Multi-user communication resource allocation method and system

Abstract

The invention relates to the technical field of communication, in particular to a multi-user communication resource allocation method and system, and the method comprises the following steps: regularly collecting the position information, bandwidth, delay and signal strength of a user terminal, monitoring the network state in real time, generating a load index, predicting the future network environment change according to the load index, a user track and the signal strength, and carrying out the resource allocation. Evaluating channel quality, calculating interference probability, adjusting base station spectrum and power, analyzing network load state and flow demand, combining user application and time period prediction demand, and dynamically optimizing bandwidth and resource configuration. According to the method, the network load state is dynamically monitored through real-time collection and analysis of the data uploaded by the user terminal, so that the distribution of communication resources is more accurate, the future moving trend of the user is calculated according to the user position information and the historical moving track, the network environment change which the user is about to face is predicted, and the user experience is improved. Therefore, interference management and channel quality optimization are performed in advance.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a multi-user communication resource allocation method and system. Background Art

[0002] Communication technologies cover processes such as information transmission, switching, and reception, mainly involving technologies for long-distance information transmission using media such as electromagnetic waves, light waves, radio waves, etc. This field includes, but is not limited to, various communication methods such as wireless communication, wired communication, satellite communication, and optical fiber communication. With the development of the information society, communication technologies are increasingly closely related to data transmission, network architecture, communication protocols, and signal processing, giving rise to modern communication technologies such as 5G / 6G communication, Internet of Things (IoT), and cloud computing.

[0003] Among them, the multi-user communication resource allocation method involves how to reasonably allocate limited communication resources (such as spectrum, time, power, etc.) in a wireless communication network to achieve effective communication among multiple users. As the number of users in the network increases, how to optimize resource allocation has become a key issue in communication network design. The purpose of this method is to improve the overall efficiency of the network, ensure that the communication needs of each user are met, and avoid resource waste.

[0004] Existing technologies are unable to accurately predict changes in user behavior and traffic demand when dealing with an increasing number of users and complex communication environments, fail to effectively consider user mobility and changes in network status, and have lagging spectrum allocation and power control, resulting in waste of network resources and fluctuations in user communication quality. Especially in the case of high load and large interference, existing technologies are unable to effectively optimize spectrum usage, leading to a serious decline in communication quality. In addition, due to the failure to accurately predict and timely adjust resources, existing technologies are relatively rough in handling traffic demand fluctuations, affecting the overall efficiency of the network and user experience. Summary of the Invention

[0005] The object of the present invention is to solve the deficiencies existing in the prior art, and to propose a multi-user communication resource allocation method and system.

[0006] To achieve the above object, the present invention adopts the following technical solution: A multi-user communication resource allocation method, including the following steps,

[0007] S1: Regularly collect the location information, bandwidth usage, communication delay, and signal strength data uploaded by user terminals, real-time monitor the communication network status, judge whether the network is in an overloaded or underloaded state by analyzing the network performance, and generate a network load index;

[0008] S2: According to the network load index, based on the current user location information and historical movement trajectory, the user's future movement trend is estimated, the network environment changes that the user will face are predicted, and the user's relative position and movement speed are calculated to determine the quality of the channel where the user is located, and obtain channel quality prediction data;

[0009] S3: Analyze potential interference sources in the area where the user is located according to the channel quality prediction data, judge the severity of the interference by comparing the current spectrum usage and the signal-to-noise ratio, and adjust the spectrum allocation and power control of the base station to obtain the interference management configuration;

[0010] S4: Based on the interference management configuration, the network load status is classified into low load, normal load and overload status, the changes in user traffic demand under different load states are analyzed, and the future traffic demand is predicted in combination with the user's common application types and online time periods to obtain the traffic demand analysis results.

[0011] The present invention has improvements in that the network load index includes network status fluctuation, packet loss event frequency, and bandwidth load distribution; the channel quality prediction data includes user signal reception quality, future channel interference probability, and signal attenuation rate; the interference management configuration includes power control level and interference source identification results; and the traffic demand analysis results include peak period traffic prediction results and resource demand information under low load conditions.

[0012] The present invention is improved in that the steps of obtaining the network load indicator are specifically as follows:

[0013] S111: regularly collect location information, bandwidth usage, communication delay and signal strength data uploaded by user terminals, monitor the communication network status in real time, preliminarily analyze the status data, evaluate the current network status, and obtain the preliminary evaluation result of the communication network;

[0014] S112: Based on the initial evaluation results of the communication network, the packet loss rate, delay and bandwidth utilization are calculated, key indicator points are screened, and it is determined whether the network is in an overload or underload state, using the formula:

[0015]

[0016] Generate network load index TR, where m tr Indicates the number of network load data points, PLR i Represents the packet loss rate of the i-th data point, THR PLR Represents the packet loss rate threshold, LAT i represents the communication delay of the ith data point, BW i Represents the bandwidth utilization of the i-th data point.

