A network-assisted full-duplex system energy efficiency optimization method and system

Abstract

The present invention provides a method and system for optimizing energy efficiency of a network-assisted full-duplex system, comprising: obtaining a set of channel vectors between an uplink user device, a downlink user device, and uplink and downlink remote radio frequency heads; constructing a joint energy collection and transmission optimization model based on the channel vector set, with the goal of maximizing system energy efficiency and with user quality of service, fronthaul constraints, energy collection requirements, and transmitter transmit power as constraints; determining the optimal operating mode of the uplink user and downlink user devices using an energy collection and information receiving antenna selection algorithm; and solving the joint energy collection and transmission optimization model for the optimal value in the optimal operating mode to obtain the target result of maximizing system energy efficiency. The present invention provides joint energy collection and transmission optimization under certain constraints for a network-assisted full-duplex system, achieving the optimal goal of maximizing system energy efficiency.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a network-assisted full-duplex system energy efficiency optimization method and system. Background Art

[0002] With the popularization of the fifth generation mobile communication technology (5G), people have higher and higher requirements for the quality of service (QoS) of communication systems. 5G has overcome the data rate and delay of uplink and downlink in cellular systems to a certain extent.

[0003] 5G New Radio (5G-NR) supports flexible duplexing technologies, including dynamic time division duplexing (TDD) and flexible frequency division duplexing (FDD) in paired and unpaired spectrum. Spatial domain flexible duplexing has been studied in many fields in recent years. Co-frequency co-time full duplex (CCFD) promises to double the spectral efficiency of wireless links compared to half-duplex by enabling downlink and uplink transmissions on the same time-frequency resources. However, ultra-dense deployment of access points (APs) leads to severe cross-link interference (CLI), i.e., interference from downlink APs to uplink APs and from uplink users to downlink users. This is also a major challenge facing CCFD, flexible duplexing, or broadband distribution networks (BDNs).

[0004] Network-Assisted Full Duplex (NAFD) can be regarded as a unified implementation of flexible duplex, CCFD, and hybrid duplex in a cellular-free network architecture. Therefore, the NAFD solution is considered to be a technology that truly realizes flexible duplex. In the cellular-free massive Multiple-in Multiple-out (MIMO) technology based on NAFD, all APs are connected to the central processing unit (CPU) through a high-speed fronthaul link. Therefore, how to eliminate CLI and how to cope with the high demand for forward and backhaul links are two major problems of the system. Most existing work focuses more on suppressing or reducing CLI by designing suitable flexible duplex transceivers. Although cellular-free massive MIMO based on NAFD can provide considerable spectrum gain, the ultra-dense deployment of APs will greatly increase the power consumption of the system. Therefore, in order to effectively utilize cross-link interference energy harvesting and design downlink beamforming, uplink receiver, uplink power control and forward backhaul compression strategies, the energy efficiency maximization problem under network-assisted full-duplex cell-free massive MIMO system is proposed with transmit power, limited capacity forward backhaul, energy harvesting and QoS as constraints. Currently, there is no research on energy-efficient resource allocation of QoS and CLI energy harvesting under NAFD scheme. Summary of the Invention

[0005] The present invention provides a network-assisted full-duplex system energy efficiency optimization method and system, which are used to solve the defect that there is no system for energy efficiency resource allocation of the system under network-assisted full-duplex in the prior art.

[0006] In a first aspect, the present invention provides a network-assisted full-duplex system energy efficiency optimization method, comprising:

[0007] Acquire a channel vector set between an uplink user equipment, a downlink user equipment, and an uplink and downlink remote radio frequency head;

[0008] Based on the channel vector set, a joint energy harvesting and transmission optimization model is constructed with the goal of maximizing system energy efficiency and with specified user quality of service, fronthaul constraints, energy harvesting requirements, and transmitter transmit power as constraints;

[0009] Determine the optimal operating mode for uplink user and downlink user equipment using an energy harvesting and information receiving antenna selection algorithm;

[0010] In the optimal working mode, based on a preset iterative algorithm and an iterative convex approximation algorithm, the optimal value of the joint energy collection and transmission optimization model is solved to obtain the target result of maximizing the system energy efficiency.

[0011] In a second aspect, the present invention further provides a network-assisted full-duplex system energy efficiency optimization system, comprising:

[0012] An acquisition module, configured to acquire a channel vector set between an uplink user equipment, a downlink user equipment, and an uplink and downlink remote radio frequency head;

[0013] A construction module is configured to construct a joint energy harvesting and transmission optimization model based on the channel vector set, with the goal of maximizing system energy efficiency and with specified user quality of service, fronthaul constraints, energy harvesting requirements, and transmitter transmit power as constraints;

[0014] A determination module, configured to determine an optimal operating mode for an uplink user and the downlink user equipment using an energy collection and information receiving antenna selection algorithm;

[0015] The processing module is used to solve the optimal value of the joint energy collection and transmission optimization model under the optimal working mode to obtain the target result of maximizing the system energy efficiency.

[0016] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the network-assisted full-duplex system energy efficiency optimization method as described above is implemented.

[0017] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described network-assisted full-duplex system energy efficiency optimization methods.

[0018] In a fifth aspect, the invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described network-assisted full-duplex system energy efficiency optimization methods.

[0019] The network-assisted full-duplex system energy efficiency optimization method and system provided by the present invention provide joint energy collection and transmission optimization for the network-assisted full-duplex system under user service quality requirements, fronthaul optimization, energy collection requirements, and access point and user transmission power constraints, thereby achieving the optimal goal of maximizing system energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 1 is a flow chart of a method for optimizing energy efficiency of a network-assisted full-duplex system provided by the present invention;

[0022] Figure 2 It is a system model diagram provided by the present invention;

[0023] Figure 3 This is a schematic diagram of the NAFD non-cellular energy collection / information receiving antenna selection provided by the present invention;

[0024] Figure 4 It is a schematic diagram of the EE convergence behavior and iteration number performance curve provided by the present invention;

[0025] Figure 5 2 is a schematic diagram comparing the performance curves of EE and the number of antennas M provided by the present invention;

[0026] Figure 6 1 is a schematic diagram comparing the interference Δ performance curves between EE and AP provided by the present invention;

[0027] Figure 7 Schematic diagram of the performance curve of the convergence behavior and number of iterations of the EE provided by the present invention;

[0028] Figure 8 2 is a schematic diagram comparing the EE and fronthaul rate performance curves provided by the present invention;

[0029] Figure 9 2 is a schematic diagram comparing the sum of collected energy and transmitted power under different receiver scenarios provided by the present invention;

[0030] Figure 10 It is a structural diagram of the network-assisted full-duplex system energy efficiency optimization system provided by the present invention;

[0031] Figure 11 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0033] Figure 1 FIG is a flow chart of the network-assisted full-duplex system energy efficiency optimization method provided by the present invention, such as Figure 1 Shown, including:

[0034] Step S1, obtaining a channel vector set between an uplink user equipment, a downlink user equipment, and uplink and downlink remote radio frequency heads;

[0035] First, the present invention models various nodes and scenarios in the model in the system, mainly including uplink user equipment, downlink user equipment and multiple channel vectors between uplink and downlink remote radio frequency heads.

