Indoor wireless signal rapid coverage method and system

Abstract

The invention belongs to the technical field of mobile communication, and discloses an indoor wireless signal rapid coverage method and system, and the method comprises the steps: receiving power distribution based on a collected reference signal, and calculating a power deviation index of M mobile radio frequency units per unit time; based on the area function and the power level, obtaining the combined spatial characteristics of each mobile radio frequency unit and the power level; performing feature engineering operation on the monitoring data and the time attribute set collected by each mobile radio frequency unit per unit time, and the power level, the power deviation index and the spatial features to obtain historical feature data of each mobile radio frequency unit per unit time; based on the historical feature data of each mobile radio frequency unit per unit time, defining a preset window length, setting training data, and training a prediction model of each mobile radio frequency unit; according to the invention, the accuracy of predicting the transmitting power level of the mobile radio frequency unit is obviously improved, and the optimal balance between the service quality and the energy consumption is realized.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and more particularly to a method and system for rapid indoor wireless signal coverage. Background Art

[0002] Semi-enclosed spaces, such as underground parking lots, experience severe attenuation and multiple reflections of RF signals in reinforced concrete and metal structures, creating coverage blind spots. Macro base station signals are virtually impervious to penetration, necessitating the deployment of distributed antenna systems (DAS) to improve indoor coverage. However, traditional threshold-based energy-saving strategies (such as underload power reduction and sleep mode) struggle to balance energy conservation and service quality due to their static configuration. They consume significant standby power even in low traffic conditions, while wake-up delays in high traffic conditions lead to interruptions and reduced throughput. While digital DAS offers the advantages of flexibility and programmability, it lacks precise prediction and intelligent resource management, hindering its full potential.

[0003] With the advancement of 5G / 6G technologies, network energy efficiency and spectrum efficiency are equally important. In wireless access networks, power consumption by base stations or mobile radio frequency units (RFUs) dominates. In underground parking lots, most RFUs are often underloaded or idle. To promote green communications and reduce operating costs, a strategy for dynamically adjusting the RFU state is urgently needed.

[0004] In view of this, the present invention proposes a method and system for rapid indoor wireless signal coverage to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions: a method for rapid indoor wireless signal coverage, comprising:

[0006] Based on the collected reference signal received power distribution, calculate the power deviation index per unit time of the M mobile radio frequency units;

[0007] Obtaining a spatial feature of each combination of mobile radio units and power levels based on the area function and the power levels;

[0008] Perform feature engineering on the monitoring data and time attribute set collected per unit time for each mobile radio frequency unit, as well as the power level, power deviation index, and spatial characteristics, to obtain historical feature data per unit time for each mobile radio frequency unit.

[0009] Based on the historical feature data of each mobile radio unit per unit time, a preset window length is defined, training data is set, and a prediction model for each mobile radio unit is trained;

[0010] A prediction result is obtained based on the prediction model of each mobile radio frequency unit and the historical characteristic data per unit time, and the power level of each mobile radio frequency unit in the next unit time is adjusted in advance based on the prediction result.

[0011] Furthermore, the method for constructing the area function includes:

[0012] For each combination of mobile radio frequency unit (MRU) and power level, a pseudo-random uniform sampling algorithm is used to generate n measurement points throughout the underground parking lot. The reference signal received power (RSRP) at each of the n measurement points is measured using a handheld drive tester or portable spectrum analyzer. Outliers are removed through statistical filtering to obtain the reference signal received power at n′ preprocessing locations. Signal strength levels are calculated based on the RSRP to obtain the signal strength levels of the n′ preprocessing locations. Preprocessing locations with effective signal strength levels are retained to obtain the effective signal strength levels of n″ effective signal locations. The functional types of the spatial regions containing the n″ effective signal locations are determined. For each combination of effective signal strength level and functional type, the number of effective signal locations is counted to obtain the equal strength and similarity quantity for each combination of mobile radio frequency unit (MRU), power level, effective signal strength level, and functional type. The total area of ​​the underground parking lot is calculated and divided by n to obtain the average area of ​​each measurement point. The equal strength and similarity quantity is multiplied by the average area to obtain the equal strength and similarity area for each combination of mobile radio frequency unit (MRU), power level, effective signal strength level, and functional type. An area function is derived using the MRU, power level, effective signal strength level, and functional type as independent variables and the equal strength and similarity area as the dependent variable.

[0013] Furthermore, the method for constructing the power level includes: dividing the collected signal transmission power into intervals to obtain the power level;

[0014] The method for constructing the signal strength level includes: dividing the collected reference signal reception power into intervals to obtain a power level;

[0015] The effective strength level is a signal strength level that is higher than a preset strength threshold.

