5G signal processing method and device, electronic equipment and readable medium

Abstract

The embodiment of the invention provides a 5G signal processing method and device, electronic equipment and a readable medium. The method comprises the following steps: receiving a 5G signal state message under the condition that equipment is accessed to a 5G network; inputting the 5G signal state message into a preset neural network model, and determining beam splitting parameters output by the neural network model; dividing the 5G signal into sub-signal streams of at least one frequency band based on the beam splitting parameter; allocating a weight to the at least one frequency band based on the beam splitting parameter; and integrating the sub-signal streams of at least one frequency band into a complete signal stream based on the weight of the frequency band, and sending the complete signal stream to downstream equipment. Therefore, beam splitting processing can be more accurately carried out on the 5G signal, and the 5G signal transmission efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a 5G signal processing method, a 5G signal processing device, an electronic device and a computer-readable medium. Background Art

[0002] With the rapid development of 5G technology, the demand for high-speed, low-latency communications is growing, especially in application scenarios such as intelligent transportation, industrial automation, and telemedicine, which place higher demands on the stability and efficiency of signal transmission. Existing signal splitting technologies mostly use fixed algorithms to process and transmit signals, which cannot effectively cope with the interference caused by environmental changes and the diversity of signal characteristics. In traditional splitting methods, signals are usually simply divided into multiple sub-signals, which has obvious deficiencies in spectrum utilization and processing efficiency. Summary of the invention

[0003] The embodiments of the present invention provide a 5G signal processing method, device, electronic device and computer-readable storage medium to solve the problem of improving the transmission efficiency of 5G signals.

[0004] An embodiment of the present invention discloses a method for processing a 5G signal, the method comprising:

[0005] When the device is connected to a 5G network, receive 5G signal status messages;

[0006] Inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model;

[0007] Based on the beam splitting parameters, the 5G signal is divided into sub-signal streams of at least one frequency band;

[0008] assigning a weight to the at least one frequency band based on the beam splitting parameter;

[0009] Based on the weights of the frequency bands, the sub-signal streams of the at least one frequency band are integrated into a complete signal stream and sent to a downstream device.

[0010] Optionally, the step of receiving a 5G signal status message when the device accesses a 5G network includes:

[0011] When the device is connected to a 5G network, at least one of signal strength, signal spectrum distribution, noise level, and signal delay is received as a 5G signal status message.

[0012] Optionally, the step of inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model includes:

[0013] At least one of the signal strength, the signal spectrum distribution, the noise level, and the signal delay is input into a preset neural network model to obtain the beam splitting parameters output by the neural network model.

[0014] Optionally, the beam splitting parameters include a spectrum division strategy;

[0015] The step of dividing the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameter includes:

[0016] Based on the spectrum division strategy, the 5G signal is decomposed and processed from the time domain and the frequency domain to obtain a sub-signal stream of at least one frequency band.

[0017] Optionally, the step of assigning a weight to the at least one frequency band based on the beam splitting parameter comprises:

[0018] Extracting frequency characteristics and time domain variation trends in the sub-signal stream of the at least one frequency band;

[0019] A weight is allocated to the at least one frequency band based on the beam splitting parameters, the frequency characteristics in the sub-signal stream of the at least one frequency band, and the time domain variation trend.

[0020] Optionally, the step of extracting the frequency characteristics and time domain variation trend in the sub-signal stream of the at least one frequency band includes:

[0021] At least one computing thread is used to respectively extract the frequency characteristics and the time domain variation trend in the sub-signal stream of the at least one frequency band.

[0022] Optionally, the method further comprises:

[0023] Detect at least one of the signal strength, signal spectrum distribution, noise level, and signal delay of the output complete signal stream, and adjust the model parameters in the neural network model.

[0024] An embodiment of the present invention further provides a 5G signal processing device, the device comprising:

[0025] A status receiving module, used to receive 5G signal status messages when the device is connected to a 5G network;

[0026] A beam splitting parameter determination module, used to input the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model;

[0027] A signal division module, used to divide the 5G signal into sub-signal streams of at least one frequency band based on the beam splitting parameters;

[0028] A weight allocation module, configured to allocate a weight to the at least one frequency band based on the beam splitting parameter;

[0029] The signal sending module is used to integrate the sub-signal streams of the at least one frequency band into a complete signal stream based on the weight of the frequency band and send it to the downstream device.

[0030] Optionally, the status receiving module includes:

[0031] The status message acquisition submodule is used to receive at least one of signal strength, signal spectrum distribution, noise level, and signal delay as a 5G signal status message when the device is connected to the 5G network.

[0032] Optionally, the beam splitting parameter determination module includes:

[0033] The beam splitting parameter acquisition submodule is used to input at least one of the signal strength, the signal spectrum distribution, the noise level, and the signal delay into a preset neural network model to obtain the beam splitting parameters output by the neural network model.

[0034] Optionally, the beam splitting parameters include a spectrum division strategy;

[0035] The signal division module comprises:

[0036] The sub-signal stream acquisition sub-module is used to decompose the 5G signal from the time domain and the frequency domain based on the spectrum division strategy to obtain a sub-signal stream of at least one frequency band.

