Resource allocation method and device, electronic equipment and computer readable storage medium

Abstract

The invention discloses a resource allocation method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: determining a plurality of access points and a plurality of pieces of user equipment; the method comprises the following steps: sending orthogonal pilot signals to a plurality of access points through a plurality of user equipment, and obtaining channel estimation results and channel estimation errors between the plurality of access points and the plurality of user equipment; splitting the original transmission signal into a plurality of sub-signals to obtain split transmission signals; based on the channel estimation result and the channel estimation error, establishing a reachable rate model for splitting the transmission signal; and under the condition that the reachable rate model reaches a predetermined target, determining a resource allocation strategy, the resource allocation strategy comprising working modes corresponding to the plurality of access points, and transmitting powers corresponding to the plurality of access points and the plurality of uplink user equipment. According to the invention, the technical problem that the maximization of the spectrum efficiency cannot be realized when resources are allocated to a communication system consisting of an access point and user equipment in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular to a resource allocation method, device, electronic device, and computer-readable storage medium. Background Art

[0002] With the development of communication networks, the density of networks has gradually increased, and ultra-dense networks are becoming more and more common. However, there are serious inter-cell interference problems in ultra-dense networks. Ultra-dense networks built in traditional cellular form also have the problem of high complexity of massive Multiple Input Multiple Output (mMIMO) architecture. Therefore, Cell-Free massive MIMO with Network-Assisted Full Duplex (CF-mMIMO-NAFD) has been proposed as an advanced wireless communication system architecture to solve the above problems in ultra-dense networks. For a distributed architecture such as CF-mMIMO-NAFD, related technologies cannot maximize spectrum efficiency when determining the mode selection and power allocation of access points.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] Embodiments of the present invention provide a resource allocation method, apparatus, electronic device, and computer-readable storage medium to at least address the technical problem in related technologies of being unable to maximize spectrum efficiency when allocating resources to a communication system consisting of access points and user equipment.

[0005] According to one aspect of an embodiment of the present invention, a resource allocation method is provided, comprising: determining multiple access points and multiple user equipment, wherein the operating mode of the multiple access points is an uplink mode or a downlink mode, and the multiple user equipment include multiple uplink user equipment and multiple downlink user equipment; sending orthogonal pilot signals to the multiple access points through the multiple user equipment to obtain channel estimation results and channel estimation errors between the multiple access points and the multiple user equipment; splitting an original transmission signal into multiple sub-signals to obtain split transmission signals, wherein the split transmission signals are used to transmit between the multiple access points and the multiple user equipment; establishing a achievable rate model for the split transmission signal based on the channel estimation results and the channel estimation errors; and determining a resource allocation strategy when the achievable rate model reaches a predetermined target, wherein the resource allocation strategy includes the operating modes corresponding to the multiple access points, and the transmit powers corresponding to the multiple access points and the multiple uplink user equipment.

[0006] According to another aspect of the present invention, a resource allocation apparatus is provided, comprising: a first determining module, configured to determine a plurality of access points and a plurality of user equipments, wherein the operating mode of the plurality of access points is an uplink mode or a downlink mode, and the plurality of user equipments include a plurality of uplink user equipments and a plurality of downlink user equipments; an acquiring module, configured to send orthogonal pilot signals to the plurality of access points through the plurality of user equipments to obtain channel estimation results and channel estimation errors between the plurality of access points and the plurality of user equipments; a splitting module, configured to split an original transmission signal into a plurality of sub-signals to obtain split transmission signals, wherein the split transmission signals are used for transmission between the plurality of access points and the plurality of user equipments; an establishing module, configured to establish a achievable rate model for the split transmission signals based on the channel estimation results and the channel estimation errors; and a second determining module, configured to determine a resource allocation strategy when the achievable rate model meets a predetermined target, wherein the resource allocation strategy includes the operating modes corresponding to the plurality of access points, and the transmit powers corresponding to the plurality of access points and the plurality of uplink user equipments.

[0007] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the resource allocation methods described above.

[0008] According to another aspect of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned resource allocation methods when running.

[0009] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any one of the resource allocation methods are implemented.

[0010] In an embodiment of the present invention, a plurality of access points and a plurality of user equipment are determined, wherein the operating mode of the plurality of access points is an uplink mode or a downlink mode, and the plurality of user equipment includes a plurality of uplink user equipment and a plurality of downlink user equipment; orthogonal pilot signals are sent by the plurality of user equipment to the plurality of access points to obtain channel estimation results and channel estimation errors between the plurality of access points and the plurality of user equipment; an original transmission signal is split into a plurality of sub-signals to obtain split transmission signals, wherein the split transmission signals are used for transmission between the plurality of access points and the plurality of user equipment; a achievable rate model of the split transmission signals is established based on the channel estimation results and the channel estimation errors; and when the achievable rate model meets a predetermined target, a resource allocation strategy is determined, wherein the resource allocation strategy includes the operating modes corresponding to the plurality of access points, and the transmit powers corresponding to the plurality of access points and the plurality of uplink user equipment, thereby achieving the goal of maximizing system reachability and rate, thereby achieving the technical effect of improving spectrum resource utilization, thereby solving the technical problem that related technologies cannot maximize spectrum efficiency when allocating resources for a communication system composed of access points and user equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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:

[0012] Figure 1 is a flow chart of a resource allocation method according to an embodiment of the present invention;

[0013] Figure 2 This is a flowchart of a CF-mMIMO-NAFD system transceiver solution and resource allocation method integrating RSMA according to an optional embodiment of the present invention;

[0014] Figure 3 is a schematic diagram of a CF-mMIMO-NAFD system structure according to an optional embodiment of the present invention;

[0015] Figure 4 is a schematic diagram of the relationship between the residual channel error between APs and the spectrum efficiency in different duplex modes according to an optional embodiment of the present invention;

[0016] Figure 5 is a schematic diagram of the relationship between system spectrum efficiency and the number of uplink and downlink UEs according to an optional embodiment of the present invention;

[0017] Figure 6 is a schematic diagram of the relationship between system spectrum efficiency and the number of APs according to an optional embodiment of the present invention;

[0018] Figure 7is a schematic diagram of the relationship between system spectrum efficiency and the number of antennas per AP according to an optional embodiment of the present invention;

[0019] Figure 8 is a schematic diagram of the relationship between system spectrum efficiency and number of iterations under different numbers of APs according to an optional embodiment of the present invention;

[0020] Figure 9 is a schematic diagram of the relationship between system spectrum efficiency and number of iterations under different numbers of UEs according to an optional embodiment of the present invention;

[0021] Figure 10 is a schematic diagram of the relationship between system spectrum efficiency and the number of APs under different optimization schemes according to optional embodiments of the present invention;

[0022] Figure 11 is a schematic diagram of the relationship between system spectrum efficiency and the number of UEs under different optimization schemes according to optional embodiments of the present invention;

[0023] Figure 12 It is a structural block diagram of a resource allocation device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] 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.

