Communication method and communication apparatus

Abstract

The application provides a communication method and a communication device, which are applied to the technical field of communication. For example, the communication method is suitable for a MIMO scene or a super-large-scale MIMO scene, and the method comprises the following steps: compared with a pre-defined static port selection scheme (for example, selecting ports according to a specific arrangement mode based on rows, columns or polarization directions at equal intervals), a pre-constructed pilot pattern library (for example, a mapping relationship or a corresponding relationship between a channel scene and a pilot pattern) is introduced in the embodiment of the application; a suitable pilot pattern is dynamically selected in the pilot pattern library by combining a current channel condition, so that antenna ports (the antenna ports are key ports with excellent channel conditions and bearing main spatial energy) are activated based on the selected pilot pattern, the pilot overhead can be compressed, the channel estimation performance (for example, the accuracy of channel estimation) is improved, and the reliability of channel estimation is improved.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically, to a communication method and a communication device. Background Technology

[0002] With the development of communication technologies, the 3rd Generation Partnership Project (3GPP) introduced MIMO scenarios for the New Radio (NR) interface of 5G. Furthermore, the future 6th Generation Mobile Communication Technology (6G) may introduce ultra-large-scale MIMO scenarios. To achieve higher spectral efficiency in MIMO or ultra-large-scale MIMO applications, the number of antenna ports is expected to increase significantly. This increase will lead to a substantial increase in reference signal (RS) overhead and a sharp decline in spectrum utilization. Therefore, it is necessary to research methods to reduce RS overhead, keeping it within an acceptable range while ensuring the performance of channel estimation / demodulation.

[0003] Existing CSI-RS spatial compression schemes employ a "predefined" static port selection method. While this reduces overhead, it suffers from poor channel estimation reliability. Therefore, it is necessary to propose a method to address this issue. Summary of the Invention

[0004] In view of this, this application provides a communication method, communication device, chip system, computer-readable storage medium, computer program product, and communication system that can improve channel estimation performance (such as improving the accuracy of channel estimation) while compressing pilot overhead, and helps to improve the reliability of channel estimation.

[0005] In a first aspect, a communication method is provided, which can be executed by a user equipment (UE), or by a component configured in the UE (such as a circuit, chip, or chip system), or by a logic module or software capable of implementing all or part of the UE's functions. This application does not limit this method.

[0006] Specifically, the method includes: a UE receiving a first message sent from a network device, the first message including a first pilot pattern, the first pilot pattern being determined in a pilot pattern library based on the current channel conditions, the first pilot pattern being a pilot pattern in the pilot pattern library that satisfies the current channel conditions; the pilot pattern library being determined based on a particle swarm optimization algorithm, the pilot pattern library including pilot patterns corresponding to different channel environments, each pilot pattern corresponding to a set of antenna port indices; and receiving a channel state information reference signal (CSI-RS) based on the first pilot pattern.

[0007] Based on the above technical solution, compared with relying on predefined static port selection schemes (such as selecting ports according to a specific arrangement based on equal intervals, rows and columns, or polarization directions), this application embodiment introduces a pre-built pilot pattern library (such as the mapping relationship or correspondence between channel scenarios and pilot patterns); the UE receiving network device dynamically selects appropriate pilot patterns from the pilot pattern library by combining the current channel conditions, thereby receiving reference signals at the corresponding antenna ports of the pilot patterns (these antenna ports are key ports with good channel conditions and carrying the main spatial energy), which can improve channel estimation performance (such as improving the accuracy of channel estimation) while compressing pilot overhead, and helps to improve the reliability of channel estimation.

[0008] Furthermore, by using the pilot pattern library to determine the most suitable pilot pattern for the current channel, pilots can be adaptively allocated according to real-time channel characteristics, significantly reducing pilot overhead while ensuring reconstruction accuracy.

[0009] Optionally, the first pilot pattern is a pilot pattern from a pilot pattern library that satisfies the current channel conditions. This includes: the first pilot pattern is a pilot pattern corresponding to a first channel environment in the pilot pattern library, where the first channel environment is the channel environment that best matches the current channel environment among various channel environments. Therefore, by using the pilot pattern library, the first channel environment closest to the current channel environment can be determined, thereby determining the pilot pattern corresponding to the first channel environment, and using it as the pilot pattern for the current channel environment.

[0010] In some embodiments, the first pilot pattern is the pilot pattern corresponding to a first sub-channel characteristic selected based on the effective channel rank of the current channel; the first channel environment corresponds to multiple sub-channel characteristics; the first sub-channel characteristic is the sub-channel characteristic among the multiple sub-channel characteristics that has the same effective channel rank as the current channel. Therefore, after finding the first channel environment closest to the current channel environment, the sub-channel characteristics in the first channel environment can be further judged to achieve a more refined distinction, thereby determining the sub-channel characteristic with the same effective channel rank as the current channel environment, and using the pilot pattern of this sub-channel characteristic as the pilot pattern of the current channel environment. Furthermore, for the first pilot pattern determined by the network device, the UE can also perform fine-tuning locally to obtain a pilot pattern that better matches the current channel conditions.

[0011] Optionally, the method further includes: performing adjustment processing on the first pilot pattern to obtain a second pilot pattern; and sending a second message to the network device, the second message including the second pilot pattern. Therefore, the UE obtains a pilot pattern that better suits the current channel conditions through online fine-tuning and sends it to the network device, so that the network device can activate the corresponding antenna port based on the second pilot pattern determined by the UE.

[0012] Optionally, the UE performs adjustment processing on the first pilot pattern, including: executing a lightweight particle swarm optimization algorithm locally to obtain the second pilot pattern. Therefore, the UE can fine-tune the pilot pattern by running a lightweight particle swarm optimization algorithm, thereby determining a pilot pattern that better suits the current channel conditions without increasing the memory space occupied by the UE.

[0013] This application does not specifically limit the first message in its embodiments. Optionally, the first message is an RRC reconfiguration message.

[0014] For a description of the pilot pattern library, please refer to the description in the second part. For the sake of brevity, it will not be repeated here.

[0015] Secondly, a communication method is provided, which can be executed by a network device, or by a component (such as a circuit, chip, or chip system) configured in the network device, or by a logic module or software capable of implementing all or part of the functions of the network device. This application does not limit this method.

[0016] Specifically, the method includes: a network device determining a first pilot pattern from a pilot pattern library based on the current channel conditions, wherein the first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions; the pilot pattern library includes pilot patterns corresponding to different channel scenarios; and the pilot pattern library is determined based on a particle swarm optimization algorithm.

[0017] Send a first message to the UE, the first message including the first pilot pattern;

[0018] Activate the antenna port corresponding to the first pilot pattern according to the first pilot pattern, and send CSI-RS to the UE through the antenna port corresponding to the first pilot pattern.

[0019] Based on the above technical solution, compared with relying on predefined static port selection schemes (such as selecting ports according to a specific arrangement based on equal intervals, rows and columns, or polarization directions), this application embodiment introduces a pre-built pilot pattern library (such as the mapping relationship or correspondence between channel scenarios and pilot patterns); the network device dynamically selects appropriate pilot patterns from the pilot pattern library by combining the current channel conditions, thereby activating antenna ports based on the selected pilot patterns (these antenna ports are key ports with good channel conditions and carrying the main spatial energy), which can improve channel estimation performance (such as improving the accuracy of channel estimation) while compressing pilot overhead, and helps to improve the reliability of channel estimation.

[0020] Optionally, the first pilot pattern is a pilot pattern from a pilot pattern library that satisfies the current channel conditions. This includes: the first pilot pattern is a pilot pattern corresponding to a first channel environment in the pilot pattern library, where the first channel environment is the channel environment that best matches the current channel environment among various channel environments. Therefore, by using the pilot pattern library, the first channel environment closest to the current channel environment can be determined, thereby determining the pilot pattern corresponding to the first channel environment, and using it as the pilot pattern for the current channel environment.

[0021] In some embodiments, the first pilot pattern is the pilot pattern corresponding to a first sub-channel characteristic selected based on the effective channel rank of the current channel; the first channel environment corresponds to multiple sub-channel characteristics; the first sub-channel characteristic is the sub-channel characteristic among the multiple sub-channel characteristics that has the same effective channel rank as the current channel. Therefore, after finding the first channel environment closest to the current channel environment, the sub-channel characteristics in the first channel environment can be further judged to achieve a more refined distinction, thereby determining the sub-channel characteristic with the same effective channel rank as the current channel environment, and using the pilot pattern of this sub-channel characteristic as the pilot pattern of the current channel environment. Optionally, the method further includes: the network device receiving a second message from the UE, the second message including a second pilot pattern, the second pilot pattern being determined by the UE based on the first pilot pattern; activating the antenna port corresponding to the second pilot pattern according to the second pilot pattern, and sending CSI-RS to the UE through the antenna port corresponding to the second pilot pattern. Therefore, the network device receives a pilot pattern that is more consistent with the current channel conditions obtained by the UE through online fine-tuning.

[0022] In one possible implementation, the network device determines a first pilot pattern from a pilot pattern library based on the current channel conditions. This includes: determining a similarity parameter between the current channel scenario and each channel scenario in the pilot pattern library, resulting in multiple similarity parameters; determining a first similarity parameter from among the multiple similarity parameters, where the first similarity parameter characterizes the degree of similarity between the first channel scenario and the current channel scenario; the first similarity parameter being a similarity parameter among the multiple similarity parameters that satisfies a preset condition; and determining the first pilot pattern based on the first channel scenario and the effective rank of the preceding channel. Therefore, by calculating the similarity parameter, the network device can find the channel environment in the pilot pattern library that is closest to the current channel environment, thereby determining the pilot pattern that best matches the current channel environment.

[0023] For example, each similarity parameter satisfies the following formula:

[0024] ;

[0025] in, For weighted Euclidean distance;

[0026] ;in, , , ..., The weights for each feature, , , ..., The sum of is 1; to Represents the various characteristics of the current channel; to These represent the various features of a channel scenario in the pilot pattern library.

