A baseband task processing method and device, a network device and a storage medium

Abstract

Embodiments of the present application disclose a baseband task processing method and device, network equipment and a storage medium. The method comprises: obtaining a first task; the first task being one of a plurality of tasks included in a baseband processing process; determining a first optimization strategy based on the first task, adjusting system parameters according to the first optimization strategy, integrating and processing corresponding sub-tasks in the first task according to the adjusted system parameters to execute the first task, or performing clustering processing on a signal to be processed during execution of the first task according to the first optimization strategy.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically to a baseband task processing method, apparatus, network device, and storage medium. Background Technology

[0002] Baseband processing is a crucial component of wireless communication systems. It is responsible for converting digital signals into a format suitable for transmission over wireless channels and processing received signals to recover the original data. This process often involves numerous complex and computationally intensive tasks. Accelerators can be optimized for these specific tasks, offloading the computational burden from the main processor and significantly improving the overall system's processing speed and efficiency. Simultaneously, accelerators can support parallel processing and multi-task scheduling, enhancing the overall system performance and flexibility.

[0003] Currently, accelerators typically employ a user-specific processing approach, meaning that baseband processing is performed independently for each user's data. However, this approach imposes strict sequential control on the baseband processing flow, ensuring that each step is executed in a predetermined order. This means that during processing, a new step can only begin after the previous step has been completed. While this sequential processing mechanism guarantees the accuracy and consistency of data processing, it also introduces limitations on processing efficiency. Summary of the Invention

[0004] To address the existing technical problems, embodiments of the present invention provide a baseband task processing method, apparatus, network device, and storage medium.

[0005] To achieve the above objectives, the technical solution of this invention is implemented as follows:

[0006] This invention provides a baseband task processing method, the method comprising:

[0007] Obtain the first task; the first task is one of multiple tasks included in the baseband processing process;

[0008] Based on the first task, a first optimization strategy is determined, the system parameters are adjusted according to the first optimization strategy, and the corresponding sub-tasks in the first task are integrated and processed according to the adjusted system parameters to execute the first task. Alternatively, during the execution of the first task according to the first optimization strategy, the signals to be processed are clustered.

[0009] In the above scheme, the system parameters include at least one of the following: number of users, number of antennas, number of symbols, and number of physical resource blocks (PRBs).

[0010] In the above scheme, adjusting the system parameters according to the first optimization strategy includes: adjusting the number of antennas from N1 to 1 and adjusting the number of PRBs to N1 times the original value, where N1 is a positive integer.

[0011] In the above scheme, the step of integrating and processing the corresponding sub-tasks in the first task according to the adjusted system parameters to execute the first task includes: executing the sub-tasks corresponding to N1 antennas at a time during the execution of the first task according to the adjusted system parameters.

[0012] In the above scheme, adjusting the system parameters according to the first optimization strategy includes: configuring system parameters for multiple users according to the first optimization strategy, adjusting the number of users corresponding to the multiple users to 1, and adjusting the PRB number to N2 times the original value, where N2 is the number of the multiple users.

[0013] In the above scheme, the step of integrating and processing the corresponding subtasks in the first task according to the adjusted system parameters to execute the first task includes: executing the subtasks corresponding to the multiple users at one time during the execution of the first task according to the adjusted system parameters.

[0014] In the above scheme, adjusting the system parameters according to the first optimization strategy includes: adjusting the number of symbols from N3 to 1 and adjusting the number of users to N3 times the original value, where N3 is a positive integer, according to the first optimization strategy.

[0015] In the above scheme, the step of integrating and processing the corresponding subtasks in the first task according to the adjusted system parameters to execute the first task includes: executing the subtasks corresponding to N3 symbols at one time during the execution of the first task according to the adjusted system parameters.

[0016] In the above scheme, adjusting the system parameters according to the first optimization strategy includes: adjusting the PRB number from N4 to 1 and adjusting the number of users to N4 times the original value, where N4 is a positive integer.

[0017] In the above scheme, the step of integrating and processing the corresponding subtasks in the first task according to the adjusted system parameters to execute the first task includes: executing N4 subtasks corresponding to PRBs at one time during the execution of the first task according to the adjusted system parameters.

[0018] In the above scheme, the clustering of signals to be processed includes: uniformly storing all signals of the same type that need to be processed within the first task scope in memory, so as to uniformly process the signals to be processed according to their signal types.

[0019] In the above scheme, after executing the first task according to the adjusted system parameters, the method further includes: filling the vacant positions and / or misaligned positions of the resources occupied by the user's signal.

[0020] This invention also provides a baseband task processing device, the device comprising: a first processing unit and a second processing unit; wherein,

[0021] The first processing unit is configured to obtain a first task; the first task is one of a plurality of tasks included in the baseband processing process; and is further configured to determine a first optimization strategy based on the first task.

[0022] The second processing unit is configured to adjust system parameters according to the first optimization strategy, integrate and process the corresponding sub-tasks in the first task according to the adjusted system parameters to execute the first task, or, during the execution of the first task according to the first optimization strategy, perform clustering processing on the signals to be processed.

