CN114885423A

CN114885423A - Network-assisted full-duplex system energy efficiency optimization method and system

Info

Publication number: CN114885423A
Application number: CN202210322087.9A
Authority: CN
Inventors: 夏心江; 王东明; 卜颖澜; 孙文菲; 凌捷; 张子扬; 尤肖虎
Original assignee: Network Communication and Security Zijinshan Laboratory
Current assignee: Network Communication and Security Zijinshan Laboratory
Priority date: 2022-03-29
Filing date: 2022-03-29
Publication date: 2022-08-09
Also published as: WO2023185077A1

Abstract

The invention provides a method and a system for optimizing energy efficiency of a network-assisted full duplex system, which comprises the following steps: acquiring a channel vector set among uplink user equipment, downlink user equipment and uplink and downlink remote radio frequency heads; constructing a combined energy collection and transmission optimization model which aims at maximizing system energy efficiency and takes user service quality, forward transmission constraint, energy acquisition requirement and transmitter transmitting power as constraint conditions on the basis of a channel vector set; determining the optimal working modes of uplink users and downlink user equipment by adopting an energy acquisition and information receiving antenna selection algorithm; and under the optimal working mode, carrying out optimal value solution on the combined energy collection and transmission optimization model to obtain a system energy efficiency maximization target result. The invention provides joint energy collection and transmission optimization under certain constraint conditions aiming at a network-assisted full-duplex system, and realizes the optimal aim of maximizing the energy efficiency of the system.

Description

Network-assisted full-duplex system energy efficiency optimization method and system

Technical Field

The invention relates to the technical field of wireless communication, in particular to a method and a system for optimizing energy efficiency of a network-assisted full-duplex system.

Background

With the popularity of the fifth Generation Mobile Communication Technology (5G), the Quality of Service (QoS) requirements of Communication systems are increasing, and the 5G overcomes the data rate and delay of the uplink and downlink in cellular systems to some extent.

Since a 5G New Radio (5G New Radio, 5G-NR) supports a flexible Duplex technology including dynamic Time Division Duplex (TDD) and flexible Frequency Division Duplex (FDD) in paired and unpaired spectrum, in recent years, a spatial domain flexible Duplex technology has been studied in many fields, and Co-Frequency simultaneous Full Duplex (Co-Frequency Co-Time Full Duplex, CCFD) is expected to double the spectrum efficiency of a wireless link by realizing downlink and uplink transmission on the same Time-Frequency resource. However, ultra-dense deployment of Access Points (APs) will cause severe Cross Link Interference (CLI), i.e., Interference of downlink APs to uplink APs and Interference of uplink users to downlink users, which is also the most difficult problem faced by CCFD, flexible duplex or Broadband Distribution Network (BDN) networks.

Network-Assisted Full Duplex (NAFD) can be seen as a unified implementation of flex Duplex, CCFD, and hybrid Duplex without cellular Network architecture. Therefore, the NAFD scheme is considered to be a technique that truly implements flexible duplexing. In NAFD based cellless massive Multiple-in Multiple-out (MIMO) technology, all APs are connected to a Central Processing Unit (CPU) through a high speed fronthaul link. Therefore, how to eliminate CLI and how to deal with the high demand of the fronthaul link are two major issues of the system. Much of the work available has focused more on suppressing or reducing CLI by designing suitable flex duplex transceivers. Although NAFD-based cellular-free massive MIMO can provide considerable spectral gain, the ultra-dense deployment of APs can cause the power consumption of the system to increase substantially. Therefore, in order to effectively utilize cross-link interference energy collection and design downlink beamforming, an uplink receiver, uplink power control and a forward backhaul compression strategy, and to take transmission power, limited capacity forward backhaul, energy collection and QoS as constraints, the energy efficiency maximization problem under a network-assisted full-duplex non-cellular large-scale MIMO system is proposed, and energy efficiency resource allocation of QoS and CLI energy collection under a NAFD scheme is not researched at present.

Disclosure of Invention

The invention provides a method and a system for optimizing energy efficiency of a network-assisted full-duplex system, which are used for solving the defect that no system exists in the prior art and aims at the distribution of energy efficiency resources of the system under the network-assisted full duplex.

In a first aspect, the present invention provides a method for optimizing energy efficiency of a network-assisted full duplex system, including:

acquiring a channel vector set among uplink user equipment, downlink user equipment and uplink and downlink remote radio frequency heads;

constructing a combined energy collection and transmission optimization model which aims at maximizing system energy efficiency and takes the specified user service quality, forward transmission constraint, energy acquisition requirement and transmitter transmitting power as constraint conditions on the basis of the channel vector set;

determining the optimal working modes of uplink users and the downlink user equipment by adopting an energy acquisition and information receiving antenna selection algorithm;

and under the optimal working mode, based on a preset iterative algorithm and an iterative convex approximation algorithm, carrying out optimal value solution on the combined energy collection and transmission optimization model to obtain a system energy efficiency maximization target result.

