CN117715078A - Self-adaptive monitoring control method based on wireless communication - Google Patents
Self-adaptive monitoring control method based on wireless communication Download PDFInfo
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Abstract
The invention belongs to the technical field of wireless communication monitoring, in particular to a self-adaptive monitoring control method based on wireless communication, which comprises the following steps: the invention calculates and adjusts the parameters and configuration of the system by using an adaptive algorithm based on the information of the monitoring index, the user demand index and the environment perception, including modulation mode, modulation parameter, transmission power, resource allocation and scheduling strategy, communication frequency band and communication frequency; and the system can perform real-time feedback and control according to the results of parameter calculation and adjustment, including adjustment of transmission power, adjustment of modulation mode and adjustment of related operations of resource allocation, thereby realizing self-adaptive monitoring and control of wireless communication data.
Description
Technical Field
The invention belongs to the technical field of wireless communication monitoring, and particularly relates to a self-adaptive monitoring control method based on wireless communication.
Background
Wireless communication is a communication method for exchanging information by utilizing the characteristic that electromagnetic wave signals can propagate in free space, and in the field of information communication in recent years, the most rapidly developed and most widely applied wireless communication technology is the wireless communication technology. Wireless communication implemented in mobile is also known as mobile communication, and both are commonly referred to as wireless mobile communication.
The adaptive monitoring control of wireless communication means that in a wireless communication system, the adaptive algorithm and the monitoring technology are utilized to realize the real-time monitoring and control of the system performance. The system can automatically adjust the parameters and configuration of the system according to the real-time communication environment and the requirements of users so as to provide better communication quality and service.
The adaptive monitoring control mainly comprises the following aspects:
adaptive modulation: according to the channel condition and the data transmission requirement, automatically selecting the most suitable modulation mode and modulation parameters; for example, higher order modulation may be selected to increase the data transmission rate when the channel quality is good; and low order modulation may be selected to improve signal reliability when channel quality is poor.
Adaptive power control: and automatically adjusting the transmission power according to the channel state and the user requirement. For example, transmit power may be reduced to reduce interference when channel quality is good; and when the channel quality is poor, the transmission power can be increased to improve the receiving performance of the signal.
And (3) adaptive scheduling: according to the priority and communication demands of users, the allocation and scheduling strategy of the resources is automatically adjusted, for example, a priority scheduling algorithm can be adopted to ensure the transmission of important data in the case of high load; and at low load, a fair scheduling algorithm may be employed to balance the resource utilization of the users.
Adaptive spectrum management: according to the current spectrum utilization condition and the user demand, automatically selecting the most suitable frequency band and frequency resource; for example, an unused frequency band may be selected for communication when the spectrum is congested; while higher frequency resources may be selected to increase the data transmission rate when the spectrum is idle.
While adaptive monitoring control of wireless communications has many advantages, there are also some drawbacks, including:
1. calculation and processing delays: the adaptive monitoring control needs to monitor and analyze the real-time environment and adjust according to the result, which involves a large amount of calculation and processing, and may introduce a certain delay to affect the response time and performance of the system.
2. Resource consumption: adaptive monitoring control requires additional computational and communication resources to enable monitoring and control of the system. This may increase the power consumption and resource consumption of the system, reducing the efficiency and reliability of the system.
The present invention thus provides an adaptive monitoring control method based on wireless communication to solve the above-mentioned problems.
Disclosure of Invention
In order to solve the technical problems, the invention provides a self-adaptive monitoring control method based on wireless communication, which can automatically select the most suitable modulation mode and modulation parameters according to channel conditions and data transmission requirements by designing a self-adaptive modulation algorithm, and the algorithms can automatically adjust the modulation mode and the modulation parameters according to indexes such as channel quality, signal-to-noise ratio, bit error rate and the like so as to improve the data transmission rate and the reliability of signals, thereby solving the problems in the background technology.