[0026] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following interpretations:

[0027] Cell-Free Massive MIMO with Network-Assisted Full Duplex (CF-mMIMO-NAFD) is an advanced wireless communication system architecture that combines cell-free massive MIMO technology with network-assisted full-duplex technology. It aims to address key challenges in traditional cellular networks and full-duplex communications, enabling more efficient and flexible wireless communication networks. Cell-free massive MIMO technology distributes multiple access points (APs), each equipped with multiple antennas, across a service area to form a dense wireless access network. Compared to traditional cellular networks, this architecture reduces inter-cell interference, improves spectrum efficiency, and improves overall system throughput. In this cell-free architecture, APs are connected to a processor via a high-speed fronthaul link, with the processor responsible for global resource allocation and signal processing. Network-assisted full-duplex technology allows the same device to transmit and receive signals simultaneously at the same time and on the same frequency band, theoretically doubling the spectral efficiency of the communication system. However, a key obstacle to achieving full-duplex is managing self-interference (SI), which refers to the interference of transmitted signals with received signals. Network-assisted full-duplex (NAFD) enables full-duplex communication by optimizing the network structure and signal processing algorithms to mitigate the impact of SI while managing interference from other devices. In CF-mMIMO-NAFD, the AP can flexibly switch between full-duplex (FD) and half-duplex (HD) modes to adapt to different communication scenarios and interference environments. The CPU optimizes the AP's duplex mode and signal power through key technologies such as channel estimation and resource allocation (such as power allocation and mode selection) to maximize the system's reachability and rate (i.e., the overall data transmission rate).

[0028] An access point (AP) acts as a bridge between the network and wireless devices in a wireless network, sending and receiving wireless signals and providing network access services. An AP can be a hotspot in a wireless local area network (WLAN) or a small base station (e.g., micro, pico, or macro) in a cellular network.

[0029] User Equipment (UE) is any terminal device that can communicate with a wireless network, such as smartphones, laptops, and IoT devices. In uplink and downlink communications, UEs can act as both transmitters and receivers. However, in specific communication scenarios, UEs can be clearly divided into uplink UEs and downlink UEs, which focus on sending data to the network (uplink) or receiving data from the network (downlink), respectively.

[0030] Rate Splitting Multiple Access (RSMA) is an advanced multiple access technology that provides a more flexible and efficient resource allocation scheme based on traditional multiple access technologies (such as orthogonal frequency division multiple access and non-orthogonal multiple access). In particular, RSMA can effectively manage interference, optimize spectrum efficiency, and ensure user fairness in large-scale MIMO systems and ultra-dense networks. The core concept of RSMA is to split the information sent by the AP into a public part and a private part, and then adopt different coding and transmission strategies. Specifically: Information Splitting: The user's data stream is divided into two parts: a public stream shared by all users and a private stream unique to each user. Coding and Transmission: The public stream is encoded using a common codebook and transmitted via broadcast, while the private stream is encoded using a user-specific codebook and transmitted as an independent stream. Reception and Decoding: The receiver (such as the base station) first decodes the public stream and then uses interference cancellation techniques to decode the private stream using the decoding results of the public stream. The user device simultaneously receives and decodes the public and private streams to reconstruct the complete information. By splitting the downlink signal into public and private streams, RSMA can effectively manage interference, especially in massive MIMO systems, mitigating the impact of inter-user interference (IUI) and intra-action interference (IAI). RSMA enables partial overlapping transmission between different user data streams, thereby improving spectrum utilization, especially in high-density and high-load network environments. By rationally designing public and private streams, RSMA can provide better user fairness and guarantee the minimum rate requirements for each user even in congested network conditions. RSMA can adapt to different network scenarios and user needs, optimizing overall system performance by dynamically adjusting the split ratio of public and private streams.

[0031] According to an embodiment of the present invention, an embodiment of a resource allocation method 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 can be executed in an order different from that shown here.

[0032] Figure 1 is a flow chart of a resource allocation method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0033] Step S102: determining a plurality of access points and a plurality of user equipments, wherein the working mode of the plurality of access points is an uplink mode or a downlink mode, and the plurality of user equipments includes a plurality of uplink user equipments and a plurality of downlink user equipments.

[0034] As an optional embodiment, the method of this embodiment may be executed by a central processor in a CF-mMIMO-NAFD network architecture. The central processor is configured to execute the resource allocation method and perform global resource allocation for the network architecture based on the obtained resource allocation policy. The method of this embodiment may also be executed by a processor located on multiple access points or multiple user devices. The processor may be a separate device or located on an access point or user device. The processor may communicate with multiple access points and multiple user devices to obtain information about the multiple access points and multiple user devices and allocate resources to the multiple access points and multiple user devices.

[0035] As an optional embodiment, the CF-mMIMO-NAFD network architecture may include a central processor, multiple access points and multiple user devices, wherein the multiple access points can switch their working modes between uplink mode and downlink mode, that is, the multiple access points can select uplink or downlink transmission on the same time and frequency resources. The multiple user devices include multiple uplink user devices and multiple downlink user devices, which serve the needs of data upload and data download respectively. The multiple access points can establish a fronthaul link to the central processor through multiple antennas, and their working modes are uniformly managed by the central processor. The multiple access points can also provide services for multiple uplink user devices and multiple downlink user devices through multiple antennas. By determining multiple access points and multiple user devices, the objects to be allocated resources can be clarified, and different transmission modes and transmission directions can also be clarified, thereby enhancing the flexibility of the communication network and the potential for resource management.

[0036] Step S104: sending orthogonal pilot signals to multiple access points via multiple user equipments to obtain channel estimation results and channel estimation errors between the multiple access points and the multiple user equipments.

[0037] As an optional embodiment, sending orthogonal pilot signals to multiple access points via multiple user devices facilitates accurate channel state information estimation. When sending orthogonal pilot signals to multiple access points via multiple user devices to obtain channel estimation results and channel estimation errors between the multiple access points and the multiple user devices, various methods can be employed. For example, channel models can be established between an access point operating in downlink mode and multiple downlink user devices, and between an access point operating in uplink mode and multiple uplink user devices, respectively. Channel gain models can be established between multiple downlink user devices and multiple uplink user devices, as well as interference channel models between the multiple access points. Based on the channel models, channel gain models, and interference channel models, interference between the multiple access points can be eliminated. Channel estimation results can be obtained using a minimum mean square error channel estimation method by sending orthogonal pilot signals to the multiple access points via multiple user devices, where the channel estimation results include channel estimation models between the access point operating in downlink mode and multiple downlink user devices, and between the access point operating in uplink mode and multiple uplink user devices. Channel estimation errors can be obtained based on the channel models and the channel estimation results. The use of orthogonal pilot signals, combined with the minimum mean square error (MMSE) channel estimation method, improves channel estimation accuracy, reduces channel estimation error, and indirectly enhances overall system performance. The establishment of channel gain and interference channel models eliminates interference between multiple access points, thereby improving system spectrum efficiency.

[0038] Step S106: Split the original transmission signal into multiple sub-signals to obtain split transmission signals, wherein the split transmission signals are used for transmission between multiple access points and multiple user equipments.

[0039] As an optional embodiment, by splitting the original transmission signal into multiple sub-signals, encoding and transmission rules are formulated for each of the sub-signals, thereby improving spectrum efficiency and managing inter-user interference. Various methods can be used to split the original transmission signal into multiple sub-signals to obtain the split transmission signals. For example, the original downlink signal can be split into a public signal and a private signal to obtain a split downlink signal, where the split downlink signal is used to transmit from an access point operating in downlink mode to multiple downlink user devices; the original uplink signal can be split into multiple uplink sub-signals to obtain a split uplink signal, where the split uplink signal is used to transmit from multiple uplink user devices to an access point operating in uplink mode. Splitting the original downlink signal into a public signal and a private signal, where the public signal is shared by all user devices and the private signal is unique to each user device, helps separate the processing of shared information and personalized information, thereby improving spectrum utilization efficiency. By splitting the original uplink signal into multiple uplink sub-signals, each uplink sub-signal can be transmitted by a user device according to its service requirements, helping to reduce interference between uplink user devices and increase uplink bandwidth.

[0040] Step S108: establishing a achievable rate model for the split transmission signal based on the channel estimation result and the channel estimation error.

