CN108400948B - Environment self-adaptive perception wireless communication channel estimation and signal reconstruction method - Google Patents

Environment self-adaptive perception wireless communication channel estimation and signal reconstruction method Download PDF

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CN108400948B
CN108400948B CN201810074910.2A CN201810074910A CN108400948B CN 108400948 B CN108400948 B CN 108400948B CN 201810074910 A CN201810074910 A CN 201810074910A CN 108400948 B CN108400948 B CN 108400948B
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optimal recovery
base station
channel state
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徐宗本
薛江
孟德宇
赵谦
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Xian Jiaotong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms

Abstract

The invention relates to an environment self-adaptive perception wireless communication channel estimation and signal reconstruction algorithm based on machine learning induced optimal recovery measurement, which comprises the following steps: storing pilot signals adjacent to each other in time and space, channel state information and recovery measurement information by using a base station storage device; after receiving the pilot signal sent by the transmitting end, the receiving end combines the base station storage information and utilizes a machine learning method to realize the environment self-adaptive perception channel state and determine the optimal recovery measurement; and the channel state information and the optimal recovery measurement are utilized to realize the reconstruction of the anti-interference and de-noising transmission signal. The invention is based on effective information stored by utilizing a communication base station and a machine learning principle, quickly and effectively learns, estimates and eliminates the influence of complex noise on a wireless communication system, and further realizes the steady estimation and signal reconstruction of a wireless communication channel in the same or shorter time through a self-adaptive mode.

Description

Environment self-adaptive perception wireless communication channel estimation and signal reconstruction method
Technical Field
The invention relates to the field of communication and machine learning, in particular to an environment self-adaptive perception wireless communication channel estimation and signal reconstruction method based on machine learning induced optimal recovery measurement.
Background
Accurate information transmission is always the first objective sought by wireless communication, and how to better eliminate interference and noise influence in communication in a complex interference and noise environment, so that more accurate communication is a key problem to be solved urgently. In recent years, the development and maturity of machine learning related theories, particularly machine learning technologies, make it possible to innovatively apply a machine learning theory related method to solve the problem of accurate information transmission of a wireless communication system under the background of complex interference and noise.
The wireless communication system which is more effective, intelligent and adaptive to complex environment is a necessary trend for the development of future communication systems by organically combining machine learning related theories and the wireless communication system. For the inevitable noise problem in wireless systems, the currently applied wireless communication theory is mostly based on idealized white noise, however, this assumption is almost impossible to exist in a realistic communication environment. This makes most algorithms based on the white noise assumption inapplicable in reality, such as the signal source detection algorithms AIC and MDL, which have an extremely high error rate in a real communication environment. As another example, least squares are currently applied at the receiving end to achieve reconstruction of the transmitted signal. Under the assumption of white noise, least square is the optimal algorithm, but under colored noise, the least square method obtains a non-optimal solution. For the problem of co-channel interference in a communication system, the influence of interference on the system can be reduced only by complex coding or simply increasing the transmission power. However, by using the relevant method and theory of machine learning, the influence of interference and noise can be well eliminated, and further, the state information of the communication channel can be accurately described, and the intelligence and the performance of the communication system are improved.
In addition, the base station in the wireless communication system processes a large amount of information every day, but the base station is not fully utilized at present, so that a large amount of valuable information is wasted and lost. How to better utilize the information of the base station and to mine the potential of the base station is another focus of attention in the future communication field.
Disclosure of Invention
The invention aims to provide an environment self-adaptive perception wireless communication channel estimation and signal reconstruction method based on machine learning induced optimal recovery measurement. The time delay influence of a machine learning-based channel and communication environment adaptive estimation algorithm facing a wireless communication system on the communication system is reduced by utilizing the user positioning information acquired by the base station and the stored communication environment information, and the robust and accurate estimation of the wireless communication channel is realized in the same or shorter time.