[0023] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the baseband task processing method described in this invention.

[0024] This invention also provides a network device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the baseband task processing method described in this invention.

[0025] This invention also provides a computer program product, including computer program instructions that cause a computer to perform the steps of the baseband task processing method described in this invention.

[0026] The present invention provides a baseband task processing method, apparatus, network device, and storage medium. The method includes: obtaining a first task; the first task is one of multiple tasks included in the baseband processing process; determining a first optimization strategy based on the first task; adjusting system parameters according to the first optimization strategy; and integrating and processing corresponding sub-tasks in the first task according to the adjusted system parameters to execute the first task; or, clustering signals to be processed during the execution of the first task according to the first optimization strategy. The technical solution of the present invention sets different optimization strategies for different tasks, and different optimization strategies can correspond to different system parameters. The network device adjusts the system parameters according to the first optimization strategy, thereby executing the first task according to the adjusted system parameters. Specifically, it merges the sub-tasks in the first task according to the adjusted system parameters, that is, a single call or execution can complete the work that originally required multiple calls or executions, achieving fine-grained control of the processing flow; or it clusters the signals to be processed according to the first optimization strategy, achieving unified and efficient optimization of signals of the same type, reducing the additional burden caused by signal type switching. The baseband processing flow is flexibly adjusted based on the usage of computing and storage resources, breaking the original pipelined operation between modules, making full use of computing and storage resources, and improving processing efficiency and flexibility. Attached Figure Description

[0027] Figure 1 A schematic diagram of the task scheduling mechanism for baseband processing;

[0028] Figure 2 This is a flowchart illustrating the baseband task processing method according to an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram illustrating the scheduling of multi-dimensional transformation in the baseband task processing method according to an embodiment of the present invention;

[0030] Figure 4 This is a schematic diagram of resource optimization in the baseband task processing method according to an embodiment of the present invention;

[0031] Figure 5 This is a schematic diagram of the composition structure of the baseband task processing device according to an embodiment of the present invention;

[0032] Figure 6 This is a schematic diagram of the hardware composition structure of a network device according to an embodiment of the present invention. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0034] The technical solutions of this invention can be applied to various communication systems, such as GSM (Global System of Mobile communication), LTE (Long Term Evolution), or 5G systems. Optionally, a 5G system or 5G network can also be referred to as a New Radio (NR) system or NR network.

[0035] For example, the communication system used in this embodiment of the invention may include network devices and terminal devices (also referred to as terminals, communication terminals, etc.); the network device may be a device that communicates with the terminal device. The network device can provide communication coverage within a certain area and can communicate with terminals located within that area. Optionally, the network device may be a base station in various communication systems, such as an evolved Node B (eNB) in an LTE system, or a gNB in ​​a 5G or NR system.

[0036] It should be understood that devices with communication functions in the network / system of this application embodiment can be referred to as communication devices. Communication devices may include network devices and terminals with communication functions. Network devices and terminal devices can be the specific devices described above, which will not be repeated here. Communication devices may also include other devices in the communication system, such as network controllers, mobility management entities, and other network entities. This embodiment of the present invention does not limit these.

[0037] It should be understood that the terms "system" and "network" are often used interchangeably in this document. The term "and / or" in this document 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, or B alone. Furthermore, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0038] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0039] Before describing the technical solutions of the embodiments of the present invention in detail, the baseband processing will be briefly explained first.

[0040] During data transfer, baseband data to be processed is transferred from the main processor or other storage devices to the accelerator via Direct Memory Access (DMA) or other efficient data transfer mechanisms to minimize the impact of data transfer on the main processor's performance. After processing the data, the accelerator sends the processing results back to the main processor or storage device for further processing or storage. Upon receiving a task, the accelerator processes the data according to preset algorithms and parameters.

[0041] Currently, when the accelerator processes the entire baseband link, it strictly follows the functional division of subtasks in a sequential manner. These steps are relatively independent within the entire link. Within specific functional modules, processing tasks are subdivided into arithmetic operation units and matrix operation units. Arithmetic operation units are typically simpler and more direct, suitable for processing data that doesn't require complex calculations. Matrix operation units, on the other hand, leverage the parallelism and correlation of data to accelerate the processing through efficient matrix operations. In the arithmetic operation unit, communication tasks for different users are currently separated using a user-specific approach. This allows the system to provide services to multiple users simultaneously, with each user having its own independent processing resources (such as processor cores and memory). The system can dynamically allocate resources based on the needs and priorities of different users, thereby improving processing efficiency and system throughput. Figure 1 A schematic diagram of the task scheduling mechanism for baseband processing, as shown below. Figure 1 As shown, Task 1, Task 2, and Task 3 are three consecutive processing units, and Task x.1, Task x.2, and Task x.3 (x = 1, 2, 3) represent three users respectively. Method 1 processes users sequentially within these consecutive processing units. First, it processes the sub-tasks of the three users for Task 1. After completion, it processes the sub-tasks of the three users for Task 2, and finally, it processes the sub-tasks of the three users for Task 3. Method 2 completes all functional modules for User 1 before processing User 2. Specifically, it executes the sub-tasks of Task 1, Task 2, and Task 3 for User 1 first, then for User 2, and finally for User 3.

