Resource scheduling processing method and device of CF-RAN system and electronic equipment

Abstract

The invention discloses a resource scheduling processing method and device of a CF-RAN system and electronic equipment. The method relates to the technical field of network communication, and comprises the following steps: acquiring a downlink sum rate and an uplink sum rate in a CF-RAN system; constructing a transceiver setting model by taking the maximum uplink and downlink sum rate in the CF-RAN system as a target; the transceiver setting model is sent to an edge processing unit in the CF-RAN system, so that the edge processing unit obtains a transceiver setting result based on the transceiver setting model, and the transceiver setting result comprises preprocessing parameters for preprocessing signals by adopting a receiver and a transmitter; the pre-processing is used for performing resource scheduling by taking minimization of signal loss caused by a channel environment and signal transmission interference as a target. According to the invention, the technical problems of low spectrum resource utilization rate and poor communication quality of a full duplex resource scheduling scheme in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a resource scheduling processing method, device, and electronic device for a CF-RAN system. Background Art

[0002] The rapid development of wireless communication technology has driven the rise of Cell-Free Radio Access Networks (CF-RAN) to meet the growing demand for data transmission and optimize network resource allocation. CF-RAN systems abandon the concept of traditional cellular networks and adopt an architecture of distributed access points (APs) and centralized processing units (CPs) or edge processing units (EDUs), aiming to provide wider coverage and more efficient spectrum utilization. However, the full-duplex (FD) communication mode—which allows devices to transmit and receive signals simultaneously on the same frequency and time—faces the dual challenges of low spectrum resource utilization and poor communication quality when applied to CF-RAN systems.

[0003] In the related art, full-duplex resource scheduling schemes attempt to improve spectrum efficiency, but due to the existence of self-interference and cross-link interference, the allocation of spectrum resources is often limited, which in turn leads to a decline in spectrum efficiency and communication quality. Especially in densely deployed AP environments, these interference sources will increase significantly, causing a surge in the complexity of resource scheduling, which directly affects the sum of the downlink and uplink rates, that is, the total data transmission rate of the system. In addition, the centralized full-duplex resource scheduling scheme has a heavy computational burden when dealing with large-scale user equipment (UE), and it is difficult to respond to network changes in real time, further weakening the potential spectrum efficiency advantage of the CF-RAN system. In summary, the full-duplex resource scheduling scheme in the related art has technical problems such as low spectrum resource utilization and poor communication quality.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] Embodiments of the present invention provide a resource scheduling processing method, apparatus, and electronic device for a CF-RAN system, to at least solve the technical problems of low spectrum resource utilization and poor communication quality in the full-duplex resource scheduling solution in the related art.

[0006] According to one aspect of an embodiment of the present invention, a resource scheduling processing method for a CF-RAN system is provided, comprising: obtaining a downlink sum rate and an uplink sum rate in the CF-RAN system, wherein the downlink sum rate is the sum of rates at which a central processor in the CF-RAN system receives downlink signals from downlink user equipment at any sampling moment, and the uplink sum rate is the sum of rates at which the central processor receives uplink signals from uplink user equipment at any sampling moment; constructing a transceiver setting model with the goal of maximizing the uplink and downlink sum rates in the CF-RAN system, wherein the uplink and downlink sum rates are the sum of the downlink sum rate and the uplink sum rate; and transmitting the transceiver setting model to an edge processing unit in the CF-RAN system, so that the edge processing unit obtains a transceiver setting result based on the transceiver setting model, wherein the transceiver setting result includes preprocessing parameters for preprocessing signals using a receiver and a transmitter, and the preprocessing is used to perform resource scheduling with the goal of minimizing signal loss caused by a channel environment and signal transmission interference.

[0007] According to another aspect of an embodiment of the present invention, another resource scheduling processing method for a CF-RAN system is provided, comprising: receiving a transceiver setting model sent by a central processor in the CF-RAN system, wherein the transceiver setting model is constructed by the central processor with the goal of maximizing the uplink and downlink sum rates in the CF-RAN system, the uplink and downlink sum rate being the sum of the downlink sum rate and the uplink sum rate in the CF-RAN system, the downlink sum rate being the sum of the rates at which downlink signals of downlink user equipment are received by the central processor in the CF-RAN system, and the uplink sum rate being the sum of the rates at which uplink signals of uplink user equipment are received by the central processor; and obtaining a transceiver setting result based on the transceiver setting model, wherein the transceiver setting result includes preprocessing parameters for preprocessing signals using a receiver and a transmitter, the preprocessing being used to minimize signal loss caused by a channel environment and signal transmission interference.

[0008] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium stores a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing any one of the resource scheduling processing methods of the CF-RAN system.

[0009] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising one or more processors and a memory, wherein the memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the resource scheduling processing methods for the CF-RAN system.

[0010] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements any one of the steps of the resource scheduling processing method for the CF-RAN system.

[0011] In an embodiment of the present invention, a downlink sum rate and an uplink sum rate in a CF-RAN system are obtained, wherein the downlink sum rate is the sum of the rates at which a central processor in the CF-RAN system receives downlink signals of downlink user equipment at any sampling moment, and the uplink sum rate is the sum of the rates at which the central processor receives uplink signals of uplink user equipment at any sampling moment; a transceiver setting model is constructed with the goal of maximizing the uplink and downlink sum rates in the CF-RAN system, wherein the uplink and downlink sum rates are the sum of the downlink sum rate and the uplink sum rate; the transceiver setting model is sent to an edge processing unit in the CF-RAN system, so that the edge processing unit obtains a transceiver setting result based on the transceiver setting model, wherein the transceiver setting result includes the signal processing of the receiver and the transmitter. Preprocessing parameters are used for preprocessing. Preprocessing is used to schedule resources with the goal of minimizing the signal loss caused by the channel environment and signal transmission interference, achieving the goal of maximizing the uplink and downlink rates. Combining distributed transceiver design and centralized uplink power control strategy, the central processor is allowed to calculate the total rate, and the edge processing unit performs local optimization based on the received model. By adjusting the preprocessing parameters, the influence of the channel environment and signal transmission interference is minimized, and effective resource management and scheduling are achieved, thereby effectively suppressing co-channel interference and improving communication quality. At the same time, the total rate of the uplink and downlink of the CF-RAN system is improved, and the technical effect of efficient utilization of spectrum resources is achieved, thereby solving the technical problems of low spectrum resource utilization and poor communication quality in the full-duplex resource scheduling scheme in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0013] Figure 1 This is a flowchart of a resource scheduling processing method for a CF-RAN system according to an embodiment of the present invention;

[0014] Figure 2 is a schematic diagram of an optional CF-RAN system structure according to an embodiment of the present invention;

[0015] Figure 3 is a flowchart of another resource scheduling processing method of a CF-RAN system according to an embodiment of the present invention;

[0016] Figure 4 is a graph of outer loop convergence and spectral efficiency of an optional algorithm according to an embodiment of the present invention;

[0017] Figure 5 1 is a schematic diagram of an optional EDU quantity-precoding update convergence and downlink sum rate according to an embodiment of the present invention;

[0018] Figure 6 This is a time diagram of an optional number of EDUs in different serial and parallel situations according to an embodiment of the present invention;

[0019] Figure 7 is an optional channel residual error-spectral efficiency diagram according to an embodiment of the present invention;

[0020] Figure 8 is an optional UE number-spectrum efficiency diagram according to an embodiment of the present invention;

[0021] Figure 9 is an optional AP antenna quantity-spectrum efficiency diagram according to an embodiment of the present invention;

[0022] Figure 10 This is a comparison diagram of spectrum efficiencies of different optional transceiver solutions according to an embodiment of the present invention;

[0023] Figure 11 is a schematic diagram of a resource scheduling processing device of a CF-RAN system according to an embodiment of the present invention;

[0024] Figure 12 2 is a schematic diagram of another resource scheduling processing device of a CF-RAN system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] First, to facilitate understanding of the embodiments of the present invention, some of the terms or nouns involved in the present invention are explained below:

[0028] The Jacobi method is an iterative approach for solving systems of linear equations, widely used in numerical analysis and linear algebra. In multivariable optimization problems, particularly in distributed systems, the Jacobi method is used as a parallel update algorithm. This allows each variable or node to be updated based on the current value of all variables, rather than waiting for all variables to be updated before proceeding to the next iteration. This improves the parallelism and convergence speed of the algorithm.

[0029] According to an embodiment of the present invention, an embodiment of a method for resource scheduling processing of a CF-RAN system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0030] Figure 1 FIG. 1 is a flow chart of a resource scheduling processing method of a CF-RAN system according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0031] Step S102: Acquire a downlink sum rate and an uplink sum rate in the CF-RAN system, where the downlink sum rate is the sum of rates at which a central processor in the CF-RAN system receives downlink signals from downlink user equipment at any sampling moment, and the uplink sum rate is the sum of rates at which the central processor receives uplink signals from uplink user equipment at any sampling moment.

[0032] The execution entity of steps S102 to S108 may be a central processing unit (CPU) in the CF-RAN system. Figure 2This is a schematic diagram of an optional CF-RAN system structure according to an embodiment of the present invention. The method of this embodiment can be applied to Figure 2 In the CF-RAN system shown, the CF-RAN system also includes: user-centric distributed units, edge distributed units (EDUs), and access point devices (APs) configured on the edge distributed units. Specifically, the CF-RAN system includes X edge processing units and M wireless access point devices. Each AP is equipped with N antennas and is connected to the edge processing unit via optical fiber, jointly serving K single-antenna downlink user devices and J single-antenna uplink user devices. Let Represents the EDU set in the CF-RAN system, Represents the AP set in the CF-RAN system, Indicates the uplink UE set in the CF-RAN system, = represents the downlink UE set in the CF-RAN system. Similar sets will not be described in detail later. Due to the flexibility and scalability of the CF-RAN system architecture, any EDU x and variable M x APs are associated, and the number of wireless access point devices M associated with any edge processing unit EDUx in the CF-RAN system x The total number of optical fibers connecting M wireless access point devices and X edge processing units in the CF-RAN system, based on dynamic changes in network requirements and / or system configuration Among them, M x (x=1, 2, ..., X) represents the total number of optical fibers connecting any edge processing unit (EDU x) among the X edge processing units and the access point device.

[0033] In an optional embodiment, obtaining a downlink sum rate and an uplink sum rate in a CF-RAN system includes: obtaining a channel estimation matrix between a user equipment and an access point device in the CF-RAN system; determining a signal-to-interference-plus-noise ratio of a downlink signal from the downlink user equipment received by a central processor based on a downlink channel coefficient in the channel estimation matrix, and determining a downlink sum rate based on the signal-to-interference-plus-noise ratio of the downlink signal, where the downlink channel coefficient is a link channel coefficient between the downlink user equipment and the access point device; determining a signal-to-interference-plus-noise ratio of an uplink signal from the uplink user equipment received by the central processor based on an uplink channel coefficient in the channel estimation matrix, and determining an uplink sum rate based on the signal-to-interference-plus-noise ratio of the uplink signal, where the uplink channel coefficient is a link channel coefficient between the uplink user equipment and the access point device.

[0034] As can be understood, by obtaining the channel estimation matrix between user devices and access points in the CF-RAN system, the central processor accurately determines the downlink and uplink signal-to-interference-and-noise ratios (SINRs), and thus calculates the sum rate of the CF-RAN system. This process, through a distributed channel estimation mechanism, collects and analyzes real-time channel state information between users and access points, ensuring that resource scheduling decisions are based on the latest and most accurate channel conditions. Accurate SINR estimation enables the CF-RAN system to efficiently allocate spectrum resources, optimize signal transmission paths, and control power, significantly improving spectrum efficiency and communication quality. This approach not only addresses the resource waste and performance degradation caused by inaccurate channel state information in traditional wireless networks, but also facilitates more flexible duplex mode switching, enhancing the CF-RAN system's responsiveness to dynamic network changes. Furthermore, accurate SINR estimation provides a powerful basis for subsequent transceiver design, ensuring optimized signal transmission.

