Multi-frequency multi-mode intelligent digital indoor distribution method and system

Abstract

The application discloses a multi-frequency multi-mode intelligent digital indoor distribution method and system. Radio frequency signals are digitized and transmitted through wired media, avoiding signal attenuation and noise accumulation problems in long-distance transmission of traditional analog distribution systems. Meanwhile, flexible wiring is supported. Software-defined network (SDN) and network function virtualization (NFV) technologies are introduced. Resources and coverage can be dynamically adjusted according to real-time loads, spectral utilization and network capacity are significantly improved, multi-frequency multi-mode processing is supported, unified access and broadcasting of various communication systems such as 2G / 3G / 4G / 5G are realized, and complexity and cost of multi-system coexistence are reduced.

Description

Technical Field

[0001] This invention relates to the field of base station control, and in particular to a multi-frequency, multi-mode intelligent digital indoor distribution method and system. Background Technology

[0002] With the rapid development of mobile communication technology, evolving from 4G to 5G and even the future 6G, user data traffic has experienced explosive growth, and business scenarios are becoming increasingly diversified (such as high-definition video, VR / AR, and industrial IoT). Statistics show that over 70% of mobile data services occur in indoor settings (such as large shopping malls, office buildings, airports, and subway stations). However, due to the shielding and attenuation of wireless signals by building walls, macro base station signals often struggle to penetrate deep into indoor spaces, leading to increasingly prominent problems such as numerous indoor coverage blind spots, poor signal quality, and insufficient capacity. Therefore, Indoor Distributed Antenna Systems (DAS) have become a key infrastructure for solving indoor deep coverage and capacity supplementation issues.

[0003] Existing indoor distribution systems have evolved primarily from traditional passive DAS to active electronic DAS, and then to digital indoor distribution systems (such as pRRU architecture). Although digital indoor distribution systems have greatly improved coverage performance, they still face significant technical bottlenecks when confronted with the new challenges of multi-frequency and multi-mode coexistence, drastic changes in business dynamics, and enormous pressure for energy conservation and emission reduction in the 5G era.

[0004] In summary, a multi-frequency, multi-mode intelligent digital indoor distribution method and system are needed to address the shortcomings of existing technologies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-frequency, multi-mode intelligent digital indoor distribution method and system, aiming to solve the aforementioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-frequency, multi-mode intelligent digital indoor distribution method, comprising the following steps:

[0007] Step S1: Signal digitization and transmission. Receive radio frequency signals from the base station through a digital signal source, convert them into digital signals, and transmit the digital signals to various distribution points indoors through a wired transmission medium.

[0008] Step S2: Signal restoration and broadcasting. At the distribution point, the received digital signal is converted back into an RF signal using a remote RF unit or repeater and then broadcast.

[0009] Step S3: Intelligent resource scheduling, based on software-defined networking and network function virtualization technology, constructs a centralized control plane, monitors network load and coverage requirements in real time, dynamically schedules and allocates network resources, and adjusts signal strength and coverage range;

[0010] Step S4: Multi-frequency and multi-mode processing. In the signal digitization and transmission steps and the signal restoration and broadcasting steps, it supports signal access, processing and broadcasting of multiple communication standards and multiple frequency bands, and enables compatible communication of multi-standard terminals.

[0011] Step S5: Centralized monitoring and management, through a central management platform to monitor the working status of each distribution point in real time, diagnose faults, and perform visualized operation and maintenance management;

[0012] Step S6: Energy efficiency optimization. Based on network load, intelligently adjust device power consumption through energy management mechanisms.

[0013] Optionally, step S1 is implemented in the following manner:

[0014] Step S11: Radio frequency signal acquisition and filtering. The digital signal source receives analog radio frequency signals from the macro base station or micro base station through the feeder. The input multi-band mixed radio frequency signal is bandpass filtered to remove out-of-band noise and interference signals, and the signal is amplified with low noise.

[0015] Step S12: Down-conversion and analog-to-digital conversion, converting the high-frequency analog radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, using a local oscillator and mixer to shift the radio frequency signal frequency to the intermediate frequency or directly to the baseband, and using a high-speed, high-precision analog-to-digital converter to sample and quantize the analog signal according to the Nyquist sampling theorem to generate the original digital bit stream;

[0016] Step S13: Digital signal processing and encapsulation, mapping the digital signal to a specific transmission protocol frame structure, performing digital filtering and decimation, protocol mapping, channel coding and time division / frequency division multiplexing on the original digital signal to generate a multi-standard composite data stream adapted to fiber optic or network cable transmission standards;

[0017] Step S14: Electro-optic conversion and modulation: The digital data stream is modulated into an optical signal through an electro-optic conversion module and injected into the optical fiber transmission medium;

[0018] Step S15: Digital transmission. The optical signal is transmitted to the indoor distribution point via optical fiber, and at the distribution point, it is converted back into a digital data stream by a photoelectric conversion module, thus completing the digital transmission of the signal.

[0019] Optionally, step S2 is implemented in the following manner:

[0020] Step S21: Digital signal reception and clock recovery. Receive digital data streams at the distribution points, perform frame parsing and clock recovery, and extract multi-standard digital baseband signals.

[0021] Step S22: Digital signal processing and demultiplexing, demultiplexing and digital predistortion processing are performed on the digital baseband signal to generate a pre-compensated digital signal;

[0022] Step S23: Digital-to-analog conversion and upconversion. The pre-compensated digital signal is converted into an analog signal using a digital-to-analog converter, and then upconverted to the target radio frequency band using a mixer.

[0023] Step S24: Power amplification and linearization correction: The radio frequency signal is amplified by a power amplifier, and nonlinear distortion is corrected in real time based on a feedback mechanism;

[0024] Step S25: RF filtering and combining, filtering and purifying the amplified RF signal and performing multi-frequency combining;

[0025] Step S26: Antenna radiation and spatial broadcasting: The processed radio frequency signal is fed to the antenna unit and converted into electromagnetic waves for directional or omnidirectional broadcasting into the indoor space.

[0026] Optionally, step S3 is implemented in the following manner:

[0027] Step S31: Network-wide status awareness and data acquisition. Through the southbound interface of the centralized control plane, real-time data on traffic load, signal quality indicators, and user location distribution at each distribution point are collected.

