Intelligent regulation and control method for signal intensity of communication terminal equipment

Abstract

The invention discloses an intelligent regulation and control method for signal strength of communication terminal equipment, which relates to the technical field of intelligent control and optimization of the communication terminal equipment and comprises the following steps: a strategy generation terminal inputs a channel interference suppression coefficient alpha, a spectrum efficiency optimization coefficient beta, a link stability coefficient gamma and a behavior correction factor epsilon into a self-adaptive regulation and control decision model; outputting a dynamic power regulation index delta; and the execution control terminal adjusts the bias voltage of the radio frequency front-end power amplifier according to the dynamic power regulation index delta. According to the method, the adaptive regulation and control decision model is constructed through the strategy generation terminal, the channel interference suppression coefficient alpha, the spectrum efficiency optimization coefficient beta, the link stability coefficient gamma and the behavior correction factor epsilon are input into the adaptive regulation and control decision model, the dynamic power regulation and control index delta is output, and multi-factor collaborative intelligent regulation and control decision is realized. The defect that traditional static threshold regulation and control cannot adapt to a dynamic scene is avoided, and a regulation and control strategy better meets actual service and environment requirements.

Description

Technical Field

[0001] This invention relates to the field of intelligent control and optimization technology for communication terminal equipment, and in particular to a method for intelligent signal strength regulation of communication terminal equipment. Background Technology

[0002] With the iteration of communication technology and the expansion of application scenarios for terminal devices, communication terminals have shifted from basic voice tools to multi-service carriers. Users demand low latency and stability for real-time voice and video streaming services, as well as low power consumption for background transmission services. At the same time, the terminal usage environment is becoming increasingly complex. Surrounding electromagnetic interference, multipath fading, dynamic changes in spectrum occupancy, and differences in user movement trajectories all place higher demands on signal strength control, requiring a balance between service quality, battery life, and chip heat dissipation.

[0003] Currently, traditional communication terminal signal strength regulation is mostly based on static thresholds or single parameters. Early methods relied on RSSI and RSRP feedback from base stations to set fixed rules, such as increasing power when the signal is below -95dBm and decreasing power when it is above -80dBm, which is difficult to adapt to dynamic environments. Although some subsequent improved technologies have introduced spectrum occupancy or single-band interference considerations, they have not achieved multi-dimensional data collaboration and generally ignore user behavior characteristics and the device's own status. They cannot distinguish user mobility differences and service type requirements, nor do they include battery capacity and chip junction temperature in the regulation.

[0004] Existing technologies have significant limitations: First, they lack dynamic adaptability. Static thresholds or single parameters cannot cope with complex electromagnetic environments and user behaviors, easily leading to frequent power fluctuations, increased power consumption, and difficulty in ensuring link stability. Second, they lack multi-factor collaborative optimization. They handle channel interference, spectrum efficiency, and link stability in isolation, without integrating environmental awareness data, user behavior data, and device status data, making it easy to overlook certain aspects during regulation. Third, the matching degree between service priority and device status is low. They cannot dynamically adjust weights according to service importance, nor can they provide targeted regulation when power is low or junction temperature is high, which can easily lead to damage to core service experience or increased equipment wear.

[0005] Therefore, it is essential to invent an intelligent signal strength control method for communication terminal equipment to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method for intelligent signal strength control of communication terminal equipment to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent signal strength control of a communication terminal device, comprising an environmental sensing terminal, a user behavior analysis terminal, a device status acquisition terminal, a strategy generation terminal, and an execution control terminal, specifically including the following steps: S1. The environmental sensing terminal collects electromagnetic environment data in the area surrounding the communication terminal equipment through multi-band radio frequency sensors to form a comprehensive signal environment dataset. S2. The user behavior analysis terminal collects user movement trajectory data through accelerometers and gyroscopes, and captures application layer business type data through network interfaces to form a user behavior feature dataset. S3. The strategy generation terminal performs pattern recognition on the user behavior feature dataset to generate a behavior correction factor ε. S4. The strategy generation terminal performs time-frequency domain analysis on the comprehensive signal environment dataset to obtain the channel interference suppression coefficient α, the spectrum efficiency optimization coefficient β, and the link stability coefficient γ. S5. The strategy generation terminal inputs the channel interference suppression coefficient α, the spectrum efficiency optimization coefficient β, the link stability coefficient γ, and the behavior correction factor ε into the adaptive control decision model, and outputs the dynamic power control index δ. S6. The execution control terminal adjusts the bias voltage of the RF front-end power amplifier according to the dynamic power regulation index δ. S7. The device status acquisition terminal monitors the remaining battery capacity C and chip junction temperature T in real time and feeds them back to the strategy generation terminal.

[0008] Preferably, the comprehensive signal environment dataset includes environmental interference intensity data, spectrum occupancy rate data, and multipath fading characteristic data; the environmental interference intensity data includes background noise power spectral density P in a specific frequency band. n (f) and the amplitude A of the co-channel interference signal int The spectrum occupancy rate data includes the percentage of licensed frequency band occupancy time T. occ Number of unlicensed frequency band conflicts N coll The multipath fading characteristic data includes the Doppler frequency shift standard deviation σ. f and delay spread τ rms .