[0035] Optionally, in a network-assisted free duplex system, each AP is a half-duplex (HD) device, and its working mode is uniformly scheduled by the CPU, so the duplex mode can be flexibly selected to better manage cross-link interference. For the convenience of description, the AP working in the downlink mode is referred to as the downlink access point device TAP, and the AP working in the uplink mode is referred to as the uplink access point device RAP. It is assumed that there are L TAPs and Z RAPs in the current CF-RAN system, satisfying M = L + Z. Similarly, the number of APs associated with EDU x satisfies M x =L x +Z x L and Z can be dynamically adjusted according to the uplink and downlink traffic requirements. Define the binary mode selection variable μ D,m ,μ U,m ∈{0,1} satisfies μ D,m +μ U,m =1, where μ D,m =1 indicates that APm is a TAP, otherwise APm is a RAP, wherein the binary mode selection variable is used to select the working mode of the AP to determine whether the AP works in the uplink mode or the downlink mode.

[0036] Considering a frequency-flat fading channel, the channel coefficient h between any two antennas a and b is ab , which can be modeled as where β ab is the large-scale fading coefficient, is the small-scale fading coefficient. Then define the channel between all UEs and APs as According to the above, we can get the mode vector composed of the working modes of all APs in the CF-RAN system: μ U=1-μ D By selecting the function g(·), the uplink channel H between UE and RAP required for transceiver design can be obtained. U =g(H,μ U ) and the downlink channel H between UE and TAP D =g(H,μ D ), where the downlink transmission channel is obtained by using the reciprocity of the uplink and downlink channels in the Time Division Duplexing (TDD) mode. To facilitate subsequent deduction, To facilitate subsequent derivation, the channel composition form is given here, H D,x =[h D,x1 h D,x2 …h D,xK ], H U,x =[h U,x1 h U,x2 … h U,xJ ], H D,x Indicates the channel between the TAP associated with EDU x and the downlink UE, H U,x It represents the channel between the RAP associated with EDU x and the uplink UE, and the corresponding estimated channel is (i.e. downlink channel estimation) and (i.e. uplink channel estimation), the estimation errors are and

[0037] The process of determining the downlink link rate can be understood as the modeling process of the downlink data transmission model. In the downlink data transmission phase, L TAPs are managed by X EDUs and serve K downlink UEs in a coordinated manner. Then the kth downlink UE receives signal y D,k for:

[0038]

[0039] in, represents the downlink channel coefficient vector between downlink UE k and AP in the channel estimation matrix; w xk represents the precoding vector of downlink UE k at EDU x; represents the symbol sent to downlink UE k; w xk′ represents the precoding vector of the downlink UE k′ at EDU x; Indicates the symbol sent to the next UE k′; h IUI,kj represents the interference channel between uplink UE j and downlink UE k, p U,j represents the uplink transmit power of UE j, represents the transmission symbol of uplink UE j, represents the additive white Gaussian noise at the downlink UE k. The signal to interference and noise ratio γ of the downlink UE k D,k Obtained by:

[0040]

[0041] The corresponding downlink rate R D Obtained by:

[0042]

[0043] Among them, R D,k Indicates the transmission rate of the downlink signal corresponding to downlink UE k.

[0044] The process of determining the uplink link rate can be understood as the modeling process of the uplink data transmission model. In the uplink data transmission phase, the uplink UE sends a signal to the RAP, and the RAP forwards the received signal to the EDU. At this time, the received signal y at the EDU x is D,x Aggregated into:

[0045]

[0046] Among them, h U,xj′ represents the uplink channel coefficient vector between uplink UE j′ and RAP in the channel estimation matrix; p U,j′ represents the uplink transmit power of UE j′; represents the transmission symbol of uplink UE j′; represents the interference channel between the TAP associated with EDU x′ and the RAP associated with EDU x; represents the thermal noise at the receiver. Since EDUs may collaborate with each other, this means that EDU x is aware of the downlink baseband signals sent by other EDUs x′. Therefore, using inter-AP channel estimation techniques, we can reconstruct the inter-AP interference signal and effectively suppress the cross-link interference between APs. However, due to inherent channel estimation errors, it is not feasible to completely eliminate inter-access point interference (IAI). Considering the residual IAI, the uplink signal received at EDU x can be transformed into:

[0047]

[0048] in, is the estimated channel between EDUs, is the estimation error, The elements satisfy the Gaussian distribution, that is, in represents the error gain remaining due to the non-ideal interference cancellation in the analog-to-digital domain; Represents an identity matrix whose dimensions are represented by NZ T and L Z′ The product of NZ T Indicates the number of TAPs associated with EDU x, that is, the number of access points used for downlink data transmission; L Z′ Indicates the number of uplink UEs associated with EDU x′.

[0049] In order to detect the uplink transmission signal of UE j, let represents the receiving vector of uplink UE j at EDU x, then the uplink data demodulated at EDU x is for:

[0050]

[0051] Among them, n x Represents the noise experienced by the receiving end of EDU x.

[0052] The user data demodulated in the EDU is transmitted to the CPU via the backhaul link, and the uplink data from UE j is combined in the following way to obtain the combined uplink data

[0053]

[0054] Where n represents the independent noise experienced by each EDU when receiving the signal.

[0055] The uplink signal-to-interference-and-noise ratio γ of UE j is obtained as follows U,j :

[0056]

[0057] The corresponding uplink sum rate R U for:

[0058]

[0059] Among them, R U,j Indicates the transmission rate of the uplink signal corresponding to uplink UE j.

[0060] Step S104: constructing a transceiver configuration model with the goal of maximizing the uplink and downlink sum rate in the CF-RAN system, where the uplink and downlink sum rate is the sum of the downlink sum rate and the uplink sum rate.

[0061] Optionally, with maximizing both uplink and downlink data rates in the CF-RAN system as the overall goal, the optimization objective is set to maximizing the total data transmission rate in both the uplink and downlink directions for all users in the CF-RAN system. This directly reflects the optimal performance of the CF-RAN system in meeting user needs, ensuring maximum spectrum utilization efficiency and communication quality, while minimizing network latency and improving system throughput. Based on the maximizing uplink and downlink data rates objectives, a transceiver configuration model can be constructed through model transformation to optimize the transceiver design results. The transceiver configuration model includes optimization objectives (which can be represented by objective functions) and constraints related to the transceiver configuration.

[0062] As an optional embodiment, it is also possible, but not limited to, to simultaneously construct a transceiver configuration model and an uplink power control model through model transformation, with the goal of maximizing both uplink and downlink rates in the CF-RAN system. The transceiver configuration model and the uplink power control model each correspond to their own optimization objectives (which can be represented by objective functions) and constraints, and are used to optimize the transceiver design and uplink power control strategy, respectively. In this embodiment, the edge processing unit (EDU) undertakes some distributed computing tasks to optimize the transceiver design, while the central processing unit (CPU) centrally handles uplink power control. This combined distributed and centralized resource scheduling strategy can further enhance the CF-RAN system's performance in terms of spectrum efficiency and communication quality.

[0063] In an optional embodiment, a transceiver setting model is constructed with the goal of maximizing uplink and downlink rates in a CF-RAN system, including: constructing an initial optimization model with the goal of maximizing uplink and downlink rates in the CF-RAN system; transforming the initial optimization model to obtain an equivalent optimization model with the goal of minimizing mean squared errors of uplink and downlink rates, wherein the equivalent optimization model is an optimization model based on mean squared error; obtaining a transceiver setting model based on the equivalent optimization model; constructing an uplink power control model with the goal of maximizing uplink and downlink rates in the CF-RAN system includes: constructing an initial optimization model with the goal of maximizing uplink and downlink rates in the CF-RAN system; transforming the initial optimization model to obtain an equivalent optimization model with the goal of minimizing mean squared errors of uplink and downlink rates, wherein the equivalent optimization model is an optimization model based on mean squared error; and obtaining an uplink power control model based on the equivalent optimization model.

[0064] It can be understood that when constructing the transceiver setting model and the uplink power control model at the same time, for a given AP duplex mode, the above initial optimization model is transformed using the transceiver design concept based on Mean Square Error (MSE). Because minimizing the user's MSE can maximize the tight lower bound of its rate, when using a minimum mean square error (MMSE) receiver, the MSE-based optimization model is equivalent to the maximum signal-to-interference-plus-noise ratio (SINR)-based optimization model, satisfying Therefore, rate-based optimization (log2(1+SINR)) can be further transformed into MSE-based optimization, i.e., -log2(MSE). The initial complex optimization model for maximizing the sum rates of uplink and downlink in the CF-RAN system is transformed into an equivalent MSE-based optimization model, which is further decomposed into two sub-models: transceiver configuration and uplink power control. This approach exploits the equivalence between MSE and sum rate—that is, minimizing MSE is equivalent to maximizing the tight lower bound of the sum rate—to transform the non-convex initial optimization model into an easily solvable convex optimization form. By transforming the initial optimization model, not only can the computational complexity be significantly reduced, but it also enables the coordinated use of distributed computing between the edge processing unit (EDU) and the central processing unit (CPU), improving the resource allocation efficiency and communication performance of the overall CF-RAN system. This conversion and decomposition strategy ensures the robustness of transceiver design and the convergence of optimization. It also allows effective information exchange between the EDU and CPU, leveraging local and global information to optimize transceiver parameters and uplink power control, ultimately improving the CF-RAN system's spectrum efficiency and anti-interference capabilities.

[0065] As an optional embodiment, an initial optimization model is constructed with the goal of maximizing uplink and downlink rates in the CF-RAN system, including: constructing an initial optimization model with the goal of maximizing uplink and downlink rates in the CF-RAN system, using a first constraint, a second constraint, a third constraint, and a fourth constraint as constraint conditions, wherein: the first constraint is used to indicate that a first preprocessing parameter (such as a precoding matrix) set by each edge processing unit for a corresponding downlink user equipment is determined by corresponding channel state information; the second constraint is used to indicate that a second preprocessing parameter (such as a receiver matrix) set by each edge processing unit for a corresponding uplink user equipment is determined by corresponding channel state information; the third constraint is used to indicate that the local state information of each edge processing unit is obtained based on an uplink channel estimate and a downlink channel estimate mastered by the corresponding edge processing unit, after a predetermined function operation; and the fourth constraint is used to indicate that the total transmit power of each access point device does not exceed a preset power threshold.

[0066] Optionally, an initial optimization model is constructed with the goal of maximizing both uplink and downlink system rates, and four key constraints are set to guide the optimization process. The first constraint specifies that the design of the precoding matrix is ​​limited to the channel state information (CSI) of the edge processing unit (EDU), ensuring computational locality and feasibility. The second constraint specifies that the receiver matrix is ​​entirely dependent on the CSI of the corresponding EDU, enhancing the distributed nature of the transceiver design. The third constraint specifies that the construction of local state information integrates uplink and downlink channel estimation, employing specific functions. This not only enhances the EDU's information processing capabilities but also provides a foundation for decoupling the subsequent optimization model. The fourth constraint caps the total transmit power of each access point (AP), ensuring energy efficiency and fair allocation of network resources in the CF-RAN system. These four constraints effectively address the challenges of resource management and signal transmission in high-density wireless network environments. They allow each EDU to utilize limited local information for precoding and receiver design. Furthermore, through global CPU coordination, they overcome the computational complexity bottleneck caused by the reliance on complete CSI in traditional systems. Furthermore, by ensuring that AP power does not exceed a threshold, self-interference and cross-AP interference caused by excessive transmit power can be effectively prevented, maintaining network stability and user communication experience. This combination of layered optimization, distributed processing, and power control significantly improves the spectrum efficiency and anti-interference capabilities of the CF-RAN system in dynamic duplex mode, providing key technical support for building efficient, reliable, and adaptable future wireless communication networks. Especially in ultra-dense network scenarios, it effectively balances spectrum resource utilization and signal purity, achieving both improved communication quality and system performance.

