Multi-cell isac power allocation method and system based on successive convex approximation

Abstract

The application provides a multi-cell ISAC power distribution method and system based on successive convex approximation, belongs to the technical field of ISAC power distribution, and constructs and rate optimization models by optimizing the power distribution of each user and base station; the and rate optimization model is solved to obtain the final power distribution and the corresponding maximum and rate. The application comprehensively considers the minimum communication rate requirement of each communication user, the total transmission power budget and the maximum Cramer-Rao lower bound constraint of target position estimation, realizes the maximum communication transmission and rate of the system, effectively improves the total rate of the system while meeting the sensing requirement, and achieves the purpose of resource allocation trade-off between sensing and communication.

Description

Technical Field

[0001] This invention relates to the field of ISAC power allocation technology, specifically to a multi-cell ISAC power allocation method and system based on successive convex approximation. Background Technology

[0002] Integrated sensing and communication (ISAC) has been identified as a key technology for next-generation wireless networks. It enhances communication services through sensing to meet the needs of emerging applications such as connected vehicles, telemedicine, smart homes, and autonomous driving. With the development of coordinated multiple points (CoMP), cloud wireless access networks, and cellless MIMO systems, multi-cell ISAC systems have become a promising system architecture. Through deep integration of sensing and communication functions, multi-cell ISAC systems offer significant comprehensive advantages in resource allocation and waveform design, providing efficient technical solutions for complex scenarios such as vehicle-to-everything (V2X) and industrial internet.

[0003] Despite the numerous advantages of multi-cell ISAC systems, they present new technical challenges in radio resource allocation, namely, how to appropriately balance the performance trade-offs between sensing and communication. Resource allocation typically encompasses multiple dimensions such as power, frequency band, and spatial degrees of freedom. Power resources, as one of the key resources in multi-cell ISAC systems, directly determine the communication and sensing performance of the system through their allocation method. Early studies on multi-cell ISAC focused on optimizing system performance while meeting the requirements for communication user quality of service and sensing target positioning accuracy. However, they did not fully consider the resource allocation problem in multi-cell CoMP-ISAC scenarios, and further research is needed on aspects such as the joint power allocation considering users and rates.

[0004] To address this issue, this patent proposes a power allocation algorithm based on SCA for multi-cell CoMP-ISAC systems. This algorithm optimizes power allocation for each user and base station to maximize user communication transmission and speed while simultaneously satisfying the minimum SINR requirement for each user, the total transmit power budget, and the maximum CRLB constraint for target location estimation, thus balancing the performance trade-offs between sensing and communication. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-cell ISAC power allocation method and system based on successive convex approximation, so as to solve at least one of the technical problems existing in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a multi-cell ISAC power allocation method based on successive convex approximation, comprising:

[0008] By optimizing the power allocation for each user and base station, the transmission and rate of communication are maximized while satisfying the minimum SINR requirement for each user's communication, the total transmit power budget, and the maximum CRLB constraint for target location estimation, and a rate optimization model is constructed.

[0009] Solving the aforementioned sum-rate optimization model yields the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to ensure that these initial power allocations satisfy the minimum SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power allocation is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. Solving this subproblem yields a new power allocation and SINR value. This iteration is repeated until the change in the objective function value is less than a preset convergence threshold, indicating convergence. The final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is defined as follows: the objective function is linear, the SINR constraint becomes a convex constraint after linearization via Taylor expansion, and both the total transmit power and CRLB constraint are convex sets; therefore, the subproblem is a convex optimization problem.

[0010] As a further limitation of the first aspect of the invention, consider a [component] A CoMP-ISAC system consists of several cells, each with one base station, one communication user, and one sensing target. Base stations within different cells connect to a central controller for joint signal processing. Each base station is equipped with independent transmit and receive antennas, providing communication services to its respective communication user, and simultaneously collaborating with multiple base stations to estimate the location of a sensing target. Each community is recorded as The corresponding base station is denoted as Base station Serving its communication users and sense its target. The coordinates of the perceived target are used express.