[0009] Preferably, the user behavior feature dataset includes movement trajectory data and service type data; the movement trajectory data includes three-dimensional spatial displacement vectors. and the standard deviation of movement speed σ v The service type data includes the real-time voice service identifier I. voice Video Stream Service Identifier video and background transmission service identifier I bg .

[0010] Preferably, the behavior modification factor ε is specifically: , Where v0 is the velocity reference value, σ vLet μ1 be the standard deviation of the mobile speed, μ2 be the service type weight coefficients, μ1 + μ2 + μ3 = 1 and μ1, μ2 and μ3 ∈ [0, 1].

[0011] Preferably, the channel interference suppression coefficient α is specifically: , Where, k p P is the slope parameter for environmental adaptation. n (f) represents the background noise power spectral density in a specific frequency band, P th For the noise power threshold, A int For the amplitude of the co-channel interference signal, A max The maximum permissible interference amplitude is given by e, which is a natural constant.

[0012] Preferably, the spectral efficiency optimization coefficient β is specifically: , Among them, T occ N represents the percentage of time the licensed frequency band is occupied. coll λ1 represents the number of unlicensed frequency band conflicts, λ2 represents the weighting factor for licensed frequency band conflicts, and λ3 represents the penalty factor for unlicensed frequency band conflicts.

[0013] Preferably, the link stability coefficient γ is specifically: , Where, σ f f is the standard deviation of Doppler frequency shift. c For the carrier frequency, τ rms For time delay spread, τ0 is the time delay spread reference value, and e is the natural constant.

[0014] Preferably, the adaptive control decision model is as follows: , Where α is the channel interference suppression coefficient, β is the spectrum efficiency optimization coefficient, γ is the link stability coefficient, ε is the behavior correction factor, η is the decision sensitivity parameter, θ is the decision threshold, δ is the dynamic power regulation index, e is the natural constant, ω1, ω2 and ω3 are weight coefficients, ω1+ω2+ω3=1 and ω1, ω2 and ω3∈[0,1].

[0015] Preferably, the adjustment amount of the bias voltage needs to meet the following requirements: , Where C is the remaining battery capacity, T is the chip junction temperature, and V is the voltage. max For the maximum allowable bias voltage, C max T represents the nominal capacity of the battery. a For ambient temperature, T maxThe maximum allowable junction temperature is given by δ, which is the dynamic power regulation index. This is a power constraint term. This is a temperature constraint term.

[0016] Preferably, the power constraint term satisfies: when When the value is less than 0.15, the output value of the dynamic power regulation index δ is limited to no more than 0.4. The temperature constraint term satisfies: when When the value is greater than 0.7, a forced setting is required. .

[0017] The technical effects and advantages of this invention are as follows: 1. This invention uses an accelerometer and gyroscope to collect three-dimensional spatial displacement vectors and the standard deviation σ of movement velocity through a user behavior analysis terminal. v Combined with the real-time voice service identifier I captured by the network interface voice Video Stream Service Identifier video and background transmission service identifier I bg This generates a user behavior feature dataset and a behavior correction factor ε, enabling the control strategy to accurately match the user's mobile status and business type requirements, avoiding problems such as real-time business lag and background business power consumption waste caused by traditional control ignoring differences in user behavior. 2. This invention constructs an adaptive control decision model through a strategy generation terminal. The channel interference suppression coefficient α, the spectrum efficiency optimization coefficient β, the link stability coefficient γ, and the behavior correction factor ε are input into the adaptive control decision model, and the dynamic power control index δ is output. This realizes intelligent control decision-making with multi-factor collaboration, avoiding the shortcomings of traditional static threshold control that cannot adapt to dynamic scenarios, and making the control strategy more in line with actual business and environmental needs. 3. This invention, through the execution control terminal based on the dynamic power regulation index δ, combined with the battery remaining capacity C and chip junction temperature T fed back by the device status acquisition terminal, precisely adjusts the bias voltage of the RF front-end power amplifier according to the bias voltage adjustment formula containing power and temperature constraints. While ensuring signal strength and service quality, it effectively controls device power consumption and prevents chip overheating, achieving a balance between service experience, battery life and device safety. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the device connection according to the present invention.

[0019] Figure 2 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention provides, for example Figure 1 The device connection diagram shown includes an environmental sensing terminal, a user behavior analysis terminal, a device status acquisition terminal, a policy generation terminal, and an execution control terminal.

