CN114584188B - Anti-eavesdrop communication method based on multi-station cooperation - Google Patents

Anti-eavesdrop communication method based on multi-station cooperation Download PDF

Info

Publication number
CN114584188B
CN114584188B CN202210035446.2A CN202210035446A CN114584188B CN 114584188 B CN114584188 B CN 114584188B CN 202210035446 A CN202210035446 A CN 202210035446A CN 114584188 B CN114584188 B CN 114584188B
Authority
CN
China
Prior art keywords
base station
user
rate
algorithm
angle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210035446.2A
Other languages
Chinese (zh)
Other versions
CN114584188A (en
Inventor
丁国如
李岩
王海超
徐以涛
谷江春
李京华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Army Engineering University of PLA
Original Assignee
Army Engineering University of PLA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Army Engineering University of PLA filed Critical Army Engineering University of PLA
Priority to CN202210035446.2A priority Critical patent/CN114584188B/en
Publication of CN114584188A publication Critical patent/CN114584188A/en
Application granted granted Critical
Publication of CN114584188B publication Critical patent/CN114584188B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/60—Context-dependent security
    • H04W12/63—Location-dependent; Proximity-dependent
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Security & Cryptography (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

A multi-station collaboration-based anti-eavesdrop communication method comprises a description module, an establishment module and an optimization module; the description module is used for describing the relationship between the distribution of the neutralization capacity and the main beam azimuth in the multi-station collaboration-based anti-eavesdropping system; the establishing module is used for establishing a mathematical model based on the distribution of the user position and the capacity and the main beam azimuth; the optimization module is used for optimizing the data model; the method effectively avoids the defects that the heuristic algorithm has certain blindness and is not optimal at the anti-eavesdropping angle by combining with other structures or methods.

