CN118802425B - Channel estimation optimization method, device, equipment and medium of base station MIMO wireless communication system - Google Patents
Channel estimation optimization method, device, equipment and medium of base station MIMO wireless communication system Download PDFInfo
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Abstract
The application relates to a channel estimation optimization method, a device, equipment and a medium of a base station MIMO wireless communication system, which relate to the field of data processing, wherein the method comprises the steps of obtaining channel lead codes in various communication connection requests; the method comprises the steps of determining pulse release rates of a plurality of neurons corresponding to channel preambles in a preset channel estimation optimization model based on the channel preambles in a communication connection request, carrying out poisson sampling according to the pulse release rates by using poisson distribution in the neurons of the channel estimation optimization model, randomly generating a time interval for next pulse release, recording channel pulse codes of the channel preambles corresponding to the current time point when the accumulated time interval reaches or exceeds a preset time interval threshold, and inputting the channel pulse codes of the channel preambles to a variable self-encoder in the channel estimation optimization model to reconstruct the channel preambles. The application improves the accuracy and efficiency of channel estimation and simultaneously obviously reduces the energy consumption of the MIMO wireless communication system.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a channel estimation optimization method for a base station MIMO wireless communication system, a corresponding apparatus, an electronic device, and a computer readable storage medium.
Background
In a base station MIMO wireless communication system, signals are reflected and refracted by multiple paths from a transmitting end to a receiving end, so that the received signals are superposition of multiple delayed versions, i.e. multipath propagation. With the wide use of high-band spectrum such as millimeter wave in 5G signal systems, signals in these bands are more susceptible to multipath propagation and attenuation, and 5G base stations are typically equipped with a large number of antenna elements (e.g., 64 or more), forming a massive MIMO wireless communication system. With this configuration, the characteristics of each antenna channel may be different, and accurate channel estimation is critical to achieving beamforming, spatial multiplexing, and improving spectral efficiency.
In recent years, artificial Neural Networks (ANNs) have evolved rapidly and have enjoyed great success in the fields of computer vision and NLP. However, artificial neural networks typically require a large amount of computing resources, which is a challenge in situations where computing resources are limited. Traditional neural network models are based on data-driven rather than event-driven. Because the base station user connection process takes place for a short time, and the base station basically works normally before and after connection, the process of connecting the base station to the user for channel estimation takes place. Conventional channel prediction networks tend to consume a significant amount of power for normal operating ineffective computations.
At present, the conventional artificial neural network mainly has the problems that the conventional artificial neural network generally processes continuous value input, has limited expression capability in a time dimension, cannot naturally capture and utilize accurate time information in time sequence data, continuously processes input data even though no obvious information changes exist when the conventional artificial neural network performs calculation, and causes high energy consumption, and the conventional artificial neural network may need additional strategies to process sparse data to avoid overfitting and the like.
In summary, the present inventors have made a corresponding search in consideration of solving the problem that the conventional artificial neural network in the prior art often consumes a large amount of power consumption for ineffective computation of normal operation, and the conventional artificial neural network generally processes continuous value input, and has limited expression capability in a time dimension, and cannot naturally capture and utilize accurate time information in time series data.
Disclosure of Invention
An object of the present application is to solve the above-mentioned problems and provide a channel estimation optimization method for a base station MIMO wireless communication system, a corresponding apparatus, an electronic device, and a computer-readable storage medium.
In order to meet the purposes of the application, the application adopts the following technical scheme:
a channel estimation optimization method of a base station MIMO wireless communication system according to one of the objects of the present application includes:
Acquiring channel preambles in respective communication connection requests in response to the respective communication connection requests from the user equipment;
determining pulse release rates of a plurality of neurons corresponding to channel preambles in a preset channel estimation optimization model based on the channel preambles in the communication connection requests, wherein each communication connection request comprises a channel preamble, and each channel preamble corresponds to a plurality of neurons;
In the neuron of the channel estimation optimization model, poisson sampling is carried out according to the pulse release rate by using poisson distribution, a next pulse release time interval is randomly generated, and when the accumulated time interval reaches or exceeds a preset time interval threshold value, the channel pulse code of the channel preamble corresponding to the current time point is recorded;
And inputting the channel pulse codes of the channel preamble codes to a variable self-encoder in the channel estimation optimization model to reconstruct the channel preamble codes so as to complete the channel estimation optimization of the base station MIMO wireless communication system.
Optionally, the step of determining, based on the channel preamble in the communication connection request, the pulse emission rates of the plurality of neurons corresponding to the channel preamble in the preset channel estimation optimization model includes:
Determining a channel lead code in the communication connection request, and extracting a characteristic value combination containing key information from the channel lead code, wherein the characteristic value combination comprises any multiple items of channel gain, noise level and transmission delay;
Inputting the extracted characteristic value combination into an input layer of a preset channel estimation optimization model, and distributing the input characteristic value combination to a plurality of neurons corresponding to the channel lead codes in the channel estimation optimization model;
Multiplying and summing each characteristic value in the input characteristic value combination with the corresponding weight by adopting an integral pulse issuing function to obtain the membrane potential of each neuron;
and determining the pulse emission rate of each neuron corresponding to the channel preamble according to the membrane potential of each neuron.