[0067] Optionally, the initial optimization model can be constructed by, but is not limited to, the following methods:

[0068]

[0069] Among them, the four constraints are arranged in order as the first constraint, the second constraint, the third constraint and the fourth constraint, w xk =w xk (S x ),v xj =v xj (S x ) represent the downlink precoding obtained by the x-th EDU, the uplink receiver is part of the observation information S x Function. x (·) is the observation function, which means that for any EDU x, the processing unit can not only obtain the local channel estimation information Contains the estimated channels of downlink UE-TAP (associated with EDU x) and uplink UE-RAF (associated with EDU x), that is, At the same time, there is cooperation between EDUs. EDU x can receive the shared channel status information of other EDUs, which can be called sideband information. When signaling is exchanged between each EDU in the CF-RAN system, If there is no signaling exchange, the sideband information at this time represents the transmit power constraint of each TAP, where p AP The AP power threshold limits the transmit power of a group of antennas deployed on a single AP in practical applications. This is more reasonable than the total system power constraint.

[0070] In an optional embodiment, the initial optimization model is transformed to obtain an equivalent optimization model, including: using a weighted minimum mean square error method to remodel the downlink and rate in the initial optimization model to obtain a first mean square error corresponding to the downlink and rate, wherein the first mean square error is used to indicate the mean square error between the signal received by any downlink user device and the expected signal under a given scalar receiving filter, precoding matrix and transmission power of the uplink user device; using a weighted minimum mean square error method to remodel the uplink and rate in the initial optimization model to obtain a second mean square error corresponding to the uplink and rate, wherein the second mean square error is used to indicate the mean square error between the signal received by any uplink user device and the expected signal under a given uplink user device receiving vector, uplink user device transmission power and precoding matrix; based on the first mean square error function and the second mean square error function, the initial optimization model is transformed to obtain an equivalent optimization model.

[0071] As can be understood, the weighted minimum mean square error (WMMSE) method is used to transform the initial optimization model for maximizing uplink and downlink sum rates in the CF-RAN system, resulting in an equivalent optimization model based on mean square error. The WMMSE method converts the originally difficult-to-optimize sum rate objective into the easily manageable first mean square error (downlink signal error) and second mean square error (uplink signal error). The functional relationship between the mean square error and the signal-to-interference-plus-noise ratio (SINR) is leveraged to simplify and decompose the complex optimization model. The equivalent optimization based on the WMMSE method allows for independent optimization of resource scheduling (such as the precoding matrix, receiver matrix, and transmit power settings) on each link, without directly addressing the non-convex sum rate maximization problem. This not only reduces the complexity of the optimization model but also enables distributed computing between the edge processing unit (EDU) and the central processing unit (CPU). By minimizing the first and second mean square errors, signal transmission quality can be effectively improved, signal errors can be reduced, and the overall spectrum efficiency and communication performance of the CF-RAN system can be enhanced.

[0072] Optionally, the WMMSE method is used to remodel the rate term in the initial optimization model. For downlink transmission, the scalar receiving filter u of the downlink UE k is first introduced. D,k , combined with the downlink received signal, calculate its mean square error (MSE) as follows:

[0073]

[0074] Among them, e D,k (u D,k ,W,p U ) represents the specific scalar receiving filter u given D,k , precoding matrix W and uplink UE power vector p U The mean square error between the signal received by the kth downlink user equipment and the expected signal. In the optimization process, the goal is to adjust u D,k , W and p U To minimize e D,k , thereby improving the reliability and efficiency of data transmission; u k represents the receiving vector of downlink UE k, represents the conjugate transpose of the downlink UE k’s received vector; is the correlation matrix of the channel estimation error between EDU x and downlink UE k, β IUI,kj is the large-scale fading from uplink UE j to downlink UE k; W represents the precoding matrix, p U represents the transmit power vector of the uplink user equipment; pU,j represents the uplink transmit power vector of UE j.

[0075] Optional, for using v j The uplink UE j receiving the vector, combined with the uplink demodulated signal, calculates its MSE as follows:

[0076]

[0077] Among them, e U,j (v j ,p U ,W) represents the given receiving vector v j , uplink UE transmit power vector p U , and the MSE between the signal received by UE j and the expected signal under the precoding matrix W. In the optimization process, the goal is to adjust v j , p U and W, to minimize e U,j ; Indicates that UE j transmits symbol s U,j estimated value of; represents the conjugate transpose of the received vector of UE j at the edge processing unit EDU x; v xj represents the receive vector designed for UE j at EDU x.

[0078] As an optional embodiment, an auxiliary variable α is further introduced D =[α D,1 ,…,α D,K ] T With α U =[α U,1 ,…,α U,J ] T , using the functional relationship between mean square error and signal to interference plus noise ratio (i.e. MSE-SINR relationship), the initial optimization model is transformed into an equivalent optimization model in the following form:

[0079]

[0080] in: χ D (W,p U ,α D ,u D ) is used to indicate that while considering the downlink UE reception quality, the precoding matrix W (i.e., the first preprocessing parameter), the uplink UE transmit power p U , regularization parameter α D and the downlink UE receiving parameter u D The process of optimizing the downlink maximum sum rate or signal quality is achieved by joint optimization ofU (W,p U ,V,α U ) is used to indicate that the uplink UE transmit power p is adjusted by optimizing the precoding matrix W. U , design the uplink UE receiver matrix V (i.e., the second preprocessing parameter) and set the uplink regularization parameter α U , in order to minimize the signal loss or increase and rate caused by the channel environment and interference between UEs during uplink signal transmission.

[0081] In an optional embodiment, the transceiver setting model includes a first sub-model and a second sub-model, wherein the first sub-model is used to obtain a second preprocessing parameter for preprocessing the signal using a receiver, and the second sub-model is used to obtain a first preprocessing parameter for preprocessing the signal using a transmitter, wherein the second preprocessing parameter can be a receiver matrix and the first preprocessing parameter can be a precoding matrix.

[0082] Optionally, an iterative approach can be used to optimize each variable one by one while keeping other optimization variables unchanged until convergence. The optimization problem is decomposed into three sub-model blocks (i.e., the first sub-model, the second sub-model, and the uplink power control model), and the corresponding closed-form solution of each sub-model block is derived, and partially distributed computing is performed to improve the overall optimization efficiency and computational efficiency. An innovative approach is proposed to decompose the transceiver setting model into two sub-models, where the first sub-model focuses on the optimization of the receiver matrix, while the second sub-model is dedicated to solving the precoding matrix, thereby achieving efficient optimization of CF-RAN network-assisted free duplex system resource scheduling and transceiver design. The above approach decouples precoding and receiver design, allowing these two key components to be optimized independently while keeping the complexity of the CF-RAN system under control, thereby improving the overall system performance and spectrum efficiency.

[0083] In an optional embodiment, the first sub-model aims to minimize the weighted mean square error of the uplink sum rate, and uses the second constraint and the third constraint as constraint conditions, wherein the second constraint is used to indicate that the second preprocessing parameter set by each edge processing unit for the corresponding uplink user equipment is determined by the corresponding channel state information; the third constraint is used to indicate that the local state information of each edge processing unit is obtained through a predetermined function operation based on the uplink channel estimate and the downlink channel estimate mastered by the corresponding edge processing unit.

[0084] Optionally, the second constraint can be understood as a local generation constraint of the second preprocessing parameter (i.e., the receiver matrix), which means that the receiver design is distributed, and each EDU is independent of other units and optimizes based on the information it has, reflecting the distributed computing characteristics of the system. This design allows the EDU to generate the receiver matrix that best suits the users it serves, taking into account its own channel conditions, interference environment, and possible collaborative information, while reducing the need to share large amounts of data within the entire CF-RAN system and reducing the burden on the fronthaul link. The third constraint can be understood as a channel estimation dependency constraint on state information. When each EDU designs a transceiver, its decision-making basis is directly related to the estimation of the channel conditions between the UE and the AP within the CF-RAN system. In this way, even with limited sharing of channel state information, the EDU can make optimization decisions based on the current best channel knowledge, which helps reduce interference within the CF-RAN system and improve signal reception quality and spectrum efficiency.

[0085] It should be noted that in the first sub-model design, this implementation maximizes summation rate by minimizing the uplink weighted mean square error (MSE), while strictly adhering to two key constraints: ensuring that the receiver matrix designed for each edge processing unit (EDU) is based solely on the channel state information (CSI) of its directly associated uplink user equipment (UE), and that the EDU's local state information is derived by fusing uplink and downlink channel estimates through a specific function. This approach cleverly decouples the receiver design from the rest of the system, achieving a balance between local optimization and global collaboration. This significantly enhances the resource scheduling flexibility and efficiency of the CF-RAN network-assisted free duplex system in handling large-scale users and high-density access points, thereby effectively improving the spectrum utilization and overall communication performance of the CF-RAN system. In particular, it demonstrates excellent results in managing cross-link interference and ensuring signal quality, enabling it to adapt to dynamically changing network conditions while reducing signaling overhead, making the system more robust and responsive.

[0086] Optionally, it can be but not limited to taking the minimum weighted mean square error of the uplink sum rate as the goal, taking the second constraint and the third constraint as the constraint conditions, for a given {α U ,p U} and {α D ,u D ,W}, the first sub-model is constructed as follows to obtain the optimal receiver matrix V (i.e., the optimal second preprocessing parameters) of the CF-RAN system:

[0087]

[0088] in, Each e U,j (vj ) represents the minimum mean square error (MMSE) receiver design for a specific uplink UE j, and α U,j It may represent a weight associated with the UE, reflecting its relative importance or priority in the system, or a coefficient related to interference management. The overall physical meaning of f1(V) is to minimize the overall error of the uplink UE received signal through minimum mean square error (MMSE) receiver design in the process of maximizing the uplink rate or optimizing the uplink signal quality, while taking into account the relative weights of different UEs to achieve efficient resource allocation and optimize network performance.

[0089] In an optional embodiment, the second submodel aims to minimize the negative impact of downlink signal interference and noise on communication quality, and uses the first constraint, the third constraint, and the fourth constraint as constraint conditions, wherein the first constraint is used to indicate that the first preprocessing parameter set by each edge processing unit for the corresponding downlink user equipment is determined by the corresponding channel state information, and the fourth constraint is used to indicate that the total transmit power of each access point device does not exceed a preset power threshold.

[0090] Optionally, by minimizing the negative impact of signal interference and noise on communications while satisfying strict constraints to construct a second sub-model, it is also possible to ensure that, in the design of the first preprocessing parameters (i.e., the precoding matrix), each edge processing unit (EDU) determines the precoding matrix based solely on the channel state information (CSI) of its directly associated downlink user equipment (UE), while maintaining the total transmit power within a safe threshold to avoid overload. The above approach can effectively improve the stability and efficiency of downlink transmission in the CF-RAN network-assisted free duplex system. By precisely controlling the transmit power and utilizing local CSI for precoding, inter-AP interference (IAI) and inter-user interference (IUI) are reduced, thereby significantly enhancing the clarity and speed of data transmission in dense network environments, ensuring the quality of user communications and the energy-efficient operation of the CF-RAN system. This approach is particularly suitable for processing scenarios with large-scale users and highly concurrent data streams.

[0091] Optionally, it is possible but not limited to minimize the negative impact of downlink signal interference and noise on communication quality as the goal, with the first constraint, the third constraint, and the fourth constraint as constraint conditions, for a given {α U ,p U , V}, the second sub-model is constructed as follows to obtain the optimal precoding matrix (i.e., the optimal first preprocessing parameters):

[0092]

[0093] in, where w lkrepresents the precoding vector of the kth downlink UE associated with the lth TAP. D ,u D The overall physical meaning of ) is to optimize downlink signal transmission while considering and minimizing the negative impact of inter-AP interference. Specifically, the goal is to ensure the signal transmission quality and efficiency (given by χ D (W,α D ,u D ) while minimizing the additional signal loss caused by inter-AP interference (represented by This objective function is designed to optimize the maximum downlink sum rate in the CF-RAN system while effectively suppressing IAI and improving the overall network performance.

