CN115037341A - D2D-assisted multi-group multicast non-cellular large-scale MIMO system architecture - Google Patents

D2D-assisted multi-group multicast non-cellular large-scale MIMO system architecture Download PDF

Info

Publication number
CN115037341A
CN115037341A CN202210694739.1A CN202210694739A CN115037341A CN 115037341 A CN115037341 A CN 115037341A CN 202210694739 A CN202210694739 A CN 202210694739A CN 115037341 A CN115037341 A CN 115037341A
Authority
CN
China
Prior art keywords
system architecture
expressed
mimo system
assisted
mth
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210694739.1A
Other languages
Chinese (zh)
Other versions
CN115037341B (en
Inventor
周猛
李金文
郑淑琰
颉满刚
万安平
谭伟强
袁建涛
杨龙祥
朱洪波
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University City College ZUCC
Original Assignee
Zhejiang University City College ZUCC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University City College ZUCC filed Critical Zhejiang University City College ZUCC
Priority to CN202210694739.1A priority Critical patent/CN115037341B/en
Publication of CN115037341A publication Critical patent/CN115037341A/en
Application granted granted Critical
Publication of CN115037341B publication Critical patent/CN115037341B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
    • 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

Landscapes

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

Abstract

The invention relates to a D2D-assisted multi-group multicast honeycomb-free large-scale MIMO system architecture, which comprises a central processing unit CPU, an access point AP, a multi-antenna honeycomb-free user CFUE and a terminal direct-connected D2D user pair. The invention also calculates the energy efficiency of the system architecture based on the sum rate and energy loss model of the system architecture. The invention has the beneficial effects that: the invention can provide services for multi-antenna cellular-free users and D2D users on the same time or frequency resource block, constructs an energy loss model, and can calculate the energy efficiency of the system, thereby facilitating the improvement of the system.

