Energy efficiency optimization method for cognitive sensing integrated system

Abstract

The invention discloses a cognitive radio network-oriented sensing and communication integrated system energy efficiency optimization method, which is suitable for an ISAC system consisting of a main base station, a secondary base station, a main user, a secondary user and a sensing target, and the secondary base station integrates communication and sensing functions. In order to effectively improve the energy efficiency of the downlink transmission system, the invention provides an energy efficiency optimization method for a CRN-ISAC system. Specifically, the method takes a communication signal to interference plus noise ratio, a sensing signal to noise ratio and base station transmitting power as constraints, and transmitting beam forming vectors of a main base station and a secondary base station are jointly optimized to maximize the energy efficiency of the secondary base station. A simulation result shows that compared with reference schemes such as zero-forcing precoding, the scheme provided by the invention remarkably improves the energy efficiency of the CN-ISAC system.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an energy efficiency optimization method for a cognitive wireless network and a sensory communication integrated system. Background Art

[0002] Cognitive Radio Networks (CRNs) utilize a flexible spectrum management technology that adapts to environmental changes by real-time modifying system parameters such as transmission power, carrier frequency, and modulation scheme, thereby achieving high spectrum utilization. Consequently, CRN systems have shown broad application prospects in wireless communications. However, in CRN systems, the use of spectrum sensing by secondary base stations to locate spectrum holes for communication requires additional energy consumption, necessitating a trade-off between spectrum efficiency and energy efficiency.

[0003] At the same time, Integrated Sensing and Communication (ISAC) technology integrates sensing and communication functions, enabling wireless systems to provide reliable communication services while also sensing the environment. This integration not only improves the overall system performance but also provides new insights for optimizing energy utilization. In an ISAC system, base stations can simultaneously use the same spectrum resources for data transmission and radar detection, effectively utilizing radio resources and improving overall system energy efficiency. Therefore, combining the ISAC system with cognitive wireless networks addresses the energy efficiency issues of CRN systems and improves their communication performance. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides an energy efficiency optimization method for a CRN-ISAC system, which is applicable to a CRN-ISAC system consisting of a primary base station, a secondary base station, a primary user, a secondary user and a sensing target, wherein the secondary base station integrates communication and sensing functions and is equipped with N transmit / receive dual-function antennas, the primary base station is equipped with N transmit / receive antennas, and both users are equipped with single antennas.

[0005] In order to solve the above technical problems, the present invention adopts the following technical means:

[0006] Obtain channel state information from the primary base station to the user, the secondary base station to the user, and the sensing target respectively;

[0007] Based on the acquired channel state information, the user's communication signal-to-interference-and-noise ratio, the secondary base station's perceived signal-to-noise ratio, and energy efficiency are calculated.

[0008] Taking the primary user's communication signal-to-interference-and-noise ratio, the secondary base station's perceived signal-to-noise ratio, and the maximum transmit power of the primary and secondary base stations as constraints, the transmit beamforming vectors of the primary and secondary base stations are jointly optimized to maximize the secondary base station's energy efficiency.

[0009] Furthermore, the signals transmitted by the primary base station and the secondary base station can be respectively expressed as:

[0010] x PBS =ν a x a (1)

[0011] x SBS =ν c x c +ν s x s (2)

[0012] Among them, x PBS Indicates the transmission signal of the main base station, x SBS Indicates the transmission signal of the secondary base station, ν a represents the transmit beamforming vector of the primary base station to the primary user, ν c represents the transmit beamforming vector of the secondary base station to the secondary user, ν s represents the dedicated sensing signal beamforming vector of the secondary base station, x a Indicates the information sent by the primary base station to the primary user, x c Indicates the information sent by the secondary base station to the secondary user, x s represents the signal sent by the secondary base station to the sensing target, and {x a ,x c ,x s} are statistically independent of each other, satisfying

[0013] represents the mathematical expectation. Therefore, the transmit power of the secondary base station can be expressed as:

[0014] P SBS =||ν s || 2 +||ν c || 2 +N*P a (3)

[0015] Among them, ||·|| represents the two-norm operation on the vector, N represents the number of transmitting antennas of the base station, P a Indicates the static circuit loss of each antenna.