[0094] In an optional embodiment, the uplink power control model aims to minimize the negative impact of uplink signal interference and noise on communication quality, and uses a third constraint as a constraint condition, wherein the third constraint is used to indicate that the local state information of each edge processing unit is obtained through a predetermined function operation based on the uplink channel estimate and downlink channel estimate mastered by the corresponding edge processing unit.

[0095] Optionally, we target uplink signal interference and noise to minimize their negative impact on communication quality by optimizing uplink power control while adhering to the third constraint: information sharing for each EDU. This strategy effectively improves the clarity and efficiency of uplink communications in CF-RAN networks, ensuring stable data transmission in complex network environments. Furthermore, by controlling power allocation, we avoid excessive energy consumption, achieving dual optimization of communication quality and energy efficiency.

[0096] Optionally, it is possible but not limited to take the goal of minimizing the negative impact of uplink signal interference and noise on communication quality as the third constraint as the constraint condition, by fixing {α D ,u D ,W}, {α U ,V}, construct the following uplink power control model to optimize the uplink power control strategy p U :

[0097]

[0098]

[0099] in, χ U (p U ) indicates the signal quality of the uplink. Part of the focus is on the impact of downlink on uplink signal quality, α D,kIt can be understood as a regularization parameter to balance signal quality and power efficiency; u D,k is the receiving weight or receiver parameter for the kth downlink UE, |u D,k | 2 Indicates its power; therefore, this part reflects the comprehensive consideration of the potential impact of all downlink UE receiver parameters on the uplink signal quality in the downlink, that is, the degree of interference caused by the downlink UE's receiving activity to the uplink UE's received signal. The overall physical significance of the above objectives is to achieve the goal of reducing the uplink UE's transmission power p by adjusting the uplink UE's transmission power p. U This optimization simultaneously considers inter-user interference within the uplink and the impact of downlink UE receiver parameters on uplink signal quality to minimize signal distortion and interference, optimizing the overall uplink speed. In the CF-RAN NAFD system, this power control ensures high-quality uplink signal transmission while mitigating inter-user interference, thereby improving network performance and efficiency. This is particularly true in dense duplex communication environments, effectively managing signal transmission quality and resource allocation.

[0100] It should be noted that since the precoding and combining matrices are both distributedly calculated at the EDU, in order to solve the sub-model, the information required by the CPU may no longer be the complete uplink estimated channel, but the equivalent channel after receiver processing, which effectively reduces signaling overhead.

[0101] Step S106: Sending the transceiver configuration model to an edge processing unit in the CF-RAN system, where the edge processing unit obtains a transceiver configuration result based on the transceiver configuration model. The transceiver configuration result includes preprocessing parameters for preprocessing signals using a receiver and a transmitter. The preprocessing is used to perform resource scheduling with the goal of minimizing signal loss caused by channel environment and signal transmission interference.

[0102] Optionally, after building the transceiver setting model, the CPU will send the transceiver setting model to the edge processing unit (EDU). The EDU solves the transceiver setting model in a distributed manner, and can design efficient transceiver design results (such as precoding matrix and receiver matrix) based on local channel state information (CSI) and collaboration between edges. The above method allows the central processor to calculate the total rate, and the edge processing unit performs local optimization based on the received model, minimizing the impact of the channel environment and signal transmission interference by adjusting the preprocessing parameters, thereby achieving effective resource management and scheduling. At the same time, distributed processing can fully utilize the computing power of the EDU, reduce the computational burden and latency of centralized processing, and enhance the adaptability and robustness of the CF-RAN system to dynamic environments.

[0103] In an optional embodiment, when there are multiple edge processing units, the transceiver setting model is sent to the edge processing unit in the CF-RAN system, including: sending the first sub-model in the transceiver setting model to the multiple edge processing units in the following manner: converting the first sub-model into multiple first decomposition sub-models, wherein the multiple first decomposition sub-models correspond one-to-one to the multiple edge processing units; sending the multiple first decomposition sub-models to the corresponding edge processing units, so that the corresponding edge processing units process the received first decomposition sub-models based on their own channel state information and shared channel state information, and obtain the second preprocessing parameters set by the corresponding edge processing units for the associated uplink user equipment.

[0104] Optionally, when the transceiver setting model includes a first sub-model and a second sub-model, and there are multiple edge processing units, the CPU can convert the first sub-model into a first decomposition sub-model equal to the number of multiple edge processing units (EDUs), and send each sub-model to the corresponding EDU. Each EDU can be given {α U ,p U} and {α D ,u D ,W}, the first submodel is solved. By decomposing the first submodel in the complex transceiver configuration model—the receiver matrix solution—into multiple first decomposition submodels corresponding to each uplink user equipment in the CF-RAN system, efficient parallel processing based on the EDU is achieved. Based on its association with the uplink UE, the EDU independently processes the corresponding decomposition submodel, utilizing local CSI combined with CSI shared from other EDUs to collaboratively optimize the local receiver matrix (i.e., the second preprocessing parameter). This approach significantly improves the resource allocation capability and computational efficiency of the CF-RAN network-assisted free duplex system in ultra-dense network environments, effectively reducing the computational burden on the central processing unit (CPU). A coordination mechanism ensures the consistency of distributed optimization and overall system performance, significantly enhancing the CF-RAN system's spectrum efficiency and communication quality. Through decomposition and distributed processing, the CF-RAN system maintains high performance and low latency even in scenarios with rapidly growing network scale, demonstrating high flexibility and robustness and significantly optimizing network operation.

[0105] Optionally, the weighted sum MSE minimization model is further approximately decomposed into J MSE minimization models (i.e., multiple first decomposition sub-models). Then, for any uplink UE j, the first decomposition sub-model is constructed as follows to obtain the optimal receiver vector v j :

[0106]

[0107] Among them, eu,j(v j ) represents the MMSE of the signal of the j-th uplink user equipment UE at its receiving end in the uplink.

[0108] In order to solve the above model and realize distributed computing, based on team theory, the traditional Linear Minimum Mean Square Error Estimation (LMMSE) method is extended to the CF-RAN system. By utilizing the EDU cooperation and exchange capabilities, an EDU-TMMSE receiving scheme is proposed, which enables each EDU to independently calculate the local receiver matrix V based on its own information and sideband information. x At the same time, it can adjust the amount of sideband shared information in real time according to the backhaul load pressure and user rate requirements, achieving a dynamic balance between signaling and performance, and has flexible scalability. The specific implementation process is as follows:

[0109] First, continue processing e D,k (u D,k ,W,p U ), the MSE function e of uplink UE j is U,j (v j ) can be restated as follows:

[0110]

[0111] in, Denotes the random variable H U Seeking expectations, e j represents a unit vector whose jth element is 1, Ω IAI is the residual interference matrix; the average power of the interference signal is in, represents the effective channel estimation matrix between the x-th EDU and the x'th EDU due to the potential interference between their associated TAP and RAP. This matrix reflects the additional interference components contained in the received signal due to the interference between TAP and RAP. x′k represents the precoding vector associated with the k-th TAP managed by the x'-th EDU. Indicates w x′k The Hermitian transpose (conjugate transpose) of . express The Hermitian transpose of . represents the non-zero entry indicator matrix associated with the x-th EDU.

[0112] By utilizing the independent nature of the channel estimation errors between different EDUs, we can finally obtain the block diagonal IAI matrix

[0113] In order to obtain the optimal distributed receiver, let F U =H U P 1 / 2 , and the above e U,j (v j ) is further expanded:

[0114]

[0115] Among them, F U represents the effective transmission matrix or equivalent channel matrix of the uplink, P represents the transmit power matrix, and f U,j Denotes the strength or quality measurement value of the signal transmitted by the jth uplink UE. but At this time, each EDU needs to share its precoding power with other EDUs to obtain a complete IAI matrix. To simplify the analysis, let And let the total transmit power of all TAPs in the CF-RAN system be where p D,x represents the total transmit power of the TAP associated with the x-th EDU. Then the IAI matrix can be further simplified to Under the current assumption, there is no need for any two EDUs to share their own precoding power. Instead, they first set the transmission power p D,x Reported to the CPU, after unified calculation by the CPU, the total transmission power p is shared with all EDUs D,total , which can effectively reduce the number of signaling exchanges. Finally, the MSE of uplink UE j can be simplified to:

[0116]

[0117] in, Then order The receiver optimization target for uplink UE j is (i.e. the optimal receiver vector) can be expressed as a typical quadratic team model:

[0118]

[0119] Using the stationary condition of the quadratic team decision model: For any EDU x, in the existing observation information S x Under the condition, the optimal receiver vector for a given EDU x is like and If established, With a stationary solution In order to obtain the receiver matrix on each EDU, we further The optimization objective function is expressed as vxj Function:

[0120]

[0121] in, Will About v xj The partial derivative of is set to 0, and the corresponding stationary conditions are as follows:

[0122]

[0123] in, represents the optimal receiver vector for a given EDU x, Indicates U xx For uplink channel H U In S x Expectations under conditional information, and the rest are similar.

[0124] In order to improve the robustness of the receiving scheme, the channel estimation error between the uplink UE and RAP is also considered to meet in and Represent the estimated channel and the error channel respectively. In the CF-RAN system, EDU can use the MMSE channel estimation algorithm. The channel estimation algorithm can be implemented by any channel estimation algorithm in the relevant technology, which will not be described here. Due to the orthogonality of MMSE estimation, and Independent of each other, the estimated channel and error channel of uplink UE j estimated by EDU x are expressed as and represent the uplink channel estimation and uplink channel estimation error respectively.

[0125] Based on the above analysis, we can further derive all the conditional expectations under the above stationary conditions.

[0126] For the second term in the stationary bar, we can get:

[0127]

[0128] in, Indicates the effective noise power of the uplink.

[0129] For the second item in the stationary condition, we can further obtain:

[0130]

[0131] Similarly, the third item is:

[0132]

[0133] Among them, e j represents a unit vector whose element at the jth position is 1 and whose elements at all other positions are 0.

[0134] Finally it simplifies to:

[0135]

[0136] in, represents the estimation error correlation matrix of the interference channel associated with uplink UE j; e U,x The vector form of the channel estimation error between the uplink UEj and the RAP associated with EDU x.

[0137] At this time, the part of the sideband information S that EDU x can obtain x In the case of uplink UE j, the optimal receiver vector at EDU x is for:

[0138]

[0139] Among them, A x The representation is as follows.

[0140]

[0141] By adding EDU processing, the collaborative receiving capability can be further enhanced based on LMMSE, which is called A x is the EDU basic receiver (ie, the basic receiver matrix), and based on the basic receiver matrix, the optimal receiver matrix is ​​obtained.

[0142] Optionally, after constructing the initial receiver matrix using the local receiver matrix calculated by each edge processing unit (EDU), a compensation strategy is further implemented to correct for performance losses caused by channel estimation errors and improve overall reception efficiency. This strategy can significantly enhance the anti-interference capabilities of the CF-RAN network-assisted free duplex system, especially against residual inter-user interference (IUI) and inter-AP interference (IAI). This enables the system to accurately demodulate signals in more complex communication environments, effectively improving spectrum efficiency and data transmission rates, and ensuring a high-quality communication experience. The compensation mechanism not only compensates for imperfect channel estimation but also promotes more efficient utilization of system resources.