Description

D2D-assisted multi-group multicast non-cellular large-scale MIMO system architecture
Technical Field
The present invention relates to the field of wireless communications technologies, and more particularly, to a D2D-assisted multi-group multicast large-scale MIMO system architecture without cellular.
Background
In recent years, since the cellular-free large-scale (MIMO) technology enables relatively few users to perform joint communication on the same time/frequency resource block, and has potential advantages in terms of spatial multiplexing gain, coverage probability, Spectral Efficiency (SE), and Energy Efficiency (EE), it is considered as one of the main architectures of the development of the upcoming post-5G/sixth generation communication system (B5G/6G), which also has seriously triggered the growing research interests of the academic, industrial, and standardized organizations.
However, there is no direct-to-Device (D2D) assisted multi-group multicast cellular-less massive MIMO system architecture in the prior art, and the energy efficiency of the novel system architecture has not been studied.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a D2D-assisted multi-group multicast non-cellular massive MIMO system architecture.
In a first aspect, a D2D-assisted multi-group multicast cellless massive MIMO system architecture is provided, including: a Central Processing Unit (CPU), an Access Point (AP), a multi-antenna cellular-Free User (CFUE) and a D2D User pair;
wherein the AP, CFUE, and D2D user pairs are each configured with an antenna; all APs coordinate and connect backhaul links with the CPU through interfaces.
In a second aspect, a method for analyzing a D2D-assisted multi-group multicast cellular-free massive MIMO system architecture is provided, including:
s1, establishing a D2D assisted multi-group multicast non-cellular massive MIMO system architecture;
s2, obtaining a closed expression of the downlink rate of the D2D-assisted multi-group multicast large-scale MIMO system architecture, and calculating the sum rate of the system architecture;
s3, constructing an energy loss model;
and S4, calculating the energy efficiency of the system architecture according to the sum rate of the system architecture and the energy loss model.
Preferably, S1 includes:
s101, assuming that the D2D-assisted multi-group multicast honeycomb-free large-scale MIMO system architecture consists of a CPU, M contacts AP, JXK multi-antenna honeycomb-free users CFUE and L terminal direct-connected D2D user pairs, wherein M is more than or equal to J; each AP, CFUEs, and DUEs (D2D users) has N, respectively A N and N D An antenna;
s102, channel matrix of k CFUE in m-th AP to j-th group
Figure BDA0003702035440000021
Modeling was performed, expressed as:
Figure BDA0003702035440000022
wherein the content of the first and second substances,
Figure BDA0003702035440000023
β mjk ,
Figure BDA0003702035440000024
representing a large scale fading coefficient, H mjk A random variable which represents a small-scale fading matrix and has components which are independent and distributed, and obeys (0, 1);
s103, for the I D2D user pair, the transmitter
Figure BDA0003702035440000025
And a receiver
Figure BDA0003702035440000026
Channel matrix of (2)
Figure BDA0003702035440000027
Modeling was performed, expressed as:
Figure BDA0003702035440000028
wherein the content of the first and second substances,
Figure BDA0003702035440000029
Figure BDA00037020354400000210
representing large-scale fading coefficients, H ll′ Represents small scale fading and satisfies [ H ] ll′ ] mn ～CN(0,1)。
Preferably, S1 further includes:
s104, in the uplink channel estimation stage, adopting a time division duplex mode; at a length of τ c Within the coherence interval of (d), the length is τ p Is represented as
Figure BDA00037020354400000211
Satisfy the requirement of
Figure BDA00037020354400000227
Wherein, the pilot sequence sets used by the CFUE and the DUE are phi and omega respectively, phi is xi,
Figure BDA00037020354400000212
assume CFUE k And
Figure BDA00037020354400000213
respectively of pilot sequences of
Figure BDA00037020354400000214
Figure BDA00037020354400000215
And
Figure BDA00037020354400000216
Figure BDA00037020354400000217
with a representation dimension of τ p A square matrix of (a);
s105, assuming that different users in each group are allocated the same pilot sequence, the pilot signal received by the mth AP is represented as:
Figure BDA00037020354400000218
wherein the content of the first and second substances,
Figure BDA00037020354400000219
and
Figure BDA00037020354400000220
respectively represent CFUE k And
Figure BDA00037020354400000221
the normalized signal-to-noise ratio of (c),
Figure BDA00037020354400000222
and
Figure BDA00037020354400000223
Figure BDA00037020354400000224
respectively characterize the m < th > AP to the l < th > AP
Figure BDA00037020354400000225
And additive white gaussian noise at the mth AP;
Figure BDA00037020354400000226
the received pilot signal is represented as:
Figure BDA0003702035440000031
wherein the content of the first and second substances,
Figure BDA0003702035440000032
representing additive white gaussian noise, whose components are random variables satisfying independent equal distribution,
Figure BDA0003702035440000033
represents the transport channel of the D2D user to the communication link;
s106, performing despreading operation on the pilot signal received by the mth AP, where the processed signal is represented as:
Figure BDA0003702035440000034
wherein the content of the first and second substances,
Figure BDA0003702035440000035
from N A X N independent elements in the same distribution;
s107, performing low resolution quantization at the mth AP, and further representing the pilot signal received after quantization as:
Figure BDA0003702035440000036
wherein λ is m ,
Figure BDA0003702035440000037
Number of representation and quantization bits
Figure BDA0003702035440000038
Correlated linear gain, and additive Gaussian quantization of noise signal matrix
Figure BDA0003702035440000039
The covariance of (a) is expressed as:
Figure BDA00037020354400000310
estimated channel response via MMSE
Figure BDA00037020354400000311
Is shown as
Figure BDA00037020354400000312
Figure BDA00037020354400000313
The covariance of the term is accordingly given mathematically