[0042] In this processing method, the accelerator implements strict sequential control over the baseband processing flow, ensuring that each step is performed in a predetermined order. This means that during processing, a process step can only begin after the previous step has been completed. While this sequential processing mechanism guarantees the accuracy and consistency of data processing, it also introduces limitations on processing efficiency.

[0043] Specifically, when the accelerator is processing multi-user data, the processor gets stuck in a large number of loop statements, repeatedly executing the same functional modules to process the resources of each user one by one. This approach can lead to a significant increase in processing time, especially when dealing with large amounts of user data and limited resources.

[0044] Furthermore, because the processing of each user's data is independent, the processor cannot effectively utilize parallel processing techniques to improve efficiency. Even in multi-core processor architectures, this strictly sequential processing mode limits the full potential of the processor's performance. Therefore, this processing method not only affects the processing speed of individual user data but also reduces the throughput of the entire system, thus impacting overall processing efficiency.

[0045] Based on this, the following embodiments of the present invention are proposed.

[0046] This invention provides a baseband task processing method. Figure 2 This is a flowchart illustrating the baseband task processing method according to an embodiment of the present invention; as shown below. Figure 2 As shown, the method includes:

[0047] Step 101: Obtain the first task; the first task is one of the multiple tasks included in the baseband processing process;

[0048] Step 102: Determine a first optimization strategy based on the first task, adjust system parameters according to the first optimization strategy, integrate and process the corresponding sub-tasks in the first task according to the adjusted system parameters to execute the first task, or, during the execution of the first task according to the first optimization strategy, perform clustering processing on the signals to be processed.

[0049] The baseband task processing method in this embodiment is applied to a network device, which may specifically be an access network device, such as a base station.

[0050] The first task in this embodiment refers to one task in a series of baseband processing or baseband signal processing processes. For example, the baseband processing or baseband signal processing process may include multiple steps such as sampling, quantization, channel coding, modulation, demodulation, channel estimation, equalization, descrambling, and decoding. These multiple steps are related; for example, encoding, modulation, and decoding operations are performed on the digital signal from the radio frequency unit to recover the original communication information. Therefore, the first task can refer to any of the processing tasks in the aforementioned steps.

[0051] In this embodiment, the network device performs fine-grained task decomposition, refining a series of baseband processing tasks into a series of independently operable but interconnected tasks (such as the first task); and sets different optimization strategies for different tasks, adjusting system parameters during task execution according to the optimization strategy (first optimization strategy) corresponding to the task, and executing sub-tasks within the scope of each first task according to the adjusted system parameters, for example, ... Figure 1 In the first task, subtasks 1.1 and 1.2, which originally required sequential processing, are merged and processed together, allowing a single call to complete the work that would otherwise have required multiple calls. Alternatively, during the execution of the first task according to the corresponding optimization strategy (first optimization strategy), the signals to be processed are clustered.

[0052] It can be understood that the embodiments of the present invention set corresponding optimization strategies for different tasks. The optimization strategies may include two processing methods: one is to adjust the system parameters and integrate the corresponding sub-tasks of the first task according to the adjusted system parameters, that is, a single call can complete the work that originally required multiple calls; the other is to perform clustering processing on signals. For example, for data signals and reference signals, the usual processing mode is to use sequential processing according to the distribution of resources. However, in this embodiment, signals of the same type can be processed on a continuous memory distribution. For example, clustering processing is performed on data signals that occupy more resources.

[0053] Subtasks are tasks defined within the task scope and categorized according to system parameters, targeting different dimensions. Optionally, the system parameters include at least one of the following: number of users, number of antennas, number of symbols, and number of Physical Resource Blocks (PRBs). Specifically, the number of symbols may refer to the number of Orthogonal Frequency Division Multiplexing (OFDM) symbols. For example, within the first task scope, each user can correspond to one or more subtasks. Similarly, within the first task scope, each antenna can correspond to one or more subtasks. In this embodiment, the network device achieves fine-grained control of the baseband processing flow by adjusting system parameters.

[0054] In this embodiment, the first processing method is described as follows:

[0055] The processing dimensions of network devices can include: users, antennas, symbols (OFDM symbols), PRB, etc., as shown in Figure 3 before conversion (left figure).

[0056] User: Refers to the terminal device that uses the service (such as 5G service), such as a smartphone or tablet. Each user has a unique identifier in the network, used for network management and service provision.

[0057] Antenna: Both base stations and user equipment are equipped with antennas for transmitting and receiving wireless signals. For example, 5G networks support massive MIMO (Multiple Input Multiple Output) technology, meaning that base stations can use multiple antennas to serve multiple users simultaneously, improving data transmission rates and network capacity.

[0058] OFDM Symbol: OFDM is a modulation technique used in 5G that divides a broadband channel into multiple subcarriers, each of which transmits data independently. The OFDM symbol is the basic time unit for OFDM transmission.