[0041] As an optional embodiment, when establishing a achievable rate model for split transmission signals based on the channel estimation results and the channel estimation error, a variety of methods can be used. For example, a downlink achievable rate model can be established based on the split downlink signal, the channel estimation results and the channel estimation error; an uplink achievable rate model can be established based on the split uplink signal, the channel estimation results and the channel estimation error; and a achievable rate model can be established based on the downlink achievable rate model and the uplink achievable rate model. The establishment of a achievable rate model provides quantitative indicators for the formulation of resource allocation strategies, transforming the optimization problem from a theoretical level to the optimization of specific numerical values, thereby improving the feasibility of optimization. Through this model, the system can intuitively evaluate the performance under different resource configurations to achieve the best balance between spectrum efficiency and network performance.

[0042] As an optional embodiment, when establishing a downlink achievable rate model based on the split downlink signal, channel estimation results, and channel estimation errors, a variety of methods can be used. For example, the first transmission signals of multiple access points can be determined based on the public signal and the private signal, the transmission power corresponding to the public signal and the private signal, and the coding vectors corresponding to the public signal and the private signal; the first received signals of multiple downlink user devices can be determined based on the first transmission signal, the channel estimation results, and the channel estimation error; based on the signal-to-interference-plus-noise ratio corresponding to the public signal and the private signal in the first received signal, downlink achievable rate sub-models corresponding to the public signal and the private signal are established, and a downlink achievable rate model is established. By determining the first transmission signals of multiple access points, the first received signals of multiple downlink user devices, and the signal-to-interference-plus-noise ratio corresponding to the public signal and the private signal in the first received signal, a downlink achievable rate model under non-ideal channel state information can be accurately established, so that the transmission efficiency of downlink data can be evaluated based on the downlink achievable rate model.

[0043] Among them, when establishing the downlink achievable rate sub-models corresponding to the public signal and the private signal respectively based on the signal-to-interference-plus-noise ratios corresponding to the public signal and the private signal in the first received signal, and establishing the downlink achievable rate model, a variety of methods can be used. For example, based on the number of multiple access points, the number of antennas at each access point, the number of multiple downlink user devices, the working mode selection of the mth access point, the public flow power and private flow power of the mth access point, and the effective noise at the public flow, the signal-to-interference-plus-noise ratio corresponding to the public signal in the first received signal can be determined, and then based on the signal-to-interference-plus-noise ratio corresponding to the public signal, a downlink achievable rate sub-model corresponding to the public signal can be established. Among them, the signal-to-interference-plus-noise ratio corresponding to the public signal in the first received signal can be determined in the following manner:

[0044]

[0045] Where, M is the number of access points, N is the number of antennas at each access point; K is the number of multiple downlink user devices, q D,m and q U,m Indicates the mode selection factor of the mth access point. When the mth access point works in downlink mode, q D,m =1,q U,m = 0, when the mth access point works in uplink mode, q D,m =0,q U,m =1;β D,mk is the large-scale fading coefficient of the signal transmitted between the mth access point and the kth downlink user equipment; p D,mc and {pD,mk} k∈K denote the public flow power and private flow power of the mth access point respectively; ξ mc and ξ mk denote the public flow power normalization factor and private flow power normalization factor of the mth access point respectively; It is the effective noise at the public flow; Where, τ p is the pilot length, p τ is the pilot power, σ 2 is the received noise power.

[0046] Based on the signal-to-interference-and-noise ratio (SINR) of the public signal, the downlink achievable rate sub-model corresponding to the public signal is established in the following manner:

[0047] R D,c =log2(1+γ D,c )

[0048] Where,

[0049] For another example, a signal-to-interference-and-noise ratio corresponding to the private signal in the first received signal can be determined based on the number of access points, the number of antennas at each access point, the number of downlink user devices, the number of uplink user devices, the operating mode selected by the mth access point, the public flow power and private flow power of the mth access point, and the fth sub-information flow power of the jth uplink user device. Based on the signal-to-interference-and-noise ratio corresponding to the private signal, a downlink achievable rate sub-model corresponding to the private signal can be established. The signal-to-interference-and-noise ratio corresponding to the private signal in the first received signal can be determined using the following method:

[0050]

[0051] Where, J is the number of uplink user equipments, is the downlink power normalization factor; p U,jf is the power of the fth sub-information stream of the jth uplink user equipment; β IUI,kj is the large-scale fading coefficient of the signal transmitted between the kth downlink user equipment and the jth uplink user equipment; is the average power of the noise received at the kth downlink user equipment.

[0052] Based on the signal-to-interference-and-noise ratio (SINR) of the private signal, the downlink achievable rate sub-model for the private signal is established as follows:

[0053] R D,k =log2(1+γ D,k )

[0054] Based on the downlink achievable rate sub-model corresponding to the public signal and the downlink achievable rate sub-model corresponding to the private signal, the downlink achievable rate model is established in the following manner:

[0055]

[0056] As an optional embodiment, when establishing the uplink achievable rate model based on the split uplink signal, the channel estimation result, and the channel estimation error, multiple approaches can be adopted. For example, second transmitted signals of multiple uplink user devices can be determined based on multiple uplink sub-signals, multiple data streams obtained by encoding the multiple uplink sub-signals, and the transmit powers corresponding to the multiple uplink sub-signals; second received signals of multiple access points can be determined based on the second transmitted signals, the channel estimation result, and the channel estimation error; multiple data streams in the second received signal are decoded in a predetermined order, and interference between the multiple uplink user devices is eliminated using a serial interference cancellation method to obtain a third received signal; and uplink achievable rate sub-models corresponding to the multiple uplink sub-signals are established based on the signal-to-interference-plus-noise ratios corresponding to the multiple uplink sub-signals in the third received signal, and an uplink achievable rate model is established. By determining the second transmitted signals of multiple uplink user devices, the third received signals of multiple access points, and the signal-to-interference-plus-noise ratios corresponding to the multiple uplink sub-signals in the third received signal, an uplink achievable rate model can be accurately established under non-ideal channel state information, thereby enabling the transmission efficiency of uplink data to be evaluated based on the uplink achievable rate model.

[0057] When establishing uplink achievable rate sub-models corresponding to the multiple uplink sub-signals based on the signal to interference plus noise ratios corresponding to the multiple uplink sub-signals in the third received signal, and establishing the uplink achievable rate model, a variety of methods can be used. For example, the number of multiple access points, the number of antennas at each access point, the number of multiple uplink user devices, the power of the first sub-information stream of the j-th uplink user device, and the effective noise at the first sub-information stream of the j-th uplink user device can be used to determine the signal to interference plus noise ratio corresponding to the first sub-signal of the multiple uplink sub-signals in the third received signal, and based on the signal to interference plus noise ratio corresponding to the first sub-signal of the multiple uplink sub-signals, establish an uplink achievable rate sub-model corresponding to the first sub-signal of the multiple uplink sub-signals. Among them, the signal to interference plus noise ratio corresponding to any one of the multiple uplink sub-signals in the third received signal can be determined in the following manner:

[0058]

[0059] Where,

[0060] p U,j1is the power of the first sub-information stream of the j-th uplink user equipment; β U,mj is the large-scale fading coefficient of the signal transmitted between the mth access point and the jth uplink user equipment; is the effective noise at the first sub-information stream of the j-th uplink user equipment.

[0061] Similarly, the signal-to-interference-and-noise ratios (SINRs) corresponding to multiple uplink sub-signals are determined, and an uplink achievable rate model is established using the following method:

[0062]

[0063] Where,

[0064] Step S1010: When the achievable rate model reaches a predetermined target, a resource allocation strategy is determined, wherein the resource allocation strategy includes working modes corresponding to the plurality of access points, and transmit powers corresponding to the plurality of access points and the plurality of uplink user equipments.

