CN113708803A - Method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO - Google Patents

Method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO Download PDF

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CN113708803A
CN113708803A CN202110684805.2A CN202110684805A CN113708803A CN 113708803 A CN113708803 A CN 113708803A CN 202110684805 A CN202110684805 A CN 202110684805A CN 113708803 A CN113708803 A CN 113708803A
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users
cluster
multiple access
user
orthogonal multiple
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吴少川
隋秋怡
李壮
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Beijing Mechanical And Electrical Engineering General Design Department
Harbin Institute of Technology
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Beijing Mechanical And Electrical Engineering General Design Department
Harbin Institute of Technology
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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
    • 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
    • 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

Abstract

The invention provides a method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-freemassiveMIMO, which divides all users into L clusters, the number of users in each cluster is 1 or 2, the users in each cluster use the same pilot frequency, different clusters use mutually orthogonal pilot frequencies, and the number of the orthogonal pilot frequencies is L; the AP is randomly divided into two parts, wherein one part provides services for OMA users, and the other part provides services for NOMA users; the data transmission of the downlink depends on conjugate beam forming to obtain the frequency spectrum efficiency of single-user downlink data transmission; the power control algorithm P1 which uses the power allocation algorithm to maximize the minimum SINR converts the power control into a convex optimization problem P2; a binary search method is used for the convex optimization problem P2 to find a solution of the downlink maximum minimum SINR, orthogonal multiple access and non-orthogonal multiple access are combined according to the specific user number and the channel correlation time condition, and the optimal spectrum efficiency can be obtained under the conditions of different user numbers and correlation time.

Description

Method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO.
Background
(1) Non-orthogonal multiple access (nonorthogonal multiple access) is one of the most important core technologies in wireless communication, and the multiple access technology has been a major focus of 5G research. The Multiple Access technology adopted in the 4G system is Orthogonal Frequency Division Multiple Access (OFDMA), which makes the allocation and utilization of time-Frequency resources more reasonable and efficient by dividing subcarriers, thereby improving communication efficiency and obtaining higher communication quality. However, as the number of users increases, non-orthogonal shared resource access between users has become necessary.
The Non-Orthogonal Multiple Access (NOMA) in power domain carries out simple linear superposition to Multiple user signals in power domain, each time-frequency unit in the base station carries Multiple user signals, and the users are distinguished by the transmitting power of the downlink signals of the users. The downlink transmitting power allocated to the user with good channel is weak, and the downlink transmitting power allocated to the user with poor channel is strong. On the terminal side, all user signals are successively detected according to the sequence of strong first and weak second according to the principle of Successive Interference Cancellation (SIC) reception. Compared with the conventional Orthogonal Multiple Access (OMA), the power domain NOMA has significant improvements in both spectral efficiency and throughput.
(2) With the increasing demand for communication and the continuous progress of communication technology, Cell-free Massive MIMO technology is considered as one of the key technologies in the 5G era. However, when this technique is used, the user throughput at the cell edge is low, the reliability is poor, and the communication delay is large, which becomes a limitation to which the massive MIMO technique is applied. Therefore, a distributed, network-based massive MIMO technology, Cell-Free massive MIMO technology, is proposed and receives increasing attention. In the Cell-Free massive MIMO system, a plurality of users occupy the same time-frequency resource in a space multiplexing mode, so that the problems are solved, and the performance of the system is greatly improved. In Cell-Free mMIMO, a CPU is connected with a large number of scattered APs through high-speed error-Free links, and all the APs cooperate with each other to provide service for all users. In this way, each AP only needs to complete simple signal processing, and those complex operations are completed by the CPU, so that the price and power consumption of the AP are relatively low. In CF-mimo, the uplink and downlink operate in TDD mode, and the coherence interval is typically divided into three phases: uplink training, downlink payload data transmission and uplink payload data transmission. The uplink training completes the estimation of the channel, for TDD, the channel gains in the uplink and downlink are the same, and the channel estimation information obtained in the training process is applied to process the data signal in the processes of downlink payload data transmission and uplink payload data transmission.