[0016] Secondly, the received signals of the primary user and the secondary user can be expressed as:

[0017]

[0018] Among them, y a represents the received signal of the primary user, y c represents the received signal of the secondary user, represents the channel state information vector from the primary base station to the primary user, represents the channel state information vector from the primary base station to the secondary user, represents the channel state information vector from the secondary base station to the secondary user, represents the channel state information vector from the secondary base station to the primary user, Indicates the channel state information from the nth antenna of the primary base station to the primary user, Indicates the channel state information from the nth antenna of the primary base station to the secondary user, Indicates the channel state information from the nth antenna of the secondary base station to the secondary user, Indicates the channel state information from the nth antenna of the secondary base station to the primary user; (·) H represents the conjugate transpose, n a represents the additive Gaussian white noise of the primary user, n c represents the additive Gaussian white noise of the secondary user, both of which have zero mean and variance respectively. and The complex Gaussian distribution of

[0019] According to the received signals y of the primary user and the secondary user a and y c , the signal-to-interference-noise ratio of the two can be expressed as:

[0020]

[0021] Finally, the echo signal received by the secondary base station can be expressed as:

[0022]

[0023] Among them, y s represents the echo signal received by the secondary base station, u represents the receive beamforming vector of the secondary base station, represents the channel state information vector from the secondary base station to the sensing target, represents the channel state information vector from the sensing target to the secondary base station, where Indicates the channel state information from the nth antenna of the base station to the sensing target, Indicates the channel state information from the sensing target to the nth antenna of the base station, represents the additive white Gaussian noise vector at the base station, represents the additive white Gaussian noise at the nth antenna of the base station, n s It has a mean of zero and a variance of The complex Gaussian distribution of Among them I N Represents the N-dimensional identity matrix.

[0024] According to the echo signal y received by the secondary base station s , the perceived signal-to-noise ratio of the secondary base station can be expressed as:

[0025]

[0026] Where SNR is the generalized Rayleigh entropy, According to the optimality principle of generalized Rayleigh entropy, the maximum value of SNR corresponds to the generalized eigenvalue problem Au = λBu, which is equivalent to the standard eigenvalue problem B -1 Au = λu, that is, the maximum value of SNR is obtained when u is the eigenvector corresponding to the maximum eigenvalue. Therefore, the maximum perceived signal-to-noise ratio of the secondary base station can be expressed as:

[0027]

[0028] Based on this, the energy efficiency of the secondary base station can be expressed as:

[0029]

[0030] By jointly optimizing the transmit beamforming vectors of the primary and secondary base stations, the energy efficiency of the secondary base station is maximized. The optimization problem is as follows:

[0031]

[0032] Where (12a), (12b), (12c) and (12d) represent the secondary base station perception signal-to-noise ratio constraint, the primary user's communication signal-to-interference-and-noise ratio constraint, the primary base station's power constraint and the secondary base station's power constraint, respectively. s and γ a They represent the minimum thresholds of the secondary base station’s perceived signal-to-noise ratio and the primary user’s communication signal-to-interference-and-noise ratio, respectively. and Represents the maximum transmit power of the primary base station and the secondary base station respectively.

[0033] Furthermore, the problem and solution are as follows:

[0034] For the objective function of this problem, let We can get:

[0035]

[0036] Furthermore, using the DinkelBach algorithm: Perform fractional optimization and use the lemma: when t = 1 / x, x>0, transform the original problem into a convex optimization problem:

[0037]

[0038] stλ s (Tr(G 34 V s )+Tr(G 34 V c ))≥γ s (14a)

[0039]

[0040] According to the lemma, for a fixed V a , V c and V s , t can be obtained in closed form with the following expression:

[0041] t * =(λ c (Tr(G1V s )+Tr(H2V a ))+1) -1 (15)

[0042] At the same time, the optimal value of η can be obtained by the DinkelBach algorithm, and the expression is as follows:

[0043]

[0044] At this point, the problem is transformed into a convex optimization problem through the SDR algorithm and lemma, and the DinkelBach algorithm is used to optimize the fractions in the problem.