[0065] As an optional embodiment, by determining the resource allocation strategy when the achievable rate model reaches the predetermined target, efficient use of spectrum resources and effective control of interference can be achieved, ultimately achieving optimization of the overall system performance. When the achievable rate model reaches the predetermined target, a variety of methods can be used to determine the resource allocation strategy. For example, multiple constraints can be determined based on the first transmission power constraint of multiple uplink user devices, the second transmission power constraint of multiple access points, and the working mode constraint of multiple access points as uplink mode or downlink mode; under the constraints of multiple constraints, the resource allocation strategy is determined with the achievable rate model reaching the maximum as the predetermined target. By defining clear constraints and optimization goals, the determined resource allocation strategy can not only meet the actual network conditions, but also maximize the achievable rate of the system as a whole, effectively improving the spectrum efficiency of the network and the user service quality, especially in high user density and complex network environments.

[0066] As an optional embodiment, under the constraints of multiple constraints, with the achievable rate model reaching the maximum as the predetermined goal, a variety of methods can be used to determine the resource allocation strategy. For example, a power control factor can be determined, wherein the power control factor includes an uplink power control factor and a downlink power control factor; the multiple constraints and the achievable rate model are converted into multiple target constraints and a target achievable rate model based on the power control factor representation; and a hybrid Harris Hawk optimization algorithm is used to determine the resource allocation strategy under the constraints of multiple target constraints with the target achievable rate model reaching the maximum as the predetermined goal, wherein the hybrid Harris Hawk optimization algorithm includes a binary Harris Hawk optimization algorithm and a continuous Harris Hawk optimization algorithm. By introducing the power control factor, the constraints and the achievable rate model can be represented as power control factors and converted into an optimization problem for these factors, which facilitates algorithm processing. By combining the binary Harris Hawk optimization algorithm and the continuous Harris Hawk optimization algorithm, a rapid search can be performed to find the optimal working modes corresponding to multiple access points and the transmit power combinations corresponding to multiple access points and multiple uplink user devices.

[0067] As an optional embodiment, when using a hybrid Harris Hawk optimization algorithm, under the constraints of multiple target constraints, with the target achievable rate model reaching the maximum as the predetermined goal, and determining the resource allocation strategy, multiple methods can be adopted. For example, the working mode selection corresponding to multiple access points can be used as the first variable, and the transmit power control corresponding to the multiple access points and multiple uplink user equipment can be used as the second variable; multiple groups of first values ​​of the first variable are traversed, and for any group of first values, under the constraints of multiple target constraints, with the target achievable rate model reaching the maximum as the predetermined goal, the second value of the second variable is determined, and the initial achievable rate corresponding to the first value and the second value when the resource allocation strategy is used is determined; based on the multiple initial achievable rates corresponding to the multiple groups of first values, the target achievable rate is determined, and the target first value and target second value corresponding to the target achievable rate are determined; based on the target first value and the target second value, the resource allocation strategy is determined. By distinguishing the first variable from the second variable, the solution process can be simplified, and the duplex mode and transmit power can be controlled more accurately. By determining the corresponding second value of the second variable and the corresponding initial achievable rate for any set of first values ​​of the first variable, it is possible to obtain transmit power control results corresponding to multiple access points and multiple uplink user equipment that can maximize the corresponding initial achievable rate under different operating mode selections. That is, the optimal transmit power control results are determined for different operating mode selections. By selecting the maximum initial achievable rate from multiple initial achievable rates as the target achievable rate, and selecting the target first value and target second value corresponding to the target achievable rate as the resource allocation strategy, it is possible to determine the optimal combination of operating mode selection and transmit power control results, thereby maximizing the target achievable rate model.

[0068] The invention relates to a method for maximizing system reachability and rate by determining multiple access points and multiple user equipment, wherein the working mode of the multiple access points is an uplink mode or a downlink mode, and the multiple user equipment include multiple uplink user equipment and multiple downlink user equipment; sending orthogonal pilot signals to the multiple access points through the multiple user equipment to obtain channel estimation results and channel estimation errors between the multiple access points and the multiple user equipment; splitting the original transmission signal into multiple sub-signals to obtain split transmission signals, wherein the split transmission signals are used to transmit between the multiple access points and the multiple user equipment; establishing a achievable rate model of the split transmission signal based on the channel estimation results and the channel estimation errors; and determining a resource allocation strategy when the achievable rate model reaches a predetermined target, wherein the resource allocation strategy includes the working modes corresponding to the multiple access points, and the transmit powers corresponding to the multiple access points and the multiple uplink user equipment, thereby achieving the purpose of maximizing system reachability and rate, thereby realizing the technical effect of improving spectrum resource utilization, and further solving the technical problem that the related technology cannot maximize spectrum efficiency when allocating resources for a communication system composed of access points and user equipment.

[0069] In combination with the above embodiments and optional embodiments, an optional implementation method is provided. In this optional implementation method, a CF-mMIMO-NAFD system transceiver scheme and resource allocation method integrating RSMA are proposed. In order to enhance the scalability of the system and reduce the fronthaul overhead and CPU computing pressure, the maximum ratio transmission / maximum ratio reception is adopted as the transceiver scheme, and spectrum efficiency analysis is performed on this basis. When the number of uplink UEs increases, IUI as an interference item will seriously affect the system spectrum efficiency, thereby achieving system reachability and rate maximization. The optimization algorithm is run by the CPU and is responsible for global resource allocation. The AP, as a controlled node, receives instructions to perform duplex mode switching and power adjustment, ensuring the optimality of global resource allocation while reducing the AP computing complexity, so as to solve the problem that the power control strategy in the related technology cannot guarantee the maximization of spectrum efficiency under the CF-mMIMO-NAFD architecture.

[0070] Figure 2 is a schematic diagram of a CF-mMIMO-NAFD system transceiver solution and resource allocation method integrating RSMA according to an optional embodiment of the present invention, such as Figure 2As shown, the method includes the following processing: considering orthogonal pilot signals and completing uplink channel estimation to obtain statistical information of the estimated value and error value; integrating RSMA technology to determine the closed-form expression of the achievable rate of downlink public / private streams and uplink user sub-information based on non-ideal CSI; analyzing the impact of the number of antennas, the number of APs, and the number of uplink and downlink UEs on the overall system performance; introducing a mode selection factor based on the flexible selection of duplex mode by each AP in the network-assisted free duplex system; taking the system achievable sum rate as the optimization goal, based on the above derivation results, constructing an intelligent optimization algorithm to achieve joint long-term optimization of AP duplex mode, public / private streams, and uplink UE sub-information power. The system achievable sum rate is the sum of the sum of the uplink rate and the sum of the downlink rate of the entire communication system.

[0071] Figure 3 Schematic diagram of the CF-mMIMO-NAFD system structure according to an optional embodiment of the present invention, as shown Figure 3 As shown in the figure, when the CPU is deployed independently of user devices and access points, the CF-mMIMO-NAFD system includes M HD APs serving K single-antenna downlink UEs and J single-antenna uplink UEs on the same time-frequency resources. Each AP is equipped with N antennas and connected to the CPU via a fronthaul link. Its operating mode is centrally managed by the CPU. M, K, and J represent the AP, downlink UE, and uplink UE index sets, respectively.

[0072] In such Figure 3 Under the system architecture shown, execute Figure 2 The method shown includes the following steps.

[0073] S1, uplink pilot training stage model.

