Cellular network signal-to-interference ratio distribution analysis method based on stochastic geometry

Abstract

The invention discloses a random geometry-based cellular network signal-to-interference ratio distribution analysis method, which comprises the following steps of: constructing a cellular space node distribution, service time arrival and equipment energy state three-dimensional model; therefore, a receiving end signal to interference ratio expression, a conditional transmission success probability of the equipment and an overall active probability of the equipment in a stable state are obtained, a moment generation function of a final transmission success probability is obtained by utilizing random geometry, and finally an expression of cellular network signal to interference ratio element distribution is obtained by utilizing a Gil-Pelaez theorem. And the proportion of the successful transmission probability of the equipment in the cellular network can be analyzed by using the expression. The method disclosed by the invention is applied to a large-scale cellular network with randomly distributed equipment positions, the characteristics of equipment energy, storage and the like are considered, the system performance can be effectively evaluated, and the influence of parameters such as a signal-to-interference ratio threshold value and an energy arrival probability on signal-to-interference ratio element distribution is analyzed.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless network communication, and in particular to a method for analyzing the distribution of signal-to-interference ratio elements in a decellularized network based on random geometry. Background Art

[0002] With the rapid development of wireless communication technology, the communication industry is entering the era of large-scale Internet of Things (IoT), involving many "new infrastructure" application fields such as smart transportation and smart agriculture. On the one hand, in the large-scale IoT, the number of devices has exploded, and traditional cellular network technology can no longer meet people's demand for communication quality. In order to reduce interference between devices and provide high-quality services for all IoT devices, the decellularization technology proposed in recent years has become a research hotspot. The decellularized network adopts a user-centric architecture, so it has the characteristics of considerable macro diversity, extremely high channel capacity and link reliability.

[0003] In terms of modeling node positions in wireless networks, the irregularity of network topology has promoted random geometry to become a powerful mathematical tool for network analysis. With the help of random geometry, it can help capture the impact of spatiotemporal interference correlation in large-scale wireless networks, thereby helping to characterize and evaluate system-level performance. However, current research mainly focuses on using random geometry to analyze the performance indicator of conditional transmission success probability, which is obtained by averaging all links in the point process. The conditional transmission success probability only quantifies the overall average performance of the entire network, but cannot reflect the change of signal-to-interference ratio (SIR) or its distribution of performance on each link. It also does not consider the impact of factors such as energy arrival process and channel access on the transmission performance of links in the network. Summary of the invention

[0004] In view of the defects existing in the above-mentioned background technology, the present invention proposes a method for analyzing the signal-to-interference ratio element distribution of a decellularized network based on random geometry, which can effectively measure the system performance. The present invention establishes a large-scale decellularized network signal-to-interference ratio element distribution analysis framework based on random geometry, evaluates the impact of energy collection on the probability of successful link transmission, and provides a powerful tool for evaluating and predicting network performance. The method of the present invention is applied to large-scale decellularized networks with randomly distributed device locations, taking into account the characteristics of device energy, storage, etc., and can effectively evaluate system performance and analyze the impact of parameters such as signal-to-interference ratio threshold value and energy arrival probability on the signal-to-interference ratio element distribution.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A random geometry-based method for analyzing the signal-to-interference ratio distribution of a decellularized network comprises the following steps:

[0007] S1. During the uplink transmission process, a three-dimensional model of node distribution, service arrival time, and device energy status in the decellularized space is constructed;

[0008] S2. Based on the three-dimensional model of node distribution, service arrival time, and device energy state in the decellularized space, an expression of signal-to-interference ratio of the receiving end is obtained;

[0009] S3, based on the decellularized spatial node distribution, service arrival time, device energy state three-dimensional model and the signal-to-interference ratio expression of the receiving end, obtain the conditional transmission success probability expression of the device;

[0010] S4, based on the three-dimensional model of the decellularized spatial node distribution, service arrival time, and device energy state, obtain the overall activity probability of the device in a stable state;

[0011] S5. Based on the conditional transmission success probability expression of the device obtained in step S3, the moment generating function of the final transmission success probability is obtained using the random geometry method. Combined with the signal-to-interference ratio expression of the receiving end in step S2 and the overall active probability of the device in the stable state in step S4, the expression of the signal-to-interference ratio element distribution of the de-cellular network is obtained based on the Gil-Pelaez theorem; the proportion of the successful transmission probability of the devices in the de-cellular network is analyzed using the expression of the signal-to-interference ratio element distribution of the de-cellular network.