[0011] As a further limitation of the first aspect of the present invention, the base station Broadcast a modulated signal To perceive the target and simultaneously with communication, If it is a random variable with zero mean and zero variance, then calculate the communication user. The received signal, thus obtaining the communication user SINR of communication, calculate user The communication rate is obtained by summing the rates of all communication users.

[0012] As a further limitation of the first aspect of the present invention, in order to ensure the comparability of the power of signals transmitted by different base stations and to avoid deviations in sensing or communication performance due to differences in signal energy, it is assumed that signal processing is performed at intervals... The process is carried out on top and lasts for a period of time. The interval is long enough that The power is normalized to 1.

[0013] As a further limitation of the first aspect of the present invention, in order to avoid multi-user interference, different signals and Orthogonal in time, that is , ,in Represents the conjugate operator; assuming different ISAC base stations achieve time and frequency synchronization in the cellular network via backhaul links, then calculate the base station... The echo signal reflected from the common sensing target was received.

[0014] As a further limitation of the first aspect of the present invention, the location of the sensed target is represented by two-dimensional planar coordinates, and the base station Location Base station Location The location of the perceived target is unknown; let its location be denoted as . So, base station The distance to the target being perceived is , For base stations Calculate the propagation delay based on the distance to the target; calculate the target's location. and channel parameters It is unknown, needs to be estimated, and is built upon , Fisher's information matrix.

[0015] Secondly, the present invention provides a multi-cell ISAC power allocation system based on successive convex approximation, comprising:

[0016] The building module is used to maximize the transmission and rate of communication by optimizing the appropriate power allocation for each user and base station, while satisfying the minimum communication SINR requirement, total transmit power budget and maximum CRLB constraint of target location estimation for each user, and to build and rate optimization models.

[0017] The solution module is used to solve the sum-rate optimization model to obtain the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to adjust these initial power allocations to meet the minimum communication SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. This subproblem is solved to obtain a new power allocation and SINR value. The iteration is repeated until the change in the objective function value is less than a preset convergence threshold, i.e., convergence is achieved, and the final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is as follows: the objective function is linear, the SINR constraint is linearized through Taylor expansion to become a convex constraint, and the total transmit power and CRLB constraint are both convex sets; therefore, the subproblem is a convex optimization problem.

[0018] Thirdly, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the multi-cell ISAC power allocation method based on successive convex approximation as described in the first aspect.

[0019] Fourthly, the present invention provides a computer device including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the multi-cell ISAC power allocation method based on successive convex approximation as described in the first aspect.

[0020] Fifthly, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the multi-cell ISAC power allocation method based on successive convex approximation as described in the first aspect.

[0021] The beneficial effects of this invention are: by comprehensively considering the minimum communication rate requirement of each communication user, the total transmission power budget, and the maximum Cramer-Rao lower bound constraint of target position estimation, the communication transmission and rate of the system are maximized; while meeting the sensing requirements, the overall rate of the system is effectively improved, achieving the goal of resource allocation trade-off between sensing and communication.

[0022] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of a CoMP-ISAC system model consisting of multiple cells, as described in an embodiment of the present invention.

[0025] Figure 2 This is a flowchart of the multi-cell ISAC power allocation algorithm based on successive convex approximation as described in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of a network topology with 3 cells according to an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram illustrating how the sum rate changes with the number of iterations, as described in an embodiment of the present invention.

[0028] Figure 5 This is a schematic diagram comparing communication and rate under different maximum CRLB constraints according to an embodiment of the present invention.

[0029] Figure 6 This is a schematic diagram comparing communication and rate under different SINR thresholds as described in an embodiment of the present invention.

[0030] Figure 7 This is a schematic diagram comparing communication and rate under different total transmit power budgets according to an embodiment of the present invention. Detailed Implementation

[0031] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0032] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as here.