[0027] Optionally, the method further includes: the network device determining whether a first parameter exceeds a first threshold, wherein the first parameter characterizes the degree of difference between the current channel measurement value and the previous channel measurement value, and the first parameter is determined based on the current channel measurement value and the previous channel measurement value; wherein, determining the first pilot pattern in the pilot pattern library based on the current channel conditions includes determining the first pilot pattern in the pilot pattern library based on the current channel conditions when the first parameter exceeds the first threshold. Therefore, the network device determines a pilot pattern for the current channel only when the first parameter exceeds the first threshold (or in other words, the difference between the current channel measurement value and the previous channel measurement value is large, or the channel is unstable), thereby avoiding frequent adjustments to the pilot pattern.

[0028] For example, the first parameter satisfies the following formula:

[0029] ;

[0030] in, This represents the first parameter. This is the current channel measurement value. superscript This represents the conjugate transpose operation. This is the value from the previous channel measurement.

[0031] Optionally, the method further includes: using the previous pilot pattern if the first parameter does not exceed a first threshold.

[0032] In one possible implementation, the pilot pattern corresponding to each channel environment in the pilot pattern library is determined in the following manner:

[0033] Initialize the following parameters:

[0034] No. i The initial position of each particle , wherein the initial position It is a vector containing elements 0 or 1, and The total number of elements containing 1 is M, where, ;

[0035] No. i The speed of each particle , t Represents the number of iterations. ;

[0036] No. i The initial fitness value of each particle ;

[0037] No. i The individual optimal fitness of each particle ;

[0038] No. i Optimal position of each particle ;

[0039] Global optimal fitness ;

[0040] Global optimal position ;

[0041] After initializing the parameters, the following iterative process is executed:

[0042] Calculate inertia weight ;

[0043] Based on inertia weight And input parameters, determine the first i The position of each particle and speed ;

[0044] Calculate the first i Fitness value of each particle ;

[0045] According to the fitness value Update the individual optimal fitness value for each particle. ;

[0046] Based on the obtained fitness values ​​of the G particles, update the global optimal fitness value. and the global optimal position ;

[0047] After performing T iterations, output the globally optimal fitness value. and the global optimal position Wherein, the global optimal position The corresponding index is the optimal port set.

[0048] Using the particle swarm optimization algorithm described above, the optimal port set for each channel scenario can be calculated, thereby constructing a pilot pattern library.

[0049] It should be noted that in the particle swarm optimization algorithm of this application embodiment, each particle represents a port selection scheme. At the receiving end, the ports of the port selection scheme are reconstructed through AI channel estimation, and the port selection is updated with minimizing the error as the optimization objective, ultimately obtaining the optimal port set, thereby achieving the optimal trade-off between pilot overhead and estimation accuracy. Furthermore, the scheme of this application embodiment can achieve higher channel estimation accuracy with the same overhead, or significantly reduce overhead with the same accuracy.

[0050] For example, the calculation of the first i Fitness value of each particle ,include:

[0051] ;

[0052] in, This represents the squaring operation of the 2-norm, where, This represents the channel response of the complete port; This represents the actual antenna port channel response.

[0053] Optionally, based on inertia weights And input parameters, determine the first i The position of each particle and speed ,include:

[0054] Based on inertia weight and the speed of input parameter calculation ;

[0055] Using a velocity mapping probability function, the velocity is... Mapped to multiple probability values;

[0056] Based on the plurality of probability values, M probability values ​​are determined, and based on the M probability values, the first probability value is obtained. i The position of each particle .

[0057] For example, sort multiple probability values ​​in descending order; based on the multiple probability values ​​sorted in descending order, select the first M probability values.

[0058] For example, sort multiple probability values ​​in ascending order; then select the last M probability values ​​based on the sorted probability values.

[0059] For example, by setting a certain threshold, the probability values ​​that are greater than the threshold can be filtered out from multiple probability values ​​and used as M probability values.

[0060] Of course, the value of M is less than the number of the aforementioned probability values. It should be understood that the examples of determining M probability values ​​shown above are merely illustrative descriptions, and the embodiments of this application are not limited thereto.

[0061] For example, the velocity mapping probability function is The function, or the hyperbolic tangent function, or the soft step function.

[0062] For example, the first i The position of each particle and speed Satisfy the following formula:

[0063] ;

[0064] in, Denotes the particle in the (t+1)th iteration. i speed, For inertial weights, Denotes the particle in the t-th iteration. i speed, Indicates the first t In the next iteration, particles i The optimal position of an individual It is the first t The global optimal position of all particles in the next iteration. It is the first t In the next iteration, particles i Location, and All are particle learning factors (e.g., a value of 2). and All are random numbers between 0 and 1; among them, The function is used to map continuous velocity values ​​to probability values ​​of taking the value 1; Used to select the top M values ​​from a set of probability values ​​sorted in descending order.

[0065] Therefore, by introducing a Top-M deterministic mapping mechanism into the binary particle swarm optimization algorithm, replacing the traditional update method based on random probability, the optimization efficiency and stability of the method are significantly improved while ensuring the feasibility of the solution.

[0066] Optionally, the first message is an RRC reconfiguration message.

[0067] It should be noted that the explanations, supplements, and descriptions of the beneficial effects of the pilot pattern library can apply to both the first and second aspects, and will not be repeated here.

[0068] Thirdly, a communication apparatus is provided, comprising modules or units for performing the method in any possible implementation of the first aspect described above.

[0069] In one design, the communication device may include modules that perform the methods / operations / steps / actions described in the foregoing aspects. These modules may be hardware circuits, software, or a combination of hardware circuits and software.

[0070] In one design, the communication device is a communication chip, which may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0071] In another design, the communication device is a communication equipment, which may include a transmitter for sending information or data and a receiver for receiving information or data.

[0072] In another design, the communication device is used to perform the method in any possible implementation of the first aspect described above. The communication device may be configured in the UE, or the communication device itself may be the UE.

[0073] Fourthly, a communication apparatus is provided, comprising modules or units for performing the method in any possible implementation of the second aspect described above.

[0074] In one design, the communication device may include modules that perform the methods / operations / steps / actions described in the foregoing aspects. These modules may be hardware circuits, software, or a combination of hardware circuits and software.

[0075] In one design, the communication device is a communication chip, which may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0076] In another design, the communication device is a communication equipment, which may include a transmitter for sending information or data and a receiver for receiving information or data.

[0077] In another design, the communication device is used to perform the method in any possible implementation of the second aspect described above. The communication device may be configured in the network device described above, or the communication device itself may be a network device.

[0078] Alternatively, the network device may be a satellite, an access network device (e.g., a gNB), or a network element in the core network.

[0079] Fifthly, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the first aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0080] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0081] In another implementation, the communication device is a chip configured in the UE. When the communication device is a chip configured in the UE, the communication interface can be an input / output interface.

[0082] In a sixth aspect, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the second aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0083] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0084] In another implementation, the communication device is a chip configured in a network device. When the communication device is a chip configured in a network device, the communication interface can be an input / output interface.

[0085] In a seventh aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute a method in any possible implementation of any aspect.

[0086] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to and transmitted by a transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as both the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0087] Eighthly, a communication device is provided, including a processor and a memory. The processor is used to read instructions stored in the memory, receive signals via a receiver, and transmit signals via a transmitter to execute the method in any possible implementation of any of the preceding aspects.

[0088] Optionally, the processor may be one or more, and the memory may be one or more.

[0089] Optionally, the memory may be integrated with the processor, or the memory may be separated from the processor.

[0090] In the specific implementation process, the memory can be a non-transitory memory, such as read-only memory (ROM), which can be integrated with the processor on the same chip or set on different chips. The embodiments of this application do not limit the type of memory or the way the memory and processor are set.

[0091] It should be understood that the relevant data interaction process, such as sending indication information, can be the process of the processor outputting indication information, and receiving capability information can be the process of the processor receiving input capability information. Specifically, the data output by the processor can be sent to the transmitter, and the input data received by the processor can come from the receiver. Here, the transmitter and receiver can be collectively referred to as a transceiver.

[0092] The processing device mentioned in the eighth aspect above can be one or more chips. The processor in the processing device can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, integrated circuit, etc.; when implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0093] Ninthly, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code or instructions) that, when the computer program is run, causes a computer to perform a method in any possible implementation of any of the above aspects.

[0094] In a tenth aspect, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the methods in any possible implementation of any of the preceding aspects.

[0095] Eleventhly, embodiments of this application provide a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0096] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0097] In a twelfth aspect, a communication system is provided, including the aforementioned UE and network device. The UE is used to execute any possible implementation of the first aspect. The network device is used to execute any possible implementation of the second aspect.

[0098] Optionally, the communication system may also include other devices that communicate with the UE and / or network devices. Attached Figure Description

[0099] Figure 1 This is an example diagram of a communication system;

[0100] Figure 2 This is an example diagram of spatial sparsity of CSI-RS ports;

[0101] Figure 3 This is an example interaction diagram of the communication method according to an embodiment of this application;

[0102] Figure 4These are example diagrams of pilot patterns for different channel scenarios according to embodiments of this application;

[0103] Figure 5 This is an example diagram illustrating the process of generating sparse pilot masks based on the binary particle swarm optimization algorithm in an embodiment of this application;

[0104] Figure 6 These are example diagrams illustrating simulation results of embodiments of this application;

[0105] Figure 7 This is a schematic block diagram of the communication device provided in the embodiments of this application;

[0106] Figure 8 This is another schematic block diagram of the communication device provided in the embodiments of this application. Detailed Implementation

[0107] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0108] In this application embodiment, "multiple" can be understood as "at least two"; "multiple items" can be understood as "at least two items".

[0109] In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in this application merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or the order of execution, and that the words "first" and "second" do not necessarily imply that they are different.

[0110] This application can be applied to communication systems. Mobile communication systems include, but are not limited to, the following systems: Long Term Evolution (LTE) systems, Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) systems, 5th Generation (5G) mobile communication systems or new radio access technology (NR) and future mobile communication systems; vehicle-to-X (V2X), where V2X can include vehicle-to-network (V2N), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), etc.; Long Term Evolution-Vehicle (LTE-V) technology for vehicle-to-everything (V2V) communication; vehicle-to-everything (V2X) communication; machine-type communication (MTC); Internet of Things (IoT); Long Term Evolution-Machine (LTE-M) technology for machine-to-machine (M2M) communication; and machine-to-machine (M2M) communication. 5G mobile communication systems can include non-standalone (NSA) and / or standalone (SA) networks.

