5G indoor-macro base station collaborative coverage system for multi-campus scenarios

Abstract

The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios involves the field of communication technology, including: setting up data acquisition terminals in designated areas according to needs, and obtaining signal data in the area through the data acquisition terminals; encrypting and verifying the signal data; obtaining signal prediction data for each signal sub-area, dividing the signal sub-area into super-quantum areas or quantum-deficient areas based on the signal prediction data of each signal sub-area and the upper limit of spectrum resources, and performing resource collaborative scheduling for the quantum-deficient areas; performing interference suppression judgment on user terminals in each signal sub-area, and performing lightweight interference suppression operations or adaptive collaborative interference suppression operations based on the judgment results; performing data link switching operations based on the user terminal's signal strength, service type and mobility status, targetedly solving core problems in multi-campus 5G coverage such as "uneven coverage, resource waste, severe interference, switching jams, and data insecurity."

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and specifically to a 5G indoor-macro base station collaborative coverage system for multi-campus scenarios. Background Art

[0002] A Chinese patent with publication number CN116887315A discloses a method and device for intelligent optimization of indoor and outdoor interference collaboration for 5G, including: obtaining uplink interference background noise data and working parameter data of each 5G cell; determining uplink high-interference indoor cells from each 5G cell based on the uplink interference background noise data and working parameter data; determining the strongly associated macro cell of the uplink high-interference indoor cell; obtaining configuration parameters of the uplink high-interference indoor cell and the strongly associated macro cell, and optimizing the configuration parameters of the uplink high-interference indoor cell based on the configuration parameters of the strongly associated macro cell.

[0003] A Chinese patent with publication number CN117979397A discloses a 5G base station collaborative energy-saving method based on proximal strategy optimization, including: combining reinforcement learning algorithm technology, with the goal of reducing 5G network energy consumption, using a proximal strategy optimization algorithm, and comprehensively optimizing the interactive collaboration between base station sleep and antenna configuration, renewable energy generation, energy storage batteries, and air conditioning operation.

[0004] With the rapid development of 5G technology, the education industry is increasingly demanding high-speed and stable networks. Multi-campus scenarios, characterized by vast campuses, complex building structures, and high and uneven user density, pose numerous challenges to 5G network coverage.

[0005] In traditional 5G network coverage, macro base stations primarily provide wide-area signal coverage outdoors. However, indoors, signal attenuation is severe due to obstacles such as building walls, making it difficult to meet the high-speed data service needs of indoor users. While distributed indoor systems can effectively address indoor coverage issues, they lack effective coordination between different campuses, between different buildings within a campus, between distributed indoor systems and macro base stations, and between distributed indoor systems. This leads to problems such as choppy signal switching, severe interference, and low network resource utilization.

[0006] For example, when students enter campus from outside, or move between buildings like teaching buildings, libraries, and dormitories on different campuses, network signal interruptions and data rate drops are common, severely impacting the user experience. Furthermore, due to the lack of intelligent resource scheduling and interference coordination mechanisms, network congestion is prone to occur in densely populated areas, such as during class time in teaching buildings and during evening rest periods in dormitories, making it impossible to meet the service needs of large numbers of users simultaneously online. Therefore, an innovative 5G indoor-to-macro base station coordinated coverage system is needed to address the complex network coverage issues in multi-campus scenarios. Summary of the Invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide a 5G indoor-macro base station collaborative coverage system for multi-campus scenarios, including the following steps:

[0008] The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios includes a cloud-based communication connection with a data acquisition module, a data encryption module, a resource collaborative scheduling module, an interference coordination module, and a fast switching execution module.

[0009] The data acquisition module is used to set up data acquisition terminals in designated areas according to needs, and obtain signal data (including signal strength, number of users, service type, etc.) in the area through the data acquisition terminals;

[0010] The data encryption module is used to encrypt and verify signal data;

[0011] The resource collaborative scheduling module is used to obtain signal prediction data for each signal sub-region, divide the signal sub-region into super-quantum regions or quantum-deficient regions based on the signal prediction data of each signal sub-region and the spectrum resource upper limit, and perform resource collaborative scheduling for the quantum-deficient regions;

[0012] The interference coordination module is used to perform interference suppression judgment on user terminals in each signal sub-area, and perform lightweight interference suppression operation or adaptive cooperative interference suppression operation according to the judgment result;

[0013] The fast switching execution module is used to execute the data link switching operation according to the signal strength, service type and mobility status of the user terminal.

[0014] Furthermore, the process of obtaining signal data includes:

[0015] Obtain the location characteristics and coverage of several indoor devices and macro base stations in a specified area. Macro base stations are responsible for providing large-scale outdoor coverage on campus and wide-area signal support. Indoor distributed antennas (DAS) achieve deep indoor coverage and resolve signal blind spots caused by building obstructions. Based on the location characteristics and coverage of several indoor devices and macro base stations, the specified area is divided into several signal sub-areas.

[0016] A data acquisition module is installed in each signal sub-area. The data acquisition module is used to acquire signal data in the signal sub-area to which the data acquisition module belongs, and an acquisition period T is set.

[0017] Furthermore, the process of encrypting and verifying the signal data by the data encryption module includes:

[0018] The public key and private key of the data acquisition module corresponding to each signal sub-area are preset using an asymmetric encryption algorithm, and the signal data within each acquisition period T of the data acquisition module is digitally signed using the private key of the data acquisition module (first, the SHA-256 cryptographic hash function is used to calculate the hash value of the signal data, and then the hash value is encrypted using the private key and the asymmetric encryption algorithm to generate a digital signature);

[0019] The validity of the digitally signed signal data sent to the cloud by the data acquisition module is verified by the public key of the data acquisition module. If the validity verification of the signal data fails, the signal data is discarded and an abnormal acquisition warning signal of the data acquisition module is generated.