[0074] Using the standard block fading model, the channel is in a coherent time interval τ c All channels experience Rayleigh fading due to the rich scattering environment. The downlink channel between UEk and APm is The uplink channel between UE j and APm is where β D,mk (β U,mj ) is the large-scale fading coefficient, g D,mk (g U,mj )∈C N×1 is a small-scale fading vector whose elements are independent and identically distributed CN(0,1) random variables. Next, the channel gain between uplink UE j and downlink UE k is defined as where β IUI,kj is the large-scale fading coefficient, g IUI,kj ~CN(0,1).

[0075] According to the channel model, the central processor is scheduled to eliminate the cross-link interference between APs, including: Define the interference channel H between APm and APm′ IAI,mm′ ∈C N×N , whose elements are independent and identically distributed CN(0,β IAI,mm′ ) random variable, when m=m′ IAI,mm′ = 0. In which, the central processor performs unified scheduling to eliminate cross-link interference between APs in the analog-digital domain. Considering the residual error caused by the imperfect channel estimation between APs, let Represents the channel estimation error, whose elements are independent and identically distributed random variables, where is the residual error power.

[0076] According to the reciprocity of uplink and downlink channels, the downlink precoding design is completed. For a coherent interval τ c , using τ p time slots for uplink pilot training, and the remaining τ d It is used for uplink and downlink data transmission. In the uplink pilot phase, the uplink UE and downlink UE synchronously transmit pilot sequences to all APs, and the AP uses the received pilot signal to estimate the channel between the UE and the AP. Orthogonal pilots are used, that is, the pilot length τ p ≥K+J for performance analysis.

[0077] For the uplink and downlink channels between UE k and APm as well as Using MMSE channel estimation, we can obtain:

[0078] in p τ is the pilot power, σ 2 is the received noise power.

[0079] The channel estimation error is and in

[0080] S2, network-assisted free duplex mode selection variable.

[0081] Define a binary variable q D,m and q U,m Indicates the mode selection of APm. When APm works in downlink mode, q D,m =1,q U,m=0, then APm is a Transmitting Access Point (TAP); when APm works in uplink mode, q D,m =0,q U,m =1, then APm is the receiving access point (RAP). Since AP is an HD device, the mode selection variable satisfies q D,m +q U,m =1.

[0082] S3, derivation of the closed expression for the downlink rate.

[0083] The CF-mMIMO-NAFD system determines the transmission rate of the downlink signal received by the central processor, including:

[0084] The central processor transmits the downlink UE k's information W D,k Split into common part W D,c,k and private part W D,p,k , the common part of all downlink UEs {W D,c,k} k∈K Use the public codebook to encode into a public stream s D,c , where E{|s D,c | 2}=1, downlink UEk private part W D,p,k Independently encoded as a private stream D,k , where E{|s D,k | 2}=1, these data streams are distributed by the central processor to all TAPs in the system for transmission at the same time.

[0085] For APm, if q D,m =1, the AP works in downlink transmission mode. At this time, APm uses linear precoding W m =[w mc ,w m1 ,...,w mK ]∈C N×(K+1) Map the transmitted symbols to the transmitting antennas, where w mc is the public precoding vector of APm, w mk is the private precoding vector of downlink UE k at APm, then the transmitted signal x of APm is D,m ∈C N×1 for:

[0086]

[0087] Among them, p D,mc and {p D,mk} k∈KRepresent the public flow and private flow power in APm respectively.

[0088] Then the downlink UE k's received signal y D,k for:

[0089]

[0090] Where F is the number of uplink UE sub-information, Indicates the downlink UE k receives noise, the average power is If q D,m =0, then APm does not send any signal, then set p D,mc ={p D,mk} k∈K =0.

[0091] When choosing the MRT precoding scheme, most of the processing can be done locally at the AP, and there is no need to exchange channel state information between the AP and the central processor. Then the APm common stream precoding vector is:

[0092]

[0093] in, Represents the common flow power normalization factor.

[0094] For private streams, the precoding is:

[0095]

[0096] in, Indicates the private stream power normalization factor.

[0097] Since there is no pilot signal in the downlink, the channel information at the UE is limited and signal detection can only be completed using statistical information. D,c , the downlink received signal of UE k can be rewritten as:

[0098]

[0099] in, Indicates the strength of the downlink expected signal;

[0100] represents the beamforming gain uncertainty;

[0101] represents the private flow interference caused by downlink UEk′;

[0102] represents the cross-link interference caused by uplink UE j.

[0103] The last four items of the downlink received signal are considered as effective noise, which are uncorrelated with the desired signal. Using the use-and-then-forget (UatF) capacity-bounding technique, the Signal to Interference plus Noise Ratio (SINR) of the common stream at downlink UE k is given as:

[0104]

[0105] use Satisfying the relationship of zero mean and mutual independence, we can further obtain:

[0106]

[0107] Using the fact that the variance of the sum of independent random variables is equal to the sum of their variances, the uncertainty in the beamforming gain can be transformed into:

[0108]

[0109] Based on this, it can be simplified as:

[0110]

[0111] According to the discussion of k′, the final result is:

[0112]

[0113] Therefore, the closed form of the SINR of the common stream at downlink UE k is:

[0114]

[0115] in,

[0116] Represents the effective noise at the public flow. Based on the above content, the expression of the public flow rate at the user is obtained:

[0117] R D,c =log2(1+γ D,c )

[0118] in,

[0119] After serial interference cancellation is performed on the common stream, the received signal of downlink UE k is:

[0120]

[0121] in,

[0122]

[0123]

[0124] The private flow SINR of downlink UE k is expressed as:

[0125]

[0126] in,

[0127] The closed form of the private flow SINR of downlink UE k is:

[0128]

[0129] in,

[0130] Therefore, the private flow rate achievable by downlink UE k is:

[0131] R D,k =log2(1+γ D,k )

[0132] The final downlink reachable sum rate of the CF-mMIMO-NAFD system is:

[0133]

[0134] S4, derivation of the closed expression for uplink rate.

[0135] In the uplink data transmission phase of the CF-mMIMO-NAFD system, Uplink UE j sends its information W U,j Split into F sub-information {W U,j1 ,W U,j2 ,...,W U,jF}, and encoded into different data streams s U,jf , corresponding to the transmission power p U,jf , satisfying E{|s U,jf | 2}=1, F = 2, the uplink signal sent by UE j is:

[0136]

[0137] For APm, if q U,m =1, the AP works in uplink receiving mode. At this time, the received signal y U,m ∈C N×1 for:

[0138]

[0139] in, Indicates the TAP interference signal received by APm, Represents the Additive White Gaussian Noise (AWGN) vector.

[0140] When q U,m = 0, APm does not receive any signal, that is, y U,m =0.

[0141] APm uses the estimated channel information Maximum ratio reception The uplink UE j's transmitted signal is demodulated and forwarded to the CPU for combination. For uplink UE j, the aggregated received signal at the CPU is:

[0142]

[0143] The uplink rate is improved by decoding the sub-information of uplink UE j using serial interference cancellation and the decoding order is optimized. First, the sum of the distances d between uplink UE j and the current RAP is calculated. j , and reorder the uplink UE numbers from small to large according to the distance to satisfy d1 <d2<...<d J , when d j <d j+1 , decoding order π j <π j+1 , that is, uplink UE j is decoded before j+1. After serial interference cancellation is performed on users with numbers less than j, the combined signal corresponding to the different sub-information of uplink UE j is:

[0144]

[0145] Using the UatF capacity-limiting technology, the SINR of uplink UE j sub-information 1 is:

[0146]

[0147] in,

[0148] For the gain error, we have:

[0149]

[0150] Since there are no related terms, we get:

[0151]

[0152] The SINR associated with the cross-link interference between APs can be further simplified as:

[0153]

[0154] Substituting this into the equation, we get the closed-form expression for the SINR of uplink UE j sub-information 1:

[0155]

[0156] in represents the effective noise composed of IAI and received noise,

[0157] Similarly, the SINR of uplink UE j sub-information 2 is:

[0158]

[0159] in,

[0160] The remaining terms are the same as sub-information 1, so similar to the above derivation, here we directly give the final closed expression:

[0161]

[0162] in,

[0163] The achievable uplink rate of UE j is:

[0164]

[0165] The final uplink reachable sum rate of the CF-mMIMO-NAFD system is:

[0166]

[0167] S5, Resource allocation problem of CF-mMIMO-NAFD system integrated with RSMA.