[0017] The present invention is improved in that the step of acquiring the channel quality prediction data is specifically as follows:

[0018] S211: Calculate the user's future movement trend according to the network load indicator, current user location information and historical movement trajectory, and obtain user movement trend data;

[0019] S212: Based on the user movement trend data, combined with the current signal strength, network load and interference situation, predict the network environment changes that the user will face, and calculate the user's relative position and movement speed according to the prediction result;

[0020] S213: According to the relative position and moving speed of the user and the network load, the formula is used:

[0021]

[0022] Determine the quality status QS of the channel where it is located and obtain the channel quality prediction data, where Se represents the current signal strength, Te represents the predicted user movement trend, Le represents the network load, De represents the user's relative position and movement speed prediction data, Ie represents the interference intensity, and Ne represents the interference noise value.

[0023] The present invention is improved in that the step of obtaining the interference management configuration is specifically:

[0024] S311: Analyze the area where the user is currently located according to the channel quality prediction data, evaluate the location of potential interference sources by comparing the difference channel quality and the signal-to-noise ratio, and generate interference source analysis results;

[0025] S312: Based on the interference source analysis result, the formula is used:

[0026]

[0027] Calculate the probability of interference occurring PG YU , judge the severity of interference, and adjust the base station spectrum allocation and power control to obtain the interference management configuration, where IU q represents the interference intensity of the qth interference source, SU q represents the signal-to-noise ratio of the qth channel, PU q represents the power of the qth channel, FU q represents the spectrum usage of the qth channel, NU w represents the noise level of the wth spectrum, n pg and m pg Represent the number of interference sources and the number of spectra respectively.

[0028] The present invention is improved in that the steps of obtaining the flow demand analysis result are specifically as follows:

[0029] S411: Obtain the network load status based on the interference management configuration, classify the real-time network load by monitoring network performance data into low load, normal load, and overload states, and generate a load status classification result;

[0030] S412: Based on the load status classification result, analyze the change in traffic demand of users under different load states by combining historical traffic data and real-time load changes, and use the formula:

[0031]

[0032] Obtain the traffic demand change trend TY under the load status d , where LY o represents the o-th load status, FY o represents the traffic demand under the o-th load status, and n ty represents the number of load statuses;

[0033] S413: Based on the traffic demand change trend under the load status, combine the user's common application types and online periods to predict the future traffic demand and obtain a traffic demand analysis result.

[0034] The improvement of the present invention is that the above steps further include:

[0035] S5: Based on the traffic demand analysis result, dynamically adjust network resources, identify high-traffic users and low-load areas, preferentially increase bandwidth for high-traffic users, and release unnecessary resources during low load, and dynamically configure bandwidth and power to obtain an optimized configuration of communication resources;

[0036] The optimized configuration of communication resources includes a bandwidth adjustment result, a power control result, and a low-load resource release plan.

[0037] The improvement of the present invention is that the obtaining steps of the optimized configuration of communication resources are specifically as follows:

[0038] S511: Based on the traffic demand analysis result, identify high-traffic users and low-load area data, and analyze the current bandwidth utilization rate and power settings to generate preliminary demand information for bandwidth and power;

[0039] S512: According to the preliminary demand information for bandwidth and power, dynamically configure the bandwidth and power of each area, preferentially increase bandwidth for high-traffic areas, and release unnecessary resources in low-load areas to obtain an optimized configuration of communication resources.

[0040] A multi-user communication resource allocation system, the system includes:

[0041] The load monitoring module regularly collects the location information, bandwidth usage, communication latency, and signal strength data uploaded by the user terminal, monitors the communication network status in real time, determines whether the network is in a state of overloading or underloading, and generates network load metrics;

[0042] The movement prediction module, based on the network load metrics, according to the current user location information and historical movement trajectories, calculates the future movement trends of the user, predicts the upcoming changes in the network environment faced by the user, calculates the relative position and movement speed of the user, and determines the quality status of the channel where the user is located, obtaining channel quality prediction data;

[0043] The interference management module, based on the channel quality prediction data, analyzes the potential interference sources in the area where the user is located, calculates the probability of interference occurrence, determines the severity of the interference, and adjusts the spectrum allocation and power control of the base station, obtaining interference management configurations;

[0044] The traffic prediction module, based on the interference management configurations, classifies the network load status, analyzes the changes in traffic demands of users under different load states according to the real-time network performance, combines the types of commonly used applications and online time periods of users, and predicts future traffic demands, obtaining traffic demand analysis results;