The specific technical scheme of the invention is as follows:
a self-adaptive monitoring control method based on wireless communication comprises the following steps:
step 1: collecting signals; firstly, receiving a wireless signal from a transmitting end through a receiving end in a self-adaptive monitoring system;
step 2: signal processing; demodulating, filtering and spectrum analyzing the wireless signals acquired in the step 1 to obtain the characteristics and parameters of the signals;
step 3: monitoring index calculation; calculating monitoring indexes for evaluating channel quality and link performance according to the characteristics and parameters of the signals obtained in the step 2; the monitoring indexes comprise signal strength, signal-to-noise ratio, bit error rate and packet loss rate;
step 4: obtaining a user demand index; acquiring user demand indexes from user equipment or a network through a QoS mechanism; the user demand index comprises bandwidth demand, time delay demand and service quality;
step 5: sensing environment; acquiring environment perception information through a sensor, base station position information and a geographic information system; the environment perception information comprises user distribution, interference sources and moving speed;
step 6: parameter acquisition and self-adaptive adjustment; according to the monitoring index, the user demand index and the environment sensing information obtained in the steps 3 to 5, obtaining the most suitable channel quality tuning parameters by using a self-adaptive algorithm, and adjusting the system parameters according to the obtained channel quality tuning parameters; the system parameters after the adjustment are fed back to the self-adaptive monitoring system in real time; the channel quality tuning parameters include: modulation mode, modulation parameters, transmission power, resource allocation and scheduling strategy, communication frequency band and communication frequency.
Preferably, in step 2, the signal processing filters through a digital filter arc to remove noise and interference, and performs spectrum analysis on the signal, and fourier transform is used to obtain frequency characteristics of the signal.
Preferably, in step 6, the adaptive algorithm includes an adaptive modulation algorithm, an adaptive power control algorithm, an adaptive scheduling algorithm, an adaptive spectrum management algorithm, and an adaptive parameter optimization algorithm; the adaptive algorithm uses existing channel conditions and data transmission requirements.
Preferably, the adaptive modulation algorithm combines the characteristics and parameters of the signals acquired in the step 1 with the channel conditions and data transmission requirements, automatically selects the modulation mode and the modulation parameters, automatically adjusts the modulation mode and the modulation parameters according to the monitoring index in the step 3, and improves the data transmission rate and the reliability of the signals.
Preferably, the adaptive power control algorithm automatically adjusts the transmission power according to the channel condition and the user requirement index, dynamically adjusts the transmission power based on the requirements of the received signal quality, the interference source, the interference degree and the system capacity, and realizes the balance of transmission performance and power consumption.
Preferably, the self-adaptive scheduling algorithm automatically adjusts the allocation and scheduling strategy of the resources according to the user demand index, dynamically allocates the bandwidth and the resources based on the service type, the load condition and the channel condition of the user, meets different demands of the user, and improves the overall performance and fairness of the system.
Preferably, the adaptive spectrum management algorithm automatically selects the most suitable frequency band and frequency resource according to the current channel quality and user demand index, and improves the utilization efficiency of the spectrum and the capacity of the system by monitoring and adjusting the allocation of the spectrum resource in real time.
Preferably, the adaptive parameter optimization algorithm is used for optimizing parameter configuration in the adaptive algorithm, automatically adjusting parameters of the algorithm based on historical data and real-time feedback, and improving performance and adaptability of the system.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention calculates and adjusts the parameters and configuration of the system by using the self-adaptive algorithm based on the monitoring index, the user demand index and the environment sensing information, including the modulation mode, the modulation parameter, the transmitting power, the resource allocation and scheduling strategy, the communication frequency band and the communication frequency, and feeds back and controls in real time.
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FIG. 1 is a flow chart of the monitoring method of the present invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings and examples. The following examples are illustrative of the invention but are not intended to limit the scope of the invention.