Optionally, in the neuron of the channel estimation optimization model, poisson sampling is performed according to the pulse release rate by using poisson distribution, a time interval of next pulse release is randomly generated, and when the accumulated time interval reaches or exceeds a preset time interval threshold, a step of recording channel pulse coding of the channel preamble corresponding to the current time point includes:
determining the pulse release rate of each neuron corresponding to the channel preamble in a preset channel estimation optimization model;
Randomly generating a next pulse issuing time interval according to the pulse issuing rate by adopting a preset Poisson distribution probability density function;
Accumulating the time intervals of each pulse emission to determine an accumulated time interval, detecting whether the accumulated time interval reaches or exceeds a preset time interval threshold, and recording the channel pulse codes of the channel preamble codes corresponding to the current time point if the accumulated time interval reaches or exceeds the preset time interval threshold;
Resetting the accumulated time interval to zero and repeating the above steps.
Optionally, the step of inputting the channel pulse code of the channel preamble to a variable self-encoder in the channel estimation optimization model to reconstruct the channel preamble includes:
Mapping, in an encoder of the variational self-encoder, channel pulse codes of the channel preambles to a potential space and outputting probability distributions of potential variables in the potential space;
Sampling in a decoder of the variational self-encoder from the probability distribution of the latent variable output by the encoder to determine a specific latent variable so as to generate a reconstructed channel preamble;
Calculating and determining a difference value between probability distribution and prior distribution of potential variables in the potential space to determine a KL divergence loss value, and completing reconstruction of the channel preamble when the KL divergence loss value is lower than a preset KL divergence loss value.
Optionally, after the step of mapping the channel pulse code of the channel preamble to a potential space and outputting the probability distribution of the potential variable in the potential space in the encoder of the variation self-encoder, the method includes:
sampling from the probability distribution of the latent variable output by the encoder in the latent space to determine a specific latent variable;
And adding Gaussian noise to the specific latent variable, and inputting the specific latent variable added with the Gaussian noise into a decoder of the variable self-encoder to generate a reconstructed channel preamble.
Optionally, after the step of acquiring the channel preamble in each communication connection request, the method includes:
responding to a data preprocessing instruction, and converting a channel lead code in the communication connection request into binary time sequence data;
and inputting the binary time series data corresponding to the channel preamble into a preset channel estimation optimization model to determine the pulse emission rates of a plurality of neurons corresponding to the channel preamble in the preset channel estimation optimization model.
Optionally, the pulse release rate characterizes the frequency or the frequency of generating pulses in a neuron unit time in a channel estimation optimization model, and a basic network architecture of the channel estimation optimization model is a pulse neural network.
A channel estimation optimizing apparatus of a base station MIMO wireless communication system according to another object of the present application comprises:
A preamble extraction module configured to obtain channel preambles in respective communication connection requests from a user equipment in response to the respective communication connection requests;
the pulse issuing rate determining module is configured to determine pulse issuing rates of a plurality of neurons corresponding to channel preambles in a preset channel estimation optimization model based on the channel preambles in the communication connection requests, wherein each communication connection request comprises one channel preamble, and each channel preamble corresponds to a plurality of neurons;
the pulse code determining module is arranged for carrying out poisson sampling according to the pulse release rate by adopting poisson distribution in neurons of the channel estimation optimization model, randomly generating a time interval of next pulse release, and recording channel pulse codes of the channel preamble corresponding to the current time point when the accumulated time interval reaches or exceeds a preset time interval threshold;
And the channel estimation optimization module is used for inputting the channel pulse codes of the channel preambles to a variable self-encoder in the channel estimation optimization model so as to reconstruct the channel preambles, so as to complete the channel estimation optimization of the base station MIMO wireless communication system.
An electronic device adapted to another object of the present application comprises a central processor and a memory, said central processor being adapted to invoke the steps of executing a computer program stored in said memory for performing the channel estimation optimization method of the base station MIMO wireless communication system according to the present application.
A computer readable storage medium adapted to another object of the present application stores a computer program implemented according to a channel estimation optimization method of the base station MIMO wireless communication system in the form of computer readable instructions, which when invoked by a computer, performs steps included in the corresponding method.
As can be seen from the foregoing embodiments, compared with the prior art, the present application aims at the problems that the conventional artificial neural network usually consumes a large amount of power consumption for the ineffective calculation of normal operation in the prior art, and the conventional artificial neural network usually processes continuous value input, which has limited expressive power in the time dimension, and cannot naturally capture and utilize accurate time information in time series data, and the present application includes, but is not limited to, the following advantages:
firstly, according to the channel estimation optimization method of the base station MIMO wireless communication system, the impulse neural network can obviously enhance the expression capacity in the time dimension, and can accurately and rapidly capture and utilize the accurate time information in the time sequence data;
Secondly, according to the channel estimation optimization method of the base station MIMO wireless communication system, the impulse neural network only sends impulse when the neuron reaches the threshold value, so that the impulse neural network is closer to an energy-saving mechanism of a biological neural system, is applied to a base station with more close energy sensitivity, and obviously reduces the energy consumption of the MIMO wireless communication system;
Thirdly, according to the channel estimation optimization method of the base station MIMO wireless communication system, the base station channel estimation often involves sparse wireless signal measurement, the impulse neural network can encode information through an impulse release mode, and the sparsity of data can be effectively utilized to accurately and efficiently process the sparse data;
fourth, the channel estimation optimization method of the base station MIMO wireless communication system of the application encodes the signal strength by the frequency of pulse emission, and has stronger adaptability to the signal variation with larger dynamic range, such as channel fluctuation caused by multipath propagation, than the traditional neural network based on continuous activation values.