[0045] The resource optimization module, based on the traffic demand analysis results, identifies high-traffic users and low-load areas, preferentially increases the bandwidth and resources for high-traffic users, releases unnecessary resources during low load, and dynamically configures the bandwidth and power, obtaining optimized communication resource configurations.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0047] In the present invention, through the real-time collection and analysis of the data uploaded by the user terminal, the network load status is dynamically monitored, enabling more accurate allocation of communication resources. By calculating the future movement trends of the user according to the user location information and historical movement trajectories, it helps to predict the upcoming changes in the network environment faced by the user, thereby enabling early interference management and channel quality optimization. By combining the network load, signal strength, and spectrum usage, the spectrum allocation and power control are adjusted, avoiding the untimely and rough interference management in traditional methods and ensuring the stability of network communication. Through refined traffic demand prediction, for different network load states, the resource allocation is dynamically adjusted, preferentially ensuring the needs of high-traffic users while avoiding resource waste in the case of low load, achieving intelligent management of communication resources, improving the overall efficiency of the network, and optimizing the communication experience of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart of a multi-user communication resource allocation method proposed by the present invention;

[0049] Figure 2 It is the flowchart for obtaining network load metrics in the present invention;

[0050] Figure 3 It is the flowchart for obtaining channel quality prediction data in the present invention;

[0051] Figure 4 It is the flowchart for obtaining interference management configuration in the present invention;

[0052] Figure 5 It is the flowchart for obtaining traffic demand analysis results in the present invention;

[0053] Figure 6 It is the flowchart for obtaining optimized allocation of communication resources in the present invention. Detailed implementation manners

[0054] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0056] Embodiment

[0057] Please refer to Figure 1 , the present invention provides a technical solution: a multi-user communication resource allocation method, including the following steps:

[0058] S1: Regularly collect the location information, bandwidth usage, communication latency, and signal strength data uploaded by user terminals, monitor the communication network status in real time, analyze the network performance, and judge whether the network is in an overloaded or underloaded state based on the packet loss rate, latency, and bandwidth utilization rate, and generate network load metrics;

[0059] S2: According to the network load metrics, based on the current user location information and historical movement trajectories, deduce the future movement trends of users, combine the current signal strength, network load, and interference conditions, predict the upcoming changes in the network environment faced by users, calculate the relative positions and movement speeds of users, and judge the quality status of the channels they are in to obtain channel quality prediction data;

[0060] S3: According to the channel quality prediction data, analyze the potential interference sources in the area where the user is located, calculate the probability of interference occurrence, judge the severity of interference by comparing the current spectrum usage and the signal-to-noise ratio, and adjust the spectrum allocation and power control of the base station to obtain the interference management configuration;

[0061] S4: Based on the interference management configuration, classify the network load status into low load, normal load, and overloaded status, analyze the change in traffic demand of users under different load statuses according to the real-time network performance, and combine the user's common application types and online time periods to predict the future traffic demand to obtain the traffic demand analysis result;

[0062] S5: According to the traffic demand analysis result, dynamically adjust the network resources, identify high-traffic users and low-load areas, preferentially increase the bandwidth for high-traffic users, and release unnecessary resources during low load, and dynamically configure the bandwidth and power to obtain the optimized configuration of communication resources.

[0063] The network load indicators include network status fluctuations, packet loss event frequencies, and bandwidth load distributions. The channel quality prediction data includes user signal reception quality, future channel interference probability, and signal attenuation rate. The interference management configuration includes power control levels and interference source identification results. The traffic demand analysis result includes peak-hour traffic prediction results and resource demand information under low-load conditions. The optimized configuration of communication resources includes bandwidth adjustment results, power control results, and low-load resource release plans.

[0064] Please refer to Figure 2 , and the specific steps for obtaining the network load indicators are as follows:

[0065] S111: Regularly collect the location information, bandwidth usage, communication delay, and signal strength data uploaded by user terminals, monitor the communication network status in real time, preliminarily analyze the status data, and evaluate the current network status to obtain the preliminary evaluation result of the communication network;

[0066] Regularly collect the data uploaded by user terminals, including location information, bandwidth usage, communication latency, and signal strength. The data provides the basic situation of the network status and helps to monitor the health of the communication network in real time. For example, a user terminal uploads its location information, bandwidth usage (such as 100 Mbps), communication latency (such as 50 ms), and signal strength (such as -65 dBm) once per minute. In actual application scenarios, continuously obtain the terminal status data, generate a data stream, and store it in the server. Through the data, the current network status can be evaluated in real time. For example, when the bandwidth utilization rate exceeds 80%, or the communication latency is higher than 100 ms, the network is in a relatively busy state. When the signal strength is lower than -80 dBm, it means that the signal quality is poor and the user experience is affected. The basic data collected, after preliminary analysis and processing, generates a preliminary evaluation result of the communication network to help further analyze whether the network is operating normally or whether corresponding adjustments are needed. For example, a network provider receives the average bandwidth usage data uploaded by users in a region. If the bandwidth usage of users in this region is generally high (such as an average bandwidth of 95 Mbps), but the latency situation is relatively low (such as 50 ms), combined with the signal strength (-65 dBm) as a reference, a preliminary network status evaluation is made to confirm that the network in this region is currently in a normal load state.