As shown in fig. 1, the present invention provides an adaptive monitoring control method based on wireless communication, where the monitoring control method includes:
step 1: collecting signals; firstly, receiving a wireless signal from a transmitting end through a receiving end in a self-adaptive monitoring system;
step 2: signal processing; demodulating, filtering and spectrum analyzing the wireless signals acquired in the step 1 to obtain the characteristics and parameters of the signals;
step 3: monitoring index calculation; calculating monitoring indexes for evaluating channel quality and link performance according to the characteristics and parameters of the signals obtained in the step 2; the monitoring indexes comprise signal strength, signal-to-noise ratio, bit error rate and packet loss rate;
step 4: obtaining a user demand index; acquiring user demand indexes from user equipment or a network through a QoS mechanism; the user demand index comprises bandwidth demand, time delay demand and service quality;
step 5: sensing environment; acquiring environment perception information through a sensor, base station position information and a geographic information system; the environment perception information comprises user distribution, interference sources and moving speed;
step 6: parameter acquisition and self-adaptive adjustment; according to the monitoring index, the user demand index and the environment sensing information obtained in the steps 3 to 5, obtaining the most suitable channel quality tuning parameters by using a self-adaptive algorithm, and adjusting the system parameters according to the obtained channel quality tuning parameters; the system parameters after the adjustment are fed back to the self-adaptive monitoring system in real time; the channel quality tuning parameters include: modulation mode, modulation parameter, transmitting power, resource allocation and scheduling strategy, communication frequency band and communication frequency;
embodiment one:
as shown in fig. 1, in this embodiment, the adaptive monitoring control may automatically adjust transmission parameters according to communication requirements and network environment characteristics of different devices, so as to adapt to different application scenarios of the internet of things for explanation.
Firstly, a system receives related wireless signals through receiving end equipment, processes the signals through a digital filter, comprises signal demodulation, filtering and spectrum analysis to obtain characteristics and parameters of the signals, and calculates monitored indexes, wherein the monitored indexes comprise signal strength, signal-to-noise ratio, bit error rate and packet loss rate; calculating signal strength by measuring a power level of the received signal; calculating a signal-to-noise ratio (SNR) by measuring a signal power and an ambient background noise power; calculating by comparing the difference between the received data and the transmitted data, dividing the number of erroneous bits by the total number of bits to obtain a percentage of the bit error rate or expressed in other forms, and then calculating the bit error rate by transmitting a known bit sequence and comparing with the received bit sequence; calculating a packet loss rate by counting the number of transmitted data packets and received data packets; the method comprises the steps of calculating the transmission time of a data packet from a transmitting end to a receiving end, recording time stamps at the transmitting end and the receiving end, calculating the transmission time difference of the data packet to calculate delay, and calculating a signal index by the method.
The method comprises the steps of starting to acquire the communication requirements and service types of a user, including related indexes of bandwidth requirements, time delay requirements and service quality, acquiring by adopting a QoS mechanism, selecting a Best-effect as a QoS service model, sending any number of messages by the user at any time without informing a network, and sending the messages by the network as much as possible when providing the Best-effect service, wherein no guarantee is provided for performances such as time delay, packet loss rate and the like. The Best-effect service model is suitable for the service with low performance requirements on time delay, packet loss rate and the like, is a default service model of the Internet at present, is suitable for most network applications, and further realizes the acquisition of communication requirements and service type information of users.
The communication environment is sensed, including user distribution, interference sources and moving speed related information, through a sensor, base station position information and a geographic information system;
then, based on the monitoring index, the user demand index and the environment perceived information, calculating and adjusting parameters and configuration of the system by using an adaptive algorithm, wherein the parameters and configuration comprise a modulation mode, modulation parameters, transmission power, a resource allocation and scheduling strategy, a communication frequency band and a communication frequency;
the calculation steps of the adaptive modulation algorithm are as follows:
(1) Channel estimation: first the current channel conditions need to be estimated. Channel estimation techniques, such as Minimum Mean Square Error (MMSE) estimation, equalizer, precoding, etc., may be used to estimate the response and characteristics of the channel;
(2) Estimating the error rate: based on the estimated channel conditions, the Bit Error Rate (BER) is estimated by transmitting a series of known modulation symbols and receiving and demodulating the received symbols. Calculating the error rate by using a statistical method or a sampling method;
(3) Modulation mode selection: and selecting the most suitable modulation mode by using a modulation mode selection algorithm according to the estimated channel condition and the target error rate. Selecting a modulation scheme based on a bit error rate-signal-to-noise ratio (SNR) curve, or other evaluation criteria;
(4) Modulation parameter selection: for the selected modulation scheme, appropriate modulation parameters, such as modulation order, modulation depth, modulation index, etc., need to be selected. Determining according to factors such as channel conditions, bit error rate requirements, system capacity and the like;
(5) Modulation operation: and carrying out modulation operation on the data to be transmitted according to the selected modulation mode and parameters. Implemented using a modulator, maps digital data to modulation symbols in the analog signal space.