[0034] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0035] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0036] To facilitate understanding of the present invention, the present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0037] Those skilled in the art should understand that the accompanying drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0038] Integrated sensing and communication (ISAC) has been identified as a key technology for next-generation wireless networks. However, the introduction of coordinated multipoint systems brings new technical challenges to radio resource management in ISAC systems, particularly in optimizing resource allocation strategies. To address this, this invention proposes a multi-cell ISAC power allocation algorithm based on successive convex approximation. This algorithm comprehensively considers the minimum communication rate requirement for each user, the total transmit power budget, and the maximum Cramer-Rao lower bound constraint for target location estimation, thereby maximizing the system's communication transmission and rate, achieving a trade-off between sensing and communication resource allocation. Since this optimization problem involves highly coupled non-convex terms, an iterative algorithm based on successive convex approximation combined with multiple random initialization strategies is designed to improve the quality of the solution. Simulation results show that the proposed power allocation scheme can effectively improve the system's communication and rate, achieving better results than other algorithms.

[0039] Example 1

[0040] In this embodiment 1, a multi-cell ISAC power allocation system based on successive convex approximation is first provided. The system includes: a construction module for maximizing the transmission and rate of communication by performing appropriate power allocation optimization for each user and base station, while satisfying the minimum communication SINR requirement, total transmit power budget and maximum CRLB constraint of target location estimation for each user, and constructing and rate optimization models. The solution module is used to solve the sum-rate optimization model to obtain the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to adjust these initial power allocations to meet the minimum communication SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. This subproblem is solved to obtain a new power allocation and SINR value. The iteration is repeated until the change in the objective function value is less than a preset convergence threshold, i.e., convergence is achieved, and the final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is as follows: the objective function is linear, the SINR constraint is linearized through Taylor expansion to become a convex constraint, and the total transmit power and CRLB constraint are both convex sets; therefore, the subproblem is a convex optimization problem.

[0041] In this embodiment, based on the above system, a multi-cell ISAC power allocation method based on successive convex approximation is implemented, including: optimizing the power allocation for each user and base station to maximize the transmission and rate of communication, while simultaneously satisfying the minimum SINR requirement, total transmit power budget, and maximum CRLB constraint for target location estimation for each user; constructing a rate optimization model; solving the rate optimization model to obtain the final power allocation and the corresponding maximized rate; wherein, firstly, multiple sets of different initial feasible power allocations are randomly assigned, then normalized, and these initial power allocations are adjusted to satisfy the minimum SINR requirement for all communication users, and the target location estimation maximum CRLB constraint; The algorithm first defines the CRLB constraint for target location estimation; then calculates the initial SINR corresponding to each initial power set; next, for each initial point, it independently runs the SCA algorithm. In the k-th iteration, based on the current iteration point, it constructs a convex optimization subproblem; solves this subproblem to obtain the new power allocation and SINR value; it iterates repeatedly until the change in the objective function value is less than the preset convergence threshold, i.e., convergence is achieved, and the final convergence result of the SCA operation is obtained, including the power allocation and the corresponding maximization and rate. The convex optimization subproblem is as follows: the objective function is linear, the SINR constraint is linearized through Taylor expansion and becomes a convex constraint, and the total transmit power and CRLB constraint are both convex sets. Therefore, the subproblem is a convex optimization problem.

[0042] Among them, consider a by A CoMP-ISAC system consists of several cells, each with one base station, one communication user, and one sensing target. Base stations within different cells connect to a central controller for joint signal processing. Each base station is equipped with independent transmit and receive antennas, providing communication services to its respective communication user, and simultaneously collaborating with multiple base stations to estimate the location of a sensing target. Each community is recorded as The corresponding base station is denoted as Base station Serving its communication users and sense its target. The coordinates of the perceived target are used Indicates. Base station Broadcast a modulated signal To perceive the target and simultaneously with communication, If it is a random variable with zero mean and zero variance, then calculate the communication user. The received signal, thus obtaining the communication user SINR of communication, calculate user The communication rate is obtained by summing the rates of all communication users.