[0016] Furthermore, the spatial region division method includes:

[0017] Create a digital map of the underground parking lot, divide the underground parking lot into different spatial areas according to functional types, and mark the functional type of each spatial area; functional types include access area, charging area, and parking area.

[0018] Furthermore, the method for obtaining spatial features includes:

[0019] For each combination of mobile radio frequency unit, power level and effective intensity level, the equal intensity similar area corresponding to each functional type is obtained by the area function; the sum of the equal intensity similar areas corresponding to all functional types is calculated to obtain the total equal intensity area; the total equal intensity area and the equal intensity similar areas corresponding to all functional types are written in the form of column vectors according to a preset order to obtain the equal intensity vectors corresponding to each combination of mobile radio frequency unit, power level and effective intensity level; for each combination of mobile radio frequency unit and power level, the equal intensity vectors corresponding to each effective intensity level are written in the form of a matrix according to a preset order to obtain the spatial characteristics corresponding to each combination of mobile radio frequency unit and power level.

[0020] Furthermore, the monitoring data includes: uplink throughput, downlink throughput, physical resource block utilization, and user carrying capacity.

[0021] Furthermore, the method for collecting signal transmission power, monitoring data, and reference signal reception power distribution includes:

[0022] Through the network management system of the digital distributed antenna system, at the beginning of each unit time, the signal transmission power, monitoring data and real-time data of the reference signal receiving power distribution of each mobile radio frequency unit are read and the timestamp of each data is recorded.

[0023] Furthermore, the calculation method of the power deviation index includes:

[0024] For each mobile radio frequency unit, the reference signal received power value of the preset percentile is calculated according to the reference signal received power distribution per unit time to obtain the cell signal strength per unit time; when the cell signal strength is greater than or equal to the upper limit of the preset quality level, the power deviation index per unit time of the mobile radio frequency unit is -1; when the cell signal strength is less than the lower limit of the preset quality level, the power deviation index per unit time of the mobile radio frequency unit is 1; when the real-time signal strength is within the range of the preset quality level, the power deviation index per unit time of the mobile radio frequency unit is 0

[0025] Furthermore, the method for adjusting the power level in advance includes:

[0026] For each mobile radio frequency unit, a preset number of unit times including the current unit time is used as a preset window length, and historical feature data of the preset window length is extracted and input into the prediction model to obtain the predicted values ​​of the power level and power deviation index of each mobile radio frequency unit in the next unit time, which are marked as the predicted power level and the predicted deviation index respectively; when the predicted deviation index is -1, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to the power level one level lower than the predicted power level; when the predicted deviation index is 1, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to the power level one level higher than the predicted power level; when the predicted deviation index is 0, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to the predicted power level.

[0027] An indoor wireless signal rapid coverage system is used to implement an indoor wireless signal rapid coverage method, the system comprising:

[0028] The first processing module calculates the power deviation index per unit time of the M mobile radio frequency units based on the collected reference signal reception power distribution;

[0029] A second processing module obtains a spatial feature of each combination of mobile radio frequency unit and power level based on the area function;

[0030] The feature engineering module performs feature engineering operations on the monitoring data and time attribute sets collected per unit time by each mobile radio unit, as well as the power level, power deviation index, and spatial characteristics, to obtain the historical feature data per unit time for each mobile radio unit.

[0031] A training module, which defines a preset window length and sets training data based on the historical feature data of each mobile radio unit per unit time, and trains a prediction model for each mobile radio unit;

[0032] The control module predicts a result based on the prediction model of each mobile radio frequency unit and the historical characteristic data per unit time, and adjusts the power level of each mobile radio frequency unit in the next unit time in advance based on the prediction result.

[0033] Beneficial effects of the method and system for rapid indoor wireless signal coverage of the present invention:

[0034] By finely dividing the underground parking lot into functional areas such as passage areas, charging areas, and parking areas, and combining it with quantitative analysis of the signal coverage area of ​​mobile radio frequency units at different power levels, the accuracy of the mobile radio frequency unit transmission power level prediction has been significantly improved.

[0035] Pseudo-random sampling and Z-score anomaly filtering are used to strictly screen drive test data to ensure the reliability of the coverage area function and eliminate error accumulation and outlier interference.

[0036] The prediction model is trained using historical feature data to accurately predict future loads, and the transmit power of the mobile radio frequency unit is intelligently adjusted in advance to achieve the optimal balance between service quality and energy consumption, avoiding the energy waste caused by traditional static threshold strategies.

