WO2023185077A1 - Energy efficiency optimization method and system for network-assisted full-duplex system - Google Patents

Abstract

Provided in the present application are an energy efficiency optimization method and system for a network-assisted full-duplex system. The method comprises: acquiring a set of channel vectors among an uplink user equipment, a downlink user equipment and uplink and downlink remote radio-frequency heads (S1); on the basis of the set of channel vectors, constructing a joint energy collection and transmission optimization model, which aims at maximizing the energy efficiency of a system, and takes the user quality of service, a fronthaul constraint, an energy collection requirement and transmit power of a transmitter as constraint conditions (S2); determining an optimal working mode for the uplink user equipment and the downlink user equipment by using an energy collection and information receiving antenna selection algorithm (S3); and in the optimal working mode, solving the optimal value for the joint energy collection and transmission optimization model, so as to obtain a target result of maximizing the energy efficiency of the system (S4). In the present application, joint energy collection and transmission optimization under certain condition constraints are provided for a network-assisted full-duplex system, and an optimal target result of maximizing the energy efficiency of the system is realized.

Description

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

Cross-references to related applications

This application claims the priority of the Chinese patent application with application number 202210322087.9 submitted on March 29, 2022, and the invention title is "A network-assisted full-duplex system energy efficiency optimization method and system", which is fully incorporated by reference This article.

Technical field

The present application relates to the field of wireless communication technology, and in particular to a network-assisted full-duplex system energy efficiency optimization method and system.

Background technique

With the popularization of the fifth generation mobile communication technology (5th Generation Mobile Communication Technology, 5G), people have higher and higher requirements for the quality of service (QoS) of the communication system. 5G has overcome the cellular system to a certain extent. Data rate and latency in uplink and downlink.

Because 5G New Radio (5G-NR) supports flexible duplex technologies, including dynamic time division duplexing (TDD) and flexible frequency division duplexing (Frequency Division) in paired and unpaired spectrums Duplexing (FDD), in recent years, spatial domain flexible duplex technology has been studied in many fields. Co-frequency Co-time Full Duplex (CCFD) realizes downlink on the same time-frequency resource. and uplink transmission, which is expected to double the spectral efficiency of wireless links compared to half-duplex. However, the ultra-dense deployment of Access Points (APs) will lead to severe Cross Link Interference (CLI), that is, interference from downlink APs to uplink APs and interference from uplink users to downlink users. Interference is also the most difficult problem faced by CCFD, flexible duplex or Broadband Distribution Network (BDN) networks.

Network-Assisted Full Duplex (NAFD) can be regarded as a unified implementation of flexible duplex, CCFD and hybrid duplex under a cellular network architecture. Therefore, the NAFD solution is considered to be a technology that truly realizes flexible duplexing. In the cellular-less massive multiple-in multiple-out (MIMO) technology based on NAFD, all APs are connected to the central processing unit (CPU) through high-speed fronthaul links. Therefore, how to eliminate CLI and how to cope with the high demand on the forward backhaul link are two major problems of the system. Most of the existing works focus more on suppressing or reducing CLI by designing suitable flexible duplex transceivers. Although cellular-less 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 to transmit power, limited capacity forward backhaul, energy harvesting and QoS As a constraint, the energy efficiency maximization problem under network-assisted full-duplex cellular-free massive MIMO system is proposed. There has been no research on energy efficiency resource allocation of QoS and CLI energy collection under NAFD scheme.

Contents of the invention

This application provides a method and system for optimizing energy efficiency of a network-assisted full-duplex system to solve the problem in the existing technology that there is no system for allocating energy-efficiency resources of the system under network-assisted full-duplex.

In the first aspect, this application provides a network-assisted full-duplex system energy efficiency optimization method, including:

Obtain the set of channel vectors between uplink user equipment, downlink user equipment, and uplink and downlink remote radio frequency heads;

Based on the set of channel vectors, a joint energy collection and transmission optimization model is constructed 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;

Use energy collection and information receiving antenna selection algorithms to determine the optimal working mode of uplink users and the downlink user equipment;

In the optimal working mode, based on the preset iterative algorithm and the 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.

In the second aspect, this application also provides a network-assisted full-duplex system energy efficiency optimization system, including:

The acquisition module is used to acquire the channel vector set between the uplink user equipment, the downlink user equipment, and the uplink and downlink remote radio frequency heads;

A building module for constructing a joint energy collection and transmission optimization based on the channel vector set 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. Model;

A determination module used to determine the optimal working mode of the uplink user and the downlink user equipment by using energy collection and information receiving antenna selection algorithms;

A processing module, configured to solve the optimal value of the joint energy collection and transmission optimization model in the optimal working mode to obtain the target result of maximizing system energy efficiency.

In a third aspect, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, any one of the above is implemented. This paper describes the energy efficiency optimization method of network-assisted full-duplex system.

In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the network-assisted full-duplex system energy efficiency optimization as described in any of the above is implemented. method.

In a fifth aspect, the invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the computer program implements any of the above network-assisted full-duplex system energy efficiency optimization methods.

The network-assisted full-duplex system energy efficiency optimization method and system provided by this application provide joint energy collection under user service quality requirements, fronthaul optimization, energy collection requirements, and access point and user transmission power constraints for network-assisted full-duplex systems. and transmission optimization to achieve the optimal goal of maximizing system energy efficiency.

Description of drawings

In order to explain the technical solutions in this application or the prior art more clearly, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are of the present invention. For some embodiments of the application, those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is a schematic flow chart of the network-assisted full-duplex system energy efficiency optimization method provided by this application;

Figure 2 is a system model diagram provided by this application;

Figure 3 is a schematic diagram of NAFD non-cellular energy collection/information receiving antenna selection provided by this application;

Figure 4 is a schematic diagram of the EE convergence behavior and iteration number performance curve provided by this application;

Figure 5 is a schematic diagram comparing the performance curves of EE and the number of antennas M provided by this application;

Figure 6 is a schematic diagram comparing the interference Δ performance curves between EE and AP provided by this application;

Figure 7 is a schematic diagram of the convergence behavior and iteration number performance curve of EE provided by this application;

Figure 8 is a schematic diagram comparing the EE and fronthaul rate performance curves provided by this application;

Figure 9 is a schematic diagram comparing the sum of collected energy and transmit power in different receiver scenarios provided by this application;

Figure 10 is a schematic structural diagram of the network-assisted full-duplex system energy efficiency optimization system provided by this application;

Figure 11 is a schematic structural diagram of an electronic device provided by this application.