Furthermore, the channel estimation optimization method of the base station MIMO wireless communication system of the application can achieve similar and even better performance by using smaller model size based on the variation self-coding encoder of the pulse network, is beneficial to deployment on the base station, and provides a Poisson coding process based on the pulse neural network according to the sparsity of user connection in the pulse coding process under the condition of MIMO multi-user, thereby greatly improving the robustness of signal transmission so as to resist multipath interference, noise and other unfavorable channel conditions;
For user connection event driven channel estimation, the impulse neural network can optimize device signal connection delay while being easy to implement in hardware, particularly using Application Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs), thereby speeding up computation and reducing power consumption.
Furthermore, the channel estimation optimization method of the base station MIMO wireless communication system ensures that the system can quickly respond to channel change through the instantaneity of channel estimation, maintains connection quality, and particularly can more accurately match signal transmission and reception through effective channel estimation in applications sensitive to time such as vehicle communication, unmanned aerial vehicle control and the like, so that energy waste is reduced, and the overall energy efficiency of the system is improved. Meanwhile, the optimized resource allocation and signal processing technology improves the frequency spectrum utilization rate and supports simultaneous transmission of more users and higher data rates.
The application combines the time coding capability of the impulse neural network and the generation modeling capability of the variable self-encoder (VAE), and can help understand and optimize the channel condition in a complex communication environment, especially in a mobile communication system. The application improves the accuracy and efficiency of channel estimation, simultaneously obviously reduces the energy consumption of the MIMO wireless communication system, and has important significance for the performance optimization of the MIMO wireless communication system.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
Fig. 1 is a flow chart of a channel estimation optimization method of a base station MIMO wireless communication system according to the present application;
FIG. 2 is an exemplary network architecture for pulsed network low power consumption channel estimation in accordance with an embodiment of the present application;
fig. 3 is a flowchart illustrating a process of converting a channel preamble into binary time-series data according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating a method for determining a burst firing rate corresponding to each neuron in a channel estimation optimization model according to an embodiment of the present application;
fig. 5 is a schematic flow chart of channel pulse coding for recording a channel preamble corresponding to a current time point in an embodiment of the present application;
FIG. 6 is a flow chart of reconstructing a channel preamble from an encoder based on a variation in an embodiment of the present application;
FIG. 7 is an exemplary network architecture of a variant-coded channel reconstruction process for a pulsed neural network in accordance with an embodiment of the present application;
FIG. 8 is a schematic flow chart of adding Gaussian noise to latent variables in an embodiment of the application;
FIG. 9 is a block diagram of a channel estimation process performed by a impulse neural network according to an embodiment of the present application;
Fig. 10 is a schematic block diagram of a channel estimation optimization device of a base station MIMO wireless communication system according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, "client," "terminal device," and "terminal device" are understood by those skilled in the art to include both devices that include only wireless signal receivers without transmitting capabilities and devices that include receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include cellular or other communication devices such as Personal computers, tablet computers, cellular or other communication devices having a single-wire or multi-wire display or no multi-wire display, PCS (Personal Communications Service, personal communication system) which may combine voice, data processing, facsimile and/or data communication capabilities, PDA (Personal DIGITAL ASSISTANT ) which may include a radio frequency receiver, pager, internet/intranet access, web browser, notepad, calendar and/or GPS (Global Positioning System ) receiver, conventional laptop and/or palmtop computer or other device having and/or including a radio frequency receiver. As used herein, "client," "terminal device" may be portable, transportable, installed in a vehicle (aeronautical, maritime, and/or land-based), or adapted and/or configured to operate locally and/or in a distributed fashion, at any other location(s) on earth and/or in space. As used herein, a "client," "terminal device," or "terminal device" may also be a communication terminal, an internet terminal, or a music/video playing terminal, for example, may be a PDA, a MID (Mobile INTERNET DEVICE ), and/or a Mobile phone with a music/video playing function, or may also be a device such as a smart tv, a set top box, or the like.
The application refers to hardware such as a server, a client, a service node, and the like, which essentially is an electronic device with personal computer and other functions, and is a hardware device with necessary components disclosed by von neumann principles such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, an output device, and the like, wherein a computer program is stored in the memory, and the central processing unit calls the program stored in the memory to run, executes instructions in the program, and interacts with the input and output devices, thereby completing specific functions.
It should be noted that the concept of the present application, called "server", is equally applicable to the case of server clusters. The servers should be logically partitioned, physically separate from each other but interface-callable, or integrated into a physical computer or group of computers, according to network deployment principles understood by those skilled in the art. Those skilled in the art will appreciate this variation and should not be construed as limiting the implementation of the network deployment approach of the present application.