[0028] Step S32: Business demand analysis and prediction. Based on the collected data, use a preset algorithm to analyze the current business scenario and predict the short-term traffic surge trend and service quality requirements.

[0029] Step S33: Resource scheduling strategy generation. Based on the analysis results, a resource scheduling strategy is dynamically generated. The strategy includes a flexible scaling scheme for virtual network functions, a spectrum resource allocation scheme, and a remote unit transmit power adjustment scheme.

[0030] Step S34: Control command issuance and configuration update. Flow table configuration is issued through the software-defined network controller, and the creation, migration, or destruction of virtual network function instances is performed through the network function virtualization orchestrator.

[0031] Step S35: Virtual cell reconstruction and interference coordination. The virtual cell topology is dynamically reconstructed according to the scheduling strategy. Multiple physical remote units are logically merged or split, and antenna weights are adjusted in a coordinated manner to suppress inter-cell interference.

[0032] Step S36: Effect verification and closed-loop optimization. Monitor network performance indicators after scheduling execution. If the expected results are not met, automatically trigger policy correction or configuration rollback to form closed-loop optimization control.

[0033] Optionally, step S4 is implemented in the following manner:

[0034] Step S41: Multi-standard signal convergence and broadband reception. At the signal source end, analog radio frequency signals of multiple communication standards are converged through a broadband radio frequency front end and then amplified in a broadband low-noise manner.

[0035] Step S42: Wideband analog-to-digital conversion and spectrum slicing. The high-speed wideband analog-to-digital converter is used to directly sample the converged wideband analog signal to generate a wideband digital signal. The signal is then logically sliced ​​into independent baseband data streams corresponding to different standards and frequency bands through digital down-conversion technology.

[0036] Step S43: Unify digital baseband processing and virtualization mapping. On a unified digital processing platform, computing resources are dynamically allocated based on network function virtualization technology to perform parallel protocol processing and timing alignment of baseband data streams of various standards.

[0037] Step S44: Multi-frequency digital multiplexing and transmission encapsulation, multiplexing the processed multi-standard digital baseband streams into the same transmission data frame, adding a quality of service priority flag before sending it to the transmission medium;

[0038] Step S45: Remote digital demultiplexing and multimode restoration. At the distribution point, the transmitted data frame is parsed, and the digital baseband streams of each standard are separated. After multi-channel digital-to-analog conversion and digital up-conversion synthesis, it is restored into a wideband analog radio frequency signal containing multiple standard signals.

[0039] Step S46: Wideband power amplification and linearization broadcasting. The wideband analog radio frequency signal is linearized and corrected, and then broadcast synchronously through a wideband antenna after power amplification, so as to achieve seamless compatible access for multi-standard terminals.

[0040] Optionally, the centralized monitoring and management in step S5 is implemented in the following ways:

[0041] Step S51: Multidimensional data acquisition and standardization. The performance indicators, hardware status and alarm information of each distribution point are collected in real time through the southbound interface, and the multi-source heterogeneous data is converted into a unified standard format.

[0042] Step S52: Real-time status monitoring and topology visualization. Based on the collected data, dynamically render the physical and logical topology of the entire network, generate a coverage heat map and a capacity load view, and realize the visual monitoring of the status.

[0043] Step S53: Intelligent fault diagnosis and root cause analysis. Using association rule mining and root cause analysis algorithms, concurrent alarms are suppressed and located, the root cause of the fault is identified and potential risks are predicted.

[0044] Step S54: Automated operation and maintenance and fault self-healing. For recoverable faults, automatically execute remote reset, configuration rollback or service migration scripts to achieve fault self-healing;

[0045] Step S55: Asset and Lifecycle Management. For faults that cannot heal themselves, automatically generate options including fault selection. The energy efficiency optimization in step S5 is implemented in the following ways:

[0046] Step S61: Energy consumption modeling and real-time metering, establish equipment power consumption model, monitor current and voltage data at each distribution point in real time and calculate real-time energy consumption;

[0047] Step S62: Load perception and service forecasting, analyze the current network load characteristics, use artificial intelligence algorithms to predict short-term traffic trends, and identify low-load periods and areas;

[0048] Step S63: Multi-dimensional energy-saving strategy generation. Based on the load prediction results, an energy-saving strategy is dynamically generated. The strategy includes one or more combinations of symbol silencing, channel shutdown, carrier sleep, and dynamic adjustment of transmit power.

[0049] Step S64: Policy execution and smooth handover. Before executing the shutdown policy, the affected user terminals are smoothly migrated to an adjacent coverage layer or frequency point, and then the device hibernation operation is executed in a progressive order.

[0050] Step S65: Effect evaluation and closed-loop optimization. Real-time monitoring of service quality indicators and energy saving effect during the energy saving period. If the indicators are abnormal, the fast wake-up mechanism is immediately triggered and the energy saving strategy parameters are corrected to form a closed-loop optimization.

[0051] Optionally, in step S1, the digital signal source includes a baseband processing unit or a radio frequency remote unit, the wired transmission medium is an optical fiber or a high-speed Ethernet cable, and the conversion process includes analog-to-digital conversion and digital signal encoding.

[0052] A multi-frequency multi-mode intelligent digital indoor distribution system, employing the aforementioned multi-frequency multi-mode intelligent digital indoor distribution method, includes a signal digitization and transmission module, a signal restoration and broadcasting module, an intelligent resource scheduling module, a multi-frequency multi-mode processing module, a centralized monitoring and management module, and an energy efficiency optimization module;

[0053] Signal digitization and transmission module; used to receive analog radio frequency signals from base stations, perform filtering, low-noise amplification, down-conversion and analog-to-digital conversion, perform protocol mapping, encoding and multiplexing of digital signals, modulate data streams into optical signals through electro-optic conversion, and transmit digital signals to various distribution points indoors using wired media such as optical fiber or high-speed Ethernet cable.

[0054] The signal restoration and broadcasting module is used to receive digital data streams at distribution points, perform clock recovery, frame parsing and demultiplexing, extract multi-standard digital baseband signals, convert high-frequency analog signals into digital baseband or intermediate frequency signals, restore digital signals to analog radio frequency signals through digital-to-analog conversion and up-conversion, and after power amplification, linearization correction and filtering, broadcast electromagnetic waves to indoor space through the antenna unit.