[0043] To ensure the comparability of signal power transmitted by different base stations and avoid deviations in sensing or communication performance due to differences in signal energy, it is assumed that signal processing occurs at intervals... The process is carried out on top and lasts for a period of time. The interval is long enough that The power is normalized to 1. To avoid interference from multiple users, different signals are... and Orthogonal in time, that is , ,in Represents the conjugate operator; assuming different ISAC base stations achieve time and frequency synchronization in the cellular network via backhaul links, then calculate the base station... The echo signal reflected from the common sensing target was received.

[0044] The base station represents the location of the sensed target using two-dimensional plane coordinates. Location Base station Location The location of the perceived target is unknown; let its location be denoted as . So, base station The distance to the target being perceived is , For base stations Calculate the propagation delay based on the distance to the target; calculate the target's location. and channel parameters It is unknown, needs to be estimated, and is built upon , Fisher's information matrix.

[0045] Specifically, such as Figure 1 As shown, in this embodiment, consider a... A CoMP-ISAC system consists of several cells, each with one base station, one communication user, and one sensing target. Base stations in different cells connect to a central controller (e.g., a centralized cloud in C-RAN) for joint signal processing. Each base station is equipped with independent transmit and receive antennas, providing communication services to its respective communication user while collaborating with multiple base stations to estimate the location of a sensing target. Each community is recorded as The corresponding base station is denoted as Base station Serving its communication users and sense its target. The coordinates of the perceived target are used... express.

[0046] base station Broadcast a modulated signal To perceive the target and simultaneously with communication, It is a random variable with zero mean and zero variance. Then the communication user... The received signal is represented as:

[0047] (1)

[0048] in For base stations The transmission power; Indicates from base station To communication users The channel coefficients; the first term represents the channel coefficients from the base station. The useful signal; the second item is inter-cell interference. Indicates from base station To communication users The channel coefficient; the third term Indicates communication user Noise at the receiver, It is a function with a mean of zero and a variance of . Additive white Gaussian noise, i.e. From formula (1), it can be seen that the communication user The communication SINR is:

[0049] (2)

[0050] Then, the user The communication rate is:

[0051] (3)

[0052] The sum of the rates for all communication users is expressed as:

[0053] (4)

[0054] To ensure the comparability of signal power transmitted by different base stations and avoid deviations in sensing or communication performance due to differences in signal energy, it is assumed that signal processing occurs at intervals... The process is carried out on top and lasts for a period of time. The interval is long enough that The power is normalized to 1; to avoid interference from multiple users, different signals are normalized. and Orthogonal in time, that is , ,in This represents the conjugate operator. Assuming different ISAC base stations can achieve time and frequency synchronization in the cellular network via backhaul links, then the base station... The received echo signal reflected from the commonly sensed target is represented as:

[0055] (5)

[0056] in, Indicates at the base station Gaussian white noise with zero mean and variance . ,Right now ; This indicates the transmitting base station. From common sensing target to receiving base station Composite channel gain; for The propagation delay.

[0057] The base station represents the location of the sensed target using two-dimensional plane coordinates. Location Base station Location The location of the perceived target is unknown; let its location be denoted as . So, base station The distance to the target being perceived is , For base stations Distance to the target being sensed, propagation delay for:

[0058] (6)

[0059] In the formula, The speed of electromagnetic waves.

[0060] Detect target location and channel parameters It is unknown and needs to be estimated. Built on , The Fisher Information Matrix (FIM) on the above is:

[0061] (7)

[0062] in It is the conditional probability density function of the received signal. Taking the partial derivative, we get the following equation:

[0063] (8)

[0064] (9)

[0065] FIM's Each element is defined as

[0066] (10)

[0067] in . For signal The effective bandwidth, which satisfies , yes Frequency domain transformation, yes The bandwidth.

[0068] simplify for ,

[0069] (11)

[0070] Each transmitting base station The contribution matrix depends on all paths of the receiving BS. In summary, the target location is perceived... The CRLB matrix on is represented as

[0071] (12)

[0072] Therefore, estimate and The sum of CRLB is expressed as Assuming the perceived target location is roughly prior, the corresponding CRLB can be optimized to improve the accuracy of real-time estimation.