[0045] Finally, the CVX toolkit is used to iterate the above problem alternately until the objective function of the optimization problem meets the convergence condition, and the optimal solution of the secondary base station energy efficiency is obtained. At the same time, the singular value decomposition is used to restore the rank-one constraint based on the solution result to obtain the optimized base station beamforming vector and

[0046] Compared to existing technologies, the present invention offers the following advantages: In traditional cognitive radio networks, base stations detect spectrum holes through sensing and implement dynamic spectrum access, significantly improving spectrum efficiency. However, single communication functionality is no longer sufficient to meet the demands of modern communication architectures. Meanwhile, ISAC technology enables both high-precision environmental perception and ultra-reliable data transmission, effectively resolving the resource duplication inherent in separately deployed radar-communication systems. However, the complexity of dynamic resource allocation in ISAC systems can lead to reduced system performance. Therefore, introducing dual-function base stations integrating sensing and communication into cognitive radio networks effectively addresses this functional monolithic issue, further utilizing the dynamic spectrum sensing capabilities of cognitive radio networks while further improving the system's spectrum efficiency. Furthermore, through the cognitive radio network's real-time spectrum detection and environmental learning capabilities, it can proactively avoid congested frequency bands and implement dynamic resource allocation for the ISAC. Finally, compared to existing baseline solutions, this solution significantly improves energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a schematic diagram of a flow chart of an embodiment of the present invention;

[0048] Figure 2 is a system model diagram of an embodiment of the present invention;

[0049] Figure 3 It is a simulation diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be further described below with reference to the accompanying drawings:

[0051] The present invention provides an energy efficiency optimization method for a CRN-ISAC system. The method is applicable to a CRN-ISAC system comprising a primary base station, a secondary base station, a primary user, a secondary user, and a sensing target. The secondary base station integrates communication and sensing functions and is equipped with N dual-function transmit / receive antennas. The primary base station is equipped with N transmit / receive antennas, and both users are equipped with single antennas. The method uses the primary user's communication signal-to-interference-plus-noise ratio, the secondary base station's sensing signal-to-noise ratio, and the maximum transmit power of the primary and secondary base stations as constraints to jointly optimize the transmit beamforming vectors of the primary and secondary base stations to maximize the secondary base station's energy efficiency. Simulation results show that compared with benchmark schemes such as zero-forcing precoding, the proposed scheme significantly improves the energy efficiency of the CRN-ISAC system. The method comprises the following steps:

[0052] Obtain channel state information from the primary base station to the user, the secondary base station to the user, and the sensing target respectively;

[0053] Based on the acquired channel state information, the user's communication signal-to-interference-and-noise ratio, the secondary base station's perceived signal-to-noise ratio, and energy efficiency are calculated.

[0054] Taking the primary user's communication signal-to-interference-and-noise ratio, the secondary base station's perceived signal-to-noise ratio, and the maximum transmit power of the primary and secondary base stations as constraints, the transmit beamforming vectors of the primary and secondary base stations are jointly optimized to maximize the secondary base station's energy efficiency.

[0055] Furthermore, the signals transmitted by the primary base station and the secondary base station can be respectively expressed as:

[0056] x PBS =ν a x a (1)

[0057] x SBS =ν c x c +ν s x s (2)

[0058] Among them, x PBS Indicates the transmission signal of the main base station, x SBS Indicates the transmission signal of the secondary base station, ν a represents the transmit beamforming vector of the primary base station to the primary user, ν c represents the transmit beamforming vector of the secondary base station to the secondary user, ν s represents the dedicated sensing signal beamforming vector of the secondary base station, x a Indicates the information sent by the primary base station to the primary user, x c Indicates the information sent by the secondary base station to the secondary user, x s represents the signal sent by the secondary base station to the sensing target, and {x a ,x c ,x s} are statistically independent of each other, satisfying

[0059] represents the mathematical expectation. Therefore, the transmit power of the secondary base station can be expressed as:

[0060] P SBS =‖ν s ‖ 2 +‖ν c ‖ 2 +N*P a (3)

[0061] Among them, ‖·‖ represents the two-norm operation on the vector, N represents the number of transmitting antennas of the base station, and P a Indicates the static circuit loss of each antenna.