One or more technical features of the present application, unless specified in the clear, may be deployed either on a server for implementation and the client remotely invokes an online service interface provided by the acquisition server for implementation of the access, or may be deployed and run directly on the client for implementation of the access.
The neural network model cited or possibly cited in the application can be deployed on a remote server and can be used for implementing remote call on a client, or can be deployed on a client with sufficient equipment capability for direct call, unless specified by plaintext, and in some embodiments, when the neural network model runs on the client, the corresponding intelligence can be obtained through migration learning so as to reduce the requirement on the running resources of the hardware of the client and avoid excessively occupying the running resources of the hardware of the client.
The various data related to the present application, unless specified in the plain text, may be stored either remotely in a server or in a local terminal device, as long as it is suitable for being invoked by the technical solution of the present application.
It will be appreciated by those skilled in the art that the various methods of the application, although described based on the same concepts as one another in common, may be performed independently of one another unless otherwise indicated. Similarly, for the various embodiments disclosed herein, all concepts described herein are presented based on the same general inventive concept, and thus, concepts described herein with respect to the same general inventive concept, and concepts that are merely convenient and appropriately modified, although different, should be interpreted as equivalents.
The various embodiments of the present application to be disclosed herein, unless the plain text indicates a mutually exclusive relationship with each other, the technical features related to the various embodiments may be cross-combined to flexibly construct a new embodiment as long as such combination does not depart from the inventive spirit of the present application and can satisfy the needs in the art or solve the deficiencies in the prior art. This variant will be known to the person skilled in the art.
Referring to fig. 1 and 2, in one embodiment, the channel estimation optimization method of the base station MIMO wireless communication system of the present application includes:
step S10, responding to each communication connection request from user equipment and acquiring a channel preamble in each communication connection request;
The receiving end in the base station MIMO wireless communication system can respond to each communication connection request from the user equipment to acquire the channel lead codes in each communication connection request, the receiving end in the base station MIMO wireless communication system can be a signal processing unit or a baseband processor, the signal processing unit comprises a channel estimation module or a digital signal processing module, the channel estimation module is used for estimating Channel State Information (CSI) and helping to correct signal distortion caused by channel fading and noise, and the user equipment can be a mobile phone, a tablet computer, a portable computer or other terminal equipment and can be used as the user equipment of the application.
In some embodiments, a receiving end in a base station MIMO wireless communication system receives communication connection requests sent by user equipment such as a mobile phone, a tablet computer or a laptop computer, each communication connection request may include a plurality of information including information such as data, a control signal and a channel preamble, and the receiving end in the base station MIMO wireless communication system extracts the channel preamble from each communication connection request, where the channel preamble is a special signal identifier for identifying and estimating a channel state, and using the extracted channel preamble, the base station can perform channel estimation so as to know propagation characteristics and signal quality of a signal in a wireless link.
In some embodiments, the base station MIMO wireless communication system is a wireless communication system utilizing multiple-input multiple-output (MIMO) technology. It is equipped with multiple transmit and receive antennas through the base station to achieve higher data transmission rates, enhanced signal coverage, and greater interference rejection.
In some embodiments, referring to fig. 3, after the step of acquiring the channel preamble in each communication connection request, the method includes:
Step S101, responding to a data preprocessing instruction, and converting a channel lead code in the communication connection request into binary time sequence data;
Step S102, inputting the binary time series data corresponding to the channel preamble into a preset channel estimation optimization model to determine the pulse emission rates of a plurality of neurons corresponding to the channel preamble in the preset channel estimation optimization model.
Specifically, the basic network architecture of the channel estimation optimization model is a pulse neural network, the input characteristics of the pulse neural network for channel estimation of the base station are binary time series data, and the channel preambles (pilot) in each communication connection request of the user equipment are complex signal values, so that the channel preambles in the communication connection request are converted into binary time series data, and the binary time series data corresponding to the channel preambles are input into a preset channel estimation optimization model to determine the pulse release rates of a plurality of neurons corresponding to the channel preambles in the preset channel estimation optimization model.
Step S20, determining pulse release rates of a plurality of neurons corresponding to channel preambles in a preset channel estimation optimization model based on the channel preambles in the communication connection requests, wherein each communication connection request comprises a channel preamble, and each channel preamble corresponds to a plurality of neurons;
The basic network architecture of the channel estimation optimization model is a pulse neural network, after channel lead codes in all communication connection requests are acquired, pulse release rates of a plurality of neurons corresponding to the channel lead codes in a preset pulse neural network are determined based on the channel lead codes in the communication connection requests, each communication connection request comprises a channel lead code, each channel lead code corresponds to a plurality of neurons, and the pulse release rates represent the frequency or the frequency of pulse generation in the unit time of the neurons in the channel estimation optimization model, namely, the pulse release rates represent the frequency or the frequency of pulse generation in the unit time of the neurons in the pulse neural network.