[0143] Optionally, further convert the basic receiver A xDenoted as EMMSE, since EMMSE only uses the EDU's own channel estimation information for reception, the sideband information is 0, so the performance is limited. To further improve the overall performance, it is necessary to make full use of the existing information in the CF-RAN system for appropriate compensation. Therefore, considering a two-layer receiver, the reception vector of EDU x for uplink UE j can be expressed as: v xj =A x B x e j , where A x is the instantaneous estimated channel The function is composed of instantaneous information. For the compensation matrix B x , which can dynamically adjust its components according to demand, and it is the sideband information function, we can prove that B x It can be obtained by the linear system constructed by signaling. The following focuses on the derivation of B x Matrix explicit expression. Since the double-layer combiner satisfies V x =A x B x form, substitute it into For any EDU x, it satisfies:

[0144]

[0145] For the complex conditional expectation model mentioned above, an inter-EDU sharing mechanism is used to process it. Since the EDU itself can share information with other EDUs through signaling on the fronthaul link as a server, it is assumed that each EDU has two states, namely shared (On) and non-shared (Off). If EDU x is in the shared state, the EDU shares the local instantaneous equivalent channel with other EDUs in the CF-RAN system. Otherwise, the CPU shares the statistical equivalent channel information of EDU x with the rest of the EDUs. Here, it is assumed that the CPU knows the statistical information of the entire system. Unlike real-time sharing of information, this shared information does not need to be shared in real time and only needs to be shared once. In summary, for any EDU x, its partial observation information S x There is other EDU information in the data, but its form is determined by the status of other EDUs. In this case, you can follow the following guidelines to handle it:

[0146]

[0147] Finally, EDU can solve a linear system of equations locally:

[0148] Get B x , however at this time It is still implicit, which makes the equations difficult to solve. It should be noted that due to the use of the EDU sharing mechanism, for any EDU x, it will receive the instantaneous information of the shared EDU in real time and the statistical information of the non-shared EDU obtained in advance as the public information of the entire CF-RAN. Each EDU makes full use of this public information, and finally B x It can be solved by the following linear system:

[0149]

[0150] make:

[0151]

[0152] Therefore, it can be further transformed into ΦB=C, then B=Φ -1 C.

[0153] It can be found that the shared EDU information in the CF-RAN system will change the conditional expectation in Φ. When the number of shared EDUs increases, more statistical information will be replaced by instantaneous information, and the performance of the CF-RAN system will also improve. Therefore, the number of shared EDUs can be dynamically adjusted to achieve a balance between backhaul load and performance. When all EDUs are shared, the performance is optimal. Finally, the EDU x receiving matrix V is obtained x =A x B x , and realizes the distributed calculation of the optimal receiver matrix. The solution algorithm corresponding to the first sub-model is shown in Table 1 below.

[0154] Table 1

[0155]

[0156] In an optional embodiment, when there are multiple edge processing units, the transceiver setting model is sent to the edge processing unit in the CF-RAN system, including: sending the second submodel in the transceiver setting model to the multiple edge processing units in the following manner: converting the second submodel into multiple second decomposition submodels, wherein the multiple second decomposition submodels correspond one-to-one to the multiple edge processing units; sending the multiple second decomposition submodels to the corresponding edge processing units, for the corresponding edge processing units to process the received second decomposition submodels based on the second preprocessing parameters set for the associated uplink user equipment using the Lagrange multiplier method of the Karl-Kuhn-Tucker condition (KKT condition) to obtain the first preprocessing parameters set by the corresponding edge processing unit for the associated downlink user equipment.

[0157] Optionally, when the transceiver setting model includes a first sub-model and a second sub-model, and there are multiple edge processing units, the CPU can convert the second sub-model into a second decomposition sub-model equal to the number of multiple EDUs, and send each sub-model to a corresponding EDU. Each EDU has a given {α U ,p U ,V} conditions, the second sub-model is solved. Through a distributed computing framework, the resource scheduling and signal processing efficiency in the CF-RAN network-assisted free duplex system can be further optimized. Specifically, with the help of the edge processing unit (EDU), the second sub-model - that is, the model for designing the precoding matrix for the downlink user equipment - is decomposed into a series of second decomposition sub-models corresponding to their respective associated UEs, thereby achieving parallel processing and local optimization. Based on the obtained optimal receiver matrix (i.e., the second preprocessing parameters), the EDU uses the Lagrange multiplier method under the KKT condition to independently solve the decomposition sub-model it is responsible for, ensuring that the design of the precoding matrix (i.e., the first preprocessing parameters) can not only reduce inter-user interference and cross-AP interference, but also comply with the power threshold constraint, thereby achieving efficient data transmission and optimized signal quality in a dense network environment. This approach not only significantly improves the spectrum utilization and communication performance of the CF-RAN system, but also effectively reduces the computational burden on the CPU, promotes balanced distribution of computing tasks across EDUs, reduces signaling overhead, ensures rapid algorithm convergence and low-latency system response. It is particularly suitable for network scenarios with large-scale users and high-density access points, demonstrates strong scalability and robustness, and provides strong technical support for free duplex communication within the CF-RAN architecture. This approach enables the CF-RAN system to maintain high performance and service quality even under highly dynamic network conditions and rapidly changing user needs.

[0158] Optionally, considering {α D ,u D ,W} are all related to downlink transmission and have a strong correlation. Based on the block optimization idea, in order to speed up the convergence of the overall algorithm, the current sub-model will be optimized in the inner loop.

[0159] The alternating optimization (AO) method can be used to fix {α U ,p U ,V} and {α D ,W}, about It is concave and unconstrained, so the precoding coefficient u of the kth downlink user equipment (UE) in the edge processing unit (EDU) is D,k By taking the partial derivative and setting it to 0, we can obtain the optimal precoding coefficient of the kth downlink user equipment (UE) in the local optimization process of the edge processing unit (EDU) Similarly, by fixing {α U ,pU ,V} and {u D ,W}, can obtain the optimal and The closed expression is as follows:

[0160]

[0161] Among them, β IU1,kj It indicates the intensity or impact of the inter-user interference IUI caused by uplink UE k on downlink UE j).

[0162] For a fixed {α U ,p U ,V} and {α D ,u D Since the objective function is a convex function with respect to the precoding matrix W and has convex constraints, the solution can be achieved using the Lagrange multiplier method based on the KKT condition. This simple alternating optimization has been shown to monotonically improve the sum rate over iterations.

[0163] In the transceiver design of this embodiment, due to the symmetry of uplink and downlink transmission, the uplink Enhanced Team-based Minimum Mean Square Error (ETMMSE) receiving scheme and the downlink Enhanced Team-based Regularized Zero Forcing (ETRZF) transceiver design scheme based on team decision theory are highly similar. If distributed precoding is directly used, the computational complexity will be effectively reduced. However, since different downlink UEs are subject to different uplink user equipment interference, the regularization term of ETRZF cannot include inter-user interference. In addition, the potential constraints of ETRZF are system and power constraints. In actual applications, in order to make each AP meet the transmission power constraints, heuristic proportional scaling must be performed, which will greatly reduce the downlink transmission performance.

[0164] It should be noted that traditional WMMSE algorithms are typically applied to single-base station scenarios or centrally computed on the CPU, which cannot fully utilize the EDU computing power in CF-RAN systems. Furthermore, as the number of users, access points, and AP antennas in a CF-RAN system increases, the computational complexity of centralized WMMSE precoding increases, which is unbearable for the CF-RAN system. Therefore, the algorithms in related technologies are not effectively applicable to current CF-RAN systems. To reduce the complexity of traditional WMMSE algorithms and make them scalable, a distributed serial-parallel update EDU-WMMSE algorithm is proposed to obtain the precoding coefficients for each EDU. This avoids direct signaling between EDUs while achieving parallel accelerated processing.

[0165] To fully utilize the EDU computational power and reduce the computational complexity of W, the optimization model is decoupled into multiple sub-models based on the MSE of UE k. Alternating optimization iterations are still used to obtain the optimal solution for each sub-model. Considering the TAP transmit power constraint, the optimal precoding coefficient obtained by each sub-model should correspond to a single TAP. This decomposition decomposes the original WMMSE model into L TAFs. For the lth optimization model, the optimal solution can be obtained by fixing the other L-1 sub-models. The specific implementation is as follows:

[0166] First, define w A,l =vec(W l ), where W l The precoding matrix calculated for the EDU associated with TAP 1 is fixed with {α D ,u D ,w A,l′≠l The optimized model is reorganized as follows:

[0167]

[0168] in:

[0169]

[0170] in addition, is the channel estimation error gain between TAP l and downlink UE k Represents a complex vector The real part of the calculation result; w A,l I and I represent the receiving vector weights at the EDU associated with TAP 1. NK , I l , I K They represent unit matrices of different dimensions respectively, and the subscripts indicate the corresponding dimension information.

[0171] Furthermore, for the current w A,l The sub-model is transformed into an unconstrained model using the Lagrange multiplier method, and w is obtained through the first-order KKT condition. A,l Local optimal solution:

[0172] w A,l =-(Ψ l +λ l I NK ) -1 q l

[0173] Among them, λ l ≥0 is the Lagrange multiplier. In order to satisfy the TAP power constraint, the current TAP l power is calculated as:

[0174]

[0175] It can be found that p(λ l ) is about λ l ≥0, so when p(0) <p AP When l = 0, the AP will send underpower. Otherwise, use the bisection method to find the solution that satisfies p(λ l )=p AP The dual variable λ l and substitute w A,l , obtain the optimal precoding coefficient, and update the precoding coefficients of other TAPs using this scheme.

[0176] In addition, in the CF-RAN system, since each TAP is associated with an EDU, the TAP only performs functions such as sending and forwarding, while functions such as channel estimation and precoding calculation are implemented at the corresponding EDU. The set of TAPs associated with EDU x is defined as The collection size is Therefore, EDU x can be completed at one time Each sub-model is optimized and updated in serial iteration. If EDUs can communicate directly with each other, then after EDU x is updated, the shared information can be handed over to EDU x+1 for serial update. However, although full serial update can disperse computing power, it takes a long time. For this reason, this embodiment proposes a serial-parallel combined update solution, in which serial update is implemented within the EDU and parallel computing is implemented between EDUs. After all EDUs complete their own internal serial updates, a parallel update is implemented to all EDUs through the CPU. The specific operation process is as follows.

[0177] For the t-th TAP on the x-th EDU, the corresponding q (x,t) , change q l Rewrite it as follows:

[0178]

[0179] Where n represents the nth parallel iteration:

[0180]

[0181] in, and denote the channel estimation and precoding matrix of the t″th TAP in the nth iteration EDU x″, respectively; and represents the channel estimate and optimal precoding coefficient of the t′th TAP in the nth iteration EDU x; represents the optimal precoding coefficient of the t′th TAP in the (n+1)th iteration EDU x.

[0182] At this time, each EDU receives the shared amount obtained from the nth parallel iteration from the CPU:

[0183]

[0184] in, represents the conjugate transpose of the channel estimate of the t-th user associated with EDU; represents the precoding matrix of the t-th user associated with EDU.

[0185] For EDU x, if we want to calculate the precoding matrix on the t-th TAP associated with it, we can get the precoding matrix from the shared quantity ε according to the information in each EDU. (n) Separate and obtain comprehensive shared information of other EDUs This shared information remains unchanged during the serial iteration of EDU x, and the shared information of EDU x itself after some serial updates can also be obtained. and calculate the new At this time The computational complexity does not increase with the number of APs. At this time, the optimal precoding coefficient of the t-th TAP in the n-th iteration EDU x is for:

[0186]

[0187] After completing the serial update of the corresponding TAP sub-model in each EDU, it is necessary to share information with the CPU. For EDUx, it is recorded as and update and Until convergence.

[0188] It should be noted that parallel updates may cause the sum rate to fluctuate dramatically with iterations. This is because the vector q of TAP(x,t) (x,t) Reliance on information from other EDUs Because their updated versions can only be obtained in subsequent parallel iterations. To solve this model, the Jacobi best response scheme method is adopted to slowly update the precoding coefficients in the following way:

[0189]

[0190] Here, η represents the step size factor.

[0191] The biggest advantage of the serial-parallel update solution is that it distributes the computational tasks, fully utilizing the EDU computing power in CF-RAN and reducing computational complexity. Furthermore, it ensures algorithm stability while balancing convergence speed. The solution algorithm corresponding to the second sub-model is shown in Table 2 below:

[0192] Table 2

[0193]

[0194]

[0195] In an optional embodiment, the method further includes: constructing an uplink power control model with the goal of maximizing uplink and downlink rates in the CF-RAN system; and obtaining an uplink power control strategy for the CF-RAN system based on the uplink power control model, wherein the uplink power control strategy is used to perform resource scheduling by adjusting the power of uplink signals sent by uplink user equipment to access point devices.

