CN108260215A - The resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code - Google Patents

The resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code Download PDF

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CN108260215A
CN108260215A CN201810052000.4A CN201810052000A CN108260215A CN 108260215 A CN108260215 A CN 108260215A CN 201810052000 A CN201810052000 A CN 201810052000A CN 108260215 A CN108260215 A CN 108260215A
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base station
user
resource allocation
noma
subcarrier
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CN108260215B (en
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费泽松
于含笑
叶能
李斌
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Beijing Institute of Technology BIT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0001—Arrangements for dividing the transmission path
    • H04L5/0003—Two-dimensional division
    • H04L5/0005—Time-frequency
    • H04L5/0007—Time-frequency the frequencies being orthogonal, e.g. OFDM(A), DMT
    • H04L5/0008—Wavelet-division
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0091—Signaling for the administration of the divided path
    • H04L5/0094—Indication of how sub-channels of the path are allocated
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality

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

Abstract

The present invention proposes the resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code, belongs to wireless communication field.There will be this phenomenons of different channels situation between user by matching for the method for the present invention, it is proposed a kind of resource distribution mode that can maximize system and rate, the channel condition information of the user obtained by base station, under conditions of sparsity constraints and power constraint is ensured, establish the optimization problem maximized with rate, and pass through and convert the non-convex optimization problem as a series of convex problems, and solved by iterative algorithm, obtain RE mappings and the power allocation scheme of near-optimization.Relative to existing resource allocation methods, improve low-density code NOMA systems and rate, and improve block error rate (BLER) performance.