[0055] The intelligent resource scheduling module is used to collect real-time data on network traffic load, signal quality, and user location, analyze service requirements and predict traffic trends, dynamically generate resource scheduling strategies based on software-defined networking and network function virtualization technologies, reconstruct virtual cell topology, suppress interference, and form closed-loop optimization control.

[0056] The multi-frequency multi-mode processing module supports the convergence, broadband reception and processing of multiple communication standards and multi-frequency band signals. It is used to process baseband data streams of various standards in parallel on a unified platform using broadband analog-to-digital conversion and spectrum slicing technology, so as to realize the multiplexing and transmission of multi-standard signals and the demultiplexing and synchronous broadcasting at a remote end.

[0057] The centralized monitoring and management module is used to collect performance indicators and hardware status of each distribution point, dynamically render the entire network topology, coverage heat map and capacity load view, use algorithms to perform intelligent fault location and root cause analysis, execute automated operation and maintenance scripts to achieve fault self-healing, or generate electronic work orders for asset management.

[0058] The energy efficiency optimization module is used to establish a power consumption model to measure energy consumption in real time, use AI algorithms to predict traffic trends to identify low load periods, dynamically execute energy-saving strategies such as symbol silencing, channel shutdown, carrier sleep or power adjustment according to the load, and achieve smooth sleep and wake-up of equipment while ensuring service quality.

[0059] The beneficial effects of this invention are:

[0060] 1. In this invention, by digitizing radio frequency signals and transmitting them through wired media, the signal attenuation and noise accumulation problems of long-distance transmission in traditional analog distributed systems are avoided. At the same time, it supports flexible cabling, introduces software-defined networking (SDN) and network function virtualization (NFV) technologies, and can dynamically adjust resources and coverage according to real-time load, which significantly improves spectrum utilization and network capacity. It supports multi-frequency and multi-mode processing, realizes unified access and broadcasting of multiple communication standards such as 2G / 3G / 4G / 5G, and reduces the complexity and cost of multi-system coexistence.

[0061] 2. In this invention, out-of-band noise and interference are effectively filtered out through bandpass filtering and low-noise amplification, improving the signal-to-noise ratio of the input signal. High-precision analog-to-digital conversion is performed according to the Nyquist sampling theorem, ensuring that the analog-to-digital signal conversion is distortion-free or low-distortion. After digital filtering, encoding and multiplexing, the generated data stream is more suitable for fiber optic or network cable transmission, improving transmission efficiency and anti-interference capability. Centralized monitoring and management realize rapid fault diagnosis and visualized operation and maintenance, reducing manual maintenance costs. Power consumption is intelligently adjusted according to the load, effectively reducing the energy consumption of the system during low-load periods.

[0062] 3. In this invention, phase errors and nonlinear distortions during transmission and processing are compensated through clock recovery and digital predistortion processing, ensuring the quality of the restored signal. Power amplification combined with linearization correction ensures the strength and linearity of the transmitted signal, reduces adjacent channel interference, supports directional or omnidirectional broadcasting, and can flexibly adjust the coverage mode according to specific indoor scenarios to eliminate blind spots. Based on network-wide status awareness and traffic prediction, it realizes elastic scaling and dynamic allocation of resources, can cope with sudden traffic demands such as "tidal effect", and has effect verification and strategy correction mechanisms to ensure that the network is always in the optimal operating state, thus improving the robustness of the system. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of a method flow of the present invention.

[0064] Figure 2 This is a schematic diagram of step S1 of the present invention.

[0065] Figure 3 This is a schematic diagram of step S2 of the present invention.

[0066] Figure 4 This is a schematic diagram of step S3 of the present invention.

[0067] Figure 5 This is a schematic diagram of step S4 of the present invention.

[0068] Figure 6 This is a schematic diagram of a centralized monitoring and management process according to the present invention.

[0069] Figure 7 This is a schematic diagram of a step-by-step energy efficiency optimization process according to the present invention. Detailed Implementation

[0070] To more clearly illustrate the technical solutions in the embodiments of the invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] like Figures 1 to 7 As shown, a multi-frequency, multi-mode intelligent digital indoor distribution method includes the following:

[0072] Step S1: Signal digitization and transmission. Receive radio frequency signals from the base station through a digital signal source, convert them into digital signals, and transmit the digital signals to various distribution points indoors through a wired transmission medium.

[0073] Specifically, it will be implemented in the following ways:

[0074] Step S11. Radio frequency signal acquisition and filtering: A digital signal source, such as an extended unit EU or a baseband unit BBU connected to the radio frequency front end, receives analog radio frequency signals from a macro base station or micro base station via a feeder.

[0075] The input multi-band mixed radio frequency signal is bandpass filtered to remove out-of-band noise and interference signals, and the signal is amplified with low noise to ensure that the signal-to-noise ratio meets the requirements of subsequent analog-to-digital conversion.

[0076] Step S12. Down-conversion and analog-to-digital conversion: convert the high-frequency analog radio frequency signal into a digital baseband signal or a digital intermediate frequency signal.

[0077] By using a local oscillator and mixer, the frequency of the radio frequency signal is shifted to the intermediate frequency or directly to the baseband. The analog signal is then sampled and quantized according to the Nyquist sampling theorem through a high-speed, high-precision analog-to-digital converter to generate the original digital bit stream.

[0078] Step S13. Digital signal processing and packaging: Process the original digital signal to adapt it to fiber optic or network cable transmission protocols.

[0079] Remove the mirror frequency, reduce the data rate, map the digital signal to a specific transmission protocol frame structure, such as CPRI, eCPRI, O-RAN fronthaul protocol, or proprietary optical transmission protocol, add error correction codes, and improve the transmission error resistance capability.

[0080] If multi-frequency and multi-mode support is supported, digital signals of different standards (2G / 3G / 4G / 5G) can be multiplexed into the same data stream.

[0081] Step S14. Electro-optical conversion and modulation: converting digital data in electrical signal form into optical signals for low-loss transmission in optical fiber medium.

[0082] Drive the laser or modulator to change the intensity or phase / frequency of the light according to the "0" and "1" of the digital signal. If the transmission is made by network cable, serialization and line encoding are performed, eliminating the need for this optical conversion step, but impedance matching is required.

[0083] Step S15. Wired media transmission and relay, physical propagation of signals in the physical medium and quality assurance of long-distance transmission.

