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CN117979325B - 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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CN117979325B
CN117979325B CN202410371522.6A CN202410371522A CN117979325B CN 117979325 B CN117979325 B CN 117979325B CN 202410371522 A CN202410371522 A CN 202410371522A CN 117979325 B CN117979325 B CN 117979325B
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scattering
user equipment
equipment
power
individual
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CN117979325A (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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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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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 Access pointsIndividual user equipments are distributed in a network service area, each user equipment being provided in the vicinity thereofAssociated scattering devices, i.e. in the service areaA scattering device (a)The individual scattering devices represent the firstThe first user equipmentThe 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: Wherein Indicating the efficiency of the power collection,Represent the firstThe reflection coefficient of the individual scattering devices,Represent the firstThe 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, Represent the firstSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,，，，Represent the firstIndividual user equipment pairsThe reception of the combined vectors by the individual access points,，Representing the result of the direct link channel estimation,，Represent the firstThe transmit power of the individual user equipment is,，，，，，，，Represent the firstThe reflection coefficient of the 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, Represent the firstSignal-to-interference-and-noise ratio of the data symbols of the individual scattering devices,Wherein，，Represent the firstPairs of scattering devicesThe reception of the access point incorporates the vector,，，，。
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,，Represent the firstThe achievable spectral efficiency of the individual user equipment,Represent the firstThe achievable spectral efficiency of the individual scattering devices,，，AndRepresenting the length of the coherence time block and the data transmission time block respectively,Representation and variableRelated firstSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,Representation and variableRelated firstSignal-to-interference-and-noise ratio of the data symbols of the individual scattering devices,Represent the firstThe reception of the individual scattering devices combines the vectors,Represent the firstThe reception of the combined vector by the individual user equipments,，Represent the firstThe transmit power of the individual user equipment is,Representing the maximum threshold of transmit power for all user devices,Represent the firstThe reflection coefficient of the individual scattering devices,，，Represent the firstThe power collected by the individual scattering devices,Represent the firstThe 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:；
optimizing the merging vectors sub-problem Equivalent to two optimization problemsAnd：，，Representation and variableRelated firstSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,Representation and variableRelated firstSignal-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 factors Using a secondary transformation tool toThe conversion is as follows: wherein, the method comprises the steps of, wherein, ，，AndIs an auxiliary variable introduced by the secondary transformation tool;，，， the specific expression is as follows: ，，，；
power-reflection coefficient optimization sub-problem of i+1th round iteration with redundancy factor removed Equivalent to：；
Fixing the power coefficient and reflection coefficient of the user equipment by making the partial derivative0, Get the (i+1) th round of outer layer iterationThe 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 sub-problem of the user equipment The method comprises the following steps:；
solving using an accelerated near-end gradient tool After 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 sub-problem 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 recorded as the objective function value of (2)；
Step 4,；
Step 5, pairingPerforming forward domain transformation;
Step 6, ，AndRepresenting gradient calculation and feasible region projection respectively;
Step 7, ；
Step 8,；
Step 9, ifUpdatingReturning to the step 4; if it isOutputting the optimized result。
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 a user equipment by solving a problemThe method comprises the following steps: ; obtaining the initial feasible solution of the power coefficient of the user equipment 。
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 formulaFormula (VI)And formula (VI)Calculated to obtainAnd，Represent the firstPower harvesting efficiency of the individual scattering devices;
Step 3, calculating And is recorded as the objective function value of (2)；
Step 4, according to the formulaFormula (VI)Calculated to obtain；
Step 5, setting；
Step 6, setting；
Step 7, calculatingAnd is recorded as the objective function value of (2)；
Step 8, by the formulaCalculated to obtain；
Step 9, fixingBased on accelerating proximal gradient tool；
Step 10, fixingBased on accelerating proximal gradient tool；
Step 11, updatingIf (if)Returning to the step 8; if it isStep 12 is implemented;
step 12, updating UpdatingIf (if)Returning to the step 4; if it isOutputting an optimization result:。
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 Access pointsIndividual user equipments are distributed in a network service area, each user equipment being provided in the vicinity thereofAssociated scattering devices, i.e. in the service areaA scattering device (a)The individual scattering devices represent the firstThe first user equipmentThe 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,Access pointsIndividual user equipment is distributed in a network service area. Each user equipment is provided with a nearbyAssociated scattering devices, i.e. in the service areaAnd a scattering device. First, theThe individual scattering devices represent the firstThe first user equipmentAnd 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 the firstIndividual user equipment to the firstDirect channel and slave of individual access pointsThe individual user equipment goes through the firstScattering devices to the firstScattering channels for the individual access points. From the firstThe individual user equipment via the firstScattering devices to the firstCascade backscatter channels of individual access points may pass throughAnd (5) calculating to obtain the product. Wherein,AndRespectively represent from the firstScattering devices to the firstPropagation channel and slave of each access pointScattering devices to the firstThe propagation channels of the individual user equipments,Representing variablesIs a function of the variance of (a),Representing variablesIs a variance of (c).
