CN113438746B - Large-scale random access method based on energy modulation - Google Patents

Large-scale random access method based on energy modulation Download PDF

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Publication number
CN113438746B
CN113438746B CN202110991607.0A CN202110991607A CN113438746B CN 113438746 B CN113438746 B CN 113438746B CN 202110991607 A CN202110991607 A CN 202110991607A CN 113438746 B CN113438746 B CN 113438746B
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user
signal
random access
codebook
optimal
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CN113438746A (en
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黄川�
崔曙光
黄坚豪
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Chinese University of Hong Kong Shenzhen
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W74/00—Wireless channel access
    • H04W74/08—Non-scheduled access, e.g. ALOHA
    • H04W74/0833—Random access procedures, e.g. with 4-step access
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • 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
    • H04L25/0204—Channel estimation of multiple channels
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a large-scale random access method based on energy modulation, which comprises the following steps: s1, constructing a random access model based on energy modulation; s2, estimating a combined signal of user information and a channel by using a message transmission algorithm; s3, detecting the transmitting information and the active state of each user by utilizing an algorithm based on the maximized posterior probability; and S4, designing an optimal constellation point codebook. The large-scale random access scheme provided by the invention obtains the approximate optimal constellation point, effectively reduces the error rate and improves the communication performance.

Description

Large-scale random access method based on energy modulation
Technical Field
The invention designs user random access, and particularly relates to a large-scale random access method based on energy modulation.
Background
With the rapid development of communication technology, base stations are more and more widely applied in social life, and the base stations are often required to be accessed to a large number of users and support uplink transmission of the large number of users; the access method of the user is very important at this time.
The traditional access strategy and the data transmission strategy are independent and are divided into two steps: firstly, active users are detected, and then channel estimation and data detection are carried out on the detected active users. This discrete strategy requires the user to complete activity detection and channel estimation through the pilot before data transmission, which can generate huge time delay and performance overhead. Therefore, it is difficult for such a conventional communication mode to satisfy the communication demand of high energy efficiency and low communication delay in a large-scale scenario.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a large-scale random access method based on energy modulation, so that an approximately optimal codebook constellation point is obtained, the error rate is effectively reduced, and the communication performance is improved.
The purpose of the invention is realized by the following technical scheme: a large-scale random access method based on energy modulation comprises the following steps:
s1, constructing a random access model based on energy modulation;
s2, joint signal of user information and channel by using message transfer algorithm
Figure 565716DEST_PATH_IMAGE001
Carrying out estimation;
s3, detecting the transmitting information and the active state of each user by utilizing an algorithm based on the maximum posterior probability, and outputting an optimal threshold
Figure DEST_PATH_IMAGE002
And detecting the signal
Figure 951698DEST_PATH_IMAGE003
And determining the theoretical expression of the bit error rate according to the bit error rate
Figure DEST_PATH_IMAGE004
；
And S4, carrying out optimal constellation point codebook design.
Further, the step S1 includes:
s101, for the content containing
Figure 335363DEST_PATH_IMAGE005
Communication system comprising a single antenna subscriber and a receiver, each subscriber transmitting information to the receiver with a certain probability in each transmission time slot, wherein the receiver is provided with
Figure DEST_PATH_IMAGE006
A root antenna; by random variables
Figure 199414DEST_PATH_IMAGE007
To describe the user
Figure 154731DEST_PATH_IMAGE008
The active nature of the slot, at each time slot,
Figure 180456DEST_PATH_IMAGE009
satisfies the following conditions:
Figure 713069DEST_PATH_IMAGE010
；
wherein the content of the first and second substances,
Figure 115231DEST_PATH_IMAGE009
is satisfied with
Figure 190634DEST_PATH_IMAGE011
，
Figure 449577DEST_PATH_IMAGE012
，gThe probability of being an active state is,
Figure 610431DEST_PATH_IMAGE013
；
s102, each user adopts a random access scheme based on energy modulation; each user is pre-assigned a dedicated pilot sequence prior to transmission
Figure 550706DEST_PATH_IMAGE014
Wherein
Figure 339670DEST_PATH_IMAGE015
For pilot length, the elements of each pilot are derived from an independent and identically distributed Gaussian distribution, i.e.