[0012] In the above technical solution, further, in step S1, considering the randomness of the device spatial position, data packet arrival, and energy collection, a three-dimensional model of decellularized space node distribution, service time arrival, and device energy state is established; the three-dimensional model of decellularized space node distribution, service time arrival, and device energy state includes a decellularized space node distribution model, a service time arrival model, and a device energy state model;

[0013] The decellularized spatial node distribution model is specifically as follows: based on a decellularized mMIMO (massive multiple input multiple output) network for uplink transmission, the positions of all access points in the network are Φ a The density is λ a Poisson point process. Each access point has M antennas and is connected to a central processing unit (CPU). All IoT devices are equipped with a single antenna. The location of the IoT device is Φ d The density is λ d The transmission power of each device is P, which is composed of the device x i The center of the circle is itself and the radius is R a All associated access points within range are collectively device x iService. The channel characteristics between the device and the access point antenna are determined by the large-scale path loss and the small-scale Rayleigh fading with unit mean. The calculation of the path loss is based on the bounded path loss model, and the specific expression is as follows:

[0014] l(r)=max(R0,r) -α (8)

[0015] Where: r is device x i To its associated access point y m distance, R0 is the reference distance, and α is the path loss exponent.

[0016] The distribution of r is used to evaluate the associated access point received from device x i signal from device x i To any of its associated access points y m The probability density function of the distance r is:

[0017]

[0018] The service time arrival model is specifically as follows: time is divided into equally spaced time slots, and the generation and transmission of data packets occur at the boundaries of time slots. Each data packet has the same size, and its transmission requires one time slot. When the energy number of the IoT device is not 0, it will generate a data packet with probability p and send it to all associated access points. The associated access point then uses a reliable forwarding link to forward the received data to the CPU, and the time of the forwarding process is negligible. In particular, energy cannot be collected while transmitting data packets.

[0019] The device energy state model is specifically as follows: each device collects energy from the surrounding nature (the device can convert wind energy, heat energy, and solar energy in nature into energy for storage), and sends data when the energy is sufficient. The battery capacity of the device is E units. When the battery is not full, each time slot independently collects one unit of energy with a probability η. Sending a data packet consumes one unit of energy. Without loss of generality, in the present invention, E>3.

[0020] Further, in step S2, the method for obtaining the signal-to-interference ratio expression of the receiving end is specifically:

[0021] For device x i , calculate its correlation with the access point y m The specific method is: m As the origin, with y m Point to x i The direction of the x-axis is used to establish a coordinate system, then x i With y m The distance is the device x i The horizontal axis of

[0022] In time slot t, for device x i Associate access point y to it m Send data packets on this communication link, y m The received signal-to-interference ratio SIR is expressed as:

[0023]

[0024] in: Is device x i and its associated access point y m The total channel gain between them follows the gamma distribution Ga(M,1), h jm Used to describe interference devices x j and access point y m The Rayleigh fading between follows an exponential distribution with a mean of 1, Φ d \{x i} indicates interference device x j The location set of the device is specifically to remove the device x under study from the location set of the device i The location is obtained after; is the indicator function, device x j The value is 1 when time slot t is in the active state, otherwise it is 0; l(||x i ||) represents device x i and access point y m The path loss between j ||) indicates the interfering device x j With access point m The path loss between .

[0025] Furthermore, in step S3, the method for obtaining the conditional transmission success probability expression of the device is:

[0026] For the decellularized network architecture, all access points are connected to the CPU via a fronthaul link. As long as any of the device's associated access points successfully receives a data packet from the device, the CPU and the device can successfully communicate. In order to obtain the expression for the probability of successful transmission, the interruption probability of a single device transmission can be obtained first. Down, Devicex i Probability of interruption of data packet transmission to the associated access point:

[0027]

[0028] Among them, SIR im,t Represents device x iThe signal-to-interference ratio during the transmission of a data packet to the associated access point in time slot t; θ represents the set SIR threshold value. If the SIR during the transmission process is lower than the threshold value θ, the transmission is considered to have failed, otherwise it is considered to have succeeded.

