CN110492915A - A kind of power distribution method based on the short packet transmission of MIMO-NOMA - Google Patents

A kind of power distribution method based on the short packet transmission of MIMO-NOMA Download PDF

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CN110492915A
CN110492915A CN201910476565.XA CN201910476565A CN110492915A CN 110492915 A CN110492915 A CN 110492915A CN 201910476565 A CN201910476565 A CN 201910476565A CN 110492915 A CN110492915 A CN 110492915A
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user
cluster
noma
indicate
mimo
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冉静学
马传连
石梦倩
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Minzu University of China
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Minzu University of China
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    • 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
    • H04B7/0426—Power distribution
    • 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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/06—TPC algorithms
    • H04W52/14—Separate analysis of uplink or downlink
    • H04W52/143—Downlink power control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
    • H04W52/42—TPC being performed in particular situations in systems with time, space, frequency or polarisation diversity
    • 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
    • H04W72/30—Resource management for broadcast services
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services

Abstract

The invention discloses a kind of power distribution methods based on the short packet transmission of MIMO-NOMA.This method uses short packet transmission correlation theory for the first time, analyzes the remote user communication quality in MIMO-NOMA system；And using the transmission power of user in alternative and iterative algorithm combined optimization cluster and maximum reachable transmission rate under the premise of guaranteeing the communication quality of nearly user, finally obtaining one kind can be realized the remote maximized power distribution method of user throughput.

Description

A kind of power distribution method based on the short packet transmission of MIMO-NOMA
Technical field
The invention belongs to wireless communication fields more particularly to the short packet transimission power of MIMO-NOMA to distribute optimization method.
Background technique
With the development of mobile communication, Internet of Things has become one of the Key driving factors of 5G Communication Development, and most of The non-NOMA of communicating requirement that the application of Internet of Things needs to meet super reliable low time delay can be simultaneously multiple use by power sharing Family provides service, improves throughput of system, to realize that super reliable low time delay communication provides great potential.In order to further increase System spectral efficiency increases the connection number of devices of system, proposes the multi-antenna technology of input multi output (MIMO).MIMO- The combination of NOMA technology provides additional freedom degree, can reduce interference, realizes large-scale connection, further improves system Performance.
In order to meet the requirement of the low time delay in 5G communication, led to using the short packages with limited block length code Letter, it is theoretical no longer suitable for having the case where asymptotic long code word based on Shannon capacity formula in the limited situation of bandwidth With, it is therefore desirable to the analyzing system performance there are short packages.
Currently, it is more using the research that short packet transmission reduces propagation delay time, but studied very in MIMO-NOMA system It is few；Not considering the fairness between user couple in existing research, the effective throughput for having ignored remote user is smaller, thus So that the problem that remote user's time delay is larger.
Summary of the invention
In order to improve the reliability and delay performance in MIMO-NOMA system, the invention discloses one kind to be based on MIMO- The power distribution method of the short packet transmission of NOMA.
The invention discloses a kind of power distribution methods based on the short packet transmission of MIMO-NOMA, include the following steps:
Step A establishes the short packet transmission system model based on MIMO-NOMA, determines transmission rate and handling capacity；
Step B, according to the system model of step A, using broken zero beam forming (Zero Forcing Beam-Forming, ZFBF) technology is interfered between eliminating cluster, is determined and is received signal；
Step C determines distance user's received signal and interference by the performance of user in short packet transmission analysis of strategies cluster Plus noise ratio (Signal to Interference plus Noise Ratio, SINR) and block transmission error probability；
Step D establishes objective optimization model according to the performance of the step C far and near user acquired, with the handling capacity of remote user Maximum turns to optimization aim, and the communication quality to guarantee nearly user, and the transmission rate and system power distribution work of remote user For optimal conditions；
Step E, solving optimization problem realize the optimization of the effective throughput of remote user by power distribution and transmission rate Target.
