SPMA protocol priority threshold design method based on stochastic geometry and queuing theory
By optimizing the priority threshold of the SPMA protocol through random geometry and queuing theory models, the balance problem between channel utilization and priority scheduling is solved, flexible adaptability and low-cost priority threshold design is achieved, and low-priority transmission performance is improved.
Patent Information
- Application Number
- CN202510771002.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-10
- Publication Date
- 2025-09-19
AI Technical Summary
The existing SPMA protocol priority threshold design method cannot effectively balance channel utilization and priority scheduling, has poor flexibility and adaptability, and has high testing costs.
A discrete-time Markov chain model is established based on random geometry and queuing theory. The priority threshold is determined through mathematical deduction. The design of each priority threshold is optimized by combining the path loss and Rayleigh fading models.
It achieves the goal of improving low-priority transmission performance while ensuring high-priority transmission performance, increasing channel utilization, adapting to topology changes and business fluctuations, and reducing testing costs.
Smart Images
Figure CN120675953A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of SPMA protocol performance optimization, and in particular relates to a SPMA protocol priority threshold design method based on random geometry and queuing theory. Background Art
[0002] The Statistical Priority-based Multiple Access Protocol (SPMA) is a random access protocol. Compared to traditional MAC protocols such as Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA), SPMA supports multiple priority services, adapts to dynamic network topology changes, and ensures low latency and high reliability for high-priority services. Therefore, SPMA can provide real-time communication guarantees in complex environments and has attracted increasing attention.
[0003] The SPMA protocol dynamically monitors channel load statistics, quantifying channel busyness and idleness in real time. It then categorizes and schedules data based on a multi-priority queue mechanism. After packets arrive at the MAC layer, they enter the corresponding queue based on service priority. Each queue has a differentiated sending threshold and backoff window. During transmission, the system checks each queue, starting with the highest-priority queue. If the current channel load falls below the queue threshold, data is immediately sent; otherwise, a backoff mechanism is triggered, with the backoff duration increasing as the priority level decreases.
[0004] Therefore, the design of priority thresholds is a key issue affecting the performance of the SPMA protocol, directly determining whether a data packet can be sent immediately. If the threshold is set too high, the channel load will be excessive, increasing the probability of channel collisions and failing to ensure the reliable transmission of high-priority data. If the threshold is set too low, data will be suppressed and unable to be transmitted, resulting in unnecessary waste of channel resources. Therefore, an appropriate threshold must be set for each priority queue to accurately control the channel load and ensure the reliable and real-time transmission of high-priority data.
[0005] Although SPMA protocol priority threshold design methods have been extensively studied, most are based on the performance metric of the highest priority packet delivery success rate. By testing the data link layer, they determine the lowest or highest priority threshold, and then determine other priority thresholds based on the arrival rate of services at each priority level. This type of priority threshold setting method is relatively simple to implement, but it has the following problems: (1) Unable to effectively balance channel utilization and priority scheduling. Since this type of design method lacks theoretical guidance to set the priority threshold, it is difficult to find an optimal solution that can maximize channel utilization and ensure priority scheduling.
[0006] (2) Poor flexibility and adaptability. Changes in the number of nodes, the arrival rate of services of different priorities, and the network topology require retesting the data link layer to obtain the lowest priority threshold or the highest priority threshold before calculating other priority thresholds.
[0007] (3) High testing cost. In order to determine the appropriate threshold, a large number of data link layer tests are required, which consumes a lot of time and resources. Moreover, when the network scale is large, the testing process may be more complicated. Summary of the Invention
[0008] The purpose of this invention is to solve the problems that existing methods cannot obtain the best solution that can maximize channel utilization and ensure priority scheduling, have poor flexibility and adaptability, and have high testing costs, and propose a priority threshold design method for the SPMA protocol based on random geometry and queuing theory.
[0009] The technical solution adopted by the present invention to solve the above technical problems is: a SPMA protocol priority threshold design method based on random geometry and queuing theory, the method specifically comprising the following steps: Step 1: The transmitter and receiver in the network obey the density of The independent Poisson point process, and the distance between each pair of transmitters and receivers is Each transmitter only processes packets of one priority level. The probability of a packet with different priorities is expressed as ,satisfy ; Step 2: Use queuing theory to A discrete-time Markov chain model is established for each priority queue, wherein the discrete-time Markov chain model includes the arrival process of data packets of various priorities and the distribution of data packet service time; wherein: No. The arrival process of priority data packets is subject to the parameters The geometric distribution of , ; Step 3: Determine the highest priority threshold based on the established discrete-time Markov chain model; Step 4: Determine the threshold of each low-priority data packet according to the highest priority threshold.
