Non-cellular network downlink communication method based on steerable antenna and related equipment

Abstract

The embodiment of the invention provides a cellular-network-free downlink communication method based on a steerable antenna and related equipment, and belongs to the technical field of wireless communication. The method comprises the following steps: establishing a cellular-free system model containing a steerable antenna, and deducing a closed expression of a user receiving signal sum rate; constructing a non-convex optimization problem for jointly optimizing an AP-user pairing relationship and an antenna pointing vector so as to maximize a sum rate; in order to solve the problem, firstly, a low-complexity two-stage AP-user matching strategy is provided, and initial pairing and residual resource allocation are realized by minimizing an association distance; then, the optimization problem of the antenna pointing vector is converted into a series of convex problems to be iteratively solved through fractional programming and a continuous convex approximation method; according to the method, the antenna beam direction is dynamically adjusted, so that the signal energy pointing to the target user is effectively improved, the interference among the users is remarkably suppressed, and the maximization of the user sum rate is realized.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a non-cellular network downlink communication method and related equipment based on a steerable antenna. Background Technology

[0002] With the evolution from 5G to 6G, emerging services such as the Internet of Things (IoT) and the Internet of Vehicles (IoV) place higher demands on the latency, reliability, and spectrum efficiency of wireless communication networks. Cellular-free communication systems, through the distributed deployment of a large number of access points (APs) and the coordinated service to users via a central processing unit, can effectively eliminate cell boundaries and alleviate the edge effects and co-channel interference of traditional cellular networks.

[0003] However, most existing non-cellular systems use fixed or omnidirectional antennas, whose radiation patterns cannot be dynamically adjusted according to user distribution and channel conditions. This limits the spatial freedom that the system can exploit and hinders performance improvement.

[0004] Rotatable antennas (RAs), as an emerging technology, can dynamically adjust the beam axis direction through mechanical or electrical means, reconstructing the radiation pattern without changing the physical location, thus introducing new spatial degrees of freedom into the system. Compared with the more complex six-dimensional rotatable antennas, RAs retain only the rotational degree of freedom, achieving a good balance between complexity, cost, and control precision, and are more promising for practical deployment.

[0005] Currently, research on introducing steerable antennas into large-scale non-cellular networks and jointly optimizing AP-user association and antenna pointing vectors is still lacking. How to design a low-complexity, high-performance joint optimization method to fully unleash the potential of steerable antennas in non-cellular networks has become an urgent technical problem to be solved. Summary of the Invention

[0006] The main objective of this application is to propose a downlink communication method, electronic device, storage medium, and program product for non-cellular networks based on a steerable antenna. By jointly optimizing AP-user matching and antenna pointing vector, the received signal energy of users is effectively improved, and interference between users is suppressed, thereby maximizing the overall system rate.

[0007] To achieve the above objectives, one aspect of this application proposes a downlink communication method for non-cellular networks based on a steerable antenna, the method comprising: Establish a system model for a non-cellular downlink communication system, in which multiple access points are equipped with a single steerable antenna and multiple users are equipped with a unidirectional antenna; Based on the system model, a closed-form expression for the sum rate of user received signals is derived. Based on the closed-form expression of the total rate, combined with the pointing vector magnitude constraint of the steerable antenna and the constraint of the access point-user matching scheme, a joint optimization problem P1 is constructed with the goal of maximizing the total user rate. By employing a two-stage access point-user matching strategy, the integer programming subproblem of access point-user pairing in the joint optimization problem P1 is solved to obtain the access point-user association matrix. Based on the determined access point-user association matrix, the optimal solution for the steerable antenna pointing vector in the joint optimization problem P1 is obtained by using fractional programming and continuous convex approximation methods.

[0008] In some embodiments, the system model includes: Set up a network consisting of L access points and K users, where L ≥ K; Each access point is connected to the central processing unit via the fronthaul network and collaboratively serves users on the same time and frequency resources; The pointing vector of the steerable antenna at each access point is a three-dimensional unit vector.

[0009] In some embodiments, the derivation of the closed-form expression for the sum rate of the user-received signals includes: The directional gain of the access point facing the user is calculated based on a general directional gain model for steerable antennas. The channel is modeled as a quasi-static flat fading channel containing large-scale fading and small-scale fading, wherein the small-scale fading follows a Rice distribution. Normalized conjugate beamforming is used as the precoding scheme. Based on the directional gain, channel state information, and precoding scheme, the received signal-to-interference-plus-noise ratio and achievable rate for each user are calculated, and the summation is obtained to obtain the total rate.