In order to achieve the purpose, the invention adopts the technical scheme that:
the core elements of the present invention include: the base station is used as a storage and processing node of a physical layer of a wireless communication network, and the communication environment and pilot frequency information stored by the base station are utilized to assist self-adaptive channel state estimation and determine the optimal recovery metric, so that the optimal recovery metric is utilized to reconstruct a signal. The method comprises the following specific steps:
1) storing pilot signals adjacent to each other in time and space, channel state information and optimal recovery measurement information by using base station storage equipment;
2) after receiving the pilot signal sent by the transmitting end, the receiving end combines the base station storage information and utilizes a machine learning method to realize the channel state estimation and the optimal recovery metric estimation of environment self-adaptive perception;
3) and the channel state information and the optimal recovery measurement are utilized to realize the reconstruction of the anti-interference and de-noised transmission signal, thereby improving the information transmission accuracy.
In the step 1), the base station stores the pilot signal, the channel state information and the optimal recovery metric information within the service area for a certain time by using the self storage device, and the base station stores the pilot signal, the channel state information and the optimal recovery metric information within the service area as a whole according to the communication scene of the service area, or divides the service area into a plurality of sub-service areas according to the scene within the service area and respectively stores the pilot signal, the channel state information and the optimal recovery metric information within the service area for a certain time.
The step 2) comprises the following substeps:
a) when a new user sends a service request, the receiving end of the base station receives a pilot signal X sent by the user1，Y1And simultaneously according to the time and position information of the user, the related stored pilot signal X of the space-time adjacent of the user is called0And Y0(ii) a Channel state information H0And optimal recovery metric information alpha0(ii) a Let Y be { Y ═ Y0,Y1}，X＝{X0,X1The preset recovery metric base is:
Figure BDA0001559248400000031
such as order p1＝1/2，p2＝1，p3Let t be 2, where:
x is NtX S matrix, NtThe number of antennas at the transmitting end is S, and the total number of used pilot signals is S;
y is NrX S matrix, NrThe number of the antennas at the receiving end;
X1is Nt×S1Matrix, S1Transmitting the number of pilot signals for the current user;
Y1is Nr×S1A matrix;
X0is Nt×S0Matrix, S0Storing the number of pilot signals for the current receiving end;
Y0is Nr×S0A matrix;
H0is Nr×NtA channel state information matrix;
α0is a Kx 1 vector, represents the optimal recovery metric information, and K represents the recovery metric base number;
Birepresents the ith column of matrix B, BiRepresents the ith component of vector b;
b) according to the machine learning maximum entropy principle, the optimal recovery metric parameter alpha of the former stept-1As an initial value, determining the current optimal recovery metric as:
Figure BDA0001559248400000032
whereinIs the solution of the following standard maximum entropy optimization model:
minαg(t)(α) (2)
here, the
Figure BDA0001559248400000034
c) Using the current optimal recovery metric, in Ht-1For the initial value, the following model is solved by iteration
Figure BDA0001559248400000041
To obtain an updated channel estimate HtWherein
Figure BDA0001559248400000045
Is given by formula (1).
d) Let t: ═ t +1, re-enter step b), iterate until convergence to produce the optimal channel estimate H*. The iterative convergence rule updates the difference II H for two adjacent stepst-1-Ht‖2The time delay is less than a preset threshold value, and the threshold value is set to achieve optimal balance between the requirement of the estimation precision and the system time delay according to different communication scenes and the requirement of the system on the time delay in real application;
e) obtaining an optimal channel estimate H*Then, the optimum recovery degree is measured as
Figure BDA0001559248400000042
The step 3) comprises the following steps:
a) optimal channel estimate H obtained with adaptation*And an optimal recovery metric
Figure BDA0001559248400000044
And the reconstruction of the Y to the transmitting signal X is realized at the receiving end by solving the following optimization problem:
b) storing the relevant information in the base station for communication of a new user;
the information saved includes: optimal channel estimation H for communication location*And an optimal recovery metric parameter alpha*The optimal channel estimation and the optimal recovery measurement parameter updating of the space-time correlation neighborhood are realized; and storing part or all of the pilot frequency information for later user machine learning data expansion.
Compared with the prior art, the invention has the beneficial effects that:
1) compared with the existing channel state estimation method, the method can fully utilize the base station to store valuable information, including pilot frequency information, self-adaptive estimation channel state information, optimal recovery measurement and the like, quickly and effectively eliminate the influence of complex noise and interference on communication, and further realize the steady and accurate estimation of the wireless communication channel in the same or shorter time.
2) The invention considers the machine learning method, does not need to know any wireless communication environment information in advance by using the method, and can effectively depict the complex noise and interference environment in the wireless communication system, thereby greatly improving the estimation accuracy and the adaptability of the wireless communication channel in the complex environment, essentially improving the system performance and having wide application prospect.
Drawings
FIG. 1 is a flow chart of an environment adaptive sensing wireless communication channel estimation and signal reconstruction algorithm based on machine learning induced optimal recovery metric;
FIG. 2 is a diagram of a conventional wireless communication system in an embodiment;
fig. 3a and b are bit error rate comparison graphs of an environment adaptive sensing wireless communication channel estimation and signal reconstruction algorithm by adopting a least square method and a machine learning-based method to induce an optimal recovery metric under different interference and noise environments, respectively.
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific embodiments.
Example (b): as shown in fig. 1, an environment adaptive perceptual wireless communication channel estimation and signal reconstruction algorithm based on a machine learning induced optimal recovery metric. The core elements of the method comprise: the base station is used as a storage and processing node of a physical layer of a wireless communication network, and the communication environment and pilot frequency information stored by the base station are utilized to assist self-adaptive channel state estimation and determine the optimal recovery metric, so that the optimal recovery metric is utilized to reconstruct a signal. The method comprises the following specific steps:
1) storing pilot signals adjacent to each other in time and space, channel state information and optimal recovery measurement information by using base station storage equipment;
2) after receiving the pilot signal sent by the transmitting end, the receiving end combines the base station storage information and utilizes a machine learning method to realize the channel state estimation and the optimal recovery metric estimation of environment self-adaptive perception;
3) and the channel state information and the optimal recovery measurement are utilized to realize the reconstruction of the anti-interference and de-noised transmission signal, thereby improving the information transmission accuracy.
In step 1), the base station stores pilot signals, channel state information and optimal recovery measurement information within a service area of the base station for a certain time by using a self storage device. The base station can store the relevant information by taking the service area as a whole according to the specific communication scene of the service area, or divide the service area into a plurality of sub-service areas according to different scenes in the service area to respectively store the relevant information.
Step 2) comprises the following substeps:
a) when new user sends service requirement, base station receives pilot signal X sent by user1，Y1And simultaneously according to the time and position information of the user, the related stored pilot signal X of the space-time adjacent of the user is called0And Y0(ii) a Channel state information H0And optimal recovery metric information alpha0(ii) a Let Y be { Y ═ Y0,Y1}，X＝{X0,X1}. The preset recovery metric base is:
Figure BDA0001559248400000061
such as order p1＝1/2，p2＝1，p3And let t be 1. Here:
x is NtX S matrix, NtThe number of antennas at the transmitting end is S, and the total number of used pilot signals is S;
y is NrX S matrix, NrThe number of the antennas at the receiving end;
X1is Nt×S1Matrix, S1Transmitting the number of pilot signals for the current user;
Y1is Nr×S1A matrix;
X0is Nt×S0Matrix, S0Storing the number of pilot signals for the current receiving end;
Y0is Nr×S0A matrix;
H0is Nr×NtA channel state information matrix;
α0is a Kx 1 vector, represents the optimal recovery metric information, and K represents the recovery metric base number;
Birepresents the ith column of matrix B, BiRepresents the ith component of vector b;
b) according to the machine learning maximum entropy principle, the optimal recovery metric parameter alpha of the former stept-1As an initial value, determining the current optimal recovery metric as:
wherein
Figure BDA0001559248400000063
Is the solution of the following standard maximum entropy optimization model:
minαg(t)(α) (2)
here, the
Figure BDA0001559248400000071
c) Using the current optimal recovery metric, in Ht-1For the initial value, the following model is solved by iteration
Figure BDA0001559248400000072
To obtain an updated channel estimate HtWherein
Figure BDA0001559248400000076
Is given by formula (1).
d) Let t: ═ t +1, re-enter step b), iterate until convergence to produce the optimal channel estimate H*. The iterative convergence rule updates the difference II H for two adjacent stepst-1-Ht‖2Less than a preset threshold, said threshold being set according toDifferent communication scenes and system requirements on time delay in real application reach optimal balance between the requirement of estimation precision and the requirement of system time delay;
e) obtaining an optimal channel estimate H*Then, the optimum recovery degree is measured as
Figure BDA0001559248400000073
The step 3) comprises the following steps:
a) optimal channel estimate H obtained with adaptation*And an optimal recovery metric
Figure BDA0001559248400000075
And the reconstruction of the Y to the transmitting signal X is realized at the receiving end by solving the following optimization problem:
b) and storing the relevant information in the base station for communication of the new user. The information saved includes: optimal channel estimation H for communication location*And an optimal recovery metric parameter alpha*The optimal channel estimation and the optimal recovery measurement parameter updating of the space-time correlation neighborhood are realized; and storing part or all of the pilot frequency information for later user machine learning data expansion.
As shown in fig. 2, in a generalized wireless communication system, a base station provides communication services to users in its serving cell, but is affected by co-channel interference and noise from other cells or signal sources. In the figure, general signal transmission is represented by solid lines; interference is represented by dotted solid lines; noise is represented by dotted lines.
In the implementation process of the patent, base station storage equipment is utilized to store pilot signals, channel state information and recovery measurement information which are adjacent in time and space; a receiving end receives and acquires a pilot signal sent by a transmitting end, and utilizes a machine learning method to realize channel state information of environment self-adaption perception and optimal recovery measurement estimation; and the anti-interference, denoising and transmission signal reconstruction are realized by utilizing the channel state information and the optimal recovery measurement, and the information transmission accuracy is improved. The base station provides communication service for the user a according to the previously stored information. And then, the base station updates and stores the related estimation result within a certain time according to the communication environment. If the new user B requests the base station to provide service during a predetermined time interval, the base station will use the saved data as an initialization condition for its channel state and other information estimation.
Fig. 3 is a simulation comparison diagram of Bit Error Rate (Bit Error Rate) of an environment adaptive sensing wireless communication channel estimation and signal reconstruction algorithm under a complex interference and noise environment by using a least square method and a machine learning-based optimal recovery metric. In the simulation, Binary Phase Shift Keying (BPSK) is adopted in the invention, and complex interference and noise are generated by a mixed Gaussian method. The diagrams a and b are simulated implementations in different interference and noise environments, respectively. It can be seen that the environment adaptive sensing wireless communication channel estimation and signal reconstruction algorithm based on the machine learning method induced the optimal recovery metric gives better results at low signal-to-noise ratio. It should be noted that the advantages of the environment adaptive perceptual wireless communication channel estimation and signal reconstruction algorithm of the present invention based on machine learning induced optimal recovery metric will be more apparent if other phase shift keying is employed, such as Quadrature Phase Shift Keying (QPSK), 8 phase shift keying (8PSK), or 16 phase shift keying (16PSK), etc., under the same conditions.