(3) And the non-orthogonal multiple access of Cell-free Massive MIMO.
Disclosure of Invention
The invention provides a method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO, which combines the orthogonal multiple access and the non-orthogonal multiple access, can exert the advantages of the orthogonal multiple access and the non-orthogonal multiple access, and can obtain the optimal frequency spectrum efficiency under the conditions of different user numbers and related time.
The invention is realized by the following scheme:
in the method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO,
dividing all users into L clusters, wherein the number of users in each cluster is 1 or 2, the users in each cluster use the same pilot frequency, different cluster users use mutually orthogonal pilot frequencies, and the number of the orthogonal pilot frequencies is L;
the AP is randomly divided into two parts, one part provides service for OMA users, namely a cluster with the number of users being 1 in each cluster, namely kl1, the set of APs serving OMA users is denoted by the symbol ΦOTo represent; the other part provides services for NOMA users, namely clusters with the number of users being 2 in each cluster, namely klPhi 1, the set in which the AP providing service for NOMA users is locatedNTo represent;
divide all users into 2 parts, some users use OMA, and use symbol omegaOTo represent the set of clusters where users using OMA are located; another part of the users use NOMA, with the symbol omegaNTo represent a set of clusters in which users using NOMA are located;
the combination method comprises the following steps:
the method comprises the following steps: the data transmission of the downlink depends on conjugate beam forming to obtain the frequency spectrum efficiency of single-user downlink data transmission;
step two: the power control algorithm P1 which uses the power allocation algorithm to maximize the minimum SINR converts the power control into a convex optimization problem P2;
step three: a binary search method is used for the convex optimization problem P2 to find the solution of the downlink maximum minimum SINR.
Further, the air conditioner is provided with a fan,
let τ be the correlation time length of the channel, i.e., the discretization length, τ be equal to the product of the channel correlation time and the channel correlation bandwidth, τpFor the duration of the uplink training, τuAnd τdFor uplink and downlink data transmission time, respectively, τ ═ τ is obtainedp+τu+τd；
Setting the transmission time of uplink and downlink data equal, i.e. tauu＝τdIf τ is τp+2τd；
In the training phase, all K users in the system automatically send a length tau to the APpThe pilot signal of (a); order to
Figure BDA0003124209870000031
Wherein
Figure BDA00031242098700000313
Is the pilot sequence assigned to the kth user, K ═ 1,2, …, K;
let the channel gain model be:
Figure BDA0003124209870000032
where beta represents a large scale fading, hmkRepresents small scale fading, with M1, 2.. M, K1, 2.. K., K being independently equally distributed;
the signal received by the mth AP is expressed as:
Figure BDA0003124209870000033
where ρ ispIs the normalized signal-to-noise ratio, w, of each pilot symbolmIs additive white Gaussian noise, w, of the mth AP endmObey independent CN (0,1) distribution;
by means of a received pilot signal ymThe channel gain g can be obtainedmkIs estimated value of
Figure BDA0003124209870000034
By using
Figure BDA0003124209870000035
Denotes ymIn that
Figure BDA0003124209870000036
Projection of (2):
Figure BDA0003124209870000037
because any two pilots are orthogonal or identical, the MMSE algorithm is used
Figure BDA0003124209870000038
Estimating the channel gain gmkThe value of (c):
Figure BDA0003124209870000039
wherein
Figure BDA00031242098700000310
βmkIs a large scale fading between the mth AP and the kth user;
setting m APs in the system, dividing all users into L clusters, and if there are K users in each cluster, then estimating the channel fmlComprises the following steps:
Figure BDA00031242098700000311
using MMSE algorithm to obtain fmlEstimated value of (a):
Figure BDA00031242098700000312
wherein the content of the first and second substances,
Figure BDA0003124209870000041
βmlk′large scale fading for the mth AP and the kth user in the ith cluster.
