CN112910517A - Massive MIMO relay system downlink model construction method based on low-precision quantization - Google Patents

Massive MIMO relay system downlink model construction method based on low-precision quantization Download PDF

Info

Publication number
CN112910517A
CN112910517A CN202110067128.XA CN202110067128A CN112910517A CN 112910517 A CN112910517 A CN 112910517A CN 202110067128 A CN202110067128 A CN 202110067128A CN 112910517 A CN112910517 A CN 112910517A
Authority
CN
China
Prior art keywords
relay
base station
channel
model
user
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
CN202110067128.XA
Other languages
Chinese (zh)
Other versions
CN112910517B (en
Inventor
张昀
李双
徐钦晨
于舒娟
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.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
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 Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202110067128.XA priority Critical patent/CN112910517B/en
Publication of CN112910517A publication Critical patent/CN112910517A/en
Application granted granted Critical
Publication of CN112910517B publication Critical patent/CN112910517B/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
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3911—Fading models or fading generators
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Radio Transmission System (AREA)

Abstract

The invention discloses a MassiveMIMO relay system downlink model construction method based on low precision quantization, firstly, modeling a channel coefficient from a base station to a relay and a channel from the relay to a target user into a Leise fading model; then, dividing signals of a Rice fading model downlink into two time slots for processing in sequence, wherein the first time slot is a signal processing process from a base station to a relay, and the second time slot is a signal processing process from the relay to a target user; secondly, deducing a real reachable total rate according to a received signal expression of a user and a Shannon formula; and finally, deriving a closed expression of the total rate by using the high-order statistic. The invention considers the condition that LOS paths exist between a base station to a relay and between the relay and a target user in the downlink process of the MassiveMIMO relay system, so that the model is more consistent with the actual communication condition, and the problem of high power consumption caused by multiple antennas is solved.