[0037] Optionally, the weight allocation module includes:

[0038] An information extraction submodule, used to extract the frequency characteristics and time domain variation trend in the sub-signal stream of the at least one frequency band;

[0039] The frequency band weight allocation submodule is used to allocate a weight to the at least one frequency band based on the beam splitting parameters, the frequency characteristics in the sub-signal stream of the at least one frequency band, and the time domain variation trend.

[0040] Optionally, the information extraction submodule includes:

[0041] The thread extraction unit is used to use at least one computing thread to respectively extract the frequency characteristics and the time domain variation trend in the sub-signal stream of the at least one frequency band.

[0042] Optionally, the device further comprises:

[0043] The model parameter adjustment module is used to detect at least one of the information of signal strength, signal spectrum distribution, noise level and signal delay of the output complete signal flow, and adjust the model parameters in the neural network model.

[0044] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0045] The memory is used to store computer programs;

[0046] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0047] The embodiment of the present invention further discloses one or more computer-readable media on which instructions are stored. When executed by one or more processors, the processors are enabled to execute the method described in the embodiment of the present invention.

[0048] The embodiments of the present invention include the following advantages:

[0049] The 5G signal processing method provided by the embodiment of the present invention receives a 5G signal status message when the device is connected to the 5G network; inputs the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model; divides the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameters; assigns weights to at least one frequency band based on the beam splitting parameters; and integrates the sub-signal streams of at least one frequency band into a complete signal stream based on the weights of the frequency bands and sends them to downstream devices. In this way, the 5G signal can be beam split more accurately, improving the efficiency of 5G signal transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flowchart of a method for processing a 5G signal provided in an embodiment of the present invention;

[0051] Figure 2 is a structural block diagram of a 5G signal processing device provided in an embodiment of the present invention;

[0052] Figure 3 is a block diagram of an electronic device provided in an embodiment of the present invention;

[0053] Figure 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] Reference Figure 1 , shows a flowchart of a 5G signal processing method provided in an embodiment of the present invention, which may specifically include the following steps:

[0056] Step 101, when the device is connected to a 5G network, receiving a 5G signal status message;

[0057] It should be noted that the device may be an upstream device, and an upstream device (Upstream Device) may refer to a device located upstream of a data transmission path in a communication network. It is usually responsible for sending data to a downstream device (such as a user device, a router, or a switch) and performing data processing, encoding, modulation, and transmission. The upstream device may be various types of network equipment, servers, or intermediate nodes. In an embodiment of the present invention, the 5G signal may be processed when the device is connected to a 5G network.

[0058] 5G signals may refer to radio signals used by the fifth generation of mobile communication technology (5G). These signals are transmitted via radio waves between base stations and user equipment to achieve high-speed, low-latency communication. In an embodiment of the present invention, status information of 5G signals may be received.

[0059] 5G signal status messages may refer to various messages and indications used to report and monitor the status of wireless signals in a 5G network. These messages can enable network operators and users to understand the current network connection quality, signal strength, data transmission status, and other information. In an embodiment of the present invention, a 5G signal status message may be received when a device is connected to a 5G network.

[0060] In an embodiment of the present invention, a 5G signal status message can be received when the device accesses a 5G network.

[0061] In a specific implementation, when a device first connects to a 5G network, it can first obtain signal status information through a specific protocol. This information can be used to initialize the beam splitting parameters to ensure optimal signal processing performance in a dynamic environment.

[0062] The specific protocol may be an RRC (Radio Resource Control, control plane) protocol, and the RRC protocol may obtain and report signal status information by sending and receiving RRC messages.

[0063] The specific protocol may also be a NAS (Non-Access Stratum) protocol, which is located between the user equipment and the core network and is responsible for managing user sessions and mobility. Signal status information may be obtained and reported by sending and receiving NAS messages.

[0064] The specific protocol may also be other protocols that can be used to obtain 5G signal status messages, and the present invention is not limited to this.

[0065] Step 102, inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model;

[0066] It should be noted that the beamforming parameters are parameters that need to be referenced when dividing the received 5G signal into multiple sub-signals. The 5G signal status message can be input into the preset neural network model to determine the beamforming parameters output by the neural network model.

[0067] The beam splitting parameters may include at least one of beam splitting direction, power allocation and spectrum division strategy.

[0068] In a specific implementation, an adaptive beam splitting algorithm can be used to dynamically adjust the beam splitting parameters through deep learning and optimization algorithms. First, the signal receiving module collects data such as 5G signal status messages in real time and inputs them into the constructed neural network model. The neural network model can adjust the beam splitting direction, power allocation, and spectrum division strategy in real time by analyzing historical data and current environmental characteristics. In an embodiment of the present invention, the neural network model can predict the channel state information (CSI) through a gradient descent optimization algorithm, and combine reinforcement learning technology to quickly iterate the optimal beam splitting scheme, thereby realizing adaptive adjustment of signal beam splitting in a dynamic environment and improving transmission efficiency.

[0069] Among them, Gradient Descent can be an optimization algorithm, which is applied in the fields of machine learning and deep learning and can be used to minimize the loss function or cost function. The gradient descent algorithm can gradually approach the optimal solution by iteratively updating the model parameters.

