Sensitivity integrated beam optimization method and related device

Abstract

The embodiment of the invention provides a communication and sensing integrated beam optimization method and a related device. Whether independent sensing signals are introduced to assist in ISAC transmission or not is judged according to the spatial distribution condition of a sensing target and a communication user. For example, in a scene where the spatial correlation between a sensing target and any communication user cluster is large, it is not needed to provide an independent sensing signal for the sensing target to assist in achieving ISAC transmission, and the sensing target can be covered only through a communication beam in the direction of the communication user cluster with the large spatial correlation. Namely, the communication user and the sensing target can be covered simultaneously through the communication beam. In a scene in which the spatial correlation between a sensing target and each communication user cluster is relatively small, a separate sensing signal is added for the sensing target to provide service for the sensing target. Therefore, the communication beam only needs to cover the communication user, the sensing target is covered by the corresponding sensing beam, and the resource utilization rate is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a synaesthesia integrated beam optimization method and related devices. Background Art

[0002] Integrated Sensing and Communication (ISAC) is a key feature of 5G systems and a key application scenario for 6G systems. ISAC is a novel technology system that deeply integrates wireless communication and environmental perception, leveraging the same hardware, spectrum resources, and signal waveforms to achieve both efficient communication and precise perception. ISAC leverages the reflection and scattering properties of communication signals during transmission to simultaneously perform sensing functions such as target ranging, velocity measurement, and imaging, enabling perceptual exploration of the physical world.

[0003] However, as the system scale continues to expand, the problem of coupling communication and perception resources has gradually become prominent in complex scenarios involving multiple communication users and multiple perception targets. There is an urgent need to further explore resource allocation solutions that can effectively balance communication and perception performance. Summary of the Invention

[0004] In view of this, the present application provides a synaesthesia integrated beam optimization method and related devices to improve the spatial reuse efficiency of resources. The disclosed technical solutions are as follows:

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

[0006] As can be seen, when the spatial correlation between the sensing target and each communication user cluster is small, a separate sensing signal is added to serve 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, improving resource utilization.

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

[0008] In one possible implementation, a joint optimization problem is iteratively optimized with the optimization objective of maximizing the communication perception performance sum to obtain the beamforming vector, power allocation, and communication perception performance sum. This involves fixing either the power allocation variable or the beamforming vector, solving the subproblem corresponding to the other variable, and alternately iteratively optimizing the two subproblems until global convergence. The beamforming vector, power allocation results, and the objective function value are then output. This solution, by fixing one variable and solving the result of the other variable for alternating iterative optimization, reduces the complexity of the optimization process and improves its timeliness.

[0009] In one possible implementation, with the communication perception performance and maximization as the optimization goal, one of the power allocation variable and the beamforming vector is fixed, and the sub-problem corresponding to the other variable is solved, and the two sub-problems are alternately iterated and optimized until global convergence, and the beamforming vector, the power allocation result, and the objective function value are output, including: fixing the power allocation variable, solving the sub-problem corresponding to the beamforming vector to obtain the beamforming vector; fixing the beamforming vector, solving the sub-problem corresponding to the power allocation variable to obtain the optimal power allocation, and updating the communication perception performance and, the beamforming vector includes the beamforming vector of the communication signal and the beamforming vector of the perception signal; if the number of iterations does not reach the maximum number of iterations, and the difference between the communication perception performance and the sum obtained by 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 communication perception performance and the sum are output; if the number of iterations reaches the maximum number of iterations, and the difference between the communication perception performance and the sum obtained by two consecutive iterations is not less than the convergence accuracy, the convergence accuracy is adjusted to continue the alternating iterative optimization until global convergence. This solution first fixes the power allocation variable to solve the beamforming vector result, then fixes the beamforming vector to the obtained result, solves the power allocation variable, and determines whether the desired convergence accuracy is achieved. If the convergence accuracy is achieved, the final beamforming vector, power allocation result and communication perception 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 perception performance model, including: taking the achievable rate of communication information as a communication performance indicator, taking the power of the transmitted signal at the perception target as a perception performance indicator, and establishing an objective function with communication perception performance and maximization as optimization goals.

[0011] In one possible implementation, the communication perception performance sum is the sum of the communication performance weighted value and the perception performance weighted value. The communication performance weighted value is the product of the communication performance sum and the communication weight parameter, and the perception performance weighted value is the product of the perception performance sum of all perception targets and the perception weight parameter. This solution uses the communication weight parameter and the perception weight parameter to balance communication performance and perception performance, achieving a balance between communication performance and perception performance.

[0012] In one possible implementation, constraints are established based on multi-objective perception fairness and the service quality requirements of communication users. These constraints include: a first constraint that the achievable communication information rate of any communication user is greater than or equal to a preset threshold; a second constraint that the power difference of the perception target in different perception directions is less than or equal to a preset power threshold; a third constraint that the power requirements of the perception signal power and the communication signal power are met when any communication user can eliminate the perception signal using serial interference cancellation technology; and a fourth constraint that the sum of the power allocation parameters of all communication users in the same communication user cluster is less than 1, and that the power allocation parameter of any communication user is greater than or equal to 0. This solution implements constraints on the achievable communication information rate through the first constraint, ensures similar power levels in different perception directions through the second constraint, and ensures that communication users can successfully eliminate interference caused by perception signals through the third constraint, thereby reducing the adverse effects of the introduced perception signals on communication performance.

[0013] In a second aspect, the present application further provides a communication device, comprising a module for executing the method of the first aspect or any possible implementation method of the first aspect.

[0014] In a third aspect, the present application also provides a communication device, comprising: a memory for storing computer instructions; a processor for executing a computer program or computer instructions stored in the memory, so that the communication device performs a method as in the first aspect or any possible implementation of the first aspect.