[0029] According to the interruption probability, we can further obtain the device x with k associated access points: i The transmission success probability of the transmitted data packet:

[0030]

[0031] In a given space Next, device x i The conditional transfer success probability is defined as the CPU successfully acquires device x i The probability of a data packet is expressed as:

[0032]

[0033] Where χ(k) is the probability that a device has k (∈{0,1,...,∞}) associated access points; according to the properties of the Poisson point process, χ(k) is expressed as:

[0034]

[0035] Furthermore, in step S4, the stable state is defined as the state distribution in the system tends to be stable over time and no longer changes over time; the method for obtaining the overall active probability of the device in the stable state is: the energy amount in the server battery of multiple IoT devices in the network will change with the time slot. As long as the energy amount in the battery is not 0, the device has a probability of being active in the next time slot. The change of the energy amount in the battery is modeled by a one-dimensional Markov chain, then the active probability a of the device x0 i You can write:

[0036]

[0037] Furthermore, in step S5, the moment generating function of the final transmission success probability is specifically,

[0038]

[0039] Where b represents the order of the moment generating function;

[0040] The expression of the signal-to-interference ratio element distribution of the cellular network is specifically:

[0041] According to the Gil-Pelaez theorem, the distribution of the signal-to-interference ratio of the cellular network is obtained, that is, the expression of the cumulative distribution function of the conditional transmission success probability, which is specifically expressed as:

[0042]

[0043] in:

[0044]

[0045] Where χ(0) represents the device x i The probability of associating with 0 access points, θ represents the probability of successful transmission, ρ represents the integral variable, Im{·} represents the imaginary part of the complex number, k2,v0,v1,...v M By the polynomial theorem The resulting term, exp{·}, indicates exponential calculation.

[0046] The inventive principle of the present invention is:

[0047] In large-scale mMIMO cellular networks, many IoT devices use the same spectrum resources to communicate with the CPU. Due to the limited spectrum resources, not too many devices can transmit at the same time, otherwise it will cause network congestion, resulting in a large number of transmission failures and poor network performance.

[0048] The present invention first analyzes the signal-to-interference ratio (SIR) at the receiving end, and constructs a universal SIR expression through a mathematical model. Taking into account the uniqueness of decellularized networks, the principle of probability theory is introduced to evaluate the conditional interruption probability of the device, from which the conditional transmission success probability of the device can be inferred. In view of the fluctuation of the number of active devices in different time slots, the Markov chain steady-state theory is introduced in the present invention to simulate the dynamic changes in the energy state of the device. By solving the Markov chain problem, the average activity probability of the devices in the entire network can be estimated. On this basis, combined with the principles of random geometry and the Gil-Pelaez theorem, the precise distribution of the conditional transmission success rate of the device, that is, the meta-distribution of SIR, can be derived. This method not only reveals the key statistical characteristics of network performance, but also provides a theoretical basis for optimizing network design, ensuring that high-quality communication services can be maintained even at peak network loads.

[0049] The advantages and beneficial effects of the present invention are:

[0050] This paper proposes a random geometry-based method for analyzing the signal-to-interference ratio element distribution of decellularized mMIMO networks, and develops a theoretical framework to evaluate the signal-to-interference ratio element distribution of reference devices. Experimental simulations can demonstrate that the theoretical method is well consistent with the simulation results, proving that the algorithm proposed in this paper can make a good fit estimate of the communication transmission of devices in an infinite network.

[0051] The present invention innovatively proposes a method for analyzing the signal-to-interference ratio element distribution for decellularized mMIMO networks. The method constructs a comprehensive theoretical framework for accurately evaluating the SIR statistical characteristics of devices in the network. Through extensive experimental simulation verification, the method of the present invention has demonstrated excellent accuracy, and the theoretical predictions are in good agreement with the actual simulation results, confirming the effectiveness and reliability of the algorithm. The method proposed by the present invention can accurately estimate and model the communication performance of devices in an infinite network environment, providing strong theoretical support for the design and optimization of mMIMO systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a curve diagram showing the distribution of conditional transmission success probability under 5dB, 10dB and 15dB signal-to-noise ratios. The theoretical analysis method proposed in the present invention can be well consistent with the simulation results, proving the accuracy of the proposed theory.

[0053] Figure 2 The distribution of the probability of successful transmission under all transmission link conditions in the network under different signal-to-noise ratio thresholds is further explored.

