamic game, and the game model is expressed as

[0056] G= <N,{S i} i∈N ,{u i} i∈N > Formula 1,

[0057] In the formula, N is the set of players in the game, S i is the strategy space of player i, u i represents the utility function of the ith player. The ultimate goal of the game is to achieve Nash equilibrium. Nash equilibrium refers to For The optimal choice of player i under the condition is

[0058]

[0059] The Nash equilibrium to be achieved in the network is an ideal situation in which each node selects the appropriate channel and power to maximize its communication rate while ensuring that information is transmitted according to priority.

[0060] 1.1 Network System Model

[0061] like Figure 1 As shown in the figure, a schematic diagram of the wireless communication network topology of the maritime unmanned boat formation is given.

[0062] The node set in the formation is K = {1, 2, ..., N}, the nodes have different node priorities, and the communication objects are random. In the cognitive spectrum within the system, in addition to the public control channel, the set of reusable channels is C = {1, 2, ..., M}, and it is assumed that each channel has the same background noise n0 and bandwidth W. The generation of information is random. According to the generation node and delay requirements of the information, the information priority is specifically refined into four levels, as shown in Table 1.

[0063] Table 1 Information priority and corresponding generation method

[0064]

[0065] Based on the two indicators of total information S and maximum transmission delay requirement T, and in order to reflect the importance of the maximum transmission delay requirement between the two, the information weight is defined as

[0066]

[0067] Where α is a constant coefficient. When nodes have different priorities, the corresponding α values ​​will also be different. For high-priority nodes, α is 30. For low-priority nodes, α is 10.

[0068] The channel selected by node k is denoted as The destination of node k is denoted as d. The transmission power on express, The data rate of node k can be described as

[0069]

[0070] In the formula, h jk represents the channel gain from the transmitter of node j to the receiver of node k, h kd represents the channel gain from the transmitter of node k to the receiver of node d.

[0071] The algorithm goal that each node needs to achieve after selecting the appropriate channel and power is

[0072]

[0073] Since the ADP algorithm has the excellent property of quickly converging to the global optimal solution, it is introduced to design the utility function. However, the traditional ADP algorithm has the problem of unreasonable interference price pricing. Improving the definition of interference price is the key to further optimizing the ADP algorithm.

[0074] 1.2 Analysis and Improvement of ADP Algorithm

[0075] In the traditional ADP algorithm, the interference price signal used by users for exchange represents the negative impact of interference, which reflects the decrease in the marginal utility of user k caused by each unit increase in the reception interference of user k.

[0076] The definition of interference price is

[0077]

[0078] Formula 6 is specifically described as

[0079]

[0080] In the formula, Indicates that node k is in the channel The same frequency interference on Indicates that node k is in the channel The signal-to-noise ratio on .

[0081] Analyzing Formula 7, we can see that it still has certain limitations. If the communication capacity of the channel occupied by user A is much greater than the actual communication rate, user B can be allowed to reuse the same channel with a certain transmission power. B hopes that the interference price paid when reusing the channel is as small as possible, but the interference price actually paid by B will increase with the increase of A's signal-to-interference-noise ratio, which may cause B to eventually give up reusing the same channel with A. This will lead to an unreasonable phenomenon that the more remaining communication capacity a user has, the less likely other users will reuse the same channel with him. In view of the above analysis, an improvement to the traditional ADP algorithm is proposed.

[0082] Based on the total amount of information transmitted and the maximum delay requirement, the average rate of information transmission U can be obtained. average , and then find the average Interference value I average , expressed as

[0083]

[0084] Will I average Set as the threshold, the interference value I of the interference price function is redefined as

[0085] I′=|II average | Formula 9,

[0086] The expression for the interference price is converted to

[0087]

[0088] After improvement, the closer to I average The higher the interference price, the farther away from I average The lower the interference price, the greater the interference price. This allows B to reuse the channel without paying too much interference price when A has a large communication margin.