[0022] This invention provides, for example Figure 2 The method for intelligent signal strength control of a communication terminal device, as shown, specifically includes the following steps: S1. The environmental sensing terminal collects electromagnetic environment data in the area surrounding the communication terminal equipment through multi-band radio frequency sensors to form a comprehensive signal environment dataset. Furthermore, in the above technical solution, the comprehensive signal environment dataset includes environmental interference intensity data, spectrum occupancy rate data, and multipath fading characteristic data; the environmental interference intensity data includes background noise power spectral density P in a specific frequency band. n (f) and the amplitude A of the co-channel interference signal int The spectrum occupancy rate data includes the percentage of licensed frequency band occupancy time T. occ Number of unlicensed frequency band conflicts N coll The multipath fading characteristic data includes the Doppler frequency shift standard deviation σ. f and delay spread τ rms .

[0023] It is important to know that the background noise power spectral density P in the specific frequency band n(f) Data is acquired using the following method: The multi-band radio frequency sensor mounted on the environmental sensing terminal needs to cover the target communication frequency band of the communication terminal equipment, such as the 4G LTE 1.8GHz band with a frequency range of 1710MHz~1785MHz. The sensor needs to meet the performance requirements of noise figure ≤3dB and sampling rate ≥3.6GHz. The acquisition process needs to be carried out during the silent period when the communication terminal equipment has no signal transmission, and the silent duration is set to ≥10ms to cover the statistical characteristics of noise. At the same time, the target frequency band is continuously scanned with a scanning step size of 1kHz. The time-domain sampling signal acquired by the sensor is transmitted to the signal processing unit and then subjected to fast Fourier transform processing. The number of FFT points is set to 1024, corresponding to a frequency resolution of approximately 3.5kHz. Hanning window is used to suppress spectral leakage. After converting the time-domain signal into a frequency-domain power distribution, the power values ​​of all frequency points in the target frequency band are extracted. First, outliers exceeding 3dB of the average power of the frequency band are removed to eliminate instantaneous pulse interference. Then, the average value of the remaining power values ​​is taken as the power spectral density value of the corresponding frequency point, and finally P is formed. n (f) The functional relationship between frequency and data is stored in the signal environment comprehensive dataset; The amplitude A of the co-frequency interference signal int The data was acquired using the following method: a multi-band radio frequency sensor of the environmental sensing terminal and the acquisition P... n (f) uses the same device with a sampling rate ≥3.6GHz. This sensor captures the time-domain waveform of non-target signals in the same frequency band in real time within the operating frequency band of the communication terminal device, such as 1710MHz~1785MHz. The capture duration is set to ≥1μs to ensure coverage of the minimum period of the interference signal and avoid incomplete waveform acquisition. After the captured time-domain waveform is transmitted to the signal processing unit, the peak hold algorithm is used to calculate the absolute value of the interference signal amplitude. The peak hold time is set to 100ns, and the judgment condition that the amplitude values ​​of three consecutive sampling points exceed the average amplitude of the background noise by 3dB is required to avoid instantaneous noise being misjudged as interference. Only then is the peak value recognized as the effective interference amplitude. At the same time, the carrier frequency of the interference signal is recorded synchronously with a measurement accuracy of ±1kHz and the timestamp of occurrence with a time accuracy of ±1μs. Finally, the effective interference amplitude value is determined as A. int And it is incorporated into the comprehensive dataset of signal environment; The percentage of time occupied by the authorized frequency band T occData was collected using the following method: The multi-band radio frequency sensor of the environmental sensing terminal monitored the licensed frequency band corresponding to the communication terminal equipment in real time, such as the 4G LTE 1.8GHz band (1710MHz~1785MHz). The monitoring period was set to 1 second. Within each monitoring period, the sensor identified whether there were signals from non-target terminals within the frequency band. The identification method was to distinguish the signals based on the differences in carrier frequency and modulation method compared to the target terminal signals, excluding the time periods when the target terminal itself transmitted signals. Simultaneously, the cumulative duration t of the licensed frequency band being occupied by non-target signals within that period was calculated. occ t within each monitoring period occ The ratio of the total monitoring cycle duration (1 second) to the total duration of the monitoring cycle is taken as the percentage of authorized frequency band usage time in that cycle. The ratio data is collected continuously for 10 monitoring cycles, and the average value is taken after removing the maximum and minimum values ​​as T. occ And store it in the comprehensive dataset of signal environment; The number of unlicensed frequency band collisions N coll Data was collected using the following method: The multi-band RF sensor of the environmental sensing terminal continuously monitored unlicensed frequency bands that the communication terminal equipment might access, such as the 5.8GHz ISM band (5725MHz~5875MHz). The monitoring duration was set to 1 minute, and the sampling interval was set to 10ms. At each sampling moment, if the sensor captured two or more non-target signals simultaneously within the unlicensed frequency band, with signal amplitude ≥ -60dBm and signal bandwidth overlap ≥ 50%, it was considered one unlicensed frequency band conflict. The number of conflicts meeting the above criteria was counted within the 1-minute monitoring period. Simultaneously, signal interference from the target terminal itself accessing the unlicensed frequency band was eliminated by identifying the target terminal's access timing and signal characteristics. Finally, N was obtained. coll And it is incorporated into the comprehensive dataset of signal environment; The standard deviation of Doppler frequency shift σ f The following methods were used to collect data: The multi-band RF sensor of the environmental sensing terminal captured downlink signals between the communication terminal equipment and the target base station, such as the 1.8GHz reference signal transmitted by the base station. The signal acquisition duration was set to ≥50ms and the sampling rate to ≥3.6GHz to ensure that enough signal samples were collected to reflect frequency shift changes. Frequency domain analysis was performed on the collected signals to calculate the signal frequency offset value corresponding to each sampling point. The calculation method was to use the nominal frequency of the base station reference signal as a reference and calculate the difference between the actual received frequency and the nominal frequency. After obtaining the frequency offset values ​​of all sampling points, the average frequency offset was first calculated, and then the standard deviation of the frequency offset value was calculated according to the standard deviation calculation formula, removing values ​​exceeding μ±2σ. f After recalculating the abnormal frequency offset values ​​within the range, σ is finally obtained. f And store it in the signal environment comprehensive dataset, where μ is the average frequency offset; The time delay spread τrms The data was collected using the following method: Multi-band RF sensors of the environmental sensing terminal received multipath signals from the vicinity of the communication terminal equipment. Taking the narrowband reference signal transmitted by the target base station as an example, the impulse response estimation method was used to obtain the time-domain impulse response of the multipath signals. The main path signal with the largest amplitude in the impulse response was determined, as well as all secondary path signals with amplitudes ≥ the main path signal amplitude - 10dB. The time delay value of each secondary path signal relative to the main path signal was recorded. The arrival time of the main path signal was recorded as the reference, with the unit being ns. The sampling rate was set to ≥100MHz to ensure that the time delay measurement resolution was ≤10ns. The root mean square formula for time delay spread was used. Calculate the root mean square time delay of all secondary path signals, where τ i P is the time delay of the i-th secondary path. i Let τ be the power of the i-th secondary path, and N be the total number of samples. rms It is also included in the comprehensive dataset of signal environment.