Description

Anti-eavesdrop communication method based on multi-station cooperation
Technical Field
The invention relates to the technical field of anti-eavesdropping communication, in particular to an anti-eavesdropping communication method based on multi-station cooperation.
Background
The openness of the wireless channel makes it possible for the communication content to be eavesdropped by an eavesdropper, which may pose a risk to the security of the communication. With the rapid development of the wireless communication field, users have put higher demands on the security of communication services. Therefore, physical layer security techniques for the purpose of achieving secure transmission of information have been widely studied.
Most of the existing work is based on passive and active eavesdropping schemes. The passive eavesdropping scheme mostly uses artificial noise, so that a legal user can recognize and filter the noise, an eavesdropper cannot recognize the noise, and the signal-to-interference-and-noise ratio of the eavesdropper user is greatly reduced. In the active eavesdropping scene, the eavesdropper aims at the beam of the eavesdropper instead of the legal user through sending the pilot frequency which is the same as that of the legal user to the base station, so that the eavesdropper can obtain good signal receiving quality. Meanwhile, technologies such as coordinated multipoint (CoMP) and Multiple Input Multiple Output (MIMO) are adopted to provide secure transmission for the communication system. The multi-point coordination is applied to the fields of heterogeneous network security coverage, unmanned aerial vehicle security communication, multi-beam satellite communication and the like.
The above work does not consider that the cooperation characteristic of the multipoint cooperative communication and the further combination of the sparse characteristic of the channel in the angle domain in the MIMO technology are adopted at the same time, which is often carried out separately, and indicates that other aspects of improving the security of the communication system exist.
Disclosure of Invention
In order to solve the above problems, the present invention proposes to simultaneously and effectively use the advantages of MIMO and CoMP to solve the anti-eavesdropping problem, and proposes a cooperative beam forming method in the angular domain, which can further use the spatial distribution characteristics of the capacity in the MIMO system to improve the security. The method comprises the following steps:
an anti-eavesdrop communication method based on multi-station cooperation comprises the following steps:
step 1: describing the relation between the distribution of the reachable velocity and the main beam azimuth in the multi-station collaboration-based anti-eavesdropping system;
step 2: establishing a mathematical model of the distribution of the achievable rates and the main beam azimuth based on the user position;
step 3: establishing an optimization problem according to a mathematical model;
step 4: base stations and beam angle selection algorithms based on Nelder-Mead are designed.
Preferably, in step 1 of the present invention, the relationship between the distribution of the achievable rate and the main beam azimuth in the multi-station collaboration-based anti-eavesdropping system is described, and the description includes:
the information source sends the original message to the core network, the core network divides the original message into M sub-messages bit by bit, and sends the M sub-messages to the corresponding base station through the wired channel; m base stations send sub-messages to users equipped with single antennas through line-of-sight MIMO channels; the user recovers the original message by combining the sub-messages;
the users in the beam overlapping area receive M sub-messages and recover the original message at the same time, and the users outside the beam overlapping area can only receive partial sub-messages or no sub-messages.
Preferably, the establishing a mathematical model of the distribution of the achievable rates and the main beam azimuth based on the user position in the step 2 of the present invention comprises the following contents:
base station i is equipped with N i A uniform linear array of half wavelength intervals of the root antenna, each antenna array element uniformly covering the arrival angle interval of [0, pi); the transmitted signal of the base station i antenna array is represented as
Figure BDA0003468180440000031
Wherein P is i Representing the transmit power, f i Is a precoding vector and f i ||=1, s represents a transmission symbol and |s|=1;
when the distance between the user and the base station is far greater than the distance between the adjacent antenna units, the directions from each antenna unit to the user are the same; let x be i And y i For the abscissa and ordinate of base station i, l ε 2×1 For the user's location, the angle of arrival is expressed as
Figure BDA0003468180440000032
Wherein: l represents the position of the user, x (l) and y (l) represent the abscissa and ordinate of l, respectively, and the abscissa and ordinate of the base station i are x, respectively i And y i The array direction of the base station i is gamma i (shown in FIG. 2), let the distance from base station i to user be d i ，
Figure BDA0003468180440000033
Lambda, a large scale fading of the radiation direction characteristics of an antenna element i For the base station i carrier wavelength,
Figure BDA0003468180440000034
for variance sigma 2 Is the noise of the base station i received from the user as
Figure BDA0003468180440000035
Wherein the method comprises the steps of
Figure BDA0003468180440000036
Is Gaussian white noise, v (θ) i ) For base station i about the departure angle θ i Is defined as the array response vector of (a)
Figure BDA0003468180440000037
The signal to noise ratio of the signal received by the base station is