[0036] Step S2: Based on the channel vector set, construct a joint energy collection and transmission optimization model with the goal of maximizing system energy efficiency and with specified user service quality, fronthaul constraints, energy collection requirements, and transmitter transmit power as constraints;

[0037] Based on the obtained multiple channel vectors, a joint energy harvesting and transmission optimization model is constructed with the overall goal of maximizing system energy efficiency, and the specified user service quality, fronthaul constraints, energy harvesting requirements and transmitter transmission power are used as model constraints.

[0038] Step S3, using an energy collection and information receiving antenna selection algorithm to determine the optimal operating mode of the uplink user and the downlink user equipment;

[0039] Furthermore, the present invention is used to select the optimal working mode of uplink user and downlink user equipment through the proposed energy collection and information receiving antenna selection algorithm.

[0040] Step S4: Under the optimal working mode, the optimal value of the joint energy collection and transmission optimization model is solved to obtain the target result of maximizing the system energy efficiency.

[0041] Finally, to solve the joint energy harvesting and transmission optimization model, the present invention adopts a two-level iterative algorithm based on Dinkelbach and uses a series of convex approximation methods to deal with the highly non-convex energy efficiency maximization optimization problem.

[0042] The present invention provides joint energy collection and transmission optimization for a network-assisted full-duplex system under user quality of service requirements, fronthaul optimization, energy collection requirements, and access point and user transmit power constraints, thereby achieving the optimal goal of maximizing system energy efficiency.

[0043] Based on the above embodiment, step S1 of the method includes:

[0044] Acquire a system model of a network-assisted full-duplex system, the system model including a plurality of transmitting access nodes, a plurality of receiving access nodes, a plurality of downlink users, and a plurality of uplink users;

[0045] Determining that a compression strategy is adopted as a forward return strategy for the downlink, and determining a received signal of any transmission access node, a received signal of any downlink user, energy information of any downlink user, and a signal to interference plus noise ratio of any downlink user based on the compression strategy;

[0046] Determining signal information of any receiving access node, energy information of any receiving access node, and total energy information of any receiving access node in an uplink;

[0047] Determining the interference plus noise ratio of a received signal at any receiving access node and any uplink user signal;

[0048] Determining any uplink fronthaul link allocation rate and any downlink fronthaul link allocation rate;

[0049] Get downlink fronthaul power consumption and uplink fronthaul power consumption;

[0050] Obtaining a downlink total power consumption based on the downlink fronthaul power consumption, and obtaining an uplink total power consumption based on the uplink fronthaul power consumption;

[0051] The system circuit power and the total network energy are acquired, and the total power consumed by the system is obtained according to the downlink fronthaul power consumption, the uplink fronthaul power consumption, the system circuit power and the total network energy.

[0052] It should be noted that if Figure 2 In the system model shown, the transmitting remote radio head obtains non-ideal channel state information between it and all downlink user equipment and the receiving remote radio head through channel estimation. The uplink user also obtains non-ideal channel state information between it and all downlink user equipment and the receiving remote radio head through channel estimation. The system of the present invention assumes that the system uses a time division duplex (TDD) format based on network-assisted full-duplex mode, and that the channel exhibits flat fading, meaning that the channel coefficients remain constant within the channel coherence time.

[0053] Based on any of the foregoing embodiments, a system model of a network-assisted full-duplex system is obtained, where the system model includes a plurality of transmitting access nodes, a plurality of receiving access nodes, a plurality of downlink users, and a plurality of uplink users, including:

[0054] Determining that each transmitting access node includes at least one information transmitting antenna, and each receiving access node includes at least one information receiving antenna and one energy harvesting antenna;

[0055] Determining that each uplink user includes an information transmission antenna and an energy collection antenna, and each downlink user includes an information receiving antenna;

[0056] A transmission access node set and a downlink user index set are constructed respectively, and a reception access node set and an uplink user index set are constructed.

[0057] Specifically, assume that the NAFD system contains L T-APSs, Z R-APSs, K downlink users, and J uplink users. Each T-AP and R-AP has M information transmission antennas and M information reception antennas, and each R-AP is equipped with an energy harvesting antenna. Each uplink user has one information transmission antenna and one energy harvesting antenna, and each downlink user has one information reception antenna. Assume and denote the set of T-AP and downlink user indexes respectively, where represents the set of R-AP and uplink user indices, respectively. In practical applications, the total number of antennas equipped in the zth R-AP can be equal to l T-APs. Here, for simplicity, the present invention assumes that each R-AP is equipped with M+1 antennas, and selects one of them as the energy harvesting antenna.

[0058] Based on any of the foregoing embodiments, determining that a downlink forward backhaul strategy adopts a compression strategy, and determining, based on the compression strategy, a received signal at any transmission access node, a received signal at any downlink user, energy information of any downlink user, and an interference plus noise ratio of any downlink user signal, includes:

[0059] quantizing and forwarding the baseband signal of each transmission access node on the fronthaul link using the compression strategy, and obtaining a received signal of any transmission access node based on a beamforming vector of any downlink user data stream, an expected signal of any downlink user, an energy beam vector, and quantization noise of any transmission access node in the downlink channel;

[0060] Obtaining a received signal for any downlink user based on a channel vector from all transmission access nodes to any downlink user, a signal from any uplink user, additive white Gaussian noise including that of any downlink user, a channel coefficient from an information transmission antenna in any uplink user to any downlink user, and an uplink transmission power of any uplink user;

[0061] Obtaining energy information of any downlink user based on energy conversion efficiency, energy collection and information detection power separation factor of any downlink user, and a received signal of any downlink user;

[0062] Determine a covariance interference matrix for any receiver, and obtain a signal to interference plus noise ratio (SINR) of any downlink user based on an energy collection and information detection power separation factor for any downlink user, a channel vector from all transmission access nodes to any downlink user, a beamforming vector for any downlink user data stream, and the covariance interference matrix.

[0063] Specifically, for the downlink in the model, a compression-based fronthaul strategy is adopted. On the fronthaul link, the CPU centrally compresses the baseband signal of each T-AP through quantization and forwarding. Each T-AP sends the compressed signal received from the CPU to the downlink user.

[0064] Signal received at the lth T-AP: in represents the beamforming vector of the data stream of the kth downlink user, s D,k ~CN(0,1) is the expected signal of the kth downlink user, and the energy beam vector The elements are zero-mean complex Gaussian random variables, that is: v D,E ~CN(0,V D,E ). Where V D,E It is v D,E The covariance matrix of represents the quantization noise at the lth T-AP in the downlink channel, μ D,l represents the downlink compressed noise power at the lth T-AP. The received signal of the kth downlink user is modeled as:

[0065]

[0066] in represents the channel vector from all T-APs to downlink user k, s U,j ~CN(0,1) is the signal of uplink user j, is additive white Gaussian noise, h IUI,j,k represents the channel coefficient from the information transmission antenna in the uplink user j to the downlink user k, P U,j is the uplink transmission power of uplink user j. Assume that the transmission signal sent to the downlink user is used for information detection and is separated by power (ratio is ρ D,k ) is also used for energy harvesting.