[0015] In a fourth aspect, the present application further provides a computer storage medium for storing a computer program, which, when executed, is used to implement the method of the first aspect or any possible implementation of the first aspect.

[0016] In a fifth aspect, the present application further provides a computer program product, wherein when the computer program is run, the method of the first aspect or any possible implementation of the first aspect is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a synaesthesia integration system provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the structure of an access network device provided in an embodiment of the present application;

[0019] Figure 3 A flowchart of the synaesthesia integrated beam optimization method provided in an embodiment of the present application;

[0020] Figure 4 A flowchart of an alternate iterative optimization process for a joint optimization problem provided in an embodiment of the present application;

[0021] Figure 5 A schematic diagram of a beamforming optimization result without introducing a separate perception signal example provided in an embodiment of the present application;

[0022] Figure 6 A schematic diagram of a beamforming optimization result for an example of introducing a separate perception signal provided in an embodiment of the present application;

[0023] Figure 7 Schematic diagram showing the performance comparison between the synaesthesia integrated beam optimization method according to an embodiment of the present application and other methods;

[0024] Figure 8 A schematic diagram of the structure of a communication device provided in an embodiment of the present application;

[0025] Figure 9 A schematic diagram of the structure of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. The terms used in the following embodiments are only for the purpose of describing specific embodiments and are not intended to be limiting of the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include expressions such as "one or more", unless there is a clear contrary indication in the context. It should also be understood that in the embodiments of the present application, "one or more" refers to one, two or more; "and / or" describes the association relationship of associated objects, indicating that three relationships may exist; for example, A and / or B can represent: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship.

[0027] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in 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 "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] The "multiple" involved in the embodiments of the present application means greater than or equal to two. It should be noted that in the description of the embodiments of the present application, the words "first" and "second" are only used for the purpose of distinguishing the description and cannot be understood as indicating or implying relative importance or order.

[0029] See Figure 1 , shows a schematic diagram of a synaesthesia integration system provided by an embodiment of the present application. Figure 1 As shown, the synaesthesia integration system may include an ISAC base station, multiple user clusters, and multiple sensing targets, wherein the user clusters can be divided according to the relevance of the users. The sensing targets may include vehicles, pedestrians, obstacles, drones, etc.

[0030] The embodiments of the present application can be applied to various communication systems, which may include but are not limited to the following systems, such as: second generation (2G) communication systems, third generation (3G) communication systems, long term evolution (LTE) systems, universal mobile telecommunication systems (UMTS), world wide interoperability for micro wave access (WiMAX) communication systems, fifth generation (5G) communication systems, and the like. th generation, 5G) system or new radio (NR), 5.5G system or sixth generation (6 th The 6G generation (6G) system and future mobile communication systems, vehicle to other devices (vehicle to X, V2X); V2X may include vehicle to network (V2N), vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrian (V2P), etc., long-term evolution-vehicle (LTE-V), Internet of Vehicles, machine type communication (MTC), Internet of Things (IOT), ambient Internet of Things (AIOT), long-term evolution-machine (LTE-M), machine to machine (M2M), etc.

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

[0032] The communication system of the present application includes an access network device and a terminal device communicating with the access network device. Optionally, the system may also include a core network element communicating with the access network device.

[0033] Access network equipment is a device deployed in a radio access network to provide wireless communication capabilities. Access network equipment, which can also be referred to as an access network node, RAN (radio access network) node, RAN entity, or access node, is located on the network side of the aforementioned communication system 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 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.

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

[0035] In an NTN, all or some of the functional modules of access network equipment can be deployed on airborne platforms, satellites, or other forms of communication equipment deployed high in the sky. Accordingly, access network equipment can refer to airborne platforms, satellites, or other similar devices that connect terminal devices to core network equipment. Airborne platforms can include at least one of the following: satellites, drones, or hot air balloons.

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

[0037] In different systems, the CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meanings. For example, in the ORAN system, the CU may also be called an O-CU (Open CU), the DU may also be called an O-DU, the CU-CP may also be called an O-CU-CP, the CU-UP may also be called an O-CU-UP, and the RU may also be called an O-RU. Any of the CU (or CU-CP, CU-UP), DU, and RU in this application may be implemented as a software module, a hardware module, or a combination of software and hardware modules. The 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, 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-DU separation. A CU can be connected to one or more DUs. The CU and DU can be divided based on the protocol layers of the wireless network: for example, the functions of the PDCP layer and above (such as the RRC layer and SDAP layer) are located in the CU, while the functions of the protocol layers below the PDCP layer (such as the RLC layer, media access control (MAC) layer, and PHY layer) are located in the DU. Another example is that the functions of the protocol layers above the PDCP layer are located in the CU, while the functions of the protocol layers below the PDCP layer are located in the DU, without limitation. When a CU includes a CU-CP and a CU-UP, the CU-CP implements the control plane functions of the CU, and the CU-UP implements 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, the CU-CP implements the RRC layer functions and the control plane functions of the PDCP layer, and the CU-UP implements the SDAP layer functions and the user plane functions of the PDCP layer. This application does not limit the names of the CU and DU. The above division of the processing functions of CU and DU according to the protocol layer is only an example, and they can also be divided in other ways.