Wherein, step A is specifically included:
A1, the MIMO-NOMA system for constructing downlink carry out the system channel model of broadcast communication using short packet, wherein Transmitting terminal configures N number of transmitting antenna, and total number of users is NK and each user is equipped with M receiving antenna, and user is divided into N number of cluster, Each cluster can be supported in K user, it is assumed that there are two users in each cluster, and nearly user and remote user respectively indicate ui,1ui,2,i ∈ 1,2 ..., N, i.e., | hi,1|2≥|hi,2|2, hi,kIndicate the channel gain of k-th of user in the i-th cluster；
A2, according to the system model that A1 is constructed, x=[x can be expressed as by sending signal1,x2,...,xN]T si,kExpression is sent to user ui,kInformation, k-th of the i-th cluster use Family distribution power is qi,k, k=1,2, total transimission power is
A3 gives block error probability ε for receiving endi,kAny user ui,k, when transmitting signal under the control of time delay Rate representation beWherein, Li,kIndicate transmission si,kWhen data packet Length, Q-1() indicatesInverse function, Vi,kIndicate channel dispersion, and Vi,k=1- (1+ γi,k)-2, γi,kFor Signal to Interference plus Noise Ratio (the Signal to Interference plus Noise for receiving signal Ratio, SINR)；
A4, according to the R of A3i,kAnd εi,k, utilize handling capacity Ti,kIt measures the performance of system, is expressed as Ti,k=Ri,k(1- εi,k)(bps/Hz)。
Wherein, step B is specifically included:
B1, according to the Transmission system model that step A is determined, transmitting terminal uses normalization beam forming vector wiBelieve sending Number precoding is carried out, the beam forming vector of receiving end is expressed as vi,k, meet | | vi,k||2=1, it is possible to determine and receive letter It number is expressed asWherein Hi,k∈ CM×NIndicate the channel matrix from AP to user, transmitting terminal and user terminal all known channel distributed intelligences, i.e.,di,kIt indicates the distance between from AP end to user, c indicates path loss index, Gi,k∈CM×NClothes From Rayleigh fading, Gi,k~CN (0, ∑i,k), ∑I, kIndicate channel covariance matrices,Indicate additivity White Gaussian noise, I indicate unit matrix；
B2 is interfered between eliminating cluster using ZFBF, and beamforming matrix meets wiIt is the pre-coding matrix of N × N, the constraint representation of k-th of user in n-th of cluster isWherein hn,nkIt has been be eliminated that, indicate Hn,kThe i-th column element；
B3 determines v according to B2n,k, vn,k=Un,kτn,k, wherein Un,kComprising corresponding to zero singular value Hn,kAll left sides it is unusual Vector, τn,kIt is a normalized vector and can be obtained by using maximum-ratio combing
Wherein, step C is specifically included:
C1, the characteristics of according to NOMA multiple access technique, nearly user can pass through serial interference elimination (Successive Interference Cancellation, SIC) interference of remote user is eliminated, and user directly decodes reception signal, i-th of cluster In remote user ui,2Reception signal beWherein
C2, due to | hi,1|2≥|hi,2|2, so ui,2Directly decode si,2, SINR isui,2Decode si,2Block error probability be
C3, ui,1First decode si,2Simultaneously by si,1It is considered as interference noise, decodes si,2SINR beui,1Decode si,2Block error probability be
C4, according to C3, if ui,1S is eliminated in successi,2Interference, then SINR be ui,1Solution Code si,1Block error probability be
Wherein, step D is specifically included:
D1 determines that optimization aim is according to above stepConstraint condition includesqi,k>=0,Wherein front two is the limitation of power, third table Show the limitation of outage probability, γiIndicate that nearly user decodes the limitation of the link SINR of weak user；
D2, according to step D1, the constraint of outage probability can be expressed as,Constraint condition It is expressed asWhereinvi,1hi,1It is that a Gauss becomes Amount, | vi,1hi,1|2Obeying parameter isExponential distribution, be expressed as by maximum operation above formula constraint condition
Include q since the inequality left side is one in D3, step D2i,2And vi,1The non-convex complicated function of two variables, table It is non-convex form up to formula, solves the problems, such as this using SDP technology and first order Taylor approximation, i.e.,First order Taylor approximate solution can be passed throughConstraint condition is converted into convex formula
Wherein, step E is specifically included:
E1, in given feasible qi,1And qi,2To determine remote user throughput Ti,2Ri,2Value, judges relative to Ri,2's Monotonicity and concavity and convexity, Ti,2(Ri,2)=Ri,2(1-εi,2)=Ri,2(1-Q(f(γi,2,Ri,2))), being led by second order may determine that Ti,2It is Ri,2Concave function, local extremum is exactly its global extremum, thus by setting Ti,2=0, and changed using linear search The R of maximum effective throughput is realized for algorithmi,2Optimal value is expressed as
E2, in qi,1And qi,2Feasible region according to the R of optimizationi,2Determine optimization power distribution qi,2, Ti,2(Ri,2, qi,2)=Ri,2(1-εi,2), wherein εi,2It is γi,2And Ri,2Function, γi,2It is qi,2Function, q is judged by derivationi,2It is Ri,2Convex function；
E3 optimizes q using linear search iterative algorithm co-design by giving the range of feasible power distributioni,2And Ri,2Determine ui,2Maximum throughput step, initializing variable,The number of iterations i=0,Convergence precision η >=0；
E4 judges convergence, whenWhen, i=i+1；
E5, ifPass through formula Ti,2(Ri,2)=Ri,2(1-εi,2)=Ri,2 (1-Q(f(γi,2,Ri,2))) calculate optimal transmission rate
E6 is updated according to step E5Calculate optimal transmission power distribution
E7 meets convergence precisionWhen, iteration terminates, and passes through formula Ti,2=Ri,2(1-εi,2) calculate Handling capacity, output
In order to improve the reliability and delay performance of MIMO-NOMA system, the present invention proposes a kind of based on MIMO-NOMA The power distribution method of short packet transmission, utilizes alternative and iterative algorithm combined optimization under the outage probability constraint for guaranteeing nearly user The transmission rate and power distribution of system, maximise the handling capacity of remote user.It may be implemented more compared with MIMO-OMA technology High effective throughput, and under the premise of throughput demands are consistent, when MIMO-NOMA can reduce transmission than MIMO-OMA Prolong.
