A cognitive beamforming method for UAV based on imperfect channel information

Abstract

The present invention discloses a UAV cognitive beamforming method based on imperfect channel information. The control center obtains the channel state information between the UAV and the user by means of channel estimation technology. The satellite network is used as the main network, and the UAV network is used as the secondary network and shares the spectrum resources of the main network. The power received by the secondary user is maximized as the criterion. At the same time, the interference to the primary user is limited to be less than the threshold value and the constraint condition of the transmission power budget of the UAV is satisfied. The corresponding wireless cognitive transmission optimization problem is established. Then, considering the situation that the channel state information from the two UAVs to the primary user is not perfectly known, the system control center calculates the beamforming weight vector. The control center sends the calculated beamforming weight vector to the UAV through an optical link to realize the cognitive downlink transmission of the UAV. The present invention maximizes the received power of the secondary user while the interference to the primary user is less than the threshold value, thereby improving the spectrum utilization.

Description

Technical Field

[0001] The present invention belongs to the field of cognitive communication, and in particular relates to a cognitive beamforming method for unmanned aerial vehicles based on imperfect channel information. Background Art

[0002] Cognitive wireless networks meet the growing demand for big data by dynamically managing primary and secondary network spectrum resources. They are an effective technology to solve the scarcity and underutilization of spectrum resources in wireless communications, and are also considered to be the core technology of next-generation wireless communications and networks. They can not only adapt to changes in the wireless communication environment, but also effectively optimize the management and use of spectrum resources in complex communication environments by changing working characteristics and various parameters.

[0003] Cognitive radio technology limits the interference of secondary users to primary users, enables them to intelligently access the authorized spectrum, and realizes the effective use of spectrum resources. Since secondary users and primary users share the same spectrum resources, the mutual interference between the two is an important factor affecting the performance of cognitive radio networks. In the cognitive network of unmanned aerial vehicles, how to ensure that the primary user is interfered by the unmanned aerial vehicle network without affecting its service quality is a hot topic and difficulty in current research. Among them, beamforming technology has attracted extensive attention from scholars at home and abroad due to its advantages of suppressing interference between users, realizing frequency reuse, and improving the spectrum efficiency of cognitive networks. At present, the beamforming method in the cognitive network of unmanned aerial vehicles mainly limits the interference of the unmanned aerial vehicle network to the primary user, that is, the interference power received by the primary user is less than a given threshold, and at the same time maximizes the receiving power of the unmanned aerial vehicle user. In addition, considering that the channel state information between the unmanned aerial vehicle and the primary user is difficult to be perfectly known, the present invention considers the channel state information between the unmanned aerial vehicle and the primary user obtained under two error conditions, and provides a cognitive beamforming method for unmanned aerial vehicles with imperfect channel information to achieve effective transmission of wireless cognitive information. Summary of the invention

[0004] Purpose of the invention: In view of the effective communication problems existing in the existing UAV cognitive network, the purpose of the present invention is to propose a UAV cognitive beamforming method based on imperfect channel information. Under the conditions that the interference power received by the primary user is less than the threshold value and the transmission power of the UAV does not exceed the maximum transmission power, the method optimizes the design of the UAV transmission beamforming weight vector with the maximum power received by the secondary user as the optimization goal. Under the condition that the UAV network and the satellite network share spectrum resources, the spectrum utilization rate is improved while ensuring the effective and reliable transmission of information.

[0005] Technical solution: In order to achieve the above-mentioned invention object, the present invention adopts the following technical solution:

[0006] A UAV cognitive beamforming method based on imperfect channel information: This method is based on a wireless cognitive network with spectrum coexistence, in which the satellite network is used as the primary network and the UAV network is used as the secondary network to share the spectrum resources of the primary network. The specific implementation steps are as follows:

[0007] Step 1: The control center of the system uses channel estimation technology to obtain perfect channel state information between the UAV and the secondary user within the coverage area, and imperfect channel state information between the UAV and the primary user;

[0008] Step 2: Based on the acquired channel state information, under the condition that the satellite network and the UAV network share spectrum resources, the objective function is to maximize the transmit power received by the secondary user, and the interference received by the primary user is less than the threshold value and the UAV transmit power budget is constrained to construct the corresponding wireless cognitive transmission optimization problem;

[0009] Step 3: Considering the situation where the channel state information from the two drones to the primary user is not perfectly known, the control center of the system uses the beamforming algorithm to calculate the beamforming weight vector;

[0010] Step 4: The control center sends the calculated beamforming weight vector to the UAV through the optical link for beamforming, so that the secondary user's receiving power is maximized while ensuring that its interference to the primary user is less than the threshold, achieving reliable information transmission between the two networks and improving spectrum utilization, completing the optimization design of the entire UAV cognitive transmission.