[0020] Furthermore, the process of the resource collaborative scheduling module acquiring the signal prediction data of each signal sub-area includes:

[0021] A signal prediction model is constructed based on a machine learning algorithm. Signal data from several historical acquisition cycles of each signal sub-area is obtained as training data. The signal prediction model is trained using the training data to obtain a trained signal prediction model. Signal prediction data for each signal sub-area in the current acquisition cycle is output according to the signal prediction model.

[0022] Furthermore, the process of dividing the signal sub-region into the super quantum region or the quantum-deficient region includes:

[0023] Obtain the spectrum resource upper limit for each signal sub-area, extract the used bandwidth time series sequence from the signal prediction data of each signal sub-area in the current acquisition cycle, compare the used bandwidth time series sequence of each signal sub-area with the spectrum resource upper limit, and obtain the cumulative time that the used bandwidth is continuously greater than or equal to the spectrum resource upper limit;

[0024] A preset error threshold is set, and the cumulative time of each signal sub-region is compared with the error threshold. If the cumulative time of the signal sub-region is greater than or equal to the error threshold, the signal sub-region is marked as a quantum-deficient region. If the cumulative time of the signal sub-region is less than the error threshold, the signal sub-region is marked as a super-quantum region.

[0025] Furthermore, the process of performing resource collaborative scheduling on the quantum-deficient region includes:

[0026] Obtain the time period t in which the bandwidth used in the quantum-deficient region exceeds the upper limit of the spectrum resource and the maximum value of the excess bandwidth (the difference between the bandwidth used and the upper limit of the spectrum resource) in the time period t, obtain the super quantum region S1 closest to the quantum-deficient region, obtain the minimum idle bandwidth of the super quantum region S1 in the time period t, and determine whether the minimum idle bandwidth is greater than or equal to the maximum bandwidth. If so, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super quantum region S1 to the quantum-deficient region. If less than, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super quantum region S1 to the quantum-deficient region, eliminate the super quantum region S1, and then obtain the excess bandwidth in the quantum-deficient region in the time period t (the sum of the used bandwidth and the minimum idle bandwidth and the spectrum The maximum value of the excess bandwidth (the difference between the used bandwidth and the upper limit of the resource) is obtained, and the other super quantum region S2 closest to the quantum-deficient region is obtained. The minimum idle bandwidth of the other super quantum region S2 in time period t is obtained, and it is determined whether the minimum idle bandwidth is greater than or equal to the maximum bandwidth. If so, the spectrum resources corresponding to the minimum idle bandwidth of the other super quantum region S2 are allocated to the quantum-deficient region. If less than, the spectrum resources corresponding to the minimum idle bandwidth of the other super quantum region S2 are allocated to the quantum-deficient region, and the other super quantum region S2 is eliminated. Then, the maximum value of the excess bandwidth (the difference between the sum of the used bandwidth and the minimum idle bandwidth and the upper limit of the spectrum resource) in time period t of the quantum-deficient region is obtained, and the other super quantum region S3 closest to the quantum-deficient region is obtained;

[0027] Repeat the above judgment process until the minimum idle bandwidth of the super quantum region is greater than or equal to the maximum bandwidth, and allocate the spectrum resources corresponding to the minimum idle bandwidth of the super quantum region to the quantum-deficient region.

[0028] Furthermore, the interference coordination module performs interference suppression determination on user terminals in each signal sub-area, and performs a lightweight interference suppression operation or an adaptive coordinated interference suppression operation according to the determination result. The process includes:

[0029] Extracting the interference signal power, interference frequency range, used signal power and used frequency of each user terminal from the signal data collected in real time in the signal sub-area;

[0030] Determine whether the interference frequency range of the user terminal is adjacent to or overlaps with the used frequency. If so, obtain the signal-to-interference-plus-noise ratio (SIN) of the user terminal based on the interference signal power and the used signal power (the ratio of the interference signal power to the used signal power). Preset a SIN threshold. If the SIN is less than the SIN threshold, mark the user terminal as an interference mitigation terminal.

[0031] Obtain the number and location characteristics of user terminals and interference suppression terminals within the signal sub-area, divide the signal sub-area into several grid cells of the same size (for example, 10m×10m), and obtain the interference suppression density (the ratio of the number of interference suppression terminals to the number of user terminals) of each grid cell based on the number and location characteristics of user terminals and interference suppression terminals.

[0032] A density threshold is preset. If the interference suppression density of a grid cell is less than or equal to the density threshold and an interference suppression terminal exists in the grid cell, a lightweight interference suppression operation is performed on the interference suppression terminal in the grid cell.

[0033] If the interference suppression density of the grid cell is greater than the density threshold, an adaptive collaborative interference suppression operation is performed on the coverage area of ​​the grid cell.

[0034] Furthermore, the process of performing the lightweight interference suppression operation includes:

[0035] Acquire several subcarrier frequencies in the communication link between the interference suppression terminal and the indoor distributed equipment or macro base station in the signal sub-area, extract subcarrier frequencies that are not adjacent to or overlap with the interference frequency range, and mark the subcarrier frequencies as subcarrier frequencies to be allocated;

[0036] Obtain the bit error rate of each subcarrier frequency to be allocated, screen out the subcarrier frequency to be allocated with the lowest bit error rate, allocate the bandwidth of the used frequency to the subcarrier frequency to be allocated, remove the original used frequency, and mark the subcarrier frequency to be allocated as the used frequency.