Detailed description of the invention
The method flow diagram of Fig. 1 embodiment of the present invention；
Multiuser MIMO-NOMA downlink system model in the method for Fig. 2 embodiment of the present invention；
Multi-user data packet transmission structure chart in the method for Fig. 3 embodiment of the present invention；
The nearly user power distribution of outage probability constraint GV.S. of SIC in the method for Fig. 4 embodiment of the present invention；
Remote user can realize the MAR of the remote user of handling capacity V.S. in the method for Fig. 5 embodiment of the present invention；
Remote user can realize handling capacity V.S. power distribution in the method for Fig. 6 embodiment of the present invention；
Remote user can realize handling capacity V.S. data packet length in the method for Fig. 7 embodiment of the present invention.
Specific embodiment
MIMO-NOMA technology can increase equipment connection quantity, the spectrum efficiency of system be effectively improved, for super reliable low Short packet is needed to transmit in time delay applied business, and the MIMO-NOMA system of multi-user's cluster is with cluster user that the remote user in gulps down The method of the lower problem of the amount of spitting, this research and utilization singular value decomposition and maximum-ratio combing is interfered between eliminating cluster, considers nearly user The block error probability of remote user is decoded lower than under the conditions of given threshold value, utilizes the hair of user in alternative and iterative algorithm combined optimization cluster It send power and maximum up to transmission rate, realizes the maximization of remote user throughput.Simulation result shows to transmit with short packet MIMO-OMA system is compared, and under conditions of guaranteeing that block error probability is lower than 10-5, the handling capacity of remote user improves 32%.
It is emulated by MIMO-NOMA system of the MATLAB to short packet transmission.Simulation parameter is configured, transmitting terminal Antenna amount N=4, receiving end antenna amount M=4, the data packet length L of transmissioni,1=Li,2=100, from the end AP to nearly user Transmission range with remote user is respectively di,1=1m, di,2=10m, path loss index c=2.The general power of each cluster is The SINR threshold value that nearly user decodes remote user information is γi=10dB.
With reference to the accompanying drawings and detailed description, the present invention will be further described.
Specific step is as follows for the system model modeling process of the short packet transmission of MIMO-NOMA system:
Step A1, the MIMO-NOMA system for constructing downlink carry out the system model of broadcast communication using short packet；
Step A2 is determined according to the system model that A1 is constructed and is sent signal x=[x1,x2,...,xN]T si,kExpression is sent to user ui,kInformation；
Step A3 determines transmission signalWherein Li,kIndicate transmission si,kWhen data packet length, Q-1() indicatesInverse function, Vi,kIndicate channel dispersion, and Vi,k=1- (1+ γi,k)-2, γi,kFor the SINR for receiving signal；
Process is interfered between elimination MIMO-NOMA system cluster, the specific steps are as follows:
Step B1 determines that receiving signal is expressed as according to the transmission channel model that step A is determined
Step B2 is interfered between eliminating cluster using ZFBF, and beamforming matrix meets wiIt is the pre-coding matrix of N × N；
Step B3 determines v according to B2n,k, vn,k=Un,kτn,k, wherein Un,kComprising corresponding to zero singular value Hn,kIt is all Left singular vector, τn,kIt is a normalized vector and can be obtained by using maximum-ratio combing
The reception SINR and block error probability process of tactful user couple are transmitted by short packet, the specific steps are as follows:
Step C1, eliminating the reception signal of receiving end after interfering between cluster is
Step C2, ui,2Directly decode si,2SINR beBlock error probability is
Step C3, ui,1Decode si,2SINR beBlock error probability is
Step C4, ui,1S is eliminated in successi,2Interference, then SINR beBlock error probability is
Construct objective optimization model process, the specific steps are as follows:
Step D1 determines that optimization aim is according to above stepConstraint condition includesqi,k>=0,Wherein front two is the limitation of power, third table Show the limitation of outage probability, γiIndicate that nearly user decodes the limitation of the link SINR of weak user；
Step D2 determines that the constraint of outage probability can be expressed as according to step D1,
Step D3 converts convex formula for non-convex problem using SDP technology and first order Taylor approximation
Optimize transmission rate and power distribution process, the specific steps are as follows:
Step E1, in given feasible qi,1And qi,2To determine remote user throughput Ti,2Ri,2Value, is judged by derivation Relative to Ri,2Monotonicity and concavity and convexity；
Step E2, in qi,1And qi,2Feasible region according to the R of optimizationi,2Determine optimization power distribution；
Step E3 is optimized by giving the range of feasible power distribution using linear search iterative algorithm co-design qi,2And Ri,2Determine ui,2Maximum throughput step, initializing variable,The number of iterations i=0,Convergence precision η >=0；
Step E4, judges convergence, whenWhen, i=i+1；
Step E5, ifCalculate optimal transmission rate
Step E6 is updated according to step E5Calculate optimal transmission power distribution
Step E7, when meeting convergence precision, iteration terminates, and passes through formula TI, 2=RI, 2(1-εI, 2) calculate handling capacity, output
Short packet transmission in cordless communication network based on MIMO-NOMA system can reduce propagation delay time, increases equipment and connects Quantity is connect, the spectrum efficiency of system is effectively improved.