Figure 972777DEST_PATH_IMAGE016
The pilot sequences of all users are stored in the receiving end;
s103, each active user synchronously transmits pilot frequency sequence and information in each transmission time slot
Figure DEST_PATH_IMAGE017
To the receiving end, the received signal is represented as
Figure 886506DEST_PATH_IMAGE018
，
Wherein
Figure 364892DEST_PATH_IMAGE019
Is Gaussian noise, each element satisfiesThe mean value of the independent distribution is zero variance
Figure 414888DEST_PATH_IMAGE020
(ii) a gaussian distribution of;
Figure 15633DEST_PATH_IMAGE021
representing a usernThe channel parameters to the receiving end satisfy the fading channel model: all channel parameters remain unchanged at each slot, but vary from slot to slot;
s104, consider
Figure 947817DEST_PATH_IMAGE022
Wherein
Figure 433156DEST_PATH_IMAGE023
Representing attenuation coefficient, transmitting information
Figure 196713DEST_PATH_IMAGE024
Based on energy constellation points
Figure 374884DEST_PATH_IMAGE025
I.e. by
Figure 522926DEST_PATH_IMAGE026
，
Figure 936590DEST_PATH_IMAGE027
. The probability of transmission per constellation point is
Figure 430019DEST_PATH_IMAGE028
Each codebook satisfying an average power constraint, i.e.
Figure 575830DEST_PATH_IMAGE029
Order to
Figure 279343DEST_PATH_IMAGE030
Obtaining a matrix expression of the received signal,
Figure 903223DEST_PATH_IMAGE031
。
further, the step S2 includes:
s201, initialization: inputting a received signal
Figure 313476DEST_PATH_IMAGE032
Sparse parameters of usersgAttenuation parameter of channel
Figure 833450DEST_PATH_IMAGE033
And code book
Figure 24260DEST_PATH_IMAGE034
(ii) a Order to
Figure 451830DEST_PATH_IMAGE035
；
S202, iterative processing: first, thetThe process of the secondary iteration is as follows:
Figure 919851DEST_PATH_IMAGE036
Figure 469782DEST_PATH_IMAGE037
Figure 288833DEST_PATH_IMAGE038
wherein t is an integer greater than zero when the condition is satisfied
Figure 520094DEST_PATH_IMAGE039
Is stopped at the moment
Figure 232835DEST_PATH_IMAGE040
Is a set threshold;
Figure 563454DEST_PATH_IMAGE041
，
Figure DEST_PATH_IMAGE042
representing the action of a noise remover onnThe column signals are then transmitted to the display device,
Figure 932118DEST_PATH_IMAGE043
representing the first derivative of the denoiser function; the design of the denoiser will depend on the received signalYAnd transmitting the signalXThe statistical properties of; because the statistical characteristics of each user are the same, the de-noiser design of each user is the same, so as to avoid the confusion of symbols
Figure 701491DEST_PATH_IMAGE044
；
A noise remover based on the minimum mean square error is designed,
Figure 409684DEST_PATH_IMAGE045
expressed as:
Figure DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 239100DEST_PATH_IMAGE047
Figure DEST_PATH_IMAGE048
Figure 298322DEST_PATH_IMAGE049
Figure 92227DEST_PATH_IMAGE050
Figure 920505DEST_PATH_IMAGE051
is the noise variance of the AMP algorithm in t iterations, also referred to as the t-th state;
and S203, calculating the noise variance.
Figure 920823DEST_PATH_IMAGE052
And (3) calculating: in the first placetAfter the second iteration, the signal
Figure DEST_PATH_IMAGE053
Is shown as
Figure 998500DEST_PATH_IMAGE054
Wherein
Figure DEST_PATH_IMAGE055
Is Gaussian noise, satisfies
Figure 250621DEST_PATH_IMAGE056
,
Figure 464565DEST_PATH_IMAGE052
The approximate calculation is obtained according to the following formula,
Figure DEST_PATH_IMAGE057
Figure 635783DEST_PATH_IMAGE058
，
wherein
Figure DEST_PATH_IMAGE059
。
S204, outputting an estimation signal after iteration is stopped
Figure 92434DEST_PATH_IMAGE060
。
Further, the step S3 includes:
s301, initialization: input estimation signal
Figure DEST_PATH_IMAGE061
Code book
Figure 272880DEST_PATH_IMAGE062
；
Computing estimation informationEnergy of horn, order
Figure 279013DEST_PATH_IMAGE063
(ii) a When the number of the antennas is large, the distribution of the signal energy is close to Gaussian distribution;
the detection process is represented as follows,
Figure 949029DEST_PATH_IMAGE064
wherein
Figure 729860DEST_PATH_IMAGE065
The optimal threshold based on the maximum posterior probability is an orthosolution of the following quadratic equation:
Figure 120521DEST_PATH_IMAGE066
Figure 309057DEST_PATH_IMAGE067
wherein the content of the first and second substances,
Figure 149974DEST_PATH_IMAGE068
，
Figure DEST_PATH_IMAGE069
。
s302, outputting the optimal threshold
Figure 564906DEST_PATH_IMAGE070
And detecting the signal
Figure DEST_PATH_IMAGE071
. Further, based on the detection process of S3 and the optimal threshold expression, a theoretical expression of the bit error rate may be obtained:
Figure 290416DEST_PATH_IMAGE072
Figure 333459DEST_PATH_IMAGE073
further, the step S4 includes:
s401, initialization: inputting into S3 to obtain theoretical expression of bit error rate
Figure 79698DEST_PATH_IMAGE074
。
S402 equates the optimal codebook design to solving the optimization problem as follows:
Figure 778664DEST_PATH_IMAGE075
Figure 839024DEST_PATH_IMAGE076
，
the optimization problem obtains a near-optimal power constellation point through an iterative algorithm:
A. initialization: inputting the number of usersNLength of sequenceDCodebook sizeLSparse parameter g, initialization power constellation point
Figure 798889DEST_PATH_IMAGE077
Wherein
Figure 856975DEST_PATH_IMAGE078
Tolerance threshold
Figure 105554DEST_PATH_IMAGE079
Let us order
Figure 766342DEST_PATH_IMAGE080
；
B. And (3) iterative calculation:
step one, the following steps are carried out
Figure 518398DEST_PATH_IMAGE081
Substituting into the following formula to calculate:
Figure 12964DEST_PATH_IMAGE082
wherein
Figure 545577DEST_PATH_IMAGE083
；
Step two: computing
Figure DEST_PATH_IMAGE084
Step three: calculating a threshold
Figure 354264DEST_PATH_IMAGE085
，
Figure 23143DEST_PATH_IMAGE086
Wherein
Figure 688610DEST_PATH_IMAGE087
；
Step four: computing
Figure 640343DEST_PATH_IMAGE088
Figure 642934DEST_PATH_IMAGE089
The following convex problems are solved by using an interior point method and a gradient descent method:
Figure 572843DEST_PATH_IMAGE090
Figure 205950DEST_PATH_IMAGE091
Figure 447576DEST_PATH_IMAGE092
obtaining new power constellation points
Figure 191541DEST_PATH_IMAGE093
；
Step five: calculate and see if the condition is satisfied
Figure 241536DEST_PATH_IMAGE094
(ii) a If not, will
Figure 842282DEST_PATH_IMAGE095
Carrying out iteration again in the first step, and if the iteration is met, outputting
Figure 774466DEST_PATH_IMAGE095
As an optimal power point;
step six: constructing constellation points according to the obtained power points; let the constellation point codebook be
Figure 259805DEST_PATH_IMAGE096
The obtained new constellation point codebook is used as a transmitting signal codebook of the user and is used as a codebook for signal estimation, transmitting information of the user and active state detection; namely: and when the actual user accesses, taking the obtained new constellation point codebook as the transmission signal codebook of the user, and replacing the constellation point codebooks in the steps S2 and S3 to perform signal estimation, transmission information of the user and active state detection. By designing the optimal constellation points, the detection error rate of the emission information and the active state can be obviously reduced, and the performance of the whole large-scale network is optimized.
The invention has the beneficial effects that: in the large-scale random access scheme provided by the invention, a decoding algorithm with low complexity is provided, so that an approximate optimal codebook constellation point is obtained, the error rate is effectively reduced, and the communication performance is improved. In particular, message-passing based detection algorithms have a complexity that is linear to the number of users, greatly reducing the time overhead incurred by decoding. The optimal codebook design can effectively reduce the error rate and improve the energy efficiency.
Drawings
FIG. 1 is a diagram of a large scale access channel
FIG. 2 is a flow chart of a method of the present invention;
FIG. 3 is a schematic diagram of an optimal power constellation point design in an embodiment;
fig. 4 is a diagram illustrating comparison of the performance of the random access policy when L =4 in the embodiment;
fig. 5 is a diagram illustrating comparison of the performance of the random access policy when L =2 in the embodiment;
fig. 6 is a schematic diagram illustrating the trend of the performance of the random access policy with the number of antennas.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
Aiming at the problem of large-scale random access in 5G communication, the invention designs a random access method based on energy modulation, which comprises the following steps: a random transmission strategy comprising energy modulation; a message-passing based decoding method; an optimal energy constellation point design method. Considering a large-scale random access channel as shown in fig. 1, a base station needs to support uplink transmission of a large number of users at the same time. At one transmission moment, only a few users are in an active state to transmit information to the base station, and other users are in a dormant state. The base station needs to identify the active users and decode the information sent by the active users; regarding the base station as a receiving end, the specific access method is as follows:
as shown in fig. 2, a large-scale random access method based on energy modulation includes the following steps:
s1, constructing a random access model based on energy modulation;
the step S1 includes:
s101, for the content containing
Figure DEST_PATH_IMAGE097
Communication system comprising a single antenna subscriber and a receiver, each subscriber transmitting information to the receiver with a certain probability in each transmission time slot, wherein the receiver is provided with
Figure 226624DEST_PATH_IMAGE098
A root antenna; by random variables
Figure 139216DEST_PATH_IMAGE007
To describe the user
Figure 621013DEST_PATH_IMAGE008
The active nature of the slot, at each time slot,
Figure 910043DEST_PATH_IMAGE009
satisfies the following conditions:
Figure 465789DEST_PATH_IMAGE010
；
wherein the content of the first and second substances,
Figure 939496DEST_PATH_IMAGE009
is satisfied with
Figure 518376DEST_PATH_IMAGE011
，
Figure 142256DEST_PATH_IMAGE012
，gThe probability of being an active state is,
Figure 880404DEST_PATH_IMAGE013
；
s102, each user adopts a random access scheme based on energy modulation; each user is pre-assigned a dedicated pilot sequence prior to transmission
Figure 134799DEST_PATH_IMAGE099
Wherein
Figure 263292DEST_PATH_IMAGE015
For pilot length, the elements of each pilot are derived from an independent and identically distributed Gaussian distribution, i.e.
Figure 753180DEST_PATH_IMAGE016
The pilot sequences of all users are stored in the receiving end;
s103, each active user synchronously transmits pilot frequency sequence and information in each transmission time slot
Figure 486780DEST_PATH_IMAGE017
To the receiving end, the received signal is represented as
Figure 968534DEST_PATH_IMAGE018
，
Wherein
Figure 646640DEST_PATH_IMAGE019
Is Gaussian noise, each element satisfies the condition that the mean value of independent homodistribution is zero variance
Figure 612322DEST_PATH_IMAGE020
(ii) a gaussian distribution of;
Figure 934850DEST_PATH_IMAGE021
representing a usernThe channel parameters to the receiving end satisfy the fading channel model: all channel parameters remain unchanged at each slot, but vary from slot to slot;
s104, consider
Figure 593365DEST_PATH_IMAGE022
Wherein
Figure 758767DEST_PATH_IMAGE023
Representing attenuation coefficient, transmitting information
Figure 528140DEST_PATH_IMAGE024
Based on energy constellation points
Figure 970753DEST_PATH_IMAGE025
I.e. by
Figure 862486DEST_PATH_IMAGE026
，
Figure 452867DEST_PATH_IMAGE027
. The probability of transmission per constellation point is
Figure 698035DEST_PATH_IMAGE028
Each codebook satisfies the flatConstrained by the mean power, i.e.
Figure 57472DEST_PATH_IMAGE029
Order to
Figure 385685DEST_PATH_IMAGE030
Obtaining a matrix expression of the received signal,
Figure 135467DEST_PATH_IMAGE031
。
s2, estimating a combined signal of user information and a channel by using a message transmission algorithm;
the step S2 includes:
s201, initialization: inputting a received signal
Figure 840117DEST_PATH_IMAGE032
Sparse parameters of usersgAttenuation parameter of channel
Figure 54061DEST_PATH_IMAGE033
And code book
Figure 162963DEST_PATH_IMAGE034
(ii) a Order to
Figure 462357DEST_PATH_IMAGE035
；
S202, iterative processing: first, thetThe process of the secondary iteration is as follows:
Figure 439540DEST_PATH_IMAGE036
Figure 711253DEST_PATH_IMAGE037
Figure 53372DEST_PATH_IMAGE100
wherein t is an integer greater than zero when the condition is satisfied
Figure 167959DEST_PATH_IMAGE039
Is stopped at the moment
Figure 558620DEST_PATH_IMAGE040
Is a set threshold;
Figure 764734DEST_PATH_IMAGE101
，
Figure 340072DEST_PATH_IMAGE102
representing the action of a noise remover onnThe column signals are then transmitted to the display device,
Figure 817321DEST_PATH_IMAGE043
representing the first derivative of the denoiser function; the design of the denoiser will depend on the received signalYAnd transmitting the signalXThe statistical properties of; because the statistical characteristics of each user are the same, the de-noiser design of each user is the same, so as to avoid the confusion of symbols
Figure 73990DEST_PATH_IMAGE044
；
A noise remover based on the minimum mean square error is designed,
Figure 444928DEST_PATH_IMAGE045
expressed as:
Figure 66533DEST_PATH_IMAGE103
wherein the content of the first and second substances,
Figure 562237DEST_PATH_IMAGE104
Figure 684914DEST_PATH_IMAGE105
Figure 582462DEST_PATH_IMAGE106
Figure 640548DEST_PATH_IMAGE107
Figure 951444DEST_PATH_IMAGE051
is the noise variance of the AMP algorithm in t iterations, also referred to as the t-th state;
and S203, calculating the noise variance.
Figure 753178DEST_PATH_IMAGE052
And (3) calculating: in the first placetAfter the second iteration, the signal
Figure 505233DEST_PATH_IMAGE053
Is shown as
Figure 124433DEST_PATH_IMAGE054
Wherein
Figure 532412DEST_PATH_IMAGE055
Is Gaussian noise, satisfies
Figure 403416DEST_PATH_IMAGE056
,
Figure 275557DEST_PATH_IMAGE052
The approximate calculation is obtained according to the following formula,
Figure 800079DEST_PATH_IMAGE108
Figure 226513DEST_PATH_IMAGE109
，
wherein
Figure 432366DEST_PATH_IMAGE059
；
S204, stop iterationOutputting the estimated signal after the end
Figure 34380DEST_PATH_IMAGE060
。
S3, detecting the transmitting information and the active state of each user by utilizing an algorithm based on the maximum posterior probability, and outputting an optimal threshold
Figure 130469DEST_PATH_IMAGE110
And detecting the signal
Figure 309777DEST_PATH_IMAGE111
And determining the theoretical expression of the bit error rate according to the bit error rate
Figure 788163DEST_PATH_IMAGE112
；
The step S3 includes:
s301, initialization: input estimation signal
Figure 369317DEST_PATH_IMAGE061
Code book
Figure 235642DEST_PATH_IMAGE062
；
Calculating the energy of the estimated signal, order
Figure 902247DEST_PATH_IMAGE063
(ii) a When the number of the antennas is large, the distribution of the signal energy is close to Gaussian distribution;
the detection process is represented as follows,
Figure 387586DEST_PATH_IMAGE064
wherein
Figure 88826DEST_PATH_IMAGE065
The optimal threshold based on the maximum posterior probability is an orthosolution of the following quadratic equation:
Figure 860473DEST_PATH_IMAGE066
Figure 483215DEST_PATH_IMAGE067
wherein the content of the first and second substances,
Figure 568983DEST_PATH_IMAGE068
，
Figure 187046DEST_PATH_IMAGE069
。
s302, outputting the optimal threshold
Figure 270539DEST_PATH_IMAGE113
And detecting the signal
Figure 911736DEST_PATH_IMAGE071
. Further, based on the detection process of S3 and the optimal threshold expression, a theoretical expression of the bit error rate may be obtained:
Figure 129091DEST_PATH_IMAGE072
Figure 477027DEST_PATH_IMAGE114
and S4, carrying out optimal constellation point codebook design.
The step S4 includes:
s401, initialization: inputting into S3 to obtain theoretical expression of bit error rate
Figure 793739DEST_PATH_IMAGE074
。
S402, designing an optimal codebook equivalently to solve the following optimization problems:
Figure 250128DEST_PATH_IMAGE075
Figure 615381DEST_PATH_IMAGE076
the optimization problem obtains a near-optimal power constellation point through an iterative algorithm:
A. initialization: inputting the number of usersNLength of sequenceDCodebook sizeLSparse parameter g, initialization power constellation point
Figure 473616DEST_PATH_IMAGE077
Wherein
Figure 961229DEST_PATH_IMAGE078
Tolerance threshold
Figure 249122DEST_PATH_IMAGE079
Let us order
Figure 474524DEST_PATH_IMAGE080
；
B. And (3) iterative calculation:
step one, the following steps are carried out
Figure 921686DEST_PATH_IMAGE081
Substituting into the following formula to calculate:
Figure DEST_PATH_IMAGE115
wherein
Figure 986725DEST_PATH_IMAGE083
；
Step two: computing
Figure 417706DEST_PATH_IMAGE084
Step three: calculating a threshold
Figure 124762DEST_PATH_IMAGE116
，
Figure 364114DEST_PATH_IMAGE086
Wherein
Figure 521425DEST_PATH_IMAGE087
；
Step four: computing
Figure 783911DEST_PATH_IMAGE088
Figure DEST_PATH_IMAGE117
The following convex problems are solved by using an interior point method and a gradient descent method:
Figure 888133DEST_PATH_IMAGE090
Figure 450832DEST_PATH_IMAGE091
Figure 513466DEST_PATH_IMAGE092
obtaining new power constellation points
Figure 325564DEST_PATH_IMAGE093
；
Step five: calculate and see if the condition is satisfied
Figure 640002DEST_PATH_IMAGE094
(ii) a If not, will
Figure 916263DEST_PATH_IMAGE095
Carrying out iteration again in the first step, and if the iteration is met, outputting
Figure 87481DEST_PATH_IMAGE095
As the optimum power point.
Step six: constructing constellation points according to the obtained power points; let the constellation point codebook be
Figure 590138DEST_PATH_IMAGE096
The obtained new constellation point codebook is used as a transmitting signal codebook of the user and is used as a codebook for signal estimation, transmitting information of the user and active state detection; that is to say: and when the actual user accesses, taking the obtained new constellation point codebook as the transmission signal codebook of the user, and replacing the constellation point codebooks in the steps S2 and S3 to perform signal estimation, transmission information of the user and active state detection. By designing the optimal constellation points, the detection error rate of the emission information and the active state can be obviously reduced, and the performance of the whole large-scale network is optimized.
In the embodiments of the present application, some simulation results are given to verify the feasibility of the proposed random access scheme. The experimental parameters were selected as: number of usersN=2000, sequence lengthD=500，
Figure 567321DEST_PATH_IMAGE118
. First, a common coherent random access method is introduced as a comparison: firstly, carrying out joint detection and estimation on the active state and the channel of a user by using an AMP algorithm; and then, the active users adopt a PAM modulation mode for modulation and decoding. By choosing a suitable slot length, the sum rate of our random access scheme can be made consistent with the coherent random access strategy.
We verified the proposed optimal constellation point algorithm. As shown in fig. 3, we compare the power constellation points obtained by the algorithm with the optimal constellation points obtained by the search algorithm. It can be observed from the figure that the algorithm obtains the design of the constellation point with the constellation power point approaching the optimal.
Next, the performance of the proposed random access policy and the coherent random access policy are compared. As shown in fig. 4 and 5, by plotting the trend of symbol error rate varying with the signal-to-noise ratio, it can be observed that the theoretical analysis of the symbol error probability of the proposed random access scheme is very consistent with the experimental results and better than the coherent access strategy. For example, when L =4, M =40, the performance of the scheme proposed herein can be up to 2 dB better than the performance of the coherent scheme with a signal-to-noise ratio of less than 15 dB. This is because when the transmission packet is short and the signal-to-noise ratio is low, the performance of the coherent scheme becomes poor due to erroneous channel estimation.
Then, the effect of the variation of the number of different antennas on the proposed random access scheme was tested. Consider, as shown in FIG. 6L=4, it can be observed that the error rate is continuously decreasing with the increase of the number of antennas, and the method proposed in the present application is better than the conventional coherent random access method. For example, to achieve an error rate of 0.001, the number of antennas required for the random access policy is 20 less than that of the coherent access policy.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (3)

1. A large-scale random access method based on energy modulation is characterized in that: the method comprises the following steps:
s1, constructing a random access model based on energy modulation;
s2, joint signal of user information and channel by using message transfer algorithm
Figure 232092DEST_PATH_IMAGE001
Carrying out estimation;
s201, initialization: inputting a received signal
Figure 73009DEST_PATH_IMAGE002
Sparse parameters of usersgAttenuation parameter of channel
Figure 409312DEST_PATH_IMAGE003
And code book
Figure 42812DEST_PATH_IMAGE004
(ii) a Order to
Figure 413751DEST_PATH_IMAGE005
；
S202, iterative processing: first, thetThe process of the secondary iteration is as follows:
Figure 159990DEST_PATH_IMAGE006
Figure 531059DEST_PATH_IMAGE007
Figure 450474DEST_PATH_IMAGE008
wherein t is an integer greater than zero when the condition is satisfied
Figure 410340DEST_PATH_IMAGE009
Is stopped at the moment
Figure 842327DEST_PATH_IMAGE010
Is a set threshold value of the threshold value,
Figure 215539DEST_PATH_IMAGE011
Figure 876328DEST_PATH_IMAGE012
，
Figure 503749DEST_PATH_IMAGE013
representing the action of a noise remover onnThe column signals are then transmitted to the display device,
Figure 857370DEST_PATH_IMAGE014
representing the first derivative of the denoiser function; the design of the denoiser will depend on the received signalYAnd transmitting the signalXThe statistical properties of; because the statistical characteristics of each user are the same, the de-noiser design of each user is the same, so as to avoid the confusion of symbols
Figure 655562DEST_PATH_IMAGE015
；
Figure 446274DEST_PATH_IMAGE016
，
Figure 115153DEST_PATH_IMAGE017
A dedicated pilot sequence pre-allocated to each user prior to transmission, wherein
Figure 905254DEST_PATH_IMAGE018
The element of each pilot frequency is obtained by independent Gaussian distribution with the same distribution as the pilot frequency length;
Figure 738212DEST_PATH_IMAGE019
，
Figure 740803DEST_PATH_IMAGE020
representing the number of users;
a noise remover based on the minimum mean square error is designed,
Figure 795347DEST_PATH_IMAGE021
expressed as:
Figure 802355DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure 43981DEST_PATH_IMAGE023
Figure 115842DEST_PATH_IMAGE024
，
Figure 837941DEST_PATH_IMAGE025
，
Figure 438687DEST_PATH_IMAGE026
.