[0084] Digital optical signals are transmitted in single-mode or multi-mode optical fibers. If the transmission distance exceeds the single-span limit (e.g., >20km), the attenuated optical signal is regenerated or amplified by an optical amplifier or opto-electro-optical repeater. Clock synchronization is maintained during transmission, such as through the 1588v2 protocol or synchronous Ethernet, to ensure that the remote unit can accurately restore the signal timing.

[0085] Step S16. Preparation for reverse processing after the remote photoelectric conversion and decapsulation signal arrives at the indoor distribution point.

[0086] The optical module of the remote unit receives the optical signal, converts it back into an electrical signal, parses the transmission protocol frame, extracts the original baseband digital signal, and extracts the clock signal from the data stream for synchronization in subsequent digital-to-analog conversion.

[0087] Step S2: Signal restoration and broadcasting. At the distribution point, the received digital signal is converted back into an RF signal using a remote RF unit or repeater and then broadcast.

[0088] Specifically, it will be implemented in the following ways:

[0089] Step S21. Digital signal reception and clock recovery: The remote radio frequency unit receives the digital data stream from the transmission medium and reconstructs the synchronization clock.

[0090] If the input is an optical signal, it is converted into an electrical signal through photoelectric detection by the optical module; if it is an electrical signal, it is shaped, the data frame header is identified, the transmission protocol is parsed, the payload (I / Q baseband data) is extracted, the high-frequency clock signal is extracted from the data stream, or the local clock is calibrated using an external synchronization source (such as 1588v2) to ensure accurate synchronization of the sampling rate of the digital-to-analog conversion and avoid code slippage or phase noise.

[0091] Step S22. Digital signal processing and demultiplexing: preprocess the extracted digital signals to separate signals of different formats and frequency bands.

[0092] The multiplexed multi-carrier data stream is separated into independent single-carrier or multi-band data channels. For example, the I / Q data of 2G, 4G and 5G are separated. Based on the nonlinear characteristics of the power amplifier, the digital signal is pre-computed with inverse compensation to offset the distortion generated by subsequent analog amplification. This is a key step to improve linearity and energy efficiency. If the remote unit supports a multi-antenna array, the digital signal is weighted at this stage to form a specific beam pattern.

[0093] Step S23. Digital-to-analog conversion and up-conversion: converting the digital baseband signal into an analog radio frequency signal.

[0094] A high-precision, high-speed digital-to-analog converter is used to restore digital I / Q signals to analog baseband or intermediate frequency signals. Low-pass or band-pass filters are used to filter out the image frequency and quantization noise generated by the DAC. A local oscillator is used to generate a carrier frequency. A mixer is used to shift the baseband / intermediate frequency signal to the target radio frequency band, such as 2.6GHz or 3.5GHz, to initially amplify the weak radio frequency signal so that it meets the input level requirements of the power amplifier.

[0095] Step S24. Power amplification and linearization correction: Amplify the RF signal to sufficient power to cover the target area and correct distortion in real time.

[0096] The signal is amplified to the rated output power by a high linearity power amplifier. The coupled signal at the output of the power amplifier is collected and fed back to the digital processing unit. The DPD coefficient is adjusted in real time to ensure the signal linearity in multi-frequency and multi-mode concurrent scenarios, reduce EVM, reduce adjacent channel leakage, and dynamically adjust the amplification factor according to intelligent scheduling instructions to achieve dynamic power regulation.

[0097] Step S25. RF filtering and combining: clean up the signal spectrum and combine multi-band signals.

[0098] Filter out harmonics and spurious signals generated by the power amplifier to ensure that the transmission spectrum complies with radio management regulations. If the system supports simultaneous transmission of multiple frequency bands, use a combiner to combine radio frequency signals of different frequency bands to the same feeder or antenna port while ensuring isolation.

[0099] Step S26. Antenna radiation and space broadcasting convert electrical signals into electromagnetic waves and radiate them into the indoor space.

[0100] The antenna interface circuit ensures efficient transmission of radio frequency energy to the antenna and reduces reflections. Depending on the antenna type, electromagnetic waves are projected onto the target coverage area with a specific beamwidth and gain. By using dual-polarized antenna technology, the signal reception probability and resistance to multipath fading in complex indoor environments are improved.

[0101] Step S3: Intelligent resource scheduling, based on software-defined networking and network function virtualization technology, constructs a centralized control plane, monitors network load and coverage requirements in real time, dynamically schedules and allocates network resources, and adjusts signal strength and coverage range;

[0102] Specifically, it will be implemented in the following ways:

[0103] Step S31. Network-wide status awareness and data acquisition: The centralized control plane collects the operational data of all nodes in the network in real time through the southbound interface.

[0104] Collect uplink / downlink data throughput, number of users, and physical resource block utilization of each remote unit; collect signal quality indicators such as Reference Signal Received Power (RSRP), Signal-to-Noise Ratio (SINR), packet loss rate, latency, and handover success rate; obtain real-time distribution heatmaps of users indoors based on measurement reports or fingerprint positioning technology; and monitor the CPU / memory load and physical link status of each Virtual Network Function (VNF) instance.

[0105] Step S32. Business demand analysis and forecasting: Analyze the collected data to identify current and future network demand patterns.

[0106] It automatically identifies the business scenarios in the current area, such as high-density meetings, peak video streaming, and low call volume periods. Using machine learning algorithms, it predicts the traffic surge effect and user movement trajectory in the short future time window based on historical data. According to the service level agreement requirements of different vertical industry applications, it analyzes their specific requirements for bandwidth, latency, and reliability.

[0107] Step S33. Resource scheduling strategy generation: Based on the analysis results, the SDN controller and NFV orchestrator collaboratively generate the optimal resource configuration scheme.

[0108] Decide whether to instantiate a new virtual baseband processing unit or destroy an idle instance, adjust the virtual machine's computing resource quota, formulate a carrier aggregation strategy, dynamically allocate carriers of different frequency bands to high-load cells, or shut down some carriers to save energy when under low load, calculate the optimal transmit power value for each remote unit, plan the boundaries of the virtual cell, determine which units should be merged into a logical cell to eliminate interference, or split to expand capacity, calculate the optimal data transmission path for the fronthaul network, and avoid congestion.

[0109] Step S34. Control command issuance and configuration update: The generated strategy is converted into specific configuration commands and issued to the execution layer through a standardized interface.