Further, it is characterized in that:
the transmission signal of the mth AP is:
Figure BDA0003124209870000042
wherein eta ismlkThe power allocated to k users in the ith cluster for the mth AP,
Figure BDA0003124209870000043
for the co-yoke of channel estimates, slkSymbols, p, for k users in the ith clusterdIn order to normalize the downlink transmit power,
then the normalized transmit signal power expression for the mth AP is:
Figure BDA0003124209870000044
wherein the content of the first and second substances,
Figure BDA0003124209870000045
each AP needs to satisfy the power control conditions as follows:
Figure BDA0003124209870000046
wherein, N is the number of antennas on each AP;
equation (10) needs to be satisfied to eliminate the successive interference:
Figure BDA0003124209870000047
wherein the content of the first and second substances,
Figure BDA0003124209870000048
representing the signal-to-interference-and-noise ratio when the user j in the first cluster decodes the user k in the same cluster when carrying out SIC algorithm; based on this, the achievable rate of user k in the final ith cluster can be written as:
Figure BDA0003124209870000049
the actual signals for decoding after SIC processing are:
user using NOMA:
Figure BDA0003124209870000051
users using OMA:
Figure BDA0003124209870000052
Figure BDA0003124209870000061
the signal to interference plus noise ratio thus derived is:
user using NOMA:
Figure RE-GDA0003292979190000062
Figure BDA0003124209870000063
users using OMA:
Figure BDA0003124209870000064
Figure BDA0003124209870000065
the finally obtained spectrum efficiency formula of the single-user downlink data transmission is as follows:
Figure BDA0003124209870000071
wherein the content of the first and second substances,
Figure BDA0003124209870000072
further, the air conditioner is provided with a fan,
the power allocation algorithm used is a power control algorithm which maximizes the minimum SINR, and the power control conditions are as follows:
P1：
Figure BDA0003124209870000073
Figure BDA0003124209870000074
the optimization problem in equation (19) is a non-convex problem(ii) a Order to
Figure BDA0003124209870000075
And introducing a relaxation variable
υm，
Figure BDA0003124209870000076
λlk′j，λlk′kConverting P1 into a convex optimization problem P2:
P2：
Figure BDA0003124209870000077
Figure BDA0003124209870000078
Figure BDA0003124209870000079
Figure BDA00031242098700000710
Figure BDA00031242098700000711
Figure BDA0003124209870000081
wherein the content of the first and second substances,
Figure BDA0003124209870000082
Figure BDA0003124209870000083
vlj，1＝[λl1j…λl(k-1)j]T，vlj，1＝[λl1j…λl(k-1)j]T，
Figure BDA0003124209870000084
Figure BDA0003124209870000085
further, the air conditioner is provided with a fan,
considering equation (20) as a second order cone, P2 is a standard second order cone optimization problem, i.e., a convex optimization problem;
the problem P2 is solved using a binary search method to find the solution of the downlink maximum minimum SINR:
setting the maximum value boundary t of the achievable SINRmaxAnd a minimum boundary tmin(ii) a Let initial target SINR t be (t)max+tmin)/2；
When the problem P2 is solvable for this target SINR t, then the minimum value t is determinedminIs set as t; if not, the maximum value t is determinedmaxIs set as t;
the binary search method is continued until the difference between the upper and lower bounds is less than a preset threshold epsilon, i.e. (t)max-tmin)＜ε。
The invention has the beneficial effects
The invention combines the orthogonal multiple access and the non-orthogonal multiple access according to the specific user number and the channel correlation time condition, can exert the advantages of the orthogonal multiple access and the non-orthogonal multiple access, and can obtain the optimal spectrum efficiency under the conditions of different user numbers and correlation time.
Compared with the non-orthogonal multiple access in which the number of users per cluster must be 2, the method is more flexible and can combine the advantages of the two access modes when the relevant time is not long or short: some users have a shorter pilot length by non-orthogonal multiple access, and some users have a higher sum rate by orthogonal multiple access.