[0073] The goal of this embodiment is to maximize communication transmission and speed by optimizing power allocation for each user and base station, while simultaneously meeting the minimum SINR requirement for each user's communication. Total transmit power budget Maximum CRLB constraint for target location estimation The problem of maximizing the summation rate can be formulated as follows:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] in, It is the maximum CRLB constraint for target location estimation; The maximum available transmit power for each base station; It is a communication user The minimum SINR threshold.

[0080] yes A linear function, yes The inverse matrix, For positive definite matrices It is a convex function, therefore the constraint A convex set is defined. The objective function for communication rate is still non-convex, requiring approximation using the SCA method. The objective function is rewritten as:

[0081] (13)

[0082] Due to the logarithmic function It is non-convex and can be implemented at the current iteration point. Perform a first-order Taylor expansion at the given location to construct a lower bound approximation, namely...

[0083] (14)

[0084] Therefore, the lower bound of the objective function is approximately:

[0085] (15)

[0086] In SCA, maximizing the original objective function is equivalent to maximizing its lower bound; therefore, the objective function of the subproblem can be set as follows:

[0087] (16)

[0088] user of Since it is non-convex, the equation needs to be rewritten as:

[0089] (17)

[0090] At the iteration point and At the point, for nonlinear terms Perform a first-order Taylor expansion:

[0091] (18)

[0092] Substituting the original constraints, we obtain the linearized approximation:

[0093] (19)

[0094] In each iteration, the original problem is transformed into the following convex optimization subproblem.

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] objective function It is linear. The SINR constraint becomes a convex constraint after being linearized by Taylor expansion. The total transmit power and CRLB constraints are both convex sets. Therefore, the subproblem is a convex optimization problem.

[0102] Because this optimization problem contains highly coupled nonconvex terms, we employ an iterative algorithm based on successive convex approximation of SCA to address this issue, combined with a multiple random initialization strategy to improve the quality of the solution. The algorithm flowchart is shown below. Figure 2 As shown.

[0103] First, random allocation Groups of different initial feasible power allocations ,in .exist Each is generated uniformly and randomly within the range Then normalize it to satisfy At the same time, it is necessary to verify or adjust these initial power allocations to ensure that they meet the minimum communication SINR for all communication users. and the CRLB constraint for target location estimation. Calculate the initial SINR corresponding to each initial power group, i.e. Secondly, for each set of initial points and Run the SCA algorithm independently. In the k-th iteration, based on the current iteration point... Construct the aforementioned convex optimization subproblem. Solve this subproblem efficiently using the convex optimization tool CVX to obtain a new power allocation. and SINR value Repeat the iteration until the change in the objective function value is less than the preset convergence threshold. This means convergence has been achieved. Record the final convergence result of this SCA run, including the final power allocation. And the corresponding maximization and rate Finally, compare the final sum rate obtained from all N SCA runs and select the one with the largest sum rate. Corresponding power allocation As the final local optimum.

[0104] The improved SCA algorithm proposed in this embodiment is simulated to verify and discuss its performance. The improved SCA algorithm is referred to as the optimized allocation algorithm below. In the simulation, it is assumed that the number of cells L=3, the location of base station 1 is (100,0)m, the location of base station 2 is (-50,85)m, the location of base station 3 is (-50,-85)m, and the coordinates of the sensing target are (20,30)m. The network topology is as follows: Figure 3 As shown in Table 1, the target position is considered a known parameter in the optimization process, and its estimated value is provided by the sensing result at the previous time step and remains unchanged within the current optimization cycle. The simulation parameters for this embodiment are shown in Table 1.