[0062] Secondly, the received signals of the primary user and the secondary user can be expressed as:

[0063]

[0064] Among them, y a represents the received signal of the primary user, y c represents the received signal of the secondary user, represents the channel state information vector from the primary base station to the primary user, represents the channel state information vector from the primary base station to the secondary user, represents the channel state information vector from the secondary base station to the secondary user, represents the channel state information vector from the secondary base station to the primary user, Indicates the channel state information from the nth antenna of the primary base station to the primary user, Indicates the channel state information from the nth antenna of the primary base station to the secondary user, Indicates the channel state information from the nth antenna of the secondary base station to the secondary user, Indicates the channel state information from the nth antenna of the secondary base station to the primary user; (·) H represents the conjugate transpose, n a represents the additive Gaussian white noise of the primary user, n c represents the additive Gaussian white noise of the secondary user, both of which have zero mean and variance respectively. and The complex Gaussian distribution of

[0065] According to the received signals y of the primary user and the secondary user a and y c , the signal-to-interference-noise ratio of the two can be expressed as:

[0066]

[0067] Finally, the echo signal received by the secondary base station can be expressed as:

[0068]

[0069] Among them, y s represents the echo signal received by the secondary base station, u represents the receive beamforming vector of the secondary base station, represents the channel state information vector from the secondary base station to the sensing target, represents the channel state information vector from the sensing target to the secondary base station, where Indicates the channel state information from the nth antenna of the base station to the sensing target, Indicates the channel state information from the sensing target to the nth antenna of the base station, represents the additive white Gaussian noise vector at the base station, represents the additive white Gaussian noise at the nth antenna of the base station, n s It has a mean of zero and a variance of The complex Gaussian distribution of Among them I N Represents the N-dimensional identity matrix.

[0070] According to the echo signal y received by the secondary base station s , the perceived signal-to-noise ratio of the secondary base station can be expressed as:

[0071]

[0072] Where SNR is the generalized Rayleigh entropy, According to the optimality principle of generalized Rayleigh entropy, the maximum value of SNR corresponds to the generalized eigenvalue problem Au = λBu, which is equivalent to the standard eigenvalue problem B -1 Au = λu, that is, the maximum value of SNR is obtained when u is the eigenvector corresponding to the maximum eigenvalue. Therefore, the maximum perceived signal-to-noise ratio of the secondary base station can be expressed as:

[0073]

[0074] Based on this, the energy efficiency of the secondary base station can be expressed as:

[0075]

[0076] By jointly optimizing the transmit beamforming vectors of the primary and secondary base stations, the energy efficiency of the secondary base station is maximized. The optimization problem is as follows:

[0077]

[0078] Where (12a), (12b), (12c) and (12d) represent the secondary base station perception signal-to-noise ratio constraint, the primary user's communication signal-to-interference-and-noise ratio constraint, the primary base station's power constraint and the secondary base station's power constraint, respectively. s and γ a They represent the minimum thresholds of the secondary base station’s perceived signal-to-noise ratio and the primary user’s communication signal-to-interference-and-noise ratio, respectively. and Represents the maximum transmit power of the primary base station and the secondary base station respectively.

[0079] Furthermore, the problem and solution are as follows:

[0080] For the objective function of this problem, let We can get:

[0081]

[0082] Furthermore, using the DinkelBach algorithm: Perform fractional optimization and use the lemma: when t = 1 / x, x>0, transform the original problem into a convex optimization problem:

[0083]

[0084] stλ s (Tr(G 34 V s )+Tr(G 34 V c ))≥γ s (14a)

[0085]

[0086] According to the lemma, for a fixed V a , V c and V s , t can be obtained in closed form with the following expression:

[0087] t * =(λ c (Tr(G1V s )+Tr(H2V a ))+1) -1 (15)

[0088] At the same time, the optimal value of η can be obtained by the DinkelBach algorithm, and the expression is as follows:

[0089]

[0090] At this point, the problem is transformed into a convex optimization problem through the SDR algorithm and lemma, and the DinkelBach algorithm is used to optimize the fractions in the problem.

[0091] Finally, the CVX toolkit is used to iterate the above problem alternately until the objective function of the optimization problem meets the convergence condition, and the optimal solution of the secondary base station energy efficiency is obtained. At the same time, the singular value decomposition is used to restore the rank-one constraint based on the solution result to obtain the optimized base station beamforming vector and

[0092] The following is an example of implementing the invention using MATLAB on a computer. Figure 2The system includes a primary base station, a secondary base station, a primary user, a secondary user, and a sensing target. Each base station is equipped with four transmitting antennas and four receiving antennas, and each user terminal is equipped with a single antenna. In the simulation example, we consider a two-dimensional Cartesian coordinate system in meters, where the primary base station, secondary base station, primary user, secondary user, and sensing target are located at (-10,0), (10,0), (-10,10), (10,10), and (15,0), respectively. The large-scale path loss is modeled as ξ0(d / d0) -α , where ζ0=10 -3 is the path loss value at the reference distance d0 = 1m, d is the distance between the two nodes, and α is the path loss exponent. The path loss exponent α of the link from the base station to the user terminal is BR Both are set to α BR =5; path loss index α of the link from the secondary base station to the sensing target and the link from the sensing target to the secondary base station BT Both are set to α BT =2.5. The base station to user terminal and the sensing target link are both modeled using Rayleigh channels. Other parameters are set as follows: Noise power Transmit power of the primary base station Transmit power of the secondary base station Communication signal-to-interference-and-noise ratio threshold γ s =0dB, the perceptual signal-to-noise ratio threshold γ a =3dB. The algorithm iteration process is as follows:

[0093]

[0094] Figure 3 This chart compares the proposed CRN-ISAC system solution with benchmark solutions such as zero-forcing precoding. As can be seen, system energy efficiency increases with the number of base station antennas. Furthermore, this solution jointly optimizes the transmit beamforming vectors of the primary and secondary base stations using the SDR and DinkelBach algorithms. Given the same communication and perception performance, this solution significantly outperforms benchmark solutions such as zero-forcing precoding for any number of base station transmit antennas.

[0095] The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. 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. An energy efficiency optimization method for a cognitive wireless network and sensory communication integrated system, characterized in that: The method is applicable to a CRN-ISAC system consisting of a primary base station, a secondary base station, a primary user, a secondary user, and a sensing target, wherein the secondary base station integrates communication and sensing functions and is equipped with N transmit / receive dual-function antennas, the primary base station is equipped with N transmit / receive antennas, and both users are equipped with single antennas. The method comprises the following steps: Obtain channel state information from the primary base station to the user, the secondary base station to the user, and the sensing target respectively; Based on the acquired channel state information, the user's communication signal-to-interference-and-noise ratio, the secondary base station's perceived signal-to-noise ratio, and energy efficiency are calculated. Taking the primary user's communication signal-to-interference-and-noise ratio, the secondary base station's perceived signal-to-noise ratio, and the maximum transmit power of the primary and secondary base stations as constraints, the transmit beamforming vectors of the primary and secondary base stations are jointly optimized to maximize the secondary base station's energy efficiency.

2. The energy efficiency optimization method for the cognitive-sensory integration system according to claim 1 is characterized in that: The signals transmitted by the primary base station and the secondary base station can be expressed as: x PBS =n a x a (1) x SBS =n c x c +n s x s (2) Among them, x PBS Indicates the transmission signal of the main base station, x SBS Indicates the transmission signal of the secondary base station, ν a represents the transmit beamforming vector of the primary base station to the primary user, ν c represents the transmit beamforming vector of the secondary base station to the secondary user, ν s represents the dedicated sensing signal beamforming vector of the secondary base station, x a Indicates the information sent by the primary base station to the primary user, x c Indicates the information sent by the secondary base station to the secondary user, x s represents the signal sent by the secondary base station to the sensing target, and {x a ,x c ,x s } are statistically independent of each other, satisfying represents the mathematical expectation. Therefore, the transmit power of the secondary base station can be expressed as: P SBS =||n s || 2 +||n c || 2 +N*P a (3) Among them, ||·|| represents the two-norm operation on the vector, N represents the number of transmitting antennas of the base station, P a Indicates the static circuit loss of each antenna.

3. The energy efficiency optimization method for the cognitive-sensory integration system according to claim 1 is characterized in that: The received signals of the primary user and the secondary user can be expressed as: Among them, y a represents the received signal of the primary user, y c represents the received signal of the secondary user, represents the channel state information vector from the primary base station to the primary user, represents the channel state information vector from the primary base station to the secondary user, represents the channel state information vector from the secondary base station to the secondary user, represents the channel state information vector from the secondary base station to the primary user, Indicates the channel state information from the nth antenna of the primary base station to the primary user, Indicates the channel state information from the nth antenna of the primary base station to the secondary user, Indicates the channel state information from the nth antenna of the secondary base station to the secondary user, Indicates the channel state information from the nth antenna of the secondary base station to the primary user; (·) H represents the conjugate transpose, n a represents the additive Gaussian white noise of the primary user, n c represents the additive Gaussian white noise of the secondary user, both of which have zero mean and variance respectively. and The complex Gaussian distribution of 4. The energy efficiency optimization method for the cognitive-sensory integration system according to claim 1 is characterized in that: According to the received signals y of the primary user and the secondary user a and y c , their signal-to-interference-noise ratios can be expressed as: Finally, the echo signal received by the secondary base station can be expressed as: Among them, y s represents the echo signal received by the secondary base station, u represents the receive beamforming vector of the secondary base station, represents the channel state information vector from the secondary base station to the sensing target, represents the channel state information vector from the sensing target to the secondary base station, where Indicates the channel state information from the nth antenna of the base station to the sensing target, Indicates the channel state information from the sensing target to the nth antenna of the base station, represents the additive white Gaussian noise vector at the base station, represents the additive white Gaussian noise at the nth antenna of the base station, n s It has a mean of zero and a variance of The complex Gaussian distribution of Among them I N Represents the N-dimensional identity matrix.

5. The energy efficiency optimization method for the cognitive-sensory integration system according to claim 1 is characterized in that: According to the echo signal y received by the secondary base station s , the perceived signal-to-noise ratio of the secondary base station can be expressed as: Where SNR is the generalized Rayleigh entropy, According to the optimality principle of generalized Rayleigh entropy, the maximum value of SNR corresponds to the generalized eigenvalue problem Au = λBu, which is equivalent to the standard eigenvalue problem B -1 The maximum SNR is obtained when Au = λu, that is, u is the eigenvector corresponding to the maximum eigenvalue. Therefore, the maximum perceived SNR of the secondary base station can be expressed as: Based on this, the energy efficiency of the secondary base station can be expressed as:

6. The energy efficiency optimization method for the cognitive-sensory integration system according to claim 1 is characterized in that: The energy efficiency maximization problem of the constructed secondary base station is as follows: Where (12a), (12b), (12c) and (12d) represent the secondary base station perception signal-to-noise ratio constraint, the primary user's communication signal-to-interference-and-noise ratio constraint, the primary base station's power constraint and the secondary base station's power constraint, respectively. s and γ a They represent the minimum thresholds of the secondary base station’s perceived signal-to-noise ratio and the primary user’s communication signal-to-interference-and-noise ratio, respectively. and Represents the maximum transmit power of the primary base station and the secondary base station respectively.

7. The energy efficiency optimization method for the cognitive-sensory integration system according to claim 1 is characterized in that: The method uses the primary user communication signal-to-interference-and-noise ratio, the secondary base station perceived signal-to-noise ratio, and the maximum transmit power of the primary and secondary base stations as constraints to jointly optimize the transmit beamforming vectors of the primary and secondary base stations to maximize the energy efficiency of the secondary base station. The solution steps are as follows: First, the original problem can be transformed into a convex optimization problem through a semidefinite relaxation algorithm and a lemma. Second, the DinkelBach algorithm is used to optimize the fraction in the problem. Finally, the convex optimization problem is iterated alternately using the CVX toolkit until the objective function meets the convergence conditions. Through the above steps, the optimized solutions for the beamforming vectors of the primary and secondary base stations and the energy efficiency of the secondary base station can be obtained respectively.

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