[0037] By comprehensively considering spatial characteristics (coverage area of ​​each functional area), time attributes (weekdays / holidays, peak and valley periods) and real-time signal indicators (transmitting power, throughput, physical resource block utilization, user carrying capacity, reference signal receiving power distribution), the power configuration is dynamically evaluated and adjusted in real time. This not only effectively fills blind spots, but also can flexibly respond to sudden traffic fluctuations, thereby significantly improving user experience and system robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a module diagram of an indoor wireless signal rapid coverage system according to the present invention;

[0039] Figure 2 This is a flow chart of a method for rapid indoor wireless signal coverage according to the present invention;

[0040] Figure 3 The figure is a schematic diagram of a scenario of an indoor wireless signal rapid coverage system according to the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Example 1

[0043] See Figure 1 The indoor wireless signal rapid coverage system described in this embodiment includes: a first processing module, a second processing module, a feature engineering module, a training module and a control module.

[0044] The Digital Distributed Antenna System (Digital DAS) consists of two main parts: the Baseband Unit (BBU) and the Remote Radio Unit (RRU). Figure 3The Remote Radio Unit (RRU), also known as the Mobile Radio Unit (MRU), is responsible for digitizing analog RF signals from the carrier's core network (including demodulation, modulation, channel coding / decoding, and multi-carrier multiplexing) to generate a unified digital baseband data stream, which is then distributed to multiple Mobile Radio Units. Furthermore, the Mobile Radio Unit receives uplink digital baseband data streams from each Mobile Radio Unit, performs reverse processing, and then transmits them to the carrier's core network. The Mobile Radio Unit is deployed within the service area (such as the various sections of the underground parking lot in this system). Its primary function is to convert analog RF signals into digital baseband data streams. Specifically, the Mobile Radio Unit receives the digital baseband data stream from the BBU and converts it back to an analog RF signal using a DAC (digital-to-analog converter). Simultaneously, the Mobile Radio Unit receives uplink analog RF signals transmitted by the end-user equipment (UE), performs low-noise amplification, and performs analog-to-digital conversion (ADC). The resulting data is then packaged into a digital baseband data stream and transmitted back to the BBU. This system ignores transmission delays between the BBU and the Mobile Radio Units (the two are connected via a high-speed, low-latency method, such as fiber optics). The digital distributed antenna system is an existing technology and will not be described in detail here.

[0045] Signal transmission power directly affects the coverage and penetration of mobile radio frequency units. Uplink throughput measures the ability of users to initiate upload services (such as cloud storage synchronization, live video streaming, or large file uploads), reflecting the network's uplink carrying capacity. For video conferencing or real-time monitoring, insufficient uplink throughput can result in video freezes and intermittent voice chatter. Downlink throughput directly reflects the user experience quality of downlink services (such as web page loading, video on demand, and file downloads). High downlink throughput supports smooth 4K / 8K video playback and fast large file downloads. If downlink throughput is insufficient, users are prone to video buffering and slow web page response. Physical Resource Block (PRB) utilization measures the intensity of cell radio resource usage. High PRB utilization indicates resource constraints, which may result in queuing or transmission delays. When PRB utilization consistently exceeds 85%, average user throughput will significantly decrease, impairing the service experience. By monitoring PRB utilization, scheduling algorithms can be dynamically adjusted or capacity expanded to ensure service quality during peak periods. User carrying capacity directly reflects the cell load level and is a key indicator for determining whether capacity expansion or resource allocation optimization is needed. More users means that the total bandwidth is shared among more terminals, and the throughput that can be allocated to a single user naturally decreases. Reference Signal Received Power (RSRP) is defined as the average power of a specific reference signal received by a user device from the serving cell. The higher the RSRP value, the stronger the signal received by the user device. The RSRP distribution is the cumulative distribution function of the RSRP values ​​reported by user devices within the coverage area of ​​a mobile radio unit. The RSRP distribution can be used to calculate the mean, median (50th percentile), standard deviation, minimum, maximum, and various percentiles (such as the 5th, 10th, and 90th percentiles).

[0046] Monitoring data includes: uplink throughput, downlink throughput, physical resource block utilization, and user carrying capacity.

[0047] The method for collecting signal transmission power, monitoring data, and reference signal reception power distribution includes: using the network management system (NMS) of a digital distributed antenna system (such as Huawei iMasterNCE-Campus), at the beginning of each unit of time (such as every 60 seconds), reading the real-time data of the signal transmission power, monitoring data, and reference signal reception power distribution of each mobile radio frequency unit and recording the timestamp of each data item.

[0048] The first processing module calculates the power deviation index per unit time of the M mobile radio frequency units based on the collected reference signal receiving power distribution.

[0049] The calculation method of the power deviation index includes: for each mobile radio frequency unit, calculating the reference signal received power value of a preset percentile based on the reference signal received power distribution per unit time, and obtaining the cell signal strength per unit time; when the cell signal strength is greater than or equal to the upper limit of the preset quality level, the power deviation index of the mobile radio frequency unit per unit time is -1; when the cell signal strength is less than the lower limit of the preset quality level, the power deviation index of the mobile radio frequency unit per unit time is 1; when the real-time signal strength is within the range of the preset quality level, the power deviation index of the mobile radio frequency unit per unit time is 0.