[0105] Table 1 Simulation Parameter Table

[0106]

[0107] In this embodiment, it is given that... SINR threshold When the transmit power is 0dB, the proposed optimization algorithm achieves higher communication and data rates under different total transmit power constraints. As the number of iterations changes, the simulation results are as follows: Figure 4 As shown. From Figure 4 It can be seen that the SCA algorithm proposed in this embodiment can quickly converge to the near-optimal solution of the sum and rate when the number of iterations is 10. It can achieve a relatively fast convergence speed while improving communication and rate. Once a certain level is reached, allowing the system to maintain high performance, the algorithm exhibits good convergence. It can be observed that the total transmit power constraint of 43dB stabilizes after approximately 10 iterations, the total transmit power constraint of 40dB stabilizes after approximately 12 iterations, and the total transmit power constraint of 37dB stabilizes after nearly 14 iterations, although initial fluctuations are slightly larger. This suggests that higher power may allow the algorithm to find the optimal solution more quickly.

[0108] To fully demonstrate the performance of the optimized allocation algorithm proposed in this embodiment, the optimized allocation algorithm proposed in this embodiment is compared with the uniform power allocation algorithm and the random power allocation algorithm within the feasible region. Figure 5 Three algorithms were demonstrated under different maximum CRLB constraints. Communication and speed The changes in these parameters illustrate the trade-off between sensing accuracy and communication performance under different power allocation algorithms. The overall trend of the curves for the three algorithms is that they increase with the sensing constraints. Become stricter, communication and speed The lower the CRLB value, the worse the communication performance. Among these, the optimized allocation algorithm, under the same CRLB constraints... This algorithm achieves maximum communication and speed, significantly outperforming the other two algorithms. Under perceptual constraints... When the optimal allocation algorithm curve is at the top and its descent is the most gradual, it indicates that the algorithm maintains communication performance to the maximum extent while ensuring high-precision sensing. When sensing constraints... Relaxed to While the performance gap between the algorithms narrowed somewhat, the optimized allocation algorithm still maintained its lead. In contrast, the uniform allocation algorithm consistently performed worse than the optimized allocation algorithm, with its curve always lying at the bottom and its slope showing little change, making it difficult to dynamically adapt to different constraints. The random allocation algorithm performed the worst and exhibited potentially greater fluctuations, confirming its instability. This comparative experiment fully validates the effectiveness of the proposed algorithm in solving the communication-aware resource contention problem.

[0109] Figure 6 This shows the effect of considering two different CRLB constraints. Below, communication and speed With SINR threshold The relationship, where the CRLB constraints are respectively set as follows: and It can be observed that within the feasible region, under the same CRLB constraint, the communication and rate of the uniform allocation algorithm remain constant, while the communication and rate of the random allocation algorithm remain constant. At SINR threshold At lower SINR thresholds, its performance is close to that of the uniform allocation algorithm, and decreases as the SINR threshold increases, but overall it is lower than the performance of the optimized allocation algorithm and most uniform allocation algorithms. In contrast, the optimized allocation algorithm has better communication and rate performance. With SINR threshold The number of feasible regions decreases with the increase of the CRLB constraint, and the feasible regions are all much larger than the other two algorithms. Stricter CRLB constraints. This will lead to a general decrease in the sum and rate of all algorithms because meeting stricter sensing accuracy requirements may require some resources to be used for sensing, thus impacting communication performance. Stricter CRLB constraints The optimized allocation algorithm has an overall lower sum rate than While achieving optimal allocation performance, it is still significantly better than the other two algorithms under the same CRLB constraints. Regardless of the CRLB constraints, the optimal allocation algorithm consistently achieves the highest system and rate, demonstrating its excellent performance in balancing communication and sensing performance.

[0110] Figure 7 This paper presents the communication and rate variation trends of the optimized allocation algorithm proposed in this embodiment compared with the power allocation algorithms, uniform allocation algorithms, and random allocation algorithms disclosed in existing literature (1) (ZOU JP, ZHONG ZH F, WANG JT, et al. Power Allocation for Coordinated Multi-Point Aided ISAC Systems[J]. arXiv preprint arXiv: 2503. 07139, 2025.). The optimized allocation algorithm achieves maximum communication and rate under the same power budget, significantly outperforming other algorithms. The increase in [something] widens the gap between the optimization allocation algorithm and other algorithms. With a sum rate of 38dB, optimize the allocation algorithm. Roughly equivalent to the uniform distribution algorithm in With a value of 42dB, the performance gain is 5dB. With a sum rate of 38dB, optimize the allocation algorithm. Roughly equivalent to the algorithm in the aforementioned literature. The result of 39dB indicates a performance gain of 1dB. Therefore, compared to other benchmarks, the optimized allocation algorithm proposed in this embodiment demonstrates superior performance.