[0010] In some embodiments, the joint optimization problem P1 is mathematically expressed as:

[0011]

[0012]

[0013]

[0014] Where B is the access point-user association matrix, Let be the set of all steerable antenna pointing vectors. For users The achievable rate, For the first The pointer vectors of each access point To indicate the first The access point and the first A binary indicator variable indicating whether a user is paired; It is a mathematical symbol that means "for all", "any", or "any one"; L The number of APs in the system. K The number of users in the system. Represents the set of indices of AP. For the user's index set.

[0015] In some embodiments, the two-stage access point-user matching strategy includes: Phase 1 - Initial Association: Initialize the set of unpaired access points and the set of users to the complete set respectively; Iteratively select the user pair with the smallest distance from the unpaired set, and remove the pair from the unpaired set until all users are assigned an access point; Phase Two - Allocation of Remaining Access Points: The remaining unpaired access points are assigned one by one to the user closest to them.

[0016] In some embodiments, the specific steps of the first phase - initial association include: Each unpaired access point is denoted as . Find the user closest to it ,Right now:

[0017] in, Indicates the first The AP and the first The distance between users This represents the set of user indices obtained after the Kth iteration of the algorithm.

[0018] Access point Assigned to user The allocation relationship is recorded in matrix B; the final output association matrix B is the overall access point-user association result.

[0019] In some embodiments, solving for the optimal solution of the steerable antenna pointing vector in the joint optimization problem P1 using fractional programming and continuous convex approximation methods includes: The joint optimization problem P1 is simplified to obtain the optimization problem P3 which only concerns the pointing vector; By performing a second transformation on the signal-to-interference-plus-noise ratio (SINR) term in problem P3 and introducing auxiliary variables, the objective function is transformed into a more manageable form. The auxiliary variable is fixed, and the pointing vector is optimized; the pointing vector is fixed, and the auxiliary variable is updated; this process is repeated iteratively. When optimizing the pointing vector, the softplus activation function is used to approximate the piecewise function in the directional gain model; Based on the first-order Taylor expansion and the continuous convex approximation, the non-convex optimization problem is transformed into a series of convex optimization subproblems for iterative solution.

[0020] In some embodiments, when solving the convex optimization subproblem of the pointing vector, the unit magnitude of the pointing vector is constrained. Relaxed to .

[0021] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0022] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0023] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0024] Compared with the prior art, this application has the following beneficial effects: 1) Increased spatial freedom: By introducing a steerable antenna and optimizing its pointing, this application brings a new spatial dimension control capability to non-cellular systems, breaking through the performance limitations of fixed antennas.

[0025] 2) Significantly improve system performance: By jointly optimizing AP-user matching and antenna pointing, this method can effectively concentrate signal energy on the target user, while significantly reducing interference to other users, thereby greatly improving the total system rate and the average user rate.

[0026] 3) Ensures user fairness: The proposed two-stage matching strategy ensures that each user is served by at least one AP, avoiding coverage blind spots and improving the business experience for all users.

[0027] 4) Combining high performance and low complexity: The designed optimization algorithm ensures near-optimal performance while having low computational complexity and fast convergence speed, making it suitable for deployment in practical systems. Attached Figure Description

[0028] Figure 1 This is an optional flowchart of a non-cellular system design based on a steerable antenna provided in the embodiments of this application.

[0029] Figure 2 This is a schematic diagram of a non-cellular communication system in an embodiment of this application. Figure 3 This is a convergence curve of the proposed optimization algorithm provided in the embodiments of this application.

[0030] Figure 4 The total user rate and number of APs provided in this application embodiment are... The relationship curve.

[0031] Figure 5 The average user speed and number of users provided in the embodiments of this application. The relationship curve.

[0032] Figure 6 This is a cumulative distribution curve of the user rates provided in the embodiments of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0035] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0036] 1) Rotatable antenna (RA) is a technology that dynamically adjusts the orientation of the main lobe of an antenna through mechanical or electronic control. It is designed to improve the adaptability and performance of wireless communication systems, especially suitable for high dynamic scenarios such as 6G.