[0111] This application can also be applied to systems that integrate mobile communication systems and satellite communication systems. Satellite communication systems include, but are not limited to, non-terrestrial network (NTN) systems such as high altitude platform station (HAPS) communication, for example, the Global Navigation Satellite System (GNSS). Optionally, satellite communication systems include geostationary orbit (GEO) satellites and non-geostationary earth orbit (NGEO) satellites; or various terrestrial network (TN) systems.

[0112] Figure 1 This is a schematic diagram of a communication system 100 used in an embodiment of this application. The communication system 100 may include network devices, such as... Figure 1 The network device 110 is shown. The communication system 100 may also include terminal devices, such as... Figure 1 The terminal device 120 shown. The network device 110 and the terminal device 120 can communicate via a wireless link.

[0113] Figure 1 An exemplary network device 110 and a terminal device 120 are shown. Optionally, the communication system 100 may also include multiple network devices and / or multiple terminal devices.

[0114] The network equipment in this application can be network-side equipment such as access network equipment and core network equipment. Access network equipment is sometimes also called access node. Access network equipment has wireless transceiver capabilities and is used to communicate with terminals. Access network equipment includes, but is not limited to, base stations, evolved NodeBs (eNodeBs), transmission reception points (TRPs) in the above-mentioned communication systems, next-generation NodeBs (gNBs) in 5G mobile communication systems, access network equipment or modules of access network equipment in open RAN (ORAN) systems, satellites in NTN communication systems, base stations in future mobile communication systems, or access nodes in WiFi systems. Access network equipment can also be modules or units that can implement some of the functions of a base station. Access network equipment can be macro base stations, micro base stations or indoor stations, relay nodes or donor nodes, or wireless controllers in cloud radioaccess network (CRAN) scenarios. Optionally, access network equipment can also be servers, wearable devices, or vehicle-mounted equipment, etc. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). Multiple access network devices in a communication system can be base stations of the same type or different types. Base stations can communicate with terminals directly or via relay stations. Terminals can communicate with multiple base stations using different access technologies. The embodiments of this application do not limit the specific technology or equipment form used in the access network equipment. In this application, access network equipment or core network equipment can be simply referred to as network equipment.

[0115] In this application, the means for implementing the functions of a network device can be a network device itself, or a means capable of supporting the network device in implementing those functions, such as a processor, circuit, chip, or chip system. This means can be installed in or connected to the network device. In the technical solutions provided in this application, the example of a network device being used to implement the functions of a network device is used to describe the technical solutions provided in this application.

[0116] The terminal device in this application can be a wireless terminal device capable of receiving network device scheduling and instruction information. The wireless terminal device can be a device providing voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. For example, the terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can also be referred to as a terminal, user equipment (UE), mobile station, mobile terminal, etc. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), ultra-reliable low-latency communication (URLLC), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, or satellite communication, etc. The terminal can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, aircraft (such as drone, helicopter, airplane), hot air balloon, ship, robot, robotic arm, or smart home device, etc. The embodiments of this application do not limit the form of the terminal device.

[0117] In this application, the apparatus for implementing the functions of a terminal device can be the terminal device itself, or any apparatus capable of supporting the terminal device in implementing those functions, such as a processor, circuit, chip, or chip system. This apparatus can be installed in or connected to the terminal device. In the technical solutions provided in this application, the example of a terminal device being used to implement the functions of a terminal device is used to describe the technical solutions provided in this application.

[0118] Access network equipment and / or terminal equipment can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites. This application does not limit the application scenarios of the access network equipment and terminal equipment. They can be deployed in the same or different scenarios; for example, both can be deployed on land simultaneously; or the access network equipment can be deployed on land while the terminal equipment is deployed on water, etc., and so on.

[0119] In practical applications, multiple network devices can collaborate to assist terminals in achieving wireless access, with different network devices each implementing a portion of the base station's functions. For example, network devices can be central units (CUs), distributed units (DUs), CUs (control planes, CPs), CUs (user planes, UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0120] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0121] To facilitate understanding of the embodiments of this application, the terminology used in this application will be briefly explained first. For example, the explanation of some terms can also be found in the interpretation of the 3rd Generation Partnership Project (3GPP) standard protocol.

[0122] 1. Reference signal (RS)

[0123] A reference signal is a predefined signal transmitted over a physical channel to support specific functions. Reference signals can also be called pilot signals, reference sequences, etc. Reference signals can be used for channel measurements, such as the channel state information reference signal (CSI-RS) for downlink channel measurements; they can also be used for data demodulation, such as the demodulated reference signal (DMRS); or they can be used for positioning, such as the positioning reference signal (PRS).

[0124] It is understood that the various types of reference signals shown above are merely examples, and the embodiments of this application are not limited thereto. For instance, as standard protocols evolve, if other reference signals are defined to achieve the same or similar functions, then the embodiments of this application may also be applicable.

[0125] The transmitter precodes the DMRS and data before sending them to the receiver. The DMRS and data use the same precoding, making the precoding invisible to the receiver. The precoding used for the DMRS can be determined based on channel state information (CSI) obtained from the CSI-RS or the sounding reference signal (SRS).

[0126] 2. Antenna port

[0127] An antenna port is a logical concept. One antenna port can correspond to one or more physical transmit antennas. From the UE's perspective, regardless of whether the channel is formed by a single physical transmit antenna or multiple physical transmit antennas, the type of reference signal corresponding to that antenna port defines the antenna port. For example, an antenna port corresponding to DMRS is a DMRS port, and an antenna port corresponding to CSI-RS is a CSI-RS port. One antenna port can correspond to one channel, and the UE can estimate the channel and demodulate data based on the reference signal corresponding to the antenna port. Each antenna port corresponds to a time-frequency resource grid and has a corresponding reference signal.

[0128] For example, network devices send channel state information-reference signals (CSI-RS) to the UE. CSI-RS is a reference signal sent by the network device to the terminal device to measure the quality of the downlink channel. By measuring CSI-RS, the UE feeds back channel state information, including channel quality indication, precoding matrix indication, rank indication, etc., helping the base station optimize signal transmission paths and improve communication efficiency.

[0129] To reduce antenna port overhead, the concept of CSI-RS port spatial sparsity has emerged. For example... Figure 2 As shown, for all antenna ports (e.g., 32 ports), according to a certain compression ratio ( Figure 2 Examples of 1 / 2 compression ratio and 1 / 4 compression ratio are shown respectively to achieve spatial sparsity of the CSI-RS port, thereby reducing the reference signal overhead. Of course, while keeping the RS overhead within an acceptable range, the performance of channel estimation or channel demodulation must be guaranteed at the same time.

[0130] This application proposes to introduce an offline-trained pilot pattern library to obtain the optimal antenna port set for each channel scenario, thereby ensuring the performance of channel estimation or channel demodulation.

[0131] 3. Binary Particle Swarm Optimization (BPSO) Algorithm

[0132] The BPSO algorithm simulates the flight and foraging process of birds. Each particle in the population represents a solution in the solution space, possessing two attributes: velocity and position. The position vector represents the solution corresponding to that particle, while the velocity vector is used to adjust the particle's next flight, thereby updating its position and searching for a new solution set. During flight, particles adjust their flight direction and velocity based on their own historical flight experience and the flight experience of other particles in the population. The optimal position of each particle during its historical flight is called its individual optimal solution. pbest The optimal location the entire population passed through during its historical flight was gbest This is called the global optimal solution. Particles communicate through... pbest, gbest Sharing information allows them to influence the search behavior of a population during evolution.

[0133] This application proposes to use a binary particle swarm optimization algorithm to solve for the optimal port set (CSI-RS) for each channel scenario. For multiple channel scenarios, the binary particle swarm optimization algorithm can be used to solve for the corresponding optimal port set, thereby forming a pilot pattern library.

[0134] It should be understood that the technical terminology used in the embodiments of this application is for illustrative purposes only and not as a limitation. As technology evolves, technical terminology may also change; however, other technical terms with the same technical meaning should also be applicable to the embodiments of this application.

[0135] Currently, 3GPP uses a "predefined" static port selection scheme, such as selecting antenna ports by equal intervals, row / column-based selection, or polarization-based selection in a specific arrangement. While this method reduces overhead, it suffers from poor channel estimation performance and reliability.

[0136] In view of this, embodiments of this application propose a communication method that dynamically selects suitable pilot patterns from a pilot pattern library based on the current channel conditions. This activates antenna ports (key ports with excellent channel conditions and carrying the majority of spatial energy) based on the selected pilot patterns. This method can improve channel estimation performance (e.g., increase the accuracy of channel estimation) while reducing pilot overhead, thus contributing to improved reliability of channel estimation. Embodiments of this application consider the differences in channel characteristics between different ports. By dynamically identifying and prioritizing the activation of key ports with excellent channel conditions and carrying the majority of spatial energy, the method achieves improved reliability of channel estimation while reducing pilot overhead.

[0137] The following detailed explanation of the solution provided in this application, in conjunction with the corresponding flowcharts, illustrates the method in detail. It is understood that the illustrative flowcharts provided in this application primarily use different devices (e.g., UE, network devices) as examples of the execution entities for this interactive illustration, but this application does not limit the execution entities of the interactive illustrations. For example, the devices (e.g., UE, network devices) in the illustrative flowcharts can also be chips, chip systems, or processors that support the implementation of this method on the device, or logical modules or software capable of implementing all or part of the device's functions.

[0138] As a general statement, the message or signaling interactions involved in the interaction process of this application embodiment can be standard messages or signaling or newly introduced messages or signaling. This application embodiment does not make specific limitations on this.

[0139] Figure 3 This is an example flowchart illustrating a communication method according to an embodiment of this application. It can be understood that... Figure 3 The UE in the middle can be Figure 1 The term "terminal device 120" can also refer to devices within the terminal device 120 (such as processors, chips, or chip systems). Network devices can be... Figure 1 The network device 110 mentioned here can also refer to the devices within the network device 110 (such as processors, chips, or chip systems). Figure 3 As shown, the method includes at least the following steps:

[0140] Step 310: The network device determines a first pilot pattern from the pilot pattern library based on the current channel conditions. The first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions. The pilot pattern library includes pilot patterns corresponding to different channel scenarios, and each pilot pattern corresponds to a set of antenna port indices. The pilot pattern library is determined based on a particle swarm optimization algorithm (e.g., binary particle swarm optimization algorithm).