Description

Massive MIMO relay system downlink model construction method based on low-precision quantization
Technical Field
The invention belongs to the technical field of wireless communication signal processing, and particularly relates to a Massive MIMO relay system downlink model construction method based on low-precision quantization.
Background
In order to meet the technical indexes of the fifth generation mobile communication system in the aspects of thousand times of data volume, ultra-low delay, 10Gbit/s transmission rate, supporting diversified applications and the like and meet the requirements of high spectral efficiency, high energy efficiency and high cost efficiency, large-scale MIMO technology is provided in both academic circles and industrial circles.
The main idea of MIMO is to install hundreds of antennas at the base station and serve tens of users on the same time-frequency resource block. The large-scale MIMO system greatly improves the antenna array gain and spatial degree of freedom at the base station end, so that the interference influence between users can be reduced or even eliminated only by adopting a simple linear signal processing method, such as Maximum Ratio Combining (MRC) and Zero-Forcing (ZF), at the base station end, thereby effectively improving the system capacity and the spectral efficiency. Therefore, the research on the system capacity of the Massive MIMO system becomes more important under different transmission and reception schemes and different channels.
In addition, in order to increase the transmission rate of mobile communication, improve the small cell coverage radius and overcome the disadvantage that the mobile terminal is not properly configured with multiple antennas, a relay transmission technology is proposed and becomes one of the important technologies to solve the above problems. The relay technology can significantly improve the transmission reliability, multiplexing gain and coverage. For multi-user multiple-input multiple-output (MU-MIMO) networks, relay stations are widely deployed to enhance the quality of communication between base stations and users.
To benefit from the advantages of massive MIMO and relay technologies, some excellent domestic and foreign researchers have studied massive MIMO relay networks. Some MRT/MRC and ZF processing used in the relay is utilized to obtain an asymptotic sum rate, and the sum rate performance between the MRT/MRC and ZF under different powers is compared. There are several pairs of massive MIMO uplinks with AF relays where both the base station and the relay are equipped with massive antennas. Existing research is based on arranging a large number of antennas at the relay to improve spatial multiplexing, and does not consider using large-scale antennas at the base station.
However, the large number of antennas greatly complicates the hardware design implementation of massive MIMO systems. Especially, each receiving antenna in the system needs to be configured with an analog-to-digital converter (ADC) unit, and the use of a large number of antennas means that a large number of ADC units are required. The exponential increase in overhead and power consumption has caused high-bit analog-to-digital converters to become a major bottleneck in implementing massive MIMO systems.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a method for constructing a Massive MIMO relay system downlink model based on low-precision quantization, which constructs a wireless communication model and analyzes the wireless communication model, so that the model is more in line with the actual communication situation, and the problem of high power consumption caused by multiple antennas is solved.
The invention content is as follows: the invention provides a Massive MIMO relay system downlink model construction method based on low-precision quantization, which specifically comprises the following steps:
(1) constructing a channel model: modeling a channel coefficient from a base station to a relay and a channel from the relay to a target user into a Leise fading model;
(2) processing the signal transmission process of the Rice fading model downlink: the method comprises the following steps that the two time slots are sequentially processed, wherein the first time slot is a signal processing process from a base station to a relay, and the second time slot is a signal processing process from the relay to a target user;
(3) calculating the true achievable total rate of the Rice fading model: deducing a real reachable total rate according to a received signal expression of a user and a Shannon formula;
(4) calculating the theoretical total rate of the Rice fading model: deducing a closed expression of the total rate by using the high-order statistic;
(5) the system capacity is obtained by analyzing a closed expression of the achievable total rate.
Further, the channel coefficients in step (1) are written in the form of a matrix as follows:
Figure BDA0002904583510000021
Figure BDA0002904583510000022
wherein the content of the first and second substances,
Figure BDA0002904583510000023
and
Figure BDA0002904583510000024
fast fading matrix, M, for modeling base station to relay and relay to destination user1And M1Respectively representing the number of antennas of the base station and the relay, K being the number of users, and [ Hi]km＝hi,km,i＝1,2；D1And D2Represents a large scale fading diagonal matrix of K x K, and [ D1]kk＝αk,[D2]kk＝βk；
Fast fading channel matrix H1And H2Can be expressed as follows:
Figure BDA0002904583510000025
Figure BDA0002904583510000026
wherein the content of the first and second substances,
Figure BDA0002904583510000031
and
Figure BDA0002904583510000032
which represents the random component of the channel and,
Figure BDA0002904583510000033
and