[0037] With the rapid development of global information and communication technologies and the continuous evolution from 5G to 6G, numerous emerging business scenarios (such as the Internet of Things, connected vehicles, industrial internet, and ultra-high-definition video) are placing higher demands on wireless access networks, especially in terms of low latency, high reliability, and high spectrum efficiency. To meet these needs, the more dense deployment of access points (APs) in communication systems has become a mainstream trend, improving overall system performance by shortening link distances and increasing spatial reuse. Against this backdrop, a promising network architecture is the cellular-free communication system. In this system, a large number of APs distributed throughout the service area are connected to the central processing unit via fronthaul links and collaboratively provide services to all users without cellular boundaries. Theoretically, this can significantly alleviate the cell edge effect and severe co-channel interference problems in traditional cellular networks, improving the service experience for all users.

[0038] However, existing non-cellular communication systems still face many challenges in practical deployment. Traditional non-cellular communication systems typically employ isotropic and fixed antenna structures, lacking the ability to flexibly adjust the antenna radiation direction and energy distribution in the spatial domain, making it difficult to adaptively optimize according to user distribution and environmental changes. This inherent limitation restricts the spatial freedom available to the system, thus hindering the further exploration of the potential communication performance of non-cellular systems to some extent.

[0039] To overcome the aforementioned limitations, rotatable antennas have gained widespread attention as an emerging technology in recent years. These antennas dynamically adjust the three-dimensional beam axis direction, actively reconstructing the radiation pattern without changing the antenna's installation position. This unlocks new spatial degrees of freedom, enabling precise spatial control of desired and interfering signals. Compared to six-dimensional rotatable antennas (6DMA), which require simultaneous control of both position and rotation, RA, as a simplified and attractive variant of 6DMA, retains only the flexibility of rotation (without changing the antenna position). This significantly reduces the complexity of the hardware structure, the required control precision, and the actual deployment cost, making it more suitable for deployment in existing wireless access networks.

[0040] Although existing literature has demonstrated the potential of RA technology from aspects such as system modeling and typical scenarios, research on introducing RA into large-scale distributed non-cellular communication networks and integrating it with its unique AP-user association mechanism for unified design remains relatively scarce. Especially in downlink transmission scenarios, how to jointly consider the AP-user association relationship and the RA beam axis design to fully release the spatial freedom provided by RA and improve the overall service capability of non-cellular systems remains an important unsolved problem.

[0041] In view of this, this application provides a downlink communication method, electronic device, storage medium, and program product for non-cellular networks based on a steerable antenna. The method comprises the following steps: Based on the constructed non-cellular communication system, a closed-form expression for the total user received signal rate is derived; based on the closed-form expression for the total rate, and combined with the antenna direction vector magnitude constraint and the access point-user matching scheme constraint, an optimization problem P1 maximizing the total user rate is constructed; the integer programming subproblem involving AP-user pairing in problem P1 is solved using a two-stage AP-user matching strategy; and the optimal solution for the antenna pointing vector is obtained using fractional programming and continuous convex approximation methods. The downlink communication method for non-cellular networks based on a steerable antenna proposed in this application can utilize the new spatial degrees of freedom brought by the steerable antenna, thereby effectively improving the user received signal energy in the non-cellular system, reducing inter-user interference, and thus maximizing the total user rate.

[0042] like Figure 1 As shown, this embodiment provides a downlink communication method for non-cellular networks based on a steerable antenna, enhancing the communication performance of non-cellular systems. The method specifically includes the following steps: S1: Establish a system model for a non-cellular downlink communication system, in which multiple access points are equipped with a single steerable antenna and multiple users are equipped with a unidirectional antenna; S2: Based on the system model, derive a closed-form expression for the sum rate of the user-received signals; S3: Based on the closed-form expression of the total rate, combined with the pointing vector magnitude constraint of the steerable antenna and the constraint of the access point-user matching scheme, construct a joint optimization problem P1 with the goal of maximizing the total user rate; S4: Solve the integer programming subproblem of access point-user pairing in the joint optimization problem P1 using a two-stage access point-user matching strategy to obtain the access point-user association matrix; S5: Based on the determined access point-user association matrix, the optimal solution of the steerable antenna pointing vector in the joint optimization problem P1 is solved using fractional programming and continuous convex approximation methods.