Inventors have found that non-orthogonal multiple access technology (Non-Orthogonal Multiple Access, NOMA) can To improve spectrum efficiency, increase the connection quantity of equipment；Multiple-input and multiple-output (Multiple Input and Multiple Output, MIMO) technology can efficiently use space resources, and system transfer rate is improved, the bit error rate is reduced.When super reliable low It is small to prolong (Ultra-Reliable and Low-Latency Communication, URLLC) communication scenes transmission error probability In 10-5, propagation delay time is lower than 1ms, and in order to reduce the propagation delay time of physical layer, it is short that URLLC usually utilizes finite length to be grouped Packet (Short Packets, SP) is transmitted.
In short packet transmission, Shannon is approximately no longer applicable in up to capacity criterion, maximum achievable rate (Maximal Achievable Rate, MAR) and limited piece of error probability (Finite Packet Error Probability, FPEP) point Not Cheng Wei system effectiveness and reliability index.Short packet point-to-point transmission system MAR in some documents analysis shows, not only It is related with channel distribution, while also being influenced by block error probability；In order to improve the MAR of system, so that propagation delay time is reduced, Some documents give the short packet communication system space -time code transmission method of point-to-point MIMO, utilize time and space diversity gain, drop The block error probability of low system；The spatial multiplexing gain for analyzing system reduces the propagation delay time of system.Some document utilization networks Differential method gives the propagation delay time of the short packet communication of multiuser MIMO, and application close-to zero beam shapes (Zero Forcing Beam-Forming, ZFBF) method under conditions of Delay Constraint, minimizes the block error probability of system.It can be seen that short packet In communication system, MIMO can use spatial degrees of freedom, the MAR of comprehensive optimization system, FPEP and time delay.NOMA can benefit With power resource, realizes a large amount of equipment access, meet the needs of Internet of Things large-scale equipment access.Therefore some document analysis NOMA short packet communication propagation delay time, and pass through the Block Error Rate of power distribution method optimization system；Some document application power point Method of completing the square sends the power distribution user poor to channel quality for more, optimizes handling capacity, the decoding block of the short packet system of NOMA Error probability and propagation delay time.It can be seen that NOMA can effectively improve the transmission performance of short packet system.MIMO-NOMA exists In short packet communication, some documents have derived the propagation delay time under descending multi-user scene, and limited in time delay and block error probability Under conditions of, it minimizes and sends power.Can be seen that the short packet of MIMO-NOMA according to above research can effectively solve the problem that big rule Mould equipment accesses problem, and utilizes space and power resource, when improving the validity and reliability of system, and reducing transmission Prolong；But the effective throughput of remote user is smaller, so that remote user's time delay is larger.
In response to this problem, the present invention passes through power distribution side in the short packet transmission of MIMO-NOMA under conditions of time delay is controlled Method improves the handling capacity of remote user.It is divided into different clusters according to the DYNAMIC DISTRIBUTION information of user, using between ZFBF elimination cluster Interference eliminates interference in cluster using serial interference elimination (Successive Interference Cancellation, SIC)； Since in short packet communication, the block error probability of receiving end is not zero, and not can guarantee the realization of SIC, considers that nearly user's decoding is remote and use The block error probability (outage probability of SIC) at family is lower than 10-5Under conditions of, the power distribution of combined optimization distance user and it is The MAR of system, to maximize the effective throughput of remote user.Utilize first order Taylor approximation and semi definite programming (Semidefinite Programming, SDP) by optimization problem convert convex formula；Then pass through alternative and iterative algorithm combined optimization distance user's The MAR of power and system realizes the globally optimal solution of remote user throughput.Simulation result shows compared to short packet transmission The handling capacity of MIMO-OMA system, remote user improves 26%.
Method provided by the invention is specific as follows:
1 system model
As shown in Fig. 2, the MIMO-NOMA system of downlink carries out broadcast communication using short packet, the system transmitting terminal Multiple antennas access point (Access Point, AP) configures N number of transmitting antenna, and total number of users is NK and each user is equipped with M Receiving antenna[11].User is divided into N number of cluster, is eliminated using ZFBF and is interfered between cluster, nearly user/strong is distributed in each cluster User and remote user/weak user assume that there are two users in each cluster to study to facilitate, in this case, NK user In N number of cluster and N number of beam forming vector can support 2N user.Nearly user and remote user respectively indicate ui,1ui,2,i∈ 1,2 ..., N, i.e., | hi,1|2≥|hi,2|2, hi,kIndicate the channel gain of k-th of user in the i-th cluster.