In some embodiments, the concept of impulse neural networks derives from observations of neuronal activity in the biological brain. In biological nerve systems, information is conveyed by electrical signals emitted by neurons, pulses or spikes (spikes). This mode of delivery is highly time dependent and event driven, and differs essentially from continuous signal delivery in conventional artificial neural networks. As the interest in the artificial intelligence field increases, the impulse neural network (SNNs) has been attributed to its potentially low power consumption characteristics. The impulse neural network (SNNs) is activated only when there is information (impulses), allowing for higher computational and energy efficiency, particularly suitable for a base station MIMO wireless communication system.
In some embodiments, channel preambles are not typically in one-to-one correspondence with channels in a base station MIMO wireless communication system, which is typically used for synchronization and channel estimation, the MIMO wireless communication system uses shared preambles for multiple channel estimation. Rather than assigning a unique preamble to each channel, the channel preambles may be multiplexed on different antennas or channels, the primary purpose of the preambles being to help the receiving end estimate and compensate for channel effects to optimize decoding and transmission of the signal. Thus, if multiple channels use the same preamble or preambles for synchronization, then the shared preambles can be used in the neural network to adjust the behavior of multiple neurons in the impulse neural network, each channel preamble corresponding to multiple neurons, the preamble of each communication connection request affecting the impulse firing rate of multiple neurons, such mapping facilitating the impulse neural network to capture and process dynamic changes in the channels.
Further, each channel preamble corresponds to a plurality of neurons, and it is understood that each channel is associated with a plurality of neurons, each channel preamble is mapped to a plurality of neurons because each channel corresponds to a plurality of neurons, which can improve the sensitivity and adaptability of the impulse neural network to channel variations, and each communication connection request can include one or more channel preambles, which can adjust the impulse firing rate of the neurons according to the channel preambles, thereby reflecting different channel conditions.
Referring to fig. 4, the step of determining, based on the channel preamble in the communication connection request, the burst firing rate corresponding to each neuron in the preset channel estimation optimization model includes:
Step S201, determining a channel lead code in the communication connection request, and extracting a characteristic value combination containing key information from the channel lead code, wherein the characteristic value combination comprises any multiple of channel gain, noise level and transmission delay;
Step S202, inputting the extracted characteristic value combination into an input layer of a preset channel estimation optimization model, and distributing the input characteristic value combination to a plurality of neurons corresponding to the channel preamble in the channel estimation optimization model;
step S203, multiplying and summing the corresponding weights of the characteristic values in the input characteristic value combination by adopting an integral pulse issuing function to obtain the membrane potential of each neuron;
Step S204, determining the pulse release rate of each neuron corresponding to the channel preamble according to the membrane potential of each neuron.
In some embodiments, the integral impulse firing function (INTEGRATE-and-Fire, I & F) is a model in a pulsed neural network (SNNs) whose core idea is to simulate the potential accumulation and firing process of biological neurons. The basic principle of operation is that the membrane potential of a neuron increases with an input signal (e.g. a synaptic input), a process similar to the integration process. When the membrane potential reaches a certain threshold, the neuron fires a pulse (spike) and resets the membrane potential to an initial state.
Specifically, the characteristic value combination in the channel lead code is extracted to construct a characteristic value combination, the characteristic value combination comprises any multiple of channel gain, noise level and transmission delay, the characteristic value combination is used as the input of neurons, the extracted characteristic value combination is input to an input layer of a preset impulse neural network, the input characteristic value combination is distributed to a plurality of neurons corresponding to the channel lead code in the impulse neural network, the integral impulse issuing function in the impulse neural network is adopted to multiply and sum the characteristic values in the input characteristic value combination with the corresponding weights of the characteristic value combination to obtain the membrane potential of each neuron, and the impulse issuing rate of each neuron corresponding to the channel lead code is determined according to the membrane potential of each neuron.
More specifically, the number or frequency of pulses generated per unit time via a neuron, commonly referred to as the firing rate (SPIKING RATE), refers to the average number of pulses fired by the neuron over a given time interval. Expressed by the formula:
Pulse delivery rate = number of pulses/time interval;
For example, if a neuron fires 50 pulses within 1 second, its firing rate is 50 hertz (Hz). This frequency reflects the intensity of the activity of the neurons and can be used in information encoding and neural network learning processes.
From the above steps, it is clear that impulse neural networks encode signal strength by pulsing frequencies, which may be more adaptive in handling signal variations with a larger dynamic range (e.g., channel fluctuations due to multipath propagation) than conventional artificial neural networks based on continuous activation values. The frequency of the pulses can accurately reflect the intensity variations of the input signal, thereby more effectively processing complex signal environments.