[0067] S112: Based on the preliminary evaluation result of the communication network, calculate the packet loss rate, latency, and bandwidth utilization rate, screen the key index points, and judge whether the network is in an overloaded or underloaded state. Use the formula:

[0068]

[0069] Generate the network load index TR, where m tr represents the number of network load data points, PLR i represents the packet loss rate of the i-th data point, indicating the proportion of packets lost during data transmission. THR PLR represents the packet loss rate threshold, which is used to judge the normal range and abnormal range of the packet loss rate. LAT i represents the communication latency of the i-th data point, which refers to the time required for data to travel from the source to the destination. BW i represents the bandwidth utilization rate of the i-th data point, indicating the usage of the network bandwidth;

[0070] Deeply analyze the key indicators of network load, especially the packet loss rate (PLR), latency (LAT), and bandwidth utilization (BW). Determine whether the network is in an overloaded or underloaded state based on these indicators. At this time, calculate the packet loss rate, latency, and bandwidth utilization for each data point, and then perform screening to select the most representative several data points. If the number of network load data points is 2, where the packet loss rate (PLR) for each data point is 0.02 and 0.05; the latency (LAT) is 30 ms and 40 ms; the bandwidth utilization (BW) is 70% and 80%, substitute the data points into the formula, where the packet loss rate threshold (THR PLR ) is 0.03, the latency unit is ms, and the bandwidth unit is %, according to the above data, for the first data point:

[0071] PLR1 = 0.02, LAT1 = 30 ms, BW1 = 70%;

[0072] |PLR1 - THR PLR | = |0.02 - 0.03| = 0.01;

[0073]

[0074] The calculated value of the first data point is:

[0075] 0.01 + 0.4286 = 0.4386;

[0076] For the second data point:

[0077] PLR2 = 0.05, LAT2 = 40 ms, BW2 = 80%;

[0078] |PLR2 - THR PLR | = |0.05 - 0.03| = 0.02;

[0079]

[0080] The calculated value of the second data point is:

[0081] 0.02 + 0.5 = 0.52;

[0082] Add up the calculated values of the first and second data points to obtain the network load index:

[0083] TR = 0.4386 + 0.52 = 0.9586;

[0084] The calculation result TR = 0.9586 represents the load index of the network area. Through the comprehensive analysis of the packet loss rate, bandwidth utilization rate, and latency, an overall assessment of the network load can be obtained, which reflects the load status of the network at these two data points. Combining with the actual situation, it can be used to determine whether the network is in an overloaded or underloaded state.

[0085] Please refer to Figure 3 , and the steps for obtaining the channel quality prediction data are specifically as follows:

[0086] S211: According to the network load index, based on the current user location information and historical movement trajectory, infer the future movement trend of the user and obtain the user movement trend data;

[0087] By obtaining the current location and historical trajectory of the user, combined with the existing network load index, use the inference method to judge the future movement path of the user. For example, if the current location of the user is at the edge of the coverage area of a certain base station and its historical trajectory shows that it often moves along a certain path, then it can be predicted that the user will continue to move in that direction. At this time, the system collects the user's historical trajectory, fits its recent movement trend, and infers the future movement direction and speed to obtain the user future movement trend data, that is, the position change of the user in the next period of time. For example, if the average movement speed of the user in the past 5 minutes is 5 km / h and its movement trajectory shows a certain stability, then the system can predict that the user will continue to move in the specified direction at this speed in the next 5 minutes, thus obtaining the user's future movement trend data.

[0088] S212: Based on the user movement trend data, combined with the current signal strength, network load, and interference situation, predict the upcoming network environment changes for the user, and calculate the relative position and movement speed of the user according to the prediction results;

[0089] By monitoring the current signal strength, obtain the real-time data of the signal quality. If the signal strength is weak at this time, such as only -85 dBm, and there are many interference sources around (such as the high-frequency bandwidth usage of other users), then the network load and interference will have a greater impact on the user. Based on this, the system combines the current network load situation and the strength of the interference source to evaluate the upcoming network environment changes for the user. For example, when the user is about to approach a traffic-intensive area (such as inside a shopping mall) where the network load has reached more than 90%, the upcoming network environment changes for the user indicate that the network quality will drop significantly. Finally, through prediction calculation, obtain the relative position and speed prediction data of the user, that is, the movement speed and direction of the user in this network environment. The predicted data will provide a reference for subsequent channel quality assessment.