[0067] The energy obtained at downlink user k is:

[0068]

[0069] where η EH,k∈(0,1] represents the energy conversion efficiency, It is used to model the additional circuit noise caused by phase offset and nonlinearity during baseband conversion; the SINR of downlink user k is:

[0070]

[0071] in:

[0072]

[0073] ρ D,k is the power separation factor for energy harvesting and information detection at the kth downlink user, γ D,k is the covariance interference matrix at receiver k.

[0074] Based on any of the foregoing embodiments, determining signal information of any receiving access node, energy information of any receiving access node, and total energy information of any receiving access node in an uplink includes:

[0075] Obtaining signal information of any receiving access node based on a channel vector from any uplink user to any receiving access node, an uplink transmission power of any uplink user, a signal of any uplink user, a receiving antenna channel matrix from all transmitting access nodes to any receiving access node, a downlink baseband transmit signal, and additive white Gaussian noise including a covariance matrix;

[0076] Obtaining energy information of any receiving access node based on channel state information from any uplink user to an energy harvesting antenna of any receiving access node, uplink transmission power of any uplink user, any uplink user signal, channel state information from all transmitting access nodes to an energy harvesting antenna of any receiving access node, and additive white Gaussian noise including the energy harvesting antenna of any receiving access node;

[0077] Based on the RF energy conversion efficiency of any receiving access node, the uplink transmission power of any uplink user, the channel state information from any uplink user to the energy collection antenna of any receiving access node, the channel state information from all transmission access nodes to the energy collection antenna of any receiving access node, any downlink user data stream beamforming vector, energy beam vector, downlink compressed noise power of any transmission access node and uplink compressed noise power of any receiving access node, the total energy information of any receiving access node is obtained.

[0078] The determining of the interference plus noise ratio of a signal received by any receiving access node and any uplink user signal includes:

[0079] Obtaining an interference covariance matrix between the uplink and downlink access nodes from the channel estimation error elements between the uplink and downlink access nodes, performing interference cancellation on the interference covariance matrix, and obtaining a received signal at the any receiving access node based on all uplink user channel vectors, the uplink transmission power of any uplink user, the any uplink user signal, the receiving antenna channel matrix from all transmitting access nodes to any receiving access node, the downlink baseband transmit signal, and the effective baseband signal;

[0080] Obtaining interference plus noise power of any uplink user and a receive beamforming vector for detecting the any uplink user signal in a central processing unit, and obtaining a signal to interference plus noise ratio of the any uplink user based on the interference plus noise power of the any uplink user, the receive beamforming vector, the channel vectors of all uplink users, and the uplink transmission power of the any uplink user.

[0081] Specifically, for the uplink, get the uplink R-AP z The signal information and energy information received at are:

[0082]

[0083]

[0084] in is the channel vector from uplink user j to AP z, is the channel matrix from all T-APs to the information receiving antenna at R-AP z, that is, the IAI channel between all T-APs and R-AP z, n U,z represents a matrix with zero mean and covariance matrix Additive Gaussian noise. EH,U,j,z represents the channel state information between user j and the uplink energy harvesting antenna of R-APz, is the channel state information between the energy harvesting antennas at all T-APs and R-AP z. represents the additive Gaussian noise received by the energy harvesting antenna at R-AP z.

[0085] The total energy transferred at R-AP z is:

[0086]

[0087] Where η U,zrepresents the RF energy conversion efficiency of R-AP z. Assuming that the AP and CPU are connected via a limited-capacity wired fronthaul link, the uplink signal can be further forwarded to the CPU. Similar to the downlink compression strategy, the R-AP will compress the received signal before forwarding it to the CPU. The effective baseband signal sent by R-AP z is is the uplink compression noise, μ U,z is the uplink compressed noise power at R-APz. The signal received by the CPU is:

[0088]

[0089] in:

[0090]

[0091]

[0092] Although the CPU can fully know the downlink baseband transmission signal x D Theoretically, the inter-AP interference can be eliminated by using the channel state information between R-APs and T-APs. However, due to the existence of channel estimation error, the actual elimination effect may be non-ideal. In practice, assuming that the channel estimation error Each element in follows a Gaussian distribution, that is, in represents the residual interference power due to non-ideal inter-AP interference cancellation in the digital or analog domain, The inter-AP interference covariance matrix between R-APz and T-APl is After proper interference cancellation, the signal received by AP z can be modeled as:

[0093]

[0094] in The SINR of uplink user j is expressed as:

[0095]

[0096] in:

[0097]

[0098] is the interference plus noise power of uplink user j, It is used in the CPU to detect s U,j The receive beamforming vector of .

[0099] Based on any of the foregoing embodiments, determining any uplink fronthaul link allocation rate and any downlink fronthaul link allocation rate includes:

[0100] Obtaining the allocated rate of any uplink fronthaul link based on any downlink user data stream beamforming vector, energy beam vector, number of information receiving antennas, uplink compressed noise power of any receiving access node, residual interference power, additional circuit noise, uplink transmission power of any uplink user, channel vector from any uplink user to any receiving access node, and downlink compressed noise power of any transmitting access node;

[0101] The any downlink fronthaul link allocation rate is obtained based on the any downlink user data stream beamforming vector, the energy beam vector, and the any receiving access node uplink compressed noise power.

[0102] Specifically, the channel vectors of uplink and downlink received signals, energy information, etc. obtained in the above embodiment are used to further obtain the zth uplink fronthaul link allocation rate C U,z and the rate C allocated on the lth downlink fronthaul link D,l :

[0103]

[0104] The uplink user is equipped with an energy harvesting antenna to capture the downlink signal, so the received signal at uplink user j is in is the channel state information between all T-APs and the energy harvesting antenna at uplink user j, is additive white Gaussian noise. The harvested energy obtained at uplink user j is:

[0105]

[0106] Based on any of the foregoing embodiments, obtaining downlink fronthaul power consumption and uplink fronthaul power consumption includes:

[0107] Obtaining the downlink fronthaul power consumption based on a downlink fronthaul front-end transmission capacity of any receiving access node, a downlink fronthaul front-end transmission capacity power loss of any receiving access node, and any downlink fronthaul link allocation rate;

[0108] The uplink fronthaul power consumption is obtained based on the uplink fronthaul front-end transmission capacity of any receiving access node, the uplink fronthaul front-end transmission capacity power loss of any receiving access node, and the any uplink fronthaul link allocation rate.

[0109] The obtaining of the downlink total power consumption based on the downlink fronthaul power consumption and the obtaining of the uplink total power consumption based on the uplink fronthaul power consumption include:

[0110] Obtaining the total downlink power consumption based on any downlink user data stream beamforming vector, energy beam vector, radio frequency power amplifier drain efficiency, the number of information receiving antennas, the number of transmission access nodes, the dynamic power consumption associated with all circuit loop power radiation of any transmission access node in each active radio frequency chain, the static power consumption associated with all circuit loop power radiation of any transmission access node in each active radio frequency chain, and the downlink fronthaul power consumption;

[0111] The total uplink power consumption is obtained based on the uplink transmission power of any uplink user, the drain efficiency of the RF power amplifier, the number of receiving access nodes, the number of information receiving antennas, the dynamic power consumption associated with all circuit loop power radiation of any receiving access node in each active RF chain, the static power consumption associated with all circuit loop power radiation of any receiving access node in each active RF chain, the dynamic power consumption associated with all circuit loop power radiation of any uplink user in each active RF chain, the static power consumption associated with all circuit loop power radiation of any uplink user in each active RF chain and the uplink fronthaul power consumption.