[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 DU can be separated. Figure 4As shown, these functions can be implemented by a radio unit (RU). The RU can have radio frequency functions. This application does not limit the name of the RU. The DU and RU can be split or separated at the PHY layer. For example, the DU can implement high-level functions in the PHY layer, and the RU can implement low-level functions in the PHY layer, or implement these low-level functions and radio frequency functions. High-level functions in the PHY layer include functions closer to the MAC layer, and low-level functions in the PHY layer include functions closer to the radio frequency. For example, high-level functions in the PHY layer include one or more of the following: forward error correction (FEC) encoding / decoding, scrambling, or modulation / demodulation. Low-level functions in the PHY layer include one or more of the following: fast Fourier transform (FFT) / inverse fast Fourier transform (IFFT), beamforming, or physical random access channel (PRACH) extraction and filtering. The RU can communicate radio frequency signals with terminal devices over the air interface. The PHY layer code precoding function can be located in the DU or the RU. The DU and RU can be split in various ways, without limitation. An interface exists between the DU and RU. For example, depending on the split method, the interface between the DU and RU can be a common public radio interface (CPRI) or an enhanced common public radio interface (eCPRI).

[0041] Optionally, any one of the above-mentioned CU, CU-CP, CU-UP, DU and RU can be a software module, a hardware structure, or a software module plus a hardware structure, without limitation. The existence forms of different entities can be the same or different. 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 one by one. These modules and their execution methods are also within the scope of protection of the embodiments of the present application. For example, when the method of the embodiment of the present 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] The embodiments of this application do not limit the form of the access network device. The device used to implement the functions of the access network device can be the access network device; it can also be a device that supports the access network device to implement the functions, 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 embodiment of the present application, the terminal may be a terminal device with a transceiver function, or may be a chip or chip system provided in the terminal device. In the embodiment of the present application, the terminal device may be in various forms, for example, a mobile phone, a tablet computer, a computer with a wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a vehicle-mounted terminal device, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a wearable terminal device, etc. The terminal device of the present application may also be an on-board module, an on-board module, an on-board component, an on-board chip or an on-board unit built into a vehicle as one or more components or units. The terminal device may also be other devices with terminal functions, for example, the terminal device may also be a device that serves as a terminal function in D2D communication.

[0044] A terminal may also be sometimes 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 may also be a fixed terminal or a mobile terminal.

[0045] The embodiments of this application do not limit the device form factor of the terminal. The device used to implement the functions of the terminal device can be the terminal device; it can also be a device that supports the terminal device to implement 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 include chips and other discrete devices.

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

[0047] In practical applications, there is uncertainty in the distribution of perception targets and communication hotspots. 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 perception targets and improve the spatial multiplexing efficiency of spectrum resources.

[0048] The integrated synaesthesia beam optimization method provided in this application can adaptively adjust the ISAC transmission scheme based on the spatial distribution of communication users and perception targets. For example, when communication users and perception targets are concentrated, there is no need to introduce perception signals; communication and perception performance can be met using only communication signals, thereby improving spatial resource utilization efficiency. When communication users and perception targets are dispersed, the introduction of perception signals can assist in forming independent perception beams, thereby improving resource utilization.

[0049] The following will be combined Figure 3 Introducing the synaesthesia integrated beam optimization method provided by this application, such as Figure 3 As shown, the method may include the following steps:

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

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

[0052] According to the channel correlation of communication users, communication users are divided into M communication user clusters. Specifically, users with high channel correlation are divided into the same user cluster, and users with low channel correlation are divided into different user clusters. Users in the same user cluster share the same beam. Among them, the mth user cluster contains K m users, denoted as {U m,1 , U m,2，……， U m,Km The starting angle of the mth user cluster is recorded as , the maximum angle and minimum angle of the users in the cluster are expressed as , , the distribution range of communication users within the cluster can be expressed as .

[0053] The azimuth angle between the lth sensing target and the ISAC base station is recorded as The smaller the difference between the azimuth angle of the perception target and the starting angle of the communication user cluster, the stronger the channel correlation between the perception target and the communication user cluster; otherwise, the channel correlation is weaker.

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

[0055] (1)

[0056] In formula 1 Indicates large-scale channel fading, Indicates small-scale fading.

[0057] During their propagation, radio waves are affected by various buildings, trees, vegetation, and terrain, causing energy absorption and reflection, scattering, and diffraction of radio waves, and suffer attenuation or loss in different ways, including path loss, large-scale fading, and small-scale fading.

[0058] Large-scale fading refers to the loss of radio waves caused by obstruction by buildings, hills, etc. on the propagation path. It reflects the trend of change in the average value of the received signal level within 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 in the received signal level within a small area, on the order of tens of wavelengths. Multipath refers to the phenomenon in which radio waves travel through multiple paths from the transmitting antenna to the receiving antenna. Multipath propagation is caused by atmospheric scattering, ionosphere reflection and refraction, and reflection from surface objects such as mountains and buildings. Ultimately, the signal received by the receiver is a composite of the direct wave and multiple reflected waves.

[0060] S102: For any sensing target, determine whether to introduce sensing signal-assisted ISAC transmission based on the correlation between the sensing target and each user cluster. S103 will be executed regardless of whether sensing signal-assisted transmission is introduced.

[0061] For the lth sensing target, whether to introduce the sensing signal auxiliary transmission is determined based on the spatial correlation between the sensing target and any communication user cluster (such as the mth user cluster). Specifically, the azimuth of the sensing target can be used to determine whether to introduce the sensing signal auxiliary transmission. The starting angle of the mth user cluster The relationship between the beamwidth and the transmission parameters determines whether to introduce a perceptual signal to assist the transmission.

[0062] Among them, the starting angle of the mth user cluster is Beamwidth on It can be expressed as:

[0063] (2)

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

[0065] When the target's azimuth is sensed satisfy When , it indicates that the sensing target is distributed within the communication beam in the direction of the mth user cluster. In this scenario, the power at the sensing target can be guaranteed by the communication signal alone. The signals of multiple users in the cluster are simply superimposed and transmitted using non-orthogonal multiple access (NOMA). The receiving end uses successive interference cancellation (SIC) technology to jointly demodulate the aliased signals and improve spectrum utilization. SIC technology eliminates interference step by step according to signal power.