Detailed ways

In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application. , not all examples. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

Figure 1 is a schematic flow chart of the network-assisted full-duplex system energy efficiency optimization method provided by this application. As shown in Figure 1, it includes:

Step S1: Obtain the channel vector set between the uplink user equipment, the downlink user equipment, and the uplink and downlink remote radio frequency heads;

First, this application models each node and scenario in the model in the system, which mainly includes multiple channel vectors between uplink user equipment, downlink user equipment, and uplink and downlink remote radio frequency heads.

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 specifying user service quality, fronthaul constraints, energy collection requirements, and transmitter transmit power as constraints;

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

Step S3: Use energy collection and information receiving antenna selection algorithms to determine the optimal working mode of the uplink user and the downlink user equipment;

Furthermore, this application uses the proposed energy collection and information receiving antenna selection algorithm to select the optimal working mode of the uplink user and downlink user equipment.

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

Finally, in order to solve the joint energy collection and transmission optimization model, this application adopts a two-layer 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.

This application provides joint energy collection and transmission optimization for network-assisted full-duplex systems under user quality of service requirements, fronthaul optimization, energy collection requirements, and access point and user transmit power constraints, achieving the optimal goal of maximizing system energy efficiency. Target.

Based on the above embodiment, the method step S1 includes:

Obtain a system model of a network-assisted full-duplex system, where the system model includes several transmission access nodes, several receiving access nodes, several downlink users and several uplink users;

Determine the forward backhaul strategy of the downlink using a compression strategy. Based on the compression strategy, determine the signal received by any transmission access node, the signal received by any downlink user, the energy information of any downlink user, and the signal received by any downlink user. Link user signal to interference plus noise ratio;

Determine the signal information of any receiving access node, the energy information of any receiving access node, and the total energy information of any receiving access node in the uplink;

Determine the signal to interference plus noise ratio of any receiving access node and any uplink user signal;

Determine the allocation rate of any uplink fronthaul link and the allocation rate of any downlink fronthaul link;

Obtain the downlink fronthaul power consumption and uplink fronthaul power consumption;

The total power consumption of the downlink is obtained based on the downlink fronthaul power consumption, and the total uplink power consumption is obtained based on the uplink fronthaul power consumption;

The system circuit power and the total network energy are obtained, and the total power consumed by the system is obtained based on the downlink fronthaul power consumption, the uplink fronthaul power consumption, the system circuit power and the total network energy.

It should be noted that in the system model shown in Figure 2, the sending remote radio frequency head obtains the non-ideal channel state information between all downlink user equipment and the receiving remote radio frequency head through channel estimation, and the uplink user obtains it through channel estimation. It sends non-ideal channel status information between all downstream user equipment and the receiving remote radio head. It is assumed that the system of this application adopts the time division duplex system based on the network-assisted full-duplex mode, and the channel obeys flat fading, that is, the channel coefficient remains unchanged during the channel coherence time.

Based on any of the above embodiments, a system model of a network-assisted full-duplex system is obtained. The system model includes several transmission access nodes, several receiving access nodes, several downlink users and several uplink users. ,include:

Determining that each transmitting access node includes at least one information transmission antenna, and each receiving access node includes at least one information receiving antenna and one energy collection antenna;

Determine that each uplink user includes an information transmission antenna and an energy collection antenna, and each downlink user includes an information receiving antenna;

The transmission access node set and the downlink user index set are constructed respectively, and the receiving access node set and the uplink user index set are constructed respectively.

Specifically, assume that the NAFD system includes L T-APS, Z R-APS, 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 collection antenna. Each uplink user has an information transmitting antenna and an energy harvesting antenna, and each downlink user has an information receiving antenna. set up

and

Represents the set of T-AP and downlink user indexes respectively,

Represents the set of R-AP and uplink user indexes respectively. In practical applications, the total antennas equipped in the z-th R-AP can be equal to l T-APs; here, for simplicity, this application assumes that each R-AP is equipped with M+1 antennas, and selects one of them as Energy harvesting antenna.

Based on any of the above embodiments, it is determined that the forward backhaul strategy of the downlink adopts a compression strategy, and based on the compression strategy, it is determined that any transmission access node receives a signal, any downlink user receives a signal, and any downlink User energy information and any downlink user signal to interference plus noise ratio, including:

The compression strategy is used to quantize and forward compress the baseband signal of each transmission access node on the fronthaul link, based on any downlink user data stream beamforming vector, any downlink user expected signal, energy beam vector and Any transmission access node in the downlink channel quantifies the noise to obtain the signal received by the any transmission access node;

Based on all transmission access nodes to any downlink user channel vector, any uplink user signal, including any downlink user's additive Gaussian white noise, any uplink user information transmission antenna to any The received signal of any downlink user is obtained from the downlink user channel coefficient and the uplink transmission power of any uplink user;

Obtain the energy information of any downlink user based on the energy conversion efficiency, any downlink user energy collection and information detection power separation factor, and the any downlink user received signal;

Determine the covariance interference matrix of any receiver, based on the energy collection and information detection power separation factor of any downlink user, the channel vectors of all transmission access nodes to any downlink user, the any downlink user energy collection and information detection power separation factor, The beamforming vector of the downlink user data flow and the covariance interference matrix are used to obtain the signal to interference plus noise ratio of any downlink user.

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

Signal received at l-th T-AP:

in

Represents the beamforming vector of the data flow 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, namely: v _D,E ~CN(0,V _D,E ). where V _D,E is the covariance matrix of v _D,E , that is

represents the quantization noise at the l-th T-AP in the downlink channel, μ _D,l represents the downlink compression noise power at the l-th T-AP. The received signal of the kth downlink user is modeled as:

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 Gaussian white noise, h _IUI,j,k represents the channel coefficient from the information transmission antenna in uplink user j to downlink user k, and P _U,j is the uplink transmission of uplink user j power. It is assumed that the transmission signal sent to the downlink users is used for information detection and simultaneously used for energy harvesting through power separation (ratio ρ _D,k ).