Through pilot training, the direct link channel estimation result can be obtained asThe direct link channel estimation error isThe scattered link channel estimation result isThe scattered link channel estimation error is，Representing direct link channel estimation errorsIs a function of the variance of (a),Representing scattered link channel estimation errorsIs a variance of (c).
A. Data transmission model
Assume the firstThe transmission symbols of the individual user equipments areAnd the transmission symbol satisfies. Then the firstThe received signals of the individual scattering devices are:
(1)
Wherein, Represent the firstThe transmit power of the individual user equipment is,Represent the firstA scattering device and a firstChannel noise between the individual user equipments,Representing the statistical variance of the noise, i.e. the noise strength. Next, the firstBased on its reflection coefficientTo from the firstThe radio frequency signals received by the individual user equipment are decomposed. In particular, receiving signalsPartly for modulation and back-scattering of its own data, while the remainder of the received signalPart is used to collect power. Thus, the firstThe collected power of the individual scattering devices is:
(2)
Wherein, It is the power collection efficiency that is a function of the power collection efficiency,Represent the firstA scattering device and a firstVariance of transmission channels between individual user equipments. Assume the firstThe transmission symbols of the individual scattering devices beingAnd the transmission symbol satisfies. Then the firstThe 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 the firstThe received combining vector of the individual user equipment and the received signal set vector of the access point. First, theThe data symbols of the individual user equipments will be transmitted fromIs obtained by decoding. To get the firstThe spectrum efficiency expression of each user equipment, developing (4) into:
(5)
Wherein, ，，，，. For the firstDecoding of data symbols for each user equipment, the first item in (5) being the desired signal, the remaining items being interference and noise. Thus, the decoding can be obtainedThe 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 a cascade backscatter channelIs used to estimate the result of the estimation of (c),，Representing a scattered link channel estimate.
，. Thus, the firstThe 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 the firstThe data symbols of the scattering devices are received and combined by the CPU, and the result is that:
(12)
Wherein, Represent the firstThe reception of the individual scattering devices combines the vectors. First, theThe data symbols of the individual scattering devices will be derived fromIs obtained by decoding. To get the firstThe 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, the decoding can be obtainedThe 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,，Represent the firstThe achievable spectral efficiency of the individual user equipment,Represent the firstThe achievable spectral efficiency of the individual scattering devices,，，AndRepresenting the length of the coherence time block and the data transmission time block respectively,Representation and variableRelated firstSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,Representation and variableRelated firstSignal-to-interference-and-noise ratio of the data symbols of the 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.Represent the firstThe reception of the individual scattering devices combines the vectors,Represent the firstThe reception of the combined vector by the individual user equipments,，Represent the firstThe transmit power of the individual user equipment is,Represent the firstThe reflection coefficient of the individual scattering devices,，，Represent the firstThe power collected by the individual scattering devices,Represent the firstThe 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, Is thatAn identity matrix of dimensions. Furthermore, since an alternate iterative algorithm is employed, it is necessary to findIs set to be a constant value. Will beThe 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 Is the objective function of (2)Is a non-convex function, soIs a non-convex problem that is difficult to solve directly. Thus, the redundancy factor is removedAnd apply the secondary transformation tool toThe conversion is as follows:
(29)
Wherein,
(30)
(31)
Wherein,AndIs an auxiliary variable introduced by the secondary transformation tool.，，，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 of the target function of (a), AndIs a linear constraint. Thus, the first and second substrates are bonded together,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 Is an accelerated near-end gradient algorithm
Initializing: setting up，Respectively representing search step length and convergence accuracy;
Setting up ；
Calculation ofAnd is recorded as the objective function value of (2)；
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, the 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 value 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 recorded as the objective function value of (2)；
And (3) loop execution:
From (24), (25) ；
Setting up，Represents convergence accuracy of the inner layer cycle;
Setting up ；
Calculation ofAnd is recorded as the objective function value of (2)；
And (3) loop execution:
Calculated from (38) ；
FixingAccelerating near-end gradient tool acquisition based on algorithm table 1；
FixingBased 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 (6)

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 Access pointsIndividual user equipments are distributed in a network service area, each user equipment being provided in the vicinity thereofAssociated scattering devices, i.e. in the service areaA scattering device (a)The individual scattering devices represent the firstThe first user equipmentThe 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;
The method comprises the steps of 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, taking the sum of the maximum spectrum efficiency of the user equipment and the scattering equipment as a target, taking the meeting of the power collection requirement of the scattering equipment as a constraint condition, and establishing a problem P1 of maximizing the sum of the spectrum efficiency of the user equipment and the scattering equipment;
Decomposing a problem P1 of maximizing the sum of the frequency spectrum efficiency of the user equipment and the scattering equipment into a sub-problem of optimizing a receiving combination vector of the user equipment and the scattering equipment and a sub-problem of optimizing a power-reflection coefficient, solving the two sub-problems by using an alternate iteration method, and in each iteration, firstly fixing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment 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; repeating iteration until convergence is met, and obtaining a receiving combination vector of the user equipment and the scattering equipment, a power coefficient of the user equipment and an optimization result of a reflection coefficient of the scattering equipment;
The data transmission model is the first The specific expression of the power collected by each scattering device is as follows: Wherein Indicating the efficiency of the power collection,Represent the firstThe reflection coefficient of the individual scattering devices,Represent the firstThe transmit power of the individual user equipment is,Representing variablesIs a function of the variance of (a),The representation is from the firstScattering devices to the firstPropagation channels of the individual user equipments;
First, the The 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, Represent the firstSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,，，，Represent the firstIndividual user equipment pairsThe reception of the combined vectors by the individual access points,，Representing the result of the direct link channel estimation,，Represent the firstThe transmit power of the individual user equipment is,，，，，，，，Representing concatenated backscatter channelsIs used to estimate the result of the estimation of (c),，Representing scattered link channel estimation errorsIs a function of the variance of (a),Represent the firstThe reflection coefficient of the individual scattering devices,，Representing direct link channel estimation errorsIs a function of the variance of (a),Representing scattered link channel estimation errorsIs a variance of (2);
First, the The 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, Represent the firstSignal-to-interference-and-noise ratio of the data symbols of the individual scattering devices,Wherein，，Represent the firstPairs of scattering devicesThe reception of the access point incorporates the vector,，，，，Representing the intensity of the noise and,，Represent the firstThe reflection coefficient of the individual scattering devices,；
The sum of the spectral efficiency of the user equipment and the scattering equipment maximizes the problem P1, expressed as follows:
，
Wherein, ，，，Representing the reflection coefficient of the scattering device,，Represent the firstThe achievable spectral efficiency of the individual user equipment,Represent the firstThe achievable spectral efficiency of the individual scattering devices,，，AndRepresenting the length of the coherence time block and the data transmission time block respectively,Representation and variableRelated firstSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,Representation and variableRelated firstSignal-to-interference-and-noise ratio of the data symbols of the individual scattering devices,Represent the firstThe reception of the individual scattering devices combines the vectors,Represent the firstThe reception of the combined vector by the individual user equipments,，Represent the firstThe transmit power of the individual user equipment is,Representing the maximum threshold of transmit power for all user devices,Represent the firstThe reflection coefficient of the individual scattering devices,，，Represent the firstThe power collected by the individual scattering devices,Represent the firstThe individual scattering devices need to collect the lowest threshold of power.
2. The resource allocation method for symbiotic descellular massive MIMO system according to claim 1, wherein the power coefficient of the user equipment and the reflection coefficient of the scattering device are fixed, and the best combining vector of the user equipment and the scattering device is found, and the specific steps include:
fixing the power coefficient of the user equipment and the reflection coefficient of the scattering equipment to obtain the combined vector optimization sub-problem P2 of the (i+1) th iteration: ；
the merging vector optimization sub-problem P2 is equivalent to two optimization problems P3 and P4: ，， Representation and variable Related firstSignal-to-interference-and-noise ratio of the data symbols of the individual user equipments,Representation and variableRelated firstSignal-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 of An identity matrix of dimensions.
3. The resource allocation method for a symbiotic descellular massive MIMO system according to claim 2, wherein the combination of the received combining vectors of the fixed user equipment and the scattering equipment, and the combination of the power coefficient of the optimized 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 P5 of the (i+1) th iteration as follows: ；
Removing redundancy factors Using a secondary transformation tool toThe conversion is as follows: wherein, the method comprises the steps of, wherein, ，，AndIs an auxiliary variable introduced by the secondary transformation tool;，，， the specific expression is as follows: ，，，；
The power-reflection coefficient optimization sub-problem P5 of the (i+1) th iteration with the redundancy factor removed is equivalent to P6:
；
Fixing the power coefficient and reflection coefficient of the user equipment by making the partial derivative For 0, the auxiliary variable optimization result of the j-th round of inner layer iteration of the i+1-th round of outer layer iteration is as follows:，
The reflection coefficient and the auxiliary variable are fixed, and the obtained power coefficient optimization sub-problem P7 of the user equipment is as follows:
；
After the optimization result of the power coefficient of the user equipment is obtained by solving P7 by adopting an accelerating near-end gradient tool, the power coefficient of the user equipment and the merging vector are fixed, and the optimization sub-problem P9 of the reflection coefficient is obtained as follows: ；
And solving P9 by adopting an accelerating near-end gradient tool to obtain an optimization result of the reflection coefficient.
4. The resource allocation method for a symbiotic descelation massive MIMO system according to claim 3, wherein the power coefficient optimization sub-problem P7 of the user equipment is solved by using an accelerated near-end gradient tool, and specifically comprises the following steps:
Step 1, initializing: setting up ，Respectively representing search step length and convergence accuracy;
Step 2, setting ；
Step 3, calculatingAnd is recorded as the objective function value of (2)；
Step 4,；
Step 5, pairingPerforming forward domain transformation;
Step 6, ，AndRepresenting gradient calculation and feasible region projection respectively;
Step 7, ；
Step 8,；
Step 9, ifUpdatingReturning to the step 4; if it isOutputting the optimized result。
5. A resource allocation method for symbiotic de-cellular massive MIMO systems according to claim 3, characterized in that the initial feasible solution of the reflection coefficients of the scattering devices before the alternate iterative solution is set toThe initial feasible solution of the power coefficient of the user equipment is obtained by solving the problem P10: ; obtaining the initial feasible solution of the power coefficient of the user equipment ，Representing variablesIs a function of the variance of (a),The representation is from the firstScattering devices to the firstPropagation channels of the individual user equipments.
6. A resource allocation method for symbiotic descellular massive MIMO system according to claim 3, wherein the process of solving the problem P1 of maximizing the sum of the spectral efficiency of the user equipment and the scattering equipment 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)Calculated to obtainAnd，Represent the firstPower harvesting efficiency of the individual scattering devices;
Step 3, calculating And is recorded as the objective function value of (2)；
Step 4, according to the formulaFormula (VI)Calculated to obtain；
Step 5, setting，Represents convergence accuracy of the inner layer cycle;
Step 6, setting ；
Step 7, calculatingAnd is recorded as the objective function value of (2)；
Step 8, by the formulaCalculated to obtain ；
Step 9, fixingBased on accelerating proximal gradient tool；
Step 10, fixingBased on accelerating proximal gradient tool；
Step 11, updatingIf (if)Returning to the step 8; if it isStep 12 is implemented;
step 12, updating UpdatingIf (if)Returning to the step 4; if it isOutputting an optimization result:。
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