[0059] PRB: In OFDM systems, a PRB is the basic unit of spectrum used for data transmission. A PRB consists of a certain number of subcarriers and is the basic unit of data transmission resources allocated to users or services.

[0060] In traditional baseband task processing, the processing of each user's data is independent. When processing multi-user data, the processor needs to repeatedly execute the same functional modules in order to process the data of each user one by one.

[0061] The technical solution in this embodiment sets different optimization strategies for different tasks, and different optimization strategies can correspond to different system parameters. The network device adjusts the system parameters according to the first optimization strategy, thereby executing the first task according to the adjusted system parameters. Specifically, the subtasks in the first task are merged according to the adjusted system parameters, that is, a single call or execution can complete the work that originally required multiple calls or executions, realizing the transformation of tasks that were originally executed serially to efficient parallel processing, and achieving fine-grained control of the processing flow. The baseband processing flow is flexibly adjusted according to the usage of computing and storage resources, breaking the original pipelined operation between modules, making full use of computing and storage resources, effectively improving system processing efficiency and flexibility, and providing solid technical support for the efficient and stable operation of 5G / NR networks.

[0062] In this embodiment of the invention, the network device performs transformations based on processing dimensions such as user, antenna, symbol (OFDM symbol), and PRB, thereby merging the corresponding sub-tasks in the first task. Specific dimension transformation methods include the following:

[0063] As a first implementation, adjusting the system parameters according to the first optimization strategy includes: adjusting the number of antennas from N1 to 1 and adjusting the number of PRBs to N1 times the original value, where N1 is a positive integer, according to the first optimization strategy.

[0064] Accordingly, the step of integrating and processing the corresponding subtasks in the first task according to the adjusted system parameters to execute the first task includes: executing the subtasks corresponding to N1 antennas at a time during the execution of the first task according to the adjusted system parameters.

[0065] The conversion method in this embodiment can be referred to as converting multi-antenna multi-PRB processing into single-antenna multi-PRB processing.

[0066] A multi-antenna system refers to a wireless communication system equipped with multiple antennas at both the transmitting and receiving ends. By flexibly adjusting the spatial resources among these antennas, signal reliability and transmission rate can be improved. In traditional baseband processing, loop statements (such as for or while loops) are typically used to iterate through all antennas and execute the same functional modules for each user connected to each antenna. These functional modules may include key steps such as channel estimation, precoding / decoding, power allocation, and modulation / demodulation, which together constitute the core flow of baseband processing. However, as the number of antennas increases, this traditional processing method may face problems such as a sharp increase in computational complexity, excessive processor resource consumption, and increased processing latency.

[0067] This embodiment proposes a single-logic-stream parallelized multi-antenna processing architecture, simplifying complex multi-antenna scenarios into a single-antenna processing logic framework. When determining system parameters, a first optimization strategy is determined based on the first task, and the system parameters are adjusted according to this strategy: the number of antennas is reduced from N1 to 1, and the allocation of Physical Resource Blocks (PRBs) is adjusted accordingly, increasing the number of PRBs to N1 times the original amount. For example, see [reference needed]. Figure 3 The modified method 1 adjusts the original three tasks (task 1, task 2, and task 3) which required three antennas to be executed in a single operation (considered as having one antenna and three times the PRB number). This adjustment logically integrates the tasks of multiple antennas into a single processing flow, completing tasks that originally required multiple iterations in a single operation. This method not only reduces the number of loop iterations and processor overhead but also improves processing efficiency, providing a more flexible and efficient solution for the practical application of MIMO systems.

[0068] For example, in the second filtering based on Minimum Mean Square Error (MMSE), the filter coefficients are calculated based on the signal-to-noise ratio (SNR) value estimated after the first filtering. The channel coefficient value after channel estimation and the filter coefficient value are multiplied to obtain the second filtering result. Since the SNR value is different for each user, the configuration must be based on the actual number of users. The matrix operation module of the network device can process the filtering process of two antennas in a single operation. Assuming the number of users is 1, the original antenna parameters are configured as 4, and the number of antennas is adjusted to 1, the number of PRBs is configured to four times the original, thus achieving the processing of multiple antennas in a single operation. In this example, the first task can be the second filtering task based on MMSE.

[0069] As a second implementation, adjusting the system parameters according to the first optimization strategy includes: configuring system parameters for multiple users according to the first optimization strategy, adjusting the number of users corresponding to the multiple users to 1, and adjusting the PRB number to N2 times the original value, where N2 is the number of the multiple users.

[0070] Accordingly, the step of integrating and processing the corresponding subtasks in the first task according to the adjusted system parameters to execute the first task includes: executing the subtasks corresponding to the multiple users at one time during the execution of the first task according to the adjusted system parameters.

[0071] The conversion method in this embodiment can be called multi-user multi-PRB conversion to single-user multi-PRB processing.

[0072] Multi-user processing refers to the requirement for the baseband processing unit of a network device to simultaneously process baseband signals from multiple users (or terminals). In traditional multi-user processing, the system often follows a linear, sequential processing logic, that is, assigning an independent set of parameters to each user and executing the same functional modules one by one in a loop. Although this processing method is intuitive and easy to implement, its processing efficiency will decrease significantly as the number of users increases, because the system needs to frequently switch contexts between different users and repeatedly execute the same calculation process.