The energy obtained at downlink user k is:

where η _EH,k ∈ (0,1] represents the energy conversion efficiency,

Used to model the additional circuit noise caused by phase offset and nonlinearity during baseband conversion; the SINR of downlink user k is:

in:

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

Based on any of the above embodiments, determining the signal information of any receiving access node, the energy information of any receiving access node, and the total energy information of any receiving access node in the uplink include:

Based on any uplink user to any receiving access node channel vector, any uplink user uplink transmission power, any uplink user signal, all transmitting access nodes to any receiving access node Receive the antenna channel matrix, the downlink baseband transmission signal and the additive Gaussian white noise including the covariance matrix to obtain the signal information of any receiving access node;

Based on the channel state information of any uplink user to any receiving access node energy collection antenna, the uplink transmission power of any uplink user, the signal of any uplink user, all transmission access The channel state information from the node to the energy collection antenna of any receiving access node and the additive Gaussian white noise including the energy collection antenna of any receiving access node are used to obtain the energy information of any receiving access node;

Based on the radio frequency energy conversion efficiency of any receiving access node, the uplink transmission power of any uplink user, the channel state information from the any uplink user to the energy collection antenna of any receiving access node, Channel state information from all transmission access nodes to any receiving access node energy collection antenna, any downlink user data stream beamforming vector, energy beam vector, any transmission access node downlink compressed noise power and the uplink compression noise power of any receiving access node to obtain the total energy information of any receiving access node.

Wherein, determining the ratio of any receiving access node's received signal and any uplink user signal to interference plus noise includes:

The interference covariance matrix between uplink and downlink access nodes is obtained from the channel estimation error elements between uplink and downlink access nodes. After interference elimination is performed on the interference covariance matrix, based on all uplink user channel vectors, any uplink Link user uplink transmission power, any uplink user signal, receiving antenna channel matrix from all transmission access nodes to any receiving access node, the downlink baseband transmission signal and effective baseband signal, and obtain the signal received by any receiving access node;

Obtain the interference plus noise power of any uplink user and the reception beamforming vector used to detect the signal of any uplink user in the central processing unit, based on the interference plus noise power of any uplink user, the The beamforming vector, the channel vectors of all uplink users and the uplink transmission power of any uplink user are received to obtain the signal to interference plus noise ratio of any uplink user.

Specifically, for the uplink, the signal information and energy information received at the uplink R-AP z are obtained, respectively:

in

in

is the channel vector from uplink user j to AP z,

is the channel matrix of the information receiving antenna from all T-AP to R-AP z, that is, the IAI channel between all T-AP and R-AP z, n _U,z represents a matrix with zero mean and covariance

additive Gaussian noise. h _EH,U,j,z represents the channel state information between user j and R-APz uplink energy harvesting antenna,

is the channel state information between all T-AP and R-AP energy harvesting antennas at z.

Represents the additive Gaussian noise received by the energy harvesting antenna at R-AP z. The total energy transferred at R-AP z is:

In the formula, η _U,z represents the radio frequency energy conversion efficiency of R-AP z. It is assumed that the AP and CPU are connected through a limited-capacity wired fronthaul link, so 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 and μ _U,z is the uplink compression noise power at R-APz. The signal received by the CPU is:

in:

Although the CPU can fully know the _downlink baseband transmission signal is non-ideal. In practice, it is assumed 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, where

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 appropriate interference cancellation, the signal received by AP z can be modeled as:

in

The SINR of uplink user j is expressed as:

in:

is the interference plus noise power of uplink user j,

is the receive beamforming vector used in the CPU to detect s _U,j .

Based on any of the above embodiments, determining any uplink fronthaul link allocation rate and any downlink fronthaul link allocation rate includes:

Based on any downlink user data stream beamforming vector, energy beam vector, number of information receiving antennas, any receiving access node uplink compression noise power, residual interference power, additional circuit noise, any uplink user uplink The link transmission power, the channel vector from any uplink user to any receiving access node and the downlink compression noise power of any transmitting access node are used to obtain the any uplink fronthaul link allocation rate;

Based on the beamforming vector of any downlink user data flow, the energy beam vector, and the uplink compression noise power of any receiving access node, the allocation rate of any downlink fronthaul link is obtained.

Specifically, from the channel vectors such as uplink and downlink received signals and energy information obtained in the previous embodiment, the z-th uplink fronthaul link allocation rate C _U,z and the l-th downlink fronthaul link allocation rate C _D, are further obtained. _l :

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 Gaussian white noise. The harvested energy obtained at uplink user j is:

Based on any of the above embodiments, obtaining downlink fronthaul power consumption and uplink fronthaul power consumption includes:

The downlink fronthaul power is obtained based on the downlink fronthaul front-end transmission capacity of any receiving access node, the downlink fronthaul front-end transmission capacity power loss of any receiving access node, and the any downlink fronthaul link allocation rate. consume;

The uplink fronthaul power 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. Consumption.

Wherein, the total downlink power consumption is obtained based on the downlink fronthaul power consumption, and the total uplink power consumption is obtained based on the uplink fronthaul power consumption, including:

Based on any downlink user data flow beamforming vector, energy beam vector, radio frequency power amplifier drain efficiency, number of information receiving antennas, number of transmission access nodes, any transmission access node in each active radio frequency chain and The dynamic power consumption associated with the power radiation of all circuit loops, the static power consumption associated with the power radiation of all circuit loops of any transmission access node in each active radio frequency chain and the downlink fronthaul power consumption are obtained by Total link power consumption;

Based on the uplink transmission power of any uplink user, the drain efficiency of the radio frequency power amplifier, the number of receiving access nodes, the number of information receiving antennas, any receiving access node in each active radio frequency chain and The dynamic power consumption associated with the power radiation of all circuit loops, the static power consumption associated with the power radiation of all circuit loops of any receiving access node in each active radio frequency chain, the static power consumption associated with the power radiation of all circuit loops of any uplink user in each active radio frequency chain. the dynamic power consumption associated with the power radiation of all circuit loops in the radio frequency chain, the static power consumption associated with the power radiation of all circuit loops of any uplink user in each active radio frequency chain and said uplink fronthaul power consumption, Obtain the total power consumption of the uplink.