[0141] The first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions. It can be understood as: the first pilot pattern is the pilot pattern in the pilot pattern library that best matches (or is closest to) the current channel environment. For example, the pilot pattern library includes one or more typical channel environments, each corresponding to one or more sets of pilot patterns. By searching among multiple typical channel environments for the channel environment closest to the current channel conditions, the pilot pattern corresponding to that closest channel environment (such as the first channel scenario) is selected and applied to the current channel.

[0142] Furthermore, in the first channel environment, there are multiple sub-channel characteristics (sub-channel characteristics can be represented by the effective channel rank), and these multiple sub-channel characteristics include at least the first sub-channel characteristic. The first pilot pattern is selected based on the effective channel rank of the current channel and corresponds to the first sub-channel characteristic; that is, the effective channel rank of the current channel is the same as the effective channel rank corresponding to the first sub-channel characteristic. Therefore, after finding the first channel environment that is closest to the current channel environment, the sub-channel characteristics in the first channel environment can be further judged to achieve a more refined distinction, thereby determining the sub-channel characteristic with the same effective channel rank as the current channel environment, and using the pilot pattern of this sub-channel characteristic as the pilot pattern of the current channel environment.

[0143] This application does not specifically limit the parameters used to characterize channel scenarios. In some embodiments, a channel scenario is defined by one or more of the following parameters: time delay, Doppler, angle spread, and effective channel rank.

[0144] Optionally, when performing the matching of the current channel with the channel scene in the pilot pattern library, a two-step matching method can be adopted, specifically including: (1) using multi-dimensional features (e.g., through one or more of the following parameters: time delay, Doppler, angle spread, and effective channel rank, etc.) to perform a rough matching of the macroscopic scene; (2) after obtaining the channel scene through rough matching, the effective channel rank ( This further involves determining the specific spatial complexity under this type of channel environment. These two matching steps enable accurate differentiation of channel scenarios, thus providing a reliable basis for subsequent pilot compression.

[0145] The effective rank of a channel is the number of significantly greater than 0 singular values ​​obtained after performing singular value decomposition on the channel matrix. For example, the channel matrix is ​​represented as... ; through the Perform singular value decomposition: ,in, Describes a left singular vector matrix. Represents the singular value matrix, Represents the conjugate transpose of a right singular vector matrix, where the superscript... This represents the conjugate transpose operation. The number of singular values ​​is That is, the effective rank of the channel is For a detailed explanation of the singular value decomposition process, please refer to the relevant mathematical principles. For the sake of brevity, it will not be elaborated upon here.

[0146] For the coarse matching in (1) above, multiple similarity parameters are obtained by calculating the similarity parameters between the current channel scenario and each channel scenario in the pilot pattern library; the first similarity parameter is used to characterize the degree of similarity between the first channel scenario and the current channel scenario; the first similarity parameter is the similarity parameter that meets the preset conditions among the multiple similarity parameters (for example, the first similarity parameter is the one with the largest value among the multiple similarity parameters; or, for example, the first similarity parameter is the parameter that is within the preset range among the multiple similarity parameters).

[0147] Optionally, in some embodiments, each similarity parameter satisfies the following formula: ;in, For weighted Euclidean distance;

[0148] .

[0149] in, , , ..., The weights for each feature, , , ..., The sum of is 1. to Represents the various characteristics of the current channel; to This represents the features of a specific channel scenario (e.g., the first channel scenario) in the pilot pattern library. 'n' represents the number or index of the main channel features; for example, 'n' can be 4. The four features are time delay... Doppler , Angle expansion Effective rank of the channel .

[0150] For example, the main characteristics of the current channel (delay) Doppler , Angle expansion Effective rank of the channel ) is represented as: .

[0151] Optionally, , , ..., In this context, the weights related to the angle and the weights related to the effective rank of the channel can be greater than other weight parameters.

[0152] It is understood that other calculation methods can be used to determine the above similarity parameters, and this application does not impose specific limitations. For example, the minimum mean square error or standard deviation method can be used, etc.

[0153] Therefore, network devices calculate similarity parameters to find (or retrieve) the channel environment that is closest to the current channel environment in the pilot pattern library, thereby determining the pilot pattern that best matches the current channel environment (or channel characteristics).

[0154] After obtaining the first similarity parameter, it can be determined that the current channel is most similar to the first channel scenario. The first channel scenario can include different sub-channel characteristics, meaning that different spatial complexities can be further subdivided within this type of channel environment. Optionally, the effective rank of the current channel can be used for further matching to obtain the sub-channel characteristics that are most similar to the current channel environment, thereby obtaining a pilot pattern that is more consistent with the current channel environment.

[0155] The aforementioned pilot pattern library can be a pre-built sparse pilot library. Optionally, the pilot pattern library can be understood as a mapping relationship (or correspondence) between channel scenarios and pilot patterns; or, a mapping relationship between channel scenarios, sub-channel characteristics, and pilot patterns. For example, channel scenario 1 corresponds to pilot pattern 1; channel scenario 2 corresponds to pilot pattern 2. In some embodiments, for a certain channel scenario, the corresponding pilot pattern (or CSI-RS port sparse mask) can be further found through sub-channel characteristics and spatial correlation. To facilitate understanding of pilot pattern libraries in different scenarios, the following description is based on examples in Table 1.

[0156] Table 1

[0157]

[0158] It should be noted that Table 1 above is merely an exemplary description of possible implementations of the pilot pattern library, and the embodiments of this application are not limited thereto. In fact, Table 1 may also include more or fewer items than shown in Table 1. For example, Table 1 may only include the correspondence between the channel scenarios in the first column and the pilot patterns in the fourth column. Or, for example, Table 1 may include the correspondence between the channel scenarios in the first column, the channel characteristics in the second column, and the pilot patterns in the fourth column.

[0159] The first pilot pattern is a pilot pattern from the pilot pattern library that satisfies the current channel conditions. It can be understood as a pilot pattern that matches the current channel conditions. For example, if the channel scenario closest to the current channel condition is channel scenario 1, the pilot pattern corresponding to channel scenario 1 can be obtained by looking up the pattern in Table 1 above. Furthermore, considering the channel characteristics of the current channel, such as… Therefore, the pilot pattern can be identified as ID1-1: a pilot pattern with extremely high spatial compression.

[0160] To facilitate understanding of the different pilot patterns corresponding to different channel scenarios, the following description uses examples from section 4 as a reference. Figure 4 As shown in the figure above, the pilot pattern for the Urban Micro (UMi) channel scenario is shown in the upper figure (the filled-in diagonal line represents the optimal port set for the UMi channel scenario); the pilot pattern for the Urban Macro (UMa) channel scenario is shown in the lower figure (the filled-in diagonal line represents the optimal port set for the UMi channel scenario). It can be seen that, under the same compression ratio (e.g., 1 / 2 compression ratio), the pilot pattern for the UMi channel scenario differs from that for the UMi scenario.

[0161] Among them, the UMA scenario usually refers to the urban macrocell scenario, where the base station antenna is relatively high (generally higher than the surrounding buildings), with a large coverage area and long propagation distance. The channel is mainly characterized by large-scale path loss and shadow fading, with relatively small multipath angular spread, strong spatial correlation, and the effective rank of the channel is usually low to medium. The overall spatial structure is relatively stable.

[0162] UMi scenarios refer to urban microcell scenarios, where base stations are deployed at lower heights (usually close to or below building heights) and have smaller coverage radii. Users and base stations are more susceptible to scattering from streets, buildings, and other objects, resulting in richer multipath and angular spread, lower spatial correlation, and channel effective rank that is usually higher than UMi, exhibiting more pronounced spatial diversity characteristics.

[0163] It should be understood that Figure 4 The description uses only UMA and UMi scenarios as examples, and the embodiments in this application are not limited thereto. Furthermore, Figure 4The examples provided only illustrate pilot patterns for the UMA and UMi scenarios using a 1 / 2 compression ratio, and the embodiments in this application are not limited to this. In fact, there can be many other channel scenarios, and different pilot patterns can be used for different channel scenarios. Furthermore, different pilot patterns can be used for the same channel scenario at different compression ratios, without specific limitations.

[0164] This application does not specifically limit the triggering conditions for the network device to determine the pilot pattern for the current channel. In some embodiments, the network device determines the channel change by comparing the channel measurements of two consecutive channels, and decides whether to determine the pilot pattern based on the channel change. If the channel is stable, i.e., the difference between the current channel and the previous channel is small, then the pilot pattern used last time can still be used; if the channel change is large, then a pilot pattern matching the current channel environment can be found in the pilot pattern library and applied, thereby realizing a scheme for adaptive pilot allocation based on real-time channel characteristics.

[0165] For example, network devices perform channel scene identification, or calculate channel measurements, based on the channel characteristics of user uplink signals.

[0166] This application does not specifically limit the method by which the network device determines the degree of channel change. Optionally, the method further includes: the network device determining the current channel measurement value and the previous channel measurement value; determining a first parameter based on the current channel measurement value and the previous channel measurement value; the first parameter is used to characterize the degree of difference between the current channel measurement value and the previous channel measurement value.

[0167] Optionally, step 310 includes: if the first parameter exceeds the first threshold, determining a first pilot pattern in the pilot pattern library according to the current channel conditions.

[0168] Optionally, the method further includes: using the previous pilot pattern if the first parameter does not exceed a first threshold.

[0169] In other words, if the first parameter exceeds the first threshold, it indicates a significant difference between the current channel measurement and the previous channel measurement. Therefore, the previous pilot pattern cannot be used, and a new pilot pattern needs to be determined from the pilot pattern library based on the current channel conditions to ensure a suitable pilot pattern for the current channel environment. If the first parameter does not exceed the first threshold, it indicates a small difference between the current and previous channel measurements, or that the channel is stable. In this case, the previously used pilot pattern can be directly selected. Therefore, network devices determine a pilot pattern for the current channel only when the first parameter exceeds the first threshold (or when the difference between the current and previous channel measurements is significant, or when the channel is unstable), thus avoiding frequent adjustments to the pilot pattern.