Figure BDA00037020354400000314
Thus, the estimation matrix
Figure BDA00037020354400000315
Restated as
Figure BDA00037020354400000316
Wherein the content of the first and second substances,
Figure BDA00037020354400000317
represents a beamforming matrix, denoted as
Figure BDA00037020354400000318
Similarly, the estimated channel matrix between the D2D links
Figure BDA00037020354400000319
Is shown as
Figure BDA0003702035440000041
Wherein, C ll Is shown as
Figure BDA0003702035440000042
Preferably, S1 further includes:
s108, in the downlink data transmission phase, implementing a linear CB decoder at the AP to separate signals sent by multiple users, where the transmission signal of the mth AP is represented as:
Figure BDA0003702035440000043
where ρ is a Representing the maximum transmit power, η, of the AP mjk Denotes the power distribution coefficient, q j Representing the independent gaussian signal required for the jth multicast group,
Figure BDA0003702035440000044
the transmission signal of the mth AP is quantized and expressed as:
Figure BDA0003702035440000045
wherein alpha is m Number of representation and quantization bits
Figure BDA0003702035440000046
The relative linear gain of the signal is,
Figure BDA0003702035440000047
representing additive Gaussian quantization noise, alpha m And
Figure BDA0003702035440000048
are independent of each other and are provided with a plurality of groups,
Figure BDA0003702035440000049
follows a gaussian distribution with a mean value of zero;
Figure BDA00037020354400000410
the covariance matrix of (a) is expressed as:
Figure BDA00037020354400000411
the power constraint of the AP is expressed as:
Figure BDA00037020354400000412
s109, for the D2D link, assume MRC encoding is used for
Figure BDA00037020354400000413
Transmitting signal
Figure BDA00037020354400000414
Expressed as:
Figure BDA00037020354400000415
where ρ is d Is the maximum signal-to-noise ratio, u, of the l-th DUE transmitter l Denotes the power distribution coefficient, q l Indicating the expected independent multicast gaussian data signal for the ith DUE receiver,
Figure BDA00037020354400000416
Figure BDA00037020354400000417
to represent
Figure BDA00037020354400000418
And a receiver
Figure BDA00037020354400000419
An estimated channel matrix therebetween;
the power constraint for the DUE is expressed as:
Figure BDA00037020354400000420
s110, the received signal of the kth CFUE in the jth group is represented as:
Figure BDA0003702035440000051
wherein n is jk Is additive noise, and n jk ～CN(0,I N )。
Preferably, in S2, the lower limit of the achievable rate of the kth CFUE in the jth group is represented as:
Figure BDA0003702035440000052
wherein, theta mj Expressed as:
Figure BDA0003702035440000053
preferably, in S2, the lower limit of the achievable rate of the ith DUE receiver is expressed as:
Figure BDA0003702035440000054
wherein, Delta l Expressed as:
Figure BDA0003702035440000055
preferably, in S2, the downlink total energy efficiency is defined as:
Figure BDA0003702035440000056
the sum rate of the system architecture is represented as:
Figure BDA0003702035440000057
wherein L ═ D 2 λ d To represent
Figure BDA0003702035440000058
Average of λ d Indicating that the density of DUEs obeys an independent homogenous poisson point process.
Preferably, in S3, the total power consumption of the downlink is represented as:
Figure BDA0003702035440000059
wherein B represents the system bandwidth, P m ，P l ，P bt,m And P 0,m Respectively represent the m-th AP and the l-th AP
Figure BDA00037020354400000510
Power cost of (d), traffic front power associated with the mth access point, and fixed power consumption for each front edge link; p m Expressed as:
Figure BDA0003702035440000061
wherein, 0 is not less than delta m ≤1，N 0 Represents the power amplifier efficiency;
power consumption P of mth AP tc,m Expressed as:
P tc,m ＝N A (c ADC,m P AGC,m +P ADC,m )+N A ·P res,m +N A (c ADC,m P AGC,m +P DAC,m ),
wherein, P AGC,m And P res,m Power consumption of the remaining components representing the automatic generation control and the mth AP;
P ADC,m expressed as:
Figure BDA0003702035440000062
P DAC,m is shown as
Figure BDA0003702035440000063
Figure BDA0003702035440000064
Wherein, V dd 、C p 、f cor And I 0 Respectively representing the power supply of the converter, the parasitic capacitance of each switch in the converter, the angular frequency of 1/f and the unit current source related to the least significant bit, limited by the noise floor and the device mismatch;
c ADC,m and c DAC,m For indicating symbols relating to the accuracy of the low-resolution analog-to-digital converter ADC and the low-resolution digital-to-analog converter DAC, respectively
Figure BDA0003702035440000065
And
Figure BDA0003702035440000066
the following formula can be given:
Figure BDA0003702035440000067
in a third aspect, a computer storage medium having a computer program stored therein is provided; when the computer program runs on a computer, the computer executes the method for analyzing a multi-group multicast cellular-free massive MIMO system architecture assisted by D2D according to any one of the second aspects
In a fourth aspect, a computer program product is provided, which when run on a computer, causes the computer to perform the method for analyzing a multi-group multicast cellular-free massive MIMO system architecture assisted by D2D according to any one of the second aspects.
The invention has the beneficial effects that:
(1) the D2D-assisted multi-group multicast cell-free massive MIMO system architecture provided by the invention can provide services for multi-antenna cell-free users and D2D users on the same time/frequency resource block.
(2) The access point of the invention adopts the low-resolution analog-to-digital converter/digital-to-analog converter so as to effectively reduce hardware damage and energy loss.
(3) The invention constructs an energy loss model, can calculate the energy efficiency of the system and is convenient for improving the system.
Drawings
Fig. 1 is a flow chart of a method of analyzing a D2D-assisted multi-group multicast cellless massive MIMO system architecture;
fig. 2 is a diagram of the spectral efficiency of the system.
Detailed Description