Figure BDA0003468180440000038
To maximize the orientation of
Figure BDA0003468180440000039
Gamma of the receiver of (2) i With conjugate beam forming, the precoding vector is set to
Figure BDA0003468180440000041
Substituting (6) into (5) to obtain the direction of the base station i
Figure BDA0003468180440000042
In the beam forming, in the direction theta i Is expressed as the signal to noise ratio of the user of (2)
Figure BDA0003468180440000043
When the number of antennas approaches infinity, the array response vectors for different angles are asymptotically orthogonal, i.e
Figure BDA0003468180440000044
The original signal is divided into M sub-signals, which propagate through M channels, respectively; let B i Is the bandwidth of the BSi signaling, then the rate of BSi to the user at l
Figure BDA0003468180440000045
Can be expressed as
Figure BDA0003468180440000046
Wherein the method comprises the steps of
Figure BDA0003468180440000047
Is the base station i main beam angle.
Definition vector s epsilon {0,1} M×1 An i-th element of s is 1, which indicates which base stations are selected, and a 0 indicates that the i-th base station is not selected; defining the achievable rate as the maximum rate of error-free transmission of information in the system, expressed as
Figure BDA0003468180440000048
The relation between the reachable velocity and the user position is described by a formula (10), the sparsity of the angle domain is described by a formula (8), and the reachable velocities of different positions also show similar characteristics; in particular, when the number of antennas provided in the base station is large, the coverage area of the main beam of all the selected base stations is defined as an effective reception area, and when the number of antennas approaches infinity, the effective reception area converges to a point.
Preferably, in step 3 of the present invention, the establishing an optimization problem according to a mathematical model specifically includes:
find out
Figure BDA0003468180440000051
To maximize the user's achievable rate R (l), i.e. the user's location should be the sum capacity maximum, for the targetExpressed mathematically as
Figure BDA0003468180440000052
Where is the real set and ζ is any receiver position. C1 illustrates that the location of the user is covered by all selected base stations; c2 illustrates that at least k base stations are selected; c3 is a guarantee of safe rate.
Preferably, the base station and beam angle selection algorithm based on Nelder-Mead is designed in step 4 of the present invention, which specifically includes:
to more effectively deal with the problem P1, a scheme s and beam angles under a given scheme are selected for the base station
Figure BDA0003468180440000053
Optimizing; specifically, a two-layer structure is adopted, the beam angle is optimized on the lower layer, and the base station selects the upper layer; the lower layer is used to find +.>
Figure BDA0003468180440000054
And the corresponding user achievable rate given s, the upper layer optimizing s by the results returned by the lower layer;
step 4-1: optimizing the beam angle: adjustment of
Figure BDA0003468180440000055
To minimize the distance between the user location and the maximum achievable rate point until convergence to 0; the beam angle selection problem only considers the communication via the main beam,/->
Figure BDA0003468180440000056
The optimization problem of (1) is expressed as
Figure BDA0003468180440000057
The previous constraint is absorbed by the target, and the beam angle is adjusted under the constraint of C1 until the maximum point of the achievable rate moves to the user position;
for P2, an algorithm is proposed to obtain
Figure BDA0003468180440000058
An adaptive Nelder-Mead algorithm is adopted, and for the scenes of k base stations, the complexity is O (klogk);
solving for
Figure BDA0003468180440000061
Setting a user position l as an initial point, taking the reachable rate as an objective function, and substituting the initial point into a self-adaptive Nelder-Mead algorithm to obtain a coordinate xi of the maximum reachable rate point and the reachable rate thereof;
the Nelder-Mead algorithm can only solve the unconstrained problem, and therefore, P2 needs to be converted into
P2.1:
Figure BDA0003468180440000062
Wherein->
Figure BDA0003468180440000063
Constraints for absorbing P2, expressed as
Figure BDA0003468180440000064
Setting up
Figure BDA0003468180440000065
For the initial point, the target of P2.1 is the target function
Figure BDA0003468180440000066
(13) Final value and error ∈>
Figure BDA0003468180440000067
Is obtained by an algorithm;
if the delta obtained converges to 0, i.e. C3 is satisfied, what is obtained
Figure BDA0003468180440000068
Is an effective solution; otherwise, it is indicated that there is a point with a higher reachable rate than the user, and the currently given base station selection scheme is not suitable for the current user location;
step 4-2: optimizing base station selection: based on the beam angle design proposed in step 4-1, the problem P1 is rewritten as
Figure BDA0003468180440000069
R (l, s) represents the achievable rate of the user located at l under the conditions of base station selection scheme s and corresponding beam angle obtained in step 4-1, and if scheme u does not fit the current user position, R (l, u) =0 is defined,
if P3.1 is searched in the poor, the compliance (15) constraint is satisfied in total
Figure BDA0003468180440000071
For the scene of M base stations, the complexity of the poor search is O (2 M MlogM)；
Firstly, selecting all base stations, if the selection is not suitable for the position of the current user, closing the base station closest to the user, and repeating the steps until a proper scheme appears or the minimum selection number of the base stations is reached; step 4-2 cycle M-k+1 times, the time complexity of the algorithm is O (M 2 logM)。
Compared with the prior art, the invention has the following advantages:
1. the invention introduces an optimization algorithm based on Nelder-head to realize the maximization of users and capacity under the security limit of a physical layer;
2. the invention can solve the problem of anti-eavesdropping in the cooperative communication scene from the angle domain;
3. the proposed low-complexity base station selection algorithm can solve the problem of high complexity of the poor search method.
Drawings
Fig. 1 is a flow chart of a planning method of the multi-station collaboration-based anti-eavesdropping system of the present invention.
Fig. 2 is a schematic diagram of a system scenario in an embodiment of the present invention.
FIG. 3 is a graph of angular relationship in an embodiment of the present invention.
Fig. 4 is a position profile of normalized achievable rates near the user.
Fig. 5 is a schematic diagram of parameter settings for evaluating the proposed beam azimuth optimization algorithm.
Fig. 6 shows a distribution diagram of the distance between the effective user s and the maximum point s of the capacity and the total capacity difference between the effective user s and the maximum and capacity.
Fig. 7 is a schematic diagram of simulation results for 8, 16, 32, 64 and 128 antenna scenarios.
Fig. 8 shows a schematic diagram of the position distribution of the optimized achievable rate in the 8-antenna scenario.
Fig. 9 shows a schematic diagram of the position distribution of the achievable rate in a 32 antenna scenario.
Detailed Description
For a given user position, after the core network calculates the main beam azimuth angle of the corresponding base station, the base station crosses the beam in a small area containing the user, and only terminals in the area can receive multiple paths of information at the same time and combine and restore the original information. Users outside the area lack at least one path of information and can not complete combination, so that the original information can not be known.
The invention will be further described with reference to the drawings and examples.
An anti-eavesdrop communication method based on multi-station cooperation comprises the following steps:
step 1: describing the relation between the distribution of the reachable velocity and the main beam azimuth in the multi-station collaboration-based anti-eavesdropping system;
the content of the description includes:
the source sends the original message to the core network. Then, the core network divides the original message into M sub-messages bit by bit, and sends the M sub-messages to the corresponding base station through the wired channel. M base stations transmit sub-messages to users equipped with single antennas over line-of-sight MIMO channels (only line-of-sight paths are considered). Finally, the user recovers the original message by combining the sub-messages. So a user located in the beam overlap area can receive M sub-messages and recover the original message at the same time, while a user not located in the beam overlap area can only receive part of the sub-messages or no sub-messages, which is insufficient to recover the original message.
Step 2: establishing a mathematical model of the distribution of the achievable rates and the main beam azimuth based on the user position; the method comprises the following steps:
base station i is equipped with N i A uniform linear array of half wavelength intervals of the root antenna, each antenna element uniformly covering the angle of arrival interval of [0, pi). The transmitted signal of the base station i antenna array can be expressed as
Figure BDA0003468180440000081
Wherein P is i Representing the transmit power, f i Is a precoding vector and f i ||=1, s represents a transmission symbol and |s|=1.
When the distance between the user and the base station is far greater than the distance between the adjacent antenna units, the directions from each antenna unit to the user are the same; let x be i And y i For the abscissa and ordinate of base station i, l ε 2×1 For the user's location, the angle of arrival is expressed as
Figure BDA0003468180440000091
Wherein: l represents the position of the user, x (l) and y (l) represent the abscissa and ordinate of l, respectively, and the abscissa and ordinate of the base station i are x, respectively i And y i The array direction of the base station i is gamma i (shown in FIG. 2), let the distance from base station i to user be d i ，
Figure BDA0003468180440000092
Lambda, a large scale fading of the radiation direction characteristics of an antenna element i For the base station i carrier wavelength,
Figure BDA0003468180440000093
for variance sigma 2 Is the noise of the base station i received from the user as
Figure BDA0003468180440000094
Wherein the method comprises the steps of
Figure BDA0003468180440000095
Is white gaussian noise. v (θ) i ) For base station i about the departure angle θ i Is defined as the array response vector of (a)
Figure BDA0003468180440000096
The signal to noise ratio of the signal received by the base station is
Figure BDA0003468180440000097
To maximize the orientation of
Figure BDA0003468180440000098
Gamma of the receiver of (2) i With conjugate beam forming, the precoding vector is set to
Figure BDA0003468180440000099
Substituting (6) into (5) to obtain the direction of the base station i
Figure BDA00034681804400000910
In the beam forming, in the direction theta i The signal to noise ratio of the user of (a) can be expressed as
Figure BDA0003468180440000101
When the number of antennas approaches infinity, the array response vectors for different angles are asymptotically orthogonal, i.e
Figure BDA0003468180440000102
In fig. 2, the original signal is divided into M sub-signals, which propagate through M channels, respectively; let B i Is the bandwidth of the BSi signaling, then the rate of BSi to the user at l
Figure BDA0003468180440000103
Can be expressed as
Figure BDA0003468180440000104