Step S30, in the neuron of the channel estimation optimization model, poisson sampling is carried out according to the pulse release rate by using poisson distribution, a time interval of next pulse release is randomly generated, and when the accumulated time interval reaches or exceeds a preset time interval threshold, the channel pulse code of the channel preamble corresponding to the current time point is recorded;
After pulse release rates of a plurality of neurons corresponding to the channel lead codes in a preset pulse neural network are determined, poisson sampling is carried out on the neurons of the pulse neural network according to the pulse release rates by using poisson distribution, a next pulse release time interval is randomly generated, and when the accumulated time interval reaches or exceeds a preset time interval threshold, channel pulse codes of the channel lead codes corresponding to the current time point are recorded;
In some embodiments, referring to fig. 5, in the neuron of the channel estimation optimization model, poisson sampling is performed according to the pulse emission rate by using poisson distribution, a time interval of next pulse emission is randomly generated, and when the accumulated time interval reaches or exceeds a preset time interval threshold, a step of recording channel pulse coding of the channel preamble corresponding to the current time point includes:
step S301, determining the pulse release rate of each neuron corresponding to the channel preamble in a preset channel estimation optimization model;
step S302, randomly generating a next pulse issuing time interval according to the pulse issuing rate by adopting a preset Poisson distribution probability density function;
step S303, accumulating the time intervals of each pulse emission to determine an accumulated time interval, detecting whether the accumulated time interval reaches or exceeds a preset time interval threshold, and if so, recording the channel pulse codes of the channel preamble corresponding to the current time point;
step S304, resetting the accumulated time interval to zero, and repeating the steps.
The method comprises the steps of determining pulse issuing rate of each neuron corresponding to a channel preamble in a preset pulse neural network based on the steps, randomly generating a next pulse issuing time interval according to the pulse issuing rate by adopting a preset Poisson distribution probability density function, accumulating each pulse issuing time interval to determine an accumulated time interval, detecting whether the accumulated time interval reaches or exceeds a preset time interval threshold, recording channel pulse codes of the channel preamble corresponding to a current time point if the accumulated time interval reaches or exceeds the preset time interval threshold, resetting the accumulated time interval to zero, and repeating the steps.
More specifically, in the impulse neural network, it is first required to determine the impulse firing rate (SPIKING RATE) of each neuron, where the impulse firing rate may be dynamically determined according to the channel preamble through the steps in the above embodiment, or may be preset based on the actual service requirement of the base station MIMO wireless communication system, and the impulse firing rate is usually expressed in units of hertz (Hz), and represents the average impulse firing number of neurons in unit time. For example, if the firing rate of a neuron is 20 Hz, it fires an average of 20 pulses in 1 second.
Based on the firing rate of the neurons, the time intervals of firing of the pulses can be randomly generated using a poisson distribution. Poisson distribution is a probability distribution describing the number of occurrences of a certain event in a unit time. The next pulse is issued at a time interval that is generated from the probability density function of the poisson distribution whose probability density function expression is expressed as follows:
,
Wherein, Is the pulse delivery rate and the pulse delivery rate,Is the time interval over which the data is to be stored,Is the number of pulses, average interval time of poisson distributionRepresenting the average time that the neuron fires the next pulse at a given firing rate.
After the time intervals for each pulse delivery are generated, these intervals are accumulated to obtain an accumulated time interval. Each time a new time interval is generated it is added to the accumulation time interval, and each time the accumulation time interval is updated it needs to be detected whether it meets or exceeds a preset time interval threshold (threshold).
The preset time interval threshold may be designed based on the actual traffic demands of the base station MIMO wireless communication system for deciding when to record a pulse code. For example, if the threshold is set to 0.1 seconds, the recording is triggered when the cumulative time interval reaches or exceeds 0.1 seconds. When the accumulated time interval reaches or exceeds a preset time interval threshold, recording channel pulse codes of the channel preamble corresponding to the current time point, which means that pulse information at a specific time point is recorded, and the information can be used for subsequent processing or analysis;
And repeating the steps to continuously generate a pulse sending time interval, accumulating the time interval, detecting whether the accumulated time interval reaches or exceeds a preset time interval threshold value, and recording the pulse codes until all required operations are completed or until the system stops running.
And S40, inputting the channel pulse codes of the channel preamble codes to a variable self-encoder in the channel estimation optimization model to reconstruct the channel preamble codes so as to finish the channel estimation optimization of the base station MIMO wireless communication system.
After the channel pulse codes of the channel leading codes corresponding to the current time point are recorded, the channel pulse codes of the channel leading codes are input to a variable self-encoder in the channel estimation optimization model to reconstruct the channel leading codes, so that the channel estimation optimization of the base station MIMO wireless communication system is completed.
In some embodiments, referring to fig. 6 and 7, the step of inputting the channel pulse code of the channel preamble to a variable self-encoder in the channel estimation optimization model to reconstruct the channel preamble includes:
Step S401, in the encoder of the variation self-encoder, mapping the channel pulse code of the channel preamble to a potential space, and outputting probability distribution of potential variables in the potential space;
step S402, in a decoder of the variable self-encoder, sampling from probability distribution of the potential variables output by the encoder to determine specific potential variables so as to generate a reconstructed channel preamble;
Step S403, calculating a difference value between the probability distribution and the prior distribution of the potential variables in the potential space to determine a KL divergence loss value, and completing the reconstruction of the channel preamble when the KL divergence loss value is lower than a preset KL divergence loss value.