[0050] For example, when the preset percentile is the fifth percentile (ensuring that the signal quality of most users is not lower than the preset quality level), the preset quality level is signal available, and the reference signal received power value of the fifth percentile of the reference signal received power distribution of a certain mobile radio frequency unit in a certain unit time is -105dBm, -90dBm is the upper limit of the preset quality level, -100dBm is the lower limit of the preset quality level, the cell signal strength of a certain mobile radio frequency unit in a certain unit time is -105dBm (5% of the user equipment receive signals with reference signal received power values ​​less than -105dBm), and the power deviation index of a certain mobile radio frequency unit in a certain unit time is 1 (indicating that the power level of a certain mobile radio frequency unit in a certain unit time needs to be improved).

[0051] The power deviation index is a key data indicator for intelligent control and energy efficiency optimization. The power deviation index not only accurately quantifies the degree of compliance of the current cell signal strength with the preset quality level, providing an indispensable objective basis for the subsequent feature engineering module to construct high-quality historical feature data, but is also a key component of predictive model learning and prediction targets (i.e., the prediction deviation index). It is through the precise calculation and application of the power deviation index and the prediction deviation index that the control module is able to go beyond traditional threshold judgments and achieve refined, proactive adjustments to the power level of the mobile radio frequency unit. When the prediction deviation index is -1, the power is appropriately reduced; when it is 1, it is promptly increased; and when it is 0, it maintains the optimized state. This closed-loop feedback and predictive control mechanism based on the power deviation index is a fundamental way to effectively solve the problem of insufficient dynamic adjustment capabilities of existing technologies and the inability to balance service quality and energy consumption. It directly contributes to the key beneficial effect of "significantly improving the accuracy of mobile radio frequency unit transmit power level prediction and achieving the optimal balance between service quality and energy consumption."

[0052] The second processing module obtains the spatial characteristics of each combination of mobile radio frequency unit and power level based on the area function.

[0053] The method for constructing the power level includes: dividing the collected signal transmission power into intervals to obtain the power level.

[0054] Power levels include: Deep Sleep, Light Sleep, Active Low Power, and Active Full Power. Deep Sleep has the lowest power consumption (on the order of a few milliwatts) and is used in scenarios where service is not needed for an extended period. Light Sleep has a lower power consumption (in the tens to hundreds of milliwatts) than Low Power Activation, but higher than Deep Sleep; it is used in scenarios where no one is using the device for a short period but a quick response is required. Low Power Activation has a medium power consumption level (30% to 50% of full power) and is used in light-load periods with only a small number of users. Full Power Activation has the highest power consumption (on the order of tens to hundreds of watts) and is used to ensure maximum coverage and throughput during peak traffic. In actual applications, the signal transmission power levels can be divided into intervals of 3dBm (decibel milliwatts) to achieve more precise power adjustment (the signal transmission power is not adjusted continuously, but discretely adjusted according to the power level).

[0055] The method for constructing the signal strength level includes: dividing the collected reference signal receiving power into intervals to obtain the power level.

[0056] Signal strength levels include: excellent signal, good signal, available signal, poor signal, and unavailable signal. In this embodiment, RSRP ≥ -80dBm is defined as "excellent signal", -90 ≤ RSRP < -80dBm is defined as "good signal", -100 ≤ RSRP < -90dBm is defined as "available signal", -110 ≤ RSRP < -100dBm is defined as "poor signal", and RSSI < -110dBm is defined as "unavailable signal".

[0057] The effective strength level is the signal strength level above the preset strength threshold. For example, if the preset strength threshold is "Unusable Signal," all preprocessed locations with a signal strength level of "Unusable Signal" will be excluded. The preset strength threshold is used to distinguish between effective coverage locations and insufficient coverage locations. By excluding insufficient coverage locations, the effective strength level can significantly narrow the domain of the area function and reduce storage space.

[0058] Functional types include: access area, charging area, and parking area. It is worth noting that in actual applications, the functional types of this embodiment can be further subdivided; for example, the access area can be subdivided into the entrance access area, exit access area, etc., and the parking area can be subdivided into the first-floor parking area, the second-floor parking area, etc.

[0059] The spatial area division method includes: creating a digital map of the underground parking lot, dividing the underground parking lot into different spatial areas according to functional types, and marking the functional type of each spatial area.

