CN116701882B - Self-adaptive multi-beam alignment method based on question-answer learning - Google Patents

Self-adaptive multi-beam alignment method based on question-answer learning Download PDF

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CN116701882B
CN116701882B CN202310920681.2A CN202310920681A CN116701882B CN 116701882 B CN116701882 B CN 116701882B CN 202310920681 A CN202310920681 A CN 202310920681A CN 116701882 B CN116701882 B CN 116701882B
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周琳
白琳
孙椿淞
苏阳
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Abstract

The invention discloses a self-adaptive multi-beam alignment method based on question-answer learning, which belongs to the technical field of multi-beam alignment and comprises the following steps: s1: constructing a mathematical model of the beam alignment problem and the beam search process; s2: initializing the probability of targets existing in each subspace; s3: randomly generating a fixed length beam search vector; s4: determining an antenna weight vector according to the beam search vector; s5: multiplying the antenna weight vector with the signal and squaring the multiplied antenna weight vector to calculate the signal power; s6: comparing the signal power with a threshold value to obtain a beam search result; s7: updating the posterior probability corresponding to each subspace according to the generated beam search vector and the corresponding search result; s8: if the maximum posterior probability is greater than the error probability, the target is considered to exist in the subspace corresponding to the maximum posterior probability; s9: the algorithm is iteratively performed until no distinguishable new users are found after the full scan of the base station.

Description

Self-adaptive multi-beam alignment method based on question-answer learning
Technical Field
The invention belongs to the technical field of multi-beam alignment, and particularly relates to a self-adaptive multi-beam alignment method based on question-answer learning.
Background
Beamforming techniques are one of the spatial division multiple access (Space Division Multiple Access, SDMA) techniques that rely on massive multiple input multiple output (Multiple Input Multiple Output, MIMO) techniques to "steer" an antenna array beam into a directional beam of high gain in a desired direction, but low gain or even no gain in a desired direction, by weighted summation of the individual element signals. Beamforming techniques are largely divided into two types, analog beamforming and digital beamforming, with the latter relying largely on channel estimation techniques. However, in a large-scale MIMO communication system, the channel environment is very complex, and the calculation overhead of the conventional channel estimation method is too large to apply. Therefore, massive MIMO communication systems mainly employ analog beamforming techniques. In analog beamforming techniques, the process of finding the desired direction to obtain the maximum beamforming gain is called beam alignment, also called beam training or beam searching, which is the first step of the beam alignment technique.
In consideration of the fact that the actual communication environment is complex and changeable and mutual interference among multiple users exists, deviation is easy to generate in the beam alignment process. Conventional robust adaptive beam alignment algorithms are largely divided into correlation algorithms based on steering vector correction and correlation algorithms based on Interference-plus-Noise Covariance (INC) matrix reconstruction. However, in an actual scene, the generation factors of the deviation cannot be accurately determined, and the possibility of the combined action of multiple generation factors cannot be eliminated, so that the applicability of most robust adaptive beam alignment algorithms is relatively narrow. In addition, most of the algorithms consider a single-user scenario, a single-base-station multi-user downlink scenario or a single-base-station multi-user uplink scenario of non-simultaneous (TDMA) non-same frequency (OFDMA) non-same received signal strength (NOMA), and less research is conducted on the single-base-station multi-user uplink scenario of simultaneous same frequency and same received signal strength. The current multi-beam alignment method based on classical sortPM algorithm expansion mainly focuses on multi-user joint search, namely, the search range is set as the combination of the distribution conditions of each user, and the posterior probability of all combinations is updated according to the search result. The method has the advantages of low algorithm convergence speed, large calculation amount and high complexity, and is difficult to meet the requirement of low time delay in an actual scene.
Disclosure of Invention
Accordingly, the present invention is directed to a robust low-complexity adaptive multi-beam alignment method suitable for the single-base-station multi-user uplink scenario with the same-frequency and same-received signal strength.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an adaptive multi-beam alignment method based on question-answer learning comprises the following steps:
s1: constructing a mathematical model of the beam alignment problem and the beam search process;
s2: initializing the probability of targets existing in each subspace;
s3: randomly generating a fixed length beam search vector;
s4: determining an antenna weight vector according to the beam search vector;
s5: multiplying the antenna weight vector with the signal and squaring the multiplied antenna weight vector to calculate the signal power;
s6: comparing the signal power with a threshold value to obtain a beam search result;
s7: updating the posterior probability corresponding to each subspace according to the generated beam search vector and the corresponding search result;
s8: judging whether the maximum posterior probability is greater than 1-Wherein->Indicating an error probability threshold set by the base station; if the number is smaller than the preset number, returning to the step S3; if the target is larger than the target, the target exists in the subspace corresponding to the maximum posterior probability;
s9: and the base station performs full scanning to judge whether distinguishable new users exist, if so, the step S2 is returned to for carrying out beam searching again until the distinguishable new users are not found after the full scanning of the base station.
Further, the mathematical model for constructing the beam alignment problem and the beam searching process in step S1 specifically includes:
abstracting the base station and the surrounding space into sphere-shaped space, transmitting signals emitted by each user from the sphere to the sphere center, transmitting the signals to the base stationThe space of the size is divided into->Equal-sized subspace->；
Equivalent beam alignment problem as inSearching some random variables on subspace, the resolution of beam search is +.>The method comprises the steps of carrying out a first treatment on the surface of the In length of +.>If +.>The value of each element is 1, which means that the sub-beam search will determine whether the user is at +.>In the subspace corresponding to the element, if +.>The value of each element is 0, which means that the sub-beam search does not determine whether the user is at +.>The subspace corresponding to the individual elements.
Further, the step S2 specifically includes:
in the initial stage, a base station firstly performs full-wave beam scanning once to confirm whether user access exists; assume thatIndividual userDistributed at->The probabilities within the subspaces are the same and independent of each other, then for anySubspace->The probability of the presence of a target is initialized to +.>。
Further, the step S3 specifically includes: at the position ofFirst, theIn the secondary beam search, it is assumed that the array antenna of the base station receives the signals from this +.>The signal of the same frequency and signal strength of the individual user +.>The method comprises the steps of carrying out a first treatment on the surface of the The base station is probability +.>Randomly generating a length of +.>Binary beam search vector +.>Wherein->。
Further, the steps S4-S6 specifically include: according toFind the corresponding antenna weight vector +.>And uses it and signal->Multiplying and squaring to obtain signal power processed by array antenna>And comparing the signal power with a preset signal power threshold; if the signal power exceeds the threshold value, the result of this beam search is +.>A reference numeral 1, indicating the presence of a target in the search area; if the signal power does not exceed the threshold value,the result of this beam search is +.>A flag of 0 indicates that no target is present in the search area.
Further, the step S7 specifically includes: through the firstSearching sub-beam to obtain search result +.>Then, search vector is searched by combining binary wave beams>Updating posterior probability corresponding to each subspace>The specific formula is as follows
Assuming that the base station is interfered with, the calculated signal power thereofThe probability of falsely being above or below the set threshold is +.>Abstracting it to a probability +.>The posterior probability update factorThe definition is as follows
。
Further, in step S8, let the first stepSubscript +.>Is that
Then assume that the error probability threshold set by the base station isThrough->After the secondary beam search, if
Then consider the subspace asTargets are present.
Further, in step S9, the probability corresponding to each subspace is reinitialized toAnd repeating from step S3, wherein a binary beam search vector is generated +.>When in use, let->Other operations are unchanged; through->After the sub-beam search, another subspace +.>。
Advancing oneIn practice, multiple users may be located in the same subspace, in which case the users should be considered indistinguishable, forRepresenting a distinguishable target number; if no distinguishable new user is found after the base station is scanned completely, the algorithm is ended, beam alignment is realized between a single base station and multiple users, and the total search frequency is +.>。
The invention has the beneficial effects that: analysis and simulation show that in the single-base station multi-user uplink scene, compared with the traditional joint self-adaptive multi-beam alignment algorithm, the algorithm provided by the invention has higher accuracy and smaller fluctuation, the number of beam searching times required for reducing the same angle error is smaller, the beam searching time is obviously smaller, and the beam searching time is slowly increased along with the continuous increase of the subspace base. In summary, the invention is a low-complexity adaptive multi-beam alignment algorithm based on question-answer learning, which can realize rapid and accurate beam alignment in a single-base-station multi-user uplink scene.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
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In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a diagram of a single-base-station multi-user uplink scenario;
FIG. 2 is a schematic diagram of a uniform planar array antenna according to the present invention;
FIG. 3 is a schematic diagram of antenna direction angle and pitch angle;
FIG. 4 is a diagram of a binary symmetric channel;
FIG. 5 is a flow chart of a non-adaptive beam alignment algorithm;
FIG. 6 is a flow chart of an adaptive beam alignment algorithm;
FIG. 7 is a schematic diagram of a beam search process;
fig. 8 is a flowchart of a multi-beam alignment algorithm according to the present invention;
fig. 9 is a pseudo code of a multi-beam alignment algorithm according to the present invention;
FIG. 10 is a simulation of the accuracy of a question-and-answer beam alignment algorithm;
FIG. 11 is a simulation graph of angle error and beam search times for a question-and-answer beam alignment algorithm;
FIG. 12 is a simulation diagram of beam search time and number of beam searches for a solution beam alignment algorithm;
fig. 13 is a simulation diagram of the beam search time and subspace radix of the solution beam alignment algorithm.
Detailed Description
The invention provides a self-adaptive multi-beam alignment method based on question-answer learning, which assumes that the incoming wave direction of a signal received by a base station is the direction of a user transmitting the signal, and considers signal interference among multiple users to consider that each judgment of beam searching is in error according to a certain specific probability. Therefore, the method provided by the invention has the simple process that a plurality of users send signals to one base station simultaneously with the same frequency and the same received signal strength, and after the base station receives the superimposed signals, the direction angles of the users are accurately obtained through a question-answer learning method under the condition that errors are possible to be judged, and the system structure diagram is shown in figure 1.
Considering a single base station multi-user uplink scenario, assuming that the array antennas on the base station are uniform planar arrays (Uniform Planar Array, UPA) as shown in FIG. 2, the steering vector for signals from a certain direction is
Wherein there is in the horizontal directionA root antenna with +.>Root antenna->Representing the wavelength of the millimeter wave signal, ">Representing antenna array spacing, +.>And->Respectively azimuth angle and pitch angle in physical space and are independent of each other, as shown in FIG. 3,/->，/>。
Assuming simultaneous co-frequency presenceThe signal strength of the signals transmitted by the users received by the base station is basically the same. Base station antenna array is->The signals received at the moment are:
wherein,,representation->Time->Signals of individual users in the channel. The signal processed by the base station UPA is that
Wherein,,representing the antenna weight vector of the UPA.
If only one of the spaces comes from the directionIs directed to a signal of (2) whose vector is +.>. When the antenna weight vector acts +.>When (I)>Maximum, a guiding and positioning effect is achieved thereby. If there are multiple signals from different directions in the space +.>According to the angular domain separation strategy, the antenna weight vector can be madeWherein->Representing the beam fusion coefficient vector, ">. Therefore, the base station changes the direction angle and pitch angle according to the beam search codebook, obtains the corresponding weighted guide vector group, and then obtains the signals received by the transposed right-multiplied antenna array +.>If the output value exceeds a certain set threshold value after squaring, the user exists in the area corresponding to the weighted guide vector group, otherwise, the user does not exist.
From the base station received by the wave interferenceThe signals of the individual users interfere with each other, so that the following two erroneous judgments occur when judging whether the user exists in the search area. Taking two users as an example, if the phases of the signals received by the base station are basically the same, the two signals are mutually overlapped, and the signals received by the antenna array are +.>Larger, also in case of search area errors, may be higher than the set threshold; if the phases of the signals received by the base station are basically opposite, the two signals cancel each other, and the signals received by the antenna array are +>Smaller, also below a set threshold in case the search area is correct.
Considering the above two misjudgment cases, assuming that the probability of misjudgment is p, a binary symmetric channel (Binary Symmetric Channel, BSC) with the probability of p can be used for representing, and the true answer is input as whether the search area has the target or not, and the actual answer is output as the interfered actual answer, as shown in fig. 4. In the beam alignment scene based on question-answer learning, there are two beam alignment modes as well, and both modes have advantages. One is that the search area for each beam search is randomly generated and the number of searches is fixed, this way is called non-adaptive beam alignment, as shown in fig. 5; another is that the search area of each beam search is generated from the previous search area and its corresponding search result and the number of searches is not fixed, which is called adaptive beam alignment, as shown in fig. 6. Compared with the non-adaptive beam alignment algorithm, the adaptive beam alignment algorithm has low error rate, and needs less beam searching times, but has complex algorithm and high calculation complexity, and needs to research the low-complexity algorithm to meet the requirement of low time delay in actual engineering scenes.
Specifically, as shown in fig. 8, the invention provides a low-complexity query number self-adaptive multi-beam alignment algorithm based on question-answer learning, and the specific flow is described as follows:
1. the base station and the surrounding space are abstracted into sphere-shaped space, and the signals emitted by each user are emitted to the sphere center from the sphere. Will beThe space of the size is divided into->Equal-sized subspace->The beam alignment problem can be equated to +.>Searching some random variables on subspace, the resolution (dividing value) of beam searching is +.>. The area corresponding to the single beam search process is shown in fig. 7 from the sphere. In length of +.>If +.>The value of each element is 1, which means that the sub-beam search will determine whether the user is at +.>In the subspace corresponding to the element, if +.>The value of each element is 0, which means that the sub-beam search does not determine whether the user is at +.>The subspace corresponding to the individual elements.
2. In the initial stage of the algorithm, the base station firstly performs full-wave beam scanning once to confirm whether user access exists. Assume thatPersonal user->Distributed at->The probabilities within the subspaces are the same and independent of each other, then for anySubspace->The probability of the presence of a target can be initialized to +.>。
3. In the first placeIn the secondary beam search, it is assumed that the array antenna of the base station receives the signals from this +.>The signal of the same frequency and signal strength of the individual user +.>. The base station is probability +.>Randomly generating a length of +.>Binary beam search vector +.>Wherein->According to->Find the corresponding antenna weight vector +.>And uses it and signal->Multiplying and squaring to obtain signal power processed by array antennaAnd compared with a preset signal power threshold. If the signal power exceeds the threshold value, the result of this beam search is +.>A reference numeral 1, indicating the presence of a target in the search area; if the signal power does not exceed the threshold value, the result of this beam search is +.>A flag of 0 indicates that no target is present in the search area.
4. Through the firstSearching sub-beam to obtain search result +.>Then, search vector is searched by combining binary wave beams>Updating posterior probability corresponding to each subspace>The specific formula is as follows
Assuming that the base station is interfered with, the calculated signal power thereofThe probability of falsely being above or below the set threshold is +.>Abstracting it to a probability +.>The posterior probability update factorThe definition is as follows
5. Let the first orderSubscript +.>Is that
Then assume that the error probability threshold set by the base station isThrough->After the secondary beam search, if
Then it can be considered as subspaceTargets are present.
6. Reinitializing the probability corresponding to each subspace asAnd repeating the above operation from the third step, wherein the binary beam search vector is generated +.>When in use, let->Other operations are unchanged. Thus, go through->After the sub-beam search, another subspace +.>. In a practical scenario, multiple users may be located in the same subspace, in which case these users may be considered indistinguishable, with +.>Representing the number of distinguishable targets. If no distinguishable new user is found after the base station is scanned completely, the algorithm is ended, beam alignment is realized between a single base station and multiple users, and the total search frequency is +.>。
Fig. 9 is a pseudo code of the multi-beam alignment algorithm according to the present invention, and the simulation results of the present invention are as follows:
(1) When subspace radixNumber of targets->Beam search vector generation probability/>Error probability of single beam search +.>Error probability threshold +.>When, as shown in fig. 10, compared with the joint multi-beam alignment algorithm expanded based on the classical sortPM algorithm, the accuracy of the algorithm provided by the invention is generally higher and the fluctuation is smaller.
(2) When subspace radixNumber of targets->Beam search vector generation probability +.>Error probability of single beam search +.>Error probability threshold +.>When, as shown in fig. 11, compared with the joint multi-beam alignment algorithm expanded based on the classical sortPM algorithm, the beam search frequency required by the algorithm to be reduced to the same angle error is less.
(3) When the target numberBeam search vector generation probability +.>Single beam search error probabilityError probability threshold +.>When, as shown in fig. 12, compared with the joint multi-beam alignment algorithm expanded based on the classical sortPM algorithm, the beam search time of the algorithm provided by the invention is significantly less, and as the subspace radix +_j->The beam search time of the conventional algorithm increases rapidly as it is continuously increased.
(4) When the target numberBeam search vector generation probability +.>Single beam search error probabilityError probability threshold +.>At this time, as shown in FIG. 13, with subspace radix +.>The beam search time of the proposed algorithm grows very slowly.
Finally, it is noted that the above-mentioned preferred embodiments are only intended to illustrate rather than limit the invention, and that, although the invention has been described in detail by means of the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims (7)

1. An adaptive multi-beam alignment method based on question-answer learning is characterized in that: the method comprises the following steps:
s1: constructing a mathematical model of the beam alignment problem and the beam search process;
s2: initializing the probability of targets existing in each subspace;
s3: randomly generating a fixed length beam search vector;
s4: determining an antenna weight vector according to the beam search vector;
s5: multiplying the antenna weight vector with the signal and squaring the multiplied antenna weight vector to calculate the signal power;
s6: comparing the signal power with a threshold value to obtain a beam search result;
s7: updating the posterior probability corresponding to each subspace according to the generated beam search vector and the corresponding search result;
s8: judging whether the maximum posterior probability is greater thanWherein->Indicating an error probability threshold set by the base station; if the number is smaller than the preset number, returning to the step S3; if the target is larger than the target, the target exists in the subspace corresponding to the maximum posterior probability;
s9: the base station performs full scanning to judge whether distinguishable new users exist, if so, the step S2 is returned to for beam searching again until no distinguishable new users are found after the full scanning of the base station;
the mathematical model for constructing the beam alignment problem and the beam searching process in step S1 specifically includes:
abstracting the base station and the surrounding space into sphere-shaped space, the signals emitted by each user are emitted to the sphere center from the sphere, and the signals are transmitted to the sphere center by [0,2 pi ] 2 Space of size is divided into M 2 Equal-sized subspaces
Equivalent beam alignment problem to M 2 Searching some random variables on subspace, and the resolution of beam searching is thatAt length M 2 Is to beam search of (a)In the cable vector, if the value of the ith element is 1, the secondary beam search is indicated to judge whether the user is in the subspace corresponding to the ith element, and if the value of the ith element is 0, the secondary beam search is indicated to not judge whether the user is in the subspace corresponding to the ith element;
the step S7 specifically comprises the following steps: after the t-th beam search is carried out to obtain a search result b (t), updating the posterior probability pi corresponding to each subspace by combining the binary beam search vector A (t) i (t) the specific formula is as follows
Wherein the method comprises the steps ofThe base station is assumed to be interfered so that the calculated signal power is Y (t) 2 If the probability of erroneously being higher or lower than the set threshold is g (p) ∈ [0,0.5 ], which is abstracted into a BSC channel with probability of g (p), the posterior probability update factor f (b (t) |i, a (t)) is defined as follows
2. The adaptive multi-beam alignment method based on learning-by-questioning-and-answering according to claim 1, wherein: the step S2 specifically comprises the following steps:
in the initial stage, a base station firstly performs full-wave beam scanning once to confirm whether user access exists; let k users beDistributed at M 2 The probabilities within the subspaces are the same and independent of each other, then for any i ε [ M ] 2 ]Subspace I i The probability of the presence of a target is initialized to +.>
3. The adaptive multi-beam alignment method based on learning-by-questioning-and-answering of claim 2, wherein: the step S3 specifically comprises the following steps: in the t-th beam search, the array antenna of the base station is assumed to simultaneously receive signals X (t) with the same frequency and the same signal strength from the k users; the base station follows the Bernoulli distribution probability p E (0, 0.5)]Randomly generating a length M 2 Binary beam search vector a (t), wherein
4. The adaptive multi-beam alignment method based on learning-by-questioning-and-answering of claim 3, wherein: the steps S4-S6 specifically comprise: at the time of the t-th beam search, a corresponding antenna weight vector W is obtained from the binary beam search vector A (t) α (t) multiplying the signal by the signal X (t) and squaring to obtain the signal power Y (t) after processing by the array antenna 2 And comparing the signal power with a preset signal power threshold; if the signal power exceeds the threshold value, marking the result b (t) of the beam search as 1, wherein the result b (t) indicates that a target exists in the search area; if the signal power does not exceed the threshold, the result b (t) of this beam search is marked as 0, indicating that no target is present in the search area.
5. The adaptive multi-beam alignment method based on learning-by-questioning-and-answering of claim 4, wherein: in step S8, let the subscript i of the maximum posterior probability at the time of the t-th beam search max (t) is
Then assume that the error probability threshold set by the base station isE (0, 1), pass t 1 After the secondary beam search, if
Then consider the subspace asTargets are present.
6. The adaptive multi-beam alignment method based on learning-by-questioning-and-answering of claim 5, wherein: in step S9, the probability corresponding to each subspace is initialized againAnd repeats from step S3, where a binary beam search vector is generated +.>When in use, let->Other operations are unchanged; through t 2 After the sub-beam search, another subspace +.>
7. The adaptive multi-beam alignment method based on learning-by-questioning-and-answering of claim 6, wherein: in a practical scenario, multiple users may be located in the same subspace, in which case these users should be considered indistinguishable, forRepresenting a distinguishable target number; if no distinguishable new user is found after the base station is scanned completely, the algorithm is ended, beam alignment is realized between a single base station and multiple users, and the total search frequency is +.>
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