[0110] Through protocols such as OpenFlow, NETCONF / YANG, flow table entries or configuration parameters are sent to switches, gateways, and remote RF units. The NFV orchestrator interface is called to execute VNF instantiate, scale-in / out, migrate, or terminate operations, dynamically modifying RF parameters such as frequency, power, and antenna weights of remote units without interrupting services.

[0111] Step S35. Virtual cell reconstruction and interference coordination: Based on the scheduling results, dynamically change the logical network topology and optimize the coverage pattern.

[0112] Multiple physical remote units are dynamically combined into a logical "super cell". Users can move within the area without switching, achieving a "zero handover" experience. In response to co-channel interference in adjacent areas, the frequency reuse mode is dynamically adjusted or collaborative multi-point transmission technology is applied to enable multiple units to work together to serve edge users. This forces or guides some users in high-load cells to switch to neighboring cells or different frequency bands, balancing the load of the entire network.

[0113] Step S36. Effect verification and closed-loop optimization: Monitor the network status after scheduling execution, verify whether the expected goal has been achieved, and perform iterative optimization.

[0114] By comparing key performance indicators before and after scheduling, the effectiveness of the scheduling strategy is evaluated. If scheduling is found to cause performance degradation or failure, a rollback mechanism is automatically triggered to restore the previous stable configuration. The input and output data of this scheduling are stored in the knowledge base to train the AI ​​model and optimize the scheduling algorithm weights for the next round.

[0115] Step S4: Multi-frequency and multi-mode processing. In the signal digitization and transmission steps and the signal restoration and broadcasting steps, it supports signal access, processing and broadcasting of multiple communication standards and multiple frequency bands, and enables compatible communication of multi-standard terminals.

[0116] Specifically, it will be implemented in the following ways:

[0117] Step S41. Multi-standard signal aggregation and broadband reception: At the signal source end, multi-band, multi-standard analog radio frequency signals from different base stations or different boards of the same base station are physically aggregated.

[0118] By using wideband combiners or filter banks, signals from different frequency bands such as 2G, 3G, 4G, and 5G can be combined into one or more wideband channels. By using RF front-end circuits that support ultra-wideband, flat gain response and low noise figure are ensured over an extremely wide frequency range, such as 600MHz - 6000MHz, without the need to configure a separate receiver link for each frequency band.

[0119] Step S42. Wideband analog-to-digital conversion and spectrum slicing: The combined wideband signal is sampled in one go using a high-speed, high-bandwidth ADC and then separated in the digital domain.

[0120] Using a high sampling rate analog-to-digital converter, the entire broadband signal is directly digitized, avoiding the losses and spurious signals caused by multi-stage frequency conversion. In FPGA or ASIC, the broadband digital signal is logically "sliced" into multiple independent narrowband baseband signal streams through digital down-conversion technology, each corresponding to a different communication standard and carrier frequency band. The standard type of each slice signal is automatically identified, such as LTE TDD / FDD, NR TDD, and the corresponding metadata is tagged for subsequent processing.

[0121] Step S43. Unify digital baseband processing and virtualization mapping. On a unified digital processing platform, perform parallel processing and resource mapping of baseband data of different standards.

[0122] By leveraging NFV technology, virtual baseband processing functions for different standards are run on general-purpose computing resources, enabling software-based coexistence of 2G / 3G / 4G / 5G protocol stacks. A shared digital signal processing resource pool is established, and DSP or CPU computing power is dynamically allocated according to the real-time traffic volume of each standard to avoid one module being idle while another is overloaded. The frame structure and clock of signals of different standards are uniformly aligned to ensure timing consistency during multi-mode concurrency, especially prioritizing the scheduling of low-latency services such as 5G URLLC.

[0123] Step S44. Multi-frequency digital multiplexing and transmission encapsulation: The processed multi-standard digital signals are repackaged and adapted to the transmission network.

[0124] Multiple digital baseband streams of different frequency bands and standards are multiplexed into the same high-bandwidth transmission protocol frame, such as the eCPRI RoE frame. Based on the bandwidth limitations of the transmission medium, lossless or lossy compression is performed on the signals of each standard, such as block floating-point compression, to ensure that multimode signals can be transmitted losslessly under limited bandwidth. QoS priority labels are set for data packets of different standards or different service types, such as voice vs. data.

[0125] Step S45. Remote digital demultiplexing and multimode restoration: At the remote distribution point, the composite digital stream is separated and restored into multi-mode radio frequency signals.

[0126] The remote unit parses the transmission frame, extracts the independent digital baseband streams belonging to different standards and frequency bands, and uses a multi-channel DAC or wideband DAC to convert multiple digital baseband signals into analog signals simultaneously. In the digital domain, the signals of each standard are shifted to their respective center frequencies and synthesized in the digital domain to generate a wideband digital intermediate frequency / radio frequency signal containing all standards.

[0127] Step S46. Wideband power amplification and linearization broadcasting: amplify and radiate the synthesized multimode wideband signal to ensure that the signals of different standards do not interfere with each other and that the quality meets the standards.

[0128] To address intermodulation interference caused by concurrent transmission of multiple standards, a multi-carrier digital predistortion algorithm is used to perform nonlinear compensation on the power amplifier, ensuring linearity when 2G / 3G / 4G / 5G signals are transmitted simultaneously. Out-of-band spurious signals are filtered out by an ultra-wideband filter, and multi-mode signals are simultaneously broadcast to the indoor space via a broadband antenna. The broadcast signal field simultaneously contains pilot signals of each standard, allowing terminals with different capabilities to automatically search for and camp on the optimal standard network.

[0129] Step S5: Centralized monitoring and management step, through the central management platform to monitor the working status of each distribution point in real time, diagnose faults and perform visualized operation and maintenance management;

[0130] Specifically, it will be implemented in the following ways:

[0131] Step S51. Multidimensional data acquisition and standardization: Raw operational data is collected in real time from various distribution points (pRRU / Hub / switch) through the southbound interface.

[0132] Collect KPI data such as traffic throughput, PRB utilization, number of users, bit error rate, and latency; obtain hardware status such as device temperature, voltage, current, optical module power, and fan speed; capture alarm events such as link interruption, over-temperature, abnormal VSWR, and synchronization loss in real time; convert private protocol data of different manufacturers and different standards of equipment into a unified standard format (such as YANG model); and perform noise reduction and missing value filling.