Among them, the system circuit power and the total network energy are obtained, and the total power consumed by the system is obtained based on the downlink fronthaul power consumption, the uplink fronthaul power consumption, the system circuit power and the total network energy, including:

Based on the information about the number of receiving antennas, the number of transmitting access nodes, the number of receiving access nodes, the dynamics of any transmitting access node associated with the power radiation of all circuit loops in each active radio frequency chain Power consumption, the static 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 receiving access node in each active radio frequency chain Dynamic power consumption associated with circuit loop power radiation, static power consumption associated with all circuit loop power radiation of any receiving access node in each active radio frequency chain, any uplink user in each active radio frequency chain The dynamic power consumption associated with the power radiation of all circuit loops in each active radio frequency chain and the static power consumption associated with the power radiation of all circuit loops in each active radio frequency chain of any uplink user are obtained. The system circuit power;

Based on the uplink transmission power of any uplink user, the data stream beamforming vector of any downlink user, the energy beam vector and the radio frequency power amplifier drain efficiency, the total network energy is obtained ;

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.

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

where C _D,max,l is the downlink front-end transmission capacity of R-AP z,

represents the corresponding power loss. Therefore, the total power consumption of NAFD’s downlink cellular-less massive MIMO is:

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. All circuit loops in each active RF chain of T-AP l where, P _D,l,st is the static power consumption of T-AP l power supply and cooling system, etc.

The total power consumption in the uplink channel is:

in:

The definitions of P _U,z,dy , P _U,j,dy , P _U,z,st and P _U,j,st are respectively 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-AP z,

Indicates the corresponding power consumption. Define the total circuit power consumption of the system:

The total power consumption of the system is obtained as:

in:

S2 includes:

Based on any downlink user data flow beamforming vector, the receive beamforming vector in the central processing unit for detecting the any uplink user signal, any uplink user uplink transmission power, any transmission The maximum value of the downlink compressed noise power of the access node, the uplink compressed noise power of any receiving access node and the energy beam vector, combined with the service quality of any downlink user, the service quality of any uplink user and the system Consume the total power consumption and build a joint energy collection and transmission optimization model;

The first constraint is determined to be an expression consisting of any downlink user data flow beamforming vector, energy beam vector, downlink compression noise power of any transmission access node and the number of receiving information antennas, which satisfies any transmission access node. Ingress node and any uplink user power budget;

It is determined that the second constraint is that the service quality of any downlink user is greater than or equal to the downlink service quality target value, and the third constraint is that the service quality of any uplink user is greater than or equal to the uplink service quality target value;

It is determined that the fourth constraint is that the energy collection constraint of any receiving access node is greater than or equal to the energy collection target value of any receiving access node, and the fifth constraint is that the energy collection constraint of any downlink user is greater than or equal to any downlink user. Energy collection target value, the sixth constraint is that any uplink user energy collection constraint is greater than or equal to any uplink user energy collection target value;

The seventh constraint is determined to be that the uplink transmission power of any uplink user is equal to zero;

It is determined that the eighth constraint is that any uplink front-end rate is less than or equal to the downlink fronthaul capacity of the user's compressed transmission signal to any transmission access node, and the ninth constraint is that any downlink front-end rate is less than or equal to the receiving access node. The ingress node transmits the user's compressed received signal to the uplink fronthaul capacity of the central processing unit.

Specifically, when constructing the joint energy collection and transmission optimization model of the system, this application takes maximizing energy efficiency of the entire transmission system as the criterion and uses 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:

C1:

C2:

C3:

C4:

C5:

C6:

C7:

C8:

C9:

Among them, C1 to C9 correspond to the first constraint to the ninth constraint respectively.

and P _D,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 R -Energy collection constraints for AP z, downlink user k and uplink user j; E _EH,min,z , E _EH,min,k and E _EH,min,j are the corresponding energy collection targets; constraints C6 and C7 means that each uplink user uses its collected energy to send information to the R-AP. C _D,min,l and C _U,min,z respectively define the downlink fronthaul capacity for transmitting the user's compressed transmit signal to T-APl and the uplink fronthaul capacity for transmitting the user's compressed receive signal from R-AP z to the CPU. .

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

Obtain the channel vectors from all transmitting access nodes to any receiving access node, the channel vectors from all receiving access nodes to any uplink user, the channel vectors from all transmitting access nodes to any uplink user and all uplinks User to any antenna channel vector in any access node;

Traverse all receiving access nodes, determine the energy collection antenna corresponding to when any antenna channel vector from all uplink users to any access node is the minimum value, and determine the corresponding information transmission receiving antenna from the energy collection antenna ;

Traverse the information transmission and receiving antennas, and update 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 in the channel vectors from all receiving access nodes to any uplink user is greater than the second channel vector, then determine that all receiving access nodes are connected to any uplink user. The link user channel vector is the first channel vector, and the channel vector from all transmitting access nodes to any uplink user is the second channel vector. Otherwise, it is determined that all receiving access nodes are connected to any uplink user channel vector. The uplink user channel vector is the second channel vector, and the channel vectors of all transmission access nodes to any uplink user are the first channel vector.

Specifically, as shown in Figure 3, in the energy collection/information receiving antenna selection stage, each R-AP and uplink user are equipped with M+1 and 2 antennas respectively. Before optimization, it is necessary to decide which antenna should be selected as the energy collection antenna. For R-APz, the energy collection antenna mainly obtains the interference (IAI) energy between the uplink receiving AP and the downlink transmitting AP from all T-APs, and interferes with IAI Energy comparison, uplink signal power can be omitted. In the same way, for uplink user j, the energy harvesting antenna obtains the downlink signal power from the T-AP.

Therefore, if the channel gain between all T-APs and the mth antenna in R-APz is significantly stronger, while the channel gain between the uplink user and the mth antenna in R-APz is poor, then The m-th antenna should work in energy harvesting mode; if the m-th antenna works in information receiving mode, its uplink throughput contribution to all uplink users is minimal because IAI elimination requires more power consumption. of.

At the same time, in this case, the energy obtained at R-AP z is also smaller than the energy collected when the m-th antenna works in energy collection mode. On the contrary, the mth antenna should work in the information receiving mode. In addition, for uplink user j, due to limited transmission power, the main goal of this application is to ensure that the uplink information is transmitted. If the channel gain between the R-AP and one of the antennas in the uplink user j is significantly greater than the channel gain between the R-AP and the other antenna in the uplink user j, then the nth antenna should be used in the information transmission work in mode.

If the nth antenna works in energy harvesting mode and the other antenna works in information transmission mode, its useful signal contribution to uplink user j is minimal, further leading to 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 n-th antenna works in the information transmission mode, the uplink user j will consume less transmit power, and the energy collected by the energy harvesting antenna can easily meet the transmit power demand.

The specific energy harvesting/information receiving antenna selection process is as follows:

in

represents the channel vector from all T-APs to R-APz,

is the channel vector from all T-APs to antenna m of R-APz, and

is the channel vector from all uplink users to antenna m in APz,

represents the channel vector from all R-APs to uplink user j,

represents the channel vector from all T-APs to uplink user j,

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

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

Among them, 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 system energy efficiency maximization target result includes:

Using path tracing algorithm and Dinkelbach algorithm to convert non-convex functions in the joint energy collection and transmission optimization model and the constraints into convex functions;

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.

Specifically, in order to solve the optimal solution of the model, this application uses a method based on Successful Convex Approximation (SCA) to solve the EE maximization problem to provide NAFD-based cellular wireless energy-carrying communication (Simultaneous Wireless Information and Power Transfer, SWIPT) transceiver design solution. First, after converting the non-convex objective function into a convex function through the path tracing algorithm and Dinkelbach method, the non-convex feasible region is processed using the SCA method to solve the sum SE, that is

Use the following inequality:

Among them, a>0, b>0,

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

Contains linear constraints

C10:

in:

Assume that the feasible point is

The lower bound of R _D,k is

However, finding a lower bound for R _D,k is challenging since transceiver beamforming, uplink transmit power, quantization power, and receive power separation ratio are tightly coupled. This application 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 }, the following inequalities are obtained:

C11:

C12:

C13:

C14:

C15:

It is obvious that all equations are non-convex except C11. at the feasible point

at , according to the inequality

and

inferred:

C16:

C17:

C18:

C19:

in:

The original problem can be approximated as:

The linear constraints transform into:

C20:β _U,j ≥0

in:

R _U,j can be defined as:

This application uses the lower bound of the objective function to approximate SE. Since there is a non-convex expression P _Total in the objective function, the objective function is still non-convex. All expressions are convex except for the total power consumption of the uplink/downlink fronthaul link, i.e.

Because ln(det(D)) is a convex function when D≥0, its upper bound can be obtained by applying the first-order Taylor expansion.

ln(det(D))≤ln(det(D ⁽ⁿ⁾ ))+Tr((D ⁽ⁿ⁾ ) ^-1 (DD ⁽ⁿ⁾ )).

Therefore, by applying the above equation to

P _Total can be approximated as

C21:

C21 also defines

and

They are:

Among them, X ⁽ⁿ⁾ and Y ⁽ⁿ⁾ are:

The objective function is approximately:

in:

Objective C1 has been transformed into a concave superlinear function. Constraints C2-C6, C8 and C9 are still highly non-convex constraints. After conversion, this application respectively utilizes

and ln(det(D))≤ln(det(D ⁽ⁿ⁾ ))+Tr((D ⁽ⁿ⁾ ) ^-1 (DD ⁽ⁿ⁾ )). Approximate internal constraints C2-C6, C8 and C9:

C22:

C23:

C24:

C25:

C26:

in:

Through the above steps, the convex set is solved at the n+1th iteration, and the following approximation problem is obtained:

s.t.C1,C7,C10,C11,C16,C17,C18,C19,C21,C22,C23,C24,C25,C26

in

This problem belongs to the concave and convex fractional programming category and can be solved using the Dinkelbach algorithm, which can be used to solve the global maximization fractional function of polynomial complexity. if and only if

The optimal solution to this problem can only be obtained when is the only zero point of the auxiliary function Ξ(λ), where:

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} )

Solve the following auxiliary problems to find the optimal

Ξ(λ)

s.t.C1,C7,C10,C11,C16,C17,C18,C19,C22,C23,C24,C25,C26

The problem is a two-level iterative problem. The internal iteration problem should make Ξ(λ) converge to some value of a given λ. Then, the outer iteration problem aims to find a typical value

to establish the equation

This application solves the two-level iteration problem by proposing two algorithms, the external solution algorithm based on Dinkelbach iteration and the internal solution algorithm using the iterative algorithm based on SCA are used to solve the external and internal iteration problems respectively.

Further, multiple performance comparison experiments are used to illustrate the advantages of the solution of this application:

Figure 4 shows the relationship between the convergence behavior of EE and the number of iterations, where the number of antennas for each access point M is 2 or 4, the fronthaul constraint C=10bps/Hz, and the number of T-AP/R-AP Z=L is 3 , 5 or 12, IAI interference is -10dB or -20dB. As can be seen from Figure 4, it takes approximately 6-10 iterations to achieve convergence.

Figure 5 shows the relationship between EE and each T-AP/R-AP under the fixed L=Z=3, Δ=-10dB, C _D,max,l =C _U,max,z =10bps/Hz. The relationship between the number of antennas M. It can be clearly found from Figure 5 that the EE performance of the proposed NAFD scheme is better than both the CCFD and TDD cases of C-RAN. The EE of the three duplex mode solutions first reaches a peak value at a certain M value, and then shows a downward trend. Furthermore, 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 gain of EE. But when M becomes large, using more antennas beyond the optimal point (i.e., M=4) does not improve the performance of EE. Employing more antennas can improve SE, but the total power consumed is much greater. Therefore, when M=14, EE's income is 30.72%-32.10% lower than when M=2.

Figure 6 shows the situation when M = 2 is fixed, the number of T-AP/R-AP Z = L is 3 to 13, Δ = -10dB, C _D,max,l = C _U,max,z = 10bps/Hz Below, EE performance in the three schemes. Similar to Figure 5, the EE of the system increases first and reaches the best EE performance when L=Z=5, and then decreases after L=Z=5.

Figure 7 compares the energy efficiency performance of NAFD, C-RAN CCFD and TDD under different IAI conditions Δ. As expected, under the conditions of Δ≤25dB and Δ≤20dB, compared with TDD, NAFD and C-RAN CCFD can achieve higher energy efficiency performance. However, under the conditions of Δ≥25dB and Δ≥20dB, the performance of the aforementioned two design solutions is slightly worse than the latter solution. This is because the strong IAI interference of NAFD and C-RAN CCFD systems will damage energy efficiency performance. Under any IAI intensity, the energy efficiency performance of the NAFD system proposed in this article is better than that of the traditional C-RAN CCFD system.

Figure 8 compares the energy efficiency performance of the three solutions 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 under each scheme also increases. This is because greater fronthaul power can be used to increase spectral efficiency gain. When the forward transmission 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 ≥16bps/Hz, the spectral efficiency performance under the three schemes is limited by the different interference between receivers, and further affects the energy efficiency performance.

Figure 9 shows the energy of different receivers as the transmission energy increases under the setting of M=2, L=Z=8, Δ=-10dB, CD _,max,l =C _U,max,z =10bps/Hz. Collection performance. As expected, as the transmission power constraint increases, the sum of energy collected at different receivers increases. In particular, the most energy is collected at R-AP, followed by downlink users, and then by uplink users. This is because the signal gains collected by R-APs are higher than those of uplink and downlink users. These signal gains include the IAI between R-APs and T-APs and the uplink signals transmitted by uplink users. Similarly, the data signal and interference power collected by downlink users are also higher than the interference power collected by uplink users.

The network-assisted full-duplex system energy efficiency optimization system provided by this application 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 correspond to each other.

Figure 10 is a schematic structural diagram of the network-assisted full-duplex system energy efficiency optimization system provided by this application. As shown in Figure 10, it includes: an acquisition module 1001, a construction module 1002, a determination module 1003 and a processing module 1004, where:

The acquisition module 1001 is used to acquire the channel vector set between the uplink user equipment, the downlink user equipment, and the uplink and downlink remote radio frequency heads; the construction module 1002 is used to build a set of channel vectors based on the channel vector set with the goal of maximizing system energy efficiency, and A joint energy collection and transmission optimization model with specified user service quality, fronthaul constraints, energy collection requirements and transmitter transmit power as constraints; the determination module 1003 is used to determine the uplink user and information receiving antenna selection algorithm using energy collection and information receiving antenna selection algorithms. The optimal working mode of the downlink user equipment; the processing module 1004 is configured to solve the optimal value of the joint energy collection and transmission optimization model in the optimal working mode to obtain the system energy efficiency maximization target result.

This application provides joint energy collection and transmission optimization for network-assisted full-duplex systems under user quality of service requirements, fronthaul optimization, energy collection requirements, and access point and user transmit power constraints, achieving the optimal goal of maximizing system energy efficiency. Target.

Figure 11 illustrates a schematic diagram of the physical structure of an electronic device. As shown in Figure 11, the electronic device may include: a processor (processor) 1110, a communications interface (Communications Interface) 1120, a memory (memory) 1130 and a communication bus 1140. Among them, the processor 1110, the communication interface 1120, and the memory 1130 complete communication with each other through the communication bus 1140. The processor 1110 can call logical instructions in the memory 1130 to perform network-assisted full-duplex system energy efficiency optimization. The method includes: obtaining a set of channel vectors between uplink user equipment, downlink user equipment, and uplink and downlink remote radio frequency heads; based on The channel vector set constructs a joint energy collection and transmission optimization model with the goal of maximizing system energy efficiency and specifying user service quality, fronthaul constraints, energy collection requirements, and transmitter transmit power as constraints; using energy collection and The information receiving antenna selection algorithm determines the optimal working mode of the uplink user and the downlink user equipment; in the optimal working mode, the optimal value of the joint energy collection and transmission optimization model is solved to obtain the system Energy efficiency maximizes target outcomes.

In addition, the above-mentioned logical instructions in the memory 1130 can be implemented in the form of software functional units 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 application is essentially or the part that contributes to the existing technology 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, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .

On the other hand, the present application also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can Perform network-assisted full-duplex system energy efficiency optimization provided by the above methods. The method includes: obtaining a set of channel vectors between uplink user equipment, 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 energy collection and information receiving antenna selection algorithms, determine The optimal working mode of the uplink user and the downlink user equipment; in the optimal working mode, the optimal value of the joint energy collection and transmission optimization model is solved to obtain the system energy efficiency maximization target result.

On the other hand, the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored. The computer program is implemented when executed by a processor to perform the network-assisted full-duplex system energy efficiency optimization provided by the above methods. , the method includes: obtaining a set of channel vectors between uplink user equipment, downlink user equipment, and uplink and downlink remote radio frequency heads; based on the set of channel vectors, constructing a system with the goal of maximizing system energy efficiency and specifying user service quality , fronthaul constraints, energy collection requirements and transmitter transmit power as constraints, a joint energy collection and transmission optimization model; using energy collection and information receiving antenna selection algorithms to determine the optimal working mode of the uplink user and the downlink user equipment ; In the optimal working mode, solve the optimal value of the joint energy collection and transmission optimization model to obtain the target result of maximizing system energy efficiency.

The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.

Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the part of the above technical solution that essentially 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, magnetic disk, optical disk, etc., including a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent substitutions are made to some of the technical features; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions in the embodiments of the present application.

Claims (18)

1. A network-assisted full-duplex system energy efficiency optimization method, including:

   Obtain the set of channel vectors between uplink user equipment, downlink user equipment, and uplink and downlink remote radio frequency heads;

   Based on the set of channel vectors, a joint energy collection and transmission optimization model is constructed 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;

   Use energy collection and information receiving antenna selection algorithms to determine the optimal working mode of uplink users and the downlink user equipment;

   In 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 system energy efficiency.
2. The network-assisted full-duplex system energy efficiency optimization method according to claim 1, wherein obtaining a set of channel vectors between uplink user equipment, downlink user equipment, and uplink and downlink remote radio frequency heads includes:

   Obtain a system model of a network-assisted full-duplex system, where the system model includes several transmission access nodes, several receiving access nodes, several downlink users and several uplink users;

   Determine the forward backhaul strategy of the downlink using a compression strategy. Based on the compression strategy, determine the signal received by any transmission access node, the signal received by any downlink user, the energy information of any downlink user, and the signal received by any downlink user. Link user signal to interference plus noise ratio;

   Determine the signal information of any receiving access node, the energy information of any receiving access node, and the total energy information of any receiving access node in the uplink;

   Determine the signal to interference plus noise ratio of any receiving access node and any uplink user signal;

   Determine the allocation rate of any uplink fronthaul link and the allocation rate of any downlink fronthaul link;

   Obtain the downlink fronthaul power consumption and uplink fronthaul power consumption;

   The total power consumption of the downlink is obtained based on the downlink fronthaul power consumption, and the total uplink power consumption is obtained based on the uplink fronthaul power consumption;

   The system circuit power and the total network energy are obtained, and the total power consumed by the system is obtained based on the downlink fronthaul power consumption, the uplink fronthaul power consumption, the system circuit power and the total network energy.
3. The energy efficiency optimization method of the network-assisted full-duplex system according to claim 2, wherein a system model of the network-assisted full-duplex system is obtained, and the system model includes several transmission access nodes, several receiving access nodes, several downlink users and several uplink users, including:

   Determining that each transmitting access node includes at least one information transmission antenna, and each receiving access node includes at least one information receiving antenna and one energy collection antenna;

   Determine that each uplink user includes an information transmission antenna and an energy collection antenna, and each downlink user includes an information receiving antenna;

   The transmission access node set and the downlink user index set are constructed respectively, and the receiving access node set and the uplink user index set are constructed respectively.
4. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, wherein the forward backhaul strategy for determining the downlink adopts a compression strategy, and based on the compression strategy, it is determined whether any transmission access node receives a signal, any downlink Downlink user received signal, any downlink user energy information and any downlink user signal to interference plus noise ratio, including:

   The compression strategy is used to quantize and forward compress the baseband signal of each transmission access node on the fronthaul link, based on any downlink user data stream beamforming vector, any downlink user expected signal, energy beam vector and Any transmission access node in the downlink channel quantifies the noise to obtain the signal received by the any transmission access node;

   Based on all transmission access nodes to any downlink user channel vector, any uplink user signal, including any downlink user's additive Gaussian white noise, any uplink user information transmission antenna to any The received signal of any downlink user is obtained from the downlink user channel coefficient and the uplink transmission power of any uplink user;

   Obtain the energy information of any downlink user based on the energy conversion efficiency, any downlink user energy collection and information detection power separation factor, and the any downlink user received signal;

   Determine the covariance interference matrix of any receiver, based on the energy collection and information detection power separation factor of any downlink user, the channel vectors of all transmission access nodes to any downlink user, the any downlink user energy collection and information detection power separation factor, The beamforming vector of the downlink user data flow and the covariance interference matrix are used to obtain the signal to interference plus noise ratio of any downlink user.
5. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, wherein the signal information of any receiving access node, the energy information of any receiving access node and the energy information of any receiving access node in the uplink are determined. Total energy information, including:

   Based on any uplink user to any receiving access node channel vector, any uplink user uplink transmission power, any uplink user signal, all transmitting access nodes to any receiving access node Receive the antenna channel matrix, the downlink baseband transmission signal and the additive Gaussian white noise including the covariance matrix to obtain the signal information of any receiving access node;

   Based on the channel state information of any uplink user to any receiving access node energy collection antenna, the uplink transmission power of any uplink user, the signal of any uplink user, all transmission access The channel state information from the node to the energy collection antenna of any receiving access node and the additive Gaussian white noise including the energy collection antenna of any receiving access node are used to obtain the energy information of any receiving access node;

   Based on the radio frequency energy conversion efficiency of any receiving access node, the uplink transmission power of any uplink user, the channel state information from the any uplink user to the energy collection antenna of any receiving access node, Channel state information from all transmission access nodes to any receiving access node energy collection antenna, any downlink user data stream beamforming vector, energy beam vector, any transmission access node downlink compressed noise power and the uplink compression noise power of any receiving access node to obtain the total energy information of any receiving access node.
6. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, wherein determining the ratio of the received signal of any receiving access node and any uplink user signal to interference plus noise includes:

   The interference covariance matrix between uplink and downlink access nodes is obtained from the channel estimation error elements between uplink and downlink access nodes. After interference elimination is performed on the interference covariance matrix, based on all uplink user channel vectors, any uplink Link user uplink transmission power, any uplink user signal, receiving antenna channel matrix from all transmission access nodes to any receiving access node, the downlink baseband transmission signal and effective baseband signal, and obtain the signal received by any receiving access node;

   Obtain the interference plus noise power of any uplink user and the reception beamforming vector used to detect the signal of any uplink user in the central processing unit, based on the interference plus noise power of any uplink user, the The beamforming vector, the channel vectors of all uplink users and the uplink transmission power of any uplink user are received to obtain the signal to interference plus noise ratio of any uplink user.
7. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, wherein determining any uplink fronthaul link allocation rate and any downlink fronthaul link allocation rate includes:

   Based on any downlink user data stream beamforming vector, energy beam vector, number of information receiving antennas, any receiving access node uplink compression noise power, residual interference power, additional circuit noise, any uplink user uplink The link transmission power, the channel vector from any uplink user to any receiving access node and the downlink compression noise power of any transmitting access node are used to obtain the any uplink fronthaul link allocation rate;

   Based on the beamforming vector of any downlink user data flow, the energy beam vector, and the uplink compression noise power of any receiving access node, the allocation rate of any downlink fronthaul link is obtained.
8. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, wherein obtaining the downlink fronthaul power consumption and the uplink fronthaul power consumption includes:

   The downlink fronthaul power is obtained based on the downlink fronthaul front-end transmission capacity of any receiving access node, the downlink fronthaul front-end transmission capacity power loss of any receiving access node, and the any downlink fronthaul link allocation rate. consume;

   The uplink fronthaul power 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. Consumption.
9. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, wherein the total downlink power consumption is obtained based on the downlink fronthaul power consumption, and the total uplink power consumption is obtained based on the uplink fronthaul power consumption. ,include:

   Based on any downlink user data flow beamforming vector, energy beam vector, radio frequency power amplifier drain efficiency, number of information receiving antennas, number of transmission access nodes, any transmission access node in each active radio frequency chain and The dynamic power consumption associated with the power radiation of all circuit loops, the static power consumption associated with the power radiation of all circuit loops of any transmission access node in each active radio frequency chain and the downlink fronthaul power consumption are obtained by Total link power consumption;

   Based on the uplink transmission power of any uplink user, the drain efficiency of the radio frequency power amplifier, the number of receiving access nodes, the number of information receiving antennas, any receiving access node in each active radio frequency chain and The dynamic power consumption associated with the power radiation of all circuit loops, the static power consumption associated with the power radiation of all circuit loops of any receiving access node in each active radio frequency chain, the static power consumption associated with the power radiation of all circuit loops of any uplink user in each active radio frequency chain. the dynamic power consumption associated with the power radiation of all circuit loops in the radio frequency chain, the static power consumption associated with the power radiation of all circuit loops of any uplink user in each active radio frequency chain and said uplink fronthaul power consumption, Obtain the total power consumption of the uplink.
10. The network-assisted full-duplex system energy efficiency optimization method according to claim 2, wherein the system circuit power and the total network energy are obtained. and the total network energy to obtain the total power consumed by the system, including:

    Based on the information about the number of receiving antennas, the number of transmitting access nodes, the number of receiving access nodes, the dynamics of any transmitting access node associated with the power radiation of all circuit loops in each active radio frequency chain Power consumption, the static 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 receiving access node in each active radio frequency chain Dynamic power consumption associated with circuit loop power radiation, static power consumption associated with all circuit loop power radiation of any receiving access node in each active radio frequency chain, any uplink user in each active radio frequency chain The dynamic power consumption associated with the power radiation of all circuit loops in each active radio frequency chain and the static power consumption associated with the power radiation of all circuit loops in each active radio frequency chain of any uplink user are obtained. The system circuit power;

    Based on the uplink transmission power of any uplink user, the data stream beamforming vector of any downlink user, the energy beam vector and the radio frequency power amplifier drain efficiency, the total network energy is obtained ;

    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, wherein based on the channel vector set, a construction is constructed with the goal of maximizing system energy efficiency and specifying user service quality, fronthaul constraints, energy collection requirements and The joint energy collection and transmission optimization model with transmitter transmit power as a constraint includes:

    Based on any downlink user data flow beamforming vector, the receive beamforming vector in the central processing unit for detecting the any uplink user signal, any uplink user uplink transmission power, any transmission The maximum value of the downlink compressed noise power of the access node, the uplink compressed noise power of any receiving access node and the energy beam vector, combined with the service quality of any downlink user, the service quality of any uplink user and the system Consume the total power consumption and build a joint energy collection and transmission optimization model;

    The first constraint is determined to be an expression consisting of any downlink user data flow beamforming vector, energy beam vector, downlink compression noise power of any transmission access node and the number of receiving information antennas, which satisfies any transmission access node. Ingress node and any uplink user power budget;

    It is determined that the second constraint is that the service quality of any downlink user is greater than or equal to the downlink service quality target value, and the third constraint is that the service quality of any uplink user is greater than or equal to the uplink service quality target value;

    It is determined that the fourth constraint is that the energy collection constraint of any receiving access node is greater than or equal to the energy collection target value of any receiving access node, and the fifth constraint is that the energy collection constraint of any downlink user is greater than or equal to any downlink user. Energy collection target value, the sixth constraint is that any uplink user energy collection constraint is greater than or equal to any uplink user energy collection target value;

    The seventh constraint is determined to be that the uplink transmission power of any uplink user is equal to zero;

    It is determined that the eighth constraint is that any uplink front-end rate is less than or equal to the downlink fronthaul capacity of the user's compressed transmission signal to any transmission access node, and the ninth constraint is that any downlink front-end rate is less than or equal to the receiving access node. The ingress node transmits the user's compressed received signal to the uplink fronthaul capacity of the central processing unit.
12. The network-assisted full-duplex system energy efficiency optimization method according to claim 1, wherein an energy collection and information receiving antenna selection algorithm is used to determine the optimal working mode of the uplink user and the downlink user equipment, including:

    Obtain the channel vectors from all transmitting access nodes to any receiving access node, the channel vectors from all receiving access nodes to any uplink user, the channel vectors from all transmitting access nodes to any uplink user and all uplinks User to any antenna channel vector in any access node;

    Traverse all receiving access nodes, determine the energy collection antenna corresponding to when any antenna channel vector from all uplink users to any access node is the minimum value, and determine the corresponding information transmission receiving antenna from the energy collection antenna ;

    Traverse the information transmission and receiving antennas, and update 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 in the channel vectors from all receiving access nodes to any uplink user is greater than the second channel vector, then determine that all receiving access nodes are connected to any uplink user. The link user channel vector is the first channel vector, and the channel vector from all transmitting access nodes to any uplink user is the second channel vector. Otherwise, it is determined that all receiving access nodes are connected to any uplink user channel vector. The uplink user channel vector is the second channel vector, and the channel vectors of all transmission access nodes to any uplink user are the first channel vector.
13. The network-assisted full-duplex system energy efficiency optimization method according to claim 1, wherein in the optimal working mode, the optimal value of the joint energy collection and transmission optimization model is solved to obtain the maximum system energy efficiency. Target results include:

    Based on the preset iterative algorithm and the iterative convex approximation algorithm, the joint energy collection and transmission optimization model is solved to obtain the target result of maximizing the system energy efficiency.
14. The network-assisted full-duplex system energy efficiency optimization method according to claim 13, wherein the joint energy collection and transmission optimization model is solved based on a preset iterative algorithm and an iterative convex approximation algorithm to obtain the maximum energy efficiency of the system target results, including:

    Using path tracing algorithm and Dinkelbach algorithm to convert non-convex functions in the joint energy collection and transmission optimization model and the constraints into convex functions;

    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, including:

    The acquisition module is used to acquire the channel vector set between the uplink user equipment, the downlink user equipment, and the uplink and downlink remote radio frequency heads;

    A building module for constructing a joint energy collection and transmission optimization based on the channel vector set 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. Model;

    A determination module used to determine the optimal working mode of the uplink user and the downlink user equipment by using energy collection and information receiving antenna selection algorithms;

    A processing module, configured to solve the optimal value of the joint energy collection and transmission optimization model in the optimal working mode to obtain the target result of maximizing system energy efficiency.
16. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, any one of claims 1 to 14 is implemented. The energy efficiency optimization method of the network-assisted full-duplex system described in the item.
17. A non-transitory computer-readable storage medium with a computer program stored thereon, wherein when the computer program is executed by a processor, the network-assisted full-duplex system energy efficiency optimization method as described in any one of claims 1 to 14 is implemented .
18. A computer program product includes a computer program, wherein 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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