[0066] When the target's azimuth is sensed satisfy or When the communication beam in the direction of the mth cluster cannot effectively cover the sensing target, the communication signal in the direction of the mth cluster has low power in the direction of the sensing target. Therefore, it is necessary to introduce the sensing signal to assist the ISAC transmission and supplement the sensing target airspace. At the same time, to reduce the interference of the sensing signal on the communication signal, the sensing signal is constructed into a virtual communication signal. The communication signal of the communication user is superimposed with the sensing signal and sent in a NOMA manner. The receiving end performs decoding and cancellation through SIC.

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

[0068] (3)

[0069] In formula 3, is the beamforming vector of the ISAC base station, s is the communication information flow, is the added perception signal, where represents a complex set, Used to control whether the perception signal is introduced. When , it means the sensory signal is introduced. Indicates that there is only communication signal. The communication information flows between different communication users are independent of each other, have a mean of zero, and have unit power. The communication information flow of M user clusters can be expressed as:

[0070] (4)

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

[0072] The introduced perception signal is independent of the communication signal and is transmitted using multiple beams, that is, the covariance matrix of the perception signal is has general rank, and (express is a positive semidefinite matrix). By performing eigenvalue decomposition, the perception signal can be decomposed into multiple perception beams:

[0073] (5)

[0074] In formula 5, represents the covariance matrix of the perception signal, is the eigenvalue, is the corresponding eigenvector, express The transposed conjugate matrix of represents the beamforming vector of the i-th sensing signal, for The transposed conjugate matrix of .

[0075] In order to achieve the superposition of perception signals and communication signals, information is embedded in part of the perception signals and processed as virtual communication signals. That is, the perception signals can be expressed as:

[0076] (6)

[0077] In formula 6, , express The general rank of To decompose the independent unit power signals and separate them from the residual perception signal Independent of each other.

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

[0079] (7)

[0080] It can be seen that the covariance matrix of the residual perception signal It also has a general rank, indicating that the residual sensing signal is consistent with the actual transmission scenario.

[0081] S103: Establish a communication performance model and a perception performance model.

[0082] Inter-communication user interference and inter-sensory signal interference are determined and a communication performance model is established; and perception power as a key metric is determined and a perception performance model is established.

[0083] Communication performance is measured by the reachable rate of information from communicating users, while perception performance is measured by the power at the perception target. Below, we will calculate the communication performance metric (reachable rate) and the perception performance metric (power at the perception 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 expressed as:

[0086] (8)

[0087] In formula 8, represents the channel coefficient of the i-th user in the m-th cluster The transposed conjugate matrix of represents the information flow of the kth user in the mth cluster, Indicates the corresponding transmit power, represents the beamforming vector of the mth cluster, where the first Represents the signal received in the direction of the mth cluster, the second represents the interference signal between the jth cluster and the mth cluster, the third term represents the perceived interference signal that can be eliminated by the SIC technology, and the fourth term represents the residual perceived interference signal. The mean is 0 and the variance is complex Gaussian white noise.

[0088] Assume that the index of the users in the mth cluster is increasing relative to the strength of their large-scale fading, that is, the channel quality of the first user in the mth cluster is the strongest, and the channel quality of the Kth user is the strongest. m The channel quality of each user is the weakest, that is, , users with weaker channel quality will be allocated larger power. When users receive the signal, SIC technology is used to eliminate the interference between users in the cluster in descending order of power level. When the sensing signal is introduced, in order to ensure that all users in the cluster can eliminate the interference caused by the sensing signal, its power must meet , that is, all users first eliminate the perceived interference signal during decoding. Therefore, when the i-th user in the m-th cluster decodes the n-th user signal, the remaining signal after eliminating the perceived interference signal can be expressed as:

[0089] (9)

[0090] In formula 9, represents the information flow of the nth user in the mth cluster, represents the information flow of the kth user in the mth cluster, represents the information flow of the kth user in the jth cluster, is the corresponding transmit power, represents the beamforming vector of the mth cluster, represents the beamforming vector of the jth cluster, The mean is 0 and the variance is The first term in this formula represents the effective signal of the nth user in the mth cluster, the second term represents the interference caused by all user signals with indexes less than n in the cluster when the i-th user in the m-th cluster decodes the n-th user signal, the third term represents the inter-cluster interference caused by the j-th cluster to the m-th cluster, and the fourth term represents the residual perceived signal interference.

[0091] The achievable rate at which the i-th user in the m-th cluster decodes the n-th user information It can be expressed as:

[0092] (10)

[0093] In formula 10, represents the signal-to-noise ratio (SNR) of the i-th user in the m-th cluster when decoding the n-th user signal, and is obtained according to Formula 9 and the calculation formula of the SNR.

[0094] If it is determined in S102 that a separate perception signal needs to be introduced to assist ISAC transmission, then If it is determined in S102 that there is no need to introduce a separate perception signal, then .

[0095] All users in the mth cluster (K m In the decoding process of the SIC technology for users in the same cluster, users with better channel quality than the nth user must first successfully parse and eliminate the information of the nth user before they can successfully extract their own useful information. As mentioned above, the index of the user in the mth cluster increases with the strength of its large-scale fading. Therefore, the user in the mth cluster with better channel quality than the nth user is The stronger the channel quality, the smaller the allocated power. Therefore, the achievable rate of the nth user information in the mth cluster is the minimum value of the achievable rate of decoding the information by users with indexes 1 to n in the cluster. That is, the achievable rate of the nth user information in the mth cluster can be expressed as:

[0096] (11)

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

[0098] The metric of the sensing performance is the power at the sensing target, i.e., the transmit beam pattern, which is the covariance matrix of the base station's transmitted signal. The decision can be expressed as:

[0099] (12)

[0100] In formula 12, is the beamforming vector of cluster i, is the beamforming vector of the i-th sensing signal, represents the covariance matrix of the residual perception signal. If it is determined in S102 that a separate perception signal needs to be introduced to assist ISAC transmission, then If it is determined in S102 that there is no need to introduce a separate perception signal, then .