[0133] Step S52. Real-time status monitoring and topology visualization: Present the overall network operation status on the central management platform and provide an intuitive operation and maintenance view.

[0134] Automatically generate and update physical connection topology and logical service topology in real time, distinguish device health by color (green / yellow / red), generate indoor coverage heat map and capacity load heat map based on user distribution and signal strength data, and display key indicators in real time on the dashboard, such as total network traffic, total number of alarms, and total energy consumption.

[0135] Step S53. Intelligent fault diagnosis and root cause analysis: Utilize algorithms to automatically analyze abnormal data, locate the source of the fault, and reduce false alarms.

[0136] Analyze the spatiotemporal correlation between alarms, identify derivative alarms, and suppress alarm storms. For example, if an optical path interruption causes all downstream units to go offline, only the root cause alarm is retained. Based on topology and expert knowledge base, the root cause of the fault is automatically inferred, such as whether it is a fiber break, equipment crash, or configuration error. Time series analysis is used to predict potential faults, such as early warning of a slow decline in optical module power, to achieve preventive maintenance.

[0137] Step S54. Automated operation and maintenance and fault self-healing: Execute preset automated repair scripts for common faults to shorten business interruption time.

[0138] Perform remote soft reboot or hard reset operations on remote units with frozen software. When a sharp drop in performance is detected after a new configuration is issued, automatically roll back to the previous stable version configuration. When the primary link or equipment fails, automatically switch to the backup link or neighboring cell to carry services. For hardware faults that cannot be automatically repaired, automatically generate an electronic work order and dispatch it to the maintenance personnel in the corresponding area, along with fault location information.

[0139] Step S55. Asset and lifecycle management: digitally manage the entire lifecycle of network devices.

[0140] Automatically synchronize device model, serial number, firmware version, installation location and other information, monitor firmware version consistency across the network, plan and execute batch upgrade tasks, and provide alerts for devices that are about to expire.

[0141] Step S6: Energy efficiency optimization. Based on network load, intelligently adjust device power consumption through energy management mechanisms.

[0142] Specifically, it will be implemented in the following ways:

[0143] Step S61. Energy consumption modeling and real-time metering: Establish a power consumption model for the equipment and monitor actual energy consumption data in real time.

[0144] Based on parameters such as device type, frequency band, bandwidth, and transmit power, mathematical models of static and dynamic power consumption are constructed. Through smart meters or internal sensors of the device, current and voltage data of each distribution point and each carrier are collected in real time to calculate real-time power consumption. Based on historical data and industry standards, energy efficiency benchmarks for different scenarios are set, such as bit / Joule.

[0145] Step S62. Load awareness and service forecasting: Analyze the current network load and predict future traffic trends to provide input for energy-saving strategies.

[0146] Identify the patterns of busy and slow periods in different areas such as office areas, residential areas, and shopping malls. Use AI algorithms to predict traffic change trends in the next few minutes to hours. Set thresholds, such as PRB utilization rate <5% and number of users <2, to determine whether a cell or carrier is a candidate for entering deep sleep state.

[0147] Step S63. Generate a multi-dimensional energy-saving strategy and dynamically select the optimal energy-saving combination strategy based on the load conditions.

[0148] Power amplifiers are turned off during OFDM symbol periods with no data transmission or on idle antenna channels to reduce dynamic power consumption. During periods of extremely low load, non-basic coverage carriers are automatically turned off or multiple cells are merged. Redundant radio frequency units are turned off. Based on the signal quality of edge users, the transmit power is dynamically reduced to the minimum value that meets coverage requirements. During absolutely idle times such as at night, some distribution points are controlled to enter micro-sleep or deep sleep mode, retaining only heartbeat detection.

[0149] Step S64. Execute the strategy and switch smoothly, issue energy-saving instructions, and ensure that the user is unaware of the energy-saving process.

[0150] Before shutting down a carrier or cell, users are smoothly migrated to adjacent coverage layers or frequencies by adjusting switching parameters. The process is carried out gradually in the order of reducing power, shutting down some channels, shutting down carriers, and shutting down equipment to avoid service abrupt changes. A fast wake-up process based on user access requests or traffic surges is established to ensure full power operation is restored in milliseconds.

[0151] Step S65. Effect evaluation and closed-loop optimization: Monitor the network performance and power saving effect after energy saving, and dynamically adjust the strategy parameters.

[0152] Real-time monitoring of call drop rate, handover success rate, and user complaint rate during energy-saving periods ensures that QoS is not compromised. It calculates the energy consumption difference before and after policy execution and generates energy-saving reports. If a policy is found to cause performance degradation or insignificant power saving, it automatically adjusts the trigger threshold or switches the policy mode, forming a closed loop of monitoring, decision-making, execution, and evaluation.

[0153] A multi-frequency multi-mode intelligent digital indoor distribution system, employing the aforementioned multi-frequency multi-mode intelligent digital indoor distribution method, includes a signal digitization and transmission module, a signal restoration and broadcasting module, an intelligent resource scheduling module, a multi-frequency multi-mode processing module, a centralized monitoring and management module, and an energy efficiency optimization module;

[0154] Signal digitization and transmission module; used to receive analog radio frequency signals from base stations, perform filtering, low-noise amplification, down-conversion and analog-to-digital conversion, perform protocol mapping, encoding and multiplexing of digital signals, modulate data streams into optical signals through electro-optic conversion, and transmit digital signals to various distribution points indoors using wired media such as optical fiber or high-speed Ethernet cable.

[0155] The signal restoration and broadcasting module is used to receive digital data streams at distribution points, perform clock recovery, frame parsing and demultiplexing, extract multi-standard digital baseband signals, convert high-frequency analog signals into digital baseband or intermediate frequency signals, restore digital signals to analog radio frequency signals through digital-to-analog conversion and up-conversion, and after power amplification, linearization correction and filtering, broadcast electromagnetic waves to indoor space through the antenna unit.

[0156] The intelligent resource scheduling module is used to collect real-time data on network traffic load, signal quality, and user location, analyze service requirements and predict traffic trends, dynamically generate resource scheduling strategies based on software-defined networking and network function virtualization technologies, reconstruct virtual cell topology, suppress interference, and form closed-loop optimization control.