[0170] The embodiments of this application do not specifically limit the method for determining the first parameter.

[0171] Optionally, the first parameter satisfies the following formula: ;in, This represents the first parameter (or correlation coefficient). This is the current channel measurement value. superscript This represents the conjugate transpose operation. This is the value from the previous channel measurement. r Represents a moment.

[0172] This application does not specifically limit the value of the first threshold in its embodiments. The first threshold can be a priori value or a value set by the network device based on the actual application scenario, thus limiting this step. For example, the first threshold is expressed as... .

[0173] Optionally, the network device calculates the current channel measurement value according to a predetermined monitoring cycle or CSI reporting cycle in order to decide whether the pilot pattern needs to be adjusted, thereby avoiding frequent adjustments to the pilot pattern.

[0174] In step 320, the network device sends a first message to the UE, the first message including a first pilot pattern. Correspondingly, the UE receives the first message.

[0175] In other words, after the network device determines the first pilot pattern through the pilot pattern library, it sends the first pilot pattern to the UE through the first message so that the UE can know the first pilot pattern.

[0176] This application embodiment does not specifically limit the message type or signaling name of the first message. Optionally, the first message is an RRC reconfiguration message.

[0177] In step 330, the network device activates the antenna port corresponding to the first pilot pattern according to the first pilot pattern, and sends CSI-RS to the UE through the antenna port corresponding to the first pilot pattern. Correspondingly, the UE receives the CSI-RS.

[0178] In this embodiment, compared to relying on predefined static port selection schemes (such as selecting ports in a specific arrangement based on equal intervals, rows and columns, or polarization directions), this embodiment dynamically selects appropriate pilot patterns from the pilot pattern library based on the current channel conditions, thereby activating antenna ports (these antenna ports are key ports with excellent channel conditions and carrying the main spatial energy) based on the selected pilot patterns. This can improve channel estimation performance (such as improving the accuracy of channel estimation) while compressing pilot overhead, and helps to improve the reliability of channel estimation.

[0179] The first pilot pattern selected by the network device from the pilot pattern library can be used as the initial pilot pattern. The network device activates the corresponding antenna port based on the initial pilot pattern and transmits CSI-RS to the UE. To further optimize the pilot pattern, adjustments can also be made to the pilot pattern selected by the network device on the UE side.

[0180] Optionally, the method further includes:

[0181] Step 340: The UE performs adjustment processing on the first pilot pattern to obtain the second pilot pattern.

[0182] In some embodiments, the UE fine-tunes the first pilot pattern by running a lightweight algorithm (such as a binary particle swarm optimization algorithm) to obtain a pilot pattern, such as a second pilot pattern, that better matches the current channel environment. The principle of the lightweight algorithm is explained later. Figure 5 The process shown is similar, but requires fewer iterations and has lower requirements for device storage space or capabilities, thus avoiding excessive storage space consumption on the UE side.

[0183] Optionally, for the pilot pattern determined by the network device when the similarity is high, the UE can perform the update by using a small number of iterations; for the pilot pattern determined by the network device when the similarity is low, the UE can perform the update by increasing the number of iterations of the particle swarm optimization to obtain a pilot pattern that is more compatible with the current channel environment.

[0184] This application does not specify the method for determining the similarity level. As mentioned above, the network device selects the pilot pattern corresponding to the first channel scenario that is closest to the current channel environment based on the first similarity parameter. Optionally, the similarity level can be determined by the relationship between the first similarity parameter and a preset threshold. For example, if the first similarity parameter exceeds the preset threshold, then the first channel scenario is considered to have a high similarity to the current channel; if the first similarity parameter does not exceed the preset threshold, then the first channel scenario is considered to have a low similarity to the current channel.

[0185] It should be noted that this description uses the first pilot pattern determined by the UE from the pilot pattern library by the network device as an example, and the embodiments of this application are not limited to this. After performing the aforementioned judgment of the first parameter and the first threshold, if the network device uses the previous pilot pattern (i.e., the pilot pattern used in the previous communication), the UE can also perform fine-tuning processing on the previous pilot pattern in combination with the current channel conditions in order to optimize the pilot pattern.

[0186] In step 350, the UE sends a second message to the network device, the second message including a second pilot pattern. Correspondingly, the network device receives the second message.

[0187] In other words, the UE feeds back the fine-tuned pilot pattern to the network device so that the network device can configure itself based on the pilot pattern fed back by the UE.

[0188] This application does not specifically limit the message type or signaling type of the second message in its embodiments. For example, the UE's RRC layer encapsulates the second pilot pattern in a dedicated RRC uplink message and sends it to the network device through the PUSCH scheduled by the network device.

[0189] Step 360: The network device activates the antenna port corresponding to the second pilot pattern according to the second pilot pattern, and sends CSI-RS to the UE through the antenna port corresponding to the second pilot pattern. Correspondingly, the UE receives the CSI-RS.

[0190] After receiving the second pilot pattern sent by the UE, the network device activates a designated subset of ports based on the second pilot pattern and transmits CSI-RS to the UE through the activated subset of antenna ports.

[0191] Therefore, once the network device has determined the first pilot pattern, the UE can further fine-tune the first pilot pattern based on the current channel conditions to obtain the second pilot pattern, thereby obtaining a pilot pattern that is more consistent with the current channel conditions, which helps to further improve the channel estimation performance.

[0192] The pilot pattern library (or pilot library) of the embodiments of this application can be constructed offline. In some embodiments, in the offline phase, the binary particle swarm optimization algorithm is used to construct an optimal CSI-RS port sparse mask that can ensure channel estimation performance for each typical channel scenario, and a pilot pattern library is formed.

[0193] A typical channel scenario can be understood as a relatively common or representative channel scenario in actual communication. Exemplarily, typical channel scenarios include: LOS scenario, NLOS scenario, high-speed mobile scenario, low-speed / quasi-static scenario, weak coverage scenario, medium / strong coverage scenario, etc.

[0194] For each channel scenario, the embodiments of this application model the port selection scheme as a combinatorial optimization problem and introduce the binary particle swarm optimization algorithm to solve it. In the binary particle swarm optimization algorithm, each particle represents a port selection scheme.

[0195] In some embodiments, under a certain airspace CSI-RS port compression ratio, in order for the receiving end to accurately reconstruct the channel state information of all CSI-RS ports from some CSI-RS ports, it is necessary to select an optimal port set from all CSI-RS ports with high spatial correlation to transmit CSI-RS. Suppose an antenna port set D composed of M active state antenna ports is selected from all CSI-RS ports ( ), so that the complete port channel response can be estimated based on the antenna port set (or antenna port subset) (which can be expressed as ), and can approximate the true complete port channel response (which can be expressed as ) as much as possible.

[0196] The value of M above is determined according to the required port compression ratio (or port compression proportion). The compression ratio of the antenna port = M / N, where M < N and N is the total number of ports.

[0197] In some embodiments, the problem of solving for M antenna ports can be modeled as the following formula (1):

[0198] (1);

[0199] Where, P0 represents the objective function, that is, the objective function of the optimization problem, is the channel response of the complete port estimated by the receiving end using the AI method; represents the true complete channel response of the antenna port; being 1 means selecting this CSI-RS port, and being 0 means not selecting this CSI-RS port; where the total number of 1s is M.

[0200] Optionally, in this embodiment of the application, a binary particle swarm optimization algorithm is used to solve the above objective function to obtain the optimal CSI-RS port set D.

[0201] For example, in the binary particle swarm optimization algorithm, the solution to each problem to be optimized can be regarded as a particle in the search space (i.e., a transmission scheme of an antenna port set). The particle has two key attributes: velocity and position. Among them, velocity determines the speed and direction of the particle's movement. In each iteration, the particle updates its velocity and position according to the following formula (2):

[0202] (2);

[0203] in, Denotes the particle in the (t+1)th iteration. i speed, For inertial weights, Denotes the particle in the t-th iteration. i speed, Indicates the first t In the next iteration, particles i The optimal position of an individual It is the first t The global optimal position of all particles in the next iteration. It is the first t In the next iteration, particles i Location, and All are particle learning factors (e.g., a value of 2). and All are random numbers (or randomly distributed values) between 0 and 1; among them, The function is used to map continuous velocity values ​​to probability values ​​of taking the value 1; This is used to select the top M values ​​from multiple probability values ​​sorted in descending order. In other words, the M values ​​with the highest probability are set to 1, and all other values ​​are set to 0, resulting in the updated particle position.

[0204] For example, Update according to the following formula (3):

[0205] (3);

[0206] in, This represents the maximum inertia weight. This represents the minimum inertia weight. T This represents the maximum number of iterations.

[0207] It should be noted that the following is adopted: The reason for the function is that, through The closer the value after function mapping is to 1, the more important it is, indicating that this port is crucial for improving overall performance; the closer it is to 0, the less important it is, or that closing this port has little or no impact on performance, or may even be beneficial. Furthermore, it is necessary to combine... Select the M probability values ​​that are closest to 1.

[0208] Compared to traditional speed conversion function algorithms, the embodiments of this application employ... Functions and Combinations The strategy controls the number of ports selected, ensuring that the number of selected ports is M, thus helping to select the M ports with the best performance and construct an optimal port set D of size M. Therefore, by introducing a Top-M deterministic mapping mechanism into the binary particle swarm optimization algorithm, replacing the traditional update method based on random probability, the optimization efficiency and stability of the method are significantly improved while ensuring the feasibility of the solution.

[0209] Additionally, it should be noted that in this application... The function is only one possible implementation method, and the embodiments in this application are not limited thereto. The purpose of the function is to convert the continuous velocity information of a particle into discrete decision probabilities where the binary position is 0 or 1. The function can also be replaced with other functions that have similar functionality. For example, The function can be replaced with other saturated activation functions that can constrain the velocity value within the (0,1) interval and be monotonic, so as to ensure that the larger the absolute value of the velocity, the higher the probability of the particle position flipping.

[0210] In one possible implementation, formula (2) The function can also be replaced with other saturated functions that satisfy the above principles (such as hyperbolic tangent function, soft step function) to achieve the conversion from velocity to probability.