The present invention will be further described with reference to the following examples. The following examples are set forth merely to aid in the understanding of the invention. It should be noted that, for a person skilled in the art, several modifications can be made to the invention without departing from the principle of the invention, and these modifications and modifications also fall within the protection scope of the claims of the present invention.
Example 1:
the application provides a D2D-assisted multi-group multicast non-cellular massive MIMO system architecture, comprising: a Central Processing Unit (CPU), an Access Point (AP), a multi-antenna Cell Free User Equipment (CFUE) and a D2D User pair;
wherein the AP, CFUE, and D2D user pairs are each configured with an antenna; all APs interface with the CPU and connect to the backhaul link, which can provide error-free and unlimited capacity.
The geographically distributed APs provide services for multi-antenna cellular-less users and D2D users on the same time/frequency resource block, and in addition, low-resolution Analog-to-Digital converters (ADCs)/Digital-to-Analog converters (DACs) are adopted in the APs to effectively reduce hardware and energy losses.
Example 2:
the application provides a method for analyzing a multi-group multicast non-cellular large-scale MIMO system architecture assisted by D2D, which comprises the steps of firstly obtaining imperfect Channel State Information (CSI) by a standard Minimum Mean Square Error (MMSE) method, and then obtaining the lower bound of the downlink rates of CFUEs and DUEs; in addition, the energy efficiency of the system is discussed according to the established power consumption model; finally, the numerical simulation was evaluated to verify the analysis results, as shown in fig. 1, including:
s1, establishing a D2D assisted multi-group multicast non-cellular massive MIMO system architecture;
s2, obtaining a closed expression of the downlink rate of the D2D-assisted multi-group multicast large-scale MIMO system architecture, and calculating the sum rate of the system architecture;
s3, constructing an energy loss model;
and S4, calculating the energy efficiency of the system architecture according to the sum rate and the energy loss model of the system architecture.
S1 includes:
s101, assuming that a multi-group multicast honeycomb-free large-scale MIMO system architecture assisted by D2D consists of a CPU, M contacts AP, a plurality of JXK multi-antenna honeycomb-free users CFUE and L terminal direct-connected D2D user pairs, wherein M is more than or equal to J; each AP, CFUEs, and DUEs has N, respectively A N and N D An antenna;
s102, for the convenience of analysis, assume that the wireless channel adopts an incoherent rui channel. Modeling a channel matrix of the mth AP to the kth CFUE in the jth group, expressed as:
Figure BDA0003702035440000081
wherein the content of the first and second substances,
Figure BDA0003702035440000082
β mjk ,
Figure BDA0003702035440000083
representing a large scale fading coefficient, H mjk A random variable representing a small-scale fading matrix and having components of independent and identical distribution (i.i.d.), obeying (0, 1);
s103, for the I D2D user pair, the transmitter
Figure BDA0003702035440000084
And a receiver
Figure BDA0003702035440000085
The channel matrix between them is modeled as:
Figure BDA0003702035440000086
wherein the content of the first and second substances,
Figure BDA0003702035440000087
Figure BDA0003702035440000088
representing large-scale fading coefficients, H ll′ Represents small scale fading and satisfies [ H ] ll′ ] mn ～CN(0,1)。
S1 further includes:
s104, in the uplink channel estimation stage, a Time Division Duplex (TDD) mode is adopted; at a length of τ c Within the coherence interval of (d), the length is τ p Is represented as
Figure BDA0003702035440000089
Satisfy the requirement of
Figure BDA00037020354400000826
Figure BDA00037020354400000827
Wherein, the pilot sequence sets used by the CFUE and the DUE are phi and omega respectively, phi is xi,
Figure BDA00037020354400000810
assume CFUE k And
Figure BDA00037020354400000811
respectively of pilot sequences of
Figure BDA00037020354400000812
And
Figure BDA00037020354400000813
Figure BDA00037020354400000814
with the expression dimension τ p A square matrix of (a);
s105, in order to effectively reduce the overhead of the limited pilot resource, assuming that different users in each group are allocated with the same pilot sequence, the pilot signal received by the mth AP is represented as:
Figure BDA00037020354400000815
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00037020354400000816
and
Figure BDA00037020354400000817
respectively represent CFUE k And
Figure BDA00037020354400000818
normalized Signal-to-Noise Ratio (SNR),
Figure BDA00037020354400000819
and
Figure BDA00037020354400000820
respectively characterize the m < th > AP to the l < th > AP
Figure BDA00037020354400000821
And Additive White Gaussian Noise (AWGN) at the mth AP;
Figure BDA00037020354400000822
the received pilot signal is represented as:
Figure BDA00037020354400000823
wherein the content of the first and second substances,
Figure BDA00037020354400000824
representing additive white gaussian noise, whose components are random variables satisfying independent equal distribution,
Figure BDA00037020354400000825
represents the transport channel of the D2D user to the communication link;
s106, performing despreading operation on the pilot signal received by the mth AP, where the processed signal is represented as:
Figure BDA0003702035440000091
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003702035440000092
from N A X N independent elements in the same distribution;
and S107, considering the huge hardware cost and energy consumption of the AP, performing low-resolution quantization at the mth AP. By using the AQNM quantization model, a low resolution quantization is performed at the mth AP, and the pilot signal received after quantization is further represented as:
Figure BDA0003702035440000093
wherein λ is m ,
Figure BDA00037020354400000917
Number of representation and quantization bits
Figure BDA0003702035440000094
Correlated linear gain, and additive Gaussian quantization noise signal matrix
Figure BDA0003702035440000095
Is expressed as:
Figure BDA0003702035440000096
estimated channel response via MMSE
Figure BDA0003702035440000097
