Antenna Beam and Power Optimization Method Based on Millimeter-Wave Full-Duplex Self-Organizing Network

Abstract

This application provides an antenna beam and power optimization method, device, and storage medium based on a millimeter-wave full-duplex ad hoc network, relating to the field of communication technology. The method includes: configuring network information and transmission requirements for millimeter-wave full-duplex multi-data streams; obtaining, based on the transmission requirements, a first positional relationship between the transmitting and receiving endpoints in a first link and a first three-dimensional pointing angle of the transmitting endpoint's antenna beam; modeling the channel of the first link based on the first positional relationship and the first three-dimensional pointing angle to obtain first channel gain information; and obtaining a first reachability of the first link based on the first channel gain information and a first transmit power to model the reachability optimization problem; and optimizing the first three-dimensional pointing angle and the first transmit power using a block coordinate descent method to obtain a target three-dimensional pointing angle and a target transmit power. The method of this application achieves efficient end-to-end transmission of multi-stream data in a millimeter-wave full-duplex ad hoc network.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to an antenna beam and power optimization method, device and storage medium based on millimeter-wave full-duplex self-organizing network. Background Technology

[0002] With the development of wireless communication network technology, high spectral efficiency has always been a common pursuit for wireless communication systems due to the scarcity of spectrum resources. On the one hand, advanced self-interference cancellation technology combined with in-band full-duplex communication technology offers the potential to double frequency efficiency and reduce end-to-end communication latency. On the other hand, millimeter-wave technology, due to its abundant bandwidth resources, has been widely considered a key component of next-generation wireless networks. To compensate for the inherent drawback of severe signal attenuation in high-frequency transmission, high-gain directional antennas or beamforming techniques are typically used to achieve millimeter-wave directional communication. Therefore, combining millimeter-wave technology with full-duplex technology has become a promising direction for application.

[0003] In multi-stream, multi-hop networks, the combination of millimeter-wave technology and full-duplex technology offers numerous advantages. Due to directional transmission, network endpoints can concurrently transmit within the same frequency band via spatial multiplexing, significantly improving network throughput. Most existing work focuses on designing self-interference cancellation techniques in integrated access and backhaul systems using beamforming technology. Considering the potential limitations and performance costs of beamforming alone in self-interference cancellation, the synergistic use of beamforming technology with traditional analog and digital self-interference cancellation filters not only enhances self-interference suppression but also helps reduce the gain loss from directional transmission beamforming. With the future development of seamlessly connected networks, it is necessary to allocate millimeter-wave resources to achieve the network performance of full-duplex, multi-stream, multi-hop networks with high concurrent transmission volumes.

[0004] Because the sidelobes of directional antenna transmit / receive antennas can leak signals in undesirable directions, millimeter-wave full-duplex multi-stream multi-hop networks still encounter significant concurrent interference. Furthermore, in real-world scenarios such as UAV interconnection and integrated air-ground networks, the 3dB beamwidth of the main lobe of the beam pattern cannot be too narrow to ensure stable connections between transceivers, which can also lead to interference leakage. Therefore, effectively allocating communication resources, such as antenna beamwidth and transmit power, is essential. In addition, links with high-quality channels are typically prioritized to maximize network throughput, but other links with poor channel conditions may suffer a sharp performance degradation, resulting in extremely low end-to-end achievable communication rates for data streams with poor channel conditions. Therefore, existing technologies suffer from poor end-to-end data stream transmission efficiency. Summary of the Invention

[0005] This application provides an antenna beam and power optimization method, device, and storage medium based on millimeter-wave full-duplex self-organizing network to solve the technical problem of poor end-to-end data flow transmission efficiency in the prior art.

[0006] In a first aspect, this application provides an antenna beam and power optimization method based on a millimeter-wave full-duplex self-organizing network, including:

[0007] Configure the network information and transmission requirements for millimeter-wave full-duplex multi-data streams. Based on the transmission requirements, obtain the first positional relationship between the transmitting endpoint and the receiving endpoint in the first link and the first three-dimensional pointing angle of the antenna beam of the transmitting endpoint; wherein, the data stream transmits data through multiple links, and the first link is any one of the multiple links;

[0008] Based on the first positional relationship and the first three-dimensional pointing angle, the channel of the first link is modeled to obtain the first channel gain information of the first link. Based on the first channel gain information and the first transmit power of the transmitting endpoint, the first reachability of the first link is obtained to model the reachability optimization problem.

[0009] Based on the reachability optimization problem, the block coordinate descent method is used to optimize the first three-dimensional pointing angle and the first transmit power to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam.

[0010] Optionally, based on the reachability optimization problem, a block coordinate descent method is used to optimize the first three-dimensional pointing angle and the first transmit power to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam, including:

[0011] Reconfigure the second three-dimensional pointing angle and second transmit power of the antenna beams at the transmit endpoints of multiple links, and configure the preset number of iterations in the block coordinate descent method;

[0012] Based on the second three-dimensional pointing angle and second transmit power of multiple links, the first channel gain information is updated to obtain the second channel gain information of each link.

[0013] Based on the second three-dimensional pointing angle in the second channel gain information of multiple links, the block coordinate descent method is used for iterative processing of a preset number of iterations. The continuous convex approximation technique is used to process the iteration results to obtain the third transmit power. It is then determined whether the third transmit power has converged; if so, the target transmit power is obtained.