[0211] For example, in formula (2) The function can also be replaced by the following hyperbolic tangent function: Alternatively, replace it with a soft step function: ,in, It is an adjustable parameter. This is the offset parameter.

[0212] Of course, if The function is replaced, then in formula (2) It also needs to be replaced accordingly. For example, if the hyperbolic tangent function is used, If a soft step function is used, .

[0213] The following describes the sparse pilot mask generation process based on the binary particle swarm optimization algorithm. For example, the input to the binary particle swarm optimization algorithm includes one or more of the following parameters: for a specific channel model (represented as a channel scenario). Number of ports to be selected M Maximum number of iterations T Maximum inertia weight coefficient Minimum inertia weight coefficient Particle learning factor and Total number of ports N The total number of particles is G； The output of the binary particle swarm optimization algorithm includes the following parameter: the global optimal port selection index. (i.e., the set of indices of the optimal ports) (or the globally optimal location), and, with The corresponding optimal fitness value .

[0214] Figure 5 An example diagram illustrating the process of generating sparse pilot masks based on the binary particle swarm optimization algorithm is shown. Figure 5 As shown, it includes the following steps 1 to 9:

[0215] Step 1, initialize the following parameters:

[0216] a. For each particle i ,and , ( (representing the total number of particles), a number is randomly generated at the sending end. dimensional vector ,vector It contains elements that are either 0 or 1, and the total number of elements that are 1 is M. Among them, This represents a port selection scheme, i.e., the sending end selects according to... To determine the CSI-RS port for transmitting pilot signals.

[0217] b. For the receiver, the velocity of randomly generated particles .

[0218] c. The receiver performs channel estimation on the CSI-RS signals received from a portion of the CSI-RS ports, and uses the AI ​​method to estimate the CSI for all ports. Then, it calculates the initial fitness value for each particle. ;

[0219] Among them, fitness value The following formula (4) is used for calculation:

[0220] (4);

[0221] in, The full-port channel information output by the AI ​​channel estimator (where, (Parameters of the AI ​​channel estimator) This is the initial channel estimate obtained using the pilot signal received from the selected port.

[0222] d. For the receiver, initialize the individual optimal fitness value for each particle: ;

[0223] And initialize the optimal position for each particle: ;

[0224] Furthermore, initialize the global optimal fitness of the particles. and the global optimal position Specifically, this includes: finding all The minimum value in G (i.e., the minimum of G) (the minimum value in the data), and the value corresponding to the minimum value. and optimal fitness value Assigned to and .

[0225] After initializing all parameters, the following iterative process can be performed to obtain the global optimal fitness value and the global optimal position.

[0226] Step 2: Determine if the iteration number t is greater than T.

[0227] Where T represents the maximum number of iterations. The value of T can be chosen based on actual needs, and this application does not impose specific limitations on the embodiments.

[0228] If the result of step 2 is yes, proceed to step 9; if the result of step 2 is no, proceed to step 3.

[0229] Step 3: If the number of iterations t is not greater than T, calculate the inertia weight according to formula (3). .

[0230] In other words, the maximum inertia weighting coefficient Minimum inertia weight coefficient Input into formula (3) to calculate the inertia weight. Among them, the maximum inertia weight coefficient Minimum inertia weight coefficient Prior values ​​can be used.

[0231] Step 4: Combine the inertia weights obtained in Step 3. And the various parameters input into the algorithm mentioned above (for example, one or more of the following parameters: number of candidate ports). M Maximum number of iterations T Maximum inertia weight coefficient Minimum inertia weight coefficient Particle learning factor and Random numbers between 0 and 1 and Total number of ports N Total number of particles G The velocity and position of particle i (or each particle) are updated according to the aforementioned formula (2).

[0232] Based on the calculation in step 4, the position of each particle i is obtained as follows: The speed is .

[0233] It should be noted that step 4 only uses formula (2) as an example to calculate or update the velocity and position of particle i. The embodiments of this application are not limited to this.

[0234] For the velocity mapping probability function (such as formula (2)) Multiple probability values ​​obtained by mapping (function) can be used to determine M probability values, and there are multiple ways to implement this.

[0235] For example, sort multiple probability values ​​in descending order; based on the multiple probability values ​​sorted in descending order, select the first M probability values.

[0236] For example, sort multiple probability values ​​in ascending order; then select the last M probability values ​​based on the sorted probability values.

[0237] For example, by setting a certain threshold, the probability values ​​that are greater than the threshold can be filtered out from multiple probability values ​​and used as M probability values.

[0238] Of course, the value of M is less than the number of the aforementioned probability values. It should be understood that the examples of determining M probability values ​​shown above are merely illustrative descriptions, and the embodiments of this application are not limited thereto.

[0239] Step 5: Calculate the fitness value for each particle.

[0240] For example, the fitness value of a particle Satisfy the following formula:

[0241] ;in, This represents the squaring operation of the 2-norm.

[0242] Step 6: Calculate the fitness value of each particle based on the results of Step 5. Update the individual optimal fitness value of each particle. .

[0243] In some embodiments, step 6 includes: if the fitness value of the current particle is less than its own optimal fitness value (i.e. If a particle's own optimal fitness value is not found, then the particle's fitness value will be updated to that value. If the current particle's fitness value is greater than or equal to its own optimal fitness value, then no update is performed.

[0244] It should be understood that the update methods shown above are merely exemplary descriptions, and the embodiments of this application are not limited thereto.

[0245] Step 7, Update the global optimal fitness value and the global optimal position .

[0246] In some embodiments, step 7 includes: if, among the G particles, there exists a particle whose fitness value is smaller than the known global optimal fitness value, then the global optimal fitness value is updated to the fitness value of that particle, and the global optimal position is updated to the position of that particle.

[0247] Step 8: Update the iteration count t.

[0248] For example, the update iteration count means that the iteration count increases by 1 with each iteration.

[0249] After step 8, you can return to step 2 to determine again whether the maximum number of iterations has been reached, and decide whether to execute step 3 or step 9 based on the result.

[0250] Step 9: Output the global optimal fitness value and the global optimal position.

[0251] At the end of the maximum number of iterations (i.e., after T iterations), the final output is selected based on the least-norm squared value of the channel estimation. The index and the corresponding optimal fitness value In other words, each particle corresponds to a design scheme, and each design scheme corresponds to a fitness value (i.e., the squared value of the L2 norm), and the final selection... This is the solution corresponding to the smallest fitness value. The final result obtained through step 9... The index is the optimal set of ports.

[0252] based on Figure 5 The process shown can determine the optimal set of ports for each channel scenario. For example, determining the pilot pattern in the last column of Table 1. That is, based on Figure 5 The algorithm process shown can construct the optimal CSI-RS port sparse mask that guarantees channel estimation performance for each typical channel scenario, thereby forming a pilot pattern library. This facilitates the adaptive selection of appropriate pilot patterns based on real-time channel characteristics, thereby dynamically configuring sparse CSI-RS ports and reducing pilot overhead while ensuring reconstruction accuracy.

[0253] It should be understood that Figure 5 The algorithm process shown is merely an example, and the embodiments of this application are not limited thereto. For example, those skilled in the art can use other algorithms to solve the optimization problem modeled above to determine the optimal port set corresponding to each channel scenario.

[0254] It should be noted that in the particle swarm optimization algorithm of this application embodiment, each particle represents a port selection scheme. At the receiving end, the ports of the port selection scheme are reconstructed through AI channel estimation, and the port selection is updated with minimizing the error as the optimization objective, ultimately obtaining the optimal port set, thereby achieving the optimal trade-off between pilot overhead and estimation accuracy. Furthermore, the scheme of this application embodiment can achieve higher channel estimation accuracy with the same overhead, or significantly reduce overhead with the same accuracy.

[0255] Optionally, the above Figure 5 The calculation process shown can be completed offline, that is, for each channel scenario or the characteristics of sub-channels under each channel scenario, the corresponding pilot pattern is determined, thereby constructing a pilot pattern library, which is convenient for network devices to query or call.

[0256] Optionally, the pilot pattern library can be built offline; the built pilot pattern library can be deployed on a cloud server, and network devices (or UEs) can call it.

[0257] It should be noted that, in some embodiments, the embodiments of this application are not only applicable to CSI-RS port selection schemes, but also to other application scenarios that require selecting the optimal subset from limited resources and performing closed-loop optimization through AI estimation or learning model performance metrics, or to scenarios in various communication, sensing, and intelligent network systems that require efficient subset selection. For example, in systems that need to select a small number of antennas, subarrays, beams, measurement points, or devices from a large number of candidate antennas, subarrays, beams, measurement points, or devices for communication, sensing, or reconstruction tasks, the method of the embodiments of this application can be used to ensure a fixed selection quantity while maximizing the estimation accuracy or reconstruction quality of the AI ​​model on the target task.

[0258] To illustrate the performance advantages of the embodiments of this application, the following is combined with... Figure 6 The simulation comparison example is described. Figure 6 A comparative example graph of simulation results is shown. It assumes the Uma channel model is used, the carrier frequency is 3.5 GHz, the carrier spacing is 15 kHz, the system bandwidth is 10 MHz, and the network equipment's CSI-RS configuration uses 32 ports; the number of antennas on the UE side is 2. For example... Figure 5 As shown, the horizontal axis represents SNR, and the vertical axis represents performance parameters, such as Squared Generalized Cosine Similarity (SGCS). Figure 6 In this study, for a 32-port network, the channel estimation performance of the method described in this application and that of the existing uniform port selection method were tested when the CSI-RS port overhead was 1 / 2, 1 / 4, 1 / 8, and 1 / 16. Figure 6 As shown, for different port overheads (or CSI-RS port compression ratios), the simulation results using this application are as follows: solid lines containing circles, solid lines containing squares, solid lines containing equilateral triangles, and solid lines containing inverted triangles. The simulation results using the uniform selection algorithm are as follows: dashed lines containing circles, dashed lines containing squares, dashed lines containing equilateral triangles, and dashed lines containing inverted triangles. For the full-port MMSE method, the simulation result is a solid line containing a pentagram.

[0259] from Figure 6 The simulation results show that, under the same CSI-RS port overhead, the method of this application (or this invention) can achieve better performance; for example, for the case where the CSI-RS port compression ratio is 1 / 16, the method of this application (i.e., the result shown by the solid line containing the inverted triangle) has significantly better performance than the existing uniform selection method (i.e., the result shown by the dashed line containing the inverted triangle).