[0090] S213: Based on the user's relative position and moving speed, combined with the network load situation, use the formula:

[0091]

[0092] Judge the quality status QS of the channel where it is located to obtain channel quality prediction data. Among them, Se represents the current signal strength, indicating the reception quality of the signal. The larger the value, the better the signal quality. Te represents the predicted value of the user's moving trend, indicating the future moving direction and speed predicted based on the user's current position and historical moving trajectory. Le represents the network load, indicating the current load of the network. The larger the value, the higher the network load. De represents the prediction data of the user's relative position and moving speed, indicating the relative position and moving speed of the user under the channel coverage. Ie represents the interference intensity, indicating the intensity of the interference signal existing in the network environment. Ne represents the interference noise value, indicating the intensity of the noise in the signal;

[0093] Obtain the current signal strength (set to -80 dBm), the predicted value of the user's moving trend (set to 3 km / h), and perform preliminary calculations based on the network load (set to 80%). When the user is at the edge of the channel coverage in terms of relative position and speed, the predicted channel quality index is:

[0094]

[0095] The calculated channel quality prediction data is 1.075. This value represents the status of the channel quality. If this result exceeds the threshold (for example, 1.0), it indicates that the current channel quality is poor, resulting in a poor user experience. If the channel quality value is lower than a certain standard, the system can automatically switch the user to a base station with a stronger signal or adjust through network optimization to ensure the communication quality.

[0096] Please refer to Figure 4 , and the specific steps for obtaining the interference management configuration are as follows:

[0097] S311: Based on the channel quality prediction data, analyze the area where the user is currently located. By comparing the differential channel quality and the signal-to-noise ratio, evaluate the potential interference source location and generate an interference source analysis result;

[0098] First, it is necessary to obtain the channel quality data of the user's location and the data of the surrounding signal environment. For example, obtain the signal strength, frequency usage, and noise level data of the user's location from the base station and wireless access point. The signal quality prediction data of the user shows that the signal strength is -50dBm, while the signal strength of the surrounding interference sources is between -70dBm and -60dBm. By comparing the data, potential interference sources can be identified. If the signal-to-noise ratio (SNR) is low in a certain frequency band, it is because the spectrum usage in this frequency band is high or there are more interference sources in the surrounding environment, resulting in poor signal quality in this frequency band. For example, when the signal-to-noise ratio is lower than a certain threshold, such as 10dB, it indicates the existence of potential interference sources, and the quality of the signal will be affected at this time. Therefore, the generated interference source analysis results can help identify the specific location of the interference source and provide data support for subsequent interference management decisions.

[0099] S312: Based on the interference source analysis results, use the formula:

[0100]

[0101] Calculate the probability PG of interference occurrence YU , judge the severity of the interference, and adjust the base station spectrum allocation and power control to obtain the interference management configuration, where IU q represents the interference intensity of the qth interference source, indicating the degree of influence of the interference source on the signal quality in a specific channel, and SU q represents the signal-to-noise ratio of the qth channel, that is, the ratio of the signal to the noise, reflecting the quality of the signal, and PU q represents the power of the qth channel, indicating the power value of signal transmission, and FU q represents the spectrum usage of the qth channel, indicating the spectrum occupancy rate or usage degree of this channel at a specific moment, and NU w represents the noise level of the wth spectrum, indicating the noise intensity of this spectrum, which affects the signal transmission quality, and n pg and m pg represent the number of interference sources and the number of spectrums respectively;

[0102] Assume that the number of interference sources and the number of spectrums are both 2 (n pg = 2, m pg = 2):

[0103] Interference source 1:

[0104] The interference intensity IU1 = 65dBm (the normalized value is 0.5), the signal-to-noise ratio SU1 = 15dB (the normalized value is 0.75), the channel power PU1 = 20W (the normalized value is 0.4), the spectrum usage situation FU1 = 0.7 (the normalized value is 0.7), and the noise level NU1 = 80dBm (the normalized value is 0.8);

[0105] Interference source 2:

[0106] The interference intensity IU2 = 60 dBm (normalized value is 0.2), the signal-to-noise ratio SU2 = 20 dB (normalized value is 1.0), the channel power PU2 = 30 W (normalized value is 0.6), the spectrum usage situation FU2 = 0.6 (normalized value is 0.6), and the noise level NU2 = 85 dBm (normalized value is 0.85);

[0107] Substitute into the formula to calculate the numerator:

[0108]

[0109] Calculate the denominator:

[0110]

[0111] Calculate the noise part:

[0112]

[0113] Substitute into the formula to get:

[0114]

[0115] Convert the result to a percentage:

[0116] PG YU = 0.785×100% ≈ 78.5%;

[0117] This result indicates that the calculated interference probability is lower than 100%, which means that under the current configuration, the severity of the interference is not strong and no further measures need to be taken.