In a second aspect, the present invention further provides a system for optimizing energy efficiency of a network-assisted full duplex system, including:

the system comprises an acquisition module, a transmission module and a control module, wherein the acquisition module is used for acquiring a channel vector set between uplink user equipment, downlink user equipment and an uplink and downlink remote radio frequency head;

the construction module is used for constructing a combined energy collection and transmission optimization model which aims at maximizing system energy efficiency and takes the specified user service quality, the forwarding constraint, the energy acquisition requirement and the transmitter transmitting power as constraint conditions based on the channel vector set;

the determining module is used for determining the optimal working modes of the uplink user and the downlink user equipment by adopting an energy acquisition and information receiving antenna selection algorithm;

and the processing module is used for solving the optimal value of the combined energy collection and transmission optimization model in the optimal working mode to obtain the target result of maximizing the energy efficiency of the system.

In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement any of the above network assisted full duplex system energy efficiency optimization methods.

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

In a fifth aspect, the invention also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for energy efficiency optimization of a network-assisted full-duplex system is implemented as any one of the above methods.

The energy efficiency optimization method and the energy efficiency optimization system for the network-assisted full-duplex system provided by the invention aim at providing combined energy collection and transmission optimization under the constraints of user service quality requirements, forwarding optimization, energy collection requirements, access points and user transmitting power for the network-assisted full-duplex system, and realize the optimal target of maximizing the system energy efficiency.

Drawings

In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

FIG. 1 is a schematic flow chart of a method for optimizing energy efficiency of a network-assisted full-duplex system according to the present invention;

FIG. 2 is a diagram of a system model provided by the present invention;

figure 3 is a schematic diagram of NAFD no-cellular energy harvesting/information receiving antenna selection provided by the present invention;

FIG. 4 is a schematic view of EE convergence behavior versus iteration number performance curves provided by the present invention;

FIG. 5 is a comparison of the performance curves of EE and the number M of antennas provided by the present invention;

FIG. 6 is a comparative diagram of the performance curve of the interference Δ between EE and AP provided by the present invention;

FIG. 7 is a graph illustrating EE convergence behavior versus iteration performance provided by the present invention;

FIG. 8 is a graphical comparison of EE and forward rate performance curves provided by the present invention;

FIG. 9 is a schematic diagram of the collected energy versus the sum of the transmitted power for different receiver scenarios provided by the present invention;

FIG. 10 is a schematic structural diagram of a network assisted full duplex system energy efficiency optimization system provided by the present invention;

fig. 11 is a schematic structural diagram of an electronic device provided in the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 1 is a schematic flow chart of a method for optimizing energy efficiency of a network-assisted full-duplex system according to the present invention, as shown in fig. 1, including:

step S1, acquiring a channel vector set among uplink user equipment, downlink user equipment and uplink and downlink remote radio frequency heads;

firstly, the invention models each node and scene in the model in the system, which mainly comprises a plurality of channel vectors between uplink user equipment, downlink user equipment and uplink and downlink remote radio frequency heads.

Step S2, constructing a combined energy collection and transmission optimization model which takes the system energy efficiency as the maximum as the target and takes the appointed user service quality, the forward transmission constraint, the energy collection requirement and the transmitter transmitting power as the constraint conditions based on the channel vector set;

based on the obtained multiple channel vectors, a combined energy collection and transmission optimization model is constructed with the system energy efficiency maximization as an overall target, and the specified user service quality, the forward transmission constraint, the energy collection requirement and the transmitter transmitting power are used as constraint conditions of the model.

Step S3, determining the optimal working mode of the uplink user and the downlink user equipment by adopting an energy collection and information receiving antenna selection algorithm;

furthermore, the invention is used for selecting the optimal working mode of the uplink user equipment and the downlink user equipment through the proposed energy collection and information receiving antenna selection algorithm.

And step S4, in the optimal working mode, carrying out optimal value solution on the combined energy collection and transmission optimization model to obtain a system energy efficiency maximization target result.

Finally, in order to solve the combined energy collection and transmission optimization model, the method adopts a double-layer iteration algorithm based on Dinkelbach and uses a series of convex approximation methods to process the optimization problem of high non-convex energy efficiency maximization.

The invention provides the combined energy collection and transmission optimization under the constraints of user service quality requirements, forward transmission optimization, energy collection requirements, access points and user transmitting power aiming at a network-assisted full duplex system, and realizes the optimal aim of maximizing the system energy efficiency.

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

acquiring a system model of a network-assisted full-duplex system, wherein the system model comprises a plurality of transmission access nodes, a plurality of receiving access nodes, a plurality of downlink users and a plurality of uplink users;

determining a compression strategy adopted by a forward feedback strategy of a downlink, and determining any transmission access node received signal, any downlink user energy information and any downlink user signal-to-interference-plus-noise ratio based on the compression strategy;

determining any receiving access node signal information, any receiving access node energy information and any receiving access node total energy information in an uplink;

determining any receiving access node received signal and any uplink user signal to interference plus noise ratio;

determining the allocation rate of any uplink forward link and the allocation rate of any downlink forward link;

acquiring downlink forward transmission power consumption and uplink forward transmission power consumption;

obtaining total power consumption of a downlink based on the downlink forward power consumption, and obtaining total power consumption of an uplink based on the uplink forward power consumption;

and obtaining system circuit power and total network energy, and obtaining system consumption total power according to the downlink forwarding power consumption, the uplink forwarding power consumption, the system circuit power and the total network energy.