[0024] S2. The user behavior analysis terminal collects user movement trajectory data through accelerometers and gyroscopes, and captures application layer business type data through network interfaces to form a user behavior feature dataset. Furthermore, in the above technical solution, the user behavior feature dataset includes movement trajectory data and service type data; the movement trajectory data includes three-dimensional spatial displacement vectors. and the standard deviation of movement speed σ v The service type data includes the real-time voice service identifier I. voice Video Stream Service Identifier video and background transmission service identifier I bg .

[0025] It is important to know that the three-dimensional spatial displacement vector The data was collected using the following method: An accelerometer and gyroscope mounted on the user behavior analysis terminal collaboratively acquired the vector. The accelerometer's range was set to ±16g with a measurement accuracy of ±0.01g, and the gyroscope's range was set to ±2000° / s with a measurement accuracy of ±0.1° / s. Both sensors had a sampling interval of 10ms. During acquisition, a three-dimensional coordinate system was first established, with the x-axis pointing horizontally east, the y-axis pointing horizontally north, and the z-axis pointing vertically upward. The accelerometer acquired acceleration data in the three-dimensional direction, and the gyroscope acquired angular velocity data in the three-dimensional direction. After transmitting both types of data to the data processing unit, the data was fused using a Kalman filter algorithm to eliminate accumulated errors. The fused acceleration data was then integrated once to obtain the velocity data in the three-dimensional direction, and then integrated twice to obtain the displacement data in the three-dimensional direction. The acquisition duration was set to ≥100ms. The displacement difference between the start and end times within the acquisition period was taken as the displacement components d of the x, y, and z axes, respectively. xd y d z Ultimately, a three-dimensional spatial displacement vector is formed. And store it in the user behavior feature dataset; The real-time voice service identifier I voice Data is collected using the following method: The user behavior analysis terminal captures application layer data packets from the communication terminal device through a network interface such as a TCP / IP Ethernet interface. Protocol parsing and feature identification are performed on the data packets. First, the port number of the data packet is extracted, such as the commonly used ports 5060 (SIP protocol) and 5061 (SIPS protocol) for VoLTE real-time voice services, and the transmission protocol type, such as SIP / H.264 / RTP. Then, the frame length characteristics of the data packet are analyzed, i.e., the frame length of real-time voice services is typically 20ms / frame, corresponding to a data packet size of approximately 30-50 bytes, and the bit rate characteristics, i.e., a typical bit rate of 12.2kbps-23.85kbps. If the captured data packet simultaneously satisfies port number matching, protocol type conformance, and frame length and bit rate within the above ranges, then the real-time voice service is identified as I. voice Setting it to "1" indicates that real-time voice service is currently running; otherwise, setting it to "0" indicates that real-time voice service is not running, and then setting I... voice The values ​​are stored in the user behavior feature dataset; The video stream service identifier I video Data is collected using the following method: The user behavior analysis terminal captures application layer data packets from the communication terminal device through the network interface. The data packets are parsed to extract port numbers (e.g., 554 for video streaming services, corresponding to the RTSP protocol; 8554 for RTSP-over-HTTP protocol); and transport protocols (e.g., RTSP / RTP / RTCP). Simultaneously, the frame length characteristics of the data packets are analyzed: video streaming frame length is typically 33ms / frame, corresponding to 30fps, and the data packet size varies with the bitrate, typically ≥1000 bytes. Bitrate characteristics are also analyzed: standard definition video bitrate ≥1Mbps, and high definition video bitrate ≥4Mbps. If the data packet meets the requirements for port number matching, protocol type conformance, and frame length and bitrate meeting the above video streaming service characteristics, then the video streaming service is identified as I. video Setting it to "1" indicates that the video streaming service is currently running; otherwise, setting it to "0" indicates that the video streaming service is not running, and then setting I... video Values ​​are incorporated into the user behavior feature dataset; The background transmission service identifier I bg Data was collected using the following method: The user behavior analysis terminal captures application layer data packets from the communication terminal device through the network interface, first excluding those already identified as real-time voice services (i.e., I...). voice =1 and video streaming services, i.e., I videoFor data packets with a value of 1, further feature analysis is performed on the remaining data packets. Port numbers are extracted, such as FTP (File Transfer Protocol) ports 21 / 22, SMTP (Small Mail Transfer Protocol) port 25, and HTTP background transmission ports 80 / 443. Transmission rate is calculated by determining the average transmission rate over 10 seconds, and latency is calculated by determining the average latency from sending to receiving the data packet. If the remaining data packets meet the following criteria: port number corresponds to the background transmission protocol, transmission rate fluctuation ≤ 10% with no significant real-time bandwidth requirement, and average latency ≥ 100ms with no low-latency requirement, then the background transmission service is identified as I. bg Setting it to "1" indicates that a background transmission service is currently running; otherwise, setting it to "0" indicates that a background transmission service is not running, and then setting I... bg The values ​​are stored in the user behavior feature dataset.