In particular, the KL divergence loss value represents a difference value between a probability distribution and a priori distribution of potential variables in a potential space, the variance self-encoder (Variational Autoencoder, VAE) is a deep learning model combining a generation model and a self-encoder concept, is mainly used for learning an efficient representation of data, and is capable of generating new samples similar to training data. The encoder portion of the VAE is responsible for mapping the input data onto a distribution of hidden variables (latent variable) rather than directly onto a fixed vector as in a conventional self-encoder. This distribution is typically gaussian (mean valueSum of variances) For each input, the encoder outputs the parameters of the distribution (mean vector and logarithmic variance vector). In order to be able to train models with random variables efficiently in gradient descent, the variational self-encoder employs a re-parameterization technique. It does not directly sample hidden variablesBut rather samples a standard normal distribution of noiseThen pass throughAnd calculating the mean and variance of the encoder output to obtain hidden variablesThis ensures that the gradient can flow through the entire network.
,
And the error employed for the encoder and decoder is calculated as:
,
Wherein, Channel preamble hidden variable distribution for measuring encoder output(Posterior distribution) and a predefined prior distributionThe difference between them, wherein,Satisfy normal distributionBy minimizing this total loss function, the variational self-encoder can optimize the reconstruction quality of the data while also forcing the hidden variable distribution to approach a preset prior distribution, thereby achieving efficient data encoding and decoding.
Further, referring to fig. 8, after the step of mapping the channel pulse code of the channel preamble to a potential space and outputting the probability distribution of the potential variable in the potential space in the encoder of the variable self-encoder, the method includes:
step S4001, sampling from probability distribution of the latent variable output by the encoder in the latent space, to determine a specific latent variable;
Step S4002, adding gaussian noise to the specific latent variable, and inputting the specific latent variable after adding gaussian noise to the decoder of the variable self-encoder, so as to generate a reconstructed channel preamble.
Specifically, in the channel reconstruction process, gaussian noise needs to be added so that the decoder can learn the data characteristics of pulse coding better, wherein the channel preamble is composed of complex numbers, and the gaussian noise adding step comprises the following expression:
(1) The expression of the signal power is:
,
Wherein, The signal power is represented by a signal power,
(2) The expression of the noise power is:
,
Wherein, Which represents the power of the noise and,Representing the signal-to-noise ratio;
(3) The gaussian noise superposition is expressed as:
,
Wherein, Is an input signal which is provided with a signal,Is the sign of the absolute value of the symbol,Is a square root sign of the sign,Is a standard normal distributed sample of the sample,The sum of the original signal and the random gaussian noise signal is characterized.
Based on the above formula, gaussian noise can be added to specific potential variables, so that the decoder can learn the data characteristics of pulse coding better.
In some embodiments, referring to fig. 9, during channel preamble reconstruction, when the ue is event driven, the process ① triggers a sufficient number of pulses to enable the burst network to fire. And the node is only triggered by a user connection event to drive data input, and the process can filter a large amount of calculation power consumption, so that the energy consumption caused by network calculation under the drive of data is prevented.
In the process of variational self-coding reconstruction, the process ② is calculated from the KL-divergence, which theoretically means that the hidden variable distribution learned by the encoder is exactly identical to the a priori distribution if the KL-divergence value is 0, which is the most ideal case, since it shows that our model can perfectly match the preset simple a priori, and that the learned representation is an unconventional norm and easy to interpret. However, in practical applications, a KL divergence of 0 is not always a achievable or desirable result. On the one hand, too strictly pursuing a KL divergence of 0 may limit the learning ability of the model, resulting in the complexity that it cannot adequately capture data. On the other hand, non-zero KL divergence may actually help the model maintain a certain diversity, facilitate the generation capability, and in some cases, moderate KL divergence values may balance the reconstruction error of the model and the capability to generate new samples. In this process, the threshold value super-parameter is set according to the load of the base station and the signal processing qualityThat is, KL divergence value is lower thanThe value of the channel reconstruction effect is better.
As can be seen from the foregoing embodiments, compared with the prior art, the present application aims at the problems that the conventional artificial neural network usually consumes a large amount of power consumption for the ineffective calculation of normal operation in the prior art, and the conventional artificial neural network usually processes continuous value input, which has limited expressive power in the time dimension, and cannot naturally capture and utilize accurate time information in time series data, and the present application includes, but is not limited to, the following advantages:
firstly, according to the channel estimation optimization method of the base station MIMO wireless communication system, the impulse neural network can obviously enhance the expression capacity in the time dimension, and can accurately and rapidly capture and utilize the accurate time information in the time sequence data;
Secondly, according to the channel estimation optimization method of the base station MIMO wireless communication system, the impulse neural network only sends impulse when the neuron reaches the threshold value, so that the impulse neural network is closer to an energy-saving mechanism of a biological neural system, is applied to a base station with more close energy sensitivity, and obviously reduces the energy consumption of the MIMO wireless communication system;
Thirdly, according to the channel estimation optimization method of the base station MIMO wireless communication system, the base station channel estimation often involves sparse wireless signal measurement, the impulse neural network can encode information through an impulse release mode, and the sparsity of data can be effectively utilized to accurately and efficiently process the sparse data;
fourth, the channel estimation optimization method of the base station MIMO wireless communication system of the application encodes the signal strength by the frequency of pulse emission, and has stronger adaptability to the signal variation with larger dynamic range, such as channel fluctuation caused by multipath propagation, than the traditional neural network based on continuous activation values.