[0070] The present invention uses an adaptive beam splitting algorithm based on deep learning and intelligent optimization technology. By constructing a neural network model, the received signal characteristics are analyzed in real time. First, the deep learning model will learn the changing characteristics of the network environment based on historical transmission data, and improve the accuracy of signal beam splitting by continuously optimizing model parameters. During operation, the algorithm dynamically adjusts the direction, intensity and spectrum allocation strategy of the signal beam splitting according to the signal characteristics of the real-time input to adapt to different transmission environments. This adaptability ensures that the signal can maintain stable transmission in high-density or high-speed mobile scenarios, effectively improving the transmission efficiency and reliability of the system.

[0071] Step 103: divide the 5G signal into sub-signal streams of at least one frequency band based on the beam splitting parameters;

[0072] In an embodiment of the present invention, the 5G signal may be divided into sub-signal streams of at least one frequency band based on the beam splitting parameters. Thus, the 5G signal may be segmented.

[0073] The frequency band refers to the frequency range of radio waves such as 5G signals. Different frequency bands have different characteristics and application scenarios and are applicable to various wireless communications and broadcasting systems. In an embodiment of the present invention, the sub-signal streams may be in different frequency bands.

[0074] In an embodiment of the present invention, the beam splitting parameters may include parameters for indicating the sub-signal stream segmentation method. By segmenting the 5G signal based on the beam splitting parameters, the beam splitting method of the 5G signal can be more accurate, and the 5G signal as a whole can be more inclined to use an undisturbed frequency band for signal transmission, thereby improving the transmission efficiency of the 5G signal.

[0075] Step 104, assigning a weight to the at least one frequency band based on the beam splitting parameter;

[0076] In the specific implementation, weight allocation can be performed based on the beam splitting parameters. The 5G signal as a whole is more inclined to use the frequency band that has not been interfered with for signal transmission. For the frequency band that may be interfered with, the signal transmission power can be increased, thereby improving the transmission efficiency of the 5G signal.

[0077] Step 105: Based on the weights of the frequency bands, the sub-signal streams of the at least one frequency band are integrated into a complete signal stream and sent to a downstream device.

[0078] It should be noted that downstream devices can be devices located downstream of the data transmission path in a communication network. These devices usually receive data from upstream devices (such as base stations, routers or servers) and process or forward it. Downstream devices can be various types of terminal devices, network devices or intermediate nodes.

[0079] In an embodiment of the present invention, based on the weight of the frequency band, the sub-signal streams of at least one frequency band can be integrated into a complete signal stream and sent to the downstream device. In this way, the 5G signal as a whole is more inclined to use the frequency band that has not been interfered with for signal transmission. For the frequency band that may be interfered with, the signal transmission power can be increased, thereby improving the transmission efficiency of the 5G signal.

[0080] In a specific implementation, during the decomposition of the sub-signal stream, the sub-signal stream can be decomposed in the time domain and frequency domain based on short-time Fourier transform or wavelet transform. When it is necessary to continue to send the sub-signal stream, the sub-signal can be reintegrated into a complete signal stream and transmitted to the downstream device through an optimized encoding method. The system can use a signal quality monitoring module to analyze the bit error rate, power intensity and delay of the output signal in real time, and input the feedback data into the adaptive optimization module for dynamic adjustment. For example, by adjusting the beam splitting parameters or reallocating the spectrum weights in real time, it is ensured that the signal stability and high transmission rate can be maintained in a high interference environment, thereby forming a closed-loop optimization mechanism.

[0081] The 5G signal processing method provided by the embodiment of the present invention receives a 5G signal status message when the device is connected to the 5G network; inputs the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model; divides the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameters; assigns weights to at least one frequency band based on the beam splitting parameters; and integrates the sub-signal streams of at least one frequency band into a complete signal stream based on the weights of the frequency bands and sends them to downstream devices. In this way, the 5G signal can be beam split more accurately, improving the efficiency of 5G signal transmission.

[0082] In one embodiment of the present invention, the step of receiving a 5G signal status message when the device accesses a 5G network includes:

[0083] S11, when the device is connected to a 5G network, at least one of receiving signal strength, signal spectrum distribution, noise level, and signal delay is received as a 5G signal status message.

[0084] It should be noted that the received signal strength (RSRP) is one of the key indicators for measuring the strength of 5G signals. It can represent the power level of the received reference signal in dBm. A higher RSRP value indicates a stronger signal, and a lower RSRP value indicates a weaker signal. The device can obtain the RSRP value through the reference signal received power measurement function of the physical layer (L1). In an embodiment of the present invention, the RSRP value can be sent to the network operator or user equipment as part of the 5G signal status message.

[0085] Signal spectrum distribution may refer to the distribution of the signal in the frequency domain, including spectrum occupancy and spectrum efficiency. The device may obtain signal spectrum distribution information through the spectrum analysis function of the physical layer (L1). In an embodiment of the present invention, the signal spectrum distribution information may be sent to a network operator or user equipment as part of a 5G signal status message.