[0112] The obtaining of the system circuit power and the total network energy, wherein the obtaining of the total system power consumption according to the downlink fronthaul power consumption, the uplink fronthaul power consumption, the system circuit power, and the total network energy, includes:

[0113] Obtaining the system circuit power based on the number of information receiving antennas, the number of transmitting access nodes, the number of receiving access nodes, the dynamic power consumption associated with all circuit loop power radiation of any transmitting access node in each active radio frequency chain, the static power consumption associated with all circuit loop power radiation of any transmitting access node in each active radio frequency chain, the dynamic power consumption associated with all circuit loop power radiation of any receiving access node in each active radio frequency chain, the static power consumption associated with all circuit loop power radiation of any receiving access node in each active radio frequency chain, the dynamic power consumption associated with all circuit loop power radiation of any uplink user in each active radio frequency chain, and the static power consumption associated with all circuit loop power radiation of any uplink user in each active radio frequency chain;

[0114] Obtaining the total network energy based on the uplink transmission power of any uplink user, the beamforming vector of any downlink user data stream, the energy beam vector, and the radio frequency power amplifier drain efficiency;

[0115] The total power consumed by the system is obtained by summing the total network energy, the system circuit power, the downlink fronthaul power consumption, and the uplink fronthaul power consumption.

[0116] Specifically, for the power consumption vector in the system, the downlink power consumption includes the power consumption of the T-AP and the downlink fronthaul. The downlink fronthaul power consumption is:

[0117]

[0118] Among them C D,max,l is the downlink front-end transmission capacity of R-AP z, = ∑ i = 1, i = 1, i + 2 ...

[0119]

[0120] Where ξ∈(0,1] is the drain efficiency of the RF power amplifier, P D,l,dy is the dynamic power consumption related to power radiation. In all circuit loops of each active RF chain of T-AP l, P D,l,st It is the static power consumption of T-AP l power supply and cooling system, etc.

[0121] The total power consumption in the uplink channel is:

[0122]

[0123] in:

[0124]

[0125] P U,z,dy , P U,j,dy , P U,z,st and P U,j,st The definitions are similar to P D,l,dy and P D,l,st , where C U,max,z is the uplink front-end transmission capacity of R-APz, Indicates the corresponding power consumption. Define the total circuit power consumption of the system:

[0126]

[0127] The total power consumption of the system is obtained as:

[0128]

[0129] in:

[0130] Based on any of the above In this embodiment, step S2 of the method includes:

[0131] Based on any downlink user data stream beamforming vector, a receive beamforming vector for detecting any uplink user signal in a central processing unit, any uplink user uplink transmission power, any transmitting access node downlink compressed noise power, any receiving access node uplink compressed noise power, and a maximum value of the energy beam vector, combined with any downlink user service quality, any uplink user service quality, and total system power consumption, a joint energy harvesting and transmission optimization model is constructed;

[0132] Determining a first constraint condition as an expression consisting of a beamforming vector of any downlink user data stream, an energy beam vector, a downlink compressed noise power of any transmission access node, and the number of receiving information antennas, satisfying a power consumption budget of any transmission access node and any uplink user;

[0133] Determining a second constraint condition that any downlink user service quality is greater than or equal to a downlink service quality target value, and a third constraint condition that any uplink user service quality is greater than or equal to an uplink service quality target value;

[0134] Determining the fourth constraint condition that any receiving access node energy collection constraint is greater than or equal to any receiving access node energy collection target value, the fifth constraint condition that any downlink user energy collection constraint is greater than or equal to any downlink user energy collection target value, and the sixth constraint condition that any uplink user energy collection constraint is greater than or equal to any uplink user energy collection target value;

[0135] Determining a seventh constraint condition that an uplink transmission power of any uplink user is equal to zero;

[0136] The eighth constraint condition is determined to be that any uplink front-end rate is less than or equal to the downlink fronthaul capacity of the user compressed transmission signal to any transmission access node, and the ninth constraint condition is that any downlink front-end rate is less than or equal to the uplink fronthaul capacity of transmitting the user compressed reception signal from the receiving access node to the central processing unit.

[0137] Specifically, when constructing the joint energy collection and transmission optimization model of the system, the present invention takes the entire transmission system as the criterion to maximize the energy efficiency, and takes the power and QoS constraints as constraints to solve the joint optimization of the transmission system {P U,j ,u U,j,z ,w D,k}problem, the model is established as:

[0138]

[0139] Among them, C1 to C9 correspond to the first to ninth constraints respectively. and PD,l are the power consumption budgets of T-AP l and uplink user j respectively; C2 and C3 are the QoS constraints of downlink user k and uplink user j respectively; C4, C5 and C6 are the energy harvesting constraints of R-AP z, downlink user k and uplink user j respectively; E EH,min,z 、E EH,min,k and E EH,min,j is the corresponding energy collection target; constraints C6 and C7 indicate that each uplink user uses its collected energy to send information to the R-AP. D,min,l and C U,min,z The downlink fronthaul capacity for transmitting the user compressed transmit signal to T-AP1 and the uplink fronthaul capacity for transmitting the user compressed receive signal from R-AP z to the CPU are defined respectively.

[0140] Based on any of the above embodiments, step S3 of the method includes:

[0141] Obtain channel vectors from all transmitting access nodes to any receiving access node, channel vectors from all receiving access nodes to any uplink user, channel vectors from all transmitting access nodes to any uplink user, and channel vectors from all uplink users to any antenna in any access node;

[0142] Traversing all receiving access nodes, determining an energy harvesting antenna corresponding to a minimum value of any antenna channel vector from all uplink users to any access node, and determining a corresponding information transmission receiving antenna based on the energy harvesting antenna;

[0143] Traversing the information transmission and receiving antennas, updating any antenna channel vector from all receiving access nodes to any uplink user to the information transmission and receiving antenna channel vector from all receiving access nodes to any uplink user;

[0144] Traverse all uplink users, and if it is determined that the first channel vector among the channel vectors from all receiving access nodes to any uplink user is greater than the second channel vector, determine that the channel vector from all receiving access nodes to any uplink user is the first channel vector, and the channel vector from all transmitting access nodes to any uplink user is the second channel vector; otherwise, determine that the channel vector from all receiving access nodes to any uplink user is the second channel vector, and the channel vector from all transmitting access nodes to any uplink user is the first channel vector.

[0145] Specifically, if Figure 3As shown in the figure, during the energy harvesting / information receiving antenna selection phase, each R-AP and uplink user is equipped with M+1 and 2 antennas, respectively. Before optimization, it is necessary to determine which antenna should be selected as the energy harvesting antenna. For R-APz, the energy harvesting antenna primarily captures the interference (IAI) energy between the uplink receiving AP and the downlink transmitting AP from all T-APs. Compared to the IAI interference energy, the uplink signal power can be omitted. Similarly, for uplink userj, the energy harvesting antenna captures the downlink signal power from the T-AP.