[0073] In this embodiment, instead of assigning a set of system parameters to each user, system parameters are configured for multiple users. The number of users in the system parameters corresponding to multiple users is adjusted from N² to 1, abstracting and integrating the concept of N² users into a single user, achieving a leap in processing logic. Specifically, during the parameter configuration phase, setting the number of users to 1 indicates that multiple users are treated as a unified, enhanced "super user." Simultaneously, to accommodate the resource demands originally distributed across various users, the number of Physical Resource Blocks (PRBs) is adjusted to N² times the original number, ensuring that the system can serve all the demands of the original N² users simultaneously within a single processing cycle with higher resource utilization and parallelism. For example, refer to... Figure 3 The converted method 2 changes the original two users' corresponding tasks 1, 2 and 3 to be executed at once (considered as the number of users being 1 and 2 times the number of PRBs).

[0074] This transformation not only simplifies the system's complexity but also significantly improves the execution efficiency of functional modules. Within a single processing cycle, the system can seamlessly and concurrently process tasks that previously required multiple iterations, reducing context switching overhead and shortening overall processing time. Furthermore, due to centralized resource management and efficient utilization, the system is better able to address the issue of uneven resource allocation, further enhancing user experience and overall system performance.

[0075] For example, regarding the descrambling process (descrambling is the inverse process of scrambling, which refers to the process of restoring a scrambled or encrypted signal to its original form, requiring the receiver to use a pseudo-random sequence), in complex multi-user communication systems, traditional methods require configuring a descrambling sequence separately for each user and processing each user's descrambling task serially, which leads to an extended processing cycle and inefficient use of system resources.

[0076] To overcome this limitation, this embodiment proposes the concept of a "super user," which involves configuring system parameters for multiple users, effectively converting multiple users into a single user. This "super user" can accommodate the resource demands previously distributed among the users, and the number of Physical Resource Blocks (PRBs) is configured to be N² times the original. By configuring a descrambling sequence for this "super user," the descrambling tasks for multiple users that would normally require N² loops can be processed in one go. This method offers significant advantages in improving system processing efficiency and simplifying system structure. In this example, the first task can be the descrambling task.

[0077] As a third implementation, adjusting the system parameters according to the first optimization strategy includes: adjusting the number of symbols from N3 to 1 and adjusting the number of users to N3 times the original value, where N3 is a positive integer, according to the first optimization strategy.

[0078] Accordingly, the step of integrating and processing the corresponding subtasks in the first task according to the adjusted system parameters to execute the first task includes: executing the subtasks corresponding to N3 symbols at a time during the execution of the first task according to the adjusted system parameters.

[0079] The conversion method in this embodiment can be called multi-symbol multi-user conversion to single-symbol multi-user conversion.

[0080] In the process of processing multiple symbols and multiple users, the traditional solution completes the same functional module processing by looping through multiple symbols, that is, looping through each symbol.

[0081] This embodiment logically integrates multiple symbols into a single processing procedure, thus doubling the number of users and enabling simultaneous processing of multiple users and symbols. For example, refer to... Figure 3 In the transformed method 3, it is assumed that the original two symbols corresponding to tasks 1, 2 and 3 are adjusted to be executed once (considered as the number of symbols being 1 and 2 times the number of users).

[0082] For example, in the channel estimation process of Physical Uplink Control Channel (PUCCH) format 3, the user's demodulation reference signal (DMRS) symbol and the local DMRS sequence are multiplied. Since the number of DMRS symbols per time slot in format 3 is 2, two multiplications are required to complete the channel estimation process. This embodiment treats multiple DMRS symbols as a single DMRS symbol, adjusting the symbol count from 2 to 1, and correspondingly doubling the number of users, thus achieving a single completion of the PUCCH format 3 channel estimation process. Therefore, in this example, the first task can be the PUCCH format 3 channel estimation task.

[0083] As a fourth implementation method, adjusting the system parameters according to the first optimization strategy includes: adjusting the PRB number from N4 to 1 and adjusting the number of users to N4 times the original value, where N4 is a positive integer, according to the first optimization strategy.

[0084] Accordingly, the step of integrating and processing the corresponding subtasks in the first task according to the adjusted system parameters to execute the first task includes: executing N4 subtasks corresponding to PRBs at one time during the execution of the first task according to the adjusted system parameters.

[0085] The conversion method in this embodiment can be called multi-user multi-PRB conversion to multi-user single PRB.

[0086] Multi-user, multi-PRB processing refers to a situation in wireless communication systems where network devices (such as baseband processing units (BBUs)) need to simultaneously process requests from multiple users and allocate resources across multiple PRBs to satisfy these requests. This requires the system to have efficient resource scheduling and allocation capabilities to ensure that multiple users can share wireless resources fairly and efficiently. In traditional multi-user, multi-PRB processing, the system typically processes requests cyclically along the PRB dimension, that is, processing all user requests one by one for each PRB. This approach can lead to significant processing latency and efficiency degradation when handling a large number of users and PRBs.