[0014] Based on the second transmission power of multiple links, a block coordinate descent method is used for iterative processing of a preset number of iterations. The particle swarm algorithm is used to process the iteration results to obtain the third three-dimensional pointing angle. It is then determined whether the third three-dimensional pointing angle has converged; if so, the target three-dimensional pointing angle is obtained.

[0015] Optionally, the third three-dimensional pointing angle is obtained by processing the iteration results using a particle swarm optimization algorithm, including:

[0016] Configure the preset number of iterations for the particle swarm algorithm and the position and velocity sets of multiple particles. Iterate through the preset number of iterations, update the position and velocity sets of multiple particles, calculate the fitness of multiple particles, and obtain the third three-dimensional pointing angle based on the fitness.

[0017] Optionally, the process iterates through a preset number of iterations, updates the position and velocity sets of multiple particles, calculates the fitness of the multiple particles, and obtains the third three-dimensional pointing angle based on the fitness, including:

[0018] The process iterates through the preset number of iterations, updates the position and velocity sets of multiple particles, calculates the fitness of multiple particles, and obtains the global target particle and the iterative local target particle.

[0019] Determine whether the number of iterations in the particle swarm optimization algorithm meets the preset number of iterations for the particle swarm optimization algorithm;

[0020] If so, then the third three-dimensional pointing angle is obtained.

[0021] Optionally, based on the first positional relationship and the first three-dimensional pointing angle, the channel of the first link is modeled to obtain the first channel gain information of the first link, including:

[0022] Based on the first positional relationship and the first three-dimensional pointing angle, the deviation angles of the antenna beam in the horizontal and vertical directions are obtained;

[0023] Based on the deviation angle, the channel of the first link is modeled to obtain the antenna attenuation parameters of the antenna beam in the horizontal and vertical directions.

[0024] Based on the antenna attenuation parameters, the first channel gain information of the first link is obtained.

[0025] Optionally, a first reachability of the first link is obtained based on the first channel gain information and the first transmit power of the transmitting endpoint, in order to model the reachability optimization problem, including:

[0026] The signal-to-interference-plus-noise ratio (SIR) of the first link is obtained based on the first channel gain information and the first transmit power of the transmitting endpoint.

[0027] Based on the signal-to-interference-plus-noise ratio (SINR), the first reachability of the first link is obtained to model the reachability optimization problem.

[0028] Optionally, after modeling the reachability optimization problem, it includes:

[0029] The reachability optimization problem is transformed into the link reachability optimization problem by maximizing the minimum end-to-end reachability of the data flow, based on the optimization objective of maximizing the minimum end-to-end reachability of the data flow.

[0030] Secondly, this application provides an antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network, comprising:

[0031] The acquisition module is used to configure the network information and transmission requirements of millimeter-wave full-duplex multi-data streams. Based on the transmission requirements, it acquires the first positional relationship between the transmitting end and the receiving end in the first link and the first three-dimensional pointing angle of the antenna beam of the transmitting end. The data stream is transmitted through multiple links, and the first link is any one of the multiple links.

[0032] The first processing module is used to model the channel of the first link according to the first positional relationship and the first three-dimensional pointing angle, to obtain the first channel gain information of the first link, and to obtain the first reachability of the first link according to the first channel gain information and the first transmit power of the transmitting endpoint, so as to model the reachability optimization problem.

[0033] The second processing module is used to optimize the first three-dimensional pointing angle and the first transmit power using a block coordinate descent method based on the reachability optimization problem, so as to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam.

[0034] Optionally, the first processing module is also used for:

[0035] Based on the first positional relationship and the first three-dimensional pointing angle, the deviation angles of the antenna beam in the horizontal and vertical directions are obtained;

[0036] Based on the deviation angle, the channel of the first link is modeled to obtain the antenna attenuation parameters of the antenna beam in the horizontal and vertical directions.

[0037] Based on the antenna attenuation parameters, the first channel gain information of the first link is obtained.

[0038] Optionally, the first processing module is also used for:

[0039] The signal-to-interference-plus-noise ratio (SIR) of the first link is obtained based on the first channel gain information and the first transmit power of the transmitting endpoint.

[0040] Based on the signal-to-interference-plus-noise ratio (SINR), the first reachability of the first link is obtained to model the reachability optimization problem.

[0041] Optionally, the first processing module is also used for:

[0042] The reachability optimization problem is transformed into the link reachability optimization problem by maximizing the minimum end-to-end reachability of the data flow, based on the optimization objective of maximizing the minimum end-to-end reachability of the data flow.

[0043] Optionally, the second processing module is also used for:

[0044] Reconfigure the second three-dimensional pointing angle and second transmit power of the antenna beams at the transmit endpoints of multiple links, and configure the preset number of iterations in the block coordinate descent method;

[0045] Based on the second three-dimensional pointing angle and second transmit power of multiple links, the first channel gain information is updated to obtain the second channel gain information of each link.

[0046] Based on the second three-dimensional pointing angle in the second channel gain information of multiple links, the block coordinate descent method is used for iterative processing of a preset number of iterations. The continuous convex approximation technique is used to process the iteration results to obtain the third transmit power. It is then determined whether the third transmit power has converged; if so, the target transmit power is obtained.