[0089] 1.3 Algorithm utility function design

[0090] Based on the improved ADP algorithm, the utility function is defined as

[0091]

[0092] To ensure that high-priority information is transmitted first, information weight is added to the utility function. Due to the influence of information weight, the node that transmits high-priority information can obtain a greater utility value, so a greater transmission power is used to ensure the transmission quality of high-priority information. The redefined utility function is

[0093]

[0094] After analyzing Formula 12, it is found that this definition method will lead to unreasonable allocation of communication capacity. The definition method of information weight makes the weight value of high-priority information with small maximum transmission delay requirements and the weight value of low-priority information have a large gap. This gap will cause the node transmitting high-priority information to use a transmission power far greater than the actual demand, resulting in excessive remaining communication capacity. In view of this situation, it is necessary to reasonably adjust the cost of transmitting information of different priorities. The improved utility function is

[0095]

[0096] Where U acquire is the communication rate that the node can obtain, U average is the average transmission rate of transmitted information. For nodes transmitting high-priority information, this is also the minimum transmission rate. After the improvement, the transmission cost of high-priority information is increased, and the node will choose to reduce the power; the transmission cost of low-priority information is reduced, and the node will choose to increase the power. This ensures that high-priority information is transmitted in a timely manner, while allocating channel communication capacity on demand.

[0097] 1.4 Algorithm flow description

[0098] The nodes are in a cooperative dynamic game, and the nodes decide the distribution order based on the information priority. The detailed algorithm flow is as follows:

[0099] Step 1: At the beginning of a time slot, if a node needs to transmit information but has not obtained a channel, the node obtains the priority of its own information to be transmitted according to Table 1, so that the node can select the channel and power according to the priority order; if a channel has been allocated before, the node continues to use the previously allocated spectrum and power.

[0100] Step 2, jointly allocate spectrum and power to node A whose information transmission priority is 1. Node A detects the information transmission status of each channel and selects an idle channel or a channel with the lowest information transmission priority to monopolize it. If there is already a node transmitting information on the channel, all nodes transmitting information on the channel take avoidance measures, and the nodes taking avoidance measures are the nodes to be avoided. The nodes to be avoided regard the remaining channels that do not transmit priority 1 information as candidate channels and participate in the subsequent dynamic game. The game process is shown in step 3. If the utility value on the candidate channel is 0, the node to be avoided still selects the original channel and adjusts the transmission power to 0. After the transmission of information with priority 1 is completed, among the avoided nodes, if there is a node with a transmission information priority of 2, the maximum power is used; if there is a node with a transmission information priority of 3 or 4. The original power is restored.

[0101] Step 3: The nodes with priority 2 to 4 for information to be transmitted are jointly allocated spectrum and power in order. The node detects the background noise and interference power on the candidate channel through spectrum sensing technology. At the same time, it receives the weight and interference price of the information being transmitted. The node calculates the weight and average transmission rate of the information to be transmitted according to formula 3, selects different transmission powers, and calculates the available utility value according to formula 13. The utility values ​​of all candidate channels are compared, the channel with the maximum utility value is selected, and the transmission power corresponding to the maximum utility value is used.

[0102] Step 4: The node that obtains the channel calculates its own interference price according to equations 8 to 10 and announces it to the public. It also announces the weight and priority of the information being transmitted.

[0103] Step 5: The remaining nodes update the interference price in this time slot and announce it to the public to ensure the real-time nature of the interference price.

[0104] Step 6: Repeat the above process when the next time slot arrives.

[0105] 2 Algorithm Analysis

[0106] 2.1 Algorithm Performance Analysis

[0107] (1) Fairness

[0108] The algorithm of this application will consider the communication capacity requirements of each node when allocating channel communication capacity. However, it should be noted that the allocation is based on the sacrifice of the transmission rate of low-priority information. In order to ensure that high-priority information is given priority and transmitted quickly, high-priority information nodes must obtain a communication capacity greater than the actual communication rate requirement. Information priority has a greater impact on the allocation of channel capacity. Therefore, the algorithm of this application is not very fair.

[0109] (2) Convergence

[0110] Taking the second-order derivative of equation 13, we get

[0111]

[0112] From formula (14), we can see that the utility function is a variable The concave function of , so the allocation result has a global optimal solution. It has been proved in the literature that when there are multiple users in the network, the ADP algorithm can quickly converge to the global optimal solution, so it will not be repeated here.

