A sensor-assisted communication method, device, equipment, and readable storage medium

Abstract

The present invention discloses a sensor-assisted communication method, apparatus, device, and readable storage medium, comprising obtaining hypothetical conditions and airborne base station communication parameters; constructing an airborne base station communication model, including constructing an ISAC signal model, an omnidirectional communication and radar model, and a directional communication and radar model; transmitting an omnidirectional beam to each IoT device based on the ISAC signal model, the omnidirectional communication and radar model, and performing a receiving step: receiving echo information from each IoT device; performing an optimization step: extracting sensor information from the echo information and optimizing the beam using a non-convex algorithm based on the sensor information; performing a transmitting step: transmitting a directional beam to each IoT device based on the directional communication and radar model and the optimized beam; and repeating the receiving step, the optimizing step, and the transmitting step in sequence. The present invention has low communication overhead, higher matched filter gain, no quantization error, can achieve more accurate sensing effects, and ensures maximum communication rate.

Description

Technical Field

[0001] The present invention relates to the field of base station communication technology, and in particular to a sensor-assisted communication method, apparatus, device, and readable storage medium. Background Art

[0002] At present, traditional base station wireless communication methods require quantization and then uplink feedback, which has high communication overhead and requires pilot blocks in the communication frame, resulting in low matched filter gain and large quantization error, which cannot achieve higher beam gain and cannot bring more accurate perception effects.

[0003] Therefore, a sensor-assisted communication method, device, equipment and readable storage medium are developed to solve the above problems. Summary of the Invention

[0004] The present invention proposes a sensor-assisted communication method, device, equipment and readable storage medium to solve the problems of high communication overhead, low filtering gain and large quantization error in existing base station wireless communication methods.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] A first aspect of the present invention provides a sensor-assisted communication method, comprising:

[0007] Acquiring first information, where the first information includes an assumption and an air base station communication parameter;

[0008] Constructing an airborne base station communication model based on the first information, including constructing an ISAC signal model, constructing an omnidirectional communication and radar model, and constructing a directional communication and radar model;

[0009] Sending an omnidirectional beam to each of the IoT devices according to the ISAC signal model, omnidirectional communication, and radar model, and performing a receiving step: receiving echo information from each of the IoT devices;

[0010] performing an optimization step: extracting sensing information from the echo information, and optimizing a beam using a non-convex algorithm based on the sensing information;

[0011] Performing a transmitting step: transmitting a directional beam to each of the IoT devices according to the directional communication and radar model and the optimized beam;

[0012] The receiving step, the optimizing step, and the transmitting step are repeated in sequence to implement a sensing-assisted communication method.

[0013] Specifically, obtaining the first information includes:

[0014] Obtain assumptions, including assuming that the aerial base station is good at sensing nearby single-antenna IoT devices and facilitating downlink communications with them, assuming that the aerial base station senses and communicates with IoT devices only through line-of-sight channels, and assuming that in the initial stage, the aerial base station has nearby There are potentially unknown numbers of devices without channel information distributed around the space base station.

[0015] Acquire the communication parameters of the aerial base station, the communication parameters of the aerial base station include: the aerial base station is equipped with The radiating uniform linear array of antennas contains Different antennas receive uniform linear arrays, the communication protocol of the aerial base station continues time intervals, each of which is .

[0016] Specifically, the ISAC signal model is constructed, including:

[0017] The ISAC signal model for:

[0018] (1)

[0019] Indicates that the first device is in the time slot Transmitted communication information, Indicates the Devices in time slot The communication information transmitted, t represents the continuous time, Represents the transpose of a matrix;

[0020] After the beamforming process, the transmitted signal is obtained for:

[0021] (2)

[0022] in represents the beamforming matrix.

[0023] Specifically, the omnidirectional communication and radar model is constructed, including:

[0024] During communication, the air base station The channel vector of each device Expressed as:

[0025] (3)

[0026] In the formula Indicates reference distance The path loss at the air base station and the The distance between devices is , for devices The steering vector Expressed as:

[0027] (4)

[0028] Where, represents an imaginary number, represents the wavelength, Indicates the gap between two adjacent antennas. is the angle of device k, so the The signal received by the device for:

[0029] (5)

[0030] in represents the additive white Gaussian noise (AWGN) at the receiving end, It means it obeys the complex Gaussian distribution with a mean of 0 and a variance of , Represents the conjugate transpose of the channel, and assumes the transmission power is a unit value. According to the principle of omnidirectional beam pattern, it is necessary to ensure The orthogonality of When , the spatial covariance matrix of the omnidirectional beam is:

[0031] (6)

[0032] in and denote the conjugate transpose of the transmit signal and the beamforming matrix, respectively. for The identity matrix of Receive signal-to-interference-and-noise ratio (SINR) of each device Expressed as:

[0033] (7)

[0034] Since the channel does not change with time, Abbreviated as ;

[0035] if , the linear precoder is redundant; thus, it can be directly manipulated Antennas transmit ISAC signal, the received SINR is Similarly, in summary, IoT devices In the The total communication rate that can be achieved in a time slot is recorded as for ;

[0036] Since the MIMO orthogonal waveform echo is still orthogonal, the radar can identify the reflection of each device, so for the device , the received echo reflection Expressed as:

[0037] (9)

[0038] Where, is the conjugate transpose of the steering vector, After a delay The transmission signal, To receive the steering vector, its structure is similar to same, The variance is Zero-mean complex additive Gaussian white noise, and Representation device In the time slot The reflection coefficient and time delay within the ,in For radar cross section (RCS), using the classic matched filtering method, the signal delay can be estimated and the refined output vector can be obtained for:

[0039] (10)

[0040] In the formula is the signal processing gain after matching filtering, the noise matrix Independent, zero-mean, and complex Gaussian distributed variance In addition, when there is a delay, the distance is measured by ranging The model is derived and the distance is measured The model is , Expressing the speed of light, using Capon's method or the generalized likelihood ratio test (GLRT), ABS and equipment The angle between them is derived as , because the angle does not change with time, so and Equivalently, the measurement models of distance and angle both adopt Gaussian distribution model, that is, and Represent zero mean and variance respectively. and Gaussian noise, considering the acquisition and The variance of the challenge, due to its unbiased estimate and the ability to provide lower MSE bounds, uses the Cramer-Rao bound (CRB) as the perceptual metric:

[0041] (11)

[0042] (12)

[0043] The preset constant is and , which is related to the system structure, the radar echo signal-to-noise ratio is recorded as , the square of the effective bandwidth is , the RMS aperture width of the beam pattern is , where σ 2 is the variance of the echo noise.

[0044] Specifically, the directional communication and radar model is constructed, including:

[0045] Set to ;

[0046] During the communication process, the device The received signal is represented as

[0047] (13)

[0048] In short, the device The SINR can be expressed as

[0049] (14)

[0050] Among them, the beamforming vector For The extracted Column vector, for time slots , i represents the i-th object, Indicates from The i-th column vector extracted from The associated total communication rate is ;

[0051] Under massive MIMO conditions, the steering vectors at different angles are asymptotically orthogonal, so the reflected echoes do not interfere with each other. Similarly, the characteristics of the received echo reflections are

[0052] (15)

[0053] in After a delay The original transmission signal;

[0054] The signal-to-noise ratio of the radar echo signal can be described as

[0055] (16).

[0056] Specifically, extracting sensing information from the echo information and optimizing the beam using a non-convex algorithm based on the sensing information includes:

[0057] Construct an objective function, which is:

[0058] (17)

[0059] Constructing constraints, wherein the constraints include:

[0060] (18)

[0061] (19)

[0062] (20)

[0063] (twenty one)

[0064] The optimized variable is the beamforming matrix of the air base station to each device, where represents the minimum communication rate of each device, H represents the conjugate transpose, represents the estimated angle of the aerial base station to the object k in time slot n-1, Indicates all satisfaction The steering vector of the coverage angle, represents the trace of the matrix, represents the beam coverage angle, l represents the beam width control coefficient, is the variance of the estimated angle of object k by the aerial base station in time slot n-1, B k is the beam smoothness control coefficient;

[0065] Constraint C1 represents a strict limit on the transmission power, constraint C2 represents a semidefinite constraint, a Hermitian constraint, and a rank-one constraint, and constraint C4 ensures the minimum communication rate for each device. ,Limit C5 represents the strict control of beam coverage;

[0066] Relax the rank-one constraint, use SCA to approximate the non-concave objective function to a concave objective function, and solve it iteratively; first perform a first-order Taylor expansion on the objective function to obtain:

[0067] (twenty two)

[0068] in, is defined as ,Will As the new objective function, The iterative objective function becomes convex and can be solved by CVX;

[0069] Assumptions is the iterative optimal solution, which is generally not rank one. The IRM algorithm is used to make the solution gradually approach rank one. and Replace and And they represent the iterative optimal solution, The solution of the iteration, Necessary and sufficient conditions must be met To become a rank one matrix, express × The identity matrix, yes of The matrix composed of the eigenvectors corresponding to the smaller eigenvalues, is a positive number that tends to 0. The IRM problem is constructed as follows:

[0070] Objective function:

[0071] (twenty three)

[0072] Constraints:

[0073] (twenty four)

[0074] (25)

[0075] (26)

[0076] (27)

[0077] (28)

[0078] in:

[0079]

[0080] ,

[0081] in, Represents the penalty coefficient. After multiple iterations, when Enough hours, It is a rank-one solution.

[0082] A second aspect of the present invention provides a sensing-assisted communication device, comprising:

[0083] an acquisition module, configured to acquire first information, wherein the first information includes an assumption and communication parameters of an aerial base station;

[0084] A construction module, the construction module being configured to construct an aerial base station communication model according to the first information, including constructing an ISAC signal model, constructing an omnidirectional communication and radar model, and constructing a directional communication and radar model;

[0085] A first execution module is configured to send an omnidirectional beam to each of the IoT devices according to the ISAC signal model, the omnidirectional communication model, and the radar model, and to perform a receiving step: receiving echo information from each of the IoT devices;

[0086] A second execution module, configured to execute an optimization step: extracting sensing information from the echo information, and optimizing a beam using a non-convex algorithm based on the sensing information;

[0087] A third execution module, configured to execute a transmitting step: transmitting a directional beam to each of the IoT devices according to the directional communication and radar model and the optimized beam;

[0088] A repeating module is used to repeat the receiving step, the optimizing step and the transmitting step in sequence to implement a sensor-assisted communication method.

[0089] A third aspect of the present invention further provides a sensor-assisted communication device, a memory for storing a computer program;

[0090] A processor is configured to implement the steps of the sensing-assisted communication method when executing the computer program.

[0091] A fourth aspect of the present invention further provides a storage medium, which is a readable storage medium and stores a computer program. When the computer program is executed by a processor, the steps of the sensor-assisted communication method are implemented.

[0092] The beneficial effects of the present invention are:

[0093] The sensor-assisted communication method, device, equipment and readable storage medium proposed in the present invention have low communication overhead, higher matched filter gain, no quantization error, can achieve more accurate perception effects, and ensure the maximum communication rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 This is a flow chart of a sensor-assisted communication method according to an embodiment of the present application;

[0095] Figure 2This is a frame structure diagram of the aerial base station sensor-assisted communication protocol in an embodiment of the present application;

[0096] Figure 3 This is a simulation diagram of the change of communication rate in different time slots compared with the traditional pilot signal and the method based only on omnidirectional sensing in the embodiment of the present application;

[0097] Figure 4 This is a simulation diagram of the dynamic changes of the optimized beam in different communication time slots in the embodiment of the present application. DETAILED DESCRIPTION

[0098] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present invention. It should be understood that the described embodiments are only a portion of the embodiments of the present invention, not all of them. Generally, the components of the embodiments of the present invention described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations.

[0099] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0100] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0101] In the description of the present invention, it should be understood that the terms "upper", "lower", "inside", "outside", "left", "right", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is conventionally placed when in use, or are the orientations or positional relationships conventionally understood by those skilled in the art. These are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0102] Furthermore, the terms “first”, “second”, etc. are merely used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0103] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, terms such as "disposed" and "connected" should be understood in a broad sense. For example, "connected" can mean a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can also mean internal communication between two components. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0104] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0105] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0106] like Figure 1 As shown, the first aspect of the present invention provides a sensor-assisted communication method, comprising:

[0107] S1: Acquire first information, where the first information includes an assumption and air base station communication parameters;

[0108] S2: Constructing an aerial base station communication model based on the first information, including constructing an ISAC signal model, constructing an omnidirectional communication and radar model, and constructing a directional communication and radar model;

[0109] S3: Sending an omnidirectional beam to each of the IoT devices according to the ISAC signal model, omnidirectional communication, and radar model, and performing a receiving step: receiving echo information from each of the IoT devices;

[0110] S4: performing an optimization step: extracting sensing information from the echo information, and optimizing the beam using a non-convex algorithm based on the sensing information;

[0111] S5: Execute a transmitting step: transmit a directional beam to each of the IoT devices according to the directional communication and radar model and the optimized beam;

[0112] S6: Repeat the receiving step, the optimizing step, and the transmitting step in sequence to implement the sensor-assisted communication method.

[0113] In some embodiments, step S1 specifically includes:

[0114] Obtain assumptions, including assuming that the aerial base station is good at sensing nearby single-antenna IoT devices and facilitating downlink communications with them, assuming that the aerial base station senses and communicates with IoT devices only through line-of-sight channels, and assuming that in the initial stage, the aerial base station has nearby There are potentially unknown numbers of devices without channel information distributed around the space base station.

[0115] Acquire the communication parameters of the aerial base station, the communication parameters of the aerial base station include: the aerial base station is equipped with The radiating uniform linear array of antennas contains Different antennas receive uniform linear arrays, the communication protocol of the aerial base station continues time intervals, each of which is .

[0116] In some embodiments, step S2 specifically includes:

[0117] The ISAC signal model for:

[0118] (1)

[0119] Indicates the Devices in time slot Transmitted communication information, Indicates the Devices in time slot The communication information transmitted, t represents the continuous time, Represents the transpose of a matrix;

[0120] After the beamforming process, the transmitted signal is obtained for:

[0121] (2)

[0122] in represents the beamforming matrix.

[0123] Build omnidirectional communication and radar models, including:

[0124] During communication, the air base station The channel vector of each device Expressed as:

[0125] (3)

[0126] In the formula Indicates reference distance The path loss at the air base station and the The distance between devices is , for devices The steering vector Expressed as:

[0127] (4)

[0128] Where, represents an imaginary number, represents the wavelength, Indicates the gap between two adjacent antennas. is the angle of device k, so the The signal received by the device for:

[0129] (5)

[0130] in represents the additive white Gaussian noise (AWGN) at the receiving end, It means it obeys the complex Gaussian distribution with a mean of 0 and a variance of , Represents the conjugate transpose of the channel, and assumes the transmission power is a unit value. According to the principle of omnidirectional beam pattern, it is necessary to ensure The orthogonality of When , the spatial covariance matrix of the omnidirectional beam is:

[0131] (6)

[0132] in and denote the conjugate transpose of the transmit signal and the beamforming matrix, respectively. for The identity matrix of Receive signal-to-interference-and-noise ratio (SINR) of each device Expressed as:

[0133] (7)

[0134] Since the channel does not change with time, Abbreviated as ;

[0135] if , the linear precoder is redundant; thus, it can be directly manipulated Antennas transmit ISAC signal, the received SINR is Similarly, in summary, IoT devices In the The total communication rate that can be achieved in a time slot is recorded as for ;

[0136] Since the MIMO orthogonal waveform echo is still orthogonal, the radar can identify the reflection of each device, so for the device , the received echo reflection Expressed as:

[0137] (9)

[0138] Where, is the conjugate transpose of the steering vector, After a delay The transmission signal, To receive the steering vector, its structure is similar to same, The variance is Zero-mean complex additive Gaussian white noise, and Representation device In the time slot The reflection coefficient and time delay within the ,in For radar cross section (RCS), using the classic matched filtering method, the signal delay can be estimated and the refined output vector can be obtained for:

[0139] (10)

[0140] In the formula is the signal processing gain after matching filtering, the noise matrix Independent, zero-mean, and complex Gaussian distributed variance In addition, when there is a delay, the distance is measured by ranging The model is derived and the distance is measured The model is , Expressing the speed of light, using Capon's method or the generalized likelihood ratio test (GLRT), ABS and equipment The angle between them is derived as , because the angle does not change with time, so and Equivalently, the measurement models of distance and angle both adopt Gaussian distribution model, that is, and Represent zero mean and variance respectively. and Gaussian noise, considering the acquisition and The variance of the challenge, due to its unbiased estimate and the ability to provide lower MSE bounds, uses the Cramer-Rao bound (CRB) as the perceptual metric:

[0141] (11)

[0142] (12)

[0143] The preset constant is and , which is related to the system structure, the radar echo signal-to-noise ratio is recorded as , the square of the effective bandwidth is , the RMS aperture width of the beam pattern is , where σ 2 is the variance of the echo noise.

[0144] In some embodiments, step S2 specifically includes:

[0145] Build directional communication and radar models, including:

[0146] Set to ;

[0147] During the communication process, the device The received signal is represented as

[0148] (13)

[0149] In short, the device The SINR can be expressed as

[0150] (14)

[0151] Among them, the beamforming vector For The extracted Column vector, for time slots , i represents the i-th object, Indicates from The i-th column vector extracted from The associated total communication rate is ;

[0152] Under massive MIMO conditions, the steering vectors at different angles are asymptotically orthogonal, so the reflected echoes do not interfere with each other. Similarly, the characteristics of the received echo reflections are

[0153] (15)

[0154] in After a delay The original transmission signal;

[0155] The signal-to-noise ratio of the radar echo signal can be described as

[0156] (16).

[0157] Extracting sensing information from the echo information and optimizing a beam using a non-convex algorithm based on the sensing information, including:

[0158] Construct an objective function, which is:

[0159] (17)

[0160] Constructing constraints, wherein the constraints include:

[0161] (18)

[0162] (19)

[0163] (20)

[0164] (twenty one)

[0165] The optimized variable is the beamforming matrix of the air base station to each device, where represents the minimum communication rate of each device, H represents the conjugate transpose, represents the estimated angle of the aerial base station to the object k in time slot n-1, Indicates all satisfaction The steering vector of the coverage angle, represents the trace of the matrix, represents the beam coverage angle, l represents the beam width control coefficient, is the variance of the estimated angle of object k by the aerial base station in time slot n-1, B k is the beam smoothness control coefficient;

[0166] Constraint C1 represents a strict limit on the transmission power, constraint C2 represents a semidefinite constraint, a Hermitian constraint, and a rank-one constraint, and constraint C4 ensures the minimum communication rate for each device. ,Limit C5 represents the strict control of beam coverage;

[0167] Relax the rank-one constraint, use SCA to approximate the non-concave objective function to a concave objective function, and solve it iteratively; first perform a first-order Taylor expansion on the objective function to obtain:

[0168] (twenty two)

[0169] in, is defined as ,Will As the new objective function, The iterative objective function becomes convex and can be solved by CVX;

[0170] Assumptions is the iterative optimal solution, which is generally not rank one. The IRM algorithm is used to make the solution gradually approach rank one. and Replace and And they represent the iterative optimal solution, The solution of the iteration, Necessary and sufficient conditions must be met To become a rank one matrix, express × The identity matrix, yes of The matrix composed of the eigenvectors corresponding to the smaller eigenvalues, is a positive number that tends to 0. The IRM problem is constructed as follows:

[0171] Objective function:

[0172] (twenty three)

[0173] Constraints:

[0174] (twenty four)

[0175] (25)

[0176] (26)

[0177] (27)

[0178] (28)

[0179] in:

[0180]

[0181] ,

[0182] in, Represents the penalty coefficient. After multiple iterations, when Enough hours, It is a rank-one solution.

[0183] A comparative overview of the protocol for conventional beam training and the method of the present application is given in Figure 2As shown in the figure, conventional approaches involve the airborne base station transmitting a pilot signal to capture target parameters, which is then fed back for beamforming and data transmission. In contrast, the proposed method performs sensing and communication simultaneously, avoiding the need for a dedicated downlink pilot and replacing uplink feedback with radar echoes, thereby improving communication efficiency and reducing time overhead. In the initial phase, the airborne base station transmits an omnidirectional beam to sense the environment. The sensing information extracted from the echo assists directional beamforming in the next time slot. The directional phase then generates more accurate sensing information, which feeds back into beamforming in the next time slot, and so on. Furthermore, protocol interconnection is facilitated by periodic omnidirectional beams. These beams capture data for both newly detected and previously known objects. For new objects, directional beams are crafted based on the sensing data and improve over time. For previously known objects, sufficient data is already available, and a two-step prediction is performed based on the object's last known position in the previous frame, quickly restoring the communication rate to near-optimal levels. The proposed beam prediction exhibits good stability under high target maneuverability.

[0184] like Figure 3 As shown, the communication rate of the present application has the highest communication rate compared with the traditional pilot method and the method based only on omnidirectional sensing.

[0185] like Figure 4 As shown in FIG, it is a simulation diagram of the dynamic change of the optimized beam in different communication time slots in the embodiment of the present application. Figure 4 As shown in the figure, a target is placed at 60 degrees and 120 degrees respectively. n is the time slot. When n=1, the aerial base station transmits an omnidirectional beam, which means that the beam gain (beam pattern) at each angle is the same. After that, the aerial base station transmits a directional beam, forming a better gain in the directions of 60 degrees and 120 degrees. The later the time slot, the narrower the beam and the higher the gain.

[0186] A second aspect of the present invention provides a sensing-assisted communication device, comprising:

[0187] an acquisition module, configured to acquire first information, wherein the first information includes an assumption and communication parameters of an aerial base station;

[0188] A construction module, the construction module being configured to construct an aerial base station communication model according to the first information, including constructing an ISAC signal model, constructing an omnidirectional communication and radar model, and constructing a directional communication and radar model;

[0189] A first execution module is configured to send an omnidirectional beam to each of the IoT devices according to the ISAC signal model, the omnidirectional communication model, and the radar model, and to perform a receiving step: receiving echo information from each of the IoT devices;

[0190] A second execution module, configured to execute an optimization step: extracting sensing information from the echo information, and optimizing a beam using a non-convex algorithm based on the sensing information;

[0191] A third execution module, configured to execute a transmitting step: transmitting a directional beam to each of the IoT devices according to the directional communication and radar model and the optimized beam;

[0192] A repeating module is used to repeat the receiving step, the optimizing step and the transmitting step in sequence to implement a sensor-assisted communication method.

[0193] A third aspect of the present invention further provides a sensor-assisted communication device, a memory for storing a computer program;

[0194] A processor is configured to implement the steps of the sensing-assisted communication method when executing the computer program.

[0195] A fourth aspect of the present invention further provides a storage medium, which is a readable storage medium and stores a computer program. When the computer program is executed by a processor, the steps of the sensor-assisted communication method are implemented.

[0196] The sensor-assisted communication method, apparatus, device, and readable storage medium proposed in this invention achieve lower communication overhead because pilot blocks are no longer required in the communication frame, and channel information is extracted from radar echoes. This also results in higher matched filter gain. As an integrated signal, the entire communication frame can be used for sensing, resulting in a high matched filter gain and no quantization error. Based on the sensing information from the initial time slot, the beam pattern for the next time slot can be optimized, achieving higher beam gain and more accurate sensing. This can then further assist in beam optimization for the next time slot. This process is repeated until convergence, ensuring maximum communication rate.

[0197] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A sensor-assisted communication method, characterized in that: include: Acquiring first information, where the first information includes an assumption and an air base station communication parameter; Constructing an airborne base station communication model based on the first information, including constructing an ISAC signal model, constructing an omnidirectional communication and radar model, and constructing a directional communication and radar model; Sending an omnidirectional beam to each IoT device according to the ISAC signal model, omnidirectional communication and radar model, and performing a receiving step: receiving echo information from each IoT device; performing an optimization step: extracting sensing information from the echo information, and optimizing a beam using a non-convex algorithm based on the sensing information; Performing a transmitting step: transmitting a directional beam to each of the IoT devices according to the directional communication and radar model and the optimized beam; Repeating the receiving step, the optimizing step, and the transmitting step in sequence to implement a sensor-assisted communication method; Obtaining first information, including: Obtaining assumptions, including assuming that the aerial base station is adept at sensing nearby single-antenna IoT devices and facilitating downlink communications with them, assuming that the aerial base station senses and communicates with IoT devices only through line-of-sight channels, assuming that in an initial stage, the aerial base station has inaccurate channel information for K nearby devices, and assuming that a potentially unknown number of devices without channel information are distributed around the aerial base station; Acquire the communication parameters of the aerial base station, the communication parameters of the aerial base station include: the aerial base station is equipped with The radiating uniform linear array of antennas contains The antenna has different receiving uniform linear arrays. The communication protocol of the aerial base station lasts for N time intervals, and the length of each time interval is ; Build the ISAC signal model, including: The ISAC signal model for: (1)， Indicates the communication information transmitted by the first device in time slot n, represents the communication information transmitted by the Kth device in time slot n, t represents the continuous time, and T represents the transpose of the matrix; After the beamforming process, the transmitted signal is obtained for: (2)， in represents the beamforming matrix; Build omnidirectional communication and radar models, including: In communication, the channel vector from the air base station to the kth device is Expressed as: (3)， In the formula Indicates reference distance The path loss at , the distance between the aerial base station and the kth device is , the steering vector for device k Expressed as: ， In the formula, j represents an imaginary number, represents the wavelength, d represents the gap between two adjacent antennas, is the angle of device k, so the signal received by the kth device for: ， in represents the additive white Gaussian noise (AWGN) at the receiving end, It means it obeys the complex Gaussian distribution with a mean of 0 and a variance of , Represents the conjugate transpose of the channel, and assumes the transmission power is a unit value. According to the principle of omnidirectional beam pattern, it is necessary to ensure The orthogonality of When , the spatial covariance matrix of the omnidirectional beam is: ， in and denote the conjugate transpose of the transmit signal and the beamforming matrix, respectively. for The identity matrix of the kth device, the received signal-to-interference-and-noise ratio (SINR) Expressed as: ， Since the channel does not change with time, Abbreviated as ; if , the linear precoder is redundant; therefore, K antennas can be directly manipulated to transmit K ISAC signals respectively, and the received SINR is the same as Equation (7). The total communication rate that IoT device k can achieve in the nth time slot is recorded as for ; Since the MIMO orthogonal waveform echoes are still orthogonal, the radar can identify the reflections of each device. Therefore, for device k, the received echo reflection Expressed as: ， Where, is the conjugate transpose of the steering vector, After a delay The transmission signal, To receive the steering vector, its structure is similar to same, The variance is Zero-mean complex additive Gaussian white noise, and It represents the reflection coefficient and delay of device k in time slot n. The reflection coefficient is expressed as ,in For radar cross section (RCS), using the classic matched filtering method, the signal delay can be estimated and the refined output vector can be obtained for: ， In the formula is the signal processing gain after matching filtering, the noise matrix Independent, zero-mean, and complex Gaussian distributed variance In addition, when there is a delay, the distance is measured by ranging The model is derived and the distance is measured The model is , Denotes the speed of light. Using the Capon method or the generalized likelihood ratio test (GLRT), the angle between ABS and device k is derived as , because the angle does not change with time, so and Equivalently, the measurement models of distance and angle both adopt Gaussian distribution model, that is, and Represent zero mean and variance respectively. and Gaussian noise, considering the acquisition and The variance of the challenge, due to its unbiased estimate and the ability to provide lower MSE bounds, uses the Cramer-Rao bound (CRB) as the perceptual metric: ， ， The preset constant is and , which is related to the system structure, the radar echo signal-to-noise ratio is recorded as , the square of the effective bandwidth is , the RMS aperture width of the beam pattern is , where σ 2 is the variance of the echo noise; Build directional communication and radar models, including: Set to ; During the communication process, the signal received by device k is expressed as ， In short, the SINR of device k can be expressed as ， Among them, the beamforming vector For The kth column vector extracted from , for time slot n, i represents the i-th object, Indicates from The total communication rate associated with IoT device k is the i-th column vector extracted from ; Under massive MIMO conditions, the steering vectors at different angles are asymptotically orthogonal, so the reflected echoes do not interfere with each other. Similarly, the characteristics of the received echo reflections are ， in After a delay The original transmission signal; The signal-to-noise ratio of the radar echo signal can be described as ； Extracting sensing information from the echo information and optimizing a beam using a non-convex algorithm based on the sensing information, including: Construct an objective function, which is: ， Constructing constraints, wherein the constraints include: ， ， ， ， The optimized variable is the beamforming matrix of the air base station to each device, where represents the minimum communication rate of each device, H represents the conjugate transpose, represents the estimated angle of the aerial base station to the object k in time slot n-1, Indicates all satisfaction The steering vector of the coverage angle, represents the trace of the matrix, represents the beam coverage angle, l represents the beam width control coefficient, is the variance of the estimated angle of object k by the aerial base station in time slot n-1, B k is the beam smoothness control coefficient; Constraint C1 represents a strict limit on the transmission power, constraint C2 represents a semidefinite constraint, a Hermitian constraint, and a rank-one constraint, and constraint C4 ensures the minimum communication rate for each device. ,Limit C5 represents the strict control of beam coverage; Relax the rank-one constraint, use SCA to approximate the non-concave objective function to a concave objective function, and solve it iteratively; first perform a first-order Taylor expansion on the objective function to obtain: ， in, is defined as ,Will As the new objective function, It becomes a convex iterative objective function and is solved by CVX; Assumptions The iterative optimal solution is obtained, and the IRM algorithm is used to make the solution gradually approach rank one. and Replace and And they represent the iterative optimal solution, The solution of the iteration, Necessary and sufficient conditions must be met To become a rank one matrix, express × The identity matrix, yes of The matrix consists of the eigenvectors corresponding to the smaller eigenvalues, r is a positive number tending to 0, The IRM problem is constructed as follows: Objective function: （23）， Constraints: （24）， （25）， （26）， （27）， （28）， in: ， ， in, Represents the penalty coefficient. After multiple iterations, when Enough hours, It is a rank-one solution.

2. A sensor-assisted communication device, characterized in that: memory for storing computer programs; A processor is configured to implement the steps of the sensing-assisted communication method as claimed in claim 1 when executing the computer program.

3. A storage medium, characterized in that: The storage medium is a readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the sensor-assisted communication method according to claim 1 are implemented.

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