It should be noted that, in the system model shown in fig. 2, the transmitting remote radio frequency head obtains the non-ideal channel state information between it and all downlink user equipments and the receiving remote radio frequency head through channel estimation, and the uplink user obtains the non-ideal channel state information between it and all downlink user equipments and the receiving remote radio frequency head through channel estimation. The system of the invention is supposed to adopt a time division duplex system based on a network-assisted full duplex mode, and the channel is subject to flat fading, i.e. the channel coefficient is kept unchanged within the channel coherence time.

Based on any of the above embodiments, obtaining a system model of a network-assisted full duplex system, where the system model includes a plurality of transmission access nodes, a plurality of reception access nodes, a plurality of downlink users, and a plurality of uplink users, includes:

determining that each transmission access node comprises at least one information transmission antenna, and each receiving access node comprises at least one information receiving antenna and an energy collecting antenna;

determining that each uplink user comprises an information transmission antenna and an energy collection antenna, and each downlink user comprises an information receiving antenna;

and respectively constructing a transmission access node set and a downlink user index set, and constructing a receiving access node set and an uplink user index set.

Specifically, the NAFD system is designed to include L T-APS, Z R-APS, K downlink users and J uplink users. Each T-AP and each R-AP are provided with M information transmission antennas and M information receiving antennas, and each R-AP is respectively provided with an energy collecting antenna. Each uplink user has one information transmitting antenna and one energy harvesting antenna, and each downlink user has one information receiving antenna. Is provided with

And

respectively representing sets of T-AP and downlink user indices,

representing sets of R-AP and uplink user indices, respectively. In practical application, the total antenna equipped in the z-th 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 an energy harvesting antenna.

Based on any of the above embodiments, determining that the downlink fronthaul strategy employs a compression strategy, and determining, based on the compression strategy, any transmission access node received signal, any downlink user energy information, and any downlink user signal-to-interference-plus-noise ratio, includes:

quantizing, forwarding and compressing the baseband signal of each transmission access node on the forward link by using the compression strategy, and obtaining a received signal of any transmission access node based on any downlink user data stream beam forming vector, any downlink user expected signal, energy beam vector and quantization noise of any transmission access node in a downlink channel;

obtaining any downlink user receiving signal based on channel vectors from all transmission access nodes to any downlink user, any uplink user signal, additive white Gaussian noise including any downlink user, channel coefficient from information transmission antenna in any uplink user to any downlink user and uplink transmission power of any uplink user;

obtaining any downlink user energy information based on energy conversion efficiency, any downlink user energy collection and information detection power separation factor and any downlink user receiving signal;

and determining a covariance interference matrix of any receiver, and obtaining a signal-to-interference-plus-noise ratio of any downlink user based on the energy collection and information detection power separation factor of any downlink user, a channel vector from all the transmission access nodes to any downlink user, a beam forming vector of any downlink user data stream and the covariance interference matrix.

Specifically, for the downlink in the model, a compression-based forward-transmission strategy is adopted, and a CPU intensively compresses a baseband signal of each T-AP through quantization and forwarding on a forward-transmission link. Each T-AP transmits the compressed signal received from the CPU to the downlink user.

Signal received at the l-th T-AP:

wherein

Beamforming vector, s, representing the data stream of the k-th downlink user _D,k CN (0,1) is the desired signal, energy beam vector, of the kth downlink user

The elements are zero-mean complex gaussian random variables, namely: v. of _D,E ～CN(0,V _D,E ). Wherein V _D,E Is v _D,E Of covariance matrix, i.e.

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

wherein

Representing the channel vector, s, from all T-APs to downlink user k _U,j CN (0,1) is the signal of uplink user j,

is additive white Gaussian noise, h _IUI,j,k Indicating a slave uplinkChannel coefficient, P, from information transmission antenna in user j to downlink user k _U,j Is the uplink transmission power of the uplink user j. It is assumed that the transmission signal sent to the downlink user is used for information detection and is separated by power (ratio ρ) _D,k ) While being used for energy harvesting.

The energy acquired at downlink user k is:

wherein eta _EH,k ∈(0,1]Which represents the efficiency of the energy conversion,

for modeling additional circuit noise caused by phase offset and non-linearity during analog baseband conversion; the SINR for downlink user k is:

wherein:

ρ _D,k is the power separation factor for energy collection and information detection at the kth downlink user,

γ _D,k is the covariance interference matrix at the receiver k.

Based on any of the above embodiments, determining any one of the received access node signal information, any one of the received access node energy information, and any one of the received access node total energy information in the uplink includes:

obtaining signal information of any receiving access node based on a channel vector from any uplink user to any receiving access node, uplink transmission power of any uplink user, signals of any uplink user, receiving antenna channel matrixes from all the transmitting access nodes to any receiving access node, downlink baseband emission signals and additive white Gaussian noise comprising a covariance matrix;

obtaining energy information of any receiving access node based on channel state information from any uplink user to any receiving access node energy collecting antenna, uplink transmission power of any uplink user, signals of any uplink user, channel state information from all transmitting access nodes to any receiving access node energy collecting antenna and additive white Gaussian noise comprising any receiving access node energy collecting antenna;

and obtaining total 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 any uplink user to any receiving access node energy collection antenna, the channel state information from all the transmitting access nodes to any receiving access node energy collection antenna, the data stream beam forming vector of any downlink user, the energy beam vector, the downlink compression noise power of any transmitting access node and the uplink compression noise power of any receiving access node.

Wherein the determining of any receiving access node received signal and any uplink user signal to interference plus noise ratio comprises:

obtaining an interference covariance matrix between uplink and downlink access nodes by using channel estimation error elements between the uplink and downlink access nodes, and obtaining a received signal of any receiving access node based on all uplink user channel vectors, any uplink user uplink transmission power, any uplink user signal, a receiving antenna channel matrix from any transmitting access node to any receiving access node, the downlink baseband transmission signal and an effective baseband signal after the interference covariance matrix is subjected to interference elimination;

acquiring any uplink user interference plus noise power and a receiving beam forming vector used for detecting any uplink user signal in a central processing unit, and acquiring any uplink user signal to interference plus noise ratio based on any uplink user interference plus noise power, the receiving beam forming vector, all uplink user channel vectors and any uplink user uplink transmission power.

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

wherein

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

channel matrix for all T-AP to R-AP z information receiving antennas, i.e. IAI channel between all T-AP and R-AP z, n _U,z Representation with zero mean and covariance matrices

Additive gaussian noise. h is _EH,U,j,z Representing channel state information between user j and R-AP z uplink energy harvesting antennas,

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

Representing the additive gaussian noise received by the energy harvesting antenna at R-AP z.

The total energy delivered at R-AP z is:

in the formula eta _U,z The efficiency of the conversion of radio frequency energy of R-AP z is shown. It is assumed that the AP and the CPU are connected by 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 transmitted by R-AP z is

Is the uplink compression noise, mu _U,z Is the uplink compression noise power at R-AP z. The signals received by the CPU are:

wherein:

although the downlink baseband transmit signal x can be fully known at the CPU _D And channel state information between the R-APs and the T-APs, the inter-AP interference can be eliminated theoretically, but the actual elimination effect may be non-ideal due to the existence of channel estimation errors. In practice, channel estimation errors are assumed

Wherein the elements follow a Gaussian distribution, i.e.

Wherein

Representing the residual interference power due to non-ideal inter-AP interference cancellation in the digital or analog domain, wherein

Representing 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-AP z and T-AP l is

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

wherein

The SINR for uplink user j is expressed as:

wherein:

is the interference plus noise power of uplink user j,

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

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

obtaining any uplink forward transmission link distribution rate based on any downlink user data stream beam forming vector, energy beam vector, information receiving antenna number, any receiving access node uplink compressed noise power, residual interference power, additional circuit noise, any uplink user uplink transmission power, any uplink user to any receiving access node channel vector and any transmitting access node downlink compressed noise power;

and obtaining the allocation rate of any downlink forwarding link based on the beam forming vector of any downlink user data stream, the energy beam vector and the compressed noise power of any receiving access node uplink.

Specifically, the z-th uplink forward link allocation rate C is further obtained from the channel vectors such as the uplink and downlink received signals and the energy information obtained in the foregoing embodiment _U,z And the rate C allocated on the l-th downlink preceding link _D,l ：

The uplink user is equipped with an energy harvesting antenna for capturing the downlink signal, so that the received signal at uplink user j is

Wherein

Channel between energy harvesting antennas at j for all T-APs and uplink usersThe status information is transmitted to the mobile station via the wireless communication network,

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

based on any of the above embodiments, obtaining downlink forwarding power consumption and uplink forwarding power consumption includes:

obtaining downlink forwarding power consumption based on the transmission capacity of a downlink forwarding front end of any receiving access node, the transmission capacity power loss of the downlink forwarding front end of any receiving access node and the allocation rate of any downlink forwarding link;

and obtaining the uplink forward transmission power consumption based on the uplink forward transmission front end transmission capacity of any receiving access node, the uplink forward transmission front end transmission capacity power loss of any receiving access node and the uplink forward transmission link allocation rate.

Obtaining total power consumption of a downlink based on the downlink forwarding power consumption, and obtaining total power consumption of an uplink based on the uplink forwarding power consumption, wherein the obtaining of the total power consumption of the downlink based on the downlink forwarding power consumption comprises the following steps:

obtaining the total downlink power consumption based on any downlink user data stream beam forming vector, energy beam vector, radio frequency power amplifier drain efficiency, information receiving antenna number, transmission access node number, dynamic power consumption of any transmission access node in each active radio frequency chain and associated with all circuit loop power radiation, static power consumption of any transmission access node in each active radio frequency chain and associated with all circuit loop power radiation, and downlink forward power consumption;

the total uplink power consumption is obtained based on uplink transmission power of any uplink user, drain efficiency of the radio frequency power amplifier, the number of receiving access nodes, the number of information receiving antennas, dynamic power consumption of any receiving access node in each active radio frequency chain, static power consumption of any uplink user in each active radio frequency chain, dynamic power consumption of any uplink user in each active radio frequency chain, static power consumption of any uplink user in each active radio frequency chain, and uplink forward power consumption.

Obtaining system circuit power and total network energy, wherein the obtaining of the total system power consumption according to the downlink forwarding power consumption, the uplink forwarding power consumption, the system circuit power and the total network energy comprises:

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

obtaining the total network energy based on the uplink transmission power of any uplink user, the data stream beam forming vector of any downlink user, the energy beam vector and the drain efficiency of the radio frequency power amplifier;

and summing the total network energy, the system circuit power, the downlink forward power consumption and the uplink forward power consumption to obtain the total power consumed by the system.

Specifically, for a power consumption vector in the system, the downlink power consumption includes power consumption of the T-AP and downlink forwarding, and the downlink forwarding power consumption is:

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

indicating the corresponding power loss. Thus, the total power consumption of the downlink cellless massive MIMO for NAFD is:

wherein xi is belonged to (0,1)]Is the drain efficiency, P, of the RF power amplifier _D,l,dy Is the dynamic power consumption associated with power radiation. In all the circuit loops in each active radio frequency chain of T-AP l, P _D,l,st Is the static power consumption of the T-AP l power supply and cooling system, etc.

The total power consumption in the uplink channel is:

wherein:

P _U,z,dy ，P _U,j,dy ，P _U,z,st and P _U,j,st Are each defined as P _D,l,dy And P _D,l,st In which C is _U,max,z Is the uplink front end transmission capacity of R-AP z,

indicating the corresponding power consumption.

Defining the total circuit power consumption of the system:

the total power consumption of the system is obtained as follows:

wherein:

based on any of the above embodiments, the method step S2 includes:

based on any downlink user data stream beam forming vector, a receiving beam forming vector used for detecting any uplink user signal in a central processing unit, any uplink user uplink transmission power, any transmission access node downlink compression noise power, any receiving access node uplink compression noise power and the maximum value of an energy beam vector, combining any downlink user service quality, any uplink user service quality and system consumption total power, and constructing a joint energy collection and transmission optimization model;

determining a first constraint condition as an expression formed by any downlink user data stream beam forming vector, an energy beam vector, any transmission access node downlink compression noise power and the number of receiving information antennas, and meeting power consumption budgets of any transmission access node and any uplink user;

determining a second constraint condition that the service quality of any downlink user is greater than or equal to a downlink service quality target value, and determining a third constraint condition that the service quality of any uplink user is greater than or equal to an uplink service quality target value;

determining a 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, a fifth constraint condition that any downlink user energy collection constraint is greater than or equal to any downlink user energy collection target value, and a 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 that the uplink transmission power of any uplink user is equal to zero;

an eighth constraint is determined that any uplink front end rate is less than or equal to the downlink fronthaul capacity of the user compressed transmit signal to any transmitting access node, and a ninth constraint is determined that any downlink front end rate is less than or equal to the uplink fronthaul capacity of the user compressed receive signal transmitted from the receiving access node to the central processing unit.

Specifically, when a combined energy collection and transmission optimization model of the system is constructed, the combined optimization { P } of the transmission system is solved by taking the maximum energy efficiency of the whole transmission system as a criterion and taking power and QoS constraints as constraints _U,j ,u _U,j,z ,w _D,k Problem, the modeling is:

C1:

C2:

C3:

C4:

C5:

C6:

C7:

C8:

C9:

wherein C1 to C9 correspond to the first constraint to the ninth constraint, respectively,

and P _D,l Power consumption budgets for T-AP l and uplink user j, respectively; c2 and C3 are QoS constraints for downlink user k and uplink user j, respectively; c4, C5, and C6 are energy harvesting constraints for R-AP z, downlink user k, and uplink user j, respectively; e _EH,min,z 、E _EH,min,k And E _EH,min,j Are respective energy harvesting targets; constraints C6 and C7 indicate that each uplink user transmits information to the R-AP using its collected energy. C _D,min,l And C _U,min,z Downlink forward capacity for transmitting user compressed transmit signals to the T-APl and uplink forward capacity for transmitting user compressed receive signals from the R-AP z to the CPU are defined separately.

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

acquiring channel vectors of all transmission access nodes to any receiving access node, channel vectors of all receiving access nodes to any uplink user, channel vectors of all transmission access nodes to any uplink user and channel vectors of all uplink users to any antenna in any access node;

traversing all receiving access nodes, determining a corresponding energy collecting antenna when any antenna channel vector of all uplink users to any access node is the minimum value, and determining a corresponding information transmission receiving antenna by the energy collecting antenna;

traversing the information transmission receiving antenna, and updating any antenna channel vector of all receiving access nodes to any uplink user into the information transmission receiving antenna channel vector of all receiving access nodes to any uplink user;

traversing all uplink users, if judging that a first channel vector in channel vectors of all receiving access nodes to any uplink user is larger than a second channel vector, determining that the channel vector of all receiving access nodes to any uplink user is the first channel vector, and the channel vector of all transmitting access nodes to any uplink user is the second channel vector, otherwise, determining that the channel vector of all receiving access nodes to any uplink user is the second channel vector, and the channel vector of all transmitting access nodes to any uplink user is the first channel vector.

Specifically, as shown in fig. 3, 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, a decision needs to be made as to which antenna should be selected as an energy harvesting antenna, and for R-AP z, the energy harvesting antenna mainly acquires interference (IAI) energy between an uplink receiving AP and a downlink transmitting AP from all T-APs, and compared with the IAI interference energy, uplink signal power can be omitted. Similarly, for uplink user j, the energy harvesting antenna obtains downlink signal power from the T-AP.

Therefore, if the channel gain between all T-APs and the mth antenna in R-AP z is significantly stronger and the channel gain between the uplink user and the mth antenna in R-AP z is worse, the mth antenna should operate in energy harvesting mode; if the mth antenna is operating in the information reception mode, its uplink throughput contributes very little to all uplink users because IAI cancellation requires more power consumption.

Also in this case, the energy obtained at R-AP z is less than the harvested energy when the mth antenna is operating in energy harvesting mode. Otherwise, the mth antenna should operate in the information receiving mode. Furthermore, for uplink user j, due to the limited transmit power, the main object of the present invention is to ensure that uplink information is transmitted. If the channel gain between the R-AP and one of the antennas of uplink user j is significantly larger than the channel gain between the R-AP and the other antenna of uplink user j, then the nth antenna should operate in the information transmission mode.

If the nth antenna is operating in energy harvesting mode and the other antenna is operating in information transmission mode, its useful signal contribution to uplink user j is very small, further resulting in a small throughput contribution. The uplink user j has to consume more power to guarantee uplink information transmission, which will result in more IUI. Otherwise, if the nth antenna is operating in the information transfer 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 requirement.

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

inputting:

h _U,z,m

and (3) outputting: h _IAI,z ,h _EH,IAI,z ,h _U,j ,h _EH,j

Repeating: z is performed 1: Z:

repeating M for 1: M to perform:

updating

End repetition

Updating

End repetition

Updating

Repeating: j is performed 1: J:

if it is not

Executing:

otherwise:

end repetition

Wherein

Representing the channel vectors from all T-APs to R-AP z,

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

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

representing the channel vectors from all R-APs to uplink user j,

representing the channel vectors from all T-APs to uplink user j,

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

and solving the combined energy collection and transmission optimization model based on a preset iterative algorithm and an iterative convex approximation algorithm to obtain a system energy efficiency maximization target result.

The method for obtaining the system energy efficiency maximization target result by solving the combined energy collection and transmission optimization model based on the preset iterative algorithm and the iterative convex approximation algorithm comprises the following steps:

converting the non-convex function in the combined energy collection and transmission optimization model and the constraint condition into a convex function by utilizing a path tracking algorithm and a Dinkelbach algorithm;

and solving the convex function based on a continuous convex approximation SCA algorithm to obtain a system energy efficiency maximization target result.

Specifically, in order to solve the optimal solution of the model, the invention adopts a method based on Sequential Convex Approximation (SCA) to solve the EE maximization problem, so as to provide a design scheme of a cellular Wireless energy Transfer (SWIPT) transceiver based on NAFD. Firstly, converting a non-convex target function into a convex function through a path tracking algorithm and a Dinkelbach method, then processing a non-convex feasible domain by using an SCA-based method, solving and SE (quadratic element analysis), namely

The following inequality is utilized:

wherein a is more than 0, b is more than 0,

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

involving linear constraints

C10:

Wherein:

suppose a feasible point is

R _D,k Has a lower bound of

However, since the transceiver beamforming, uplink transmit power, quantized power, and received power separation ratio are closely coupled together, R is found _D,k The lower bound of (2) is challenging. The invention firstly uses SCA method to approximate R _D,k . By introducing a series of variationsQuantity { alpha } _D }，{t _DU,j }，{χ _U,j,z }，{ε _U,j,j′ And { beta ] _U,j Get the following inequality:

C11:

C12:

C13:

C14:

C15:

it is clear that all equations, except C11, are non-convex. At the feasible point

According to the inequality

And

to obtain:

C16:

C17:

C18:

C19:

wherein:

the original problem can be approximated as:

the linear constraint translates into:

C20:β _U,j ≥0

wherein:

R _U,j can be defined as:

the present invention approximates SE using the lower bound of the objective function due to the presence of the non-convex expression P in the objective function _Total 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. all expressions are convex

Since ln (det (D)) is in

When D is more than or equal to 0, the function is a convex function, and the upper bound of the function can be obtained by applying a first-order Taylor expansion

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

Therefore, by applying the above formula to

P _Total Can be approximated as

C21:

C21 at the same time defines

And

respectively as follows:

wherein, X ⁽ⁿ⁾ And Y ⁽ⁿ⁾ Respectively as follows:

the objective function is approximated as:

wherein:

target C1 has been converted to concave superlinearityA function. Constraints C2-C6, C8 and C9 are still highly non-convex constraints. After conversion, the invention respectively utilizes

And ln (det (D) ≦ ln (det (D)) ⁽ⁿ⁾ ))+Tr((D ⁽ⁿ⁾ ) ^-1 (D-D ⁽ⁿ⁾ ) ). approximate internal constraints C2-C6, C8 and C9:

C22:

C23:

C24:

C25:

C26:

wherein:

through the steps, the convex set is solved in the (n + 1) th iteration, and the following approximate problem is obtained:

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

wherein

The problem belongs to the concave-convex fractional programming class, can be solved by using a Dinkelbach algorithm, and can be used for solving a polynomial complexity global maximization fractional function. If and only if

Is the only zero of the auxiliary function xi (λ), the optimal solution to the problem is obtained, 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 )

solving the following additional problem to find the optimum

Ξ(λ)

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

The problem is a two-layer iteration problem. The internal iterative problem should cause xi (λ) to converge to some value given λ. Then, the external iteration problem aims to find a typical value

To establish an equation

The invention solves the two-stage 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 are respectively used for solving the external and internal iteration problems.

Further, the advantages of the inventive scheme are illustrated in several performance comparison experiments:

fig. 4 shows the convergence behavior of EE as a function of the number of iterations, where the number of antennas per access point M is 2 or 4, the fronthaul constraint C is 10bps/Hz, the number of T-AP/R-AP Z is 3, 5 or 12, and the IAI interference is-10 dB or-20 dB. As can be seen from fig. 4, approximately 6-10 iterations are required to achieve convergence.

Fig. 5 shows that when L ═ Z is 3, Δ ═ 10dB, C is fixed _D,max,l ＝C _U,max,z EE is related to the number of antennas M per T-AP/R-AP, 10 bps/Hz. It can clearly be seen from fig. 5 that the proposed NAFD scheme has EE performance superior to both CCFD and TDD cases of C-RAN. The EE of the three duplex mode schemes reaches a peak value at a certain M value and then always shows a descending 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 increase SE and thus EE gain. But when M becomes large, using more antennas beyond the optimum (i.e., M-4) does not improve EE performance. Using more antennas may improve SE but the total power consumed is much larger. Thus, EE is 30.72% -32.10% less profitable when M is 14 than when M is 2.

Fig. 6 shows that at a fixed M-2, the number Z-L of T-AP/R-AP is 3 to 13, Δ -10dB, C _D,max,l ＝C _U,max,z EE performance in three scenarios, 10 bps/Hz. Similar to fig. 5, the EE of the system increases first and reaches the best EE performance when L ═ Z ═ 5, and 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, NAFD and C-RAN CCFD can achieve higher energy performance than TDD under Δ ≦ 25dB and Δ ≦ 20dB conditions. However, under the conditions of Δ ≧ 25dB and Δ ≧ 20dB, the two aforementioned designs are slightly inferior to the latter because of the energy efficiency performance penalty due to the strong IAI interference of NAFD with the C-RAN CCFD system. The NAFD system proposed herein is superior in energy efficiency performance to the conventional C-RAN CCFD system at any IAI strength.

In FIG. 8The energy efficiency performance of the three schemes as the fronthaul capacity increases when M is 2, L is 8, and Δ is-10 dB are compared. It can be seen that as the fronthaul capacity limit increases, the energy efficiency under each scenario also increases. This is because more forward power can be used to increase the spectral efficiency gain. When the fronthaul capacity limit is higher than 16bps/Hz, the growth trend will be slow. This is because when C _D,max,l ＝C _U,max,z The spectral efficiency performance under the three schemes is limited by different interference among receivers when the frequency is larger than or equal to 16bps/Hz, and further influences the energy efficiency performance.

Fig. 9 shows the results when M ═ 2, L ═ Z ═ 8, Δ ═ -10dB, C _D,max,l ＝C _U,max,z The energy harvesting performance of the different receivers varies with increasing transmission energy at a setting of 10 bps/Hz. As expected, as the transmit power constraint increases, the sum of the energy collected at the different receivers will increase. Specifically, the most energy is collected at the R-AP, followed by downlink users, and then uplink users. This is because the signal gains collected by the R-APs, including the IAI between the R-APs and the T-APs and the uplink signals transmitted by the uplink users, are higher than those of the uplink and downlink users. Similarly, the data signals and interference power collected by the downlink users are also higher than the interference power collected by the uplink users.

The energy efficiency optimization system of the network-assisted full-duplex system provided by the invention is described below, and the energy efficiency optimization system of the network-assisted full-duplex system described below and the energy efficiency optimization method of the network-assisted full-duplex system described above can be referred to correspondingly.

Fig. 10 is a schematic structural diagram of a network-assisted energy efficiency optimization system of a full-duplex system according to the present invention, and as shown in fig. 10, the system includes: an obtaining module 1001, a constructing module 1002, a determining module 1003 and a processing module 1004, wherein:

the obtaining module 1001 is configured to obtain a channel vector set between an uplink user equipment, a downlink user equipment, and an uplink and downlink remote radio frequency head; the constructing module 1002 is configured to construct, based on the channel vector set, a joint energy collection and transmission optimization model that aims at maximizing system energy efficiency and that takes specified user service quality, fronthaul constraint, energy acquisition requirement, and transmitter transmission power as constraint conditions; the determining module 1003 is configured to determine an optimal operating mode of an uplink user and the downlink user equipment by using an energy acquisition and information receiving antenna selection algorithm; the processing module 1004 is configured to perform optimal value solution on the joint energy collection and transmission optimization model in the optimal operating mode to obtain a target result of maximizing system energy efficiency.

Fig. 11 illustrates a physical structure diagram of an electronic device, and as shown in fig. 11, the electronic device may include: a processor (processor)1110, a communication Interface (Communications Interface)1120, a memory (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. Processor 1110 may invoke logic instructions in memory 1130 to perform network assisted full duplex system energy efficiency optimization, the method comprising: acquiring a channel vector set among uplink user equipment, downlink user equipment and uplink and downlink remote radio frequency heads; constructing a combined energy collection and transmission optimization model which aims at maximizing system energy efficiency and takes the specified user service quality, forward transmission constraint, energy acquisition requirement and transmitter transmitting power as constraint conditions on the basis of the channel vector set; determining the optimal working modes of uplink users and the downlink user equipment by adopting an energy acquisition and information receiving antenna selection algorithm; and under the optimal working mode, carrying out optimal value solution on the combined energy collection and transmission optimization model to obtain a system energy efficiency maximization target result.

In addition, the logic instructions in the memory 1130 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of performing the network assisted full duplex system energy efficiency optimization provided by the above methods, the method comprising: acquiring a channel vector set among uplink user equipment, downlink user equipment and uplink and downlink remote radio frequency heads; constructing a combined energy collection and transmission optimization model which aims at maximizing system energy efficiency and takes the specified user service quality, forward transmission constraint, energy acquisition requirement and transmitter transmitting power as constraint conditions on the basis of the channel vector set; determining the optimal working modes of uplink users and the downlink user equipment by adopting an energy acquisition and information receiving antenna selection algorithm; and under the optimal working mode, carrying out optimal value solution on the combined energy collection and transmission optimization model to obtain a system energy efficiency maximization target result.

In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the network assisted full duplex system energy efficiency optimization provided by performing the above methods, the method comprising: acquiring a channel vector set among uplink user equipment, downlink user equipment and uplink and downlink remote radio frequency heads; constructing a combined energy collection and transmission optimization model which aims at maximizing system energy efficiency and takes the specified user service quality, forward transmission constraint, energy acquisition requirement and transmitter transmitting power as constraint conditions on the basis of the channel vector set; determining the optimal working modes of uplink users and the downlink user equipment by adopting an energy acquisition and information receiving antenna selection algorithm; and under the optimal working mode, carrying out optimal value solution on the combined energy collection and transmission optimization model to obtain a system energy efficiency maximization target result.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy efficiency of a network-assisted full duplex system is characterized by comprising the following steps:

and under the optimal working mode, carrying out optimal value solution on the combined energy collection and transmission optimization model to obtain a system energy efficiency maximization target result.

2. The method of claim 1, wherein obtaining a set of channel vectors between an uplink user equipment, a downlink user equipment, and an uplink and a downlink remote radio frequency head comprises:

and obtaining system circuit power and total network energy, and obtaining system total power consumption according to the downlink forwarding power consumption, the uplink forwarding power consumption, the system circuit power and the total network energy.

3. The method according to claim 2, wherein obtaining a system model of the network assisted full duplex system, the system model including a plurality of transmission access nodes, a plurality of reception access nodes, a plurality of downlink users, and a plurality of uplink users, comprises:

4. The method of claim 2, wherein determining the downlink fronthaul strategy employs a compression strategy, and determining any tx access node received signal, any downlink user energy information, and any downlink user signal-to-interference-plus-noise ratio based on the compression strategy comprises:

5. The method of claim 2, wherein determining any received access node signal information, any received access node energy information, and any received access node total energy information in the uplink comprises:

6. The method of claim 2, wherein the determining any receiving access node received signal and any uplink user signal-to-interference-plus-noise ratio comprises:

7. The method of claim 2, wherein determining any uplink fronthaul link allocation rate and any downlink fronthaul link allocation rate comprises:

8. The method for optimizing the energy efficiency of the network-assisted full-duplex system according to claim 2, wherein the obtaining of the downlink forwarding power consumption and the uplink forwarding power consumption comprises:

9. The method according to claim 2, wherein the obtaining of the downlink total power consumption based on the downlink forward power consumption and the obtaining of the uplink total power consumption based on the uplink forward power consumption comprises:

10. The method according to claim 2, wherein the step of obtaining system circuit power and total network energy, and the step of obtaining total system power consumption according to the downlink forwarding power consumption, the uplink forwarding power consumption, the system circuit power and the total network energy, comprises:

11. The method of claim 1, wherein constructing a joint energy collection and transmission optimization model based on the channel vector set, the joint energy collection and transmission optimization model aiming at maximizing system energy efficiency and taking specified user service quality, forwarding constraint, energy collection requirement and transmitter transmission power as constraint conditions, comprises:

12. The method of claim 1, wherein determining the optimal operating mode of the uplink user and the downlink user equipment using an energy harvesting and information receiving antenna selection algorithm comprises:

13. The method according to claim 1, wherein in the optimal operating mode, the optimal value solution is performed on the joint energy collection and transmission optimization model to obtain a system energy efficiency maximization target result, and the method comprises:

14. The method according to claim 13, wherein the step 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 comprises:

15. A network assisted full duplex system energy efficiency optimization system, comprising:

16. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the network assisted full duplex system energy efficiency optimization method of any of claims 1 to 14.

17. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the network assisted full duplex system energy efficiency optimization method according to any of claims 1 to 14.

18. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the network assisted full duplex system energy efficiency optimization method according to any of claims 1 to 14.