[0168] The uplink and downlink reachable rates derived above are used as performance indicators for the CF-mMIMO-NAFD system (i.e., the subsequent CF-RS-network-assisted free duplex system) integrated with RSMA, namely:

[0169] R=R D +R U

[0170] When adopting the MR transceiver scheme, the key factors affecting the performance of the CF-RS-network assisted free duplex system are the AP working mode and the uplink and downlink transmission power. Due to the inherent cross-link interference of the FD system, a reasonable scheduling AP mode can reduce the impact of IAI and IUI. Analyzing the reachability and rate of the downlink public and private streams, the downlink performance increases with the increase in the number of TAPs and the number of antennas configured for each TAP. Assuming that all uplink UEs are transmitting at full power, when the number of uplink UEs increases, the increase in IUI will seriously affect the downlink performance, so it is necessary to reasonably control the uplink UE transmission power. For the uplink reachability and rate, the increase in the number of RAPs and the number of antennas configured for each RAP will improve the uplink performance. In addition, since the interference signal is reconstructed using the channel state information between TAP-RAP, IAI can be effectively suppressed. However, due to the influence of the residual channel estimation error, when the number of TAPs increases or the downlink transmission power is large, the uplink performance will still be reduced. Finally, based on the above-obtained closed-form expressions for the reachability and rate of CF-RS-network assisted free duplex, the results are derived to maximize R=R D +R U As the optimization target, the optimization problem is established by combining TAP with uplink UE transmit power constraint.

[0171] The constraints of this optimization problem include: the total transmit power of each AP in downlink mode (including the sum of public flow power and all private flow powers) must not exceed its maximum allowed power; the total transmit power of each uplink UE on different sub-information must not exceed its maximum allowed power; all power variables (including downlink public flow power, downlink private flow power, and uplink UE power) must be non-negative real numbers; at the same time, since each AP can only operate in pure uplink or pure downlink mode at a certain moment, this is achieved through binary variable constraints, that is, the uplink and downlink mode selection variables of each AP must satisfy a mutually exclusive and complete relationship, and when the AP operates in uplink mode, all its corresponding downlink transmit powers (including public flows and private flows) must be forcibly set to zero.

[0172] For example, the optimization problem can be expressed as follows:

[0173]

[0174] Where X = {p D,c ,p D,p ,p U ,q D ,q U} represents the set of optimized variables, p D,c ∈C M×1 Indicates the common flow power allocated to all APs, p D,p ∈C MK×1 Indicates the private flow power allocated to all APs, pU ∈C JF×1 Indicates the power allocated to all uplink UEs with different sub-information. Constrains the maximum transmit power of each AP p AP , Constrains the maximum transmit power p of each UE UE Finally, the AP is constrained to be an HD device and can only work in uplink or downlink mode.

[0175] In order to adapt to the intelligent optimization algorithm, all power variables are normalized by introducing power control factors so that their value range is unified between 0 and 1. Finally, the optimization variables are divided into a set of discrete variables that determine the system structure and a set of continuous variables responsible for power allocation, among which discrete variables have a higher optimization priority. For example, in order to adapt to the subsequent intelligent optimization algorithm, the uplink and downlink power control factor α can be introduced D,lk With α U,jf , and meet At this time, the optimization variables related to the transmit power have a unified upper and lower bound of 0 and 1. In order to deal with the mixed integer programming problem, the optimization variable set X is split into two parts for processing X1 = {q D ,q U} and X2={p D,c ,p D,p ,p U}, we can find that X1={q D ,q U} takes precedence over As X1 determines the overall system structure, we call it a continuous planning problem Based on , the original optimization problem (i.e., the aforementioned constraints and achievable rate model) can be modified into the target constraints and target achievable rate model expressed in the following form:

[0176]

[0177] These optimization tasks are difficult to solve using classical numerical methods. Instead, we employ the Harris Hawk Optimization (HHO) algorithm to address these optimization problems. Compared to other heuristic algorithms that rely on a single search strategy, HHO employs multiple optimization strategies tailored to the specific search process, achieving a perfect balance between exploration and exploitation.

[0178] S6, Harris Eagle intelligent optimization algorithm.

[0179] The HHO algorithm is inspired by the cooperative foraging activities of hawks in nature. It achieves optimization by simulating their hunting process, including three stages: exploration, conversion, and exploitation. The prey is the best candidate solution after each iteration (the best position so far), based on the objective function value f(Xi ), the position X of each eagle i Keep updating until you catch your prey.

[0180] The escape energy of the prey can be defined as:

[0181]

[0182] Where t represents the current number of iterations, T represents the maximum number of iterations, and E0(t) is a random number in the interval (-1,1).

[0183] The HHO for solving binary problems can be called binary HHO (BHHO). Correspondingly, the HHO for solving continuous problems can be called continuous HHO (CHHO). The combination of the two can be called hybrid HHO (HHHO).

[0184] In the HHO iteration process, the computational complexity of initializing the population position is O(N P N D ), where N P is the number of individuals in the population, N D To optimize the problem dimension.

[0185] Therefore, the HHHO algorithm can be used to solve the optimization problem. BHHO and CHHO are applied to mode selection and power control respectively. The optimal power factor is searched for a given AP mode, and the AP mode is updated according to the BHHO update criterion until convergence.

[0186] At this point, the optimization problem is transformed into a double-loop problem. The outer loop corresponds to the mode selection, and its optimization variable dimension is M, which corresponds to the eagle individual in BHHO. The inner loop corresponds to power control, and its optimization variable dimension is L(K+1)+2J, which corresponds to the eagle individual in CHHO In addition, the outer loop fitness function is:

[0187]

[0188] Among them, (1) is the case where the constraints are satisfied, and (2) is the case where the constraints are not satisfied.

[0189] Correspondingly, the inner loop is given X O,i Under the condition of , the fitness function is:

[0190]

[0191] In summary, based on the derived closed-form expressions for uplink and downlink reachability and rates of CF-RS-network-assisted free duplex, combined with the meta-heuristic hybrid Harris Hawk optimization algorithm, the optimal AP mode selection and fast search for uplink and downlink transmit power are achieved. The specific process is shown in Table 1.

[0192] Table 1 CF-RS-network-assisted free duplex resource allocation scheme based on hybrid Harris Hawk optimization

[0193]

[0194] Monte Carlo simulations were performed to verify the accuracy of the closed-form expressions for the reachability and rate of the CF-RS-network-assisted free duplex system using the MR transceiver scheme. The Monte Carlo simulation results were obtained by averaging 500 randomly generated trials. Consider a circular area with a radius of 100m, with M randomly distributed access points (APs), K downlink UEs, and J uplink UEs. The APs are equipped with N antennas. During the uplink pilot phase, the pilot power of each UE, p, is τ =100mW, pilot length τ p =K+J. In addition, in the uplink and downlink data transmission phase of this section, unless otherwise specified, all flows adopt the average power allocation scheme, and AP adopts the random mode equalization scheme to ensure that the number of L TAPs and Z RAPs is equal. Noise power For computational convenience, we let the residual estimation error between TAP l and RAP z be More simulation parameters are shown in Table 1, where d represents the distance in meters.