[0086] The noise level may refer to the noise component in the received signal, measured in dBm. A higher noise level indicates a poor signal quality, and a lower noise level indicates a better signal quality. The device may obtain the noise level information through the noise measurement function of the physical layer (L1). In an embodiment of the present invention, the noise level information may be sent to a network operator or user equipment as part of a 5G signal status message.

[0087] Signal delay may refer to the transmission time of a signal from a transmitting end to a receiving end, measured in milliseconds (ms). A lower signal delay indicates a faster signal transmission speed, and a higher signal delay indicates a slower signal transmission speed. The device may obtain signal delay information through delay measurement functions of the physical layer (L1) and the data link layer (L2). In an embodiment of the present invention, the signal delay information may be sent to a network operator or user equipment as part of a 5G signal status message.

[0088] In an embodiment of the present invention, when the device accesses a 5G network, at least one of signal strength, signal spectrum distribution, noise level, and signal delay can be received as a 5G signal status message.

[0089] In one embodiment of the present invention, the step of inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model includes:

[0090] S21, input at least one of the signal strength, the signal spectrum distribution, the noise level, and the signal delay into a preset neural network model to obtain the beam splitting parameters output by the neural network model.

[0091] In an embodiment of the present invention, at least one of signal strength, signal spectrum distribution, noise level, and signal delay may be input into a preset neural network model to obtain the beam splitting parameters output by the neural network model.

[0092] In one embodiment of the present invention, the beam splitting parameters include a spectrum partitioning strategy;

[0093] It should be noted that the spectrum division strategy may be a division method of 5G signals to optimize spectrum utilization efficiency and improve system performance. In an embodiment of the present invention, the 5G signal may be decomposed according to the spectrum division strategy.

[0094] The step of dividing the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameter includes:

[0095] S31, based on the spectrum division strategy, decompose the 5G signal from the time domain and the frequency domain to obtain a sub-signal stream of at least one frequency band.

[0096] It should be noted that the time domain may refer to the representation of a signal on the time axis. In the time domain, the characteristics of a signal (such as amplitude, phase, waveform, etc.) change over time. Time domain analysis mainly focuses on the changes and fluctuations of a signal over time. In an embodiment of the present invention, the 5G signal may be decomposed from the time domain.

[0097] Frequency domain refers to the representation of a signal on the frequency axis. In the frequency domain, the characteristics of a signal (such as frequency components, amplitude spectrum, phase spectrum, etc.) vary with frequency. Frequency domain analysis mainly focuses on the frequency components and spectral characteristics of a signal. In an embodiment of the present invention, the 5G signal can also be decomposed from the frequency domain.

[0098] In an embodiment of the present invention, based on a spectrum division strategy, the 5G signal can be decomposed and processed from the time domain and the frequency domain to obtain a sub-signal stream of at least one frequency band.

[0099] In the specific implementation, advanced signal processing technologies such as short-time Fourier transform (STFT) and wavelet transform can be used to decompose and process the received 5G signal in both the time domain and the frequency domain. Specifically, the signal is first split into short-time segments using a window function to form sub-signal streams in multiple time windows. Subsequently, the wavelet transform can be used to perform multi-resolution analysis on each sub-signal to extract the local frequency characteristics and time domain change trends of the signal.

[0100] Among them, short-time Fourier transform is a method to decompose a signal into time and frequency components. By sliding a window in time and performing Fourier transform on the signal in each window, the distribution of the signal in time and frequency can be obtained.

[0101] Wavelet transform is a method to decompose a signal into wavelet components of different scales and positions. Multi-resolution analysis of the signal can be performed by sliding the wavelet function in time and scale.

[0102] The present invention can analyze and decompose 5G signals from both the time domain and the frequency domain through advanced signal processing methods such as short-time Fourier transform (STFT) and wavelet transform. During the time-frequency analysis process, the signal can be decomposed into multiple sub-signal streams in different frequency bands, and each sub-signal stream is processed independently within a specific frequency and time window. It can effectively capture the instantaneous change characteristics of the signal and identify potential interference sources and noise distribution. The refined processing of time-frequency analysis can improve the accuracy of signal splitting, make more efficient use of spectrum resources, and ensure that the data transmission rate and signal stability in different application scenarios are optimized.

[0103] In one embodiment of the present invention, the step of assigning a weight to the at least one frequency band based on the beam splitting parameter comprises:

[0104] S41, extracting frequency characteristics and time domain variation trends in the sub-signal stream of the at least one frequency band;

[0105] It should be noted that frequency characteristics refer to the distribution and characteristics of a signal on the frequency axis, including the frequency components, amplitude spectrum, phase spectrum, etc. of the signal. Frequency characteristic analysis mainly focuses on the frequency components and spectrum characteristics of the signal. In a specific implementation, the frequency characteristics of a sub-signal stream of at least one frequency band can be obtained.

[0106] The time domain variation trend refers to the variation and fluctuation of the signal on the time axis, including the waveform, amplitude, period, frequency, phase, etc. of the signal. The time domain analysis mainly focuses on the variation and fluctuation of the signal over time. In an embodiment of the present invention, the time domain variation trend in the sub-signal stream of at least one frequency band can be obtained.