[0087] In this embodiment, the network device converts the PRB dimension into a user dimension and sets the number of PRBs to 1 during parameter configuration, while doubling the number of users. The "doubling of the number of users" refers to merging user requests originally allocated to multiple PRBs into a single "virtual PRB" for processing, rather than actually increasing the number of physical users, thereby avoiding the duplication of processing and inefficient loops in traditional methods.

[0088] Specifically, when configuring system parameters, the number of PRBs is set to 1, and user requests are mapped to corresponding "virtual users" according to user identifiers (IDs). These "virtual users" actually represent user requests that would originally be allocated to different PRBs. For example, if the original number of users is 2 and the number of PRBs is N; using the technical solution of this embodiment, the number of PRBs is set to 1, and the number of users is set to 2N. These 2N users can be regarded as "virtual users," and a mapping relationship between actual users and "virtual users" can be established through the mapping relationship of user IDs. In addition, since the number of PRBs has not actually changed, a mapping relationship between actual PRBs and "virtual users" can be established, and / or a mapping relationship between actual PRBs and actual users can be established. For example, refer to... Figure 3 In the transformed method 3, it is assumed that the original two PRBs corresponding to tasks 1, 2 and 3 are adjusted to be executed once (considered as the number of PRBs being 1 and 2 times the number of users).

[0089] For example, in the SNR calculation process of PUCCH format3, the average power of the channel estimate and the average power of the noise are calculated. The SNR is obtained by multiplying the reciprocal of the noise by the Reference Signal Receiving Power (RSRP). Assuming that the number of symbols for each antenna corresponding to each user is 2, the number of symbols is configured to 2, the PRB is configured to 1, and the number of users is configured as: number of users * number of RBs per user. The power calculation for all users on a single antenna can be achieved by calling the matrix operation module once, and then the SNR is obtained by taking the corresponding power value according to the user arrangement.

[0090] In some optional embodiments of the present invention, after performing the first task according to the adjusted system parameters, the method further includes: filling the vacant positions and / or misaligned positions of the resource positions occupied by the signals corresponding to each user.

[0091] In 5G or NR communication systems, resource alignment typically ensures the orderly allocation and scheduling of different signals across time-frequency resources. This orderly allocation aims to improve overall network resource utilization efficiency and system processing efficiency.

[0092] To achieve this goal, in certain signal processing or scheduling scenarios, such as during the execution of a first task according to the adjusted system parameters described above, or during the integration of corresponding subtasks within the first task, some resource positions in the PRB may become empty or misaligned. In such cases, resource filling is performed on the empty positions to achieve specific resource configuration or optimization. This resource filling method allows for flexible configuration and adjustment of specific resource blocks without interfering with other resource blocks. For example, empty positions in the PRB can be filled with 0s for resource filling, or, in the case of resource misalignment, 0s can be filled in the high (or head) or low (or tail) bits of the PRB for resource alignment. This approach allows for flexible configuration and adjustment of specific resource blocks without interfering with other resource blocks.

[0093] For example, de-resource mapping is the process of mapping a received signal back to its original data stream or channel from its position on the resource grid. Taking de-resource mapping as an example, in the process of performing the first task using the technical solution of this embodiment of the invention, the frequency domain data of all users are first arranged. Since each user occupies a different number of PRBs, if the arranged data is less than 4 PRBs, the frequency domain resources are padded with zeros until 4 PRBs are filled. Then, the resources are mapped uniformly according to 4 PRBs, without distinguishing the specific number of resources occupied by each user. The de-resource mapping process for all users is completed in one go, which improves the processing efficiency compared to the traditional solution that iterates according to the number of users.

[0094] Based on this, the technical solution of this embodiment can further integrate network resources, ensuring the orderly arrangement and allocation of different resource blocks. This not only avoids time-frequency domain misalignment and chaos during resource integration, ensuring unified processing even with differences in quantity, but also improves the flexibility and efficiency of resource utilization.

[0095] In this embodiment, the second processing method is addressed as follows:

[0096] The first task can be the processing of multiple signal types (or symbol types), such as data signals (or the symbols occupied by data) and reference signals (or the symbols occupied by reference signals). The reference signal is a known signal provided by the transmitter to the receiver for channel estimation or channel sounding, helping the base station understand the channel conditions between the terminal and the base station. Data signals are used in the communication system to transmit actual information, carrying the data to be exchanged, such as text, voice, and video. The resource distribution of different types of signals varies depending on the specific task (or task module).

[0097] Traditional processing schemes store signals in memory according to the resources corresponding to the signals when processing different types of signals. This is often limited by the physical distribution of resources, leading to a sequential processing mode based on resource distribution, which increases the complexity and time consumption of the processing flow. To address this problem, this invention proposes a clustering processing strategy. Its core idea is to break free from the constraints of traditional resource location and logically rearrange and integrate signals based on their type.

[0098] In some optional embodiments, the clustering of signals to be processed includes: uniformly storing all signals of the same type that need to be processed within the first task scope in memory, so as to uniformly process the signals to be processed according to their signal types.