Drawings
FIG. 1 is a system diagram of the combination of NOMA and OMA in Cell-Free massive MIMO according to the present invention;
FIG. 2 is a comparison of the combination of NOMA and OMA of the present invention with the spectral efficiencies of OMA and NOMA.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments; all other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The current methods all discuss the case of K ═ 2. Obviously, it is not reasonable nor flexible to forcibly specify that the number of users in each cluster is equal, and the maximum value K of the number of users in a cluster should be set according to the specific number of users and the channel correlation time conditionmaxAnd specify kl≤KmaxWherein k islThe number of users in the ith cluster.
All users are divided into L clusters, the number of users in each cluster is 1 or 2, the users in each cluster use the same pilot frequency, different cluster users use mutually orthogonal pilot frequencies, and therefore the number of the orthogonal pilot frequencies is L. In order to be able to serve all users, we also randomly split the AP into two parts, one part serving OMA users (i.e. a cluster of 1 user per cluster) and one part serving NOMA users (a cluster of more than 1 user per cluster). The system block diagram is shown in FIG. 1:
as shown in FIG. 1, all APs and UEs are divided into 2 parts, one part of users uses non-orthogonal multiple access (light UE), and each cluster has more than 1 user, i.e. kl> 1, all klThe same pilot is used by each user, and the cluster set in which this part of users is located is denoted by the symbol ΩNTo indicate. Serving users non-orthogonally multiple-accessed by a portion of APs (light-colored APs), the set in which this portion of APs is located being marked by the symbol ΦNTo indicate. Another part of the users use orthogonal multiple access (dark UE), and there are only 1 user in each cluster, i.e. kl1, the cluster set where this part of users is located is denoted by the symbol ΩoTo indicate. And served by another part of the APs (dark colored APs), the set in which this part of the APs is located being denoted by the symbol ΦOTo indicate.
The invention is realized by the following scheme:
in the method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO,
dividing all users into L clusters, wherein the number of users in each cluster is 1 or 2, the users in each cluster use the same pilot frequency, different cluster users use mutually orthogonal pilot frequencies, and the number of the orthogonal pilot frequencies is L;
the AP is randomly divided into two parts, one part provides service for OMA users, namely a cluster with the number of users being 1 in each cluster, namely kl1, the set of APs serving OMA users is denoted by the symbol ΦOTo represent; the other part provides services for NOMA users, namely clusters with the number of users being 2 in each cluster, namely klPhi 1, the set in which the AP providing service for NOMA users is locatedNTo represent;
divide all users into 2 parts, some users use OMA, and use symbol omegaOTo represent the set of clusters where users using OMA are located; another part of the users use NOMA, with the symbol omegaNTo represent a set of clusters in which users using NOMA are located;
the combination method comprises the following steps:
the method comprises the following steps: the data transmission of the downlink depends on Conjugate beam forming (CB), and the spectrum efficiency of single-user downlink data transmission is obtained;
step two: the power control algorithm P1 which uses the power allocation algorithm to maximize the minimum SINR converts the power control into a convex optimization problem P2;
step three: a binary search method is used for the convex optimization problem P2 to find the solution of the downlink maximum minimum SINR.