[0107] In the embodiment of the present invention, the frequency characteristics and the time domain variation trend in the sub-signal stream of at least one frequency band may be extracted;

[0108] S42: Allocate a weight to the at least one frequency band based on the beam splitting parameter, the frequency characteristics in the sub-signal stream of the at least one frequency band, and the time domain variation trend.

[0109] In a specific implementation, the beam splitting parameters may include information such as beam splitting direction, beam splitting strength, and spectrum allocation. At the same time, the sub-signal streams may be analyzed to extract the frequency characteristics and time domain variation trends therein.

[0110] Afterwards, a weight can be assigned to at least one frequency band by considering the beam splitting parameters, the frequency characteristics in the sub-signal stream of at least one frequency band, and the time domain change trend. Thus, a higher weight can be assigned to a frequency band with higher beam splitting strength, better frequency response, wider bandwidth, higher signal-to-noise ratio, stable amplitude change, smaller phase change, and shorter time delay, so that the 5G signal can tend to use a better frequency band for signal transmission as a whole, thereby improving transmission efficiency. A higher weight can also be assigned to a frequency band with lower beam splitting strength, lower frequency response, lower bandwidth, lower signal-to-noise ratio, unstable amplitude change, larger phase change, and longer time delay, so that the 5G signal can be better transmitted on the interfered frequency band, thereby improving transmission stability.

[0111] In a specific implementation, weights can be dynamically assigned to the decomposed frequency bands to avoid wasting resources in high-interference frequency bands, thereby achieving accurate decomposition of the signal and maximizing spectrum utilization.

[0112] In one embodiment of the present invention, the step of extracting the frequency characteristics and the time domain variation trend in the sub-signal stream of the at least one frequency band includes:

[0113] S51, using at least one computing thread to respectively extract frequency characteristics and time domain variation trends in the sub-signal stream of the at least one frequency band.

[0114] It should be noted that a thread is a basic unit for executing program code in an operating system. A process can contain multiple threads, which share the resources of the process (such as memory, file handles, etc.), but each thread has its own execution path and stack space. The concept and use of threads are very important in multitasking, concurrent programming, and performance optimization. In an embodiment of the present invention, the frequency characteristics and time domain variation trends in the sub-signal stream can be extracted through multiple threads.

[0115] In the embodiment of the present invention, at least one computing thread may be used to respectively extract the frequency characteristics and the time domain variation trend in the sub-signal stream of at least one frequency band.

[0116] In the specific implementation, an efficient parallel processing architecture can be built based on multi-threading and distributed computing technologies. Each received sub-signal stream is assigned to an independent thread in a multi-core processor according to priority, and the signal processing tasks are run in parallel. Task scheduling algorithms, such as dynamic load balancing, can be used to reasonably allocate computing resources to prevent overloading of certain threads and cause performance bottlenecks. In addition, through a distributed computing architecture, high-bandwidth signals can be processed in parallel across multiple nodes, significantly improving overall processing efficiency while ensuring low latency and high throughput.

[0117] Among them, a multi-core processor can refer to a processor that integrates multiple processing cores on a single chip. Each core can execute instructions independently, thereby achieving parallel processing and improving computing power and system performance. A multi-core processor can address the bottleneck of single-core processors in performance improvement and improve overall processing power through parallel computing.

[0118] Task Scheduling Algorithm can refer to the algorithm used in the operating system to manage and allocate processor time to different tasks. Task scheduling algorithms can optimize system performance, improve resource utilization, ensure fairness and response time. Here are some task scheduling algorithms and their detailed explanations:

[0119] First-Come, First-Served (FCFS) can schedule tasks according to the order in which they arrive. The first tasks to arrive will be executed first. It is simple to implement and highly fair.

[0120] Shortest Job First (SJF) allows you to select the task with the shortest execution time first, with the shortest average waiting time and shorter response time.

[0121] Priority Scheduling: Each task can be assigned a priority, and tasks with higher priorities will be executed first. Tasks can be scheduled based on their importance and urgency.

[0122] Round Robin (RR) can allocate a fixed-length time slice to each task in sequence, and switch to the next task after the time slice is used up. It has high fairness and short response time, and is suitable for interactive systems.

[0123] Multi-Level Feedback Queue (MLFQ) can arrange multiple queues by priority, move tasks between different queues, and adjust the priority according to the execution status of tasks. It can combine the advantages of priority scheduling and time slice rotation and has strong adaptability.

[0124] Shortest Remaining Time First (SRTF) can select the task with the shortest remaining execution time first, which is suitable for preemptive scheduling. The average waiting time is the shortest and the response time is short.

[0125] Highest Response Ratio Next (HRRN) can select the task with the highest response ratio to execute first. Response ratio = (waiting time + execution time) / execution time. This balances the waiting time and execution time.

[0126] Fair Share Scheduling can allocate a certain amount of processor time to each user or group to ensure that each user or group gets a fair share of resources. It has high fairness and is suitable for multi-user environments.

[0127] Real-Time Scheduling can ensure that tasks are completed within the deadline and is suitable for real-time systems. It meets real-time requirements and is suitable for real-time applications.