[0157] The multi-frequency multi-mode processing module supports the convergence, broadband reception and processing of multiple communication standards and multi-frequency band signals. It is used to process baseband data streams of various standards in parallel on a unified platform using broadband analog-to-digital conversion and spectrum slicing technology, so as to realize the multiplexing and transmission of multi-standard signals and the demultiplexing and synchronous broadcasting at a remote end.

[0158] The centralized monitoring and management module is used to collect performance indicators and hardware status of each distribution point, dynamically render the entire network topology, coverage heat map and capacity load view, use algorithms to perform intelligent fault location and root cause analysis, execute automated operation and maintenance scripts to achieve fault self-healing, or generate electronic work orders for asset management.

[0159] The energy efficiency optimization module is used to establish a power consumption model to measure energy consumption in real time, use AI algorithms to predict traffic trends to identify low load periods, dynamically execute energy-saving strategies such as symbol silencing, channel shutdown, carrier sleep or power adjustment according to the load, and achieve smooth sleep and wake-up of equipment while ensuring service quality.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-frequency, multi-mode intelligent digital indoor distribution method, characterized in that, Includes the following steps: Step S1: Signal digitization and transmission. Receive radio frequency signals from the base station through a digital signal source, convert them into digital signals, and transmit the digital signals to various distribution points indoors through a wired transmission medium. Step S2: Signal restoration and broadcasting. At the distribution point, the received digital signal is converted back into an RF signal using a remote RF unit or repeater and then broadcast. Step S3: Intelligent resource scheduling, based on software-defined networking and network function virtualization technology, constructs a centralized control plane, monitors network load and coverage requirements in real time, dynamically schedules and allocates network resources, and adjusts signal strength and coverage range; Step S4: Multi-frequency and multi-mode processing. In the signal digitization and transmission steps and the signal restoration and broadcasting steps, it supports signal access, processing and broadcasting of multiple communication standards and multiple frequency bands, and enables compatible communication of multi-standard terminals. Step S5: Centralized monitoring and management, through a central management platform to monitor the working status of each distribution point in real time, diagnose faults, and perform visualized operation and maintenance management; Step S6: Energy efficiency optimization. Based on network load, intelligently adjust device power consumption through energy management mechanisms.

2. The multi-frequency, multi-mode intelligent digital indoor distribution method according to claim 1, characterized in that, Step S1 is implemented in the following manner: Step S11: Radio frequency signal acquisition and filtering. The digital signal source receives analog radio frequency signals from the macro base station or micro base station through the feeder. The input multi-band mixed radio frequency signal is bandpass filtered to remove out-of-band noise and interference signals, and the signal is amplified with low noise. Step S12: Down-conversion and analog-to-digital conversion, converting the high-frequency analog radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, using a local oscillator and mixer to shift the radio frequency signal frequency to the intermediate frequency or directly to the baseband, and using a high-speed, high-precision analog-to-digital converter to sample and quantize the analog signal according to the Nyquist sampling theorem to generate the original digital bit stream; Step S13: Digital signal processing and encapsulation, mapping the digital signal to a specific transmission protocol frame structure, performing digital filtering and decimation, protocol mapping, channel coding and time division / frequency division multiplexing on the original digital signal to generate a multi-standard composite data stream adapted to fiber optic or network cable transmission standards; Step S14: Electro-optic conversion and modulation: The digital data stream is modulated into an optical signal through an electro-optic conversion module and injected into the optical fiber transmission medium; Step S15: Digital transmission. The optical signal is transmitted to the indoor distribution point via optical fiber, and at the distribution point, it is converted back into a digital data stream by a photoelectric conversion module, thus completing the digital transmission of the signal.

3. The multi-frequency, multi-mode intelligent digital indoor distribution method according to claim 2, characterized in that, Step S2 is implemented in the following manner: Step S21: Digital signal reception and clock recovery. Receive digital data streams at the distribution points, perform frame parsing and clock recovery, and extract multi-standard digital baseband signals. Step S22: Digital signal processing and demultiplexing, demultiplexing and digital predistortion processing are performed on the digital baseband signal to generate a pre-compensated digital signal; Step S23: Digital-to-analog conversion and upconversion. The pre-compensated digital signal is converted into an analog signal using a digital-to-analog converter, and then upconverted to the target radio frequency band using a mixer. Step S24: Power amplification and linearization correction: The radio frequency signal is amplified by a power amplifier, and nonlinear distortion is corrected in real time based on a feedback mechanism; Step S25: RF filtering and combining, filtering and purifying the amplified RF signal and performing multi-frequency combining; Step S26: Antenna radiation and spatial broadcasting: The processed radio frequency signal is fed to the antenna unit and converted into electromagnetic waves for directional or omnidirectional broadcasting into the indoor space.

4. The multi-frequency, multi-mode intelligent digital indoor distribution method according to claim 1, characterized in that, Step S3 is implemented in the following manner: Step S31: Network-wide status awareness and data acquisition. Through the southbound interface of the centralized control plane, real-time data on traffic load, signal quality indicators, and user location distribution at each distribution point are collected. Step S32: Business demand analysis and prediction. Based on the collected data, use a preset algorithm to analyze the current business scenario and predict the short-term traffic surge trend and service quality requirements. Step S33: Resource scheduling strategy generation. Based on the analysis results, a resource scheduling strategy is dynamically generated. The strategy includes a flexible scaling scheme for virtual network functions, a spectrum resource allocation scheme, and a remote unit transmit power adjustment scheme. Step S34: Control command issuance and configuration update. Flow table configuration is issued through the software-defined network controller, and the creation, migration, or destruction of virtual network function instances is performed through the network function virtualization orchestrator. Step S35: Virtual cell reconstruction and interference coordination. The virtual cell topology is dynamically reconstructed according to the scheduling strategy. Multiple physical remote units are logically merged or split, and antenna weights are adjusted in a coordinated manner to suppress inter-cell interference. Step S36: Effect verification and closed-loop optimization. Monitor network performance indicators after scheduling execution. If the expected results are not met, automatically trigger policy correction or configuration rollback to form closed-loop optimization control.