[0118] Please refer to Figure 5 , and the specific steps for obtaining the traffic demand analysis result are as follows:

[0119] S411: Based on the interference management configuration, obtain the network load status. By monitoring network performance data, classify the real-time network load into low load, normal load, and overload status, and generate a load status classification result;

[0120] Interference management configuration is achieved by continuously monitoring the network environment to collect network performance data from different nodes. The data includes network bandwidth, latency, packet loss rate, throughput, etc. By analyzing the performance metrics, the current network load condition can be inferred. For example, if the bandwidth utilization of some nodes exceeds 70% and the latency time reaches the preset benchmark value, it can be determined that the area is in an overloaded state; conversely, if the node bandwidth utilization is low and the latency time is short, it is determined to be in a low-load state. Through monitoring the data, the system can real-time judge whether the network is in a low-load, normal-load or overloaded state, and adjust the subsequent traffic management according to the load state classification result, generating the load state classification result.

[0121] S412: Based on the load state classification result, combined with historical traffic data and real-time load changes, analyze the traffic demand changes of users under different load states, using the formula:

[0122]

[0123] Obtain the traffic demand change trend TY under the load state d , representing the change trend value of future traffic demand under different network load states, where LY o represents the o-th load state, indicating the specific load state of the network (such as low load, normal load or overloaded state), FY o represents the traffic demand under the o-th load state, indicating the traffic demand value under a specific load state, n ty represents the number of load states;

[0124] Extract the traffic demand change information under different load states from the historical data and compare it with the current real-time network load state data. There are three load states: low load, normal load and overloaded. The traffic demand under each load state is different. Generally, the traffic demand is low under the low-load state, relatively stable under the normal-load state, and high under the overloaded state. For example, in the low-load state, some user groups will have a low traffic demand, while in the overloaded state, a sharp increase in demand will be observed. In this process, a formula is used to predict the traffic demand change. If there are three load states and the traffic demands are: low-load state: the traffic demand is 30Mbps, normal-load state: the traffic demand is 70Mbps, and overloaded state: the traffic demand is 120Mbps, and the frequency or weight of the traffic demand appearing in each state is equal, that is, the proportion of the traffic demand under each load state is 1 (i.e., the frequencies are equal). Therefore:

[0125] LY1 = 30Mbps (low load), LY2 = 70Mbps (normal load), LY3 = 120Mbps (overloaded state);

[0126] While FY1 = 30, FY2 = 70, and FY3 = 120 Mbps also correspond to the traffic demands under each load state respectively, and are calculated according to the formula:

[0127]

[0128] This result indicates that the changing trends of traffic demands under different load states have been calculated, and the changing trend of traffic demand is 5400 Mbps. This value can provide support for future traffic demand prediction.

[0129] S413: Based on the changing trend of traffic demand under the load state, combined with the user's common application types and online time periods, predict the future traffic demand to obtain the traffic demand analysis result;

[0130] Combined with the user's common application types and online time periods, further predict the future traffic demand. In the actual implementation process, it is necessary to evaluate the traffic demand trend of the user under different time periods and load states according to the user's behavior data. For example, assume that a certain user usually uses online video streaming media during peak periods and uses social media applications more during off-peak periods. At this time, it is necessary to combine the traffic demand patterns of the applications and the load state trends for prediction, predict the changes in the user's traffic demand at specific time periods in the future, so as to adjust the allocation of network resources. The prediction process uses historical data and real-time network status information to determine the traffic demand at a certain future moment, thereby helping the communication network management to make preparations in advance.

[0131] Please refer to Figure 6 , and the specific steps for obtaining the optimized allocation of communication resources are as follows:

[0132] S511: Based on the traffic demand analysis result, identify the data of high-traffic users and low-load areas, and analyze the current bandwidth utilization rate and power settings to generate preliminary bandwidth and power demand information;

[0133] Based on the traffic demand analysis result, identify the data of high-traffic users and low-load areas. First, collect the real-time network traffic information of each area, combine the current bandwidth and power configurations of each area, and calculate the usage of current network resources. The bandwidth demand is estimated by comparing the ratio of the bandwidth usage of each user to the network traffic, and the power demand is calculated based on the change of traffic in each area and the power consumption of the device. For example, in a certain high-traffic area, the bandwidth demand of the user is 20 Mbps and the power demand is 30 W, while the bandwidth demand of the low-load area is 5 Mbps and the power demand is 15 W. By analyzing the data, generate preliminary bandwidth and power demand information to provide data basis for subsequent resource allocation.