[0128] Load Balancing Scheduling can evenly distribute tasks to multiple processors or computing nodes to optimize system performance and resource utilization. It can improve system performance and resource utilization and is suitable for distributed systems.

[0129] Distributed computing architecture can refer to distributing computing tasks on multiple independent computing nodes, communicating and collaborating through the network to jointly complete complex computing tasks. Distributed computing architecture can improve the scalability, fault tolerance and performance of the system.

[0130] The present invention can efficiently process multiple sub-signal streams by setting up a parallel processing architecture based on multi-threaded computing and distributed computing technology. In 5G communication, each sub-signal stream needs to be processed in a very short time to ensure low latency and high efficiency. Through multi-threading technology, each sub-signal stream can run in parallel in different processing units or cores, greatly improving the signal processing speed. At the same time, the distributed computing architecture can coordinate processing tasks on multiple computing nodes, optimize system resource utilization, and avoid the bottleneck problem of a single computing unit. The parallel processing architecture not only shortens the signal processing time, but also ensures the real-time response performance of the entire system, meeting the strict requirements of the 5G network for low latency and high throughput.

[0131] In one embodiment of the present invention, the method further comprises:

[0132] S61, detecting at least one of the signal strength, signal spectrum distribution, noise level, and signal delay of the output complete signal stream, and adjusting the model parameters in the neural network model.

[0133] It should be noted that the model parameters in the neural network model may refer to the parameters that need to be learned and optimized during the training process. These parameters determine the structure and function of the neural network. The optimization of model parameters is the core task of neural network training. By optimizing the model parameters, the neural network can learn the mapping relationship between input data and output labels. In an embodiment of the present invention, the optimization of the neural network model can be achieved by adjusting the model parameters. The model parameters may be one or more of the following parameters:

[0134] Weights are parameters that connect neurons in a neural network and can be used to calculate the weighted sum of input signals. Weights determine the degree of influence of input signals on neuron outputs.

[0135] Biases are additional parameters for each neuron in a neural network that can be used to adjust the activation threshold of the neuron. The bias determines the activation state of the neuron when there is no input signal.

[0136] Activation function is a nonlinear transformation function of each neuron in a neural network. It can be used to introduce nonlinear characteristics and enhance the expressiveness of the neural network. Common activation functions include Sigmoid, Tanh, ReLU, Leaky ReLU, etc.

[0137] The loss function is a function used to measure the difference between the output of a neural network and the true label, and can be used to guide the optimization of model parameters. Common loss functions include mean square error (MSE), cross-entropy loss, etc.

[0138] Optimizer is an algorithm used to update model parameters. It can optimize model parameters by minimizing the loss function. Optimizers can include Gradient Descent, Stochastic Gradient Descent (SGD), Adam, RMSprop, etc.

[0139] In the embodiment of the present invention, at least one of the information of signal strength, signal spectrum distribution, noise level, and signal delay of the output complete signal stream can be detected to adjust the model parameters in the neural network model.

[0140] In the specific implementation, during the signal transmission process, the environment and network status can be continuously monitored, including channel quality, user mobility and interference level. By introducing intelligent optimization algorithms, such as the multi-objective optimization model based on genetic algorithms, the system can dynamically optimize the beam splitting parameters and resource allocation strategies under multiple constraints. At the same time, the adaptive model can be trained using historical transmission data to continuously improve the prediction ability and response speed of complex channel environments, thereby achieving long-term stability and performance improvement of signal transmission.

[0141] Channel quality refers to the degree of interference and attenuation of a signal during transmission. The quality of a channel directly affects the transmission and reception quality of a signal. Channel quality can be evaluated by measuring indicators such as the signal-to-noise ratio (SNR), bit error rate (BER), and signal strength (RSSI).

[0142] User mobility refers to the change in the location of user equipment in the network. User mobility affects the signal transmission path and channel quality. Positioning technology (such as GPS, base station positioning) and mobility prediction algorithms can be used to monitor user location changes and movement speed in real time.

[0143] Interference level refers to the degree of interference caused by other signals during the transmission of a signal. The interference level directly affects the transmission effect and reception quality of the signal. The interference level can be evaluated by measuring indicators such as the signal's interference to noise ratio (INR) and interference power.

[0144] Intelligent optimization algorithm can refer to an algorithm that finds the optimal solution by simulating the optimization process in nature (such as genetic algorithm, particle swarm optimization, ant colony algorithm, etc.). Intelligent optimization algorithm can be applied to complex multi-objective optimization problems. In the process of signal transmission, intelligent optimization algorithm can be used to dynamically optimize beam splitting parameters and resource allocation strategies to improve signal transmission performance and stability.

[0145] Genetic Algorithm (GA) can be an optimization algorithm that simulates natural selection and genetic mechanisms. Through operations such as selection, crossover and mutation, genetic algorithms can find the optimal solution in complex multi-objective optimization problems. In the process of signal transmission, genetic algorithms can be used to optimize beam splitting parameters and resource allocation strategies to improve signal transmission performance and stability.