[0260] Furthermore, compared to the full-port scheme (including the solid line of the pentagram), the CSI-RS port overhead of the scheme in this application embodiment can be reduced by at least 50% (for example, the CSI-RS port overhead used in the scheme of this application embodiment is 1 / 2, 1 / 4, 1 / 8 and 1 / 16 respectively), and can still have good estimation accuracy or performance with lower pilot overhead (for example, overhead reduction of 93.75%).

[0261] It should also be understood that Figures 1 to 6 The flowcharts or scene diagrams shown are for illustrative purposes only and are not intended to limit the embodiments of this application to the examples illustrated. In fact, those skilled in the art can interpret the embodiments based on... Figures 1 to 6 The examples in the document can be transformed into equivalent ways to obtain more implementations.

[0262] The above text combined Figures 1 to 6This document describes in detail the communication method provided in the embodiments of this application. The following will combine... Figure 7 and Figure 8 The device embodiments of this application are described in detail below. It should be understood that the communication device of this application embodiment can execute the various communication methods of the foregoing embodiments of this application, that is, the specific working processes of the various products below can be referred to the corresponding processes in the foregoing method embodiments.

[0263] In the embodiments described above, the UE can execute some or all of the steps in each embodiment; the network device can execute some or all of the steps in each embodiment. These steps or operations are merely examples, and the embodiments of this application can also perform other operations or variations of various operations. Furthermore, the steps can be executed in different orders as presented in the embodiments, and it is not necessary to execute all the operations in the embodiments of this application. Moreover, the sequence number of each step does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0264] Figure 7 This is a schematic block diagram of a communication device provided in an embodiment of this application. Figure 7 As shown, the communication device 1500 may include a communication module 1520. The communication module 1520 can implement corresponding communication functions, which can be internal communication functions of the communication device 1500 or communication functions between the communication device 1500 and other devices. Optionally, the communication module 1520 may also be referred to as a communication interface or transceiver module. Optionally, the communication device 1500 further includes a processing module 1510. The processing module 1510 can implement corresponding processing functions.

[0265] Optionally, the communication device 1500 further includes a storage module, which can be used to store instructions and / or data; the processing module 1510 can read the instructions and / or data in the storage module so that the communication device 1500 can implement the aforementioned method embodiments.

[0266] In one possible design, the communication device 1500 may correspond to the UE in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the UE. The communication device 1500 may be used to perform the steps or processes performed by the UE in any of the above method embodiments.

[0267] In one possible design, the communication module 1520 is used to receive a first message sent from a network device. The first message includes a first pilot pattern, which is determined in a pilot pattern library based on the current channel conditions. The first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions. The pilot pattern library is determined based on a particle swarm optimization algorithm and includes pilot patterns corresponding to different channel environments. Each pilot pattern corresponds to a set of antenna port indices.

[0268] The communication module 1520 is used to receive the Channel State Information Reference Signal (CSI-RS) based on the first pilot pattern.

[0269] Optionally, as an embodiment, the first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions, including: the first pilot pattern is a pilot pattern corresponding to a first channel environment in the pilot pattern library, and the first channel environment is the channel environment that best matches the current channel environment among multiple channel environments.

[0270] Optionally, as an embodiment, the first pilot pattern is a pilot pattern corresponding to a first sub-channel characteristic selected based on the effective channel rank of the current channel; the first channel environment corresponds to multiple sub-channel characteristics; the first sub-channel characteristic is a sub-channel characteristic among the multiple sub-channel characteristics that has the same effective channel rank as the current channel. Optionally, as an embodiment, the processing module 1510 is used to perform adjustment processing on the first pilot pattern to obtain a second pilot pattern;

[0271] The communication module 1520 is also used to send a second message to the network device, the second message including the second pilot pattern.

[0272] Optionally, as an embodiment, the processing module 1510 is used to perform adjustment processing on the first pilot pattern, including: executing a lightweight particle swarm optimization algorithm locally to obtain the second pilot pattern.

[0273] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0274] Alternatively, in another possible design, the communication device 1500 may correspond to the network device in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the network device. The communication device 1500 can be used to perform the steps or processes executed by the network device in any of the above method embodiments.

[0275] For example, the processing module 1510 is used to determine a first pilot pattern in the pilot pattern library according to the current channel conditions. The first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions. The pilot pattern library includes pilot patterns corresponding to different channel scenarios. The pilot pattern library is determined based on the particle swarm optimization algorithm.

[0276] The communication module 1520 is used to send a first message to the UE, the first message including the first pilot pattern;

[0277] Activate the antenna port corresponding to the first pilot pattern according to the first pilot pattern, and send CSI-RS to the UE through the antenna port corresponding to the first pilot pattern.

[0278] Optionally, as an embodiment, the first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions, including: the first pilot pattern is a pilot pattern corresponding to a first channel environment in the pilot pattern library, and the first channel environment is the channel environment that best matches the current channel environment among multiple channel environments.

[0279] Optionally, as an embodiment, the first pilot pattern is the pilot pattern corresponding to the first sub-channel characteristic selected based on the effective channel rank of the current channel; the first channel environment corresponds to multiple sub-channel characteristics; the first sub-channel characteristic is the sub-channel characteristic among the multiple sub-channel characteristics that has the same effective channel rank as the current channel.

[0280] Optionally, as an embodiment, the communication module 1520 is further configured to receive a second message from the UE, the second message including a second pilot pattern, the second pilot pattern being determined by the UE based on the first pilot pattern; the processing module 1510 is further configured to activate the antenna port corresponding to the second pilot pattern according to the second pilot pattern, and send CSI-RS to the UE through the antenna port corresponding to the second pilot pattern.

[0281] Optionally, as an embodiment, the processing module 1510 is used to determine a first pilot pattern in the pilot pattern library according to the current channel conditions, including: determining a similarity parameter between the current channel scenario and each channel scenario in the pilot pattern library to obtain multiple similarity parameters; determining a first similarity parameter among the multiple similarity parameters, wherein the first similarity parameter is used to characterize the degree of similarity between the first channel scenario and the current channel scenario; the first similarity parameter is a similarity parameter among the multiple similarity parameters that satisfies a preset condition; and determining the first pilot pattern according to the first channel scenario and the effective rank of the previous channel.

[0282] Optionally, as an embodiment, the processing module 1510 is further configured to determine whether the first parameter exceeds a first threshold, wherein the first parameter is used to characterize the degree of difference between the current channel measurement value and the previous channel measurement value, and the first parameter is determined based on the current channel measurement value and the previous channel measurement value; wherein, determining the first pilot pattern in the pilot pattern library based on the current channel conditions includes determining the first pilot pattern in the pilot pattern library based on the current channel conditions when the first parameter exceeds the first threshold.

[0283] Optionally, as an embodiment, the processing module 1510 is further configured to use the previous pilot pattern if the first parameter does not exceed a first threshold.

[0284] Optionally, as an embodiment, the pilot pattern corresponding to each channel environment in the pilot pattern library is determined in the following manner:

[0285] Initialize the following parameters:

[0286] No. i The initial position of each particle , wherein the initial position It is a vector containing elements 0 or 1, and The total number of elements containing 1 is M, where, ;

[0287] No. i The speed of each particle , t Represents the number of iterations. ;

[0288] No. i The initial fitness value of each particle ;

[0289] No. i The individual optimal fitness of each particle ;

[0290] No. i Optimal position of each particle ;

[0291] Global optimal fitness ;

[0292] Global optimal position ;

[0293] After initializing the parameters, the following iterative process is executed:

[0294] Calculate inertia weight ;

[0295] Based on inertia weight And input parameters, determine the first i The position of each particle and speed ;

[0296] Calculate the first i Fitness value of each particle ;

[0297] According to the fitness value Update the individual optimal fitness value for each particle. ;

[0298] Based on the obtained fitness values ​​of the G particles, update the global optimal fitness value. and the global optimal position ;

[0299] After performing T iterations, output the globally optimal fitness value. and the global optimal position Wherein, the global optimal position The corresponding index is the optimal port set.

[0300] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0301] Figure 8 This is another schematic block diagram of the communication device 1600 provided in the embodiments of this application. The communication device 1600 may be a chip, chip system, or processor, etc., in a terminal device (such as a UE) or network device that implements the above methods. The communication device 1600 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0302] like Figure 8 As shown, the communication device 1600 may include one or more processors 1610, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 1610 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device 1600 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0303] In an alternative design, the processor 1610 may also store instructions and / or data that can be executed by the processor 1610 to cause the communication device 1600 to perform the methods described in the above method embodiments.

[0304] In another alternative design, the communication device 1600 may include a communication interface 1620 for implementing receiving and transmitting functions. For example, the communication interface 1620 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0305] Optionally, the communication device 1600 may include one or more memories 1630, which may store instructions that can be executed on the processor 1610, causing the communication device 1600 to perform the methods described in the above method embodiments. Optionally, the memories 1630 may also store data. Optionally, the processor 1610 may also store instructions and / or data. The processor 1610 and the memories 1630 may be provided separately or integrated together.

[0306] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0307] In one implementation, the communication device 1600 may correspond to a terminal device (e.g., a UE) in the above method embodiments, and may be used to execute various steps and / or processes performed by the terminal device (e.g., the UE) in the above method embodiments. The processor 1610 may be used to execute instructions stored in the memory 1630, and when the processor 1610 executes the instructions stored in the memory, the processor 1610 is used to execute various steps and / or processes of the above method embodiments corresponding to the terminal device.

[0308] In another implementation, the communication device 1600 may correspond to a network device (such as a satellite or base station) in the above method embodiments, and may be used to execute the various steps and / or processes executed by the network device in the above method embodiments. The processor 1610 may be used to execute instructions stored in the memory 1630, and when the processor 1610 executes the instructions stored in the memory, the processor 1610 is used to execute the various steps and / or processes of the above method embodiments corresponding to the network device.