[0113] The reluctance to accept a transaction with uncertain returns, relative to accepting a transaction with insurance but lower expected returns, can be quantified in economics by the risk aversion coefficient. Given that the power corresponding to the maximum value of the utility function does not necessarily maximize the communication rate, and the node does not know the interference threshold of other nodes, blindly increasing the power will cause changes in the interference price of other nodes, thereby affecting network performance. If this is regarded as a risk, and the increase in its own communication rate after increasing the power is regarded as a benefit, this application introduces the relative risk aversion coefficient in economics to evaluate the willingness of the node to increase the return by increasing the power under the premise of increased risk after converging to the maximum value of the utility function. Relative risk aversion coefficient

[12] Defined as

[0114]

[0115] Taking the derivative of equation 15, we get

[0116]

[0117] From Formula 16, we can see that the node is relatively risk-averse in an increasing manner. This shows that after the node adopts the transmission power corresponding to the maximum utility value, it is very "averse" to increasing the power too much to improve its communication rate, which is consistent with the assumed requirements.

[0118] 2.2 Algorithm simulation analysis

[0119] In order to verify the effectiveness of this algorithm, it is compared with the algorithm for allocating channels based on node priority. Assume that there are 10 nodes in the formation wireless network, set nodes 1 and 2 as high priority nodes, and nodes 3 to 10 as low priority nodes. The nodes are distributed in an area of ​​20km×20km. The system has a total of 6 reusable channels, and the bandwidth of each channel is 25kHz. The channel gain between nodes h = 1 / d 1.6 , d is the distance between two nodes. The maximum transmission power of the node is 10W.

[0120] Figure 2 The simulation results of the throughput obtained by the two allocation methods are given when the background noise is 100mW. Figure 2 It can be seen that in terms of the overall network throughput, the allocation method based on information priority has a slight advantage over the allocation method based on node priority. Figure 3 The sum of data transmission volume achieved by each node in 100 time slots of the two algorithms is given. Figure 4 The total amount of data transmission achieved by the two algorithms with different priority levels within 100 time slots is given. Figure 3 and Figure 4It can be seen that the algorithm of this application can provide more communication rate guarantees for high-priority information.

[0121] Figure 5 A comparison chart of the packet loss of the two algorithms is given. It can be seen from the figure that the packet loss of the two algorithms is not much different. Figure 6 A comparison chart of the total amount of packet loss at each node within 100 time slots is given. Figure 7 A comparison chart of the total amount of packet loss of information with different priorities within 100 time slots is given. Figure 8 The successful transmission rate of different priority information packets is given. Figure 6 to Figure 7 It can be seen that by setting different information weights, the algorithm of the present application can ensure the transmission of high-priority information at the expense of low-priority information. Figure 8 The iterative update of channels for different nodes in one allocation is given. As can be seen from the figure, after 4 iterations, the nodes can converge to the Nash equilibrium state. Since nodes 1 and 2 are high-priority nodes, they can occupy a channel in this allocation according to the algorithm. Since multiple iterations are required, the algorithm of this application will perform multiple multiplication operations, and the algorithm complexity is O(4(MN h )(NN h )+1), N h Indicates the number of nodes that generate level 1 information.

