Communication sensing integrated beam forming method based on imperfect CSI (Channel State Information)

Abstract

The invention relates to a communication perception integrated beam forming method based on imperfect CSI, and belongs to the technical field of communication. The method comprises the following steps: constructing a multi-input single-output downlink communication perception integrated model considering channel uncertainty; establishing a resource allocation model aiming at maximizing the total throughput of the system by considering the minimum transmission rate constraint of each communication user, the sensing performance Cramer-Rao bound constraint, the maximum transmitting power constraint of the base station and the channel uncertainty constraint; converting an unsmooth optimization problem into a smooth optimization problem by using a variable substitution method, a continuous convex approximation method and a Scherr complement condition; the constraint containing the channel uncertainty is converted into a deterministic constraint through an S process; an iteration-based robust throughput maximization resource allocation algorithm is provided, and an optimal resource allocation solution is obtained by combining a positive semi-definite relaxation method and a first-order Taylor expansion. According to the method, the robustness and transmission fairness of the system can be improved, the throughput of the system is increased, and the interruption probability of communication is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology and relates to a communication perception integrated beamforming method based on imperfect CSI. Background Art

[0002] With the rapid development of wireless communication technology, future wireless networks need to support higher transmission rates, lower latency, and higher service reliability. At the same time, the deep integration of wireless communication and environmental perception has become a research hotspot. The integrated communication and perception technology can not only effectively improve the efficiency of spectrum utilization, but also realize the coordinated operation of multiple functions. It has broad application prospects in the fields of intelligent transportation, unmanned driving, and intelligent manufacturing. However, the design of the integrated communication and perception system faces many challenges, among which the imperfection of channel state information (CSI) is a key issue.

[0003] In actual wireless communication environments, the CSI obtained by the base station is often imperfect due to factors such as channel estimation error, quantization error or feedback delay. This uncertainty will have a significant impact on the beamforming performance, thereby reducing communication reliability. Therefore, how to design a robust beamforming method under this condition and improve communication and perception performance at the same time is an important topic of current research. Existing beamforming methods usually assume that the CSI is perfect. Under this idealized assumption, the system performance can be optimized. However, in actual scenarios, these methods lack robustness to CSI uncertainty, which can easily lead to performance degradation or fail to meet the requirements of communication and perception tasks. Some studies have attempted to introduce channel uncertainty models, but their optimization methods are complex and difficult to implement in practical applications. In addition, most optimization algorithms fail to effectively take into account both communication throughput and perception performance, making it difficult for system design to achieve a good balance between the two.

[0004] The present invention considers the impact of imperfect CSI acquisition in advance and proposes a robust and anti-disturbance maximization system total throughput optimization method, which enables the system to achieve a balance between system performance and user transmission fairness, as well as a trade-off between communication and perception under bounded channel uncertainty conditions. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a communication-aware integrated beamforming method based on imperfect CSI.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A communication-aware integrated beamforming method based on imperfect CSI, the method comprising the following steps:

[0008] S1: Construct a multi-input single-output multi-communication user downlink communication perception integrated model; this downlink communication perception integrated system includes a dual-function base station with N antennas and K single-antenna communication users. The dual-function signal transmitted by the base station is transmitted through Calculate; where, represents the data vector sent to K users at the lth discrete time, satisfying I K represents the K×K dimensional identity matrix, (·) H represents the conjugate transpose operation;

[0009] The signal received by the kth communication user is based on Calculate; where w k represents the beamforming vector sent by the base station to the kth user, h k represents the channel from the base station to the kth user, It means that the mean value of the kth user is 0 and the variance is Additive Gaussian white noise;

[0010] The target echo signal received by the dual-function base station can be s (l) = αa(θ)a H (θ)x(l)+n s (l) Calculation; where a(θ) = [1, e j2πΔsin(θ) ,...,e j2π(N-1)Δsin(θ) ] T represents the steering vector of the transmitting antenna, θ represents the azimuth of the target relative to the dual-function base station, Δ represents the normalized spacing between adjacent antennas, and a H (θ) represents the steering vector of the receiving antenna, α represents the complex amplitude proportional to the radar cross section of the target and the round-trip path loss, It means that the mean at the base station receiver is 0 and the variance is Additive Gaussian white noise;