Is shown as
Figure BDA0003702035440000098
For the sake of compactness in the symbol,
Figure BDA0003702035440000099
the covariance of the term is accordingly given mathematically
Figure BDA00037020354400000910
Thus, the estimation matrix
Figure BDA00037020354400000911
Restated as
Figure BDA00037020354400000912
Wherein the content of the first and second substances,
Figure BDA00037020354400000913
represents a beamforming matrix, denoted as
Figure BDA00037020354400000914
Similarly, the estimated channel matrix between the D2D links
Figure BDA00037020354400000915
Is shown as
Figure BDA00037020354400000916
Wherein, C ll Is shown as
Figure BDA0003702035440000101
As shown in fig. 2, due to the effect of low resolution quantization on system performance, when the number of quantization bits is less than 5, the total SE achieved by the system increases with the increase of the number of quantization bits, and reaches a maximum constant value at 5 or more. This fully illustrates that the higher quantization bits can be replaced by the lower resolution quantization bits without affecting the system SE performance, which will effectively reduce the hardware consumption of the system, which further indicates the superiority of the lower resolution quantization in the proposed system architecture. Furthermore, studies have also shown that increasing the number of APs can effectively increase the SE of the system. This is mainly due to the fact that the deployment of a large number of APs can effectively bring a large multiplexing gain and spatial freedom to the system. S1 further includes:
s108, in the downlink data transmission phase, implementing a linear CB decoder at the AP to separate signals sent by multiple users, where the transmission signal of the mth AP is represented as:
Figure BDA0003702035440000102
where ρ is a Representing the maximum transmit power, η, of the AP mjk Denotes the power distribution coefficient, q j Representing the independent gaussian signal required for the jth multicast group,
Figure BDA0003702035440000103
the transmission signal of the mth AP is quantized and expressed as:
Figure BDA0003702035440000104
wherein alpha is m Number of representation and quantization bits
Figure BDA0003702035440000105
The relative linear gain of the signal is,
Figure BDA0003702035440000106
representing additive Gaussian quantization noise, alpha m And
Figure BDA0003702035440000107
are independent of each other and are provided with a plurality of independent,
Figure BDA0003702035440000108
follows a gaussian distribution with a mean value of zero;
Figure BDA0003702035440000109
the covariance matrix of (a) is expressed as:
Figure BDA00037020354400001010
to satisfy power constraints of each AP, i.e.
Figure BDA00037020354400001011
Of APThe power constraint is expressed as:
Figure BDA00037020354400001012
can be further expressed as:
Figure BDA00037020354400001013
s109, for the D2D link, assume MRC encoding is used for
Figure BDA00037020354400001014
Transmitting signal
Figure BDA00037020354400001015
Expressed as:
Figure BDA00037020354400001016
where ρ is d Is the maximum signal-to-noise ratio, u, of the l-th DUE transmitter l Denotes the power distribution coefficient, q l Indicating the desire for an individual multicast gaussian data signal for the ith DUE receiver,
Figure BDA0003702035440000111
Figure BDA0003702035440000112
to represent
Figure BDA0003702035440000113
And a receiver
Figure BDA0003702035440000114
An estimated channel matrix therebetween;
in order to satisfy the power constraints for each DUE,
Figure BDA0003702035440000115
power of DUEThe constraint is expressed as:
Figure BDA0003702035440000116
further, the following expression can be obtained:
Figure BDA0003702035440000117
wherein, gamma is ll Can be expressed as
Figure BDA0003702035440000118
S110, the received signal of the kth CFUE in the jth group is represented as:
Figure BDA0003702035440000119
wherein n is jk Is additive noise, and n jk ～CN(0,I N )。
In S2, the lower limit of the achievable rate of the kth CFUE in the jth group is represented as:
Figure BDA00037020354400001110
wherein, theta mj Expressed as:
Figure BDA00037020354400001111
in S2, the lower limit of the achievable rate of the ith DUE receiver is represented as:
Figure BDA00037020354400001112
wherein, Delta l Expressed as:
Figure BDA00037020354400001113
in S2, the downlink total energy efficiency is defined as:
Figure BDA00037020354400001114
the sum rate of the system architecture is represented as:
Figure BDA0003702035440000121
wherein L ═ D 2 λ d To represent
Figure BDA0003702035440000122
Average of (a) d Indicating that the density of the DUEs is amenable to an independent homogeneous Poisson-Point-Process (PPP).
In S3, the total power consumption of the downlink is represented as:
Figure BDA0003702035440000123
wherein B represents the system bandwidth, P m ，P l ，P bt,m And P 0,m Respectively represent the mth AP and the lth AP
Figure BDA0003702035440000124
Power cost, traffic front power associated with the mth access point, and fixed power consumption for each front link; p m Expressed as:
Figure BDA0003702035440000125
wherein, 0 is not less than delta m ≤1，N 0 Represents the power amplifier efficiency;
power consumption P of mth AP tc,m Expressed as:
P tc,m ＝N A (c ADC,m P AGC,m +P ADC,m )+N A ·P res,m +N A (c ADC,m P AGC,m +P DAC,m ),
wherein, P AGC,m And P res,m Power consumption of the remaining components representing the automatic generation control and the mth AP;
P ADC,m expressed as:
Figure BDA0003702035440000126
P DAC,m is shown as
Figure BDA0003702035440000127
Figure BDA0003702035440000128
Wherein, V dd 、C p 、f cor And I 0 Respectively representing the power supply of the converter, the parasitic capacitance of each switch in the converter, the angular frequency of 1/f and the unit current source related to the least significant bit, limited by the noise floor and the device mismatch;
c ADC,m and c DAC,m For indicating signs relating to the accuracy of the low-resolution analog-to-digital converter ADC and the low-resolution digital-to-analog converter DAC, respectively
Figure BDA0003702035440000129
And
Figure BDA00037020354400001210
the following formula can be given:
Figure BDA00037020354400001211
the energy efficiency of the system can be obtained through algebraic calculation according to the formula.