[0196] Optionally, based on transceiver design results, the uplink power control model is centrally optimized at the central processing unit (CPU). Optimizing the uplink power control strategy ensures that device transmit power in the uplink is minimized while meeting uplink and downlink maximum speed targets, reducing energy consumption and potential interference, especially in dense deployment environments.

[0197] Optionally, after the second sub-model has converged internally, the next step is to fix {α D ,u D ,W}, {α U ,V}, and solve the uplink power control model at the CPU to centrally optimize the uplink power control strategy.

[0198] Optionally, for ease of processing, let the optimization variable Set the partial derivative to 0, that is, The local optimal solution for the uplink UE j can be obtained:

[0199]

[0200] Finally {α D ,u D ,W} and {p D ,V}, V is no longer considered a random variable, and is substituted into e D,k (u D,k ,W,p U ), calculate the uplink MSE of UE j, and complete α U Update:

[0201]

[0202] At this point, all optimization variables are updated, and the solution algorithm of the uplink power control model is shown in Table 3 below.

[0203] Table 3

[0204]

[0205] In an optional embodiment, the method further includes: constructing a working mode selection model for the access point device with the goal of maximizing uplink and downlink rates; after obtaining the transceiver setting result and the uplink power control strategy, using a greedy search algorithm based on the working mode selection model to obtain a target working mode selection result for the access point device in the CF-RAN system.

[0206] Optionally, a greedy search algorithm can be introduced to optimize access point (AP) operating mode selection for dynamic resource management in CF-RAN network-assisted free duplex systems. By combining uplink and downlink rate maximization objectives, transceiver design results, and uplink power control strategies, this method effectively solves the complex optimization model of AP duplex mode selection, ensuring high performance and efficiency of the CF-RAN system in various communication scenarios. Through intelligent dynamic adjustment of AP modes, this approach effectively balances uplink and downlink communication demands, significantly reducing inter-user and cross-AP interference, thereby maximizing spectrum efficiency under limited spectrum resources and improving overall network throughput and user experience. Furthermore, the greedy search algorithm not only simplifies the computational process and reduces central processing unit (CPU) load, but also promotes collaboration among edge processing units (EDUs), enabling dynamic optimization and rapid response in resource scheduling. This approach is particularly suitable for high-density users and rapidly changing communication environments.

[0207] It can be understood that the transceiver design and uplink power control model solution of the aforementioned embodiment can be understood as a joint optimization model based on the EDU and CPU. The above joint optimization discussion is performed under a given AP mode. A fifth constraint of the following form can be added to the constraint conditions of the initial optimization model so that the initial optimization model can also be used to solve the working mode selection model of the access point device (i.e., AE mode selection):

[0208]

[0209] Among them, the fifth constraint μ D,m ,μ U,m ∈{0,1},μ D,m +μ U,m =1, can be understood as an AP duplex mode selection constraint, which means that a single AP can only operate in half-duplex mode. This can eliminate the large internal self-interference of the AP in Cellular Cloud-Assisted Full Duplex (CCFD) communication mode.

[0210] It should be noted that the above joint optimization discussions are all performed under a given AP mode, and due to the constraint μ D,m ,μ U,m ∈{0,1},μ D,m +μ U,m =1 makes the mode selection model become a 0-1 integer programming model. The most direct way is to determine the best duplex mode for each AP through exhaustive search. For the current system with M APs, it is necessary to implement 2 M However, such complexity is unbearable in the CF-RAN system. Therefore, a greedy search algorithm is proposed. The specific implementation process is as follows:

[0211] First, a duplex mode is randomly selected for M APs, and then AP numbers 1-M are traversed. For the mode selection vector μ D , using the bit flipping strategy, the best mode is selected for the currently traversed AP. If the current AP can bring a higher sum rate to the overall system during downlink transmission than uplink reception, μ D The corresponding position is set to 1, μ U The corresponding position is set to 0, and so on. It can be seen that the greedy search-based mode selection algorithm can be completed within M optimizations, greatly reducing complexity. The algorithm representation of the AP mode selection process based on greedy search is shown in Table 4 below.

[0212] Table 4

[0213]

[0214]

[0215] In an optional embodiment, after obtaining a transceiver setting result and an uplink power control policy, a target working mode selection result of an access point device in the CF-RAN system is obtained using a greedy search algorithm based on a working mode selection model, including: after obtaining the transceiver setting result and the uplink power control policy, updating an initial working mode selection result of the access point device in the CF-RAN system based on the working mode selection model to obtain an updated working mode selection result; updating the transceiver setting result and the uplink power control policy based on the updated working mode selection result using a greedy search algorithm, and updating the updated working mode selection result after obtaining the updated transceiver setting result based on an edge processing device and the updated uplink power control policy based on a central processing unit; repeating the above operations until a predetermined termination condition is met; and using the updated working mode selection result obtained when the predetermined termination condition is met as the target working mode selection result.

[0216] Optionally, in a CF-RAN system, APs can operate in different modes, such as half-duplex or full-duplex, each of which has a different impact on communication quality. The operating mode selection model is constructed with the goal of maximizing the uplink and downlink rates of the CF-RAN system, taking into account the signal interference and network performance variations that may be caused by different AP operating modes. A greedy search algorithm selects the currently optimal option at each step in the hope of ultimately reaching a global optimal solution. In this embodiment, after the current transceiver settings and uplink power control strategy are generated, the algorithm first updates the initial operating mode selection results for the APs in the CF-RAN system. This process involves evaluating network performance under different operating modes, such as sum, rate, and interference level, to select the mode that performs best under the current conditions. After updating the AP's operating mode, the new transceiver settings and uplink power control strategy are re-evaluated, and the greedy search algorithm is reapplied to adjust the AP's operating mode. The new transceiver settings and uplink power control strategy are obtained in the same manner as in the previous embodiment and will not be further described here. This process repeats until a predetermined termination condition is met, such as when performance improvement is no longer significant or the maximum number of iterations has been reached. When this condition is met, the algorithm stops iterating and uses the last updated operating mode selection result as the target operating mode selection result. This result represents the optimal AP operating mode selection for the current CF-RAN system configuration, aiming to maximize the overall performance of the CF-RAN system.

[0217] This approach intelligently adjusts AP operating modes to reduce signal interference, optimize resource allocation, and ultimately improve the communication quality and efficiency of the CF-RAN system. This approach combines dynamic resource scheduling with mode selection. Through iterative and greedy search strategies, it flexibly responds to changes in the network environment, providing the system with dynamic optimization capabilities and ensuring efficient utilization of network resources.

[0218] Through the above steps S102 to S106, the goal of maximizing uplink and downlink rates can be achieved by combining a distributed transceiver design with a centralized uplink power control strategy, thereby effectively suppressing co-channel interference and improving communication quality. This also increases the total uplink and downlink rate of the CF-RAN system, achieving the technical effect of efficient spectrum resource utilization, and thus resolving the technical issues of low spectrum resource utilization and poor communication quality in related full-duplex resource scheduling schemes. Specifically, the edge processing unit (EDU) undertakes some distributed computing tasks and optimizes the transceiver design, while the central processing unit (CPU) centrally handles uplink power control. This resource scheduling strategy, which combines distributed and centralized methods, can significantly enhance the system's performance in terms of spectrum efficiency and communication quality.

[0219] According to an embodiment of the present invention, another resource scheduling processing method of a CF-RAN system is provided. Figure 3 FIG. 1 is a flow chart of another resource scheduling processing method of a CF-RAN system according to an embodiment of the present invention. Figure 3 As shown, the method includes:

[0220] S302: Receive a transceiver configuration model sent by a central processor in the CF-RAN system, where the transceiver configuration model is constructed by the central processor with the goal of maximizing uplink and downlink sum rates in the CF-RAN system, where the uplink and downlink sum rate is the sum of the downlink sum rate and the uplink sum rate in the CF-RAN system, the downlink sum rate is the sum of rates at which the central processor in the CF-RAN system receives downlink signals from downlink user equipment, and the uplink sum rate is the sum of rates at which the central processor in the CF-RAN system receives uplink signals from uplink user equipment;

[0221] S304, based on the transceiver setting model, obtain a transceiver setting result, wherein the transceiver setting result includes preprocessing parameters for preprocessing the signal using the receiver and the transmitter, and the preprocessing is used to minimize the signal loss caused by the channel environment and signal transmission interference.

[0222] The execution subject of the above steps S302 to S304 can be a target edge processing unit (EDU) in the CF-RAN system. The target edge processing unit is any one of the multiple EDUs included in the CF-RAN system. The method of this embodiment can also be applied to Figure 2 In the CF-RAN system shown in FIG. , steps S302 to S304 can achieve the goal of maximizing the sum of uplink and downlink rates, combining a distributed transceiver design with a centralized uplink power control strategy. This allows the central processor to calculate the sum rate, while the edge processing unit performs local optimization based on the received model, minimizing the impact of the channel environment and signal transmission interference by adjusting preprocessing parameters. This effectively manages and schedules resources, thereby effectively suppressing co-channel interference and improving communication quality. This also improves the sum of the uplink and downlink rates of the CF-RAN system, achieving efficient spectrum resource utilization, and thereby resolving the technical issues of low spectrum resource utilization and poor communication quality in related full-duplex resource scheduling solutions.

[0223] In an optional embodiment, the transceiver setting model includes a first sub-model and a second sub-model, wherein the first sub-model is used to obtain a second preprocessing parameter for preprocessing the signal using a receiver, and the second sub-model is used to obtain a first preprocessing parameter for preprocessing the signal using a transmitter; corresponding to the first decomposition sub-model obtained by the central processor converting the first sub-model, and the second decomposition sub-model obtained by the central processor converting the second sub-model, a transceiver setting result is obtained based on the transceiver setting model, including: obtaining the second preprocessing parameter in the transceiver setting result in the following manner: based on the channel state information of the target edge processing unit itself, and the shared channel state information between the target edge processing unit and other edge processing units, the received first decomposition sub-model is processed to obtain the second preprocessing parameter set by the target edge processing unit for the associated uplink user equipment, wherein the second preprocessing parameter represents the preprocessing parameter for preprocessing the signal using the corresponding receiver.

[0224] Optionally, when the transceiver setting model includes a first sub-model and a second sub-model, and there are multiple edge processing units, the CPU can convert the first sub-model into a first decomposition sub-model equal to the number of the multiple edge processing units (EDUs), and send each sub-model to the corresponding EDU. After receiving the first decomposition sub-model sent by the CPU, the target EDU can U ,p U} and {α D ,u D,W}, the first submodel is solved. By decomposing the first submodel in the complex transceiver configuration model—the solution of the receiver matrix—into multiple first decomposition submodels corresponding to each uplink user equipment in the CF-RAN system, efficient parallel processing based on the EDUs can be achieved. For example, the target EDU can independently process the received first decomposition submodel based on its association with the uplink UE, and optimize the local receiver matrix (i.e., the second preprocessing parameter) in a collaborative manner using local CSI combined with CSI shared from other EDUs. This approach significantly improves the resource allocation capability and computational efficiency of the CF-RAN network-assisted free duplex system in ultra-dense network environments, effectively reducing the computational burden on the CPU. A coordination mechanism ensures the consistency of distributed optimization and overall system performance, significantly enhancing the CF-RAN system's spectrum efficiency and communication quality. Through decomposition and distributed processing, the CF-RAN system can maintain high performance and low latency even in scenarios with rapidly growing network scale, demonstrating high flexibility and robustness and significantly optimizing network operation.

[0225] In an optional embodiment, based on the transceiver setting model, a transceiver setting result is obtained, including: obtaining a first preprocessing parameter in the transceiver setting result by: based on the second preprocessing parameter set by the target edge processing unit for the associated uplink user equipment, the Lagrange multiplier method of the KKT condition, processing the received second decomposition sub-model to obtain the first preprocessing parameter set by the target edge processing unit for the associated downlink user equipment.