[0011] In the aforementioned UAV cognitive beamforming method based on imperfect channel information, the control center obtains the channel vector from the UAV to the secondary user and the primary user by means of channel estimation technology as g s , g p .

[0012] In the aforementioned UAV cognitive beamforming method based on imperfect channel information, the wireless cognitive transmission optimization problem in step 2 is expressed as:

[0013]

[0014] stI≤I th

[0015] ||w|| 2 ≤P max

[0016] Among them, st represents the constraints of the optimization problem, g s represents the channel vector from the UAV to the secondary user, w represents the required beamforming weight vector, I represents the interference power of the UAV on the primary user, and I th Indicates the interference power threshold that the primary user can tolerate, Pmax Indicates the maximum transmission power of the drone, [·] H represents the operation of taking the conjugate transpose of a vector, |·| represents the modulus of a complex number, and ||·|| represents the 2-norm of a vector.

[0017] In the aforementioned UAV cognitive beamforming method based on imperfect channel information, the channel state information from the two UAVs to the primary user in step 3 is imperfectly known and expressed as:

[0018] ① The channel vector between the UAV and the primary user is not perfectly known, that is,

[0019]

[0020] in, is the actual channel vector between the UAV and the primary user, g p is the estimated channel vector, represents the estimation error, ζ p is the upper bound of the estimation error;

[0021] Then the interference power of the UAV received by the primary user is expressed as:

[0022]

[0023] ② The autocorrelation matrix of the channel vector between the UAV and the primary user is not perfectly known, that is,

[0024]

[0025] in, is the actual autocorrelation matrix of the channel vector between the UAV and the primary user, G p is the estimated autocorrelation matrix, which can be expressed as E[·] means taking the expectation, and ΔG means the estimation error.

[0026] Then the interference power of the UAV received by the primary user is expressed as:

[0027]

[0028] The aforementioned UAV cognitive beamforming method based on imperfect channel information, the method for solving the wireless cognitive transmission optimization problem is: converting the original non-convex optimization problem into a second-order cone convex optimization problem, and using a standard convex optimization tool to solve and obtain the UAV transmit beamforming weight vector.

[0029] Beneficial technical effects of the present invention: The UAV cognitive beamforming method based on imperfect channel information proposed in the present invention not only maximizes the secondary user's receiving power through beamforming technology, but also makes the interference power received by the primary user less than the threshold value, thereby ensuring effective and reliable transmission of information while improving spectrum utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the cognitive downlink beamforming model of the UAV in the implementation case of the present invention;

[0031] Figure 2 It is a flow chart of a specific implementation method of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to have a more detailed understanding of the present invention, the technical solution of the present invention is more clearly described below in conjunction with the drawings in this embodiment, but the protection scope of the present invention is not limited thereto.

[0033] The present invention proposes a UAV cognitive beamforming method based on imperfect channel information, which not only ensures effective and reliable information transmission, but also improves spectrum utilization. Figure 1 The following is a schematic diagram of the UAV cognitive downlink beamforming model. Figure 1 As shown, the present invention studies the UAV cognitive downlink transmission system. It consists of a multi-beam communication satellite as the main network serving M main users, a control center and UAVs as secondary networks sharing the spectrum resources of the main network to serve L secondary users. The multi-beam satellite uses a multi-feed single-reflector onboard antenna, equipped with N s (N s >M) feed sources, UAV configuration N u (N u >L) uniform planar array elements, and both the primary user and the secondary user are configured with a single antenna.