[0134] S512: Dynamically configure the bandwidth and power of each area according to the preliminary bandwidth and power demand information, preferentially increase the bandwidth for high-traffic areas, and release unnecessary resources in low-load areas to obtain an optimized configuration of communication resources;

[0135] Dynamically configure the bandwidth and power of each area according to the preliminary bandwidth and power demand information. According to the traffic demand and the current bandwidth usage situation, preferentially increase the bandwidth of high-traffic areas to ensure that the bandwidth of high-traffic areas can meet user needs. For example, in a certain high-traffic area, if its bandwidth demand exceeds 1.5 times the current bandwidth configuration, bandwidth resources need to be increased to ensure the smooth transmission of data traffic. At the same time, in low-load areas, unnecessary bandwidth and power resources will be appropriately released according to the actual needs of the area. For example, if the bandwidth demand in a low-load area drops to 3Mbps, a 40% reduction compared to the preliminary demand, the remaining bandwidth can be released at this time to reduce power consumption, thereby optimizing resource allocation and generating an optimized communication resource configuration to ensure that the bandwidth and power configuration of each area reaches the optimal state, avoiding resource waste and ensuring the network quality of high-traffic areas.

[0136] A multi-user communication resource allocation system, the system includes:

[0137] The load monitoring module regularly collects the location information, bandwidth usage, communication delay, and signal strength data uploaded by user terminals, monitors the communication network status in real time, determines whether the network is in a state of overloading or low load, and generates a network load index;

[0138] The movement prediction module, based on the network load index, according to the current user location information and historical movement trajectories, calculates the future movement trend of users, predicts the upcoming changes in the network environment for users, calculates the relative position and movement speed of users, and determines the quality status of the channels they are in to obtain channel quality prediction data;

[0139] The interference management module, based on the channel quality prediction data, analyzes the potential interference sources in the area where users are located, calculates the probability of interference occurrence, determines the severity of interference, and adjusts the spectrum allocation and power control of the base station to obtain an interference management configuration;

[0140] The traffic prediction module classifies the network load status based on the interference management configuration, analyzes the changes in traffic demand of users under different load states according to the real-time network performance, combines the types of commonly used applications and online time periods of users, and predicts future traffic demand to obtain a traffic demand analysis result;

[0141] Based on the result of traffic demand analysis, the resource optimization module identifies high-traffic users and low-load areas, preferentially increases bandwidth and resources for high-traffic users, releases unnecessary resources during low load, dynamically configures bandwidth and power, and obtains an optimized configuration of communication resources.

[0142] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A multi-user communication resource allocation method, characterized in that, It includes the following steps: S1: Regularly collect the location information, bandwidth usage, communication latency, and signal strength data uploaded by user terminals, monitor the communication network status in real time, analyze the network performance, judge whether the network is in a state of overloading or low load, and generate network load metrics; S2: Based on the network load metrics, according to the current user location information and historical movement trajectories, deduce the future movement trends of users, predict the upcoming changes in the network environment faced by users, calculate the relative position and movement speed of users, and judge the quality status of the channels they are in to obtain channel quality prediction data; S3: Based on the channel quality prediction data, analyze the potential interference sources in the area where the user is located, judge the severity of interference by comparing the current spectrum usage and signal-to-noise ratio, and adjust the spectrum allocation and power control of the base station to obtain interference management configurations; S4: Based on the interference management configurations, classify the network load status into low load, normal load, and overload states, analyze the changes in traffic demand of users under different load states, and combine the types of commonly used applications and online periods of users to predict future traffic demand and obtain traffic demand analysis results.

2. The multi-user communication resource allocation method according to claim 1, characterized in that, The network load metrics include network state fluctuations, packet loss event frequencies, and bandwidth load distributions. The channel quality prediction data includes user signal reception quality, future channel interference probabilities, and signal attenuation rates. The interference management configurations include power control levels and interference source identification results. The traffic demand analysis results include peak-hour traffic prediction results and resource demand information under low load conditions.

3. The multi-user communication resource allocation method according to claim 1, wherein The specific steps for obtaining the network load metrics are as follows: S111: Regularly collect the location information, bandwidth usage, communication latency, and signal strength data uploaded by user terminals, monitor the communication network status in real time, preliminarily analyze the status data, and evaluate the current network status to obtain the initial evaluation results of the communication network; S112: Based on the initial evaluation results of the communication network, calculate the packet loss rate, latency, and bandwidth utilization rate, screen the key metric points, judge whether the network is in a state of overloading or low load, and use the formula: Generate the network load indicator TR, where m tr represents the number of network load data points, and PLR i represents the packet loss rate of the i-th data point, and THR PLR represents the packet loss rate threshold, and LAT i represents the communication delay of the i-th data point, and BW i represents the bandwidth utilization rate of the i-th data point.