[0099] Specifically, taking signals including data signals and reference signals as an example, according to traditional processing schemes, different types of signals are stored in memory according to their corresponding resources, meaning that reference signals may be interspersed among data signals. However, the technical solution of this embodiment processes the reference signals, which were originally interspersed among data signals, either post-processing or pre-processing them in memory, allowing signals of the same type (such as digital signals) to be processed centrally in a contiguous memory segment. This adjustment not only simplifies the processing flow and reduces the additional burden caused by signal type switching, but also reduces resource idleness and waste caused by segmented processing.

[0100] Figure 4 This is a schematic diagram illustrating resource optimization in the baseband task processing method according to an embodiment of the present invention; as shown below. Figure 4 Taking the example shown, with channel equalization as the first task, data signals need to be processed during channel equalization. Since the resource memory location of the reference signal (such as DMRS) is interspersed in the data signals, traditional solutions need to process them separately, sequentially processing multiple stages such as module 1 (data signal), module 2 (reference signal), and module 3 (data signal). Each of these modules can be referred to as a sub-task in the first task.

[0101] The clustering processing strategy proposed in this embodiment of the invention merges subtasks and places the estimated values ​​of the data signals obtained after channel estimation in a contiguous memory location. Therefore, the equalization process can continuously read the data signals and process all data signals (module 1 + module 3) at once, and then process the reference signal after the data signal processing is completed. This processing method not only improves processing efficiency but also reduces resource idleness and waste caused by segmented processing, further enhancing the overall performance of the communication system and the user experience.

[0102] Based on the above embodiments, this invention also provides a baseband task processing device, which is applied to a network device. Figure 5 This is a schematic diagram of the composition structure of the baseband task processing device according to an embodiment of the present invention; as shown... Figure 5 As shown, the device includes: a first processing unit 21 and a second processing unit 22; wherein,

[0103] The first processing unit 21 is configured to obtain a first task; the first task is one of a plurality of tasks included in the baseband processing process; and is further configured to determine a first optimization strategy based on the first task.

[0104] The second processing unit 22 is used to adjust system parameters according to the first optimization strategy, integrate and process the corresponding sub-tasks in the first task according to the adjusted system parameters to execute the first task, or, during the execution of the first task according to the first optimization strategy, perform clustering processing on the signals to be processed.

[0105] In some optional embodiments of the present invention, the system parameters include at least one of the following: number of users, number of antennas, number of symbols, and number of physical resource blocks (PRBs).

[0106] In some optional embodiments of the present invention, the second processing unit 22 is used to adjust the number of antennas from N1 to 1 and the number of PRBs to N1 times the original value, where N1 is a positive integer, according to the first optimization strategy.

[0107] In some optional embodiments of the present invention, the second processing unit 22 is used to execute N1 sub-tasks corresponding to antennas at a time during the execution of the first task according to the adjusted system parameters.

[0108] In some optional embodiments of the present invention, the second processing unit 22 is configured to configure system parameters for multiple users according to the first optimization strategy, adjust the number of users corresponding to the multiple users to 1, and adjust the PRB number to N2 times the original value, where N2 is the number of multiple users.

[0109] In some optional embodiments of the present invention, the second processing unit 22 is used to execute the sub-tasks corresponding to the plurality of users at one time during the execution of the first task according to the adjusted system parameters.

[0110] In some optional embodiments of the present invention, the second processing unit 22 is used to adjust the number of symbols from N3 to 1 and the number of users to N3 times the original value, where N3 is a positive integer, according to the first optimization strategy.

[0111] In some optional embodiments of the present invention, the second processing unit 22 is used to execute N3 subtasks corresponding to symbols at one time during the execution of the first task according to the adjusted system parameters.

[0112] In some optional embodiments of the present invention, the second processing unit 22 is used to adjust the number of PRBs from N4 to 1 and the number of users to N4 times the original value, where N4 is a positive integer, according to the first optimization strategy.

[0113] In some optional embodiments of the present invention, the second processing unit 22 is used to execute N4 sub-tasks corresponding to PRBs at a time during the execution of the first task according to the adjusted system parameters.

[0114] In some optional embodiments of the present invention, the second processing unit 22 is used to uniformly store all signals of the same type that need to be processed within the scope of the first task module in memory, so as to uniformly process the signals to be processed according to the signal type.

[0115] In some optional embodiments of the present invention, the second processing unit 22 is further configured to fill in the vacant positions and / or misaligned positions of the resource positions occupied by the signals corresponding to each user.

[0116] In this embodiment of the invention, the first processing unit 21 and the second processing unit 22 in the device can both be implemented by a central processing unit (CPU), a digital signal processor (DSP), a microcontroller unit (MCU), or a field-programmable gate array (FPGA) in the terminal in practical applications.