Let τ be the correlation time length of the channel, i.e. the discretization lengthτ is equal to the product of the channel correlation time and the channel correlation bandwidth, τpFor the duration of the uplink training, τuAnd τdFor uplink and downlink data transmission time, respectively, τ ═ τ is obtainedp+τu+τd；
Setting the transmission time of uplink and downlink data equal, i.e. tauu＝τdIf τ is τp+2τd；
In the training phase, all K users in the system automatically send a length tau to the APpThe pilot signal of (a); order to
Figure BDA0003124209870000101
Wherein
Figure BDA0003124209870000102
Is the pilot sequence assigned to the kth user, K ═ 1,2, …, K;
let the channel gain model be:
Figure BDA0003124209870000103
where beta represents a large scale fading, hmkRepresents small scale fading, with M1, 2.. M, K1, 2.. K., K being independently equally distributed;
since the APs and the users are discretely distributed over a large area, h can be considered asmkM1, 2,. M, K1, 2,. K, K are independently and identically distributed;
the signal received by the mth AP is expressed as:
Figure BDA0003124209870000111
where ρ ispIs the normalized signal-to-noise ratio, w, of each pilot symbolmIs additive white Gaussian noise, w, of the mth AP endmObey independent CN (0,1) distribution;
by means of a received pilot signal ymCan be used forObtain the channel gain gmkIs estimated value of
Figure BDA0003124209870000112
By using
Figure BDA0003124209870000113
Denotes ymIn that
Figure BDA0003124209870000114
Projection of (2):
Figure BDA0003124209870000115
because any two pilots are orthogonal or identical, the MMSE algorithm is used
Figure BDA0003124209870000116
Estimating the channel gain gmkThe value of (c):
Figure BDA0003124209870000117
wherein
Figure BDA0003124209870000118
βmkIs a large scale fading between the mth AP and the kth user;
setting m APs in the system, dividing all users into L clusters, and if there are K users in each cluster, then estimating the channel fmlComprises the following steps:
Figure BDA0003124209870000119
using MMSE algorithm to obtain fmlEstimated value of (a):
Figure BDA00031242098700001110
wherein the content of the first and second substances,
Figure BDA00031242098700001111
βmlk′large-scale fading for the mth AP and the kth user in the ith cluster;
the transmission signal of the mth AP is:
Figure BDA00031242098700001112
wherein eta ismlkThe power allocated to k users in the ith cluster for the mth AP,
Figure BDA0003124209870000121
for the co-yoke of channel estimates, slkSymbols, p, for k users in the ith clusterdIn order to normalize the downlink transmit power,
then the normalized transmit signal power expression for the mth AP is:
Figure BDA0003124209870000122
wherein the content of the first and second substances,
Figure BDA0003124209870000123
each AP needs to satisfy the power control conditions as follows:
Figure BDA0003124209870000124
wherein, N is the number of antennas on each AP;
equation (10) needs to be satisfied to eliminate the successive interference:
Figure BDA0003124209870000125
wherein the content of the first and second substances,
Figure BDA0003124209870000126
representing the signal-to-interference-and-noise ratio when the user j in the first cluster decodes the user k in the same cluster when carrying out SIC algorithm; based on this, the achievable rate of user k in the final ith cluster can be written as:
Figure BDA0003124209870000127
the actual signals for decoding after SIC processing are:
user using NOMA:
Figure BDA0003124209870000128
Figure BDA0003124209870000131
users using OMA:
Figure BDA0003124209870000132
the signal to interference plus noise ratio thus derived is:
user using NOMA:
Figure RE-GDA0003292979190000142
Figure BDA0003124209870000142
users using OMA:
Figure BDA0003124209870000143
Figure BDA0003124209870000144
the finally obtained spectrum efficiency formula of the single-user downlink data transmission is as follows:
Figure BDA0003124209870000145
wherein the content of the first and second substances,
Figure BDA0003124209870000146
the power allocation algorithm used is a power control algorithm which maximizes the minimum SINR, and the power control conditions are as follows:
P1：
Figure BDA0003124209870000151
Figure BDA0003124209870000152
the optimization problem in formula (19) is a non-convex problem; order to
Figure BDA0003124209870000153
And introducing a relaxation variable upsilonm，
Figure BDA0003124209870000154
λlk′j，λlk′kConverting P1 into a convex optimization problem P2:
P2：
Figure BDA0003124209870000155
Figure BDA0003124209870000156
Figure BDA0003124209870000157
Figure BDA0003124209870000158
Figure BDA0003124209870000159
Figure BDA00031242098700001510
wherein the content of the first and second substances,
Figure BDA00031242098700001511
Figure BDA00031242098700001512
vlj，1＝[λl1j…λl(k-1)j]T，vlj，1＝[λl1j…λl(k-1)j]T，
Figure BDA0003124209870000161
Figure BDA0003124209870000162
considering equation (20) as a Second Order Cone (SOC), P2 is a standard Second Order Cone optimization problem, i.e., a convex optimization problem;
the problem P2 is solved using a binary search method to find the solution of the downlink maximum minimum SINR:
setting the maximum value boundary t of the achievable SINRmaxAnd a minimum boundary tmin(ii) a Let initial target SINR t be (t)max+tmin)/2；
When the problem P2 is solvable for this target SINR t, then the minimum value t is determinedminIs set as t; if not, the maximum value t is determinedmaxIs set as t;
the binary search method is continued until the difference between the upper and lower bounds is less than a preset threshold epsilon, i.e. (t)max-tmin)＜ε。
The numerical simulation result of the method is given as follows:
the APs are uniformly distributed in a square area of 1km x 1km, and all users are randomly distributed. The path loss model is used here as follows:
Figure BDA0003124209870000163
wherein d ismlkDenotes the distance between user k and base station m in the ith cluster, and PLmlkRepresenting the path loss between the user k in the ith cluster and the base station;
wherein the content of the first and second substances,
L ＝46.3+33.9log10(f)-13.82log10(hAP)-(1.1log10(f)-0.7)hu+(1.56log10(f)- 0.8)
the rest simulation parameters are shown in Table 1
Parameter name Parameter value
Carrier frequency 1.9GHz
Bandwidth of 20MHz
Noise figure 9dB
AP antenna height 15m
Number of antennas per AP (N) 10
Height of user antenna 1.65m
Pilot transmission power 100mW
Downlink data transmission power 200mW
Shadow fading variance 8dB
TABLE 1
The power allocation problem for users using NOMA and OMA is optimized separately using the power allocation algorithm described above. A total of 40 APs are provided, wherein 20 of the APs provide services for NOMA users and 20 of the APs provide services for OMA users; there are 20 users, 10 of which use NOMA technology, divided into 5 clusters, 2 users per cluster, and 10 users use OMA technology, divided into 10 clusters, and 1 user per cluster. The final results are shown in FIG. 2:
spectral efficiency results the plots show that the spectral efficiency of cell-free massive MIMO combining NOMA and OMA is higher than that of NOMA or OMA alone under certain relevant time conditions.
This is because the advantage of orthogonal multiple access is that there is no interference from users using the same pilot and therefore the sum rate (R in the spectral efficiency formula) is higher than that of non-orthogonal multiple access at the same downlink transmission time. However, the orthogonal multiple access requires a large number of orthogonal pilots, and the length of the pilots is longer, so that the time occupied for transmitting the pilots is longer, which results in shorter time for uplink and downlink data transmission, which may seriously affect the spectrum efficiency when the correlation time is shorter and the number of users is larger. The advantages of non-orthogonal multiple access are that the number of needed orthogonal pilot frequencies is less, the length of the pilot frequency is shorter, and more time can be used for uplink and downlink data transmission. However, non-orthogonal multiple access has intra-cluster interference, influence and rate, when the correlation time is longer, the influence of the pilot frequency length on the spectrum efficiency is smaller, and the influence of the intra-cluster interference causes the spectrum efficiency to be lower than that of orthogonal multiple access.
The combination of the orthogonal multiple access and the non-orthogonal multiple access is a compromise implementation mode, one part of users in the system use the orthogonal access (the number of users per cluster is 1), one part of users use the non-orthogonal access (the number of users per cluster is 2), compared with the non-orthogonal multiple access, the system is more flexible in that the number of users per cluster is required to be 2, and the advantages of the two access modes can be combined when the relevant time is not long or short (the pilot frequency length is shortened by the non-orthogonal multiple access of one part of users, and the rate is higher by the non-orthogonal multiple access of one part of users). But when the correlation time is small, because a part of the users use the orthogonal multiple access, the length of the needed orthogonal pilot frequency is larger than that of all the users using the non-orthogonal multiple access; when the correlation time is large, the intra-cluster interference causes the sum rate to be reduced and the spectrum efficiency is influenced because a part of users use the non-orthogonal multiple access.
The method for combining orthogonal multiple access and non-orthogonal multiple access under Cell-free Massive MIMO proposed by the present invention is described in detail above, and the principles and embodiments of the present invention are explained in the present document by applying numerical simulation examples, and the description of the above examples is only used to help understanding the method and core ideas of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and as described above, the content of the present specification should not be construed as a limitation to the present invention.

Claims (5)

1. The method for combining the orthogonal multiple access and the non-orthogonal multiple access under the Cell-freeMassiveMIMO is characterized in that:
dividing all users into L clusters, wherein the number of users in each cluster is 1 or 2, the users in each cluster use the same pilot frequency, different cluster users use mutually orthogonal pilot frequencies, and the number of the orthogonal pilot frequencies is L;
the AP is randomly divided into two parts, one part provides service for OMA users, namely a cluster with the number of users being 1 in each cluster, namely kl1, the set of APs serving OMA users is denoted by the symbol ΦOTo represent; the other part provides services for NOMA users, namely clusters with the number of users being 2 in each cluster, namely klSymbol phi for set where AP serving NOMA users is located > 1NTo represent;
divide all users into 2 parts, some users use OMA, and use symbol omegaOTo represent the set of clusters where users using OMA are located; another part of the users use NOMA, with the symbol omegaNTo represent a set of clusters in which users using NOMA are located;
the combination method comprises the following steps:
the method comprises the following steps: the data transmission of the downlink depends on conjugate beam forming to obtain the frequency spectrum efficiency of single-user downlink data transmission;
step two: the power control algorithm P1 which uses the power allocation algorithm to maximize the minimum SINR converts the power control into a convex optimization problem P2;
step three: a binary search method is used for the convex optimization problem P2 to find the solution of the downlink maximum minimum SINR.
2. The method of claim 1, wherein:
let τ be the correlation time length of the channel, i.e., the discretization length, τ be equal to the product of the channel correlation time and the channel correlation bandwidth, τpFor the duration of the uplink training, τuAnd τdRespectively the upstream and downstream rowsThe data transmission time is τ ═ τ -p+τu+τd；
Setting the transmission time of uplink and downlink data equal, i.e. tauu＝τdIf τ is τp+2τd；
In the training phase, all K users in the system automatically send a length tau to the APpThe pilot signal of (a); order to
Figure FDA0003124209860000011
Wherein
Figure FDA0003124209860000012
Is the pilot sequence assigned to the kth user, K ═ 1,2, …, K;
let the channel gain model be:
Figure FDA0003124209860000013
where beta represents a large scale fading, hmkRepresents small scale fading, M1, 2.. M, K1, 2.. K., K is independently identically distributed;
the signal received by the mth AP is expressed as:
Figure FDA0003124209860000021
where ρ ispIs the normalized signal-to-noise ratio, w, of each pilot symbolmIs additive white Gaussian noise, w, of the mth AP endmObey independent CN (0,1) distribution;
by means of a received pilot signal ymThe channel gain g can be obtainedmkIs estimated value of
Figure FDA0003124209860000022
By using
Figure FDA0003124209860000023
Denotes ymIn that
Figure FDA0003124209860000024
Projection of (2):
Figure FDA0003124209860000025
because any two pilots are orthogonal or identical, the MMSE algorithm is used
Figure FDA00031242098600000211
Estimating the channel gain gmkThe value of (c):
Figure FDA0003124209860000026
wherein
Figure FDA0003124209860000027
βmkIs a large scale fading between the mth AP and the kth user;
if m APs are arranged in the system, all users are divided into L clusters, and each cluster has K users, then the channel estimation fmlComprises the following steps:
Figure FDA0003124209860000028
using MMSE algorithm to obtain fmlEstimated value of (a):
Figure FDA0003124209860000029
wherein the content of the first and second substances,
Figure FDA00031242098600000210
βmlk′is the mth AP and the mth APlarge scale fading for the k' th user in cluster.