[0011] S2: Considering the transmission power constraints of dual-function base stations, perception performance constraints, and the minimum data rate constraints of each communication user, a robust resource allocation model for the communication-perception integrated system with the total throughput maximization as the optimization goal is established based on bounded channel uncertainty;

[0012] S3: The non-smooth optimization problem is transformed into a smooth optimization problem by using the variable substitution method, continuous convex approximation method and Schur complement condition; the constraints containing channel uncertainty are transformed into deterministic constraints by using the S process; an iterative robust throughput maximization resource allocation algorithm is proposed, and the optimal resource allocation solution is obtained by combining the semi-definite relaxation method and the first-order Taylor expansion;

[0013] S4: Use the CVX toolbox to solve the convex optimization problem and obtain a robust resource allocation solution that maximizes the total system throughput.

[0014] Furthermore, the signal to interference and noise ratio of the kth communication user of the system is:

[0015]

[0016] The data rate of the kth communication user is calculated according to R k =log2(1+γ k )calculate.

[0017] Furthermore, the Cramer-Rao bound of the sensing target relative to the dual-function base station is calculated according to the following formula:

[0018]

[0019] in, represents the covariance matrix of the transmitted signal x(l), W = [w1,w2,...,w k ] represents the beamforming vector transmitted by the base station, and L represents the communication frame length.

[0020] Furthermore, the channel uncertainty set between the dual-function base station and the communication user is modeled as in, represents the channel uncertainty set, represents the estimated channel vector from the dual-function base station to the kth communication user, Δh k and k Denotes the corresponding channel estimation error and the upper bound of the channel estimation error.

[0021] Furthermore, the robust resource allocation model of the communication perception integrated system with maximizing the total system throughput as the optimization goal is:

[0022]

[0023] in, represents the total throughput of the system, CRLB represents the Cramer-Rao bound that measures the perceived performance, β represents the threshold of the Cramer-Rao bound, and P max Indicates the maximum transmit power of a dual-function base station. represents the minimum data rate of the kth communication user; C1 ensures the accuracy of target perception, C2 ensures that the maximum transmission power of the dual-function base station is not higher than its threshold, C3 ensures that the minimum data rate of the kth communication user is not lower than its threshold, and C4 is the channel uncertainty set.

[0024] Furthermore, in S3, the non-smooth problem is converted into a smooth problem by using the variable substitution method:

[0025]

[0026] Among them, t is an auxiliary variable used to relax the non-smooth part of the objective function.

[0027] Furthermore, in S3, the original uncertain non-convex optimization problem is transformed into a deterministic convex problem by using the continuous convex approximation method, the Schur complement condition and the S process method. Specifically,

[0028]

[0029] Among them, φ k , χ k and r k is the slack variable, and is the slack variable, and They are and The value of the previous iteration, λ1 and λ2 are auxiliary variables; A(θ) = a(θ)a H (θ), B1=W k , is a set of auxiliary variables;

[0030] The convex optimization problem is solved based on the CVX tool to obtain the optimal solution for resource allocation, that is, the robust throughput maximization method is obtained.

[0031] The beneficial effects of the present invention are as follows: the present invention takes into account the imperfect CSI in the actual radio environment, and proposes a robust resource allocation method for maximizing system throughput by optimizing the beamforming matrix; utilizes the variable substitution method, the continuous convex approximation method and the Schur complement condition to transform the non-smooth optimization problem into a smooth optimization problem; transforms the constraints containing channel uncertainty into deterministic constraints through the S process; proposes an iterative robust throughput maximization resource allocation algorithm, and combines the semi-positive relaxation method and the first-order Taylor expansion to obtain the optimal resource allocation solution; compared with the prior art, the present invention takes into account the imperfect CSI, can effectively improve the robustness and transmission fairness of the system, significantly reduce the probability of communication interruption, and is more conducive to deployment in actual systems.