Figure BDA0002904583510000034
representing the deterministic component of the channel, omega1And Ω2The Rician-factor diagonal matrix of K multiplied by K can be expressed, and the Rician-factor of the K user can respectively express [ omega ]1]kk＝μkAnd [ omega ]2]kk＝εk。
Further, the step (2) comprises the steps of:
(21) dividing the transmission signal of the whole downlink process into two time slots for processing;
(22) in the first time slot, the base station transmits the source signal to the relay after the maximum ratio transmission precoding processing, and the received signal of the relay end is as follows:
Figure BDA0002904583510000035
Figure BDA0002904583510000036
wherein the source signal is
Figure BDA0002904583510000037
And x satisfies the normalization E { x · x [ ] xH}＝IK，G1Is K M between base station and relay1The channel matrix comprises a fast fading part and a large-scale fading part; since MRT is used at the base station to process the signals, the precoding matrix is
Figure BDA0002904583510000038
Depend on
Figure BDA0002904583510000039
Figure BDA00029045835100000310
Is G1Conjugate transpose matrix of G1Is K M between base station and relay1The matrix of the channels is then used,
Figure BDA00029045835100000311
as a precoding matrix, yBSFor signals to be transmitted at the base station, puFor each user's transmit power, nRSRepresents complex white Gaussian noise independently and equally distributed and
Figure BDA00029045835100000312
(23) ADCs are configured at the relay terminal, and an additive quantization noise model is adopted to carry out on the received signal yRSAnd (3) quantification:
Figure BDA00029045835100000313
where ξ ═ 1- ρ is the linear quantization gain, nqIs quantization noise, and yRSNot related; ρ is the ratio of the quantizer error variance and the input variance; receiving signal yRS,qAnd performing power amplification at the relay, wherein the signals after power amplification are as follows:
yAF＝a·yRS,q
wherein, yAFSatisfies E { yAF·yAF H}＝pRA is an amplification factor satisfying the total transmit power constraint at the relay end; the amplification factor a that satisfies the total transmit power constraint at the relay, which can be obtained from the power constraint condition, is:
Figure BDA00029045835100000314
will yAFProcessing the signals by MRT to obtain signals
Figure BDA00029045835100000315
Comprises the following steps:
Figure BDA0002904583510000041
(24) in the second time slot, the relay will
Figure BDA0002904583510000042
And forwarding to the destination user, wherein the received signals reaching the destination user are as follows:
Figure BDA0002904583510000043
wherein G is2Is that fast fading and large-scale fading are contained between the relay and the target user2Of the channel matrix nUIs complex additive white gaussian noise at the destination user and
Figure BDA0002904583510000044
the user receiving signal based on Rician channel Massive MIMO relay system is:
Figure BDA0002904583510000045
further, the step (3) is realized as follows:
the received signal of the kth destination user is as follows:
Figure BDA0002904583510000046
signal to interference plus noise ratio SINR:
Figure BDA0002904583510000047
the achievable rate for the kth user is:
Figure BDA0002904583510000048
wherein:
Figure BDA0002904583510000049
further, the step (4) is realized as follows:
the achievable total rate of the Massive MIMO relay system under the Rician channel is as follows:
Figure BDA00029045835100000410
for ease of calculation, the following variables are defined:
Figure BDA0002904583510000051
Figure BDA0002904583510000056
using delta1,k,Δ2,k,Φ1,ik,φ2,ik,γ1,ki,γ2,kiIs S in the above formulak,Ik,N1,k,N2,kCan be expressed as:
Figure BDA0002904583510000052
Figure BDA0002904583510000053
Figure BDA0002904583510000054
Figure BDA0002904583510000055
has the advantages that: compared with the prior art, the invention has the beneficial effects that: the invention considers the condition that LOS paths exist between a base station to a relay and between the relay and a target user in the downlink process of the Massive MIMO relay system, so that the model is more consistent with the actual communication condition; and in order to solve the high power consumption problem brought by many aerials; configuring low-precision ADCs for the antenna at the relay to perform quantization processing; through the simulation result, the total rate curve obtained through Monte-Carlo simulation almost completely coincides with the total rate curve obtained through analysis; analyzing the downlink performance of a Massive MIMO relay system under a Rician fading channel based on low-precision quantization, and deriving an approximate closed expression of the downlink reachable rate of the Massive MIMO relay system; the large-scale MIMO system with low-precision quantization can obtain satisfactory throughput performance and spectral efficiency under a Rician channel
Drawings
FIG. 1 is a diagram of a structural implementation of a Rician channel Massive MIMO relay system downlink model based on low-precision quantization;
FIG. 2 is a graph of total rate as a function of the number of relay antennas that can be achieved by the present invention;
FIG. 3 is a graph of the total rate achievable with the present invention as a function of Rician-factor;
fig. 4 is a graph of achievable rate as a function of the number of quantization bits.
Detailed Description
The technical scheme of the invention is clearly and completely described below with reference to the accompanying drawings.
In an actual communication system, due to the existence of severe shadowing and path LOSs between a base station and a destination user, a direct link is not available, signals transmitted by the base station need to be relayed to be received by the user, and LOS (line-of-sight) exists between the base station and the relay and the user. .