[0010] Furthermore, the channel model between the transmitter and the receiver includes path loss and Rayleigh fading.
[0011] Furthermore, the path loss is defined as: in, represents the path loss; is the path loss exponent.
[0012] Furthermore, the Rayleigh fading obeys a unit mean exponential distribution, that is, , represents Rayleigh fading, represents an exponential distribution with unit mean.
[0013] Furthermore, the data packet service time obeys a stage-type distribution, which specifically includes an idle phase, a channel load statistics phase, and a backoff phase.
[0014] Furthermore, the specific process of step three is: Step 3.1. The transmitter of the priority data packet is denoted as ,transmitter The channel load statistics detected do not exceed the The priority threshold corresponding to the priority data packet The probability is: in, It is a transmitter The channel load statistics detected do not exceed the The priority threshold corresponding to the priority data packet The probability of represents the channel access rate; Indicates transmitter Estimated number of Indicates transmitter The channel load statistics detected are ; Indicates transmitter The channel load statistics detected are The probability mass function of Step 32: Transmitter The packet transmission success rate Specifically: in, is the path loss exponent; Indicates transmitter Corresponding to the thermal noise of the receiver Laplace transform of Conforms to Gaussian distribution; For transmitter The transmission power; is the decoding threshold; Indicates the transmitter Of all transmitters with interference, the one used to process the density of transmitters of packets of different priorities; Step 3: Based on channel access rate and packet transmission success rate Calculate the The success rate of sending data packets of different priorities : in, Indicates the number of groups into which the data packet is divided; Indicates the minimum number of packets that must be received to recover the original data packet; Indicates the number of combinations, ; Step 3 and 4: Solve the problem Priority threshold ,in, Indicates the success rate of sending priority 0 packets. Indicates the priority threshold for packets of priority 0, which is the highest priority threshold in the network.
[0015] Furthermore, the transmitter The channel load statistics detected are The probability mass function of is: in, Indicates transmitter In the time slot window No. The number of packets detected in a time slot is The probability mass function of ; Indicates the The maximum number of packets detected in a time slot; , the weights satisfy ; Indicates that for all channels that meet the channel load statistics value Number of groups The probability mass function of is summed.
[0016] Furthermore, the transmitter The estimated quantity is:
[0017] in, Indicates transmitter The probability of being in a perceived state.
[0018] Furthermore, the pair of transmitters Of all transmitters with interference, the one used to process The density of transmitters of packets of different priorities for: in, Indicates the total number of time-frequency resource blocks in the data link network.
[0019] Furthermore, the specific process of step 4 is as follows: Step 4.1 Initialization ; Step 4.2 The priority threshold and The minimum threshold difference of the priority threshold for: in, Indicates the The priority threshold; Step 43. The priority thresholds are: in, Indicates the The priority threshold; Step 4. Determine whether ; If satisfied , then it ends; If not satisfied , then let , return to step 42.