[0195] Furthermore, a number of performance comparison experiments are used to illustrate the advantages of this optional implementation. In which, all access points AP are randomly associated with edge distributed units (EDUs), and each edge distributed unit EDU processes M z The channel model parameters are shown in Table 2.

[0196] Table 2 Channel model parameters

[0197] parameter Numerical TAP total transmit power 30dBm Uplink UE total transmit power 20dBm Path loss 128.1+37.6log10(d) Rayleigh fading 0dB Shadow Fading 8dB

[0198] Figure 4 FIG. 1 is a schematic diagram showing the relationship between the residual channel error between APs and the spectrum efficiency in different duplex modes according to an optional embodiment of the present invention. Figure 4The closed-form expressions for system achievable and rate derived above are applicable to traditional multiple access (MA) and RSMA-enabled time division duplex (TDD), co-channel full duplex (CCFD), and network-assisted free duplex scenarios. Figure 4 As shown in the figure, when Δ≤-5dB, CCFD and network-assisted free duplexing outperform TDD. Furthermore, it can be found that RS-network-assisted free duplexing has better interference management capabilities than other schemes. When Δ≥0dB, the RS-network-assisted free duplexing system performance is lower than TDD. This verifies that network-assisted free duplexing has better system performance than TDD and CCFD under good interference suppression.

[0199] Figure 5 Schematic diagram of the relationship between the system spectrum efficiency and the number of uplink and downlink UEs according to an optional embodiment of the present invention. When △ = -15dB, M = 16, N = 8, the relationship between the system spectrum efficiency and the number of uplink and downlink UEs is as follows: Figure 5 The theoretically derived achievable sum rate is very close to that obtained through Monte Carlo simulation, further validating the correctness of the derivation. Furthermore, increasing the number of uplink and downlink UEs improves system spectrum efficiency. However, as the number of UEs increases, the performance growth rate slows due to increased inter-user interference, which affects the downlink rate. Finally, compared to random decoding or traditional multiple access schemes, using RSMA with optimized decoding can significantly improve performance.

[0200] Figure 6 : is a schematic diagram of the relationship between the system spectrum efficiency and the number of APs according to an optional embodiment of the present invention. When △ = -15dB, K = 8, J = 8, N = 4, the relationship between the system spectrum efficiency and the number of APs is as follows: Figure 6 The theoretical results for reachability and rate are consistent with the simulation results. As the number of APs in the system increases, the rate of spectrum efficiency growth slows. This is because, under random mode averaging, the increase in the number of TAPs leads to increased inter-AP interference, which in turn affects the uplink rate.

[0201] Figure 7 FIG is a schematic diagram showing the relationship between the system spectrum efficiency and the number of antennas per AP according to an optional embodiment of the present invention. Figure 7 As shown in Figure 2, the theoretical results of the achievable sum rate are consistent with the simulation results. As the number of antennas per AP increases, the sum rate grows more slowly. This is because when the number of antennas increases to a certain value, the system capacity tends to stabilize.

[0202] Simulations validated the superior performance of the RSMA-based network-assisted free duplexing scheme under both randomized mode sharing and average power allocation schemes. This also verified the correctness of the closed-form expressions for uplink and downlink reachability and rate. Based on these closed-form expressions, we will now perform a long-term joint optimization of the AP duplexing mode and uplink and downlink power. Maintaining consistent simulation parameters, we will first consider power control based on CHHO in fixed AP mode.

[0203] Figure 8 : is a schematic diagram of the relationship between system spectrum efficiency and number of iterations under different numbers of APs according to an optional embodiment of the present invention, such as Figure 8 As shown in the figure, when K = J = 4 and the number of antennas per AP N = 4, the number of required convergence times increases as the total number of APs M in the system increases. It can be seen that the algorithm can complete convergence within 20 iterations, showing a considerable convergence speed and a performance gain of approximately 12%.

[0204] Figure 9 is a schematic diagram of the relationship between system spectrum efficiency and number of iterations under different numbers of UEs according to an optional embodiment of the present invention, such as Figure 9 As shown in the figure. When M = 24 and N = 4, the algorithm converges within 20 iterations, but only achieves a performance gain of approximately 9%. Therefore, achieving mode selection in a network-assisted free duplex system is key to further improving system performance and speed.

[0205] Figure 10 FIG. 1 is a schematic diagram of the relationship between system spectrum efficiency and the number of APs under different optimization schemes according to an optional embodiment of the present invention, such as Figure 10 When N=4, K=J=4, hybrid optimization can further improve the system reachability and rate, and can bring a performance gain of nearly 24% compared to the average allocation scheme.

[0206] Figure 11 is a schematic diagram of the relationship between system spectrum efficiency and the number of UEs under different optimization schemes according to optional embodiments of the present invention, such as Figure 11 When N=4 and M=24, hybrid optimization can bring a 22% performance gain, breaking through the gain limitation of the original random mode that only optimizes power.

[0207] According to an embodiment of the present invention, a resource allocation device is provided. Figure 12 is a structural block diagram of a resource allocation device according to an embodiment of the present invention. Figure 12 As shown, the device includes: a first determination module 1202, an acquisition module 1204, a splitting module 1206, a building module 1208 and a second determination module 1210. The device is described below.

[0208] A first determining module 1202 is configured to determine multiple access points and multiple user equipments, wherein the operating mode of the multiple access points is an uplink mode or a downlink mode, and the multiple user equipments include multiple uplink user equipments and multiple downlink user equipments. An acquiring module 1204 is connected to the first determining module 1202 and configured to send orthogonal pilot signals to the multiple access points via the multiple user equipments to obtain channel estimation results and channel estimation errors between the multiple access points and the multiple user equipments. A splitting module 1206 is connected to the acquiring module 1204 and configured to split an original transmission signal into multiple sub-signals to obtain split transmission signals, wherein the split transmission signals are used for transmission between the multiple access points and the multiple user equipments. An establishing module 1208 is connected to the splitting module 1206 and configured to establish a achievable rate model for the split transmission signals based on the channel estimation results and the channel estimation errors. A second determining module 1210 is connected to the establishing module 1208 and configured to determine a resource allocation strategy when the achievable rate model meets a predetermined target, wherein the resource allocation strategy includes the operating modes corresponding to the multiple access points and the transmit powers corresponding to the multiple access points and the multiple uplink user equipments.

[0209] It should be noted here that the above-mentioned first determination module 1202, acquisition module 1204, splitting module 1206, establishment module 1208 and second determination module 1210 correspond to steps S102 to S1010 in the embodiment, and the instances and application scenarios implemented by multiple modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0210] According to an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program runs, the device where the computer-readable storage medium is located is controlled to execute any one of the resource allocation methods described above.

[0211] According to an embodiment of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor configured to run the program, wherein the program executes any one of the above-mentioned resource allocation methods when running.

[0212] According to an embodiment of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0213] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0214] 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.

[0215] 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 units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0216] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0217] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0218] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable 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 storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned 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, etc. Various media that can store program codes.

[0219] The above is only a preferred embodiment 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 within the scope of protection of the present invention.