[0309] It should be understood that the aforementioned processing device can be one or more chips. For example, the processing device can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0310] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0311] According to the method provided in the embodiments of this application, this application also provides a chip system, which includes one or more processors for calling and executing instructions stored in memory, thereby causing the method described in the embodiments of this application to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0312] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0313] According to the method provided in the embodiments of this application, this application also provides a communication system, which includes the aforementioned network device and UE.

[0314] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the network device or UE in any of the foregoing method embodiments.

[0315] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the various steps or processes performed by the network device or UE in any of the foregoing method embodiments.

[0316] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or it may include both volatile memory and non-volatile memory.

[0317] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0318] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0319] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0320] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0321] Furthermore, the terms "system" and "network" are often used interchangeably in this paper. The term "and / or" in this paper merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this paper generally indicates that the preceding and following related objects have an "or" relationship. For example, A / B can represent A or B.

[0322] In the embodiments of this application, the terms "information," "signal," "message," "channel," and "signaling" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0323] To clarify, the specific implementation of "predefined" can include any of the following: protocol predefined, manufacturer-specified, defined by the communication equipment, pre-installed in the communication equipment at the time of manufacture, or agreed upon in advance by other agreed methods.

[0324] The terms (or designations) "first," "second," etc., appearing in the embodiments of this application are for descriptive purposes only, that is, only to distinguish different objects, and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first," "second," etc., may explicitly or implicitly include one or more features. In the description of the embodiments of this application, "at least one (item)" refers to one or more. "Multiple" means two or more. "At least one (item) below" or similar expressions refer to any combination of these items, including any combination of a single (item) or a plurality of (items).

[0325] For example, expressions like "the item includes at least one of the following: A, B, and C" appearing in the embodiments of this application generally mean, unless otherwise specified, that the item can be any one of the following: A; B; C; A and B; A and C; B and C; A, B and C; A and A; A, A and A; A, A and B; A, A and C, A, B and B; A, C and C; B and B, B, B and B, B, B and C, C and C; C, C and C, and other combinations of A, B, and C. The above uses three elements, A, B, and C, as examples to illustrate the possible entries for the item. When expressed as "the item includes at least one of the following: A, B, ..., and X," that is, when the expression contains more elements, then the applicable entries for the item can also be obtained according to the aforementioned rules.

[0326] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A communication method, characterized in that, Applied to a user equipment (UE), the method includes: The system receives a first message from a network device. The first message includes a first pilot pattern, which is determined from a pilot pattern library based on the current channel conditions. The first pilot pattern is a pilot pattern in the pilot pattern library that satisfies the current channel conditions. The pilot pattern library is determined based on the particle swarm optimization algorithm and includes pilot patterns corresponding to different sub-channel characteristics in multiple different channel environments. Each pilot pattern corresponds to a set of antenna port indices. The Channel State Information Reference Signal (CSI-RS) is received based on the first pilot pattern; The first pilot pattern is a pilot pattern from the pilot pattern library that satisfies the current channel conditions, including: The first pilot pattern is the pilot pattern corresponding to the first channel environment in the pilot pattern library. The first channel environment is the channel environment that best matches the current channel environment among multiple channel environments. The first pilot pattern is the pilot pattern corresponding to the first sub-channel characteristic selected based on the effective channel rank of the current channel; the first channel environment corresponds to multiple sub-channel characteristics; the first sub-channel characteristic is the sub-channel characteristic among the multiple sub-channel characteristics that has the same effective channel rank as the current channel.

2. The method according to claim 1, characterized in that, The method further includes: The first pilot pattern is adjusted to obtain the second pilot pattern; A second message is sent to the network device, the second message including the second pilot pattern.

3. The method according to claim 2, characterized in that, The adjustment process performed on the first pilot pattern includes: The second pilot pattern is obtained by executing a lightweight particle swarm optimization algorithm locally.

4. The method according to any one of claims 1 to 3, characterized in that, The first message is an RRC reconfiguration message.

5. A communication method, characterized in that, Applied to network devices, the method includes: A first pilot pattern is determined from the pilot pattern library based on the current channel conditions. This first pilot pattern is a pilot pattern in the library that satisfies the current channel conditions. The pilot pattern library includes pilot patterns corresponding to different sub-channel characteristics in each channel scenario across multiple different channel scenarios. Each pilot pattern corresponds to a set of antenna port indices. The pilot pattern library is determined based on the particle swarm optimization algorithm. The first pilot pattern, which satisfies the current channel conditions, includes: the first pilot pattern being a pilot pattern corresponding to a first channel environment in the pilot pattern library, where the first channel environment is the channel environment that best matches the current channel environment among multiple channel environments; the first pilot pattern corresponding to a first sub-channel characteristic selected based on the effective rank of the current channel; multiple sub-channel characteristics corresponding to the first channel environment; and the first sub-channel characteristic being the sub-channel characteristic among the multiple sub-channel characteristics that has the same effective rank as the current channel. Send a first message to the UE, the first message including the first pilot pattern; Activate the antenna port corresponding to the first pilot pattern according to the first pilot pattern, and send CSI-RS to the UE through the antenna port corresponding to the first pilot pattern.

6. The method according to claim 5, characterized in that, The method further includes: The UE receives a second message, which includes a second pilot pattern, which is determined by the UE based on the first pilot pattern. Activate the antenna port corresponding to the second pilot pattern according to the second pilot pattern, and send CSI-RS to the UE through the antenna port corresponding to the second pilot pattern.

7. The method according to claim 5 or 6, characterized in that, The step of determining the first pilot pattern from the pilot pattern library based on the current channel conditions includes: Determine the similarity parameters between the current channel scenario and each channel scenario in the pilot pattern library to obtain multiple similarity parameters; A first similarity parameter is determined among the plurality of similarity parameters. The first similarity parameter is used to characterize the degree of similarity between the first channel scenario and the current channel scenario. The first similarity parameter is a similarity parameter among the plurality of similarity parameters that meets a preset condition. The first pilot pattern is determined based on the first channel scenario and the effective rank of the current channel.

8. The method according to claim 7, characterized in that, Each similarity parameter satisfies the following formula: ； in, For weighted Euclidean distance; ;in, , , ..., The weights for each feature, , , ..., The sum of is 1; to Represents the various characteristics of the current channel; to These represent the various features of a channel scenario in the pilot pattern library.

9. The method according to claim 5, characterized in that, The method further includes: Determine whether the first parameter exceeds the first threshold. The first parameter is used to characterize the degree of difference between the current channel measurement value and the previous channel measurement value. The first parameter is determined based on the current channel measurement value and the previous channel measurement value. The step of determining the first pilot pattern from the pilot pattern library based on the current channel conditions includes: If the first parameter exceeds the first threshold, a first pilot pattern is determined from the pilot pattern library based on the current channel conditions.

10. The method according to claim 9, characterized in that, The first parameter satisfies the following formula: ； in, This represents the first parameter. This is the current channel measurement value. This is the value from the previous channel measurement.

11. The method according to claim 9, characterized in that, The method further includes: If the first parameter does not exceed the first threshold, the previous pilot pattern is used.

12. The method according to claim 5 or 6, characterized in that, The pilot pattern corresponding to each channel environment in the pilot pattern library is determined in the following manner: Initialize the following parameters: No. i The initial position of each particle , wherein the initial position It is a vector containing elements 0 or 1, and The total number of elements containing 1 is M, where, ; No. i The speed of each particle , t Represents the number of iterations. ; No. i The initial fitness value of each particle ; No. i The individual optimal fitness of each particle ; No. i Optimal position of each particle ; Global optimal fitness ; Global optimal position ; After initializing the parameters, the following iterative process is executed: Calculate inertia weight ; Based on inertia weight And input parameters, to determine the first i The position of each particle and speed ; Calculate the first i Fitness value of each particle ; According to the fitness value Update the individual optimal fitness value for each particle. ; Based on the obtained fitness values ​​of the G particles, update the global optimal fitness value. and the global optimal position ; After performing T iterations, output the globally optimal fitness value. and global optimal position Wherein, the global optimal position The corresponding index is the optimal port set.

13. The method according to claim 12, characterized in that, The calculation of the first i Fitness value of each particle ,include: ； in, This represents the squaring operation of the 2-norm, where, This represents the channel response of the complete port; This represents the actual antenna port channel response.

14. The method according to claim 12, characterized in that, The inertial weight And input parameters, to determine the first i The position of each particle and speed ,include: Based on inertia weight and the speed of input parameter calculation ; Using a velocity mapping probability function, the velocity is... Mapped to multiple probability values; M probability values ​​are determined based on the plurality of probability values, and the first probability value is obtained based on the M probability values. i The position of each particle .

15. The method according to claim 14, characterized in that, The velocity mapping probability function is: The function, or the hyperbolic tangent function, or the soft step function.

16. The method according to any one of claims 12 to 14, characterized in that, The first i The position of each particle and speed Satisfy the following formula: ； in, Denotes the particle in the (t+1)th iteration. i speed, For inertial weights, Denotes the particle in the t-th iteration. i speed, Indicates the first t In the next iteration, particles i The optimal position of an individual It is the first t The global optimal position of all particles in the next iteration. It is the first t In the next iteration, particles i Location, and All are particle learning factors. and A random number in [0,1]; where, The function maps continuous velocity values ​​to probability values ​​of 1; N represents the total number of antenna ports. Used to select the top M values ​​from a set of probability values ​​sorted in descending order.

17. The method according to claim 5 or 6, characterized in that, The first message is an RRC reconfiguration message.

18. A communication device, characterized in that, The device includes at least one processor coupled to a memory storing a program or instructions, wherein the processor executes the program or instructions to cause the communication device to perform the method as claimed in any one of claims 1 to 4, or to cause the communication device to perform the method as claimed in any one of claims 5 to 17.

19. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they cause the computer to perform the method as described in any one of claims 1 to 4, or cause the computer to perform the method as described in any one of claims 5 to 17.

20. A communication system, characterized in that, Includes the communication device as described in claim 18.

21. A chip system, characterized in that, The chip system includes one or more processors, which are configured to retrieve and execute instructions stored in memory, such that the method as described in any one of claims 1 to 4 is executed, or that the method as described in any one of claims 5 to 17 is executed.

22. A computer program product, characterized in that, It includes computer program instructions that cause a computer to perform the method as described in any one of claims 1 to 4; or, cause a computer to perform the method as described in any one of claims 5 to 17.

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