[0043] To better understand the above technical solution, the following will describe in more detail a downlink communication method for non-cellular networks based on a steerable antenna disclosed in this embodiment, in conjunction with the accompanying drawings and specific implementation methods.

[0044] This embodiment provides a downlink communication method for non-cellular networks based on a steerable antenna, including the following steps: Step 1: Establish a system model for a non-cellular downlink communication system based on a steerable antenna, where the AP is equipped with a single steerable antenna and the user is equipped with a single omnidirectional antenna.

[0045] In this embodiment, see Figure 2 Consider a downlink communication network based on a steerable antenna, which consists of... AP and Composed of 1 user group, among which Each access point (AP) is equipped with one radiation detector (RA), and each user is equipped with one omnidirectional antenna. All APs are connected to the Central Processing Unit (CPU) via a fronthaul network and collaboratively provide services to users on the same time-frequency resources. and Let represent the sets of indices for APs and users, respectively. Without loss of generality, assume that all APs and users reside in the same global Cartesian Coordinate System (CCS). Let and They represent the first The AP and the first The location vector of each user. Let be... For the first The AP and the first The distance between users, where This represents the Euclidean norm.

[0046] Step 2: Based on the constructed cellular-free downlink communication system, calculate the closed-form expression for the total rate of user received signals.

[0047] In this embodiment, a general RA directional gain model is adopted: (1) in, This represents the pair of incident angles relative to the line-of-sight direction (beam principal axis direction) of RA. As a directional factor, This represents the maximum gain in the line-of-sight direction. Let... Indicates the first The pointing vector of RA at the i-th AP. Therefore, the i-th Each AP is for users The directional gain of RA in the direction can be expressed as ,in Representing vectors and The projection cosine between, and Indicates from the first The AP points to the first Normalized direction vectors for each user.

[0048] Assuming from the first AP to the first The channel of a user experiences quasi-static flat fading, which can be modeled as follows: (2) in, This illustrates the large-scale channel power gain caused by distance-dependent path loss and shadow fading. This indicates small-scale channel fading. Specifically, The model is as follows: (3) in, Indicates reference distance Channel power gain at point m Let be the path loss exponent. Further, we assume... It follows an independent Ricean fading distribution, which is determined by the Ricean factor. Characterization: (4) in, This represents the line-of-sight (LoS) component of the channel. This represents the non-line-of-sight (NLoS) channel component exhibiting Rayleigh fading characteristics.

[0049] Since a single access point (RA) can only point its antenna beam axis in one direction at any given time, serving multiple users simultaneously would cause signal power divergence. Therefore, to simplify the analysis, we assume that each AP serves only one user at a time. Furthermore, we utilize a matrix... The AP-user pairing relationship is represented in the following form: (5) in, For binary indicators, it represents the first... The AP and the first Pairing relationships between users: when When, it indicates the user By AP Provide services; otherwise .

[0050] As a prerequisite for precoding and subsequent optimization, we assume that the CPU can fully acquire global channel state information (CSI) through the fronthaul link. Furthermore, to achieve coherent superposition and maximize received signal power, we employ normalized conjugate beamforming, with precoding coefficients set to... Therefore, the first Downlink transmission signal of each AP It can be represented as: (6) in, This indicates the AP's transmit power. To be sent to users The data signal, and Therefore, users The received signal can be represented as: (7) in, It is additive white Gaussian noise (AWGN). Therefore, the user The received signal-to-interference-plus-noise ratio (SINR) can be expressed as: (8) Based on this, the user The reachable rate can be expressed as: (9) Step 3: Based on the closed-form expression in Step 2 and combined with the antenna directional vector magnitude constraint and the AP-user matching scheme constraint, construct the optimization problem P1 to maximize the total user rate.

[0051] The goal of this embodiment is to maximize the overall system rate by jointly optimizing the AP-user pairing relationship and the RA pointing vector. Accordingly, the optimization problem can be formulated as: (10a) (10b) (10c) (10d) in, Let represent the set of all RA pointing vectors. Constraint (10b) requires each pointing vector to satisfy the unit norm condition; constraint (10c) ensures that each user is served by at least one AP to reflect fairness; and constraint (10d) ensures that each AP serves only one user. It should be noted that since the objective function (10a) is non-concave and the constraint (10b) is non-convex, problem (P1) is difficult to solve directly. To efficiently solve this problem, we first propose a two-stage strategy to obtain the AP-user association matrix, and then design an algorithm based on fractional programming to optimize the RA pointing vectors.

[0052] Step 4: Solve the integer programming subproblem involving AP-user pairing in problem P1 using a two-stage AP-user matching strategy.

[0053] To mitigate the adverse effects of distance-dependent path loss, this embodiment proposes a distance-based AP-user pairing strategy. Specifically, this involves calculating the distance between all possible AP-user pairs. We refactor the original association problem into minimizing AP – total user association distance. Therefore, problem (P1) can be expressed as: (11a) (11b) (11c) (11d) Among these constraints, (11b) defines the problem as a 0–1 optimization problem, (11c) ensures that each user is associated with at least one AP, and (11d) ensures that each AP serves only one user. To solve this non-convex problem, we propose a two-stage AP-user association strategy based on AP-user distance, which includes two stages: 1) Initial AP – User Association: In this phase, our goal is to assign each user to a unique AP while ensuring fairness, and to minimize the association distance between each AP-user pair. First, the index sets of unpaired APs and users are initialized as follows: and ,at this time , Next, calculate the distance set of all possible AP-user pairs. Based on these distances, we iteratively... Among the APs, Each user is assigned an AP until each user is associated with a unique AP. Specifically, in the... In the next iteration, let and They represent the first time. The set of unpaired APs and user indices after the next iteration. and Then, select the AP-user pair with the smallest corresponding distance from the unpaired set. : (12) According to equation (12), AP Assigned to user and in the matrix The allocation relationship is recorded in the middle: (13) Subsequently, AP and users The indices are from the set respectively and Removed from: (14a) (14b) When completed After the next iteration, the set of unpaired users becomes empty. This ensures that each user is served by exactly one AP, satisfying constraint (11c).

[0054] 2) Remaining AP allocation: At this stage, our goal is to [redacted] the remaining [redacted]. Each unpaired AP is assigned to its nearest user. For each unpaired AP (denoted as...), ... Find the user closest to it. ,Right now (15) Subsequently, AP Assigned to user and in the matrix This allocation relationship is recorded in the database. The final output is the association matrix. This refers to the overall AP – user association results.

[0055] This algorithm guarantees that each user is served by at least one access point (AP) and that there are no idle APs. It's worth noting that the computational complexity is extremely low, involving only sorting, comparisons, and basic mathematical operations. Specifically, the time complexity of solving problem (P2) is O(log n). .

[0056] Step 5: Use fractional programming and continuous convex approximation to obtain the optimal solution for the antenna pointing vector.

[0057] After completing the AP-user association, our goal is to optimize the pointing vector of RA. Therefore, problem (P1) simplifies to the following form: (16a) (16b) Due to the achievable rate The problem (P3) contains a complex fractional structure, making direct solution difficult. Therefore, we perform a quadratic transformation on the SINR term to obtain the equivalent problem as follows: (17a) (17b) in, Users introduced through quadratic transformation new variables, express The set. The quadratic transformation will change the SINR in the objective function. Rewritten in the following form: (18) Next, we will optimize alternately. and To maximize the objective function (17a). For a fixed... Optimal The following closed-form solutions exist: (19) Next, we will fix Focus on optimization .because piecewise functions in The objective function (17b) remains non-convex. To address this issue, we use the softplus activation function to approximate it. This function provides a smooth and differentiable alternative expression: (20) in, We control the smoothness of the approximation. Furthermore, we apply a continuous convex approximation, replacing the approximation with a convex approximation function iteratively (17a). For simplicity, we show the... The process of the nth iteration, where the nth The iteration yielded Recorded as Through Use a first-order Taylor expansion at this point. It can be linearized to The objective function (17a) can then be expressed as Its specific expression is as follows: (twenty one) (twenty two) Therefore, problem (P4) can be transformed into the following problem: (23a) (23b) However, due to With the unit norm constraint on, problem (P5) remains nonconvex. To address this, we relax the equality constraint (16b) to... This leads to the following question: (24a) (24b) It can be verified that problem (P6) is convex and can be solved using the CVX solver. Since the optimal objective function is monotonically increasing during iteration, the algorithm guarantees convergence. Regarding computational complexity, the update... The cost is negligible, while optimization In problem (P6), the complexity of each iteration is O(n). ,in This is the convergence accuracy threshold. Therefore, the overall complexity of solving problem (P5) is... ,in This represents the required number of iterations.

[0058] To better illustrate the technological advancements of the method proposed in this application, a downlink communication method for non-cellular networks based on a steerable antenna, as described in this application, is compared with different benchmark schemes on the MATLAB platform. These other benchmark schemes include: 1) Directional antenna-based scheme: The AP is equipped with a directional antenna, and the antenna pointing vector is fixed at [1,0,0].

[0059] 2) Scheme based on omnidirectional antenna: The AP is equipped with an omnidirectional antenna, that is, the antenna gain in equation (1) is set to .

[0060] 3) Tiltable antenna pointing to user: The AP is equipped with a tiltable antenna, and the antenna pointing vector is set to point to the user it is paired with.

[0061] This application proposes a two-stage pairing strategy for AP-user pairing relationship design in a cellular system based on steerable antennas, and proposes a fractional programming-based algorithm for the pointing vector design of steerable antennas, thereby improving the total user rate of the cellular system.

[0062] Figure 3 This is the convergence curve of the proposed optimization algorithm provided in the embodiments of this application. Observations show that for all given APs... The system efficiency and convergence rate both increase monotonically with the number of iterations. Although increasing the number of APs slightly reduces the convergence speed, the algorithm converges within 10 iterations in all configurations, thus verifying its effectiveness.

[0063] Figure 4 The total user rate and number of APs provided in this application embodiment are... The relationship curve. Figure 4 Showing a fixed number of users Below, the system efficiency and rate of operation vary with the number of APs for different schemes. The performance curves show changes. It can be seen that in all schemes, as... With the increase in power, both the system efficiency and power efficiency are improved. Furthermore, the proposed RA-based scheme and the RA-aligned user scheme consistently outperform other benchmark schemes throughout the entire range. This performance gain is mainly due to the RA's ability to dynamically point its beam axis towards the target user, thus concentrating the radiated signal power more effectively. Notably, the proposed RA scheme also demonstrates a significant performance improvement over the baseline RA-aligned user scheme, highlighting the effectiveness of the joint optimization algorithm presented in this paper: this algorithm not only focuses on maximizing the target user's signal power but also fully considers the impact of inter-user interference.

[0064] Figure 5 The average user speed and number of users provided in the embodiments of this application. The relationship curve. Figure 5 This demonstrates how the average user rate varies with the number of users under different schemes. The changing relationship. It can be seen that, with... As the beamwidth increases, the average user rate decreases across all schemes because more users lead to more severe multi-user interference. Notably, the proposed RA-based scheme consistently achieves the best performance across the entire range and exhibits greater robustness to performance degradation. This improvement is primarily due to the RA's ability to adaptively rotate its beam axis, thus concentrating signal power more precisely on the target user and reducing interference to other users caused by signal energy leakage. In contrast, the performance of the fixed directional antenna scheme is even worse than that of the isotropic antenna scheme because its fixed beam axis direction results in limited coverage and poor interference suppression. This phenomenon further demonstrates the importance of directional flexibility for improving overall communication quality.

[0065] Figure 6 This is a cumulative distribution curve of the user rates provided in the embodiments of this application. Figure 6The cumulative distribution function of user rates under each scheme is presented. Compared with the RA-based directional user scheme, the proposed RA-based scheme has a larger proportion of high data rate users, indicating that optimized beam axis control can significantly improve single-user performance. Furthermore, the fixed directional antenna scheme results in zero transmission rate for some users, mainly because its beam axis direction cannot be adaptively adjusted, thus creating coverage blind spots.

[0066] In summary, the novel cellular-free downlink communication system based on steerable antennas proposed in this application can maximize the overall system rate by adjusting the pointing vector of the RA to change its directional gain mode. Specifically, we design a two-stage strategy to solve the AP-user association problem and propose a fractional programming-based algorithm to obtain a high-quality solution for the RA pointing vector. Simulation results show that by optimizing the pointing vector of each RA, the RA-based AP can significantly improve the directional gain in the direction of the target user while suppressing interference in undesired directions, thus achieving a higher system rate than many benchmark schemes.

[0067] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0068] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0069] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0070] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0071] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0072] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0073] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0074] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0075] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0078] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0079] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0081] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0083] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A downlink communication method for non-cellular networks based on a steerable antenna, characterized in that, The method includes the following steps: Establish a system model for a non-cellular downlink communication system, in which multiple access points are equipped with a single steerable antenna and multiple users are equipped with a unidirectional antenna; Based on the system model, a closed-form expression for the sum rate of user received signals is derived. Based on the closed-form expression of the total rate, combined with the pointing vector magnitude constraint of the steerable antenna and the constraint of the access point-user matching scheme, a joint optimization problem P1 is constructed with the goal of maximizing the total user rate. By employing a two-stage access point-user matching strategy, the integer programming subproblem of access point-user pairing in the joint optimization problem P1 is solved to obtain the access point-user association matrix. Based on the determined access point-user association matrix, the optimal solution for the steerable antenna pointing vector in the joint optimization problem P1 is obtained by using fractional programming and continuous convex approximation methods.

2. The method according to claim 1, characterized in that, The system model includes: Set up a network consisting of L access points and K users, where L ≥ K; Each access point is connected to the central processing unit via the fronthaul network and collaboratively serves users on the same time and frequency resources; The pointing vector of the steerable antenna at each access point is a three-dimensional unit vector.

3. The method according to claim 2, characterized in that, The derivation of the closed-form expression for the sum rate of the user received signals includes: The directional gain of the access point facing the user is calculated based on a general directional gain model for steerable antennas. The channel is modeled as a quasi-static flat fading channel containing large-scale fading and small-scale fading, wherein the small-scale fading follows a Rice distribution. Normalized conjugate beamforming is used as the precoding scheme. Based on the directional gain, channel state information, and precoding scheme, the received signal-to-interference-plus-noise ratio and achievable rate for each user are calculated, and the summation is obtained to obtain the total rate.

4. The method according to claim 1, characterized in that, The mathematical formulation of the joint optimization problem P1 is as follows: Where B is the access point-user association matrix, Let be the set of all steerable antenna pointing vectors. For users The achievable rate, For the first The pointer vectors of each access point To indicate the first The access point and the first A binary indicator variable indicating whether a user is paired; L The number of access points in the system. K The number of users in the system. Represents the set of indexes for access points. For the user's index set.

5. The method according to claim 4, characterized in that, The two-stage access point-user matching strategy includes: Phase 1 - Initial Association: Initialize the set of unpaired access points and the set of users to the complete set respectively; Iteratively select the user pair with the smallest distance from the unpaired set, and remove the pair from the unpaired set until all users are assigned an access point; Phase Two - Allocation of Remaining Access Points: The remaining unpaired access points are assigned one by one to the user closest to them.

6. The method according to claim 5, characterized in that, The specific steps of the first stage - initial association include: Each unpaired access point is denoted as . Find the user closest to it ,Right now: in, Indicates the first The AP and the first The distance between users This represents the set of user indices obtained after the Kth iteration of the algorithm. Access point Assigned to user The allocation relationship is recorded in matrix B; the final output association matrix B is the overall access point-user association result.

7. The method according to claim 1, characterized in that, The method of using fractional programming and continuous convex approximation to solve the optimal solution of the steerable antenna pointing vector in the joint optimization problem P1 includes: The joint optimization problem P1 is simplified to obtain the optimization problem P3 which only concerns the pointing vector; By performing a second transformation on the signal-to-interference-plus-noise ratio (SINR) term in problem P3 and introducing auxiliary variables, the objective function is transformed into a more manageable form. The auxiliary variable is fixed, and the pointing vector is optimized; the pointing vector is fixed, and the auxiliary variable is updated; this process is repeated iteratively. When optimizing the pointing vector, the softplus activation function is used to approximate the piecewise function in the directional gain model; Based on the first-order Taylor expansion and the continuous convex approximation, the non-convex optimization problem is transformed into a series of convex optimization subproblems for iterative solution.

8. The method according to claim 7, characterized in that, When solving the convex optimization subproblem of the pointing vector, the unit magnitude of the pointing vector is constrained. Relaxed to .

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

Images