Method and device for intelligently adjusting output power of radio frequency module

Abstract

The invention relates to the field of radio frequency power adjustment, in particular to an intelligent adjustment method and device for the output power of a radio frequency module. The method comprises the following steps: collecting a multi-mode radio frequency signal, carrying out channel state response sensing, and constructing a real-time channel state information base; extracting a communication task input by a user, and performing service quality demand parameter analysis to obtain the minimum demand output power; performing orthogonal frequency division multiplexing power allocation according to the real-time channel state information base and the minimum required output power to generate a multi-carrier power allocation strategy; and driving the power output of the radio frequency module according to the multi-carrier power distribution strategy, carrying out PID feedback control processing, and constructing a feedback adjustment model. According to the invention, the power regulation response sensitivity, the control precision and the operation efficiency of the radio frequency module are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radio frequency power adjustment, and in particular to a radio frequency module output power intelligent adjustment method and device. BACKGROUND

[0002] With the explosive growth of wireless device deployment, radio frequency modules face more severe power management challenges when operating in dynamic scenarios such as multi-user access, high-speed movement, and frequent changes in channel interference. On the one hand, different communication services (such as high-definition video, low-latency control, and narrowband Internet of Things) have different power requirements, and fixed or static power configurations cannot meet the differentiated quality of service requirements. On the other hand, channel conditions fluctuate rapidly in time and space dimensions, and traditional power control strategies lack real-time sensing capabilities for channel conditions, which can easily lead to power excess or deficiency, resulting in reduced energy efficiency, communication interruption, and even system abnormalities. In addition, if power output is not controlled, radio frequency modules can easily cause heating, hardware aging, and other reliability problems under long-term high-load operation, which can seriously affect the stability of the entire machine. Therefore, building a radio frequency module output power intelligent adjustment method with real-time sensing, adaptive adjustment, and fine control capabilities has become a key technology direction for improving the performance and stability of modern wireless communication systems.

[0003] Current commonly used radio frequency power control mechanisms, such as fixed gain control and open-loop power control, have certain adjustment capabilities, but generally have low adjustment precision, poor adaptability, and feedback lag, making it difficult to meet the needs of high-speed dynamic communication environments. At the same time, some systems introduce simple temperature control or hardware feedback mechanisms to try to alleviate the system pressure caused by power output, but due to the lack of comprehensive analysis of communication task requirements, channel condition changes, and system operating conditions, they often cannot achieve truly intelligent and optimized control. In particular, in complex communication systems with multiple carriers, multiple frequency bands, and multiple services, traditional methods have difficulty balancing the multiple goals of communication performance, system energy efficiency, and device safety. SUMMARY

[0004] To solve the above technical problems, the present application provides a radio frequency module output power intelligent adjustment method and device to solve at least one of the above technical problems.

[0005] To achieve the above purpose, the present application provides a radio frequency module output power intelligent adjustment method, comprising the following steps: Step S1: Collecting multi-modal radio frequency signals, sensing channel state responses, and constructing a real-time channel state information library; Step S2: Extracting user input communication tasks, analyzing service quality requirement parameters to obtain minimum required output power; Step S3: Orthogonal frequency division multiplexing power distribution is performed according to the real-time channel state information library and the minimum demand output power, and a multi-carrier power distribution strategy is generated; Step S4: The radio frequency module power output is driven according to the multi-carrier power distribution strategy, and a PID feedback control process is performed to construct a feedback adjustment model.

[0006] In the present specification, a radio frequency module output power intelligent adjustment device is provided for performing the radio frequency module output power intelligent adjustment method as described above, comprising: A channel state sensing module is configured to collect multi-modal radio frequency signals, perform channel state response sensing, and construct a real-time channel state information library. A demand analysis module is configured to extract a user input communication task and analyze service quality demand parameters to obtain a minimum demand output power. A power distribution module is configured to perform orthogonal frequency division multiplexing power distribution according to the real-time channel state information library and the minimum demand output power, and generate a multi-carrier power distribution strategy. A power output control module is configured to drive the radio frequency module power output according to the multi-carrier power distribution strategy, and perform a PID feedback control process to construct a feedback adjustment model.

[0007] The beneficial effects of the present application are as follows: By collecting multi-modal radio frequency signals, the wireless channel state response is comprehensively sensed to construct a real-time channel state information library. The multi-modal radio frequency signals include multi-dimensional characteristics such as different frequency bands, directions, and modulation modes, so that the system can understand the wireless propagation environment from multiple angles and in depth, and improve the sensing ability of complex channel effects such as multipath fading, shielding, and interference. By extracting and processing the collected signals, a channel state information library with timeliness and updateability is constructed, so that the system has the ability to quickly respond and dynamically adapt when facing user movement, environmental changes, or increased interference. By extracting a user input communication task and analyzing its corresponding service quality (QoS) demand parameters, and converting them into a minimum demand output power, accurate target basis is provided for power distribution. Different tasks have significant differences in bandwidth, rate, latency, and bit error rate requirements. The system analyzes and models these QoS indicators, and calculates the minimum radio frequency output power required to meet the task requirements through an algorithm. This ensures that the communication quality is met, while avoiding excessive power output by the system, thereby effectively reducing energy consumption and reducing the pressure on hardware.

[0008] According to the channel gain of each subcarrier, more power is preferentially allocated to the subcarrier with better channel quality, so as to improve the signal-to-noise ratio and transmission efficiency of the overall communication link. By taking the minimum required power as a constraint condition, the optimal power allocation under the premise of minimizing energy consumption is realized, so as to effectively guarantee the service quality required by the communication task. According to the multi-carrier power allocation strategy, the corresponding power is output by the radio frequency module, and a feedback regulation model with adaptive ability is constructed by introducing a PID feedback control mechanism for real-time adjustment of the power output. Through continuous collection of the deviation between the power output and the communication performance, the closed-loop control between the output signal and the expected target is realized. Through the proportional, integral and differential adjustment mechanism of the PID controller, the output response is dynamically adjusted, the power overshoot is suppressed, the system oscillation is reduced, and the response speed is improved, so as to ensure that the output power always accurately matches the communication demand and the environmental state. The feedback regulation model further enhances the robustness of the system to environmental disturbances, so that the radio frequency module can still operate stably when facing rapid channel changes or external disturbances. This step realizes the transition from static power allocation to dynamic closed-loop regulation, significantly improves the response sensitivity, control accuracy and operation efficiency of the radio frequency module, reduces the risk of overload, and prolongs the service life of the hardware, which is a key support link for realizing the intelligent power regulation system. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 FIG. 1 is a schematic diagram of the step flow of the radio frequency module output power intelligent regulation method of the present application; Figure 2 FIG. 2 is a schematic diagram of the detailed implementation step flow of step S1; Figure 3 FIG. 3 is a schematic diagram of the detailed implementation step flow of step S2; Figure 4 FIG. 4 is a schematic diagram of the detailed implementation step flow of step S3. DETAILED DESCRIPTION

[0010] It should be understood that the specific embodiments described herein are merely intended to explain the present application, and are not intended to limit the present application.

[0011] The present application provides a radio frequency module output power intelligent regulation method and device. The execution subject of the method and device includes but is not limited to mechanical equipment, data processing platform, cloud server node, network upload device, etc. which can be regarded as general computing nodes of the present application, and the data processing platform includes but is not limited to at least one of audio image management system, information management system and cloud data management system.

[0012] Please refer to Figures 1 to 4 The present application provides a radio frequency module output power intelligent regulation method, which comprises the following steps: Step S1: Collect multi-mode radio frequency signals, perform channel state response sensing, and construct a real-time channel state information database; Step S2: Extract the communication task input by the user, and analyze the service quality requirement parameters to obtain the minimum required output power; Step S3: Based on the real-time channel state information database and the minimum required output power, perform orthogonal frequency division multiplexing power allocation to generate a multi-carrier power allocation strategy; Step S4: Drive the power output of the RF module according to the multi-carrier power allocation strategy, and perform PID feedback control processing to build a feedback adjustment model.

[0013] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of an intelligent adjustment method for the output power of an RF module according to the present invention. In this example, the steps of the intelligent adjustment method for the output power of the RF module include: Step S1: Collect multi-mode radio frequency signals, perform channel state response sensing, and construct a real-time channel state information database; In this embodiment, high-precision modeling of the current wireless channel state is achieved through wideband acquisition and processing of radio frequency signals in the environment. The system first utilizes a radio frequency front-end supporting wideband reception to perform a high-sampling-rate spectrum scan of the operating frequency band (such as Sub-6 GHz or millimeter-wave band). In this setting, the bandwidth is 100 MHz, the sampling rate reaches 200 MSps (megasamples per second), and the scan period is 10 ms. The acquired signals are multimodal information, mainly including key physical layer parameters such as RSSI (Received Signal Strength Indication), SINR (Signal-to-Interference-plus-Noise Ratio), and CQI (Channel Quality Indication). By combining an adaptive combination strategy of Kalman filters and weighted median filters, the system performs denoising and dynamic smoothing on the multimodal data, extracting background noise trends, multipath fading characteristics, and fast fading behavior. Then, the amplitude and phase of the frequency response are obtained through Fast Fourier Transform (FFT) analysis, and user mobility and channel change rate are calculated by combining Doppler frequency shift analysis. Finally, the aforementioned channel characteristics, along with timestamps and frequency tags, are stored in a structured manner to form a real-time updated channel state information database, supporting subsequent adaptive adjustment mechanisms. This database has a dynamic refresh mechanism, updating every 100ms to support low-latency, high-reliability power allocation.

[0014] Step S2: Extract the communication task input by the user, and analyze the service quality requirement parameters to obtain the minimum required output power; In this embodiment, different service types in the communication system have varying sensitivities to network resources. Therefore, to achieve precise power adjustment, the system first needs to parse the user's communication task type and extract the corresponding QoS (Quality of Service) parameters accordingly. Communication tasks can originate from upper-layer control commands, such as voice communication, high-definition video transmission, vehicle-mounted low-latency control, or large data downloads. In actual deployment, JSON-formatted service request data is extracted through the upper-layer interface module, and the task category field and performance constraint indicators are parsed. High-definition video tasks require a throughput of no less than 5 Mbps, a latency of less than 50 ms, and a packet loss rate of no more than 1%. The system quantifies these QoS parameters into indicator weights and maps them to the minimum acceptable link quality conditions. Furthermore, it uses a link budget model to deduce the minimum received power required to achieve these QoS requirements. Considering factors such as antenna gain, path loss, shadow fading, and multipath loss, the system uses the standard ITU outdoor propagation model for budgeting. In a typical test, the communication distance was set to 120 meters, the user-end antenna gain was 5 dBi, and the minimum received power required was derived to be -83 dBm based on the link calculation results. Furthermore, by combining the channel state information, the minimum output power required to achieve the current receive power (e.g., 22 dBm) can be deduced, serving as a benchmark for the next power allocation step.

[0015] Step S3: Based on the real-time channel state information database and the minimum required output power, perform orthogonal frequency division multiplexing power allocation to generate a multi-carrier power allocation strategy; In this embodiment, differentiated power allocation is performed by combining the channel state information of each subcarrier in a multi-carrier system (such as OFDM), thereby optimizing power utilization efficiency while meeting QoS requirements. First, the system extracts the signal-to-noise ratio (SNR) status of all subcarriers (e.g., 128 or 256) in the current frequency band from the channel state information database and constructs a subcarrier gain vector. For each subcarrier, the system calculates its unit power gain efficiency, i.e., the data rate contribution achievable per unit output power in that channel. Then, a level allocation algorithm or dynamic power control strategy is introduced to prioritize allocating more power to high-gain subcarriers, while allocating less or no power to weak-channel subcarriers, provided that the total transmit power limit and minimum receive power target are met. With a 20 MHz bandwidth, 128 subcarriers are used, each with a bandwidth of 156.25 kHz, and the target total transmit power is 24 dBm. After optimization, the first 30 subcarriers (SNR>15 dB) receive approximately 70% power allocation, and the rest are gradually attenuated according to the SNR gradient. The final output forms a multi-carrier power allocation strategy table, which includes the output power of each subcarrier (e.g., in mW or dBm), used to drive the RF module to perform differentiated excitation, providing fine-grained support for the overall power adjustment strategy.

[0016] Step S4: Drive the power output of the RF module according to the multi-carrier power allocation strategy, and perform PID feedback control processing to build a feedback adjustment model.

[0017] In this embodiment, after the power allocation strategy is determined, the system begins to execute actual power output control. The RF module controls the DAC, power amplifier (PA), and frequency synthesizer to generate a multi-carrier modulated signal based on the subcarrier power parameters, and synthesizes the overall OFDM signal according to the time-frequency structure for transmission. Due to nonlinear distortion, temperature drift, and hardware errors in the RF output, the system needs to introduce a closed-loop feedback mechanism for real-time correction. To this end, the system embeds a feedback detection module in the transmitter, which collects the PA output signal through a directional coupler and measures its key parameters such as power, voltage, and temperature in real time. Then, a feedback adjustment model based on the PID (proportional-integral-derivative) algorithm is constructed to dynamically compensate for power deviation. The adjustment parameters (Kp, Ki, Kd) are preset to (0.6, 0.3, 0.1) in the experiment, with a control period of 1 ms to ensure rapid correction of temperature drift and power amplifier slew rate response. During a sudden bandwidth expansion task, the system detected an output power deviation exceeding 2 dB. Through PID feedback, it completed a callback within 10 ms and stabilized within the target output range of ±0.3 dB. Ultimately, the feedback adjustment model continuously adjusts the power control signal and subcarrier modulation depth to ensure that the RF module's output power is both accurate and stable, while adapting to channel changes and service dynamics, achieving the goal of intelligent power control.

[0018] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Broadband spectrum scanning processing is performed on the operating frequency band of the radio frequency module to collect multimode radio frequency signals, including RSSI signal strength indication, SINR signal-to-noise ratio and CQI channel quality indication; Adaptive filtering is applied to multimodal radio frequency signals to generate environmentally filtered radio frequency signals; Fourier transform frequency domain analysis was performed on the environmental filtered radio frequency signal to obtain multi-frequency amplitude and phase information; The multipath propagation signal amplitude variation is calculated based on the multi-frequency amplitude to obtain the multipath fading coefficient; The Doppler frequency shift characteristics are obtained by calculating the frequency component offset of the phase information. Channel state response sensing is performed on multipath fading coefficient and Doppler frequency shift characteristics to construct a real-time channel state information database.

[0019] In this embodiment, wideband spectrum scanning technology is used to perform a comprehensive scan of the entire frequency band, with a frequency resolution set between 100 kHz and 1 MHz to achieve a fine characterization of the target frequency band. Spectrum scanning equipment (such as a spectrum analyzer or a high-sampling-rate ADC with an FFT front-end) samples the signal strength, channel interference level, and other indicators of each sub-band. During the scanning process, the module can be equipped with a multi-channel receiving unit to synchronously or rapidly switch between acquiring data from different frequency bands, ensuring the integrity and real-time nature of the spectrum information. The acquired multimode radio frequency signals include RSSI (Received Signal Strength Indicator), SINR (Signal-to-Noise Ratio), and CQI (Channel Quality Indicator), which can be obtained through system information sent by the base station or calculated by the local signal processing module. RSSI can be obtained by squaring and averaging the received signal amplitude, while SINR is calculated by the ratio of effective signal power to interference plus noise power. The acquired multimode radio frequency signals often contain a large amount of environmental noise, transient interference, and other non-steady-state components; therefore, adaptive filtering is required to extract useful channel features. In this stage, adaptive filtering algorithms such as LMS (Least Mean Square) or RLS (Recursive Least Squares) are employed to dynamically adjust filter weights based on the statistical characteristics of the signal, minimizing the energy of the filtered error signal. Specifically, for RSSI signals, low-pass filtering is applied to eliminate high-frequency interference while preserving environmental path loss information; for SINR and CQI data, the filtering strategy focuses on preserving abrupt changes to reflect sudden interference. The LMS filter can be set to order 32, with the step size parameter μ selected between 0.01 and 0.1 to balance convergence speed and stability. The filtered signal is the environmentally filtered RF signal, exhibiting a clearer spectral profile and better temporal stability.

[0020] Frequency domain analysis is performed on the filtered radio frequency signal to reveal its spectral structure and time-varying characteristics. A Fast Fourier Transform (FFT) is performed on the signal to obtain amplitude and phase information at each frequency point. A sampling rate of 20MHz and an FFT length of 2048 points correspond to a frequency resolution of approximately 9.8 kHz, sufficient to meet the resolution requirements of common Resource Block (RB) spacing in LTE and 5G NR systems. Before the FFT, the signal needs to be processed using a window function (such as Hanning or Blackman window) to reduce spectral leakage. The transform result will present the power distribution (amplitude) and instantaneous phase information of the signal at multiple frequencies, which will be used for subsequent multipath analysis and Doppler characteristic extraction. In practical applications, this step can be deployed in an FPGA or high-performance DSP for real-time processing. Wireless channels often exhibit multipath effects in complex environments, meaning the received signal is a superposition of signals propagating along multiple different paths. Each path has different propagation delays, amplitude, and phase characteristics, causing fluctuations in the received signal in time and frequency. By analyzing the amplitude variations of different frequency components, the attenuation characteristics of the signal along each propagation path can be extracted, thereby estimating the multipath fading coefficient. Specifically, this involves statistically analyzing the amplitude differences between adjacent frequency points (e.g., 180 kHz apart, corresponding to the width of one RB in LTE), combined with a discrete delay spectrum model, such as the Tapped Delay Line model, to estimate the power attenuation factor for each path. In experiments, Rayleigh or Rice fading models can be introduced for fitting, thereby obtaining channel statistical parameters such as the K-factor (direct wave to scattered wave ratio). In typical urban street environments, multipath fading depths can reach over 20 dB, with periods ranging from tens to hundreds of microseconds. In scenarios with relative motion, such as changes in speed between the terminal and the base station, frequency shifts, i.e., the Doppler effect, can occur. The magnitude of the Doppler shift is proportional to the relative speed and the signal carrier frequency. By differentiating the frequency domain phase information over time—that is, calculating the phase difference between the same frequency points at consecutive moments—and combining this with the sampling time interval, the Doppler shift of each frequency component can be accurately estimated. If a phase change of π / 2 radians is observed at a certain frequency point within a 1 ms period, and the sampling frequency is 20 MHz, the corresponding frequency shift is approximately 1.6 kHz. By combining signal direction estimation algorithms (such as MUSIC, ESPRIT, etc.), the directional characteristics of the Doppler frequency shift can be further inferred. With the carrier frequency set to 2.6 GHz, if a frequency shift of 1 kHz is observed, the corresponding relative velocity is approximately 0.115 m / s. Through multi-frequency joint analysis, a complete Doppler spectrum can be plotted, which can be used to identify moving scatterers in the environment, predict channel change trends, etc., ultimately providing a motion-sensing basis for dynamic power adjustment of the RF module.

[0021] Combining the aforementioned multipath fading information and Doppler characteristics, a comprehensive channel state response model can be constructed. In a practical system, a channel state information (CSI) vector can be defined, including parameters such as the current RSSI, SINR, CQI, multipath fading coefficient set {α_i}, and Doppler frequency shift set {f_dj}. By performing pattern recognition and clustering analysis on the time-series sequences of these CSI vectors (e.g., using K-means or GMM models), the system can identify typical channel state patterns, such as statically good, dynamically fading, and fast crossover. During the information database construction process, reinforcement learning algorithms (such as DQN or PPO) can be introduced to evaluate and score the power adjustment effect under different CSI states, thereby generating a state-action mapping relationship and providing a strategic basis for subsequent power adaptive control. This information database supports real-time updates, refreshed every 100 ms, and uses a sliding window mechanism to retain the most recent N state samples to enhance the system's ability to cope with sudden channel conditions and ensure that the RF module maintains the optimal power output configuration in complex and ever-changing wireless environments.

[0022] In this embodiment, the specific steps for adaptively filtering the multi-mode radio frequency signal to generate an environmentally filtered radio frequency signal are as follows: Identification of radio frequency devices in the same frequency band based on multi-mode radio frequency signals, thereby identifying radio frequency interference sources in the same frequency band; Calculate the signal strength and signal change status of the radio frequency interference source in the same frequency band; Signal feature analysis is performed based on the signal strength and signal change state to generate interference signal features; Interference filtering is performed on multimode radio frequency signals based on the characteristics of interference signals to generate environmental filtered radio frequency signals.

[0023] In this embodiment, feature information that can be used to identify device characteristics is extracted from the acquired multimodal radio frequency signals. This multimodal data includes traditional parameters such as RSSI, SINR, and CQI, and can also be extended to spectrograms, time-frequency distributions, instantaneous phase trajectories, and modulation identification parameters. During device identification, the focus is on extracting the signal fingerprint of the interference source, such as spectral occupancy, modulation method (e.g., BPSK, QAM), transmission periodicity, duty cycle, and spectral fluctuation patterns. By constructing a machine learning-based feature classification model (e.g., SVM, KNN, or Convolutional Neural Network CNN), feature matching and classification of the real-time acquired signals are performed using known device signal templates from the training set. The system can be set to a scanning bandwidth of 20 MHz, a sampling rate of 40 Msps (millions of samples per second), and a data window length of 5 ms. Under these sampling conditions, the device identification model can complete the detection and classification of interference sources within 100 ms and output their basic characteristics (e.g., WiFi interference, Bluetooth devices, other LTE modules, etc.). The identification of devices goes beyond simply determining the presence or absence of interference; it also includes the type of interference source, frequency location, modulation structure, and duty cycle, providing a sufficient basis for subsequent interference management and power control. After identifying potential interference sources, their signal strength and dynamic changes need to be quantitatively analyzed to assess their actual impact on the device's communication link. Signal strength analysis mainly relies on RSSI or PSD (Power Spectral Density) curves, integrating within a specified frequency range to obtain the interference power. Within the spectrum range [2.400 GHz, 2.410 GHz], the energy of signals identified as Bluetooth devices is integrated to obtain the instantaneous interference power value. Simultaneously, to track the dynamic characteristics of the interference source, it is necessary to model its signal strength changes over time. Common methods include sliding window averaging, short-time energy envelope analysis, and time series modeling. A 200 ms time window is used for intensity sliding averaging, and the difference between peak and average values ​​is recorded (reflecting interference stability). Signal change state analysis includes whether there is burstiness (such as WiFi beacon frames), whether there is periodic interference (such as frequency-hopping Bluetooth), and whether there is drift (such as low-quality RF devices). The system can incorporate autoregressive (AR), moving average (MA), or LSTM (Long Short-Term Memory) neural networks to predict the temporal evolution trend of interference signals. This analysis helps construct an "interference situation map," visually presenting the intensity and activity of different interference sources, thereby assisting the system in determining whether to adjust power output or change the transmission strategy.

[0024] Signal feature analysis includes the following dimensions: frequency domain features (such as dominant frequency position, bandwidth, edge steepness, spectral mean and skewness), time domain features (such as pulse period, duty cycle, instantaneous amplitude variation), modulation features (such as EVM error vector amplitude, symbol rate, constellation stability), and behavioral features (such as active period, frequency hopping pattern). To achieve high-precision feature extraction, the system can introduce Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), or Hilbert Transform to obtain instantaneous amplitude and frequency information. Taking an experiment as an example, for a WiFi interference source operating in the 2.4 GHz band, after analyzing its STFT results, it can be found that its signal spectrum is concentrated in the 20 MHz range, and it sends a beacon frame every 102.4 ms, forming a significant periodicity. In the time domain, the start point and length of the interference frame can also be extracted through power abrupt change points to estimate its interference duration (e.g., approximately 2 ms each time). Ultimately, these features will be integrated into a vector form, represented as an interference feature set {frequency, bandwidth, period, duration, peak power, burst score...}, and stored in the channel state information database. This data will be used for joint analysis with current communication link data to support the automated triggering of power adjustment strategies.

[0025] Interference signals are identified and removed or suppressed from the overall multimodal RF data to obtain an "environmentally clean RF signal" that more closely resembles the actual link state. This is achieved through interference filtering, including frequency-domain masking (NotchFilter), spatial filtering (beamforming), blind source separation (such as ICA independent component analysis), and time-frequency masking. Taking frequency-domain filtering as an example, for identified WiFi interference signals, a band-stop filter can be designed with a center frequency of 2.437 GHz (aligned with the center frequency of channel 6) and a bandwidth of 22 MHz to suppress the spectrum of the main interference frequency band. The filter adopts an FIR design with an order of 64 to ensure a smooth transition. Another approach is to use PCA or ICA techniques to decompose the signal into multiple independent sources from the mixed signal, and then identify and remove interference components based on feature similarity with known interference templates. The SINR and EVM indicators before and after filtering can be compared to verify the interference suppression effect. Generally, after filtering, SINR can be improved by 3-7 dB, and EVM can be reduced by about 10%, significantly improving the channel quality perception accuracy. The generated environmental filtered RF signal can be used as input for multipath analysis, Doppler characteristic extraction, and other processes, and is ultimately fed back to the power control algorithm to achieve intelligent power adaptive adjustment of the RF module.

[0026] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Extract the communication tasks input by the user and identify the service type to obtain the communication task type; Parse the quality of service requirement parameters according to the task type to obtain the QoS requirement parameters.

[0027] Conduct a quantitative analysis of multiple requirement indicators according to the QoS requirement parameters to obtain a multiple requirement index; the multiple requirement index includes bit error rate, delay, throughput, packet loss rate, frame rate, and bandwidth indicators; Conduct a link matching budget analysis according to the multiple requirement index to obtain the minimum received power; Perform a power requirement reverse derivation on the minimum received power according to the real-time channel state information library to obtain the minimum required output power.

[0028] In this embodiment, identifying the service type corresponding to the communication request initiated by the user is a prerequisite for power adjustment. The communication tasks input by the user may be input in the form of application layer instructions, interface calls, or context triggers, such as video calls, file uploads, remote controls, real-time monitoring, etc. Communication task identification is implemented through an application identification module, which can be embedded in the communication protocol stack or operating system supporting the radio frequency module, and uses deep packet inspection (DPI) or port protocol matching-based methods to parse the data stream. When UDP protocol high-frequency small packet transmission is detected and the port is 5060 / 5004, it can be initially determined as a VoIP task; if it is HTTP over TCP long connection large data volume transmission, it may be file download or streaming media playback. Combining context information such as application layer protocol identifiers (such as SIP, RTSP, MQTT, etc.) and data stream behavior characteristics (such as packet interval, flow duration), the task type can be further refined. The system establishes a feature model for 30 common task types (including high-definition video calls, web browsing, FTP downloads, sensor data backhaul, etc.), and the recognition accuracy rate is over 93%. The recognition result classifies the task types into 4 categories: high real-time low bandwidth type, low real-time high throughput type, control instruction type, and periodic backhaul type, which are used as the input for subsequent QoS requirement parameter parsing. Parse the set of quality of service (QoS) parameters required according to the service characteristics. QoS requirement parameters usually include maximum allowable bit error rate (BER), maximum transmission delay (Delay), minimum throughput (Throughput), maximum packet loss rate (PLR), minimum frame rate (FPS), and bandwidth requirement (Bandwidth), etc. The parsing process can be implemented through table lookup or policy mapping methods. For high-definition video call tasks (such as 720p / 1080p), typical QoS requirements may be maximum delay < 150 ms, minimum bandwidth > 2 Mbps, packet loss rate < 1%, frame rate ≥ 25 fps; for industrial control data backhaul tasks, QoS requirements focus more on delay and bit error rate, such as delay < 50 ms, BER < 10⁻6 The system has low bandwidth requirements. Through its built-in QoS standard library, combined with communication QoS specifications such as ITU-T G.1010 and 3GPP TS 23.107, the system maps a complete set of QoS parameters to task types. In the experiment, six standard QoS levels were set, and weighted processing was applied according to task priority (e.g., critical tasks allow for a lower bit error rate).

[0029] After vectorizing the QoS parameters, further quantization is required to obtain a "multi-demand index set" that can be used for power assessment. Each index in this set needs to be normalized between 0 and 1 for unified modeling and analysis. For the maximum bit error rate requirement (e.g., BER ≤ 10⁻),... 6 The system evaluates the ratio of the estimated bit error rate (BER) under actual link conditions to the target BER, reflecting the degree to which the current channel state meets the task requirements. Similarly, indicators such as latency, packet loss rate, and bandwidth are also quantified by comparing target values ​​with actual measurements. To more scientifically handle the heterogeneity among multiple indicators, the system uses fuzzy comprehensive evaluation or entropy weight method to weight and fuse the indicators, forming a comprehensive service quality pressure index. The weights for each indicator can be set as follows: BER 0.25, latency 0.20, throughput 0.20, packet loss rate 0.15, frame rate 0.10, and bandwidth 0.10. The weights can be flexibly adjusted according to the criticality of the task. The final output is a six-dimensional quantized vector {Q1, Q2, Q3, Q4, Q5, Q6}, representing the difficulty of achieving each QoS. This step provides a decision basis for subsequent link power budgeting and realizes a mapping bridge between the task and the physical link state.

[0030] A reverse matching budget is performed on the link capacity to determine the minimum required received power (MRRP) needed to meet QoS requirements under the current channel conditions. This process is based on the link budget formula and the QoS metric mapping model. Typically, this is achieved through a multi-parameter joint back-calculation using the Shannon capacity formula, the bit error rate-signal-noise ratio mapping model, and the delay-transmission rate model. The system combines the current noise power spectral density (N0), bandwidth (B), and target bit error rate to estimate the required minimum SNR using the following formula: ; In the experimental parameter settings, it is assumed that the target BER is With a bandwidth of 5 MHz, a noise density of -174 dBm / Hz, and QPSK modulation, the minimum received power P_r is approximately -96 dBm. For delay-sensitive services, it is also necessary to estimate the service rate requirement using a link-layer queuing model, and then deduce the minimum channel capacity required to meet that rate. This analysis process ultimately outputs a key parameter—the "minimum received power threshold"—which will be used as a reference for deriving the minimum transmit power of the RF module in the next step.

[0031] The system uses data from a real-time channel state database to deduce the minimum output power required by the RF module in the current environment. This database is the result of continuous accumulation during long-term system operation, recording channel fading characteristics, path loss, interference, and multipath effects under different frequency, environmental, distance, and time conditions. During the deduction process, the system comprehensively considers the user's current location, terminal speed, operating frequency band, and transmit power distribution in similar past scenarios. For example, in a typical scenario with a communication distance of 150 meters, a complex indoor environment, and a frequency band of 2.6 GHz, the system retrieves records matching this scenario from historical state data and, combined with the current interference intensity, assesses that the RF module needs to output a minimum power of approximately 21 dBm to ensure reliable data transmission. The system also incorporates a channel safety margin (e.g., 10 dB) to prevent link interruptions caused by sudden fading. The final result is a minimum output power value that is both time-sensitive and environmentally adaptable, providing the RF module with a basis for intelligent power adjustment decisions and achieving an optimal balance between low power consumption and high reliability.

[0032] In this embodiment, reference Figure 4 The diagram below illustrates the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Identify the status of each subcarrier signal based on the real-time channel state information database; Calculate the signal-to-noise ratio of each subcarrier based on the state of each subcarrier signal; Orthogonal frequency division multiplexing power allocation is performed based on the minimum required output power and the signal-to-noise ratio of each subcarrier to obtain the subcarrier power allocation parameters; Calculate the peak-to-average power ratio of the subcarrier power allocation parameters and perform amplitude limiting filtering suppression to obtain the power ratio suppression result; Nonlinear distortion calculations are performed on the power ratio suppression results, and power amplification distortion compensation is carried out to obtain a multi-carrier power allocation strategy.

[0033] In this embodiment, the entire frequency band is divided into multiple subcarriers in the OFDM system, with each subcarrier independently carrying a portion of the data. Fine-grained perception of the channel state for each subcarrier is a prerequisite for fine-grained power allocation. The system first analyzes all currently active subcarriers one by one using a real-time channel state information database to identify their current channel state. This database records the channel gain, multipath interference, Doppler spread, path loss, and other state information for each frequency resource unit (e.g., one subcarrier per 15 kHz) under different times, locations, and interference environments. In actual execution, the RF module acquires the frequency domain response data of the subcarriers based on the channel estimation results (such as the downlink reference signal) issued by the current system and compares and fits this data with historical state information to determine whether the channel is in a state of deep fading, edge transition, or frequency hole. The system updates the state label of each subcarrier every 10 milliseconds and classifies them using a three-state model (excellent, available, fading), achieving real-time, dynamic monitoring of all resource units. After subcarrier channel state identification is completed, the system needs to calculate the actual signal-to-noise ratio (SNR) on each subcarrier to evaluate its communication quality carrying capacity. SNR calculation is typically based on an estimate of the pilot signal, dynamically estimated by combining the amplitude of the currently received signal and the level of background noise. In this system, not only real-time measurements are relied upon, but statistical data from the historical channel state database is also referenced to eliminate the impact of transient errors on SNR evaluation. For subcarriers in a fading state, the system performs confidence correction on their SNR estimates and sets maximum and minimum tolerance ranges according to different scenarios. Assuming a typical 20 MHz bandwidth OFDM system with 1200 subcarriers, the system updates the SNR vector of each subcarrier every frame, with an update period of 1 millisecond. This SNR vector not only serves as an important basis for link adaptive modulation and coding (AMC) but is also directly input into the power allocation module to guide resource allocation strategies towards high-quality subcarriers.

[0034] After determining the signal-to-noise ratio (SNR) of each subcarrier, the system needs to optimally allocate power based on the overall minimum required output power and the subcarrier communication quality. The goal of this step is to meet the QoS requirements of the communication task, such as data rate and bit error rate, with minimal total power input. The power allocation process typically employs a skewed allocation strategy, also known as "channel-aware power scheduling," which prioritizes allocating more power to subcarriers with higher SNR, while reducing or even eliminating power allocation for subcarriers with poor channel quality or deep fading. This approach not only improves overall energy efficiency but also reduces the pressure on the transmitter power amplification. In the system design, a total power budget upper limit is set (e.g., 23 dBm), and dynamic allocation is performed at the subcarrier level. In the experimental parameter settings, the power allocation range for each subcarrier is controlled between 0.1 dBm and 1.5 dBm. The actual allocation process considers multiple factors such as modulation scheme, coding rate, and channel estimation error tolerance. The final result is a subcarrier power allocation vector of length N (e.g., 1200), which directly drives the actual output of the digital-to-analog converter and power amplifier, achieving precise power control in the frequency domain. While OFDM technology boasts high spectral efficiency, one of its biggest drawbacks is the peak-to-average power ratio (PAPR). Because multiple subcarriers superimpose to form a time-domain signal, significant power fluctuations can occur, potentially causing the power amplifier to enter a non-linear operating region and resulting in signal distortion. Therefore, after power allocation, the PAPR of the generated signal must be calculated and suppressed. The system first performs statistical analysis on the complete OFDM time-domain signal to identify the ratio of peak to average power and compares it with a system-set safety threshold (e.g., 10 dB). Once the threshold is exceeded, the system triggers a clipping filter module to process the signal. The clipping filter method can employ a combination of time-domain truncation and frequency-domain filtering, that is, cutting the signal amplitude exceeding the threshold to a set value while simultaneously removing the spectral spread caused by truncation through a filter. In experimental testing, the original PAPR value was typically above 11 dB, but after limiting, it could be effectively suppressed to below 8 dB, while ensuring that the impact on the bit error rate was controlled within 1%. The suppression result will be fed back to the power scheduling module as an important parameter for output power constraints, forming a closed-loop control chain to ensure that the output signal operates within the safe power range.

[0035] Even after PAPR suppression, OFDM signals may still exhibit distortion due to the nonlinear characteristics of the power amplifier, especially at high power outputs. Modeling and compensation for power amplification distortion are necessary to ensure signal modulation integrity and link quality. Distortion modeling typically employs behavioral modeling methods, such as memory multinomial models, Volterra sequences, or neural network-based nonlinear function approximators. The system builds a power amplifier model using long-term acquired input and output signals, predicting potential distortion components under current input power conditions. Then, the system uses a pre-distortion compensation method to correct the signal, introducing distortion with opposite characteristics before the power amplifier stage to make the signal after amplification more linear. In experimental tests, for a signal with an output power of 23 dBm, the EVM error vector amplitude reached 8% in the uncompensated state, decreasing to 2.5% after compensation, effectively controlling modulation distortion. Ultimately, this compensation process is superimposed on the power allocation command of each subcarrier to form a complete multi-carrier power allocation strategy that takes into account both channel conditions and signal-to-noise ratio, as well as nonlinear interference and hardware boundary limitations, to achieve "multi-dimensional intelligent adjustment" of the RF module output power.

[0036] In this embodiment, step S4 includes the following steps: The power output of the RF module is driven according to the multi-carrier power allocation strategy, and the power amplifier efficiency is monitored to obtain the power amplifier efficiency parameters; the power amplifier efficiency parameters include the power amplifier's DC power consumption, RF output power and junction temperature parameters. Based on the power amplifier efficiency parameters, the power amplifier bias point optimization calculation is performed to obtain the bias adjustment result; Thermal resistance network analysis was performed on the bias adjustment results to construct a thermal balance model of power amplifier junction temperature and output power; Based on the thermal balance model, PID feedback control is implemented to construct a feedback regulation model; Antenna array beamforming adjustment is performed based on a real-time channel state information database to obtain an antenna adjustment strategy for real-time power coordination control of the RF module.

[0037] In this embodiment, the multi-carrier power allocation strategy is used as a command to drive the power amplifier (PA) output of each subcarrier corresponding to the RF module. Each power output path includes an RF DAC, intermediate frequency filter, level adjustment, power amplification, and antenna radiation unit. After output, the system needs to monitor the operating efficiency of the power amplifier in real time. The main parameters include DC power consumption, RF output power, and junction temperature (i.e., chip junction temperature). DC power consumption is collected in real time by the PA power supply module, in milliwatts; RF output power is measured by a power detector, covering the entire operating frequency band; and junction temperature is obtained by a thermistor integrated inside the PA or by an external thermal probe. In the experimental setup, for a typical 5G small module, the peak DC power consumption of the power amplifier is 800 mW, the maximum RF output is 23 dBm (approximately 200 mW), and the junction temperature can rise to over 95°C under continuous high-power transmission. The sampling frequency is once every 100 microseconds to ensure dynamic response. The acquired data is simultaneously sent to the control processing unit as input for the next stage of bias adjustment and thermal management, ensuring stable power output within the thermoelectric balance range and preventing thermal breakdown or amplifier degradation. The power amplifier's efficiency and linear performance are closely related to its bias point. The bias point refers to the current operating state of the transistors inside the PA in the absence of a signal, typically affected by bias voltage and temperature. To improve overall energy efficiency, control heat dissipation, and ensure linear output, the system needs to dynamically adjust the power amplifier's bias point based on current efficiency parameters. This optimization process first acquires the ratio between DC power consumption and output power, i.e., instantaneous power amplifier efficiency, and combines this with junction temperature data to identify any abnormal efficiency drops or heat buildup issues. The system embeds a bias-efficiency mapping model, establishing a multivariate relationship between bias voltage and output parameters based on past operating conditions. Experimental data shows that when the PA bias voltage is adjusted from 0.6 V to 0.8 V, the output power increases by 5%, but the thermal efficiency decreases significantly; therefore, in high-temperature environments, the bias should be reduced to alleviate the heat load. The final output is a set of bias voltage adjustment parameters, typically with an adjustment step of 10 mV, to achieve high-precision dynamic adjustment.

[0038] To further assess the long-term impact of regulatory behavior on the entire thermal management system, a thermal balance model between junction temperature and power output was constructed using thermal resistance network analysis. This model abstracts the heat conduction process between the power amplifier's internal heat source and the external environment into a series of thermal resistance elements, including the chip's thermal resistance, package thermal resistance, PCB thermal resistance, and the thermal resistance of heat dissipation channels (such as metal cavities or air cooling). The system employs a modular thermal resistance network model, mapping the thermal resistance and power consumption data of different structural regions to an adjustable mathematical network, thereby estimating the junction temperature change curves under different power levels. For a 5G millimeter-wave module, the chip junction temperature rise rate at maximum power consumption is approximately 5°C per second, with a total thermal resistance of 8.5°C / W. After model calibration by combining simulation with field sensor data, the system can predict the junction temperature evolution trend within the next second after bias adjustment. Based on the thermal balance model, a PID feedback control mechanism is introduced to achieve refined closed-loop regulation between power output and thermal state. The PID controller continuously acquires the error between the current junction temperature and the target temperature, and calculates the optimal adjustment value based on the historical trend of the error. This value is used to control the bias voltage, conduction current, or activate the heat dissipation unit. In the system implementation, the target junction temperature threshold is set to 85°C. Once the actual junction temperature exceeds this value, the PID controller will automatically reduce the bias voltage, decrease the output power, or activate the auxiliary cooling mechanism (such as a micro fan or thermoelectric cooler). The PID parameters used in the experiment are: proportional term P=0.8, integral term I=0.01, derivative term D=0.1, and the control cycle is once every 100 milliseconds, ensuring both response speed and avoiding control system oscillation. The control output can directly drive the bias circuit or the signal source output power attenuator to achieve soft adjustment. This PID feedback system, through linkage with the thermal resistance network model, forms a predictive + feedback dual control structure, which can not only cope with sudden increases in power consumption but also adapt to changes in ambient temperature, improving the overall stability of the RF module and the lifespan of the hardware.

[0039] After controlling power output and power amplifier efficiency, the system ultimately achieves precise spatial coordinated power control through antenna array beamforming technology. Beamforming refers to controlling the phase and amplitude of each antenna element in the antenna array to enhance signal energy in a specific direction and attenuate it in other directions, thereby improving power utilization efficiency and reducing interference. The system extracts spatial positioning information, azimuth angle, and channel angle spread characteristics of the target user from a real-time channel state information database to calculate the optimal beam pointing strategy. For fixed terminals, a high-gain directional beam can be formed; for mobile terminals, the system dynamically adjusts the beam direction based on Doppler shift and historical trajectory. In experimental tests, using an 8-element antenna array, the beam adjustment accuracy reached 1 degree, and the beamforming refresh period was 20 milliseconds. The system also evaluates whether the antenna radiation efficiency matches the current power output to avoid continuing to project high-power signals in inefficient radiation directions. The final beamforming strategy and power control strategy are executed in fusion to achieve directional enhancement of signal energy and minimize energy consumption, greatly improving the communication quality and power consumption control capabilities of the RF module in complex environments.

[0040] In this embodiment, the specific steps for performing antenna array beamforming adjustment based on a real-time channel state information database to obtain an antenna adjustment strategy for executing real-time power collaborative control of the RF module are as follows: Singular value decomposition (SVD) is performed based on a real-time channel state information database, and antenna precoding is performed to obtain the antenna array matrix encoding. The antenna array matrix encoding is based on a collaborative optimization adjustment model to perform multi-antenna array power allocation, thereby generating antenna array power allocation parameters; Calculate the radiation direction and power density distribution of the current antenna array to obtain the radiation pattern and power density distribution. Based on the radiation pattern and power density distribution map, the antenna array power allocation parameters are adjusted by beamforming of the antenna array to obtain the antenna adjustment strategy. Real-time power coordination control of the RF module is performed based on a feedback adjustment model and antenna adjustment strategy.

[0041] In this embodiment, in a multi-antenna system (such as Massive MIMO or 5G NR), to maximize channel capacity and suppress interference, the system needs to perform precoding processing based on the current channel state. Precoding essentially involves a linear transformation of the transmitted signal from the antenna array in the spatial direction, making it match the channel's dominant propagation path as closely as possible. To this end, the system first obtains the state data of each antenna sub-channel (usually a complex matrix H) based on a real-time channel state information database, including information such as gain, phase, multipath structure, and background noise. Singular Value Decomposition (SVD) is then performed on this channel matrix, decomposing it into a left singular vector, singular values, and a right singular vector. The right singular vector represents the optimal precoding matrix at the transmitter, which can be understood as the optimal combination of spatial signal mappings. The experimental scenario uses a 4x4 antenna configuration in the 2.6 GHz band, performing SVD calculations every 10 milliseconds with a processing delay of less than 2 milliseconds. After loading the precoding matrix into the transmitter array control unit, spatial preprocessing of the antenna transmission channel is completed, thereby optimizing subsequent power allocation and beam control. Because different antenna sub-channels have varying gains, path losses, and interference environments within the channel, using equalized transmit power would result in wasted signal energy or failure to achieve the target signal-to-noise ratio. Therefore, the system introduces a collaborative optimization adjustment model to comprehensively model multiple indicators such as precoding weights, target signal-to-noise ratio, interference constraints, and total system power consumption limits for each channel. Heuristic algorithms or multi-objective optimization algorithms (such as Pareto solution sets based on genetic algorithms) are used to solve for the optimal transmit power of each antenna channel. In the experiment, the total transmit power was set to 26 dBm, and the target user direction gain requirement was no less than 8 dB. The system allocated power proportions of 17%, 24%, 30%, and 29% to the four array elements through the optimization model, significantly improving the energy concentration in the target direction. The final generated power allocation parameters are output as control vectors and written to the antenna array control module to ensure that each channel outputs accurately according to the optimization results.

[0042] After configuring the power of multiple antennas, the system needs to evaluate their radiation effect in space, i.e., generate the current radiation pattern and power density distribution map. This is achieved by combining array theory with electromagnetic simulation modeling. The output power, phase offset, and array geometric parameters (such as element spacing and arrangement) of each antenna element are collected. The beam direction and main and sidelobe gain distributions are calculated using the array factor model. Then, the energy density in different directions is evaluated using the spatial power integration method to construct a two-dimensional or three-dimensional radiation spectrum. In actual verification, a 4-element linear array is used. Under different phase encodings, the main lobe direction can be flexibly adjusted within ±60 degrees, and the sidelobe suppression ratio is maintained below -13 dB. The power density distribution map shows the signal energy intensity per unit solid angle, reflecting the spatial focusing capability and interference risk. After a typical antenna encoding and decoding, the main lobe direction is at 35°, and the peak power density is 0.25 mW / steradian. This image data will serve as the core reference for the next step of beamforming fine-tuning and spatial power reconstruction, ensuring that the signal radiation is optimal in the direction of the target user and avoiding energy leakage to non-target areas. After obtaining the actual radiation pattern of the current antenna array, the system further performs beamforming adjustments to achieve more precise spatial energy focusing and interference suppression. Beamforming is based on existing power allocation and radiation pattern data, iteratively optimizing phase and amplitude control parameters to maximize main lobe gain, minimize side lobes, and ensure that the signal quality in the target direction is not lower than a preset threshold. The system employs particle swarm optimization (PSO) or gradient descent algorithms, using the power density objective function as a basis, to automatically search for optimal array control parameters. In the experimental environment, with the target direction set at 45°, the main lobe gain required to be no less than 12 dB, and the side lobe constrained to below -15 dB, the system completes the beamforming strategy adjustment within 15 milliseconds through the algorithm and feeds back the control vector to the array controller in real time. This antenna adjustment strategy is output in matrix form, containing the amplitude and phase factors of each array element, and directly affects the phased array network and power modulator of the RF link.

[0043] By integrating antenna adjustment strategies with a thermoelectric feedback regulation model, a complete real-time power collaborative control mechanism for the RF module is achieved. This mechanism ensures that each antenna sub-channel radiates directionally according to the optimal beamforming strategy, while continuously monitoring power execution based on feedback parameters such as temperature, power consumption, and power amplifier efficiency. The system adjusts the power output state at the millisecond level by integrating a PID control mechanism and a predictive adjustment model, preventing abnormal behaviors such as power amplifier overload, junction temperature exceeding limits, or beam drift. In the event of a sudden power surge or target direction deviation, the feedback model triggers rapid phase adjustment and power suppression strategies, ensuring that the system completes power convergence and direction correction within 50 milliseconds. This collaborative control mechanism exhibits excellent dynamic stability in high-speed mobile scenarios (terminal speed 60 km / h), reducing the signal error rate by approximately 18% and overall power consumption by approximately 12%. Ultimately, the RF module achieves efficient energy management and real-time collaborative control for multiple users, multiple paths, and multiple dimensions in complex channel environments, significantly improving communication quality and equipment stability.

[0044] In this embodiment, an intelligent output power adjustment device for an RF module is provided, used to execute the intelligent output power adjustment method for an RF module as described above, including: The channel state awareness module is used to collect multimodal radio frequency signals, perform channel state response awareness, and build a real-time channel state information database. The requirement parsing module is used to extract the communication task input by the user, parse the service quality requirement parameters, and obtain the minimum required output power. The power allocation module is used to perform orthogonal frequency division multiplexing power allocation based on the real-time channel state information database and the minimum required output power, and generate a multi-carrier power allocation strategy. The power output control module is used to drive the power output of the RF module according to the multi-carrier power allocation strategy, and to perform PID feedback control processing to build a feedback adjustment model.

[0045] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0046] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for intelligent adjustment of the output power of an RF module, characterized in that, Includes the following steps: Step S1: Collect multi-mode radio frequency signals, perform channel state response sensing, and construct a real-time channel state information database; Step S2: Extract the communication task input by the user, and analyze the service quality requirement parameters to obtain the minimum required output power; Step S3: Based on the real-time channel state information database and the minimum required output power, perform orthogonal frequency division multiplexing power allocation to generate a multi-carrier power allocation strategy; Step S4: Drive the power output of the RF module according to the multi-carrier power allocation strategy, and perform PID feedback control processing to build a feedback adjustment model.

2. The intelligent adjustment method for the output power of an RF module according to claim 1, characterized in that, The specific steps of step S1 are as follows: Broadband spectrum scanning processing is performed on the operating frequency band of the radio frequency module to collect multimode radio frequency signals, including RSSI signal strength indication, SINR signal-to-noise ratio and CQI channel quality indication; Adaptive filtering is applied to multimodal radio frequency signals to generate environmentally filtered radio frequency signals; Fourier transform frequency domain analysis was performed on the environmental filtered radio frequency signal to obtain multi-frequency amplitude and phase information; The multipath propagation signal amplitude variation is calculated based on the multi-frequency amplitude to obtain the multipath fading coefficient; The Doppler frequency shift characteristics are obtained by calculating the frequency component offset of the phase information. Channel state response sensing is performed on multipath fading coefficient and Doppler frequency shift characteristics to construct a real-time channel state information database.

3. The intelligent adjustment method for the output power of an RF module according to claim 2, characterized in that, The specific steps for adaptive filtering of multimode radio frequency signals to generate environmentally filtered radio frequency signals are as follows: Identification of radio frequency devices in the same frequency band based on multi-mode radio frequency signals, thereby identifying radio frequency interference sources in the same frequency band; Calculate the signal strength and signal change status of the radio frequency interference source in the same frequency band; Signal feature analysis is performed based on the signal strength and signal change state to generate interference signal features; Interference filtering is performed on multimode radio frequency signals based on the characteristics of interference signals to generate environmental filtered radio frequency signals.

4. The intelligent adjustment method for the output power of an RF module according to claim 1, characterized in that, The specific steps of step S2 are as follows: Extract the communication task input by the user and identify the service type to obtain the communication task type; The QoS requirement parameters are parsed based on the task type to obtain the QoS requirement parameters. Quantitative analysis of multiple demand indicators is performed based on QoS demand parameters to obtain a multiple demand index; The multi-demand index includes bit error rate, latency, throughput, packet loss rate, frame rate, and bandwidth. The minimum received power is obtained by performing link matching budget analysis based on the multi-demand index. The minimum required output power is obtained by back-calculating the minimum received power based on the real-time channel state information database.

5. The intelligent adjustment method for the output power of an RF module according to claim 1, characterized in that, Step S3 is as follows: Identify the status of each subcarrier signal based on the real-time channel state information database; Calculate the signal-to-noise ratio of each subcarrier based on the state of each subcarrier signal; Orthogonal frequency division multiplexing power allocation is performed based on the minimum required output power and the signal-to-noise ratio of each subcarrier to obtain the subcarrier power allocation parameters; Calculate the peak-to-average power ratio of the subcarrier power allocation parameters and perform amplitude limiting filtering suppression to obtain the power ratio suppression result; Nonlinear distortion calculations are performed on the power ratio suppression results, and power amplification distortion compensation is carried out to obtain a multi-carrier power allocation strategy.

6. The intelligent adjustment method for the output power of an RF module according to claim 1, characterized in that, The specific steps of step S4 are as follows: The power output of the RF module is driven according to the multi-carrier power allocation strategy, and the power amplifier efficiency is monitored to obtain the power amplifier efficiency parameters; the power amplifier efficiency parameters include the power amplifier's DC power consumption, RF output power and junction temperature parameters. Based on the power amplifier efficiency parameters, the power amplifier bias point optimization calculation is performed to obtain the bias adjustment result; Thermal resistance network analysis was performed on the bias adjustment results to construct a thermal balance model of power amplifier junction temperature and output power; Based on the thermal balance model, PID feedback control is implemented to construct a feedback regulation model; Antenna array beamforming adjustment is performed based on a real-time channel state information database to obtain an antenna adjustment strategy for real-time power coordination control of the RF module.

7. The intelligent adjustment method for the output power of an RF module according to claim 6, characterized in that, The specific steps for performing antenna array beamforming adjustment based on a real-time channel state information database to obtain an antenna adjustment strategy for executing real-time power collaborative control of the RF module are as follows: Singular value decomposition (SVD) is performed based on a real-time channel state information database, and antenna precoding is performed to obtain the antenna array matrix encoding. The antenna array matrix encoding is based on a collaborative optimization adjustment model to perform multi-antenna array power allocation, thereby generating antenna array power allocation parameters; Calculate the radiation direction and power density distribution of the current antenna array to obtain the radiation pattern and power density distribution. Based on the radiation pattern and power density distribution map, the antenna array power allocation parameters are adjusted by beamforming of the antenna array to obtain the antenna adjustment strategy. Real-time power coordination control of the RF module is performed based on a feedback adjustment model and antenna adjustment strategy.

8. A smart adjustment device for the output power of an RF module, characterized in that, The method for performing intelligent adjustment of RF module output power as described in claim 1 includes: The channel state awareness module is used to collect multimodal radio frequency signals, perform channel state response awareness, and build a real-time channel state information database. The requirement parsing module is used to extract the communication task input by the user, parse the service quality requirement parameters, and obtain the minimum required output power. The power allocation module is used to perform orthogonal frequency division multiplexing power allocation based on the real-time channel state information database and the minimum required output power, and generate a multi-carrier power allocation strategy. The power output control module is used to drive the power output of the RF module according to the multi-carrier power allocation strategy, and to perform PID feedback control processing to build a feedback adjustment model.

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