The calculation steps of the self-adaptive power control algorithm are as follows:
(1) Initial power setting: the initial value of the transmission power is initialized, and the initial power level may be empirically set or preset through the system.
(2) And (3) transmission power adjustment: a power control algorithm is used to adjust the transmit power based on the channel estimate and the target performance requirements. Common algorithms include closed loop control algorithms (e.g., PID control), open loop control algorithms (e.g., successive approximation), and the like.
(3) Monitoring performance indexes: and calculating actual performance indexes such as error rate, packet loss rate and the like according to the adjusted transmission power.
(4) Judging and adjusting: and comparing the actual performance index with the target performance requirement, and judging whether the requirement is met. If not, the increase or decrease in transmit power is adjusted according to a specific algorithm and strategy.
(5) And (5) repeating the steps 2 to 4 until the target performance requirement is met or the maximum iteration number is reached.
The calculation steps of the self-adaptive scheduling algorithm are as follows:
(1) User demand index acquisition: acquiring the communication requirement and service type of a user through QoS mechanism in user equipment or a network; acquiring indexes such as bandwidth requirement, time delay requirement, service quality and the like from user equipment or a network; such information may be obtained using a related protocol or interface.
(2) Environmental perception: acquiring related information of a communication environment by using technologies such as a sensor, base station position information, a geographic information system and the like; perceived environmental characteristics of user distribution, interference sources, moving speed and the like are converted into usable data.
(3) Resource assessment: and evaluating the condition of the current system resource according to the user demand and the environment-aware information. Including available bandwidth, remaining time slots, channel quality, etc. The resource assessment may be performed using correlation algorithms and models.
(4) User priority calculation: and calculating the priority of each user according to the information of the user demands, the environment awareness and the resource assessment. Some evaluation criteria may be used, such as maximizing system capacity, minimizing latency, maximizing user satisfaction, etc.
(5) And (3) resource allocation: a resource allocation algorithm is used to determine the resources and time slots allocated to each user based on user priority. Common algorithms include maximum rate priority, minimum latency priority, perceptual rate control, and the like.
(6) Monitoring performance indexes: according to the result of the resource allocation, the actual performance index such as bandwidth utilization, time delay, throughput and the like is calculated.
The calculation steps of the self-adaptive parameter optimization algorithm are as follows:
(1) Initial parameter setting: the parameters of the initialization system may be empirically set or initial parameters set in advance by the system.
(2) Performance evaluation: and calculating the performance index of the system according to the current parameter setting. The performance evaluation may be performed using actual data or analog data.
(3) Setting an objective function: according to the optimization target, an objective function is set as a guiding criterion of parameter optimization. The objective function may be to minimize error, maximize system capacity, minimize latency, etc.
(4) Optimizing system parameters: an optimization algorithm, such as a gradient descent method, a genetic algorithm, particle swarm optimization, etc., is used to optimize the objective function to update the parameters of the system. And the optimization algorithm calculates the optimal parameter adjustment direction and step length according to the current parameter setting and the gradient information of the objective function.
(5) And (3) system parameter adjustment: and adjusting parameters of the system according to an optimization result obtained by the optimization algorithm. Parameters can be updated step by step according to the set adjustment strategy and step size.
(6) Monitoring performance indexes: according to the adjusted parameters, actual performance indexes such as errors, capacity, time delay and the like are calculated, and according to the actual performance indexes and target performance requirements, whether the requirements are met or not is judged. If not, the increase or decrease of the parameter is adjusted according to the specific algorithm and strategy.
After the calculation by the self-adaptive algorithm, according to the results of parameter calculation and adjustment, the system can perform real-time feedback and control, including related operations such as a modulation mode, modulation parameters, transmission power, resource allocation and scheduling strategies, communication frequency bands, communication frequencies and the like, so as to optimize the performance and adaptability of the system, further realize self-adaptive monitoring of wireless communication and timely control the occurrence of emergency.