[0111] Example 3

[0112] This embodiment 3 provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, they implement the multi-cell ISAC power allocation method based on successive convex approximation as described above. The method includes:

[0113] By optimizing the power allocation for each user and base station, the transmission and rate of communication are maximized while satisfying the minimum SINR requirement for each user's communication, the total transmit power budget, and the maximum CRLB constraint for target location estimation, and a rate optimization model is constructed.

[0114] Solving the aforementioned sum-rate optimization model yields the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to ensure that these initial power allocations satisfy the minimum SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power allocation is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. Solving this subproblem yields a new power allocation and SINR value. This iteration is repeated until the change in the objective function value is less than a preset convergence threshold, indicating convergence. The final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is defined as follows: the objective function is linear, the SINR constraint becomes a convex constraint after linearization via Taylor expansion, and both the total transmit power and CRLB constraint are convex sets; therefore, the subproblem is a convex optimization problem.

[0115] Example 4

[0116] This embodiment 4 provides a computer device, including a memory and a processor. The processor and the memory communicate with each other. The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute the multi-cell ISAC power allocation method based on successive convex approximation as described above. The method includes:

[0117] By optimizing the power allocation for each user and base station, the transmission and rate of communication are maximized while satisfying the minimum SINR requirement for each user's communication, the total transmit power budget, and the maximum CRLB constraint for target location estimation, and a rate optimization model is constructed.

[0118] Solving the aforementioned sum-rate optimization model yields the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to ensure that these initial power allocations satisfy the minimum SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power allocation is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. Solving this subproblem yields a new power allocation and SINR value. This iteration is repeated until the change in the objective function value is less than a preset convergence threshold, indicating convergence. The final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is defined as follows: the objective function is linear, the SINR constraint becomes a convex constraint after linearization via Taylor expansion, and both the total transmit power and CRLB constraint are convex sets; therefore, the subproblem is a convex optimization problem.

[0119] Example 5

[0120] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions implementing the multi-cell ISAC power allocation method based on successive convex approximation as described above. The method includes:

[0121] By optimizing the power allocation for each user and base station, the transmission and rate of communication are maximized while satisfying the minimum SINR requirement for each user's communication, the total transmit power budget, and the maximum CRLB constraint for target location estimation, and a rate optimization model is constructed.

[0122] Solving the aforementioned sum-rate optimization model yields the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to ensure that these initial power allocations satisfy the minimum SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power allocation is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. Solving this subproblem yields a new power allocation and SINR value. This iteration is repeated until the change in the objective function value is less than a preset convergence threshold, indicating convergence. The final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is defined as follows: the objective function is linear, the SINR constraint becomes a convex constraint after linearization via Taylor expansion, and both the total transmit power and CRLB constraint are convex sets; therefore, the subproblem is a convex optimization problem.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative effort should be included within the scope of protection of the present invention.

Claims

1. A multi-cell ISAC power allocation method based on successive convex approximation, characterized in that, include: By optimizing the power allocation for each user and base station, the transmission and rate of communication are maximized while satisfying the minimum SINR requirement for each user's communication, the total transmit power budget, and the maximum CRLB constraint for target location estimation, and a rate optimization model is constructed. Solving the aforementioned sum-rate optimization model yields the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to ensure that these initial power allocations satisfy the minimum SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power allocation is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. Solving this subproblem yields a new power allocation and SINR value. This iteration is repeated until the change in the objective function value is less than a preset convergence threshold, indicating convergence. The final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is defined as follows: the objective function is linear, the SINR constraint becomes a convex constraint after linearization via Taylor expansion, and both the total transmit power and CRLB constraint are convex sets; therefore, the subproblem is a convex optimization problem.