Furthermore, the channel estimation optimization method of the base station MIMO wireless communication system of the application can achieve similar and even better performance by using smaller model size based on the variation self-coding encoder of the pulse network, is beneficial to deployment on the base station, and provides a Poisson coding process based on the pulse neural network according to the sparsity of user connection in the pulse coding process under the condition of MIMO multi-user, thereby greatly improving the robustness of signal transmission so as to resist multipath interference, noise and other unfavorable channel conditions;
For user connection event driven channel estimation, the impulse neural network can optimize device signal connection delay while being easy to implement in hardware, particularly using Application Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs), thereby speeding up computation and reducing power consumption.
Furthermore, the channel estimation optimization method of the base station MIMO wireless communication system ensures that the system can quickly respond to channel change through the instantaneity of channel estimation, maintains connection quality, and particularly can more accurately match signal transmission and reception through effective channel estimation in applications sensitive to time such as vehicle communication, unmanned aerial vehicle control and the like, so that energy waste is reduced, and the overall energy efficiency of the system is improved. Meanwhile, the optimized resource allocation and signal processing technology improves the frequency spectrum utilization rate and supports simultaneous transmission of more users and higher data rates.
The application combines the time coding capability of the impulse neural network and the generation modeling capability of the variable self-encoder (VAE), and can help understand and optimize the channel condition in a complex communication environment, especially in a mobile communication system. The application improves the accuracy and efficiency of channel estimation, simultaneously obviously reduces the energy consumption of the MIMO wireless communication system, and has important significance for the performance optimization of the MIMO wireless communication system.
Referring to fig. 10, a channel estimation optimization device of a base station MIMO wireless communication system according to one of the objects of the present application includes a preamble extraction module 1100, a burst release rate determination module 1200, a burst coding determination module 1300, and a channel estimation optimization module 1400. The system comprises a preamble extraction module 1100, a pulse distribution rate determination module 1200 and a channel estimation optimization module 1400, wherein the preamble extraction module 1100 is configured to respond to each communication connection request from user equipment to obtain channel preambles in each communication connection request, the pulse distribution rate determination module 1200 is configured to determine pulse distribution rates of a plurality of neurons corresponding to the channel preambles in a preset channel estimation optimization model based on the channel preambles in the communication connection requests, each communication connection request comprises one channel preamble, each channel preamble corresponds to the plurality of neurons, the pulse coding determination module 1300 is configured to perform poisson sampling according to the pulse distribution rate in the neurons of the channel estimation optimization model by using poisson distribution to randomly generate a time interval of next pulse distribution, and when the accumulated time interval reaches or exceeds a preset time interval threshold, the channel pulse coding of the channel preambles corresponding to the current time point is recorded, and the channel estimation optimization module 1400 is configured to input the channel pulse coding of the channel preambles to a variable self-encoder in the channel estimation optimization model so as to reconstruct the channel preambles to complete the channel estimation optimization model of the wireless base station.
On the basis of any embodiment of the present application, referring to fig. 11, another embodiment of the present application further provides an electronic device, which may be implemented by a computer device, and as shown in fig. 11, an internal structure of the computer device is schematically shown. The computer device includes a processor, a computer readable storage medium, a memory, and a network interface connected by a system bus. The computer readable storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store a control information sequence, and when the computer readable instructions are executed by a processor, the processor can realize a channel estimation optimization method of the base station MIMO wireless communication system. The processor of the computer device is used to provide computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may store computer readable instructions that, when executed by the processor, cause the processor to perform the channel estimation optimization method of the base station MIMO wireless communication system of the present application. The network interface of the computer device is for communicating with a terminal connection. It will be appreciated by those skilled in the art that the structure shown in FIG. 11 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The processor in this embodiment is configured to execute specific functions of each module and its sub-module in fig. 10, and the memory stores program codes and various data required for executing the above-mentioned modules or sub-modules. The network interface is used for data transmission between the user terminal or the server. The memory in this embodiment stores program codes and data required for executing all modules/sub-modules in the channel estimation optimizing apparatus of the base station MIMO wireless communication system of the present application, and the server can call the program codes and data of the server to execute the functions of all sub-modules.
The present application also provides a storage medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of a channel estimation optimization method of a base station MIMO wireless communication system according to any of the embodiments of the present application.
The present application also provides a computer program product comprising computer programs/instructions which when executed by one or more processors implement the steps of a channel estimation optimization method for a base station MIMO wireless communication system according to any of the embodiments of the present application.
Those skilled in the art will appreciate that all or part of the processes implementing the methods of the above embodiments of the present application may be implemented by a computer program for instructing relevant hardware, where the computer program may be stored on a computer readable storage medium, where the program, when executed, may include processes implementing the embodiments of the methods described above. The storage medium may be a computer readable storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (RandomAccess Memory, RAM).
The foregoing is only a partial embodiment of the present application, and it should be noted that it will be apparent to those skilled in the art that modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations are intended to be comprehended within the scope of the present application.
In summary, the present application combines the time encoding capability of a pulsed neural network with the generation modeling capability of a variational self-encoder (VAE) to help understand and optimize channel conditions in a complex communication environment, particularly in a mobile communication system. The method can improve the accuracy and efficiency of channel estimation, and has important significance for performance optimization of the MIMO wireless communication system.
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