3. The method of claim 2, wherein:
the transmission signal of the mth AP is:
Figure FDA0003124209860000031
wherein eta ismlkThe power allocated to k users in the ith cluster for the mth AP,
Figure FDA0003124209860000032
is the conjugate of the channel estimate, slkSymbols, p, for k users in the ith clusterdIn order to normalize the downlink transmit power,
then the normalized transmit signal power expression for the mth AP is:
Figure FDA0003124209860000033
wherein the content of the first and second substances,
Figure FDA0003124209860000034
each AP needs to satisfy the power control conditions as follows:
Figure FDA0003124209860000035
wherein, N is the number of antennas on each AP;
equation (10) needs to be satisfied to eliminate the successive interference:
Figure FDA0003124209860000036
wherein the content of the first and second substances,
Figure FDA0003124209860000037
representing the signal-to-interference-and-noise ratio when the user j in the first cluster decodes the user k in the same cluster when carrying out SIC algorithm; based on this, the achievable rate of user k in the final ith cluster can be written as:
Figure FDA0003124209860000038
the actual signals for decoding after SIC processing are:
user using NOMA:
Figure FDA0003124209860000039
Figure FDA0003124209860000041
users using OMA:
Figure FDA0003124209860000042
the signal to interference plus noise ratio thus derived is:
user using NOMA:
Figure DEST_PATH_FDA0003292979180000051
Figure DEST_PATH_FDA0003292979180000052
users using OMA:
Figure FDA0003124209860000052
Figure FDA0003124209860000053
the finally obtained spectrum efficiency formula of the single-user downlink data transmission is as follows:
Figure FDA0003124209860000054
wherein the content of the first and second substances,
Figure FDA0003124209860000061
4. the method of claim 3, wherein:
the power allocation algorithm used is a power control algorithm which maximizes the minimum SINR, and the power control conditions are as follows:
P1：
Figure FDA0003124209860000062
s.t.
Figure FDA0003124209860000063
the optimization problem in formula (19) is a non-convex problem; order to
Figure FDA0003124209860000064
And introducing a relaxation variable vm，
Figure FDA0003124209860000065
λlk′j，λlk′kConverting P1 into a convex optimization problem P2:
P2：
Figure FDA0003124209860000066
s.t.
Figure FDA0003124209860000067
Figure FDA0003124209860000068
Figure FDA0003124209860000069
Figure FDA00031242098600000610
Figure FDA00031242098600000611
wherein the content of the first and second substances,
Figure FDA0003124209860000071
Figure FDA0003124209860000072
vlj，1＝[λl1j … λl(k-1)j]T，vlj，1＝[λl1j … λl(k-1j]T，
Figure FDA0003124209860000073
Figure FDA0003124209860000074
5. the method of claim 4, wherein:
considering equation (20) as a second order cone, P2 is a standard second order cone optimization problem, i.e., a convex optimization problem;
the problem P2 is solved using a binary search method to find the solution of the downlink maximum minimum SINR:
setting the maximum value boundary t of the achievable SINRmaxAnd a minimum boundary tmin(ii) a Let initial target SINR t be (t)max+tmin)/2；
When the problem P2 is solvable for this target SINR t, then the minimum value t is determinedminIs set as t; if not, the maximum value t is determinedmaxIs set as t;
the binary search method is continued until the difference between the upper and lower bounds is less than a preset threshold epsilon, i.e. (t)max-tmin)＜ε。
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