In downlink multiuser MIMO-NOMA system, data packet when using NOMA and OMA technical transmission information Length frame diagram is as shown in Figure 3.When using OMA technical transmission, each user data packet length is Lk, andAnd it utilizes When NOMA technical transmission, transmitting terminal is communicated with all users during entire transmission simultaneously by the data bit of L symbol, is mentioned High efficiency of transmission.
X=[x can be expressed as by sending signal1,x2,...,xN]T si,kTable Show and is sent to user ui,kInformation, k-th of user's distribution power of the i-th cluster is qi,k, k=1,2, total transimission power isBlock error probability ε is given for receiving endi,kAny user ui,k, under the control of time delay (time delay D=Lt, The time used in the data bit information of L symbol is transmitted, t indicates the duration of each symbol), it is obtained according to existing literature It is to rate representation when transmitting signal
Wherein, Li,kIndicate transmission si,kWhen data packet length, Q-1() indicatesInverse function, Vi,kIndicate channel dispersion, and Vi,k=1- (1+ γi,k)-2, γi,kFor the Signal to Interference plus Noise Ratio (Signal for receiving signal To Interference plus Noise Ratio, SINR), when being communicated using NOMA technology, Li,1=Li,2=L.For Balance MAR and block error probability, the performance of system are measured using handling capacity, the effective throughput of different user can indicate For
Ti,k=Ri,k(1-εi,k)(bps/Hz) (2)
In this system model, transmitting terminal uses normalization beam forming vector wiPrecoding is carried out to signal is sent, is connect The beam forming vector of receiving end is expressed as vi,k, meet | | vi,k||2=1, so reception signal is
Wherein Hi,k∈CM×NIndicate the channel matrix from AP to user, it is assumed that transmitting terminal and user terminal all known channels are distributed Information[14], i.e.,di,kIt indicates the distance between from AP end to user, c indicates path loss index, Gi,k∈CM ×NObey Rayleigh fading, Gi,k~CN (0, ∑i,k), ∑i,kIndicate channel covariance matrices,Indicate that additivity is high This white noise, I indicate unit matrix.It is interfered between cluster to completely eliminate, beamforming matrix meets wiIt is the pre-coding matrix of N × N, therefore the constraint representation of k-th of user in n-th of cluster is
Wherein hn,nkIt has been be eliminated that, indicate Hn,kThe i-th column element.It enablesIt can be with It was found thatIt is Hn,kSubmatrix, in order to further analyze its performance, utilizeV can be obtainedn,k, i.e. vn,k=Un,kτn,k, Wherein Un,kComprising corresponding to zero singular value Hn,kAll left singular vectors, τn,kIt is a normalized vector and can be by making It is obtained with maximum-ratio combing
So using beamforming matrix v is receivedI, kIt can eliminate and interfere between cluster.
2 short packet transmission strategies
In addition to interfering between cluster, with the user in cluster, there is also interference problems, according to the spy of NOMA multiple access technique Point, nearly user can eliminate the interference of remote user by SIC, and user directly decodes reception signal.Therefore user in i-th of cluster uI, 2Reception signal be
WhereinDue to | hi,1|2≥|hi,2|2, so ui,2Directly decode si,2, SINR is
ui,2Decode si,2Block error probability be
And ui,1First decode si,2Simultaneously by si,1It is considered as interference noise, decodes si,2SINR be
ui,1Decode si,2Block error probability be
If ui,1S is eliminated in successi,2Interference, then SINR be
ui,1Decode si,1Block error probability be
U is obtained by (10) and (12)i,1Block error probability be
3 power distribution strategies
3.1 power distribution method
In order to maximize the handling capacity of remote user, optimizes power distribution and MAR meets different user service feature and pre- The block error probability demand for defining nearly user distributes combination for each given user, in order to further increase in each cluster The performance of MIMO-NOMA optimizes power partition coefficient according to the channel condition of each cluster.Optimization aim can indicate are as follows:
qi,k≥0 (13c)
(13b), (13c) indicate the limitation of transimission power, and (13d) indicates the limitation of SIC outage probability.In order to optimize this Problem makes user ui,1It being capable of successful decoding user ui,2Information first have to list the closure expression formula of SIC outage probability, then Optimize range of power division by iterative algorithm.User ui,1Decode si,2Link down probability expression be
Due to | | vi,k||2=1, it is the vector of a unit norm, so given vi,kWhen, vi,khi,kIt is still a height This variable, so | vi,khi,k|2Obeying parameter isExponential distribution, majorized function can indicate are as follows:
qi,k≥0 (15c)
Wherein(15d) the inequality left side is one and includes qi,2And vi,1The non-convex complicated letter of two variables Number, in order to solve to it, both sides take logarithm to obtain first
That is
Although maximum operation is convex calculating process, but cannot influence internal concavity and convexity, convex approximation can be used Method solving optimization problem.In order to obtain maximum throughput under conditions of outage probability constrains, enableDue to power distribution qi,2By beam forming vector vi,1Influence, therefore can use multinomial The method that formula is found a function carrys out optimized variable, i.e.,
Therefore the complicated outage probability that formula (17) indicates is converted into a convex formula, and optimization problem can indicate again are as follows:
s.t.