Claims (10)

1. A D2D-assisted multi-group multicast cellless massive MIMO system architecture, comprising: the system comprises a Central Processing Unit (CPU), an Access Point (AP), a multi-antenna cellular-free user CFUE and a terminal direct-connected D2D user pair;
wherein the AP, CFUE, and D2D user pairs are each configured with an antenna; the AP coordinates and connects with the CPU through an interface and a backhaul link.
2. A method for analyzing D2D-assisted multi-group multicast cellular-free massive MIMO system architecture, applied to the D2D-assisted multi-group multicast cellular-free massive MIMO system architecture of claim 1, comprising:
s1, establishing a D2D assisted multi-group multicast non-cellular massive MIMO system architecture;
s2, obtaining a closed expression of the downlink rate of a D2D assisted multi-group multicast large-scale MIMO system architecture, and calculating the sum rate of the system architecture;
s3, constructing an energy loss model;
and S4, calculating the energy efficiency of the system architecture according to the sum rate of the system architecture and the energy loss model.
3. The method for analyzing multi-group multicast cellless massive MIMO system architecture assisted by D2D according to claim 2, wherein S1 comprises:
s101, assuming that the D2D-assisted multi-group multicast honeycomb-free large-scale MIMO system architecture consists of a CPU, M contacts AP, JXK multi-antenna honeycomb-free users CFUE and L terminal direct-connected D2D user pairs, wherein M is more than or equal to J; each AP, CFUEs, and DUEs has N, respectively A N and N D An antenna;
s102, modeling channel matrixes from the mth AP to the kth CFUE in the jth group, and expressing as follows:
Figure FDA0003702035430000017
wherein the content of the first and second substances,
Figure FDA0003702035430000011
representing a large scale fading coefficient, H mjk A random variable which represents a small-scale fading matrix and has components which are independently and equally distributed obeys (0, 1);
s103, for the I D2D user pair, the transmitter
Figure FDA0003702035430000012
And a receiver
Figure FDA0003702035430000013
The channel matrix between them is modeled as:
Figure FDA0003702035430000014
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003702035430000015
Figure FDA0003702035430000016
representing large-scale fading coefficients, H ll′ Represents small scale fading and satisfies [ H ] ll′ ] mn ～CN(0,1)。
4. The method for analyzing multi-group multicast cellless massive MIMO system architecture assisted by D2D, according to claim 3, wherein S1 further comprises:
s104, in the uplink channel estimation stage, adopting a time division duplex mode; at a length of τ c Within the coherence interval of (d), the length is τ p Is represented as
Figure FDA0003702035430000021
Satisfy the requirement of
Figure FDA0003702035430000022
τ p ≥K+LN D (ii) a Wherein, the pilot sequence sets used by the CFUE and the DUE are phi and omega respectively, phi is xi,
Figure FDA00037020354300000226
assume CFUE k And
Figure FDA0003702035430000023
respectively of pilot sequences of
Figure FDA0003702035430000024
And
Figure FDA0003702035430000025
Figure FDA0003702035430000026
with a representation dimension of τ p A square matrix of (a);
s105, assuming that different users in each group are allocated the same pilot sequence, the pilot signal received by the mth AP is represented as:
Figure FDA0003702035430000027
wherein the content of the first and second substances,
Figure FDA0003702035430000028
and
Figure FDA0003702035430000029
respectively represent CFUE k And
Figure FDA00037020354300000210
the normalized signal-to-noise ratio of (c),
Figure FDA00037020354300000211
and
Figure FDA00037020354300000212
Figure FDA00037020354300000213
respectively characterize the m < th > AP to the l < th > AP
Figure FDA00037020354300000214
And additive white gaussian noise at the mth AP;
Figure FDA00037020354300000215
the received pilot signal is represented as:
Figure FDA00037020354300000216
wherein the content of the first and second substances,
Figure FDA00037020354300000217
representing additive white gaussian noise, whose components are random variables satisfying independent equal distribution,
Figure FDA00037020354300000218
represents the transport channel of the D2D user to the communication link;
s106, performing despreading operation on the pilot signal received by the mth AP, where the processed signal is represented as:
Figure FDA00037020354300000219
wherein the content of the first and second substances,
Figure FDA00037020354300000220
from N A X N independent elements in the same distribution;
s107, performing low resolution quantization at the mth AP, and further representing the pilot signal received after quantization as:
Figure FDA00037020354300000221
wherein the content of the first and second substances,
Figure FDA00037020354300000222
number of representation and quantization bits
Figure FDA00037020354300000223
Correlated linear gain, and additive Gaussian quantization of noise signal matrix
Figure FDA00037020354300000224
The covariance of (a) is expressed as:
Figure FDA00037020354300000225
Figure FDA0003702035430000031
estimated channel response via MMSE
Figure FDA0003702035430000032
Is shown as
Figure FDA0003702035430000033
Figure FDA0003702035430000034
The covariance of the term is accordingly given mathematically
Figure FDA0003702035430000035
Thus, the estimation matrix
Figure FDA0003702035430000036
Restated as
Figure FDA0003702035430000037
Wherein the content of the first and second substances,
Figure FDA0003702035430000038
represents a beamforming matrix, denoted as
Figure FDA0003702035430000039
Similarly, the estimated channel matrix between the D2D links