[0026] S3. The strategy generation terminal performs pattern recognition on the user behavior feature dataset to generate a behavior correction factor ε. Furthermore, in the above technical solution, the behavior modification factor ε is specifically: , Where v0 is the velocity reference value, σ v Let μ1 be the standard deviation of the mobile speed, μ2 be the service type weight coefficients, μ1 + μ2 + μ3 = 1 and μ1, μ2 and μ3 ∈ [0, 1].

[0027] It is important to know that the speed reference value v0 is set as the walking reference speed of the communication terminal device under typical usage scenarios. Specifically, during the pre-installation and debugging phase before the device leaves the factory, at least 10 sets of standard walking data are collected using accelerometers and gyroscopes. In these data sets, the user moves in a straight line at a speed of 1.0 m / s ± 0.2 m / s for 30 seconds, and the standard deviation σ of each speed set is calculated. v The average value is taken as 1.5 times the average value as the initial v0 value and ensured to be within the reasonable range of [0.8, 1.2] m / s. After the device is enabled, it continuously monitors the user's movement speed. When the average speed is detected to fall into the range of [0.3v0, 0.7v0] or [1.3v0, 1.8v0] for 24 consecutive hours, vv0 is automatically updated with the 90th percentile of the speed standard deviation for that period, and the update operation interval is not less than 72 hours. The service type weight coefficients μ1, μ2, and μ3 are determined based on the service quality level requirements of communication services and through a preset priority mapping table on the device: μ1 corresponds to the weight of real-time voice services, set to 0.5, because voice services are most sensitive to latency jitter and require the highest weight to ensure signal stability; μ2 corresponds to the weight of video streaming services, set to 0.3, because video services require a medium weight to balance bandwidth and latency requirements; μ3 corresponds to the weight of background transmission services, set to 0.2, because background transmission can tolerate higher latency, hence it is given the lowest weight; when the device status acquisition terminal detects that the remaining battery capacity C < 0.2 × C max When the chip junction temperature T > 0.7 × T, the power-saving mode is automatically activated, adjusting μ1, μ2, and μ3 to 0.6, 0.25, and 0.15 respectively, prioritizing the continuity of voice services; when the chip junction temperature T > 0.7 × T max When this occurs, a thermal protection mode is triggered, reducing μ2 to 0.2 and increasing μ3 to 0.3 to distribute the data processing load and reduce heat generation.

[0028] S4. The strategy generation terminal performs time-frequency domain analysis on the comprehensive signal environment dataset to obtain the channel interference suppression coefficient α, the spectrum efficiency optimization coefficient β, and the link stability coefficient γ. Furthermore, in the above technical solution, the channel interference suppression coefficient α is specifically: , Where, k p P is the slope parameter for environmental adaptation. n (f) represents the background noise power spectral density in a specific frequency band, P th For the noise power threshold, A int For the amplitude of the co-channel interference signal, A max The maximum permissible interference amplitude is given by e, which is a natural constant.