[0020] The beneficial effects of the present invention are: The present invention integrates random geometry and queuing theory. Based on random geometry, it obtains the position distribution of transmitters and receivers and the arrival process of data packets of various priorities. It obtains the distribution obeyed by data packet service time through queuing theory, and then clarifies the constraint relationship between priority threshold and data packet sending success rate through mathematical deduction. It solves the defect of traditional empirical methods that lack theoretical guidance, provides a theoretical guidance method for priority threshold design, and realizes that while ensuring high-priority transmission performance, it provides more channel access opportunities for low-priority groups, improves channel utilization, and ensures a dynamic balance between maximizing channel utilization and multi-priority scheduling. In addition, the method of the present invention establishes a theoretical model by considering node density and distance between nodes, which can reveal the interaction between different nodes, comprehensively describe the communication process in a dynamic environment, and realize adaptive threshold adjustment of topology changes and business fluctuations. It has the ability to adapt in a highly dynamic environment and has extremely strong flexibility: The present invention replaces a large number of data link layer tests with theoretical model analysis, which can effectively reduce testing costs and reduce consumption of time and resources. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 It is a flow chart of a method for designing priority threshold of SPMA protocol based on random geometry and queuing theory of the present invention; Figure 2 It is a schematic diagram of the node spatial distribution model; Figure 3 It is a schematic diagram of the priority queue model of the transmitting node; Figure 4 This is a comparison chart of the throughput of priority 0 under different threshold setting methods; Figure 5 This is a comparison chart of the throughput of priority 1 under different threshold setting methods; Figure 6 This is a comparison chart of the throughput of priority 2 under different threshold setting methods; Figure 7 This is a comparison chart of the throughput of the entire network under different threshold setting methods. DETAILED DESCRIPTION
[0022] Specific implementation method 1: Combination Figure 1 This embodiment describes a method for designing a priority threshold for an SPMA protocol based on random geometry and queuing theory, and the method specifically includes the following steps: Step 1: Figure 2 As shown, the transmitter and receiver in the network obey the density of The independent Poisson point process (PPP) is a process where the distance between each pair of transmitters and receivers is Each transmitter only processes packets of one priority level. The probability of a packet with different priorities is expressed as ,satisfy ; Step 2: Figure 3 As shown, using queuing theory to A discrete-time Markov chain model is established for each priority queue, wherein the discrete-time Markov chain model includes the arrival process of data packets of various priorities and the distribution of data packet service time; wherein: No. The arrival process of priority data packets is subject to the parameters The geometric distribution of , ; It should be noted that: The smaller the value, the higher the priority of the data packet. The probability of data packets of various priorities arriving in each time slot is fixed and independent of each other. Step 3: Determine the highest priority threshold (i.e., the priority threshold of the 0th priority data packet) based on the established discrete-time Markov chain model; Step 4: Determine the thresholds of each low-priority data packet (ie, the other priority thresholds except the 0th priority data packet) according to the highest priority threshold.
[0023] While traditional methods focus primarily on the highest-priority transmission success rate, this method utilizes a theoretical analysis model to simultaneously constrain throughput and packet success rate, improving low-priority transmission performance while ensuring high-priority transmission performance. Furthermore, through key parameter optimization, this method overcomes the limitations of the traditional SPMA protocol, which is limited to high-priority, time-sensitive target strike scenarios, and supports flexible adaptation for multiple service types (such as reconnaissance, command, and general data transmission).
[0024] Specific embodiment 2: This embodiment differs from specific embodiment 1 in that the channel model between the transmitter and the receiver includes large-scale path loss and small-scale Rayleigh fading.
[0025] Other steps and parameters are the same as those in the first embodiment.
[0026] Specific embodiment 3: This embodiment differs from specific embodiments 1 or 2 in that the path loss is defined as: in, represents the path loss; is the path loss exponent.
[0027] Other steps and parameters are the same as those in the first or second embodiment.
[0028] Specific embodiment 4: This embodiment differs from any one of specific embodiments 1 to 3 in that the Rayleigh fading obeys a unit mean exponential distribution, i.e. , represents Rayleigh fading, represents an exponential distribution with unit mean.
[0029] The other steps and parameters are the same as those in the first to third embodiments.
[0030] Specific embodiment 5: This embodiment differs from any one of the specific embodiments 1 to 4 in that the data packet service time obeys a stage-type distribution, which specifically includes an idle phase ( Idle ), channel load statistics phase (S) and backoff phase (B).
[0031] The other steps and parameters are the same as those in the first to fourth embodiments.
[0032] The idle phase, channel load statistics phase, and backoff phase are used to describe the arrival, backoff process, and service time of each priority data packet. Each priority data packet needs to be divided into multiple packets for transmission. The phase before the packet reaches the transmitter node is called the idle phase. After the packet arrives at the transmitter node, channel load statistics need to be performed. If the channel load statistics value is less than the priority threshold of the data packet, there is no need to backoff at the moment and the packet data can be sent directly. Otherwise, if the channel load statistics value is greater than or equal to the priority threshold of the data packet, backoff is required. time slots (the number of time slots that need to be retreated for each priority level data packet is different. The higher the priority of the data packet, the The smaller the value of After a time slot, the channel load statistics are performed again.
[0033] Specific embodiment 6: This embodiment differs from any one of specific embodiments 1 to 5 in that the specific process of step 3 is as follows: Step 3.1. The transmitter of the priority data packet is denoted as ,transmitter The channel load statistics detected do not exceed the The priority threshold corresponding to the priority data packet The probability is: in, It is a transmitter The channel load statistics detected do not exceed the The priority threshold corresponding to the priority data packet The probability of represents the channel access rate; Indicates transmitter Estimated number of Indicates transmitter The channel load statistics detected are ; Indicates transmitter The channel load statistics detected are The probability mass function of Step 3.2: Based on the channel status and interference distribution in the spatiotemporal analysis model, the packet transmission success rate The signal-to-interference-plus-noise ratio (SINR) at the receiving end exceeds the decoding threshold. probability; transmitter The packet transmission success rate Specifically: in, is the path loss exponent; Indicates transmitter Corresponding to the thermal noise of the receiver Laplace transform of Conforms to Gaussian distribution; For transmitter The transmission power; is the decoding threshold; Indicates the transmitter Of all transmitters with interference, the one used to process the density of transmitters of packets of different priorities; Step 3. Since the physical layer of TTNT uses Turbo coding, even if packets collide, the data packets can still be recovered as long as the number of received packets exceeds a certain threshold.
[0034] According to the channel access rate and packet transmission success rate Calculate the The success rate of sending data packets of different priorities : in, Indicates the number of groups into which the data packet is divided; Indicates the minimum number of packets that must be received to recover the original data packet; Indicates the number of combinations, ; Step 3 and 4: Use the numerical iterative algorithm based on gradient descent to solve the problem that satisfies Priority threshold ,in, Indicates the success rate of sending priority 0 packets. Indicates the priority threshold for packets of priority 0, which is the highest priority threshold in the network.
[0035] The other steps and parameters are the same as those in the first to fifth embodiments.
[0036] Specific embodiment 7: This embodiment differs from any one of specific embodiments 1 to 6 in that the transmitter The channel load statistics detected are The probability mass function of is: in, Indicates transmitter In the time slot window No. The number of packets detected in a time slot is The probability mass function of ; Indicates the The maximum number of packets detected in a time slot; , the weights satisfy , that is, the time slot closer to the current moment has a higher weight; Indicates that for all channels that meet the channel load statistics value Number of groups The probability mass function of is summed.
[0037] The other steps and parameters are the same as those in the first to sixth embodiments.
[0038] Specific embodiment eight: This embodiment differs from any one of specific embodiments one to seven in that the transmitter The estimated quantity is: in, Indicates transmitter The probability of being in a perceived state.
[0039] The other steps and parameters are the same as those in the first to seventh embodiments.
[0040] Specific embodiment 9: This embodiment differs from any one of specific embodiments 1 to 8 in that the transmitter Of all transmitters with interference, the one used to process The density of transmitters of packets of different priorities for: in, Indicates the total number of time-frequency resource blocks in the data link network.
[0041] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.
[0042] Specific embodiment 10: This embodiment differs from specific embodiments 1 to 9 in that Priority threshold The constraint should be satisfied as the low priority threshold Below high priority threshold The specific process of step 4 is as follows: Step 4.1 Initialization ; Step 4.2 The priority threshold and The minimum threshold difference of the priority threshold for: in, Indicates the The priority threshold; Step 43. The priority thresholds are: in, Indicates the The priority threshold; Step 4. Determine whether ; If satisfied , then it ends; If not satisfied , then let , return to step 42.
[0043] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.
[0044] Simulation part To verify the effectiveness of the proposed method, a series of simulation experiments were conducted through model design to compare two different priority threshold setting methods: (1) Traditional method: The highest priority threshold is obtained by testing the data link layer, and then other priority thresholds are determined based on the arrival rate of services of different priorities. (2) The proposed method: The highest priority threshold is analyzed by establishing a theoretical model of the data link network, and then other priority thresholds are determined based on the minimum threshold difference.
[0045] The simulation experiment settings are as follows: Throughput changes over time: By monitoring the changes in the throughput of each priority service over time, the impact of the method of the present invention on the transmission performance of services of different priorities is verified, such as Figures 4 to 7 As shown, the throughput changes of the 0th priority, the 1st priority, the 2nd priority and the entire network service are shown in sequence.
[0046] Simulation experiments not only validated the theoretical feasibility of the proposed method, but also demonstrated its effectiveness in practical applications through the resulting data. The experimental results show that the proposed method can improve the transmission performance of low-priority services while ensuring the transmission performance of high-priority services, thereby increasing channel utilization.
[0047] The above examples are merely illustrative of the calculation model and process of the present invention and are not intended to limit the embodiments of the present invention. Persons skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. This list of embodiments is not exhaustive; however, any obvious variations or modifications derived from the technical solution of the present invention remain within the scope of protection of the present invention.
Claims
1. A method for designing priority thresholds for the SPMA protocol based on random geometry and queuing theory, characterized in that: The method specifically comprises the following steps: Step 1: The transmitter and receiver in the network obey the density of The independent Poisson point process, and the distance between each pair of transmitters and receivers is Each transmitter only processes packets of one priority level. The probability of a packet with different priorities is expressed as ,satisfy ; Step 2: Use queuing theory to A discrete-time Markov chain model is established for each priority queue, wherein the discrete-time Markov chain model includes the arrival process of data packets of various priorities and the distribution of data packet service time; wherein: No. The arrival process of priority data packets is subject to the parameters The geometric distribution of , ; Step 3: Determine the highest priority threshold based on the established discrete-time Markov chain model; Step 4: Determine the threshold of each low-priority data packet according to the highest priority threshold.
2. A method for designing priority thresholds for SPMA protocol based on random geometry and queuing theory according to claim 1, characterized in that: The channel model between the transmitter and the receiver includes path loss and Rayleigh fading.
3. A method for designing priority thresholds for SPMA protocol based on random geometry and queuing theory according to claim 2, characterized in that: The path loss is defined as: in, represents the path loss; is the path loss exponent.
4. A method for designing priority thresholds for SPMA protocol based on random geometry and queuing theory according to claim 2, characterized in that: The Rayleigh fading obeys the unit mean exponential distribution, that is, , represents Rayleigh fading, represents an exponential distribution with unit mean.
5. The SPMA protocol priority threshold design method based on random geometry and queuing theory according to claim 3 is characterized in that: The data packet service time obeys a stage-type distribution, which specifically includes an idle phase, a channel load statistics phase, and a backoff phase.
6. A method for designing priority thresholds for SPMA protocol based on random geometry and queuing theory according to claim 5, characterized in that: The specific process of step three is: Step 3.
1. The transmitter of the priority data packet is denoted as ,transmitter The channel load statistics detected do not exceed the The priority threshold corresponding to the priority data packet The probability is: in, It is a transmitter The channel load statistics detected do not exceed the The priority threshold corresponding to the priority data packet The probability of represents the channel access rate; Indicates transmitter Estimated number of Indicates transmitter The channel load statistics detected are ; Indicates transmitter The channel load statistics detected are The probability mass function of Step 32: Transmitter The packet transmission success rate Specifically: in, is the path loss exponent; Indicates transmitter Corresponding to the thermal noise of the receiver Laplace transform of Conforms to Gaussian distribution; For transmitter The transmission power; is the decoding threshold; Indicates the transmitter Of all transmitters with interference, the one used to process the density of transmitters of packets of different priorities; Step 3: Based on channel access rate and packet transmission success rate Calculate the The success rate of sending data packets of different priorities : in, Indicates the number of groups into which the data packet is divided; Indicates the minimum number of packets that must be received to recover the original data packet; Indicates the number of combinations, ; Step 3 and 4: Solve the problem Priority threshold ,in, Indicates the success rate of sending priority 0 packets. Indicates the priority threshold for packets of priority 0, which is the highest priority threshold in the network.
7. The SPMA protocol priority threshold design method based on random geometry and queuing theory according to claim 6 is characterized in that: The transmitter The channel load statistics detected are The probability mass function of is: in, Indicates transmitter In the time slot window No. The number of packets detected in a time slot is The probability mass function of ; Indicates the The maximum number of packets detected in a time slot; , the weights satisfy ; Indicates that for all channels that meet the channel load statistics value Number of groups The probability mass function of is summed.
8. The SPMA protocol priority threshold design method based on random geometry and queuing theory according to claim 7 is characterized in that: The transmitter The estimated quantity is: in, Indicates transmitter The probability of being in a perceived state.
9. The SPMA protocol priority threshold design method based on random geometry and queuing theory according to claim 8, characterized in that: The pair of transmitters Of all transmitters with interference, the one used to process The density of transmitters of packets of different priorities for: in, Indicates the total number of time-frequency resource blocks in the data link network.
10. The method for designing priority threshold of SPMA protocol based on random geometry and queuing theory according to claim 9, characterized in that: The specific process of step 4 is as follows: Step 4.1 Initialization ; Step 4.2 The priority threshold and The minimum threshold difference of the priority threshold for: in, Indicates the The priority threshold; Step 43. The priority thresholds are: in, Indicates the The priority threshold; Step 4. Determine whether ; If satisfied , then it ends; If not satisfied , then let , return to step 42.