4. The multi-user communication resource allocation method according to claim 1, wherein The specific steps for obtaining the channel quality prediction data are as follows: S211: Based on the network load metrics, according to the current user location information and historical movement trajectories, deduce the future movement trends of users and obtain user movement trend data; S212: Based on the user movement trend data, combine the current signal strength, network load, and interference conditions, predict the upcoming changes in the network environment faced by users, and calculate the relative position and movement speed of users according to the prediction results; S213: According to the relative position and movement speed of users, combined with the network load situation, use the formula: Judge the quality status QS of the channel they are in to obtain channel quality prediction data, where Se represents the current signal strength, Te represents the predicted value of the user movement trend, Le represents the network load, De represents the predicted data of the relative position and movement speed of the user, Ie represents the interference intensity, and Ne represents the interference noise value.

5. The multi-user communication resource allocation method according to claim 1, characterized in that, The specific steps for obtaining the interference management configurations are as follows: S311: Analyze the area where the user is currently located based on the channel quality prediction data, evaluate the potential interference source locations by comparing the differential channel quality and signal-to-noise ratio, and generate an interference source analysis result; S312: Based on the interference source analysis result, use the formula: Calculate the probability PG of interference occurrence YU , determine the severity of interference, and adjust the base station spectrum allocation and power control to obtain the interference management configuration, where IU q represents the interference intensity of the q-th interference source, SU q represents the signal-to-noise ratio of the q-th channel, PU q represents the power of the q-th channel, FU q represents the spectrum usage of the q-th channel, NU w represents the noise level of the w-th spectrum, n pg and m pg represent the number of interference sources and the number of spectrums respectively.

6. The multi-user communication resource allocation method according to claim 1, characterized in that The specific steps for obtaining the traffic demand analysis result are as follows: S411: Based on the interference management configuration, obtain the network load status, classify the real-time network load by monitoring network performance data into low load, normal load, and overload statuses, and generate a load status classification result; S412: Based on the load status classification result, combine historical traffic data with real-time load changes, analyze the traffic demand changes of users under different load statuses, and use the formula: Obtain the changing trend TY of traffic demand under the load state d , where LY o represents the o-th load state, and FY o represents the traffic demand under the o-th load state, and n ty represents the number of load states; S413: Based on the traffic demand change trend under the load status, combine the user's common application types and online time periods to predict future traffic demands and obtain a traffic demand analysis result.

7. The multi-user communication resource allocation method according to claim 1, wherein, The steps further include: S5: Based on the traffic demand analysis result, dynamically adjust network resources, identify high-traffic users and low-load areas, preferentially increase the bandwidth for high-traffic users, and release unnecessary resources during low load, dynamically configure bandwidth and power, and obtain an optimized configuration of communication resources; The optimized configuration of communication resources includes a bandwidth adjustment result, a power control result, and a low-load resource release plan.

8. The multi-user communication resource allocation method according to claim 7, wherein The specific steps for obtaining the optimized configuration of communication resources are as follows: S511: Based on the traffic demand analysis result, identify data of high-traffic users and low-load areas, and analyze the current bandwidth usage rate and power settings to generate preliminary bandwidth and power demand information; S512: According to the preliminary bandwidth and power demand information, dynamically configure the bandwidth and power of each area, preferentially increase the bandwidth for high-traffic areas, and release unnecessary resources in low-load areas to obtain an optimized configuration of communication resources.

9. A multi-user communication resource allocation system, characterized in that, The system is used to implement the multi-user communication resource allocation method described in any one of claims 1-8. The system includes: A load monitoring module regularly collects location information, bandwidth usage, communication latency, and signal strength data uploaded by user terminals, real-time monitors the communication network status, determines whether the network is in a load overload or low load state, and generates a network load index; A movement prediction module, based on the network load index, according to the current user location information and historical movement trajectories, estimates the user's future movement trend, predicts the upcoming changes in the network environment faced by the user, calculates the relative position and movement speed of the user, and determines the quality status of the channel where the user is located to obtain channel quality prediction data; An interference management module, based on the channel quality prediction data, analyzes potential interference sources in the area where the user is located, calculates the probability of interference occurrence, determines the severity of interference, and adjusts the spectrum allocation and power control of the base station to obtain an interference management configuration; A traffic prediction module, based on the interference management configuration, classifies the network load status, analyzes the traffic demand changes of users under different load statuses according to real-time network performance, combines the user's common application types and online time periods, and predicts future traffic demands to obtain a traffic demand analysis result; Based on the traffic demand analysis results, the resource optimization module identifies high-traffic users and low-load areas, preferentially increases bandwidth and resources for high-traffic users, releases unnecessary resources during low load, and dynamically configures bandwidth and power to obtain an optimized configuration of communication resources.

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