Figure FDA00037020354300000310
Is shown as
Figure FDA00037020354300000311
Wherein, C ll Is shown as
Figure FDA00037020354300000312
5. The method for analyzing multi-group multicast cellless massive MIMO system architecture assisted by D2D, according to claim 4, wherein S1 further comprises:
s108, in the downlink data transmission phase, implementing a linear CB decoder at the AP to separate signals sent by multiple users, where the transmission signal of the mth AP is represented as:
Figure FDA00037020354300000313
where ρ is a Representing the maximum transmit power, η, of the AP mjk Denotes the power distribution coefficient, q j Representing the independent gaussian signal required for the jth multicast group,
Figure FDA00037020354300000314
the transmission signal of the mth AP is quantized and expressed as:
Figure FDA00037020354300000315
wherein alpha is m Number of representation and quantization bits
Figure FDA00037020354300000316
The relative linear gain of the signal is,
Figure FDA00037020354300000317
representing additive Gaussian quantization noise, alpha m And
Figure FDA00037020354300000318
are independent of each other and are provided with a plurality of groups,
Figure FDA0003702035430000041
follows a gaussian distribution with a mean value of zero;
Figure FDA0003702035430000042
the covariance matrix of (a) is expressed as:
Figure FDA0003702035430000043
the power constraint of the AP is expressed as:
Figure FDA0003702035430000044
s109, for the D2D link, assume MRC encoding is used for
Figure FDA0003702035430000045
Transmitting signal
Figure FDA0003702035430000046
Expressed as:
Figure FDA0003702035430000047
where ρ is d Is the maximum signal-to-noise ratio, u, of the l-th DUE transmitter l Denotes the power distribution coefficient, q l Indicating the expected independent multicast gaussian data signal for the ith DUE receiver,
Figure FDA0003702035430000048
Figure FDA0003702035430000049
to represent
Figure FDA00037020354300000410
And a receiver
Figure FDA00037020354300000411
An estimated channel matrix therebetween;
the power constraint for the DUE is expressed as:
Figure FDA00037020354300000412
s110, the received signal of the kth CFUE in the jth group is represented as:
Figure FDA00037020354300000413
wherein n is jk Is additive noise, and n jk ～CN(0,I N )。
6. The method for analyzing architecture of multi-group multicast cellless massive MIMO system aided by D2D of claim 5, wherein the lower limit of the achievable rate of the kth CFUE in the jth group in S2 is expressed as:
Figure FDA00037020354300000414
wherein, theta mj Expressed as:
Figure FDA00037020354300000415
7. the method for analyzing multi-group multicast cellless massive MIMO system architecture assisted by D2D, according to claim 6, wherein in S2, the lower bound of the achievable rate of the ith DUE receiver is expressed as:
Figure FDA00037020354300000416
wherein, Delta l Expressed as:
Figure FDA0003702035430000051
8. the method for analyzing multi-group multicast cellless massive MIMO system architecture assisted by D2D, according to claim 7, wherein in S2, the downlink aggregate energy efficiency is defined as:
Figure FDA0003702035430000052
the sum rate of the system architecture is represented as:
Figure FDA0003702035430000053
wherein L ═ D 2 λ d Represent
Figure FDA0003702035430000054
Average of (a) d Indicating that the density of the DUEs obeyed an independent homogenous poisson point process.
9. The method for analyzing multi-group multicast cellless massive MIMO system architecture assisted by D2D according to claim 8, wherein the total power consumption of the downlink in S3 is represented as:
Figure FDA0003702035430000055
wherein B represents the system bandwidth, P m ，P l ，P bt,m And P 0,m Respectively represent the m-th AP and the l-th AP
Figure FDA0003702035430000056
Power cost of (d), traffic front power associated with the mth access point, and fixed power consumption for each front edge link; p m Expressed as:
Figure FDA0003702035430000057
wherein, 0 is not less than delta m ≤1，N 0 Representative power amplifierEfficiency;
power consumption P of mth AP tc,m Expressed as:
P tc,m ＝N A (c ADC,m P AGC,m +P ADC,m )+N A ·P res,m +N A (c ADC,m P AGC,m +P DAC,m ),
wherein, P AGC,m And P res,m Power consumption of the remaining components representing the automatic generation control and the mth AP;
P ADC,m expressed as:
Figure FDA0003702035430000058
P DAC,m is shown as
Figure FDA0003702035430000059
Figure FDA00037020354300000510
Wherein, V dd 、C p 、f cor And I 0 Respectively representing the power supply of the converter, the parasitic capacitance of each switch in the converter, the angular frequency of 1/f and the unit current source related to the least significant bit, limited by the noise floor and the device mismatch;
c ADC,m and c DAC,m For indicating symbols relating to the accuracy of the low-resolution analog-to-digital converter ADC and the low-resolution digital-to-analog converter DAC, respectively
Figure FDA00037020354300000511
And
Figure FDA00037020354300000512
the following formula can be given:
Figure FDA0003702035430000061
10. a computer storage medium, wherein a computer program is stored in the computer storage medium; the computer program, when run on a computer, causes the computer to perform the method of D2D-assisted analysis of multi-group multicast cellless massive MIMO system architecture as claimed in any of claims 2 to 9.
CN202210694739.1A 2022-06-20 2022-06-20 D2D-assisted multi-group multicast honeycomb-free large-scale MIMO system architecture Active CN115037341B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210694739.1A CN115037341B (en) 2022-06-20 2022-06-20 D2D-assisted multi-group multicast honeycomb-free large-scale MIMO system architecture

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210694739.1A CN115037341B (en) 2022-06-20 2022-06-20 D2D-assisted multi-group multicast honeycomb-free large-scale MIMO system architecture

Publications (2)

Publication Number Publication Date
CN115037341A true CN115037341A (en) 2022-09-09
CN115037341B CN115037341B (en) 2023-12-01

Family

ID=83124524

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210694739.1A Active CN115037341B (en) 2022-06-20 2022-06-20 D2D-assisted multi-group multicast honeycomb-free large-scale MIMO system architecture

Country Status (1)

Country Link
CN (1) CN115037341B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103095423A (en) * 2013-01-31 2013-05-08 西安电子科技大学 Multi-user cooperative transmission method based on dimension to dimension (D2D) inter-group communication
US20200052761A1 (en) * 2018-08-10 2020-02-13 At&T Intellectual Property I, L.P. Multiple-input multiple-output system performance using advanced receivers for 5g or other next generation networks
CN110855335A (en) * 2019-10-24 2020-02-28 暨南大学 Downlink virtual MIMO-NOMA method based on power and rate joint optimization
KR20210097546A (en) * 2020-01-30 2021-08-09 숙명여자대학교산학협력단 A method for determining communication mode and transmission power of a terminal and an apparatus for the method
CN114641018A (en) * 2022-04-26 2022-06-17 东南大学 RIS-assisted D2D communication system and performance optimization method thereof

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103095423A (en) * 2013-01-31 2013-05-08 西安电子科技大学 Multi-user cooperative transmission method based on dimension to dimension (D2D) inter-group communication
US20200052761A1 (en) * 2018-08-10 2020-02-13 At&T Intellectual Property I, L.P. Multiple-input multiple-output system performance using advanced receivers for 5g or other next generation networks
CN110855335A (en) * 2019-10-24 2020-02-28 暨南大学 Downlink virtual MIMO-NOMA method based on power and rate joint optimization
KR20210097546A (en) * 2020-01-30 2021-08-09 숙명여자대학교산학협력단 A method for determining communication mode and transmission power of a terminal and an apparatus for the method
CN114641018A (en) * 2022-04-26 2022-06-17 东南大学 RIS-assisted D2D communication system and performance optimization method thereof

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
HAMED MASOUMI: "Coexistence of D2D Communications and Cell-Free Massive MIMO Systems With Low Resolution ADC for Improved Throughput in Beyond-5G Networks", IEEE TRANSACTIONS ON COMMUNICATIONS, pages 999 - 1013 *
XU QIAO: "Downlink Achievable Rate of D2D Underlaid Cell-Free Massive MIMO Systems With Low-Resolution DACs", IEEE SYSTEMS JOURNAL, pages 3855 - 3866 *
颉满刚;贾向东;周猛;: "基于大规模MIMO和D2D技术的混合网络能量效率研究", 信号处理, no. 01 *
颉满刚;贾向东;周猛;: "大规模MIMO蜂窝网与D2D混合网络物理层安全性能研究", 重庆邮电大学学报(自然科学版), no. 01 *

Also Published As

Publication number Publication date
CN115037341B (en) 2023-12-01

Similar Documents

Publication Publication Date Title
CN110099017B (en) Channel estimation method of hybrid quantization system based on deep neural network
CN107453795B (en) Beam allocation method of multi-user millimeter wave communication system, device and system thereof
CN113179109B (en) Honeycomb-removing large-scale MIMO uplink spectrum efficiency optimization method
CN113078930B (en) Method for establishing downlink de-cellular large-scale MIMO system
CN108063634B (en) Optimal regular pre-coding method in low-precision quantitative large-scale MIMO
CN104617996B (en) The Precoding Design method of minimum signal to noise ratio is maximized in extensive mimo system
CN110518952B (en) Adaptive grouping broadband hybrid beam forming design method based on codebook
JP2009153139A (en) Pre-coding processing method and apparatus for mimo downlink, and base station
Ding et al. Performance analysis of mixed-ADC massive MIMO systems over spatially correlated channels
CN105162503B (en) Multi-user beam forms the co-design method with day line options in a kind of extensive mimo system
WO2017005086A1 (en) Precoding method and device
CN111865844B (en) Channel estimation method and device for large-scale MIMO full-duplex relay system
CN110149133B (en) Large-scale uplink transmission method based on beam space
CN117240331A (en) No-cellular network downlink precoding design method based on graph neural network
CN109951219B (en) Low-cost large-scale non-orthogonal multi-access method
CN115037341A (en) D2D-assisted multi-group multicast non-cellular large-scale MIMO system architecture
CN107104715B (en) Interference alignment method based on antenna selection
CN102647247B (en) One transmits preliminary treatment sending method and device
CN111130611B (en) Improved distributed MIMO system antenna selection decrement algorithm
CN108418615B (en) User scheduling method based on MU-MIMO limited feedback system
JP4358241B2 (en) Spatial multiplexing transmission method and transmitter
CN106788645B (en) Energy efficiency optimization method in multi-user MIMO distributed base station system
CN105656666A (en) Total power joint optimization method for collaborative network downlink under non-perfect channel
CN105119869A (en) Space-shift keying method based on time-space matrix constellation diagram
Sande et al. Fast convergence for efficient beamforming in massive MIMO heterogeneous networks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant