Sensing auxiliary communication method, device and equipment and readable storage medium

Abstract

The invention discloses a sensing auxiliary communication method, a sensing auxiliary communication device, sensing auxiliary communication equipment and a readable storage medium. The sensing auxiliary communication method comprises the following steps: acquiring assumed conditions and air base station communication parameters; constructing an air base station communication model, including constructing an ISAC signal model, constructing an omnidirectional communication and radar model and constructing a directional communication and radar model; according to the ISAC signal model and the omni-directional communication and radar model, sending an omni-directional wave beam to each piece of Internet of Things equipment, and executing a receiving step: receiving echo information of each piece of Internet of Things equipment; executing an optimization step: extracting sensing information from the echo information, and optimizing a wave beam by adopting a non-convex algorithm based on the sensing information; executing a transmitting step: transmitting a directional wave beam to each piece of Internet of Things equipment according to a directional communication and radar model and the optimized wave beam; and repeating the receiving step, the optimizing step and the transmitting step in sequence. The method is low in communication overhead, has higher matched filtering gain, has no quantization error, can realize a more accurate sensing effect, and ensures the 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, device, equipment and readable storage medium. Background Art

[0002] At present, the traditional base station wireless communication method requires quantization and then uplink feedback, which has a relatively high communication overhead and requires pilot blocks in the communication frame, resulting in low matched filter gain and large quantization errors. It is impossible to achieve higher beam gain and 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 relatively 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] Acquire first information, wherein the first information includes an assumption and an aerial base station communication parameter;

[0008] Constructing 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;

[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 of each of the IoT devices;

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

[0011] 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;

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

[0013] Specifically, obtaining the first information includes:

[0014] Obtaining assumptions, including assuming that the base station is good at sensing proximal 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 information about K devices in its vicinity, and that a potential unknown number of devices without channel information are also distributed around;

[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 N t The transmitting uniform linear array of N antennas and r The antennas are different receiving uniform linear arrays, and the communication protocol of the aerial base station lasts for N time intervals, each time interval is ΔT in length.

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

[0017] The ISAC signal model S n (t) is:

[0018] s n (t) = [s 1,n (t),...,s K,n (t)] T (1)

[0019] S 1,n (t) represents the communication information transmitted by the first device in time slot n, S K,n (t) represents the communication information transmitted by the Kth device in time slot n, t represents continuous time, and T represents the transpose of the matrix;

[0020] After the beamforming process, the transmitted signal x is obtained n (t) is:

[0021] x n (t) = W n s n (t) (2)

[0022] in represents the beamforming matrix.

[0023] Specifically, build an omnidirectional communication and radar model, including:

[0024] In communication, the channel vector h from the air base station to the kth device is k,n It is expressed as:

[0025]

[0026] Where α0 represents the path loss at the reference distance d0 = 1m, and the distance between the aerial base station and the kth device is dk , for the steering vector a(θ k ) is expressed as:

[0027]

[0028] In the formula, j represents an imaginary number, λ represents the wavelength, d represents the gap between two adjacent antennas, and θ k ∈(-π,π) is the angle of device k, so the signal y received by the kth device k,n (t) is:

[0029]

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

[0031]

[0032] in and denote the conjugate transpose of the transmit signal and the beamforming matrix, respectively. N t ×N t The unit matrix of the kth device is the received signal-to-interference-plus-noise ratio (SINR) γ k,n It is expressed as:

[0033]

[0034] Since the channel does not change over time, h k,n Abbreviated as h k ;

[0035] If K <N t , the linear precoder is redundant; therefore, the K antennas can be directly controlled to transmit K ISAC signals respectively, and the received SINR is the same as equation (7). In summary, the total communication rate that can be achieved by IoT device k in the nth time slot is denoted by R k,n For R k,n =log2(1+γ k,n );

[0036] 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 r k,n (t) is expressed as:

[0037] r k,n (t) = β k b(θ k ) H (θ k )x n (t-τ k,n )+z k,n (t) (9)

[0038] In the formula, a H (θ k ) is the conjugate transpose of the steering vector, x n (t-τ k,n )+ is the time delay τ k,n The transmitted signal, b(θ k ) is the received steering vector, and its structure is similar to a(θ k ) is the same, z k,n (t) represents the variance σ 2 Zero-mean complex additive Gaussian white noise, β k and τ k,n represents the reflection coefficient and delay of device k in time slot n. The reflection coefficient is expressed as Where ε is the 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]

[0040] Where G m is the signal processing gain after matched filtering, the noise matrix Z r Independent, zero-mean, and complex Gaussian-distributed variance Composition, in addition, ranging d k,n The model is c represents the speed of light. When there is a delay, the distance can be simply derived. Using the Capon method or the generalized likelihood ratio test (GLRT), the angle between the ABS and the device k is derived as Since the angle does not change with time, θ k With θ k,n Equivalently, the measurement models of distance and angle both adopt Gaussian distribution model, that is, and Respectively represent zero mean and variance and Gaussian noise, considering the acquisition and The variance of is challenged, and the Cramer-Rao Bound (CRB) is used as the perceptual metric due to its unbiased estimation and ability to provide lower MSE bounds:

[0041]

[0042]

[0043] The preset constants are α1 and α2, which are related to the system structure. The radar echo signal-to-noise ratio is recorded as The square of the effective bandwidth is κ 2 , 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 n=2,...,N;

[0046] During the communication process, the signal received by device k is expressed as

[0047]

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

[0049]

[0050] Among them, the beamforming vector w k,n For W n The kth column vector extracted from , for time slot n, i represents the i-th object, w i,n Indicates that from W n The total communication rate associated with IoT device k is R k,n =log2(1+γ k,n );

[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] r k,n (t) = β k b(θ k ) H (θ k )W n s(t-τ k,n )+z k,n (t)(15)

[0053] Where s(t-τ k,n ) is the time delay τ k,n The original transmission signal;

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

[0055]

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

[0057] Construct an objective function, which is:

[0058]

[0059] Construct constraints, including:

[0060]

[0061]

[0062]

[0063]

[0064] The optimized variable is the beamforming matrix of the aerial base station for each device, where Γ k represents the minimum communication rate of each device, H represents the conjugate transpose, represents the estimated angle of the aerial base station to object k in time slot n-1, Indicates that all satisfaction The steering vector of the coverage angle, tr(W k，n ), 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 C3 ensures the minimum communication rate Γ for each device. k ,Limit C4 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 iterate to solve it; first perform a first-order Taylor expansion on the objective function to obtain:

[0067]

[0068] in, is defined as Will As the new objective function, the objective function (24) becomes a convex iterative objective function, which can be solved by CVX;

[0069] Assumptions is the iterative optimal solution. This solution is generally not rank one. The IRM algorithm is used to make the solution gradually approach rank one. and {M k,n} respectively replace and and represent the iterative optimal solution respectively. The solution of the iteration, Necessary and sufficient conditions must be met To become a rank one matrix, N t-1 ×N t-1 The identity matrix, yes N t -1 smaller eigenvalues ​​corresponding to the eigenvector matrix, r is a positive number tending to 0, in the pth iteration, the IRM problem is constructed as follows:

[0070] Objective function:

[0071]

[0072] Constraints:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] in:

[0079]

[0080]

[0081] Among them, w p Represents the penalty coefficient. After multiple iterations, when r p Enough hours, It is a rank one solution.

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

[0083] An acquisition module, the acquisition module is used to acquire first information, the first information includes an assumption condition and an aerial base station communication parameter;

[0084] A construction module, wherein the construction module is used 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, wherein the first execution module is used to send an omnidirectional beam to each of the IoT devices according to the ISAC signal model, the omnidirectional communication and the radar model, and perform a receiving step: receiving echo information of each of the IoT devices;

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

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

[0088] A repeating module, wherein the repeating module is used to sequentially repeat the receiving step, the optimizing step and the transmitting step 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 used to implement the steps of the sensor-assisted communication method when executing the computer program.

[0091] The 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 effect, 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 in an embodiment of the present application;

[0095] Figure 2 This is a frame structure diagram of the air 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 method of the present application in the embodiment of the present application and the traditional pilot signal and the method based on omnidirectional sensing only;

[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] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various 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 claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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, further definition and explanation thereof is not required 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 directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use, or are directions or positional relationships commonly understood by those skilled in the art. These directions or positional relationships are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, 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 is also necessary to explain that, unless otherwise clearly specified and limited, the terms such as "setting" and "connection" should be understood in a broad sense. For example, "connection" can be 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, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0104] The specific implementation modes of the present invention are described in detail below in conjunction with 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 assumptions and air base station communication parameters;

[0108] S2: constructing 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;

[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 of 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] Obtaining assumptions, including assuming that the base station is good at sensing proximal 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 information about K devices in its vicinity, and that a potential unknown number of devices without channel information are also distributed around;

[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 N t The transmitting uniform linear array of N antennas and r The antennas are different receiving uniform linear arrays, and the communication protocol of the aerial base station lasts for N time intervals, each time interval is ΔT in length.

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

[0117] The ISAC signal model S n (t) is:

[0118] s n (t) = [s 1,n (t),...,s K,n (t)] T (1)

[0119] S 1,n (t) represents the communication information transmitted by the first device in time slot n, S K,n (t) represents the communication information transmitted by the Kth device in time slot n, t represents continuous time, and T represents the transpose of the matrix;

[0120] After the beamforming process, the transmitted signal x is obtained n (t) is:

[0121] x n (t) = W n s n (t) (2)

[0122] in represents the beamforming matrix.

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

[0124] In communication, the channel vector h from the air base station to the kth device is k,n It is expressed as:

[0125]

[0126] Where α0 represents the path loss at the reference distance d0 = 1m, and the distance between the aerial base station and the kth device is d k , for the steering vector a(θ k ) is expressed as:

[0127]

[0128] In the formula, j represents an imaginary number, λ represents the wavelength, d represents the gap between two adjacent antennas, and θ k∈(-π,π) is the angle of device k, so the signal y received by the kth device k,n (t) is:

[0129]

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

[0131]

[0132] in and denote the conjugate transpose of the transmit signal and the beamforming matrix, respectively. N t ×N t The unit matrix of the kth device is the received signal-to-interference-plus-noise ratio (SINR) γ k,n = is expressed as:

[0133]

[0134] Since the channel does not change over time, h k,n Abbreviated as h k ;

[0135] If K <N t , the linear precoder is redundant; therefore, the K antennas can be directly controlled to transmit K ISAC signals respectively, and the received SINR is the same as equation (7). In summary, the total communication rate that can be achieved by IoT device k in the nth time slot is denoted by R k,n For R k,n =log2(1+γ k,n );

[0136] 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 r k,n (t) is expressed as:

[0137] r k,n (t) = β k b(θ k ) H (θ k )xn (t-τ k,n )+z k,n (t) (9)

[0138] In the formula, a H (θ k ) is the conjugate transpose of the steering vector, x n (t-τ k,n )+ is the time delay τ k,n The transmitted signal, b(θ k ) is the received steering vector, and its structure is similar to a(θ k ) is the same, z k,n (t) represents the variance σ 2 Zero-mean complex additive Gaussian white noise, β k and τ k,n represents the reflection coefficient and delay of device k in time slot n. The reflection coefficient is expressed as Where ε is the 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]

[0140] Where G m is the signal processing gain after matched filtering, the noise matrix Z r Independent, zero-mean, and complex Gaussian-distributed variance Composition, in addition, ranging d k,n The model is c represents the speed of light. When there is a delay, the distance can be simply derived. Using the Capon method or the generalized likelihood ratio test (GLRT), the angle between the ABS and the device k is derived as Since the angle does not change with time, θ k With θ k,n Equivalently, the measurement models of distance and angle both adopt Gaussian distribution model, that is, and Respectively represent zero mean and variance and Gaussian noise, considering the acquisition and The variance of is challenged, and the Cramer-Rao Bound (CRB) is used as the perceptual metric due to its unbiased estimation and ability to provide lower MSE bounds:

[0141]

[0142]

[0143] The preset constants are α1 and α2, which are related to the system structure. The radar echo signal-to-noise ratio is recorded as The square of the effective bandwidth is κ 2 , 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 n=2,...,N;

[0147] During the communication process, the signal received by device k is expressed as

[0148]

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

[0150]

[0151] Among them, the beamforming vector w k,n For W n The kth column vector extracted from , for time slot n, i represents the i-th object, w i,n Indicates that from W n The total communication rate associated with IoT device k is R k,n =log2(1+γ k,n );

[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] r k,n (t) = β k b(θ k ) H (θ k )W n s(t-τ k,n )+z k,n (t) (15)

[0154] Where s(t-τ k,n ) is the time delay τ k,n The original transmission signal;

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

[0156]

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

[0158] Construct an objective function, which is:

[0159]

[0160] Construct constraints, including:

[0161]

[0162]

[0163]

[0164]

[0165] The optimized variable is the beamforming matrix of the aerial base station for each device, where Γ k represents the minimum communication rate of each device, H represents the conjugate transpose, represents the estimated angle of the aerial base station to object k in time slot n-1, Indicates that all satisfaction The steering vector of the coverage angle, tr(W k，n ), 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 C3 ensures the minimum communication rate Γ for each device. k ,Limit C4 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 iterate to solve it; first perform a first-order Taylor expansion on the objective function to obtain:

[0168]

[0169] in, is defined as Will As the new objective function, the objective function (24) becomes a convex iterative objective function, which can be solved by CVX;

[0170] Assumptions is the iterative optimal solution. This solution is generally not rank one. The IRM algorithm is used to make the solution gradually approach rank one. and {M k,n} respectively replace and and represent the iterative optimal solution respectively. The solution of the iteration, Necessary and sufficient conditions must be met To become a rank one matrix, N t-1 ×N t-1 The identity matrix, yes N t -1 smaller eigenvalues ​​corresponding to the eigenvector matrix, r is a positive number tending to 0, in the pth iteration, the IRM problem is constructed as follows:

[0171] Objective function:

[0172]

[0173] Constraints:

[0174]

[0175]

[0176]

[0177]

[0178]

[0179] in:

[0180]

[0181]

[0182] Among them, w p Represents the penalty coefficient. After multiple iterations, when r p 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. The traditional method involves the aerial base station transmitting a pilot signal to capture the target parameters, and then feeding back beamforming and data transmission. In contrast, the method proposed in this application performs sensing and communication simultaneously, avoids the need for a dedicated downlink pilot, and replaces uplink feedback with radar echoes, thereby improving communication efficiency while reducing time overhead. In the initial stage, the aerial base station transmits an omnidirectional beam to sense the environment, and assists the directional beamforming of the next time slot based on the sensing information extracted from the echo. Subsequently, the directional stage can obtain more accurate sensing information to further feed back the beamforming of the next time slot, and so on. In addition, protocol interconnection is facilitated by periodic omnidirectional beams. These beams capture data of newly detected objects and previously known objects. For new objects, directional beams are made based on the sensing data and improve over time. For previously known objects, sufficient data has been obtained, and we perform a two-step prediction based on the last known position of the object in the previous frame, thereby quickly restoring the communication rate to a near-optimal level. The proposed beam prediction has good stability in the case of high target mobility.

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

[0185] like Figure 4 As shown in FIG. 1 , 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, 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, and the later the time slot, the narrower the beam and the higher the gain.

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

[0187] An acquisition module, the acquisition module is used to acquire first information, the first information includes an assumption condition and an aerial base station communication parameter;

[0188] A construction module, wherein the construction module is used 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, wherein the first execution module is used to send an omnidirectional beam to each of the IoT devices according to the ISAC signal model, the omnidirectional communication and the radar model, and perform a receiving step: receiving echo information of each of the IoT devices;

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

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

[0192] A repeating module, wherein the repeating module is used to sequentially repeat the receiving step, the optimizing step and the transmitting step 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 used to implement the steps of the sensor-assisted communication method when executing the computer program.

[0195] The 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, device, equipment and readable storage medium proposed in the present invention solve the following problems: Lower communication overhead. Because pilot blocks are no longer required in communication frames, channel information is extracted from radar echoes; higher matched filter gain. As an integrated signal, the communication frame can be used entirely for perception, with a higher matched filter gain; and no quantization error. Based on the perception information of the initial time slot, the beam pattern of the next time slot can be optimized to achieve a higher beam gain for a more accurate perception effect, which can further assist the beam optimization of the next time slot, and this process is repeated until convergence, thereby ensuring the 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 the scope of protection of the present invention.

Claims

1. A sensor-assisted communication method, characterized in that: include: Acquire first information, wherein the first information includes an assumption and an aerial base station communication parameter; Constructing 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; 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 of each of the IoT devices; Performing an optimization step: extracting sensor information from the echo information, and optimizing a beam using a non-convex algorithm based on the sensor information; 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; The receiving step, the optimizing step and the transmitting step are repeated in sequence to implement the sensor-assisted communication method.

2. A sensor-assisted communication method according to claim 1, characterized in that: Obtain the first information, including: Obtaining assumptions, including assuming that the base station is good at sensing proximal 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 information about K devices in its vicinity, and that a potential unknown number of devices without channel information are also distributed around; 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 N t The transmitting uniform linear array of N antennas and r The antennas are different receiving uniform linear arrays, and the communication protocol of the aerial base station lasts for N time intervals, each time interval is ΔT in length.

3. A sensor-assisted communication method according to claim 2, characterized in that: Build the ISAC signal model, including: The ISAC signal model S n (t) is: s n (t)=[s 1,n (t),...,s K,n (t)] T (1) S 1,n (t) represents the communication information transmitted by the first device in time slot n, S K,n (t) represents the communication information transmitted by the Kth device in time slot n, t represents continuous time, and T represents the transpose of the matrix; After the beamforming process, the transmitted signal x is obtained n (t) is: x n (t)=W n s n (t) (2) in represents the beamforming matrix.

4. A sensor-assisted communication method according to claim 3, characterized in that: Build omnidirectional communication and radar models, including: In communication, the channel vector h from the air base station to the kth device is k,n It is expressed as: Where α0 represents the path loss at the reference distance d0 = 1m, and the distance between the aerial base station and the kth device is d k , for the steering vector a(θ k ) is expressed as: In the formula, j represents an imaginary number, λ represents the wavelength, d represents the gap between two adjacent antennas, and θ k ∈(-π,π) is the angle of device k, so the signal y received by the kth device k,n (t) is: in represents the additive white Gaussian noise (AWGN) at the receiving end, It means that it obeys the complex Gaussian distribution, with a mean of 0 and a variance of Represents the conjugate transpose of the channel, assuming the transmission power P T is a unit value. According to the principle of omnidirectional beam pattern, it is necessary to ensure that x n (t) orthogonality, when K ≥ N t 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. N t ×N t The unit matrix of the kth device is the received signal-to-interference-plus-noise ratio (SINR) γ k,n It is expressed as: Since the channel does not change over time, h k,n Abbreviated as h k ; If K <N t , the linear precoder is redundant; therefore, the K antennas can be directly controlled to transmit K ISAC signals respectively, and the received SINR is the same as equation (7). In summary, the total communication rate that can be achieved by IoT device k in the nth time slot is denoted by R k,n For R k,n =log2(1+γ k,n ); 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 r k,n (t) is expressed as: r k,n (t)=β k b(θ k )a H (i k )x n (t-t k,n )+z k,n (t) (9) In the formula, a H (θ k ) is the conjugate transpose of the steering vector, x n (t-τ k,n )+ is the time delay τ k,n The transmitted signal, b(θ k ) is the received steering vector, and its structure is similar to a(θ k ) is the same, z k,n (t) represents the variance σ 2 Zero-mean complex additive Gaussian white noise, β k and τ k,n represents the reflection coefficient and delay of device k in time slot n. The reflection coefficient is expressed as Where ε is the 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: Where G m is the signal processing gain after matched filtering, the noise matrix Z r Independent, zero-mean, and complex Gaussian-distributed variance Composition, in addition, ranging d k,n The model is c represents the speed of light. When there is a delay, the distance can be simply derived. Using the Capon method or the generalized likelihood ratio test (GLRT), the angle between the ABS and the device k is derived as Since the angle does not change with time, θ k With θ k,n Equivalently, the measurement models of distance and angle both adopt Gaussian distribution model, that is, and Respectively represent zero mean and variance and Gaussian noise, considering the acquisition and The variance of is challenged, and the Cramer-Rao Bound (CRB) is used as the perceptual metric due to its unbiased estimation and ability to provide lower MSE bounds: The preset constants are α1 and α2, which are related to the system structure. The radar echo signal-to-noise ratio is recorded as The square of the effective bandwidth is κ 2 , the RMS aperture width of the beam pattern is where σ 2 is the variance of the echo noise.

5. A sensor-assisted communication method according to claim 4, characterized in that: Build directional communication and radar models, including: Set n=2,...,N; During the communication process, the signal received by device k is expressed as In short, the SINR of device k can be expressed as That In beamforming Vector w k,n For W n The kth column vector extracted from , for time slot n, i represents the i-th object, w i,n Indicates that from W n The total communication rate associated with IoT device k is R k,n =log2(1+γ k,n ); 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 r k,n (t)=β k b(θ k )a H (i k )W n s(t-τ k,n )+z k,n (t) (15) Where s(t-τ k,n ) is the time delay τ k,n The original transmission signal; The signal-to-noise ratio of the radar echo signal can be described as 6. A sensor-assisted communication method according to claim 5, characterized in that: Extracting sensing information from the echo information, and optimizing the beam using a non-convex algorithm based on the sensing information, including: Construct an objective function, which is: Construct constraints, including: The optimized variable is the beamforming matrix of the aerial base station for each device, where Γ k represents the minimum communication rate of each device, H represents the conjugate transpose, represents the estimated angle of the aerial base station to object k in time slot n-1, Indicates that all satisfaction The steering vector of the coverage angle, tr(W k，n ), 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 C3 ensures the minimum communication rate Γ for each device. k ,Limit C4 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 iterate to solve it; first perform a first-order Taylor expansion on the objective function to obtain: in, is defined as Will As the new objective function, the objective function (24) becomes a convex iterative objective function, which can be solved by CVX; Assumptions is the iterative optimal solution. This solution is generally not rank one. The IRM algorithm is used to make the solution gradually approach rank one. and {M k,n } respectively replace and and represent the iterative optimal solution respectively. The solution of the iteration, Necessary and sufficient conditions must be met To become a rank one matrix, N t-1 ×N t-1 The identity matrix, yes N t -1 smaller eigenvalues ​​corresponding to the eigenvector matrix, r is a positive number tending to 0, in the pth iteration, the IRM problem is constructed as follows: Objective function: Constraints: in: Among them, w p Represents the penalty coefficient. After multiple iterations, when r p Enough hours, It is a rank one solution.

7. A sensor-assisted communication device, characterized in that: include: An acquisition module, the acquisition module is used to acquire first information, the first information includes an assumption condition and an aerial base station communication parameter; A construction module, wherein the construction module is used 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; A first execution module, wherein the first execution module is used to send an omnidirectional beam to each of the IoT devices according to the ISAC signal model, the omnidirectional communication and the radar model, and perform a receiving step: receiving echo information of each of the IoT devices; A second execution module, the second execution module is used to execute an optimization step: extracting sensor information from the echo information, and optimizing a beam using a non-convex algorithm based on the sensor information; A third execution module, the third execution module is used to execute the transmitting step: transmitting a directional beam to each of the Internet of Things devices according to the directional communication and radar model and the optimized beam; A repeating module, wherein the repeating module is used to sequentially repeat the receiving step, the optimizing step and the transmitting step to implement a sensor-assisted communication method.

8. A sensor-assisted communication device, characterized in that: Memory for storing computer programs; A processor, configured to implement the steps of a sensor-assisted communication method as claimed in any one of claims 1 to 6 when executing the computer program.

9. 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 any one of claims 1 to 6 are implemented.

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