2. The multi-cell ISAC power allocation method based on successive convex approximation according to claim 1, characterized in that, Consider a A CoMP-ISAC system consists of several cells, each with one base station, one communication user, and one sensing target. Base stations within different cells connect to a central controller for joint signal processing. Each base station is equipped with independent transmit and receive antennas, providing communication services to its respective communication user, and simultaneously collaborating with multiple base stations to estimate the location of a sensing target. Each community is recorded as The corresponding base station is denoted as Base station Serving its communication users and sense its target. The coordinates of the perceived target are used express.

3. The multi-cell ISAC power allocation method based on successive convex approximation according to claim 2, characterized in that, base station Broadcast a modulated signal To perceive the target and simultaneously with communication, If it is a random variable with zero mean and zero variance, then calculate the communication user. The received signal, thus obtaining the communication user SINR of communication, calculate user The communication rate is obtained by summing the rates of all communication users.

4. The multi-cell ISAC power allocation method based on successive convex approximation according to claim 3, characterized in that, To ensure the comparability of signal power transmitted by different base stations and avoid deviations in sensing or communication performance due to differences in signal energy, it is assumed that signal processing occurs at intervals... The process is carried out on top, and the duration is... The interval is long enough that The power is normalized to 1.

5. The multi-cell ISAC power allocation method based on successive convex approximation according to claim 4, characterized in that, To avoid interference from multiple users, so that different signals and Orthogonal in time, that is , ,in Represents the conjugate operator; assuming different ISAC base stations achieve time and frequency synchronization in the cellular network via backhaul links, then calculate the base station... The echo signal reflected by the common sensing target was received.

6. The multi-cell ISAC power allocation method based on successive convex approximation according to claim 5, characterized in that, The base station represents the location of the sensed target using two-dimensional plane coordinates. Location Base station Location The location of the perceived target is unknown; let its location be denoted as . So, base station The distance to the target being perceived is , For base stations Calculate the propagation delay based on the distance to the target; calculate the target's location. and channel parameters It is unknown, needs to be estimated, and is built upon , Fisher's information matrix.

7. A multi-cell ISAC power allocation system based on successive convex approximation, characterized in that, include: The building module is used to maximize the transmission and rate of communication by optimizing the appropriate power allocation for each user and base station, while satisfying the minimum communication SINR requirement, total transmit power budget and maximum CRLB constraint of target location estimation for each user, and to build and rate optimization models. The solution module is used to solve the sum-rate optimization model to obtain the final power allocation and the corresponding maximized sum-rate. First, multiple sets of different initial feasible power allocations are randomly assigned and then normalized to adjust these initial power allocations to meet the minimum communication SINR for all communication users and the CRLB constraint for target location estimation. The initial SINR corresponding to each initial power is calculated. Second, for each initial point, the SCA algorithm is run independently. In the k-th iteration, a convex optimization subproblem is constructed based on the current iteration point. This subproblem is solved to obtain a new power allocation and SINR value. The iteration is repeated until the change in the objective function value is less than a preset convergence threshold, i.e., convergence is achieved, and the final convergence result of the SCA run, the power allocation, and the corresponding maximized sum-rate are obtained. The convex optimization subproblem is as follows: the objective function is linear, the SINR constraint is linearized through Taylor expansion to become a convex constraint, and the total transmit power and CRLB constraint are both convex sets; therefore, the subproblem is a convex optimization problem.

8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the multi-cell ISAC power allocation method based on successive convex approximation as described in any one of claims 1-6.

9. A computer device, characterized in that, The system includes a memory and a processor, which communicate with each other. The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the multi-cell ISAC power allocation method based on successive convex approximation as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions that implement the multi-cell ISAC power allocation method based on successive convex approximation as described in any one of claims 1-6.

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