[0060] The system's precise spatial division of service environments, such as underground parking lots, into functional areas (e.g., "access area," "charging area," and "parking area"), along with the functional type of each area, forms a key component in achieving accurate prediction and intelligent resource management. This functional-type-based spatial division enables the system to transcend the limitations of traditional reliance on network management system data and gain a deeper understanding of the unique user behavior and data demand patterns within areas of different functional types. The resulting area function, which captures the coverage of each functional type, and the further extracted spatial features, provide the feature engineering module with refined spatial contextual information reflecting the differences in real-world service scenarios. Incorporating these spatial features, rich in regional functional attributes, into historical feature data and using them to train the prediction model is a key element in significantly improving the accuracy of mobile radio unit transmit power level predictions. This meticulous consideration and utilization of spatial areas and their functional types enables power control decisions to fully account for the differentiated needs of different areas, effectively supporting the ultimate goal of achieving the optimal balance between service quality and energy consumption, ensuring intelligent and efficient wireless signal coverage in complex indoor environments.

[0061] The construction method of the area function includes: for each combination of mobile radio frequency unit and power level, n measurement point positions are generated within the entire underground parking lot through a pseudo-random uniform sampling algorithm (ensuring the spatial representativeness of the measurement positions), and the reference signal received power at the n measurement point positions is measured using a handheld road test instrument (such as Huawei's GENEX Probe) or a portable spectrum analyzer (such as Keysight's FieldFox series), and outliers are removed through statistical filtering (such as using the Z-score method, and setting that when the absolute value of the Z-score is greater than 3, it is an outlier), obtaining the reference signal received power at n' (n' < n) preprocessed positions; calculating the signal strength level based on the reference signal received power, obtaining the signal strength levels at the n' preprocessed positions; retaining the preprocessed positions with valid strength levels, obtaining the valid strength levels at n'' (n'' < n') valid signal positions; obtaining the functional type of the spatial area where the n'' valid signal positions are located; for each combination of valid strength level and functional type, counting the number of valid signal positions, obtaining the equal-strength and same-type quantity for each combination of mobile radio frequency unit, power level, valid strength level, and functional type (for example, for a certain combination of mobile radio frequency unit and low-power activation, if the number of valid signal positions belonging to the charging area with extremely good signal is 15, then the corresponding equal-strength and same-type quantity is equal to 15); calculating the total area of the underground parking lot, dividing the total area by n to obtain the average area of each measurement point position; multiplying the equal-strength and same-type quantity by the average area to obtain the equal-strength and same-type area for each combination of mobile radio frequency unit, power level, valid strength level, and functional type; using the mobile radio frequency unit, power level, valid strength level, and functional type as independent variables and the equal-strength and same-type area as the dependent variable to obtain the area function.

[0062] The area function plays a key role in accurately quantifying the relationship between a mobile radio unit's power level and its actual signal coverage effectiveness. The area function systematically measures and statistically filters the reference signal received power through pseudo-random uniform sampling. It then maps the functional type of the spatial region to establish a mapping relationship between mobile radio units, power levels, effective strength levels, and functional types to areas of equal strength and similarity. This refined, multi-dimensional modeling of coverage provides a solid and reliable foundation for the subsequent extraction of spatial features that truly reflect the coverage characteristics of different functional types. Leveraging these precise spatial features derived from the reliable area function, the system's prediction model can more accurately learn and predict the signal coverage requirements and corresponding power level configurations for specific functional types, directly supporting a fundamental improvement over existing technologies that suffer from inaccurate predictions and fail to effectively adapt to the dynamic demands of complex indoor environments. Therefore, the construction and application of the area function not only ensures the quality of spatial features, eliminating error accumulation and outlier interference, but also significantly improves the accuracy of mobile radio unit transmit power level predictions, providing a key technical guarantee for ultimately achieving the beneficial effect of "optimizing service quality and energy consumption."

[0063] The method for obtaining spatial features includes: for each combination of mobile radio frequency unit, power level and effective intensity level, obtaining the equal-intensity and similar areas corresponding to each functional type by an area function; calculating the sum of the equal-intensity and similar areas corresponding to all functional types to obtain the equal-intensity total area; writing the equal-intensity total area and the equal-intensity and similar areas corresponding to all functional types in the form of a column vector according to a preset order, to obtain the equal-intensity vector corresponding to each combination of mobile radio frequency unit, power level and effective intensity level (for example, taking the equal-intensity total area as the first element of the equal-intensity vector, taking the equal-intensity and similar areas of the channel area as the second element of the equal-intensity vector, taking the equal-intensity and similar areas of the charging area as the third element of the equal-intensity vector, and taking the equal-intensity and similar areas of the parking area as the fourth element of the equal-intensity vector); for each combination of mobile radio frequency unit and power level, writing the equal-intensity vector corresponding to each effective intensity level in the form of a matrix according to a preset order, to obtain the spatial features corresponding to each combination of mobile radio frequency unit and power level.

[0064] For example, when the preset strength threshold is signal unavailable, if the signal strength vector corresponding to a combination of mobile radio unit and power level is excellent is the column vector (4.0, 1.4, 1.0, 1.6) T , the equal strength vector with good signal is the column vector (6.2, 1.5, 2.4, 2.3) T , the signal available equal strength vector is the column vector (5.5, 2.2, 3.3, 0) T , the equal strength vector with poor signal is the column vector (8.9, 2.1, 2.8, 4.0) T, then the spatial feature is the matrix

[0065]

[0066] Spatial features are key data structures that carry detailed information about a mobile radio unit's coverage effectiveness for different functional types at specific power levels. These features are derived from a deep analysis of area functions. They systematically organize information such as the areas of equal intensity and the total area of ​​equal intensity for each functional type, for example, into a matrix format for different effective intensity levels. This provides the feature engineering module with quantified, structured, and refined spatial context. These spatial features, which precisely describe the specific associations between coverage and functional types, enable the prediction model to go beyond single signal or load metrics during learning and gain a deep understanding of the actual impact of different power level configurations on coverage across various functional types (e.g., "access areas," "charging areas," and "parking areas"). Therefore, the introduction and application of spatial features plays a crucial role in resolving the bottleneck of existing technologies, which suffer from inaccurate transmit power level predictions in complex indoor environments and the difficulty of dynamically adapting to the differentiated needs of various functional areas. They are an essential technical support for significantly improving the accuracy of mobile radio unit transmit power level predictions and ultimately achieving the core benefit of "optimizing the balance between service quality and energy consumption."

[0067] The feature engineering module performs feature engineering operations on the monitoring data and time attribute set collected per unit time of each mobile radio frequency unit, as well as the power level, power deviation index and spatial characteristics, to obtain the historical feature data of each mobile radio frequency unit per unit time.

[0068] The method for obtaining the time attribute set includes: obtaining all time attributes for each unit time to form a time attribute set for each unit time.

[0069] Time attributes include: work attributes and peak and valley attributes.

[0070] The method for obtaining work attributes includes: obtaining the unit time to be processed, extracting the corresponding date information, and determining whether the extracted date is a working day or a non-working day (weekend / legal holiday) by calling the system calendar service or the built-in holiday table; if it is a working day, the work attribute is 1; if it is a non-working day, the work attribute is 0.

[0071] The method for obtaining the peak and valley attributes includes: obtaining the unit time to be processed, extracting the corresponding hours and minutes, and comparing them with the preset time period thresholds of the morning peak (such as 07:00-09:00), noon peak (such as 11:30-13:30), evening peak (such as 17:00-19:00) and night valley (such as 23:00-5:00 the next day); if it falls within the time period threshold of the morning peak, the peak and valley attribute is 0; if it falls within the time period threshold of the noon peak, the peak and valley attribute is 1; if it falls within the time period threshold of the evening peak, the peak and valley attribute is 2; if it falls within the time period threshold of the night valley, the peak and valley attribute is 3.

[0072] Feature engineering operations include: cleaning, encoding, standardization, and unified timestamp format.

[0073] The training module defines a preset window length based on the historical feature data of each mobile radio frequency unit per unit time, sets the training data, and trains the prediction model of each mobile radio frequency unit.

[0074] A preset number of unit times including the current unit time is used as a preset window length.

[0075] The method for setting training data includes: presetting the window length as y, taking the historical feature data of y unit time from the kth to the k+y-1th unit time as input samples, taking the power level and power deviation index of the k+yth day as output samples, and forming a set of training data with the input samples and the output samples.

[0076] The prediction model training method includes: for each mobile radio unit, historical feature data per unit time is divided into a training set and a test set (the ratio can be set to 7:3). The selected machine learning model is trained using the training set, and tested using the test set. The model converges when the prediction accuracy reaches a preset prediction accuracy. Finally, a prediction model capable of predicting power level and power deviation index is obtained. The machine learning model can be a long short-term memory network (LSTM) model.

[0077] The control module predicts a result based on the prediction model of each mobile radio frequency unit and the historical characteristic data per unit time, and adjusts the power level of each mobile radio frequency unit in the next unit time in advance based on the prediction result.

[0078] The method for adjusting the power level in advance includes: for each mobile radio frequency unit, extracting historical feature data of a preset window length and inputting it into a prediction model to obtain the predicted values ​​of the power level and power deviation index of each mobile radio frequency unit in the next unit time, which are marked as the predicted power level and the predicted deviation index respectively; when the predicted deviation index is -1, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to a power level one level lower than the predicted power level; when the predicted deviation index is 1, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to a power level one level higher than the predicted power level; when the predicted deviation index is 0, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to the predicted power level.

[0079] This embodiment proposes an indoor wireless signal rapid coverage system, which effectively solves the problem in the prior art of being unable to predict and adjust the transmission power level of each mobile radio frequency unit in real time.

[0080] Specifically, this system is implemented through the following methods:

[0081] The signal transmission power, monitoring data, and reference signal receiving power distribution of each mobile radio frequency unit are collected in real time. The spatial areas of the underground parking lot are divided according to functional types (channel area, charging area, parking area). An area function is constructed through pseudo-random sampling and drive test data to quantify the signal coverage area of ​​spatial areas of each functional type at different power levels. The power deviation index is calculated based on the reference signal receiving power distribution to determine whether the transmission power needs to be adjusted. In combination with time attributes (weekdays / holidays, peak and valley periods) and spatial characteristics (a functional area coverage matrix), a prediction model is trained using historical data through a sliding window method to achieve dynamic adaptive adjustment of the transmission power level of the mobile radio frequency unit.

[0082] Compared with existing technologies, this system has significant advantages:

[0083] The system divides underground parking lots into functional areas, such as access areas, charging areas, and parking areas. This system also improves prediction accuracy by combining the coverage of mobile radio frequency unit signals at various power levels for each functional area (quantified using an area function). Drive test signal measurements are rigorously screened using pseudo-random sampling and Z-score anomaly filtering to ensure the accuracy of the area function and effectively avoid error accumulation and outlier interference.

[0084] Based on historical feature data, the prediction model is trained through a sliding window to achieve accurate prediction of future loads and adjust the mobile radio frequency unit transmission power level in advance to balance service quality and energy consumption, reducing the energy waste caused by static threshold strategies.

[0085] The comprehensive functional area coverage area (spatial characteristics), time attributes (weekdays / holidays, peak and valley periods) and real-time signal indicators (transmitting power, throughput, physical resource block utilization, user carrying capacity, reference signal receiving power distribution) are used to evaluate and dynamically adjust the power configuration in real time. This not only effectively fills coverage blind spots, but also can flexibly respond to sudden load peaks or valley fluctuations, significantly improving user experience and system robustness.

[0086] Example 2

[0087] See Figure 2 As shown, this embodiment provides a method for rapid indoor wireless signal coverage, including:

[0088] Based on the collected reference signal received power distribution, calculate the power deviation index per unit time of the M mobile radio frequency units;

[0089] Obtaining a spatial feature of each combination of mobile radio units and power levels based on the area function and the power levels;

[0090] Perform feature engineering on the monitoring data and time attribute set collected per unit time for each mobile radio frequency unit, as well as the power level, power deviation index, and spatial characteristics, to obtain historical feature data per unit time for each mobile radio frequency unit.

[0091] Based on the historical feature data of each mobile radio unit per unit time, a preset window length is defined, training data is set, and a prediction model for each mobile radio unit is trained;

[0092] A prediction result is obtained based on the prediction model of each mobile radio frequency unit and the historical characteristic data per unit time, and the power level of each mobile radio frequency unit in the next unit time is adjusted in advance based on the prediction result.

[0093] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0094] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0095] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for rapid indoor wireless signal coverage, characterized in that: include: Based on the collected reference signal received power distribution, calculate the power deviation index per unit time of the M mobile radio frequency units; Obtaining a spatial feature of each combination of mobile radio units and power levels based on the area function and the power levels; Perform feature engineering on the monitoring data and time attribute set collected per unit time for each mobile radio frequency unit, as well as the power level, power deviation index, and spatial characteristics, to obtain historical feature data per unit time for each mobile radio frequency unit. Based on the historical feature data of each mobile radio unit per unit time, a preset window length is defined, training data is set, and a prediction model for each mobile radio unit is trained; A prediction result is obtained based on the prediction model of each mobile radio frequency unit and the historical characteristic data per unit time, and the power level of each mobile radio frequency unit in the next unit time is adjusted in advance based on the prediction result.

2. The method for rapid indoor wireless signal coverage according to claim 1, characterized in that: The construction methods of area function include: For each combination of mobile radio frequency unit (MRU) and power level, a pseudo-random uniform sampling algorithm is used to generate n measurement points throughout the underground parking lot. The reference signal received power (RSRP) at each of the n measurement points is measured using a handheld drive tester or portable spectrum analyzer. Outliers are removed through statistical filtering to obtain the reference signal received power at n′ preprocessing locations. Signal strength levels are calculated based on the RSRP to obtain the signal strength levels of the n′ preprocessing locations. Preprocessing locations with effective signal strength levels are retained to obtain the effective signal strength levels of n″ effective signal locations. The functional types of the spatial regions containing the n″ effective signal locations are determined. For each combination of effective signal strength level and functional type, the number of effective signal locations is counted to obtain the equal strength and similarity quantity for each combination of mobile radio frequency unit (MRU), power level, effective signal strength level, and functional type. The total area of ​​the underground parking lot is calculated and divided by n to obtain the average area of ​​each measurement point. The equal strength and similarity quantity is multiplied by the average area to obtain the equal strength and similarity area for each combination of mobile radio frequency unit (MRU), power level, effective signal strength level, and functional type. An area function is derived using the MRU, power level, effective signal strength level, and functional type as independent variables and the equal strength and similarity area as the dependent variable.

3. The method for rapid indoor wireless signal coverage according to claim 2, characterized in that: The method for constructing the power level includes: dividing the collected signal transmission power into intervals to obtain the power level; The method for constructing the signal strength level includes: dividing the collected reference signal reception power into intervals to obtain a power level; The effective strength level is a signal strength level that is higher than a preset strength threshold.

4. The method for rapid indoor wireless signal coverage according to claim 2, wherein: The methods for dividing spatial regions include: Create a digital map of the underground parking lot, divide the underground parking lot into different spatial areas according to functional types, and mark the functional type of each spatial area; functional types include access area, charging area, and parking area.

5. The method for rapid indoor wireless signal coverage according to claim 1, characterized in that: The methods for obtaining spatial features include: For each combination of mobile radio frequency unit, power level and effective intensity level, the equal intensity similar area corresponding to each functional type is obtained by the area function; the sum of the equal intensity similar areas corresponding to all functional types is calculated to obtain the total equal intensity area; the total equal intensity area and the equal intensity similar areas corresponding to all functional types are written in the form of column vectors according to a preset order to obtain the equal intensity vectors corresponding to each combination of mobile radio frequency unit, power level and effective intensity level; for each combination of mobile radio frequency unit and power level, the equal intensity vectors corresponding to each effective intensity level are written in the form of a matrix according to a preset order to obtain the spatial characteristics corresponding to each combination of mobile radio frequency unit and power level.

6. The method for rapid indoor wireless signal coverage according to claim 1, characterized in that: Monitoring data includes: uplink throughput, downlink throughput, physical resource block utilization, and user carrying capacity.

7. The method for rapid indoor wireless signal coverage according to claim 1, characterized in that: The methods for collecting signal transmission power, monitoring data, and reference signal receiving power distribution include: Through the network management system of the digital distributed antenna system, at the beginning of each unit time, the signal transmission power, monitoring data and real-time data of the reference signal receiving power distribution of each mobile radio frequency unit are read and the timestamp of each data is recorded.

8. The method for rapid indoor wireless signal coverage according to claim 1, characterized in that: The calculation method of the power deviation index includes: For each mobile radio frequency unit, the reference signal received power value of the preset percentile is calculated according to the reference signal received power distribution per unit time to obtain the cell signal strength per unit time; when the cell signal strength is greater than or equal to the upper limit of the preset quality level, the power deviation index of the mobile radio frequency unit per unit time is -1; when the cell signal strength is less than the lower limit of the preset quality level, the power deviation index of the mobile radio frequency unit per unit time is 1; when the real-time signal strength is within the range of the preset quality level, the power deviation index of the mobile radio frequency unit per unit time is 0.

9. The method for rapid indoor wireless signal coverage according to claim 1, characterized in that: Methods for early adjustment of power levels include: For each mobile radio frequency unit, a preset number of unit times including the current unit time is used as a preset window length, and historical feature data of the preset window length is extracted and input into the prediction model to obtain the predicted values ​​of the power level and power deviation index of each mobile radio frequency unit in the next unit time, which are marked as the predicted power level and the predicted deviation index respectively; when the predicted deviation index is -1, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to the power level one level lower than the predicted power level; when the predicted deviation index is 1, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to the power level one level higher than the predicted power level; when the predicted deviation index is 0, the actual value of the power level configured for the corresponding mobile radio frequency unit in the next unit time is equal to the predicted power level.

10. An indoor wireless signal rapid coverage system, characterized in that: For implementing the method for rapid indoor wireless signal coverage according to claim 1, the system comprises: The first processing module calculates the power deviation index per unit time of the M mobile radio frequency units based on the collected reference signal reception power distribution; A second processing module obtains a spatial feature of each combination of mobile radio frequency unit and power level based on the area function; The feature engineering module performs feature engineering operations on the monitoring data and time attribute sets collected per unit time by each mobile radio unit, as well as the power level, power deviation index, and spatial characteristics, to obtain the historical feature data per unit time for each mobile radio unit. A training module, which defines a preset window length and sets training data based on the historical feature data of each mobile radio unit per unit time, and trains a prediction model for each mobile radio unit; The control module predicts a result based on the prediction model of each mobile radio frequency unit and the historical characteristic data per unit time, and adjusts the power level of each mobile radio frequency unit in the next unit time in advance based on the prediction result.

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