[0146] Therefore, if the channel gain between all T-APs and the mth antenna in R-APz is significantly stronger, and the uplink users and R-AP z If the channel gain between the mth antennas in the network is poor, the mth antenna should operate in the energy harvesting mode; if the mth antenna operates in the information receiving mode, its uplink throughput contribution to all uplink users is negligible because IAI elimination requires more power consumption.

[0147] At the same time, in this case, in R-AP z The energy collected at the mth antenna is also less than the energy collected when the mth antenna operates in energy collection mode. Conversely, the mth antenna should operate in information reception mode. Furthermore, for uplink user j, due to limited transmit power, a primary goal of the present invention is to ensure that uplink information is transmitted. If the channel gain between the R-AP and one antenna of uplink user j is significantly greater than the channel gain between the R-AP and another antenna of uplink user j, the nth antenna should operate in information transmission mode.

[0148] If the nth antenna operates in energy harvesting mode while the other antenna operates in information transmission mode, its useful signal contribution to uplink user j is negligible, resulting in a smaller throughput contribution. Uplink user j must consume more power to ensure uplink information transmission, which will result in more IUI. Otherwise, if the nth antenna operates in information transmission mode, uplink user j will consume less transmit power, and the energy collected by the energy harvesting antenna can easily meet the transmit power requirement.

[0149] The specific energy collection / information receiving antenna selection process is as follows:

[0150] Input: H IAI,z ,H U,j ,H EH,j ,h U,z,m

[0151] Output: H IAI,z ,h EH,IAI,z ,h U,j,h EH,j

[0152] Repeat: z=1:Z Execute:

[0153]

[0154] Repeat m=1:M execution:

[0155] renew

[0156] End repetition

[0157] renew

[0158] End repetition

[0159] renew

[0160] Repeat: j=1:J Execute:

[0161] if implement:

[0162]

[0163] otherwise:

[0164]

[0165] End repetition

[0166] in From all T-APs to R-APs z The channel vector of From all T-APs to R-APs z The channel vector of antenna m, and is from all uplink users to the AP z The channel vector for antenna m in , represents the channel vector from all R-APs to uplink user j, represents the channel vector from all T-APs to uplink user j,

[0167] Based on any of the above embodiments, step S4 of the method includes:

[0168] Based on a preset iterative algorithm and an iterative convex approximation algorithm, the joint energy collection and transmission optimization model is solved to obtain the target result of maximizing the energy efficiency of the system.

[0169] The method of solving the joint energy collection and transmission optimization model based on a preset iterative algorithm and an iterative convex approximation algorithm to obtain the target result of maximizing the system energy efficiency includes:

[0170] Converting the non-convex functions in the joint energy harvesting and transmission optimization model and the constraints into convex functions using a path tracking algorithm and a Dinkelbach algorithm;

[0171] The convex function is solved based on the continuous convex approximation (SCA) algorithm to obtain the target result of maximizing the energy efficiency of the system.

[0172] Specifically, in order to find the optimal solution of the model, the present invention adopts a method based on Successive Convex Approximation (SCA) to solve the EE maximization problem, and proposes a design scheme for a NAFD-based SWIPT (Simultaneous Wireless Information and Power Transfer) transceiver. First, the non-convex objective function is converted into a convex function through the path tracking algorithm and the Dinkelbach method. Then, the non-convex feasible region is processed using the SCA method to solve the sum SE, that is, Using the following inequality:

[0173]

[0174] Where a>0, b>0,

[0175] The SINR of downlink user k can be equivalently replaced by:

[0176]

[0177] Contains linear constraints

[0178]

[0179] in:

[0180]

[0181] Assume that the feasible point is R D,k The lower bound of

[0182]

[0183] However, since the transceiver beamforming, uplink transmit power, quantization power, and receive power split ratio are tightly coupled together, finding R D,kThe lower bound of R is challenging. This paper first uses the SCA method to approximate R D,k By introducing a series of variables {α D}, {t DU,j},{χ U,j,z},{ε U,j,j′} and {β U,j}, we get the following inequality:

[0184]

[0185] It is clear that all equations except C11 are non-convex. According to the inequality and It turns out that:

[0186]

[0187] in:

[0188]

[0189] The original problem can be approximated as:

[0190]

[0191] The linear constraints are transformed into:

[0192] C20:β U,j ≥0

[0193] in:

[0194]

[0195] R U,j It can be defined as:

[0196]

[0197] This paper uses the lower bound of the objective function to approximate SE. Since there is a non-convex expression P in the objective function Total , the objective function is still non-convex. Except for the total power consumption of the uplink / downlink fronthaul link, all expressions are convex, that is, Since ln(det(D)) is a convex function when D ≥ 0, its upper bound can be obtained by applying the first-order Taylor expansion

[0198] ln(det(D))≤ln(det(D (n) ))+Tr((D (n) ) -1 (DD (n) )).

[0199] Therefore, by applying the above formula to P Total Can be approximated as

[0200]

[0201] C21 also defines and They are:

[0202]

[0203] Among them, X (n) and Y (n) They are:

[0204]

[0205] The objective function is approximately:

[0206]

[0207] in:

[0208]

[0209] The target C1 has been converted to a concave superlinear function. Constraints C2-C6, C8 and C9 are still highly non-convex constraints. After the conversion, the present invention uses and ln(det(D))≤ln(det(D (n) ))+Tr((D (n) ) -1 (DD (n) )). Approximate internal constraints C2-C6, C8 and C9:

[0210]

[0211] in:

[0212]

[0213] Through the above steps, solving the convex set at the n+1th iteration, we get the following approximate problem:

[0214]

[0215] stC1,C7,C10,C11,C16,C17,C18,

[0216] C19,C21,C22,C23,C24,C25,C26

[0217] in

[0218] This problem belongs to the category of concave-convex fractional programming and can be solved using the Dinkelbach algorithm, which can be used to solve the global maximization fractional function of polynomial complexity. The optimal solution to the problem can be obtained when is the only zero of the auxiliary function Ξ(λ), where:

[0219] Ξ(λ)=R Total (w D,k ,v D,E ,P U,j ,u U,j )-λG(w D,k ,v D,E ,P U,j ,u U,j )

[0220] Solve the following auxiliary problem to find the optimal

[0221]

[0222] stC1,C7,C10,C11,C16,C17,C18,

[0223] C19,C22,C23,C24,C25,C26

[0224] The problem is a two-level iterative problem. The inner iterative problem should make Ξ(λ) converge to a certain value of given λ. Then, the outer iterative problem aims to find a typical value To establish the equation

[0225] The present invention solves the two-level iteration problem by proposing two algorithms, an external solution algorithm based on Dinkelbach iteration and an internal solution algorithm using an SCA-based iteration algorithm for solving the external and internal iteration problems respectively.

[0226] Furthermore, several performance comparison experiments are used to illustrate the advantages of the solution of the present invention:

[0227] Figure 4 The relationship between the convergence behavior of EE and the number of iterations is shown, where the number of antennas per access point M is 2 or 4, the forward transmission constraint C = 10 bps / Hz, the number of T-APs / R-APs Z = L is 3, 5 or 12, and the IAI interference is -10 dB or -20 dB. Figure 4 It can be seen that it takes about 6-10 iterations to reach convergence.