The embodiments of the present invention have been presented for purposes of illustration and description, but are not intended to be exhaustive or limited to the invention in the form disclosed, and although the invention has been described in detail with reference to the embodiments, it will be apparent to those skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof.
Claims (8)
1. An adaptive monitoring control method based on wireless communication is based on the operation of an adaptive monitoring system; characterized in that the monitoring control method comprises the steps of:
step 1: collecting signals; firstly, receiving a wireless signal from a transmitting end through a receiving end in a self-adaptive monitoring system;
step 2: signal processing; demodulating, filtering and spectrum analyzing the wireless signals acquired in the step 1 to obtain the characteristics and parameters of the signals;
step 3: monitoring index calculation; calculating monitoring indexes for evaluating channel quality and link performance according to the characteristics and parameters of the signals obtained in the step 2; the monitoring indexes comprise signal strength, signal-to-noise ratio, bit error rate and packet loss rate;
step 4: obtaining a user demand index; acquiring user demand indexes from user equipment or a network through a QoS mechanism; the user demand index comprises bandwidth demand, time delay demand and service quality;
step 5: sensing environment; acquiring environment perception information through a sensor, base station position information and a geographic information system; the environment perception information comprises user distribution, interference sources and moving speed;
step 6: parameter acquisition and self-adaptive adjustment; according to the monitoring index, the user demand index and the environment sensing information obtained in the steps 3 to 5, obtaining the most suitable channel quality tuning parameters by using a self-adaptive algorithm, and adjusting the system parameters according to the obtained channel quality tuning parameters; the system parameters after the adjustment are fed back to the self-adaptive monitoring system in real time; the channel quality tuning parameters include: modulation mode, modulation parameters, transmission power, resource allocation and scheduling strategy, communication frequency band and communication frequency.
2. The adaptive monitoring control method based on wireless communication according to claim 1, wherein: in step 2, the signal processing is performed with filtering by a digital filter to remove noise and interference, and the signal is subjected to spectrum analysis, and fourier transformation is used to obtain the frequency characteristics of the signal.
3. The adaptive monitoring control method based on wireless communication according to claim 1, wherein: in step 6, the adaptive algorithm comprises an adaptive modulation algorithm, an adaptive power control algorithm, an adaptive scheduling algorithm, an adaptive spectrum management algorithm and an adaptive parameter optimization algorithm; the adaptive algorithm uses existing channel conditions and data transmission requirements.
4. The adaptive monitoring control method based on wireless communication as claimed in claim 3, wherein: the self-adaptive modulation algorithm combines the characteristics and parameters of the signals acquired in the step 1 according to the used channel conditions and data transmission requirements, automatically selects a modulation mode and modulation parameters, automatically adjusts the modulation mode and the modulation parameters according to the monitoring indexes in the step 3, and improves the data transmission rate and the reliability of the signals.
5. The adaptive monitoring control method based on wireless communication as claimed in claim 3, wherein: the self-adaptive power control algorithm automatically adjusts the transmission power according to channel conditions and user demand indexes, dynamically adjusts the transmission power based on the requirements of received signal quality, interference sources, interference degrees and system capacity, and realizes balance of transmission performance and power consumption.
6. The adaptive monitoring control method based on wireless communication as claimed in claim 3, wherein: the self-adaptive scheduling algorithm automatically adjusts the allocation and scheduling strategy of the resources according to the user demand index, dynamically allocates the bandwidth and the resources based on the service type, the load condition and the channel condition of the user, meets different demands of the user, and improves the overall performance and fairness of the system.
7. The adaptive monitoring control method based on wireless communication as claimed in claim 3, wherein: the self-adaptive spectrum management algorithm automatically selects the most suitable frequency band and frequency resource according to the current channel quality and user demand index, and improves the utilization efficiency of the spectrum and the capacity of the system by monitoring and adjusting the distribution of the spectrum resource in real time.
8. The adaptive monitoring control method based on wireless communication as claimed in claim 3, wherein: the self-adaptive parameter optimization algorithm is used for optimizing parameter configuration in the self-adaptive algorithm, automatically adjusting parameters of the algorithm based on historical data and real-time feedback, and improving performance and adaptability of the system.
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