[0101] The base station transmits signals in L directions. The beam pattern in the direction can be expressed 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 antenna array's response to a signal in a specific direction. It is related to the antenna array's geometric structure, the signal's arrival angle, and the signal frequency.

[0105] S104: Based on the communication performance model and the perception performance model, a joint optimization problem of beamforming vectors, power allocation, and perception waveform parameters is established with the optimization goal of maximizing the weighted sum of communication perception performance.

[0106] Based on the communication performance model and perception performance model established in the previous step, the optimization goal is to maximize the weighted sum of communication perception performance. Considering constraints such as multi-objective perception fairness and user communication QoS requirements, a joint optimization problem for transmit beamforming vector, power allocation, and perception 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 perception performance, where communication performance is the communication performance index of all communication users in the system (i.e., the achievable rate of communication information flow). ), the perception performance is the perception performance index of all perception targets in the system (that is, the power at the perception target ) and.

[0109] C1~C7 are constraints; among them, the optimization objective 、 is the weight parameter of communication performance and perception service, which is used to balance the performance of the two. The optimization variable is the beamforming vector of the communication signal , the beamforming vector of the sensing signal , the covariance matrix of the residual perception signal , and the power allocation parameters within each cluster .

[0110] Constraint C1 is the information reachable rate constraint of the communication user, Indicates the corresponding threshold;

[0111] Constraint C2 ensures similar power levels in different sensing directions. Used to determine the fluctuation range;

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

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

[0114] Constraint C5 is the semi-positive definite constraint on the residual perception signal covariance matrix, that is, the residual perception signal conforms to the physically realizable signal statistics law;

[0115] Constraints C6 and C7 are constraints on the power allocation variables within the cluster.

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

[0117] First, fix the power allocation variables , solve the beamforming vector sub-problem, then fix the beamforming vector and solve the power allocation variable sub-problem, alternately iteratively optimize the two sub-problems until global convergence, and output the beamforming vector and power allocation results, as well as the objective function value (i.e., the weighted sum of communication perception performance).

[0118] The integrated synaesthesia 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 the communication user. Specifically, when the spatial correlation between the sensing target and any communication user cluster is large, there is no need to provide a separate sensing signal for the sensing target to assist in ISAC transmission. The sensing target can be covered by the communication beam in the direction of the communication user cluster with large spatial correlation. That is, the communication beam can simultaneously cover the communication user and the sensing target. When the spatial correlation between the sensing target and each communication user cluster is small, a separate sensing signal is added to the sensing target to provide service 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. In addition, the NOMA technology is used to alleviate the mutual interference between communication and perception, effectively improving the utilization of spectrum resources.

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

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

[0121] S202 , solving the beamforming vector subproblem with a fixed power allocation variable to obtain a beamforming vector.

[0122] S203 , fixing the beamforming vector to solve the power allocation variable sub-problem, obtain the optimal power allocation, and update the weighted sum of communication perception performance.

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

[0124] S205 , determining whether the difference between the weighted sums of the communication perception performance of two consecutive iterations is less than the convergence accuracy; if so, executing S206 ; if not, returning to executing S202 .

[0125] S206: Output a beamforming vector, a power allocation parameter, and a weighted sum of communication perception performance.

[0126] The following describes the alternating iterative optimization process in detail:

[0127] (1) Fixed power allocation variables Solve the beamforming vector subproblem.

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

[0129] (15)

[0130] The auxiliary variable of semidefinite relaxation is introduced to transform the communication signal beamforming vector , sensing signal beamforming vector , replaced by 、 in, , .

[0131] Introducing the trace operation Tr(), and making , then the achievable rate of communication user information is expressed as:

[0132] (16)

[0133] in, represents the noise power, is the scaling auxiliary variable.

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

[0135] (17)

[0136] Among them, in constraint C10, represents the threshold requirement for the information reachable rate of the communication user; 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 complicated because the local minimum is not necessarily the global minimum. In optimization problems, it is usually necessary to use some special methods to deal with non-convex problems. For example, in this embodiment, the Successive Convex Approximation (SCA) algorithm is used to solve the problem. First, the second term in Formula 16 is solved. Non-convex term at point Performing a first-order Taylor expansion, where the superscript "n" represents the nth iteration, yields:

[0137] (18)

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

[0139] (19)

[0140] Next, constrain the rank-one constraints in C8 and C9, i.e. , Add it to the objective function in the form of a penalty term. Specifically, , Can be equivalently transformed into:

[0141] (20)

[0142] (twenty one)

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

[0144] Since the matrix and is positive semidefinite, if the matrix is ​​not of rank one, then:

[0145] (twenty two)

[0146] (twenty three)

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

[0148] (twenty four)

[0149] In formula 24 and That is, the matrix and The penalty term corresponding to the rank of is the regularization parameter.

[0150] if If is 0, Approaching infinity, the penalty term has a greater impact on the optimization problem, so the matrix and The rank of will be strictly controlled. It can be initialized with a larger value , in order to find a good starting point for beam matching, and then by Gradually reduce the parameter to a small enough value, then 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-layer convergence accuracy This process terminates when:

[0153] (26)

[0154] because For non-convex items, The first-order Taylor expansion approximation can transform the non-convex term into a convex term, where the first-order Taylor expansion approximation process is:

[0155] (27)

[0156] in, Representation matrix The largest eigenvector of .

[0157] Similarly, we can also The first-order Taylor expansion approximation is:

[0158] (28)

[0159] in, Representation matrix The largest eigenvector of .

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

[0161] (29)

[0162] That is, Formula 29 is the final objective function corresponding to the fixed power allocation variable. This problem can be directly solved by the CVX convex optimization tool, and the beamforming vector result is finally obtained, that is, the beamforming vector of the communication signal and the 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 the beamforming vector of the sensing signal ), and solve for the power allocation variables.

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

[0165] (30)

[0166] In the objective function in Equation 30, only the first term contains the power allocation coefficient. Therefore, the optimization problem in this formula can be transformed into maximizing the sum of the communication rates of the communication users, that is:

[0167] (31)

[0168] Perform equivalent transformation for the non-convex constraint C1:

[0169] (32)

[0170] in, , , is the noise power, Indicates the threshold requirement corresponding to the achievable rate of information of the communication user.

[0171] Therefore, the 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, can be re-expressed as:

[0176] (35)

[0177] Therefore, the optimization problem in Equation 33 is Translates to:

[0178] (36)

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

[0180] Assume that the outer convergence accuracy is , alternately iteratively optimize the two objective functions shown in Formula 29 and Formula 36 until the difference between the weighted sum of two consecutive communication perception performances is less than the convergence accuracy , that is, after global convergence, the beamforming vector and power allocation results are output, and the objective function value, that is, the weighted sum of communication perception performance, is further obtained based on the beamforming vector and power allocation results.

[0181] The above method was simulated and verified. The ISAC base station was equipped with a ULA consisting of 8 antennas. There were 4 communication users and 2 sensing targets in the system. The 4 communication users were divided into 2 user clusters according to channel correlation. , the noise power is The path loss model is defined based on the 3GPP propagation environment as ,in, is the propagation distance; Ricean channel The factor value is 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 accuracy of the inner SCA loop is set to ; The communication perception weight parameters are set as .

[0182] See Figure 5 , which is a schematic diagram of beam image simulation for a scenario where the sensing target is close to a cluster of communication users.

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

[0184] Sensing target A is highly correlated with the spatial location of user cluster 1, meaning the communication beam in the direction of user cluster 1 can guarantee power at sensing target A. In this scenario, the signals of all users in user cluster 1 are simply superimposed and transmitted in a NOMA manner. The receiving end uses SIC technology for joint demodulation, eliminating the need for separate sensing signals and improving spectrum utilization.

[0185] Moreover, the spatial correlation between sensing target B and user cluster 2 is high, that is, sensing target B is distributed in 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 sensing target B. Only all user signals of user cluster 2 are superimposed and sent in NOMA mode. The receiving end uses SIC technology for joint demodulation, without the need to introduce a separate sensing signal, thereby improving spectrum utilization.

[0186] In this scenario, the communication beamforming and power allocation parameters can be optimized according to the distribution of users and sensing targets in the user cluster. Figure 5 As shown in the figure, the communication beam in the direction of user cluster 1 meets the needs of both user cluster 1 and sensing target A, while the communication beam in the direction of user cluster 2 meets the needs of both user cluster 2 and sensing target B. This shows that the beamforming result can simultaneously meet the communication performance and sensing performance requirements, thereby improving resource utilization.

[0187] See Figure 6 , which is a schematic diagram of beam image simulation for the user cluster scenario of the perception target principle communication.

[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 target A and user clusters 1 and 2 is low, and the channel correlation between target B and user clusters 1 and 2 is also low. In this scenario, it is difficult to cover targets A and B using only the communication beams in the direction of user clusters 1 and 2. Therefore, it is necessary to introduce sensing signals to supplement the spatial domain of the sensing targets.

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

[0190] In transmission schemes that don't introduce separate sensing beams, communication users and sensing targets are served solely through integrated interawareness beams. To ensure that the communication beams simultaneously cover dispersed communication users and sensing users, the system needs to increase transmit power and beamwidth. However, in scenarios where sensing targets and communication user clusters are dispersed, the solution of this application introduces separate sensing beams to assist ISAC transmission. This way, the communication beam only needs to cover the communication users, while the sensing targets are covered by separate sensing beams, improving resource utilization.

[0191] The following will be combined Figure 7, the transmission power and communication perception performance weighted sum of the synaesthesia integrated beam optimization method provided in the embodiment of the present application are compared with those of the traditional ISAC solution and the ISAC solution without introducing the perception signal.

[0192] Figure 7 The horizontal axis represents the total base station transmission power, and the vertical axis represents the weighted sum of communication perception performance, wherein curve 1 represents a curve diagram of the weighted sum of transmission power and communication perception performance corresponding to the transmission scheme of the present application introducing a separate perception beam, curve 2 represents a curve diagram of the weighted sum of transmission power and communication perception performance corresponding to the ISAC scheme that does not introduce an independent perception signal, and curve 3 represents a curve diagram of the weighted sum of transmission power and communication perception performance corresponding to the traditional ISAC scheme.

[0193] Among them, the traditional ISAC solution is that the communication and perception functions are independent of each other, and the communication user's receiver cannot eliminate perception interference; the ISAC solution that does not introduce independent perception signals does not introduce independent perception signals, and only uses integrated synaesthesia signals to provide services to perception targets and communication users.

[0194] like Figure 7 As shown, as the total base station transmit power increases, the maximum-minimum effective sensing power of all three schemes increases. Furthermore, at the same total transmit power level, the ISAC scheme introduced with a separate sensing signal, as proposed in this application, achieves a higher weighted sum of communication sensing performance than the comparison schemes (i.e., the traditional ISAC scheme and the ISAC scheme without the separate sensing signal). This is because the scheme proposed in this application introduces separate sensing signals to assist ISAC transmission in scenarios where sensing targets and user clusters are dispersed. This allows the communication beam to cover only the communication users, while the sensing targets are covered by the introduced separate sensing beam, improving resource utilization. Furthermore, NOMA technology is used to mitigate the mutual interference between communication and sensing, effectively improving spectrum resource utilization. In other words, at the same transmit power as the comparison scheme, a higher weighted sum of communication sensing performance can be achieved.

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

[0196] S1: A network device obtains spatial information of all communication users and sensing targets within the current network device coverage area. All communication users are divided into at least one communication user cluster based on channel correlation. 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, the communication signal corresponding to the communication user cluster and the perception signal are superimposed and sent using NOMA technology.

[0199] In ISAC transmission scenarios where a separate perception signal is required, the perception signal is constructed into a virtual communication signal to reduce its interference with the communication signal. For example, the communication user signal and the perception signal are superimposed and transmitted in a NOMA manner. After receiving the beam signal sent by the network device, the terminal device uses SIC technology to decode and eliminate the perception signal.

[0200] In an exemplary embodiment, in a scenario where a separate perception signal needs to be introduced, the network device may send indication information to the terminal device through high-layer protocol signaling, indicating that the beam signal currently sent by the terminal device includes the perception signal.

[0201] In another exemplary embodiment, the perception signal generally has a specific structure or signal feature, and the terminal device may perform feature extraction on the received signal to determine whether the perception signal is included.

[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 interawareness transmission method provided in this implementation can adaptively adjust the ISAC transmission scheme based on the spatial distribution of communication users and perception targets. For example, when communication users and perception targets are concentrated, there is no need to introduce perception signals; communication and perception performance can be met using only communication signals, thereby improving spatial resource utilization efficiency. When communication users and perception targets are dispersed, the introduction of perception signals can form independent perception beams to assist and improve resource utilization.

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

[0205] like Figure 8 As shown, the communication device may include a processing module 101, which may 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 implements the aforementioned method embodiment.

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

[0208] In one possible implementation, the processing module 101 is used to, for any of the perception targets, introduce a perception signal that can cover the perception target if the spatial correlation between the perception target and any communication user cluster is low, and establish an information flow model and a perception signal model for the communication user; establish a communication performance model and a perception performance model based on the information flow model and the perception signal model for the communication user; construct an objective function based on the communication performance model and the perception performance model, construct constraints based on multi-objective perception fairness and the service quality requirements of the communication user, 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 the maximization of the communication perception performance and the communication perception performance as the optimization goal to obtain the beamforming matrix, power allocation and communication perception performance.

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

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

[0211] In another possible implementation, the processing module 101 is specifically used to: take communication perception performance and maximization as optimization goals, fix one of the power allocation variable and the beamforming vector to solve the sub-problem corresponding to the other variable, alternately iteratively optimize the two sub-problems 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 perception performance sum, where the beamforming vector includes the beamforming vector of the communication signal and the beamforming vector of the perception signal; if the number of iterations does not reach the maximum number of iterations, and the difference between the sum of the communication perception performance and the sum obtained from two consecutive iterations is less than the convergence accuracy, determine global convergence, and output the optimal beamforming result, the optimal power allocation result and the objective function value; if the number of iterations reaches the maximum number of iterations, and the difference between the sum of the communication perception performance and the sum obtained from two consecutive iterations is not less than the convergence accuracy, adjust the convergence accuracy and continue to perform alternating iterative optimization until global convergence.

[0213] In another possible implementation, the processing module 101 is specifically used to: use the achievable rate of communication information as the communication performance indicator, use the power of the transmitted signal at the perception target as the perception performance indicator, and establish an objective function with communication perception performance and maximization as the optimization goal.

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

[0215] In another possible implementation, the processing module 101 is specifically used to: construct a first constraint based on the achievable 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 perception target in different perception directions being less than or equal to a preset power threshold; construct a third constraint based on the power requirements satisfied by the perception signal power and the communication signal power when any communication user can use serial interference cancellation technology to eliminate the perception signal; 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 a communication device provided in an embodiment of the present application.

[0217] The communication device may be a terminal device, a network device, a chip, a chip system or a processor that implements the above method, etc. The communication device may be used to implement the method described in the above method embodiment, and for details, please refer to the description in the above method embodiment.

[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 may implement certain control functions. Processor 201 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, while the central processing unit may be used to control the communication device (e.g., base station, baseband chip, user, user chip), execute software programs, and process software program data.

[0219] In an optional design, the processor 201 may also store instructions and / or data, and the instructions and / or data can be executed by the processor 201, so that the communication device executes the method described in the above method embodiment.

[0220] In another optional 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, an interface, an interface circuit, or a transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing the receiving and transmitting functions may be separate or integrated. The transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or the transceiver circuit, interface, interface circuit, or transceiver may be used for transmitting or delivering 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 method described in the above method embodiment. Optionally, the memory 203 may also store data. Optionally, the processor 201 may also store instructions and / or data. The processor 201 and memory 203 may be provided separately or integrated.

[0222] It should be understood that, in one possible design, each step in the method embodiment provided in the present application can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

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

[0224] It is understood that the processor may be one or more chips. For example, the processor may 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 the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0226] An embodiment of the present application also provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on one or more computing devices, the one or more computing devices execute the synaesthesia integrated beam optimization method described in the above embodiment.

[0227] The computer-readable storage medium may be a non-transitory computer-readable storage medium, for example, a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0228] The present application also provides a computer program product. When executed by one or more computing devices, the one or more computing devices perform any of the aforementioned methods for integrated synaesthesia beam optimization. The computer program product may be a software installation package. When any of the aforementioned methods for integrated synaesthesia beam optimization is required, the computer program product may be downloaded and executed on a computer.

[0229] The present application also provides a processor comprising an input circuit, an output circuit, and a processing circuit, wherein the processing circuit is configured to receive signals through the input circuit and transmit signals through the output circuit, so that the processor executes the synaesthesia integrated beam optimization method described in the above embodiment.

[0230] In a specific implementation, the processor may be one or more chips, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a trigger, or various logic circuits. The input signal received by the input circuit may be, for example, but not limited to, received and input by a receiver, and the signal output by the output circuit may be, for example, but not limited to, output to and transmitted by a transmitter. The input circuit and the output circuit may be the same circuit, which functions as an input circuit and an output circuit at different times. The embodiments of the present application do not limit the specific implementation of the processor and various circuits.

[0231] The present application also provides a chip system including one or more processors configured to retrieve and execute instructions stored in a memory, thereby executing the synaesthesia integrated beam optimization method described in the above embodiment. The chip system may be composed of a chip, or may include a chip and other discrete devices. The chip system may include an input circuit or interface for sending information or data, and an output circuit or interface for receiving information or data.

[0232] In the embodiments of this application, each term and English abbreviation is provided for convenience of description and shall not constitute any limitation to this application. This application does not exclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0233] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0234] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0235] It should be understood that in the various embodiments of the present application, the size of the serial number of each process does not mean 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 the present application.

[0236] In short, the above description is only 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 replacements, improvements, etc. made within the spirit and principles of this application shall be included in the scope of protection of this application.

Claims

1. A synaesthesia integrated beam optimization method, characterized in that: Applied to a synaesthesia integration system, the system includes at least one communication user and at least one perception target, the at least one communication user is divided into at least one communication user cluster according to channel correlation; the method includes: For any of the sensing targets, 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 of the communication user; Establish communication performance model and perception performance model; An objective function is constructed based on a communication performance model and a perception performance model, constraints are constructed based on multi-objective perception fairness and the service quality requirements of communication users, and a joint optimization problem of beamforming and power allocation parameters is established based on the objective function and the constraints; The joint optimization problem is iteratively optimized with maximizing the communication perception performance and as the optimization goal to obtain the beamforming vector, power allocation and communication perception performance and.

2. The method according to claim 1, characterized in that The spatial correlation between the sensing target and any communication user cluster is low, including: Obtaining the azimuth of the sensed target; If the azimuth angle is not within the beam width range corresponding to the starting angle of any communication user cluster, determining 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 perception target and the any communication user cluster is high.

3. The method according to claim 1 or 2, characterized in that The method further comprises: If the spatial correlation between the sensing target and any communication user cluster is high, the sensing target and the communication users in the any communication user cluster are covered by a communication beam in the direction of the any communication user cluster.

4. The method according to claim 1, wherein The iterative optimization of the joint optimization problem with maximizing the communication perception performance as the optimization goal to obtain the beamforming vector, power allocation and communication perception performance includes: With communication perception performance and maximization as the optimization goals, one of the power allocation variable and the beamforming vector is fixed to solve the sub-problem corresponding to the other variable. The two sub-problems are optimized alternately and iteratively until global convergence, and the beamforming vector, power allocation result, and objective function value are output.

5. The method according to claim 4, characterized in that The optimization goal is to maximize the communication perception performance and solve the subproblem corresponding to the other variable by fixing one of the power allocation variable and the beamforming vector, alternately iteratively optimizing the two subproblems until global convergence, and outputting the beamforming vector, the power allocation result, and the objective function value, including: Fix the power allocation variable and solve the sub-problem corresponding to the beamforming vector to obtain the beamforming vector; Fixing a beamforming vector, solving a subproblem corresponding to a power allocation variable to obtain a power allocation result, and updating a communication perception performance sum, wherein the beamforming vector includes a beamforming vector of a communication signal and a beamforming vector of the perception signal; If the number of iterations does not reach the maximum number of iterations, and the difference between the sum of the communication perception performance obtained in two consecutive iterations is less than the convergence accuracy, global convergence is determined, and the optimal beamforming result, optimal power allocation result, and the sum of the communication perception performance are output; If the number of iterations reaches the maximum number of iterations, and the difference between the sum of the communication perception performance obtained in two consecutive iterations is not less than the convergence accuracy, the convergence accuracy is adjusted to continue the alternating iterative optimization until global convergence is achieved.

6. The method according to claim 1, characterized in that The objective function is constructed based on the communication performance model and the perception performance model, including: Taking the achievable rate of communication information as the communication performance index and the power of the transmitted signal at the perception target as the perception performance index, an objective function with the communication perception performance and maximization as the optimization goal is established.

7. The method according to any one of claims 1 or 4 to 6, characterized in that The communication perception performance sum is the sum of the communication performance weighted value and the perception performance weighted value. The communication performance weighted value is the product of the communication performance sum and the communication weight parameter. The perception performance weighted value is the product of the perception performance sum of all perception targets and the perception weight parameter.

8. The method according to claim 1, characterized in that The constraint conditions are constructed based on multi-objective perceived fairness and the service quality requirements of communication users, including: A first constraint is established based on the achievable rate of communication information of any communication user being greater than or equal to a preset threshold; A second constraint is established based on a power difference of the sensing target in different sensing directions being less than or equal to a preset power threshold; A third constraint is established based on a power requirement that the power of the sensing signal and the power of the communication signal satisfy when any communication user can eliminate the sensing signal using a serial interference cancellation technique; The fourth constraint is constructed by 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.

9. A communication device, characterized in that: Comprising means for executing the method according to any one of claims 1 to 8.

10. A communication device, characterized in that: include: Memory, for storing computer instructions; A processor, configured to execute the computer program or computer instructions stored in the memory, so that the communication device performs the method according to any one of claims 1 to 8.

11. A computer storage medium, characterized in that Used to store a computer program, which is used to implement the method according to any one of claims 1 to 8 when executed.

12. A computer program product, characterized in that A computer program thereof, when the computer program is run, causes the method according to any one of claims 1 to 8 to be performed.

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