[0228] Figure 5 Indicates that at a fixed L=Z=3, Δ=-10dB, C D,max,l =C U,max,z=10bps / Hz, the relationship between EE and the number of antennas per T-AP / R-AP M. Figure 5 It is clearly found that the EE performance of the proposed NAFD scheme is better than that of CCFD and TDD in C-RAN. The EE of the three duplex mode schemes first reaches a peak at a certain M value and then shows a downward trend. In addition, the best EE performance can be achieved when M = 4. This is because increasing the number of antennas per T-AP / R-AP is beneficial to improving SE, thereby increasing the EE gain. However, when M becomes large, using more antennas beyond the optimal point (i.e., M = 4) does not improve the EE performance. Using more antennas can improve SE, but the total power consumption is much greater. Therefore, when M = 14, the EE gain is 30.72%-32.10% lower than when M = 2.

[0229] Figure 6 Shown with M=2, the number of T-AP / R-AP Z=L ranging from 3 to 13, Δ=-10dB, C D,max,l =C U,max,z =10bps / Hz, the EE performance of the three schemes. Figure 5 Similarly, the EE of the system increases first and reaches the best EE performance when L=Z=5, and then the EE decreases after L=Z=5.

[0230] Figure 7 The energy efficiency performance of NAFD, C-RAN CCFD, and TDD under different IAI conditions, Δ, was compared. As expected, under Δ≤25dB and Δ≤20dB, NAFD and C-RAN CCFD achieved higher energy efficiency performance compared to TDD. However, under Δ≥25dB and Δ≥20dB, the performance of the two aforementioned designs was slightly worse than that of the latter. This is because the strong IAI interference between the NAFD and C-RAN CCFD systems impairs energy efficiency. Under all IAI strengths, the proposed NAFD system outperforms the traditional C-RAN CCFD system in terms of energy efficiency.

[0231] Figure 8 The energy efficiency performance of the three schemes is compared as the fronthaul capacity increases when M = 2, L = Z = 8, and Δ = -10dB. It can be seen that as the fronthaul capacity limit increases, the energy efficiency of each scheme also increases. This is because a larger fronthaul power can be used to increase the spectral efficiency gain. When the fronthaul capacity limit is higher than 16bps / Hz, the growth trend will slow down. This is because when C D,max,l =C U,max,z When the bit rate is ≥16 bps / Hz, the spectrum efficiency performance of the three schemes is limited by the different interferences between receivers, which further affects the energy efficiency performance.

[0232] Figure 9 Displayed at M=2,L=Z=8,Δ=-10dB,C D,max,l =C U,max,z =10bps / Hz setting, the energy harvesting performance of different receivers as the transmission power increases. As expected, the sum of the energy harvested at different receivers increases with increasing transmission power constraints. Specifically, the R-AP harvests the most energy, followed by downlink users, and then uplink users. This is because the signal gain harvested by R-APs is higher than that of uplink and downlink users. This signal gain includes the IAI between R-APs and T-APs, as well as the uplink signal transmitted by uplink users. Similarly, the data signal and interference power harvested by downlink users are higher than the interference power harvested by uplink users.

[0233] The network-assisted full-duplex system energy efficiency optimization system provided by the present invention is described below. The network-assisted full-duplex system energy efficiency optimization system described below and the network-assisted full-duplex system energy efficiency optimization method described above can refer to each other.

[0234] Figure 10 Schematic diagram of the network-assisted full-duplex system energy efficiency optimization system provided by the present invention. Figure 10 As shown, it includes: an acquisition module 1001, a construction module 1002, a determination module 1003 and a processing module 1004, wherein:

[0235] The acquisition module 1001 is used to obtain a set of channel vectors between an uplink user device, a downlink user device, and uplink and downlink remote radio frequency heads; the construction module 1002 is used to construct, based on the channel vector set, a joint energy collection and transmission optimization model with the goal of maximizing system energy efficiency and with specified user service quality, fronthaul constraints, energy collection requirements, and transmitter transmission power as constraints; the determination module 1003 is used to determine the optimal working mode of the uplink user and the downlink user device by adopting an energy collection and information receiving antenna selection algorithm; the processing module 1004 is used to solve the optimal value of the joint energy collection and transmission optimization model under the optimal working mode to obtain the target result of maximizing system energy efficiency.

[0236] The present invention provides joint energy collection and transmission optimization for a network-assisted full-duplex system under user quality of service requirements, fronthaul optimization, energy collection requirements, and access point and user transmit power constraints, thereby achieving the optimal goal of maximizing system energy efficiency.

[0237] Figure 11 An example of a physical structure diagram of an electronic device is shown below. Figure 11As shown, the electronic device may include: a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other via the communication bus 1140. The processor 1110 may call the logic instructions in the memory 1130 to perform network-assisted full-duplex system energy efficiency optimization, the method comprising: obtaining a set of channel vectors between an uplink user equipment, a downlink user equipment, and uplink and downlink remote radio frequency heads; based on the set of channel vectors, constructing a joint energy collection and transmission optimization model with the goal of maximizing system energy efficiency and with specified user service quality, fronthaul constraints, energy collection requirements, and transmitter transmit power as constraints; using an energy collection and information receiving antenna selection algorithm to determine the optimal operating mode of the uplink user and the downlink user equipment; and under the optimal operating mode, solving the joint energy collection and transmission optimization model for the optimal value to obtain the target result of maximizing system energy efficiency.

[0238] In addition, the logic instructions in the above-mentioned memory 1130 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the 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, a server, or a 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: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0239] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the network-assisted full-duplex system energy efficiency optimization provided by the above-mentioned methods, and the method includes: obtaining a channel vector set between an uplink user device, a downlink user device, and an uplink and downlink remote radio frequency head; based on the channel vector set, constructing a joint energy collection and transmission optimization model with the goal of maximizing system energy efficiency and with specified user service quality, fronthaul constraints, energy collection requirements, and transmitter transmission power as constraints; using an energy collection and information receiving antenna selection algorithm to determine the optimal working mode of the uplink user and the downlink user device; under the optimal working mode, solving the optimal value of the joint energy collection and transmission optimization model to obtain the target result of maximizing system energy efficiency.

[0240] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the network-assisted full-duplex system energy efficiency optimization provided by the above-mentioned methods, the method comprising: obtaining a set of channel vectors between an uplink user device, a downlink user device, and uplink and downlink remote radio frequency heads; based on the channel vector set, constructing a joint energy collection and transmission optimization model with the goal of maximizing system energy efficiency and with specified user service quality, fronthaul constraints, energy collection requirements, and transmitter transmission power as constraints; using an energy collection and information receiving antenna selection algorithm to determine the optimal working mode of the uplink user and the downlink user device; under the optimal working mode, solving the optimal value of the joint energy collection and transmission optimization model to obtain the target result of maximizing system energy efficiency.

[0241] The device embodiments described above are merely illustrative. 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, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0242] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0243] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A network-assisted full-duplex system energy efficiency optimization method, characterized in that: include: Acquire a channel vector set between an uplink user equipment, a downlink user equipment, and an uplink and downlink remote radio frequency head; Based on the channel vector set, a joint energy harvesting and transmission optimization model is constructed with the goal of maximizing system energy efficiency and with specified user quality of service, fronthaul constraints, energy harvesting requirements, and transmitter transmit power as constraints; Determine the optimal operating mode for uplink user and downlink user equipment using an energy harvesting and information receiving antenna selection algorithm; Under the optimal working mode, the optimal value of the joint energy collection and transmission optimization model is solved to obtain the target result of maximizing the system energy efficiency.

2. The network-assisted full-duplex system energy efficiency optimization method according to claim 1, characterized in that: Obtaining a channel vector set between an uplink user equipment, a downlink user equipment, and an uplink and downlink remote radio frequency head, including: Acquire a system model of a network-assisted full-duplex system, the system model including a plurality of transmitting access nodes, a plurality of receiving access nodes, a plurality of downlink users, and a plurality of uplink users; Determining that a compression strategy is adopted as a forward return strategy for the downlink, and determining a received signal of any transmission access node, a received signal of any downlink user, energy information of any downlink user, and a signal to interference plus noise ratio of any downlink user based on the compression strategy; Determining signal information of any receiving access node, energy information of any receiving access node, and total energy information of any receiving access node in an uplink; Determining the interference plus noise ratio of a received signal at any receiving access node and any uplink user signal; Determining any uplink fronthaul link allocation rate and any downlink fronthaul link allocation rate; Get downlink fronthaul power consumption and uplink fronthaul power consumption; Obtaining a downlink total power consumption based on the downlink fronthaul power consumption, and obtaining an uplink total power consumption based on the uplink fronthaul power consumption; The system circuit power and the total network energy are acquired, and the total power consumed by the system is obtained according to the downlink fronthaul power consumption, the uplink fronthaul power consumption, the system circuit power and the total network energy.

3. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: Obtaining a system model of a network-assisted full-duplex system, the system model including a plurality of transmitting access nodes, a plurality of receiving access nodes, a plurality of downlink users, and a plurality of uplink users, including: Determining that each transmitting access node includes at least one information transmitting antenna, and each receiving access node includes at least one information receiving antenna and one energy harvesting antenna; Determining that each uplink user includes an information transmission antenna and an energy collection antenna, and each downlink user includes an information receiving antenna; A transmission access node set and a downlink user index set are constructed respectively, and a reception access node set and an uplink user index set are constructed.

4. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: Determining that a compression strategy is adopted for the forward backhaul strategy of the downlink, and determining a received signal of any transmission access node, a received signal of any downlink user, energy information of any downlink user, and an interference plus noise ratio of any downlink user based on the compression strategy, including: quantizing and forwarding the baseband signal of each transmission access node on the fronthaul link using the compression strategy, and obtaining a received signal of any transmission access node based on a beamforming vector of any downlink user data stream, an expected signal of any downlink user, an energy beam vector, and quantization noise of any transmission access node in the downlink channel; Obtaining a received signal for any downlink user based on a channel vector from all transmission access nodes to any downlink user, a signal from any uplink user, additive white Gaussian noise including that of any downlink user, a channel coefficient from an information transmission antenna in any uplink user to any downlink user, and an uplink transmission power of any uplink user; Obtaining energy information of any downlink user based on energy conversion efficiency, energy collection and information detection power separation factor of any downlink user, and a received signal of any downlink user; Determine a covariance interference matrix for any receiver, and obtain a signal to interference plus noise ratio (SINR) of any downlink user based on an energy collection and information detection power separation factor for any downlink user, a channel vector from all transmission access nodes to any downlink user, a beamforming vector for any downlink user data stream, and the covariance interference matrix.

5. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: Determining signal information of any receiving access node, energy information of any receiving access node, and total energy information of any receiving access node in an uplink includes: Obtaining signal information of any receiving access node based on a channel vector from any uplink user to any receiving access node, an uplink transmission power of any uplink user, a signal of any uplink user, a receiving antenna channel matrix from all transmitting access nodes to any receiving access node, a downlink baseband transmit signal, and additive white Gaussian noise including a covariance matrix; Obtaining energy information of any receiving access node based on channel state information from any uplink user to an energy harvesting antenna of any receiving access node, uplink transmission power of any uplink user, any uplink user signal, channel state information from all transmitting access nodes to an energy harvesting antenna of any receiving access node, and additive white Gaussian noise including the energy harvesting antenna of any receiving access node; Based on the RF energy conversion efficiency of any receiving access node, the uplink transmission power of any uplink user, the channel state information from any uplink user to the energy collection antenna of any receiving access node, the channel state information from all transmission access nodes to the energy collection antenna of any receiving access node, any downlink user data stream beamforming vector, energy beam vector, downlink compressed noise power of any transmission access node and uplink compressed noise power of any receiving access node, the total energy information of any receiving access node is obtained.

6. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: The determining of the signal received by any receiving access node and any uplink user signal and the interference plus noise ratio includes: Obtaining an interference covariance matrix between the uplink and downlink access nodes from the channel estimation error elements between the uplink and downlink access nodes, performing interference cancellation on the interference covariance matrix, and obtaining a received signal at any receiving access node based on all uplink user channel vectors, any uplink user uplink transmission power, any uplink user signal, all receiving antenna channel matrices from all transmitting access nodes to any receiving access node, a downlink baseband transmit signal, and an effective baseband signal; Obtaining interference plus noise power of any uplink user and a receive beamforming vector for detecting the any uplink user signal in a central processing unit, and obtaining a signal to interference plus noise ratio of the any uplink user based on the interference plus noise power of the any uplink user, the receive beamforming vector, the channel vectors of all uplink users, and the uplink transmission power of the any uplink user.

7. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: Determining any uplink fronthaul link allocation rate and any downlink fronthaul link allocation rate, including: Obtaining the allocated rate of any uplink fronthaul link based on any downlink user data stream beamforming vector, energy beam vector, number of information receiving antennas, uplink compressed noise power of any receiving access node, residual interference power, additional circuit noise, uplink transmission power of any uplink user, channel vector from any uplink user to any receiving access node, and downlink compressed noise power of any transmitting access node; The any downlink fronthaul link allocation rate is obtained based on the any downlink user data stream beamforming vector, the energy beam vector, and the any receiving access node uplink compressed noise power.

8. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: Obtain downlink and uplink fronthaul power consumption, including: Obtaining the downlink fronthaul power consumption based on a downlink fronthaul front-end transmission capacity of any receiving access node, a downlink fronthaul front-end transmission capacity power loss of any receiving access node, and any downlink fronthaul link allocation rate; The uplink fronthaul power consumption is obtained based on the uplink fronthaul front-end transmission capacity of any receiving access node, the uplink fronthaul front-end transmission capacity power loss of any receiving access node, and the any uplink fronthaul link allocation rate.

9. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: The obtaining of the downlink total power consumption based on the downlink fronthaul power consumption, and the obtaining of the uplink total power consumption based on the uplink fronthaul power consumption, includes: Obtaining the total downlink power consumption based on any downlink user data stream beamforming vector, energy beam vector, radio frequency power amplifier drain efficiency, the number of information receiving antennas, the number of transmission access nodes, the dynamic power consumption associated with all circuit loop power radiation of any transmission access node in each active radio frequency chain, the static power consumption associated with all circuit loop power radiation of any transmission access node in each active radio frequency chain, and the downlink fronthaul power consumption; The total uplink power consumption is obtained based on the uplink transmission power of any uplink user, the drain efficiency of the RF power amplifier, the number of receiving access nodes, the number of information receiving antennas, the dynamic power consumption associated with all circuit loop power radiation of any receiving access node in each active RF chain, the static power consumption associated with all circuit loop power radiation of any receiving access node in each active RF chain, the dynamic power consumption associated with all circuit loop power radiation of any uplink user in each active RF chain, the static power consumption associated with all circuit loop power radiation of any uplink user in each active RF chain and the uplink fronthaul power consumption.

10. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, characterized in that: Obtaining system circuit power and total network energy, wherein obtaining the total system power consumption according to the downlink fronthaul power consumption, the uplink fronthaul power consumption, the system circuit power, and the total network energy includes: Obtaining the system circuit power based on the number of information receiving antennas, the number of transmitting access nodes, the number of receiving access nodes, the dynamic power consumption associated with all circuit loop power radiation of any transmitting access node in each active radio frequency chain, the static power consumption associated with all circuit loop power radiation of any transmitting access node in each active radio frequency chain, the dynamic power consumption associated with all circuit loop power radiation of any receiving access node in each active radio frequency chain, the static power consumption associated with all circuit loop power radiation of any receiving access node in each active radio frequency chain, the dynamic power consumption associated with all circuit loop power radiation of any uplink user in each active radio frequency chain, and the static power consumption associated with all circuit loop power radiation of any uplink user in each active radio frequency chain; Obtaining the total network energy based on any uplink user uplink transmission power, any downlink user data stream beamforming vector, an energy beam vector, and a radio frequency power amplifier drain efficiency; The total power consumed by the system is obtained by summing the total network energy, the system circuit power, the downlink fronthaul power consumption, and the uplink fronthaul power consumption.

11. The network-assisted full-duplex system energy efficiency optimization method according to claim 1, characterized in that: Based on the channel vector set, a joint energy harvesting and transmission optimization model is constructed with the goal of maximizing system energy efficiency and subject to constraints such as user quality of service, fronthaul constraints, energy harvesting requirements, and transmitter transmit power. The model includes: Based on the beamforming vector of any downlink user data stream, the receive beamforming vector for detecting any uplink user signal in the central processing unit, the uplink transmission power of any uplink user, the downlink compressed noise power of any transmitting access node, the uplink compressed noise power of any receiving access node, and the maximum value of the energy beam vector, combined with the service quality of any downlink user, the service quality of any uplink user, and the total power consumption of the system, a joint energy harvesting and transmission optimization model is constructed; Determining a first constraint condition as an expression consisting of a beamforming vector of any downlink user data stream, an energy beam vector, a downlink compressed noise power of any transmission access node, and the number of receiving information antennas, satisfying a power consumption budget of any transmission access node and any uplink user; Determining a second constraint condition that any downlink user service quality is greater than or equal to a downlink service quality target value, and a third constraint condition that any uplink user service quality is greater than or equal to an uplink service quality target value; Determining the fourth constraint condition that any receiving access node energy collection constraint is greater than or equal to any receiving access node energy collection target value, the fifth constraint condition that any downlink user energy collection constraint is greater than or equal to any downlink user energy collection target value, and the sixth constraint condition that any uplink user energy collection constraint is greater than or equal to any uplink user energy collection target value; Determining a seventh constraint condition that an uplink transmission power of any uplink user is greater than or equal to zero; The eighth constraint condition is determined to be that any uplink front-end rate is less than or equal to the downlink fronthaul capacity of the user compressed transmission signal to any transmission access node, and the ninth constraint condition is that any downlink front-end rate is less than or equal to the uplink fronthaul capacity of transmitting the user compressed reception signal from the receiving access node to the central processing unit.

12. The network-assisted full-duplex system energy efficiency optimization method according to claim 1, characterized in that: An energy harvesting and information receiving antenna selection algorithm is used to determine the optimal operating mode of the uplink user and the downlink user equipment, including: Obtain channel vectors from all transmitting access nodes to any receiving access node, channel vectors from all receiving access nodes to any uplink user, channel vectors from all transmitting access nodes to any uplink user, and channel vectors from all uplink users to any antenna in any access node; Traversing all receiving access nodes, determining an energy harvesting antenna corresponding to a minimum value of any antenna channel vector from all uplink users to any access node, and determining a corresponding information transmission receiving antenna based on the energy harvesting antenna; Traversing the information transmission and receiving antennas, updating any antenna channel vector from all receiving access nodes to any uplink user to the information transmission and receiving antenna channel vector from all receiving access nodes to any uplink user; Traverse all uplink users, and if it is determined that the first channel vector among the channel vectors from all receiving access nodes to any uplink user is greater than the second channel vector, determine that the channel vector from all receiving access nodes to any uplink user is the first channel vector, and the channel vector from all transmitting access nodes to any uplink user is the second channel vector; otherwise, determine that the channel vector from all receiving access nodes to any uplink user is the second channel vector, and the channel vector from all transmitting access nodes to any uplink user is the first channel vector.

13. The network-assisted full-duplex system energy efficiency optimization method according to claim 1, characterized in that: Under the optimal working mode, the optimal value of the joint energy collection and transmission optimization model is solved to obtain the target result of maximizing the system energy efficiency, including: Based on a preset iterative algorithm and an iterative convex approximation algorithm, the joint energy collection and transmission optimization model is solved to obtain the target result of maximizing the energy efficiency of the system.

14. The network-assisted full-duplex system energy efficiency optimization method according to claim 13, characterized in that: Solving the joint energy collection and transmission optimization model based on a preset iterative algorithm and an iterative convex approximation algorithm to obtain the target result of maximizing the system energy efficiency includes: Converting the non-convex functions in the joint energy harvesting and transmission optimization model and the constraints into convex functions using a path tracking algorithm and a Dinkelbach algorithm; The convex function is solved based on the continuous convex approximation (SCA) algorithm to obtain the target result of maximizing the energy efficiency of the system.

15. A network-assisted full-duplex system energy efficiency optimization system, characterized in that: include: An acquisition module, configured to acquire a channel vector set between an uplink user equipment, a downlink user equipment, and an uplink and downlink remote radio frequency head; A construction module is configured to construct a joint energy harvesting and transmission optimization model based on the channel vector set, with the goal of maximizing system energy efficiency and with specified user quality of service, fronthaul constraints, energy harvesting requirements, and transmitter transmit power as constraints; A determination module, configured to determine an optimal operating mode for an uplink user and the downlink user equipment using an energy collection and information receiving antenna selection algorithm; The processing module is used to solve the optimal value of the joint energy collection and transmission optimization model under the optimal working mode to obtain the target result of maximizing the system energy efficiency.

16. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the network-assisted full-duplex system energy efficiency optimization method according to any one of claims 1 to 14 is implemented.

17. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the network-assisted full-duplex system energy efficiency optimization method according to any one of claims 1 to 14 is implemented.

18. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the network-assisted full-duplex system energy efficiency optimization method according to any one of claims 1 to 14 is implemented.

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