[0037] Furthermore, the process of performing the adaptive cooperative interference suppression operation includes:

[0038] Extracting the interference signal strength and the transmission path of the interference frequency range of the interference suppression terminal in the grid unit coverage area, obtaining the signal sub-area emitting the interference signal strength and the interference frequency range according to the transmission path, and marking the signal sub-area as an interference source;

[0039] An adaptive interference suppression model is constructed based on the adaptive beamforming algorithm. The interference signal strength and interference frequency range of the interference source at the current moment and the coverage area of ​​the grid unit are input into the adaptive interference suppression model. According to the adaptive interference suppression model, the optimal adjustment parameters (including beam direction angle, transmission power, etc.) of the indoor equipment or macro base station of the interference source are output.

[0040] Furthermore, the process of the fast switching execution module performing the data link switching operation according to the signal strength, service type and mobility status of the user terminal includes:

[0041] Extract the signal strength, service type (including HD video, live teaching, text chat, etc.) and mobility status of each user terminal from the real-time signal data collected in the signal sub-area;

[0042] When the mobile state of the user terminal is far away from the signal sub-area and the used signal strength is less than the preset signal strength threshold corresponding to the service type, the used bandwidth and used signal strength corresponding to the service type are extracted, and the idle bandwidth valley value (the lowest idle bandwidth value in the current collection period) and the used signal strength valley value (the lowest used signal strength value in the current collection period) of other signal sub-areas in the current collection period are obtained. Other signal sub-areas whose idle bandwidth valley value is greater than the used bandwidth and whose used signal strength valley value is greater than the used signal strength are obtained, and the Euclidean distance between the other signal sub-areas and the user terminal is obtained. The other signal sub-areas with the shortest Euclidean distance are screened out to perform the data link switching operation.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. Accurately adapt to the coverage requirements of multi-campus scenarios and improve communication quality and stability:

[0045] Multi-campus scenarios commonly suffer from problems such as "dispersed building layouts (independent areas such as teaching buildings, dormitories, and libraries), large indoor and outdoor signal penetration losses (the reinforced concrete structure makes it difficult for macro base station signals to cover indoor areas, and indoor signals are easily blocked by walls), and dynamic fluctuations in user distribution (significant differences in user density during class / after-get out of class periods and on weekdays / weekends)."

[0046] This system uses a data acquisition module to finely divide the area into "indoor distribution - macro base station location characteristics + coverage range," forming several signal sub-areas. Signal data from each sub-area is collected periodically, achieving "grid-like" precision perception of complex scenarios. This design avoids the blind "one-size-fits-all" approach of traditional coverage solutions and can capture details such as signal strength and interference in each area (such as a specific floor of a teaching building or a specific dormitory building) in real time. This provides an accurate data foundation for subsequent resource scheduling and interference processing, and fundamentally solves the problems of "coverage blind spots" and "signal fluctuations" across multiple campuses.

[0047] 2. Ensure data transmission security and adapt to the sensitive scenarios of multiple campuses:

[0048] Multi-campus scenarios involve a large amount of teachers' and students' personal communication data, as well as teaching and research business data (such as online exams and remote experiments). Data security is of paramount importance.

[0049] The data encryption module uses an asymmetric encryption algorithm (public-private key system) to encrypt and verify collected signal data: the private key is used for digital signatures on the data collection terminal, and the public key is used to verify data validity in the cloud. If verification fails, abnormal data is discarded and an alert is issued. This design eliminates risks such as information leakage and data tampering at the data transmission link level. It is particularly suitable for multi-campus scenarios where "teaching business data is highly sensitive and user privacy must be strictly protected," ensuring the integrity and security of communication data.

[0050] 3. Dynamically optimize spectrum resource allocation to improve resource utilization efficiency:

[0051] There are significant temporal and spatial differences in spectrum resource demand in different areas of multiple campuses (for example, resource demand in teaching buildings surges during class time, while demand in dormitory areas dominates late at night). Traditional static resource allocation methods can easily lead to "excess resources (waste) in some areas and insufficient resources (stuttering) in some areas."

[0052] The resource collaborative scheduling module dynamically determines sub-areas using signal prediction data and spectrum resource caps to divide them into "over-quantum regions" (resource surplus) and "under-quantum regions" (resource shortage). Based on the principle of "distance priority and idle bandwidth matching," it dispatches idle resources from over-quantum regions to supplement under-quantum regions. This design enables the "elastic flow" of spectrum resources. For example, during recess, when demand for resources in the teaching building sub-area decreases (transforming it into an over-quantum region), idle resources can be dispatched to the dormitory area (under-quantum region), where demand surges during the same period. This increases spectrum utilization by over 30% and avoids the conflict between idle resources and resource shortages.

[0053] 4. Hierarchical interference suppression, balancing processing efficiency and communication quality:

[0054] Interference sources in multi-campus scenarios are complex: indoor signals from distributed base stations and outdoor macro base stations can interfere with each other due to frequency proximity, reflected signals between densely populated buildings can easily cause multipath interference, and terminal signals in densely populated areas (such as auditoriums and playgrounds) can interfere with each other. Traditional interference mitigation solutions either "over-process" (using complex algorithms to address minor interference, wasting resources) or "under-process" (lacking targeted measures for severe interference, resulting in communication interruptions).

[0055] The interference coordination module solves this problem through the "interference determination-hierarchical processing" logic:

[0056] Terminals whose interference frequencies are adjacent to or overlap with the active frequency, and whose signal-to-interference-to-noise ratio (SIN) is below a threshold, are marked as "interference suppression terminals" and are treated differently based on the "interference suppression density" of each grid cell. Low-density interference areas are treated with "lightweight interference suppression" (which switches to interference-free subcarriers to quickly resolve simple interference), while high-density interference areas are treated with "adaptive collaborative interference suppression" (which uses algorithms such as beamforming to specifically eliminate strong interference). This hierarchical design ensures both efficient handling of minor interference (reducing computing resource consumption) and deep suppression of severe interference (improving communication quality in complex scenarios), reducing communication interruption rates caused by multi-campus interference by over 40%.

[0057] 5. Adapt to high user mobility and ensure business continuity:

[0058] Users (teachers and students) across multiple campuses are highly mobile (for example, moving from the dormitory to the teaching building during class, commuting across campuses, etc.). Traditional switching solutions rely solely on signal strength judgment, which can easily lead to "switching delays (such as video call freezes) and false switches (brief signal fluctuations triggering switches)."

[0059] The fast handoff execution module uses a multi-dimensional approach based on signal strength, service type, and mobility. For example, when a user moves their terminal from a playground covered by a macro base station to a teaching building covered by indoor base stations, the system triggers handoff based on factors such as the declining signal strength trend (consistently moving away from the current area), service type (e.g., online exams require low latency, prioritizing handoff speed), and direction of movement (direction toward the target sub-area). This avoids the limitations of single-signal strength judgment. This design keeps cross-area handoff latency below 10ms and reduces the probability of service interruption to below 0.1%, making it ideal for the multi-campus demand for high-frequency mobility and high-priority services (such as live classes and online labs).

[0060] 6. Cloud-based collaborative architecture improves system scalability and management efficiency:

[0061] The system is centered on the cloud, connecting all modules in a unified manner, achieving "centralized management + distributed execution" for dispersed areas across multiple campuses. Compared to the traditional solution of "independent deployment in each area and severe data silos," this architecture has the following advantages:

[0062] Can quickly adapt to new campus expansion (new areas only need to deploy data collection terminals and connect to the existing system through the cloud, without rebuilding the entire architecture);

[0063] Global optimization of resource scheduling, interference handling, and switching strategies in the cloud (based on comprehensive decision-making based on campus-wide data to avoid resource conflicts or interference overlap between regions);

[0064] Reduce operation and maintenance costs (monitor the status of each sub-area uniformly through the cloud, reducing on-site operation and maintenance workload).

[0065] In summary, the system, through its full-link design of "precise perception - secure transmission - dynamic scheduling - intelligent anti-interference - fast switching", has targetedly solved the core problems of "uneven coverage, resource waste, severe interference, switching jams, and data insecurity" in 5G coverage on multiple campuses, providing teachers and students with "continuous, stable, high-speed, reliable, secure and controllable" communication services, and at the same time providing solid network support for the implementation of smart campuses on multiple campuses (such as distance learning, Internet of Things management, emergency communications, etc.). BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a schematic diagram of the 5G indoor-macro base station collaborative coverage system for multi-campus scenarios in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0068] like Figure 1 As shown in the figure, the 5G indoor-macro base station collaborative coverage system for multi-campus scenarios includes a cloud, and the cloud communication connection has a data acquisition module, a data encryption module, a resource collaborative scheduling module, an interference coordination module, and a fast switching execution module;

[0069] The data acquisition module is used to set up data acquisition terminals in designated areas according to needs, and obtain signal data (including signal strength, number of users, service type, etc.) in the area through the data acquisition terminals;

[0070] The data encryption module is used to encrypt and verify signal data;

[0071] The resource collaborative scheduling module is used to obtain signal prediction data for each signal sub-region, divide the signal sub-region into super-quantum regions or quantum-deficient regions based on the signal prediction data of each signal sub-region and the spectrum resource upper limit, and perform resource collaborative scheduling for the quantum-deficient regions;

[0072] The interference coordination module is used to perform interference suppression judgment on user terminals in each signal sub-area, and perform lightweight interference suppression operation or adaptive cooperative interference suppression operation according to the judgment result;

[0073] The fast switching execution module is used to execute the data link switching operation according to the signal strength, service type and mobility status of the user terminal.

[0074] It should be further explained that, in the specific implementation process, the process of obtaining signal data includes:

[0075] Obtain the location characteristics and coverage of several indoor devices and macro base stations in a specified area. Macro base stations are responsible for providing large-scale outdoor coverage on campus and wide-area signal support. Indoor distributed antennas (DAS) achieve deep indoor coverage and resolve signal blind spots caused by building obstructions. Based on the location characteristics and coverage of several indoor devices and macro base stations, the specified area is divided into several signal sub-areas.

[0076] A data acquisition module is installed in each signal sub-area. The data acquisition module is used to acquire signal data in the signal sub-area to which the data acquisition module belongs, and an acquisition period T is set.

[0077] It should be further explained that, in the specific implementation process, the process of the data encryption module encrypting and verifying the signal data includes:

[0078] The public key and private key of the data acquisition module corresponding to each signal sub-area are preset using an asymmetric encryption algorithm, and the signal data within each acquisition period T of the data acquisition module is digitally signed using the private key of the data acquisition module (first, the SHA-256 cryptographic hash function is used to calculate the hash value of the signal data, and then the hash value is encrypted using the private key and the asymmetric encryption algorithm to generate a digital signature);

[0079] The validity of the digitally signed signal data sent to the cloud by the data acquisition module is verified by the public key of the data acquisition module. If the validity verification of the signal data fails, the signal data is discarded and an abnormal acquisition warning signal of the data acquisition module is generated.

[0080] It should be further explained that, in a specific implementation process, the resource collaborative scheduling module obtains the signal prediction data of each signal sub-area in the following process:

[0081] A signal prediction model is constructed based on a machine learning algorithm. Signal data from several historical acquisition cycles of each signal sub-area is obtained as training data. The signal prediction model is trained using the training data to obtain a trained signal prediction model. Signal prediction data for each signal sub-area in the current acquisition cycle is output according to the signal prediction model.

[0082] For example, based on signal data from several historical collection cycles in the past week, the signal strength at different locations in each signal sub-area in the current collection cycle, as well as the number of user connections on each floor and in each room in the indoor distribution equipment and the traffic generated by ongoing services (such as video teaching, online games, file downloads, etc.) are predicted.

[0083] It should be further explained that, in a specific implementation process, the process of dividing the signal sub-region into a super quantum region or a quantum-deficient region includes:

[0084] Obtain the spectrum resource upper limit for each signal sub-area, extract the used bandwidth time series sequence from the signal prediction data of each signal sub-area in the current acquisition cycle, compare the used bandwidth time series sequence of each signal sub-area with the spectrum resource upper limit, and obtain the cumulative time that the used bandwidth is continuously greater than or equal to the spectrum resource upper limit;

[0085] A preset error threshold is set, and the cumulative time of each signal sub-region is compared with the error threshold. If the cumulative time of the signal sub-region is greater than or equal to the error threshold, the signal sub-region is marked as a quantum-deficient region. If the cumulative time of the signal sub-region is less than the error threshold, the signal sub-region is marked as a super-quantum region.

[0086] It should be further explained that, in the specific implementation process, the process of performing resource collaborative scheduling for the sub-region with insufficient quantum includes:

[0087] Obtain the time period t in which the bandwidth used in the quantum-deficient region exceeds the upper limit of the spectrum resource and the maximum value of the excess bandwidth (the difference between the bandwidth used and the upper limit of the spectrum resource) in the time period t, obtain the super quantum region S1 closest to the quantum-deficient region, obtain the minimum idle bandwidth of the super quantum region S1 in the time period t, and determine whether the minimum idle bandwidth is greater than or equal to the maximum bandwidth. If so, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super quantum region S1 to the quantum-deficient region. If less than, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super quantum region S1 to the quantum-deficient region, eliminate the super quantum region S1, and then obtain the excess bandwidth in the quantum-deficient region in the time period t (the sum of the used bandwidth and the minimum idle bandwidth and the spectrum The maximum value of the excess bandwidth (the difference between the used bandwidth and the upper limit of the resource) is obtained, and the other super quantum region S2 closest to the quantum-deficient region is obtained. The minimum idle bandwidth of the other super quantum region S2 in time period t is obtained, and it is determined whether the minimum idle bandwidth is greater than or equal to the maximum bandwidth. If so, the spectrum resources corresponding to the minimum idle bandwidth of the other super quantum region S2 are allocated to the quantum-deficient region. If less than, the spectrum resources corresponding to the minimum idle bandwidth of the other super quantum region S2 are allocated to the quantum-deficient region, and the other super quantum region S2 is eliminated. Then, the maximum value of the excess bandwidth (the difference between the sum of the used bandwidth and the minimum idle bandwidth and the upper limit of the spectrum resource) in time period t of the quantum-deficient region is obtained, and the other super quantum region S3 closest to the quantum-deficient region is obtained;

[0088] Repeat the above judgment process until the minimum idle bandwidth of the super quantum region is greater than or equal to the maximum bandwidth, and allocate the spectrum resources corresponding to the minimum idle bandwidth of the super quantum region to the quantum-deficient region.

[0089] It should be further explained that, in a specific implementation process, the interference coordination module performs interference suppression determination on user terminals in each signal sub-area, and performs a lightweight interference suppression operation or an adaptive coordinated interference suppression operation according to the determination result. The process includes:

[0090] Extracting the interference signal power, interference frequency range, used signal power and used frequency of each user terminal from the signal data collected in real time in the signal sub-area;

[0091] Determine whether the interference frequency range of the user terminal is adjacent to or overlaps with the used frequency. If so, obtain the signal-to-interference-plus-noise ratio (SIN) of the user terminal based on the interference signal power and the used signal power (the ratio of the interference signal power to the used signal power). Preset a SIN threshold. If the SIN is less than the SIN threshold, mark the user terminal as an interference mitigation terminal.

[0092] Obtain the number and location characteristics of user terminals and interference suppression terminals within the signal sub-area, divide the signal sub-area into several grid cells of the same size (for example, 10m×10m), and obtain the interference suppression density (the ratio of the number of interference suppression terminals to the number of user terminals) of each grid cell based on the number and location characteristics of user terminals and interference suppression terminals.

[0093] A density threshold is preset. If the interference suppression density of a grid cell is less than or equal to the density threshold and an interference suppression terminal exists in the grid cell, a lightweight interference suppression operation is performed on the interference suppression terminal in the grid cell.

[0094] If the interference suppression density of the grid cell is greater than the density threshold, an adaptive collaborative interference suppression operation is performed on the coverage area of ​​the grid cell.

[0095] It should be further explained that, in a specific implementation process, the process of performing the lightweight interference suppression operation includes:

[0096] Acquire several subcarrier frequencies in the communication link between the interference suppression terminal and the indoor distributed equipment or macro base station in the signal sub-area, extract subcarrier frequencies that are not adjacent to or overlap with the interference frequency range, and mark the subcarrier frequencies as subcarrier frequencies to be allocated;

[0097] Obtain the bit error rate of each subcarrier frequency to be allocated, screen out the subcarrier frequency to be allocated with the lowest bit error rate, allocate the bandwidth of the used frequency to the subcarrier frequency to be allocated, remove the original used frequency, and mark the subcarrier frequency to be allocated as the used frequency.

[0098] It should be further explained that, in a specific implementation process, the process of performing the adaptive cooperative interference suppression operation includes:

[0099] Extract the interference signal strength and interference frequency range of the interference suppression terminal in the grid unit coverage area, obtain the signal sub-area emitting the interference signal strength and interference frequency range based on the transmission path, and mark the signal sub-area as an interference source;

[0100] An adaptive interference suppression model is constructed based on the adaptive beamforming algorithm. The interference signal strength and interference frequency range of the interference source at the current moment and the coverage area of ​​the grid unit are input into the adaptive interference suppression model. According to the adaptive interference suppression model, the optimal adjustment parameters (including beam direction angle, transmission power, etc.) of the indoor equipment or macro base station of the interference source are output.

[0101] It should be further explained that, in the specific implementation process, the process of building the adaptive interference suppression model includes:

[0102] The adaptive interference suppression model uses an adaptive beamforming algorithm to calculate the optimal adjustment parameters based on the interference signal strength and interference frequency range of the input interference source and the coverage area of ​​the grid unit;

[0103] The core optimization goal of the adaptive interference suppression model is to maximize the signal-to-interference-noise ratio (SINR) of the signal sub-area while also determining the interference scenario and selecting the appropriate beamforming algorithm based on the interference scenario (static or dynamic). The adaptive interference suppression model includes an adaptive beam optimization module and a weight mapping module.

[0104] The adaptive beam optimization module includes objective function design and adaptive algorithm selection and application;

[0105] The objective function is: ;

[0106] in, is the beamforming weight vector (N×1 dimension), for The conjugate transpose of (1×N dimensions), is the steering vector of the desired signal (N×1 dimension), is the direction (angle) of the expected signal, is the power (variance) of the desired signal, is the covariance matrix of interference plus noise (N×N dimensions), is the interference plus noise power at the output;

[0107] The adaptive algorithm selection and application process of the adaptive beam optimization module is as follows:

[0108] Preset a step size factor, obtain the interference signal strength and interference frequency range of the interference source at the previous n1 moments and the current moment, and the coverage area of ​​the grid unit based on the step size factor, and perform consistency judgment on the interference signal strength and interference frequency range of the interference source at the previous n1 moments and the current moment, and the coverage area of ​​the grid unit;

[0109] If the interference signal strength and interference frequency range of the interference source at the previous n1 moments and the current moment and the grid unit coverage area remain unchanged, the MVDR (minimum variance distortionless response) algorithm is used: while ensuring that the expected signal is distortion-free Under the constraint of , the output power (including interference and noise) is minimized, a deep null is formed for strong interference, and the computational complexity is low;

[0110] The weight solution is ;

[0111] in, is the optimal weight vector of the MVDR algorithm (N×1 dimension), is the inverse of the interference plus noise covariance matrix (N×N dimensions), is the steering vector of the desired signal (N×1 dimension), the same as in the objective function , is a normalization constant (scalar);

[0112] If the interference signal strength and interference frequency range of the interference source and the grid unit coverage area change in the previous n1 moments and the current moment, the LMS (least mean square) adaptive algorithm is used: iteratively updates the weights through real-time error feedback to track interference changes. It does not require a priori covariance matrix and has strong real-time performance, making it suitable for scenarios where the interference source changes:

[0113] Initialize the weights w(0) (i.e., the initial beam pointing in the desired direction);

[0114] Receive signals every moment , calculation error ,in, is the expected signal reference value, The conjugate transpose of the weight vector (N×1 dimension) at the nth iteration;

[0115] Iterative updates: ,in, is the weight vector for the n+1th iteration (N×1 dimension), μ is the step size factor (scalar, , for ), is the error signal of the nth iteration (scalar), The input signal vector of the nth iteration (N×1 dimension);

[0116] The weight mapping module is used to map weight vectors to device parameters. The weight vector w output by the beam optimization module is a complex number (including amplitude and phase) and needs to be converted into physically adjustable parameters for indoor equipment or macro base stations:

[0117] Phase adjustment: The phase part of the weight corresponds to the phase offset of each array element;

[0118] Amplitude adjustment: The amplitude part of the weight corresponds to the transmit / receive gain of each array element;

[0119] To ensure the stability of the model in complex scenarios (multiple interferences, occlusions, and frequency-selective fading), further training and optimization were performed using simulation and measured data:

[0120] Dataset construction:

[0121] Simulation data: Based on the ray tracing model, interference signal data for different scenarios (indoor, outdoor, and densely populated campuses) is generated, including interference sources of varying numbers (1-5), directions (0°-360°), and strengths (-100dBm to -50dBm).

[0122] Measured data: Deploy test equipment in typical sub-areas (such as classrooms and stadiums) to collect mixed data from real interference (such as Wi-Fi interference and leakage from neighboring cells) and user signals.

[0123] Hyperparameter optimization:

[0124] Adjust key parameters of the adaptive algorithm (such as the step size μ of LMS and the regularization coefficient ε of MVDR to avoid R singularity);

[0125] Optimize array manifold error compensation (a(θ) deviation caused by array element position error and channel inconsistency is corrected by calibration matrix).

[0126] Performance indicator verification:

[0127] Core indicators: User terminal SINR improvement and beam pointing accuracy (deviation between main lobe and θ_d ≤ 3°).

[0128] Extreme scenario testing: For example, when the direction angle between the interference source and the user is close (for example, θ_j = θ_d ± 10°), can the model suppress the interference while ensuring the user signal quality (achieved by introducing sidelobe constraints).

[0129] For example, if the macro base station signal on campus A is on the same frequency as the indoor equipment on campus B, the macro base station signal on campus A will interfere with the users (target users) in the teaching building on campus B. In this case:

[0130] First, analyze the strength of the macro base station interference signal and the path to the teaching building;

[0131] Determine that the target users are on the 2nd to 5th floors of the teaching building (high-quality signal is required);

[0132] Calculate the optimal parameters: Adjust the beam direction of the macro base station in Campus A so that the main lobe of the signal (the direction with the strongest energy) points to the first floor of the teaching building (not the target area, as the first floor is an empty hall with few users). At the same time, adjust the phase so that the signal is reflected and enhanced by the walls of the first floor, while the energy of the interference signal on floors 2-5 is significantly attenuated.

[0133] Final effect: The energy of the interference signal is mainly concentrated on the first floor (non-target area), the target users on floors 2-5 are almost unaffected, and the useful signal (the indoor signal of Campus B) can be clearly received.

[0134] It should be further explained that, in a specific implementation process, the process in which the fast switching execution module performs the data link switching operation according to the signal strength, service type, and mobility status of the user terminal includes:

[0135] Extract the signal strength, service type (including HD video, live teaching, text chat, etc.) and mobility status of each user terminal from the real-time signal data collected in the signal sub-area;

[0136] When a user terminal is moving away from a signal sub-area and its signal strength is less than the preset signal strength threshold for the service type, the system extracts the bandwidth and signal strength corresponding to the service type. It also obtains the idle bandwidth valley (the lowest idle bandwidth value within the current collection period) and signal strength valley (the lowest signal strength value within the current collection period) of other signal sub-areas in the current collection period. It then identifies other signal sub-areas where the idle bandwidth valley is greater than the bandwidth and the signal strength valley is greater than the signal strength. It then determines the Euclidean distance between the other signal sub-areas and the user terminal, and selects the signal sub-area with the shortest Euclidean distance to perform a data link handover. This involves optimizing the signaling process between the macro base station or indoor equipment in the other signal sub-area and the user device. Pre-synchronization technology is used to perform some signaling and synchronization operations with the macro base station or indoor equipment in the signal sub-area before the handover. Once the handover target is determined, the fast handover execution module uses fast signaling to quickly establish a data link between the user device and the macro base station or indoor equipment in the other signal sub-area, achieving seamless handover. This optimized signaling process reduces handover interruption time from the traditional 100ms to less than 50ms.

[0137] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios is characterized by: It includes a cloud, and the cloud communication connection includes a data acquisition module, a data encryption module, a resource collaborative scheduling module, an interference coordination module and a fast switching execution module; The data acquisition module is used to set up data acquisition terminals in designated areas according to needs and obtain signal data of the area through the data acquisition terminals; The data encryption module is used to encrypt and verify signal data; The resource collaborative scheduling module is used to obtain signal prediction data for each signal sub-region, divide the signal sub-region into super-quantum regions or quantum-deficient regions based on the signal prediction data of each signal sub-region and the spectrum resource upper limit, and perform resource collaborative scheduling for the quantum-deficient regions; The interference coordination module is used to perform interference suppression judgment on user terminals in each signal sub-area, and perform lightweight interference suppression operation or adaptive cooperative interference suppression operation according to the judgment result; The fast switching execution module is used to execute the data link switching operation according to the signal strength, service type and mobility status of the user terminal.

2. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 1 is characterized in that: The process of acquiring signal data includes: Obtain the location characteristics and coverage of several indoor devices and macro base stations in the specified area, divide the specified area into several signal sub-areas according to the location characteristics and coverage of several indoor devices and macro base stations, install a data acquisition module in each signal sub-area, and the data acquisition module is used to collect signal data in the signal sub-area to which the data acquisition module belongs, and set the acquisition cycle.

3. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 2 is characterized in that: The process of encrypting and verifying signal data by the data encryption module includes: The public key and private key of the data acquisition module corresponding to each signal sub-area are preset through an asymmetric encryption algorithm. The signal data within each acquisition cycle T of the data acquisition module is digitally signed by the private key. The validity of the digitally signed signal data sent to the cloud by the data acquisition module is verified by the public key. If the validity verification of the signal data fails, the signal data is discarded and an acquisition abnormality warning signal is generated.

4. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 3 is characterized in that: The process of the resource collaborative scheduling module obtaining signal prediction data for each signal sub-area includes: Build a signal prediction model, obtain the signal data of each signal sub-area in several historical acquisition cycles as training data, use the training data to train the signal prediction model, obtain the trained signal prediction model, and output the signal prediction data of each signal sub-area in the current acquisition cycle according to the signal prediction model.

5. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 4 is characterized in that: The process of dividing the signal sub-region into super quantum region or quantum-deficient region includes: Obtain the spectrum resource upper limit for each signal sub-area, extract the used bandwidth time series sequence from the signal prediction data of each signal sub-area in the current acquisition cycle, compare the used bandwidth time series sequence of each signal sub-area with the spectrum resource upper limit, and obtain the cumulative time that the used bandwidth is continuously greater than or equal to the spectrum resource upper limit; A preset error threshold is set. If the cumulative time of the signal sub-region is greater than or equal to the error threshold, the signal sub-region is marked as a quantum-deficient region. If the cumulative time of the signal sub-region is less than the error threshold, the signal sub-region is marked as a super-quantum region.

6. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 5 is characterized in that: The process of performing resource collaborative scheduling for sub-regions with insufficient quantum resources includes: Step 1: Obtain the time period t in which the bandwidth used in the quantum-deficient region exceeds the upper limit of the spectrum resource and the maximum value of the excess bandwidth in time period t, obtain the super-quantum region closest to the quantum-deficient region, obtain the minimum idle bandwidth of the super-quantum region in time period t, and determine whether the minimum idle bandwidth is greater than or equal to the maximum bandwidth. If so, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super-quantum region to the quantum-deficient region, and terminate resource coordinated scheduling. If less than, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super-quantum region to the quantum-deficient region, eliminate the super-quantum region, and execute step 2. Step 2: Obtain the maximum excess bandwidth of the missing quantum region in time period t after spectrum resource allocation, re-obtain the super quantum region closest to the missing quantum region, obtain the minimum idle bandwidth of the super quantum region in time period t, and execute step 3; Step 3: Determine whether the minimum idle bandwidth is greater than or equal to the maximum bandwidth. If so, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super-quantum area to the quantum-deficient area, and end resource collaborative scheduling. If less than, allocate the spectrum resources corresponding to the minimum idle bandwidth of the super-quantum area to the quantum-deficient area, eliminate the super-quantum area, and execute step 2.

7. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 6 is characterized in that: The interference coordination module performs interference suppression determination on user terminals in each signal sub-area and performs lightweight interference suppression operations or adaptive coordinated interference suppression operations based on the determination results. The process includes: Extracting the interference signal power, interference frequency range, used signal power and used frequency of each user terminal from the signal data collected in real time in the signal sub-area; Determine whether the interference frequency range of the user terminal is adjacent to or overlaps with the used frequency. If so, obtain the signal-to-interference-plus-noise ratio (SINR) of the user terminal based on the interference signal power and the used signal power. Preset a SINR threshold. If the SINR is less than the SINR threshold, mark the user terminal as an interference suppression terminal. Obtaining the number and location characteristics of user terminals and interference suppression terminals within the signal sub-area, dividing the signal sub-area into a number of grid cells of the same size, and obtaining the interference suppression density of each grid cell based on the number and location characteristics of user terminals and interference suppression terminals; A density threshold is preset. If the interference suppression density of a grid cell is less than or equal to the density threshold and an interference suppression terminal exists in the grid cell, a lightweight interference suppression operation is performed on the interference suppression terminal in the grid cell. If the interference suppression density of the grid cell is greater than the density threshold, an adaptive collaborative interference suppression operation is performed on the coverage area of ​​the grid cell.

8. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 7 is characterized in that: The process of performing lightweight interference suppression operation includes: Acquire several subcarrier frequencies in the communication link of the interference suppression terminal, extract subcarrier frequencies that are not adjacent to or overlap with the interference frequency range, and mark the subcarrier frequencies as subcarrier frequencies to be allocated; Obtain the bit error rate of each subcarrier frequency to be allocated, screen out the subcarrier frequency to be allocated with the lowest bit error rate, allocate the bandwidth of the used frequency to the subcarrier frequency to be allocated, remove the original used frequency, and mark the subcarrier frequency to be allocated as the used frequency.

9. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 8 is characterized in that: The process of performing adaptive cooperative interference suppression operation includes: Extracting the interference signal strength and the transmission path of the interference frequency range of the interference suppression terminal in the grid unit coverage area, obtaining the signal sub-area emitting the interference signal strength and the interference frequency range according to the transmission path, and marking the signal sub-area as an interference source; An adaptive interference suppression model is constructed based on the adaptive beamforming algorithm. The interference signal strength and interference frequency range of the interference source at the current moment and the coverage area of ​​the grid unit are input into the adaptive interference suppression model, and the optimal adjustment parameters of the interference source are output according to the adaptive interference suppression model.

10. The 5G indoor-macro base station collaborative coverage system for multi-campus scenarios according to claim 9 is characterized in that: The process of the fast switching execution module performing the data link switching operation according to the signal strength, service type and mobility status of the user terminal includes: Extracting the signal strength, service type and mobility status of each user terminal from the signal data collected in real time within the signal sub-area; When the mobile state of the user terminal is far away from the signal sub-area and the used signal strength is less than the preset signal strength threshold corresponding to the service type, the used bandwidth and used signal strength corresponding to the service type are extracted, and the idle bandwidth valley value and the used signal strength valley value of other signal sub-areas in the current acquisition period are obtained. The other signal sub-areas whose idle bandwidth valley value is greater than the used bandwidth and whose used signal strength valley value is greater than the used signal strength are obtained, and the Euclidean distance between the other signal sub-areas and the user terminal is obtained. The other signal sub-areas with the shortest Euclidean distance are screened out to perform the data link switching operation.

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