Figure 698767DEST_PATH_IMAGE027
is the noise variance of the AMP algorithm in t iterations, also referred to as the t-th state;
s203, calculating the noise variance;
Figure 357675DEST_PATH_IMAGE028
and (3) calculating: in the first placetAfter the second iteration, the signal
Figure 121231DEST_PATH_IMAGE029
Is shown as
Figure 705928DEST_PATH_IMAGE030
Wherein
Figure 453304DEST_PATH_IMAGE031
Is Gaussian noise, satisfies
Figure 601388DEST_PATH_IMAGE032
,
Figure 531036DEST_PATH_IMAGE033
The approximate calculation is obtained according to the following formula,
Figure 739164DEST_PATH_IMAGE034
Figure 708257DEST_PATH_IMAGE035
，
wherein
Figure 207502DEST_PATH_IMAGE036
;
Figure 945651DEST_PATH_IMAGE037
For receiving signals
Figure 121417DEST_PATH_IMAGE038
The variance of the gaussian noise contained in (a);
s204, outputting an estimation signal after iteration is stopped
Figure 830004DEST_PATH_IMAGE039
；
S3, detecting the transmitting information and the active state of each user by utilizing an algorithm based on the maximum posterior probability, and outputting an optimal threshold
Figure 319891DEST_PATH_IMAGE040
And detecting the signal
Figure 974863DEST_PATH_IMAGE041
And determining the theoretical expression of the bit error rate according to the bit error rate
Figure 541105DEST_PATH_IMAGE042
；
S4, carrying out optimal constellation point codebook design:
s401, initialization: inputting into S3 to obtain theoretical expression of bit error rate
Figure 219211DEST_PATH_IMAGE042
.
S402, designing an optimal codebook equivalently to solve the following optimization problems:
Figure 309527DEST_PATH_IMAGE043
Figure 5956DEST_PATH_IMAGE044
the optimization problem obtains a near-optimal power constellation point through an iterative algorithm:
A. initialization: inputting the number of usersNPilot lengthDCodebook sizeLSparse parameter g, initialization power constellation point
Figure 789104DEST_PATH_IMAGE045
Wherein
Figure 954506DEST_PATH_IMAGE046
Tolerance threshold
Figure 599246DEST_PATH_IMAGE047
Let us order
Figure 697652DEST_PATH_IMAGE048
；
B. And (3) iterative calculation:
step one, the following steps are carried out
Figure 841581DEST_PATH_IMAGE049
Substituting into the following formula to calculate:
Figure 291017DEST_PATH_IMAGE050
wherein
Figure 473868DEST_PATH_IMAGE051
；
Step two: computing
Figure 692360DEST_PATH_IMAGE052
Figure 754994DEST_PATH_IMAGE053
The number of the receiving end antennas is;
step three: calculating a threshold
Figure 675414DEST_PATH_IMAGE054
Figure 176803DEST_PATH_IMAGE055
Wherein
Figure 203796DEST_PATH_IMAGE056
；
Step four: computing
Figure 499648DEST_PATH_IMAGE057
Figure 861359DEST_PATH_IMAGE058
The following convex problems are solved by using an interior point method and a gradient descent method:
Figure 875758DEST_PATH_IMAGE059
Figure 272105DEST_PATH_IMAGE060
Figure 489591DEST_PATH_IMAGE061
obtaining new power constellation points
Figure 338598DEST_PATH_IMAGE062
；
Step five: calculate and see if the condition is satisfied
Figure 916210DEST_PATH_IMAGE063
(ii) a If not, will
Figure 416330DEST_PATH_IMAGE064
Carrying out iteration again in the first step, and if the iteration is met, outputting
Figure 257247DEST_PATH_IMAGE064
As an optimal power point;
step six: constructing constellation points according to the obtained power points; let the constellation point codebook be
Figure 655867DEST_PATH_IMAGE065
And using the obtained new constellation point codebook as a transmitting signal codebook of the user and as a codebook for signal estimation, transmitting information of the user and active state detection.
2. The massive random access method based on energy modulation as claimed in claim 1, wherein: the step S1 includes:
s101, for the content containing
Figure 725586DEST_PATH_IMAGE066
Communication system comprising a single antenna subscriber and a receiver, each subscriber transmitting information to the receiver with a predetermined probability in each transmission time slot, wherein the receiver is provided with
Figure 96524DEST_PATH_IMAGE067
A root antenna; by random variables
Figure 905080DEST_PATH_IMAGE068
To describe the user
Figure 715298DEST_PATH_IMAGE069
The active nature of the slot, at each time slot,
Figure 103554DEST_PATH_IMAGE070
satisfies the following conditions:
Figure 125736DEST_PATH_IMAGE071
；
wherein the content of the first and second substances,
Figure 793609DEST_PATH_IMAGE072
is satisfied with
Figure 166822DEST_PATH_IMAGE073
，gThe probability of being an active state, also called the sparse parameter of the user,
Figure 93189DEST_PATH_IMAGE074
；
s102, each user adopts a random access scheme based on energy modulation; each user is pre-assigned a dedicated pilot sequence prior to transmission
Figure 156829DEST_PATH_IMAGE075
Wherein
Figure 776029DEST_PATH_IMAGE076
For pilot length, the elements of each pilot are derived from an independent and identically distributed Gaussian distribution, i.e.
Figure 370959DEST_PATH_IMAGE077
The pilot sequences of all users are stored in the receiving end;
s103, each active user synchronously transmits pilot frequency sequence and information in each transmission time slot
Figure 320591DEST_PATH_IMAGE078
To the receiving end, the received signal is represented as
Figure 317366DEST_PATH_IMAGE079
Wherein
Figure 841889DEST_PATH_IMAGE080
Is Gaussian noise, each element satisfies the condition that the mean value of independent homodistribution is zero variance
Figure 910732DEST_PATH_IMAGE081
(ii) a gaussian distribution of;
Figure 444482DEST_PATH_IMAGE082
representing a usernThe channel parameters to the receiving end satisfy the fading channel model: all channel parameters remain unchanged at each slot, but vary from slot to slot;
s104, consider
Figure 233446DEST_PATH_IMAGE083
Wherein
Figure 476340DEST_PATH_IMAGE084
Representing attenuation coefficient, transmitting information
Figure 249124DEST_PATH_IMAGE085
Based on energy constellation points
Figure 55406DEST_PATH_IMAGE086
Figure 10461DEST_PATH_IMAGE087
(ii) a The probability of transmission per constellation point is
Figure 142365DEST_PATH_IMAGE088
Each codebook satisfying an average power constraint, i.e.
Figure 871287DEST_PATH_IMAGE089
Order to
Figure 294309DEST_PATH_IMAGE090
Obtaining a matrix expression of the received signal,
Figure 57866DEST_PATH_IMAGE091
。
3. the massive random access method based on energy modulation as claimed in claim 1, wherein: the step S3 includes:
s301, initialization: input estimation signal
Figure 144027DEST_PATH_IMAGE092
Code book
Figure 625824DEST_PATH_IMAGE093
；
Calculating the energy of the estimated signal, order
Figure 836225DEST_PATH_IMAGE094
；
The detection process is represented as follows,
Figure 1758DEST_PATH_IMAGE095
wherein
Figure 741044DEST_PATH_IMAGE096
The optimal threshold based on the maximum posterior probability is an orthosolution of the following quadratic equation:
Figure 693826DEST_PATH_IMAGE097
Figure 707918DEST_PATH_IMAGE098
wherein the content of the first and second substances,
Figure 180488DEST_PATH_IMAGE099
，
Figure 372566DEST_PATH_IMAGE100
，
Figure 563376DEST_PATH_IMAGE101
；
s302, outputting the optimal threshold
Figure 318842DEST_PATH_IMAGE102
And detecting the signal
Figure 237730DEST_PATH_IMAGE103
And determining a theoretical expression of the bit error rate according to the bit error rate:
Figure 787660DEST_PATH_IMAGE104
Figure 13236DEST_PATH_IMAGE105
。
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