Description

The resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code
Technical field
The present invention relates to the resource allocation methods that channel conditions in a kind of NOMA of low-density code optimize, and belong to wireless communication Field.
Background technology
Non-orthogonal multiple access (non-orthogonal multiple access, NOMA) is intended between support large number of equipment Spectral efficient data transmission, be widely regarded as the crucial skill that wireless access in the 5th generation (5G) cordless communication network designs Art.The core concept of NOMA behinds is by the Signal averaging of multiple users on identical radio resource, and using advanced more Receiver user distinguishes superposed signal.In a variety of NOMA technologies, a kind of important NOMA technologies use low-density code (low density signature, LDS) carries out signal identification, by using the openness of this yard, may be used based on message Pass-algorithm (message passing algorithm, MPA) receiver, can be in guarantee system in the complexity that can be born Spectrum efficiency well is obtained under the premise of degree.Typical such NOMA technologies include LDS Orthogonal Frequency Division Multiplexing (OFDM), non-rule Then LDS, low-density code multiple access (sparse code multiple access, SCMA) and pattern divide multiple access to access (pattern division multiple access, PDMA) etc..
Resource allocation is the key technology in the NOMA based on low-density code, it is by the signal of each user with sparse side Formula is mapped in resource grid (resource element, RE).Resource allocation mode can be roughly divided into two classes：1) all users Distribute that resource grid number is equal and user's number also equal RE mappings in each resource grid, we term it rules to map；2) Distribute resource grid number unequal RE mappings between user, we term it irregular mappings.Rational resource allocation mode can To improve throughput of system, the complexity of receiving terminal MPA is reduced.Therefore we match same asset in NOMA systems User usually there is visibly different channel condition this discovery, devise Resource Allocation Formula, enable the system to reach higher And capacity.
The existing design of resource impact pattern in NOMA systems, segmentation scheme is not according to the channel condition pair of user Mapping scheme is designed, and segmentation scheme decouples RE mappings and power allocation procedure, is designed respectively, therefore, these set It is not best to count in the target for obtaining maximum system and capacity.
In the NOMA systems for being currently based on low-density code, radio resource allocation problem is typically to enable user in stent It is selected in the allocation model of formula, and the criterion designed is typically based on computation complexity and considers or reduce user's interference.
Invention content
It is an object of the invention to overcome the problems, such as in existing radio resource allocation, usually not in view of existing between user Different channel conditions, and the different channel conditions of user to obtain not when they take different resource allocation modes Same transmission rate and bit error rate performance, it is proposed that the resource allocation side that channel conditions optimize in a kind of NOMA of low-density code Method, it is contemplated that there will be this phenomenon of different channels situation between matching user, propose a kind of system and rate of can maximizing Resource distribution mode, including RE mappings and the design of power distribution.
The present invention core concept be：It is designed in the low-density code NOMA systems that there is different channels gain in matching user Best sparse RE mappings and power allocation pattern；It is primarily based in the NOMA systems of low-density code, base station matches N number of user M subcarrier is shared, due to user distance base station distance difference, these users have different large-scale declines, due to small ruler Degree decline, it is also different in the channel parameter of each subcarrier for specific user；Base station can obtain the complete of user Channel condition information, by openness and power constraint and rate optimized problem is formulated according to channel condition information, by punishing The sparsity constraints of object function is penalized to be translated into a series of convex problems, and pass through a kind of iterative algorithm, have obtained resource point With scheme, allocation plan is fed back to user by base station, and user carries out RE mappings and power according to the Resource Allocation Formula received Distribution, the resource allocation algorithm based on subscriber channel information in a kind of non-orthogonal multiple access system of low-density code, including with Lower step：
Step 1：Base station carries out channel estimation to the N number of user that will use identical M subcarrier transmission data, obtains its letter Road information, wherein i-th of user is expressed as h in the channel condition information of j-th of subcarrieri,j；
Step 2：Base station calculates N number of user attainable and appearance on M subcarrier according to the channel condition information of acquisition It measures, is：
Wherein, S=[s1,s2,…,sN] to need the resource assignment matrix that designs, σ2For noise power spectral density, si,jFor The element of the i-th rows of S jth row, the power that i-th of user of statement will transmit signal on j-th of subcarrier, works as si,jWhen=0, i-th A user does not transmit signal on j-th of subcarrier；Log represents log operations；
Step 3：Base station is needed in view of Receiver Complexity in tolerance interval, therefore the signal on each subcarrier Number cannot be too big, therefore base station sets a maximum value nmax, and obtain shown in Receiver Complexity constraint equation below (2)：
Wherein,Jth for S arranges,For0 norm, i.e.,In non-zero element number；
Step 4：Base station considers that each user's transmission power need to be can be transmitted power by the maximum of user and be limited, therefore Obtain power constraint equation below (3)：
Step 5：Base station obtains the optimization problem maximized with capacity under the constraints of step 3 and step 4, such as public Formula (4)：
Step 6：Base station sets a functionWhereinForThe n of middle maximummaxThe sum of a element, expression formula areIt is in vector Element big i establishes new optimization object functionWherein μjFor punishment Coefficient andTherefore the optimization problem in step 5 can be converted to formula (5)：
Step 7：Base station sets iteration factor t=0, initialization changes by the optimization problem obtained in iteration settlement steps to deal 6 For when required maximum iteration tmaxWith judge the error thresholds δ needed for iteration ends, random initializtion meets condition | | si ||1≤ P, 0≤i≤N and si,j>=0 matrix S0；
Step 8：Vector is calculated in base stationWherein：
According to vectorIt is calculated again about Rsum(S) in StNeighbouring approximate functionIts expression formula For (6)：
Step 9：Base station solves the convex problem of following formula (7) using convex optimization tool：
Obtain an optimal solution S to the problem*；
Step 10：Enable rtEqual to argmax0≤r≤1Rsum(St+r(S*-St)), obtain the solution S of the t times iterationt+1；
Step 11：T=t+1 is enabled, if t=tmaxOr ‖ St+1-St‖2＜ δ, then export St+1For the resource allocation after optimization Otherwise matrix goes to step 8；
Step 12：Base station carries out resource block according to resource assignment matrix according to the resource assignment matrix scheduling user of acquisition and reflects It penetrates and power distribution；
So far, from step 1 to step 12, the resource allocation that channel conditions optimize in a kind of NOMA of low-density code is completed Method.
Advantageous effect
The resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code proposed by the present invention, with the prior art It compares, has the advantages that：
1) relative to existing Resource Allocation Formula, i.e., performance is carried out with the RE mapped modes of conventional regular and irregular Comparison, this method optimize subcarrier maps mode and power distribution, substantially increase the sum of low-density code NOMA systems simultaneously Rate, that is, improve system and capacity；
2) algorithm proposed by the invention improves block error rate (BLER) performance, and the block that can reduce system user misses Code check.
Description of the drawings
Fig. 1 is in the resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code of the present invention and embodiment 1 System structure diagram；
Fig. 2 is in the resource allocation methods embodiment 1 that channel conditions optimize in a kind of NOMA of low-density code of the present invention Simulation performance figure.
Specific embodiment
The invention will be further described with specific embodiment below in conjunction with the accompanying drawings.
Embodiment 1
Fig. 1 is in a uplink low-density code NOMA communication system, and user's number is N=6, and subcarrier number is M=4 A system structure diagram, 6 users of base station scheduling shared data on 4 subcarriers, 6 users are by 2 cell sides Edge user, 2 cell intermediate users, 2 Cell Center User compositions.Base station is distributed son and is carried according to the channel conditions of 6 users Wave carries out data transmission to each user, needs to consider that same sub-carrier carrying user number no more than 3, is prevented in assigning process Only decoding complexity is excessively high.
Fig. 2 emulates resource allocation methods proposed by the present invention, is not optimized with by this method randomly selected Systematicness resource allocation methods are compared, and it is N=6 that user, which selects number, in emulation, and subcarrier number is M=4.
It illustrates how to promote system how by constructing resource allocation methods using the present invention with reference to specific implementation step System and capacity reduce bLock error rate：
Step A：Base station carries out channel estimation to the N=6 user that will use identical M=4 subcarrier transmission data, obtains Its channel information is obtained, wherein i-th of user is expressed as h in the channel condition information of j-th of subcarrieri,j；
Step B：Base station calculates 6 users attainable and appearance on 4 subcarriers according to the channel condition information of acquisition It measures, isWherein, S=[s1,s2,…,s6] it is the resource allocation square for needing to design Battle array, si,jFor the element of the i-th rows of S jth row, represent the power that i-th of user will transmit signal on j-th of subcarrier, work as si,j When=0, i-th of user does not transmit signal on j-th of subcarrier；
Step C：Base station is needed in view of Receiver Complexity in tolerance interval, therefore the signal on each subcarrier Number cannot be too big, therefore base station needs to set a maximum value nmax=3, and obtain Receiver Complexity constraint
Wherein,Jth for S arranges,For0 norm, i.e.,In non-zero element number, which represents every Signal number no more than 3 on a subcarrier, therefore can ensure that the complexity of receiver will not be excessively high；
Step D：Each user's transmission power need to be can be transmitted power by the maximum of user and be limited, thus base station establish it is excellent Power constraint need to be considered during change condition
Power summation of i.e. each user on each subcarrier is less than P；
Step E：Base station obtains the optimization problem maximized with capacity under the constraints of step C and step D：
Step F：Base station sets a functionWhereinForThe sum of 3 elements of middle maximum, | |[i]The element big in vector i-th, builds Vertical new optimization object functionWherein μjFor penalty coefficient andTherefore the optimization problem in step 5 can be converted to
Step G：Base station sets iteration factor t=0, greatest iteration by the optimization problem obtained in iteration settlement steps to deal 6 Number tmax=10, random initializtion meets condition | | si||1≤ P, 0≤i≤6 and si,j>=0 matrix S0And smallest error threshold Value δ；
Step H：Base station, which calculates, obtains vectorWhereinDefinition For
According to the vector being calculatedIt is obtained again about Rsum(S) in StNeighbouring approximate function Expression formula is：
Step I：At this point, base station can obtain following convex problems,
This convex problem is solved, obtains an optimal solution S to the problem*；
Step J：Enable rtEqual to argmax0≤r≤1Rsum(St+r(S*-St)), obtain the solution S of the t times iterationt+1；
Step K：T=t+1 is enabled, if t=10 ‖ St+1-St‖2＜ δ, then export St+1For the resource allocation square after optimization Battle array, otherwise goes to step H；
Step L：Base station carries out resource block according to resource assignment matrix according to the resource assignment matrix scheduling user of acquisition and reflects It penetrates and power distribution.
The above is presently preferred embodiments of the present invention, and it is public that the present invention should not be limited to embodiment and attached drawing institute The content opened.It is every not depart from the lower equivalent or modification completed of spirit disclosed in this invention, both fall within the model that the present invention protects It encloses.

Claims (2)

1. a kind of resource allocation methods that channel conditions optimize in NOMA of low-density code, it is characterised in that：It is that one kind can be most The resource distribution mode of bigization system and rate is set in low-density code NOMA systems of the matching user with different channels gain The best sparse RE mappings counted and power allocation pattern；It is primarily based in the NOMA systems of low-density code, base station matches N number of use M subcarrier is shared at family, and due to user distance base station distance difference, these users have different large-scale declines, due to small Scale declines, also different in the channel parameter of each subcarrier for specific user；Base station can obtain the complete of user Whole channel condition information is formulated by openness and power constraint and rate optimized problem according to channel condition information, is passed through The sparsity constraints of punishment object function are translated into a series of convex problems, and pass through a kind of iterative algorithm, have obtained resource Allocation plan is fed back to user by allocation plan, base station, and user carries out RE mappings and work(according to the Resource Allocation Formula received Rate is distributed.
2. the resource allocation algorithm that channel conditions optimize in a kind of NOMA of low-density code according to claim 1, including Following steps：
Step 1：Base station carries out channel estimation to the N number of user that will use identical M subcarrier transmission data, obtains its channel letter Breath, wherein i-th of user is expressed as h in the channel condition information of j-th of subcarrieri,j；
Step 2：Base station calculates N number of user's attainable and capacity on M subcarrier according to the channel condition information of acquisition, For：
Wherein, S=[s1,s2,…,sN] to need the resource assignment matrix that designs, σ2For noise power spectral density, si,jFor S i-th The element of row jth row, the power that i-th of user of statement will transmit signal on j-th of subcarrier, works as si,jWhen=0, i-th of use Signal is not transmitted on j-th of subcarrier in family；Log represents log operations；
Step 3：Base station is needed in view of Receiver Complexity in tolerance interval, therefore the signal number on each subcarrier Cannot be too big, therefore base station sets a maximum value nmax, and obtain shown in Receiver Complexity constraint equation below (2)：
Wherein,Jth for S arranges,For0 norm, i.e.,In non-zero element number；
Step 4：Consider that each user's transmission power need to be can be transmitted power by the maximum of user and be limited, therefore obtain in base station Power constraint equation below (3)：
Step 5：Base station obtains the optimization problem maximized with capacity, such as formula under the constraints of step 3 and step 4 (4)：
Step 6：Base station sets a function, whereinForIn Maximum nmaxThe sum of a element, expression formula are|·|[i]It is big in vector i-th Element, establish new optimization object functionWherein μjIt is for punishment Number andTherefore the optimization problem in step 5 can be converted to formula (5)：
Step 7：Base station sets iteration factor t=0, when initializing iteration by the optimization problem obtained in iteration settlement steps to deal 6 Required maximum iteration tmaxWith judge the error thresholds δ needed for iteration ends, random initializtion meets condition | | si||1≤ P, 0≤i≤N and si,j>=0 matrix S0；
Step 8：Vector is calculated in base stationWherein：
According to vectorial BIt is calculated again about Rsum(S) in StNeighbouring approximate functionIts expression formula is (6)：
Step 9：Base station solves the convex problem of following formula (7) using convex optimization tool：
Obtain an optimal solution S to the problem*；
Step 10：Enable rtEqual to argmax0≤r≤1Rsum(St+r(S*-St)), obtain the solution S of the t times iterationt+1；
Step 11：T=t+1 is enabled, if t=tmaxOr ‖ St+1-St‖2＜ δ, then export St+1For the resource allocation square after optimization Battle array, otherwise goes to step 8；
Step 12：Base station according to acquisition resource assignment matrix scheduling user according to resource assignment matrix carry out resource block mapping and Power distribution；
So far, it from step 1 to step 12, completes the resource allocation that channel conditions optimize in a kind of NOMA of low-density code and calculates Method.
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CN108989012A (en) * 2018-07-20 2018-12-11 东北电力大学 Non-orthogonal multiple access technology power distribution method based on fairness
CN108989012B (en) * 2018-07-20 2021-01-01 东北电力大学 Fairness-based power distribution method for non-orthogonal multiple access technology
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CN110113082B (en) * 2019-03-20 2021-12-07 中山大学 Method for solving robust safety and rate optimization problem of multi-antenna non-orthogonal multiple access system based on orthogonal space-time block coding transmission
CN110868238A (en) * 2019-11-08 2020-03-06 南京邮电大学 Multi-address sequence construction method for realizing low-complexity high-spectrum efficiency
CN110868238B (en) * 2019-11-08 2021-05-11 南京邮电大学 Multi-address sequence construction method for realizing low-complexity high-spectrum efficiency
CN112261713A (en) * 2020-10-22 2021-01-22 岭南师范学院 Multi-input single-output NOMA system power distribution method based on matched filtering precoding

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