5. The multi-frequency, multi-mode intelligent digital indoor distribution method according to claim 1, characterized in that, Step S4 is implemented in the following manner: Step S41: Multi-standard signal convergence and broadband reception. At the signal source end, analog radio frequency signals of multiple communication standards are converged through a broadband radio frequency front end and then amplified in a broadband low-noise manner. Step S42: Wideband analog-to-digital conversion and spectrum slicing. The high-speed wideband analog-to-digital converter is used to directly sample the converged wideband analog signal to generate a wideband digital signal. The signal is then logically sliced ​​into independent baseband data streams corresponding to different standards and frequency bands through digital down-conversion technology. Step S43: Unify digital baseband processing and virtualization mapping. On a unified digital processing platform, computing resources are dynamically allocated based on network function virtualization technology to perform parallel protocol processing and timing alignment of baseband data streams of various standards. Step S44: Multi-frequency digital multiplexing and transmission encapsulation, multiplexing the processed multi-standard digital baseband streams into the same transmission data frame, adding a quality of service priority flag before sending it to the transmission medium; Step S45: Remote digital demultiplexing and multimode restoration. At the distribution point, the transmitted data frame is parsed, and the digital baseband streams of each standard are separated. After multi-channel digital-to-analog conversion and digital up-conversion synthesis, it is restored into a wideband analog radio frequency signal containing multiple standard signals. Step S46: Wideband power amplification and linearization broadcasting. The wideband analog radio frequency signal is linearized and corrected, and then broadcast synchronously through a wideband antenna after power amplification, so as to achieve seamless compatible access for multi-standard terminals.

6. The multi-frequency, multi-mode intelligent digital indoor distribution method according to claim 1, characterized in that, The centralized monitoring and management in step S5 is implemented in the following ways: Step S51: Multidimensional data acquisition and standardization. The performance indicators, hardware status and alarm information of each distribution point are collected in real time through the southbound interface, and the multi-source heterogeneous data is converted into a unified standard format. Step S52: Real-time status monitoring and topology visualization. Based on the collected data, dynamically render the physical and logical topology of the entire network, generate a coverage heat map and a capacity load view, and realize the visual monitoring of the status. Step S53: Intelligent fault diagnosis and root cause analysis. Using association rule mining and root cause analysis algorithms, concurrent alarms are suppressed and located, the root cause of the fault is identified and potential risks are predicted. Step S54: Automated operation and maintenance and fault self-healing. For recoverable faults, automatically execute remote reset, configuration rollback or service migration scripts to achieve fault self-healing; Step S55: Asset and lifecycle management. For faults that cannot heal themselves, automatically generate an electronic work order containing fault location information and dispatch it to the operation and maintenance terminal.

7. The multi-frequency, multi-mode intelligent digital indoor distribution method according to claim 1, characterized in that, The energy efficiency optimization in step S5 is implemented in the following ways: Step S61: Energy consumption modeling and real-time metering, establish equipment power consumption model, monitor current and voltage data at each distribution point in real time and calculate real-time energy consumption; Step S62: Load perception and service forecasting, analyze the current network load characteristics, use artificial intelligence algorithms to predict short-term traffic trends, and identify low-load periods and areas; Step S63: Multi-dimensional energy-saving strategy generation. Based on the load prediction results, an energy-saving strategy is dynamically generated. The strategy includes one or more combinations of symbol silencing, channel shutdown, carrier sleep, and dynamic adjustment of transmit power. Step S64: Policy execution and smooth handover. Before executing the shutdown policy, the affected user terminals are smoothly migrated to an adjacent coverage layer or frequency point, and then the device hibernation operation is executed in a progressive order. Step S65: Effect evaluation and closed-loop optimization. Real-time monitoring of service quality indicators and energy saving effect during the energy saving period. If the indicators are abnormal, the fast wake-up mechanism is immediately triggered and the energy saving strategy parameters are corrected to form a closed-loop optimization.

8. The multi-frequency, multi-mode intelligent digital indoor distribution method according to claim 1, characterized in that, In step S1, the digital signal source includes a baseband processing unit or a radio frequency remote unit, the wired transmission medium is an optical fiber or a high-speed Ethernet cable, and the conversion process includes analog-to-digital conversion and digital signal encoding.

9. A multi-frequency, multi-mode intelligent digital indoor distribution system, employing the multi-frequency, multi-mode intelligent digital indoor distribution method as described in any one of claims 1-8, characterized in that, It includes a signal digitization and transmission module, a signal restoration and broadcasting module, an intelligent resource scheduling module, a multi-frequency and multi-mode processing module, a centralized monitoring and management module, and an energy efficiency optimization module; Signal digitization and transmission module; It is used to receive analog radio frequency signals from base stations, perform filtering, low-noise amplification, down-conversion and analog-to-digital conversion, perform protocol mapping, encoding and multiplexing of digital signals, modulate data streams into optical signals through electro-optic conversion, and transmit digital signals to various distribution points indoors using wired media such as optical fiber or high-speed Ethernet cable. The signal restoration and broadcasting module is used to receive digital data streams at distribution points, perform clock recovery, frame parsing and demultiplexing, extract multi-standard digital baseband signals, convert high-frequency analog signals into digital baseband or intermediate frequency signals, restore digital signals to analog radio frequency signals through digital-to-analog conversion and up-conversion, and after power amplification, linearization correction and filtering, broadcast electromagnetic waves to indoor space through the antenna unit. The intelligent resource scheduling module is used to collect real-time data on network traffic load, signal quality, and user location, analyze service requirements and predict traffic trends, dynamically generate resource scheduling strategies based on software-defined networking and network function virtualization technologies, reconstruct virtual cell topology, suppress interference, and form closed-loop optimization control. The multi-frequency multi-mode processing module supports the convergence, broadband reception and processing of multiple communication standards and multi-frequency band signals. It is used to process baseband data streams of various standards in parallel on a unified platform using broadband analog-to-digital conversion and spectrum slicing technology, so as to realize the multiplexing and transmission of multi-standard signals and the demultiplexing and synchronous broadcasting at a remote end. The centralized monitoring and management module is used to collect performance indicators and hardware status of each distribution point, dynamically render the entire network topology, coverage heat map and capacity load view, use algorithms to perform intelligent fault location and root cause analysis, execute automated operation and maintenance scripts to achieve fault self-healing, or generate electronic work orders for asset management. The energy efficiency optimization module is used to establish a power consumption model to measure energy consumption in real time, use AI algorithms to predict traffic trends to identify low load periods, dynamically execute energy-saving strategies such as symbol silencing, channel shutdown, carrier sleep or power adjustment according to the load, and achieve smooth sleep and wake-up of equipment while ensuring service quality.

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