[0047] Based on the second transmission power of multiple links, a block coordinate descent method is used for iterative processing of a preset number of iterations. The particle swarm algorithm is used to process the iteration results to obtain the third three-dimensional pointing angle. It is then determined whether the third three-dimensional pointing angle has converged; if so, the target three-dimensional pointing angle is obtained.

[0048] Optionally, the second processing module is also used for:

[0049] Configure the preset number of iterations for the particle swarm algorithm and the position and velocity sets of multiple particles. Iterate through the preset number of iterations, update the position and velocity sets of multiple particles, calculate the fitness of multiple particles, and obtain the third three-dimensional pointing angle based on the fitness.

[0050] Optionally, the second processing module is also used for:

[0051] The process iterates through the preset number of iterations, updates the position and velocity sets of multiple particles, calculates the fitness of multiple particles, and obtains the global target particle and the iterative local target particle.

[0052] Determine whether the number of iterations in the particle swarm optimization algorithm meets the preset number of iterations for the particle swarm optimization algorithm;

[0053] If so, then the third three-dimensional pointing angle is obtained.

[0054] Thirdly, this application provides an antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network, comprising:

[0055] Processor and memory;

[0056] The memory stores the instructions that the computer executes;

[0057] The processor executes computer execution instructions stored in memory, causing the antenna beam and power optimization device based on millimeter-wave full-duplex self-organizing network to perform the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network of any one of the first aspects.

[0058] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network as described in any of the first aspects.

[0059] The antenna beam and power optimization method, device, and storage medium based on millimeter-wave full-duplex ad hoc networks provided in this application establish a mathematical model for optimizing the joint three-dimensional pointing angle and transmit power of the antenna beam to maximize the minimum end-to-end achievable rate of the data stream, based on pre-configured network information and transmission requirements of millimeter-wave full-duplex multi-data streams. The three-dimensional pointing angle and transmit power are optimized using a block coordinate descent method, and the target three-dimensional pointing angle and target transmit power are converged to obtain the target three-dimensional pointing angle and target transmit power, thereby realizing efficient end-to-end transmission of multi-stream data in millimeter-wave full-duplex ad hoc networks. Attached Figure Description

[0060] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0061] Figure 1 A flowchart illustrating the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks provided in this application embodiment. Figure 1 ;

[0062] Figure 2 A flowchart illustrating the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks provided in this application embodiment. Figure 2 ;

[0063] Figure 3 A flowchart illustrating the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks provided in this application embodiment. Figure 3 ;

[0064] Figure 4 A schematic diagram of an antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network provided for an embodiment of this application;

[0065] Figure 5 A schematic diagram of the hardware structure of an antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network provided in an embodiment of this application.

[0066] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0067] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0068] In multi-stream, multi-hop networks, combining millimeter-wave technology with full-duplex technology enables directional transmission, spatial multiplexing, and concurrent transmission, significantly improving network throughput. However, with future network development, directional transmitting / receiving antennas used in millimeter-wave communication may experience sidelobe leakage, diverting signals in unwanted directions and causing interference. Particularly in scenarios such as UAV interconnection and integrated air-ground networks, the 3dB beamwidth of the main lobe of the beam pattern cannot be too narrow to maintain stable connections between transceivers. This increases the probability of interference leakage and negatively impacts communication quality.

[0069] Secondly, in full-duplex multi-stream multi-hop networks, links with high-quality channels are typically given higher allocation priority to maximize network throughput. However, links with poor channel quality may suffer a sharp performance degradation, resulting in low end-to-end transmission rates. This can lead to inefficient data stream transmission, limiting the overall network performance. Therefore, existing technologies suffer from the technical problem of poor end-to-end data stream transmission efficiency.

[0070] This application provides an antenna beam and power optimization method, device, and storage medium based on millimeter-wave full-duplex ad hoc networks. Based on pre-configured network information and transmission requirements for millimeter-wave full-duplex multi-data streams, a mathematical model is established to jointly optimize the three-dimensional pointing angle and transmit power of the antenna beam to maximize the minimum end-to-end achievable data stream rate. The three-dimensional pointing angle and transmit power are then optimized using a block coordinate descent method, converging to obtain the target three-dimensional pointing angle and target transmit power, thereby achieving efficient end-to-end transmission of multi-stream data in millimeter-wave full-duplex ad hoc networks.

[0071] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0072] Figure 1 A flowchart illustrating the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks provided in this application embodiment. Figure 1 .like Figure 1 As shown in the figure, this embodiment provides an antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network, including:

[0073] S101. Configure the network information and transmission requirements of millimeter-wave full-duplex multi-data stream, and according to the transmission requirements, obtain the first positional relationship between the transmitting endpoint and the receiving endpoint in the first link and the first three-dimensional pointing angle of the antenna beam of the transmitting endpoint.

[0074] In this embodiment, the millimeter-wave full-duplex ad hoc network multi-data stream end-to-end transmission can be used in scenarios with different broadband data transmission requirements, such as emergency disaster relief, remote communication, smart cities, telemedicine, and maritime communication. The ad hoc network system built using millimeter-wave communication technology connects multiple endpoints, including emergency disaster relief bases, remote communication sites, sensor endpoints in smart cities, telemedicine equipment, and maritime communication facilities. These endpoints can be connected via links and transmitted through multiple data streams to meet the monitoring and processing needs of large amounts of images, videos, and real-time data, with data streams transmitted through multiple links.

[0075] In a three-dimensional Cartesian coordinate system, the various transmitting and receiving endpoints form a self-organizing network. Each endpoint exchanges endpoint telemetry and control information via an omnidirectional antenna operating in the Sub-6GHz band (e.g., L-band). Simultaneously, each endpoint is equipped with a directional antenna operating in the millimeter-wave band (e.g., 60GHz), employing full-duplex mode for end-to-end broadband image and video data transmission. Using traditional routing algorithms, multiple end-to-end broadband data stream requests are periodically obtained based on the telemetry and control information from the omnidirectional antennas.

[0076] For each multi-data stream transmission requirement, define the endpoint sequence. Let H be the set of path endpoints of data stream f, where H f Let f be the number of hops in the data flow f. A directed graph can be constructed using graph theory. The endpoint set is The link set is Select any one of the multiple links as the first link, and obtain the transmitting endpoint u and receiving endpoint u in the first link.re The first positional relationship between them is defined. The first azimuth angle, the first elevation angle, and the first positional relationship of the antenna beam at the transmitting end in the first link are represented, wherein the first link is any one of multiple links; the first three-dimensional pointing angle includes the first azimuth angle and the first elevation angle.

[0077] Based on geometric relationships, it can be calculated that...

[0078]

[0079]

[0080]

[0081] Where, x u y u and z u Represents the three-dimensional coordinates of endpoint u.

[0082] S102. Based on the first positional relationship and the first three-dimensional pointing angle, the channel of the first link is modeled to obtain the first channel gain information of the first link. Based on the first channel gain information and the first transmit power of the transmitting endpoint, the first reachability of the first link is obtained to model the reachability optimization problem.

[0083] In this embodiment, the channel gain information of the link refers to the ratio of the actual signal power received by the receiving end to the signal power transmitted by the transmitting end when the signal sent by the transmitting end is transmitted to the receiving end through the wireless channel at a specific frequency. Channel gain information represents the transmission quality of the channel; a higher value indicates better signal transmission quality. Channel gain is affected by various factors, including transmission distance, transmission medium, antenna gain, path loss, multipath effects, fading, and interference. Therefore, in wireless communication systems, appropriate modulation and coding of signals are necessary to improve channel capacity and system performance. Link reachability refers to the probability of successful data transmission from the transmitting end to the receiving end through the link in a wireless communication system. Reachability is an important indicator for measuring link transmission quality and stability.

[0084] S103. Based on the reachability optimization problem, the first three-dimensional pointing angle and the first transmit power are optimized using the block coordinate descent method to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam.

[0085] In this embodiment, the block coordinate descent method is an optimization algorithm used to solve multivariate optimization problems. The basic idea of ​​the block coordinate descent method is to divide the objective function into blocks according to variables, updating only one variable in each iteration while keeping the others unchanged. By iterating and updating each variable multiple times, the optimization objective is eventually achieved.

[0086] This application provides an antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network. This method configures the network information and transmission requirements of millimeter-wave full-duplex multi-data streams. Based on the transmission requirements, it obtains the first positional relationship between the transmitting and receiving endpoints in the first link and the first three-dimensional pointing angle of the transmitting endpoint's antenna beam. Based on the first positional relationship and the first three-dimensional pointing angle, it models the channel of the first link to obtain the first channel gain information. Then, based on the first channel gain information and the first transmit power of the transmitting endpoint, it obtains the first reachability of the first link to model the reachability optimization problem. Based on the reachability optimization problem, it uses a block coordinate descent method to optimize the first three-dimensional pointing angle and the first transmit power, converging to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam, ultimately achieving efficient end-to-end transmission of multi-stream data.

[0087] Figure 2 A flowchart illustrating the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks provided in this application embodiment. Figure 2 This embodiment is in Figure 1 Based on the embodiments, a detailed explanation of the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks is provided. For example... Figure 2 As shown, this embodiment provides an antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks, including:

[0088] S201. Configure the network information and transmission requirements of millimeter-wave full-duplex multi-data stream, and according to the transmission requirements, obtain the first positional relationship between the transmitting endpoint and the receiving endpoint in the first link and the first three-dimensional pointing angle of the antenna beam of the transmitting endpoint.

[0089] Step S201 is similar to step S101 above, and will not be repeated here.

[0090] S202. Based on the first positional relationship and the first three-dimensional pointing angle, the deviation angle of the antenna beam in the horizontal and vertical directions is obtained.

[0091] In this embodiment, based on the first positional relationship and the first three-dimensional pointing angle, the deviation angles of the antenna beam in the horizontal and vertical directions are obtained, respectively expressed as:

[0092]

[0093]

[0094] S203. Based on the deviation angle, model the channel of the first link to obtain the antenna attenuation parameters of the antenna beam in the horizontal and vertical directions.

[0095] In this embodiment, the channel of the first link is modeled based on the deviation angle, using a 3GPP beam radiation pattern. The antenna attenuation in the horizontal and vertical directions, in dB, is as follows:

[0096]

[0097]

[0098] Among them, A m Let θ be the maximum antenna attenuation, and for simplicity of analysis, let it be ∞. 3dB The horizontal beamwidth is 3dB, φ 3dB The vertical beamwidth is 3dB.

[0099] S204. Based on the antenna attenuation parameters, obtain the first channel gain information of the first link.

[0100] In this embodiment, based on the antenna attenuation parameters, the first link (u,u) re The antenna gain is:

[0101]

[0102] Among them, G max This represents the peak gain. Therefore, the linear form of the antenna gain is:

[0103]

[0104] The receiving antenna of the link adopts omnidirectional mode with a gain of g0. It is worth noting that the method of this embodiment is applicable to any transmit / receive antenna beam pattern. Therefore, the first link (u, u) re The first channel gain information is:

[0105]

[0106] Where λ is the carrier wavelength and α is the path attenuation coefficient. It is the small-scale attenuation coefficient that follows a zero-mean complex Gaussian distribution.

[0107] S205. Based on the first channel gain information and the first transmit power of the transmitting endpoint, obtain the signal-to-interference-plus-noise ratio of the first link.

[0108] In this embodiment, let p u Let u represent the first transmit power of the transmitting endpoint u, then the first link (u, u) re The signal-to-interference-plus-noise ratio (SIR) is:

[0109]

[0110] in, To prevent concurrent communication interference caused by the first link, To address the residual self-interference after using digital and analog self-interference cancellation filters, σ 2 The variance is the additive white Gaussian noise.

[0111] S206. Based on the signal-to-interference-plus-noise ratio, the first reachability of the first link is obtained to model the reachability optimization problem.

[0112] In this embodiment, since the loop interference gain of a full-duplex relay can be as low as -100dB, the self-interference is almost negligible compared to concurrent interference. First link (u,u) re The first reachability rate is:

[0113]

[0114] S207. Based on the optimization objective of maximizing the minimum end-to-end reachability of the data flow, the reachability optimization problem is transformed into the link reachability optimization problem of maximizing the minimum reachability.

[0115] In this embodiment, in a multi-stream network, it is necessary to guarantee the quality of service for each data stream. Therefore, the optimization objective is to maximize the minimum end-to-end reachable rate of all data streams. For multi-hop transmission, the end-to-end reachability of a given transmission path is constrained by the hop link with the minimum achievable rate, i.e. Therefore, the optimization problem is modeled as follows:

[0116]

[0117] The first two constraints ensure that the receiving endpoint must be within the main lobe coverage area of ​​the transmitting node, while the last constraint limits the first transmission power of the transmitting endpoint.

[0118] The above optimization problem can be equivalently transformed into:

[0119]

[0120] in, This means that maximizing the minimum reachable rate of all data streams end-to-end is equivalent to maximizing the minimum reachable rate of all links.

[0121] S208, reconfigure the second three-dimensional pointing angle and second transmit power of the antenna beams of the transmit endpoints of multiple links, and configure the preset number of iterations in the block coordinate descent method.

[0122] In this embodiment, for the optimization problem in step S207 above, the second three-dimensional pointing angle and second transmit power of the antenna beams at the transmitting endpoints of multiple links are optimized and calculated. First, the second three-dimensional pointing angle {ψ} of the antenna beams at the transmitting endpoints of multiple links is reconfigured. f} (0) Second transmission power {p u} (0) The preset number of iterations t in the block coordinate descent method is configured, and the initial number of iterations in the block coordinate descent method is t = 1.

[0123] S209. Based on the second three-dimensional pointing angle and the second transmit power of multiple links, update the first channel gain information to obtain the second channel gain information for each link.

[0124] In this embodiment, based on the second three-dimensional pointing angle and second transmit power of multiple links, and according to the solution process of the first channel gain information in step S204, the channel gain information of all links is updated. This allows us to obtain the second channel gain information for each link.

[0125] S210. Based on the second three-dimensional pointing angle in the second channel gain information of multiple links, the block coordinate descent method is used for iterative processing for a preset number of iterations, and the continuous convex approximation technique is used to process the iteration results to obtain the third transmit power; based on the second transmit power of multiple links, the block coordinate descent method is used for iterative processing for a preset number of iterations, and the particle swarm algorithm is used to process the iteration results to obtain the third three-dimensional pointing angle.

[0126] In this embodiment, a block coordinate descent method is used to iterate the transmit power a preset number of times to obtain the iterative result for the transmit power. A continuous convex approximation technique is then used to process the iterative result to obtain the target transmit power {p} of the antenna beam. u} (t) Specifically, it involves maintaining the second three-dimensional pointing angle {ψ} in the second channel gain information of multiple links. f} (t-1) If the three-dimensional pointing angles remain unchanged, then the channel gain information remains unchanged, and the achievable rate of the link is:

[0127]

[0128] Equation (15) is obtained by first-order Taylor expansion:

[0129]

[0130] The transmit power optimization subproblem can be written as:

[0131]

[0132] The aforementioned transmit power optimization subproblem is a convex problem, and the optimal power solution can be obtained using a convex optimization toolkit, ultimately yielding the third transmit power of the antenna beam. The convex optimization toolkit can be either the CVX toolkit in MATLAB or the CVXPY toolkit in Python.

[0133] S211. Determine whether the third transmit power and the third three-dimensional pointing angle have converged; if yes, proceed to step S212; if no, re-execute step S209.

[0134] S212. Obtain the target's transmission power and the target's three-dimensional pointing angle.

[0135] In this embodiment, the target transmission power {p} is obtained by determining whether the third transmission power and the third three-dimensional pointing angle converge to the optimization target value of formula (14) until the optimization target value of formula (14) converges. u} * ←{p u} (t) and the target's three-dimensional pointing angle {ψ f} * ←{ψ f} (t) .

[0136] This application provides an antenna beam and power optimization method based on millimeter-wave full-duplex ad hoc networks. This method obtains the signal-to-interference-plus-noise ratio (SINR) of a first link based on first channel gain information and the first transmit power of the transmitting endpoint, thereby determining the first reachability of the first link. This improves the accuracy of reachability determination, increasing the success probability of data transmission and reducing the risk of data transmission errors and loss. By reconfiguring the second three-dimensional pointing angle and second transmit power of the antenna beams at the transmitting endpoints of multiple links and updating the first channel gain information, the reachability of the links can be optimized, thereby improving the end-to-end reachability of the data stream. Iterative processing using a block coordinate descent method and continuous convex approximation technology improves the accuracy of determining the target transmit power of the antenna beam, while particle swarm optimization improves the accuracy of determining the target three-dimensional pointing angle of the antenna beam. This achieves efficient end-to-end transmission of multi-stream data in millimeter-wave full-duplex ad hoc networks.

[0137] Figure 3 A flowchart illustrating the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing networks provided in this application embodiment. Figure 3 This embodiment is in Figure 2 Based on the previous embodiment, a detailed explanation is provided regarding the iterative processing using a block coordinate descent method for a preset number of iterations based on the second transmit power of multiple links, and the use of a particle swarm optimization algorithm to process the iteration results to obtain the third three-dimensional pointing angle. For example... Figure 3 As shown in the embodiments of this application, the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network includes:

[0138] S301. Based on the second transmit power of multiple links, perform iterative processing for a preset number of iterations using the block coordinate descent method.

[0139] In this embodiment, the second transmit power of multiple links is kept constant, and the block coordinate descent method is used for iterative processing of a preset number of iterations.

[0140] S302. Configure the preset number of iterations for the particle swarm algorithm and the position and velocity sets of multiple particles.

[0141] In this embodiment, the preset iteration number T of the particle swarm optimization algorithm is configured. Initially, the iteration number of the particle swarm optimization algorithm is T = 1, and the position set of multiple particles is... With velocity set The position set represents the possible solutions for the three-dimensional pointing angle of the directional antenna beam. For a multi-stream, multi-hop network, the position information of each particle n is represented as:

[0142]

[0143] The first particle is initialized with the line-of-sight link pointing angle of the transmitting and receiving nodes, i.e.

[0144]

[0145] The second particle is initialized with the optimal solution obtained in the previous block coordinate descent iteration, and the remaining particles are randomly generated under the constraints.

[0146] Particle swarm optimization (PSO) is a algorithm that searches for global and local optima by updating the positions and velocities of multiple particles and analyzing their fitness. By continuously updating the set of particle positions and velocities and calculating their fitness, the accuracy of the solution can be gradually improved and optimized.

[0147] S303. Iterate through the preset number of iterations, update the position and velocity sets of multiple particles, calculate the fitness of multiple particles, and obtain the global target particle and the iterative local target particle.

[0148] In this embodiment, for each particle, the position-velocity set and the position set are updated according to the following formula:

[0149]

[0150] in ξ1 represents the inertia weight. c1 and c2 are the individual and global learning factors, respectively. To improve the optimality of the obtained solution, ξ1 and ξ2 are set as random parameters between 0 and 1.

[0151] Since the azimuth and elevation angles of the beam are in the range of 0 to 2π, the position set is updated according to the following formula:

[0152]

[0153] The fitness of a particle is calculated using the following formula:

[0154]

[0155] Initialize global target particles and the local target particle in the current iteration

[0156] S304. Determine whether the number of iterations of the particle swarm optimization algorithm meets the preset number of iterations of the particle swarm optimization algorithm; if yes, then execute step S305; if no, then execute step S303 again.

[0157] S305, obtain the third three-dimensional pointing angle.

[0158] In this embodiment, step S305 is repeated until the preset iteration limit of the particle swarm optimization algorithm is reached, i.e., T = T. max The third three-dimensional pointing angle {ψ} of the launch endpoint, calculated using the particle swarm optimization algorithm, is obtained. f} (t) ←q global .

[0159] This application provides an antenna beam and power optimization method based on millimeter-wave full-duplex ad hoc networks. This method optimizes the three-dimensional pointing angle of the antenna beam using a block coordinate descent method and a particle swarm optimization algorithm, and achieves better performance by calculating fitness. By accurately iteratively updating the particle position and velocity sets, calculating fitness, and selecting global and iterative local target particles, the accuracy of determining the target three-dimensional pointing angle of the antenna beam is improved, thereby achieving efficient end-to-end transmission of multi-stream data in millimeter-wave full-duplex ad hoc networks.

[0160] Figure 4 This is a schematic diagram of an antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network, provided as an embodiment of this application. The device in this embodiment can be in software and / or hardware form. For example... Figure 4 As shown in the embodiment of this application, an antenna beam and power optimization device 400 based on a millimeter-wave full-duplex self-organizing network is provided. The device includes: an acquisition module 401, a first processing module 402, and a second processing module 403.

[0161] The acquisition module 401 is used to configure the network information and transmission requirements of millimeter-wave full-duplex multi-data stream, and according to the transmission requirements, acquire the first positional relationship between the transmitting end and the receiving end in the first link and the first three-dimensional pointing angle of the antenna beam of the transmitting end.

[0162] The first processing module 402 is used to model the channel of the first link according to the first positional relationship and the first three-dimensional pointing angle, to obtain the first channel gain information of the first link, and to obtain the first reachability of the first link according to the first channel gain information and the first transmit power of the transmitting endpoint, so as to model the reachability optimization problem.

[0163] The second processing module 403 is used to optimize the first three-dimensional pointing angle and the first transmit power by using a block coordinate descent method based on the reachability optimization problem, so as to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam.

[0164] In one possible implementation, the first processing module 402 is further configured to:

[0165] Based on the first positional relationship and the first three-dimensional pointing angle, the deviation angles of the antenna beam in the horizontal and vertical directions are obtained;

[0166] Based on the deviation angle, the channel of the first link is modeled to obtain the antenna attenuation parameters of the antenna beam in the horizontal and vertical directions.

[0167] Based on the antenna attenuation parameters, the first channel gain information of the first link is obtained.

[0168] In one possible implementation, the first processing module 402 is further configured to:

[0169] The signal-to-interference-plus-noise ratio (SIR) of the first link is obtained based on the first channel gain information and the first transmit power of the transmitting endpoint.

[0170] Based on the signal-to-interference-plus-noise ratio (SINR), the first reachability of the first link is obtained to model the reachability optimization problem.

[0171] In one possible implementation, the first processing module 402 is further configured to:

[0172] The reachability optimization problem is transformed into the link reachability optimization problem by maximizing the minimum end-to-end reachability of the data flow, based on the optimization objective of maximizing the minimum end-to-end reachability of the data flow.

[0173] In one possible implementation, the second processing module 402 is further used for:

[0174] Reconfigure the second three-dimensional pointing angle and second transmit power of the antenna beams at the transmit endpoints of multiple links, and configure the preset number of iterations in the block coordinate descent method;

[0175] Based on the second three-dimensional pointing angle and second transmit power of multiple links, the first channel gain information is updated to obtain the second channel gain information of each link.

[0176] Based on the second three-dimensional pointing angle in the second channel gain information of multiple links, the block coordinate descent method is used for iterative processing of a preset number of iterations. The continuous convex approximation technique is used to process the iteration results to obtain the third transmit power. It is then determined whether the third transmit power has converged; if so, the target transmit power is obtained.

[0177] Based on the second transmission power of multiple links, a block coordinate descent method is used for iterative processing of a preset number of iterations. The particle swarm algorithm is used to process the iteration results to obtain the third three-dimensional pointing angle. It is then determined whether the third three-dimensional pointing angle has converged; if so, the target three-dimensional pointing angle is obtained.

[0178] In one possible implementation, the second processing module 402 is further used for:

[0179] Configure the preset number of iterations for the particle swarm algorithm and the position and velocity sets of multiple particles. Iterate through the preset number of iterations, update the position and velocity sets of multiple particles, calculate the fitness of multiple particles, and obtain the third three-dimensional pointing angle based on the fitness.

[0180] In one possible implementation, the second processing module 402 is further used for:

[0181] The process iterates through the preset number of iterations, updates the position and velocity sets of multiple particles, calculates the fitness of multiple particles, and obtains the global target particle and the iterative local target particle.

[0182] Determine whether the number of iterations in the particle swarm optimization algorithm meets the preset number of iterations for the particle swarm optimization algorithm;

[0183] If so, then the third three-dimensional pointing angle is obtained.

[0184] Figure 5 This is a schematic diagram of the hardware structure of an antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network, provided as an embodiment of this application. Figure 5 As shown, the antenna beam and power optimization device 500 for this millimeter-wave full-duplex self-organizing network includes:

[0185] Processor 501 and memory 502;

[0186] The memory stores the instructions that the computer executes;

[0187] The processor executes the computer execution instructions stored in memory 502, causing the antenna beam and power optimization device based on millimeter-wave full-duplex self-organizing network to perform the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network as described above.

[0188] It should be understood that the processor 501 described above can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The memory 502 may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0189] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement an antenna beam and power optimization method based on a millimeter-wave full-duplex self-organizing network.

[0190] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0191] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0192] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0193] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0194] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0195] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0196] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). 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 memory and includes several 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 memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0197] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0198] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0199] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for optimizing antenna beam and power based on millimeter-wave full-duplex self-organizing networks, characterized in that, include: Configure the network information and transmission requirements of the millimeter-wave full-duplex multi-data stream, and according to the transmission requirements, obtain the first positional relationship between the transmitting endpoint and the receiving endpoint in the first link and the first three-dimensional pointing angle of the antenna beam of the transmitting endpoint; wherein, the data stream transmits data through multiple links, and the first link is any one of the multiple links; Based on the first positional relationship and the first three-dimensional pointing angle, the channel of the first link is modeled to obtain the first channel gain information of the first link, and the first reachability of the first link is obtained based on the first channel gain information and the first transmit power of the transmitting endpoint, so as to model the reachability optimization problem. Based on the reachability optimization problem, the first three-dimensional pointing angle and the first transmit power are optimized using a block coordinate descent method to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam; The reachability optimization problem is modeled as follows: in, To maximize the minimum end-to-end reachable rate of all the data streams, For the data stream The number of jumps, The horizontal beamwidth is 3dB. The vertical beamwidth is 3dB. The first transmit power of the transmitting endpoint u, The maximum permissible transmission power of the transmitting endpoint.

2. The method according to claim 1, characterized in that, The step of optimizing the first three-dimensional pointing angle and the first transmit power using a block coordinate descent method based on the reachability optimization problem to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam includes: The second three-dimensional pointing angle and second transmit power of the antenna beams at the transmit endpoints of multiple links are reconfigured, and the preset number of iterations in the block coordinate descent method is configured. Based on the second three-dimensional pointing angle and the second transmit power of the multiple links, the first channel gain information is updated to obtain the second channel gain information of each link. Based on the second three-dimensional pointing angle in the second channel gain information of multiple links, the block coordinate descent method is used for a preset number of iterations. The iteration results are processed using a continuous convex approximation technique to obtain the third transmit power. It is then determined whether the third transmit power has converged. If so, the target transmit power is obtained. Based on the second transmit power of the multiple links, the block coordinate descent method is used to perform the preset number of iterations. The particle swarm algorithm is used to process the iteration results to obtain the third three-dimensional pointing angle. It is then determined whether the third three-dimensional pointing angle has converged. If so, the target three-dimensional pointing angle is obtained.

3. The method according to claim 2, characterized in that, The process of using a particle swarm optimization algorithm to process the iteration results to obtain the third three-dimensional pointing angle includes: Configure the preset number of iterations of the particle swarm algorithm and the position and velocity sets of multiple particles, traverse the preset number of iterations, update the position and velocity sets of the multiple particles, calculate the fitness of the multiple particles, and obtain the third three-dimensional pointing angle based on the fitness.

4. The method according to claim 3, characterized in that, The iterative processing of traversing a preset number of iterations involves updating the position and velocity sets of the multiple particles and calculating the fitness of the multiple particles. The third three-dimensional pointing angle is then obtained based on the fitness, including: The process iterates through the preset number of iterations, updates the position and velocity sets of the multiple particles, calculates the fitness of the multiple particles, and obtains the global target particle and the iterative local target particle. Determine whether the number of iterations of the particle swarm optimization algorithm satisfies the preset number of iterations of the particle swarm optimization algorithm; If so, then the third three-dimensional pointing angle is obtained.

5. The method according to claim 4, characterized in that, The step of modeling the channel of the first link based on the first positional relationship and the first three-dimensional pointing angle to obtain the first channel gain information of the first link includes: Based on the first positional relationship and the first three-dimensional pointing angle, the deviation angles of the antenna beam in the horizontal and vertical directions are obtained; Based on the deviation angle, the channel of the first link is modeled to obtain the antenna attenuation parameters of the antenna beam in the horizontal and vertical directions; Based on the antenna attenuation parameters, the first channel gain information of the first link is obtained.

6. The method according to claim 5, characterized in that, The step of obtaining the first reachability of the first link based on the first channel gain information and the first transmit power of the transmitting endpoint, in order to model the reachability optimization problem, includes: The signal-to-interference-plus-noise ratio (SIR) of the first link is obtained based on the first channel gain information and the first transmit power of the transmitting endpoint. Based on the signal-to-interference-plus-noise ratio (SINR), the first reachability of the first link is obtained to model the reachability optimization problem.

7. The method according to claim 6, characterized in that, After modeling the reachability optimization problem, the following steps are included: The reachability optimization problem is transformed into the link reachability optimization problem by maximizing the minimum end-to-end reachability of the data stream, with the optimization objective being to maximize the minimum end-to-end reachability of the data stream.

8. An antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network, characterized in that, include: The acquisition module is used to configure the network information and transmission requirements of the millimeter-wave full-duplex multi-data stream, and according to the transmission requirements, acquire the first positional relationship between the transmitting endpoint and the receiving endpoint in the first link and the first three-dimensional pointing angle of the antenna beam of the transmitting endpoint; wherein, the data stream transmits data through multiple links, and the first link is any one of the multiple links; The first processing module is used to model the channel of the first link according to the first positional relationship and the first three-dimensional pointing angle, to obtain the first channel gain information of the first link, and to obtain the first reachability of the first link according to the first channel gain information and the first transmit power of the transmitting endpoint, so as to model the reachability optimization problem. The second processing module is used to optimize the first three-dimensional pointing angle and the first transmit power using a block coordinate descent method based on the reachability optimization problem, to obtain the target three-dimensional pointing angle and target transmit power of the antenna beam. The reachability optimization problem is modeled as follows: in, To maximize the minimum end-to-end reachable rate of all the data streams, For the data stream The number of jumps, The horizontal beamwidth is 3dB. The vertical beamwidth is 3dB. The first transmit power of the transmitting endpoint u, The maximum permissible transmission power of the transmitting endpoint.

9. An antenna beam and power optimization device based on a millimeter-wave full-duplex self-organizing network, comprising: Processor and memory; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory, causing the antenna beam and power optimization device based on millimeter-wave full-duplex self-organizing network to perform the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the antenna beam and power optimization method based on millimeter-wave full-duplex self-organizing network as described in any one of claims 1 to 7.

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