[0034] like Figure 2The figure shows a flow chart of the method of the present invention. The method first obtains the channel state information between the UAV and the user through the channel estimation technology in the control center of the system; based on the obtained channel state information, when the satellite network is used as the main network and the UAV network is used as the secondary network and shares the spectrum resources of the main network, the objective function is to maximize the power received by the secondary user, and the interference to the main user is less than the threshold value and the transmission power of the UAV does not exceed the maximum transmission power as the constraint conditions, and the corresponding wireless cognitive transmission optimization problem is established; then, considering the case where the channel state information from the two UAVs to the main user is not perfectly known, the original non-convex optimization problem is converted into a second-order cone convex optimization problem, and the standard convex optimization tool is used to solve it to obtain the UAV transmit beamforming weight vector; then, the control center sends the calculated beamforming weight vector to the UAV through the optical link to perform beamforming to realize UAV cognitive downlink transmission. The detailed steps are as follows:

[0035] (1) The control center obtains the channel state information between the UAV and the user through channel estimation technology, which can be expressed as:

[0036]

[0037] where θ∈[0,π / 2) and Indicates the vertical and horizontal angles of arrival, L n represents the number of indirect paths. ρ0 and ρ l Represent the path loss of the direct path and the lth indirect path respectively. Since the direct path component is dominant, |ρ l | 2 The value of |ρ0| is usually larger than 2 5-10dB smaller. Antenna array gain In dB it can be expressed as

[0038]

[0039] Among them G max Represents the maximum gain of the drone antenna, S m Represents the sidelobe gain. and Represent the gains in the horizontal and vertical directions respectively, which can be expressed as

[0040]

[0041]

[0042] in, and Represent the 3dB beamwidth in the horizontal and vertical directions respectively. In formula (1), and are the horizontal and vertical steering vectors, which can be expressed as:

[0043]

[0044]

[0045] Where β = 2π / λ represents the wave number and λ is the wavelength. x and N y They represent the number of antennas in the horizontal and vertical directions respectively, and d1 and d2 represent the antenna spacing in the horizontal and vertical directions respectively.

[0046] (2) Based on the obtained channel state information, under the condition that the satellite network and the UAV network share spectrum resources, the objective function is to maximize the transmit power received by the secondary user, and the constraints are that the interference received by the primary user is less than the threshold value and the transmit power of the UAV does not exceed the maximum transmit power. The corresponding wireless cognitive transmission optimization problem is constructed, which can be expressed as:

[0047]

[0048] Among them, g s represents the channel vector from the drone to the secondary user, I represents the interference power of the drone to the primary user, and I th Indicates the interference power threshold that the primary user can tolerate, P max Indicates the maximum transmit power of the drone.

[0049] (3) Considering the case where the channel state information from the two drones to the primary user is not perfectly known, the control center of the system calculates the beamforming weight vector:

[0050] The first one is that the channel vector between the UAV and the primary user is not perfectly known, that is,

[0051]

[0052] in, is the actual channel vector between the UAV and the primary user, g p is the estimated channel vector, represents the estimation error, ζ p is the upper bound of the estimation error.

[0053] Then the interference power of the UAV received by the primary user can be expressed as:

[0054]

[0055] The interference constraint is converted to:

[0056]

[0057] Consider the worst case where the constraints are satisfied, that is

[0058]

[0059] According to the triangle inequality and the Cauchy-Wartz inequality, we can get

[0060]

[0061] Then the optimization problem when the channel vector between the UAV and the primary user is not perfectly known can be converted to:

[0062]

[0063] By observing the objective function and constraints of the above optimization problem, we can find that if w is the optimal solution, then there must be an arbitrary phase rotation we that satisfies problem (13) jα Without loss of generality, assume that the choice is such that If w is a real value, then problem (13) can be transformed into:

[0064]

[0065] The above optimization problem is a second-order cone convex optimization problem, which can be directly solved using the standard convex optimization toolkit to obtain the UAV transmit beamforming weight vector w.

[0066] The second type is that the autocorrelation matrix of the channel vector between the UAV and the primary user is not perfectly known, that is,

[0067]

[0068] in, is the actual autocorrelation matrix of the channel vector between the UAV and the primary user, G p is the estimated autocorrelation matrix, which can be expressed as E[·] represents the expectation, and ΔG represents the estimation error. When the estimated autocorrelation matrix G p When known, it can be decomposed into

[0069] G p =VV H (16)

[0070] Then, when there is an error in the actual autocorrelation matrix, the error of the autocorrelation matrix can be mapped to the decomposition result, which can be expressed as

[0071]

[0072] further Then the interference power of the UAV received by the primary user can be expressed as

[0073]

[0074] Consider the worst case where the constraints are satisfied, that is

[0075]

[0076] According to the triangle inequality and the Cauchy-Wartz inequality, we can get

[0077] ||(V+ΔV) H w||≤||ΔV H w||+||V H w||≤||V H w||+||ΔV||||w||≤||V H w||+δ V (20)

[0078] Then the optimization problem when the autocorrelation matrix of the channel vector between the UAV and the primary user is not perfectly known can be converted to:

[0079]

[0080] The above optimization problem is a second-order cone convex optimization problem, which can be directly solved using the standard convex optimization toolkit to obtain the UAV transmit beamforming weight vector w.