The invention provides a method for constructing a Massive MIMO relay system downlink model based on low-precision quantization, which comprises the steps of carrying out corresponding processing on signals in a communication process to obtain a receiving signal of a kth user, calculating the signal-to-noise ratio of the kth user according to the obtained receiving signal, obtaining the realization rate of the kth user through a Shannon formula, summing to obtain a real reachable rate, and finally deducing a closed expression of the reachable rate of a downlink according to high-order statistics of a channel. The method specifically comprises the following steps:
as shown in fig. 1, it is a structure diagram of a Rician channel Massive MIMO relay system downlink model construction and performance analysis method based on low precision quantization. The system consists of a base station, a relay and K single-antenna users. Due to serious shadow and path LOSs, a direct link does not exist between the base station and the target user, a signal sent by the base station needs to pass through the relay to reach the target user, and LOS paths exist between the base station and the relay and the target user. The base station and the relay system are provided with a large number of antenna arrays, the number of the antennas is M respectively1And M2(assume M1≥M2≥K)。
Step 1: constructing a channel model: and modeling the channel coefficient from the base station to the relay and the channel from the relay to the target user into a Lass fading model.
The channel coefficients are written in the form of a matrix as follows:
Figure BDA0002904583510000061
Figure BDA0002904583510000062
wherein the content of the first and second substances,
Figure BDA0002904583510000063
and
Figure BDA0002904583510000064
fast fading matrix, M, for modeling base station to relay and relay to destination user1And M1Respectively representing the number of antennas of the base station and the relay, K being the number of users, and [ Hi]km＝hi,km,i＝1,2。D1And D2Represents a large scale fading diagonal matrix of K x K, and [ D1]kk＝αk,[D2]kk＝βk。
Fast fading channel matrix H1And H2Can be expressed as follows:
Figure BDA0002904583510000071
Figure BDA0002904583510000072
here, the
Figure BDA0002904583510000073
And
Figure BDA0002904583510000074
which represents the random component of the channel and,
Figure BDA0002904583510000075
and
Figure BDA0002904583510000076
Figure BDA0002904583510000077
representing the deterministic component of the channel, omega1And Ω2The Rician-factor diagonal matrix of K multiplied by K can be expressed, and the Rician-factor of the K user can respectively express [ omega ]1]kk＝μkAnd [ omega ]2]kk＝εk。
Step 2: and processing the signal transmission process of the whole downlink: and the processing is sequentially carried out by dividing the time slot into two time slots, wherein the first time slot is a signal processing process from the base station to the relay, and the second time slot is a signal processing process from the relay to the target user.
The whole process is divided into two time slots.
In the first time slot, the base station transmits the source signal to the relay after performing Maximum Ratio Transmission (MRT) precoding processing. Assume a source signal of
Figure BDA0002904583510000078
And x satisfies the normalization E { x · x [ ] xH}＝IK，G1Is K M between base station and relay1The channel matrix comprises two parts of fast fading and large-scale fading. Signal to be transmitted y at base stationBSThat is, the source signal is simply processed through the MRT, i.e., the sum of x depends on
Figure BDA0002904583510000079
Of the receiving matrix
Figure BDA00029045835100000710
Multiplication:
Figure BDA00029045835100000711
p in the above formulauIs the transmit power of each user.
Therefore, the received signal at the relay end is:
Figure BDA00029045835100000712
wherein n isRSRepresents complex white Gaussian noise independently and equally distributed and
Figure BDA00029045835100000713
next, since the ADCs are configured at the relay end, the signal is quantized before further processing. In this model, we adopt a widely used Additive Quantization Noise Model (AQNM) to receive the signal yRSThe quantized signal obtained at the relay is:
Figure BDA00029045835100000714
where ξ ═ 1- ρ is the linear quantization gain, nqIs quantization noise, and yRSIs not relevant. ρ is the ratio of the quantizer error variance and the input varianceSince the channel input signal x is gaussian-distributed, the received signal at the relay terminal is also gaussian-distributed. As shown in table 1 for b<5, the exact values of ρ are in table 1; for the case that b is more than or equal to 6, rho can be expressed by the formula
Figure BDA0002904583510000081
And (6) calculating. Further, n isqThe conditional covariance formula of (a) can be expressed as:
Figure BDA0002904583510000082
therefore, the temperature of the molten metal is controlled,
Figure BDA0002904583510000083
TABLE 1 quantization bit number b and quantization distortion factor
Figure BDA0002904583510000084
Subsequently, the signal y is receivedRS,qAnd performing power amplification at the relay, wherein the signals after power amplification are as follows:
yAF＝a·yRS,q
the signal y here being power amplifiedAFSatisfies E { yAF·yAF H}＝pRAnd a is an amplification factor that satisfies the total transmit power constraint at the relay. The amplification factor a that satisfies the total transmit power constraint at the relay, which can be obtained from the power constraint condition, is:
Figure BDA0002904583510000085
finally, mixing yAFProcessing the signals by MRT to obtain signals
Figure BDA0002904583510000086
Comprises the following steps:
Figure BDA0002904583510000087
in the second time slot, the relay will
Figure BDA0002904583510000088
Forwarded to the destination user, so the received signal to the destination user is:
Figure BDA0002904583510000089
where G is2Is that fast fading and large-scale fading are contained between the relay and the target user2Of the channel matrix nUIs complex additive white gaussian noise at the destination user and
Figure BDA00029045835100000810
the user receiving signal based on the Rician channel Massive MIMO relay system can be obtained by expanding the above formula as follows:
Figure BDA00029045835100000811
and step 3: calculating the real achievable total rate of the model: and deducing the real total rate according to the received signal expression of the user and the Shannon formula.
Writing a received signal of a k-th destination user based on a user received signal of a Rician channel Massive MIMO relay system as follows:
Figure BDA0002904583510000091
suppose that user interference is gaussian distributed and xkRegardless, we can get the signal to interference plus noise ratio (SINR):
Figure BDA0002904583510000092
the achievable rate for the kth user can thus be found to be:
Rk＝1/2·E{log2(1+SINR)}
thus, the achievable rate for the kth user can be approximated as:
Rk≈1/2·log2(1+E{SINR})
the concrete expression is as follows:
Figure BDA0002904583510000093
and 4, step 4: calculating the theoretical total rate of the Rice fading model: a closed expression of the achievable total rate is derived using the high order statistics.
The achievable total rate of the Massive MIMO relay system under the Rician channel is as follows:
Figure BDA0002904583510000094
suppose that:
Figure BDA0002904583510000095
Figure BDA0002904583510000096
using delta1,k,Δ2,k,Φ1,ik,φ2,ik,γ1,ki,γ2,kiIs S in the above formulak,Ik,N1,k,N2,kCan be expressed as:
Figure BDA0002904583510000101
Figure BDA0002904583510000102
Figure BDA0002904583510000103
Figure BDA0002904583510000104
the following was demonstrated: the high order statistics are:
Figure BDA0002904583510000105
Figure BDA0002904583510000106
Figure BDA0002904583510000107
Figure BDA0002904583510000108
at RkIn the expression of
Figure BDA0002904583510000109
The following calculation can be made:
Figure BDA00029045835100001010
can pass through
Figure BDA00029045835100001011
The power of the desired signal is obtained as:
Figure BDA00029045835100001012
similarity can be calculated
Figure BDA0002904583510000111
Figure BDA0002904583510000112
Therefore, power I of inter-user interferencekCan be expressed in the following form:
Figure BDA0002904583510000113
the same principle is that:
Figure BDA0002904583510000114
the last term in the denominator is calculated as follows:
Figure BDA0002904583510000115
Figure BDA0002904583510000121
a using Delta1,k,Δ2,k,Φ1,ik,φ2,ik,γ1,ki,γ2,kiIt can be expressed as follows:
Figure BDA0002904583510000122
expression of true rate by a
Figure BDA0002904583510000123
The expectation of the ith element is:
Figure BDA0002904583510000124
thus obtaining the following components:
Figure BDA0002904583510000125
the third term in the denominator is:
Figure BDA0002904583510000126
in summary, in the downlink process, the model is considered as a leis fading channel model in the channels from the base station to the relay and from the relay to the user, and the leis fading channel model is more consistent with the actual communication environment compared with the traditional rayleigh channel model. And after the problem of higher hardware cost brought by multiple antennas is considered, low-precision quantized ADCs are configured in the relay so as to reduce cost and power consumption. The model also has the advantage that the channel of the entire model can be simplified according to the value of a particular Rician-factor, such as when Rician-factor is- ∞ dB (equivalent to Δ ∞ dB)1,k＝1、γ1,ki1) whose channel model changes from rice to rayleigh fading.
Fig. 2 is a simulation result of the total rate of the Rician channel Massive MIMO relay system based on low precision quantization according to the number of antennas in relay when the number of users K is 10 and Rician-factor is 10dB under perfect channel state information. Wherein the star points are the real rate obtained by taking 1000 Monte Carlo experiments, and the linear ones are the simulation results obtained by analysis. As can be seen from fig. 2, in this case, the curve obtained by the analysis almost coincides with the true curve of 1000 monte carlo simulations, which confirms the correctness of the analysis result.
Fig. 3 is a graph of the total rate as a function of Rician-factor for a number K of users of 10, a number of antennas at the relay of 200, and a transmission power of 10 dB. It can be seen from fig. 3 that the total achievable rate increases with increasing Rician-factor and the number of quantization bits. It can also be seen from fig. 3 that when Rician-factor is reduced to a relatively small value, when the channel degrades to a rayleigh fading channel, the total rate no longer changes with Rician-factor, and when Rician-factor reaches 30dB, the total rate no longer increases with increase of Rician-factor, thereby reaching a saturation state.
Fig. 4 is a graph showing the variation of the average rate of each user with the number of transmission bits for a relay antenna number of 200 and two Rician-factors of 10 dB. As can be seen from fig. 4, when the quantization bit number is less than 5, the average rate gradually increases with the increase of the quantization bit number, and when the quantization bit number is greater than 5, the average rate tends to be in a steady state and does not increase any more. Meanwhile, the performance degradation caused by the 1-bit quantization can be compensated by increasing the number of antennas.