[0226] Optionally, when the transceiver setting model includes a first sub-model and a second sub-model, and there are multiple edge processing units, the CPU can convert the second sub-model into a second decomposition sub-model equal to the number of EDUs, and send each sub-model to the corresponding EDU. After receiving the second decomposition sub-model sent by the CPU, the target EDU can U ,p U,V}, the second sub-model is solved under the conditions of ,V}. Through the distributed computing framework, the resource scheduling and signal processing efficiency in the CF-RAN network-assisted free duplex system can be further optimized. Specifically, with the help of the edge processing unit (EDU), the second sub-model - that is, the model for designing the precoding matrix for the downlink user equipment - is decomposed into a series of second decomposition sub-models corresponding to their respective associated UEs, thereby achieving parallel processing and local optimization. For example, the target EDU can independently solve the decomposition sub-model for which it is responsible based on the obtained optimal receiver matrix (i.e., the second preprocessing parameters) using the Lagrange multiplier method under the KKT condition, ensuring that the design of the precoding matrix (i.e., the first preprocessing parameters) can reduce inter-user interference and cross-AP interference while complying with the power threshold constraint, thereby achieving efficient data transmission and optimized signal quality in a dense network environment. This approach not only significantly improves the spectrum utilization and communication performance of the CF-RAN system, but also effectively reduces the computational burden on the CPU, promotes balanced distribution of computing tasks across EDUs, reduces signaling overhead, ensures rapid algorithm convergence and low-latency system response. It is particularly suitable for network scenarios with large-scale users and high-density access points, demonstrates strong scalability and robustness, and provides strong technical support for free duplex communication within the CF-RAN architecture. This approach enables the CF-RAN system to maintain high performance and service quality even under highly dynamic network conditions and rapidly changing user needs.

[0227] Based on the above embodiment and optional embodiment, the present invention proposes an optional implementation of a resource scheduling processing method for a CF-RAN system, the method comprising:

[0228] S1 is a central processor applied to a cellular-free radio access network (CF-RAN) system. The CF-RAN system further includes: a user-centric distributed unit, an edge distributed unit, and an access point configured on the edge distributed unit. The method includes:

[0229] S2, obtaining a channel estimation matrix between the user equipment and the access point;

[0230] S3, determining a signal-to-noise ratio of a downlink signal of the user equipment received by the central processor according to the channel estimation matrix, and determining a rate of the downlink signal received by the central processor according to the signal-to-noise ratio;

[0231] S4, determining a signal-to-noise ratio of an uplink signal of the user equipment received by the central processor according to the channel estimation matrix, and determining a rate of the uplink signal received by the central processor according to the signal-to-noise ratio;

[0232] S5 aims to maximize the sum of uplink and downlink rates in the CF-RAN system. Based on uplink UE and TAP transmit power constraints and EDU information constraints, it implements flexible AF mode selection, distributed transceiver design, and an uplink power control model. Specifically, with the goal of maximizing the sum of uplink and downlink rates in the CF-RAN-NAFD system, based on uplink UE and TAP transmit power constraints and EDU information constraints, a partially distributed block coordinate descent (PDBCD) algorithm is employed to decompose the sum and rate maximization objective into three optimization sub-models: a distributed MMSE transceiver design based on team decision theory, uplink power control with interference compensation factors, and an AP mode selection strategy combining an improved greedy algorithm and Monte Carlo sampling.

[0233] Furthermore, multiple performance comparison experiments are used to illustrate the advantages of the method of this embodiment. In this embodiment, MATLAB simulation is used to verify the effectiveness of the transceiver design based on PDBCD, uplink power control, and mode selection scheme based on greedy search in the CF-RAN system of this embodiment. The experiment considers a simulation scenario with an area radius of 200m. To ensure good coverage, 48 half-duplex APs are evenly distributed on the circle and randomly associated with 4 EDUs, and 6 single-antenna downlink UEs and 6 single-antenna uplink UEs are evenly distributed in the circle. Assume that the residual interference power between EDUs is equal, that is, d represents the distance in meters. The channel model parameters are shown in Table 5.

[0234] Table 5

[0235] TAP power threshold 30dBm Uplink UE power threshold 20dBm Path loss 128.1+37.6log10(d) Rayleigh fading 0dB Shadow Fading 8dB Noise power -104dBm

[0236] Figure 4 This is a graph showing the convergence and spectral efficiency of the outer loop of an optional algorithm according to an embodiment of the present invention; the convergence of the proposed PDBCD algorithm is verified, and the basic receiver EDU-MMSE is used as the initial iteration i=0, Figure 4 The figure shows the relationship between spectral efficiency and the number of outer loop iterations. Using a single-antenna AP, the inter-AP interference Δ is set to -45dB, -40dB, -35dB, or -30dB. It shows that the PDBCD algorithm has good convergence, achieving convergence within 2-3 iterations. The number of required convergence times increases slightly with increasing inter-AP interference.

[0237] Figure 5 This is an optional EDU number-precoding update convergence and downlink sum rate diagram according to an embodiment of the present invention; the relationship between the downlink sum rate and the number of iterations of the PDBCD algorithm inner loop is studied, such as Figure 4As shown in the figure, as the number of EDUs increases, the number of iterations of the proposed serial-parallel update scheme increases accordingly, and is between serial update and parallel update. When the number of EDUs is 4, it converges in 12 times and the performance is close to that of serial update.

[0238] Figure 6 1 is a time diagram of an optional EDU quantity under different serial and parallel conditions according to an embodiment of the present invention; Figure 6 As shown in the figure, serial updates take the longest time, while parallel updates take the least time. However, in practice, parallel updates have poor convergence, which increases the signaling overhead between the EDU and the CPU. Therefore, the serial-parallel update scheme proposed in the inner loop can achieve a balance between performance, time, and signaling overhead, further verifying the effectiveness of the PDBCD algorithm.

[0239] Figure 7 is an optional channel residual error-spectral efficiency diagram according to an embodiment of the present invention; Figure 6 As shown, it can be found that the solution of this embodiment can achieve a balance among performance, time and signaling overhead, thereby further verifying the effectiveness of the PDBCD algorithm.

[0240] Figure 8 This is an optional UE number-spectrum efficiency diagram according to an embodiment of the present invention; when Δ=-45dB, the relationship between different UE numbers and the spectrum efficiency of four duplexing schemes, where K=J is set, so the total number of users in the system is K+J. It can be seen that the spectrum of all duplexing schemes increases monotonically with the increase in the number of UEs. It is worth noting that increasing the number of UEs will lead to a stronger CLI. The results show that the joint optimization PDBCD scheme proposed in this embodiment can effectively manage the cross-link interference (Cross Link Interference, CLI) in the Greedy-mode-selected Network-Assisted Full Duplex (G-NAFD) and Randomly-selected Network-Assisted Full Duplex (R-NAFD) schemes. Figure 9This is an optional AP antenna number-spectrum efficiency graph according to an embodiment of the present invention. When Δ = -45dB, the relationship between system spectral efficiency and the number of antennas per AP, N, is shown. It can be seen that increasing the number of AP antennas significantly improves system performance. This is because the additional antennas may introduce additional channel gain to suppress interference between different devices. Based on the above analysis, we believe that the greedy search-based PDBCD algorithm proposed in this chapter achieves higher system gain and can effectively address the CLI challenge that NAFD systems must face compared to TDD systems.

[0241] Figure 10 This is a comparison diagram of spectrum efficiencies of different optional transceiver solutions according to an embodiment of the present invention; Figure 10 The probability distribution function diagrams of the spectrum efficiency of the NAFD system under different transceiver schemes when Δ=-45dB are compared. Among them, EDU-MMSE represents the MMSE transceiver design that relies only on the EDU's own estimated channel to achieve MMSE, CPU-MMSE represents the traditional centralized transceiver design, PDBCD-NO represents that all uplink reception schemes are basic EDU receivers, PDBCD-TEAM represents that all EDUs in the uplink reception scheme are non-shared EDUs, PDBCDDHALF represents that half of the EDUs in the uplink reception scheme are shared EDUs, PDBCD-FULL represents the case where all EDUs in the uplink reception scheme are shared EDUs, and G represents the greedy mode selection. Under high CLI interference, fully distributed transceivers perform poorly, while fully centralized solutions can achieve higher gains. However, this places enormous computational pressure on the CPU, preventing the distributed computing capabilities of the distributed system from being fully utilized. Furthermore, since the centralized MMSE solution does not effectively allocate uplink power, IUI will affect downlink performance. This explains why the CPU-MMSE performance is similar to that of the PDBCD-NO solution. Therefore, our proposed GPDBCD solution has significant advantages over existing solutions. It can also adjust the number of shared EDUs in real time based on the EDU-CPU backhaul link pressure, achieving a balance between performance and backhaul load.

[0242] This embodiment also provides a resource scheduling processing device for a CF-RAN system, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described are omitted. As used below, the terms "module" and "device" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0243] According to an embodiment of the present invention, there is also provided an embodiment of a device for implementing the resource scheduling processing method of the CF-RAN system. Figure 11FIG. 1 is a structural diagram of a resource scheduling processing device of a CF-RAN system according to an embodiment of the present invention. Figure 11 As shown, the resource scheduling processing device of the CF-RAN system includes: a rate acquisition module 1100, a model construction module 1102, and a first model solving module 1104, wherein:

[0244] The rate acquisition module 1100 is configured to acquire a downlink sum rate and an uplink sum rate in the CF-RAN system, wherein the downlink sum rate is the sum of rates at which a central processor in the CF-RAN system receives downlink signals from downlink user equipment at any sampling time, and the uplink sum rate is the sum of rates at which the central processor receives uplink signals from uplink user equipment at any sampling time.

[0245] A model building module 1102, connected to the rate acquisition module 1100, is configured to build a transceiver configuration model with the goal of maximizing the uplink and downlink sum rates in the CF-RAN system, where the uplink and downlink sum rates are the sum of the downlink sum rate and the uplink sum rate;

[0246] The first model solving module 1104 is connected to the model building module 1102 and is used to send the transceiver setting model to the edge processing unit in the CF-RAN system, so that the edge processing unit can obtain the transceiver setting result based on the transceiver setting model, wherein the transceiver setting result includes preprocessing parameters for preprocessing the signal using the receiver and the transmitter, and the preprocessing is used to perform resource scheduling with the goal of minimizing the signal loss caused by the channel environment and signal transmission interference.

[0247] According to an embodiment of the present invention, another device embodiment for implementing the resource scheduling processing method of the CF-RAN system is also provided. Figure 12 FIG. 1 is a structural diagram of another resource scheduling processing device of a CF-RAN system according to an embodiment of the present invention. Figure 12 As shown, the resource scheduling processing device of the CF-RAN system includes: a model receiving module 1202 and a transceiver setting module 1204, wherein:

[0248] a model receiving module 1202 configured to receive a transceiver configuration model sent by a central processor in a CF-RAN system, wherein the transceiver configuration model is constructed by the central processor with the goal of maximizing an uplink and downlink sum rate in the CF-RAN system, where the uplink and downlink sum rate is the sum of the downlink sum rate and the uplink sum rate in the CF-RAN system, the downlink sum rate is the sum of the rates at which the central processor in the CF-RAN system receives downlink signals from downlink user equipment, and the uplink sum rate is the sum of the rates at which the central processor receives uplink signals from uplink user equipment;

[0249] The transceiver setting module 1204 is connected to the model receiving module 1202 and is used to obtain the transceiver setting results based on the transceiver setting model, wherein the transceiver setting results include preprocessing parameters for preprocessing the signal using the receiver and transmitter, and the preprocessing is used to minimize the signal loss caused by the channel environment and signal transmission interference.

[0250] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0251] It should be noted that the rate acquisition module 1100, model construction module 1102, and first model solving module 1104 correspond to steps S102 to S106 in the embodiment; the model receiving module 1202 and transceiver setting module 1204 correspond to steps S302 to S304 in the embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that these modules, as part of the device, can be run on a computer terminal.

[0252] It should be noted that the optional or preferred implementation of this embodiment can be found in the relevant description in the embodiment, which will not be repeated here.

[0253] The resource scheduling processing device of the above-mentioned CF-RAN system may further include a processor and a memory. The above-mentioned rate acquisition module 1100, model construction module 1102, first model solving module 1104, second model solving module 1106, model receiving module 1202, transceiver setting module 1204, etc. are all stored in the memory as program modules, and the processor executes the above-mentioned program modules stored in the memory to implement corresponding functions.