However, due to the v of the constraint for any i and kikQuadratic constraints be still non-convex.It can use SDP skill Art solves the problems, such as this, i.e.,If Vi,kIt is full rank, then can obtains the solution of order one, otherwise pass through gaussian random One approximate solution of order is obtained,First order Taylor approximate solution can be passed through
So majorized function (15) can be converted into
Q is found by iterative algorithmi,2Zone of reasonableness, set ui,1Initial power range of distributionFull Judge whether to meet formula (21b) in the case where sufficient convergence precision, satisfaction then exports corresponding qi,1Value, finally obtains ui,1Power Range of distribution isIt enablesAlthough G increases with the increase of the number of iterations, due to Power distribution is limited, and is up toSo outage probability is bounded.
According toIt can further determine that qi,2Range beWherein
3.2 optimized throughput
Next for given feasible qi,1And qi,2To determine remote user throughput Ti,2Ri,2Value.In order to find out it most The figure of merit needs to study it relative to Ri,2Monotonicity and concavity and convexity, it is known that
Ti,2(Ri,2)=Ri,2(1-εi,2)=Ri,2(1-Q(f(γi,2,Ri,2))) (22)
Its first derivative is found out,
It cannot judge positive negativity, need to find out its second order and lead
Analysis can obtainPerseverance is set up, so Ti,2It is Ri,2Concave function, local extremum is exactly its global pole Value, so passing through setting Ti,2=0, and utilize the R of the maximum effective throughput of linear search iterative algorithm realizationi,2Optimal value indicates For
In qi,1And qi,2Feasible region according to the R of optimizationi,2Determine optimization power distribution qi,2, because it is known that
Ti,2(Ri,2,qi,2)=Ri,2(1-εi,2) (25)
Wherein εi,2It is γi,2And Ri,2Function, γi,2It is qi,2Function, q is analyzed according to above formulai,2(Ri,2) function Monotonicity.
γ can be derived by formula (6) and (7)I, 2It is qI, 2Increasing function, εI, 2It is γI, 2Subtraction function, soPerseverance is set up, i.e. Ti,2It is qi,2Increasing function.
Substitution formula (23), obtains
So qi,2It is Ri,2Convex function, optimization problem at this time can be expressed as
Further by giving the range of feasible power distribution, optimized using linear search iterative algorithm co-design qi,2And Ri,2And then determine ui,2Maximum throughput, improve the fairness of system.
Under iterative algorithm step institute.
Step 1 initializing variable,The number of iterations i=0,Convergence precision η≥0；
Step 2 is worked asWhen, i=i+1；
If step 3It is calculated by formula (22) optimal
Step 4 is updated according to step 3Optimal transmission power distribution is calculated by formula (30)
Step 5 meetsWhen, iteration terminates, output
Algorithm Convergence proves: due to the power limit on every transmission antenna, sequence of iterations is limited, handling capacity with The increase of the number of iterations and increase, therefore the sequence T of grey iterative generationi,2(Ri,2) it is bounded；If iteration j+ 1 iteration of jthTherefore q 'i,2(j+1) < q 'i,2(j), institute It is convergent with this algorithm.
4 simulation analysis
It is emulated by MIMO-NOMA system of the MATLAB to short packet transmission.Simulation parameter is configured, transmitting terminal Antenna amount N=4, receiving end antenna amount M=4, the data packet length L of transmissioni,1=Li,2=100, from the end AP to nearly user Transmission range with remote user is respectively di,1=1m, di,2=10m, path loss index c=2.The general power of each cluster isThe SINR threshold value that nearly user decodes remote user information is γi=10dB.
It is respectively 10 that Fig. 4, which depicts nearly user and decodes the block error probability threshold value of remote user,-3, 10-4And 10-5Under the conditions of, Meet power allocation case when formula (21b).By analogous diagram it can be found that block error probability is bigger, it is able to satisfy constraint condition Range is bigger, but in order to improve the reliability of system, uses block error probability for 10 herein-5When performance number, so working as G When≤1, qi,1Effective range be 0≤qi,1≤27(dB)。
Fig. 5 depicts the handling capacity of remote user and divides MIMO-NOMA technology and MIMO-OMA technology and fixed time slot The OMA matched is compared.Observation analogous diagram can be found that the optimal value of existence anduniquessMaximize handling capacity；Compared to OMA skill Art, the achievable maximum amount of spitting improves 22% when using NOMA technical transmission, and showing NOMA may be implemented preferably to transmit Performance；And the handling capacity that the OMA scheme of fixed time slot allocation is realized will be lower than the handling capacity of the OMA scheme of optimization, this explanation Also the length of the data packet of distribution should be optimized, and be more than simple fixed when being transmitted using OMA technology Distribution；With Ri,2Increase, Ti,2Value be gradually increased, but due to the system by decoding block error probability in short packet communication About, Ri,2T when continuing to increasei,2Value can be gradually reduced, when block error probability reaches certain value, be considered as invalid communication, handle up Amount is zero.
It is obtained by 3.2 theory analysis, handling capacity Ti,2It is qi,2Increasing function, Fig. 6 depicts in the short of MIMO-NOMA In packet communication system, work as Ri,2When respectively 6bps, 5bps, 7bps, Ti,2With qi,2Change curve.Obviously with qi,2 Increase Ti,2Increase；Ri,2T when taking 7bps ratio 5bpsi,2Value growth trend it is fast, can find that this is by comparison diagram 5 Because within this range, whenWhen Ti,2Variable quantity ratioVariable quantity it is big；In addition to be contrasted, It draws out and works as Ri,2When for 6bps under MIMO-OMA transmission plan handling capacity with qi,2Change curve, find the value of its handling capacity Less than the value under MIMO-NOMA transmission mode, it is seen that NOMA technology can effectively promote handling capacity in short packet communication.
The influence of transmission performance.It can be found that the handling capacity T of remote useri,2Increase with the increase of data packet length；And And for the length L of any given data packeti,2, the value in NOMA scheme is better than OMA scheme, such as to realize Ti,2= 5.8bps/Hz needs when needing the data bit of 100 symbols when then utilizing NOMA technology, and using OMA technology more than 200 The data bit of a symbol can be only achieved this handling capacity；Similarly to realize identical handling capacity, using used in NOMA technology Data packet length is shorter, effectively reduces propagation delay time.
As it can be seen that in the MIMO-NOMA system of URLLC, in order to guarantee the performance of remote user, this hair under the control of time delay The bright power distribution method for having obtained short packet transmission, so that decoding the block error probability of remote user lower than 10 in nearly user-5Condition Under, using the MAR and power distribution of the far and near user of alternative and iterative algorithm combined optimization, maximise the handling capacity of remote user. It eliminates due to there are interfering between cluster, proposing ZFBF method and interferes between cluster, and will using first order Taylor approximation and SDP method Optimization problem is converted into convex formula.By the feasibility of simulating, verifying mentioned method of the invention, and with MIMO-OMA technology phase Than higher effective throughput may be implemented, and under the premise of throughput demands are consistent, MIMO-NOMA can compare MIMO- OMA reduces propagation delay time.
Various pieces are described in a progressive manner in this specification, and what each some importance illustrated is and other parts Difference, same and similar part may refer to each other between various pieces.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, defined herein General Principle can realize in other embodiments without departing from the spirit or scope of the present invention.Therefore, this hair It is bright to be not intended to be limited to embodiment shown in the application, and be to fit to and principle disclosed in the present application and features of novelty phase Consistent widest scope.

Claims (1)

1. a kind of power distribution method based on the short packet transmission of MIMO-NOMA, specifically comprises the following steps:
Step A establishes the short packet transmission system model based on MIMO-NOMA, determines transmission rate and handling capacity；
Step B, according to the system model of step A, using broken zero beam forming (Zero Forcing Beam-Forming, ZFBF) technology is interfered between eliminating cluster, is determined and is received signal；
Step C is determined distance user's received signal and is interfered plus made an uproar by the performance of user in short packet transmission analysis of strategies cluster Acoustic ratio (Signal to Interference plus Noise Ratio, SINR) and block transmission error probability；
Step D establishes objective optimization model according to the performance of the step C far and near user acquired, maximum with the handling capacity of remote user Optimization aim, and the communication quality to guarantee nearly user are turned to, and the transmission rate and system power of remote user are distributed as excellent Change condition；
Step E, solving optimization problem realize the optimization mesh of the effective throughput of remote user by power distribution and transmission rate Mark.
Wherein, step A is specifically included:
A1, the MIMO-NOMA system for constructing downlink carries out the system channel model of broadcast communication using short packet, wherein sending End configures N number of transmitting antenna, and total number of users is NK and each user is equipped with M receiving antenna, and user is divided into N number of cluster, each Cluster can be supported in K user, it is assumed that there are two users in each cluster, and nearly user and remote user respectively indicate ui,1ui,2,i∈1, 2 ..., N, i.e., | hi,1|2≥|hi,2|2, hi,kIndicate the channel gain of k-th of user in the i-th cluster；
A2, according to the system model that A1 is constructed, sending signal can be expressed assi,kExpression is sent to user ui,kLetter Breath, k-th of user's distribution power of the i-th cluster are qi,k, k=1,2, total transimission power is
A3 gives block error probability ε for receiving endi,kAny user ui,k, speed when signal is transmitted under the control of time delay Rate is expressed asWherein, Li,kIndicate transmission si,kWhen data packet length, Q-1() indicatesInverse function, Vi,kIndicate channel dispersion, and Vi,k=1- (1+ γi,k)-2, γi,kFor receive signal Signal to Interference plus Noise Ratio (Signal to Interference plus Noise Ratio, SINR)；
A4, according to the R of A3i,kAnd εi,k, utilize handling capacity Ti,kIt measures the performance of system, is expressed as Ti,k=Ri,k(1-εi,k) (bps/Hz)。
Wherein, step B is specifically included:
B1, according to the Transmission system model that step A is determined, transmitting terminal uses normalization beam forming vector wiTo send signal into The beam forming vector of row precoding, receiving end is expressed as vi,k, meet | | vi,k||2=1, it is possible to determine and receive signal table It is shown asWherein Hi,k∈CM×N Indicate the channel matrix from AP to user, transmitting terminal and user terminal all known channel distributed intelligences, i.e., di,kIt indicates the distance between from AP end to user, c indicates path loss index, Gi,k∈CM×NObey Rayleigh fading, Gi,k~CN (0,∑i,k), ∑i,kIndicate channel covariance matrices,Indicate that additive white Gaussian noise, I indicate single Bit matrix；
B2 is interfered between eliminating cluster using ZFBF, and beamforming matrix meetsn∈{1,2,...,N,n≠ I }, wiIt is the pre-coding matrix of N × N, the constraint representation of k-th of user in n-th of cluster isWherein hn,nkIt has been be eliminated that, indicate Hn,kThe i-th column element；
B3 determines v according to B2n,k, vn,k=Un,kτn,k, wherein Un,kComprising corresponding to zero singular value Hn,kAll unusual arrows in a left side Amount, τn,kIt is a normalized vector and can be obtained by using maximum-ratio combing
Wherein, step C is specifically included:
C1, the characteristics of according to NOMA multiple access technique, nearly user can pass through serial interference elimination (Successive Interference Cancellation, SIC) interference of remote user is eliminated, and user directly decodes reception signal, i-th of cluster In remote user ui,2Reception signal beWherein
C2, due to | hi,1|2≥|hi,2|2, so ui,2Directly decode si,2, SINR is ui,2Decode si,2Block error probability be
C3, ui,1First decode si,2Simultaneously by si,1It is considered as interference noise, decodes si,2SINR be ui,1Decode si,2Block error probability be
C4, according to C3, if ui,1S is eliminated in successi,2Interference, then SINR beui,1Decode si,1's Block error probability is
Wherein, step D is specifically included:
D1 determines that optimization aim is according to above stepConstraint condition includesqi,k>=0,Wherein front two is the limitation of power, third table Show the limitation of outage probability, γiIndicate that nearly user decodes the limitation of the link SINR of weak user；
D2, according to step D1, the constraint of outage probability can be expressed as,Constraint condition It is expressed asWhereinvi,1hi,1It is that a Gauss becomes Amount, | vi,1hi,1|2Obeying parameter isExponential distribution, be expressed as by maximum operation above formula constraint condition
Include q since the inequality left side is one in D3, step D2i,2And vi,1The non-convex complicated function of two variables, expression formula For non-convex form, this is solved the problems, such as using SDP technology and first order Taylor approximation, i.e., First order Taylor approximate solution can be passed throughConstraint condition is converted into convex formula
Wherein, step E is specifically included:
E1, in given feasible qi,1And qi,2To determine remote user throughput Ti,2Ri,2Value, judges relative to Ri,2Monotonicity And concavity and convexity, Ti,2(Ri,2)=Ri,2(1-εi,2)=Ri,2(1-Q(f(γi,2,Ri,2))), being led by second order may determine that Ti,2It is Ri,2Concave function, local extremum is exactly its global extremum, thus by setting Ti,2=0, and calculated using linear search iteration Method realizes the R of maximum effective throughputi,2Optimal value is expressed as
E2, in qi,1And qi,2Feasible region according to the R of optimizationi,2Determine optimization power distribution qi,2, Ti,2(Ri,2,qi,2)= Ri,2(1-εi,2), wherein εi,2It is γi,2And Ri,2Function, γi,2It is qi,2Function, q is judged by derivationi,2It is Ri,2's Convex function,；
E3 optimizes q using linear search iterative algorithm co-design by giving the range of feasible power distributioni,2And Ri,2 Determine ui,2Maximum throughput step, initializing variable,The number of iterations i=0,Convergence precision η >=0；
E4 judges convergence, whenWhen, i=i+1；
E5, ifPass through formula Ti,2(Ri,2)=Ri,2(1-εi,2)=Ri,2(1-Q(f (γi,2,Ri,2))) calculate optimal transmission rate
E6 is updated according to step E5Calculate optimal transmission power distribution
E7 meets convergence precisionWhen, iteration terminates, and passes through formula Ti,2=Ri,2(1-εi,2) calculate and handle up Amount, output
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