Claims (5)

1. A Massive MIMO relay system downlink model construction method based on low-precision quantization is characterized by comprising the following steps:
(1) constructing a channel model: modeling a channel coefficient from a base station to a relay and a channel from the relay to a target user into a Leise fading model;
(2) processing the signal transmission process of the Rice fading model downlink: the method comprises the following steps that the two time slots are sequentially processed, wherein the first time slot is a signal processing process from a base station to a relay, and the second time slot is a signal processing process from the relay to a target user;
(3) calculating the true achievable total rate of the Rice fading model: deducing a real reachable total rate according to a received signal expression of a user and a Shannon formula;
(4) calculating the theoretical total rate of the Rice fading model: deducing a closed expression of the total rate by using the high-order statistic;
(5) the system capacity is obtained by analyzing a closed expression of the achievable total rate.
2. The method for constructing the Massive MIMO relay system downlink model based on the low-precision quantization according to claim 1, wherein the channel coefficients in the step (1) are written in a matrix form as follows:
Figure FDA0002904583500000011
Figure FDA0002904583500000012
wherein the content of the first and second substances,
Figure FDA0002904583500000013
and
Figure FDA0002904583500000014
fast fading matrix, M, for modeling base station to relay and relay to destination user1And M1Respectively representing the number of antennas of the base station and the relay, K being the number of users, and [ Hi]km＝hi，km，i＝1，2；D1And D2Represents a large scale fading diagonal matrix of K x K, and [ D1]kk＝αk，[D2]kk＝βk；
Fast fading channel matrix H1And H2Can be expressed as follows:
Figure FDA0002904583500000015
Figure FDA0002904583500000016
wherein the content of the first and second substances,
Figure FDA0002904583500000017
and
Figure FDA0002904583500000018
which represents the random component of the channel and,
Figure FDA0002904583500000019
and
Figure FDA00029045835000000110
representing the deterministic component of the channel, omega1And Ω2The Rician-factor diagonal matrix of K multiplied by K can be expressed, and the Rician-factor of the K user can respectively express [ omega ]1]kk＝μkAnd [ omega ]2]kk＝εk。
3. The method for constructing the Massive MIMO relay system downlink model based on the low precision quantization as claimed in claim 1, wherein the step (2) comprises the steps of:
(21) dividing the transmission signal of the whole downlink process into two time slots for processing;
(22) in the first time slot, the base station transmits the source signal to the relay after the maximum ratio transmission precoding processing, and the received signal of the relay end is as follows:
Figure FDA0002904583500000021
Figure FDA0002904583500000022
wherein the source signal is
Figure FDA0002904583500000023
And x has a power satisfying normalized Ebar xH}＝IK，G1Is K M between base station and relay1The channel matrix comprises a fast fading part and a large-scale fading part; since MRT is used at the base station to process the signals, the precoding matrix is
Figure FDA0002904583500000024
Depend on
Figure FDA0002904583500000025
Figure FDA0002904583500000026
Is G1Conjugate transpose matrix of G1Is K M between base station and relay1The matrix of the channels is then used,
Figure FDA0002904583500000027
as a precoding matrix, yBsFor the signal to be transmitted at the base station, pu is the transmit power of each user, nRSRepresents complex white Gaussian noise independently and equally distributed and
Figure FDA0002904583500000028
(23) ADCs are configured at the relay terminal, and an additive quantization noise model is adopted to carry out on the received signal yRsAnd (3) quantification:
Figure FDA0002904583500000029
where ξ ═ 1- ρ is the linear quantization gain, nqIs quantization noise, and yRsNot related; ρ is the ratio of the quantizer error variance and the input variance; receiving signal yRS，qAnd performing power amplification at the relay, wherein the signals after power amplification are as follows:
yAF＝a·yRS，q
wherein, yAFSatisfies E { yAF·yAF H}＝pRA is an amplification factor satisfying the total transmit power constraint at the relay end; the amplification factor a that satisfies the total transmit power constraint at the relay, which can be obtained from the power constraint condition, is:
Figure FDA00029045835000000210
will yAFProcessing the signals by MRT to obtain signals
Figure FDA00029045835000000211
Comprises the following steps:
Figure FDA00029045835000000212
(24) in the second time slot, the relay will
Figure FDA00029045835000000213
And forwarding to the destination user, wherein the received signals reaching the destination user are as follows:
Figure FDA0002904583500000031
wherein G is2Is that fast fading and large-scale fading are contained between the relay and the target user2Of the channel matrix nUIs complex additive white gaussian noise at the destination user and
Figure FDA0002904583500000032
the user receiving signal based on Rician channel Massive MIMO relay system is:
Figure FDA0002904583500000033
4. the method for constructing the downlink model of the Massive MIMO relay system based on the low precision quantization according to claim 1, wherein the step (3) is realized by the following steps:
the received signal of the kth destination user is as follows:
Figure FDA0002904583500000034
signal to interference plus noise ratio SINR:
Figure FDA0002904583500000035
the achievable rate for the kth user is:
Figure FDA0002904583500000036
wherein:
Figure FDA0002904583500000037
5. the method for constructing the downlink model of the Massive MIMO relay system based on the low precision quantization according to the claim 1, wherein the step (4) is realized by the following steps:
the achievable total rate of the Massive MIMO relay system under the Rician channel is as follows:
Figure FDA0002904583500000038
for ease of calculation, the following variables are defined:
Figure FDA0002904583500000041
Figure FDA0002904583500000042
using delta1，k，Δ2，k，Φ1，ik，φ2，ik，γ1，ki，γ2，kiIs S in the above formulak，Ik，N1，k，N2，kCan be expressed as:
Figure FDA0002904583500000043
Figure FDA0002904583500000044
Figure FDA0002904583500000045
Figure FDA0002904583500000046
CN202110067128.XA 2021-01-19 2021-01-19 Massive MIMO relay system downlink model construction method based on low-precision quantization Active CN112910517B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110067128.XA CN112910517B (en) 2021-01-19 2021-01-19 Massive MIMO relay system downlink model construction method based on low-precision quantization

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110067128.XA CN112910517B (en) 2021-01-19 2021-01-19 Massive MIMO relay system downlink model construction method based on low-precision quantization

Publications (2)

Publication Number Publication Date
CN112910517A true CN112910517A (en) 2021-06-04
CN112910517B CN112910517B (en) 2022-03-29

Family

ID=76115228

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110067128.XA Active CN112910517B (en) 2021-01-19 2021-01-19 Massive MIMO relay system downlink model construction method based on low-precision quantization

Country Status (1)

Country Link
CN (1) CN112910517B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107154819A (en) * 2017-03-31 2017-09-12 南京邮电大学 A kind of satellite relay selection optimization method based on geographical location information
US20170272147A1 (en) * 2011-04-19 2017-09-21 Sun Patent Trust Relay method and relay device
CN108243431A (en) * 2017-08-28 2018-07-03 南京邮电大学 The power distribution algorithm of unmanned plane relay system based on efficiency optiaml ciriterion
CN109474317A (en) * 2019-01-07 2019-03-15 河南理工大学 MR pre-processes the lower extensive MIMO bidirectional relay system power distribution method of hardware damage
CN109547139A (en) * 2018-12-29 2019-03-29 南京邮电大学 A kind of uplink model construction and method for analyzing performance based on Rician channel
CN111405596A (en) * 2020-03-24 2020-07-10 西安电子科技大学 Resource optimization method for large-scale antenna wireless energy-carrying communication system under Rice channel

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170272147A1 (en) * 2011-04-19 2017-09-21 Sun Patent Trust Relay method and relay device
US20200186235A1 (en) * 2011-04-19 2020-06-11 Sun Patent Trust Relay method and relay device
CN107154819A (en) * 2017-03-31 2017-09-12 南京邮电大学 A kind of satellite relay selection optimization method based on geographical location information
CN108243431A (en) * 2017-08-28 2018-07-03 南京邮电大学 The power distribution algorithm of unmanned plane relay system based on efficiency optiaml ciriterion
CN109547139A (en) * 2018-12-29 2019-03-29 南京邮电大学 A kind of uplink model construction and method for analyzing performance based on Rician channel
CN109474317A (en) * 2019-01-07 2019-03-15 河南理工大学 MR pre-processes the lower extensive MIMO bidirectional relay system power distribution method of hardware damage
CN111405596A (en) * 2020-03-24 2020-07-10 西安电子科技大学 Resource optimization method for large-scale antenna wireless energy-carrying communication system under Rice channel

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"韦忠忠": ""CRAHNs网络中基于Massive MIMO的预编码性能分析和资源分配方案设计"", 《中国优秀硕士学位论文全文数据库信息科技辑》 *
P. ZHU等: ""Large System Performance and Distributed Scheme of Downlink Beamforming in F-RANs With Distributed Antennas"", 《IEEE ACCESS》 *

Also Published As

Publication number Publication date
CN112910517B (en) 2022-03-29

Similar Documents

Publication Publication Date Title
Zhang et al. Joint linear transmitter and receiver design for the downlink of multiuser MIMO systems
KR101859821B1 (en) Method and system for digital-analog hybrid beamforming in the millimeter wave downlink channel
Suraweera et al. Multi-pair amplify-and-forward relaying with very large antenna arrays
Parfait et al. Performance analysis and comparison of ZF and MRT based downlink massive MIMO systems
Liu et al. Energy efficiency of massive MIMO systems with low-resolution ADCs and successive interference cancellation
Castanheira et al. Hybrid beamforming designs for massive MIMO millimeter-wave heterogeneous systems
Peng et al. Hybrid beamforming, user scheduling, and resource allocation for integrated terrestrial-satellite communication
CN108199793B (en) Hybrid pre-coding method based on time delay pre-compensation for broadband millimeter wave system
CN108063634B (en) Optimal regular pre-coding method in low-precision quantitative large-scale MIMO
Zhang et al. Optimal hybrid beamforming design for millimeter-wave massive multi-user MIMO relay systems
Ding et al. Performance analysis of mixed-ADC massive MIMO systems over spatially correlated channels
JP2009153139A (en) Pre-coding processing method and apparatus for mimo downlink, and base station
Asif et al. Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems.
Wang et al. Joint optimization of spectral efficiency and energy efficiency with low-precision ADCs in cell-free massive MIMO systems
Ding et al. SE analysis for mixed-ADC massive MIMO uplink with ZF receiver and imperfect CSI
Li et al. Multi-pair two-way massive MIMO relaying with hardware impairments over Rician fading channels
CN112910517B (en) Massive MIMO relay system downlink model construction method based on low-precision quantization
CN113922849B (en) User grouping and power distribution method under millimeter wave MIMO-NOMA system
Luo et al. Robust precoder design for MIMO relay networks over double correlated Rician channels
Pirzadeh et al. On the effect of mutual coupling in one-bit spatial sigma-delta massive MIMO systems
Sun et al. Multi-pair two-way massive MIMO AF full-duplex relaying with ZFR/ZFT and imperfect CSI
Wang et al. Antenna selections strategies for massive MIMO systems with limited-resolution ADCs/DACs
Kim et al. Downlink NOMA for short-packet Internet of Things communications with low-resolution ADCs
Han et al. Hybrid beamforming with sub-connected structure for MmWave massive multi-user MIMO relay systems
Matthaiou et al. Guest editorial: Large-scale multiple antenna wireless systems

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