[0254] The processor includes a core, which retrieves corresponding program modules from memory. There can be one or more cores. Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0255] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program is executed, the device containing the non-volatile storage medium is controlled to execute any of the above-mentioned resource scheduling processing methods for the CF-RAN system.

[0256] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0257] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-mentioned resource scheduling processing methods for the CF-RAN system.

[0258] According to an embodiment of the present application, an embodiment of a processor is further provided. Optionally, in this embodiment, the processor is configured to run a program, wherein the program, when running, executes any of the above-mentioned resource scheduling processing methods for the CF-RAN system.

[0259] According to an embodiment of the present application, an embodiment of a computer program product is also provided. When executed on a data processing device, the program is suitable for executing the steps of initializing any one of the above-mentioned resource scheduling processing methods for the CF-RAN system.

[0260] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, any one of the steps of the resource scheduling processing method for the CF-RAN system described above is implemented.

[0261] The above sequence of the embodiments of the present invention is for description only and does not represent the superiority or inferiority of the embodiments.

[0262] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0263] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the above modules can be a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

[0264] The modules described above as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0265] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0266] If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program codes.

[0267] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A resource scheduling method for a cellular-free wireless access network (CF-RAN) system, characterized in that: include: Obtaining a downlink sum rate and an uplink sum rate in the CF-RAN system, wherein the downlink sum rate is the sum of rates at which a central processor in the CF-RAN system receives downlink signals from downlink user equipment at any sampling moment, and the uplink sum rate is the sum of rates at which the central processor receives uplink signals from uplink user equipment at any sampling moment; Constructing a transceiver configuration model with the goal of maximizing the uplink and downlink sum rate in the CF-RAN system, wherein the uplink and downlink sum rate is the sum of the downlink sum rate and the uplink sum rate; The transceiver setting model is sent to an edge processing unit in the CF-RAN system, so that the edge processing unit obtains a transceiver setting result based on the transceiver setting model, wherein the transceiver setting result includes preprocessing parameters for preprocessing signals using a receiver and a transmitter, and the preprocessing is used to perform resource scheduling with the goal of minimizing signal loss caused by a channel environment and signal transmission interference.

2. The method according to claim 1, characterized in that The obtaining of a downlink sum rate and an uplink sum rate in the CF-RAN system includes: Obtaining a channel estimation matrix between a user equipment and an access point device in the CF-RAN system; determining, based on a downlink channel coefficient in the channel estimation matrix, a signal-to-interference-plus-noise ratio of a downlink signal of the downlink user equipment received by the central processor, and determining the downlink sum rate based on the signal-to-interference-plus-noise ratio of the downlink signal, wherein the downlink channel coefficient is a link channel coefficient between the downlink user equipment and an access point device; Determine, based on the uplink channel coefficient in the channel estimation matrix, a signal-to-interference-plus-noise ratio of an uplink signal of the uplink user equipment received by the central processor, and determine the uplink sum rate based on the signal-to-interference-plus-noise ratio of the uplink signal, where the uplink channel coefficient is a link channel coefficient between the uplink user equipment and an access point device.

3. The method according to claim 1, characterized in that The method further comprises: An uplink power control model is constructed with the goal of maximizing uplink and downlink rates in the CF-RAN system; An uplink power control strategy of the CF-RAN system is obtained based on the uplink power control model, wherein the uplink power control strategy is used to perform resource scheduling by adjusting the power of the uplink signal sent by the uplink user equipment to the access point device.

4. The method according to claim 3, characterized in that The transceiver configuration model is constructed with the goal of maximizing uplink and downlink rates in the CF-RAN system. The method comprises: constructing an initial optimization model with the goal of maximizing the uplink and downlink sum rates in the CF-RAN system; transforming the initial optimization model to obtain an equivalent optimization model with the goal of minimizing the mean square error of the uplink and downlink sum rates, wherein the equivalent optimization model is an optimization model based on the mean square error; and obtaining the transceiver setting model based on the equivalent optimization model; The constructing of the uplink power control model with the goal of maximizing the uplink and downlink sum rates in the CF-RAN system includes: constructing an initial optimization model with the goal of maximizing the uplink and downlink sum rates in the CF-RAN system; transforming the initial optimization model to obtain an equivalent optimization model with the goal of minimizing the mean square error of the uplink and downlink sum rates, wherein the equivalent optimization model is an optimization model based on the mean square error; and obtaining the uplink power control model based on the equivalent optimization model.

5. The method according to claim 4, characterized in that said transforming the initial optimization model, Obtaining an equivalent optimization model with the goal of minimizing the mean square error of the uplink and downlink sum rates, including: Remodeling the downlink sum rate in the initial optimization model using a weighted minimum mean square error method to obtain a first mean square error corresponding to the downlink sum rate, wherein the first mean square error is used to indicate a mean square error between a signal received by any downlink user equipment and a desired signal under a given scalar receive filter, precoding matrix, and transmit power of the uplink user equipment; Remodeling the uplink sum rate in the initial optimization model using the weighted minimum mean square error method to obtain a second mean square error corresponding to the uplink sum rate, wherein the second mean square error is used to indicate a mean square error between a signal received by any uplink user equipment and a desired signal under a given uplink user equipment receiving vector, an uplink user equipment transmit power, and a precoding matrix; Based on the first mean square error and the second mean square error, the initial optimization model is transformed to obtain the equivalent optimization model.

6. The method according to claim 1, characterized in that The transceiver setting model includes a first sub-model and a second sub-model, wherein the first sub-model is used to obtain the second preprocessing parameters for preprocessing the signal using the receiver, and the second sub-model is used to obtain the first preprocessing parameters for preprocessing the signal using the transmitter.

7. The method according to claim 6, characterized in that The first sub-model aims to minimize the weighted mean square error of the uplink sum rate, and uses a second constraint and a third constraint as constraint conditions, wherein the second constraint is used to indicate that the second pre-processing parameter set by each edge processing unit for the corresponding uplink user equipment is determined by the corresponding channel state information; and the third constraint is used to indicate that the local state information of each edge processing unit is obtained by performing a predetermined function operation based on the uplink channel estimate and the downlink channel estimate mastered by the corresponding edge processing unit; The second sub-model aims to minimize the negative impact of downlink signal interference and noise on communication quality, and uses the first constraint, the third constraint, and the fourth constraint as constraint conditions, wherein the first constraint is used to indicate that the first preprocessing parameter set by each edge processing unit for the corresponding downlink user equipment is determined by the corresponding channel state information, and the fourth constraint is used to indicate that the total transmit power of each access point device does not exceed a preset power threshold.

8. The method according to claim 3, characterized in that The uplink power control model aims to minimize the negative impact of uplink signal interference and noise on communication quality, and uses a third constraint as a constraint condition, wherein the third constraint is used to indicate that the local state information of each edge processing unit is obtained through a predetermined function operation based on the uplink channel estimate and the downlink channel estimate mastered by the corresponding edge processing unit.

9. The method according to claim 6, characterized in that In a case where there are multiple edge processing units, sending the transceiver setting model to the edge processing unit in the CF-RAN system includes: The first sub-model in the transceiver setting model is sent to a plurality of edge processing units in the following manner: Converting the first sub-model into a plurality of first decomposition sub-models, wherein the plurality of first decomposition sub-models correspond one-to-one to the plurality of edge processing units; The multiple first decomposition sub-models are sent to the corresponding edge processing unit, so that the corresponding edge processing unit processes the received first decomposition sub-model based on its own channel state information and shared channel state information to obtain the second preprocessing parameter set by the corresponding edge processing unit for the associated uplink user equipment.

10. The method according to claim 6, characterized in that In a case where there are multiple edge processing units, sending the transceiver setting model to the edge processing unit in the CF-RAN system includes: The second sub-model in the transceiver setting model is sent to a plurality of edge processing units in the following manner: Converting the second sub-model into a plurality of second decomposition sub-models, wherein the plurality of second decomposition sub-models correspond one-to-one to the plurality of edge processing units; The multiple second decomposition sub-models are sent to the corresponding edge processing unit, so that the corresponding edge processing unit processes the received second decomposition sub-model based on the second preprocessing parameter set for the associated uplink user equipment and adopts the Lagrange multiplier method of the Karl-Kuhn-Tucker condition (KKT condition) to obtain the first preprocessing parameter set by the corresponding edge processing unit for the associated downlink user equipment.

11. The method according to claim 3, characterized in that The method further comprises: With the maximum uplink and downlink speed as the goal, a working mode selection model for the access point device is constructed; After obtaining the transceiver setting result and the uplink power control strategy, a target working mode selection result of the access point device in the CF-RAN system is obtained by adopting a greedy search algorithm based on the working mode selection model.

12. The method according to claim 11, characterized in that After obtaining the transceiver setting result and the uplink power control strategy, a greedy search algorithm is used based on the working mode selection model. Obtaining a target operating mode selection result of an access point device in the CF-RAN system includes: After obtaining the transceiver setting result and the uplink power control strategy, updating the initial working mode selection result of the access point device in the CF-RAN system based on the working mode selection model to obtain an updated working mode selection result; Based on the updated working mode selection result, the greedy search algorithm is used to update the transceiver setting result and the uplink power control strategy, and after the updated transceiver setting result is obtained based on the edge processing device and the updated uplink power control strategy is obtained based on the central processing unit, the updated working mode selection result is updated; and the above operation is repeatedly performed until a predetermined termination condition is reached; The updated working mode selection result obtained when the predetermined termination condition is reached is used as the target working mode selection result.

13. A resource scheduling processing method for a CF-RAN system, characterized in that: include: receiving a transceiver setting model sent by a central processor in a CF-RAN system, wherein the transceiver setting model is constructed by the central processor with the goal of maximizing an uplink and downlink sum rate in the CF-RAN system, the uplink and downlink sum rate being the sum of a downlink sum rate and an uplink sum rate in the CF-RAN system, the downlink sum rate being the sum of rates at which the central processor in the CF-RAN system receives downlink signals from downlink user equipment, and the uplink sum rate being the sum of rates at which the central processor receives uplink signals from uplink user equipment; Based on the transceiver setting model, a transceiver setting result is obtained, wherein the transceiver setting result includes preprocessing parameters for preprocessing signals using a receiver and a transmitter, and the preprocessing is used to minimize signal loss caused by channel environment and signal transmission interference.

14. The method according to claim 13, characterized in that The transceiver setting model includes a first sub-model and a second sub-model, wherein the first sub-model is used to obtain a second preprocessing parameter for preprocessing the signal using a receiver, and the second sub-model is used to obtain a first preprocessing parameter for preprocessing the signal using a transmitter; in response to receiving a first decomposition sub-model obtained by the central processor transforming the first sub-model and a second decomposition sub-model obtained by the central processor transforming the second sub-model, obtaining a transceiver setting result based on the transceiver setting model includes: The second preprocessing parameter in the transceiver setting result is obtained by: Based on the channel state information of the target edge processing unit itself and the shared channel state information between the target edge processing unit and other edge processing units, the received first decomposition sub-model is processed to obtain a second preprocessing parameter set by the target edge processing unit for the associated uplink user equipment, wherein the second preprocessing parameter represents a preprocessing parameter for performing the preprocessing on the signal using a corresponding receiver.

15. The method according to claim 14, characterized in that The obtaining a transceiver setting result based on the transceiver setting model includes: The first preprocessing parameter in the transceiver setting result is obtained by: Based on the second preprocessing parameter set by the target edge processing unit for the associated uplink user equipment and the Lagrange multiplier method of the KKT condition, the received second decomposition sub-model is processed to obtain the first preprocessing parameter set by the target edge processing unit for the associated downlink user equipment.

16. An electronic device, characterized in that: The system comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the resource scheduling processing method of the CF-RAN system according to any one of claims 1 to 15.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the resource scheduling processing method of the CF-RAN system according to any one of claims 1 to 15 are implemented.

Images