Wherein the method comprises the steps of
Figure BDA0003468180440000105
Is the base station i main beam angle.
Definition vector s epsilon {0,1} M×1 Indicating which base stations are selected. Specifically, an i element of s of 1 indicates that the i base station is selected, and an i element of s of 0 indicates that the i base station is not selected. Defining the achievable rate as the maximum rate for error-free transmission of information in the system can be expressed as
Figure BDA0003468180440000106
Equation (10) describes the relationship of the achievable rate and the user position. (8) The sparsity of the angle domain is described, and the achievable rates at different locations also exhibit similar characteristics, especially when the number of antennas provided at the base station is large. The area covered by the main beam of all selected base stations is defined as the effective reception area. As the number of antennas approaches infinity, the effective reception area converges to a point. Thus, we can achieve anti-eavesdropping communication by intersecting the beam of the base station at the user's location.
Each eavesdropper is considered to be equipped with an antenna, which can be like a userThe sub-message is received, which means that the location distribution of the achievable rates of the eavesdropper is the same as the location distribution of the user achievable rates described above. In accordance with the discussion above, the safe rate is a function of position, expressed as [ R (l) -R (ζ) e )] + . To meet the requirements of secure communications, R (l) -R (ζ) should be guaranteed e ) Everywhere non-negative. In other words, the achievable rate of an eavesdropper is always lower than the user.
Step 3: establishing an optimization problem according to a mathematical model; the method specifically comprises the following steps:
find out
Figure BDA0003468180440000111
To maximize the user's achievable rate R (l), i.e. the user's location should be the sum capacity maximum, the goal is expressed mathematically as
Figure BDA0003468180440000112
Where is the real set and ζ is any receiver position. C1 illustrates that the location of the user is covered by all selected base stations; c2 illustrates that at least k base stations are selected; c3 is a guarantee of safe rate.
The constraint of equation (11) is critical. Without this constraint, the highest achievable rate for the user can be obtained, but it cannot guarantee the data rate required for physical layer security. However, constraints keep the eavesdropper's sum capacity always below that of the user, i.e. obtained
Figure BDA0003468180440000113
The physical layer security can be ensured.
Significant power gain can be provided for the main beam centerline user. One intuitive approach is to direct the two main beams directly to the user, i.e
Figure BDA0003468180440000114
This scheme is easy to calculate. The location distribution of the achievable capacity is shown in fig. 4, where the location of the user is not the achievable rate maximum point.
Fig. 4 is a position distribution of normalized achievable rates near the user. It can be seen that the maximum point of achievable velocity is not where the user is located as the intersection of the main beam centerlines. This is a counterintuitive phenomenon.
As can be seen from equation (10), the achievable rate is a function of distance and relative azimuth. By adding a slave
Figure BDA0003468180440000115
The angular gain or reduction determined by d n The determined path loss can achieve maximization of the achievable rate. In FIG. 4, the relative azimuth angle of the crossing is +.>
Figure BDA0003468180440000116
Representing the maximum angular gain but at a distance from the base station that is far from the optimum point, this results in a larger path loss that varies faster than the angular gain near the user.
Step 4: designing a base station and a beam angle selection algorithm based on Nelder-Mead; the method specifically comprises the following steps:
the adaptive Nelder-Mead algorithm is an algorithm that takes local minima of a multiple function, which has the advantage of not requiring the function to be guided and converging to local minima faster. For an N-ary function (where the function arguments are represented by N-dimensional vectors), the algorithm needs to provide an initial point x in the function argument space 0 The algorithm looks for a local minimum from this point. The algorithm can be applied to nonlinear programming without requiring the first derivative of the objective function.
To more effectively deal with the problem P1, an algorithm is proposed to select the scheme s and the beam angle for a given scheme for the base station
Figure BDA0003468180440000121
And (5) optimizing. Specifically, the proposed algorithm adopts a two-layer structure, the beam angle is optimized in the lower layer, and the base station selects the upper layer. That is, the lower layer is used to find +.>
Figure BDA0003468180440000122
And the corresponding user achievable rate given s, the upper layer optimizes s by the results returned by the lower layer.
Step 4-1: the beam angle is optimized. Inspired by the counterintuitive phenomenon, consider the adjustment
Figure BDA0003468180440000123
To minimize the distance between the user location and the maximum achievable rate point until convergence to 0. The beam angle selection problem only considers communication via the main beam. />
Figure BDA0003468180440000124
The optimization problem of (1) can be expressed as
Figure BDA0003468180440000125
P2 is easier to implement than P1 because it allows the previous constraint to be absorbed by the target. The beam angle may be adjusted under the constraint of C1 until the maximum achievable rate point moves to the user position.
Process P2, propose an algorithm to obtain
Figure BDA0003468180440000126
This procedure uses an adaptive Nelder-Mead algorithm, which has a complexity of O (klogk) for the scenario of k base stations.
Solving for
Figure BDA0003468180440000127
The user position l is set as an initial point and the achievable rate is an objective function. Substituting the self-adaptive Nelder-Mead algorithm can obtain the coordinate xi of the maximum point of the reachable velocity and the reachable velocity thereof.
The Nelder-Mead algorithm can only solve the unconstrained problem, and therefore, P2 needs to be converted into
P2.1:
Figure BDA0003468180440000131
Wherein->
Figure BDA0003468180440000132
Constraints for absorbing P2, which can be expressed as
Figure BDA0003468180440000133
P2.1 can be solved directly with the adaptive Nelder-Mead algorithm. Setting up
Figure BDA0003468180440000134
For the initial point, P2.1 is the target function, then +.>
Figure BDA0003468180440000135
(13) Final value and error of (2)
Figure BDA0003468180440000136
Can be obtained by an algorithm.
If the delta obtained converges to 0, i.e. C3 is satisfied, what is obtained
Figure BDA0003468180440000137
Is an effective solution. Otherwise, it is stated that there is a point with a higher achievable rate than the user, and the currently given base station selection scheme is not suitable for the current user location.
Step 4-2: and optimizing the selection of the base station. Based on the beam angle design proposed in step 4-1, the problem P1 can be rewritten as
Figure BDA0003468180440000138
R (l, s) represents the achievable rate of the user located at l under the conditions of the base station selection scheme s and the corresponding beam angle obtained in step 4-1. If scheme u does not fit the current user position, R (l, u) =0 is defined.
If P3.1 is searched in the poor, the compliance (15) constraint is satisfied in total
Figure BDA0003468180440000139
A kind of module is assembled in the module and the module is assembled in the module. For the scenario of M base stations, the complexity of the search is O (2 M Mlog M). Therefore, there is a need to develop an algorithm of low complexity.
An algorithm is presented that takes advantage of heuristics and base station selection. Specifically, all base stations are first selected as in the heuristic. If this selection does not fit in the current user's location, the base station closest to the user is turned off because an invalid solution is likely to occur when there is a base station covered by the main beam of the other base station, which often occurs when the base station is very close to the user. Repeating the steps until a proper scheme appears or the minimum base station selection number is reached.
For the worst case, step 4-2 loops M-k+1 times. The time complexity of the proposed algorithm is O (M 2 log m) is much lower than the time complexity of the poor search.
The description module is used for describing an anti-eavesdropping system based on multi-station cooperation;
the establishing module is used for establishing a mathematical model of the main beam azimuth based on the user position;
the optimization module is used for proposing an optimization problem according to a mathematical model;
the Nelder-Mead algorithm module is used for introducing a Nelder-Mead algorithm;
the algorithm module is used for calculating a base station selection scheme and a base station beam angle.
While one embodiment of the present invention is described below, the system simulation is in the Python language. The following examples examine the effectiveness of the unmanned aerial vehicle data distribution optimization method under the energy constraint designed by the invention.
In this embodiment, the proposed beam azimuth optimization algorithm is evaluated. Parameter settings as shown in fig. 5, 100 different user positions are randomly generated in the region { (x, y) | -300< x <300 and-300 < y <300 }. Setting the minimum selected base station number to 2
Fig. 6 shows a distribution of the distance between the effective user s and the maximum point s of capacity and the total capacity difference between the effective user s and the maximum and capacity, and it can be seen that the distance and the difference decrease as the number of antennas increases. A point that is not located in (0, 0) indicates that there is a point that is higher than the user's capacity. But no point is located in (0, 0). The results indicate that there is a general counter-intuitive phenomenon with C3 that is not the point of maximum capacity for users among users in different locations. Further, in the distribution corresponding to the proposed scheme, all points in fig. 6 are converged to (0, 0) after being optimized.
Fig. 7 is a simulation result for 8, 16, 32, 64 and 128 antenna scenarios. As can be seen from fig. 7, the total capacity of the users increases with the number of antennas and the average achievable rate of the users in the proposed algorithm approaches that obtained by the poor search method. It is demonstrated that the proposed algorithm can greatly reduce complexity while having little impact on performance.
Fig. 8 shows the location distribution of the optimized achievable rates in an 8 antenna scenario, it can be seen that the achievable rates for all points are less than the achievable rates for the location of the user. In fig. 4, a counterintuitive phenomenon is found and optimized according to its simulation configuration parameters. It can be seen that this phenomenon has been eliminated in fig. 8.
Fig. 9 shows the location distribution of the achievable rates in a 32 antenna scenario, where only a small fraction of the area can be seen to have good reception performance, indicating the effectiveness of the proposed algorithm.
While the invention has been described by way of examples, it will be understood by those skilled in the art that the present disclosure is not limited to the examples described above, and that various changes, modifications and substitutions may be made without departing from the scope of the invention.