Claims (3)

1. An environment self-adaptive perception wireless communication channel estimation and signal reconstruction method based on machine learning induced optimal recovery measurement is characterized by comprising the following steps:
1) storing pilot signals adjacent to each other in time and space, channel state information and optimal recovery measurement information by using base station storage equipment;
2) after receiving the pilot signal sent by the transmitting end, the receiving end combines the base station storage information and utilizes a machine learning method to realize the channel state estimation and the optimal recovery metric estimation of environment self-adaptive perception, and the steps are as follows:
a) when a new user sends a service requirement, the receiving end of the base station receives the guide sent by the userFrequency signal X1，Y1And simultaneously according to the time and position information of the user, the related stored pilot signal X of the space-time adjacent of the user is called0And Y0(ii) a Channel state information H0And optimal recovery metric information alpha0(ii) a Let Y be { Y ═ Y0，Y1}，X＝{X0，X1}; the preset recovery metric base is:
Figure FDA0002263825020000011
let p be1＝1/2，p2＝1，p32, and let t be 1,
x is NtX S matrix, NtThe number of antennas at the transmitting end is S, and the total number of used pilot signals is S;
y is NrX S matrix, NrThe number of the antennas at the receiving end;
X1is Nt×S1Matrix, S1Transmitting the number of pilot signals for the current user;
Y1is Nr×S1A matrix;
X0is Nt×S0Matrix, S0Storing the number of pilot signals for the current receiving end;
Y0is Nr×S0A matrix;
H0is Nr×NtA channel state information matrix;
α0is a Kx 1 vector, represents the optimal recovery metric information, and K represents the recovery metric base number;
Birepresents the ith column of matrix B, BiRepresents the ith component of vector b;
b) according to the maximum entropy principle of machine learning, the optimal recovery measurement parameter alpha of the former stept-1As an initial value, determining the current optimal recovery metric as:
wherein
Figure FDA0002263825020000013
Is the solution of the following standard maximum entropy optimization model:
minαg(t)(α) (2)
here, the
Figure FDA0002263825020000021
c) Using the current optimal recovery metric, in Ht-1For the initial value, the following model is solved by iteration
Figure FDA0002263825020000022
To obtain an updated channel estimate HtWhereinIs given by formula (1);
d) let t: re-entering step b) until convergence to generate optimal channel estimation H*The iterative convergence rule is that the difference H is updated by two adjacent stepst-1-Ht||2The time delay is less than a preset threshold value, and the threshold value is set to achieve optimal balance between the requirement of the estimation precision and the system time delay according to different communication scenes and the requirement of the system on the time delay in real application;
e) obtaining an optimal channel estimate H*Then, the optimum recovery degree is measured as
Figure FDA0002263825020000023
3) And the channel state information and the optimal recovery measurement are utilized to realize the reconstruction of the anti-interference and de-noised transmission signal, thereby improving the information transmission accuracy.
2. The method of claim 1 for environment adaptive perceptual wireless communication channel estimation and signal reconstruction based on machine learning induced optimal recovery metric, wherein:
in the step 1), the base station stores the pilot signal, the channel state information and the optimal recovery metric information within the service area for a certain time by using the self storage device, and the base station stores the pilot signal, the channel state information and the optimal recovery metric information within the service area as a whole according to the communication scene of the service area, or divides the service area into a plurality of sub-service areas according to the scene within the service area and respectively stores the pilot signal, the channel state information and the optimal recovery metric information within the service area for a certain time.
3. The method of claim 1 for environment adaptive perceptual wireless communication channel estimation and signal reconstruction based on machine learning induced optimal recovery metric, wherein:
the step 3) comprises the following steps:
a) optimal channel estimate H obtained with adaptation*And an optimal recovery metric
Figure FDA0002263825020000032
And the reconstruction of the Y to the transmitting signal X is realized at the receiving end by solving the following optimization problem:
Figure FDA0002263825020000031
b) storing the relevant information in the base station for communication of a new user;
the information saved includes: optimal channel estimation H for communication location*And an optimal recovery metric parameter alpha*The optimal channel estimation and the optimal recovery measurement parameter updating of the space-time correlation neighborhood are realized; and storing part or all of the pilot frequency information for later user machine learning data expansion.
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