[0032] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 It is a flow chart of the present invention;

[0035] Figure 2 A comparison diagram of the method of the present invention under different channel error upper bounds;

[0036] Figure 3 A trade-off diagram between the communication performance and the perception performance of the method of the present invention;

[0037] Figure 4 This is a comparison chart of the satisfaction probability between the method of the present invention and different methods. DETAILED DESCRIPTION

[0038] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0039] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0040] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0041] Figure 1This is the solution flow chart of the present invention. The present invention provides an integrated downlink communication perception system based on imperfect CSI. Specifically, the system consists of a dual-function base station equipped with N antennas and K single-antenna communication users, and N>K. Since the target perception signal does not need to carry useful information, it only needs to interact with the target and return to the base station to complete the perception function. Therefore, the signal transmitted by the dual-function base station has both communication and perception functions, which is used to provide services for communication users and for target perception tasks. Channel modeling takes into account both large-scale and small-scale fading effects. Large-scale fading is A0(d / d0) -α Where A0=1, d represents the distance of the transceiver, d0 is the reference distance set to 1m, and α=3 is the path loss exponent. The settings of other system simulation parameters are shown in Table 1.

[0042] Table 1

[0043]

[0044] Figure 2 It shows that under different CSI error upper bounds ζ k In the case of Obviously, for the same ζ k ,when When it is lower, the total system throughput increases as This is because the increase in Forces the system to allocate more power to each communication user to meet higher demand, thereby increasing the throughput of each communicating user. In addition, Under this condition, the total system throughput increases with ζ k This is because the larger ζ k It indicates higher channel estimation error and more challenging channel environment, resulting in reduced overall system throughput.

[0045] Figure 3 Describes the maximum transmission power P of different base stations max The relationship between the total throughput and the sensing performance of the system under the same P. Obviously, as the Cramer-Rao bound threshold increases, the system sensing performance deteriorates, while the total throughput increases. This is due to the inherent performance trade-off between communication and sensing. max The larger the Cramer-Rao bound threshold, the lower the system's requirement for sensing accuracy. Therefore, less power is directed to point targets, allowing more power to be allocated to communication data transmission, resulting in enhanced communication performance and an overall increase in total throughput. In addition, with the Cramer-Rao bound threshold unchanged, the total system throughput increases with P. max This is because Pmax The increase in expands the feasible domain of transmission power, thereby increasing the data rate.

[0046] Figure 4 Depicts different channel error upper bounds ζ under different algorithms k Obviously, the algorithm proposed in the present invention has a 100% satisfaction probability. k =0.18, the satisfaction probability of the algorithm proposed in the present invention is improved by 7.3% compared with the non-robust algorithm. This improvement is due to the introduction of bounded CSI error design, which ensures that the system can operate normally even under the most unfavorable conditions. In contrast, the satisfaction probability of non-robust algorithms and non-robust equal power allocation algorithms decreases significantly as the CSI error increases. This is because the CSI error causes a deviation between the system design and the actual operation, which in turn causes improper resource allocation, reduces the robustness of the system, and increases the probability of interruption. In addition, since the non-robust equal power allocation algorithm is less sensitive to CSI errors, its satisfaction probability is usually higher than that of the non-robust algorithm. These findings further emphasize the robustness and superiority of the method of the present invention.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A communication-aware integrated beamforming method based on imperfect CSI, characterized in that: The method comprises the following steps: S1: Construct a multi-input single-output multi-communication user downlink communication perception integrated model; this downlink communication perception integrated system includes a dual-function base station with N antennas and K single-antenna communication users. The dual-function signal transmitted by the base station is transmitted through Calculate; where, represents the data vector sent to K users at the lth discrete time, satisfying I K represents the K×K dimensional identity matrix, (·) H represents the conjugate transpose operation; The signal received by the kth communication user is based on Calculate; where w k represents the beamforming vector sent by the base station to the kth user, h k represents the channel from the base station to the kth user, It means that the mean value of the kth user is 0 and the variance is σ k 2 Additive Gaussian white noise; The target echo signal received by the dual-function base station is based on y s (l) = αa(θ)a H (θ)x(l)+n s (l) Calculation; where a(θ) = [1, e j2πΔsin(θ) ,...,e j2π(N-1)Δsin(θ) ] T represents the steering vector of the transmitting antenna, θ represents the azimuth of the target relative to the dual-function base station, Δ represents the normalized spacing between adjacent antennas, and a H (θ) represents the steering vector of the receiving antenna, α represents the complex amplitude proportional to the radar cross section of the target and the round-trip path loss, It means that the mean value at the base station receiver is 0 and the variance is σ s 2 Additive Gaussian white noise; S2: Considering the transmission power constraints of dual-function base stations, perception performance constraints, and the minimum data rate constraints of each communication user, a robust resource allocation model for the communication-perception integrated system with the total throughput maximization as the optimization goal is established based on bounded channel uncertainty; S3: The non-smooth optimization problem is transformed into a smooth optimization problem by using the variable substitution method, continuous convex approximation method and Schur complement condition; the constraints containing channel uncertainty are transformed into deterministic constraints by using the S process; an iterative robust throughput maximization resource allocation algorithm is proposed, and the optimal resource allocation solution is obtained by combining the semi-definite relaxation method and the first-order Taylor expansion; S4: Use the CVX toolbox to solve the convex optimization problem and obtain a robust resource allocation solution that maximizes the total system throughput.

2. The communication-aware integrated beamforming method based on imperfect CSI according to claim 1, characterized in that: The signal to interference noise ratio of the kth communication user of the system is: The data rate of the kth communication user is calculated according to R k =log2(1+γ k )calculate.

3. The communication-aware integrated beamforming method based on imperfect CSI according to claim 1, characterized in that: The Cramer-Rao bound of the sensing target relative to the dual-function base station is calculated according to the following formula: in, represents the covariance matrix of the transmitted signal x(l), W = [w1,w2,...,w k ] represents the beamforming vector transmitted by the base station, and L represents the communication frame length.

4. The communication-aware integrated beamforming method based on imperfect CSI according to claim 1, characterized in that: The channel uncertainty set between the dual-function base station and the communication user is modeled as: in, represents the channel uncertainty set, represents the estimated channel vector from the dual-function base station to the kth communication user, Δh k and k Denotes the corresponding channel estimation error and the upper bound of the channel estimation error.

5. The communication-aware integrated beamforming method based on imperfect CSI according to claim 1, characterized in that: The robust resource allocation model of the communication perception integrated system with maximizing the total system throughput as the optimization goal is: in, represents the total throughput of the system, CRLB represents the Cramer-Rao bound that measures the perceived performance, β represents the threshold of the Cramer-Rao bound, and P max Indicates the maximum transmit power of a dual-function base station. represents the minimum data rate of the kth communication user; C1 ensures the accuracy of target perception, C2 ensures that the maximum transmission power of the dual-function base station is not higher than its threshold, C3 ensures that the minimum data rate of the kth communication user is not lower than its threshold, and C4 is the channel uncertainty set.

6. The communication-aware integrated beamforming method based on imperfect CSI according to claim 1, characterized in that: In S3, the non-smooth problem is converted into a smooth problem by using the variable substitution method: Among them, t is an auxiliary variable used to relax the non-smooth part of the objective function.

7. The communication-aware integrated beamforming method based on imperfect CSI according to claim 6, characterized in that: In S3, the original uncertain non-convex optimization problem is transformed into a deterministic convex problem by using the continuous convex approximation method, Schur complement condition and S process method. Specifically: Among them, φ k , χ k and r k is the slack variable, and is the slack variable, and They are and The value of the previous iteration, λ1 and λ2 are auxiliary variables; A(θ) = a(θ)a H (θ), B1=W k , is a set of auxiliary variables; The convex optimization problem is solved based on the CVX tool to obtain the optimal solution for resource allocation, that is, the robust throughput maximization method is obtained.

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