Claims (1)

1. An anti-eavesdropping communication method based on multi-station cooperation is characterized by comprising the following steps:
step 1: describing the relation between the distribution of the reachable velocity and the main beam azimuth in the multi-station collaboration-based anti-eavesdropping system;
the content of the description includes:
the information source sends the original message to the core network, the core network divides the original message into M sub-messages bit by bit, and sends the M sub-messages to the corresponding base station through the wired channel; m base stations send sub-messages to users equipped with single antennas through line-of-sight MIMO channels; the user recovers the original message by combining the sub-messages;
the users in the beam overlapping area receive M sub-messages and recover the original message at the same time, and the users outside the beam overlapping area can only receive partial sub-messages or no sub-messages;
step 2: establishing a mathematical model of the distribution of the achievable rates and the main beam azimuth based on the user position;
the method comprises the following steps:
base station i is equipped with N i A uniform linear array of half wavelength intervals of the root antenna, each antenna array element uniformly covering the arrival angle interval of [0, pi); the transmitted signal of the base station i antenna array is represented as
Figure FDA0004174950980000011
Wherein P is i Representing the transmit power, f i Is a precoding vector and f i ||=1, s represents a transmission symbol and |s|=1;
when the distance between the user and the base station is far greater than the distance between the adjacent antenna units, the directions from each antenna unit to the user are the same; let x be i And y i For the abscissa and ordinate of base station i,
Figure FDA0004174950980000012
for the user's location, the angle of arrival is expressed as
Figure FDA0004174950980000013
Wherein: l represents the position of the user, x (l) and y (l) represent the abscissa and ordinate of l, respectively, and the abscissa and ordinate of the base station i are x, respectively i And y i The array direction of the base station i is gamma i Let the distance from base station i to user be d i ，
Figure FDA0004174950980000021
Lambda, a large scale fading of the radiation direction characteristics of an antenna element i For the base station i carrier wavelength,
Figure FDA0004174950980000022
for variance sigma 2 Is the noise of the base station i received from the user as
Figure FDA0004174950980000023
Wherein the method comprises the steps of
Figure FDA0004174950980000024
Is Gaussian white noise, v (θ) i ) For base station i about the departure angle θ i Is defined as the array response vector of (a)
Figure FDA0004174950980000025
The signal to noise ratio of the signal received by the base station is
Figure FDA0004174950980000026
To maximize the orientation of
Figure FDA0004174950980000027
Gamma of the receiver of (2) i With conjugate beam forming, the precoding vector is set to
Figure FDA0004174950980000028
Substituting (6) into (5) to obtain the direction of the base station i
Figure FDA0004174950980000029
In the beam forming, in the direction theta i Is expressed as the signal to noise ratio of the user of (2)
Figure FDA00041749509800000210
When the number of antennas approaches infinity, the array response vectors for different angles are asymptotically orthogonal, i.e
Figure FDA00041749509800000211
The original signal is divided into M sub-signals, which propagate through M channels, respectively; let B i Is the bandwidth of the BSi signaling, then the rate of BSi to the user at l
Figure FDA0004174950980000031
Can be expressed as
Figure FDA0004174950980000032
Wherein the method comprises the steps of
Figure FDA0004174950980000033
The angle of the main beam of the base station i;
definition vector s epsilon {0,1} M×1 An i-th element of s is 1, which indicates which base stations are selected, and a 0 indicates that the i-th base station is not selected; defining the achievable rate as the maximum rate of error-free transmission of information in the system, expressed as
Figure FDA0004174950980000034
The relation between the reachable velocity and the user position is described by a formula (10), the sparsity of the angle domain is described by a formula (8), and the reachable velocities of different positions also show similar characteristics; when the number of antennas equipped on the base station is large, defining the coverage area of the main beams of all the selected base stations as an effective receiving area, and when the number of antennas approaches infinity, converging the effective receiving area to a point;
step 3: establishing an optimization problem according to a mathematical model; the method specifically comprises the following steps:
find out
Figure FDA0004174950980000035
To maximize the user's achievable rate R (l), i.e. the user's location should be the sum capacity maximum, the goal is expressed mathematically as
Figure FDA0004174950980000036
Wherein the method comprises the steps of
Figure FDA0004174950980000037
Being a real set, ζ is any receiver position; c1 illustrates that the location of the user is covered by all selected base stations; c2 illustrates that at least k base stations are selected; c3 is a guarantee of safe rate;
step 4: designing a base station and a beam angle selection algorithm based on Nelder-Mead; the method specifically comprises the following steps:
to more effectively deal with the problem P1, a scheme s and beam angles under a given scheme are selected for the base station
Figure FDA0004174950980000041
Optimizing; specifically, a two-layer structure is adopted, the beam angle is optimized on the lower layer, and the base station selects the upper layer; the lower layer is used to find +.>
Figure FDA0004174950980000042
And the corresponding user achievable rate given s, the upper layer returning better results through the lower layerTransforming s;
step 4-1: optimizing the beam angle: adjustment of
Figure FDA0004174950980000043
To minimize the distance between the user location and the maximum achievable rate point until convergence to 0; the beam angle selection problem only considers the communication via the main beam,/->
Figure FDA0004174950980000044
The optimization problem of (1) is expressed as
Figure FDA0004174950980000045
The previous constraint is absorbed by the target, and the beam angle is adjusted under the constraint of C1 until the maximum point of the achievable rate moves to the user position;
for P2, an algorithm is proposed to obtain
Figure FDA0004174950980000046
An adaptive Nelder-Mead algorithm is adopted, and for the scenes of k base stations, the complexity is O (klogk);
solving for
Figure FDA0004174950980000047
Setting a user position l as an initial point, taking the reachable rate as an objective function, and substituting the initial point into a self-adaptive Nelder-Mead algorithm to obtain a coordinate xi of the maximum reachable rate point and the reachable rate thereof;
the Nelder-Mead algorithm can only solve the unconstrained problem, and therefore, P2 needs to be converted into
Figure FDA0004174950980000048
Wherein the method comprises the steps of
Figure FDA0004174950980000049
Constraints for absorbing P2, expressed as
Figure FDA00041749509800000410
Setting up
Figure FDA00041749509800000411
For the initial point, the target of P2.1 is the target function
Figure FDA0004174950980000051
(13) Final value and error ∈>
Figure FDA0004174950980000052
Is obtained by an algorithm;
if the delta obtained converges to 0, i.e. C3 is satisfied, what is obtained
Figure FDA0004174950980000053
Is an effective solution; otherwise, it is indicated that there is a point with a higher reachable rate than the user, and the currently given base station selection scheme is not suitable for the current user location;
step 4-2: optimizing base station selection: based on the beam angle design proposed in step 4-1, the problem P1 is rewritten as
Figure FDA0004174950980000054
Figure FDA0004174950980000055
Representing the achievable rate of the user at l under the conditions of base station selection scheme s and corresponding beam angle obtained in step 4-1, defining R (l, u) =0 if scheme u does not fit the current user position,
if P3.1 is searched in the poor, the compliance (15) constraint is satisfied in total
Figure FDA0004174950980000056
For the scene of M base stations, the complexity of the poor search is O (2 M MlogM)；
Firstly, selecting all base stations, if the selection is not suitable for the position of the current user, closing the base station closest to the user, and repeating the steps until a proper scheme appears or the minimum selection number of the base stations is reached; step 4-2 cycle M-k+1 times, the time complexity of the algorithm is O (M 2 logM)。
CN202210035446.2A 2022-01-13 2022-01-13 Anti-eavesdrop communication method based on multi-station cooperation Active CN114584188B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210035446.2A CN114584188B (en) 2022-01-13 2022-01-13 Anti-eavesdrop communication method based on multi-station cooperation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210035446.2A CN114584188B (en) 2022-01-13 2022-01-13 Anti-eavesdrop communication method based on multi-station cooperation

Publications (2)

Publication Number Publication Date
CN114584188A CN114584188A (en) 2022-06-03
CN114584188B true CN114584188B (en) 2023-06-27

Family

ID=81771069

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210035446.2A Active CN114584188B (en) 2022-01-13 2022-01-13 Anti-eavesdrop communication method based on multi-station cooperation

Country Status (1)

Country Link
CN (1) CN114584188B (en)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7526036B2 (en) * 2006-04-20 2009-04-28 Mitsubishi Electric Research Laboratories, Inc. System and method for transmitting signals in cooperative base station multi-user mimo networks
US8218422B2 (en) * 2008-06-03 2012-07-10 Nec Laboratories America, Inc. Coordinated linear beamforming in downlink multi-cell wireless networks
US8125917B2 (en) * 2008-10-10 2012-02-28 Avaya Inc. Optimisation of transmission parameters of access points in wireless networks
US10445442B2 (en) * 2016-09-01 2019-10-15 Energid Technologies Corporation System and method for game theory-based design of robotic systems
CN108834108B (en) * 2018-05-03 2021-04-02 中国人民解放军陆军工程大学 D2D cooperative relay selection method for resisting half-duplex active eavesdropping and based on virtual decision
CN108832998B (en) * 2018-08-15 2021-04-13 中国人民解放军陆军工程大学 Cooperative data distribution method in air-ground converged communication network

Also Published As

Publication number Publication date
CN114584188A (en) 2022-06-03

Similar Documents

Publication Publication Date Title
CN113225108B (en) Robust beam forming method for intelligent reflector-assisted multi-cell coordinated multi-point transmission
CN112290995B (en) Beam design method based on safety energy efficiency in satellite-ground integrated network
CN107888270A (en) Recognize the safe transmission method of physical layer in satellite ground UNE
CN110430542B (en) Rapid beam tracking method for unmanned aerial vehicle station group networking
CN114006645B (en) Relay-assisted safe transmission method and system for cognitive unmanned aerial vehicle
CN113727405B (en) Method for improving safety rate of wireless communication system based on intelligent reflecting surface
Zhu et al. Multi-UAV aided millimeter-wave networks: Positioning, clustering, and beamforming
Xu et al. Sum secrecy rate maximization for IRS-aided multi-cluster MIMO-NOMA terahertz systems
CN114337902B (en) IRS-assisted millimeter wave multi-cell interference suppression method
Xue et al. Multi-user mmWave uplink communications based on collaborative double-RIS: Joint beamforming and power control
Zhao et al. MADRL-based 3D deployment and user association of cooperative mmWave aerial base stations for capacity enhancement
Chen et al. Intelligent reflecting surface aided network under interference toward 6G applications
CN115052285A (en) Unmanned aerial vehicle intelligent reflecting surface safe transmission method based on deep reinforcement learning
Elsherbini et al. Reconfigurable intelligent surface reliable cooperative beamforming based on cascade/parallel hybrid networking
CN117375683A (en) Communication-centric RIS-assisted non-cellular ISAC network joint beam forming method
CN114584188B (en) Anti-eavesdrop communication method based on multi-station cooperation
Lyu et al. Primary rate maximization in movable antennas empowered symbiotic radio communications
CN116208971B (en) Uplink transmission method of non-orthogonal multiple access system assisted by active RIS
Huang et al. Self-attention reinforcement learning for multi-beam combining in mmWave 3D-MIMO systems
CN115278736B (en) Simultaneous same-frequency full duplex communication networking scheme based on reconfigurable intelligent surface
Zhang et al. Research On beamforming Design of ISAC System: A FP Approach
Priya et al. Spectral and energy efficient user pairing for RIS-assisted uplink NOMA systems with imperfect phase compensation
Chen et al. Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning
Zhou et al. Power Optimization for Aerial Intelligent Reflecting Surface‐Aided Cell‐Free Massive MIMO‐Based Wireless Sensor Network
CN116470938B (en) IRS auxiliary communication service quality fairness combined beam forming optimization method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant