Method and related apparatus for inter-sensing integration beam optimization

Abstract

Embodiments of the present application provide a sensing and communication integrated beam optimization method and related device, whether to introduce a separate sensing signal assisted ISAC transmission is determined according to the spatial distribution of the sensing target and the communication user. For example, in the scenario that the spatial correlation degree of the sensing target and any communication user cluster is large, a separate sensing signal does not need to be provided for the sensing target to achieve ISAC transmission, and only the communication beam in the direction of the communication user cluster with a large spatial correlation degree can cover the sensing target, that is, the communication beam can cover the communication user and the sensing target at the same time. In the scenario that the spatial correlation degree of the sensing target and each communication user cluster is small, a separate sensing signal is added for the sensing target to provide services for the sensing target. In this way, the communication beam only needs to cover the communication user, and the sensing target is covered by the corresponding sensing beam, thereby improving resource utilization.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method and apparatus for integrated sensing and beam optimization. Background Technology

[0002] Integrated Sensing and Communication (ISAC) is a crucial function of 5G systems and a key application scenario for 6G systems. ISAC is a novel technology system that deeply integrates wireless communication with environmental sensing capabilities. It utilizes the same hardware, spectrum resources, and signal waveforms to simultaneously achieve efficient communication and accurate sensing. ISAC technology leverages the reflection and scattering characteristics of communication signals during information transmission to simultaneously perform target ranging, velocity measurement, and imaging, enabling the perception and exploration of the physical world.

[0003] However, as the system scale continues to expand, the problem of coupling communication and sensing resources becomes increasingly prominent in complex scenarios involving multiple communication users and multiple sensing targets, and there is an urgent need to further explore resource allocation schemes that can effectively balance communication and sensing performance. Summary of the Invention

[0004] In view of this, this application provides a sensor-integrated beam optimization method and related apparatus to improve the spatial reuse efficiency of resources. The disclosed technical solution is as follows:

[0005] In a first aspect, this application provides a beam optimization method for integrated sensing and communication systems, applied to an integrated sensing and communication system. The system includes at least one communication user and at least one sensing target. The at least one communication user is divided into at least one communication user cluster based on channel correlation. The method includes: for any sensing target, if the spatial correlation between the sensing target and any communication user cluster is low, introducing a sensing signal that can cover the sensing target, and establishing an information flow model and a sensing signal model for the communication user; establishing a communication performance model and a sensing performance model; constructing an objective function based on the communication performance model and the sensing performance model, constructing constraints based on multi-target sensing fairness and the service quality requirements of the communication user, and establishing a joint optimization problem of beamforming and power allocation parameters based on the objective function and constraints; iteratively optimizing the joint optimization problem with maximizing the communication sensing performance as the optimization objective, to obtain the beamforming matrix, power allocation, and communication sensing performance.

[0006] It is evident that when the spatial correlation between the sensing target and each communication user cluster is relatively low, a separate sensing signal is added for the sensing target to provide services. In this way, the communication beam only needs to cover the communication users, while the sensing target is covered by the corresponding sensing beam, improving resource utilization.

[0007] In one possible implementation, determining that the spatial correlation between the sensed target and any communication user cluster is low includes: acquiring the azimuth angle of the sensed target; if the azimuth angle is not within the beamwidth range corresponding to the starting angle of any communication user cluster, determining that the spatial correlation between the sensed target and any communication user cluster is low; if the azimuth angle is within the beamwidth range corresponding to the starting angle of any communication user cluster, determining that the spatial correlation between the sensed target and any communication user cluster is high. Thus, by using the beamwidth range corresponding to the azimuth angle of the sensed target and the starting angle of the communication user cluster, the spatial correlation between the sensed target and the communication user cluster can be determined simply and effectively.

[0008] In one possible implementation, the joint optimization problem is iteratively optimized with the goal of maximizing communication-aware performance, yielding the beamforming vector, power allocation, and communication-aware performance. This involves: with the goal of maximizing communication-aware performance, fixing one variable (power allocation) and the other (beamforming vector) to solve the corresponding subproblem, alternately iterating the two subproblems until global convergence, and outputting the beamforming vector, power allocation results, and the objective function value. This scheme reduces the complexity of the optimization process and improves its timeliness by iteratively optimizing the result of solving the other variable while fixing one variable.

[0009] In one possible implementation, with maximizing communication sensing performance as the optimization objective, the subproblems corresponding to the power allocation variable and the beamforming vector are solved by fixing one of them, and iteratively optimizing the two subproblems alternately until global convergence. The beamforming vector, power allocation result, and objective function value are output. This includes: fixing the power allocation variable and solving the subproblem corresponding to the beamforming vector to obtain the beamforming vector; fixing the beamforming vector and solving the subproblem corresponding to the power allocation variable to obtain the optimal power allocation, and updating the communication sensing performance sum. The beamforming vector includes the beamforming vector of the communication signal and the beamforming vector of the sensing signal. If the number of iterations has not reached the maximum number of iterations, and the difference between the communication sensing performance sums obtained from two consecutive iterations is less than the convergence accuracy, global convergence is determined, and the optimal beamforming result, optimal power allocation result, and communication sensing performance sum are output. If the number of iterations reaches the maximum number of iterations, and the difference between the communication sensing performance sums obtained from two consecutive iterations is not less than the convergence accuracy, the convergence accuracy is adjusted, and iterative optimization continues until global convergence. This scheme first fixes the power allocation variables to solve for the beamforming vector, then fixes the beamforming vector to the obtained result, solves for the power allocation variables, and determines whether the expected convergence accuracy has been achieved. If the convergence accuracy is achieved, the final beamforming vector, power allocation result, and communication sensing performance are output, thereby reducing the complexity of the optimization process and improving the timeliness of the optimization process.

[0010] In one possible implementation, an objective function is constructed based on a communication performance model and a sensing performance model, including: using the reachability rate of communication information as a communication performance index, using the power of the transmitted signal at the sensing target as a sensing performance index, and establishing an objective function with the optimization objective of maximizing communication sensing performance.

[0011] In one possible implementation, the communication-sensing performance is the sum of the communication performance weighted value and the sensing performance weighted value. The communication performance weighted value is the product of the total communication performance and the communication weight parameter, while the sensing performance weighted value is the product of the total sensing performance of all sensing targets and the sensing weight parameter. This scheme uses communication weight parameters and sensing weight parameters to balance communication performance and sensing performance, achieving a balance between the two.

[0012] In one possible implementation, constraints are constructed based on the fairness of multi-target sensing and the service quality requirements of communication users. These constraints include: a first constraint based on the achievable rate of communication information for any communication user being greater than or equal to a preset threshold; a second constraint based on the power difference of the sensing target in different sensing directions being less than or equal to a preset power threshold; a third constraint based on the power requirements satisfied by the sensing signal power and communication signal power when any communication user can eliminate the sensing signal using serial interference cancellation technology; and a fourth constraint based on the sum of the power allocation parameters of all communication users within the same communication user cluster being less than 1, and the power allocation parameter of any communication user being greater than or equal to 0. This scheme constrains the achievable rate of communication information through the first constraint, ensures similar power levels in different sensing directions through the second constraint, and guarantees that communication users can successfully eliminate interference generated by the sensing signal through the third constraint, thus mitigating the adverse effects of the introduced sensing signal on communication performance.

[0013] Secondly, this application also provides a communication device, including a module for performing a method as described in the first aspect or any possible implementation of the first aspect.

[0014] Thirdly, this application also provides a communication device, comprising: a memory for storing computer instructions; and a processor for executing the computer program or computer instructions stored in the memory, causing the communication device to perform a method as described in the first aspect or any possible implementation thereof.

[0015] Fourthly, this application also provides a computer storage medium for storing a computer program, which, when executed, is used to implement the first aspect or any possible implementation of the first aspect.

[0016] Fifthly, this application also provides a computer program product, wherein the computer program, when run, causes the method as described in the first aspect or any possible implementation thereof to be executed. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a sensor integrated system provided in an embodiment of this application;

[0018] Figure 2 This is a schematic diagram of the structure of an access network device provided in an embodiment of this application;

[0019] Figure 3 A flowchart of the inductive beam optimization method provided in the embodiments of this application;

[0020] Figure 4 A flowchart illustrating the iterative optimization process for a joint optimization problem provided in this application embodiment;

[0021] Figure 5 A schematic diagram illustrating a beamforming optimization result that does not require the introduction of a separate sensing signal example, provided in an embodiment of this application;

[0022] Figure 6 A schematic diagram illustrating the beamforming optimization result of introducing a separate sensing signal example, provided as an embodiment of this application;

[0023] Figure 7 This is a schematic diagram comparing the performance of the inductive beam optimization method of this application with other methods;

[0024] Figure 8 This is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0025] Figure 9 This is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be a limitation of this application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "the," "the," "the," and "this" are intended to also include expressions such as "one or more," unless the context clearly indicates otherwise. It should also be understood that in the embodiments of this application, "one or more" refers to one, two, or more; "and / or" describes the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, 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.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] In this application, "multiple" refers to two or more embodiments. It should be noted that in the description of the embodiments of this application, terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance, nor as indicating or implying order.

[0029] Please see Figure 1 The diagram illustrates a sensor-integrated system according to an embodiment of this application. Figure 1 As shown, this integrated sensing system may include an ISAC base station, multiple user clusters, and multiple sensing targets. User clusters can be divided based on user relevance. Sensing targets may include vehicles, pedestrians, obstacles, drones, etc.

[0030] The embodiments of this application can be applied to various communication systems, including but not limited to the following systems: second-generation (2G) communication systems, third-generation (3G) communication systems, long-term evolution (LTE) systems, universal mobile telecommunication system (UMTS), worldwide interoperability for microwave access (WiMAX) communication systems, and fifth-generation (5G) communication systems. th 5G (5G) or new radio (NR), 5.5G or sixth generation (6G) systems th The 6G generation and future mobile communication systems include vehicle-to-X (V2X) systems. V2X can include vehicle-to-network (V2N), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), long-term evolution technology for vehicle-to-vehicle communication (LTE-V), vehicle-to-everything (V2X), machine-type communication (MTC), Internet of Things (IoT), ambient Internet of Things (AIOT), long-term evolution technology for machine-to-machine communication (LTE-M), and machine-to-machine (M2M).

[0031] The communication system can be applied to scenarios including: non-terrestrial communication (NTN), satellite communication, high-altitude platform station (HAPS) communication, vehicle-to-everything (V2X) communication, integrated access and backhaul (IAB) communication, reconfigurable intelligent surface (RIS) communication, etc.

[0032] The communication system of this application includes access network equipment and terminal equipment that communicates with the access network equipment. Optionally, the system may also include core network elements that communicate with the access network equipment.

[0033] Access network equipment is a device deployed in a radio access network to provide wireless communication functions. Access network equipment can also be called access network node, RAN (radio access network) node, RAN entity, or access node, etc. It is located on the network side of the aforementioned communication system and is used to help terminal devices achieve wireless access. It is a device with wireless transceiver capabilities, or a chip or chip system that can be installed in the device. Access network equipment includes, but is not limited to: base stations, evolved NodeBs (eNodeBs), access points (APs), transmission receiving points / transmission reception points (TRPs) or transmission points (TPs), base stations (gNodeBs or gNBs) in NR, next-generation base stations in 6G mobile communication systems, base stations in future mobile communication systems, or access nodes in Wi-Fi systems, etc.

[0034] Access network equipment can be a macro base station, micro base station, indoor station, relay node, donor node, or radio controller in a centralized radio access network (CRAN) scenario. Access network equipment can also be one or a group of antenna panels (including multiple antenna panels) of a 5G base station, or it can be a network node constituting a gNB, TRP, TP, or transmission measurement function (TMF), such as a central unit (CU), distributed unit (DU), CU-control plane (CP), CU-user plane (UP), or radio unit (RU), roadside unit (RSU) with base station functionality. Optionally, access network equipment can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, the access network equipment in V2X technology can be an RSU. All or part of the functions of the access network equipment in this application can also be implemented through software functions running on hardware, or through virtualization functions instantiated on a platform (e.g., a cloud platform). The access network device in this application may also be a logical node, logical module, or software that can implement all or part of the functions of the access network device.

[0035] In an NTN network, all or part of the functional modules of the access network equipment can be deployed on an airborne platform or satellite, or other forms of communication equipment deployed in the high atmosphere. Correspondingly, access network equipment can refer to an airborne platform, satellite, or other similar equipment that connects terminal equipment to the core network equipment. An airborne platform can include at least one of the following: a satellite, a drone, or a hot air balloon.

[0036] The CU and DU can be set up separately or included in the same network element, such as the baseband unit (BBU). The RU can be included in radio frequency equipment or radio frequency units, such as in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0037] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0038] Figure 2 This is a schematic diagram of the structure of an access network device. As an implementation example, such as... Figure 4 As shown, the access network device may include at least one CU and at least one DU. This design can be referred to as CU and DU separation. One CU can be connected to one or more DUs. CU and DU can be separated according to the protocol layer of the wireless network: for example, the functions of the PDCP layer and above (such as the RRC layer and SDAP layer, etc.) are set in the CU, and the functions of the protocol layers below the PDCP layer (such as the RLC layer, media access control (MAC) layer, and PHY layer, etc.) are set in the DU; or, for another example, the functions of the protocol layers above the PDCP layer are set in the CU, and the functions of the protocol layers below the PDCP layer are set in the DU, without limitation. When the CU includes CU-CP and CU-UP, CU-CP is used to implement the control plane functions of the CU, and CU-UP is used to implement the user plane functions of the CU. For example, when the CU is configured to implement the functions of the PDCP layer, RRC layer, and SDAP layer, CU-CP is used to implement the RRC layer functions and the PDCP layer control plane functions, and CU-UP is used to implement the SDAP layer functions and the PDCP layer user plane functions. This application does not limit the names of CU and DU. The above division of CU and DU processing functions according to the protocol layer is just one example; other methods can also be used.

[0039] The CU can be connected to the core network. Optionally, the CU can have some of the functions of the core network.

[0040] Furthermore, some functions of the DU can be separated and configured. For example... Figure 4As shown, this functionality can be implemented by a radio unit (RU). The RU can have radio frequency (RF) capabilities. This application does not limit the name of the RU. The DU and RU can be split or separated within the PHY layer. For example, the DU can implement higher-level functions in the PHY layer, and the RU can implement lower-level functions in the PHY layer, or implement both lower-level and RF functions. Higher-level functions in the PHY layer include functions closer to the MAC layer, and lower-level functions in the PHY layer include functions closer to the RF layer. For example, higher-level functions in the PHY layer include one or more of the following: forward error correction (FEC) encoding / decoding, scrambling, or modulation / demodulation. Lower-level functions in the PHY layer include one or more of the following: fast Fourier transform (FFT) / inverse fast Fourier transform (IFFT), beamforming, or extraction and filtering of the physical random access channel (PRACH), etc. The RU can communicate with the terminal device via the air interface using RF signals. The pre-coding function of the PHY layer code can be located in the DU or the RU. The separation between the DU and RU can be done in various ways without restriction. An interface exists between the DU and RU. For example, depending on the separation method, the interface between the DU and RU can be a Common Public Radio Interface (CPRI) interface or an Enhanced Common Public Radio Interface (eCPRI) interface.

[0041] Optionally, any one of CU, CU-CP, CU-UP, DU, and RU can be a software module, a hardware structure, or a combination of software and hardware structures, without limitation. The different entities can exist in the same or different forms. For example, CU, CU-CP, CU-UP, and DU are software modules, and RU is a hardware structure. For the sake of brevity, all possible combinations are not listed here. These modules and the methods they execute are also within the protection scope of the embodiments of this application. For example, when the method of the embodiments of this application is executed by an access network device, it can be specifically executed by at least one of CU, CU-CP, CU-UP, DU, or RU.

[0042] In this application embodiment, the form of the access network device is not limited. The device used to implement the function of the access network device can be the access network device itself; it can also be a device that supports the access network device in implementing the function, such as a chip system. The device can be installed in the access network device or used in conjunction with the access network device.

[0043] In the embodiments of this application, the terminal can be a terminal device with transceiver functions, or it can be a chip or chip system disposed in the terminal device. In the embodiments of this application, the terminal device can be of various forms, such as a mobile phone, tablet computer, computer with wireless transceiver functions, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal in industrial control, vehicle-mounted terminal device, wireless terminal in self-driving, wireless terminal in remote medical care, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, wearable terminal device, etc. The terminal device of this application can also be an on-board module, on-board component, on-board chip, or on-board unit built into a vehicle as one or more components or units. The terminal device can also be other devices with terminal functions; for example, the terminal device can also be a device that performs terminal functions in D2D communication.

[0044] A terminal may also be referred to as terminal equipment, user equipment (UE), access terminal equipment, vehicle-mounted terminal, industrial control terminal, UE unit, UE station, mobile station, mobile station, remote station, remote terminal equipment, mobile device, UE terminal equipment, wireless communication equipment, UE agent, or UE device, etc. A terminal can also be a fixed terminal or a mobile terminal.

[0045] The embodiments of this application do not limit the device form of the terminal. The device used to implement the functions of the terminal device can be the terminal device itself; it can also be a device that supports the terminal device in implementing the functions, such as a chip system. The device can be installed in the terminal device or used in conjunction with the terminal device. In the embodiments of this application, the chip system can be composed of chips or can include chips and other discrete components.

[0046] A core network element is a functional unit deployed in the core network to provide services to terminal devices. In systems employing different radio access technologies, the names of core network devices with similar wireless communication functions may differ. For example, when the wireless communication method of this application embodiment is applied to a 5G system, the core network device may be an access and mobility management function (AMF) network element, a session management function (SMF) network element, a user plane function (UPF) network element, etc. The UPF network element processes user plane data. The AMF and SMF network elements process control plane signaling. When the precoding method of this application embodiment is applied to an LTE system, the core network device may be a mobility management entity (MME). For ease of description only, in this application embodiment, the above-mentioned devices that can provide services to terminal devices are collectively referred to as core network devices.

[0047] In practical applications, the distribution of sensing targets and communication hotspots is uncertain, and traditional transmission schemes are difficult to achieve efficient resource utilization in diverse scenarios. Therefore, it is necessary to further explore the spatial channel correlation between communication users and sensing targets to improve the spatial reuse efficiency of spectrum resources.

[0048] The integrated sensing beam optimization method provided in this application can adaptively adjust the ISAC transmission scheme according to the spatial distribution of communication users and sensing targets. For example, when communication users and sensing targets are concentrated, there is no need to introduce sensing signals; communication signals alone are sufficient to meet both communication and sensing performance, thus improving the efficiency of spatial resource utilization. When communication users and sensing targets are dispersed, sensing signals are introduced to form an independent sensing beam assist, further improving resource utilization.

[0049] The following will combine Figure 3 This application introduces the integrated inductive beam optimization method, such as... Figure 3 As shown, the method may include the following steps:

[0050] S101, Construct an integrated sensing system, which includes one ISAC base station (BS), K downlink communication users, and L sensing targets.

[0051] The antenna array of the ISAC base station is a uniform linear array (ULA) consisting of N antennas, with each communication user and sensing target equipped with a single antenna.

[0052] Based on the channel correlation of communication users, the users are divided into M user clusters. Specifically, users with high channel correlation are grouped into the same user cluster, while users with low channel correlation are grouped into different user clusters. Users within the same user cluster share the same beam. The m-th user cluster contains K... m There are 1 user, denoted as {U}. m,1 U m,2，……， U m,Km}. The starting angle of the m-th user cluster is denoted as . The maximum and minimum angles of users within this cluster are respectively expressed as: , The distribution range of intra-cluster communication users can be represented as .

[0053] The azimuth angle between the l-th sensing target and the ISAC base station is denoted as . The smaller the difference between the azimuth angle of the sensing target and the starting angle of the communication user cluster, the stronger the channel correlation between the sensing target and the communication user cluster; conversely, the weaker the channel correlation.

[0054] Considering that typical ISAC applications are usually deployed in open outdoor environments with good line-of-sight conditions, this application uses the Ricean channel model to model the downlink channel from the base station to the communication user. The channel coefficients can be expressed as:

[0055] (1)

[0056] In formula 1 This indicates large-scale fading in the channel. This indicates small-scale fading.

[0057] During propagation, radio waves encounter various buildings, trees, vegetation, and terrain undulations, causing energy absorption and reflection, scattering, and diffraction of the waves. They suffer attenuation or loss through different pathways, including path loss, large-scale fading, and small-scale fading.

[0058] Large-scale fading refers to the loss of radio waves due to obstruction by buildings and hills along their propagation path. It reflects the average change trend of the received signal level over a medium range of several hundred times the wavelength.

[0059] Small-scale fading refers to the loss caused by multipath propagation, reflecting the average variation trend of the received signal level within a small area on the order of tens of wavelengths. Multipath propagation refers to the phenomenon where radio waves travel from the transmitting antenna through multiple paths to the receiving antenna. Atmospheric scattering, ionospheric reflection and refraction, and reflection from surface objects such as mountains and buildings all contribute to multipath propagation, ultimately resulting in the receiver receiving a signal that is a combination of the direct wave and multiple reflected waves.

[0060] S102: For any sensing target, determine whether to introduce sensing signals to assist ISAC transmission based on the correlation between the sensing target and each user cluster. Regardless of whether sensing signals are introduced to assist transmission, S103 will be executed.

[0061] For the l-th sensing target, the spatial correlation between the sensing target and any communication user cluster (such as the m-th user cluster) determines whether to introduce sensing signals to assist transmission. Specifically, this can be determined based on the azimuth angle of the sensing target. The starting angle of the m-th user cluster The relationship between beamwidths determines whether to introduce sensing signals to assist transmission.

[0062] Wherein, the starting angle of the m-th user cluster Beamwidth on It can be represented as:

[0063] (2)

[0064] In formula 2 denoted by wavelength, d represents the spacing between antenna elements, and N represents the number of antenna arrays in the base station.

[0065] When the azimuth angle of the perceived target satisfy When the target is located within the communication beam along the direction of the m-th user cluster, it indicates that the target is distributed within this beam. In this scenario, the power at the target can be guaranteed solely through communication signals. The signals from multiple users within the cluster are simply superimposed and transmitted using non-orthogonal multiple access (NOMA). The receiver then utilizes Successive Interference Cancellation (SIC) technology to jointly demodulate the aliased signals, improving spectral efficiency. Specifically, SIC technology eliminates interference sequentially based on signal power.

[0066] When the azimuth angle of the perceived target satisfy or In scenarios where the communication beam in the m-th cluster direction cannot adequately cover the sensing target, the power of the communication signal in that direction is relatively low. Therefore, a sensing signal is needed to assist ISAC transmission and supplement the spatial domain of the sensing target. Simultaneously, to reduce interference between the sensing signal and the communication signal, a virtual communication signal is constructed. This virtual signal is then superimposed on the user's communication signal and transmitted using NOMA (Normally Oscillating and Mapping). The receiving end decodes and cancels this interference using SIC (Self-Integrated Communication Card).

[0067] When it is necessary to introduce sensing signals to assist transmission, the signals transmitted by the ISAC base station can be uniformly represented as:

[0068] (3)

[0069] In formula 3, The beamforming vector for the ISAC base station, where s is the communication information flow. For the added sensing signals, where, Represents the set of complex numbers. Used to control whether a sensing signal is introduced, when Time indicates the introduction of a sensing signal. This indicates that only communication signals exist. The communication information flows between different communication users are independent, have a mean of zero, and possess unit power. The communication information flow of a cluster of M users can be represented as:

[0070] (4)

[0071] In formula 4, This represents the communication information flow of the nth user in the mth user cluster. Indicates the transmission power.

[0072] The introduced sensing signal is independent of the communication signal and is transmitted using multi-beam transmission, i.e., the covariance matrix of the sensing signal. It has a general rank, and (express (It is a positive semi-definite matrix). Eigenvalue decomposition can be used to decompose the sensed signal into multiple sense beams:

[0073] (5)

[0074] In formula 5, The covariance matrix of the sensed signal is represented. For eigenvalues, It is the corresponding feature vector. express The transpose conjugate matrix, This represents the beamforming vector of the i-th sensed signal. for The transpose conjugate matrix.

[0075] To achieve the superposition of sensing signals and communication signals, information is embedded in a portion of the sensing signals and processed as virtual communication signals. That is, the sensing signals can be represented as:

[0076] (6)

[0077] In formula 6, , express The general rank, To decompose the signals into independent unity power signals, and to combine them with the residual sensing signal They are independent of each other.

[0078] Furthermore, the covariance matrix of the residual sensed signal can be expressed as:

[0079] (7)

[0080] It can be seen that the covariance matrix of the residual sensed signal It also has a general rank, indicating that the residual sensing signal conforms to the actual transmission scenario.

[0081] S103, Establish communication performance model and perception performance model.

[0082] Interference between communication users and interference between sensing signals are identified and a communication performance model is established. In addition, sensing power, which is a key metric, is identified and a sensing performance model is established.

[0083] Communication performance is measured by the rate at which information can be delivered to the user, while sensing performance is measured by the power at the sensing target. The following sections will calculate both the communication performance metric (deliverable rate) and the sensing performance metric (power at the sensing target).

[0084] (1) The process of calculating communication performance indicators is as follows:

[0085] For the signal received by the i-th communication user in the m-th cluster It can be represented as:

[0086] (8)

[0087] In formula 8, Represents the channel coefficient of the i-th user in the m-th cluster. The transpose conjugate matrix, This represents the information flow of the k-th user in the m-th cluster. This indicates the corresponding transmission power. Let m be the beamforming vector of the m-th cluster, where the first term is... The second term represents the signal received in the direction of the m-th cluster. The first term represents the interference signal between the j-th cluster and the m-th cluster. The second term represents the sensing interference signal that can be eliminated by SIC technology. The third term represents the residual sensing interference signal. This indicates that the mean is 0 and the variance is... Complex Gaussian white noise.

[0088] Assume that the index of a user in cluster m increases relative to the strength of its large-scale fading, meaning the channel quality of the first user in cluster m is the strongest, and the index of the Kth user in cluster k is the strongest. m The channel quality for each user is the weakest, that is... Users with weaker channel quality will be allocated higher power. After a user receives a signal, SIC (Search Engine Injection) technology is used to eliminate inter-user interference within the cluster in descending order of power level. When a sensing signal is introduced, to ensure that all users within the cluster can eliminate interference from the sensing signal, their power must meet certain requirements. That is, all users first eliminate perceptual interference signals during decoding. Therefore, when the i-th user in the m-th cluster decodes the signal of the n-th user, the remaining signal after eliminating perceptual interference signals can be expressed as:

[0089] (9)

[0090] In formula 9, This represents the information flow of the nth user in the mth cluster. This represents the information flow of the k-th user within the m-th cluster. This represents the information flow of the k-th user within the j-th cluster. For the corresponding transmission power, This represents the beamforming vector of the m-th cluster. Denotes the beamforming vector of the j-th cluster. This indicates that the mean is 0 and the variance is... Complex Gaussian white noise; in this formula, the first term represents the effective signal of the nth user in the mth cluster, the second term represents the interference generated by all user signals with indices less than n in the cluster when the i-th user in the mth cluster decodes the signal of the nth user, the third term represents the inter-cluster interference generated by the j-th cluster on the m-th cluster, and the fourth term represents the residual sensing signal interference.

[0091] The achievable rate of decoding information from user n for user i in cluster m. It can be represented as:

[0092] (10)

[0093] In formula 10, The signal-to-dryness ratio (SDR) represents the signal-to-dryness ratio (SDR) of the i-th user in the m-th cluster when decoding the signal of the n-th user, which is obtained according to Formula 9 and the formula for calculating the SDR.

[0094] If it is determined in S102 that a separate sensing signal is needed to assist ISAC transmission, then in Equation 10... If it is determined in S102 that no separate sensing signal needs to be introduced, then in Formula 10... .

[0095] All users in the m-th cluster (K) m During the decoding process using SIC technology (for multiple users), users within the same cluster with channel quality stronger than the nth user must first successfully parse and eliminate the nth user's information before they can successfully extract their own useful information. As previously mentioned, the index of a user within the m-th cluster increases relative to the strength of its large-scale fading; therefore, the users within the m-th cluster with channel quality stronger than the nth user are... The stronger the channel quality, the smaller the allocated power. Therefore, the reachable rate of the nth user's information within the m-th cluster is the minimum reachable rate of users indices 1 to n within that cluster decoding the information. That is, the reachable rate of the nth user's information within the m-th cluster can be expressed as:

[0096] (11)

[0097] (2) The process of calculating the perceptual performance index of the perceptual model is as follows:

[0098] The performance metric for sensing is the power at the sensing target, i.e., the transmitted beam pattern, which is the covariance matrix of the signals transmitted by the base station. A decision can be expressed as:

[0099] (12)

[0100] In formula 12, For the beamforming vector of the i-th cluster, The beamforming vector for the i-th sensed signal, This represents the covariance matrix of the residual sensing signal. If it is determined in S102 that a separate sensing signal is needed to assist ISAC transmission, then in Equation 12... If it is determined in S102 that no separate sensing signal needs to be introduced, then in Formula 12... .

[0101] The base station transmits signals in L directions The beam pattern in the direction can be represented as:

[0102] (13)

[0103] In formula 13, For ULA to The steering vector of the direction, R represents the covariance matrix of the base station's transmitted signal.

[0104] The steering vector is a complex vector that describes the response of an antenna array to a signal in a specific direction. It is related to the geometry of the antenna array, the angle of arrival of the signal, and the signal frequency.

[0105] S104, based on the communication performance model and the sensing performance model, establishes a joint optimization problem concerning beamforming vector, power allocation and sensing waveform parameters with the goal of maximizing the weighted sum of communication and sensing performance.

[0106] Based on the communication performance model and sensing performance model established in the previous step, the optimization objective is to maximize the weighted sum of communication and sensing performance. Considering constraints such as multi-target sensing fairness and user communication QoS requirements, the joint optimization problem concerning the transmit beamforming vector, power allocation, and sensing waveform parameters is established as follows:

[0107] (14)

[0108] In Formula 14, P1 is the objective function of the joint optimization problem, which is to find the maximum value of the weighted sum of communication sensing performance. Here, communication performance refers to the communication performance index of all communication users in the system (i.e., the achievable rate of communication information flow). The sum of the power at each sensing target is the sensing performance index of all sensing targets within the system (i.e., the power at the sensing target). ) and.

[0109] C1~C7 are constraints; among them, the optimization objective is... , These are weighting parameters for communication performance and sensing services, used to balance the performance of the two. The optimization variable is the beamforming vector of the communication signal. Beamforming vector of the sensing signal The covariance matrix of the residual sensed signal and power allocation parameters within each cluster .

[0110] Constraint C1 is the information reachability rate constraint for communication users. Indicates the corresponding threshold;

[0111] Constraint C2 ensures similar power levels across different sensing directions. Used to determine the range of fluctuations;

[0112] Constraint C3 is the power constraint for successfully eliminating sensing signal interference through SIC technology;

[0113] Constraint C4 is the total transmit power constraint, where the total signal power is the trace of the transmit signal covariance matrix. , The total power threshold;

[0114] Constraint C5 is a positive semidefinite constraint on the covariance matrix of the residual sensing signal, meaning that the residual sensing signal conforms to the statistical laws of physically realizable signals.

[0115] Constraints C6 and C7 are intra-cluster power allocation variable constraints.

[0116] S105, for the joint optimization problem, fix the power allocation variable and the beamforming vector respectively to solve for another variable, and perform alternating iterative optimization to obtain the final beamforming vector, power allocation and communication sensing performance.

[0117] First, fix the power allocation variable. The beamforming vector subproblem is solved, and then the power allocation variable subproblem is solved while the beamforming vector is fixed. The two subproblems are iteratively optimized alternately until global convergence. The beamforming vector and power allocation results are output, as well as the objective function value (i.e., the weighted sum of communication sensing performance).

[0118] The sensing-integrated beam optimization method provided in this embodiment determines whether to introduce a separate sensing signal to assist ISAC transmission based on the spatial distribution of the sensing target and communication users. Specifically, when the spatial correlation between the sensing target and any communication user cluster is high, it is not necessary to provide a separate sensing signal to assist ISAC transmission for the sensing target. The sensing target can be covered by the communication beam in the direction of the communication user cluster with high spatial correlation. That is, the communication beam can cover both communication users and the sensing target simultaneously. When the spatial correlation between the sensing target and each communication user cluster is low, a separate sensing signal is added for the sensing target to provide services. In this way, the communication beam only needs to cover the communication users, while the sensing target is covered by the corresponding sensing beam, improving resource utilization. Moreover, the use of NOMA technology to mitigate mutual interference between communication and sensing effectively improves the utilization of spectrum resources.

[0119] In one exemplary embodiment, see Figure 4 The alternating optimization process may include the following steps:

[0120] S201, Initialize beamforming vector, power allocation variable, expected convergence accuracy, maximum number of iterations, and initial value of the communication sensing performance weighted sum.

[0121] S202, with fixed power allocation variables, solves the beamforming vector subproblem to obtain the beamforming vector.

[0122] S203, the fixed beamforming vector is used to solve the power allocation variable subproblem to obtain the optimal power allocation and update the communication sensing performance weighted sum.

[0123] S204, determine whether the number of iterations has reached the set maximum number of iterations; if not, execute S205; if yes and the convergence accuracy has not been reached, adjust the convergence accuracy and return to execute S202.

[0124] S205, determine whether the difference between the weighted sum of the communication sensing performance of two consecutive iterations is less than the convergence accuracy; if yes, execute S206; otherwise, return to execute S202.

[0125] S206, output beamforming vector, power allocation parameters, and a weighted sum of communication sensing performance.

[0126] The alternating iterative optimization process will be described in detail below:

[0127] (1) Fixed power distribution variables Solve the beamforming vector quantum problem.

[0128] Define the auxiliary variables for semidefinite relaxation (SDR):

[0129] (15)

[0130] Introducing a semi-definite relaxation auxiliary variable is to transform the communication signal beamforming vector. Sensing signal beamforming vector Replace with in sequence , in, , .

[0131] The way to introduce the trace operator Tr(), and let The reachable rate of communication user information is expressed as:

[0132] (16)

[0133] in, Indicates noise power. This is a scaling auxiliary variable.

[0134] The optimization problem shown in Equation 14 can be expressed as:

[0135] (17)

[0136] Among them, in constraint C10, This represents the threshold requirement for the reachability of information for communication users; constraints C1, C8, and C9 are non-convex constraints. Non-convex constraints mean that the graph of the function is not a convex set. In this case, the optimization problem usually becomes more complex because a local minimum is not necessarily a global minimum. In optimization problems, special methods are often needed to handle non-convex problems. For example, in this embodiment, the Successive Convex Approximation (SCA) algorithm is used to solve it. First, the second term in Equation 16... Non-convex terms at points Performing a first-order Taylor expansion, where the superscript "n" indicates the nth iteration, we obtain:

[0137] (18)

[0138] Furthermore, the reachable rate in constraint C1 can be expressed as:

[0139] (19)

[0140] Next, the rank-one constraints in constraints C8 and C9, i.e. , It is added to the objective function as a penalty term, specifically... , This can be equivalently transformed into:

[0141] (20)

[0142] (twenty one)

[0143] in, and This represents the nuclear norm and the spectral norm.

[0144] Due to the matrix and It is positive semi-definite. If the matrix is ​​not rank-1, then:

[0145] (twenty two)

[0146] (twenty three)

[0147] Therefore, the optimization problem P in Equation 17 can be obtained by minimizing the difference between the nuclear norm and the spectral norm. beam The approximate optimal solution is obtained by transforming the optimization problem into:

[0148] (twenty four)

[0149] Formula 24 and That is, matrix and The penalty term corresponding to the rank, It is the regularization parameter.

[0150] if If it is 0, then As the value approaches infinity, the penalty term has a greater impact on the optimization problem, and thus the matrix... and The rank is strictly controlled. It can be initialized with a large value. In order to find a good starting point for beam matching, and then by... By gradually decreasing the parameter to a sufficiently small value, the solution found will gradually approach rank one. The process is as follows:

[0151] (25)

[0152] When the penalty term is less than the predefined first-level convergence precision At that time, the process ends, that is:

[0153] (26)

[0154] because For non-convex terms, The first-order Taylor expansion approximation can transform non-convex terms into convex terms. The first-order Taylor expansion approximation process is as follows:

[0155] (27)

[0156] in, Representation matrix The largest eigenvector.

[0157] Similarly, the same can be done on The first-order Taylor expansion approximates it as follows:

[0158] (28)

[0159] in, Representation matrix The largest eigenvector.

[0160] The optimization problem P in Formula 17 beam It can be rewritten as:

[0161] (29)

[0162] Equation 29 represents the final objective function after fixing the power allocation variables. This problem can be solved directly using the CVX convex optimization tool, ultimately yielding the beamforming vector result, which is the beamforming vector of the communication signal. and beamforming vector of the sensing signal .

[0163] (2) Fix the beamforming vector (i.e., fix the beamforming vector of the communication signal obtained in the previous step). and beamforming vector of the sensing signal ), to solve for the power allocation variables.

[0164] With the beamforming vector fixed, the optimization problem P1 in Equation 14 can be expressed as:

[0165] (30)

[0166] In the objective function of Formula 30, only the first term contains the power allocation coefficient. Therefore, the optimization problem in this formula can be transformed into maximizing the total communication rate of communication users, i.e.:

[0167] (31)

[0168] For the non-convex constraint C1, perform the equivalent transformation:

[0169] (32)

[0170] in, , , For noise power, This represents the threshold requirement corresponding to the achievable rate of information for communication users.

[0171] Therefore, optimization problem 31 can be equivalently transformed into:

[0172] (33)

[0173] in, ,make The objective function in formula 33 is equivalently transformed into:

[0174] (34)

[0175] Furthermore, according to the SMW formula ,in Represents the identity matrix. It can be reformulated as:

[0176] (35)

[0177] Therefore, the optimization problem in Equation 33 Transform into:

[0178] (36)

[0179] The optimization problem of Equation 36 can be solved directly using the CVX convex optimization tool.

[0180] Let the outer layer convergence accuracy be... The two objective functions shown in Equations 29 and 36 are iteratively optimized alternately until the difference between the weighted sums of two consecutive communication sensing performance values ​​is less than the convergence accuracy. That is, after global convergence, the output beamforming vector and power allocation results are used to obtain the objective function value, i.e., the weighted sum of communication sensing performance.

[0181] The above method was simulated and verified. The ISAC base station was equipped with a ULA consisting of 8 antennas. The system had 4 communication users and 2 sensing targets. The 4 communication users were divided into 2 user clusters according to channel correlation. Base station transmit power The noise power is The path loss model, based on the 3GPP propagation environment, is defined as follows: ,in, For propagation distance; Ricean channel The factor is set to 5; the initial penalty function penalty factor is set to... The convergence accuracy of the outer layer alternating iterative optimization is set to The convergence precision of the inner SCA loop is set to The communication-aware weight parameters are set as follows: .

[0182] Please see Figure 5 This is a simulation diagram of beam imagery for sensing targets approaching a cluster of communication users.

[0183] Communication users 1 and 2 have high channel correlation and are classified into one user cluster (denoted as user cluster 1). Communication users 3 and 4 are classified into the same user cluster (denoted as user cluster 2).

[0184] The sensing target A has a high spatial correlation with user cluster 1, meaning the communication beam in the direction of user cluster 1 can guarantee the power at sensing target A. In this scenario, only the signals of all users within user cluster 1 are superimposed and transmitted in a NOMA manner. The receiver uses SIC technology for joint demodulation, eliminating the need to introduce a separate sensing signal and improving spectrum utilization.

[0185] Moreover, the sensing target B has a high spatial correlation with the location of user cluster 2, meaning that the sensing target B is distributed within the communication beam in the direction of user cluster 2. In other words, the communication beam in the direction of user cluster 2 can guarantee the power at the sensing target B. Only all user signals of user cluster 2 are superimposed and transmitted in NOMA mode. The receiver uses SIC technology for joint demodulation, without the need to introduce a separate sensing signal, thus improving the spectrum utilization.

[0186] In this scenario, communication beamforming and power allocation parameters can be optimized based on the distribution of users and sensing targets within the user cluster. Beamforming, for example... Figure 5 As shown, the communication beam in the direction of user cluster 1 simultaneously satisfies the needs of both user cluster 1 and sensing target A, while the communication beam in the direction of user cluster 2 can simultaneously satisfy the needs of both user cluster 2 and sensing target B. Therefore, this beamforming result can simultaneously meet both communication and sensing performance requirements, thereby improving resource utilization.

[0187] Please see Figure 6 This is a schematic diagram of beam image simulation for a user cluster scenario based on the principle of target perception.

[0188] Communication users 1 and 2 are grouped into user cluster 1, and communication users 3 and 4 are grouped into user cluster 2. The channel correlation between sensing target A and both user clusters 1 and 2 is low, as is the channel correlation between sensing target B and both user clusters 1 and 2. In this scenario, communication beams from user clusters 1 and 2 alone are insufficient to cover sensing targets A and B. Therefore, sensing signals need to be introduced to supplement the spatial domain of the sensing targets.

[0189] like Figure 6 As shown, the spatial correlation between sensing targets A and B is low. Therefore, it is necessary to introduce sensing signals in the directions of sensing targets A and B respectively to assist ISAC transmission, adjust the system transmission power, use the same beam in the same communication cluster to provide services to communication users, and provide separate sensing beams for sensing targets to provide sensing services.

[0190] In transmission schemes without a separate sensing beam, services to both communication users and sensing targets are provided solely through an integrated sensing beam. To ensure that the communication beam simultaneously covers both dispersed communication and sensing users, the system needs to increase transmission power and beamwidth. However, the scheme proposed in this application introduces a separate sensing beam to assist ISAC transmission in scenarios where sensing targets and communication user clusters are dispersed. This way, the communication beam only needs to cover communication users, while the sensing target is covered by a separate sensing beam, improving resource utilization.

[0191] The following will combine Figure 7The integrated sensing beam optimization method provided in this application will be compared with the traditional ISAC scheme and the weighted sum of the transmit power and communication sensing performance of the ISAC scheme without the introduction of sensing signals.

[0192] Figure 7 The horizontal axis represents the total transmit power of the base station, and the vertical axis represents the weighted sum of communication sensing performance. Curve 1 is a schematic diagram of the transmit power and the weighted sum of communication sensing performance corresponding to the transmission scheme that introduces a separate sensing beam in this application. Curve 2 is a schematic diagram of the transmit power and the weighted sum of communication sensing performance corresponding to the ISAC scheme that does not introduce an independent sensing signal. Curve 3 is a schematic diagram of the transmit power and the weighted sum of communication sensing performance corresponding to the traditional ISAC scheme.

[0193] Among them, the traditional ISAC scheme is that the communication and sensing functions are independent of each other, and the receiver of the communication user cannot eliminate sensing interference; the ISAC scheme that does not introduce independent sensing signals does not introduce independent sensing signals, and only uses integrated sensing signals to provide services to the sensing target and the communication user.

[0194] like Figure 7 As shown, with the increase of the total transmission power of the base station, the maximum and minimum effective sensing power of all three schemes increase. Moreover, under the same total transmission power level, the weighted sum of communication sensing performance of the ISAC scheme with the introduction of a separate sensing signal provided in this application is higher than that of the comparative schemes (i.e., the traditional ISAC scheme and the ISAC scheme without the introduction of a separate sensing signal). This is because the scheme provided in this application introduces a separate sensing signal to assist ISAC transmission in scenarios where the sensing target and user cluster are dispersed. In this way, the communication beam only needs to cover the communication users, while the sensing target is covered by the introduced separate sensing beam, thus improving resource utilization. Furthermore, the use of NOMA technology to alleviate mutual interference between communication and sensing effectively improves the utilization of spectrum resources. In other words, with the same transmission power as the comparative scheme, a higher weighted sum of communication sensing performance can be obtained.

[0195] This application also provides a method for integrated sensing transmission, which is applied to a communication system including terminal devices and network devices. The method may include the following steps:

[0196] S1, the network device acquires spatial information of all communication users and sensing targets within its current coverage area, wherein all communication users are divided into at least one communication user cluster based on channel correlation. Communication users with high channel correlation are grouped into the same communication user cluster, while users with low channel correlation are grouped into different communication user clusters.

[0197] S2, For any sensing target, if the spatial correlation between the sensing target and any communication user cluster is low, a sensing signal that can cover the sensing target is introduced.

[0198] S3 overlays and transmits the communication signals and sensing signals corresponding to the communication user clusters using NOMA technology.

[0199] In ISAC transmission scenarios where a separate sensing signal is required, to reduce interference from the sensing signal to the communication signal, the sensing signal is constructed as a virtual communication signal. For example, the communication user signal and the sensing signal are superimposed and transmitted using NOMA. After receiving the beam signal sent by the network device, the terminal device decodes and eliminates the sensing signal using SIC technology.

[0200] In an exemplary embodiment, in a scenario where a separate sensing signal needs to be introduced, the network device can send indication information to the terminal device via higher-layer protocol signaling, indicating that the beam signal currently transmitted by the terminal device contains a sensing signal.

[0201] In another exemplary embodiment, the sensing signal typically has specific structural or signal characteristics, and the terminal device can perform feature extraction on the received signal to determine whether it contains a sensing signal.

[0202] S4. If the spatial correlation between the sensing target and any communication user cluster is high, the sensing target is covered by the communication beam in the direction of any communication user cluster.

[0203] The integrated sensing transmission method provided in this implementation can adaptively adjust the ISAC transmission scheme according to the spatial distribution of communication users and sensing targets. For example, when communication users and sensing targets are concentrated, there is no need to introduce sensing signals; communication signals alone are sufficient to meet both communication and sensing performance, thus improving the efficiency of spatial resource utilization. When communication users and sensing targets are dispersed, sensing signals are introduced to form independent sensing beams for assistance, further improving resource utilization.

[0204] Figure 8 This is a schematic block diagram of a communication device provided in an embodiment of this application.

[0205] like Figure 8 As shown, the communication device may include a processing module 101, which can implement corresponding processing functions. Optionally, the processing module 101 may also be referred to as a processing unit.

[0206] Optionally, the communication device further includes a storage module, which can be used to store instructions and / or data; the processing module 101 can read the instructions and / or data in the storage module so that the communication device can implement the aforementioned method embodiments.

[0207] In one possible design, the communication device may correspond to the network device (which may be a RAN, a core network device, or a functional unit within a core network device) in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the network device. Alternatively, the communication device may correspond to the terminal device in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the terminal device. The communication device can be used to perform the steps or processes in any of the above method embodiments.

[0208] In one possible implementation, the processing module 101 is configured to, for any of the sensing targets, if the spatial correlation between the sensing target and any communication user cluster is low, introduce a sensing signal capable of covering the sensing target, and establish an information flow model and a sensing signal model for the communication users; establish a communication performance model and a sensing performance model based on the information flow model and the sensing signal model for the communication users; construct an objective function based on the communication performance model and the sensing performance model, construct constraints based on multi-objective sensing fairness and the service quality requirements of the communication users, and establish a joint optimization problem of beamforming and power allocation parameters based on the objective function and the constraints; iteratively optimize the joint optimization problem with maximizing the communication sensing performance as the optimization objective, and obtain the beamforming matrix, power allocation, and communication sensing performance.

[0209] In another possible implementation, the processing module 101 is specifically used to: obtain the azimuth angle of the sensing target; if the azimuth angle is not within the beamwidth range corresponding to the starting angle of any communication user cluster, determine that the spatial correlation between the sensing target and any communication user cluster is low; if the azimuth angle is within the beamwidth range corresponding to the starting angle of any communication user cluster, determine that the spatial correlation between the sensing target and any communication user cluster is high.

[0210] In another possible implementation, the processing module 101 is further configured to cover the sensing target and the communication users within any communication user cluster by means of a communication beam in the direction of any communication user cluster if the spatial correlation between the sensing target and any communication user cluster is high.

[0211] In another possible implementation, the processing module 101 is specifically used to: with communication sensing performance and maximization as optimization objectives, fix one of the power allocation variable and the beamforming vector respectively to solve the subproblem corresponding to the other variable, alternately iterate the optimization of the two subproblems until global convergence, and output the beamforming vector, power allocation result, and objective function value.

[0212] In another possible implementation, the processing module 101 is specifically used to: fix the power allocation variable, solve the sub-problem corresponding to the beamforming vector to obtain the beamforming vector; fix the beamforming vector, solve the sub-problem corresponding to the power allocation variable to obtain the optimal power allocation, and update the communication sensing performance sum, wherein the beamforming vector includes the beamforming vector of the communication signal and the beamforming vector of the sensing signal; if the number of iterations has not reached the maximum number of iterations, and the difference between the communication sensing performance sum obtained from two consecutive iterations is less than the convergence accuracy, global convergence is determined, and the optimal beamforming result, the optimal power allocation result, and the objective function value are output; if the number of iterations has reached the maximum number of iterations, and the difference between the communication sensing performance sum obtained from two consecutive iterations is not less than the convergence accuracy, the convergence accuracy is adjusted and alternating iterative optimization is continued until global convergence.

[0213] In another possible implementation, the processing module 101 is specifically used to: establish an objective function with the communication sensing performance as the optimization objective, taking the reachability rate of communication information as the communication performance index and the power of the transmitted signal at the sensing target as the sensing performance index.

[0214] In another possible implementation, the communication sensing performance sum is the sum of the communication performance weighted value and the sensing performance weighted value. The communication performance weighted value is the product of the total communication performance sum and the communication weight parameter, and the sensing performance weighted value is the product of the total sensing performance of all sensing targets and the sensing weight parameter.

[0215] In another possible implementation, the processing module 101 is specifically used to: construct a first constraint based on the reachability rate of communication information of any communication user being greater than or equal to a preset threshold; construct a second constraint based on the power difference of the sensing target in different sensing directions being less than or equal to a preset power threshold; construct a third constraint based on the power requirement that the power of the sensing signal and the power of the communication signal must satisfy when any communication user can eliminate the sensing signal using serial interference cancellation technology; and construct a fourth constraint based on the sum of the power allocation parameters of all communication users in the same communication user cluster being less than 1, and the power allocation parameter of any communication user being greater than or equal to 0.

[0216] Figure 9 This is another schematic block diagram of the communication device provided in the embodiments of this application.

[0217] The communication device can be a terminal device, a network device, a chip, chip system, or processor that implements the above methods. This communication device can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0218] like Figure 9As shown, the communication device may include one or more processors 201, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 201 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device (e.g., base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0219] In an alternative design, the processor 201 may also store instructions and / or data, which can be executed by the processor 201 to cause the communication device to perform the methods described in the above method embodiments.

[0220] In another alternative design, the communication device may include a communication interface 202 for implementing receiving and transmitting functions. For example, the communication interface 202 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0221] Optionally, the communication device may include one or more memories 203, which may store instructions that can be executed on the processor 201, causing the communication device to perform the methods described in the above method embodiments. Optionally, the memories 203 may also store data. Optionally, the processor 201 may also store instructions and / or data. The processor 201 and the memories 203 may be provided separately or integrated together.

[0222] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0223] In one implementation, the communication device may correspond to a UE or network device in the aforementioned communication system, and may be used to execute various steps and / or processes in the aforementioned method embodiments. The processor 201 may be used to execute instructions stored in the memory 203, and when the processor 201 executes instructions stored in the memory, the processor 201 is used to execute various steps and / or processes in the aforementioned method embodiments.

[0224] It is understood that the aforementioned processor can be one or more chips. For example, the processor can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0225] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0226] This application also provides a computer-readable storage medium storing instructions that, when executed on one or more computing devices, cause the one or more computing devices to perform the inductive beam optimization method described in the above embodiments.

[0227] Computer-readable storage media can be non-transitory computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices.

[0228] This application also provides a computer program product. When executed by one or more computing devices, the computer program product enables the computing devices to execute any of the methods described in the aforementioned integrated inductive beam optimization method. The computer program product can be a software installation package. When any of the aforementioned integrated inductive beam optimization methods needs to be used, the computer program product can be downloaded and executed on a computer.

[0229] This application also provides a processor, including: an input circuit, an output circuit, and a processing circuit. The processing circuit receives signals through the input circuit and transmits signals through the output circuit, causing the processor to execute the inductive beam optimization method described in the above embodiments.

[0230] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be output to, for example, but not limited to, a transmitter and transmitted by the transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0231] This application also provides a chip system including one or more processors for calling and executing instructions stored in a memory, thereby executing the inductive beam optimization method described in the above embodiments. The chip system may be composed of chips or may include chips and other discrete devices. The chip system may include input circuitry or interfaces for transmitting information or data, and output circuitry or interfaces for receiving information or data.

[0232] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0233] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0234] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units 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.

[0235] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0236] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for integrated inductive beam optimization, characterized in that, The application is applied to a sensing and communication integrated system, the system includes at least one communication user and at least one sensing target, the at least one communication user is divided into at least one communication user cluster according to channel correlation, and the method includes: For any sensing target, if the spatial correlation between the sensing target and any communication user cluster is low, a sensing signal capable of covering the sensing target is introduced, and an information flow model of the communication user and a sensing signal model are established, the sensing signal is independent of the communication beam, the communication beam only covers the communication user cluster, and the sensing signal only covers the sensing target; if the spatial correlation between the sensing target and any communication user cluster is high, the communication beam in the direction of the any communication user cluster covers the sensing target and the communication user in the any communication user cluster; A communication performance model and a sensing performance model are established; A target function is constructed based on the communication performance model and the sensing performance model, a constraint condition is constructed based on multi-target sensing fairness and quality of service requirements of the communication user, and a joint optimization problem of beamforming and power allocation parameters is established based on the target function and the constraint condition; The joint optimization problem is iteratively optimized with the maximum communication and sensing performance sum as the optimization target, and a beamforming vector of a sensing beam corresponding to the communication beam and the sensing signal, power allocation and the communication and sensing performance sum are obtained.

2. The method of claim 1, wherein, The spatial correlation between the sensing target and any communication user cluster is low, which includes: An azimuth angle of the sensing target is obtained; If the azimuth angle is not within the beam width range corresponding to the starting angle of any communication user cluster, it is determined that the spatial correlation between the sensing target and any communication user cluster is low; If the azimuth angle is within the beam width range corresponding to the starting angle of any communication user cluster, it is determined that the spatial correlation between the sensing target and the any communication user cluster is high.

3. The method of claim 1, wherein, The joint optimization problem is iteratively optimized with the maximum communication and sensing performance sum as the optimization target, and a beamforming vector of a sensing beam corresponding to the communication beam and the sensing signal, power allocation and the communication and sensing performance sum are obtained, which includes: With the maximum communication and sensing performance sum as the optimization target, one of the power allocation variable and the beamforming vector is fixed to solve the sub-problem corresponding to the other variable, and the two sub-problems are alternately iteratively optimized until global convergence, and the beamforming vector, the power allocation result and the target function value are output.

4. The method of claim 3, wherein, With the maximum communication and sensing performance sum as the optimization target, one of the power allocation variable and the beamforming vector is fixed to solve the sub-problem corresponding to the other variable, and the two sub-problems are alternately iteratively optimized until global convergence, and the beamforming vector, the power allocation result and the target function value are output, which includes: The power allocation variable is fixed, and the sub-problem corresponding to the beamforming vector is solved to obtain the beamforming vector; The beamforming vector includes a beamforming vector of a communication signal and a beamforming vector of the sensing signal, the beamforming vector is fixed, the sub-problem corresponding to the power allocation variable is solved to obtain the power allocation result, and the communication and sensing performance sum is updated; if the number of iterations does not reach the maximum number of iterations, and the difference between the communication and sensing performance and obtained by two consecutive iterations is less than the convergence precision, determining global convergence, outputting the optimal beamforming result, the optimal power allocation result and the communication and sensing performance and; if the number of iterations reaches the maximum number of iterations, and the difference between the communication and sensing performance and obtained by two consecutive iterations is not less than the convergence precision, adjusting the convergence precision to continue the alternating iterative optimization until global convergence.

5. The method of claim 1, wherein, The target function is constructed based on the communication performance model and the sensing performance model, and includes: The reachable rate of the communication information is taken as the communication performance index, the power of the transmitted signal at the sensing target is taken as the sensing performance index, and a target function with the optimization target of maximizing the communication and sensing performance and is established.

6. The method of any one of claims 1, or 3-5, wherein, The communication and sensing performance and is the sum of the communication performance weighted value and the sensing performance weighted value, the communication performance weighted value is the product of the communication performance sum and the communication weight parameter, and the sensing performance weighted value is the product of the total sensing performance sum of all sensing targets and the sensing weight parameter.

7. The method of claim 1, wherein, The constraint condition is constructed based on the multi-target sensing fairness and the quality of service demand of the communication user, and includes: A first constraint is constructed in that the reachable rate of the communication information of any communication user is greater than or equal to a preset threshold value; A second constraint is constructed in that the power difference of the sensing target in different sensing directions is less than or equal to a preset power threshold value; A third constraint is constructed in that when any communication user can eliminate the sensing signal by using the serial interference cancellation technology, the power requirement that the sensing signal power and the communication signal power satisfy; A fourth constraint is constructed in that the sum of the power allocation parameters of all communication users in the same communication user cluster is less than 1, and the power allocation parameter of any communication user is greater than or equal to 0.

8. A communication device, characterized by The method comprises the following steps of:

9. A communications device, characterized by The method comprises the following steps of: The memory is used for storing computer programs or computer instructions; The processor is used for executing the computer programs or computer instructions stored in the memory, so that the communication device executes the method.

10. A computer storage medium, characterized in that, The computer program is used for storing the computer program, and the computer program is executed to implement the method.

11. A computer program product, characterised in that, The computer program is executed to execute the method.

Images