[0146] A multi-objective optimization model can refer to an optimization model that considers multiple objective functions and constraints simultaneously during the optimization process. A multi-objective optimization model can find a balanced solution between multiple objectives. During signal transmission, a multi-objective optimization model can be used to simultaneously optimize multiple objectives such as channel quality, user mobility, and interference level to improve signal transmission performance and stability.

[0147] An adaptive model can refer to a model that dynamically adjusts model parameters based on environmental changes and historical data. An adaptive model can improve the model's adaptability to complex environments and prediction accuracy. During signal transmission, an adaptive model can be used to dynamically adjust beam splitting parameters and resource allocation strategies to improve signal transmission performance and stability.

[0148] Historical transmission data refers to historical data recorded during the signal transmission process, including channel quality, user mobility, interference level, etc. Historical transmission data can be used to train adaptive models and optimize algorithms. During the signal transmission process, historical transmission data can be used to train adaptive models to improve the model's prediction ability and response speed to complex channel environments.

[0149] A complex channel environment refers to an environment where the signal transmission process is affected by multiple interference and attenuation factors. A complex channel environment will affect the transmission effect and reception quality of the signal. Through intelligent optimization algorithms and adaptive models, the beam splitting parameters and resource allocation strategies can be dynamically optimized to improve signal transmission performance and stability.

[0150] Through the above steps, the present invention can effectively improve the beam splitting processing capability of 5G signals, reduce latency, and improve spectrum utilization. The present invention can not only optimize the performance of 5G networks, but also provide users with a more efficient and stable communication experience.

[0151] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0152] Reference Figure 2, shows a structural block diagram of a 5G signal processing device provided in an embodiment of the present invention, which may specifically include the following modules:

[0153] The status receiving module 201 is used to receive a 5G signal status message when the device is connected to a 5G network;

[0154] A beam splitting parameter determination module 202, used to input the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model;

[0155] A signal division module 203, configured to divide the 5G signal into sub-signal streams of at least one frequency band based on the beam splitting parameters;

[0156] A weight allocation module 204, configured to allocate a weight to the at least one frequency band based on the beam splitting parameter;

[0157] The signal sending module 205 is configured to integrate the sub-signal streams of the at least one frequency band into a complete signal stream based on the weights of the frequency bands and send the complete signal stream to a downstream device.

[0158] Optionally, the status receiving module includes:

[0159] The status message acquisition submodule is used to receive at least one of signal strength, signal spectrum distribution, noise level, and signal delay as a 5G signal status message when the device is connected to the 5G network.

[0160] Optionally, the beam splitting parameter determination module includes:

[0161] The beam splitting parameter acquisition submodule is used to input at least one of the signal strength, the signal spectrum distribution, the noise level, and the signal delay into a preset neural network model to obtain the beam splitting parameters output by the neural network model.

[0162] Optionally, the beam splitting parameters include a spectrum division strategy;

[0163] The signal division module comprises:

[0164] The sub-signal stream acquisition sub-module is used to decompose the 5G signal from the time domain and the frequency domain based on the spectrum division strategy to obtain a sub-signal stream of at least one frequency band.

[0165] Optionally, the weight allocation module includes:

[0166] An information extraction submodule, used to extract the frequency characteristics and time domain variation trend in the sub-signal stream of the at least one frequency band;

[0167] The frequency band weight allocation submodule is used to allocate a weight to the at least one frequency band based on the beam splitting parameters, the frequency characteristics in the sub-signal stream of the at least one frequency band, and the time domain variation trend.

[0168] Optionally, the information extraction submodule includes:

[0169] The thread extraction unit is used to use at least one computing thread to respectively extract the frequency characteristics and the time domain variation trend in the sub-signal stream of the at least one frequency band.

[0170] Optionally, the device further comprises:

[0171] The model parameter adjustment module is used to detect at least one of the signal strength, signal spectrum distribution, noise level and signal delay of the output complete signal flow, and adjust the model parameters in the neural network model.

[0172] The 5G signal processing device provided by the embodiment of the present invention receives a 5G signal status message when the device is connected to the 5G network; inputs the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model; divides the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameters; assigns a weight to at least one frequency band based on the beam splitting parameters; and integrates the sub-signal streams of at least one frequency band into a complete signal stream based on the weight of the frequency band and sends it to the downstream device. In this way, the 5G signal can be beam split more accurately, and the efficiency of 5G signal transmission can be improved.

[0173] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0174] In addition, an embodiment of the present invention further provides an electronic device, such as Figure 3 As shown, it includes a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304.

[0175] Memory 303, used for storing computer programs;

[0176] The processor 301 is used to execute the program stored in the memory 303 to implement the following steps:

[0177] When the device is connected to a 5G network, receive 5G signal status messages;

[0178] Inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model;

[0179] Based on the beam splitting parameters, the 5G signal is divided into sub-signal streams of at least one frequency band;

[0180] assigning a weight to the at least one frequency band based on the beam splitting parameter;

[0181] Based on the weights of the frequency bands, the sub-signal streams of the at least one frequency band are integrated into a complete signal stream and sent to a downstream device.

[0182] Optionally, the step of receiving a 5G signal status message when the device accesses a 5G network includes:

[0183] When the device is connected to a 5G network, at least one of signal strength, signal spectrum distribution, noise level, and signal delay is received as a 5G signal status message.