[0081] (4) The control center sends the calculated beamforming weight vector to the UAV through an optical link for beamforming, so that the secondary user's receiving power is maximized while ensuring that its interference to the primary user is less than the threshold, achieving reliable information transmission between the two networks and improving spectrum utilization, thereby completing the optimization design of the entire UAV cognitive transmission.

[0082] In addition to the above embodiments, the present invention may also have other implementation modes. Any technical solutions formed by equivalent replacement or equivalent transformation shall fall within the protection scope required by the present invention.

Claims

1. A UAV cognitive beamforming method based on imperfect channel information, based on a wireless cognitive network with spectrum coexistence, wherein a satellite network is used as a primary network and a UAV network is used as a secondary network to share the spectrum resources of the primary network, characterized in that: The method comprises the following steps: Step 1: The system consists of a multi-beam communication satellite as the primary network serving M primary users, a control center and drones as secondary networks sharing the spectrum resources of the primary network to serve L secondary users. The control center of the system uses channel estimation technology to obtain perfect channel state information between drones and secondary users within the coverage area, and imperfect channel state information between drones and primary users; Step 2: Based on the acquired channel state information, under the condition that the satellite network and the UAV network share spectrum resources, the wireless cognitive transmission optimization problem is constructed with the objective function of maximizing the transmit power received by the secondary user and the constraints that the interference received by the primary user is less than the threshold value and the UAV transmit power budget; The wireless cognitive transmission optimization problem is expressed as: s.t.I≤I th ||in|| 2 ≤P max Among them, st represents the constraints of the optimization problem, g s represents the channel vector from the UAV to the secondary user, w represents the required beamforming weight vector, I represents the interference power of the UAV on the primary user, and I th Indicates the interference power threshold that the primary user can tolerate, P max Indicates the maximum transmission power of the drone, [·] H represents the operation of taking the conjugate transpose of a vector, | · | represents the modulus of a complex number, ||·|| represents the 2-norm of a vector; Step 3: Considering the situation where the channel state information from the two drones to the primary user is not perfectly known, the control center of the system uses the beamforming algorithm to calculate the beamforming weight vector; The channel state information from the two drones to the primary user is not perfectly known as follows: ① The channel vector between the UAV and the primary user is not perfectly known, that is, in, is the actual channel vector between the UAV and the primary user, g p is the estimated channel vector, represents the estimation error, ζ p is the upper bound of the estimation error; Then the interference power of the UAV received by the primary user is expressed as: ② The autocorrelation matrix of the channel vector between the UAV and the primary user is not perfectly known, that is, in, is the actual autocorrelation matrix of the channel vector between the UAV and the primary user, G p is the estimated autocorrelation matrix, which can be expressed as E[·] means taking the expectation, and ΔG means the estimation error; Then the interference power of the UAV received by the primary user is expressed as: Step 4: The control center sends the calculated beamforming weight vector to the UAV through the optical link for beamforming, so that the secondary user's receiving power is maximized while ensuring that its interference to the primary user is less than the threshold, achieving reliable information transmission between the primary network and the secondary network and improving spectrum utilization, completing the optimization design of the entire UAV cognitive transmission.

2. The method for cognitive beamforming of unmanned aerial vehicles based on imperfect channel information according to claim 1, characterized in that: The control center uses the channel estimation technology to obtain the channel vector from the drone to the secondary user and the primary user as g s , g p .

3. The method for cognitive beamforming of unmanned aerial vehicles based on imperfect channel information according to claim 1, characterized in that: The method for solving the wireless cognitive transmission optimization problem described in step 2 is: converting the original non-convex optimization problem into a second-order cone convex optimization problem, and using a standard convex optimization tool to solve and obtain the UAV transmit beamforming weight vector.

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