[0029] It is important to know that the environmental adaptation slope parameter k p Value setting: Set the communication terminal equipment to the background noise power P n (f) From P th -10dB to P th In an environment with a dynamic noise change of +10dB, the response slope of α was measured for every 1dB change in noise. The mean of 20 sets of test data was taken as k. p Base value, ensuring k p ∈[0.04, 0.08] dB -1 For example, the typical value k of the 4G LTE 1.8GHz band p =0.05dB -1 When the environmental sensing terminal detects a change in operating frequency band, such as switching from 4G 1.8GHz to 5G 3.5GHz, it dynamically adjusts according to the formula: , where f old f is the carrier frequency before the switch. new The carrier frequency after the switch; The noise power threshold P th The typical value is -101 dBm / Hz; The maximum permissible interference amplitude A max To determine the upper limit of co-channel interference signal amplitude that a communication terminal device can tolerate under nominal operating conditions, a base value is determined by the 1dB compression point of the RF receiving link: A max =0.8×P1dB, where P 1dB The 1dB compression point power value of the low-noise amplifier in the RF front-end is obtained through actual measurement using a vector network analyzer during equipment factory testing. For example, the typical value for the 4G LTE 1.8GHz band is P1dB = -15dBm. Therefore, A... max =-18.2dBm; Furthermore, in the above technical solution, the spectral efficiency optimization coefficient β is specifically: , Among them, T occ N represents the percentage of time the licensed frequency band is occupied. coll λ1 represents the number of unlicensed frequency band conflicts, λ2 represents the weighting factor for licensed frequency band conflicts, and λ3 represents the penalty factor for unlicensed frequency band conflicts.

[0030] It should be noted that the weighting factor λ1 of the licensed frequency band is specifically... Among them, B auth This refers to the bandwidth of the currently authorized frequency band. The unlicensed frequency band conflict penalty factor λ2 is specifically: , where N max The maximum number of unlicensed frequency bands supported by the device; When the environmental sensing terminal detects a carrier frequency switch, then .

[0031] Furthermore, in the above technical solution, the link stability coefficient γ is specifically: , Where, σ f f is the standard deviation of Doppler frequency shift. c For the carrier frequency, τ rms For time delay spread, τ0 is the time delay spread reference value, and e is the natural constant.

[0032] It should be noted that the carrier frequency f cThe data acquisition is specifically achieved in the following way: The environmental sensing terminal obtains the operating frequency configuration parameters of the current communication link in real time through the communication baseband chip. The baseband chip's radio frequency control register reads the locked target frequency band center frequency value, which is determined by the radio frequency transceiver after frequency synchronization based on the synchronization signal block or channel state information reference signal sent from the network side. During the reading process, the baseband chip accesses the programming register of the radio frequency front-end local oscillator frequency synthesizer through the digital interface and directly outputs the nominal carrier frequency value of the current operating channel with millihertz precision as f. c The value; The delay spread reference value τ0 is set in the following way: During the actual deployment phase of the equipment, the environmental sensing terminal continuously collects and statistically analyzes the historical delay spread τ. rms When the effective sample size exceeds 100 groups, the 85th percentile value of the sample is calculated. If the deviation of this value from the preset τ0 exceeds ±5ns, τ0 is automatically updated to the current statistical quantile value. Simultaneously, when the device switches operating frequencies, τ0 is dynamically adjusted according to the frequency band characteristics, following the following adjustment rules: .

[0033] S5. The strategy generation terminal inputs the channel interference suppression coefficient α, the spectrum efficiency optimization coefficient β, the link stability coefficient γ, and the behavior correction factor ε into the adaptive control decision model, and outputs the dynamic power control index δ. Furthermore, in the above technical solution, the adaptive control decision model specifically refers to: , Where α is the channel interference suppression coefficient, β is the spectrum efficiency optimization coefficient, γ is the link stability coefficient, ε is the behavior correction factor, η is the decision sensitivity parameter, θ is the decision threshold, δ is the dynamic power regulation index, e is the natural constant, ω1, ω2 and ω3 are weight coefficients, ω1+ω2+ω3=1 and ω1, ω2 and ω3∈[0,1].

