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CN117979325A - Resource allocation method for symbiotic honeycomb-removing large-scale MIMO system - Google Patents

Resource allocation method for symbiotic honeycomb-removing large-scale MIMO system Download PDF

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CN117979325A
CN117979325A CN202410371522.6A CN202410371522A CN117979325A CN 117979325 A CN117979325 A CN 117979325A CN 202410371522 A CN202410371522 A CN 202410371522A CN 117979325 A CN117979325 A CN 117979325A
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scattering
user equipment
equipment
individual
power
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CN117979325B (en
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王同
黄荣强
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Harbin Institute Of Technology shenzhen Shenzhen Institute Of Science And Technology Innovation Harbin Institute Of Technology
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Harbin Institute Of Technology shenzhen Shenzhen Institute Of Science And Technology Innovation Harbin Institute Of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/22—Scatter propagation systems, e.g. ionospheric, tropospheric or meteor scatter
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/535—Allocation or scheduling criteria for wireless resources based on resource usage policies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a resource allocation method for a symbiotic descelation large-scale MIMO system, which aims at the resource allocation problem of the symbiotic descelation large-scale MIMO system accessed by multi-user equipment and multi-scattering equipment, and maximizes the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment under the condition that the power collection requirement of the scattering equipment is met; the original optimization problem is split into a plurality of sub-optimization problems through an alternate iterative algorithm, and a resource allocation method for combining the optimization merging vectors, the power coefficients and the reflection coefficients is provided by combining secondary transformation and accelerating a near-end gradient tool. Simulation results show that compared with the traditional full-power transmission method and the equal-reflection transmission method, the resource allocation method provided by the method realizes higher total spectrum efficiency performance of the system by carrying out joint optimization on a plurality of key resource variables of the system.

Description

Resource allocation method for symbiotic honeycomb-removing large-scale MIMO system
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a resource allocation method for a symbiotic honeycomb-removing large-scale MIMO system.
Background
The de-cellular large-scale Multiple Input Multiple Output (MIMO) system provides services for user equipment in a service area simultaneously by uniformly deploying a large number of low-cost service access points in the network service area, solves the problems of poor service quality at the cell edge, service failure caused by cross-cell handover and the like in the traditional centralized cellular network, and becomes one of the key technologies in sixth-generation mobile communication. The symbiotic radio system comprises two transmission links, namely direct transmission link and scattering transmission link, and the scattering equipment completes the scattering transmission of the self data by modulating the self data into a direct link signal in the environment, so that the high-energy efficiency and network differentiated service are realized, and the symbiotic radio system is widely studied in the scene of the Internet of things. The main performance constraint of symbiotic radio systems is that the performance of scattering links is greatly limited due to the effects of double path loss and therefore difficult to deploy efficiently in large service areas. The key to improving the coverage of the symbiotic radio system is to shorten the distance between the scattering device and the transmitting or receiving device.
In the research of the existing symbiotic radio combined with the de-cellular massive MIMO system, only special situations of only single user equipment or single scattering equipment in a service area are often considered, however, the access equipment in the current network is continuously increased, so that the research of the existing symbiotic radio combined with the de-cellular massive MIMO system is obviously not suitable for the actual massive Internet of things scene. In the research of the existing symbiotic radio combined with the cellular massive MIMO system, the fact that the actual scattering equipment is often low in power consumption and even passive cannot be considered, so that it is important to ensure long-time normal operation of the scattering equipment in an environment power supply mode.
The existing spectrum efficiency optimization research on the symbiotic radio combined with the de-cellular large-scale MIMO system mainly focuses on optimizing and designing the beam forming vector or the combining vector of the access point so as to improve the spectrum efficiency performance of the user equipment and the scattering equipment. The existing researches often assume that the reflection coefficient of the scattering device is fixed, that is, the existing resource allocation methods cannot effectively perform joint optimization on a plurality of resource variables, so that the reachable spectrum efficiency performance of the user device and the scattering device is underestimated, and the power collection and spectrum efficiency performance of the scattering device cannot be effectively balanced.
Disclosure of Invention
Aiming at the problems, the invention provides a resource allocation method for a symbiotic honeycomb-removing large-scale MIMO system, which aims to realize higher overall spectrum efficiency performance of the system by carrying out joint optimization on a plurality of key resource variables of the system.
The technical scheme of the invention is as follows:
A resource allocation method for a symbiotic descellular massive MIMO system comprises the following steps:
Constructing a symbiotic honeycomb-removing large-scale MIMO system model, wherein the system model comprises Individual access pointsIndividual user equipments are distributed in a network service area, each user equipment being provided withAssociated scattering devices, i.e. having/>, in service areaScattering device, no.The individual scattering devices represent theFirst/>, of individual user equipmentsThe associated scattering equipment establishes a data transmission model based on the symbiotic descellular large-scale MIMO system model, and acquires an expression of the reachable spectrum efficiency of the user equipment and the scattering equipment;
By carrying out joint optimization control on a receiving combination vector of user equipment and scattering equipment, a power coefficient of the user equipment and a reflection coefficient of the scattering equipment, aiming at maximizing the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment, and taking meeting the power collection requirement of the scattering equipment as a constraint condition, establishing the problem of maximizing the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment ；
Maximizing sum of spectral efficiency of user equipment and scattering equipmentDecomposing the sub-problems into a receiving combination vector optimization sub-problem and a power-reflection coefficient optimization sub-problem of the user equipment and the scattering equipment, solving the two sub-problems by using an alternate iteration method, and firstly fixing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment in each iteration to find the receiving combination vector of the optimal user equipment and the scattering equipment; then fixing a receiving combination vector of the user equipment and the scattering equipment, and combining and optimizing a power coefficient of the user equipment and a reflection coefficient of the scattering equipment; and repeating the iteration until convergence is met, and obtaining the receiving combination vector of the user equipment and the scattering equipment, the power coefficient of the user equipment and the optimization result of the reflection coefficient of the scattering equipment.
In some embodiments, the data transfer model is the first oneThe specific expression of the power collected by each scattering device is as follows: /(I)WhereinRepresenting power harvesting efficiency,Represents theReflection coefficient of individual scattering devices,Represents theThe transmit power of the individual user equipment.
In some embodiments, the thThe expression of the reachable spectrum efficiency of each user equipment is as follows:，、 representing the length of the coherence time block and the data transmission time block respectively, Represents theSignal-to-interference-and-noise ratio of individual user equipment data symbols,，，，Represents theIndividual user equipment pairsReception combining vector of individual access points,，Representing the result of the direct link channel estimation,，Represents theTransmission power of individual user equipments,，，，，，，，Represents theReflection coefficient of individual scattering devices,。
In some embodiments, the thThe expression of the achievable spectral efficiency of the individual scattering devices is:，、 representing the length of the coherence time block and the data transmission time block respectively, Represents theSignal-to-interference-and-noise ratio of data symbols of individual scattering devices,Wherein，，Represents theIndividual scattering device pairsReception combining vector of access point,，，，。
In some embodiments, the sum of the spectral efficiency of the user equipment and the scattering device maximizes the problemThe specific expression is as follows:
，
Wherein, ，，，Representing the reflection coefficient of the scattering device,，Represents theThe achievable spectral efficiency of the individual user equipment,Represents theReachable spectral efficiency of individual scattering devices,，，AndRepresenting the length of the coherence time block and the data transmission time block respectively,Representation and variableRelatedSignal-to-interference-and-noise ratio of individual user equipment data symbols,Representation and variableRelatedSignal-to-interference-and-noise ratio of the data symbols of the individual scattering devices,Represents theReceiving combined vector of individual scattering devices,Represents theReception combining vector of individual user equipments,，Represents theTransmission power of individual user equipments,Representing the maximum threshold of the transmission power of all user equipments,Represents theThe reflection coefficient of the individual scattering devices,，，Represents thePower collected by individual scattering devices,Represents theThe individual scattering devices need to collect the lowest threshold of power.
In some embodiments, the power coefficient of the user equipment and the reflection coefficient of the scattering equipment are fixed, and the receiving combination vector of the best user equipment and the scattering equipment is found, which comprises the following specific steps:
fixing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment to obtain the combined vector optimization sub-problem of the (i+1) th iteration The method comprises the following steps: /(I)；
Optimizing the merging vectors sub-problemEquivalent to two optimization problemsAnd：，，Representation and variableRelatedSignal-to-interference-and-noise ratio of individual user equipment data symbols,Representation and variableRelatedSignal-to-interference-and-noise ratio of the data symbols of the individual scattering devices;
the receiving combination vector optimization result of the user equipment and the scattering equipment in the (i+1) th iteration is as follows:
；
。
In some embodiments, the combination vector of the fixed user equipment and the scattering equipment, and the combination optimizing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment, specifically comprises the following steps:
After obtaining the receiving combination vector optimization result of the user equipment and the scattering equipment of the (i+1) th iteration, fixing the receiving combination vector to obtain the power-reflection coefficient optimization sub-problem of the (i+1) th iteration The method comprises the following steps: ; removing redundancy factor/> Application of a quadratic transformation tool willThe conversion is as follows: Wherein, the method comprises the steps of, wherein, ，，AndIs an auxiliary variable introduced by the secondary transformation tool; /(I)，，，The specific expression is as follows:，，，；
power-reflection coefficient optimization sub-problem of i+1th round iteration with redundancy factor removed Equivalent is：；
Fixing the power coefficient and reflection coefficient of the user equipment by making the partial derivativeIs 0, and the i+1 th round of outer layer iteration is obtainedThe auxiliary variable optimization result of the in-wheel layer iteration is as follows:
fixing the reflection coefficient and the auxiliary variable to obtain the power coefficient optimization subproblem/>, of the user equipment The method comprises the following steps: /(I)；
Solving using an accelerated near-end gradient toolAfter the optimization result of the power coefficient of the user equipment is obtained, fixing the combination vector and the power coefficient of the user equipment to obtain the optimization subproblem/>, of the reflection coefficientThe method comprises the following steps:
；
Solving using an accelerated near-end gradient tool And obtaining the optimization result of the reflection coefficient.
In some embodiments, power coefficient optimization sub-problems for user equipment using an accelerated near-end gradient toolThe method comprises the following steps of:
Step 1, initializing: setting up ；
Step 2, setting；
Step 3, calculatingAnd is denoted as；
Step 4,；
Step 5, pairingPerforming forward domain transformation;
Step 6, ，AndRepresenting gradient calculation and feasible region projection respectively;
Step 7, ；
Step 8,；
Step 9, ifUpdateReturning to the step 4; if it isOutput optimization results。
In some embodiments, an initial feasible solution of the reflection coefficient of the scattering device is set to before the alternate iterative solutionAn initially viable solution to the power coefficient of the user equipment is obtained by solving the problemThe method comprises the following steps: /(I); The initial feasible solution of the power coefficient of the user equipment is obtained。
In some embodiments, the sum of the spectral efficiency of the user equipment and the scattering device maximizes the problemThe solving process specifically comprises the following steps:
Step 1, setting ；
Step 2, from the formulaFormulaAnd formula (VI)CalculatedAnd，Represents thePower harvesting efficiency of the individual scattering devices;
step 3, calculating And is denoted as；
Step 4, according to the formulaFormulaCalculated to obtain；
Step 5, setting；
Step 6, setting；
Step 7, calculatingAnd is denoted as；
Step 8, by the formulaCalculated；
Step 9, fixingObtaining/>, based on accelerating proximal gradient tool；
Step 10, fixingObtaining/>, based on accelerating proximal gradient tool；
Step 11, updatingIfReturning to the step 8; ifStep 12 is implemented;
step 12, updating UpdateIf (if)Returning to the step 4; if it isOutputting an optimization result: /(I)。
According to the resource allocation method for the symbiotic descelation large-scale MIMO system, the descelation large-scale MIMO system is combined with the symbiotic radio system, so that the applicability of the symbiotic radio system in a large-scale Internet of things scene is further improved; aiming at the symbiotic honeycomb-removing large-scale MIMO system, under the condition of meeting the power collection constraint of scattering equipment, a resource allocation method for jointly optimizing a combination vector, a power coefficient and a reflection coefficient is provided by taking the sum of the maximum spectrum efficiency of user equipment and the scattering equipment as an objective function; meanwhile, the method considers the symbiotic honeycomb-removing large-scale MIMO system which is accessed by the multi-user equipment and the multi-scattering equipment simultaneously, and optimizes the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment under the condition of meeting the environmental power collection constraint of the scattering equipment; the method of the invention performs joint optimization on a plurality of key resource variables in the symbiotic desceling large-scale MIMO system, namely the combination vector, the power coefficient and the reflection coefficient, so as to maximize the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment in the symbiotic desceling large-scale MIMO system. The beneficial effects are as follows: considering that a large number of devices with different service requirements are accessed simultaneously in a future large-scale Internet of things scene, the method of the invention supports the resource allocation problem of the symbiotic honeycomb-removing large-scale MIMO system accessed by multi-user devices and multi-scattering devices, and maximizes the sum of the frequency spectrum efficiency of the user devices and the scattering devices under the condition of meeting the power collection requirement of the scattering devices; the original optimization problem is split into a plurality of sub-optimization problems through an alternate iterative algorithm, and a resource allocation method for combining the optimization merging vectors, the power coefficients and the reflection coefficients is provided by combining secondary transformation and accelerating a near-end gradient tool. Simulation results show that compared with the traditional full-power transmission method and the equal-reflection transmission method, the resource allocation method achieves higher total spectrum efficiency performance of the system by carrying out joint optimization on a plurality of key resource variables of the system.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention;
Fig. 1 is a schematic diagram of a symbiotic desceling massive MIMO system in accordance with an embodiment of the present invention;
FIG. 2 is a diagram of a time division duplex model of a co-generation cellular massive MIMO system in accordance with an embodiment of the present invention;
FIG. 3 is a graph of total spectral efficiency as a function of scattering device power harvesting requirements in an embodiment of the invention;
Fig. 4 is a graph showing a change of total spectrum efficiency with a maximum transmission power of a ue according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The resource allocation method for the symbiotic descellular large-scale MIMO system of the embodiment comprises the following steps:
Constructing a symbiotic honeycomb-removing large-scale MIMO system model, wherein the system model comprises Individual access pointsIndividual user equipments are distributed in a network service area, each user equipment being provided withAssociated scattering devices, i.e. having/>, in service areaScattering device, no.The individual scattering devices represent theFirst/>, of individual user equipmentsThe associated scattering equipment establishes a data transmission model based on the symbiotic descellular large-scale MIMO system model, and acquires an expression of the reachable spectrum efficiency of the user equipment and the scattering equipment;
By carrying out joint optimization control on a receiving combination vector of user equipment and scattering equipment, a power coefficient of the user equipment and a reflection coefficient of the scattering equipment, aiming at maximizing the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment, and taking meeting the power collection requirement of the scattering equipment as a constraint condition, establishing the problem of maximizing the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment ；
Maximizing sum of spectral efficiency of user equipment and scattering equipmentDecomposing the sub-problems into a receiving combination vector optimization sub-problem and a power-reflection coefficient optimization sub-problem of the user equipment and the scattering equipment, solving the two sub-problems by using an alternate iteration method, and firstly fixing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment in each iteration to find the receiving combination vector of the optimal user equipment and the scattering equipment; then fixing a receiving combination vector of the user equipment and the scattering equipment, and combining and optimizing a power coefficient of the user equipment and a reflection coefficient of the scattering equipment; and repeating the iteration until convergence is met, and obtaining the receiving combination vector of the user equipment and the scattering equipment, the power coefficient of the user equipment and the optimization result of the reflection coefficient of the scattering equipment.
Embodiments contemplate a more practical symbiotic descellular massive MIMO system model, and in particular, consider the case where multi-user devices and multi-scattering devices are distributed within a service area. Aiming at the proposed symbiotic honeycomb-removing large-scale MIMO system, a plurality of key resource variables, namely a combination vector, a power coefficient of user equipment and a reflection coefficient of scattering equipment are jointly optimized by designing a proper resource allocation method so as to maximize the sum of the spectrum efficiency of the user equipment and the scattering equipment, wherein the constraint condition is the value limit of the power coefficient and the transmission coefficient and the power collection limit of the scattering equipment. And an algorithm based on secondary transformation and accelerating a near-end gradient tool is provided for solving. Compared with the traditional resource allocation method, the resource allocation method of the embodiment has better performance.
As shown in fig. 1, a symbiotic de-cellular massive MIMO system model was constructed, and in particular,Individual access pointsIndividual user equipment is distributed in a network service area. The vicinity of each user equipment is equipped withAssociated scattering devices, i.e. having/>, in service areaAnd a scattering device. FirstThe individual scattering devices represent theFirst/>, of individual user equipmentsAnd associated scattering devices. According to the idea of a traditional de-cellular massive MIMO system, all user equipment and scattering devices can be served by all access points under the same time-frequency resource. All access points communicate with the CPU over a backhaul link. The access point performs only a simple signal reception process, and a complex calculation process is completed by the CPU.
Based on the idea of the traditional declustering large-scale MIMO system, the proposed symbiotic declustering large-scale MIMO system adopts a time division duplex working mode, and each coherent time block is divided into two processing stages: pilot training and data transmission. Due to the application of the symbiotic radio architecture, not only the direct link channel but also the backscatter link channel needs to be estimated during the pilot training phase. A specific time division duplex model of a symbiotic de-cellular massive MIMO system is shown in fig. 2.
Each user device, its associated scattering device and the serving access point constitute a localized symbiotic radio system. Each user equipment transmits a radio frequency signal carrying data and energy to an access point to form a direct link. Each scattering device is activated by the radio frequency signal of its attached user device and modulates its own data onto the radio frequency signal from the attached user device and scatters the modulated signal to the access point, thereby forming a scattering link.AndRespectively represent from thePersonal user equipment to theDirect channel and slave of individual access pointsThe individual user equipment goes through theScattering device to theScattering channels for the individual access points. From theThe individual user equipment via theScattering device to theCascading backscatter channels of individual access points may passAnd (5) calculating to obtain the product. WhereinAndRespectively represent from theScattering device to thePropagation channel and slave of individual access pointsScattering device to thePropagation channel of individual user equipments,Representing the variableVariance ofRepresenting the variableIs a variance of (c).
Through pilot training, the direct link channel estimation result can be obtained asDirect link channel estimation error isScattered link channel estimation result isScattered link channel estimation error is，Representing direct link channel estimation errorVariance ofRepresenting scattered link channel estimation errorIs a variance of (c).
A. Data transmission model
Assume the firstThe transmission symbol of the individual user equipment isAnd the transmission symbol satisfies. ThenThe received signals of the individual scattering devices are:
(1)
Wherein, Represents theTransmission power of individual user equipments,Represents thePersonal scattering device andChannel noise between individual user equipments,Representing the statistical variance of the noise, i.e. the noise strength. Next, theThe individual scattering devices are based on their reflection coefficientFor the slaveThe radio frequency signals received by the individual user equipment are decomposed. Specifically/>, of the received signalPart is used for modulation and back scattering of its own data, while the remainder/>, of the received signalPart is used to collect power. Thus, theThe collected power of the individual scattering devices is:
(2)
Wherein, Is the power collection efficiency,Represents theScattering device andVariance of transmission channels between individual user equipments. LetThe transmission sign of the individual scattering devices isAnd the transmission symbol satisfies. ThenThe received signals of the access points are:
(3)
Wherein, Is the received noise.
B. Spectral efficiency analysis
All access points forward the received signals to the CPU via the backhaul link, which completes the reception and combination of the direct link signals by multiplying the transmitted data from the access points with the corresponding combining vectors. For decoding the firstThe data symbols of the user equipment are received and combined by the CPU, and the result is that:
(4)
Wherein, AndRespectively represent theThe received combining vector of the individual user equipment and the received signal set vector of the access point. FirstThe data symbols of the individual user equipments will followIs obtained by decoding. To get theThe spectrum efficiency expression of each user equipment, developing (4) into:
(5)
Wherein, ，，，，. ForDecoding of data symbols for each user equipment, the first item in (5) being the desired signal, the remaining items being interference and noise. Thus, a decodedThe signal-to-interference-and-noise ratio of the data symbols of the individual user equipment is:/>
(6)
Wherein,，，，，，。，，Can be obtained by the following calculation
(7)
(8)
(9)
Wherein,Is cascade backscatter channelEstimation result of，Representing a scattered link channel estimate.
，. Thus, theThe expression of the reachable spectrum efficiency of the individual user equipment is:
(10)
Wherein, AndRepresenting the lengths of the coherence time block and the data transmission time block, respectively. After the data symbols of the user equipment are obtained through decoding, the CPU receives and combines the signals of the scattering equipment to decode the data symbols of the scattering equipment. At this stage, the CPU first uses successive interference cancellation techniques to reject the known portion from the direct link to reduce the signal-to-interference-and-noise ratio of the scattering device decoding, with the processing result:
(11)
Wherein, . The CPU completes the reception combining of the scattering link signals by multiplying the processed signals with the corresponding scattering device combining vectors. For decoding theThe data symbols of the scattering devices are received and combined by the CPU, and the result is that:
(12)
Wherein, Represents theThe reception of the individual scattering devices combines the vectors. FirstThe data symbols of the individual scattering devices will followIs obtained by decoding. To get theThe spectral efficiency expression for the individual scattering devices, develop (11) as:
(13)
for the first Decoding of the data symbols by the scattering device, the first term in (11) being the desired signal and the remaining terms being interference and noise. Thus, a decodedThe signal-to-interference-and-noise ratio of the data symbols of the individual scattering devices is:
(14)
Wherein, ，，，，。，，The method can be obtained by the following calculation:
(15)
(16)
(17)
Thus, the first The expression of the achievable spectral efficiency of the individual scattering devices is:
(18)
up to this point, an expression of the achievable spectral efficiency of the user equipment and the scattering device is obtained.
The sum of the spectral efficiency of the user equipment and the scattering equipment maximizes the problem;
For the symbiotic decellular large-scale MIMO system, the main aim is to maximize the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment by carrying out joint optimization control on the combination vector, the power coefficient and the reflection coefficient under the constraint of meeting the power collection requirement of the scattering equipment. The specific optimization problem is as follows:
(19)
Wherein, ，，，Representing the reflection coefficient of the scattering device,，Represents theReachable spectral efficiency of individual user equipments,Represents theReachable spectral efficiency of individual scattering devices,，，AndRepresenting the length of the coherence time block and the data transmission time block, respectively,Representation and variableRelatedSignal-to-interference-and-noise ratio of individual user equipment data symbols,Representation and variableRelatedSignal-to-interference-and-noise ratio of data symbols of individual scattering devices,AndThe power coefficient and the reflection coefficient are respectively the value constraints,Is a power harvesting requirement constraint for each scattering device. /(I)Represents theReceiving combined vector of individual scattering devices,Represents theThe reception of the combined vector by the individual user equipments,，Represents theTransmission power of individual user equipments,Represents theReflection coefficient of individual scattering devices,，，Represents thePower collected by individual scattering devices,Represents theThe individual scattering devices need to collect the lowest threshold of power.
Problem(s)The method is a complex non-convex optimization problem, solving is considered based on the idea of alternate iteration, and the original joint optimization problem is decomposed into two sub-problems of combined vector optimization and power-reflection coefficient optimization. The specific alternate iterative optimization flow is as follows:
(20)
as shown in (17), the solution process begins with a set of initially feasible solutions. In each iteration, the power coefficient and the reflection coefficient are first fixed, and the optimal combining vector is found. The combining vector is then fixed and the power coefficient and reflection coefficient are optimized in combination. The above process is repeated until convergence is satisfied.
(1) Merging vector optimizations
Firstly, fixing a power coefficient and a reflection coefficient to obtain a combined vector optimization sub-problem of the (i+1) th iteration as follows:
(21)
Since each merge vector is only used when detecting data from the respective device, i.e. the respective merge vectors are not coupled to each other. And the objective function is positively correlated with the spectral efficiency of each device. Thus, the following two optimization problems can be equated:
(22)
(23)
furthermore, the combined vector optimization result of the (i+1) th iteration can be obtained as follows:
(24)
(25)
Wherein, IsAn identity matrix of dimensions. Furthermore, since an alternating iterative algorithm is employed, it is necessary to findIs set to be a constant value. WillThe initial value of (2) is set as follows:
(26)
(27)
(2) Optimizing power-reflection coefficient; after the merging vector optimization result of the (i+1) th iteration is obtained, fixing the merging vector, and obtaining the power-reflection coefficient optimization sub-problem of the (i+1) th iteration is as follows:
(28)
Due to Target functionIs a non-convex function, soIs a non-convex problem that is difficult to solve directly. Thus, the redundancy factor/>, is removedAnd apply a quadratic transformation tool to getThe conversion is as follows:
(29)
Wherein,
(30)
(31)/>
Wherein,AndIs an auxiliary variable introduced by the secondary transformation tool. /(I)，，，The method can be obtained by the following calculation:
(32)
(33)
(34)
(35)
Further, the processing unit is used for processing the data, Can be equivalent to:
(36)
Then, solving based on alternating iteration 、And. I.e., in the i+1 outer layer iteration, the power-reflection coefficient inner layer optimization iteration is included as follows:
(37)
First, the power coefficient and the reflection coefficient are fixed by making the partial derivative Is 0. Obtain the (i+1) -th round of outer layer iterationThe auxiliary variable optimization result of the in-wheel layer iteration is as follows:
(38)
next, the reflection coefficient and the auxiliary variable are fixed, and the obtained power coefficient optimization sub-problem is:
(39)
is a concave function,/> AndIs a linear constraint. Thus,Is a convex optimization problem, which is considered to be solved by the patent by adopting an accelerating near-end gradient tool. The specific solving algorithm is shown in algorithm 1:
Algorithm 1. Solution Accelerating near-end gradient algorithm/>
Initializing: setting up，Respectively representing search step length and convergence accuracy;
Setting up ；
Calculation ofAnd is denoted as；
And (3) loop execution:
；
For a pair of Performing forward domain transformation;
；
；
；
Updating ；
Up to；
Outputting an optimization result:。
Wherein, AndRepresenting gradient calculations and feasible region projections, respectively. For solvingThe accelerating near-end gradient algorithm of (2) has three key operations: gradient computation, feasible-domain projection, and forward-domain transformation. Next, specific calculation expressions for the above three operations will be given.
A. Gradient computation
Gradients with respect to power coefficients can be calculated
Obtained. Wherein/>
(40)
Wherein,. As shown in (40), since the square root operation of the denominator term exists in the gradient operation, it is necessary to secure a variable for calculating the gradientAll elements in (a) are positive numbers. Thus, in the algorithm step, pairA forward domain transformation is performed.
B. forward domain transformation
The positive domain transformation result of (2) can be calculated as follows:
(41)
Wherein, Is set to a sufficiently small positive number. By positive domain transformation, calculation errors caused by negative square roots in the algorithm can be effectively avoided. After the iteration is finished, the iteration is equal toThe value of the variable of (2) is set back to 0.
C. Feasible region projection
Feasible region projectionEssentially, the following minimum Euclidean distance problem is solved:
(42)
Can be easily obtained The optimization result of (2) is:
(43)
after the optimized result of the power coefficient is obtained by the accelerating near-end gradient tool, the vector and the power coefficient are combined and fixed, and the optimized sub-problem of obtaining the reflection coefficient is as follows:
(44)
similarly, the problem is also a convex optimization problem, and the problem is solved by using an accelerating near-end gradient tool similar to the power coefficient optimization problem, and the description is not repeated here.
Furthermore, since an alternate iterative solution framework is employed, an initial feasible solution for the power coefficient and reflection coefficient needs to be obtained. In the present embodiment, the initial feasible solution of the reflection coefficient is set toThe initial feasible solution for the power coefficient is obtained by solving the following problem:
(45)
readily available initial feasible solution for power coefficients
(46)
To this end, an initial feasible solution for the power coefficient and the reflection coefficient is obtained. To sum up, solveThe specific algorithm flow of (a) is shown in algorithm 2.
Algorithm 2 solvingIs an alternating iterative algorithm of (a)
Initializing: setting up，Represents convergence accuracy of the outer layer cycle;
Calculated from (26), (27), (46) And；
Calculation ofAnd is denoted as；
And (3) loop execution:
From (24), (25) ；
Setting up，Represents convergence accuracy of the inner layer cycle;
Setting up ；
Calculation ofAnd is denoted as；
And (3) loop execution:
Calculated from (38) ；
FixingAccelerating near-end gradient tool acquisition/>, based on algorithm table 1；
FixingObtaining/>, based on accelerating proximal gradient tool；
Updating；
Up to；/>
Updating；
Updating；
Up to；
Outputting an optimization result:。
In order to better embody the effectiveness of the invention, the embodiment carries out a simulation experiment, and the performance of the resource allocation method provided by the invention is analyzed through simulation. The system comprises 100 access points, 20 user equipment, and 3 associated scattering devices arranged nearby each user equipment. The channels of the system are subject to independent rayleigh fading.
Fig. 3 illustrates the performance of joint resource allocation using an algorithm. Consider that the maximum transmit power of the user equipment is 20dBm at this time. The resource allocation method based on the secondary transformation and the accelerating near-end gradient tool, the full-power transmission method and the equal-reflection transmission method are compared in the aspect of the performance of the total frequency spectrum efficiency of the system. And under the full power transmission method, all power coefficients are the maximum transmission power, and then an algorithm is used for carrying out the combined optimization of the combining vector and the reflection coefficient. Under the equal reflection transmission method, all reflection coefficients are set to 0.5, and the combination vector and power coefficient are optimized at this time. Simulation shows that the reachable spectrum efficiency of the three resource allocation methods is reduced along with the increase of the power collection requirement of the scattering equipment, and the reachable spectrum efficiency of the proposed resource allocation method is optimal, because the proposed resource allocation method jointly optimizes the merging vector, the power coefficient and the reflection coefficient, the additional spectrum efficiency performance gain can be obtained. And the performance of the full power distribution method is slightly poorer than that of the maximum transmission power distribution method, which shows that the spectrum efficiency performance gain caused by optimizing the reflection coefficient relative to the power coefficient is more remarkable.
Fig. 4 shows a plot of the total spectral efficiency of the system as a function of the maximum transmit power of the user equipment. Consider that the power harvesting requirement of the scattering device is 0.005mW at this time. It can be observed that as the maximum transmit power of the ue increases, the total spectral efficiency of the system increases for all three transmission methods. This is the expected result, as an increase in the maximum transmit power of the user equipment helps to improve the signal to noise ratio, thereby reducing the effect of channel noise on spectral efficiency performance. In addition, as the maximum transmission power of the user equipment continues to increase, the rate of rise of the spectral efficiency gradually slows down, since as the signal transmission power continues to increase, a determinant affecting the spectral efficiency performance becomes inter-transmission interference, and the influence of channel noise on the spectral efficiency performance becomes weaker. Furthermore, it can be observed that the proposed resource allocation method has an optimal spectrum efficiency optimizing effect.
According to the resource allocation method for the symbiotic descelation large-scale MIMO system, the descelation large-scale MIMO system is combined with the symbiotic radio system, so that the applicability of the symbiotic radio system in a large-scale Internet of things scene is further improved; aiming at the symbiotic honeycomb-removing large-scale MIMO system, under the condition of meeting the power collection constraint of scattering equipment, a resource allocation method for jointly optimizing a combination vector, a power coefficient and a reflection coefficient is provided by taking the sum of the maximum spectrum efficiency of user equipment and the scattering equipment as an objective function; meanwhile, the method considers the symbiotic honeycomb-removing large-scale MIMO system which is accessed by the multi-user equipment and the multi-scattering equipment simultaneously, and optimizes the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment under the condition of meeting the environmental power collection constraint of the scattering equipment; the method of the invention performs joint optimization on a plurality of key resource variables in the symbiotic desceling large-scale MIMO system, namely the combination vector, the power coefficient and the reflection coefficient, so as to maximize the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment in the symbiotic desceling large-scale MIMO system. The beneficial effects are as follows: considering that a large number of devices with different service requirements are accessed simultaneously in a future large-scale Internet of things scene, the method of the invention supports the resource allocation problem of the symbiotic honeycomb-removing large-scale MIMO system accessed by multi-user devices and multi-scattering devices, and maximizes the sum of the frequency spectrum efficiency of the user devices and the scattering devices under the condition of meeting the power collection requirement of the scattering devices; the original optimization problem is split into a plurality of sub-optimization problems through an alternate iterative algorithm, and a resource allocation method for combining the optimization merging vectors, the power coefficients and the reflection coefficients is provided by combining secondary transformation and accelerating a near-end gradient tool. Simulation results show that compared with the traditional full-power transmission method and the equal-reflection transmission method, the resource allocation method achieves higher total spectrum efficiency performance of the system by carrying out joint optimization on a plurality of key resource variables of the system.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.

Claims (10)

1. The resource allocation method for the symbiotic honeycomb-removing large-scale MIMO system is characterized by comprising the following steps:
Constructing a symbiotic honeycomb-removing large-scale MIMO system model, wherein the system model comprises Individual access pointsIndividual user equipments are distributed in a network service area, each user equipment being provided withAssociated scattering devices, i.e. in the service areaScattering device, no.The individual scattering devices represent theFirst/>, of individual user equipmentsThe associated scattering equipment establishes a data transmission model based on the symbiotic descellular large-scale MIMO system model, and acquires an expression of the reachable spectrum efficiency of the user equipment and the scattering equipment;
By carrying out joint optimization control on a receiving combination vector of user equipment and scattering equipment, a power coefficient of the user equipment and a reflection coefficient of the scattering equipment, aiming at maximizing the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment, and taking meeting the power collection requirement of the scattering equipment as a constraint condition, establishing the problem of maximizing the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment ；
Maximizing sum of spectral efficiency of user equipment and scattering equipmentDecomposing the sub-problems into a receiving combination vector optimization sub-problem and a power-reflection coefficient optimization sub-problem of the user equipment and the scattering equipment, solving the two sub-problems by using an alternate iteration method, and firstly fixing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment in each iteration to find the receiving combination vector of the optimal user equipment and the scattering equipment; then fixing a receiving combination vector of the user equipment and the scattering equipment, and combining and optimizing a power coefficient of the user equipment and a reflection coefficient of the scattering equipment; and repeating the iteration until convergence is met, and obtaining the receiving combination vector of the user equipment and the scattering equipment, the power coefficient of the user equipment and the optimization result of the reflection coefficient of the scattering equipment.
2. The resource allocation method for symbiotic descellular massive MIMO system of claim 1 wherein the data transmission model is the firstThe specific expression of the power collected by each scattering device is as follows: Wherein/> Representing power harvesting efficiency,Represents theReflection coefficient of individual scattering devices,Represents theTransmission power of individual user equipments,Representing the variableVariance ofThe representation is from the firstScattering device to thePropagation channels of the individual user equipments.
3. The resource allocation method for symbiotic de-cellular massive MIMO system of claim 1 wherein the firstThe expression of the reachable spectrum efficiency of each user equipment is as follows: /(I)，、Representing the length of the coherence time block and the data transmission time block, respectively,Represents theSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,，，，Represents theIndividual user equipment pairReception combining vector of individual access points,，Representing direct link channel estimation results,，Represents theThe transmit power of the individual user equipment is,，，，，，，Represents theReflection coefficient of individual scattering devices,，Representing direct link channel estimation errorsVariance ofRepresenting scattered link channel estimation errorIs a variance of (c).
4. A resource allocation method for symbiotic de-cellular massive MIMO system according to claim 3, characterized by the firstThe expression of the achievable spectral efficiency of the individual scattering devices is: /(I)，、Representing the length of the coherence time block and the data transmission time block, respectively,Represents theSignal-to-interference-and-noise ratio of data symbols of individual scattering devices,Wherein，Represents theIndividual scattering device pairsReception combining vector of access point,，，，，Representing noise intensity,，Represents theReflection coefficient of the individual scattering devices.
5. The resource allocation method for symbiotic decell-oriented massive MIMO system of claim 1 wherein the sum of the spectral efficiency of the user equipment and the scattering equipment maximizes the problemThe specific expression is as follows: Wherein/> ，，，Representing the reflection coefficient of the scattering device,，Represents theReachable spectral efficiency of individual user equipments,Represents theReachable spectral efficiency of individual scattering devices,，，AndRepresenting the length of the coherence time block and the data transmission time block respectively,Representation and variableRelatedSignal-to-interference-and-noise ratio of individual user equipment data symbols,Representation and variableRelatedSignal-to-interference-and-noise ratio of data symbols of individual scattering devices,Represents theReceiving combined vector of individual scattering devices,Represents theReception combining vector of individual user equipments,，Represents theTransmission power of individual user equipments,Representing the maximum threshold of the transmission power of all user equipments,Represents theReflection coefficient of individual scattering devices,，，Represents thePower collected by individual scattering devices,Represents theThe individual scattering devices need to collect the lowest threshold of power.
6. The resource allocation method for symbiotic descelation massive MIMO system according to claim 5, wherein the step of fixing the power coefficient of the ue and the reflection coefficient of the scattering device, finding the best combining vector of the ue and the scattering device comprises:
fixing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment to obtain the combined vector optimization sub-problem of the (i+1) th iteration The method comprises the following steps: /(I)；
Optimizing the merging vectors sub-problemEquivalent to two optimization problemsAnd：，，Representation and variableRelatedSignal-to-interference-and-noise ratio of individual user equipment data symbols,Representation and variableRelatedSignal-to-interference-and-noise ratio of the data symbols of the individual scattering devices;
the receiving combination vector optimization result of the user equipment and the scattering equipment in the (i+1) th iteration is as follows:
；
， Representation/> An identity matrix of dimensions.
7. The resource allocation method for symbiotic descelation massive MIMO system according to claim 6, wherein the combining of the received combining vectors of the fixed user equipment and the scattering equipment, and the optimizing of the power coefficient of the user equipment and the reflection coefficient of the scattering equipment, specifically comprises the following steps:
After obtaining the receiving combination vector optimization result of the user equipment and the scattering equipment of the (i+1) th iteration, fixing the receiving combination vector to obtain the power-reflection coefficient optimization sub-problem of the (i+1) th iteration The method comprises the following steps: /(I); Removing redundancy factorApplication of a quadratic transformation tool willThe conversion is as follows: Wherein, the method comprises the steps of, wherein, ，
，AndIs an auxiliary variable introduced by the secondary transformation tool; /(I)，，，The specific expression is as follows:，，，；
power-reflection coefficient optimization sub-problem of i+1th round iteration with redundancy factor removed Equivalent is：; Fixing the power coefficient and the reflection coefficient of the user equipment, and obtaining the (i+1) th round of outer layer iteration by making the partial derivative be 0The auxiliary variable optimization result of the in-wheel layer iteration is as follows: fixing the reflection coefficient and the auxiliary variable to obtain the power coefficient optimization subproblem/>, of the user equipment The method comprises the following steps: /(I); Solving/>, using an accelerated near-end gradient toolAfter the optimization result of the power coefficient of the user equipment is obtained, fixing the combination vector and the power coefficient of the user equipment to obtain the optimization subproblem/>, of the reflection coefficientThe method comprises the following steps: /(I)；
Solving using an accelerated near-end gradient toolAnd obtaining the optimization result of the reflection coefficient.
8. The resource allocation method for symbiotic decellular massive MIMO system of claim 7 wherein the power coefficient optimization sub-problem for user equipment is adopted by an accelerated near-end gradient toolThe method comprises the following steps of:
Step 1, initializing: setting up ，Respectively representing search step length and convergence accuracy;
Step 2, setting ；
Step 3, calculatingAnd is denoted as；
Step 4,；
Step 5, pairingPerforming forward domain transformation;
Step 6, ，AndRepresenting gradient calculation and feasible region projection respectively;
Step 7, ；
Step 8,；
Step 9, ifUpdateReturning to the step 4; if it isOutput optimization results。
9. The resource allocation method for symbiotic decellular massive MIMO system of claim 7 wherein the initial feasible solution of the reflection coefficient of the scattering device is set as before the alternate iterative solutionAn initially viable solution to the power coefficient of the user equipment is obtained by solving the problemThe method comprises the following steps: /(I); The initial feasible solution of the power coefficient of the user equipment is obtained，Representing the variableVariance ofThe representation is from theScattering device to thePropagation channels of the individual user equipments.
10. The resource allocation method for symbiotic decell-oriented massive MIMO system of claim 7 wherein the sum of the spectral efficiency of the user equipment and the scattering equipment maximizes the problemThe solving process specifically comprises the following steps:
Step 1, setting ，Represents convergence accuracy of the outer layer cycle;
Step 2, from the formula ，
Formula (VI)And formula (VI)CalculatedAnd，Represents thePower harvesting efficiency of the individual scattering devices;
step 3, calculating And is denoted as；
Step 4, according to the formulaFormulaCalculated to obtain；
Step 5, setting，Represents convergence accuracy of the inner layer cycle;
Step 6, setting ；
Step 7, calculatingAnd is denoted as；
Step 8, by the formulaCalculated；
Step 9, fixingObtaining/>, based on accelerating proximal gradient tool；
Step 10, fixingObtaining/>, based on accelerating proximal gradient tool；
Step 11, updatingIfReturning to the step 8; ifStep 12 is implemented;
step 12, updating UpdateIf (if)Returning to the step 4; if it isOutputting an optimization result: /(I)。
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