[0122] This application focuses on how to improve the spectrum utilization rate of the communication network of the unmanned boat formation at sea, introduces the ADP algorithm that can quickly converge to the global optimal solution, and proposes a dynamic spectrum allocation algorithm based on information priority. In order to ensure that high-priority information is transmitted first, information weight is added to the utility function. Taking into account the problem of unreasonable pricing in the traditional ADP algorithm, the definition of interference price is improved. Taking into account the problem of unreasonable allocation of communication capacity, the cost part of the utility function is modified. The algorithm is analyzed in terms of fairness and convergence, and the algorithm performance is simulated and verified. Simulation results show that the algorithm of this application can achieve higher throughput and reliability than the allocation algorithm based on node priority.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for dynamic spectrum allocation with information priority protection, comprising a wireless network, wherein the network has a plurality of nodes and channels; the nodes transmit information through the channels, characterized in that: A dynamic spectrum allocation model is constructed in the wireless network. The dynamic spectrum allocation model adopts a complete information dynamic game. The game model is expressed as: G = <N,{S i } i∈N ,{u i } i∈N > Formula 1, In the formula, N is the set of players in the game, S i is the strategy space of player i, u i represents the utility function of the i-th player; the ultimate goal of the game is to achieve Nash equilibrium; Nash equilibrium refers to For The optimal choice of player i under the condition is The Nash equilibrium to be achieved in the network is to ensure that each node selects the appropriate channel and power to achieve the maximum communication rate under the condition of ensuring that information is transmitted according to priority. The nodes are in a cooperative dynamic game, and the nodes determine the channel allocation order based on information priority; Step 1: Information priority: At the beginning of a time slot, the information priority is 1-4, and is graded according to the information generation node and delay requirements; Step 2, the node performs a joint allocation of spectrum and power to the node A whose information transmission priority is 1. Node A detects the information transmission status of each channel and selects an idle channel or the channel where the node B with the lowest information transmission priority is located to monopolize it. If the channel originally has information of node B being transmitted, the node B transmitting information on the channel takes avoidance measures and becomes the node to be avoided. The node to be avoided regards all the remaining channels that do not transmit information of priority 1 as candidate channels. Spectrum and power are jointly allocated in order to nodes with priority levels 2 to 4 for information to be transmitted: nodes detect the background noise and interference power on candidate channels through spectrum sensing technology, and receive the weight and interference price of the information being transmitted; Step 3: Interference price, information is based on the total amount of transmission and the maximum delay requirement, and the average rate of transmission information U is obtained average , and then find the average Interference value I average , expressed as: Will I average Set as the threshold, the interference value I of the interference price function is redefined as: I′=|II average | Equation 9, The expression for the interference price is converted to: As I get closer average The higher the interference price, the farther away from I average The lower the interference price, the greater the interference price is. This allows node A with a priority of 1 to reuse the channel without paying an excessively high interference price when node B has a large communication margin. Step 4: Announcement: The node that obtains the channel calculates its own interference price and announces it to the public. It also announces the weight and priority of the information being transmitted. Step 5: The remaining nodes update the interference price in the current time slot and announce it to the public to ensure the real-time nature of the interference price. Step 6: Repeat the above process when the next time slot arrives.

2. The method for dynamic spectrum allocation for information priority protection according to claim 1, characterized in that: The priority is generated in the following manner: the short delay requirement information generated by the high priority node has a priority of 1; the short delay requirement information generated by the low priority node has a priority of 2; the long delay requirement information generated by the high priority node has a priority of 3; The low priority nodes that generate long delays and require information have a priority of 4.

3. The method for dynamic spectrum allocation for information priority protection according to claim 2, characterized in that: The node to be avoided participates in the subsequent dynamic game. If the utility value on the candidate channel is 0, the node to be avoided still selects the original channel and adjusts the transmission power to 0. After the transmission of information with priority 1 is completed, among the avoided nodes, if there is a node with a transmission information priority of 2, the maximum power is used; if there is a node with a transmission information priority of 3 or 4, the original power is restored.

4. The method for dynamic spectrum allocation for information priority protection according to claim 2, characterized in that: At the beginning of a time slot, if a node needs to transmit information but has not obtained a channel, the node derives the priority of its own information to be transmitted according to the priority generation method, so that the node can select a channel and power according to the priority order; if a channel has been allocated before, the node continues to use the previously allocated spectrum and power.

5. The method for dynamic spectrum allocation for information priority protection according to claim 1, characterized in that: The weight of the information is based on two indicators: the total amount of information S and the maximum transmission delay requirement T. At the same time, in order to reflect the importance of the maximum transmission delay requirement between the two, the information weight is defined as: In the formula, α is a constant coefficient. When nodes have different priorities, the corresponding α values ​​will also be different. For high-priority nodes, α is 30; for low-priority nodes, α is 10. The channel selected by node k is denoted as The destination of node k is denoted as d. The transmission power on express, The data rate of node k can be described as: Where hjk represents the channel gain from the transmitter of node j to the receiver of node k, and hkd represents the channel gain from the transmitter of node k to the receiver of node d. The algorithm objectives to be achieved by each node after selecting the appropriate channel and power are: Since the ADP algorithm has the excellent property of quickly converging to the global optimal solution, this algorithm is introduced to design the utility function.

6. The method for dynamic spectrum allocation for information priority protection according to claim 5, characterized in that: The utility function of the algorithm increases the transmission cost of high-priority information, so the node chooses to reduce power; reduces the transmission cost of low-priority information, so the node will choose to increase power to ensure timely transmission of high-priority information, and allocate channel communication capacity on demand. The specific method is: Where U acquire is the communication rate that the node can obtain, U average is the average transmission rate of transmitted information. For nodes transmitting high-priority information, it is the minimum transmission rate.

Images