[0034] It is important to know that during the pre-delivery debugging phase of the equipment, the decision sensitivity parameter η is adjusted using a gradient search algorithm and the response sensitivity of the dynamic power control index δ during three typical service scenarios: real-time voice, video streaming, and background transmission. The optimal η value, which results in a service interruption rate of less than 1% and a power consumption reduction of ≥15%, is selected as the base value, with a typical range of 0.8 to 1.2. During equipment operation, the parameter η is dynamically corrected based on the real-time status. When the equipment status acquisition terminal detects that the remaining battery capacity C < 0.3 × C... max At that time, according to η′=η×[0.7+0.3×(C / C max [Reduce sensitivity to suppress power consumption when chip junction temperature T > 0.8 × T] maxAt that time, η is forcibly set to 0.5 to trigger a protective desensitization strategy; The typical range of the decision threshold θ is [-1, 0.5], such as θ=-0.3 by default for 4G devices; The default values ​​for the weighting coefficients ω1, ω2, and ω3 are set to ω1=0.35, ω2=0.25, and ω3=0.4, respectively, and their allocation follows the priority of stability > interference suppression > spectral efficiency. In actual operation, the weighting coefficients are dynamically adjusted according to the real-time service type and device status: when real-time voice service is activated, ω1 is increased to 1.2 times its original value to enhance anti-interference capability, while ω3 is compressed to 0.9 times to appropriately reduce the stability weight; when video streaming service is activated, ω2 is increased to 1.3 times to prioritize bandwidth requirements; if the device enters low-power mode (i.e., remaining capacity C / C...), the weighting coefficients are adjusted accordingly. max If the value is less than 0.3, then ω2 is reduced to 0.7 times to suppress power consumption during spectrum optimization, and ω3 is increased to 1.1 times to reduce retransmission power consumption by improving stability; if the chip junction temperature exceeds the safety threshold, i.e., T / T max >0.75, ω1 drops sharply to 0.5 times to avoid power amplification exacerbating heat generation. After each dynamic adjustment, weight normalization is performed, and each weight value is forcibly constrained to be in the range of [0.15, 0.55] to prevent extreme weights from causing control imbalance.

[0035] S6. The execution control terminal adjusts the bias voltage of the RF front-end power amplifier according to the dynamic power regulation index δ. Furthermore, in the above technical solution, the adjustment amount of the bias voltage must satisfy: , Where C is the remaining battery capacity, T is the chip junction temperature, and V is the voltage. max For the maximum allowable bias voltage, C max T represents the nominal capacity of the battery. a For ambient temperature, T max The maximum allowable junction temperature is given by δ, which is the dynamic power regulation index. This is a power constraint term. This is a temperature constraint term.

[0036] It should be noted that the remaining battery capacity C is obtained through the battery management system built into the communication terminal device; The chip junction temperature T is obtained by an integrated temperature sensor built into the RF front-end power amplifier chip or baseband chip of the communication terminal equipment. The ambient temperature Ta is obtained by an NTC thermistor deployed inside the casing of the communication terminal equipment, near the edge and away from heat-generating components such as the RF power amplifier and processor. The maximum allowable bias voltage V maxSpecifically, the performance of the power amplifier was determined through actual measurements during the equipment's factory testing phase. A vector network analyzer was used to perform performance testing, first measuring the bias voltage V at which the amplifier reached the 1dB compression point in the target operating frequency band, such as the 4G LTE 1.8GHz band or the 5G 3.5GHz band. 1dB That is, the input bias voltage when the amplifier gain drops by 1dB, and then take V. 1dB 90% as V max The initial value is set to allow for a 10% safety margin to prevent distortion or damage to the amplifier due to excessive bias voltage; for different operating frequency bands, the V value of the corresponding frequency band needs to be tested separately. 1dB And calculate V max For example, the measured V of the 4G 1.8GHz band 1dB =5V, then V max =4.5V; Actual measured V in 5G 3.5GHz band 1dB =5.5V, then V max =4.95V; The nominal capacity of the battery C max The value is set based on the factory specifications of the battery installed in the communication terminal equipment. The official nominal capacity provided by the battery supplier is used as the base value. Before the equipment leaves the factory, it needs to be calibrated by three complete charge-discharge cycles through the battery management system of the equipment status acquisition terminal. Each cycle starts from the battery cutoff voltage, charges to the full charge voltage, and then discharges to the cutoff voltage. The actual capacity under the full charge state is recorded each time, and the average of the three actual capacities is taken as C. max ; The maximum allowable junction temperature T max The value is set based on the rated operating parameters of the core chips of the communication terminal equipment, including the RF power amplifier chip and the baseband processing chip. First, the rated maximum junction temperature marked in the chip datasheet is extracted, such as 125℃ for the RF power amplifier chip and 110℃ for the baseband chip. Then, redundancy adjustments are made based on the actual heat dissipation capacity of the equipment, and 90% of the rated maximum junction temperature of the chip is taken as the T value for the corresponding chip. max Such as the radio frequency power amplifier chip T max =112.5℃, baseband chip T max =99℃.

[0037] S7. The device status acquisition terminal monitors the remaining battery capacity C and chip junction temperature T in real time and feeds them back to the strategy generation terminal.

[0038] Furthermore, in the above technical solution, the power constraint term satisfies: when When the value is less than 0.15, the output value of the dynamic power regulation index δ is limited to no more than 0.4. The temperature constraint term satisfies: when When the value is greater than 0.7, a forced setting is required. .

[0039] It is important to know that when it occurs In conflict scenarios where <0.15 and δ>0.4, the conflict resolution mechanism is triggered, specifically as follows: when When the value is less than 0.15, the execution control terminal activates the dynamic clamping circuit to make the actual output value δ equal to δ. out satisfy The strategy generation terminal synchronously activates the emergency power consumption mode, shuts down non-core RF channels such as the MIMO auxiliary stream, and increases ω3 to 0.8; the device status acquisition terminal performs a C-retest every 5 seconds, and if it occurs 3 times consecutively... If the value is greater than 0.18, the conflict resolution mechanism will be exited.

[0040] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent signal strength control in a communication terminal device, characterized in that, It includes an environmental sensing terminal, a user behavior analysis terminal, a device status acquisition terminal, a policy generation terminal, and an execution control terminal, and specifically includes the following steps: S1. The environmental sensing terminal collects electromagnetic environment data in the area surrounding the communication terminal equipment through multi-band radio frequency sensors to form a comprehensive signal environment dataset. S2. The user behavior analysis terminal collects user movement trajectory data through accelerometers and gyroscopes, and captures application layer business type data through network interfaces to form a user behavior feature dataset. S3. The strategy generation terminal performs pattern recognition on the user behavior feature dataset to generate a behavior correction factor ε. S4. The strategy generation terminal performs time-frequency domain analysis on the comprehensive signal environment dataset to obtain the channel interference suppression coefficient α, the spectrum efficiency optimization coefficient β, and the link stability coefficient γ. S5. The strategy generation terminal inputs the channel interference suppression coefficient α, the spectrum efficiency optimization coefficient β, the link stability coefficient γ, and the behavior correction factor ε into the adaptive control decision model, and outputs the dynamic power control index δ. S6. The execution control terminal adjusts the bias voltage of the RF front-end power amplifier according to the dynamic power regulation index δ. S7. The device status acquisition terminal monitors the remaining battery capacity C and chip junction temperature T in real time and feeds them back to the strategy generation terminal.

2. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The comprehensive signal environment dataset includes environmental interference intensity data, spectrum occupancy rate data, and multipath fading characteristic data; the environmental interference intensity data includes background noise power spectral density P in a specific frequency band. n (f) and the amplitude A of the co-channel interference signal int The spectrum occupancy rate data includes the percentage of licensed frequency band occupancy time T. occ Number of unlicensed frequency band conflicts N coll The multipath fading characteristic data includes the Doppler frequency shift standard deviation σ. f and delay spread τ rms .

3. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The user behavior feature dataset includes movement trajectory data and service type data; the movement trajectory data includes three-dimensional spatial displacement vectors. and the standard deviation of movement speed σ v The service type data includes the real-time voice service identifier I. voice Video Stream Service Identifier video and background transmission service identifier I bg .

4. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The behavior modification factor ε is specifically: ， Where v0 is the velocity reference value, σ v Let μ1 be the standard deviation of the moving speed, μ2 be the service type weight coefficients, μ1 + μ2 + μ3 = 1 and μ1, μ2 and μ3 ∈ [0, 1].

5. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The channel interference suppression coefficient α is specifically: ， Where, k p P is the slope parameter for environmental adaptation. n (f) represents the background noise power spectral density in a specific frequency band, P th For the noise power threshold, A int For the amplitude of the co-channel interference signal, A max The maximum permissible interference amplitude is given by e, which is a natural constant.

6. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The spectral efficiency optimization coefficient β is specifically: ， Among them, T occ N represents the percentage of time the licensed frequency band is occupied. coll λ1 represents the number of unlicensed frequency band conflicts, λ2 represents the weighting factor for licensed frequency band conflicts, and λ3 represents the penalty factor for unlicensed frequency band conflicts.

7. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The link stability coefficient γ is specifically: ， Where, σ f f is the standard deviation of Doppler frequency shift. c For the carrier frequency, τ rms For time delay spread, τ0 is the time delay spread reference value, and e is the natural constant.

8. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The adaptive control decision model is specifically as follows: ， Where α is the channel interference suppression coefficient, β is the spectrum efficiency optimization coefficient, γ is the link stability coefficient, ε is the behavior correction factor, η is the decision sensitivity parameter, θ is the decision threshold, δ is the dynamic power regulation index, e is the natural constant, ω1, ω2 and ω3 are weight coefficients, ω1+ω2+ω3=1 and ω1, ω2 and ω3∈[0,1].

9. The intelligent signal strength control method for a communication terminal device according to claim 1, characterized in that, The adjustment amount of the bias voltage must meet the following requirements: ， Where C is the remaining battery capacity, T is the chip junction temperature, and V is the voltage. max For the maximum allowable bias voltage, C max T represents the battery's nominal capacity. a For ambient temperature, T max The maximum allowable junction temperature is given by δ, which is the dynamic power regulation index. This is a power constraint term. This is a temperature constraint term.

10. The intelligent signal strength control method for a communication terminal device according to claim 9, characterized in that, The energy constraint term satisfies: when When the value is less than 0.15, the output value of the dynamic power regulation index δ is limited to no more than 0.

4. The temperature constraint term satisfies: when When the value is greater than 0.7, a forced setting is required. .

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