[0054] Figure 3 According to the theoretical analysis method proposed in the present invention, the influence of two key factors, energy arrival rate η and allowed transmission probability p, on the average conditional transmission success probability of the link is explored. DETAILED DESCRIPTION

[0055] A random geometry-based method for analyzing the signal-to-interference ratio element distribution of a decellularized network comprises the following steps:

[0056] S1. During the uplink transmission process, a three-dimensional model of node distribution, service arrival time, and device energy status in the decellularized space is constructed.

[0057] Taking into account the randomness of device spatial position, data packet arrival, and energy collection, a three-dimensional model of de-cellularized space node distribution, service time arrival, and device energy status is established; the three-dimensional model of de-cellularized space node distribution, service time arrival, and device energy status includes a de-cellularized space node distribution model, a service time arrival model, and a device energy status model.

[0058] (1) The decellularized spatial node distribution model is as follows: For a decellularized mMIMO network with uplink transmission, the locations of all access points in the network are Φ a The density is λ a Poisson point process; each access point has M antennas and is connected to a CPU; all IoT devices are equipped with a single antenna, and the location of the IoT device is Φ d The density is λ d Poisson point distribution of each device x iThe transmission power is P, which is determined by the device x i The center of the circle is itself and the radius is R a All associated access points within range are collectively device x i Service; The channel characteristics between the device and the access point antenna are determined by path loss and unit mean Rayleigh fading. According to the bounded path loss model, the path loss can be expressed as follows:

[0059] l(r)=max(R0,r) -α (11)

[0060] Where: r is device x i To its associated access point y m distance, R0 is the reference distance, and α is the path loss exponent.

[0061] The distribution of r is used to evaluate the associated access point received from device x i signal from device x i To any of its associated access points y m The probability density function of the distance r is:

[0062]

[0063] (2) The service time arrival model is specifically as follows: time is divided into equally spaced time slots, and the generation and transmission of data packets occur at the boundaries of the time slots. Each data packet has the same size and requires one time slot for its transmission. When the energy count of an IoT device is not 0, it generates a data packet with probability p and sends it to all associated access points. The associated access points then use a reliable fronthaul link to forward the received data to the CPU, and the time for this process is negligible. In particular, energy cannot be harvested while transmitting data packets.

[0064] (3) The device energy state model is specifically as follows: each device collects energy from the surrounding nature (such as wind energy, thermal energy, solar energy, etc.) and sends data when the energy is sufficient. The battery capacity of the device is E units. When the battery is not full, each time slot independently collects one unit of energy with probability η. Sending a data packet consumes one unit of energy. Without loss of generality, in the present invention, E>3.

[0065] S2. Based on the three-dimensional model of node distribution, service arrival time, and device energy state in the decellularized space, an expression of the signal-to-interference ratio of the receiving end is obtained. The specific method is:

[0066] For device x i , calculate its correlation with the access point y m The specific method is: m As the origin, with y m Point to xi The direction of the x-axis is used to establish a coordinate system, then x i With y m The distance is the device x i The horizontal axis of

[0067] In time slot t, for device x i Associate access point y to it m Send data packets on this communication link, y m The received signal-to-interference ratio SIR is expressed as:

[0068]

[0069] in: Is device x i and its associated access point y m The total channel gain between them follows the gamma distribution Ga(M,1), h jm Used to describe interference devices x j and access point y m The Rayleigh fading between follows an exponential distribution with a mean of 1, Φ d \{x i} indicates interference device x j The location set of the device is specifically to remove the device x under study from the location set of the device i The location is obtained after; is the indicator function, device x j The value is 1 when time slot t is in the active state, otherwise it is 0; l(||x i ||) represents device x i and access point y m The path loss between j ||) indicates the interfering device x j With access point m The path loss between .

[0070] S3, based on the decellularized spatial node distribution, service arrival time, device energy state three-dimensional model and the signal-to-interference ratio expression of the receiving end, obtain the conditional transmission success probability expression of the device. The specific method is:

[0071] As long as any of the device's associated access points successfully receives the data packet from the device, the CPU and the device can communicate successfully. In order to obtain the expression of the transmission success probability, the interruption probability of a single device transmission can be obtained first. Down, Devicex i Probability of interruption of data packet transmission to associated access point for:

[0072]

[0073] Among them, SIR im,t Represents device x i The signal-to-interference ratio during the transmission of a data packet to the associated access point in time slot t; θ represents the set SIR threshold value. If the SIR of the transmission process is lower than the threshold value θ, the transmission is considered to have failed, otherwise it is considered to have succeeded;

[0074] According to the interruption probability, we can further obtain the device x with k associated access points: i The transmission success probability of the transmitted data packet:

[0075]

[0076] In a given space Next, device x i The conditional transfer success probability is defined as the CPU successfully acquires device x i The probability of a data packet is expressed as:

[0077]

[0078] Where χ(k) is the probability that a device has k (∈{0,1,...,∞}) associated access points; according to the properties of the Poisson point process, χ(k) is expressed as:

[0079]

[0080] S4. Based on the three-dimensional model of the de-cellularized spatial node distribution, service arrival time, and device energy state, the overall activity probability of the device in a stable state is obtained.

[0081] In step S4, the stable state is defined as the state distribution in the system tends to be stable over time and no longer changes over time. The method for obtaining the overall active probability of the device in the stable state is as follows: the energy count in the server battery of multiple IoT devices in the network will change with the time slot. As long as the energy count in the battery is not 0, the device has a probability of being active in the next time slot. The change in the energy count in the battery is modeled with a one-dimensional Markov chain. The active probability a of device x0 i You can write:

[0082]

[0083] S5. Based on the conditional transmission success probability expression of the device obtained in step S3, the moment generating function of the final transmission success probability is obtained using the random geometry method. Combined with the signal-to-interference ratio expression of the receiving end in step S2 and the overall active probability of the device in the stable state in step S4, the expression of the signal-to-interference ratio element distribution of the de-cellular network is obtained based on the Gil-Pelaez theorem; the proportion of the successful transmission probability of the devices in the de-cellular network is analyzed using the expression of the signal-to-interference ratio element distribution of the de-cellular network.

[0084] The moment generating function of the final transmission success probability is specifically,

[0085]

[0086] Where b represents the order of the moment generating function.

[0087] The expression of the signal-to-interference ratio element distribution of the cellular network is specifically:

[0088]

[0089] in:

[0090]

[0091] Where χ(0) represents the device x i The probability of associating with 0 access points, θ represents the probability of successful transmission, ρ represents the integral variable, Im{·} represents the imaginary part of the complex number, k2,v0,v1,...v M By the polynomial theorem The obtained term, exp{·} represents exponential calculation. The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0092] The communication performance of uplink transmission in the decellularized network is evaluated by Matlab simulation. The access points and devices in the network are set to follow Poisson distribution with densities of 5×10 -3 and 1.5×10 -2 . Each access point is equipped with 2 antennas, and all devices are single antennas. The service cluster of each device covers all access points within a circle with a radius of 8m and itself as the center. The channel between any IoT device and the access point antenna is affected by both large-scale path loss and small-scale Rayleigh fading with unit mean, and the path loss exponent α is 4. The reference distance is 1m. Therefore, the path loss is calculated using the bounded path loss model, which can be specifically expressed as:

[0093] l(r)=[max(1,r)] -α

[0094] The energy arrival process of each node conforms to the Bernoulli process, and 1 unit of energy can be collected independently in each time slot with a probability of 0.4. Assume that the battery capacity of the server is 30 units, and the arriving energy can only be stored when the battery is not fully charged. Sending a data packet consumes 1 unit of energy. Divide the time into equally spaced time slots, and the generation and transmission of data packets occur at the boundaries of the time slots. Each data packet has the same size and can be transmitted to the receiver within one time slot. Data packets are generated by the device and sent to all access points in the service cluster. When the number of energy in the server equipped with the IoT device is greater than or equal to 1, it will collect the surrounding environmental data with a probability of 0.7 and generate data packets to access the channel. In particular, it is assumed that energy cannot be collected while transmitting data packets.

[0095] Figure 1 The signal-to-interference ratio element distributions when the signal-to-noise ratio receiving thresholds are 5dB, 10dB, and 15dB are compared. Overall, the higher the threshold value is set, the lower the proportion of the transmission success probability is, which is consistent with common sense: a higher threshold value often requires a higher signal-to-interference ratio at the receiving end. Taking the 10dB curve as an example, it can be seen that about 80% of the link transmission success probabilities in the entire network are higher than 0.7, that is, the conditional transmission success probability of most devices is at a high level. At the same time, it can be seen that the analysis method proposed in the present invention is very consistent with the simulation results, so the method can be used to accurately analyze the distribution of conditional transmission success probabilities of devices in large-scale networks.

[0096] Depend on Figure 1 It can be seen that it is very important to set a suitable threshold value in the entire simulation process. Figure 2 The relationship between the threshold value and the probability distribution of conditional transmission success was further explored. The numbers on the line in the figure indicate the proportion of links in the network that reach this transmission probability. Overall, as the threshold value θ gradually increases, the probability of conditional transmission success gradually decreases. This is because setting a higher transmission threshold often means more stringent transmission conditions. Taking the conditional transmission success probability of 0.5 as an example, it can be seen that when the threshold value is set to 10dB, 90% of the links have a transmission success probability of 0.5, while when the threshold value is set to 20dB, only about 50% of the links can reach it. At the same time, it can be seen from the density and sparseness of the curve that as the threshold value θ increases, its influence on the conditional transmission success probability first increases and then decreases.

[0097] Figure 3Two key factors that affect the average probability of successful transmission are analyzed: the energy arrival probability η and the sending probability p. First, for any given η, as p increases, the average probability of successful transmission first decreases and then stabilizes. This is because, as p increases, the number of active links in the network also increases, increasing mutual interference. When p increases to a certain extent, energy limits the activity state of the device, so the average probability of successful transmission remains unchanged. In addition, for lower p values, as η increases, the average probability of successful transmission also shows a trend of first decreasing and then stabilizing. The reason is that the increase in η allows more devices to meet energy requirements, which in turn increases the interference between links. The final stabilization is because the transmission activity is restricted. In contrast, at higher p values, as long as there is a sufficient number of energy units, the device is likely to access the channel. Therefore, as η increases, more devices will transmit simultaneously, resulting in a continuous decrease in the average probability of successful transmission.

Claims

1. A random geometry-based method for analyzing the signal-to-interference ratio distribution of decellularized networks, characterized in that: The method comprises the following steps: S1. During the uplink transmission process, a three-dimensional model of node distribution, service arrival time, and device energy status in the decellularized space is constructed; S2. Based on the three-dimensional model of node distribution, service arrival time, and device energy state in the decellularized space, an expression of signal-to-interference ratio of the receiving end is obtained; S3, based on the decellularized spatial node distribution, service arrival time, device energy state three-dimensional model and the signal-to-interference ratio expression of the receiving end, obtain the conditional transmission success probability expression of the device; S4, based on the three-dimensional model of the decellularized spatial node distribution, service arrival time, and device energy state, obtain the overall activity probability of the device in a stable state; S5. Based on the conditional transmission success probability expression of the device obtained in step S3, the moment generating function of the final transmission success probability is obtained using the random geometry method. Combined with the signal-to-interference ratio expression of the receiving end in step S2 and the overall active probability of the device in the stable state in step S4, the expression of the signal-to-interference ratio element distribution of the de-cellular network is obtained based on the Gil-Pelaez theorem; the proportion of the successful transmission probability of the devices in the de-cellular network is analyzed using the expression of the signal-to-interference ratio element distribution of the de-cellular network.

2. According to claim 1, a random geometry-based decellularized network signal-to-interference ratio element distribution analysis method is characterized in that: In step S1, considering the randomness of the device spatial position, data packet arrival, and energy collection, a three-dimensional model of decellularized space node distribution, service time arrival, and device energy state is established; the three-dimensional model of decellularized space node distribution, service time arrival, and device energy state includes a decellularized space node distribution model, a service time arrival model, and a device energy state model; The decellularized spatial node distribution model is specifically as follows: for a decellularized mMIMO network for uplink transmission, the locations Φ of all access points in the network a The density is λ a Poisson point process; each access point has M antennas and is connected to a CPU; all IoT devices are equipped with a single antenna, and the location of the IoT device is Φ d The density is λ d Poisson point distribution of each device x i The transmission power is P, which is determined by the device x i The center of the circle is itself and the radius is R a All associated access points within range are collectively device x i service; the channel characteristics between the device and the access point antenna are determined by path loss and unit mean Rayleigh fading; The calculation of the path loss is based on a bounded path loss model, and the specific expression is as follows: l(r)=max(R0,r) -α (1) Where: r is device x i To its associated access point y m distance, R0 is the reference distance, and α is the path loss exponent; The distribution of r is used to evaluate the associated access point received from device x i signal from device x i To any of its associated access points y m The probability density function of the distance r is: The service time arrival model is specifically as follows: time is divided into equally spaced time slots, and the generation and transmission of data packets occur at the boundaries of time slots; each data packet has the same size, and its transmission requires one time slot; when the energy number of the IoT device is not 0, it will generate a data packet with probability p and send it to all associated access points; the associated access points will then use a reliable forwarding link to forward the received data to the CPU, and the time of the forwarding process is negligible; energy cannot be collected while transmitting data packets; The device energy state model is specifically as follows: each device collects energy from the surrounding nature and sends data when the energy is sufficient; the battery capacity of the device is E units, and when the battery is not fully charged, each time slot independently collects one unit of energy with a probability η; sending a data packet consumes one unit of energy; set E>3.