[0184] Optionally, the step of inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model includes:

[0185] At least one of the signal strength, the signal spectrum distribution, the noise level, and the signal delay is input into a preset neural network model to obtain the beam splitting parameters output by the neural network model.

[0186] Optionally, the beam splitting parameters include a spectrum division strategy;

[0187] The step of dividing the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameter includes:

[0188] Based on the spectrum division strategy, the 5G signal is decomposed and processed from the time domain and the frequency domain to obtain a sub-signal stream of at least one frequency band.

[0189] Optionally, the step of assigning a weight to the at least one frequency band based on the beam splitting parameter comprises:

[0190] Extracting frequency characteristics and time domain variation trends in the sub-signal stream of the at least one frequency band;

[0191] A weight is allocated to the at least one frequency band based on the beam splitting parameters, the frequency characteristics in the sub-signal stream of the at least one frequency band, and the time domain variation trend.

[0192] Optionally, the step of extracting the frequency characteristics and time domain variation trend in the sub-signal stream of the at least one frequency band includes:

[0193] At least one computing thread is used to respectively extract the frequency characteristics and the time domain variation trend in the sub-signal stream of the at least one frequency band.

[0194] Optionally, the method further comprises:

[0195] Detect at least one of the signal strength, signal spectrum distribution, noise level, and signal delay of the output complete signal stream, and adjust the model parameters in the neural network model.

[0196] The electronic device provided by the embodiment of the present invention receives a 5G signal status message when the device is connected to a 5G network; inputs the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model; divides the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameters; assigns a weight to at least one frequency band based on the beam splitting parameters; and integrates the sub-signal streams of at least one frequency band into a complete signal stream based on the weight of the frequency band and sends it to a downstream device. In this way, the 5G signal can be beam split more accurately, thereby improving the efficiency of 5G signal transmission.

[0197] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0198] The communication interface is used for communication between the above terminal and other devices.

[0199] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0200] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0201] like Figure 4 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 401 is also provided, in which instructions are stored. When the computer-readable storage medium 401 is run on a computer, the computer executes the 5G signal processing method described in the above embodiment.

[0202] In another embodiment of the present invention, a computer program product comprising instructions is also provided. When the computer program product is run on a computer, the computer executes the 5G signal processing method described in the above embodiment.

[0203] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0204] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0205] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0206] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for processing 5G signals, characterized in that: include: When the device is connected to a 5G network, receive 5G signal status messages; Inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model; Based on the beam splitting parameters, the 5G signal is divided into sub-signal streams of at least one frequency band; assigning a weight to the at least one frequency band based on the beam splitting parameter; Based on the weights of the frequency bands, the sub-signal streams of the at least one frequency band are integrated into a complete signal stream and sent to a downstream device.

2. The method according to claim 1, characterized in that The step of receiving a 5G signal status message when the device accesses a 5G network includes: When the device is connected to a 5G network, at least one of signal strength, signal spectrum distribution, noise level, and signal delay is received as a 5G signal status message.

3. The method according to claim 2, characterized in that The step of inputting the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model includes: At least one of the signal strength, the signal spectrum distribution, the noise level, and the signal delay is input into a preset neural network model to obtain the beam splitting parameters output by the neural network model.

4. The method according to claim 1, characterized in that The beam splitting parameters include spectrum partitioning strategy; The step of dividing the 5G signal into at least one sub-signal stream of a frequency band based on the beam splitting parameter includes: Based on the spectrum division strategy, the 5G signal is decomposed and processed from the time domain and the frequency domain to obtain a sub-signal stream of at least one frequency band.

5. The method according to claim 1, characterized in that The step of assigning a weight to the at least one frequency band based on the beam splitting parameter comprises: Extracting frequency characteristics and time domain variation trends in the sub-signal stream of the at least one frequency band; A weight is allocated to the at least one frequency band based on the beam splitting parameters, the frequency characteristics in the sub-signal stream of the at least one frequency band, and the time domain variation trend.

6. The method according to claim 5, characterized in that The step of extracting the frequency characteristics and time domain variation trend in the sub-signal stream of the at least one frequency band comprises: At least one computing thread is used to respectively extract the frequency characteristics and the time domain variation trend in the sub-signal stream of the at least one frequency band.

7. The method according to claim 1, characterized in that The method further comprises: Detect at least one of the signal strength, signal spectrum distribution, noise level, and signal delay of the output complete signal stream, and adjust the model parameters in the neural network model.

8. A 5G signal processing device, characterized in that: include: A status receiving module, used to receive 5G signal status messages when the device is connected to a 5G network; A beam splitting parameter determination module, used to input the 5G signal status message into a preset neural network model to determine the beam splitting parameters output by the neural network model; A signal division module, used to divide the 5G signal into sub-signal streams of at least one frequency band based on the beam splitting parameters; A weight allocation module, configured to allocate a weight to the at least one frequency band based on the beam splitting parameter; The signal sending module is used to integrate the sub-signal streams of the at least one frequency band into a complete signal stream based on the weight of the frequency band and send it to the downstream device.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 7.

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