[0117] It should be noted that the baseband task processing device provided in the above embodiments is only illustrated by the division of the above program modules when performing baseband task processing. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the baseband task processing device and the baseband task processing method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0118] This invention also provides a network device. Figure 6 This is a schematic diagram of the hardware composition structure of a network device according to an embodiment of the present invention, such as... Figure 6 As shown, the network device includes a memory 32, a processor 31, and a computer program stored in the memory 32 and executable on the processor 31. When the processor 31 executes the program, it implements the steps of the baseband task processing method of the present invention.

[0119] Optionally, the network device may also include at least one network interface 33. The various components of the network device are coupled together via a bus system 34. It is understood that the bus system 34 is used to implement communication between these components. In addition to a data bus, the bus system 34 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 6 The general labeled all buses as Bus System 34.

[0120] It is understood that memory 32 can be volatile memory or non-volatile memory, or both. 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), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. 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), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 32 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0121] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 31. Processor 31 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 31 or by instructions in software form. The processor 31 may be a general-purpose processor, DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 31 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of the present invention can be directly manifested as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory 32. Processor 31 reads the information in memory 32 and completes the steps of the aforementioned method in combination with its hardware.

[0122] In an exemplary embodiment, the network device may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, MCUs, microprocessors, or other electronic components to perform the aforementioned method.

[0123] In an exemplary embodiment, the present invention also provides a computer-readable storage medium, such as a memory 32 including a computer program, which can be executed by a processor 31 of a network device to perform the steps described in the foregoing method. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM; or it may be various devices including one or any combination of the above-mentioned memories.

[0124] The computer-readable storage medium provided in the embodiments of the present invention stores a computer program thereon, which, when executed by a processor, implements the steps of the baseband task processing method of the embodiments of the present invention.

[0125] This application also provides a computer program product, including a computer program that can be executed by a network device (such as the processor 31 of the network device) to complete the steps of any of the aforementioned baseband task processing methods.

[0126] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0127] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0128] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0130] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0132] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0133] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0134] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A baseband task processing method, characterized by, The method comprises: obtaining a first task; the first task is one of a plurality of tasks included in a baseband processing process; determining a first optimization strategy based on the first task, adjusting system parameters according to the first optimization strategy, and performing integrated processing on corresponding subtasks in the first task according to the adjusted system parameters to execute the first task, wherein the system parameters include at least one of the following: the number of users, the number of antennas, the number of symbols, and the number of physical resource blocks (PRBs).

2. The method of claim 1, wherein, The system parameters are adjusted according to the first optimization strategy, comprising: According to the first optimization strategy, the number of antennas is adjusted from N1 to 1, and the number of PRBs is adjusted to N1 times the original value, and N1 is a positive integer.

3. The method of claim 2, wherein, The corresponding subtasks of N1 antennas are executed at a time during the execution of the first task according to the adjusted system parameters. The system parameters are adjusted according to the first optimization strategy, comprising:

4. The method of claim 1, wherein, According to the first optimization strategy, the system parameters are configured for a plurality of users, the number of users corresponding to the plurality of users is adjusted to 1, and the number of PRBs is adjusted to N2 times the original value, and N2 is the number of the plurality of users. The corresponding subtasks of the plurality of users are executed at a time during the execution of the first task according to the adjusted system parameters.

5. The method of claim 4, wherein, The system parameters are adjusted according to the first optimization strategy, comprising: According to the first optimization strategy, the number of symbols is adjusted from N3 to 1, and the number of users is adjusted to N3 times the original value, and N3 is a positive integer.

6. The method of claim 1, wherein, The corresponding subtasks of N3 symbols are executed at a time during the execution of the first task according to the adjusted system parameters. The system parameters are adjusted according to the first optimization strategy, comprising:

7. The method of claim 6, wherein, According to the first optimization strategy, the number of PRBs is adjusted from N4 to 1, and the number of users is adjusted to N4 times the original value, and N4 is a positive integer. The corresponding subtasks of N4 PRBs are executed at a time during the execution of the first task according to the adjusted system parameters.

8. The method of claim 1, wherein, After executing the first task according to the adjusted system parameters, the method further comprises: Performing resource filling on the vacant positions and / or resource misaligned positions in the resource positions occupied by the signals corresponding to the users.

9. The method of claim 8, wherein, The device comprises a first processing unit and a second processing unit; wherein, The first processing unit is configured to obtain a first task; the first task is one of a plurality of tasks included in a baseband processing process; and further configured to determine a first optimization strategy based on the first task.

10. The method according to any one of claims 1 to 9, characterized in that, ​ ​ 11. A baseband task processing apparatus characterized by comprising: ​ ​ The second processing unit is configured to adjust system parameters according to the first optimization strategy, and perform integrated processing on corresponding sub-tasks in the first task according to the adjusted system parameters to execute the first task, wherein the system parameters include at least one of the following: a number of users, a number of antennas, a number of symbols, and a number of physical resource blocks (PRBs).

12. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the steps of the method of any one of claims 1-10.

13. A network device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program comprises instructions for causing the processor to perform the method of any one of claims 1 to 12. The processor, when executing the program, implements the steps of the method of any one of claims 1-10.

14. A computer program product, characterised in that, The computer program product comprises computer program instructions, which cause a computer to perform the steps of the method of any one of claims 1-10.

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