3. According to claim 2, a random geometry-based decellularized network signal-to-interference ratio element distribution analysis method is characterized in that: In step S2, the method for obtaining the signal-to-interference ratio expression of the receiving end is specifically as follows: For device x i , calculate its correlation with the access point y m The specific method is: m As the origin, with y m Point to x i The direction of the x-axis is used to establish a coordinate system, then x i With y m The distance is the device x i The horizontal axis of In time slot t, for device x i Associate access point y to it m Send data packets on this communication link, y m The received signal-to-interference ratio SIR is expressed as: in: Is device x i and its associated access point y m The total channel gain between them follows the gamma distribution Ga(M,1), h jm Used to describe interference devices x j and access point y m The Rayleigh fading between follows an exponential distribution with a mean of 1, Φ d \{x i } indicates interference device x j The location set of the device is specifically to remove the device x under study from the location set of the device i The location is obtained after; is the indicator function, device x j The value is 1 when time slot t is in the active state, otherwise it is 0; l(‖x i ‖) represents device x i and access point y m The path loss between j ||) indicates the interfering device x j With access point m The path loss between .

4. According to claim 3, a random geometry-based decellularized network signal-to-interference ratio element distribution analysis method is characterized in that: In step S3, the method for obtaining the conditional transmission success probability expression of the device is: As long as any of the device's associated access points successfully receives the data packet from the device, the CPU and the device can communicate successfully. In order to obtain the expression of the transmission success probability, the interruption probability of a single device transmission can be obtained first. Down, Devicex i Probability of interruption of data packet transmission to associated access point for: Among them, SIR im,t Represents device x i The signal-to-interference ratio during the transmission of a data packet to the associated access point in time slot t; θ represents the set SIR threshold value. If the SIR of the transmission process is lower than the threshold value θ, the transmission is considered to have failed, otherwise it is considered to have succeeded; According to the interruption probability, we can further obtain the device x with k associated access points: i The transmission success probability of the transmitted data packet: In a given space Next, device x i The conditional transfer success probability is defined as the CPU successfully acquires device x i The probability of a data packet is expressed as: Where χ(k) is the probability that a device has k (∈{0,1,...,∞}) associated access points; according to the properties of the Poisson point process, χ(k) is expressed as:

5. According to claim 4, a random geometry-based decellularized network signal-to-interference ratio element distribution analysis method is characterized in that: In step S4, the stable state is defined as the state distribution in the system tends to be stable over time and no longer changes over time; the method for obtaining the overall active probability of the device in the stable state is as follows: the energy amount in the server battery of multiple IoT devices in the network will change with the time slot; as long as the energy amount in the battery is not 0, the device has a probability of being active in the next time slot; the change of the energy amount in the battery is modeled by a one-dimensional Markov chain, then the active probability a of the device x0 i writing:

6. According to the random geometry-based method for analyzing the signal-to-interference ratio of a decellularized network as claimed in claim 1, it is characterized in that: In step S5, the moment generating function of the final transmission success probability is specifically, Where b represents the order of the moment generating function; The expression of the signal-to-interference ratio element distribution of the cellular network is specifically: in: Where χ(0) represents the device x i The probability of associating with 0 access points, represents the probability of successful transmission, ρ represents the integral variable, Im{·} represents the imaginary part of the complex number, k2,v0,v1,...v M By the polynomial theorem The resulting term, exp{·}, indicates exponential calculation.

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