Claims

1. A resource allocation method, characterized in that: include: Determining a plurality of access points and a plurality of user equipments, wherein the working mode of the plurality of access points is an uplink mode or a downlink mode, and the plurality of user equipments include a plurality of uplink user equipments and a plurality of downlink user equipments; Sending orthogonal pilot signals to the multiple access points through the multiple user equipments to obtain channel estimation results and channel estimation errors between the multiple access points and the multiple user equipments; Splitting an original transmission signal into a plurality of sub-signals to obtain split transmission signals, wherein the split transmission signals are used for transmission between the plurality of access points and the plurality of user equipments; Establishing a achievable rate model of the split transmission signal based on the channel estimation result and the channel estimation error; When the achievable rate model reaches a predetermined target, a resource allocation strategy is determined, wherein the resource allocation strategy includes working modes corresponding to the multiple access points, and transmit powers corresponding to the multiple access points and the multiple uplink user equipments.

2. The method according to claim 1, characterized in that The sending, by the multiple user equipments, orthogonal pilot signals to the multiple access points, and obtaining channel estimation results and channel estimation errors between the multiple access points and the multiple user equipments, includes: Establishing channel models between the access point operating in the downlink mode and the multiple downlink user equipments, and between the access point operating in the uplink mode and the multiple uplink user equipments, respectively; Establishing channel gain models between the multiple downlink user equipments and the multiple uplink user equipments, and interference channel models between the multiple access points respectively; Eliminating interference between the multiple access points based on the channel model, the channel gain model, and the interference channel model; Sending orthogonal pilot signals to the multiple access points through the multiple user equipments, and obtaining the channel estimation results using a minimum mean square error channel estimation method, wherein the channel estimation results include channel estimation models between the access point operating in the downlink mode and the multiple downlink user equipments, and between the access point operating in the uplink mode and the multiple uplink user equipments; The channel estimation error is obtained based on the channel model and the channel estimation result.

3. The method according to claim 1, characterized in that The step of splitting an original transmission signal into a plurality of sub-signals to obtain split transmission signals, wherein the split transmission signals are used for transmission between the plurality of access points and the plurality of user equipments, comprises: Splitting an original downlink signal into a public signal and a private signal to obtain a split downlink signal, wherein the split downlink signal is used to send the access point operating in the downlink mode to the multiple downlink user equipments; An original uplink signal is split into multiple uplink sub-signals to obtain split uplink signals, wherein the split uplink signals are used for the multiple uplink user equipments to send to the access point working in the uplink mode.

4. The method according to claim 3, characterized in that The establishing, based on the channel estimation result and the channel estimation error, a achievable rate model of the split transmission signal includes: Establishing the downlink achievable rate model based on the split downlink signal, the channel estimation result and the channel estimation error; Establishing the uplink achievable rate model based on the split uplink signal, the channel estimation result and the channel estimation error; The achievable rate model is established based on the downlink achievable rate model and the uplink achievable rate model.

5. The method according to claim 4, characterized in that The establishing the downlink achievable rate model based on the split downlink signal, the channel estimation result and the channel estimation error includes: Determining first transmit signals of the multiple access points based on the common signal and the private signal, transmit powers corresponding to the common signal and the private signal respectively, and coding vectors corresponding to the common signal and the private signal respectively; Determining first received signals of the multiple downlink user equipments based on the first transmitted signal, the channel estimation result, and the channel estimation error; Based on the signal-to-interference-plus-noise ratios (SINRs) respectively corresponding to the common signal and the private signal in the first received signal, downlink achievable rate sub-models respectively corresponding to the common signal and the private signal are established, and the downlink achievable rate model is established.

6. The method according to claim 4, characterized in that The establishing the uplink achievable rate model based on the split uplink signal, the channel estimation result and the channel estimation error includes: Determining second transmit signals for the multiple uplink user equipments based on the multiple uplink sub-signals, the multiple data streams obtained by respectively encoding the multiple uplink sub-signals, and the transmit powers corresponding to the multiple uplink sub-signals respectively; determining second received signals of the plurality of access points based on the second transmitted signal, the channel estimation result, and the channel estimation error; Decoding the multiple data streams in the second received signal in a predetermined order, and using a serial interference cancellation method to eliminate interference between the multiple uplink user equipments to obtain a third received signal; Based on the signal-to-interference-plus-noise ratios (SINRs) respectively corresponding to the multiple uplink sub-signals in the third received signal, uplink achievable rate sub-models respectively corresponding to the multiple uplink sub-signals are established, and the uplink achievable rate model is established.

7. The method according to claim 1, characterized in that The determining of a resource allocation strategy when the achievable rate model reaches a predetermined target includes: Determining a plurality of constraint conditions according to the first transmit power constraints of the plurality of uplink user equipments, the second transmit power constraints of the plurality of access points, and the working mode constraints of the plurality of access points being the uplink mode or the downlink mode; Under the constraints of the multiple constraints, the resource allocation strategy is determined with the achievable rate model reaching a maximum as the predetermined goal.

8. The method according to claim 7, characterized in that The determining of the resource allocation strategy under the constraints of the multiple constraints and taking the maximum achievable rate model as the predetermined goal includes: Determining a power control factor, wherein the power control factor includes an uplink power control factor and a downlink power control factor; Converting the plurality of constraints and the achievable rate model into a plurality of target constraints and a target achievable rate model represented based on the power control factor; A hybrid Harris Hawk optimization algorithm is used to determine the resource allocation strategy under the constraints of the multiple target constraints, with the target achievable rate model reaching the maximum as the predetermined goal. The hybrid Harris Hawk optimization algorithm includes a binary Harris Hawk optimization algorithm and a continuous Harris Hawk optimization algorithm.

9. The method according to claim 8, characterized in that The hybrid Harris Hawk optimization algorithm is used to determine the resource allocation strategy with the target achievable rate model reaching a maximum value as the predetermined goal under the constraints of the multiple target constraints, including: Using the working mode selections corresponding to the multiple access points as a first variable, and using the transmit power control corresponding to the multiple access points and the multiple uplink user equipments as a second variable; Traversing multiple groups of first values ​​of the first variable, for any group of first values, under the constraints of the multiple target constraints, taking the target achievable rate model reaching a maximum as the predetermined target, determining a second value of the second variable, and determining an initial achievable rate corresponding to when the first value and the second value are used as the resource allocation strategy; Determining a target achievable rate based on a plurality of initial achievable rates corresponding to the plurality of groups of first values, the target achievable rate corresponding to a target first value and a target second value; The resource allocation strategy is determined based on the first target value and the second target value.

10. A resource allocation device, characterized in that: include: A first determining module is configured to determine a plurality of access points and a plurality of user equipments, wherein the working mode of the plurality of access points is an uplink mode or a downlink mode, and the plurality of user equipments includes a plurality of uplink user equipments and a plurality of downlink user equipments; an acquisition module, configured to send orthogonal pilot signals to the multiple access points through the multiple user equipments, and acquire channel estimation results and channel estimation errors between the multiple access points and the multiple user equipments; a splitting module, configured to split an original transmission signal into a plurality of sub-signals to obtain split transmission signals, wherein the split transmission signals are used to be transmitted between the plurality of access points and the plurality of user equipments; An establishing module, configured to establish a achievable rate model of the split transmission signal based on the channel estimation result and the channel estimation error; The second determination module is used to determine a resource allocation strategy when the achievable rate model reaches a predetermined target, wherein the resource allocation strategy includes the working modes corresponding to the multiple access points, and the transmit powers corresponding to the multiple access points and the multiple uplink user equipments.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the computer-readable storage medium is located is controlled to execute the resource allocation method according to any one of claims 1 to 9.

12. An electronic device, characterized in that: include: a memory storing an executable program; A processor is used to run the program, wherein the program executes the resource allocation method according to any one of claims 1 to 9 when running.

13. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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